The 100 most-cited articles in Applied Sciences, 2022

Deep Residual Networks have recently been shown to significantly improve the performance of neural networks trained on ImageNet, with results beating all previous methods on this dataset by large margins in the image classification task. However, the meaning of these impressive numbers and their implications for future research are not fully understood yet. In this survey, we will try to explain what Deep Residual Networks are, how they achieve their excellent results, and why their successful implementation in practice represents a significant advance over existing techniques. We also discuss some open questions related to residual learning as well as possible applications of Deep Residual Networks beyond ImageNet. Finally, we discuss some issues that still need to be resolved before deep residual learning can be applied on more complex problems.

Particle swarm optimization (PSO) is one of the most famous swarm-based optimization techniques inspired by nature. Due to its properties of flexibility and easy implementation, there is an enormous increase in the popularity of this nature-inspired technique. Particle swarm optimization (PSO) has gained prompt attention from every field of researchers. Since its origin in 1995 till now, researchers have improved the original Particle swarm optimization (PSO) in varying ways. They have derived new versions of it, such as the published theoretical studies on various parameters of PSO, proposed many variants of the algorithm and numerous other advances. In the present paper, an overview of the PSO algorithm is presented. On the one hand, the basic concepts and parameters of PSO are explained, on the other hand, various advances in relation to PSO, including its modifications, extensions, hybridization, theoretical analysis, are included.

Background: Polyphenols are a set of bioactive compounds commonly found in plants. These compounds are of great interest, as they have shown high antioxidant power and are correlated to many health benefits. Hence, traditional methods of extraction such as solvent extraction, Soxhlet extraction and novel extraction technologies such as ultrasound-assisted extraction and subcritical water extraction (SWE) have been investigated for the extraction of polyphenols. Scope and Approach: Generally, for traditional extractions, the total phenolic content (TPC) is highest at an extraction temperature of 60–80 °C. For this reason, polyphenols are regularly regarded as heat-labile compounds. However, in many studies that investigated the optimal temperature for subcritical water extraction (SWE), temperatures as high as 100–200 °C have been reported. These SWE extractions showed extremely high yields and antioxidant capacities at these temperatures. This paper aimed to examine the relevant literature to identify and understand the mechanisms behind this discrepancy. Results: Thermal…

In the era of the fourth industrial revolution, several concepts have arisen in parallel with this new revolution, such as predictive maintenance, which today plays a key role in sustainable manufacturing and production systems by introducing a digital version of machine maintenance. The data extracted from production processes have increased exponentially due to the proliferation of sensing technologies. Even if Maintenance 4.0 faces organizational, financial, or even data source and machine repair challenges, it remains a strong point for the companies that use it. Indeed, it allows for minimizing machine downtime and associated costs, maximizing the life cycle of the machine, and improving the quality and cadence of production. This approach is generally characterized by a very precise workflow, starting with project understanding and data collection and ending with the decision-making phase. This paper presents an exhaustive literature review of methods and applied tools for intelligent predictive maintenance models in Industry 4.0 by identifying and categorizing the life cycle…

The growth of the global population coupled with a decline in natural resources, farmland, and the increase in unpredictable environmental conditions leads to food security is becoming a major concern for all nations worldwide. These problems are motivators that are driving the agricultural industry to transition to smart agriculture with the application of the Internet of Things (IoT) and big data solutions to improve operational efficiency and productivity. The IoT integrates a series of existing state-of-the-art solutions and technologies, such as wireless sensor networks, cognitive radio ad hoc networks, cloud computing, big data, and end-user applications. This study presents a survey of IoT solutions and demonstrates how IoT can be integrated into the smart agriculture sector. To achieve this objective, we discuss the vision of IoT-enabled smart agriculture ecosystems by evaluating their architecture (IoT devices, communication technologies, big data storage, and processing), their applications, and research timeline. In addition, we discuss trends and opportunities of IoT…

Financial fraud, considered as deceptive tactics for gaining financial benefits, has recently become a widespread menace in companies and organizations. Conventional techniques such as manual verifications and inspections are imprecise, costly, and time consuming for identifying such fraudulent activities. With the advent of artificial intelligence, machine-learning-based approaches can be used intelligently to detect fraudulent transactions by analyzing a large number of financial data. Therefore, this paper attempts to present a systematic literature review (SLR) that systematically reviews and synthesizes the existing literature on machine learning (ML)-based fraud detection. Particularly, the review employed the Kitchenham approach, which uses well-defined protocols to extract and synthesize the relevant articles; it then report the obtained results. Based on the specified search strategies from popular electronic database libraries, several studies have been gathered. After inclusion/exclusion criteria, 93 articles were chosen, synthesized, and analyzed. The review summarizes…

Machine learning algorithms are increasingly used in various remote sensing applications due to their ability to identify nonlinear correlations. Ensemble algorithms have been included in many practical applications to improve prediction accuracy. We provide an overview of three widely used ensemble techniques: bagging, boosting, and stacking. We first identify the underlying principles of the algorithms and present an analysis of current literature. We summarize some typical applications of ensemble algorithms, which include predicting crop yield, estimating forest structure parameters, mapping natural hazards, and spatial downscaling of climate parameters and land surface temperature. Finally, we suggest future directions for using ensemble algorithms in practical applications.

Augmented reality is a mature technology that uses the real world as a substrate and extends it by overlaying computer-generated information. It has been applied to several domains. In particular, the technology was proven to be useful for the management and preservation of Cultural Heritage. This study provides an overview of the last decade of the use of augmented reality in cultural heritage through a detailed review of the scientific papers in the field. We analyzed the applications published on Scopus and Clarivate Web of Science databases over a period of 9 years (2012–2021). Bibliometric data consisted of 1201 documents, and their analysis was performed using various tools, including ScientoPy, VOS Viewer, and Microsoft Excel. The results revealed eight trending topics of applying augmented reality technology to cultural heritage: 3D reconstruction of cultural artifacts, digital heritage, virtual museums, user experience, education, tourism, intangible cultural heritage, and gamification. Each topic is discussed in detail in the article sections, providing insight into…

The integration of sustainability in the circular economy is an emerging paradigm that can offer a long term vision to achieve environmental and social sustainability targets in line with the United Nation’s Sustainable Development Goals. Developing scalable and sustainable impacts in circular economy business models (CEBMs) has many challenges. While many advanced technology manufacturing firms start as small enterprises, remarkably little is known about how material reuse firms in sociotechnical systems transition towards circular business models. Research into CEBMs integrating sustainability and environmental conservation is still in its early stages. There has been increased interest in sustainability and circular economy research, but current research is fragmented. The innovation surrounding CEBMs eludes some firms with relatively limited evidence of the transitional perspective necessary to integrate aspects of sustainability. This lack of evidence is especially applicable to the context of circular economy practices in small and medium enterprises in the United States…

In recent years, smart cities have been significantly developed and have greatly expanded their potential. In fact, novel advancements to the Internet of things (IoT) have paved the way for new possibilities, representing a set of key enabling technologies for smart cities and allowing the production and automation of innovative services and advanced applications for the different city stakeholders. This paper presents a review of the research literature on IoT-enabled smart cities, with the aim of highlighting the main trends and open challenges of adopting IoT technologies for the development of sustainable and efficient smart cities. This work first provides a survey on the key technologies proposed in the literature for the implementation of IoT frameworks, and then a review of the main smart city approaches and frameworks, based on classification into eight domains, which extends the traditional six domain classification that is typically adopted in most of the related works.

One of the most promising techniques used in various sciences is deep neural networks (DNNs). A special type of DNN called a convolutional neural network (CNN) consists of several convolutional layers, each preceded by an activation function and a pooling layer. The feature map of the previous layer is sampled by the pooling layer (that seems to be an important layer) to create a new feature map with condensed resolution. This layer significantly reduces the spatial dimension of the input. It always accomplished two main goals. As a first step, it reduces the number of parameters or weights to minimize computational costs. The second step is to prevent the overfitting of the network. In addition, pooling techniques can significantly reduce model training time and computational costs. This paper provides a critical understanding of traditional and modern pooling techniques and highlights the strengths and weaknesses for readers. Moreover, the performance of pooling techniques on different datasets is qualitatively evaluated and reviewed. This study is expected to contribute to a…

One of the most promising technologies that is driving digitalization in several industries is Digital Twin (DT). DT refers to the digital replica or model of any physical object (physical twin). What differentiates DT from simulation and other digital or CAD models is the automatic bidirectional exchange of data between digital and physical twins in real-time. The benefits of implementing DT in any sector include reduced operational costs and time, increased productivity, better decision making, improved predictive/preventive maintenance, etc. As a result, its implementation is expected to grow exponentially in the coming decades as, with the advent of Industry 4.0, products and systems have become more intelligent, relaying on collection and storing incremental amounts of data. Connecting that data effectively to DTs can open up many new opportunities and this paper explores different industrial sectors where the implementation of DT is taking advantage of these opportunities and how these opportunities are taking the industry forward. The paper covers the applications of DT in 13…

This study scrutinizes the existing literature regarding the use of augmented reality and gamification in education to establish its theoretical basis. A systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was conducted. To provide complete and valid information, all types of related studies for all educational stages and subjects throughout the years were investigated. In total, 670 articles from 5 databases (Scopus, Web of Science, Google Scholar, IEEE, and ERIC) were examined. Based on the results, using augmented reality and gamification in education can yield several benefits for students, assist educators, improve the educational process, and facilitate the transition toward technology-enhanced learning when used in a student-centered manner, following proper educational approaches and strategies and taking students’ knowledge, interests, unique characteristics, and personality traits into consideration. Students demonstrated positive behavioral, attitudinal, and psychological changes and increased…

Industrial robots and associated control methods are continuously developing. With the recent progress in the field of artificial intelligence, new perspectives in industrial robot control strategies have emerged, and prospects towards cognitive robots have arisen. AI-based robotic systems are strongly becoming one of the main areas of focus, as flexibility and deep understanding of complex manufacturing processes are becoming the key advantage to raise competitiveness. This review first expresses the significance of smart industrial robot control in manufacturing towards future factories by listing the needs, requirements and introducing the envisioned concept of smart industrial robots. Secondly, the current trends that are based on different learning strategies and methods are explored. Current computer-vision, deep reinforcement learning and imitation learning based robot control approaches and possible applications in manufacturing are investigated. Gaps, challenges, limitations and open issues are identified along the way.

Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed…

Phytoremediation is a cost-effective and sustainable technology used to clean up pollutants from soils and waters through the use of plant species. Indeed, plants are naturally capable of absorbing metals and degrading organic molecules. However, in several cases, the presence of contaminants causes plant suffering and limited growth. In such situations, thanks to the production of specific root exudates, plants can engage the most suitable bacteria able to support their growth according to the particular environmental stress. These plant growth-promoting rhizobacteria (PGPR) may facilitate plant growth and development with several beneficial effects, even more evident when plants are grown in critical environmental conditions, such as the presence of toxic contaminants. For instance, PGPR may alleviate metal phytotoxicity by altering metal bioavailability in soil and increasing metal translocation within the plant. Since many of the PGPR are also hydrocarbon oxidizers, they are also able to support and enhance plant biodegradation activity. Besides, PGPR in agriculture can be an…

Artificial intelligence (AI) and machine learning (ML) have recently been radically improved and are now being employed in almost every application domain to develop automated or semi-automated systems. To facilitate greater human acceptability of these systems, explainable artificial intelligence (XAI) has experienced significant growth over the last couple of years with the development of highly accurate models but with a paucity of explainability and interpretability. The literature shows evidence from numerous studies on the philosophy and methodologies of XAI. Nonetheless, there is an evident scarcity of secondary studies in connection with the application domains and tasks, let alone review studies following prescribed guidelines, that can enable researchers’ understanding of the current trends in XAI, which could lead to future research for domain- and application-specific method development. Therefore, this paper presents a systematic literature review (SLR) on the recent developments of XAI methods and evaluation metrics concerning different application domains and tasks.…

Recent advances in Artificial Intelligence (AI) and machine learning have paved the way for the increasing adoption of chatbots in language learning. Research published to date has mostly focused on chatbot accuracy and chatbot–human communication from students’ or in-service teachers’ perspectives. This study aims to examine the knowledge, level of satisfaction and perceptions concerning the integration of conversational AI in language learning among future educators. In this mixed method research based on convenience sampling, 176 undergraduates from two educational settings, Spain (n = 115) and Poland (n = 61), interacted autonomously with three conversational agents (Replika, Kuki, Wysa) over a four-week period. A learning module about Artificial Intelligence and language learning was specifically designed for this research, including an ad hoc model named the Chatbot–Human Interaction Satisfaction Model (CHISM), which was used by teacher candidates to evaluate different linguistic and technological features of the three conversational agents. Quantitative and qualitative data…

Using advanced artificial intelligence (AI) technology in learning environments is one of the latest challenges for educators and education policymakers. Conversational AI brings new possibilities for alternative and innovative Information and Communication Technologies (ICT) tools, such as AI chatbots. This paper reports on field experiments with an AI chatbot and provides insights into its contribution to Content and Language Integrated Learning (CLIL). More specifically, this paper presents an experimental use case of an educational AI chatbot called AsasaraBot, designed to teach high school students cultural content in a foreign language, i.e., English or French. The content is related to the Minoan Civilization, emphasizing the characteristic figurine of the Minoan Snake Goddess. The related chatbot-based educational program has been evaluated at public and private language schools in Greece. The findings from these experiments show that the use of AI chatbot technology for interactive ICT-based learning is suitable for learning foreign languages and cultural content at the…

The present paper reviews the most recent advances regarding the effects of chemical and organic fertilizers on soil microbial communities. Based on the results from the articles considered, some details are presented on how the use of various types of fertilizers affects the composition and activity of soil microbial communities. Soil microbes have different responses to fertilization based on differences in the total carbon (C), nitrogen (N) and phosphorus (P) contents in the soil, along with soil moisture and the presence of plant species. These articles show that the use of chemical fertilizers changes the abundance of microbial populations and stimulates their growth thanks to the nutrient supply added. Overall, however, the data revealed that chemical fertilizers have no significant influence on the richness and diversity of the bacteria and fungi. Instead, the abundance of individual bacterial or fungal species was sensitive to fertilization and was mainly attributed to the changes in the soil chemical properties induced by chemical or organic fertilization. Among the…

A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no control over tumor growth. Deep learning has been argued to have the potential to overcome the challenges associated with detecting and intervening in brain tumors. It is well established that the segmentation method can be used to remove abnormal tumor regions from the brain, as this is one of the advanced technological classification and detection tools. In the case of brain tumors, early disease detection can be achieved effectively using reliable advanced A.I. and Neural Network classification algorithms. This study aimed to critically analyze the proposed literature solutions, use the Visual Geometry Group (VGG 16) for discovering brain tumors, implement a convolutional neural network (CNN) model framework, and set parameters to train the model for this challenge. VGG is used as one of the highest-performing CNN models because of its simplicity. Furthermore, the study developed an effective approach to detect brain tumors using MRI to aid in making quick, efficient, and precise…

The major criteria that control pile foundation design is pile bearing capacity (Pu). The load bearing capacity of piles is affected by the various characteristics of soils and the involvement of multiple parameters related to both soil and foundation. In this study, a new model for predicting bearing capacity is developed using an extreme gradient boosting (XGBoost) algorithm. A total of 200 driven piles static load test-based case histories were used to construct and verify the model. The developed XGBoost model results were compared to a number of commonly used algorithms—Adaptive Boosting (AdaBoost), Random Forest (RF), Decision Tree (DT) and Support Vector Machine (SVM) using various performance measure metrics such as coefficient of determination, mean absolute error, root mean square error, mean absolute relative error, Nash–Sutcliffe model efficiency coefficient and relative strength ratio. Furthermore, sensitivity analysis was performed to determine the effect of input parameters on Pu. The results show that all of the developed models were capable of making accurate…

Due to the COVID-19 pandemic, there has been a shift from in-person to remote education, with most students taking classes via video meetings. This change inhibits active class participation from students. In particular, video education has limitations in replacing practical classes, which require both theoretical and empirical knowledge. In this study, we propose a system that incorporates virtual reality and metaverse methods into the classroom to compensate for the shortcomings of the existing remote models of practical education. Based on the proposed system, we developed an aircraft maintenance simulation and conducted an experiment comparing our system to a video training method. To measure educational effectiveness, knowledge acquisition, and retention tests were conducted and presence was investigated via survey responses. The results of the experiment show that the group using the proposed system scored higher than the video training group on both knowledge tests. As the responses given to the presence questionnaire confirmed a sense of spatial presence felt by the…

Nowadays, food treatment technologies are constantly evolving due to an increasing demand for healthier and tastier food with longer shelf lives. In this review, our aim is to highlight the advantages and disadvantages of some of the most exploited industrial techniques for food processing and microorganism deactivation, dividing them into those that exploit high temperatures (pasteurization, sterilization, aseptic packaging) and those that operate thanks to their inherent chemical–physical principles (ultrasound, ultraviolet radiation, ozonation, high hydrostatic pressure). The traditional thermal methods can reduce the number of pathogenic microorganisms to safe levels, but non-thermal technologies can also reduce or remove the adverse effects that occur using high temperatures. In the case of ultrasound, which inactivates pathogens, recent advances in food treatment are reported. Throughout the text, novel discoveries of the last decade are presented, and non-thermal methods have been demonstrated to be more attractive for processing a huge variety of foods. Preserving the…

The increasing world population makes it necessary to fight challenges such as climate change and to realize production efficiently and quickly. However, the minimum cost, maximum income, environmental pollution protection and the ability to save water and energy are all factors that should be taken into account in this process. The use of information and communication technologies (ICTs) in agriculture to meet all of these criteria serves the purpose of precision agriculture. As unmanned aerial vehicles (UAVs) can easily obtain real-time data, they have a great potential to address and optimize solutions to the problems faced by agriculture. Despite some limitations, such as the battery, load, weather conditions, etc., UAVs will be used frequently in agriculture in the future because of the valuable data that they obtain and their efficient applications. According to the known literature, UAVs have been carrying out tasks such as spraying, monitoring, yield estimation, weed detection, etc. In recent years, articles related to agricultural UAVs have been presented in journals with…

Federated learning (FL) is a machine learning technique that enables distributed devices to train a learning model collaboratively without sharing their local data. FL-based systems can achieve much stronger privacy preservation since the distributed devices deliver only local model parameters trained with local data to a centralized server. However, there exists a possibility that a centralized server or attackers infer/extract sensitive private information using the structure and parameters of local learning models. We propose employing homomorphic encryption (HE) scheme that can directly perform arithmetic operations on ciphertexts without decryption to protect the model parameters. Using the HE scheme, the proposed privacy-preserving federated learning (PPFL) algorithm enables the centralized server to aggregate encrypted local model parameters without decryption. Furthermore, the proposed algorithm allows each node to use a different HE private key in the same FL-based system using a distributed cryptosystem. The performance analysis and evaluation of the proposed PPFL…

Deepfake is utilized in synthetic media to generate fake visual and audio content based on a person’s existing media. The deepfake replaces a person’s face and voice with fake media to make it realistic-looking. Fake media content generation is unethical and a threat to the community. Nowadays, deepfakes are highly misused in cybercrimes for identity theft, cyber extortion, fake news, financial fraud, celebrity fake obscenity videos for blackmailing, and many more. According to a recent Sensity report, over 96% of the deepfakes are of obscene content, with most victims being from the United Kingdom, United States, Canada, India, and South Korea. In 2019, cybercriminals generated fake audio content of a chief executive officer to call his organization and ask them to transfer $243,000 to their bank account. Deepfake crimes are rising daily. Deepfake media detection is a big challenge and has high demand in digital forensics. An advanced research approach must be built to protect the victims from blackmailing by detecting deepfake content. The primary aim of our research study is to…

The Internet of Things (IoT) is an emerging field consisting of Internet-based globally connected network architecture. A subset of IoT is the Internet of Healthcare Things (IoHT) that consists of smart healthcare devices having significant importance in monitoring, processing, storing, and transmitting sensitive information. It is experiencing novel challenges regarding data privacy protection. This article discusses different components of IoHT and categorizes various healthcare devices based on their functionality and deployment. This article highlights the possible points and reasons for data leakage, such as conflicts in laws, the use of sub-standard devices, lack of awareness, and the non-availability of dedicated local law enforcement agencies. This article draws attention to the escalating demand for a suitable regulatory framework and analyzes compliance problems of IoHT devices concerning healthcare data privacy and protection regulations. Furthermore, the article provides some recommendations to improve the security and privacy of IoHT implementation.

