The 100 most-cited articles in International Journal for Research in Applied Science and Engineering Technology, 2024

Abstract: In light of the increasing sophistication of cyberattacks and the rapid growth in network traffic, it is essential to detect network traffic anomalies or intrusions as they occur. Manual inspection is inefficient due to the large volume, speed, and variety network traffic data. This paper suggests using deep learning techniques in order to build intelligent models which can detect network traffic anomalies automatically within big data environments. We present a framework for anomaly detection using long-short-term memory models (LSTM) and convolutional neural network (CNN). The models are based on data extracted from packet captures. The models are evaluated on benchmark intrusion datasets as well as a large scale real network traffic dataset. The results show that deep learning models are able to detect anomalies more effectively than traditional shallow learning methods. Models can handle high-volume streaming data with low latency and in real time. To improve detection efficiency, we also propose optimization methods such as model compression and transfer learning.…

Abstract: Introducing an innovative IoT-based pill reminder and monitoring system, this solution aims to tackle medication nonadherence challenges effectively. Leveraging interconnected devices and advanced sensors, personalized medication reminders are delivered to users, enhancing adherence rates efficiently. Accessible via smartphones or other devices, a user-friendly interface allows patients to configure medication schedules and receive timely reminders tailored to their needs. The system tracks pill intakefor dosage accuracy and provides real-time data to healthcare providers for remote monitoring and intervention. Integration with electronic health records streamlines communication between patients and healthcare providers, fostering collaborative care. This IoT solution holds significant potential for addressing medication non-adherence issues and improving overall healthcare outcomes, with further research warranted for broader implementation and evaluation across diverse patient populations.

Abstract: Artificial intelligence, also known as AI, is a technology that enables computers and machines to emulate human intelligence and problem-solving abilities. Computers can perform a wide range of advanced functions thanks to the use of artificial intelligence (AI) technologies. These functions include the ability of machines to see, comprehend, and translate spoken and written language, analyzing data, making recommendations, and more. Artificial intelligence is considered a field of computer science that encompasses other areas like machine learning and deep learning, data analytics, linguistics, software engineering, and so on. These disciplines often involve the development of AI algorithms that are based on the decision-making processes of the human brain, that has the ability to learn and memorize from existing data and allows more precise classifications or predictions over a period of time. AI is the foundation for innovation in modern computing, discovering the value for both individuals and businesses. For illustration, AI is used to extricate content and…

This paper explores the transformative power ofAdaptive Learning Systems powered by Artificial Intelligence(ALS) to personalize educational outcomes for diverse learners in a way that adaptive learning approaches have the advantage of meeting the needs of the student’s learning style and pace, which traditional education methods have failed to do. It uses data analytics and machine learning algorithms in real time to tailor content and assessment to the specific needs of each learner. In our work, we break down mechanisms by which AI enables timely feedback, changes learning paths, and raises engagement that makes for a more effective and inclusive learningenvironment. Based on case studies and empirical analysis, the contribution of this research will show the added values and challenges AI adaptive learning solutions bring to different educational settings. The findings put so much importance on continuous assessment, hence integrating and harnessing pedagogical best practices for the maximization of AI effect on personal learning.

Cloud computing offers scalable resources and services over the internet, enabling businesses to reduce infrastructure costs while improving flexibility. However, its widespread adoption also brings significant security concerns, including data privacy, regulatory compliance, and threat prevention. This research paper aims to identify and analyze the primary security challenges in cloud computing, propose solutions, and discuss future research directions

Abstract: Time series forecasting is a critical component in various fields such as finance, economics, meteorology, and engineering. Among the multitude of methods available for time series forecasting, the Autoregressive Integrated Moving Average (ARIMA) model stands out for its simplicity and effectiveness. This paper provides a comprehensive review of ARIMA models, focusing on their application in forecasting time series data. We begin with an overview of time series analysis and the theoretical foundations of ARIMA models. Subsequently, we delve into the process of building and fitting ARIMA models, discussing the steps involved and the considerations for model selection. Furthermore, we explore advanced topics such as seasonal ARIMA (SARIMA) models and discuss their relevance in handling seasonal data patterns. Additionally, we review recent advancements and extensions of ARIMA models, including hybrid models and machine learning-based approaches. Finally, we discuss the challenges and limitations associated with ARIMA modeling and provide recommendations for future research…

Abstract: This paper delves into the dynamic intersection of finance and sustainability, focusing on the evolution and impact of Environmental, Social, and Governance (ESG) investing. With assets exceeding $30 trillion, the rise of sustainable investing is driven by ESG risk recognition, investor demand, and regulatory influences. The inquiry navigates through critical questions regarding the relationship between sustainability and financial returns, global harmonization of disclosure standards, and the efficiency of markets in pricing ESG risks and opportunities. The narrative unfolds the journey from ethical exclusions to comprehensive ESG integration, examining motivators behind investor adoption. Regulatory developments, from disclosure mandates to taxonomy frameworks, are explored alongside initiatives for ESG metric standardization. The paper also presents case studies spotlighting exemplary ESG practices in asset management, ownership, and corporate strategies. By synthesizing empirical evidence, real-world illustrations, and policy insights, the paper provides a compass for…

In recent years, the integration of Artificial Intelligence (AI) into educational settings has revolutionized traditional teaching methodologies, leading to the development of innovative teaching strategies. This paper explores how AI-enhanced approaches are transforming education by personalizing learning experiences, improving student engagement, and optimizing instructional methods. Through adaptive learning platforms, AI tailors educational content to meet individual student needs, fostering a more inclusive and effective learning environment. Intelligent tutoring systems provide real-time feedback and support, enabling students to progress at their own pace and achieve mastery in various subjects. Additionally, AI-driven analytics offer educators valuable insights into student performance and learning patterns, facilitating data-informed decisionmaking and targeted interventions. By leveraging AI technologies, educators can create dynamic, interactive, and studentcentered classrooms that cater to diverse learning styles and preferences. This paper discusses the potential…

Abstract: Our website aims to simplify education by providing a single platform for students, teachers, and parents, eliminating multiple logins. Teachers can upload grades, share resources, and send notifications, while parents can monitor progress. Students can track performance, access materials, and stay informed. Using HTML, CSS, JavaScript, MySQL, and PHP, we prioritize simplicity and accessibility. Security measures like encryption safeguard user data. With a menu-driven interface and robust data storage, we focus on enhancing the user experience for all stakeholders

Abstract: Edge computation (EC) represents a transformative architecture in which cloud computing services are decentralized to the locations where data originates. This shift has been facilitated by the integration of deep learning (DL) technologies, notably in eliminating latency issues commonly referred to as the "echo effect" across various platforms. In typical EC-enabled DL frameworks where data producers are directly involved, it is often necessary to share data with third parties or edge/cloud servers to facilitate model training. This process, however, raises significant concerns regarding synchronization with high data rates, seamless migration, and security, consequently exposing the system to privacy vulnerabilities. These challenges can be addressed through the adoption of Federated Learning (FL), which provides a robust mechanism to mitigate risks associated with data loss, ensure data freshness, and enhance privacy.

Abstract: The utilization of the internet has greatly increased in recent decades, leading to a vulnerability in networking and cybersecurity. One of the most common resulting attacks is Distributed Denial of Service (DDoS), where overwhelming amounts of data are sent to legitimate websites or servers, causing delays or denying access to legitimate users. Single source attacks are known as denial of service (DoS), while attacks from multiple sources, such as a botnet, are considered distributed denial of service (DDoS). In our project, we employed three machine learning algorithms to identify DDoS attacks, and determined the most successful algorithm based on the accuracy metric. We trained and tested our data using the standardized dataset, dataset_sdn, and obtained experimental results. Out of all the algorithms used, the XGBoost algorithm proved to be the most effective with an accuracy of 99.9%. During preprocessing, any missing data was replaced with the column's mean value

Abstract: India is the second largest populated country in the world so most of the people use public transport. During peak hours issuing tickets to each passenger manually is time consuming and tedious task and also a problem. The probability of ticket fraud is also high in this case. Since the tickets are made of papers it leads to deforestation and these tickets are of no use once when the passenger gets out of the bus. So in order to overcome all these difficulties we have a Smart ticketing system that is proposed using RFID to issue tickets to passengers. In order to ensure the passengers journey with no quarrels and mesh we employ this solution that replaces the traditional paper ticketing by electronic cards, vended through automated machine using smart cards, which improves the convenience and security of transaction. This project actually suggests a much more public friendly, automated system of ticketing with the use of RFID based tickets. Automatic Fare Collection System is implemented in this project through RFID (Smart) card. RFID card is given to the passenger and…

Abstract: Due to the growing volume and variety in data generated by cybersecurity systems, it is crucial that unstructured text be used for detecting anomalies. Natural language processing is a powerful tool for analyzing unstructured information and identifying threats. This paper presents a comprehensive review of NLP applications for cybersecurity. We first present the motivations for and challenges associated with using NLP to improve cybersecurity. Then, we provide background information on unstructured data that is relevant to cybersecurity, and discuss NLP techniques such as named entity recognition (NEAR), sentiment analysis, topic modelling, and document classifying. This paper focuses on how these techniques are used to detect anomalies and intrusions. We present a taxonomy for NLP-driven approaches, and we conduct a literature review that is categorized according to this taxonomy. We examine critically the strengths and weaknesses of current techniques. We highlight research gaps based on this analysis and propose a research agenda to advance NLP research in…

