The 100 most-cited articles in International Journal for Research in Applied Science and Engineering Technology, 2023

Abstract: We describe approaches that are essential for fine-tuning a language model further utilized for text categorization and propose Universal Language Model Fine-tuning (ULMFiT), an efficient transfer learning method that can be used for any NLP activity. Transfer learning methods have greatly impacted computer vision, but existing approaches in NLP still require taskspecific modifications. Universal Language Model Fine-tuning, or ULMFiT, is an architecture and transfer learning method that can be applied to NLP tasks. It involves a 3-layer architecture. Three steps make up the training process: pre-training for the general language model on a text taken from Wikipedia; fine-tuning the language model on a target task; and fine-tuning the classifier on the target task. Deep learning techniques enable computers to learn and comprehend natural language, facilitating human-machine interaction. Deep learning models are often employed in medical research, from medication candidate identification to picture analysis. On text classification tasks, our method greatly surpasses the…

Abstract: This study develops a newly designed, patented, bidirectional DC/DC converter (BDC) that interfaces a main energy storage (ES1), an auxiliary energy storage (ES2), and a DC-bus of different voltage levels, for application in hybrid electric vehicle systems. The proposed converter can operate in a step-up mode (i.e., low-voltage dual-source-powering mode) and a stepdown (i.e., high-voltage dc-link energy-regenerating mode), both with bidirectional power flow control. In addition, the model can independently control power flow between any two low-voltage sources (i.e., low-voltage dual-source buck/boost mode). Herein, the circuit configuration, operation, steady-state analysis, and closed-loop control of the proposed BDC are discussed according to its three modes of power transfer.

Abstract: This study explores the role of artificial intelligence (AI) in enhancing sustainable supply chain management (SSCM). As environmental awareness grows, the importance of sustainable, efficient, and socially responsible supply chains is rising, necessitating the integration of AI to meet these goals. AI's potential to bolster supply chain operations' transparency and sustainability is analyzed in detail. Employing a combination of qualitative and quantitative methods, we evaluate the various applications of AI in SSCM. These range from demand forecasting with predictive analytics, to carbon reduction through intelligent automation, real-time tracking for improved transparency, and supplier assessment via machine learning based on sustainability parameters. Our findings suggest AI's effectiveness in enhancing visibility across the supply chain, promoting ethical and sustainable practices, and optimizing resource use to reduce waste. However, the research also acknowledges obstacles to AI adoption, such as high costs, data privacy concerns, and skill gaps, while proposing…

Abstract: Nowadays, traditional markets are gradually are being replaced by many online marketplaces. Fierce competition in the online market demands excellent service from both sellers and buyers, so many online stores offer his 24-hour service. This service would certainly cost a lot of money if done manually. Chat bots can be used as a solution for automated shopping online. Second, the bot must be able to answer accurately and quickly. In this study, we propose an intelligent chatbot system based on AIML (Artificial Intelligence Markup Language) that can be used as an e-commerce assistant. Input questions from users are processed in her three phases: parsing, pattern matching, and AIML crawling the data. In the AIML process, user requests fall into his three categories: general questions, calculations, and inventory checks. Where compute requests intercept the order and payment process

Abstract: Water quality is a critical aspect of environmental health and human well-being. pH, as a fundamental parameter, plays a significant role in determining the chemical characteristics of water. This experimental study aims to investigate the impact of pH levels on water quality. The research involved collecting water samples from various sources and subjecting them to controlled pH variations in a laboratory setting. Multiple parameters, including dissolved oxygen, nutrient availability, microbial activity, heavy metal contamination, algal blooms, turbidity, and ecological health, were assessed to evaluate the effects of pH on water quality. The results revealed that pH fluctuations had a substantial influence on these parameters, indicating a direct relationship between pH levels and water quality. The study findings contribute to the understanding of the complex interactions between pH and water quality, providing valuable insights for water resource management and environmental conservation efforts. Further research is warranted to explore additional factors that may…

Abstract: This paper delves into the multifaceted role of Artificial Intelligence (AI) in inventory management, encompassing its applications, challenges, and future opportunities. AI's integration into inventory management systems has revolutionized supply chain operations, enhancing efficiency, accuracy, and decision-making processes. The paper explores various AI applications, including demand forecasting, stock optimization, and automated reordering. However, it also acknowledges the challenges in AI implementation, such as data quality, interpretability, and model transparency. The research highlights the synergy between AI and emerging technologies like the Internet of Things (IoT), pointing towards new innovative solutions that were unimaginable in the past. In conclusion, the paper presents a balanced view of AI's transformative impact on inventory management, emphasizing both its current benefits and the hurdles that need to be overcome for its successful integration. Furthermore, this comprehensive analysis not only provides insights into the current state of AI in…

Abstract: This research paper introduces a robust real-time object detection system, leveraging the cutting-edge capabilities of the YOLOv4 (You Only Look Once) deep learning framework.The integration of this system with a webcam in the Google Colab environment forms a novel and accessible platform for live object detection. The paper meticulously details the methodology behind theintegration, providing comprehensive insightsinto the intricatesteps taken to achieve seamless real-time detection. Key aspects covered include the configuration and acquisition of YOLOv4 model weights, setup of the Google Colab environment, and the challenges encountered in optimizing and streaming webcam feeds in this unique computational setting. Implementation details are articulated through Python code snippets, offering a practical guide for researchers and practitioners interested in replicating or extending the system. The code encompasses the initialization of YOLOv4, handling real-time webcam feeds, and harnessing GPU acceleration within the Google Colab framework to ensure optimalperformance.…

Abstract: The digital revolution has irreversibly transformed the healthcare landscape, revolutionizing the way health data is managed and raising critical questions about its protection. The seamless integration of technology has propelled healthcare into a new era of interconnectedness and data-driven innovation, presenting both unprecedented opportunities and challenges. In this context, this paper conducts a comprehensive comparative study of two pivotal regulatory frameworks: the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). These regulations, enacted on different continents, serve as cornerstones in the protection of health data and offer profound insights into the evolving landscape of data privacy and security in the digital age.The surge in digital health platforms, electronic medical records, and data-sharing networks has ushered in an era where personal health information is more vulnerable and valuable than ever before. The GDPR, a landmark legislation within the European Union, empowers individuals with…

Abstract: Large Language Models (LLMs) have become effective tools for natural language process-ing and have been used in many different fields. This essay offers a succinct summary of various LLM subcategories. The survey emphasizes recent developments and efforts made for various LLM kinds, including task-based financial LLMs, multilingual language LLMs, biomedical and clinical LLMs, vision language LLMs, and code language models. The survey gives a general summary of the methods, attributes, datasets, transformer models, and comparison metrics applied in each category of LLMs. Furthermore, it highlights unresolved problems in the field of developing chatbots and virtual assistants, such as boosting natural language processing, enhancing chatbot intelligence, and resolving moral and legal dilemmas. The purpose of this study is to provide readers, developers, academics, and users interested in LLM-based chatbots and virtual intelligent assistant technologies with use full information and future directions.

Abstract: Early detection of melanoma, the most dangerous form of skin cancer, is crucial for effective treatment. Melanoma has a higher propensity to metastasize to other parts of the body if not identified and treated promptly. In recent years, non-invasive medical computer vision and medical image processing techniques have emerged as valuable tools in clinical diagnosis for various diseases, including melanoma. These techniques enable automatic image analysis, facilitating accurate and efficient evaluation of skin lesions. The study follows a systematic approach, involving the collection of a dermo image database and preprocessing steps such as segmentation using thresholding. Statistical feature extraction methods, including gray level cooccurrence matrix (GLCM), glam asymmetry, border analysis, color assessment, and diameter calculation, are utilized. Principal component analysis (PCA) is employed for feature selection to reduce dimensionality. A total dermo copy score is calculated, followed by classification using a convolutional neural network (CNN). The results indicate an…

Abstract: This project proposes designing and implementing an LPG gas leakage detection and alert system for households and industries. The increase in deaths caused by LPG explosions has highlighted the need for a gas leak detection system to identify potential hazards. Various gas sensors are used in this process, including wireless and GSM technology. The gas sensor detects the presence of LPG gas in the environment, and the microcontroller processes the sensor data to trigger an alarm or shut off the gas supply. The system can also send an SMS alert to a designated phone number in case of gas leakage. The proposed system is intended to prevent accidents, fires, and explosions by providing an early warning and taking necessary actions to minimize the damage caused by gas leakage. The proposed LPG Gas Leak Detection Smart Tool effectively addresses the growing concern about LPG explosions and their catastrophic consequences.

