Volume 13 | Issue 4

 Abstract

The rapid growth of cloud-based applications has led to an exponential increase in various file formats that demand efficient and scalable storage solutions. This project presents a dynamic memory management framework designed for storage and processing of multi-format files in an AWS cloud environment. This framework provides an intelligent and automated approach to file classification, storage and lifecycle management using AWS services such as S3, Lambda, Dynamodb, and CloudWatch. Dynamic adaptation to file types, such as text, images, videos, structured data, and more ensures optimized performance, cost-effectiveness and easy access. Additionally, the framework includes actual monitoring and protocols for improved visibility and control. This solution demonstrates how to integrate cloud-native tools to create flexible and scalable architectures for modern data management challenges in heterogeneous environments.

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 Keywords

Cloud Storage, AWS, Multi format File Handling, Dynamic Storage Management, Serverless Architecture, Amazon S3, AWS Lambda

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The biological processes of human beings are governed by an internal mechanism denoted as circadian rhythm which operates on a 24 hour basis. This clock influences sleep patterns, hormone production, digestion, metabolism and a lot else. Not to mention, it significantly affects the way the body reacts to medicines. The time of day at which a medicine is taken - whether for managing blood pressure, asthma, cancer or any other ailment - often determines its efficacy and potency of side effects. This variation happens because the human body metabolizes drugs at different rates throughout the day. The present review examines the body's natural timing in relation to medication, and the necessity to take medicines at certain times for maximal safety, minimal risk, and greater efficiency. It also discusses the development of smart automated drugs and wearables that enable real time monitoring and could synchronize therapy with the patient's body clock. Understanding body rhythm allows treatment customization to ensure personal health care that is efficient.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Circadian rhythm, Chronopharmacology, Chronotherapy, Biological clock, Drug efficacy, Chronomedicine

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This system explores the core principles of client-server (C/S) architecture, emphasizing the role of socket programming in network-based communication. It outlines a structured approach to software design that enables efficient interaction between client and server processes through socket mechanisms. Additionally, it provides practical insights into implementing connection-oriented services. At the transport layer, TCP ensures reliable, sequential data transmission, while IP offers lightweight, connectionless communication, each utilizing distinct socket types. A comprehensive understanding of these protocols, their functions, and their integration within applications is crucial for optimizing network performance. By separating protocol-specific operations from application-level logic, developers can build scalable, secure, and high-performance client-server systems. Moreover, this system explores enhanced features such as a graphical user interface (GUI), video and audio calling, file sharing, and dynamic password authentication for clients. On the server side, security measures, including static login credentials, further strengthen system protection. These elements collectively contribute to a more robust and user-friendly client-server model.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Client-Server Architecture, Socket Programming, TCP/IP Protocols, Graphical User Interface (GUI), Dynamic Password Authentication, Real-time communication, Audio and Video Calling, File Sharing, and Real-time Messaging, Java Socket Programming

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Blood donation is a vital healthcare service that saves lives, yet the unavailability of donors at critical moments remains a major challenge. The lack of a centralized and efficient system often leads to delays in finding suitable donors, which can be life threatening. Traditional methods of donor search and blood bank coordination are time-consuming and inefficient, highlighting the need for a digital solution. The paper presents a web-based Blood Donation System (BDS) designed to streamline the donor-recipient connection and improve the accessibility of blood donations. The platform allows individuals to register as donors, making their availability known to hospitals and patients in need. A built-in search function enables users to find donors based on blood type and location, ensuring quicker response times in emergencies. Additionally, donor health information is securely stored in a centralized database, assisting medical professionals in making informed decisions. Unlike conventional methods, the proposed system eliminates communication barriers by facilitating direct interaction between donors and recipients. Hospitals can post blood requests, and the system will identify and notify suitable donors in real time. The approach enhances the efficiency of blood donation services, reducing delays and improving the chances of timely medical intervention. The Blood Donation System provides a user-friendly and accessible interface, ensuring that both donors and hospitals can easily navigate the platform. By leveraging digital technology, the system aims to create a structured and reliable network for blood donation, ultimately contributing to a more responsive and effective healthcare infrastructure.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blood Donation, Web-Based System, XAMPP, PHP, MySQL, Hospital Management, Healthcare Technology