Recent developments in video analysis of sports and computer vision techniques have achieved significant improvements to enable a variety of critical operations. To provide enhanced information, such as detailed complex analysis in sports such as soccer, basketball, cricket, and badminton, studies have focused mainly on computer vision techniques employed to carry out different tasks. This paper presents a comprehensive review of sports video analysis for various applications: high-level analysis such as detection and classification of players, tracking players or balls in sports and predicting the trajectories of players or balls, recognizing the team’s strategies, and classifying various events in sports. The paper further discusses published works in a variety of application-specific tasks related to sports and the present researcher’s views regarding them. Since there is a wide research scope in sports for deploying computer vision techniques in various sports, some of the publicly available datasets related to a particular sport have been discussed. This paper reviews detailed…

In this research, we proposed a novel 14-layered deep convolutional neural network (14-DCNN) to detect plant leaf diseases using leaf images. A new dataset was created using various open datasets. Data augmentation techniques were used to balance the individual class sizes of the dataset. Three image augmentation techniques were used: basic image manipulation (BIM), deep convolutional generative adversarial network (DCGAN) and neural style transfer (NST). The dataset consists of 147,500 images of 58 different healthy and diseased plant leaf classes and one no-leaf class. The proposed DCNN model was trained in the multi-graphics processing units (MGPUs) environment for 1000 epochs. The random search with the coarse-to-fine searching technique was used to select the most suitable hyperparameter values to improve the training performance of the proposed DCNN model. On the 8850 test images, the proposed DCNN model achieved 99.9655% overall classification accuracy, 99.7999% weighted average precision, 99.7966% weighted average recall, and 99.7968% weighted average F1 score. Additionally,…

Over the past few years, Internet applications have become more advanced and widely used. This has increased the need for Internet networks to be secured. Intrusion detection systems (IDSs), which employ artificial intelligence (AI) methods, are vital to ensuring network security. As a branch of AI, deep learning (DL) algorithms are now effectively applied in IDSs. Among deep learning neural networks, the convolutional neural network (CNN) is a well-known structure designed to process complex data. The CNN overcomes the typical limitations of conventional machine learning approaches and is mainly used in IDSs. Several CNN-based approaches are employed in IDSs to handle privacy issues and security threats. However, there are no comprehensive surveys of IDS schemes that have utilized CNN to the best of our knowledge. Hence, in this study, our primary focus is on CNN-based IDSs so as to increase our understanding of various uses of the CNN in detecting network intrusions, anomalies, and other types of attacks. This paper innovatively organizes the studied CNN-IDS approaches into…

The evolution of recent malicious software with the rising use of digital services has increased the probability of corrupting data, stealing information, or other cybercrimes by malware attacks. Therefore, malicious software must be detected before it impacts a large number of computers. Recently, many malware detection solutions have been proposed by researchers. However, many challenges limit these solutions to effectively detecting several types of malware, especially zero-day attacks due to obfuscation and evasion techniques, as well as the diversity of malicious behavior caused by the rapid rate of new malware and malware variants being produced every day. Several review papers have explored the issues and challenges of malware detection from various viewpoints. However, there is a lack of a deep review article that associates each analysis and detection approach with the data type. Such an association is imperative for the research community as it helps to determine the suitable mitigation approach. In addition, the current survey articles stopped at a generic detection…

The use of natural food ingredients has been increased in recent years due to the negative health implications of synthetic ingredients. Natural bioactive compounds are important for the development of health-oriented functional food products with better quality attributes. The natural bioactive compounds possess different types of bioactivities, e.g., antioxidative, antimicrobial, antihypertensive, and antiobesity activities. The most common method for the development of functional food is the fortification of these bioactive compounds during food product manufacturing. However, many of these natural bioactive compounds are heat-labile and less stable. Therefore, the industry and researchers proposed the microencapsulation of natural bioactive compounds, which may improve the stability of these compounds during processing and storage conditions. It may also help in controlling and sustaining the release of natural compounds in the food product matrices, thus, providing bioactivity for a longer duration. In this regard, several advanced techniques have been explored in recent years…

The Internet of Things (IoT) ecosystem has experienced significant growth in data traffic and consequently high dimensionality. Intrusion Detection Systems (IDSs) are essential self-protective tools against various cyber-attacks. However, IoT IDS systems face significant challenges due to functional and physical diversity. These IoT characteristics make exploiting all features and attributes for IDS self-protection difficult and unrealistic. This paper proposes and implements a novel feature selection and extraction approach (i.e., our method) for anomaly-based IDS. The approach begins with using two entropy-based approaches (i.e., information gain (IG) and gain ratio (GR)) to select and extract relevant features in various ratios. Then, mathematical set theory (union and intersection) is used to extract the best features. The model framework is trained and tested on the IoT intrusion dataset 2020 (IoTID20) and NSL-KDD dataset using four machine learning algorithms: Bagging, Multilayer Perception, J48, and IBk. Our approach has resulted in 11 and 28 relevant features (out of 86)…

A network intrusion detection model that fuses a convolutional neural network and a gated recurrent unit is proposed to address the problems associated with the low accuracy of existing intrusion detection models for the multiple classification of intrusions and low accuracy of class imbalance data detection. In this model, a hybrid sampling algorithm combining Adaptive Synthetic Sampling (ADASYN) and Repeated Edited nearest neighbors (RENN) is used for sample processing to solve the problem of positive and negative sample imbalance in the original dataset. The feature selection is carried out by combining Random Forest algorithm and Pearson correlation analysis to solve the problem of feature redundancy. Then, the spatial features are extracted by using a convolutional neural network, and further extracted by fusing Averagepooling and Maxpooling, using attention mechanism to assign different weights to the features, thus reducing the overhead and improving the model performance. At the same time, a Gated Recurrent Unit (GRU) is used to extract the long-distance dependent…

Arsenic intoxication represents a worldwide health problem and occurs mainly through drinking water. Arsenic, a metalloid and naturally occurring element, is one of the most abundant elements in the earth’s crust, whose toxicity depends on the reduction state. The trivalent arsenicals are more toxic than the pentavalent arsenicals. In the trivalent state, inorganic and organic arsenic may react with thiol groups in proteins inhibiting their activity, whereas inorganic arsenic in the pentavalent state may replace phosphate ions in several reactions. Arsenic induces various epigenetic changes in mammalian cells, both in vivo and in vitro, often leading to the development of various types of cancers, including skin, lung, liver, urinary tract, prostate, and hematopoietic cancers. Potential mechanisms of arsenic toxicity in cancer include genotoxicity, altered DNA methylation and cell proliferation, co-carcinogenesis, tumor promotion, and oxidative stress. On the other hand, the FDA-certified drug arsenic trioxide provides solutions for various diseases, including several types of…

Pneumonia is a life-threatening lung infection resulting from several different viral infections. Identifying and treating pneumonia on chest X-ray images can be difficult due to its similarity to other pulmonary diseases. Thus, the existing methods for predicting pneumonia cannot attain substantial levels of accuracy. This paper presents a computer-aided classification of pneumonia, coined Ensemble Learning (EL), to simplify the diagnosis process on chest X-ray images. Our proposal is based on Convolutional Neural Network (CNN) models, which are pretrained CNN models that have been recently employed to enhance the performance of many medical tasks instead of training CNN models from scratch. We propose to use three well-known CNNs (DenseNet169, MobileNetV2, and Vision Transformer) pretrained using the ImageNet database. These models are trained on the chest X-ray data set using fine-tuning. Finally, the results are obtained by combining the extracted features from these three models during the experimental phase. The proposed EL approach outperforms other existing state-of-the-art…

Intelligent mobile robots that can move independently were laid out in the real world around 100 years ago during the second world war after advancements in computer science. Since then, mobile robot research has transformed robotics and information engineering. For example, robots were crucial in military applications, especially in teleoperations, when they emerged during the second world war era. Furthermore, after the implementation of artificial intelligence (AI) in robotics, they became autonomous or more intelligent. Currently, mobile robots have been implemented in many applications like defense, security, freight, pattern recognition, medical treatment, mail delivery, infrastructure inspection and developments, passenger travel, and many more because they are more intelligent nowadays with artificial intelligence technology. To study the developments of mobile robots, we have studied an extensive literature survey of the last 50 years. In this article, we discuss a complete century of mobile robotics research, major sensors used in robotics, some major applications of…

The application of deep architectures inspired by the fields of artificial intelligence and computer vision has made a significant impact on the task of crack detection. As the number of studies being published in this field is growing fast, it is important to categorize the studies at deeper levels. In this paper, a comprehensive literature review of deep learning-based crack detection studies and the contributions they have made to the field is presented. The studies are categorised according to the computer vision aspect and at deeper levels to facilitate exploring the studies that utilised similar approaches to address the crack detection problem. Moreover, the authors perform a comparison between the studies which use the same publicly available data sets, in order to find the most promising crack detection approaches. Critical future directions for research are proposed, based on these reviewed studies as well as on trends and developments in areas similar to the crack detection area.