Abstract: Basalt fiber is a type of mineral fiber that is derived from basalt rock. It has excellent mechanical properties, high resistance to chemicals, superior thermal stability, and good resistance to moisture. Additionally, it is environmentally friendly as it can be easily recycled. All these properties are better when compared to E-glass, which uses many industries for structural applications. The use of basalt fiber reinforced composites (FRC) in industries is still limited because there isn't enough information available about them and their production is not yet at a large scale. This makes them more expensive to produce, which reduces their adoption in industrial applications. This paper critically evaluates the current knowledge and understanding of basalt FRC and highlights the growing interest in researching and publishing articles related to basalt composites. In addition to reviewing the current state of knowledge on basalt fiber reinforced composites, this paper also shares information about the physical, chemical, and mechanical properties of basalt fibers. It also…

Abstract: Automatic Street Light Control System is a simple yet powerful concept, which uses transistor as a switch. By using this system manual works are 100% removed. It automatically switches ON lights when the sunlight goes below the visible region of our eyes. This is done by a sensor called infrared sensor (IR) which senses the light actually like our eyes. It automatically switches OFF lights whenever the sunlight comes, visible to our eyes. By using this system energy consumption is also reduced because nowadays the manually operated street lights are not switched off even the sunlight comes and also switched on earlier before sunset. In this project, no need of manual operation like ON time and OFF time setting. This project clearly demonstrates the working of transistor in saturation region and cut-off region.

Abstract: As a binder for infrastructure development, conventional Portland cement is currently the primary material that is used. However, cement production has a substantial impact on the environment, and many pozzolanic materials can reduce their carbon footprint with conventional Portland cement. In this experimental study, replacement of OPC (ordinary portland cement) is replaced with the ash produced from rice husk (An agricultural waste) and analyzed for durability and strength performance in concrete samples and mortar as well as chemical composition analysis and microstructure observation of rice hull ash according to X-ray differential test data were also conducted in the research analysis. it is produced by burning of husk (produced by rice barley waste) of oxygen for the production of rice husk ash. Its chemical composition is rich in silicates and aluminates which promotes the binding property of ordinary Portland cement and does not affect the IST(Initial setting time), FST(Final setting time) and does not increase the fineness of the cement particles hence it gives…

Abstract: Cassava (Manihot esculenta) is a crucial food crop sustaining millions of people worldwide. However, the presence of diseases in cassava plants poses a significant threat to agricultural productivity. Traditional methods of disease detection are often labor-intensive, subjective, and prone to human error. To address these challenges, this research focuses on developing an automated system for cassava plant disease detection using machine learning. After the algorithms have been trained on the dataset, the accuracy of the algorithms is compared, the photos are categorised, and preventions for unhealthy plants are proposed. Apart from detection, this aims to support and help the greenhouse farmers in an efficient way. Plant disease identification by visual way is more laborious task and at the same time, less accurate and can be done only in limited areas. Whereas if automatic detection technique is used it will take less efforts, less time and become more accurate.

Abstract: Crack formation is very common phenomenon in concrete structure which allows the water and different type of chemical into the concrete through the cracks and decreases their durability, strength and which also affect the reinforcement when it comes in contact with water, CO2 and other chemicals. For repairing the cracks developed in the concrete, it requires regular maintenance and special type of treatment which will be very expensive. So, to overcome from this problem autonomous self-healing mechanism is introduced in the concrete which helps to repair the cracks by producing calcium carbonate crystals which block the micro cracks and pores in the concrete. The selection of the bacteria was according to their survival in the alkaline environment. The condition of growth is different for different types of bacteria. For the growth, bacteria were put in a medium containing different chemical at a particular temperature and for a particular time period. Bacteria improves the structural properties such as tensile strength, water permeability, durability and compressive…

Abstract: This paper explores the benefits, challenges, and real-world applications of automated test-case generation and bug identification using generative artificial intelligence (AI). In today's software development and testing landscape, ensuring code quality and minimizing bugs are crucial. However, manual testing methods can be time-consuming and error-prone. Generative AI algorithms, such as generative models, can automatically generate test cases based on inputs, specifications, or system behavior. These algorithms employ machine learning techniques to analyze codebases, uncover test scenarios, and produce comprehensive test cases with broad coverage. The advantages of automated test-case generation include amplified test coverage, supercharged efficiency and time savings, and seamless scalability. Generative AI models also excel in bug identification by scrutinizing codebases, execution traces, and test results. They can detect coding mistakes and identify anomalous patterns indicating potential bugs, memory leaks, or security vulnerabilities. However, challenges such as…

Abstract: This study provided a meta-synthesis of Filipino teachers' experiences with inclusive education. It used 15 of the 214 preliminary researches and was filtered using a set of selection and exclusion criteria. Furthermore, the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) flow diagram was used to arrange the chosen research papers. In six steps by Clark and Braune, constant comparison and thematic analysis yielded eight (8) themes and two (2) meta-themes. According to the findings, 21st-century teachers are progressing in adhering to continuing professional education, adopting inclusion policies, and incorporating instructional innovations and activities to promote inclusivity. Teachers experienced recurring obstacles such as insufficient preparedness and expertise for special education, a lack of educational services, and general social contexts that substantially impact teacher teaching. However, professionalism and perseverance in adjusting inclusion justify the teacher's responsibility to sustain resilience and inventiveness for their…

Abstract: This paper delves into the intricate process of integrating Artificial Intelligence (AI) into legacy inventory systems, a critical challenge in the realm of modern inventory management. It presents a comprehensive analysis, exploring the multifaceted barriers encountered in this integration, particularly in traditional industries. The study identifies and examines key technical, organizational, and financial challenges, offering a nuanced understanding of the complexities involved. Innovative solutions and strategies are proposed to address these challenges, drawing on a rich array of existing literature and real-world case studies. The paper highlights successful integrations of AI in various sectors, extracting valuable lessons and best practices. It contributes significantly to the existing body of knowledge by bridging theoretical research with practical applications, providing insights that are both profound and actionable. This research not only illuminates the path forward for traditional industries seeking to embrace AI in inventory management but also serves as a…

The landscape of global supply chains is undergoing a transformation characterized by increasing complexity and the need for agile response to demand fluctuations. At the forefront of addressing these challenges is the strategic alignment of inventory levels with volatile demand, a task that has become more sophisticated within the Just-In-Time (JIT) inventory management paradigm. This paper explores the integration of Artificial Intelligence (AI) into JIT systems, with a focus on enhancing the accuracy of demand forecasting—a critical component for inventory optimization. We present a groundbreaking hybrid AI model that combines the forward-thinking capabilities of neural networks with the reliability of classical statistical forecasting methods. This innovative approach seeks to elevate forecasting reliability and reduce errors that lead to either inventory shortfalls or surpluses. Through a series of case studies spanning various industries, the versatility and effectiveness of the hybrid model are demonstrated, highlighting improvements in forecasting accuracy and the resulting…

Abstract: This research paper scrutinizes and explores the substantial impact of Artificial Intelligence (AI) and Generative AI on the media and film industry. It delves into the continuously evolving applications of AI algorithms and advanced models, emphasizing their profound implications for content creation, production workflows, and distribution strategies. The paper offers comprehensive insights into the operational mechanics of key AI models, underscoring their direct relevance within the domain of media and film. This inquiry provides a timeless and academic perspective on the transformative influence of AI and Generative AI in these industries, facilitating a deeper understanding of their applications and implications

Abstract: In recent years, Precision Agriculture has embarked a promising role in augmenting agricultural production and various farm mechanization. The present research was conducted to design and develop a Precision Spraying System (PSS) that is controlled by an Automatic Control System (ACS). PSS was equipped with micro-controller (NodeMCU), an analog pressure regulator, solenoid valve, diaphragm pump coupled with 12V DC motor; operated on a Iot platform BLYNK. A 3D printed full cone nozzle (0.2 mm orifice dia., cone angle of 95°) was designed to achieve desired spraying efficacy as per the canopy area and the need of the crop cultivated. Integration of PSS with ACS, intrigues a proper regulation on the valves (24V, 7.92W), the pump speed, pressure head and flow rates. The spray patternator test was conducted to evaluate the Delay Time Losses (DTL), application losses, effective spraying height and pressure. The test was conducted for 4 sets of increasing pressure (5.46, 6.24, 6.86 and 7.80 bar) and 3 sets of decreasing nozzle heights (890, 760 and 620 mm). The results revealed…

Abstract: Artificial intelligence has been used in many different fields throughout its development, especially in recent years as the amount of data available has increased. Its major objective is to aid individuals in making more reliable decisions more quickly. Machine learning and artificial intelligence are being used more and more in medicine. This is particularly true in the medical field where a large number of digital files must be collected and processed to apply a variety of biomedical imaging and diagnostic techniques. Machine learning is used to analyse medical images, which helps with consistency and increases reporting accuracy. To process chest X-ray data and assist in diagnosis, this study compares transfer learning-based deep neural networks. The study focuses on comparing and evaluating deep learning methods based on convolutional neural networks for the identification of pneumonia. To establish the model that would accurately diagnose pneumonia, the researchers developed several different models. Five distinct CNN models, including VGG19, VGG16, ResNet50,…