Abstract: The integration of Artificial Intelligence (AI) in the textile industry is driving significant advancements across various domains, including manufacturing, design, quality control, and sustainability. This review explores how AI technologies, such as machine learning, computer vision, and predictive analytics, are transforming traditional processes to enhance efficiency, reduce waste, and offer innovative solutions for smart textiles and wearable technology. AI-driven automation is optimizing production workflows, while AI-powered quality control ensures higher accuracy in defect detection and fabric inspection. Additionally, AI is enabling predictive analytics in supply chains, helping manufacturers anticipate demand and manage resources more effectively. The review also highlights AI's role in sustainable textiles, where it supports eco-friendly practices by minimizing waste, promoting resource efficiency, and enabling circular fashion. Despite its transformative potential, AI implementation presents challenges, such as ethical concerns, data quality, and integration…

Abstract: COVID-19 had a huge impact on our educational system in the world which resulted in the introduction of digital technologies to be adopted in the education systems. This has brought about issues regarding the quality of teaching and learning with ICTs, especially concerning the adoption of Artificial intelligence in the education sector. The use of machine learning models such as chatGpt has changed how learners' outcomes and experiences. “Artificial Intelligence (AI) has emerged as a disruptive force in the education sector, revolutionizing teaching and learning processes. AI’s ability to adapt to individual learners’ needs, automate administrative tasks, and provide personalized feedback has transformed traditional educational practices. However, the integration of AI in education also presents challenges, including data privacy concerns, the digital divide, and the need for significant infrastructural changes. Despite these challenges, the potential benefits of AI in enhancing educational outcomes, particularly in developing countries, are substantial. This paper…

Abstract: Artificial Intelligence (AI) is revolutionizing the landscape of Engineering Education, albeit with limited empirical qualitative assessment of its potential and perceived benefits or challenges. This paper investigates the integration and experiences of academic professionals with AI tools in their pedagogical practices. The study employs a comprehensive survey asking specific questions relating to the familiarity, motivation, usage, training, benefits, challenges, ethical considerations, and future implications of AI tools like ChatGPT, Google Bard, and Microsoft Bing. The results illustrate a broad understanding and adoption of AI tools, and their instrumental role in enhancing educational methods and pedagogical approaches. However, the findings also highlight the need for adequate training and ethical guidelines for responsible AI use. Additionally, perceived challenges are revealed, underscoring the importance of addressing them to harness AI's full potential in education. Overall, this research contributes to the understanding of AI's value in education from an…

Generative AI, with its capacity to create diverse content formats, holds immense potential to revolutionize the educational landscape. This research paper delves into the multifaceted ways in which generative AI can enhance teaching and learning, fostering a symbiotic relationship between technology and human ingenuity. Personalized Learning: Generative AI can analyse vast datasets of student performance and engagement to create adaptive learning paths, tailoring instruction to individual needs and preferences. This personalized approach ensures that each learner receives the optimal level of support and challenge, maximizing their potential for growth and development. Engaging Learning Experiences AI-powered tools can generate a wide range of interactive and immersive learning materials, such as simulations, virtual labs, and gamified experiences. These innovative resources can make education more engaging and enjoyable, capturing students' attention and motivating them to actively participate in the learning process. Streamlining Administrative Tasks Generative AI can automate…

Abstract: Drowsy driver detection system is a safety technology designed to identify the potential signs of fatigue or drowsiness of the driver to prevent accidents caused as a result of not being alert. It is a significant risk factor for traffic accidents which has led to thousands of accidents, injuries, and fatalities each year. In this system we have used some techniques to monitor the driver’s behavior or state. This involves tracking facial features, eye movements or blink patterns and yawning. The system identifies signs of fatigue, such as slow eyelid closure, increased blink duration and yawn which often indicate drowsiness. As technology advances, these systems are becoming more accurate, efficient and integrated with other vehicle safety features. The continued development of drowsy driver detection technology holds significant promise for reducing traffic accidents and improving overall road safety.

Abstract: Sign language serves as a primary mode of communication for the Deaf and hard-of-hearing community. This paper presents a Sign Language Recognition System (SLRS) designed to facilitate seamless communication between individuals proficient in sign language and those who may not share this proficiency. The system employs a multi-faceted approach, integrating computer vision, machine learning, and signal processing techniques to accurately interpret and recognize sign language gestures. The methodology involves data collection from diverse signing styles, preprocessing to enhance data quality, feature extraction capturing key aspects of sign language expressions, and the selection and training of machine learning models. The system aims to represent sign language gestures effectively, providing a foundation for real-time recognition. Integration into hardware or software platforms ensures practical applications in various settings, including education, employment, and public spaces.

Water treatment and water treatment technologies are an essential line of defence to remove contaminants and bacteria before the delivery of clean, potable water supplies for consumption. Water sources can be subject to contamination and therefore require appropriate treatment to remove disease-causing agents. Public drinking water systems use a variety of methods to provide safe drinking water for their communities. Depending on the continent, country and region, different water treatment systems may be in operation depending on regional regulations and raw water input. The following article provides an overview of the New Technology used in water Distribution Network Design.

Abstract:In many different fields, there is a great need to save the information contained in printed or written documents or ima ges to the computer for later use. An easy way to save the information in these printed documents to your computer is to scan the document and save it as an image file. But it would be very difficult to read or query the text or other information in this image f ile to reuse this data. Therefore, there needs to be a technology that automatically collects and stores information (especially text ) from image files. Optical character recognition is a field of research that seeks to create computer systems that can extract and process text from images. The purpose of OCR is to alter or convert any text or document containing text (such as handwritten, p rinted, or scanned images) into a digital format that can be converted to be processed for greater depth and processing. OCR thu s enables machines to recognize text in these documents. There are some important issues that need to be recognized and address ed in order for automation to be implemented…

Abstract: The increasing adoption of connected healthcare technologies has transformed the traditional healthcare landscape, offering improved healthcare delivery. The combination of medical devices, sensors, and electronic health records has enabled real-time monitoring, remote care, and data-driven decision-making. However, the connectivity of medical devices to hospital network also introduces a vast array of challenges which include security challenges, scalability issues, and storage and interoperability issues. These challenges can compromise patient data, disrupt clinical workflows, and hinder the effective delivery of healthcare services. This article gives a detailed survey of the challenges and solutions in connected healthcare, with a focus on the technical and practical considerations of integrating medical devices into hospital networks. We explore the current state of connected healthcare, identify key challenges and limitations. Our analysis covers various aspects of connected healthcare, including device security, data privacy, network infrastructure, and standards…

Abstract: The present paper examines the intricate interplay between self-efficacy, learning performance, social loafing, and AI usage within educational contexts. Key findings unveil moderate positive correlations between self-efficacy and learning performance, juxtaposed with a subtle negative correlation between self-efficacy and AI usage. Yet, a delicate positive correlation emerges between social loafing and AI usage, signaling potential implications for student engagement and collaborative practices. The implications of these findings extend to educators, policymakers, and researchers, underscoring the imperative of ethical and informed AI integration in education. Moreover, the paper advocates for continued exploration into the multifaceted impacts of AI on student learning, offering valuable insights for enhancing educational practices in the digital era.

Abstract: Artificial Intelligence (AI) is transforming the field of online learning by providing personalized and efficient learning experiences for students. AI-powered online learning platforms can analyze data on students’ learning styles, preferences, progress, and performance, and use this information to tailor the curriculum, content, feedback, and support for each student. AI can also enhance the engagement and interaction of online learners by using chat bots, virtual assistants, and intelligent content recommendations. According to a report by eLearning Industry 1 , AI is expected to grow by 48% in the eLearning market by 2024, reaching a value of $3.68 billion. Some of the factors driving this growth are the increasing demand for personalized learning, the availability of big data and cloud computing, and the advancement of AI technologies. However, there are also some challenges and limitations that need to be addressed, such as the ethical, legal, and social implications of AI, the quality and reliability of AI systems, and the need for human oversight and intervention.…

Abstract: Precision Agriculture (PA) is a modern farming management system which can help access and get maximum return from advanced technology advantages. The present conceptual review concentrates on three main sectors such as soil health monitoring, water management, and conservation practices to highlight the role of Artificial Intelligence (AI) vision and machine learning (ML). However, several achievements are pioneering AI agriculture today, including soil quality check using AI, irrigation forecasting, conservation modelling using ML, and many more. The review finds certain progress in key areas of sustainable global food systems and suggests a practical future through improvement in resource efficiency, but notes also challenges including data standardization, technology accessibility and interdisciplinary research. The paper finally presents future research directions to overcome the challenges and advance the acceptance of AI and ML in precision agriculture.