The IoT’s quick development has brought up several security problems and issues that cannot be solved using traditional intelligent systems. Deep learning (DL) in the field of artificial intelligence (AI) has proven to be efficient, with many advantages that can be used to address IoT cybersecurity concerns. This study trained two models of intelligent networks—namely, DenseNet and Inception Time—to detect cyber-attacks based on a multi-class classification method. We began our investigation by measuring the performance of these two networks using three datasets: the ToN-IoT dataset, which consists of heterogeneous data; the Edge-IIoT dataset; and the UNSW2015 dataset. Then, the results were compared by identifying several cyber-attacks. Extensive experiments were conducted on standard ToN-IoT datasets using the DenseNet multicategory classification model. The best result we obtained was an accuracy of 99.9% for Windows 10 with DenseNet, but by using the Inception Time approach we obtained the highest result for Windows 10 with the network, with 100% accuracy. As for using the…

The connectivity of devices through the internet plays a remarkable role in our daily lives. Many network-based applications are utilized in different domains, e.g., health care, smart environments, and businesses. These applications offer a wide range of services and provide services to large groups. Therefore, the safety of network-based applications has always been an area of research interest for academia and industry alike. The evolution of deep learning has enabled us to explore new areas of research. Hackers make use of the vulnerabilities in networks and attempt to gain access to confidential systems and information. This information and access to systems can be very harmful and portray losses beyond comprehension. Therefore, detection of these network intrusions is of the utmost importance. Deep learning based techniques require minimal inputs while exploring every possible feature set in the network. Thus, in this paper, we present a one-dimensional convolutional neural network-based deep learning architecture for the detection of network intrusions. In this research, we…

With the recent development of drone technology, object detection technology is emerging, and these technologies can also be applied to illegal immigrants, industrial and natural disasters, and missing people and objects. In this paper, we would like to explore ways to increase object detection performance in these situations. Photography was conducted in an environment where it was confusing to detect an object. The experimental data were based on photographs that created various environmental conditions, such as changes in the altitude of the drone, when there was no light, and taking pictures in various conditions. All the data used in the experiment were taken with F11 4K PRO drone and VisDrone dataset. In this study, we propose an improved performance of the original YOLOv5 model. We applied the obtained data to each model: the original YOLOv5 model and the improved YOLOv5_Ours model, to calculate the key indicators. The main indicators are precision, recall, F-1 score, and mAP (0.5), and the YOLOv5_Ours values of mAP (0.5) and function loss were improved by comparing it with…

In this study, we analyze “Discrimination”, ”Bias”, “Fairness”, and “Trustworthiness” as working variables in the context of the social impact of AI. It has been identified that there exists a set of specialized variables, such as security, privacy, responsibility, etc., that are used to operationalize the principles in the Principled AI International Framework. These variables are defined in such a way that they contribute to others of more general scope, for example, the ones studied in this study, in what appears to be a generalization–specialization relationship. Our aim in this study is to comprehend how we can use available notions of bias, discrimination, fairness, and other related variables that will be assured during the software project’s lifecycle (security, privacy, responsibility, etc.) when developing trustworthy algorithmic decision-making systems (ADMS). Bias, discrimination, and fairness are mainly approached with an operational interest by the Principled AI International Framework, so we included sources from outside the framework to complement (from a conceptual…

In recent years, gesture recognition and speech recognition, as important input methods in Human–Computer Interaction (HCI), have been widely used in the field of virtual reality. In particular, with the rapid development of deep learning, artificial intelligence, and other computer technologies, gesture recognition and speech recognition have achieved breakthrough research progress. The search platform used in this work is mainly the Google Academic and literature database Web of Science. According to the keywords related to HCI and deep learning, such as “intelligent HCI”, “speech recognition”, “gesture recognition”, and “natural language processing”, nearly 1000 studies were selected. Then, nearly 500 studies of research methods were selected and 100 studies were finally selected as the research content of this work after five years (2019–2022) of year screening. First, the current situation of the HCI intelligent system is analyzed, the realization of gesture interaction and voice interaction in HCI is summarized, and the advantages brought by deep learning are selected for…

The rise in prices of traditional energy sources, the high dependence of many countries on their import, and the associated need for security of supply have led to large investments in new capacity of wind power plants. Although wind power generation is a mature technology and levelized cost of electricity low, there is still room for its improvement. A review of available literature has indicated that wind turbine development in the coming decade will be based on upscaling wind turbines and minor design improvements. These include further improvements in rotor blade aerodynamics, active control of the rotor blade rotation system, and aerodynamic brakes that will lead to increased power generation efficiency. Improvements in system maintenance and early diagnosis of transmission and power-related faults and blade surface damage will reduce wind turbine downtime and increase system reliability and availability. The manufacture of wind turbines with larger dimensions presents problems of transportation and assembly, which are being addressed by manufacturing the blades from segments.…

Brain tumors are considered one of the most serious, prominent and life-threatening diseases globally. Brain tumors cause thousands of deaths every year around the globe because of the rapid growth of tumor cells. Therefore, timely analysis and automatic detection of brain tumors are required to save the lives of thousands of people around the globe. Recently, deep transfer learning (TL) approaches are most widely used to detect and classify the three most prominent types of brain tumors, i.e., glioma, meningioma and pituitary. For this purpose, we employ state-of-the-art pre-trained TL techniques to identify and detect glioma, meningioma and pituitary brain tumors. The aim is to identify the performance of nine pre-trained TL classifiers, i.e., Inceptionresnetv2, Inceptionv3, Xception, Resnet18, Resnet50, Resnet101, Shufflenet, Densenet201 and Mobilenetv2, by automatically identifying and detecting brain tumors using a fine-grained classification approach. For this, the TL algorithms are evaluated on a baseline brain tumor classification (MRI) dataset, which is freely available on…

Insect pests are a major element influencing agricultural production. According to the Food and Agriculture Organization (FAO), an estimated 20–40% of pest damage occurs each year, which reduces global production and becomes a major challenge to crop production. These insect pests cause sooty mold disease by sucking the sap from the crop’s organs, especially leaves, fruits, stems, and roots. To control these pests, pesticides are frequently used because they are fast-acting and scalable. Due to environmental pollution and health awareness, less use of pesticides is recommended. One of the salient approaches could be to reduce the wide use of pesticides by spraying on demand. To perform spot spraying, the location of the pest must first be determined. Therefore, the growing population and increasing food demand emphasize the development of novel methods and systems for agricultural production to address environmental concerns and ensure efficiency and sustainability. To accurately identify these insect pests at an early stage, insect pest detection and classification have recently…

Bidirectional Encoder Representations from Transformers (BERT) has gained increasing attention from researchers and practitioners as it has proven to be an invaluable technique in natural languages processing. This is mainly due to its unique features, including its ability to predict words conditioned on both the left and the right context, and its ability to be pretrained using the plain text corpus that is enormously available on the web. As BERT gained more interest, more BERT models were introduced to support different languages, including Arabic. The current state of knowledge and practice in applying BERT models to Arabic text classification is limited. In an attempt to begin remedying this gap, this review synthesizes the different Arabic BERT models that have been applied to text classification. It investigates the differences between them and compares their performance. It also examines how effective they are compared to the original English BERT models. It concludes by offering insight into aspects that need further improvements and future work.

Breast cancer is a major research area in the medical image analysis field; it is a dangerous disease and a major cause of death among women. Early and accurate diagnosis of breast cancer based on digital mammograms can enhance disease detection accuracy. Medical imagery must be detected, segmented, and classified for computer-aided diagnosis (CAD) systems to help the radiologists for accurate diagnosis of breast lesions. Therefore, an accurate breast cancer detection and classification approach is proposed for screening of mammograms. In this paper, we present a deep learning system that can identify breast cancer in mammogram screening images using an “end-to-end” training strategy that efficiently uses mammography images for computer-aided breast cancer recognition in the early stages. First, the proposed approach implements the modified contrast enhancement method in order to refine the detail of edges from the source mammogram images. Next, the transferable texture convolutional neural network (TTCNN) is presented to enhance the performance of classification and the energy…

Employee attrition refers to the natural reduction in the employees in an organization due to many unavoidable factors. Employee attrition results in a massive loss for an organization. The Society for Human Resource Management (SHRM) determines that USD 4129 is the average cost-per-hire for a new employee. According to recent stats, 57.3% is the attrition rate in the year 2021. A research study needs to be implemented to find the causes of employee attrition and a learning framework to predict employee attrition. This research study aimed to analyze the organizational factors that caused employee attrition and the prediction of employee attrition using machine learning techniques. The four machine learning techniques were applied in comparison. The proposed optimized Extra Trees Classifier (ETC) approach achieved an accuracy score of 93% for employee attrition prediction. The proposed approach outperformed recent state-of-the-art studies. The Employee Exploratory Data Analysis (EEDA) was applied to determine the factors that caused employee attrition. Our study revealed that the…

Enzyme-assisted extraction (EAE) involves the use of hydrolytic enzymes for the degradation of the cell wall or other cell components. This supports the diffusion of the solvent into the plant or fungal material, leading to easier elution of its metabolites. This technique has been gaining increasing attention, as it is considered an eco-friendly and cost-effective improvement on classical or modern extraction methods. Its promising application in improving the recovery of different classes of bioactive metabolites (e.g., polyphenols, carotenoids, polysaccharides, proteins, components of essential oil, and terpenes) has been reported by many scientific papers. This review summarises information on the theoretical aspects of EAE (e.g., the components of the cell walls and the types of enzymes used) and the most recent discoveries in the effective involvement of enzyme-assisted extraction of natural products (plants, mushrooms, and animals) for nutraceutical and pharmaceutical applications.

