Admission lead management in educational institutions requires coordination among administrative staff, departments, and authorities, yet traditional methods often cause data duplication, uneven workload, poor monitoring, and limited accountability. This paper presents TrackNEnroll: Student Admission Categorization System, a centralized, role-based platform designed to streamline student lead handling. The system includes four dashboards — Admin Department, HOD, Teacher/Staff, and Principal — each responsible for controlled lead distribution and tracking. Students are added manually or via Excel upload and allocated through automatic or manual methods to ensure balanced workload. Staff interact with leads through a calling interface, and task completion is validated using a proof-based verification process involving call log screenshots, dates, and durations. The platform further provides performance analytics, lead status filtering, internal messaging, and AI-assisted support. The Principal dashboard offers institution-level insights into departmental performance and lead progress. By combining automation with verification, TrackNEnroll improves transparency, reduces duplication, strengthens accountability, and supports data-driven admission management.

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Education systems across the world are undergoing a steady transformation from rigid, classroom centered instruction toward flexible, technology enabled learning environments. This shift, commonly referred to as Education 4.0, is driven by the growing integration of digital tools into academic institutions. Rather than viewing technology as a supplementary add on, modern educational models increasingly position digital systems as foundational infrastructure supporting teaching, learning, and institutional management. Among emerging technologies, the Internet of Things, when combined with artificial intelligence and fog cloud computing, offers significant potential to enhance educational environments. IoT enables continuous data collection from classrooms, devices, and infrastructure. Artificial intelligence transforms that data into actionable insights, while fog and cloud architectures ensure efficient processing and scalability. However, many current implementations remain fragmented, resulting in isolated systems that fail to deliver cohesive intelligence. This paper proposes an integrated intelligent smart education framework built upon a layered IoT Fog Cloud architecture. The framework addresses key limitations of existing digital education systems, including latency issues, scalability constraints, and inefficient data processing. By combining real time sensing, localized decision making, and cloud level analytics, the proposed model supports adaptive pedagogy, intelligent campus resource management, and proactive security mechanisms. In addition to outlining technical architecture, this study critically examines economic, ethical, and social considerations, emphasizing the importance of privacy protection and human centered educational values. The framework ultimately presents a balanced, scalable, and ethically aware vision for future smart education ecosystems.

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Road accidents involving motorcyclists are one of the major causes of injuries and fatalities worldwide. Many accidents become more serious due to the absence of proper safety measures and delayed emergency response. This paper presents the design and development of an IoT-based Smart Helmet Safety System that improves rider safety by integrating multiple sensors and real-time communication technology. The proposed system is built around an ESP32 microcontroller integrated with several sensors such as MQ3 for alcohol detection, MQ2 for gas detection, MPU6050 for accident detection, DHT22 and BME280 for environmental monitoring, and MLX90614 for body temperature measurement. The system ensures that the motorcycle starts only when the rider wears the helmet, thereby enforcing helmet usage and improving road safety. In the event of sudden impact or abnormal motion detected by the MPU6050 sensor, the system identifies a potential accident. Once an accident is detected, the ESP32 automatically sends an emergency alert message to predefined contacts through a Telegram Bot using Wi-Fi connectivity. The system is powered by lithium batteries supported by a solar panel, providing a sustainable and efficient power source. By combining rider authentication, accident detection, environmental monitoring, and real-time alert communication, the proposed system aims to reduce accident risks and improve emergency response time.

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Old photographs often degrade over time due to fading, scratches, blur, noise, and low resolution. Preserving such photographs is important because they represent valuable memories and historical records. However, traditional photo restoration methods require manual editing and professional skills, which can be time-consuming and difficult for general users. This paper presents LifeFrame AI, an artificial intelligence-based system designed to enhance and restore old photographs using modern image processing techniques. The proposed system allows users to upload degraded images through a web interface where AI-based algorithms analyze image features and apply enhancement operations such as noise reduction, sharpening, and contrast improvement. The system uses artificial intelligence and deep learning-based techniques to reconstruct missing details and improve overall image clarity. After processing, the system generates an enhanced version of the photograph that is clearer and visually improved compared to the original image. Experimental results demonstrate that the LifeFrame AI system significantly improves the quality of degraded photographs by increasing sharpness, reducing noise, and enhancing visual details. The proposed system provides a simple and efficient solution for automatic photo restoration and helps preserve valuable memories using artificial intelligence technology.