Abstract: Artificial Intelligence has found widespread use across various industries, from optimizing manufacturing workflows to diagnosing health conditions. However, the large volumes of data required to train AI models raise privacy concerns, especially when stored in centralized databases vulnerable to leaks. Federated Learning solves this problem by training models collaboratively by avoiding centralization of the sensitive data, preserving privacy while allowing decentralized models to be exported to edge devices. This paper explores Federated Learning, focusing on its technical aspects, algorithms, and decentralized architecture. By keeping raw data localized, Federated Learning enables global models while safeguarding individual privacy, fostering collaboration across sectors like healthcare, finance, and IoT. It also addresses challenges such as privacy vulnerabilities and model aggregation across devices, proposing solutions to strengthen Federated Learning's effectiveness. Ultimately, this study highlights Federated Learning's pivotal role in the future of AI, where…

A leading player in artificial intelligence (AI) and natural language processing (NLP), Hugging Face is known for its open-source tools and libraries that enable sophisticated NLP models to be developed and deployed. A detailed review of Hugging Face is provided in this paper, highlighting its mission to democratize AI, the evolution of its products, and its core technologies, particularly the Transformers library. A variety of applications of Hugging Face are presented in the paper, showing how its models enhance efficiency and innovation in industries such as healthcare, finance, customer service, and education. Hugging Face is also discussed in relation to its integration with other technologies, its lively community, and its future prospects within the AI and NLP space. As a conclusion, the paper summarizes Hugging Face's potential and impact on AI and NLP going forward

Abstract: Industry generates significant volumes of waste water laden with various pollutants, including suspended solids, organic compounds and heavy metals presenting considerable environmental challenges. This study proposes an innovation approach for the analysis and treatment of tannery waste water using a combined filtration and coagulation treatment process. The analysis phase involves a comprehensive characterization of the tannery effluent, identifying key contaminants and their concentrations. This understanding informs the design of an efficient treatment strategy. The treatment process integrates two key techniques i.e. filtration and coagulation.

Abstracts: Our paper offers a comprehensive exploration of Generative Adversarial Networks (GANs), tracing their evolution from Ian Goodfellow's seminal work to their current state-of-the-art status. Delving into the intricacies of GAN architecture and training dynamics, we illuminate their pivotal role in diverse applications such as image synthesis, style transfer, and text-toimage conversion. Through an exhaustive literature review, we dissect the progression of GAN architectures, from Vanilla GANs [1] to advanced variants like Progressive GANs [7] and StyleGANs [8], highlighting their techniques, contributions, and performance across benchmark datasets. Moreover, we confront challenges such as training instability and mode collapse, while also presenting a meticulously curated repository of contemporary generative model advancements. This repository encapsulates the cutting edge of GAN research, showcasing innovative approaches across domains ranging from financial forecasting to image restoration. Despite hurdles and ethical considerations, GANs persist as the vanguard of…

Abstract: A new area of research that has the potential to completely transform human-computer interaction is the incorporation of emotional intelligence into artificial intelligence (AI) systems. Through the use of sentiment analysis, facial recognition, and voice tone analysis, AI assistants are now able to recognize and comprehend human emotions, allowing them to respond to users with greater emotional nuance and empathy [1] [2]. This opens up new avenues for therapeutic mental health interventions, emotionally supportive dialogues, and improved customer service encounters [3].

Artificial intelligence is a science that aims to perform tasks that require human intelligence. For the past two years, it has been used as a development tool in many areas such as prediction, health, security, and also improves the performance of production and services. Since artificial intelligence and its operations are based on too much data, algorithms, and scientific data, users cannot understand and comprehend the content and do not have the skills necessary to use this technology. Since artificial intelligence is controlled by machines and algorithms, it is difficult to determine the cause of system software and hardware crashes. Using this system requires significant funding. However, there are some facts that support the adoption of AI, such as the availability of transferable power in the cloud, the availability of simple software libraries, and the availability of data. These changes allow users to create their own algorithms.

Data mining plays a crucial role in extracting meaningful insights from large and complex data sets, with broad applications in sectors like finance, health care, and market analysis. Traditional techniques—such as classification, clustering, association rule mining, regression, and anomaly detection—are effective for analyzing structured data but struggle with sequential data due to the challenges of modeling temporal dependencies. Long Short Term Memory (LSTM) networks, a specialized form of Recurrent Neural Networks (RNNs), provide a solution to these challenges. By incorporating memory cells and gating mechanisms, LSTM effectively manage long-term dependencies and address issues like vanishing and exploding gradients. This paper reviews the impact of LSTM networks on data mining, analyzing over 60 key publications. By synthesizing concepts and recent advancements, the review underscores how LSTMs enhance the ability of data mining algorithms to capture and predict temporal patterns, reflecting current research trends and innovations

Abstract: Bioplastics originating from waste biomass present a viable remedy to the environmental dilemmas associated with traditional plastics, thereby reducing dependency on fossil fuels and mitigating carbon emissions. This review investigates the utilization of diverse waste biomass sources, encompassing agricultural residues, food waste, industrial by-products, municipal solid waste, and forest biomass residues, in the synthesis of bioplastics. Technological innovations in feedstock pretreatment, bioconversion methodologies, and polymerization techniques have markedly enhanced the efficiency and material characteristics of bioplastics, establishing them as formidable alternatives to petroleum-derived plastics. Notwithstanding these advancements, obstacles such as feedstock inconsistency, scalability issues, technological limitations, and consumer acceptance persistently impede the extensive adoption of bioplastics. The ecological advantages of bioplastics, particularly concerning their diminished carbon footprint and potential for biodegradability, are underscored through life…

Abstract: Brain tumor detection remains a critical challenge in medical diagnostics, necessitating accurate and efficient methods for early detection and diagnosis. Deep Learning (DL) techniques have emerged as promising tools in this domain due to their ability to extract intricate patterns from complex medical imaging data. This paper presents a comprehensive review and comparative analysis of various DL approaches employed in brain tumor detection. We begin by providing an overview of brain tumors, their classification, and the significance of early detection. Subsequently, we delve into the fundamentals of DL, highlighting its suitability for medical image analysis tasks. We then survey state-of-the-art DL architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants, applied to brain tumor detection from magnetic resonance imaging (MRI) and computed tomography (CT) scans. Additionally, we discuss the challenges associated with DL-based tumor detection, such as dataset size, class imbalance, and interpretability of results.…

Abstract: Through out history and across the plant kingdom has a provided a variety of medicines. The evaluation and spread of antibiotic resistance as well as the evaluation of new strains of disease causing agents ,is of great concern to the global health community .plants with the antimicrobial activity are also known to the numerous ,yet prior to a decade ago,minimal research had been conducted in the area of antifungal and antibiotic activities of the medicinal plants .our ability to the effectively that disease is dependent on the development sources of novel drugs is traditional medicines nature has been a source of a medicines agent of thousands of year and impressive number modern drug has been isolated from nature resources traditional medicines is an important source of potentially useful area source of great economic value all over the world .plants with antimicrobial activity are also known to the numerous yet prior to a decade ago, minimal research had been conducted in the area of antifungal medicinal plants this review focus major method used to asses in vitro…

Abstract: However being rare in women, breast cancer can have a substantial morbidity and fatality rate. The goal of the current review is to ascertain whether breast cancer treatment and evaluation methods used on female patients are suitable for this type of cancer. In comparison to male breast cancer, female breast cancer differs biologically in that it is more common in some regions of Africa, has a higher frequency of oestrogen receptor positive, and has more aggressive clinical behavior. It reacts to chemotherapy and hormonal therapy, yet it is unknown what the best treatment plans are for female patients. Breast cancer in women is still a rare condition. The majority of what we currently know about its biology, natural history, and methods of therapy has been derived from research on its female counterpart.

Abstract: The main aim of this research is to examine the various aspects of cyber infrastructure, encompassing both hardware and software systems, networks, enterprise networks, intranets, and the occurrence of cyber intrusions. The paper intends to elucidate the significance of network intrusions and cyber-theft, along with delving into the factors contributing to the proliferation of cybercrime. Furthermore, it provides a comprehensive definition of cyber security and its role in addressing network intrusion and cyber theft, while also exploring the drivers behind the surge in cybercrime and their repercussions. The authors conclude by suggesting preventive measures and potential solutions to mitigate the risks associated with cyber attacks. They emphasize that while technological advancements can aid in minimizing the impact of cyber attacks, human behavior and psychological tendencies remain significant vulnerabilities. Despite this, the authors express optimism in reducing cyber attacks through investment in organizational education campaigns that address psychological…

Abstract: Cloud computing is now ubiquitous and provides convenient access to computing resources on demand. Cloud environments are complex and prone to faults, which can have a negative impact on service quality. Cloud providers must be able to detect issues in a proactive manner using unsupervised anomaly detection. This does not require labeled data. This paper presents a comparison of deep neural networks and support vector machine (SVMs), both used for unsupervised anomaly identification in cloud environments. On benchmark datasets provided by cloud providers, we evaluate the performance Autoencoders with LSTM models, One Class SVMs, and Isolation Forests. Our results show that shallow Autoencoders do not capture workload patterns well, but LSTMs or Convolutional Autoencoders can. SVMs are as good or better than Autoencoders. One-Class SVMs show robust performance in all workloads. Isolation Forests perform poorly on cloud data that is seasonal. One-Class SVMs are the most accurate and low latency option for anomaly detection. Our findings offer cloud providers guidance on how…

Abstract: Frauds in credit card transactions are common today as most of us are using the credit card payment methods more frequently. This is due to the advancement of Technology and increase in online transaction resulting in frauds causing huge financial loss. Therefore, there is need for effective methods to reduce the loss. In addition, fraudsters find ways to steal the credit card information of the user by sending fake SMS and calls, also through masquerading attack, phishing attack and so on. This paper aims in using the multiple algorithms of Machine learning such as support vector machine (SVM), k-nearest neighbor (Knn) and artificial neural network (ANN) in predicting the occurrence of the fraud. Further, we conduct a differentiation of the accomplished supervised machine learning and deep learning techniques to differentiate between fraud and non-fraud transactions.

