International Research Journal on Advanced Science Hub
International Research Journal on Advanced Science Hub (IRJASH) (eISSN 2582-4376) is an open access journal that was launched by RSP Science Hub in 2019. It aims to publish high quality peer reviewed articles to disseminate the progress in the field of science and technology therby bridging the gap between researchers, Academicians and industrial expert to remain up-to-date with the latest developments. Upon acceptance all the articles are free to read and download.
Post-Implementation Evaluation and System Optimization: An Analytical Study of Performance and Sustainability
Implementation is a very important step in any research and development project where ideas are put into practice by converting the theoretical knowledge into practical systems or solutions. The true gauge of success however is the effectiveness of a system after implementation. This report contains a post-implementation analysis of a system that has been implemented, and its functionality, flexibility, and sustainability. Some of the key performance indicators (KPIs) identified in the research include accuracy, user satisfaction, scalability, and responsiveness of the system. The evaluation of comparing the pre-implementation expectations and the post-implementation results has been performed to identify the areas that can be improved and optimized. Based on the findings, it is indicated that efficiency requires continuous monitoring and feedback loops that would allow reaching long-term project goals. This research finds that the post-implementation evaluation should not be termed as the final stage of any project but a continuous learning, adapting and improving process which decides the true success of any model developed.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
Space structures are a common element in modern architecture because of their expressive form and intrinsic geometric complexity, which allow architects to produce designs that are both aesthetically pleasing and structurally sound. The majority of studies and research on space structures have traditionally concentrated on their structural behaviour, highlighting elements like geometric stability, load distribution, and material efficiency. However, it is becoming more and more crucial to look at space structures from both an engineering and an architectural design standpoint as they continue to acquire traction in architectural practice. In the present study. Researches on dynamic analysis of space structures were widely carried out across the world. The study on structural behavior of grid, domes, vaults which are subjected to earthquake were carried out through many analytical and experimental works. The present work focuses on the study of orientation of stiffness of the supporting roof, and the effect of peripheral cross-bracings on overall lateral response. Further, dynamic analysis has been carried out for grid space structure with different horizontal bracings. Finally, criticality and its locations in various configurations of the space structures are identified. Based on the results and discussions, it is concluded that, the presence of horizontal and vertical bracings will resist the lateral load efficiently and particularly in model 5 where cross bracings are provided. From linear time history analysis, it can be concluded that, the presence of fixed base, vertical bracings and horizontal bracings has significant effect on the vibration characteristics. Presence of horizontal and vertical bracings will contribute significantly in resisting the lateral load of grid space structures.
Improving Biometric Security Through Multimodal Fusion and Deep Hashing
Biometric security technologies are increasingly important for protecting sensitive information and securing access control. There are inherent problems related to spoofing, privacy and data security in traditional monomial biometric systems. In this paper, we proposed a novel deep learning framework to enhance the biometrics security by using multispectral face, iris and fingerprint information. Combining deep hashing into the proposed fusion framework, a strong binary multimodal latent representation is generated which is robust in presence of fake attempts. The proposed approach also integrates a hybrid security framework (combining cancellable biometrics and secure sketch method) for improving security of biometric templates. Furthermore, deep auto encoder algorithm is applied for feature extraction to get improved encoded features in order to boast security. The efficacy of the approach is demonstrated on a multimodal face, iris and fingerprint biometric database, resulting in improved performance along with enhanced privacy through cancelability and unlink ability of biometrics templates. Deep hashing function is also tested on an image retrieval dataset task as well standard one where the network structure could be applied’re used and it shows similar adaptability.
A Miniaturized High Fractional Bandwidth Microstrip Antenna for Wide-Band Application
This scholarly work presents the design of a compact microstrip antenna for transmitting and receiving signals. The antenna is optimized using parametric analysis and simulated using HFSS 15 on a 1.6-mm FR4 substrate with a dielectric constant 4.4 and a tangent angle of 0.02. It offers a wide impedance bandwidth of 8.5 GHz to 19.6 GHz, with a fractional bandwidth of 79%. The reflection coefficient of the antenna is remarkable, with values of -66.61 and -46.20 dB, and it has a gain of 3.42 dB. Its radiation patterns, both in the E- and H-plane, are observed, and it is suitable for wireless communication. The proposed antenna is ideal for various applications, including mobile communication devices, wireless networks, and satellite communication systems.
