International Journal of Recent Advances in Engineering and Technology
Efficient File Distribution Using Adaptive Chunking and Gossip-Based Node Discovery in DisktroByte
The exponential growth of digital data and rising concerns over privacy have highlighted the limitations of centralized storage systems, which suffer from scalability bottlenecks, single points of failure, and insufficient built-in security. To address these challenges, we present a peer-to-peer (P2P) distributed file storage system that integrates lightweight compression, strong encryption, chunk-based distribution, and replication with automated self-healing. Files are segmented, compressed using LZ4, and encrypted with authenticated encryption schemes (AES-256-GCM or ChaCha20-Poly1305). Consistent hashing distributes chunks evenly across peers, while replication ensures durability. Metadata is managed using BadgerDB to maintain file–chunk mappings, cryptographic keys, and operational logs. The system provides a command-line interface (CLI), uses HTTP endpoints for peer discovery, and leverages gRPC for efficient data transfer. Preliminary evaluation indicates reduced storage overhead, improved retrieval speed, and resilience under node churn. This work lays the foundation for future enhancements such as access control, duplication, versioning, and dashboard-based observability.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nd/4.0
A Review on Recent Advancements in Scoliosis Detection System
Scoliosis, particularly Adolescent Idiopathic Scoliosis (AIS), represents a prevalent orthopedic condition characterized by a lateral curvature and axial rotation of the spine. Early detection and intervention are critical to prevent curve progression and associated complications. This review critically examines ten significant research studies spanning clinical diagnostics [1][2], genetic predisposition analysis [5][7], artificial intelligence (AI)-based detection methods [3][4][8][9], and large-scale epidemiological investigations [10]. The integration of deep learning algorithms into scoliosis screening has demonstrated substantial potential in enhancing diagnostic accuracy while minimizing reliance on radiographic imaging [3][4]. Genetic studies have elucidated loci such as LBX1 [5], suggesting hereditary susceptibility, although clinical translation remains limited [7]. Epidemiological data underline the need for structured screening initiatives [10], particularly during adolescence. Despite these advancements, challenges persist, including the external validation of AI models, standardization of clinical protocols, and integration of multimodal data for personalized care. This review synthesizes current knowledge, identifies key research gaps, and highlights future directions toward developing comprehensive, non-invasive, and predictive scoliosis management frameworks.
Analysis of Chest Radiographs Using Deep Learning: A Multi-Model Approach for Detection of Thoracic Abnormalities
The increasing utilization of chest radiographs in medical diagnostics necessitates automated systems that can assist radiologists in accurate and efficient interpretation. This paper presents a comprehensive deep learning-based framework for automated analysis of chest X-rays, focusing on three critical diagnostic tasks: abnormal rib count detection, cardiomegaly identification, and pneumonia classification. Our multi-model ap- proach employs specialized convolutional neural network (CNN) modules including a custom lightweight regression model for rib counting achieving Mean Absolute Error (MAE) of 0.58 ribs with 89% accuracy within ±1 rib prediction, transfer learning-based architectures for pneumonia detection with 94.87% accuracy us- ing ResNet50, and cardiomegaly detection through cardiothoracic ratio analysis. The proposed framework addresses significant gaps in automated chest radiograph analysis by providing simul- taneous assessment of structural abnormalities, cardiac condi- tions, and pathological conditions. Evaluated on multiple datasets including VinDr-RibCXR, pediatric pneumonia dataset, and NIH-derived cardiomegaly subset, our system demonstrates significant potential for deployment in resource-constrained healthcare environments, offering rapid diagnostic assistance while maintaining high sensitivity and specificity rates across all three diagnostic modules.
Proactive Ransomware Early Warning System
Ransomware has emerged as one of the most severe cybersecurity threats, encrypting sensitive data and demanding ransom for decryption. Traditional antivirus mechanisms often fail to detect new and evolving ransomware variants. This paper presents a Proactive Ransomware Early Warning System designed to detect and prevent ransomware activity during its initial stages. The system continuously monitors file directories and process behaviors to identify signs of encryption, such as abnormal entropy changes and mass file access. Built using Python, the system integrates modules like Watchdog for file tracking, Psutil for process monitoring, and Tkinter with Pystray for user alerts. When suspicious activity is detected, the tool alerts the user and can terminate malicious processes to prevent damage. Experimental results show that this lightweight and real-time system effectively provides early ransomware detection for personal and small office environments.