Abstract: We are in the middle of a powerful era of cloud computing in IT. Rooted in the wide reach of the Internet, this technology provides a robust computing architecture that combines networked hardware, software, and Internet infrastructure. Its advantages go beyond traditional computing methods and go beyond grid computing and others. A key moment in the maturation of cloud computing occurred in 2006 when Amazon introduced the first cloud services. Hong Kong is particularly suitable for the deployment of cloud services due to its extensive daily data processing across various sectors.

Agriculture is experiencing a period of technological change, driven by the addition of intelligent technologies into agricultural technology. The integration of smart systems into farm machinery has greatly improved soil fertility management and crop productivity. Advanced technologies such as sensors, IoT, AI, and precision agriculture tools enable real-time monitoring of critical soil parameters, leading to targeted interventions for improving soil health. Automated machinery with GPS and AI-driven algorithms ensures efficient seed placement, precise fertilizer application, and weed management, thereby minimizing resource wastage and environmental impact. Such insights based on data allow farmers to take appropriate decisions based on changing conditions and improve farming practices sustainably, but their large-scale adaptation can be impeded due to high implementation costs, issues with privacy over the data, and expertise over technicalities. But even these challenges are seen in light of increasing yield, input costs reduced, and sustainability-promoting benefits, thereby…

Social and Emotional Learning (SEL) has emerged as a crucial component of education, equipping students with essential skills for personal and interpersonal success. This synthesis explores the interplay between digital tools and learning, with a focus on interactivity, emotional literacy, and the evolving nature of education. The integration of technology reshapes traditional educational paradigms by emphasizing performative and collaborative dimensions of learning. Theoretical frameworks such as social constructivism highlight the role of digital tools in fostering collective meaning-making and situated learning. Meanwhile, the transformative potential of technology in early childhood education underscores its dual role as both a challenge and a resource. Digital tools offer significant advantages in promoting SEL by providing interactive, accessible, and personalized learning experiences. The integration of AI and other advanced technologies further enhances the efficacy of these tools. Despite concerns about diminishing face-to-face interactions, intentional applications of…

This paper reviews recent advancements and applications of hydrological models for effective water resource management. Hydrological models have evolved significantly over the past four decades, enhancing their ability to simulate complex hydrological processes with improved spatial and temporal precision. Advances such as the integration of highresolution data, adaptive grid resolutions, and data assimilation techniques like the Ensemble Kalman Filter have improved model accuracy, especially in flood prediction and catchment-scale hydrological assessments. However, challenges persist, including high computational demands, data scarcity in certain regions, and limitations in modelling nonlinear flow dynamics in diverse environments. The review underscores the need for continued research, particularly in machine learning and remote sensing, to refine model capabilities. These advancements will be crucial for addressing the impacts of climate change on water resources and supporting sustainable watershed management strategies.

Abstract: Microbial Fuel Cells (MFCs) have emerged as a compelling technological advancement that utilizes microbial metabolic processes to produce electricity from organic waste substrates. This review examines the latest advancements, prevailing challenges, and prospective developments of MFCs in relation to renewable energy generation and ecological sustainability. MFCs present a dual benefit by facilitating wastewater treatment while concurrently generating bioelectricity, thereby rendering them appealing for decentralized energy frameworks and waste management strategies. Recent progress in the fields of genetic engineering and synthetic biology has culminated in the development of optimized microbial strains and improved biofilm stability, which significantly enhances the efficiency of electron transfer. Innovations in electrode materials, including carbon nanotubes and graphene, have further augmented the performance metrics of these systems. Nevertheless, obstacles persist in augmenting power output, minimizing material costs, and scaling MFCs for larger industrial…

Abstract: The landscape of drug discovery is undergoing a profound transformation driven by the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. This paper explores the impact of these advancements on various stages of the drug discovery process, from initial screening to clinical trials. Initially, AI and ML are revolutionizing the early stages of drug discovery by enhancing the efficiency of target identification and lead compound screening. Traditional methods, which often rely on labor-intensive processes and high costs, are being supplemented with AI-driven algorithms that analyze vast datasets to identify potential drug targets and predict the biological activity of compounds with unprecedented accuracy. In the drug design and optimization phase, ML models facilitate the prediction of drug interactions and side effects, thus accelerating the development of safer and more effective therapeutics. Advanced simulations and predictive models reduce the reliance on experimental trials, thereby streamlining the development pipeline. The clinical…

Abstract: Predictive Maintenance (PdM) stands as a revolutionary approach in industrial systems, effectively mitigating the limitations of conventional reactive maintenance strategies. In an era characterized by hyper-connectivity and data-centric operations, the incorporation of Artificial Intelligence (AI) has fundamentally transformed asset management practices. This transformation enhances operational efficiency and significantly reduces costly downtimes. This paper examines PdM, emphasizing its capacity to leverage AI in processing extensive data streams generated by sensors and equipment. Through precise failure predictions, this method enables proactive and cost-effective maintenance interventions, thereby optimizing system reliability and performance.

Abstract: This review explores notable advancements and methodologies in Natural Language Processing (NLP) and machine learning. Key innovations include convolutional neural networks capable of generating multiple language processing predictions and the Transformer-XL model, which enhances temporal coherence and dependency handling. The importance of logical reasoning and common sense in AI is emphasized, along with practical methods like web mining for building extensive knowledge repositories. The development of NLP tools for specific languages, such as Gujarati, and the utilization of BERT for bidirectional training are discussed, showcasing significant performance improvements across multiple tasks. Sentiment analysis is highlighted as a crucial tool for interpreting unstructured data from social media, achieving high accuracy through rule-based approaches and feature extraction methods. The role of social media in disseminating emotionally charged content and its impact on user behavior is examined, providing valuable insights for businesses to enhance marketing strategies.…

Abstract: This research delivers an Internet of Things-enabled smart farming system that leverages AI capabilities for better agricultural management. The system includes manual NPK value input in addition to a number of sensors, including as flame, DHT, and soil moisture sensors, for real-time monitoring of environmental conditions and soil health. The YOLO model and an image detection system with OpenCV are also used in the assessment of crop health. Crop recommendations are facilitated by a Random Forest classification system, which is based on sensor data and picture analysis results. The Raspberry Pi serves as the central processing unit, coordinating the processes of data collection, analysis, and decision-making. By providing farmers with practical information, the system aims to maximize output, minimize resource usage, and promote sustainable farming practices. Farmers can lessen risks such as diseases, pests, and other problems by keeping a close eye on things and analyzing bad weather patterns.

Abstract: Now a days Generative Artificial Intelligence is the buzz in the field of technology and science it is the implementation of the Artificial intelligence to generate different types of contents with the help of its models and ease the human life to a extend. Prompt Engineering is one of the arts of crafting instructions to guide large language models (LLMs), and has emerged as a critical technique in natural language processing (NLP). This systematic study delves into the intricacies of prompt engineering, exploring its techniques, evaluation methods, and applications. The study categorizes prompt engineering techniques into instruction-based, information-based, reformulation, and metaphorical prompts. It emphasizes the importance of evaluating prompt effectiveness using metrics like accuracy, fluency, and relevance. Additionally, the study investigates factors influencing prompt effectiveness, including prompt length, complexity, specificity, phrasing, vocabulary choice, framing, and context. The study highlights the impact of prompt engineering in enhancing LLM…

Abstract: Image classification is a very important issue in digital image analysis. CNN is type of Deep Neural Networks (DNN) that contains multiple layerssuch as Conv layers, integration layer and fully integrated layer. Convolutional neural networks (CNNs) are widely used in pattern-and picture- recognition problems as they have a number of advantages compared to others strategies. CIFAR-10is a very popular computer vision database. Thisdatabase is well read in it many types of in-depth study of object recognition. This database contains 60,000 images separated by 10 target classes, each a section containing 6000 images of 32 * 32 shapes. This database contains images of low-resolution (32 * 32), which allows researchers to experiment with new algorithms. Image classification is a fundamentaltask in computer vision that involves assigning a labelor category to an image. Convolutional Neural Networks (CNNs) have become the state-of-the-art method for image classification due to their ability toautomatically learn features from raw pixel values. Inthis project, we aim to build a CNN…