Sustainable Concrete with Bethamcherla Waste Stone and Polyvinyl-Alcohol: Mechanical Properties and NDT Evaluation
Concrete is the most widely used construction material globally, valued for its strength, durability, and versatility. With the increasing demand for sustainable construction practices, the search for alternative materials that maintain performance while conserving resources has become essential. This study explores the development of M20 and M50 grade concrete using partial replacement of conventional coarse aggregates with Bethamcherla stone at varying levels of 10%, 20%, 30%, 40%, and 50% by weight. Bethamcherla stone, known for its distinct physical characteristics, is investigated for its potential to enhance resource efficiency in concrete production. Additionally, a self-curing agent, Polyvinyl Alcohol (PVA), is incorporated at dosages of 0.03%, 0.06%, 0.12%, and 0.24% by weight of cement. PVA aids in internal moisture retention, reducing the need for external water curing particularly beneficial in water-scarce regions. The performance of the modified concrete is evaluated through tests on fresh properties such as workability and slump, as well as hardened properties like compressive strength using rebound hammer and ultrasonic pulse velocity tests conducted at 7, 14, and 28 days. The findings aim to support the development of environmentally conscious concrete that meets structural and performance standards while addressing resource and water management challenges.
Integrating Machine Learning Models with Swift for Personalized User Experiences in iOS Applications
The increased demand for the development of adaptive and intelligent mobile apps has led to the rapid implementation of machine learning models in native iOS development, specifically using Swift. In this review, Swift programming and machine learning are discussed as coming together to enable real-time personalization in iOS applications. Using the power of on-device inference, frameworks such as CoreML and SwiftData, along with powerful API solutions, developers can build user-centric experiences that are secure, responsive, and contextual. This paper focuses on recent progress in emotion-aware design, which combines machine learning methods for identifying and reacting to the emotional state of users in real time through the analysis of inputs such as facial expressions, voice tone, typing behavior, and interaction patterns. Mobile deep learning and cross-platform deployment strategies are also discussed as shaping the future of personalized applications. Through an analysis of emerging trends such as federated learning and topic modeling, this review highlights the robustness of Swift-based development in providing a solid foundation for scalable, privacy-compliant, and intelligent user experiences. The results indicate that the convergence of Swift and machine learning is likely to characterize the next generation of personalization in mobile software development.
Thermal Performance Study of an Electric-Motor Using Innovative Indirect Cooling Methods
A 70 kW Permanent Magnet Synchronous Motor (PMSM) is selected for the present thermal management study, where detailed loss calculations including stator iron losses, copper losses, rotor and magnet losses are applied as distributed heat sources in the simulation. A mesh sensitivity analysis is performed, and an optimized mesh configuration with 14 prism layers is adopted to accurately resolve near-wall heat transfer. The numerical model is validated using an energy balance, ensuring the accuracy and reliability of the thermal predictions. Two indirect liquid-cooling strategies helical and meander cooling channels are evaluated using 3D steady state CFD simulations. Although the Helical channel demonstrates approximately 20% higher thermal performance (TPF), the Meander channel is selected for further analysis due to its lower pressure drop, improved flow uniformity, and better suitability for practical motor integration. Thermal enhancement is further explored using BN and CNT nanofluids dispersed in an ethylene-glycol (EG) base fluid. Increasing the BN and CNT nanoparticle concentrations leads to a marked decrease in average temperatures across stator, windings, rotor, and magnets, indicating enhanced heat removal capability. For both nanofluids, the representative maximum reductions achieved are 8.6% at the stator, 7.7% at the slot windings, 7% at the end windings, and approximately 3.2% across the rotor and magnets. An optimal concentration range is observed for both nanofluids. BN shows a clear and substantial improvement up to 6% concentration, beyond which temperature reduction becomes marginal. Similarly, CNT exhibits strong improvement up to 1% concentration, after which further increases result in minimal additional thermal benefit. These results demonstrate that nanofluids significantly enhance PMSM cooling performance, with well-defined saturation limits guiding their effective usage in next-generation EV motor thermal management.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
Interactive Selective Spatial Feature Extractor for Small Object Detection in Challenging Scenes
Small object detection in computer vision has made great strides in accuracy and robustness. However, practical applications are still hampered by two main issues: inaccurate detection of small objects and the difficulty of deploying these models on resource- constrained devices due to their extensive parameters and high computational demands. To address these limitations, we propose the Interactive Selective Spatial Feature Extraction method. Which is designed for efficient small object detection, utilizing a modified C2f-Darknet backbone for robust feature extraction and an attention mechanism to focus on crucial spatial areas. The core innovation of this technique lies in its ability to extract and combine multi-scale information to capture fine-grained details and border content, and an internal interaction mechanism that allows communication and information sharing to selectively attend to the most relevant features while suppressing noise. Proposed a state-of-art system that is evaluated on a publicly available DOTA dataset and VisDrone dataset. The proposed methodology detects the accurate region.