Abstract: This paper presents a deep learning-based approach for the detection and classification of bone abnormalities using the Vgg16 algorithm. Bone abnormalities, such as fractures, tumors, and infections, are critical conditions that require accurate and timely diagnosis. Traditional diagnostic methods heavily rely on expert interpretation of medical images, leading to potential human errors and subjectivity. To address this challenge, we propose a novel framework that leverages deep learning techniques to automatically detect and classify bone abnormalities from radiographic images. Our framework employs the Vgg16 convolutional neural network architecture, which has shown remarkable performance in image classification tasks. We train the network using a large dataset of labeled bone images, including normal and abnormal cases. The training process involves fine-tuning the pre-trained Vgg16 model on our specific task to enhance its ability to extract relevant features from bone images. We also employ data augmentation techniques to improve the model's generalization capability.…

Abstract: A software agent that will carry out tasks or provide services in response to a user's privately supported instructions or inquiries is known as an intelligent virtual assistant (IVA) or intelligent personal assistant (IPA). A virtual assistant capable of being accessed via web chat is sometimes called a "chatbot." Online chat systems can occasionally only be used for amusement. Some virtual assistants are equipped to comprehend spoken language and answer with synthetic voices. Users can use voice commands to manage other basic chores like email, to-do lists, and calendars in addition to asking their assistants questions, controlling home automation devices, and controlling media playing. One of the best applications of artificial intelligence is the virtual personal assistant (VPA), which offers a new way for people to delegate tasks to machines. To create a Virtual Personal Assistant (VPA) and use it in various software applications, certain approaches and principles are used. To enable users to communicate with virtual assistants, speech recognition systems—also known…

Abstract: IOT leads to a wide use a mobile phones with with broadband internet. One important concept of the same is the use of mobile phones by working parents to watch the activities of baby while babysitting. This paper presents the design of intelligent baby monitoring which supports such video monitoring. This system automatically on detection of baby cry sound. It switch on buzzer and sends a notification if baby any continues till specific time. That means system cannot handle baby and require personal attention and also if the bed in the cradle is damp.

Abstract: The intensity of precipitation plays a crucial role in hydraulic engineering when creating hydraulic structures and evaluating their impact. To design hydraulic projects such as drainage networks, bridges, and road culverts, Intensity-DurationFrequency (IDF) curves are used. These curves are heavily influenced by project planning, design, and operation in the field of water resources engineering and are also used in various flood structures. In this study, the frequency of rainfall intensity duration was determined in Kamti, Khari, catchments, Nagpur Maharashtra, from 1996 to 2019. Three different frequency analysis methods, including log-normal, Gumbel, and log-Pearson type III distribution, were applied to fit the corresponding curves. Analyzing the precipitation data is of great importance in this field..

Abstract: The software development life cycle (SDLC) process plays an important role in the development and delivery of a software product. This research article provides an in-depth analysis of various SDLC approaches and their unique capabilities, benefits, and challenges. By reviewing traditional and modern SDLC models,this article aims to provide insight to software developers, project managers, and project partners to help them make decisions. Information and development into the entire software development process.

Abstract: Artificial intelligence (AI) has become increasingly popular in recent years and has been used in a range of industries to improve outcomes, streamline processes, and improve decision-making. But there are also moral questions raised by the employment of AI, particularly in light of potential bias and discrimination. In order to promote justice and reduce bias, this paper offers a thorough discussion of ethical issues and mitigation techniques in AI. The evolution of AI and its possible advantages and disadvantages are first covered in the paper. After that, it explores the different ethical issues surrounding AI, such as trust, accountability, fairness, and openness. The study emphasises the effects of bias and discrimination on AI systems as well as the possible outcomes of these problems. The study also discusses the various mitigation measures, such as algorithmic strategies, data pre-processing, and model validation, that have been suggested to mitigate bias and enhance justice in AI. In order to develop the subject of AI ethics, the study analyses the advantages and…

Abstract: Humans have been inventing machines to accomplish arduous and time-consuming tasks. Technology has advanced to enable the development of machines that perceive and acquire data, understand language, retain it as knowledge, deduce information, reason, and solve problems. The widespread deployment of artificial intelligence is a milestone in the history of the transformative aspects of modern technology. Agriculture is a vital sector that supports economic growth and human subsistence and automation in this sector is a global concern. A vast majority of people in India's agrarian economy depend on agriculture for their livelihood, so it is crucial to promote sustainable agricultural practices. Due to population growth, conventional farming techniques can no longer keep up with food demand. Various novel automation technologies are being established to meet these needs and provide employability in this field. Ensuring food security despite climate change and population growth is a challenge for Artificial Intelligence. Recent environmental changes and climate catastrophes…

Abstract: The real estate industry is seeing an increase in the use of data mining. The capacity of data mining to extricate helpful data from crude information makes it especially helpful for anticipating home estimations, essential housing characteristics, and a great many different elements. Homeowners and the real estate industry frequently feel anxious about price swings, according to research. The most useful models and important criteria for predicting home values are examined in a literature review. The adoption of Random Forest and XGBoost as the most effective models in comparison to others was confirmed by this study's findings. Additionally, our data suggest that locational and structural characteristics are significant forecasting variables for housing values. In order to identify the most effective machine learning model for conducting a study in this field and the most significant factors that influence home prices, this study will be very helpful, particularly to housing developers and academics.

Abstract: In the dynamic realm of eCommerce, the integration of Artificial Intelligence (AI) has revolutionized advertising strategies, forging a path towards highly personalized consumer experiences. This exploration delves into the multifaceted role of AI in eCommerce advertising, highlighting the efficacy of technologies such as machine learning, natural language processing, and predictive analytics. A thorough analysis of consumer behavior, underpinned by AI, reveals advancements in data collection, privacy concerns, and innovative data analysis techniques. Ethical considerations, including data privacy and bias in AI algorithms, emerge as pivotal in maintaining consumer trust. The paper presents an array of case studies, illustrating the successful application of AI across diverse industries.

Abstract: An image steganographic scheme is one kind of steganographic systems, where the secret message is hidden in a digital image with some hiding method. Someone can then use a proper embedding procedure to recover the hidden message from the image. The original image is called a cover image in steganography, and the message-embedded image is called a stego image. This Steganography technique is more popular in recent year than other steganography possibly because of the flood of electronic image information available with the advent of digital cameras and high-speed internet distribution. It can involve hiding information in the naturally occurred noise within the image. In this paper we provide a review on various image steganography techniques.

Abstract: Sign language is the only tool of communication for the person who is not able to speak and hear anything. Sign language is a boon for verbally challenged people to express their thoughts and emotion. Using this Sign Language Recognition system, the communication gap between people with hearing impairments and the general public can be cleared. In this work, a scheme of sign language recognition has been proposed for identifying the gestures in sign language. With the help of computer vision and neural networks, system can detect the signs and give the respective text as output. The major aim of this work is to build a neural network using a Long Short-Term Memory (LSTM) deep learning model using the video frames which offer the translation of gestures into text. The model is trained with the dataset that is collected using MediaPipe holistic key points from the video of the person which detects the pose, face and hand landmarks. after building the neural network, real-time sign language recognition is performed using OpenCV and a user interface is developed using…

Abstract: Large language models and generative artificial intelligence (GAI) have recently demonstrated significant promise for revolutionizing a range of industries, including healthcare. The paper investigates how these cutting-edge AI developments are transforming healthcare applications. We focus on how big language models, like GPT-3 (generative pretrained transformer), Visual ChatGPT and generative AI, such Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), can be applied to solve important problems in the healthcare sector. Medical text analysis is one of the main uses of massive language models in the visual ChatGPT healthcare industry. These models have impressive natural language processing abilities that make it possible to effectively extract important information from electronic health records (EHRs), biomedical text data from large biobanks, scholarly articles and patient notes. The Biomedical Transformer Model represents a ground-breaking development in natural language processing for the biomedical field, exhibiting outstanding performance in…

Abstract: The field of inventory management stands on the brink of a transformative era, heralded by the confluence of Artificial Intelligence (AI) and the Internet of Things (IoT). This paper conducts an in-depth analysis, offering a visionary outlook by leveraging an extensive review of scholarly articles and existing literature on Artificial Intelligence (AI) and Internet of Things (IoT). The fusion of these technologies promises to revolutionize traditional practices by enabling real-time tracking, predictive analytics, and automated replenishment—ushering in unprecedented levels of efficiency and precision in inventory control. Furthermore, the study probes into the horizon of emerging trends, casting light on the progressive strides in machine learning, edge computing, and blockchain technology. Such advancements beckon a reimagined future for inventory strategies. However, this promising future is not without its hurdles. The research underscores critical impediments, including concerns surrounding data privacy, security, and technological constraints