Abstract: Our research delves into the creation of a Virtual Fitness Trainer using Artificial Intelligence (AI) to offer personalized workout guidance and real-time feedback. This project harnesses advanced computer vision, machine learning, and natural language processing techniques to develop an intelligent system capable of accurately detecting human poses, tracking exercise repetitions, and providing corrective feedback. Utilizing Python's OpenCV library to capture live webcam feeds, processed by MediaPipe's BlazePose tool for precise pose estimation, our application employs a novel topology with 33 keypoints, enhancing the accuracy of body movement analysis. The frontend interface, designed with Flask, HTML, CSS, and Bootstrap, ensures an intuitive user experience, allowing selection from various exercises such as squats, curls, jumping jacks, push-ups, lateral raises, and pull-ups. Each exercise page offers detailed instructions and demonstration videos for correct technique execution. The system processes live video streams frame-by-frame, converting them into formats…

Abstract: Gold holds critical esteem because it is commonly utilized to back exchange exchanges. In nations such as India, China, Indonesia, and others, gold isn't as it were cherished as gems but is additionally traded as blessings or utilized in marriage settlements. Furthermore, nations with significant gold saves are regularly seen as financially affluent. Within the show period, gold rises above its conventional part as unimportant decorations and is recognized as a important speculation. Central banks around the world hold gold as collateral for outside credits and as a implies to control swelling. The worldwide economy's status essentially impacts gold costs, given the rising request and supply of this valuable metal. Thus, numerous financial specialists are turning to gold ventures, propelled by the vacillations in its advertise esteem. Be that as it may, the instability in gold costs postures dangers for financial specialists. Subsequently, the objective of "Gold Cost Prediction" is to utilize different machine learning procedures to figure gold costs. This includes…

Abstract: Currently, advancements in farm automation and technological techniques contribute significantly to boosting farm productivity, with a notable focus on cattle health monitoring systems in research. This system encompasses infrastructure, hardware, and software components aimed at enhancing cattle health, crucial for overall farm productivity. Given the challenges posed by managing numerous cattle in dairy farms, continuous monitoring of individual health becomes imperative. Leveraging sensor technology to monitor vital signs such as temperature and heart rate allows for early detection of health issues, reducing the need for frequent health inspections and long-term healthcare costs for the animals.

Abstract: The integration of artificial intelligence (AI) in organic farming has the potential to revolutionize sustainable agriculture practices. This paper explores the use of AI-powered solutions for sustainable organic farming. The study highlights the potential of AI in organic farming, including predictive analytics for pest and disease management, precision farming, and an integrated organic farming system. The review also emphasizes the importance of collaboration between the agricultural sector and AI developers to ensure that AI-driven solutions are accessible, affordable, and ethically implemented. The study concludes that by harnessing the power of AI, organic farmers can increase yields, reduce environmental impact, and meet the growing global demand for organic produce, paving the way for a more sustainable and food-secure future. Therefore, findings underscore the potential of AI to contribute to sustainable organic farming, marking a crucial step toward a technologically advanced and environmentally conscious agricultural future

The Newton-Raphson method is one of the most popular iterative techniques for solving equations of the form f(x)=0. It is widely used in numerical analysis due to its efficiency and rapid convergence properties when the initial guess is sufficiently close to the true root. This paper explores the underlying theory of the Newton-Raphson method, its mathematical formulation, convergence criteria, and practical applications. Additionally, we examine its advantages and limitations providing insight into the conditions necessary for its optimal performance.

Abstract: This article explores the transformative impact of Artificial Intelligence (AI) on education, focusing on its ability to personalize learning experiences and address longstanding educational challenges. It examines key areas where AI is making significant contributions, including adaptive learning systems, intelligent tutoring, accessibility improvements, and data-driven decision-making. The article discusses emerging trends such as augmented and virtual reality integration, emotionally intelligent AI systems, and lifelong learning support. Drawing on various studies and real-world implementations, it highlights the potential of AI to enhance student outcomes, support educators, and create more inclusive and efficient educational environments. While acknowledging the exciting possibilities, the article also addresses important considerations regarding privacy, ethics, and the evolving role of human educators in an AI-enhanced educational landscape.

Abstract: Intelligent Tutoring Systems (ITS) are a promising way to personalize and adapt learning. They use educational theory and artificial intelligence to give students individualized instruction and support. This study examines the practical aspects of ITS implementation, emphasizing important factors, difficulties, and recommended procedures. The paper begins by outlining the design concepts that guide the deployment of ITS, highlighting the significance of personalized learning, proactive participation, prompt feedback, and flexibility. The first section of the article outlines the design principles that direct the implementation of ITS, emphasizing the value of proactive engagement, timely feedback, individualized learning, and adaptability. The paper also discusses how to include instructional methodologies and domain-specific expertise into the design of ITS, emphasizing the need of matching pedagogical approaches with educational goals.

Abstract: Agriculture is a crucial profitable motorist. It's a key to healthy biosphere. People depend on a wide range of agrarian products in nearly all aspects of life. growers need to manage with climate change, and meet rising demands for further food of advanced food quality. In order to escalate the yield and growth of crops, the planter needs to be apprehensive of the climatic conditions, hence abetting its decision of growing the suitable crop, under those factors. IoT grounded Smart Farming improves the entire husbandry system by covering the field in real- time. It keeps colorful factors like moisture, temperature, soiletc. under check and gives a crystalclear real- time observation. Machine literacy in husbandry is used to ameliorate the productivity and quality of the crops in the husbandry sector. Use of applicable algorithms on the tasted data can help in recommendation of suitable crop.

Abstract: In the modern world, power is becoming more and more crucial for all tasks. Even with our existing array of renewable and nonrenewable power sources, we are still unable to meet our needs for electricity. The population of humans is one of these assets. For this project, jogging or walking is how we produce energy. Calorie burning occurs when you go up and down stairs. People can generate power with their weight when they walk on steps or platforms. The energy produced will be kept in a battery for subsequent use around the house. This technique can be applied in sites with a high pedestrian traffic volume, such as movie theaters, colleges, schools, and temples. Applying pressure causes the control mechanism's piezoelectric sensors to produce electrical energy. The energy produced by walking on the floor is pollution- and noise-free. You may use this technology's energy to charge other electrical devices, such as laptops and mobile phones.

Abstract: In light of the growing concern for air quality and its impact on public health, there is an increasing demand for air pollution monitoring systems that are both effective and efficient. This article introduces an IoT-based Air Pollution Detection System that utilizes the power of Internet of Things (IoT) technology to provide real-time monitoring and analysis of air quality parameters. The system incorporates a network of advanced air quality sensors that can measure particulate matter (PM) concentrations, as well as various gas levels such as carbon dioxide (CO2), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). These sensors are integrated with a microcontroller that enables data processing and communication capabilities.

Abstract: The paradigm shift in architectural pedagogy encompasses various facets, each contributing to a holistic transformation of the educational landscape. From computational design methodologies to generative algorithms, AI empowers students to explore a vast array of design possibilities, pushing the boundaries of creativity and problem-solving. The goal of this study is to understand the paradigm shifts in the constantly changing fields of architectural education with the incorporation of Artificial Intelligence (AI). This change transforms architectural teaching fundamentally in addition to redefining architectural practice approaches. Now that we are at the crossroads of technology innovation and human creativity, it is critical to investigate how artificial intelligence (AI) might transform architecture education and promote creativity, sustainability, and adaptation. The primary exploration reveals a significant gap in the current teaching methods, with little emphasis on AI. The study emphasizes the urgency of incorporating certain approaches into architectural education…

Abstract: The Internet of Things (IoT) technology integrated with healthcare has revolutionized the way we monitor and manage health. This research paper presents a comprehensive study and exploration of a Health Monitoring System (HMS) built upon using IoT technologies and principles. The HMS offers numerous advantages, which includes, reduced healthcare cost increased accessibility to healthcare services, enhanced patient outcomes, ultimately improving the quality of life. The core features of the HMS include a diverse array of IoT devices such as wearable sensors, smart medical equipment, and smartphones, which collect real-time health data. This data is then seamlessly transmitted, using advanced communication protocols, to centralized servers for analysis and interpretation. The system employs sophisticated data analytics and machine learning algorithms, providing healthcare professionals with invaluable insights into patient health. The technical architecture of the HMS is clarifying, illuminate on how these components synergic operate to create a robust system. However, we…

Abstract: This study investigated the collaborative role of AI and DevOps within the field of information technology and its significance for the United States. The research revealed that DevOps systems can pose significant challenges without the integration of artificial intelligence. AI and DevOps synergize to enhance efficiency in managing diverse tasks within the information technology domain. The exponential growth in data volume presents difficulties for DevOps teams in swiftly assimilating and addressing consumer issues. The automation trend has positioned DevOps as a vital component of information technology, facilitating efficient software delivery and faster market entry, resulting in more stable products. Furthermore, AIpowered technologies hold great potential for addressing critical issues such as national security. The research recommends organizations adopt AI-driven deployments within their DevOps environments. Given the limitations of computer processing capacity, artificial intelligence serves as a solution for storing, processing, and analyzing vast datasets. To…

Dyslexia, a prevalent learning difficulty affecting reading, writing, and spelling, requires specialized interventionsthat traditional educational systems often lack. This paper proposes an AI-assisted learning platform aimed at addressing the diverse needs of dyslexic students. The system first tests for dyslexia, classifying students into three zones—low, moderate, and high— based on severity. Using Natural language processing (NLP), Machine learning, speech-to-text (STT), and Text-to-speech (TTS) these are some of the AI technologies, the platform provides personalized learning practices tailored to each student's classification. Continuous assessment and real- time monitoring allow for dynamic adaptation of the learning process to accommodate individual progress, ensuring that interventions are both responsive and effective.