Interactive Selective Spatial Feature Extractor
IoT Reliability: An Approach to Enhancing Network Resilience and Fault Tolerance
The growing dependence on Internet of Things (IoT) devices highlights the essential need for fault tolerance to ensure device reliability despite common faults that can disrupt operations. Traditional techniques such as majority consensus and triple modular redundancy have been widely used but often fall short in addressing the complexities of modern IoT networks. As IoT applications evolve, there is a growing demand for innovative fault-tolerance strategies that integrate advanced technologies to enhance system resilience. This paper provides a comprehensive survey of current fault-tolerance techniques, examining their strengths and limitations. It also introduces a unique framework aimed at achieving significant improvements in network reliability through the integration of contemporary methodologies. The proposed approach offers a scalable and efficient solution to the challenges faced by modern IoT systems, paving the way for robust and reliable IoT infrastructures capable of handling dynamic environments and unpredictable faults.
Mapping and Implementation of Reinforcement Learning Algorithms for Quarter-Car Semi-Active Suspension Systems: An Analogy-Based Approach
This paper presents an analogy-based framework for integrating Reinforcement Learning (RL) algorithms into a Quarter-Car Semi-Active Suspension System to improve overall ride comfort and handling stability. The framework establishes a direct mapping between suspension parameters and RL components - state, action, reward, and policy enabling learning-driven control design. Multiple RL algorithms are investigated, including value-based (DQN), policy-based (PPO, A3C), actor–critic, and model-based approaches (DDPG, TD3, SAC). Parameter variation and performance analysis reveal that RL-based controllers effectively adapt to nonlinear suspension behaviour and varying road excitations. Simulation results show significant improvements in ride comfort, reduced sprung-mass acceleration, and enhanced tire–road contact stability compared to conventional semi-active control techniques. Among all methods, DDPG and SAC demonstrated superior adaptability and convergence. The proposed analogy-based RL framework provides a systematic pathway for developing intelligent, self-optimizing vehicle suspension systems suitable for next-generation adaptive vehicle dynamics control.
Commercial Building with Vertical Irregularities Using Etabs Software
Building on hilly slopes is always a challenge because the natural ground profile forces engineers to adopt unconventional building forms like step-back or step-back & set-back structures. These designs often create vertical irregularities, since the columns rest at different heights, which makes the structure more vulnerable to torsional effects and increased shear during an earthquake. To understand this behavior, the present study uses the Response Spectrum Method to analyses step-back and step-back & set-back frames. The focus is on key seismic responses such as fundamental time period, lateral displacement, inter-storey drift, and base shear in columns for buildings of different heights. The results clearly show that step-back & set-back frames perform better under earthquake loads compared to simple step-back frames, as they control displacement, reduce story drift, and distribute base shear more effectively.
