In today’s rapidly evolving business landscape, organizations must continuously adapt to stay competitive. AI-driven human resource (HR) analytics has emerged as a strategic tool to enhance workforce agility and inform decision-making processes. By leveraging advanced algorithms, machine learning models, and predictive analytics, HR departments can transform vast data sets into actionable insights, driving talent management, employee engagement, and overall organizational efficiency. AI’s ability to analyze patterns, forecast trends, and offer data-driven recommendations empowers HR professionals to make proactive decisions in hiring, (...) skill development, performance management, and retention. (shrink)

The core objective of this project is to recognize and reconstruct distorted facial images, particularly in the context of accidents. This involves using deep learning techniques to analyze the features of a distorted face and regenerate it into a recognizable form. Deep learning models are wellsuited for this task due to their ability to learn complex patterns and representations from data the input data consists of distorted facial images, typically obtained from MRI scans of accident victims. These images may contain (...) various types of distortions such as swelling, bruising, or other injuries that affect facial appearance. (shrink)

The shift to digital learning platforms has empowered students with access to a broader array of learning resources, interactive content, and personalized learning paths. However, millions of students, particularly those from rural areas or lower-income families, struggle to access these resources due to a lack of digital infrastructure, reliable internet connectivity, and affordability of devices. Furthermore, the role of teachers has evolved, requiring new pedagogical approaches and technical skills to effectively harness these digital tools.

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed by (...) training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. (shrink)

This project presents the development of a QR Code Based Attendance Management System using Javascript with and Reactjs, Bootstrap for frontend development and Firebase for database management. The system comprises two main entities: Student and Staff. Students register by providing comprehensive details including Roll Number, Year, Photo, Name, email id. Students can log in to the system and download their ID Card containing a unique QR code. This QR code serves as a means for students to mark their attendance by (...) presenting the ID card. The system distinguishes between valid and invalid QR codes, ensuring accurate attendance recording. Students have access to their attendance logs, allowing them to monitor their attendance record. Moreover, staff can view attendance logs for all students, ensuring comprehensive monitoring and record-keeping. Overall, the QR Code Based Attendance Management System offers a convenient and secure method for managing student attendance, promoting accountability and efficiency in academic institutions. (shrink)

The study highlights the advantages telemedicine offers, such as increased flexibility, reduced commuting time, and better time management. It also examines the challenges doctors face, including the demands of always being digitally accessible, the learning curve of new technologies, and the difficulty in maintaining boundaries between personal and professional life. Using both quantitative and qualitative data, this research compares the telemedicine adaptation experiences of doctors in public versus private hospitals, considering factors such as hospital infrastructure, support systems, and patient load.

Bitcoin, as a decentralized digital currency, has undergone extreme price fluctuations over the years. Predicting its future price presents a significant challenge due to its volatile nature and susceptibility to various external factors, including market sentiment, regulations, and technological developments. This research aims to build an advanced forecasting model to predict Bitcoin’s price movements accurately. We leverage historical price data and apply cutting-edge machine learning techniques, including Long Short-Term Memory (LSTM) networks and Gradient Boosting Machines (GBM). By comparing these methods (...) with existing models, we evaluate their performance in predicting Bitcoin’s future prices, which is vital for traders, investors, and financial institutions in making informed decisions. (shrink)

Closed-Circuit Television (CCTV) systems are essential in modern security setups because they provide continuous surveillance, acting as both a deterrent and a critical tool for monitoring and evidence collection. Unlike human guards who can be limited by fatigue and blind spots, CCTV cameras offer consistent, 24/7 coverage of key areas. They fill gaps in the current security system by enabling real-time monitoring and recording incidents for later review, ensuring that potential security breaches are detected and addressed more effectively. This enhances (...) overall security effectiveness and reduces the reliance on human intervention. The integration of Artificial Intelligence and Machine Learning (AIML) techniques with existing CCTV networks presents a promising approach to address critical challenges in urban environments. This project examines how AIML can be leveraged for crowd management, crime prevention, and work monitoring using CCTV infrastructure. For crowd management, AIML enables automated crowd counting and density estimation, facilitating efficient allocation of resources during events and emergencies. In crime prevention, AIML algorithms analyze video feeds in real-time to detect suspicious activities and identify anomalies, aiding law enforcement in proactive interventions. Additionally, AIML enhances workplace monitoring by tracking productivity metrics, ensuring compliance with safety protocols, and optimizing operational workflows. Through these applications, AIML empowers cities and organizations to improve public safety, enhance operational efficiency, and make data-driven decisions based on insights derived from CCTV footage. The integration of AIML with existing CCTV networks represents a transformative advancement in urban surveillance and management practices, offering scalable solutions for diverse urban challenges. (shrink)