Purpose: This paper proposes structured frameworks for effective Human-AI collaboration within business processes. It aims to identify and model optimal task divisions where humans contribute oversight, creativity, and strategic judgment while AI provides computational power, automation, and analytical insights. Methodology: We explore collaboration models based on role-based division, process integration, and task adaptability. We analyze real-world business applications to demonstrate the efficacy of these models in improving productivity, decision-making, and innovation. Findings: We propose three key frameworks: (1) Augmented Creativity, where AI enhances human ideation, (2) Hybrid Decision Systems, where AI assists human judgment through predictive insights, and (3) Oversight-Driven Automation, where humans maintain control over automated tasks. Implications: The study highlights pathways for achieving synergistic Human-AI interactions to optimize business outcomes, enhance agility, and ensure ethical AI deployment

This article details a study that looked at the effects of 10%, 20%, and 30% fly ash substitution on the mechanical and durability qualities of concrete. Compressive, tensile, flexural, workability, heat of hydration, and water absorption tests were performed on the resulting mixtures after OPC was partially replaced with fly ash. The best compressive, tensile, and flexural strengths along with durability characteristics were found at the 20 % replacement. The workability increases with an increase in fly ash due to its lubricating effect, which decreased the heat of hydration up to 20 % replacement and then started to rise slightly at 30 % replacement. Water absorption is significantly reduced at 20% fly ash whereas increased at 30% as a result of excess porosity and voids. These observations indicate that 20% fly ash represents the optimum level of substitution in order to strike the balance between the performance under mechanical stress as well as durability, thus would be a sustainable replacement for the conventional cement

Blood group detection is a crusial aspect of medical diagnostics, widely used in blood transfusions, organ transplants, and prenatal care. Traditional blood typing methods require blood samples and reagents, which can be invasive, timeconsuming, and costly. This research proposes a novel, non-invasive approach to identify blood group using fingerprint analysis. By using Cutting-edge techniques for image processing , we analyse unique fingerprint patterns and identify specific features linked to blood types. Our methodology incorporates image enhancement, Extracting data attributes and employing learning algorithms to classify blood groups accurately. We are planning to optimize and check the performance of the model on a robust dataset, achieving promising correctness and performance in blood group prediction. This technique holds promise to redefine blood typing processes, providing a rapid, cost-effective, and accessible solution for various medical applications, particularly in remote or resource-limited areas. Further improvements in accuracy through larger datasets and advanced…

The proposed research presents an AI-enhanced CRM framework designed to improve customer segmentation, predictive accuracy, and personalized engagement. Leveraging advanced Recency-Frequency-Monetary (RFM) segmentation, KMeans clustering, and Gradient Boosting, the model achieves a predictive accuracy of 84.3%, outperforming conventional CRM approaches. Additionally, reinforcement learning enables real-time personalization, dynamically adapting to customer behaviors and enhancing engagement, retention, and satisfaction. Comparative analysis with existing literature underscores the model’s superiority in scalability, segmentation granularity, and predictive reliability. This framework offers a robust, data-driven solution for modern CRM, effectively addressing customer needs through tailored interactions.

This paper presents a comparative study of machine learning models namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Logistic Regression on two datasets: 20Newsgroups and Wine datasets. Based on basic accuracy, and precision, and recall of the models, F1 measures were assessed. When compared with other classifiers such as KNN and Decisions Trees, SVM and Logistic regression gave better results especially in the case of the 20Newsgroups dataset dominated by textual high dimensional data. KNN had poor recall results and Decision Tree was moderate. In the Wine dataset, since the structure of data is comparatively less complex in our context then all the models yielded almost similar results with accuracy and precision factors very close to 1.0 which of course manifested that the choice of the model does not affect much on simple data. These results stress the importance of the choice of the appropriate model for tasks with a certain level of data complexity; detailed models show the greatest efficiency at the accomplishment of complicated tasks, though…

Abstract: This scholarly review investigates contemporary advancements in the conversion of lignocellulosic biomass to biofuels, emphasizing innovative pretreatment and bioconversion technologies that aim to surmount the intrinsic challenges associated with the processing of this complex biomass. Lignocellulosic materials, which are predominantly comprised of cellulose, hemicellulose, and lignin, represent a renewable and plentiful source for biofuel production; however, their structural complexity necessitates sophisticated methodologies for the effective degradation of resistant components. The review scrutinizes a variety of pretreatment methodologies, encompassing physical, chemical, and burgeoning techniques such as plasma-assisted processing, which are engineered to augment cellulose accessibility for enzymatic hydrolysis. Additionally, it elucidates the progress made in bioconversion processes, concentrating on enzymatic hydrolysis, microbial fermentation, and consolidated bioprocessing (CBP), wherein recent endeavors in genetic engineering are refining microbial strains to…

Abstract: The landscape of performance management is undergoing a significant transformation, moving away from the traditional model of annual reviews toward a more dynamic and continuous feedback approach. This shift is driven by the need to adapt to the rapidly changing work environment, where agility, employee engagement, and real-time performance improvement are becoming increasingly critical. This chapter explores the evolution of performance management, examining the limitations of annual reviews and the growing demand for more frequent, constructive feedback mechanisms. Continuous feedback not only fosters a culture of ongoing development but also aligns employee objectives with organizational goals in realtime, enhancing overall productivity and job satisfaction. The discussion highlights the technological advancements, such as performance management software and AI-driven analytics, that are enabling this shift, making it easier for organizations to implement and sustain continuous feedback systems. Additionally, the chapter delves into the challenges and considerations…

Abstract: In the oil and gas sector, supervisory control and data acquisition (SCADA) systems have become a revolutionary force that are transforming operations throughout the whole value chain. This in-depth analysis looks at the development, design, and various uses of SCADA systems in the upstream, middle, and downstream domains. It projects that the global SCADA market for oil and gas will grow to $4.52 billion by 2026. Real-time well monitoring, production optimization, and remote operations are made possible by SCADA systems in upstream operations, which greatly increase output and efficiency. The goal of midstream applications is to increase pipeline efficiency and safety by using predictive maintenance, enhanced leak detection, and flow optimization. By streamlining quality assurance, energy management, and process control, SCADA systems can completely transform refinery operations. SCADA deployments are not without difficulties, though, including data management complications, cybersecurity threats, and legacy system interoperability. In addition to addressing these…

Abstract: This paper provides a comparative study of three popular microcontrollers: Arduino Uno, Raspberry Pi 4, and ESP32. Arduino Uno is known for its simplicity and low power consumption, making it ideal for educational and small-scale projects. Raspberry Pi 4 offers high computational power and extensive connectivity, suitable for IoT gateways, media centers, and industrial automation. ESP32 combines robust wireless connectivity with efficient processing, perfect for IoT applications, smart home devices, and wearable electronics. The study evaluates their technical specifications, performance, development environments, and use cases, helping developers choose the best microcontroller for their projects

Abstract: The thyroid is very important because it helps our body function properly. It does this by secreting special hormones into our bloodstream that control how our body uses energy. Sometimes there may be problems with the thyroid gland secreting too much or too little hormone. This is because not having enough iodine, swelling of the tumor, or the body's immune system attacking the tumor. Doctors can diagnose these problems with blood tests, but sometimes the results may be confusing. Using special technology, doctors can make information easier to understand and detect how a person may have thyroid problems. This analysis includes analysis of the distribution pattern of thyroid disease and data based on the UCI Machine Learning Repository. Detection of thyroid diseases involves machine learning, which plays an vital role in detecting thyroid disease. Using this technique,doctors can better understand and treat thyroid problems.

Abstract: This paper proposes an innovative AI-based mock interview platform designed to enhance interview preparedness by assessing candidates across three key dimensions: emotions, confidence, and knowledge. Utilizing deep learning convolutional neural networks, the system analyzes facial expressions to gauge emotional responses, while speech recognition and natural language processing evaluate the candidate's confidence levels. Additionally, semantic analysis and keyword mapping assess the candidate's knowledge by comparing responses with relevant online resources. This comprehensive approach aims to reduce pre-interview anxiety, boost confidence, and refine interview skills, providing a more effective preparation tool compared to traditional methods.