Analysis and Design of Pre Engineering Building by Using STAAD Pro
This thesis presents the structural modelling, analysis, and assessment of a Pre-Engineered Building (PEB) system. The objective of the study is to evaluate the performance of the structure under various loading conditions and to verify its compliance with codal provisions. The analysis was carried out using a 3D model, and results were extracted for critical parameters such as deflection, bending moment, shear force, axial force, utilization ratio, and column end reactions. Deflection checks were performed as per IS 800 limits, and it was found that both vertical and lateral deflections of rafters, purlins, and the overall building frame were within permissible values, ensuring adequate serviceability. The bending moment analysis revealed that the structure is primarily governed by the major axis, with a maximum moment of approximately 317 kNm under gravity load, while the minor axis bending was found to be negligible. Shear force distribution indicated maximum values at supports, with a peak of about 110 kN, whereas axial forces were observed to be safe with a maximum of 189 kN. The utilization ratios of all members were below unity, confirming sufficient strength and structural stability. Column end reactions were extracted to facilitate foundation design, which can be carried out efficiently using standard spreadsheets. Overall, the study concludes that the PEB system is safe, economical, and optimized, demonstrating its effectiveness in providing lightweight, cost-efficient, and structurally reliable solutions for industrial and commercial applications.
Detection of Confirmation Bias in Horoscope Texts Using Support Vector Machine
The study addresses the challenging task of identification of confirmation bias in horoscope texts, using machine learning techniques. Bias confirmation, a cognitive bias in which the information that confirms one's prior beliefs is preferred, is a widely used personalized media such as horoscopes and has implications for both mental health and digital content analysis. For this research, a carefully selected set of horoscope responses was compiled and annotated for the occurrence of confirmation bias or its absence. Data pre-processing included methodical text cleaning—removal of unnecessary columns, normalization, whitespace trimming, and imbalanced class analysis—to make it possible to build strong predictive models. Feature extraction involved a high-dimensional TF-IDF (Term Frequency-Inverse Document Frequency) vectorization that was able to capture relevant linguistic patterns as well as n-gram structures that are highly indicative of biased content. To address the issue of class imbalance, oversampling methods like SMOTE were used together with class weighting in the Support Vector Machine (SVM) learning framework. The SVM model was adjusted for the best kernel parameters and probabilistic output calibration, while stratified train-test data splitting was used to ensure representative evaluation across bias classes. Baseline model Naïve Bayes and Logistic Regression were also set up for comparative analysis, but SVM’s margin-based classification was able to deliver competitive performance, especially for minority bias detection. Deep emphasis was placed on the model evaluation to ensure the metrics used were appropriate for an imbalanced classification such as: accuracy, precision, recall, F1-score, and ROC-AUC, with a detailed examination through confusion matrices and threshold tuning curves. Besides, the interpretability layer was also present in the study by means of feature importance visualization, thus giving a clear indication of the textual elements that influenced bias predictions the most.
Cognitive Bias Detection-IDF Feature Extraction
AI in Multidisciplinary Engineering: A Holistic Review of Technologies and Trends
Artificial Intelligence (AI) has evolved from a computational tool into a transformative force reshaping every branch of engineering. This review paper explores the depth and breadth of AI integration across major engineering disciplines—including Mechanical, Electrical, Electronics, Computer Science, Civil, Chemical, Aerospace, Biomedical, and Industrial Engineering. By analysing recent advances in machine learning, deep learning, computer vision, natural language processing, and intelligent automation, the paper highlights how AI enhances design optimization, predictive maintenance, smart manufacturing, structural health monitoring, energy management, autonomous systems, process control, and human–machine collaboration. The study also examines domain-specific applications such as digital twins, Industry 4.0, AI-driven materials discovery, intelligent transportation, personalized healthcare systems, and sustainable infrastructure development. Furthermore, the paper discusses emerging challenges involving data quality, interoperability, ethics, and the need for interdisciplinary skill development. This comprehensive review demonstrates that AI is not merely an add-on technology but a foundational enabler driving innovation, efficiency, and intelligent decision-making across all engineering fields, shaping the future of global technological advancement.