This research focuses on developing an advanced UAV detection system using state-of-theart YOLOv8 and YOLOv9 models. By training these models on diverse datasets, we aim to enhance real-time detection accuracy and precision under various environmental conditions. The system is designed to improve UAV safety and minimize bird collisions. Through rigorous benchmarking, we demonstrate significant improvements in detection performance, accuracy, and computational efficiency compared to existing approaches. A user-friendly web interface, built using HTML, CSS, and Flask, provides real-time visualization of detection (...) results and system performance. This innovative system holds immense potential for applications in defense, airspace safety, wildlife conservation, and other UAV-dependent sectors. (shrink)

This project focuses on estimating reservoir inflows by integrating rainfall data, soil moisture levels in the catchment area, and releases from upstream reservoirs, coupled with an automated gate control system to prevent flooding in the basin. Utilizing hydrological models, the methodology predicts runoff from rainfall, adjusted for current soil moisture to enhance accuracy. Real-time data from upstream releases further refines inflow predictions. The automated system leverages predictive analytics and real-time monitoring to optimize gate operations, ensuring moderate water releases to maintain (...) safe reservoir levels and mitigate downstream flooding. This approach aims to enhance flood management and sustainable water resource use through advanced modelling and automation. (shrink)

Phishing attacks are an increasingly common cyber threat that exploit the trust users place in genuine websites by mimicking their look and feel. This research proposes a novel AI-based detection system designed to identify phishing domains that imitate authentic websites. Our approach leverages machine learning algorithms to analyze visual similarities, domain patterns, and metadata between phishing and legitimate sites. Through this method, the system detects phishing attempts before users are deceived. Key results indicate a high detection accuracy and a reduction (...) in false positives, which demonstrate the potential of this method for enhancing cybersecurity. Future improvements in realtime detection and integration into security protocols are discussed. (shrink)

The AI-Enabled Water Well Predictor is a machine learning-based solution aimed at accurately predicting optimal drilling locations for water wells. This project leverages artificial intelligence to analyze vast datasets, including geological, hydrological, environmental, and meteorological data, to pinpoint areas with the highest likelihood of accessible groundwater. By integrating multiple data sources, the AI model identifies patterns and correlations that are difficult to detect through traditional methods, significantly increasing the reliability of well placement predictions. In regions where water scarcity is prevalent, (...) especially in rural and underdeveloped areas, this solution can provide a cost-effective, data-driven approach to well drilling. The AI-Enabled Water Well Predictor reduces the risks and costs associated with drilling unproductive wells, enhancing the success rate of water access initiatives. The system empowers governments, NGOs, and local communities to make informed decisions, promoting sustainable water resource management and supporting efforts to improve public health, agriculture, and economic resilience in water-stressed regions. (shrink)

Image captioning has seen remarkable advancements with the integration of deep learning techniques, notably Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, for generating descriptive captions for images. Despite these improvements, capturing intricate details and context remains a challenge. This project introduces an enhanced image captioning model that integrates transformers with an attention mechanism to address these limitations. By leveraging CNNs for feature extraction and LSTMs for sequence generation, while utilizing transformers to apply sophisticated attention to significant image (...) regions, the proposed model aims to generate more contextually rich and coherent captions. Experimental results indicate that incorporating transformers with attention mechanisms leads to a significant enhancement in caption accuracy and descriptiveness, surpassing traditional CNN-LSTM models. This advancement is particularly beneficial in various applications, including assistive technologies for the visually impaired, content-based image retrieval systems, automatic image annotation for digital asset management, and improved human-computer interaction. This approach represents a substantial step forward in achieving more precise and detailed image captioning, with potential impacts across numerous fields. (shrink)

By embracing AI-driven HR analytics, organizations can anticipate market shifts, prepare their workforce for future challenges, and stay ahead of the competition. This study outlines the essential components of AI-driven HR analytics, demonstrates its impact on workforce agility, and concludes with potential future enhancements to further optimize HR functions. Key words: Predictive Workforce Analytics, Talent Optimization, Machine Learning in.