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Agricultural waste mismanagement poses significant environmental and economic challenges. research introduces an innovative farm waste management system leveraging AI-based waste classification, digital waste tracking, and an integrated marketplace for non-disposable waste. Utilizing ONNX-based AI models, location-based recycling recommendations, and educational resources, study demonstrates the effectiveness of technology-driven solutions in reducing pollution and enhancing sustainability in the agricultural sector. The results highlight improved waste management practices, increased awareness among farmers, and a structured approach to farm waste disposal.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Farm Waste Management, AI Waste Classification, Sustainable Agriculture, Recycling, Waste Tracking

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The agricultural sector is often burdened by middlemen and brokers, leading to unfair pricing for farmers and higher costs for consumers. Many farmers struggle to receive a fair value for their produce, while customers face affordable challenges in accessing fresh, healthy fruits and vegetables. This project introduces a digital e-commerce platform to bridge this gap by directly connecting farmers with consumers. By eliminating intermediaries, the platform ensures farmers maximize their earnings, while buyers receive farm-fresh produce at reasonable prices. Farmers can list their produce, set their prices, and sell directly to consumers without relying on third-party distributors. For consumers, the platform offers a direct farm-to-table experience, ensuring high-quality, chemical-free, and organic food options. The integration of demand forecasting and AI-driven recommendations helps both farmers and customers make informed decisions, reducing wastage and optimizing supply chain efficiency. System research evaluates the platform's impact on the economic empowerment of farmers, improved market accessibility, and the overall efficiency of agricultural trade. By leveraging technology to create a sustainable and fair agricultural ecosystem, this project contributes to enhancing food security, promoting transparency, and fostering economic growth in rural communities.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

E-commerce, Agriculture, Agri-Tech, Digital marketplace, Farmer-to-consumer, Supply chain, Agricultural marketing, Rural digitization, Online fruit market, E-grocery, Agriculture innovation.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

: The rapid advancement of technology has transformed healthcare data management, especially in developing regions where data networks are vital. Research study proposes a cloud-based model for centralized patient data storage, integrating authentication, storage, and cloud messaging to enhance information sharing among healthcare providers. It also introduces Location-Based Services (LBS) for real-time access to nearby medical facilities, utilizing network analysis and the Bidirectional algorithm in ArcGIS to optimize emergency response routes. Additionally, an Android-based medical reminder system is developed to improve doctor-patient interaction and medication adherence through alarms, notifications, and access to medical resources. By combining cloud data management, LBS, and smart reminders, research aims to optimize healthcare delivery, boost patient engagement, and improve health outcomes.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Healthcare Data Management, Cloud-Based Model, Patient Data Storage, Data Integration, Cloud Messaging, Location-Based Services (LBS), Emergency Response, Network Analysis, Bidirectional Algorithm, Medical Reminder System, Medication Adherence, Doctor-Patient Interaction, Patient Engagement, Health Outcomes.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