Optimal Workflow Scheduling in Cloud Computing Based on Hybrid Bacterial Evolutionary and Bees Mating Optimization Algorithm
Cloud computing has emerged as a rapidly maturing paradigm that delivers software applications and hardware infrastructure as services through Service Level Agreements (SLAs). A critical challenge in this environment is the efficient scheduling of interdependent tasks across virtual machines (VMs) while minimizing resource consumption and meeting Quality of Service (QoS) requirements. This paper proposes a Hybrid Optimization Workflow Scheduling (HOWS) algorithm that integrates Bees Mating Optimization (BMO) for global resource exploration and Bacterial Evolutionary Algorithm (BEA) for adaptive local refinement. The hybrid framework improves workflow scheduling by ensuring balanced task allocation, optimal VM utilization, enhanced energy efficiency, and system scalability. Simulation experiments conducted in CloudSim demonstrate that the proposed model significantly outperforms traditional scheduling algorithms such as Round Robin and Shortest Job Next in terms of makespan reduction, workload balancing, and overall throughput. The results establish HOWS as an effective and secure scheduling strategy, contributing to improved resource management and robust performance in dynamic cloud environments. Future directions include
Legal documents, such as court judgments and case files, are often lengthy and contain complex information. One way to understand this information is by using computer programs that can automatically find and highlight names, such as people, laws, courts, or organisations. This process is called Named Entity Recognition (NER). Many tools can locate these names in a single document. In this paper, we take it a step further for Legal Entity Tracking Over Time, which tracks how often and where these names appear across multiple documents over time. This data would enable insights into legal behaviour, such as identifying repeat petitioners, monitoring the activity of judges, or analysing the influence of legal statutes over time. It demonstrates how temporal entity tracking can enhance legal research, support predictive analytics, and contribute to more transparent legal data systems. By organising this information, the project makes legal data easier to understand and more useful for researchers, students, and legal professionals.
AI Pet Robot for Mental Health Care and Personal Assistance
The increasing prevalence of mental health concerns and the growing demand for personalized assistance have motivated the development of intelligent robotic companions. This project, “AI Pet Robot for Mental Health and Personal Assistance”, presents a cost-effective, autonomous robotic platform capable of recognizing human emotions, understanding speech, and responding interactively to support user well-being. The system integrates a Jetson nano-based hardware setup comprising a camera, microphone, display screen for facial expressions, and Bluetooth connectivity, enabling real-time interaction. Using advanced algorithms in natural language processing (NLP) and facial emotion recognition, the robot interprets user inputs, detects emotional states, and generates contextually appropriate responses. The architecture synergizes hardware control, AI-driven decision-making, and personalized feedback to create an engaging and responsive user experience. Experimental evaluation demonstrates the robot’s potential in mental health monitoring, companionship, and adaptive interaction. This project highlights the promise of combining artificial intelligence and robotics in developing accessible, interactive, and supportive personal assistants.
AI-Powered Inclusive Career and Learning Assistant for People with Disabilities
People with disabilities (PwDs) are still confronted with huge challenges in acquiring quality education and employment because inclusive digital systems are not available. This literature review captures prevailing research and developments in AI- powered inclusive learning systems, assistive technologies, and accessible design. The review is based on recent research that investigates the application of artificial intelligence, machine learning, and human-computer interaction to personalized support in learning settings for PwDs. Although some promising breakthroughs, research indicates that there is an evident shortage of holistic, scalable solutions fully responsive to the various needs of this population. This paper aims to synthesize the current state of research, identify limitations, and highlight future directions to support inclusive education through AI.
E-R Homie: A Smart Companion System for Emotional Well-being at Home
This paper presents E-R Homie, an emotion-aware smart home system designed to enhance the emotional well-being of users by adapting the home environment in real time. The system aims to overcome the limitations of traditional smart homes by integrating emotional intelligence through multimodal sensing techniques. Voice patterns, facial expressions and optional physiological signals are captured through IoT-enabled devices and analyzed using machine learning algorithms such as CNN, SVM and LSTM. The recognized emotional states are mapped to environmental responses, including adjustments in lighting, temperature and music to promote comfort and relaxation. Experimental observations show that the system achieves an average emotion recognition accuracy of approximately 85% and executes environmental changes within two seconds. The results indicate a significant improvement in user comfort, stress reduction and emotional support. The proposed system demonstrates how emotion-aware automation can contribute to a human-centric living environment and offers a scalable solution for integrating affective computing into everyday life.