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The accelerating digitization of financial services has intensified demands for transparency, accountability, and operational efficiency within banking reward programs. Traditional centralized loyalty architectures suffer from fragmentation across institutions, susceptibility to fraudulent manipulation, limited auditability, and prohibitive overhead costs associated with third-party reward management intermediaries. This paper presents BlockArc, a novel permissioned blockchain architecture underpinned by self-executing Ethereum smart contracts, specifically engineered to address these systemic deficiencies in banking reward management. The proposed framework is structured across four tightly integrated layers: a Proof of Authority (PoA) Blockchain Layer providing immutable transaction recording; a Smart Contract Layer automating BankToken issuance, redemption, and expiration logic; an Application Layer delivering an intuitive banking portal and decentralized application (dApp); and a User Roles Layer defining distinct permission boundaries for customers, merchants, and administrators. Security is reinforced through a multi-layered defense incorporating cryptographic SHA-256 hashing, Zero-Knowledge Proofs (ZKPs) for privacy-preserving balance verification, Role-Based Access Control (RBAC) for privilege enforcement, and Chainlink decentralized oracle networks for tamper-resistant off-chain data integration. Empirical evaluation on a simulated permissioned testnet environment demonstrated system throughput of 112 transactions per second (TPS) under moderate load conditions, 100% fraud detection accuracy across all tested attack vectors, end-to-end reward processing latency of 3.2 seconds, and a 37% reduction in operational expenditure by eliminating intermediary dependencies. A pilot study involving 50 participants reported a mean satisfaction score of 4.6/5 for transparency and 4.4/5 for usability. The paper further presents a formal STRIDE-based security analysis, gas optimization strategies, a cross-institutional use case evaluation, and a comparative benchmarking of BlockArc against centralized, public Ethereum, and Hyperledger Fabric deployments. Findings establish BlockArc as a viable, scalable, and compliance-ready foundation for next-generation digital banking reward ecosystems.

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Rapid urbanisation and expanding highway networks in India have intensified demand for Pavement Quality Concrete (PQC), driving consumption of natural river sand at environmentally unsustainable rates. Simultaneously, India's marble processing and ferrous foundry industries generate millions of tonnes of marble dust (MD) and furnace slag (FS) annually — largely disposed of as waste. This investigation systematically explores the utilisation of these industrial by-products as partial fine aggregate replacements in M40-grade PQC. Three control factors — marble dust replacement (A: 0%, 10%, 20%), furnace slag replacement (B: 0%, 20%, 30%), and water-cement ratio (C: 0.35, 0.38, 0.40) — were varied using a Taguchi L9(3⁴) Orthogonal Array, reducing 27 full-factorial runs to only 9 mixes. Compressive strength (7, 28, 56 days), flexural strength (MOR), and splitting tensile strength were evaluated. Taguchi S/N analysis, ANOVA, and Multiple Linear Regression were performed in Minitab 2022. The optimal combination A₃B₃C₂ (20% MD + 30% FS + W/C 0.38) yielded CS₂₈ = 50.2 MPa (+8.7% over control; +25.5% over M40 minimum) and MOR = 5.32 MPa (+18.2% over IRC:58-2015 minimum). W/C ratio is the dominant factor (ANOVA: 31.2%), followed by MD (24.8%) and FS (5.1% individually + 12.4% synergistic interaction). Microstructural analysis confirms MD micro-filling reduces ITZ width (15–25 μm → 8–12 μm) while FS pozzolanic reaction reduces total porosity from 12.4% to 8.6%. The optimised mix achieves the lowest water absorption (3.72%) and best chemical durability among all mixes, at ₹102/m³ less than conventional PQC. The findings confirm that the combined MD+FS system simultaneously enhances PQC strength, durability, and environmental performance.