Effective water management is vital for sustainable agriculture. This project introduces an IoT-based, AI-driven irrigation system that optimizes valve control using real-time soil moisture data. Soil moisture sensors continuously assess root zone moisture and relay this data to an AI framework, determining precise irrigation needs. The system autonomously adjusts valves according to environmental conditions, minimizing water waste and enhancing crop hydration. Machine learning algorithms, notably the random forest algorithm, analyze soil moisture and weather variables to inform irrigation schedules.

Heart disease is a major challenge for global health, along with high morbidity and mortality. The earlier it is diagnosed, the better the outcome of the patient given timely intervention. This project employs a form of machine learning to train and create a risk assessment model of heart disease from the user-submitted data. The model employs the Random Forest algorithm, one of the most accurate robust algorithms available. We will use a dataset having patient records, such as age, gender, blood (...) pressure, and others. Other medical indicators. Therefore, data preprocessing and feature selection will be applied to enhance the model performance. The main idea is to design an interactive web application which can provide easy access to predict heart disease risk assessment. Users input health parameters, and the application, developed with Streamlit, indicates an immediate prediction for heart disease risk. The interface can be accessed by anyone without technical backgrounds. After the submission, it will process the data through the machine learning model, and risk assessment will be shown. This immediately outcome gotten from this type of risk assessment may prove to be an early warning for patients as they are encouraged to seek medical evaluation in appropriate times. This project, therefore, aims to bridge the gap that exists between advanced machine learning and an actual setting of practical healthcare where users can be well cared for proactively with their hearts and in an effort to ease the burden of heart diseases through early detection and intervention. (shrink)

The exponential growth of data storage requirements has become a pressing challenge in hybrid cloud environments, necessitating efficient data deduplication methods. This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework (...) achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

The rapid advancement of artificial intelligence (AI) technologies has revolutionized various industries, including the realm of education and career guidance. This project endeavors to harness the power of AI to develop a sophisticated career guidance application that offers personalized and effective recommendations to students and job seekers. The primary objective of this project is to address the limitations of traditional career guidance methods, which often lack customization and fail to adapt to individual preferences, skills, and aspirations. Through the integration of (...) AI algorithms, NLP techniques, and ML models, our application aims to provide accurate interest assessments, skill matching, and tailored educational pathways. (shrink)

In a country as linguistically diverse as India, the demand for localized video content is rapidly increasing. Dubbing English videos into Indian languages, such as Hindi, Tamil, Telugu, and Bengali, is critical to making content accessible to wider audiences. This paper presents a software solution that automates the dubbing process using a combination of technologies like Automatic Speech Recognition (ASR), Machine Translation (MT), andText-to-Speech (TTS). The process begins by converting English audio into text using ASR, followed by translation into the (...) target Indian language through advanced Natural Language Processing (NLP) models. The translated text is then synthesized into natural-sounding speech using TTS engines like Google Text-to-Speech (gTTS). Finally, the dubbed audio is synchronized with the original video to ensure proper lip-sync and timing. This automated approach streamlines the dubbing process, reducing time and cost while maintaining translation accuracy, voice quality, and synchronization. The software offers an efficient solution for content creators, businesses, and educators looking to expand their reach across India’s multilingual population. (shrink)

Substation maintenance is a critical aspect of ensuring the reliable operation and longevity of electrical power systems. This process involves a range of activities aimed at preserving the functionality and safety of substations, which serve as pivotal nodes in the electrical grid. Effective maintenance strategies encompass routine inspections, preventive measures, and corrective actions to address equipment wear and potential failures. Advances in technology have introduced innovative tools and techniques for substation maintenance, including automated diagnostic systems, predictive analytics, and real-time monitoring (...) solutions. For that an Intelligent chat bot is build, Substation maintenance is a critical aspect of ensuring the reliable operation and longevity of electrical power systems. This process involves a range of activities aimed at preserving the functionality and safety of substations, which serve as pivotal nodes in the electrical grid. Effective maintenance strategies encompass routine inspections, preventive measures, and corrective actions to address equipment wear and potential failures. Advances in technology have introduced innovative tools and techniques for substation maintenance, including automated diagnostic systems, predictive analytics, and realtime monitoring solutions. (shrink)