The proposed system introduces an intelligent hydroponics model that integrates Internet of Things (IoT) and Artificial Intelligence (AI) to enable automated, data-driven plant cultivation without the use of soil. The system employs an ESP8266 microcontroller for real-time monitoring of temperature & humidity, light intensity, and nutrient concentration using DHT11, LDR, and TDS sensors respectively. Sensor readings are displayed on LCD and transmitted wirelessly to the ThingSpeak cloud platform, which enables continuous visualization and remote supervision of environmental conditions. Based on ambient light levels, the controller automatically regulated LED grow lights through a relay module, while a constant-speed water pump ensures uninterrupted nutrient circulation. An ESP32-CAM module provides live video streaming of the plant, and a YOLOv11-based computer-vision model executes plant detection, size estimation, and growth assessment. Power management is achieved by 12-V supply with buck converters to maintain stable operation across all components.
Iris Recognition System (IRS) In Biometric World
With the rapid growth of digital technologies, the demand for reliable and secure authentication methods has become increasingly vital. Among various biometric identification techniques, iris recognition stands out as one of the most dependable approaches due to the iris’s unique and stable pattern that remains consistent throughout a person’s life. Unlike traditional password- or token-based systems, iris recognition offers a higher level of accuracy, security, and resistance to environmental and age-related variations. This paper presents a comprehensive study of the Iris Recognition System (IRS), encompassing both classical and modern methodologies. The research highlights the traditional techniques such as Daugman’s rubber sheet model and Gabor wavelet-based feature extraction, while addressing their limitations in uncontrolled or noisy conditions. To overcome these challenges, the proposed approach integrates advanced deep learning methods like Convolutional Neural Networks (CNNs) and transfer learning models such as DenseNet201, which enhance feature extraction and classification accuracy. The workflow includes essential stages such as image acquisition, preprocessing, iris localization, normalization, feature extraction, and pattern matching. The proposed system was evaluated on standard datasets including CASIA, UBIRIS.v2, and IITD, achieving a recognition accuracy of over 95% even under challenging conditions like poor lighting and off-angle captures. The paper also explores key applications of iris recognition in domains such as border control, mobile authentication, e-governance, and healthcare. Finally, it discusses potential advancements like lightweight CNN architectures, multimodal biometric systems, and edge-based iris recognition models for future research and deployment.
Dual Prediction of Blood Pressure and Blood Glucose Level using PPG Signals: Explore Deep Learning Models through Comparative Study
Monitoring blood pressure (BP) and blood glucose levels is vital for managing unremitting conditions such as hypertension and diabetes, which affect millions of individuals around the world. Conventional strategies for measuring BP, which depend exclusively on pulse amplitudes, often fail to provide exact readings, especially in patients with atherosclerosis or those who are obese, where pulse amplitudes may be frail or mutilated. Moreover, customary strategies for blood glucose estimation involve invasive strategies, causing discomfort. This research presents a novel approach to non-invasive dual prediction of BP and blood glucose levels utilizing photoplethysmogram (PPG) signals. Leveraging the power of LSTM network and XGBoostRegressor enables accurate and efficient forecast of both BP and blood glucose levels. LSTM is utilized to find time related patterns within PPG signal, while XGBoost enhances model performance by identifying the most relevant features for prediction. Then, the two pre-trained deep learning models (one for glucose prediction and another for blood pressure prediction) are loaded, and a test sample is reshaped to match the required input format for each model. These models are then used to make predictions on the reshaped sample. The results demonstrate promising performance, resulting in an RMS error of 8.014 mg/dL for blood glucose estimation and a Mean Absolute Error (MAE) of 25.336 mmHg for BP estimation. The model's performance reflects high potential for practical, non-invasive monitoring, offering a more comfortable and accessible alternative for patients requiring regular checking of both BP and glucose levels.