AAROS (Aegis Active Response Operating System)
An AAROS (Aegis Active Response Operating System), a novel retaliatory red teaming tool designed to address the limitations of passive defensive security measures. The persistent threat of unauthorized access to sensitive data often leaves organizations with reactive, post-incident forensic analysis rather than proactive countermeasures. AAROS proposes a new paradigm by employing a low-cost Raspberry Pi, configured as a Human Interface Device (HID), to serve as a decoy system. The device hosts a sensitive data folder secured with strong cryptography, requiring correct credentials for access. The core functionality of AAROS is its retaliatory mechanism: upon the detection of failed credential attempts or unauthorized access, the HID automatically executes a pre-configured C2 (Command and Control) malware payload on the attacker's system. This action not only deters malicious actors but also establishes a connection to the attacker's machine, allowing for immediate control and intelligence gathering. Our methodology details the system architecture, hardware and software components, and the cryptographic and payload deployment mechanisms. The results demonstrate the viability of this approach in controlled test environments, confirming the device's ability to reliably detect unauthorized access and successfully deploy a C2 payload to gain control over the attacker's machine. AAROS provides a unique and effective strategy for red teaming, offering a proactive, real-time response that moves beyond traditional perimeter defenses and static detection systems.
Survey of Real Time Drowsiness Detection Systems
Driver drowsiness, or getting sleepy while driving, Causes many road accidents every year. To keep road Safe, this paper reviews different way to detect when a driver is getting tired. Two main methods are studied Radar-based systems and Camera –based systems. Radar can check heart rate and breathing by sensing small Chest movements without touching the driver. Camera systems watch the driver’s face and head to see if the eyes are closing or the head is dropping. Using both methods together can give a more complete view of the driver’s condition. Studied show radar works well even in low light, and cameras can clearly spot visible signs of tiredness. Combining these methods can make a reliable system that warns drivers before accidents happen. This approach can help reduce accidents, but more work is needed to make it cheaper and easy to use in all cars.
Safe Start is an AI-powered safety system that prevents two-wheeler ignition unless a rider is wearing a helmet. Using a handle-mounted camera connected to a Raspberry Pi, the system applies computer vision with OpenCV to detect helmet presence in real time. If no helmet is detected, ignition is blocked automatically, ensuring rider safety without external enforcement. Vibration-dampening mounts and redundant sensors improve detection accuracy across varied lighting and environmental conditions. This compact and affordable hardware solution reduces head injury risks, encourages helmet compliance, and promotes road safety.Safe Start demonstrates how indigenous, low-cost AI technology can transform two-wheeler safety enforcement through smart vehicle innovation.
CallShield - Real Time Fraud Call & OTP Scam Detection App
Spam calls and OTP scams have become a major concern in recent years, affecting millions of mobile users around the world. Fraudsters often pretend to be representatives of banks or trusted organizations and trick people into revealing sensitive details such as one-time passwords (OTPs), PINs, or account numbers. Many existing spam detection methods depend on number blocking or community reports, which can only identify scams that have already been detected. These traditional methods fail to protect users from new or unknown fraud numbers. This review paper studies different approaches used for spam call and message detection, including keyword-based filtering, machine learning, and natural language processing (NLP) techniques. It also reviews the growing use of artificial intelligence (AI) for real-time fraud detection. The paper focuses on the CallShield concept, a mobile-based system that uses speech- to-text conversion, keyword spotting, and a spam number database to detect and alert users during live calls. When suspicious words like “OTP,” “PIN,” or “bank account” are detected, the system immediately warns the user to stay cautious. The review highlights the advantages of AI-powered detection, identifies gaps in existing research, and emphasizes the importance of developing secure, real-time systems to reduce financial loss and increase user awareness against fraud calls and OTP scams.
Helpdesk Chatbot: Technology and Human-Centred Design
The proliferation of advanced Large Language Models (LLMs) has catalyzed a paradigm shift in enterprise customer service, transforming helpdesk chatbots from rudimentary scripted tools into sophisticated conversational agents. This paper presents a multi-dimensional analysis of the modern helpdesk chatbot, arguing that its efficacy and long- term value are contingent not merely on its underlying technological architecture but on a holistic and nuanced integration of technological design, economic realism, and human-centered evaluation. Through a comprehensive synthesis of recent academic and industry literature from 2023- 2025, this research explores the architectural evolution from rule-based systems to Retrieval-Augmented Generation (RAG), which has emerged as a critical technology for ensuring enterprise reliability. It critically examines the economic calculus of chatbot deployment, exposing the "ROI Mirage" where a myopic focus on cost reduction often obscures the significant financial and reputational costs of poor user experience. The analysis delves into the complex dynamics of the human-AI interface, highlighting the "empathy gap" and the phenomenon of "brittle trust" as primary barriers to user acceptance. Ultimately, this paper critiques the prevailing over-reliance on purely technical metrics for system assessment and proposes a new, multi-dimensional evaluation framework encompassing Technical, Human-centered, Contextual, and Temporal pillars. This framework is positioned not as a post- deployment checklist but as a proactive design driver. The paper concludes that the future of the enterprise helpdesk lies in a hybrid model, where AI agents function not as replacements for humans but as intelligent teammates, augmenting human capabilities to create sustainable, human-centric value.