AI-generated contracts are becoming significant legal elements as artificial intelligence systems increasingly influence legal and commercial practices. The Indian legal system faces challenges regarding enforceability, liability, and regulatory compliance under the Indian Contract Act, 1872. This paper examines the existing legal framework's deficiencies and proposes necessary reforms to ensure clarity in AI-generated contractual agreements.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AI Contracts, Indian Contract Act, Legal Liability, Regulatory Compliance, Digital Transformation, Data Protection, Contract Automation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study presents a sustainable approach to utilizing human hair waste as a valuable resource for developing eco-friendly cosmetic products. Through the extraction of key biomolecule--keratin and melanin--from discarded salon hair, the project aims to create advanced formulations for sunscreens and hair care applications. Keratin contributes to hair strength and repair, while melanin offers natural UV protection. The extraction process is optimized using controlled temperature treatments and stabilized with ionic liquids to preserve biomolecule integrity. A decision tree algorithm is employed to determine the optimal processing conditions based on the quality and composition of collected hair samples. This initiative not only reduces salon waste and environmental impact but also supports the production of biodegradable and effective cosmetic alternatives. The outcomes suggest promising avenues for sustainable product innovation in the beauty industry.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Hair-derived biomolecules, Sustainable beauty products, Keratin extraction, Melanin applications, Organic waste reuse, Eco-friendly cosmetics, Decision tree optimization, Circular economy in cosmetics.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Often access to learning new skills face significant barriers for students and young professionals due to finance and a lack of access to personal learning solutions. Traditional e-learning platforms capitalize on knowledge with a transaction-based model, which may create an economic barrier for many learners. In light of the discussed barriers, this paper outlines Connectra, a new mobile application focusing on peer-to-peer skill exchange. Connectra allows users to share their knowledge, and learn from other users, without the necessity of exchanging money. The platform facilitates initial connections through an in-app chat feature, enabling users to establish rapport before sharing Google Meet links to conduct live skill exchange sessions. Connectra has a dual-application design consisting of client and admin interfaces, while implementing strong security measures and user experience features. Connectra was developed using Java, XML, the Firebase Realtime Database and Cloud Storage. The new platform provides a sustainable, learning ecosystem which meets the increasing demand for skill development in an age of digital-disruption. The study reported 93% satisfaction in user experience with the Connectra platform. The results of this study indicate that peer-to-peer skill exchange models may provide an alternative to traditional e-learning platforms by providing democratization of knowledge and development of collaborative learning communities.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

skill exchange, peer learning, mobile application, firebase, non-monetary education, collaborative learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Face recognition is a powerful and widely adopted biometric technology that allows systems to automatically identify or verify an individual based on facial features. In an era where security is a major concern, face recognition presents a noncontact and highly efficient method for personal identification. Project explores a face recognition system developed using Python and OpenCV. The system detects, stores, trains, and identifies human faces by capturing and analyzing facial data. A webcam is used to collect facial images of users, which are later used to recognize the individual in real-time.. The is simple which helps non technical user to use .Project demonstrates how artificial intelligence and image processing can work together to improve digital security and ease the authentication process in real-life scenarios like attendance tracking, access control, and digital verification.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Face Recognition , Face Detection , attendance system.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Prosthetic technology has undergone a significant transformation with the advent of intelligent systems that integrate bio-signal processing, machine learning, and real-time control mechanisms. This review paper presents an in-depth exploration of a Smart Bionic Hand system that combines low-cost hardware components such as servo motors, Raspberry Pi, Arduino microcontrollers, and various sensors (EMG, flex, and gyroscopic sensors) with artificial intelligence algorithms to enable intuitive, adaptive, and affordable prosthetic solutions. The system captures electromyographic (EMG) signals from the user's muscles, interprets them using AI models, and actuates the mechanical hand to mimic natural human gestures. A feedback loop ensures real-time response and system learning, offering a high level of customization and comfort for the user. This approach addresses the shortcomings of traditional prosthetics, including high cost, lack of feedback, and poor adaptability. The review discusses system architecture, literature background, implementation details, and analytical performance of the Smart Bionic Hand in real-world scenarios, alongside future improvements.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: Smart Bionic Hand, Prosthetics, Electromyography (EMG), Artificial Intelligence, Adaptive Control, Raspberry Pi, Gesture Recognition, Low-cost Design, Bio-mechatronics, Real-time Feedback