Abstract: Multiple linear regression is a widely used statistical tool for modeling relationships between a dependent variable and multiple explanatory variables. However, it assumes that these explanatory variables are independent, which is not always the case in practical scenarios, leading to a phenomenon known as multicollinearity. Multicollinearity occurs when explanatory variables in a regression model are strongly correlated with each other, causing several issues in regression analysis. This paper discusses the detection and remedies for multicollinearity in detail. Detection methods include examining the determinant of the correlation matrix, inspecting correlation coefficients, using partial regression coefficients, calculating Variance Inflation Factors (VIFs), and assessing the condition number and condition index. These techniques help researchers identify the presence and severity of multicollinearity in their dataset. To address multicollinearity, several remedies are proposed, including obtaining more data, dropping collinear variables, using relevant prior…

Abstract: This meta-synthesis aims to synthesize factors that affect the low reading literacy skills among pupils enrolled in elementary schools in the Philippines. Fifteen (15) studies from 2013 to 2022 were screened and selected using a set of inclusion and exclusion criteria. Published papers from Google Scholar, CrossRef, and OpenAlex databases from Publish or Perish software were used to collect studies related to the factors affecting the pupils’ low reading literacy skills. The Critical Appraisal Skills Programme (CASP) checklist was utilized to quality check data, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram was used to organize the studies that are included. By using thematic analysis, the factors affecting the low reading literacy skills in Philippines’ elementary learners were identified as the meta-theme. Accordingly, seven sub-themes emerged namely, lack of reading elements, inclusion of learners-at-risk, lack of reading enthusiasm, teacher’s incompetence, shortage in reading materials and facilities, parental…

Abstract: Healthcare changes dramatically because of technological developments, from aesthetics and antibiotics to magnetic resonance imaging scanners and radiotherapy. Future technological innovation is going to keep transforming healthcare, yet while technologies (new drugs and treatments, new devices, new social media support for healthcare, etc) will drive innovation, human factors will remain one of the stable limitations of breakthroughs. No predictions can satisfy everybody; instead, this article explores fragments of the future to see how to think more clearly about how to get where we want to go. Significance for public health technology drives healthcare more than any other force, and in the future, it will continue to develop in dramatic ways. While we can glimpse and debate the details of future trends in healthcare, we need to be clear about the drivers so we can align with them and actively work to ensure the best outcomes for society as a whole.

Abstract: The rapid urbanization and population growth in developing countries have resulted in increased generation of municipal solid waste, leading to environmental degradation and health hazards. Traditional waste management practices have become unsustainable and inefficient in addressing these challenges. This study proposes a smart waste management system that utilizes advanced technologies such as Artificial Intelligence and Internet of Things to find an optimized position for placing the dustbins using neural network; optimize route for garbage collectors using genetic algorithm; segregate waste into categories of materials and their re-use. The system also includes a waste pick-up and incentivization mechanism that invokes theories such as "Tragedy of commons" and "Invisible hands" to drive societal change. The aim is to not only advance technical aspects but also societal and environmental aspects, making future generations more sustainable.

Abstract: The increasing number of malicious URLs on the internet poses a significant threat to online security and privacy of individuals and organizations. Machine learning algorithms have been proposed as a solution to this problem, but the high volume and diversity of URLs and the constant evolution of malicious URLs pose significant challenges for researchers in this field. Ensemble models, which combine multiple models to improve the overall performance, have shown great promise in addressing these challenges. In this paper, we compare four popular ensemble algorithms for the task of classifying URLs: Random Forest, Bagging, XGBoost and AdaBoost. We use a dataset of labeled URLs and pre-process them. The performance of the models is evaluated using accuracy and run time as the evaluation metric. Our results show that all the four algorithms are able to achieve good performance with accuracy scores above 95%. The Random Forest algorithm had the highest accuracy, followed by XGBoos, Bagging and then AdaBoost. XGBoost was the fasted algorithm with runtime of 1 minute. Our study…

bstract: One of the most important aspects of life is leisure time activities, especially for students because they allow them to participate in extracurricular activities outside of the classroom and advance their development of active behaviour. The purpose of the study is to ascertain how students spend their free time and how it impacts their academic achievement. Students in higher education institutions participate in a variety of activities, including sports, leisure pursuits, social networking, etc. Our selection of the six components—physical activities, indoor activities, social activities, reading activities, dating/relationship activities, and recreational activities—is based on literature review findings showing students engage in a variety of leisure activities. College students between the ages of 18 and 30 were taken into account for this study. A questionnaire-based method was utilised to collect the data, and it was distributed to 168 students studying in various universities in India in a variety of academic fields. The research's conclusions gained significance…

Abstract: Dark web platforms have emerged as a significant concern in the context of global security, with the potential to facilitate and amplify terrorism activities. This paper presents an analysis of the evolving landscape of dark web terrorism and proposes strategies for combating this emerging threat. The study begins by examining the characteristics and functionalities of the dark web that make it attractive to terrorist organizations, including anonymity, encrypted communications, and illicit marketplaces. It further explores the methods employed by terrorists to exploit the dark web for recruitment, propaganda dissemination, and coordination of attacks. To counteract this growing menace, a comprehensive framework for combating dark web terrorism is introduced. The proposed strategies encompass various dimensions, such as technological advancements, international collaboration, legislative measures, and law enforcement efforts. Technological approaches focus on enhancing digital surveillance capabilities, developing advanced data analytics tools, and improving cybersecurity…

Abstract: User experience design (UXD) has become increasingly important in e-commerce website development, as it plays a crucial role in shaping customer satisfaction and loyalty. This study aims to investigate the impact of UXD on customer satisfaction in e-commerce websites. A mixed-methods research design was employed, involving a survey of 500 online shoppers and semi-structured interviews with 10 e-commerce website designers. The findings show that UXD has a significant positive effect on customer satisfaction, with factors such as ease of navigation, clarity of product information, visual design, and speed of page loading being the most influential. The study also reveals that personalization and customization features can enhance customer satisfaction, as they provide a more personalized and engaging shopping experience. Furthermore, the study highlights the importance of mobile optimization, as customers increasingly use mobile devices to shop online. These findings have significant implications for e-commerce website development, as they suggest that UXD should be a top…

Abstract: The Garage Management System allows the user to keep track of all garage operations. It is a web-based tool that allows the user to manage the garage's stock, check for repair estimates, and schedule deliveries, among other things. It keeps track of the vehicle’s service history as well as the time spent in the mechanic's shop. It also keeps track of the car components inventory. It will keep track of all cars that have been maintained and will be able to send service reminders to clients depending on the service dates. Admin access to the Garage Management System is restricted. The administrator will be able to keep track of various users such as supervisors, receptionists, and principals, among others. It's a smart online Web App that can help garage owners keep track of events that occurs in garage. Customers are served by garage management system based on their servicing requirements. The major goal of this initiative is to eliminate manual labour

Abstract: Image classification is a hot research topic in today's society and an important direction in the field of image processing research. Image classification is a supervised learning method used to classify images. Paper analyses four common image classification algorithms: convolution neural network, support vector machine, artificial neural network and logistic regression. In the research work, both theoretical and empirical approaches were followed. For the theoretical approach a review of both secondary data as well as data based on results obtained by application on the tools is studied. Secondary data was acquired from the research articles, text books, journals, technical reports, published thesis, websites, e-journals, software tool manuals, conference proceedings and any other research articles published in the related domain. The empirical study was carried out on the set of experiments, using software tools. The results obtained from the experiments were analyzed for the finding of the research. The paper compares the results of these four algorithms when tested on…

Abstract: Augmented reality-based education platforms are a new initiative that aims to change the way students interact with the curriculum. The program uses advanced computer vision to reconcile the physical world of books, photographs and educational models with a rich digital content system, multimedia language. By doing this, it creates an interactive and interactive learning experience that encourages students to understand complex concepts. The main goal of the program is to bridge the gap between traditional education and the dynamic digital age. The platform transforms static learning materials into interactive three-dimensional learning tools by integrating augmented reality (AR). Users simply point their mobile phone or AR-equipped glasses at a physical object andthe app will quickly find it and provide important details suchas 3D models, videos and detailed descriptions. Key elementsof an AR-based learning platform include a powerful computer vision system for object recognition, a user-friendly mobile appor AR glasses interface, a comprehensive library of educational…

Abstract: A multidisciplinary discipline called artificial intelligence (A.I.) aims to automate jobs that currently need human intelligence. Artificial intelligence (AI), while not being well known, is a technology that is revolutionising all facets of existence. In order to rethink how we mix data, analyse it, and make decisions, this article attempts to inform laypeople about AI and urge them to use it as a tool in various fields. In this post, we briefly discussed artificial intelligence (AI), its principles, and potential applications.