Real-Time Detection of USB Rubber Ducky Attacks Using Behavioral Keystroke Analysis
The USB Rubber Ducky is a malicious device that disguises itself as a standard flash drive while functioning as a keyboard to inject commands at extremely high speeds. Such attacks are file-less, exploit the inherent trust of operating systems in Human Interface Devices (HIDs), and therefore bypass traditional antivirus and endpoint security solutions. This work proposes a real-time detection system that monitors USB activity and applies behavioral analysis of keystrokes to identify anomalies such as excessive typing speed, rapid command execution, and irregular input sequences. Upon detection, the system generates alerts, maintains logs, and can optionally disable the suspicious device. The proposed solution is lightweight, cost-effective, and enhances protection against HID-based cyberattacks.
Integrated Microwave Radar and Camera for Object Identification
The rapid proliferation of unmanned aerial vehicles (UAVs) in both civilian and military sectors has introduced substantial challenges to surveillance, defense, and public safety systems. Small-scale drones, in particular, are difficult to detect due to their low radar cross-sections, silent flight capability, and ability to operate at low altitudes. Conventional single- layer detection techniques based solely on radar, acoustic sensing, or vision-based approaches often fail to deliver reliable accuracy across varying operational environments. To address these limitations, this paper presents a dual-layer UAV detection and neutralization framework that integrates low-cost microwave radar with artificial intelligence (AI)- driven computer vision. The primary detection layer employs an microwave radar module, enabling continuous 360° motion detection and real-time tracking of aerial targets. The secondary layer leverages deep learning-based computer vision, specifically a YOLO (You Only Look Once) architecture, to classify and confirm UAVs while distinguishing them from non-threatening aerial objects. Upon classification of a potential threat, the system activates a laser-based neutralization mechanism and concurrently transmits alerts to security operators for situational awareness.
Digital Storytelling and Audio Summaries: A Hybrid Approach to Inclusive Education
In today’s fast-paced digital world, children’s emotional and intellectual development faces challenges from increased screen distractions and less access to traditional mentorship from extended families. MetaGurukul, a digital approach to child mentorship, addresses this gap by combining cultural heritage, value-based education, and modern digital learning. The platform provides a curated library of age-appropriate books, stories, and cultural insights, delivered through brief, high-quality audio clips lasting 5 to 15 minutes. These audio narratives aim not just to summarize content but to spark curiosity, explain relevance, and encourage deeper exploration. By including meaningful commentary, MetaGurukul turns screen time into a useful learning experience that promotes reading habits, critical thinking, and cultural understanding. The system also acts as a helpful resource for parents, teachers, and grandparents looking for structured guidance tools. By serving as a co-mentor, MetaGurukul connects traditional wisdom with modern access, fostering holistic development and lifelong learning habits in an engaging and scalable format.
An Automated Retrieval-Augmented Generation Framework for Course Generation
This paper presents an automated framework for course generation designed to address critical inefficiencies in traditional curriculum development. The creation of high-quality educational content is a resource-intensive process that impedes the rapid deployment of customized learning materials in academic and corporate environments. Our proposed system overcomes these challenges through a web-based platform that generates complete, interactive online courses from a single natural language prompt. The architecture leverages a React frontend to capture user requirements and the n8n workflow automation platform to orchestrate a sophisticated backend process. At the core of the framework is a Retrieval-Augmented Generation (RAG) model. Upon receiving a prompt, the system employs a Large Language Model, such as Google Gemini, to first retrieve relevant information from a specified knowledge base and then generate a comprehensive course structure in a standardized JSON format. This output includes pedagogically sequenced modules, detailed lessons, multimedia placeholders, and formative assessments. RAG architecture ensures the generated content is factually grounded in trusted source materials, mitigating the risk of inaccuracies common in standalone language models. The resulting JSON is dynamically rendered by the React application into an engaging learning interface. This automated approach offers significant advantages over traditional methods by reducing development time from weeks to minutes, enabling unparalleled scalability and personalization for diverse audiences. The framework facilitates a paradigm shift from rigid, one-size-fits-all instruction to a more flexible, on-demand, and interactive educational experience.
NestEasy-Redefining Student Living with Intelligent Accomodation Matching
Nest Easy is an innovative platform designed to resolve the greatest problem of students, i.e. the right accommodations. Moving for the sake of education is often accompanied by anxiety, stress, and a few choices which, in turn, put the students into a situation where it is hard for them to find safe, cheap, and comfortable living quarters. Nest Easy changes the way the students feel about this by using a smart algorithm-powered accommodation matching. The platform does the matching of students and housing that goes with their budget, location preference, and lifestyle. Apart from that, the platform ensures trustworthiness through listings that are verified and details that are transparent. Furthermore, Nest Easy is all about the community and compatibility by offering features for the roommate-matching that will link the students with the peers who have the same interests and thus will be able to form social networks that are supportive. By using technology in combination, personalization and trust factors, Nest Easy is making the way a client finds, compares and books accommodation easier. The main idea of this company is to provide a worry-free relocation experience to students, lessen the burdens of moving to a new place and also encourage a living environment that is safe and comfortable. With such a move, Nest Easy is not only the platform you use to find rooms but it's actually a way modal to student living through smarter, safer, and student-friendly housing solutions. With the aid of technology, personalization, and trust, Nest Easy makes it super simple to do all the stuff that come with finding accommodation searching, comparing, and finalizing places to stay. The idea behind it is to have a super easy handover for students, lessen the trouble of moving, and also help to have a secure and nice place to live. Therefore, Nest Easy is no longer about just finding rooms; it is about setting the trend for student living through intelligent, safer, and student-friendly housing solutions.