Economic Dynamics Model Using q-Laplace’s Transform
Here, for the first time, we introduce the quantum Laplace transform as a tool to analyze dynamic models in economic theory. In order to do it, at first, we revisit some useful essential preliminaries of q-calculus as well as q-Laplace transform and then apply this framework to three important cases: the Walrasian price stability model, the measurement of input response in an inflationary setting, and the model of farm sponsored job training. The solutions derived through this approach are studied numerically that shows this method provides a clear and systematic way to interpret dynamic behavior in economics. A noteworthy feature of this framework is its consistency, and it is notable that as the parameter q→1, all the outcomes reduce smoothly to those obtained via the classical Laplace transform. This convergence highlights the robustness of the method that opens a promising pathway for deploying q-calculus in economic dynamics. In short, this approach opens new research opportunities for both theoretical and applied economics..
Performance Assessment of a Designed RC Slab Under Sequential Fire and Blast Loading Using a Novel Etabs-Ansys Framework
This research develops a novel, integrated workflow to evaluate the performance of a code-compliant RC slab panel designed for gravity and lateral loads in ETABS under consecutive thermal and blast loading. A prototype one-way slab panel is designed and detailed as per Indian Standards (IS 456:2000). The critical reinforcement data and geometry are then translated into a high-fidelity finite element model in ANSYS Workbench for advanced nonlinear analysis. A sequentially coupled thermo-mechanical simulation is performed. First, a Transient Thermal analysis applies the ISO-834 standard fire curve for one hour to induce thermal degradation. Subsequently, an Explicit Dynamics analysis subjects the fire-damaged slab to a blast load, modeled using the CONWEP methodology with a centrally-located charge. The results quantify the severe performance degradation in the combined fire-blast scenario compared to isolated events. The study concludes by deriving resilience-oriented design and detailing recommendations for RC slabs in disaster-prone urban environments. The primary contribution of this work is the established ETABS-to-ANSYS workflow, providing a practical tool for engineers to assess the multi-hazard resilience of as-designed structural elements.
Review Paper On: Array Manipulation Techniques
This review presents a systematic analysis of four fundamental array manipulation techniques— traversal, searching, two-pointer approach, and sliding window paradigm—that are critical for developing efficient algorithms. We examine linear and specialized traversal methods, compare brute-force and binary search algorithms, and analyse two-pointer variations (same-direction and opposite-end) for optimization problems. The sliding window technique is explored for both fixed and dynamic window scenarios in subarray problems. Beyond theoretical foundations, this work synthesizes practical insights, performance trade-offs, and implementation challenges, bridging the gap between algorithmic principles and real-world applications. By providing a structured framework for selecting and optimizing array-based solutions, this review serves as an essential resource for researchers and practitioners advancing algorithm design.
AI-Powered Lost Object and Entity Tracking Across Live and Recorded Camera Feeds
The rapid growth of video surveillance systems in urban, industrial, and institutional areas has resulted in a huge increase in visual data streams. Manually monitoring these large video feeds has become inefficient, prone to errors, and labor-intensive. This situation calls for the development of smart, automated surveillance systems. This paper proposes an AI-driven framework that can detect, identify, and track lost objects and individuals in both live and recorded CCTV footage. The system uses advanced deep learning models, in-cluding YOLOv8 for real-time multi-class object detection and CNN-based face recognition for re-identification and tracking across different camera views. The framework includes modules for data prepro-cessing, feature extraction, and temporal tracking. These components work together to ensure continuous identity tracking and object localization. It also allows for multi-camera synchronization, which enables smooth cross-view tracking of individuals and misplaced items in complicated environments. We conducted experimental analysis on benchmark datasets and real-world surveillance footage. The results showed an av-erage precision of 94.2% and a recall of 91.6%, highlighting the model's strength under various lighting, oc-clusion, and motion conditions. In addition to high detection accuracy, the framework offers real-time per-formance, scalability, and flexibility for use in airports, railway stations, shopping malls, and law enforce-ment agencies. This research bridges the gap between traditional manual surveillance and modern autono-mous monitoring systems, contributing to the field of intelligent security solutions.