Abstract: Machines utilize Natural Language Processing (NLP), a subfield of Artificial Intelligence (AI), to comprehend, evaluate, and translate human languages. Due to advancements in information and communication technology over the last ten years, NLP has gained increased awareness and has been the subject of several studies. Understanding the steps required to advance literary understanding is so crucial. The study's objective is to provide an organized review of the literature in NLP research using bibliometric analysis. In the discipline of NLP, the study highlights important research fields, research clusters, cited publications, significant authors, institutions, nations, and publishing patterns. From the Web of Science (WoS) database, 12541 NLP papers were taken out and subjected to further bibliometric analysis The outcome showed that the largest number of NLP publications occurred in 2021, with the first publication being in 1989. The leading journal with the most publications was IEEE Access, and NLP papers have earned the most citations—3174—than any other journal. Liu…

Abstract: This research introduces an Emotion-based Music Recommendation System (EMRS) using Convolutional Neural Networks (CNNs) to analyze facial expressions and recommend music tailored to individual emotional states. Unlike traditional systems, EMRS prioritizes facial expression analysis for personalization. CNNs, trained on a diverse dataset of emotional expressions linked to music, extract key emotional features. EMRS leverages this analysis to intuitively suggest music that can potentially aid in emotional regulation. This research not only advances personalized music recommendation but also opens doors for emotion-aware technology with applications in mental healthcare for emotional imbalance and trauma. EMRS has the potential to serve as a complementary tool for therapists and individuals seeking emotional well-being through music

Abstract: This paper delves into the core algorithms and techniques employed in healthcare predictive analytics, including machine learning, statistical modeling, and data mining. We explore the multifaceted applications of this technology, encompassing improved patient stratification for risk assessment, targeted interventions for disease prevention, and optimized resource allocation for healthcare systems. However, the implementation of predictive analytics necessitates careful consideration of ethical issues surrounding data privacy and potential biases within algorithms. Regulatory frameworks may also require adaptation to ensure responsible use of this technology, this research emphasizes the transformative potential of predictive analytics in healthcare, paving the way for a future of proactive medicine and personalized care.

Abstract: The deadly illness known as pneumonia develops in the lungs and is brought on by a bacterial or viral infection. Pneumonia can be difficult and prone to error to diagnose in chest X-ray pictures due to its similarities to other lung infections. The purpose of this project is to create a computer-aided pneumonia detection system to speed up the process of making a diagnosis. As a result, an ensemble convolutional neural network (CNN) technique was presented for the automated diagnosis of pediatric pneumonia. In this case, the chest X-ray dataset was used to train seven well-known CNN models (VGG-16, VGG-19, ResNet-50, Inception-V3, Xception, MobileNet, and SqueezeNet) with the proper transfer learning and fine-tuning techniques after they had been pre-trained on the ImageNet dataset. The three best-performing models out of the seven were chosen for the ensemble approach. During the test, the ensemble method was used to combine the predictions made by CNN models to get the final findings. Furthermore, a CNN model was trained from the beginning, and its outcomes were…

Abstract: The rapid proliferation of unmanned aerial vehicles (UAVs)/ drones, has brought about significant advancements in various fields such as military operations, surveillance, agriculture, and logistics. However, the misuse of drones poses substantial risks to security, privacy, and safety. This comprehensive review explores the current state of counter-drone technologies, examining trends, challenges, and future directions. We delve into various detection, tracking, and mitigation techniques, including radar, radio frequency (RF) sensing, computer vision, and artificial intelligence (AI)-driven approaches. Furthermore, the paper highlights the integration of deep reinforcement learning (DRL) in enhancing the efficacy of counterdrone systems. Key issues such as legal and ethical considerations, technological limitations, and emerging threats have been discussed in detail. The review synthesizes findings from recent literature, offering a detailed analysis of the capabilities and constraints of existing counter-drone technologies. This work aims to provide a foundation for…

Abstract: The purpose of this study was to assess the impact of Artificial Intelligence (AI) on education. Premised on a narrative and framework for assessing AI identified from a preliminary analysis, the scope of the study was limited to the application and effects of AI in administration, instruction, and learning. A qualitative research approach, leveraging the use of literature review as a research design and approach was used and effectively facilitated the realization of the study purpose. Artificial intelligence is a field of study and the resulting innovations and developments that have culminated in computers, machines, and other artifacts having human-like intelligence characterized by cognitive abilities, learning, adaptability, and decision-making capabilities. The study ascertained that AI has extensively been adopted and used in education, particularly by education institutions, in different forms. AI initially took the form of computer and computer related technologies, transitioning to webbased and online intelligent education systems, and ultimately with the use of…

Abstract: The potential of filamentous fungi and their enzymes are investigated for bioremediating pharmaceutical pollutants from wastewater thereby addressing the growing concern of emerging pollutants in water resources. Among the various microorganisms, the current abstract discusses the role of fungi in degrading antibiotics and persistent pharmaceutical compounds. These fungi allow the biodegradation via the formation of highly oxidative hydroxyl radicals. It summarizes the role of both intra (cytochrome P450) and extracellular (laccase) enzyme systems for the bioremediation of emerging clinical pollutants. It emphasizes the importance of both biosorption and biodegradation for effective pollutant removal by breaking down pharmaceuticals (e.g. antibiotics) and other hazardous compounds (e.g. endocrine-disrupting compounds, polycyclic aromatic hydrocarbons). Although the enzymes CYP450 are not directly engaged, their metabolic pathways execute mechanisms such as oxidation, the formation of reactive intermediates, and chemical modification, all of which result in the degradation…

Abstract: In the era of big data and privacy concerns, federated learning has emerged as a promising approach to training machine learning models while preserving data privacy. This paper explores the principles and applications of federated learning, highlighting its potential to revolutionize privacy-centric AI. We discuss the methodology, significance, and challenges of federated learning, providing insights into its future directions. By leveraging decentralized data and aggregating model updates, federated learning enables the development of powerful AI models without compromising individual privacy. We present real-world applications and cite relevant studies to demonstrate the transformative impact of federated learning across various domains.

Abstract: This research explores how social media influences investment decisions among Generation X, millennials, and Generation Z, known for their heavy use of digital technology. Using quantitative methods and surveys, the study examines individuals from different age groups on social media platforms. Results show a significant link between social media engagement and investment choices across all generations. Investment firms are urged to use social media for marketing and communication to reach a wider audience. The study emphasizes the need for financial literacy across generations in an era where social media plays a crucial role in financial decision-making. It advocates for tailored strategies to engage each demographic while promoting universal financial literacy.

Abstract: The "Stock Price Prediction Using Machine Learning" project aims to develop an advanced predictive model for forecasting stock prices in financial markets. The volatility and complexity of stock markets make accurate predictions challenging, and the utilization of machine learning techniques offers a promising approach to address this challenge. This project leverages historical stock data, technical indicators, and sentiment analysis to create a robust predictive model. The methodology involves collecting and preprocessing a vast dataset of historical stock prices and relevant financial indicators. Various machine learning algorithms, including but not limited to linear regression, decision trees, support vector machines, and neural networks, are employed to analyze patterns and relationships within the data. The project focuses on model evaluation and comparison to identify the most accurate and reliable prediction model. Performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and accuracy are utilized to assess the effectiveness of the…

Abstract: Security cameras and video surveillance cameras have become an important partof public safety. However, in many cities, thesesystems still manually detect high-risk situations. Understaffing in security services can lead to delays in detecting incidents or unforeseen threats, putting the public at risk. The aim of this project is to develop a low-cost, effective intelligencebased solution for real-time weapons detection and surveillance video analysis in different situations. As can be seen from many statistics, the incidence of gun and dangerous weapon crimes is increasing every year, making it difficult for the police to solve the problem in time. Crimes caused by guns or knives are very common in many places, especially in places where gun laws do not exist. Early detection of crime is critical to public safety. One way to prevent these situations is to use video surveillance to detect the presence of dangerous weapons such as guns and knives. Monitoring and control now also require monitoring and intervention. Here, we present in video a system for tracking weapons…

Abstract: As the technology is progressing, internet is growing day by day and it has became more vulnerable than ever. To prevent data from being tampered or viewed by third parties, cryptography is used. Cryptography has it’s roots from ancient egypt, where secret writings are developed to transfer messages. Now, modern cryptography is used to secure data over the network, which are classified into symmetric and asymmetric cryptography. Both cryptographic methods has it’s own limitations and advantages, which are further used to develop algorithms for better security. More and more efficient algorithms are developed which are discussed in this paper

Abstract: Soil plays an essential role in the construction of any civil engineering structure. It must be able to withstand the loads without breaking down. In some areas, the soil may not be strong enough to withstand the incoming loads. To address this, there are many methods of soil stabilization available, but some of these methods, such as chemical stabilization, lime stabilization, etc., can have an adverse effect on the soil's chemical composition. In this paper, we will be looking at the effects of fly ash on the strength gain of an expansive clay soil when combined with different percentages of fly ash, such as 0%, 5%, 10%, 15%, 20%, and 25%. We will also be looking at tests to determine the strength gain of the soil with and without fly ash. The results of the laboratory study will show that adding fly ash to the expansive clay soil reduces the soil's plasticity index and increases its MDD. The expansive clayey soil's strength characteristics have improved.