Disease Prediction and Recommendation System: A Comprehensive Study with Multi-Model Comparison and Clustering Integrations
Healthcare decision-support systems increasingly rely on machine learning to assist clinicians and patients. We present a comprehensive system that predicts diseases from symptom profiles and returns actionable recommendations (diet, medication, precautions, and workouts) using a structured health- care dataset. Five supervised learning models — Random Forest (RF), Support Vector Classifier (SVC), k-Nearest Neighbors (KNN), Naive Bayes (NB), and Decision Tree (DT) — were trained and evaluated for comparison. A Random Forest model achieved the highest accuracy of 99.05% and was selected as the final classifier. In addition, unsupervised K-Means clustering was implemented to reveal symptom similarity groups, while a Top-3 confidence mapping mechanism provided multi-disease probability outputs. We also report feature-importance ranking and interpretability analysis. The system provides interpretable, data-driven disease predictions with integrated healthcare rec- ommendations, bridging AI-based diagnosis with clinical decision support.
A Review of Retrieval-Augmented Generation for University-Specific Chatbot Systems
The rapid advancement of Artificial Intelligence (AI) and Natural Language Processing (NLP) has made Large Language Models (LLMs) pivotal in educational question-answering systems, particularly for university admission chatbots [1]. However, LLMs face critical challenges such as generating hallucinations, relying on outdated knowledge, and having non-transparent reasoning processes [10]. To address this, Retrieval-Augmented Generation (RAG) has emerged as a promising solution, incorporating knowledge from external databases to enhance the accuracy and credibility of generated responses [10]. This paper reviews the architecture and application of RAG-powered chatbots (RAGBots) designed for specific university domains [1]. A key finding is that while RAG systems like URAG, SAMCares, and Infersity v1 demonstrate utility in providing intelligent access to university resources [1, 3, 4], datasets for such closed domains are still difficult to obtain and curate [2]. Furthermore, complex RAG implementations often involve high operational costs and specialized modules [1]. The work highlights enhancements like Multi-Query and Ensemble Retrieval [6] and discusses critical challenges such as Document-Level Retrieval Mismatch (DRM) [8], concluding with a vision for reliable, domain-specific RAGBots in higher education.
Design and Development of a Web-Based Solution for Automated Solar Installation Compliance Documentation
Due to the rapid growth of renewable energy, especially solar power, managing and documenting solar projects has become increasingly complex and critical. Traditional methods that rely on manual paperwork often lead to inefficiencies, repetitive tasks, and errors in vital data such as system capacity, installation details, and compliance records. These challenges impede scalability, lower transparency, and complicate decision-making for both project developers and regulatory agencies. To tackle these issues, this project introduces the "Design and Development of a Web-Based Solution for Automated Solar Installation Compliance Documentation" (Solar Management, Automation, Registration, and Tracking System) — a digital platform aimed at streamlining and automating solar project documentation. This system provides a structured process for project registration, document uploads, validation, and real- time tracking. By utilizing automation and digitization, the platform minimizes human errors, ensures secure document storage, and accelerates approval processes. Moreover, it enhances accessibility by allowing engineers, developers, and regulatory bodies to view and monitor project information via a centralized interface. Beyond simplifying documentation, the system supports sustainable management practices by reducing paper consumption. It ensures regulatory compliance, improves data accuracy, and offers a scalable solution for managing extensive project data. In the end, the Web-Based Solution for Automated Solar Installation Compliance Documentation demonstrates how automation can transform conventional workflows into a more efficient, transparent, and reliable system, thereby facilitating the widespread adoption and effective management of solar energy projects.
Bridging Ancient Ayurveda with Modern Sensor Technology: Comprehensive Review
Nadi Pariksha, an ancient Ayurvedic method of diagnosis, evaluates the balance of the three Doshas—Vata, Pitta, and Kapha—by analyzing the pulse at the radial artery. This technique, traditionally practiced by experienced professionals, provides important information about a person’s health condition but relies on personal interpretation and extensive training. This paper introduces an IoT-based system for Nadi Pariksha that converts pulse examination into a digital process to enable more objective health tracking and early disease detection. The system uses a three-sensor setup placed at the Cun, Guan, and Chi points on the wrist to measure pulse waveforms associated with each Dosha. These signals undergo filtering and cleaning before being analyzed to identify time, frequency, and shape-based features. Machine learning algorithms then use these features to determine the balance of the three Doshas and spot irregularities that may signal cardiovascular or metabolic problems. The data is sent securely through IoT methods to a cloud platform, where it can be viewed and reviewed in real time. This approach combines traditional knowledge with current technology, allowing for non-invasive, inexpensive, and ongoing health monitoring. This can significantly improve access to preventive healthcare, especially in areas with limited resources.
