Stainless Steel Property Checker
Authors: Adinath Satpute, Khandagle Siddhi Shantaram, Mavale Vaishnavi Subhash, Shinde Neha Anil.
Abstract: The “Stainless Steel Property Checker” is an advanced, microcontroller-based embedded system designed to automate the non-destructive testing (NDT) and verification of stainless steel materi-als. Stainless steel is ubiquitous in industrial, construction, and biomedical sectors due to its high corrosion resistance and tensile strength. However, the visual homogeneity across different grades—specifically widely used austenitic grades (e.g., 304, 316) and martensitic/ferritic grades (e.g., 410, 430)—poses a significant challenge for quality assurance. The inadvertent mixing of grades or the utilization of substandard alloys can precipitate catastrophic failures, ranging from structural collapse to critical chemical contamination in food processing units. To mitigate these risks, this research proposes a portable, cost-effective, and highly reliable testing apparatus. The system integrates a suite of sensors, including Hall-effect sensors for magnetic permeability anal-ysis, conductivity probes for electrical resistivity profiling, and force sensors for surface hardness correlation. A central processing unit, utilizing platforms such as Arduino or Raspberry Pi, ac-quires real-time sensor data, processes it through predefined algorithms, and compares the results against a calibrated database of standard metallurgical properties. The final output determines the specific grade of the material and assesses its surface quality, displaying the results instantly on a digital interface. By replacing subjective manual inspections and expensive laboratory spectrosco-py with a unified, portable electronic solution, this project aims to revolutionize on-site material verification, ensuring adherence to safety standards and enhancing industrial operational efficien-cy.
DOI: https://doi.org/10.5281/zenodo.18455572
SRIM-Based Numerical Fitting And Comparison With PSTAR For Proton Stopping Power In Al, Cu, And Pb
Authors: Wafaa N. Jasim
Abstract: In this research, we conducted a theoretical study to calculate the stopping power of protons in aluminum, copper, and lead for energy ranges between 0.2 and 2.0 MeV. We used SRIM 2013 software to extract the stopping power values, and then numerically fitted these values using MATLAB 2015 to obtain a semi-empirical stopping power equation that describes the behavior of the stopping power with energy. The results of the proposed equation were compared with standard PSTAR data, and the comparison showed good agreement within acceptable deviation limits for most energy points. These results confirm the validity of the equation in representing the stopping power of protons in the studied materials and its potential use in radiation shielding applications and modeling charged particle-matter interactions.
DOI: http://doi.org/10.5281/zenodo.18468726
Pentraguard – Web Vulnerability Scanner And Security Analyzer
Authors: Mrs. S. Revathi, Sabarinathan S2, Sai Vigneshwaran A, Soundararajan T
Abstract: PentraGuard is a Python-based ethical web vulnerability scanner designed as a Dynamic Appli-cation Security Testing (DAST) tool to evaluate the security posture of real-time web applica-tions. It automates systematic testing to uncover common vulnerabilities such as SQL Injection (SQLi), Cross-Site Scripting (XSS), insecure HTTP headers, and sensitive data exposure, align-ing with the OWASP Top 10 security risks. The system functions in two core modes: Discov-ery Mode and Active Scan Mode. In Discovery Mode, the scanner safely analyzes and maps the application structure by identifying web pages, links, and input forms without using harmful payloads. Active Scan Mode performs controlled security assessments using predefined attack patterns to detect vulnerabilities. To enforce ethical usage, PentraGuard includes a domain own-ership verification feature that restricts scanning to authorized or owned sites. The tool produces comprehensive vulnerability reports containing technical evidence, impact analysis, and remedia-tion recommendations, enabling users to effectively mitigate identified risks.
Photo Catalytic Degradation Of Emerging Pollutants Using Eco Friendly Material
Authors: Dr. Rishu Agarwal, Professor RituRathor
Abstract: The continuous release of pollutants into environment has increased significant environmental pollution and public health concerns due to their persistence and biological activity. This study is based on synthesis and application of an ecofriendly TiO2biomass carbon hybrid photocatalyst for the degradation of emerging pollutants such as diclofenac, methylene blue, and bisphenol-A under visible light irradiation. Structural, morphological, and optical analyses confirmed improved surface characteristics and extended light absorption compared to pristine TiO₂. The developed photocatalyst achieved degradation efficiencies that exceed 90% for pharmaceutical and endocrine disrupting compounds within 120 minutes. Kineticof these reactionsfollow a pseudofirstorder path and the catalyst retained high activity even after multiple reuse cycles. The findings demon-strate the potential of eco friendly photocatalytic materials as sustainable solutions for advanced wastewater treatment.
DOI: https://doi.org/10.5281/zenodo.18514969
Fake News Détection In Social Média Using Machine Learning Techniques: A Compréhensive Review And Ensemble Implémentation
Authors: Krishna Prisad Bajgai, Dr. Bhoj Raj Ghimire
Abstract: The rapid spread of fake news on social media platforms poses serious threats to public trust, democratic processes, and social stability. Manual verification approaches are insufficient due to the scale and velocity of online content, necessitating automated solutions. This paper presents a comprehensive review of machine learning (ML) and deep learning (DL) approaches for fake news detection and proposes an ensemble framework combining Support Vector Machine (SVM) and Deep Neural Network (DNN) models. Thirty-four peer-reviewed studies are ana-lyzed to identify trends, performance benchmarks, and research gaps. Experimental evaluation on multiple benchmark datasets demonstrates that the proposed ensemble achieves an accuracy of 94.6% and macro-F1 score of 0.946, outperforming individual classifiers. The findings highlight the importance of robust preprocessing, dataset diversity, and hybrid learning models for reliable misinformation detection. Future work emphasizes multimodal learning, transformer architec-tures, and real-time deployment in large-scale social media systems.