Abstract: Mobile Ad Hoc Networks (MANETs) consist of digital devices, nodes are constantly moving, forming disconnected form of network with exploiting chances offered by immediate circumstances without reference to a general plan node encountering. Such transient network connections pose a challenge for the development of P2P MANETs. Traditional methods supporting P2P MANETs are flooding-based, advertisement-based and social networking method. This paper proposes a P2P content-based file sharing system, namely SPOON, for disconnected MANETs. The system uses an interest extraction algorithm to derive a node’s interests from its files for content-based file searching. For efficient file searching, SPOON groups commoninterest nodes that frequently meet with each other as communities.

Abstract: People's living standards have improved as a result of modern technologies. As a result, there was a large crowd at the mall. To control the huge crowd, we must streamline the billing procedure. A smart shopping system based on RFID is used to accomplish this. Once each item in a smart shopping cart has been read thoroughly, the bill is generated and displayed. after the last A bill is printed. The customer pays the money using the pre-charged cards that the shopping complex has provided. The objective is to reduce time consumption. It makes use of RFID technology, which can scan a large number of things and help save both customers' and the mall's valuable time.

Abstract: Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification tasks due to their ability to automatically learn hierarchical representations from raw pixel data. This paper provides a comprehensive review of CNN-based image classification methods, covering various aspects such as network architectures, training techniques, and evaluation metrics. Additionally, we discuss recent advancements, challenges, and future directions in CNN-based image classification research. The aim of this paper is to provide researchers and practitioners with a thorough understanding of CNNbased image classification and to guide future research in this field.

Abstract: Decentralized application make use of peer-to-peer networks, this ensures that no network failure can occur due to central node failure. Blockchain serves as an immutable ledger which allows messaging to take place in a decentralized manner. A decentralized application for communication and resource sharing is need in today’s world, where keeping data on a centralized server can be risky and costly experience. With the help of various consensus, we can implement different ways to share resources and communicate. Together with Blockchain and Decentralized Applications, we can create a secure and reliable messaging application that overcomes the drawbacks of traditional messaging applications.

Abstract: The rapid advancements in artificial intelligence (AI) and natural language processing have led to the development of sophisticated language models like ChatGPT, Siri, Google Assistant. These models possess the ability to generate human-like text, enabling them to engage in conversations and assist users in various domains. However, as these models become increasingly integrated into our lives, it becomes crucial to examine the ethical considerations associated with their use. This survey-based research paper aims to explore and analyze the ethical implications of deploying ChatGPT and similar language models, focusing on concerns such as bias, privacy, accountability, and transparency. The research was qualitative research as data was collected through surveys using the questionnaire tool. By identifying these ethical challenges, we can foster responsible development, deployment, and regulation of AI technologies to ensure their beneficial and ethical integration into society.

Abstract: Risky situations arise when visually challenged people attempt to identify the items in front of them while crossing the roadway. The development of a smart cane with a distance measurement system is the primary goal of this project. An ultrasonic sensor serves as the system's input, and earphones serve as its output. Additionally, the device features a GPS live finding system and a blind person's navigation guidance. Through the audio output, this device will warn blind persons of any obstructions so they can proceed safely and without incident. Blindness is a condition in which there is a loss of seeing due to physiological or neurological reasons. Total blindness is the complete absence of visual light perception, while partial blindness is the lack of integration within the development of the nervus opticus or visual centre of attention.

Abstract: Wireless communications involves the transfer of voiceand data without a cable or wires. It uses orthogonal frequencydivision multiplexing, also known as a multicarrier transmission technique. In multiple input multiple output (MIMO) wireless communication (4G and 5G technologies), channel estimation is crucial. Multiple antennas are used at both the transmit and receive sides ofa MIMO system to increase spectral efficiency and reliability. In 5G channel estimation is performed to improve the accuracy of the received signal. Least-squares estimation is a cheap method with relatively large channel estimationerrors, but it is supported in this work by using a new channel estimation method that leverages deep learning. LS (least squares) and MMSE (minimum mean squared error) are two popular traditional approaches to channel estimation, but deep learning provides much more accurate results than previous channel estimation methods.

Abstract: Biometric authentication systems have gained significant interest due to their reliable and practical authentication options. These systems utilize physiological or behavioral traits, like fingerprints, face recognition, and speech patterns, to confirm a person's identity. They offer advantages such as increased security and user convenience compared to traditional methods. However, biometric authentication also poses challenges, including privacy concerns and the potential for false positives or negatives. This research study provides a comprehensive overview of the various forms, applications, benefits, and limitations of biometric authentication systems. It emphasizes the need to address privacy concerns and establish appropriate standards and regulations. The findings inform practitioners, researchers, and policymakers about the latest advancements and implications of biometric authentication systems.

Abstract: On the undergraduate level, this book is an introduction to the Theory of Computation. A public purpose of terminology must protect a combination of different classifications. Natural languages, programming languages, mathematical languages, etc. The notation of a natural language is like English, Hindi, etc. Informal language can be defined as a system suitable for expressing specific ideas, facts, or concepts, including symbols and rules to manipulate these. The language we consider for our discussion is an abstraction of natural languages. The theory of Computation is based on Logic, Algorithms, and Theorem-proving. A machine is used to perform the user's essential tasks (requirements). So, it is equally important to know the computing models that compare the study. The model tells us how the machine will work together to do Computation. The theory of Computation abstractly provides computational models.

Abstract: Sign language is a form of communication that uses hand sign and gestures to convey meaning. we present a new approach to converting sign language into text format. Our system is designed to enable deaf and mute people to communicate with others in a more accessible and convenient way. The proposed method uses computer vision and deep learning methods to recognize hand gestures and translate them into appropriate text. The system was built using a combination of key point detection using MediaPipe, data pre-processing, label, feature generation and LSTM neural network training. This work has the potential to significantly improve communication for deaf and dumb individuals and reduce the barriers to communication with the rest of the world. The system uses key point detection algorithms such as MediaPipe to identify hand gestures and a Lstm model to translate them into corresponding text output. The data collected from the sign language is pre-processed and then used to train an LSTM neural network to accurately recognize gestures and produce text output. This method of…

Abstract: Cryptography is the technique for hiding data and information from unauthorized users. It is the art of achieving security by encoding messages to make them non-readable. When we send simple message from one location to another then this message called plain text is visible to anybody. If we want to codify the message called cipher text so that no one can easily understand the meaning of message then we use cryptography techniques. There are numerous applications where this technology is used such as ATM pin, Password, Credit Card number or any other secret communication. Cryptography can be divided into following three categories depending upon the types of key used: secret key (symmetric) cryptography, public key (asymmetric) cryptography and hash functions. In this paper we provide review of various types of cryptography techniques.

Abstract: There are number of hospitals in the world with advanced diagnostic equipment. But although having this equipment some patients cannot get proper treatments and may suffer to death. Main reason behind this is time, our medical systems lack time and it is not easy for them to manage time. With help of machine learning techniques we created project that identifies patients with major diseases like Heart disease, Kidney disease and Diabetes disease at early stage so that proper treatments can be given to them. We collected three datasets for three models from Kaggle [1], analyzed[2]them, cleaned them and choose best algorithm [3] for each dataset. We achieved 98.52% accuracy on heart disease prediction model [4], 98.73% accuracy on kidney disease prediction model [5], 80.55% accuracy on diabetes disease prediction model [6]. For all three models we used Random Forest algorithm [7] .At the end we created web application using Flask [8] for easy user interaction.

Abstract: In India, the annual production of GGBS is 15 million tons and only 55% of GGBS is used in the construction industry. It is therefore important to increase the use of industrial waste on a larger scale for environmental benefits in the concrete industry. Numerous studies have been performed on these SCMs to evaluate the properties of his GGBS-containing concrete in fresh and cured states. Using by-products of the slag industry to higher replacement levels for cement could significantly reduce the price of concrete. Paving economical and environmentally friendly concrete Green house gas (GHG) emissions are effectively reduced by 47.5% by using GGBS as a cement substitute. India is the largest producer of fly ash. In one year he produces more than 112 million tons of fly ash. 60-70% of the fly ash is consumed and the rest is spread over the ground. This is because the government encourages the use of his 100% fly ash to avoid landfills and create a greener environment.