CityServe: Online Grievance Redressal System for Municipal Services
The Online Grievance Redressal System is designed to address some of the failures of traditional grievance process. The view is that a centralized and (hopefully) technology-enabled service will help to create transparency, accountability, and a level of trust with the citizenry. While there are current frameworks for managing complaints, this project moves toward a more deliberate and joined up approach that improves and streamlines reporting processes and tracking electronically. This effort would provide a redesign of a citizen-centered service that is functional and scalable, but also a step beyond early forms of e-governance.
MentorDeskk: Intelligent E-Learning with Advanced Faculty Tools
The rapid digital transformation of education necessitates intelligent, integrated systems that simultaneously enhance student learning and empower faculty in managing complex academic workflows. This paper introduces the Smart E-Learning & Faculty Management System (SEFMS), a unified, web-based platform designed to streamline faculty responsibilities, strengthen instructional effectiveness, and promote adaptive learning experiences for students. SEFMS leverages adaptive assessment methodologies, automated assignment generation, AI-driven grading, and advanced analytics to personalize learning pathways, deliver immediate and targeted feedback, and dynamically adjust assessments based on individual student competency, behavioural patterns, and preferred learning styles. In addition to its assessment capabilities, the platform consolidates essential academic management functions—including plagiarism detection, student performance analytics, attendance monitoring, timetable management, and documentation workflows—into a single, cohesive environment. This reduces faculty dependency on multiple disparate tools, minimizes administrative workload, and ensures greater transparency and consistency in academic processes. The system’s predictive analytics and real-time dashboards further support data-driven decision-making by enabling instructors to identify at-risk students, analyses performance trends, and implement timely interventions. Pilot evaluations conducted across diverse classroom environments reveal substantial improvements in student engagement, motivation, and conceptual understanding, alongside measurable gains in faculty Productivity and grading accuracy. The integration of intelligent automation with pedagogical principles ensures that SEFMS not only enhances operational efficiency but also strengthens the overall learning ecosystem by fostering personalized, equitable, and outcome-driven education. By positioning faculty at the centre of the digital learning experience, SEFMS represents a significant paradigm shift toward smart, scalable, and sustainable academic management systems for modern educational institutions.
The project titled “Implementation of SOC using SIEM Tools” aims to design and deploy a fully functional Security Operations Centre leveraging open-source or commercial SIEM solutions. This SOC will integrate log collection, normalization, correlation, alerting, and incident response workflows. Building on the success of the malware analysis sandbox, this project focuses on broader organizational security by correlating diverse security data sources in real-time, developing actionable detection rules, and establishing robust incident response procedures. The outcome will be a comprehensive SOC environment capable of monitoring, detecting, and responding to threats, thereby minimizing risk and enhancing cybersecurity resilience.
Modernizing Land Ownership Systems through LandLedger+: Ensuring Reliability, Accessibility, and Fraud Prevention
In India, land ownership still largely depends on paper- based documentation, which is susceptible to deterioration, forgery, and administrative inefficiencies. This research introduces LandLedger+, a secure and transparent digital land registry system designed to modernize property record management through cryptographic protection, cloud integration, and user-friendly accessibility. A systematic, phased methodology was employed to ensure structured development and reliable implementation. The process began with requirement analysis and research to identify user needs, functional requirements, and appropriate cryptographic algorithms. The finalized technology stack comprised Python, Django, Firebase/Supabase, and Progressive Web App (PWA) frameworks. The frontend was developed using HTML5, CSS/Tailwind, and JavaScript (React optional) to create responsive, cross-platform interfaces, while the backend managed authentication, document handling, and real-time data synchronization. Cryptographic algorithms were implemented for secure file encryption and decryption, complemented by role- based access control and metadata-based search. Rigorous testing and debugging ensured system security, performance, and functionality, followed by documentation and deployment on Vercel and Netlify. The results demonstrated that LandLedger+ effectively prevented duplication and unauthorized alterations, ensured data immutability through hashing, and enhanced accessibility through a user-friendly web interface. The discussion highlights the system’s potential to reduce fraud, improve administrative efficiency, and enable transparent property transfers. In conclusion, LandLedger+ presents a scalable and secure framework for transforming India’s land registry system, combining cryptographic security with cloud- based architecture to promote transparency, reduce fraud, and establish a citizen-centric approach to land governance.