Nowadays, microalgae and cyanobacteria have become a promising and sustainable source of useful products, thanks to their richness in bioactive metabolites of high value (antibiotics, toxins, pharmaceutically active compounds, plant growth regulators, and others). These photoautotroph microorganisms generate biomass using photosynthesis. This review, which distinguishes microalgae and Cyanobacteria, often called blue-green microalgae, aims to present their classification and taxonomic diversity as the ecological niches occupied by them. In addition, the usages of open ponds and photobioreactors to produce various microalgae and Cyanobacteria strains and the high-value bioactive compounds from these microorganisms are summarized. Finally, the numerous commercial applications of these phytoplanktons in different fields, such as food, dietary supplements, feed, cosmetic, and biofuel applications, are reviewed.

This review is focused on the topical developments in the synthesis of nanocomposites using the simplest top-down approach, mechanochemical milling, and the related aspects of the interfacial interactions. Milling constraints include time duration of milling, ball size, the ball-to-sample content proportion, rotation speed, and energy that took part in a vital part of the structure–property relationships and composite interactions. Milled nanocomposites are being used in different structural applications for their higher performance rate and throughput. The synthesis of different nanocomposites and the effect of various parameters on the mill-ability of nanocomposites are discussed. Moreover, some of the major advancements in the energy sector are discussed in the latter part of the review.

Agriculture is considered an important field with a significant economic impact in several countries. Due to the substantial population growth, meeting people’s dietary needs has become a relevant concern. The transition to smart agriculture has become inevitable to achieve these food security goals. In recent years, deep learning techniques, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), have been intensely researched and applied in various fields, including agriculture. This study analyzed the recent research articles on deep learning techniques in agriculture over the previous five years and discussed the most important contributions and the challenges that have been solved. Furthermore, we investigated the agriculture parameters being monitored by the internet of things and used them to feed the deep learning algorithm for analysis. Additionally, we compared different studies regarding focused agriculture area, problems solved, the dataset used, the deep learning model used, the framework used, data preprocessing and augmentation method, and…

This work presents the development of an ADAS (advanced driving assistance system) focused on driver drowsiness detection, whose objective is to alert drivers of their drowsy state to avoid road traffic accidents. In a driving environment, it is necessary that fatigue detection is performed in a non-intrusive way, and that the driver is not bothered with alarms when he or she is not drowsy. Our approach to this open problem uses sequences of images that are 60 s long and are recorded in such a way that the subject’s face is visible. To detect whether the driver shows symptoms of drowsiness or not, two alternative solutions are developed, focusing on the minimization of false positives. The first alternative uses a recurrent and convolutional neural network, while the second one uses deep learning techniques to extract numeric features from images, which are introduced into a fuzzy logic-based system afterwards. The accuracy obtained by both systems is similar: around 65% accuracy over training data, and 60% accuracy on test data. However, the fuzzy logic-based system stands out…

New technologies bring opportunities to deploy AI and machine learning to the edge of the network, allowing edge devices to train simple models that can then be deployed in practice. Federated learning (FL) is a distributed machine learning technique to create a global model by learning from multiple decentralized edge clients. Although FL methods offer several advantages, including scalability and data privacy, they also introduce some risks and drawbacks in terms of computational complexity in the case of heterogeneous devices. Internet of Things (IoT) devices may have limited computing resources, poorer connection quality, or may use different operating systems. This paper provides an overview of the methods used in FL with a focus on edge devices with limited computational resources. This paper also presents FL frameworks that are currently popular and that provide communication between clients and servers. In this context, various topics are described, which include contributions and trends in the literature. This includes basic models and designs of system architecture,…

Blockchain technologies have received considerable attention from academia and industry due to their distinctive characteristics, such as data integrity, security, decentralization, and reliability. However, their adoption rate is still scarce, which is one of the primary reasons behind conducting studies related to users’ satisfaction and adoption. Determining what impacts the use and adoption of Blockchain technologies can efficiently address their adoption challenges. Hence, this systematic review aimed to review studies published on Blockchain technologies to offer a thorough understanding of what impacts their adoption and discuss the main challenges and opportunities across various sectors. From 902 studies collected, 30 empirical studies met the eligibility criteria and were thoroughly analyzed. The results confirmed that the technology acceptance model (TAM) and technology–organization–environment (TOE) were the most common models for studying Blockchain adoption. Apart from the core variables of these two models, the results indicated that trust, perceived cost, social…

In recent years, privacy concerns have become a serious issue for companies wishing to protect economic models and comply with end-user expectations. In the same vein, some countries now impose, by law, constraints on data use and protection. Such context thus encourages machine learning to evolve from a centralized data and computation approach to decentralized approaches. Specifically, Federated Learning (FL) has been recently developed as a solution to improve privacy, relying on local data to train local models, which collaborate to update a global model that improves generalization behaviors. However, by definition, no computer system is entirely safe. Security issues, such as data poisoning and adversarial attack, can introduce bias in the model predictions. In addition, it has recently been shown that the reconstruction of private raw data is still possible. This paper presents a comprehensive study concerning various privacy and security issues related to federated learning. Then, we identify the state-of-the-art approaches that aim to counteract these problems. Findings…

Mushrooms are considered to be functional foods with high nutritional, culinary, and pharmacological values, and there has been an increase in their consumption, both through the diet and in the form of dietary supplements. The present study aimed to briefly review the nutritional composition and biological properties of sixteen mushroom species, as well as to compare the mushrooms’ proximate composition to the analyses conducted at the University of Thessaly, Greece, in cooperation with the Natural History Museum of Meteora and Mushroom Museum. The macronutrient profile of each mushroom was analyzed according to the methods described in the Association of Official Analytical Chemists International, at the School of Agricultural Sciences of the University of Thessaly. The protein content of the mushrooms was found to range between 13.8 g/100 g and 38.5 g/100 g, carbohydrate content ranged between 32 g/100 g and 61.4 g/100 g, and fat content ranged between 0.4 g/100 g and 5.9 g/100 g. Additionally, a serving of 100 g of most species of mushrooms covers 15 to 30% of the daily…

This article presents the current state-of-the-art research on applying artificial intelligence (AI) technology in smart greenhouses to optimize crop yields, water, and fertilizer use efficiency, to reduce pest and disease, and to enhance agricultural sustainability. The key technologies of interest were robotic systems for pesticide application, irrigation, harvesting, bio-inspired algorithms for the automation of greenhouse processes, energy management, machine path planning and operation of UAVs (unmanned aerial vehicles), resolution of scheduling problems, and image signal processing for pest and disease diagnosis. Additionally, the review investigated the cost benefits of various energy-management and AI-based energy-saving technologies, the integration of photovoltaics and dynamic pricing based on real-time and time-of-use metrics, and the cost benefits of LoRa, Wi-Fi, Bluetooth, ZigBee, mobile, and RFID (radiofrequency identification) technologies. The review established that commercially viable AI technologies for agriculture had increased exponentially. For example,…

Sentiment analysis (SA) has been an active research subject in the domain of natural language processing due to its important functions in interpreting people’s perspectives and drawing successful opinion-based judgments. On social media, Roman Urdu is one of the most extensively utilized dialects. Sentiment analysis of Roman Urdu is difficult due to its morphological complexities and varied dialects. The purpose of this paper is to evaluate the performance of various word embeddings for Roman Urdu and English dialects using the CNN-LSTM architecture with traditional machine learning classifiers. We introduce a novel deep learning architecture for Roman Urdu and English dialect SA based on two layers: LSTM for long-term dependency preservation and a one-layer CNN model for local feature extraction. To obtain the final classification, the feature maps learned by CNN and LSTM are fed to several machine learning classifiers. Various word embedding models support this concept. Extensive tests on four corpora show that the proposed model performs exceptionally well in Roman Urdu and…

The Internet of Things (IoT) has been implemented by multiple manufacturing companies into their production chain as this technology is the main source of digitalization in a production plant. It improves the data assembling, productivity of the operation, communication efficiency, and overall manufacturing performance. IoT is also serves to be a good means for improved and efficient warehouse automation. It makes the delivery of products more efficient by calculating the least routes and also reduces the time that is consumed during the management of inventory. The basic objective of Industry 4.0 is to lessen the participation of human operators and to emphasize the automation systems. However, this objective has changed in Industry 5.0, which aims to achieve the maximum benefits through the human–machine interaction by maintaining a balance. Industry 5.0 aims to strengthen the interaction between ever-increasing powerful machinery and the productive abilities of human beings. This paper introduces a detailed overview of IoT in enabling digital transformations and Industry 4.0. The…

Rapid and accurate detection of Camellia oleifera fruit is beneficial to improve the picking efficiency. However, detection faces new challenges because of the complex field environment. A Camellia oleifera fruit detection method based on YOLOv7 network and multiple data augmentation was proposed to detect Camellia oleifera fruit in complex field scenes. Firstly, the images of Camellia oleifera fruit were collected in the field to establish training and test sets. Detection performance was then compared among YOLOv7, YOLOv5s, YOLOv3-spp and Faster R-CNN networks. The YOLOv7 network with the best performance was selected. A DA-YOLOv7 model was established via the YOLOv7 network combined with various data augmentation methods. The DA-YOLOv7 model had the best detection performance and a strong generalisation ability in complex scenes, with mAP, Precision, Recall, F1 score and average detection time of 96.03%, 94.76%, 95.54%, 95.15% and 0.025 s per image, respectively. Therefore, YOLOv7 combined with data augmentation can be used to detect Camellia oleifera fruit in complex scenes.…

Chirality plays an important role in the development of many pharmaceuticals, being a general property of ‘handedness’; nevertheless, a large number of pharmaceuticals are still marketed and administered as racemates. Chirality is all around and even within us; indeed, receptors and enzymes are chiral entities and interact in a specific manner with chiral drugs. Consequently, controlling enantiomeric purity and isolating the enantiomers from chiral drugs remains a crucial subject for analytical, clinical, and regulatory purposes, thus, improving the drug safety profile. The classical examples of spontaneous enantiomerization and severe toxicity related to chirality are represented by ibuprofen and thalidomide, respectively, but numerous other cases have been reported in the literature. This review intends to offer a brief overview on the most common chiral drugs used in therapy for the treatment of various diseases.