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Distributed network attacks are referred to, usually, as Distributed Denial of Service (DDoS) attacks. These attacks take advantage of specific limitations that apply to any arrangement asset, such as the framework of the authorized organization's site. In the existing research study, the author worked on an old KDD dataset. It is necessary to work with the latest dataset to identify the current state of DDoS attacks. This paper, used a machine learning approach for DDoS attack types classification and prediction. For this purpose, used Random Forest and XGBoost classification algorithms. To access the research proposed a complete framework for DDoS attacks prediction. For the proposed work, the UNWS-np-15 dataset was extracted from the GitHub repository and Python was used as a simulator. After applying the machine learning models, we generated a confusion matrix for identification of the model performance. In the first classification, the results showed that both Precision (PR) and Recall (RE) are _89% for the Random Forest algorithm. The average Accuracy (AC) of our proposed model is _89% which is superb and enough good. In the second classification, the results showed that both Precision (PR) and Recall (RE) are approximately 96% for the XGBoost algorithm. The average Accuracy (AC) of our suggested model is 96%. By comparing our work to the existing research works, the accuracy of the defect determination was significantly improved which is approximately 85% and 79%, respectively.

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 Keywords

CNN(Convolutional Neural Network), LCNN(Lookup based Convolutional Neural Network), RNN(Recurrent Neural Network), DEX(Dalvik Executables), TCP(Transmission Control Protocol), IP(Internet Protocol), HTTP(Hyper Text Transfer Protocol), ADT(Android Development Tool).

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Predicting water quality is essential for ensuring public health and sustainable water resource management. This study explores the application of machine learning algorithms, specifically Random Forest (RF) and Naive Bayes (NB), for effective water quality prediction. Using a dataset composed of various physicochemical parameters, we analyze and classify water quality indicators to assess its suitability for consumption and environmental health. Random Forest, an ensemble learning method, is leveraged for its robustness in handling large datasets and its ability to capture complex patterns in water quality features. Naive Bayes, a probabilistic classifier, complements this by providing a simple yet effective approach to classify water quality based on conditional probabilities. Both models are evaluated in terms of accuracy, precision, recall, and F1-score, with comparative analysis to highlight their strengths and limitations. The results demonstrate that combining the predictive accuracy of Random Forest with the interpretability of Naive Bayes offers a practical approach for water quality monitoring, supporting real-time decision-making and regulatory compliance in water resource management.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

These days people can't live without using social media either in personal life of individual or in the academics. All we accept that it is the contemporary technological era, where social media has emerged as a powerful influence across various spheres, including higher education. This study investigates the usage patterns of social media among teachers and students in Higher Education Institutions (HEIs) in West Bengal, India. With the increasing integration of platforms such as Facebook, YouTube, and live-streaming tools into academic practices, it is vital to understand both the opportunities and challenges they present. Utilizing a descriptive survey method, the study sampled 40 teachers and 200 students across five universities using multistage random sampling. Data were collected through two distinct questionnaires developed for teachers and students. Findings reveal that social media is predominantly used for educational purposes, communication, recreation, and encouraging social responsibility. Notably, the COVID-19 pandemic accelerated the shift in perceptions, positioning social media as a critical tool for sustaining education during crises. However, concerns such as privacy risks, distraction, and ethical issues also surfaced. The study underscores the need for strategic policies and training programs to maximize the educational benefits of social media while mitigating its drawbacks. This study may contribute to the growing body of knowledge on digital integration in higher education specially teacher education and offers valuable insights for educators, policymakers, and students aiming to navigate the digital learning environment more effectively.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Social Media, Teachers, Higher Education Institutions, Facebook, Instagram, Telegram, X, YouTube, WhatsApp

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Ayodhya, a city of profound religious and cultural significance, has emerged as a prominent destination for religious tourism in India. Known as the birthplace of Lord Rama and home to the recently inaugurated Ram Janmabhoomi Temple, Ayodhya has witnessed a rapid transformation driven by spiritual, historical, and cultural narratives. This study explores the development of religious tourism in Ayodhya from a socio-economic perspective, highlighting its impact on local communities, infrastructure, employment generation, and economic diversification. The research examines how religious tourism contributes to income opportunities, revitalizes traditional livelihoods such as handicrafts and hospitality, and fosters cultural preservation. Simultaneously, it addresses the challenges posed by rapid urbanization, environmental pressures, and socio-cultural shifts. By analysing the interplay between religious heritage and socio-economic development, the study underscores the need for sustainable tourism planning those balances economic growth with cultural integrity and community welfare. The findings aim to inform policy frameworks and development strategies to ensure inclusive and long-term benefits from Ayodhya's growing religious tourism sector.