Abstract: Phishing attacks pose a significant threat in cyberspace, Cybercrimes such as Phishing means accessing personal data and violating security through the Internet and its main aim is to steal the information from the users using different techniques in that the primary one is Phishing which demands effective detection mechanisms. This study evaluates the performance of Gradient Boosting Classifier, Random Forest, and Decision Tree machine learning models in conjunction with feature selection techniques namely SelectKBest and Chi-Square. Initially, a comprehensive feature set of 30 attributes achieved a baseline accuracy of 97.4%. Through SelectKBest and Chi-Square feature selection methods, 13 key features were identified, leading to a slightly decreased accuracy of 95.6% upon model retraining. This research highlights the importance of feature selection in enhancing phishing detection accuracy while maintaining model interpretability. Technologies such as NumPy, Pandas, Matplotlib, Scikit-learn, and Flask drive. The project, emphasising the exploration of ML models, EDA…

bstract: Diabetes, a chronic metabolic disorder affecting millions worldwide, requires early detection and management to mitigate its complications. Machine learning (ML) techniques have emerged as promising tools for predictive analytics in healthcare, offering the potential to improve diagnostic accuracy and patient outcomes. This paper presents a comprehensive review of ML algorithms applied to diabetes prediction, encompassing diverse methodologies and datasets. The study evaluates the performance of various ML algorithms, including but not limited to logistic regression, decision trees, support vector machines, random forests, and deep learning approaches, in predicting the onset or progression of diabetes. Additionally, feature selection techniques and data pre-processing methods are explored to enhance model robustness and interpretability. Furthermore, this review highlights the significance of dataset characteristics such as size, imbalance, and feature diversity in influencing model performance. Challenges associated with model interpretability, scalability, and deployment…

Abstract: The increasing incidence of vehicle theft poses a significant challenge to vehicle owners and law enforcement agencies worldwide. This paper presents a comprehensive solution for addressing this issue through the integration of Global System for Mobile Communications (GSM) and Global Positioning System (GPS) technologies. The proposed system aims to enhance the security of vehicles by providing real-time theft detection and efficient tracking capabilities. The GSM module facilitates communication between the vehicle and a centralized monitoring system, allowing forseamless data transmission. The system leverages the widespread coverage and reliability of GSM networks to ensure that alerts are promptly delivered to relevant stakeholders, irrespective of the vehicle's location. Furthermore, the GPS technology enables real-time tracking of the stolen vehicle, assisting law enforcement agencies in swiftly responding to the incident. The system's tracking feature provides continuous updates on the vehicle's location, allowing for efficient recovery operations. Additionally, the…

Augmented Reality (AR) stands at the forefront of technological innovation, poised to revolutionize the landscape of education.With its ability to overlay digital content onto the physical world, AR offers unprecedented opportunities to enhance learning experiences and transform traditional pedagogical approaches.This introduction aims to elucidate the significance of AR in education, emphasizing its potential to revolutionize teaching and learning paradigms.Education is undergoing a profound transformation, driven by rapid advancements in technology and changing learning preferences of students.Traditional methods of instruction are increasingly being supplemented, if not replaced, by digital tools and immersive technologies that cater to the needs of 21st-century learners.Augmented Reality represents one such transformative technology, offering educators a powerful tool to engage students, foster deeper understanding, and facilitate experiential learning.The significance of AR in education lies in its ability to bridge the gap between abstract concepts and real-world…

Abstract: The goal of Project SPACE is to create an automated solar panel cleaner that will address the adverse impact of soiling on commercial photovoltaic cells. Specifically, we hoped to create a device that increases the maximum power output of a soiled panel by 10% (recovering the amount of power lost) while still costing under $500 and operating for up to 7.0 years. A successful design should operate without the use of water.

Abstract: This research paper explores the transformative impact of artificial intelligence (AI) in the field of mental health counselling, aiming to enhance the effectiveness and accessibility of support services. As the demand for mental health care continues to rise globally, there is a growing need for innovative solutions to bridge the gap between the increasing demand for counselling and the limited availability of human counsellors. Our study focuses on the integration of AI technologies to assist mental health counsellors in various aspects of their practice. Through an extensive review of existing literature, we analyse the potential benefits and challenges associated with implementing AI in counselling settings. The paper highlights AI's ability to augment traditional counselling approaches by offering timely and personalised interventions, improving the overall efficiency of therapeutic processes. Drawing on case studies and pilot programs, this research presents empirical evidence supporting the positive outcomes and user acceptance of AI-assisted counselling…

Abstract: The study examines the concerning increase in heart disease cases and emphasizes the importance of proactive forecasting. The timely and accurate detection of this growing health issue is essential. The main aim of the study is to determine whether a patient is at the risk of heart disease or not based on the dataset having the medical history of the patient. There are a number of linear and non-linear machine learning algorithms including Logistic Regression, K-nearest Neighbor, State Vector Machine and Random Forest Classifier which are used to determine the likelihood of having cardiovascular problems in the patient. The implementation of the algorithms such as Logistic Regression and Random Forest Classifier has shown the great results by providing a higher accuracy in predicting CVDs rather than the traditionally used methods such as Naive Bayes. The proposed method is not only cost effective but also is easier to use. It just requires some basic information of the patient and based on the feeded medical history of the patient , the results are produced. It improves…

Firefighting is an inherently dangerous profession, placing human lives at high risk. With advancements in robotics, firefighting robots are becoming viable alternatives for operating in hazardous conditions. This paper explores the design, components, and technologies that power firefighting robots, such as sensors, AI-driven navigation, and suppression systems. It also discusses real-world applications, case studies, the economic and environmental impacts of robotic firefighting solutions, and the challenges in their adoption. The study concludes with insights into future technological advancements and their potential to revolutionize fire management

Antibiotic resistance (AMR) is a growing global health crisis that poses a significant threat to public health, economies, and healthcare systems worldwide. The emergence of resistant bacteria, fungi, and viruses has been driven by the overuse and misuse of antibiotics in human medicine, agriculture, and animal farming. AMR reduces the effectiveness of treatment options, leading to longer hospital stays, increased medical costs, and higher mortality rates. Furthermore, infections caused by resistant pathogens are becoming harder to treat, complicating efforts to combat common diseases such as pneumonia, tuberculosis, and urinary tract infections. The global impact of antibiotic resistance is not limited to individual countries; it is a cross-border issue that requires international cooperation and a multifaceted approach, involving surveillance, regulation, and public awareness campaigns. Low- and middle-income countries, where access to antibiotics is often unregulated, face the highest risks. In addition to human health, the economic toll of AMR is severe, with global estimates…

Red signals often make ambulances face huge delays at traffic intersections, thus impeding urgent response and patient care. This paper presents a real-time, autonomous traffic control system that applies a deep learning-based object detection approach to identify ambulances stuck in traffic. The proposed system takes the YOLOv8 model in a close system collaboration with Python and Ultralytics to detect emergency vehicles accurately. In case an ambulance is seen on a busy lane, the system automatically changes the signal on that lane from red to green and clears the path for it while also turning the signals on other lanes to red to avoid further congestion. The implementation of the project utilised OpenCV, NumPy, and other Python libraries like PyAudio, Plyer, Tkinter, Matplotlib, PushBullet for data processing, alert management, and system interface. Roboflow was used to curate and annotate the training dataset to ensure high-quality input to the model. Experimental results show that the system is capable of reducing a considerable amount of delay caused by intersections for…

The “Human Anomaly Detection System” seeks to augment real-time surveillance by pinpointing atypical human activities via a hybrid deep learning strategy that merges RGB video frame analysis with pose estimation. By employing multistream neural networks such as YOLO for object detection and MobileLSTM for the classification of temporal actions this system utilizes attention mechanisms to uncover subtle anomaliesin human behavior. The limitations of traditional surveillance methods, which usually rely on manual monitoring or rule- based frameworks that are prone to errors, are addressed by this research. Public safety, healthcare, and educational institu- tions can all benefit from the system’s notable improvements in recognizing complex, non-rigid human behaviors, despite the difficulties in combining spatial and temporal data. This technology is positioned as a powerful solution for anomaly identification in dynamic contexts since evaluation results showa high degree of accuracy and dependability. Future work will focus on improving model modularity and scalability to enable wider…

Abstract: This paper introduces CU-Events, a comprehensive event management system tailored to the unique needs of university campuses. CU-Events centralizes event organization, simplifies registration, and offers real-time participation tracking, addressing longstanding challenges in university event management. By leveraging a three-layer architecture— comprising a user-friendly interface, robust backend, and efficient database structure—CU-Events significantly enhances user engagement and administrative efficiency. This study highlights the system’s architecture, compares CU-Events with traditional methods, and presents projected improvements in user participation, feedback accuracy, and operational cost savings. Our analysis demonstrates CU-Events’ potential to streamline event processes and create a cohesive, interactive campus environment.