Abstract: This paper presents a Graphical User Interface (GUI) application for PDF processing tools and file conversion tools. The application provides a user-friendly interface for users to perform various operations on PDF documents, such as splitting, merging, extracting, rotating, and deleting pages. Depending on the user's needs, the user can perform these operations on every page, even pages, odd pages, random specific pages, all pages after some nth page, and between some specific ranges. The application is designed to be user-friendly and simple to use, allowing users to complete tasks quickly and easily. Nowadays, most universities and schools provide their students with all of their course materials online, therefore many students access these resources via PDF files. Since this application operates without an internet connection and is therefore advantageous to all users, Using the PyPDF2 library for PDFs, the Python graphics package Tkinter is used to develop the graphical user interface and UI library CustomTkinter which provides fresh, modern, and completely…

Abstract: The objective of this paper is to create a mental health prediction system through text sentimental analysis. The system will employ natural language processing technique to examine expressions and sentiments conveyed in text, aiming to predict the likelihood of an individual experiencing mental health issues such as anxiety, depression, or stress. The proposed system will make use of machine learning models to extract features that will provide with the results of sentiment analysis derived from text input. The integrated features will then be utilized to predict the likelihood of an individual experiencing mental health issues.

Abstract: The 21st century has witnessed a significant transformation in education through the integration of Information and Communication Technology (ICT). This article explores how ICT enhances educational effectiveness and equitability, focusing on student engagement, personalized learning, teacher professional development, and narrowing educational disparities. ICT adoption has positively impacted student engagement and learning outcomes, enabling educators to create dynamic and immersive learning experiences. Through adaptive learning technologies, ICT allows personalized educational content tailored to individual student needs, fostering deeper understanding and self-directed learning. To fully leverage ICT's potential, continuous professional development for educators is essential. However, the digital divide remains a challenge, with disparities in technology and internet access. Policymakers and educators must collaborate to address infrastructure limitations, privacy concerns, and resistance to change. By promoting student engagement, facilitating personalized learning,…

Abstract: Now a days, electrical accidents to the line man are increasing, while repairing the electrical lines dua to the lack of communication between the electrical substation and maintenance staff. This project gives a solution to this problem to ensure line man safety. In this proposed system the control (ON/OFF) of the electrical lines lies with line man. This project is arranged in such a way that maintenance staff or line man has to enter the password ON/OFF the electrical line. Now if there is any fault in electrical line then line man will switch off the power supply to the line by entering password and comfortably repair the electrical line, and after coming to the substation line man switch on the supply to the particular line by entering the password.

Abstract: Amidst the swift-paced digital transformation and intricate web of global markets, the imperative of astute and adaptive demand forecasting is brought into sharp focus, particularly within the context of multi-product warehousing environments. These warehouses, tasked with the meticulous management and storage of a wide array of products, are deeply entwined with the accuracy and foresight provided by proficient future demand predictions—these being crucial in streamlining inventory, curtailing waste, and amplifying profitability. While traditional forecasting approaches provide a fundamental backbone, they often exhibit shortcomings when navigating the multifaceted dynamics present, especially as data proliferates both in volume and complexity. This paper aims to illuminate the pivotal, transformative role of incorporating Machine Learning (ML) methodologies into demand forecasting practices. By delving into a meticulous examination, we seek to elucidate not only the inherent benefits and challenges but also the wider global repercussions of synthesizing avant-garde ML…

Abstract: Over the last decade, cloud computing has grown and evolved significantly. It provides cost-effective solutions as well as a wide range of adaptable services. Many businesses are using cloud services. Users mostly use cloud services to save data in a virtual environment. Users have no knowledge of or influence over what happens in the cloud, and nothing in the cloud is visible to them, which raises security and privacy problems. Malicious administrators can mess with the integrity, privacy, virtual machines, and volatile confidentiality of cloud services. Clouds are vulnerable to hacking, and their integrity, availability, and data security are all in jeopardy.

Abstract: In civil engineering practice, 1D modeling (also called line modeling) is often the most common method of modeling used for the analyses of structures. It is easy to create the model and saves time without loss of significant accuracy in the results. However, these line models are ideally not a true representation of the behavior of the structure as they are overly simplified versions. Though this simplification leads to quick modeling and reduces analyses-design cycle time, this oversimplification can lead to inaccuracies and over- design that can impact the economy of construction in repeat projects. Also, in the current era of precast modular construction, it would be prudent to analyze a structure as precisely as it can be so that optimal solution is achieved before designing as standard member that can be reused across an array of projects. In this study an effort is made to model a box bridge structure to analyze and compare the behavior of the bridge under railway vehicular train loading. A Road under Bridge (RUB) of 25m barrel length is considered and analysis is…

Abstract: There is worldwide demand for an affordable Blood Group measurement solution, which is a particularly urgent need in developing countries. The Image Processing, which is the most penetrated device in both rich and resource-constrained areas, would be a suitable choice to build this solution. This Project proposes a noninvasive Blood Group measurement processes. Also its compared the variation in data collection sites, biosignal processing techniques, theoretical foundations, photoplethysmogram (PPG) signal and features extraction process, Image Processing algorithms, and Detection models to calculate Blood Groups. This analysis was then used to recommend realistic approaches to build a Image Processing-based point-of-care tool for Blood Group measurement in a noninvasive manner. In this project, proposing approaches for blood Group measurement with the aim of recommending data collection techniques, signal extraction processes, feature calculation processes, Image Processing algorithms for developing a noninvasive Blood Group estimation using an Image

Abstract: Food safety is becoming an increasingly serious topic worldwide. To address food safety issues from a technical perspective, people need a reliable food traceability system that can track and monitor the entire food production lifecycle, including process of growing/propagating food ingredients, processing, transporting, storing and selling, etc. In this article, we propose a reliable, self-organizing, open and ecological food traceability system based on blockchain and Internet of Things (IoT) technology, which includes all parts of smart agroecosystems, even if they do not trust each other. We use IoT devices to replace as much as possible manual registration and verification, which can effectively reduce human intervention in the system. In addition, we plan to use smart contract technology to help enforcers find problems and resolve them in a timely manner.

Abstract: Water flow through soil is a critical process that affects many aspects of soil health and environmental sustainability. Soil texture, which is the relative proportion of sand, silt, and clay particles in soil, plays a significant role in regulating water flow through soils. The soil texture triangle is a widely used tool for classifying soil texture based on the proportions of sand, silt, and clay particles. This review paper aims to evaluate the effect of soil texture on water flow through soils using the soil texture triangle. The paper presents an overview of the soil texture triangle and its applications in soil texture classification, followed by a discussion of the relationship between soil texture and water flow. The paper reviews existing literature on the impact of soil texture on water flow and summarizes the key findings. The review highlights the importance of soil texture in regulating water flow through soils and discusses the implications for soil management and environmental sustainability. The paper concludes by identifying areas for future research to…

Abstract: Modern organisation’s rapid pace of software development necessitates teams delivering high-quality software products faster than ever before. In order to accomplish this, software development teams are incorporating continuous integration and continuous deployment (CI/CD) methodologies into their software development processes. CI/CD pipelines are a set of practises and tools that allow teams to efficiently and reliably automate the development, testing, and deployment of software products. CI/CD pipelines have become an essential tool for teams delivering web applications at a faster pace while ensuring scalability, security, and performance in the context of web applications. This paper provides an overview of the best practises and tools for constructing a successful CI/CD pipeline for web applications. Version control, continuous integration, automated testing, deployment automation, monitoring, and logging are among the key steps covered in the paper. The paper also discusses the advantages and disadvantages of CI/CD pipelines, such as increased productivity, shorter…

Abstract: With the rise of online job application processes, submitting a resume has become easier than ever before. As a result, a larger number of individuals are impacted by this change. Many organizations still accept resumes by mail, which can cause challenges for their human resources departments. Sorting through a large number of applications to find the most suitable candidates can be a time-consuming process for these agencies. Job applicants submit resumes in a wide range of formats, including various fonts, font sizes, colors, and other design elements. Human resources departments are responsible for reviewing each application and selecting the most qualified candidate for the job. My suggestion is to incorporate natural language processing techniques into the project's parser to assist the human resources department or recruiting manager in analyzing the information provided in resumes. This approach can involve using keyword matching and other natural language processing methods to identify the most suitable candidates and obtain the most effective resumes. By doing so,…

Abstract: Career recommendation system aims to offer direction and assist students in selecting engineering streams with the help of a WebApp. Nowadays, there are more educational courses available, making it easier for students to pick courses that interest them. However, in the 22nd century, more than half of the youths do not exercise their freedom of choice and make wellinformed decisions. A number of factors contribute to this. One of the main reasons is the lack of awareness of all the available options. The other well-known hindrance is the family pressure of following a well-known or previously followed path when it comes to choosing a career. This results in candidates ending up in the wrong fields and dropping out of college midway through the course. As a result, time and money are wasted. Moreover, it may happen that a deserving candidate was denied a seat in an institution since its capacity was full at the start of the course. The proposed WebApp aims to develop a system that would suggest a course based on certain fundamental information about the student such as…