The diagnosis and surgical resection using Magnetic Resonance (MR) images in brain tumors is a challenging task to minimize the neurological defects after surgery owing to the non-linear nature of the size, shape, and textural variation. Radiologists, clinical experts, and brain surgeons examine brain MRI scans using the available methods, which are tedious, error-prone, time-consuming, and still exhibit positional accuracy up to 2–3 mm, which is very high in the case of brain cells. In this context, we propose an automated Ultra-Light Brain Tumor Detection (UL-BTD) system based on a novel Ultra-Light Deep Learning Architecture (UL-DLA) for deep features, integrated with highly distinctive textural features, extracted by Gray Level Co-occurrence Matrix (GLCM). It forms a Hybrid Feature Space (HFS), which is used for tumor detection using Support Vector Machine (SVM), culminating in high prediction accuracy and optimum false negatives with limited network size to fit within the average GPU resources of a modern PC system. The objective of this study is to categorize multi-class…

This review summarizes the current knowledge on current and future applications of carbon nanoparticles in medicine. The carbon nanoparticle family has a large number of representatives with unique physicochemical properties that make them good candidates for use in clinical medicine. The best-known (and most researched) carbon nanoparticles include graphene, graphene oxide, and carbon nanotubes. The main direction of use involves medical diagnostics, which includes bioimaging and the detection of chemicals or metabolites present in the body. Since the question of nanoparticle toxicity has not been fully answered, the use of nanoparticles in the fields of therapeutics (drug delivery), regenerative medicine (cell scaffolding, tissue engineering), and vaccine production is still under research and many in vivo studies are ongoing. These preclinical studies suggest that carbon nanoparticles have great potential for diagnosis and treatment; the results show that the nanoparticles used do not have significant toxic effects; however, great caution is needed before nanoparticles are…

This study performs a GIS-based landslide susceptibility assessment using a convolutional neural network, CNN, in a study area of the Gorzineh-khil region, northeastern Iran. For this assessment, a 15-layered CNN was programmed in the Python high-level language for susceptibility mapping. In this regard, as far as the landside triggering factors are concerned, it was concluded that the geomorphologic/topographic parameters (i.e., slope curvature, topographical elevation, slope aspect, and weathering) and water condition parameters (hydrological gradient, drainage pattern, and flow gradient) are the main triggering factors. These factors provided the landside dataset, which was input to the CNN. We used 80% of the dataset for training and the remaining 20% for testing to prepare the landslide susceptibility map of the study area. In order to cross-validate the resulting map, a loss function, and common classifiers were considered: support vector machines, SVM, k-nearest neighbor, k-NN, and decision tree, DT. An evaluation of the results of the susceptibility assessment revealed that…

The results of the comparative quantitative study of oxygen-containing groups adsorbed on the surface of carbonized sponge scaffold (CSS), highly oriented pyrolytic graphite (HOPG), fullerite C60 and multi-walled carbon nanotubes (MWCNTs) introduced into a high vacuum from the atmosphere without any pre-treatment of the surface are discussed. The studied materials are first tested by XRD and Raman spectroscopy, and then quantitatively characterized by XPS and NEXAFS. The research results showed the presence of carbon oxides and water-dissociation products on the surfaces of materials. It was shown that main source of oxygen content (~2%) on the surface of HOPG, MWCNTs, and C60 powder is water condensed from the atmosphere in the form of an adsorbed water molecule and hydroxyl group. On the CSS surface, oxygen atoms are present in the forms of carbon oxides (4–5%) and adsorbed water molecules and hydroxyl groups (5–6%). The high content of adsorbed water on the CSS surface is due to the strong roughness and high porosity of the surface.

Adoption of hydrogen energy as an alternative to fossil fuels could be a major step towards decarbonising and fulfilling the needs of the energy sector. Hydrogen can be an ideal alternative for many fields compared with other alternatives. However, there are many potential environmental challenges that are not limited to production and distribution systems, but they also focus on how hydrogen is used through fuel cells and combustion pathways. The use of hydrogen has received little attention in research and policy, which may explain the widely claimed belief that nothing but water is released as a by-product when hydrogen energy is used. We adopt systems thinking and system dynamics approaches to construct a conceptual model for hydrogen energy, with a special focus on the pathways of hydrogen use, to assess the potential unintended consequences, and possible interventions; to highlight the possible growth of hydrogen energy by 2050. The results indicate that the combustion pathway may increase the risk of the adoption of hydrogen as a combustion fuel, as it produces NOx, which is…

Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then…

In recent years, art creation using artificial intelligence (AI) has started to become a mainstream phenomenon. One of the latest applications of AI is to generate visual artwork from natural language descriptions where anyone can interact with it to create thousands of artistic images with minimal effort, which provokes the questions: what is the essence of artistic creation, and who can create art in this era? Considering that, in this study, the theoretical communication framework was adopted to investigate the difference in the interaction with the text-to-image system between artists and nonartists. In this experiment, ten artists and ten nonartists were invited to co-create with Midjourney. Their actions and reflections were recorded, and two sets of generated images were collected for the visual question-answering task, with a painting created by the artist as a reference sample. A total of forty-two subjects with artistic backgrounds participated in the evaluated experiment. The results indicated differences between the two groups in their creation actions and their attitude…

Worker safety at construction sites is a growing concern for many construction industries. Wearing safety helmets can reduce injuries to workers at construction sites, but due to various reasons, safety helmets are not always worn properly. Hence, a computer vision-based automatic safety helmet detection system is extremely important. Many researchers have developed machine and deep learning-based helmet detection systems, but few have focused on helmet detection at construction sites. This paper presents a You Only Look Once (YOLO)-based real-time computer vision-based automatic safety helmet detection system at a construction site. YOLO architecture is high-speed and can process 45 frames per second, making YOLO-based architectures feasible to use in real-time safety helmet detection. A benchmark dataset containing 5000 images of hard hats was used in this study, which was further divided in a ratio of 60:20:20 (%) for training, testing, and validation, respectively. The experimental results showed that the YOLOv5x architecture achieved the best mean average precision (mAP) of…

Skin lesion classification has recently attracted significant attention. Regularly, physicians take much time to analyze the skin lesions because of the high similarity between these skin lesions. An automated classification system using deep learning can assist physicians in detecting the skin lesion type and enhance the patient’s health. The skin lesion classification has become a hot research area with the evolution of deep learning architecture. In this study, we propose a novel method using a new segmentation approach and wide-ShuffleNet for skin lesion classification. First, we calculate the entropy-based weighting and first-order cumulative moment (EW-FCM) of the skin image. These values are used to separate the lesion from the background. Then, we input the segmentation result into a new deep learning structure wide-ShuffleNet and determine the skin lesion type. We evaluated the proposed method on two large datasets: HAM10000 and ISIC2019. Based on our numerical results, EW-FCM and wide-ShuffleNet achieve more accuracy than state-of-the-art approaches. Additionally, the…

Nowadays, food is no longer just for nutrition. Consumers are more demanding and expect to get health benefits from their daily meals. Various areas of the food industry are in great demand of functional chemicals to enhance the taste and nutritional value of their products. Probiotic bacteria have already been part of the human’s routine for good gut microbiota maintenance in terms of pharmaceutical products. Their incorporation in food however is a challenging task that offers great opportunities but has limitations as well. Specifically, the purpose of this review is to emphasize the importance of probiotics in food, to assess their survival through gastrointestinal tract, and to highlight the recent advances in approaches for their improved viability.

Diabetes is a long-term illness caused by the inefficient use of insulin generated by the pancreas. If diabetes is detected at an early stage, patients can live their lives healthier. Unlike previously used analytical approaches, deep learning does not need feature extraction. In order to support this viewpoint, we developed a real-time monitoring hybrid deep learning-based model to detect and predict Type 2 diabetes mellitus using the publicly available PIMA Indian diabetes database. This study contributes in four ways. First, we perform a comparative study of different deep learning models. Based on experimental findings, we next suggested merging two models, CNN-Bi-LSTM, to detect (and predict) Type 2 diabetes. These findings demonstrate that CNN-Bi-LSTM surpasses the other deep learning methods in terms of accuracy (98%), sensitivity (97%), and specificity (98%), and it is 1.1% better compared to other existing state-of-the-art algorithms. Hence, our proposed model helps clinicians obtain complete information about their patients using real-time monitoring and can check…

Machine learning can be used to automate a wide range of tasks. Smart buildings, which use the Internet of Things (IoT) to connect building operations, enable activities, such as monitoring temperature, safety, and maintenance, for easier controlling via mobile devices and computers. Smart buildings are becoming core aspects in larger system integrations as the IoT is becoming increasingly widespread. The IoT plays an important role in smart buildings and provides facilities that improve human security by using effective technology-based life-saving strategies. This review highlights the role of IoT devices in smart buildings. The IoT devices platform and its components are highlighted in this review. Furthermore, this review provides security challenges regarding IoT and smart buildings. The main factors pertaining to smart buildings are described and the different methods of machine learning in combination with IoT technologies are also described to improve the effectiveness of smart buildings to make them energy efficient.