Scalable Framework for Advanced Threat Analysis
Scalable framework for advanced threat analysis is an open-source web application designed to redefine open-source intelligence (osint) by providing a centralized platform for cybersecurity investigations. It integrates powerful tools to deliver a sleek, responsive interface and robust backend. The application enables comprehensive analysis of ips, domains, emails and files, addressing critical cybersecurity challenges such as identifying network vulnerabilities, detecting data breaches, profiling threat actors, and uncovering malicious file attributes. It ensures flexibility and scalability, making osint investigator an essential tool for tackling modern cyber threats.
CampusCravings: A Scalable Full-Stack Framework for Automating Institutional Canteens
In the fast-paced environment of educational institutions, traditional canteen operations often lead to inefficiencies such as long queues, order errors, and manual record-keeping challenges. To address these issues, we present CampusCravings, a web-based platform that automates food ordering and management. The system is hosted locally via XAMPP and employs PHP for the backend, MySQL for data storage, and React for an interactive interface. It gives administrators safe controls for order processing and menu modifications while enabling easy menu viewing without requiring user registration. With modular components that guarantee flexibility to different canteen sizes, the platform prioritizes efficiency, scalability, and simplicity. It has a comprehensive design, implementation guidelines, and evaluation processes that were all created with reproducibility in mind. The evaluation approach suggests potential improvements in wait times, error reduction, and user satisfaction. In addition to technical contributions, CampusCravings aims to modernize institutional services, reduce operational burdens, and support future expansions like mobile integration and payment gateways.
Overcoming Unimodal Challenges: A Survey of Multi-Modal Fusion for Mobile Interfaces
The proliferation of mobile devices has spurred the development of interaction paradigms that extend beyond traditional touch inputs, catering to users with motor impairments and situations requiring hands-free operation. This paper presents a comprehensive survey of the primary modalities for hands-free mobile interaction: head pose estimation, eye-gaze tracking, and voice command recognition. We conduct a comparative analysis of the algorithmic evolution within each modality, tracing the progression from classical computer vision techniques to modern deep learning architectures. For head pose estimation, we evaluate the trade-offs between landmark-based and landmark-free methods, with a focus on lightweight models suitable for on-device deployment. For eye-gaze tracking, we compare model-based and appearance-based approaches, highlighting the critical role of large-scale datasets in achieving robustness. For voice, we analyze the performance characteristics of on-device versus cloud-based speech recognition and the architectural necessity of low-power keyword spotting. Furthermore, we analyze the synergistic potential of multimodal fusion as a solution to inherent unimodal challenges, most notably the "Midas Touch problem." By synthesizing findings from across the field, this survey provides a structured overview of the state of the art and identifies key considerations for designing the next generation of effective and accessible hands-free systems.
Frictionless Attendance System Using BLE
This paper presents a frictionless, low-cost, and privacy-preserving smart attendance system using Bluetooth Low Energy (BLE) beacons and mobile devices. The system preserves privacy by relying solely on anonymized device identifiers without tracking location outside designated beacon zones or collecting personal data. The proposed system enables automatic attendance recording through proximity and behaviour-based verification of signals. Unlike traditional RFID or manual systems, the approach eliminates physical friction and human intervention. The system incorporates two-step verification using Received Signal Strength Indicator (RSSI) smoothing and exit hysteresis to ensure reliable detection. The system infrastructure supports correlation-based proxy detection through continuous RSSI time series collection, enabling future server-side implementation without app changes. Experimental results demonstrate an 81% reduction in false positives (from 21% to 4%) and a 77% reduction in missed detections (from 13% to 3%), with high scalability for classroom environments.
Structured Peer-to-Peer Skill Development with Faculty Oversight
Effective skill sharing among college students enhances learning opportunities and fosters collaborative growth. However, existing platforms often lack personalized matching and verification mechanisms, limiting their effectiveness and trustworthiness. This research presents a novel peer-to-peer skill exchange platform designed to facilitate efficient matching between students and to connect them with college faculty for skill verification. Leveraging a hybrid recommendation system combining content-based and collaborative filtering, the platform intelligently pairs users based on skills, interests, and past interactions. Faculty allocation is managed through a rule-based matching algorithm that assigns domain experts for verification, ensuring quality and credibility. The system’s performance was evaluated through user engagement metrics, satisfaction surveys, and verification accuracy. To build user confidence and transparency, the platform incorporates explainable features that clarify match recommendations and verification assignments. The entire framework is deployed as a web application, offering an intuitive interface for skill exchange, real-time matching, and faculty verification. The proposed solution demonstrates how integrating intelligent matching algorithms and expert validation can create a trustworthy, scalable, and user-centric environment for peer learning in college settings.