Abstract: The quality of water refers to its suitability for different applications based on specific physical, chemical, and biological characteristics. Depending on its intended use, water may need to meet certain requirements, such as maximum levels of toxic substances for drinking water or specific temperature and pH ranges for supporting aquatic life. Therefore, water quality can be defined by various variables that limit water use. Balancing the demands of different users for quantity and quality often requires compromises when trying to improve or maintain a certain water quality.Preserving natural ecosystems is increasingly recognized as a crucial aspect of water quality management. These ecosystems are valuable for their intrinsic worth and also serve as sensitive indicators of changes or deterioration in overall water quality, providing useful additional information alongside physical, chemical, and other data.Wi-Fi is a wireless technology that uses radio frequency to transmit data through the air. With initial speeds of 1-2 Mbps, Wi-Fi transmits data in the frequency…

Abstract: This research paper provides an in-depth analysis of the impact of Artificial Intelligence (AI) on various job sectors. It explores the history of AI and highlights the top AI tools that are being used today. The primary focus of the research is on the effects of AI on different job sectors, including healthcare, finance, and transportation. Additionally, the paper examines the potential benefits and challenges of AI adoption, including the concerns about job displacement and the future of work. Ultimately, the research concludes that a responsible approach to AI adoption is necessary, with the need for ethical frameworks and investment in research and development to maximise the benefits while mitigating the risks.Artificial Intelligence (AI) is one of the most revolutionary computer technologies that is changing our world. With AI-powered tools, organisations can automate routine and repetitive tasks, enabling workers to focus on more complex and higher-value tasks. Moreover, AI is transforming the nature of work, enabling more flexible and remote work arrangements.…

Abstract: The payments industry is changing as a result of mobile payments. These services give non-banking actors a way to break into the sector. Banks, who are already involved in the traditional payments sector, must provide mobile payments in order to meet this threat. Right now, we're in a digital era. India is about to see a significant digital revolution. The increasing connectivity that the Internet provides in today's society has altered the way that money is exchanged. The use of several technologies, including smartphones, computers, iPads, tablets, and the internet, has advanced dramatically in India. Smartphones and government initiatives like Digital India are acting as catalysts for the exponential growth in the use of digital payment as a result of the increase in internet users. In India, efforts are being made to switch to electronic payments. A digital payment system is one that processes payments through digital channels and an electronic network. Every transaction involving digital payments is completed online. It is a speedy and useful way to pay. With the aim…

Abstract: An AI fitness trainer is a computer application that utilizes the capabilities of Python, OpenCV, and MediaPipe to guide users through physical fitness routines. The application uses computer vision techniques provided by OpenCV to track the user's movements and provide feedback on form and technique. MediaPipe is used to process the video data and provide realtime analysis. The application also utilizes machine learning algorithms to provide personalized fitness recommendations and progress tracking. The combination of these technologies provides a highly interactive and effective way for users to improve their physical fitness

Abstract: Public healthcare has been paid increasing attention given the exponential growth human population and medical expenses. It is well known that an effective health monitoring system can detect abnormalities of health conditions in time and make diagnoses according to the gleaned data. As a vital approach to diagnosing heart diseases, ECG monitoring is widely studied and applied. However, nearly all existing portable ECG monitoring systems cannot work without a mobile application, which is responsible for data collection and display. In this project, we propose a new method for ECG monitoring based on Internet-of-Things (IoT) techniques. ECG data are gathered using a wearable monitoring node and are transmitted directly to the IoT cloud using Wi-Fi. Both the HTTP and MQTT protocols are employed in the IoT cloud in order to provide visual and timely ECG data to users. Nearly all smart terminals with a web browser can acquire ECG data conveniently, which has greatly alleviated the cross-platform issue. Experiments are carried out on healthy volunteers in order to verify the…

bstract: Rapid technology growth has affected corporate practices. With more items and services to select from, client churning has become a big challenge and threat to all firms. We offer a machine learning-based churn prediction model for a B2B subscription-based service provider. Our research aims to improve churn prediction. We employed machine learning to iteratively create and evaluate the resulting model using accuracy, precision, recall, and F1- score. The data comes from a financial administration subscription service. Since the given dataset is mostly non-churners, we analyzed SMOTE, SMOTEENN, and Random under Sampler to balance it. Our study shows that machine learning can anticipate client attrition. Ensemble learners perform better than single base learners, and a balanced training dataset should increase classifier performance.

Abstract: This research paper delves into the advancements and potential of Optical Character Recognition (OCR) technology for revolutionizing data entry processes. The study provides a comprehensive analysis of the history, current state, and future of OCR technology. The paper begins with an overview of the evolution of OCR technology and its various applications in document scanning, indexing, and management. The research then examines the benefits of OCR technology, such as improved accuracy, faster processing times, and cost savings compared to traditional data entry methods. It also addresses the challenges faced by OCR technology, including difficulties in recognizing handwritten text and the need for continuous improvement and innovation. The paper concludes with a discussion of the future potential of OCR technology, including the integration of artificial intelligence and machine learning for improved accuracy and efficiency, and the potential for increased automation in various industries. The study highlights the importance of OCR technology for revolutionizing data…

Abstract: This research paper presents an in-depth exploration of a cost-effective fall detection system that repurposes the ADXL345 tilt sensor in conjunction with the ESP8266 (NodeMCU) microcontroller. The study details the hardware setup, algorithm development, and implementation, shedding light on how the acceleration data is utilized to repurpose the ADXL345 sensor for fall detection. Additionally, the paper discusses the adaptability of the system to other IoT boards such as Arduino, NodeMCU, and Raspberry Pi, and concludes with an expanded future scope for further enhancements

Abstract: The integration of artificial intelligence (AI) into the workforce has taken canter stage, accelerated by the COVID-19 pandemic and the imperative for remote and efficient work solutions. This research paper extensively examines the multifaceted impact of AI on employment and workforce trends in the post-pandemic era. Our study investigates the profound changes AI brings to the labour market, touching on issues such as job displacement, skills adaptation, and the evolving nature of work. Furthermore, we explore the ethical dimensions and socio-economic considerations of this transition. This paper offers a global perspective on how AI-driven workforce changes affect diverse regions and economies. Finally, it outlines strategies and recommendations for individuals and organizations to navigate this evolving landscape, ensuring a harmonious blend of AI technologies with human potential. The first dimension of this research is a prospective analysis of the current and future landscape. "Artificial Intelligence, Pandemic, and Employment: A Prospective Analysis" by John A.…

Abstract: Assessing student’s learning performance is a fundamental aspect of evaluating educational systems, playing a pivotal role in addressing challenges within the learning process and measuring learning outcomes. The emergence of educational data mining (EDM) as a research field has harnessed the power of data and knowledge to enhance education systems. EDM involves the development of techniques to analyze data collected from educational environments, offering a more comprehensive understanding of students and facilitating improvements in educational outcomes. The integration of machine learning (ML) technology has witnessed substantial growth, enabling researchers and educators to leverage data mining insights to predict and simulate educational processes, including success rates, dropouts, and more. This research paper explores the analysis of students' performance through data mining methods. It employs classification technique to discern the early-stage impact on GPA. In the classification methodology, various machine learning models are experimented with to predict…

Abstract: A various non-invasive administration have recently emerged as an alternative toconventional needles. Transdermal drug delivery system is most attractive method. The transdermal as route has numerous advantages over the more traditional drug delivery system and they include high bioavailability, absence offirst pass hepatic metabolism, steady drug plasma concentration. and the fact that therapy is noninvasive. TDDS could be applicable in not only Pharmaceuticals but also in the skin industry, including cosmetics. Transdermal drug delivery has made an important contribution to medical practice such as diabetes, hemo rrhoids arthritis, migraine and Schizophrenia treatment, but yet to fully achieve it’s potential in the treatment of Obesity. The adhesive of the transdermal drug delivery system is critical to the safety, efficacy and quality of product

Abstract: In today's fast-paced business era, data reigns supreme. From emails and social media to reviews and articles, we've amassed a treasure trove of textual information that unveils customer sentiments, market trends, and brand perceptions. However, the real challenge lies in extracting valuable insights from this textual abundance. With that, we present an intensive and thorough review of the existing methods of over the past six years, from 2018 to 2023. We found two game-changers: text analytics, the detective of text patterns, and Natural Language Processing (NLP), the language expert for computers. Together, they bring order to the chaotic world of words. Our review explores the quick development of NLP and offers suggestions for problems. Businesses can make educated decisions, outperform rivals, and make data their greatest asset with the help of these cutting-edge solutions. In order to ensure they find gold in the sea of text data, our study serves as the compass that directs them on this revolutionary journey.