Attendifyx: Smart Attendance System Using Deep Learning
With the advent of the digital age, old attendance systems like manual registers and simple biometric verification tend to be susceptible to inefficiency, human mistakes, and proxy abuse. AttendifyX mitigates these issues by an intelligent, deep learning–powered face recognition method for automated and accurate classroom attendance. The framework utilizes the Buffalo-L version of InsightFace model together with a randomized multi-capture verification that flags a student present only if identified in a minimum of 75% of sampled captures within a session—hence reducing impersonation and short-term attendance fraud. Architecturally, AttendifyX incorporates a Python-based recognition module, a Node/Express backend store MySQL, and a React + Vite frontend that includes dashboards for administrators and instructors. Key features encompass automated reconciliation of attendance, reporting analytics, schedule management, and modular APIs ready for ERP integration. By integrating cutting-edge recognition, smart verification, and an intuitive admin interface, AttendifyX guarantees precision, transparency, and scalability—translating traditional attendance monitoring into a smooth, reliable digital process.
Protection and Control Measures for Fire Safety in High-Risk Industrial Areas
The evaluation of protection & control measures for fire safety in high-risk industrial areas is a critical aspect with far-reaching implications for the safety & safeguards of personnel and the preservation of various valuable assets in an industrial establishment. This project aims to layout an overview of the comprehensive evaluation process that consists of protection & control measures for fire safety & similar incidents. This study emphasizes the significance of regulatory compliance, employee training, and the cultivation of a strong safety culture within different industrial establishments & facilities. This project underscores the importance of critical evaluation process, which is essential for wellbeing of living beings & safeguarding of assets & property, and even for the sustainable functioning of various industrial operations across different high-risk industries.
Sustainable Wildlife Movement Detection and Crop Protection System
Wildlife intrusion into farmlands poses a major threat to agricultural productivity and farmer safety, particularly in regions bordering forest areas. Traditional preventive measures such as fencing, ultrasonic repellents, and surveillance cameras are often costly, energy-intensive, and unreliable under low-light or complex environmental conditions. To address these limitations, this project proposes a Sustainable Wildlife Movement Detection and Crop Protection System that integrates IoT and AI for intelligent, eco-friendly, and automated crop protection. The proposed system employs a YOLO-based object detection algorithm for real-time identification and classification of animals from live camera feeds. The detected animal behavior and movement patterns are analyzed to assess threat levels. Based on this analysis, appropriate eco-friendly deterrents including sound, light, or eco-friendly fog/spray mechanisms are automatically activated to drive away the animals without causing harm. An offline alert mechanism ensures that nearby farmers receive notifications even in areas with limited or no internet connectivity. Additionally, a farmer-friendly dashboard provides live intrusion alerts, detection history, and manual control options. By integrating YOLO-based AI detection, IoT-based automation, and sustainable deterrent mechanisms, the proposed system offers a low-cost, scalable, and intelligent solution that minimizes crop losses, enhances farmer safety, and fosters coexistence between humans and wildlife.
Client-Side Threats in SPAs: Modeling Security Risks in Popular JavaScript Framework
Single Page Applications (SPAs) have reshaped web development by improving responsiveness and interactivity, but the shift of application logic and data handling to the client side has introduced security challenges that traditional server-centric models do not adequately address. This study proposes and validates a threat model specifically designed for SPAs, focusing on two widely adopted JavaScript frameworks, React and Vue.js. Two prototype applications with equivalent functionality were developed and evaluated using a modified STRIDE methodology, combining static analysis tools (ESLint, SonarQube, Snyk), dynamic testing tools (OWASP ZAP, Burp Suite), and manual inspection of client-side code and runtime behavior. The analysis identified common vulnerabilities across both frameworks, including DOM-based XSS, insecure token storage, broken route guards, and exposed API endpoints. React showed higher risk when unsafe rendering practices such as dangerously SetInnerHTML were used, while Vue’s vulnerabilities were linked to insecure use of v-html and un validated dynamic imports. Mitigation strategies, including input sanitization, Http Only cookie-based token storage, Content Security Policy (CSP), and strict route guards, significantly reduced vulnerabilities. This work delivers a structured SPA-specific threat model and reproducible methodology, providing developers and security practitioners with actionable guidance for building more secure client-side applications.
Single Page Applications (SPAs Threat Modeling)