Enhancing Student Success through Adaptive Learning Paths and Intelligent Resource Recommendations: A Review
Students today find it difficult to have access to resources that closely match their skills and career expectations with the educational scenario changing almost every day. E- Vidya attempts to fill this gap by bringing together advanced AI-enabled recommendations, learning through experiences and effective career guidance. And it is more than just delivering content to the students; the platform has a structured module, previous years sample papers, code exercise and also very detailed placement guide all as per student’s requirement based on what each student intend to achieve. Starting from teaching and enhancing their tech skills, learning new certifications or preparing for placements. Motivation tools like bonuses, badges, and leaderboards drive students to want to continue and push themselves in a nice space. The forum for teachers, interactive courses and quizzes all ensure that a lively and engaged interaction takes place, in which a real community is actively participating. E-Vidya ultimately serves as a reliable mentor in the journey of a learner connecting the traditional gap between classroom instruction and modern electronic teaching, so that kids feel readier, more self-assured, and more inspired for their future.
Image Based Breed Recognition for Cattle and Buffaloes of India
India's agricultural economy and genetic biodiversity are deeply rooted in its vast population of indigenous cattle and buffalo breeds. Traditional methods for breed identification are manual, subjective, and require specialized expertise, creating significant inefficiencies in livestock management and conservation. This paper proposes a hybrid automated system for cattle breed identification using deep learning. Our methodology combines two powerful computer vision techniques: (1) A fine-tuned Convolutional Neural Network (CNN) for holistic classification based on overall phenotypical features, and (2) A YOLO (You Only Look Once) object detection model to perform fine-grained biometric analysis by specifically locating and identifying muzzle patterns. This dual-approach allows the system to cross-validate a broad classification with a highly specific biometric marker. Our proof-of-concept models, trained on public datasets, achieved high accuracy for both whole-body classification (97.44% mAP@0.5) and muzzle detection (99% mAP@0.5), demonstrating the viability and robustness of this hybrid strategy. This research provides a framework for a scalable and accessible tool to aid farmers, researchers, and policymakers in the preservation and management of India's valuable livestock resources.
A Salesforce-Driven Healthcare Information and Automation Platform Enhancing Efficiency, Accuracy, and Collaboration in Modern Healthcare Ecosystems
The rapid digital transformation of the healthcare industry has highlighted the need for systems that can manage patient information efficiently, reduce manual effort, and improve overall care quality. Many healthcare institutions still depend on manual record-keeping and disconnected systems, which often cause data loss, communication gaps, and delays in patient treatment. To bridge this gap, this study introduces CareHub, a Salesforce-based healthcare management and automation platform designed to streamline hospital operations and enhance patient care. CareHub integrates different aspects of healthcare—such as patient profiles, appointments, care plans, medications, lab results, and insurance details—into one unified, cloud- driven environment. The system makes use of Salesforce’s declarative tools, including Flows and Process Builder, along with programmatic components like Apex Triggers and Apex Classes to automate routine hospital workflows. For instance, it can automatically notify doctors about abnormal lab reports, send reminders to patients who miss appointments, and verify insurance coverage before treatment approval. Through these features, CareHub minimizes administrative workload, improves accuracy in patient data management, and ensures timely medical intervention. The platform demonstrates how Salesforce can be customized beyond traditional business applications to create a secure, scalable, and patient-focused healthcare ecosystem. By integrating automation with real- time communication, CareHub represents a significant step toward achieving smarter, data- driven, and more connected healthcare services.
Design and Development of Intelligent Transportation Systems for Campus Bus Management: A Comprehensive Technical Review
Timely and safe transportation is essential for student life in colleges and universities. However, many institutions still use manual communication or fixed schedules. This leads to uncertainty, wasted time, and safety issues. To address these problems, we suggest a real-time college bus tracking system. This system allows students, drivers, and administrators to access live updates and route information through a web-based platform. Our system includes driver-side GPS tracking, a Firebase cloud database, and the Google Maps API to show accurate bus locations and estimated arrival times. The design ensures transparency, low cost, and easy access from any browser without needing extra hardware. A three-layer communication model connects students, drivers, and administrators through real-time notifications, attendance logging, and route monitoring. This solution encourages punctuality, safety, and sustainability by cutting down on wait times and improving route usage across the campus network.
From Transistors to Quantum Dots: A Comparative Study of CMOS and QCA Paradigms Technologies
(CMOS) Complementary-Metal-Oxide Semiconductor technology has been the dominant platform for digital circuit design for decades, enabling high-performance and cost-effective electronics. However, as CMOS devices scale below 5 nm, they face physical and economic challenges such as leakage currents, short-channel effects, and increased fabrication costs. Quantum- dot Cellular Automata (QCA) is an emerging nanotechnology that encodes binary information using electron polarization rather than current flow, offering ultra-low power consumption and extremely high device density. This paper presents a comparative review of CMOS and QCA technologies in terms of power, area, speed, scalability, reliability, and fabrication complexity. Challenges and future trends for both technologies are also discussed, providing an overview of their possible roles in next-generation Nanoelectronics. The paper also discusses current challenges and potential research directions for enabling a transition from CMOS to QCA in the coming decades.