The increasing demand for reliable power sources has made diesel generators essential in various industries. However, traditional monitoring methods often rely on manual inspections, resulting in inefficiencies, unplanned downtimes, and higher operational costs. This project proposes a digital monitoring system for diesel generators that leverages advanced sensor technologies and real-time data analytics to overcome these challenges. The system will utilize Internet of Things (IoT) devices to collect key operational parameters, such as fuel levels, temperature, vibration, and performance metrics, enabling comprehensive (...) monitoring of generator health. By integrating machine learning algorithms, the system will analyze historical and real-time data to predict potential failures and optimize maintenance schedules, shifting from reactive to proactive management. A user-friendly interface will allow operators to visualize generator performance and receive instant alerts for detected anomalies, enhancing decision- making processes, reducing downtime, and lowering maintenance costs—ultimately increasing the reliability and efficiency of power supply. Additionally, the system facilitates remote monitoring, providing continuous oversight regardless of location. This project not only aims to enhance the operational efficiency of diesel generators but also lays the groundwork for smarter energy management solutions in the future. The successful implementation of this digital monitoring system could serve as a model for similar applications across various sectors, contributing to sustainability and operational excellence in energy management. Through this innovative approach, we aim to ensure diesel generators operate at optimal performance levels while minimizing environmental impact. Keywords. Diesel Generators, Digital Monitoring, IoT Devices, Real-time Data Analytics, Machine Learning. (shrink)

"Genius AI" is an innovative SaaS platform poised to revolutionize the landscape of artificial intelligence tools. This dynamic platform integrates a diverse array of AI functionalities, including conversation generation, code generation, music generation, and video generation, all in one centralized hub. At its core, Smart AI harnesses cutting-edge machine learning algorithms and neural networks to empower users with unparalleled capabilities. The conversation generation feature enables seamless interaction with users through natural language processing, facilitating engaging and lifelike conversations. At its core, (...) Smart AI harnesses cutting-edge machine learning algorithms and neural networks to empower users with unparalleled capabilities. The conversation generation feature enables seamless interaction with users through natural language processing, facilitating engaging and lifelike conversations. At its core, Smart AI harnesses cutting-edge machine learning algorithms and neural networks to empower users with unparalleled capabilities. The conversation generation feature enables seamless interaction with users through natural language processing, facilitating engaging and lifelike conversations. For developers and coders, Smart AI offers a ground breaking code generation tool, streamlining the process of software development by automatically generating code snippets tailored to specific requirements and programming languages. Moreover, music aficionados can leverage Smart AI's advanced algorithms to compose original melodies, harmonies, and rhythms, personalized to suit any style or genre. Meanwhile, the video generation functionality empowers content creators with the ability to effortlessly produce high-quality videos, complete with customizable elements and visual effects. With its user-friendly interface and scalable architecture, Smart AI caters to a broad spectrum of industries and applications, from e-commerce and marketing to entertainment and education. Whether enhancing customer engagement, accelerating software development, or fostering artistic expression, Smart AI unlocks endless possibilities for innovation and creativity. Join the forefront of AI-driven transformation with Smart AI and redefine the way we interact with technology. (shrink)

Our findings reveal that implementing accelerated computing can achieve substantial improvements, often reducing computation times by more than 60% compared to traditional sequential methods. This paper details the experimental setup, including algorithm selection and parallelization techniques, and discusses the role of memory bandwidth and latency in achieving optimal performance. Based on the analysis, we propose a streamlined methodology to guide the deployment of accelerated computing frameworks in various industries. Concluding with a discussion on future directions, we highlight potential advancements in (...) hardware architectures and software optimizations that could further augment computational efficiency and scalability in accelerated computing. (shrink)

The EdTech revolution in India has emerged as a transformative force, particularly during and after the COVID-19 pandemic, when traditional education systems faced unprecedented disruptions. While digital technologies have unlocked new opportunities for teaching and learning, they have also exposed systemic inequities and deepened the existing digital divide. This paper examines how EdTech is reshaping India's education landscape by addressing these challenges, with a focus on both the opportunities it presents and the barriers it creates. The shift to digital learning (...) platforms has empowered students with access to a broader array of learning resources, interactive content, and personalized learning paths. However, millions of students, particularly those from rural areas or lower-income families, struggle to access these resources due to a lack of digital infrastructure, reliable internet connectivity, and affordability of devices. Furthermore, the role of teachers has evolved, requiring new pedagogical approaches and technical skills to effectively harness these digital tools. (shrink)

The AICTE approval process for institutions plays a critical role in regulating technical education in India, ensuring quality and adherence to established standards. However, the current system is cumbersome, leading to delays and inefficiencies. This paper proposes the development of an AI-powered assistive portal that automates key stages of the approval process. The portal aims to reduce manual errors, provide real-time feedback, and enhance user experience for stakeholders, thereby improving overall system efficiency and transparency.