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The disposal of end-of-life tyres presents a persistent environmental problem, prompting the exploration of sustainable alternatives in construction materials. Rubberized concrete has emerged as a promising solution due to its ability to reuse waste rubber while offering advantages such as enhanced ductility and energy absorption. The incorporation of rubber aggregates into concrete contributes to sustainable construction; however, its widespread structural application remains constrained by reductions in strength and stiffness. To mitigate these limitations, this study investigates the effectiveness of graphene oxide (GO) as a performance-enhancing additive in rubberized concrete. Concrete mixtures were produced by partially substituting manufactured sand with recycled rubber aggregates at 5%, 10%, and 15% replacement levels by weight. Graphene oxide was introduced as a partial cement replacement at dosages of 0.01% and 0.05% by weight. Ten different mix proportions were developed and experimentally tested to evaluate compressive, flexural, and split tensile strengths. The results reveal a notable improvement in mechanical performance with the inclusion of graphene oxide, despite the presence of rubber aggregates. This improvement is mainly attributed to enhanced interfacial bonding among the cement matrix, rubber particles, and graphene oxide, supported by the latter's high surface area and superior mechanical properties. Overall, the findings indicate that graphene oxide can effectively counteract the strength loss associated with rubber incorporation, enabling rubberized concrete to be considered for structural applications while maintaining its sustainability benefits.

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This study presents a comparative seismic analysis of a G+27 high-rise building using Conventional RCC structural systems and RCC–Steel Composite structural systems across India's four seismic zones (II, III, IV, and V). Using ETABS 16.0.3, eight 3-D models were developed and analyzed through the Response Spectrum Method as per IS 1893:2002 provisions. Key structural parameters—storey displacement, storey shear, storey drift, stiffness, and overturning moment—will evaluate to understand the influence of seismic intensity and structural configuration.

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The Dry Hand Washing Machine by Fog Disinfection to Save Water presents an innovative, contactless, and water-free hygiene solution designed to improve public sanitation while conserving natural resources. The system utilizes sensor-based automation and fog disinfection technology to sanitize hands by dispersing a fine mist of disinfectant, eliminating the need for water and physical contact. This approach significantly reduces water consumption, minimizes the risk of cross-contamination, and ensures efficient germ control in high-traffic public environments. The machine operates automatically through a microcontroller-controlled mechanism that detects hand presence, activates the fog generator, and ensures uniform disinfection within a fixed time cycle. Experimental results demonstrate reliable performance, fast operation, and effective sanitation, making the system suitable for hospitals, educational institutions, public transport areas, and rural regions. The proposed solution offers a sustainable, eco-friendly, and cost-effective alternative to conventional handwashing systems, contributing to improved hygiene standards and environmental protection.

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Smart Irrigation Water Pump system is an innovative solution designed to enhance agricultural water management through automation and remote monitoring. Traditional irrigation methods involve manual operation, leading to inefficiencies such as water wastage, high energy consumption, and equipment damage due to uncontrolled usage. This study focuses on the analysis, investigation, and research of integrating IoT based monitoring, sensor-driven control, and wireless communication into irrigation systems. The system enables farmers to remotely operate water pumps using a mobile device while incorporating automatic shutdown mechanisms to prevent damage from low water levels. Real-time data analysis optimizes water consumption patterns, promoting sustainable agriculture and energy efficiency. The findings indicate that implementing automation significantly reduces manual intervention, enhances safety, and improves resource conservation. This project contributes to precision agriculture, ensuring better irrigation control while reducing operational costs. The Smart E-Water Pump represents a step toward technological advancements in ecofriendly irrigation, supporting modern farming practices and addressing the increasing demand for efficient water management solutions.

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The EV and CNG Station Locator is a technology-driven web and mobile application designed to help vehicle users easily locate nearby Electric Vehicle charging stations and Compressed Natural Gas (CNG) fuel stations through intelligent navigation and real-time location services. With the increasing adoption of electric and eco-friendly vehicles, users often face difficulties in identifying available charging or fueling infrastructure. The proposed system addresses this challenge by providing an integrated digital platform that allows users to search, locate, and navigate to nearby EV charging stations and CNG filling stations using geographic information systems (GIS) and location-based services. The system enables users to access station details such as location coordinates, charging type, availability status, operating hours, and navigation directions through an interactive map interface. The application integrates GPS tracking, Google Maps API, and cloud-based data storage to ensure accurate station discovery and efficient route guidance. Users can also filter stations based on vehicle compatibility, distance, and fuel type, which enhances convenience and reduces travel time. On the administrative side, the platform provides centralized management for adding, updating, and monitoring station data, ensuring the system remains updated with the latest infrastructure information. The platform integrates real-time navigation modules, location tracking services, and database management systems to provide reliable and scalable station-locator functionality. By improving accessibility to EV and CNG fueling infrastructure, the system encourages sustainable transportation adoption, reduces fuel search time, and promotes environmentally friendly mobility solutions.