Compressive Strength behaviour of Recycled Aggregate Concrete
This study explores the compressive strength of Recycled Aggregate Concrete (RAC) using Recycled Coarse Aggregate (RCA) as a partial substitute for Natural Coarse Aggregate (NCA). Four mixes were tested with RCA replacement levels of 0%, 15%, 20%, and 25%. The research involved two stages: Stage I focused on early-age compressive strength (3 days) to find the optimal replacement ratio, while Stage II assessed long-term strength at 7, 14, and 28 days. Results indicated that compressive strength declined with higher RCA content due to increased porosity, but strength reduction was minimal up to 20% replacement. The 20% RCA mix achieved about 95% of the strength of natural aggregate concrete at 28 days, confirming its structural adequacy. Microstructural analyses supported that 20% RCA provides a good balance of sustainability, cost-effectiveness, and structural integrity.
Balancing fidelity and efficiency remains a core challenge in Neural Style Transfer (NST). Traditional optimization-based models generate visually rich stylizations but are computationally slow, whereas feed-forward networks achieve real-time inference at the cost of structure and detail. This paper presents a two-stage hybrid pipeline that bridges this gap. The first stage employs Adaptive Instance Normalization (AdaIN) for rapid global alignment of content and style statistics. The second stage introduces a lightweight VGG19-based refinement that optimizes a composite loss integrating Gram matrix texture matching, channel-wise mean–variance stability, and Sobel edge preservation. This combination enhances both structural integrity and color coherence in the final stylized outputs. Experiments on varied content–style pairs demonstrate significant visual improvements while maintaining computational efficiency. Furthermore, our findings expose the limitations of traditional quantitative metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), which fail to capture perceptual quality, highlighting the necessity of perceptual measures like LPIPS for evaluating generative visual tasks.
Hotel Booking Analysis: Customer Segmentation and Demand Forecasting
This study presents a thorough analytical approach to hotel booking data. By using machine learning and time series methodologies, the research integrates customer segmentation, cancellation prediction, and demand forecasting. The study used K-Means, HDBSCAN clustering to define guest segments, Random Forests with SMOTE to classify booking cancellations, and SARIMA models to forecast future demand. The findings demonstrate that this approach leads to more accurate demand predictions and a deeper understanding of booking patterns. This improved understanding can help hotels optimize their resource allocation, pricing strategies, and customer targeting.
Customer Lifetime Value Prediction for E-Commerce
Customer Lifetime Value (CLTV) is like, a really important thing that companies use in marketing and planning and stuff. It kind a tells them how much money a customer can bring over the whole time they’re with the brand not just from one buy, but like the whole journey, you know? With CLTV, businesses can kind a understand which customers are more valuable in money terms. So they can make better plans for getting new people, keeping the ones they already got, and helping them stick around longer. It also helps them not waste money on marketing and just focus on the customers who actually bring in more cash. Nowadays, everything’s super digital and competition is like, everywhere. So it's really important to keep customers happy and not losing them. CLTV looks at stuff like how many times a customer buys things, how much they usually spend, how loyal they are, and even if they get discounts or whatever. It gives a full picture of how useful a customer really is. Thanks to tech like machine learning and predictive tools and all that, CLTV models are now way more smart and also easier to use. Companies can use them to make fast decisions in real-time, which is super cool and useful. If companies use CLTV properly, they can send more targeted ads, make loyalty things, and give better support so the customers feel special and all. So yeah, CLTV is not just some number game, it's actually like a smart guide that helps build strong customer relations and stay ahead in this crazy digital market world.
Towards Greener Logistics: AI-Driven Carbon Footprint Optimization for Smart Cities
The rapid growth of logistics and computational infrastructures has substantially contributed to global carbon emissions, emphasizing the need for sustainable optimization solutions. This paper proposes an AI-powered Carbon Footprint Optimization (CFO) framework that minimizes CO2 emissions in supply chain logistics through intelligent route and resource planning. The system employs an XGBoost regression model trained on segment-level parameters such as distance, slope, cargo weight, traffic density, and weather conditions to accurately predict fuel consumption and emission levels. These predictions are integrated into a Vehicle Routing Problem (VRP), solved using Google OR-Tools, where the optimization objective focuses on minimizing carbon emissions rather than distance or time. The framework also incorporates real-time traffic and weather data, ensuring adaptive and efficient route recommendations, while results are visualized through an interactive Folium-based map. Additionally, the study integrates concepts from Green Algorithms to quantify computational carbon footprints and ECO-CHIP methodologies to promote sustainable computing practices. Experimental results demonstrate that the proposed system significantly reduces emissions and enhances route efficiency compared to traditional distance-based approaches. This work establishes a scalable foundation for sustainable logistics, with potential extensions toward multi-vehicle optimization, reinforcement learning-based decision systems, and real-time carbon aware routing.