This project explores the issue of high dropout rates in school education. We utilize a machine learning-driven approach to analyze data on student demographics, academic performance, attendance, and socioeconomic factors. By identifying at-risk students early, we aim to provide targeted interventions that will reduce dropout rates. This document outlines the structure, methodology, and findings of the project, leveraging techniques such as data preprocessing, model training, hyperparameter tuning, and risk stratification.

The study highlights the advantages telemedicine offers, such as increased flexibility, reduced commuting time, and better time management. It also examines the challenges doctors face, including the demands of always being digitally accessible, the learning curve of new technologies, and the difficulty in maintaining boundaries between personal and professional life. Using both quantitative and qualitative data, this research compares the telemedicine adaptation experiences of doctors in public versus private hospitals, considering factors such as hospital infrastructure, support systems, and patient load.

The proposed sensor device for dragline operator is designed to enhance environmental safety by detecting and alerting operators to climate change impacts and potential hazards. Utilizing advanced sensor technology, this device continuously monitors weather conditions, such as heavy rain, strong winds, and extreme temperatures. When a hazard is detected, the device provides real-time alerts through an intuitive interface, enabling operators to take immediate action to mitigate risks. This proactive approach ensures operators are well-informed about their surroundings, significantly reducing the risk (...) of accidents and promoting a safer working environment. Additionally, the sensor device seamlessly integrates with existing dragline systems, ensuring compatibility and ease of adoption. By enhancing situational awareness and facilitating timely responses to environmental threats, this sensor device represents a critical advancement in the safety and efficiency of dragline operations in the mining and construction indus. (shrink)

Accelerated computing leverages specialized hardware and software techniques to optimize the performance of computationally intensive tasks, offering significant speed-ups in scientific, engineering, and data-driven fields. This paper presents a comprehensive study examining the role of accelerated computing in enhancing processing capabilities and reducing execution times in diverse applications. Using a custom-designed experimental framework, we evaluated different methodologies for parallelization, GPU acceleration, and CPU-GPU coordination. The aim was to assess how various factors, such as data size, computational complexity, and task concurrency, (...) impact processing efficiency. (shrink)

In an increasingly interconnected world, language barriers continue to hinder effective communication across diverse populations. BharathLingo addresses this challenge by offering a user-friendly text translation platform specifically designed to facilitate translation between English and multiple Indian regional languages such as Hindi, Telugu, Bengali, Tamil, and more. Developed using Flask and powered by machine translation algorithms, BharathLingo allows users to input text manually or upload files for translation, delivering instant and accurate translations. The platform’s seamless interface and commitment to privacy make (...) it an ideal solution for students, professionals, and travellers seeking to bridge linguistic divides. This paper outlines the architecture, design choices, and challenges of developing BharathLingo, including its deployment using the Waitress web server. (shrink)

The EdTech revolution in India has emerged as a transformative force, particularly during and after the COVID-19 pandemic, when traditional education systems faced unprecedented disruptions. While digital technologies have unlocked new opportunities for teaching and learning, they have also exposed systemic inequities and deepened the existing digital divide. This paper examines how EdTech is reshaping India's education landscape by addressing these challenges, with a focus on both the opportunities it presents and the barriers it creates.

Migrating legacy database applications to modern cloud-based solutions, such as Snowflake, is becoming essential for organizations aiming to leverage scalable, efficient, and cost-effective data solutions. Legacy databases, typically bound to on-premises infrastructure, often lack the flexibility required by contemporary data analytics and storage needs. Snowflake, a cloud-native data platform, provides a versatile environment that enables efficient data storage, sharing, and processing. This research examines a structured methodology for migrating legacy database applications to Snowflake, addressing key stages from initial assessment to (...) data transformation, integration, and performance optimization. A systematic, step-by-step migration process is implemented to minimize risks, reduce downtime, and ensure data integrity. (shrink)

Results indicate substantial improvements in efficiency, accuracy, and personalized care, but also highlight the challenges of data privacy, ethical considerations, and system scalability. By providing a structured analysis, this research contributes insights into optimizing generative AI deployments for both banking and healthcare, ensuring a balance between innovation and risk management. The study concludes with recommendations for future research directions, including advanced model training, ethical guidelines, and enhanced privacy measures. These insights aim to inform practitioners on the benefits of generative AI, (...) ensuring sustainable integration into banking and healthcare ecosystems. (shrink)