Poly(lactic acid)(PLA) is an aliphatic polyester that can be derived from natural and renewable resources. Owing to favorable features, such as biocompatibility, biodegradability, good thermal and mechanical performance, and processability, PLA has been considered as one of the most promising biopolymers for biomedical applications. Particularly, electrospun PLA nanofibers with distinguishing characteristics, such as similarity to the extracellular matrix, large specific surface area and high porosity with small pore size and tunable mechanical properties for diverse applications, have recently given rise to advanced spillovers in the medical area. A variety of PLA-based nanofibrous structures have been explored for biomedical purposes, such as wound dressing, drug delivery systems, and tissue engineering scaffolds. This review highlights the recent advances in electrospinning of PLA-based structures for biomedical applications. It also gives a comprehensive discussion about the promising approaches suggested for optimizing the electrospun PLA nanofibrous structures towards the…

Acoustic emission is a nondestructive control technique as it does not involve any input of energy into the materials. It is based on the acquisition of ultrasonic signals spontaneously emitted by a material under stress due to irreversible phenomena such as damage, microcracking, degradation, and corrosion. It is a dynamic and passive-receptive technique that analyzes the ultrasonic pulses emitted by a crack when it is generated. This technique allows for an early diagnosis of incipient structural damage by capturing the precursor signals of the fracture. Recently, the scientific community is making extensive use of methodologies based on machine learning: the use of machine learning makes a machine capable of receiving a series of data, modifying the algorithms as they receive information on what they are processing. In this way, the machine can learn without being explicitly programmed, and this implies a huge use of data and an efficient algorithm to adapt. This review described the methodologies for the implementation of the acoustic emission (AE) technique in the evaluation of…

The implementation of sustainable agro-energy systems that integrate crop, livestock, and bioenergy production is attracting increasing interest from farmers. Livestock produces large amounts of animal manure which can serve as organic fertilizer for crops and pasture growth. However, the nutrients contained in manure can adversely affect air, water, and soil quality and pose a public health risk if not handled properly. Existing manure management practices vary widely on a global scale. Researchers are striving to identify appropriate manure management practices with the aim of environmental protection. Anaerobic digestion of manure and subsequent digestate (DG) processing technologies have been proposed to stabilize manure so that it can be safely used for land applications. DG, which represents digested substrate removed from the anaerobic reactor after recovery of biogas, is a rich source of N, P, K, and S, various micronutrients, and organic matter, the addition of which to the soil can stimulate soil microbial biomass metabolic activities thus improving soil ecosystem…

Virtual reality (VR) has been brought closer to the general public over the past decade as it has become increasingly available for desktop and mobile platforms. As a result, consumer-grade VR may redefine how people learn by creating an engaging “hands-on” training experience. Today, VR applications leverage rich interactivity in a virtual environment without real-world consequences to optimize training programs in companies and educational institutions. Therefore, the main objective of this article was to improve the collaboration and communication practices in 3D virtual worlds with VR and metaverse focused on the educational and productive sector in smart factory. A key premise of our work is that the characteristics of the real environment can be replicated in a virtual world through digital twins, wherein new, configurable, innovative, and valuable ways of working and learning collaboratively can be created using avatar models. To do so, we present a proposal for the development of an experimental framework that constitutes a crucial first step in the process of formalizing…

This review identifies current machine-learning algorithms implemented in building structural health monitoring systems and their success in determining the level of damage in a hierarchical classification. The integration of physical models, feature extraction techniques, uncertainty management, parameter estimation, and finite element model analysis are used to implement data-driven model detection systems for SHM system design. A total of 68 articles using ANN, CNN and SVM, in combination with preprocessing techniques, were analyzed corresponding to the period 2011–2022. The application of these techniques in structural condition monitoring improves the reliability and performance of these systems.

The Metaverse is a multi-user virtual world that combines physical reality with digital virtual reality. The three basic technologies for building the Metaverse are immersive technologies, artificial intelligence, and blockchain. Companies are subsequently making significant investments into creating an artificially intelligent Metaverse, with the consequence that cybersecurity has become more crucial. As cybercrime increases exponentially, it is evident that a comprehensive study of Metaverse security based on artificial intelligence is lacking. A growing number of distributed denial-of-service attacks and theft of user identification information makes it necessary to conduct comprehensive and inclusive research in this field in order to identify the Metaverse’s vulnerabilities and weaknesses. This article provides a summary of existing research on AI-based Metaverse cybersecurity and discusses relevant security challenges. Based on the results, the issue of user identification plays a very important role in the presented works, for which biometric methods are the most commonly…

Surveillance cameras have been increasingly used in many public and private spaces in recent years to increase the security of those areas. Although many companies still recruit someone to monitor the cameras, the person recruited is more likely to miss some abnormal events in the camera feeds due to human error. Therefore, monitoring surveillance cameras could be a waste of time and energy. On the other hand, many researchers worked on surveillance data and proposed several methods to detect abnormal events automatically. As a result, if any anomalous happens in front of the surveillance cameras, it can be detected immediately. Therefore, we introduced a model for detecting abnormal events in the surveillance camera feed. In this work, we designed a model by implementing a well-known convolutional neural network (ResNet50) for extracting essential features of each frame of our input stream followed by a particular schema of recurrent neural networks (ConvLSTM) for detecting abnormal events in our time-series dataset. Furthermore, in contrast with previous works, which mainly…

Adaptive Boosting (AdaBoost) is a distinguished representative of ensemble learning algorithm which combines weak classifiers as a strong classifier by the weighted majority voting rule. Weak classifiers of AdaBoost with Threshold Classification, trying to find the best threshold in one of the data dimensions to separate the data into two classes -1 and 1, are proved to be both convenient and efficient. However, in some case, this Weak Learn algorithm achieves not a great enough accuracy and poor generalization performance due to the following reasons: (1) In each iteration, it finds threshold in all data dimensions, but only the best one with the greatest accuracy will be referenced for classification that doesn’t take advantage of the potential value of other dimensions. (2) This classifier still has the tendency of overfitting as it may focus on training examples which is misclassified by just only one criterion in each iteration. To improve its accuracy and generalization, we proposed a new Weak Learn algorithm which classi­fies examples based on multiple thresholds not the only…

Ship position prediction plays a key role in the early warning and safety of inland waters and maritime navigation. Ship pilots must have in-depth knowledge of the future position of their ship and target ship in a specific time period when maneuvering the ship to effectively avoid collisions. However, prediction accuracy and computing efficiency are crucial issues that need to be worked out at present. To solve these problems, in this paper, the deep long short-term memory network framework (LSTM) and genetic algorithm (GA) are introduced to predict the ship trajectory of inland water. Firstly, the collected actual automatic identification system (AIS) data are preprocessed and a series of typical trajectories are extracted from them; then, the LSTM network is used to predict the typical trajectories in real time. Considering that the hyperparameters of the LSTM network have difficulty obtaining the optimal solution manually, the GA is used to optimize hyperparameters of LSTM; finally, the GA-LSTM trajectory prediction model is constructed with the optimal network parameters and…

Image-guided surgery (IGS) can reduce the risk of tissue damage and improve the accuracy and targeting of lesions by increasing the surgery’s visual field. Three-dimensional (3D) medical images can provide spatial location information to determine the location of lesions and plan the operation process. For real-time tracking and adjusting the spatial position of surgical instruments, two-dimensional (2D) images provide real-time intraoperative information. In this experiment, 2D/3D medical image registration algorithm based on the gray level is studied, and the registration based on normalized cross-correlation is realized. The Gaussian Laplacian second-order differential operator is introduced as a new similarity measure to increase edge information and internal detail information to solve single information and small convergence regions of the normalized cross-correlation algorithm. The multiresolution strategy improves the registration accuracy and efficiency to solve the low efficiency of the normalized cross-correlation algorithm.

In recent years, the use of VR games in cultural heritage has been growing. VR Games have increasingly found their way into museums and exhibitions, highlighting the increasing cultural value associated with games and the institutionalization of game culture. In particular, serious VR games have a variety of benefits for educational purposes. There are several studies that deployed VR games to improve visitor experiences in several contexts. However, there are not sufficient studies in the field that examine the benefits and drawbacks of VR gaming. This lack of classification studies is regarded as an obstacle to developing more effective games and proposing guidance on the best way of using them in cultural heritage. This review aims to analyze how VR games are used in cultural heritage settings, to explore the evolution and opportunities of this emerging field, the challenges and tensions these innovations present, and to collectively advance this work to benefit visitor experiences.