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In food processing industries and small-scale kitchens, vegetable cutting is generally performed manually. This method requires more time, labor effort, and may lead to non-uniform slicing. To overcome these limitations, a vegetable slicing machine was designed and developed. The aim of the project is to reduce manual effort and increase productivity in vegetable cutting operations. The machine consists of a 0.5 HP electric motor operating at 1300 rpm, a shaft supported by bearings, a slicing blade mechanism, belt and pulley transmission, and a stainless-steel body. The motor power is transmitted to the shaft through a belt and pulley arrangement to obtain the required speed for slicing. The rotating blade cuts vegetables into uniform slices with higher efficiency. The shaft was designed considering bending and torsional loads. Calculations showed that a theoretical shaft diameter of 10 mm was sufficient, but for safer operation a diameter of 20 mm was selected. The developed machine improves cutting speed, provides uniform slices, reduces manual effort, and increases productivity in food preparation processes.

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The Reality Ender 3 is a cost-effective fused deposition modelling (FDM) 3D printer that has gained significant popularity due to its open-source nature and upgrade flexibility. This study presents the systematic enhancement of the Ender 3 platform through a series of hardware and firmware modifications aimed at improving printing performance and operational stability. The implemented upgrades include an all-metal hot end for high-temperature printing, a CR Touch-based automatic bed levelling system, silent stepper motor drivers for noise reduction, and optimized part cooling solutions. Structural reinforcements and firmware tuning were also carried out to enhance motion accuracy and thermal consistency. Performance evaluation was conducted based on parameters such as dimensional accuracy, surface finish, print speed, and acoustic emission. The upgraded configuration demonstrated measurable improvements across all evaluated metrics when compared to the stock printer. The findings of this study emphasize the effectiveness of targeted upgrades in transforming an entry-level 3D printer into a high-performance prototyping system suitable for both educational and semi-professional applications.

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The rapid expansion of online hiring platforms has made recruitment faster in reach but more difficult in practice, as organizations now receive a very large number of applications for each open position. This volume creates pressure on recruiters to evaluate resumes quickly, often leading to inconsistent shortlisting, overlooked candidates, and decisions based on shallow keyword matches rather than meaningful fit [4][5]. Conventional recruitment software is useful for storing and filtering applications, yet it often fails to support deeper recruiter reasoning about candidate readiness, skill strength, role alignment, and interview planning [9][10]. In response to this limitation, this paper presents NextHire AI, an explainable AI framework designed to support recruitment intelligence and interview readiness rather than only resume ranking. The system combines resume parsing, job-description understanding, semantic matching, fuzzy skill detection, readiness analysis, and interview guidance into one integrated workflow [2][3][6][7]. Instead of stopping at a match score, the framework generates structured evidence about strengths, risks, missing competencies, and candidate-specific interview priorities. This approach helps recruiters move from passive filtering toward informed decision support that is more transparent, more scalable, and better aligned with real hiring workflows [4][8][10]. The study shows that AI can be used not merely to automate screening, but to improve the quality of recruiter decisions by turning unstructured candidate data into actionable hiring intelligence [1][2][9].

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Alcohol-impaired riding is a major cause of road accidents worldwide, particularly in countries where two-wheelers are the dominant mode of transport. According to the Ministry of Road Transport and Highways (MoRTH) and the World Health Organization (WHO), alcohol consumption contributes to a significant proportion of road accidents every year. Traditional enforcement methods such as breathalyzer tests conducted by traffic police are reactive and cannot continuously monitor riders before ignition. This research proposes SenCox, an embedded alcohol detection and ignition lock system designed for two-wheelers. The system uses three sensors: an enzyme-based ethanol sensor, a breath alcohol sensor, and a hand-touch ethanol detection sensor embedded in the motorcycle handlebar. When alcohol is consumed, ethanol vapors appear in breath and small traces are released through sweat. Approximately 1–2% of ethanol may be released through sweat from the palm of the hand. The system includes a micro‑porous sponge-like material placed inside the handle grip which absorbs ethanol from sweat and transfers the signal to the ethanol sensor. The sensor outputs are sent to a microcontroller which compares the detected alcohol concentration with the legally permissible limit. If the detected value exceeds the legal limit, the controller sends a signal to an ignition relay that disables the motorcycle ignition system. This preventive mechanism aims to reduce drunk driving accidents and improve road safety.

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