The primary objective of this research is to enhance the precision and speed of stock handling while minimizing human intervention and error. Our design incorporates state-of-the-art sensors, real-time tracking systems, and autonomous robots programmed with advanced algorithms for object identification, gripping, and movement. We propose a systematic workflow for automating the storage and retrieval process, starting from the identification of the stock to its precise placement and retrieval within the storage facility. The design also addresses potential challenges such as robot (...) mobility, collision avoidance, and space optimization. Performance metrics, including accuracy, time efficiency, and system scalability, are measured using simulation-based experiments in a controlled environment. The results show significant improvements in operational efficiency compared to traditional stock management approaches. This integration paves the way for future advancements in fully automated warehouses, reducing the need for human labor and increasing reliability. Finally, we discuss potential enhancements, including AI-based decision-making algorithms, multi-robot collaboration, and integration with Internet of Things (IoT) for real-time data analysis and continuous system improvement. (shrink)

he health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. (...) The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed by (...) training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

This study explores the role of government policies, public-private partnerships, and emerging technologies in bridging the digital divide, ensuring inclusive and equitable access to quality education for all students. By analyzing case studies and statistical data, this research identifies key areas where the EdTech revolution can be leveraged to close gaps in the education system while fostering innovation in pedagogical practices. Finally, the paper presents recommendations for ensuring that the EdTech revolution contributes to systemic equity, rather than exacerbating existing disparities.

Although Internet of Things (IoT) devices are being rapidly embraced worldwide, there are still several security concerns. Due to their limited resources, they are susceptible to malware assaults such as Gafgyt and Mirai, which have the ability to interrupt networks and infect devices. This work looks into methods based on machine learning to identify and categorize malware in IoT network activity. A dataset comprising both malware and benign traffic is used to assess different classification techniques, such as Random Forest, XGBoost, (...) Logistic Regression, etc., for multi-class malware detection. After a great deal of empirical testing, XGBoost comes out on top, providing 99.9% recall and accuracy. Both known and unknown malware can be detected by the trained model with remarkable precision. The use of transfer learning, where the XGBoost model is used as a basis for the rapid construction of new malware detection models, is one of the major innovations put forth. This makes it possible to quickly adjust to new dangers. More information about how real-time network traffic can be monitored with the help of the created model to find irregularities and sound the alarm. An intelligent and proactive security solution for IoT environments is offered by the machine learning technique that is being discussed. This is an efficient defense against malware because of its high accuracy, low false positive rate, real-time detection capability, and adaptability to new malware varieties changing risks associated with IoT. The suggested methods will assist in securing susceptible IoT devices and networks from obstructive malware assaults. (shrink)

We propose a systematic workflow for automating the storage and retrieval process, starting from the identification of the stock to its precise placement and retrieval within the storage facility. The design also addresses potential challenges such as robot mobility, collision avoidance, and space optimization. Performance metrics, including accuracy, time efficiency, and system scalability, are measured using simulation-based experiments in a controlled environment. The results show significant improvements in operational efficiency compared to traditional stock management approaches. This integration paves the way (...) for future advancements in fully automated warehouses, reducing the need for human labor and increasing reliability. Finally, we discuss potential enhancements, including AI-based decision-making algorithms, multi-robot collaboration, and integration with Internet of Things (IoT) for real-time data analysis and continuous system improvement. (shrink)

The health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. (...) The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. (shrink)

The rapid adoption of telemedicine has transformed healthcare delivery, especially during the COVID-19 pandemic. This digital shift has enabled medical professionals to offer consultations and manage patients remotely, ensuring continuity of care while reducing exposure risks. However, the integration of telemedicine has presented both opportunities and challenges for doctors, particularly in terms of their work-life balance. This paper explores the digital adaptation of doctors in public and private hospitals concerning telemedicine practices and its impact on their work-life harmony. The study (...) highlights the advantages telemedicine offers, such as increased flexibility, reduced commuting time, and better time management. It also examines the challenges doctors face, including the demands of always being digitally accessible, the learning curve of new technologies, and the difficulty in maintaining boundaries between personal and professional life. Using both quantitative and qualitative data, this research compares the telemedicine adaptation experiences of doctors in public versus private hospitals, considering factors such as hospital infrastructure, support systems, and patient load. (shrink)