Automatic speech recognition (ASR) is one of the ways used to transform acoustic speech signals into text. Over the last few decades, an enormous amount of research work has been done in the research area of speech recognition (SR). However, most studies have focused on building ASR systems based on adult speech. The recognition of children’s speech was neglected for some time, which means that the field of children’s SR research is wide open. Children’s SR is a challenging task due to the large variations in children’s articulatory, acoustic, physical, and linguistic characteristics compared to adult speech. Thus, the field became a very attractive area of research and it is important to understand where the main center of attention is, and what are the most widely used methods for extracting acoustic features, various acoustic models, speech datasets, the SR toolkits used during the recognition process, and so on. ASR systems or interfaces are extensively used and integrated into various real-life applications, such as search engines, the healthcare industry, biometric analysis,…

Cellulose is the most venerable and essential natural polymer on the planet and is drawing greater attention in the form of nanocellulose, considered an innovative and influential material in the biomedical field. Because of its exceptional physicochemical characteristics, biodegradability, biocompatibility, and high mechanical strength, nanocellulose attracts considerable scientific attention. Plants, algae, and microorganisms are some of the familiar sources of nanocellulose and are usually grouped as cellulose nanocrystal (CNC), cellulose nanofibril (CNF), and bacterial nanocellulose (BNC). The current review briefly highlights nanocellulose classification and its attractive properties. Further functionalization or chemical modifications enhance the effectiveness and biodegradability of nanocellulose. Nanocellulose-based composites, printing methods, and their potential applications in the biomedical field have also been introduced herein. Finally, the study is summarized with future prospects and challenges associated with the nanocellulose-based materials to promote studies…

Potholes, a kind of road defect, can damage vehicles and negatively affect drivers’ safe driving, and in severe cases can lead to traffic accidents. Efficient and preventive management of potholes in a complex road environment plays an important role in securing driver safety. It is also expected to contribute to the prevention of traffic accidents and the smooth flow of traffic. In the past, pothole detection was mainly performed via visual inspection by human experts. Recently, automated pothole-detection methods apply various technologies that converge basic technologies such as sensors and signal processing. The automated pothole-detection methods can be classified into three types according to the technology used in the pothole-recognition process: a vision-based method, a vibration-based method, and a 3D reconstruction-based method. In this paper, three methods are compared, and the strengths and weaknesses of each method are summarized. The detection process and technology proposed in the latest research related to automated pothole detection are described for each method.…

In addition to changing global demography and global warming, agricultural production systems around the world are threatened by intensive agricultural practices (overuse of land and excessive use of chemical fertilizers and pesticides) that deplete soils by affecting their dynamics and their fertility, pollute the environment, lower production, and alter biodiversity on a large scale. The use of bioformulations based on PGPMs (plant growth-promoting microorganisms) seems to be a promising and sustainable strategy to overcome these threats, thanks to their tolerance to various biotic and abiotic stresses and via their beneficial effects in promising plant growth, pest protection, bioremediation, and restoration of degraded lands. In recent years, particular attention has been paid to encapsulated formulations because they offer several advantages over conventional bioformulation (liquid and solid) related to shelf life, problems of survival and viability in the environment, and the efficiency of rhizospheric colonization. This review focuses on the types of encapsulations and the…

Melanoma is a fatal type of skin cancer; the fury spread results in a high fatality rate when the malignancy is not treated at an initial stage. The patients’ lives can be saved by accurately detecting skin cancer at an initial stage. A quick and precise diagnosis might help increase the patient’s survival rate. It necessitates the development of a computer-assisted diagnostic support system. This research proposes a novel deep transfer learning model for melanoma classification using MobileNetV2. The MobileNetV2 is a deep convolutional neural network that classifies the sample skin lesions as malignant or benign. The performance of the proposed deep learning model is evaluated using the ISIC 2020 dataset. The dataset contains less than 2% malignant samples, raising the class imbalance. Various data augmentation techniques were applied to tackle the class imbalance issue and add diversity to the dataset. The experimental results demonstrate that the proposed deep learning technique outperforms state-of-the-art deep learning techniques in terms of accuracy and computational cost.

Aiming to address the currently low accuracy of domestic industrial defect detection, this paper proposes a Two-Stage Industrial Defect Detection Framework based on Improved-YOLOv5 and Optimized-Inception-ResnetV2, which completes positioning and classification tasks through two specific models. In order to make the first-stage recognition more effective at locating insignificant small defects with high similarity on the steel surface, we improve YOLOv5 from the backbone network, the feature scales of the feature fusion layer, and the multiscale detection layer. In order to enable second-stage recognition to better extract defect features and achieve accurate classification, we embed the convolutional block attention module (CBAM) attention mechanism module into the Inception-ResnetV2 model, then optimize the network architecture and loss function of the accurate model. Based on the Pascal Visual Object Classes 2007 (VOC2007) dataset, the public dataset NEU-DET, and the optimized dataset Enriched-NEU-DET, we conducted multiple sets of comparative experiments on the Improved-YOLOv5…

Bi-metallic nanoparticles (NPs) have appeared to be more efficient as antimicrobials than mono-metallic NPs. The fungus Aspergillus terreus-mediated synthesis of bi-metallic Ag-Cu NPs was optimized using response surface methodology (RSM) to reach the maximum yield of NPs. The optimal conditions were validated using ANOVA. The optimal conditions were 1.5 mM total metal (Ag + Cu) concentration, 1.25 mg fungal biomass, 350 W microwave power, and 15 min reaction time. The structure and shape of the synthesized NPs (mostly 20–30 nm) were characterized using several analytical tools. The biological activities of the synthesized NPs were assessed by studying their antioxidant, antibacterial, and cytotoxic activity in different NP concentrations. A dose-dependent response was observed in each test. Bi-metallic Ag-Cu NPs inhibited three clinically relevant human pathogens: Klebsiella pneumoniae, Enterobacter cloacae, and Pseudomonas aeruginosa. Escherichia coli, Enterococcus faecalis, and Staphylococcus aureus were inhibited less. The DPPH and hydrogen peroxide scavenging activities of the…

The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represents one of the biggest challenges for intrusion detection. An intrusion detection system (IDS) is a tool that helps to detect intrusions by inspecting the network traffic. Although many researchers have studied and created new IDS solutions, IDS still needs improving in order to have good detection accuracy while reducing false alarm rates. In addition, many IDS struggle to detect zero-day attacks. Recently, machine learning algorithms have become popular with researchers to detect network intrusion in an efficient manner and with high accuracy. This paper presents the concept of IDS and provides a taxonomy of machine learning methods. The main metrics used to assess an IDS are presented and a review of recent IDS using machine learning is provided where the strengths and weaknesses of…

Learning information from a single or a few samples is called few-shot learning. This learning method will solve deep learning’s dependence on a large sample. Deep learning achieves few-shot learning through meta-learning: “how to learn by using previous experience”. Therefore, this paper considers how the deep learning method uses meta-learning to learn and generalize from a small sample size in image classification. The main contents are as follows. Practicing learning in a wide range of tasks enables deep learning methods to use previous empirical knowledge. However, this method is subject to the quality of feature extraction and the selection of measurement methods supports set and the target set. Therefore, this paper designs a multi-scale relational network (MSRN) aiming at the above problems. The experimental results show that the simple design of the MSRN can achieve higher performance. Furthermore, it improves the accuracy of the datasets within fewer samples and alleviates the overfitting situation. However, to ensure that uniform measurement applies to all tasks, the…

In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. However, the AVS is still far away from mass production because of the high cost of sensor fusion and a lack of combination of top-tier solutions to tackle uncertainty on roads. To reduce sensor dependency and to increase manufacturing along with enhancing research, deep learning-based approaches could be the best alternative for developing practical AVS. With this vision, in this systematic review paper, we broadly discussed the literature of deep learning for AVS from the past decade for real-life implementation in core fields. The systematic review on AVS implementing deep learning is categorized into several modules that cover activities including perception analysis (vehicle detection, traffic signs and light identification, pedestrian detection, lane and curve detection, road object localization, traffic scene…

As the vital technology of natural language understanding, sentence representation reasoning technology mainly focuses on sentence representation methods and reasoning models. Although the performance has been improved, there are still some problems, such as incomplete sentence semantic expression, lack of depth of reasoning model, and lack of interpretability of the reasoning process. Given the reasoning model’s lack of reasoning depth and interpretability, a deep fusion matching network is designed in this paper, which mainly includes a coding layer, matching layer, dependency convolution layer, information aggregation layer, and inference prediction layer. Based on a deep matching network, the matching layer is improved. Furthermore, the heuristic matching algorithm replaces the bidirectional long-short memory neural network to simplify the interactive fusion. As a result, it improves the reasoning depth and reduces the complexity of the model; the dependency convolution layer uses the tree-type convolution network to extract the sentence structure information along with the…

Interest in utilizing neural networks in a variety of scientific and academic studies and in industrial applications is increasing. In addition to the growing interest in neural networks, there is also a rising interest in video classification. Object detection from an image is used as a tool for various applications and is the basis for video classification. Identifying objects in videos is more difficult than for single images, as the information in videos has a time continuity constraint. Common neural networks such as ConvLSTM (Convolutional Long Short-Term Memory) and 3DCNN (3D Convolutional Neural Network), as well as many others, have been used to detect objects from video. Here, we propose a 3DCNN for the detection of human activity from video data. The experimental results show that the optimized proposed 3DCNN provides better results than neural network architectures for motion, static and hybrid features. The proposed 3DCNN obtains the highest recognition precision of the methods considered, 87.4%. In contrast, the neural network architectures for motion, static and…

The proliferation of sensing technologies such as sensors has resulted in vast amounts of time-series data being produced by machines in industrial plants and factories. There is much information available that can be used to predict machine breakdown and degradation in a given factory. The downtime of industrial equipment accounts for heavy losses in revenue that can be reduced by making accurate failure predictions using the sensor data. Internet of Things (IoT) technologies have made it possible to collect sensor data in real time. We found that hybrid modelling can result in efficient predictions as they are capable of capturing the abstract features which facilitate better predictions. In addition, developing effective optimization strategy is difficult because of the complex nature of different sensor data in real time scenarios. This work proposes a method for multivariate time-series forecasting for predictive maintenance (PdM) based on a combination of convolutional neural networks and long short term memory with skip connection (CNN-LSTM). We experiment with CNN, LSTM, and…