Abstract: Floods pose significant threats to communities worldwide, necessitating the development of efficient monitoring and alerting systems. This abstract presents an IoT-based Flood Monitoring and Alerting System that leverages the power of LoRaWAN (Long Range Wide Area Network) technology to provide real-time flood detection, monitoring, and timely alerts. The system consists of strategically placed water level sensors that use ultrasonic or pressure-based technology to accurately measure water levels. The sensor data is wirelessly transmitted to a central gateway through the LoRaWAN network, known for its long-range and low-power capabilities. A cloud-based platform processes and analyzes the collected data, identifying abnormal water level patterns and potential flood conditions. In case of a flood, the system triggers alerts via mobile notifications, email, and SMS to relevant authorities and stakeholders. Users can access real-time flood information, historical data, and visualizations through a user-friendly web or mobile interface. The system offers cost-effectiveness,…

Abstract: This research paper aims to explore the impact of generative artificial intelligence (AI) on IT professionals. Generative AI refers to the technology that can autonomously create new content, such as images, text, and even code. With the rapid advancements in AI, the role of IT professionals is evolving, and it is crucial to understand the implications of generative AI on their work. This paper examines the potential benefits, challenges, and ethical considerations associated with the integration of generative AI in the IT industry. Through a comprehensive analysis of existing literature, this paper sheds light on the transformation of IT professionals' responsibilities, skills, and career prospects in the era of generative AI.

Abstract: The Restaurant Management System will provide the user a new experience in the restaurant. It will allow a manager and an owner to store the data and transactions made by customer. The System will allow user to order the food by scanning QR code placed on the table. It will manage the employee master and stock details and also produce the bill receipt a per the customer convenience(i.e. via Whatsapp, Email, SMS). It will manage the workload of chefs also. That will be very beneficial for the restaurant to give more better service to their customer.

Abstract: Facial emotion recognition (FER) has become an important topic in the fields of computer vision and artificial intelligence due to its great academic and commercial potential. Although FER can be performed using multiple sensors, this review focuses on studies using facial images exclusively, since visual expressions areone of the main channels of information in human communication. Automatic emotion recognition based onfacial expressions is an interesting research area that has been applied and applied in various fields such as safety, health and human-computer interface. Researchersin this field are interested in developing techniquesto interpret, encode facial expressions and extract these features for better prediction by computer. With the remarkable success of deep learning, different types of architectures of this technique are exploited to achieve better performance. The purpose of this paperisto conduct a study ofrecent work on automatic facial emotionrecognition (FER) via deep learning. We highlight these contributions, the architectures and the databases used,…

Abstract: This research paper investigates the relationship between job satisfaction and work motivation. The paper examines existing literature on the topic, outlining various theories and studies that highlight the importance of job satisfaction and work motivation in enhancing employee performance and overall job satisfaction. The paper also explores the various factors that contribute to job satisfaction and work motivation, including job security, organizational culture, and opportunities for career growth. The study was done through online surveys to collect data from a sample of employees in IT industry. The findings suggest a strong positive correlation between job satisfaction and work motivation, highlighting the need for employers to create a conducive work environment that promotes job satisfaction and work motivation. The paper concludes with recommendations for employers on strategies for enhancing job satisfaction and work motivation among their employees. Work motivation and Job satisfaction is a study of a organizational behaviour among the employees. Both these…

Abstract: In daily life, the role of a helmet is vital for motorists. The human brain is an important organ, which is protected by the skull. So the head is to be protected by a helmet in case of an accident. From our literature survey we found that in india, the majority of motorists do not wear a helmet. This negligence causes fatal injuries. We want to minimize this risk. Our project uses ML and OPENCV tools for Helmet Detection. In this project we use a camera module to detect the face of the person. The preprocessed input is fed to the Machine Learning model. This model processes it and transports which ‘outputs’ the real-time status of the helmet. Keywords:- MachineLearning, OpenCV, Helmet Detection

Abstract: Agriculture is the practice of cultivating natural coffers to sustain mortal life and give profitable gain. It combines the creativity, imagination, and skill involved in planting crops and raising creatures with ultramodern product styles and new technologies. It's necessary element for the actuality and elaboration of the Indian frugality. Beforehand these days, crop product done by growers was practical moxie. Not growers are suitable to elect the stylish applicable crop grounded on characteristics and features of soil. So, a recommendation system has been developed that applies Machine Learning Algorithm to recommend a particular crop that can be gathered in that soil. A Neural Network arranges algorithms in a fashion that it can make accurate opinions by itself. therefore, although Machine learning models can learn from data, in the original stages, they may bear some mortal intervention. Neural networks don't bear mortal intervention as the nested layers within pass the data through scales of colorful generalities, which ultimately makes them able of learning through…

Abstract: OpenAI Codex is an AI system that converts natural language into code, OpenAI shows how the software can be used to build simple websites and rudimentary natural language games, translate between different programming languages, and answer data science queries. The user enters English commands into the software. For example, Create a web page with a menu on the side and a title at the top and Codex will translate this into code. Codex is the model behind GitHub Copilot, developed and launched in partnership with GitHub. Codex is proficient in over 10 programming languages and can interpret and execute simple natural language commands on your behalf, allowing you to create natural language interfaces to your existing applications.

Abstract: Real estate plays an important role in the national economy, but the existing system has many flaws that affect everything from finding a property to renting to buying and selling. Real estate and blockchain have the potential to work well together. In order to strongly support the premise of real estate in multiple wearing exercises, the land business process utilizes a blockchain model. Either way, the majority of digital data is enabled across multiple frameworks, putting an end to a lack of transparency and an increased rate of errors that create more obvious potential for fraud. Blockchain technology has the potential to improve apartment trading activities. A new strategy for companies that are steadily revealing some of these facts is the un-middle of blockchain for a real estate approach.

Abstract: The rapid evolution of real-time communication technologies has spurred the development of diverse messaging protocols, each tailored to specific requirements. This paper investigates the efficacy of the Message Queuing Telemetry Transport (MQTT) protocol in the context of real-time communication. Emphasis is placed on understanding MQTT's unique features and capabilities, positioning it as a versatile solution for custom chat applications. The study involves the implementation of a Python-based chat application, leveraging MQTT, and utilizes the broker.hivemq.com infrastructure to showcase the protocol's robustness in facilitating seamless communication between clients. By examining the intricacies of this custom chat application, this research aims to contribute insights into the practical applications of MQTT and its potential for enhancing real-time communication paradigms.

Abstract: This paper examines the integration of Blockchain and Artificial Intelligence (AI) in enhancing inventory management. It highlights how these technologies synergistically improve accuracy and efficiency, significantly reducing fraud and errors. The study explores Blockchain's secure ledger and AI's predictive analytics, emphasizing their practical applications in various industries. Challenges such as technical complexities and ethical considerations, including data privacy and regulatory compliance, are also addressed. The paper concludes by discussing the implications for businesses and researchers, underscoring the transformative impact of these technologies in inventory management and the necessity for ongoing innovation and ethical vigilance

High-Performance Liquid Chromatography (HPLC) has emerged as a fundamental technique in modern analytical chemistry, serving as a powerful tool for the separation, identification, and quantification of [1] diverse compounds. One critical aspect that ensures the validity and credibility of HPLC results is the concept of system suitability. This abstract highlights the importance of system suitability in HPLC analysis, emphasizing its role in ensuring accurate and reliable results. System suitability is a set of pre-defined criteria that evaluate the performance of the HPLCsystem, encompassing factors such as resolution, peak symmetry, tailing factor, retention time, and peak area reproducibility. Adhering to these criteria is essential for consistent and precise analyses, as they confirm that the instrument is operating optimally and capable of generating dependable data

Abstract: As the intricacy of web applications continues to escalate, the difficulties linked with the upkeep of testing in web-based automation have grown more apparent. Conventional automation scripts frequently encounter difficulties in adjusting to frequent modifications in web components, resulting in substantial manual endeavours and diminished testing efficacy. In reaction to these challenges, self-correcting web-based automation frameworks have emerged as a promising resolution. This critical review paper offers a comprehensive examination of web-based automation frameworks that have the ability to selfheal. It provides a thorough analysis of their principles, mechanisms, and real-world applications. Through the categorization and analysis of these frameworks' essential components, this review aims to elucidate their effectiveness in addressing the continuously evolving nature of web applications.While acknowledging the advancements made possible by self-healing frameworks, this review also explores the inherent challenges and limitations they possess. It outlines potential…