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 Keywords

Religious Tourism, Socio-economic perspective, Cultural integrity, Historical Background and Ayodhya Municipal Corporation.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

System presents a hybrid movie recommendation system designed to merge collaborative filtering, content-based filtering, and cosine similarity, offering users personalized suggestions rooted in their preferences and viewing history. Built as a web application with an HTML/CSS frontend, the system dynamically retrieves movie data via APIs to circumvent static dataset limitations. User engagement is heightened through visual comparisons of watched and recommended content. Sentiment analysis of reviews, implemented using Support Vector Machines (SVM), further refines recommendation accuracy. By integrating collaborative and content-based methods, the system addresses challenges like data sparsity and the cold start problem. Future plans include transitioning the platform to Flutter for improved interactivity and mobile compatibility. System underscores the efficacy of hybrid models in enhancing recommendation diversity and user satisfaction.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

hybrid recommendation system, collaborative filtering, content-based filtering, sentiment analysis, Api integration, mobile adaptation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid expansion of the Internet of Things (IoT) has enabled transformative applications across various sectors such as healthcare, smart cities, and industrial automation. However, this surge in connectivity has simultaneously exposed IoT networks to heightened cybersecurity threats, particularly Distributed Denial of Service (DDoS) attacks. Due to limited processing and security capabilities, IoT devices are easily compromised, making traditional Intrusion Detection Systems (IDS) inadequate in such environments. This study introduces an adaptive and lightweight IDS framework that utilizes a hybrid ensemble of bio-inspired optimization techniques--Spotted Hyena Optimizer (SHO), Parrot Optimizer (PO), and Grey Wolf Optimizer (GWO)--for feature selection. By embedding a self-attention mechanism, the system dynamically identifies key features that enhance detection accuracy while minimizing computational costs. The selected features are used to train machine learning classifiers including Decision Tree, SVM, Random Forest, XGBoost, and shallow Neural Networks. Evaluated on the UNSW-NB15 dataset, the proposed model demonstrates high performance with reduced false positives and latency, offering a scalable and real-time solution for DDoS mitigation in IoT ecosystems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Internet of Things (IoT), Distributed Denial of Service (DDoS), Intrusion Detection System (IDS), Bio-Inspired Optimization, Feature Selection, Machine Learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract: With the rapid advancement of wireless communication technologies, 5G mm Wave patch antennas have become increasingly important due to their capability to operate at high frequencies, their small size, and their support for high-speed data transmission. However, the development and analysis of these antennas often involve time-consuming full-wave electromagnetic simulations, which can slow down the design cycle. To overcome this limitation, Artificial Neural Networks (ANNs) offer a promising alternative by enabling quick prediction of essential antenna characteristics. In this work, a compact patch antenna operating in two bands 38.93-39.63GHz and 41.79 GHz to 42.92 GHz band is introduced, making it well-suited for 5G applications. The trained ANN model provides fast and accurate S11 predictions, effectively minimizing the need for lengthy simulation processes.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial neural network, 5G antenna, Machine Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

dakshin-purv asia mein bharat ki pahuch:act east niti ki uplabdhiyan aur chunautiyan

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Creative Commons Attribution 4.0 and The Open Definition

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dakshin-purv asia mein bharat ki pahuch:act east niti ki uplabdhiyan aur chunautiyan

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Creative Commons Attribution 4.0 and The Open Definition