This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage (...) needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

The study outlines the technical challenges encountered during migration, including data compatibility issues, schema conversion, and security compliance, as well as strategies to mitigate these obstacles. Testing and validation techniques are applied throughout the migration, highlighting essential checkpoints to confirm data accuracy and optimal performance in the Snowflake environment. Postmigration performance metrics are evaluated to illustrate improvements in query execution, scalability, and overall system efficiency compared to the legacy system. The results underscore the advantages of Snowflake’s architecture in handling high-volume, (...) concurrent queries without compromising speed or accuracy. (shrink)

This paper provides a comprehensive workflow for organizations considering a move from legacy database systems to Snowflake, demonstrating how a structured migration process can result in enhanced data accessibility, operational flexibility, and long-term scalability. Future directions for research are also discussed, including integration with advanced analytics and machine learning applications, which could further augment Snowflake’s utility in big data ecosystems.

The research introduces to the design and implementation of the Cybersecurity Enabled Smart Controller for the grid connected microgrid which uses the combination of the Web technologies and the authentication mechanism which enables real time monitoring with help of the Dashboards built using Chart.js which is a framework of JavaScript. Due to the role and connection of the Microgrids with the critical infrastructure they are vulnerable to the cyber threats. This project leverages the use of HTML, CSS, JavaScript and MongoDB (...) to create a secure and responsive system ensuring the operational resilience and data integrity. By focusing on simplicity the project uses user friendly dashboard for monitoring the microgrid data , controlling the grid crafted with HTML, CSS and JavaScript allowing the users to visualize the performance metrics using Chart.js for real time data representation. This methodology focuses on creating secure communication channels which enables effective data management through MongoDB providing a responsive experience on the web application. This approach allows users to monitor grid operations continuously and the access management effectively. The findings focused on creating a user friendly interface using the HTML, CSS, JavaScript which give users the access to monitor and control microgrid operations seamlessly. Utilizing the Chart.js enabled the users to visualize real time performance metrics which enables the operators to make decisions quickly addressing operational efficiencies. This paper proposes a role-based access control system (RBAC) to protect and manage the smart controller of a grid-connected microgrid. The design integrates a login page developed with HTML, JavaScript, and MongoDB for authentication and user management, with three distinct roles: Admin, Power Operator, and User. Each role has access to specific functions in the dashboard, reflecting their respective permissions. The backend was implemented using Node.js and Express to monitor network traffic, detect potential threats, and prevent unauthorized operations. The results demonstrate that the system provides effective protection against cyber threats while ensuring that different users can access the functionalities appropriate to their roles. (shrink)

The health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. (...) The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. A. (shrink)

The accuracy levels of VGG19 and 3D CNN are compared using the performance metrics. This comparison helps in identifying which model performs better in the task of facial reconstruction from distorted images. Visualizing the results in the form of a graph provides a clear and concise way to understand the comparative performance of the algorithms. The ultimate goal of this project is to develop a system that can accurately reconstruct distorted faces, which can be invaluable in identifying accident victims or (...) assisting in medical treatments. By providing a reliable method for facial reconstruction, this technology can potentially save lives and improve outcomes for individuals involved in accidents. (shrink)

The project focuses on the development of a tool for identifying and extracting Forward Error Correction (FEC) schemes from unknown demodulated signals. FEC is a vital communication technique that ensures error-free data transmission without the need for retransmission, particularly in satellite communications, digital broadcasting, and deepspace applications. The proposed solution involves using Python to preprocess signals, detect FEC schemes, and then extract the specific coding parameters. Different FEC schemes such as BCH, Convolutional Codes, Turbo Codes, and LDPC codes are explored (...) for their unique characteristics and error correction capabilities. The tool aims to automate the detection and extraction of FEC schemes, thus improving the accuracy and efficiency of signal processing in communication systems. (shrink)

The AWSAR Job Portal is developed to provide job seekers with a platform to register, log in, and apply for jobs with ease. The purpose of this project was to create a full-stack web application, ensuring smooth user experience and addressing common challenges such as responsiveness and compatibility across browsers. The project used technologies like React.js, Tailwind CSS for frontend development, and Node.js with Express.js for backend management. MongoDB was implemented for database storage. The key challenge faced was the unresponsiveness (...) of the 'Apply to a job' button post-login, which was successfully resolved through code optimization. This manuscript discusses the system's architecture, methodology, issues encountered, and future enhancements. (shrink)