DOI: http://doi.org/10.5281/zenodo.18659562
From Technology Integration To Segment-Level Substitution: Rethinking Hybrid Fiber– Wireless Access Network Design
Authors: MG Kasheera Gamith
Abstract: Hybrid fiber–wireless access networks are commonly framed in the literature as systems that integrate heterogeneous technologies to extend coverage or improve performance. While this integration-centric perspective has yielded important architectural innovations, it has also ob-scured a more fundamental driver of real-world broadband deployment outcomes: the asymmetric concentration of cost, time, and operational constraints across network segments. This paper proposes a conceptual shift from “technology integration” toward “segment-level substitution” as the dominant logic for hybrid access network design. Drawing on broadband deployment litera-ture, techno-economic studies, and infrastructure management theory, the paper argues that hybrid architectures are most effective when wireless technologies are selectively substituted for optical fiber in constraint-heavy access segments, rather than uniformly integrated across the network. A segment-based analytical model is developed to distinguish feeder, distribution, and access com-ponents of passive optical networks (PON), and to explain why deployment inefficiencies dis-proportionately arise in the last-mile segment. The paper introduces the concepts of constraint concentration and feasibility-gated substitution to formalize when and where wireless access can outperform fiber on deployment efficiency dimensions without compromising service require-ments. By reframing hybrid access networks as decision problems rather than purely technical architectures, the paper contributes to hybrid networking theory, broadband infrastructure eco-nomics, and strategic network planning. The framework provides a foundation for future empiri-cal validation and informs both operator strategy and public broadband policy.
DOI:
Enhancing Regression Testing Efficiency: A Machine Learning Approach To Test Case Prioritization
Authors: Ms. Meenakshi
Abstract: Regression testing plays a pivotal role in maintaining software quality, especially in agile envi-ronments where frequent changes are made to the code. However, traditional regression testing methods are often inefficient due to their time-consuming nature and inability to effectively handle large-scale systems. To address these challenges, machine learning techniques offer a promising approach for optimizing test case prioritization, enabling more efficient and targeted testing. This paper explores various machine learning models, including supervised, unsupervised, and rein-forcement learning, for test case prioritization in regression testing. It examines their impact on fault detection, execution time, and resource optimization. The paper also evaluates the strengths and weaknesses of each approach through real-world case studies, demonstrating the effective-ness of machine learning in enhancing testing efficiency. Furthermore, the study highlights the challenges industries face in adopting machine learning for regression testing, including issues related to data privacy, computational overhead, and the lack of skilled expertise. Overall, the application of machine learning in test case prioritization presents significant benefits, although practical barriers still need to be addressed for widespread adoption.
Privacy-Preserving Fall Detection for Elderly Care using Distributed Edge-AI and Pose Estimation
Authors: Dr. S. Dhanabal, Sreejith R S, Srinath S, Tamilarasu K
Abstract: Privacy-Preserving Fall Detection for Elderly Care using Distributed Edge-AI and Pose Estima-tion aims to deliver real-time and reliable fall detection while safeguarding user privacy. Instead of transmitting or storing raw video footage, the system applies pose estimation methods such as OpenPose or MediaPipe to extract human skeletal keypoints, thereby eliminating exposure of sensitive visual information. Lightweight deep learning models, including CNNs combined with LSTM or GRU networks, are deployed directly on edge devices such as smart cameras and IoT nodes, enabling efficient on-device processing. By analyzing temporal posture and motion pat-terns, the system effectively differentiates falls from routine daily activities. Federated learning is incorporated to enhance model performance across devices without sharing raw data. This edge-based approach ensures low latency, minimal bandwidth consumption, and robust data security. Overall, the system provides strong privacy protection, rapid emergency detection, scalability across diverse environments, and dependable operation even under limited network connectivity, making it well suited for continuous elderly monitoring in smart healthcare applications
DOI: https://doi.org/10.5281/zenodo.18754262
Assessing Biodiversity And Taxonomic Diversity Through Environmental DNA Datasets Using Deep Learning
Authors: Ms.V. Dhanalakshmi, Premkumar R, Prithvi V, Sree Hari S, K.Madhumitha
Abstract: Accurate biodiversity assessment is vital for effective ecosystem monitoring and conservation. Traditional methods are often invasive, labor-intensive, and limited in scope. Environmental DNA (eDNA) analysis provides a non-invasive approach to detect species from environmental samples such as water, soil, and air. In this study, machine learning techniques are applied to eDNA da-tasets to classify taxa, predict species richness, and enhance detection of rare or cryptic species. Features such as sequence patterns and taxonomic markers are extracted from high-throughput sequencing data. Models including support vector machines, random forests, and deep learning are evaluated using standard performance metrics. The results demonstrate that machine learning-driven eDNA analysis enables efficient, scalable, and accurate biodiversity assessment, support-ing data-driven conservation strategies.
DOI: https://doi.org/10.5281/zenodo.18754631
Physicochemical And Toxicological Characterization Of Five Mushroom Species And Their Potential Application In The Bioremediation Of Trace Metal Contaminated Soils
Authors: Umudi Ese Queen, Odimgbe Ezekiel Izudike, Anyanwu Chidinma Gogo, Andrew Ogheneovo Onofuevure, Ikechukwu Sampson Chikwe, Ndego Chukwudi. Charles, Onwugbuta Godpower Chukwuemeka, Abubakar Abdulkarim, Wilson joseph joseph, Erienu Kennedy Obruche
Abstract: Five mushroom species—Termitomyces robustus, Agaricus bisporus, Pleurotus tuber-regium, Amanita phalaoides, and Amanita verosa—were collected from eleven locations in Anambra State, Nigeria, between 2024 and 2025. The mushrooms were identified, dried at 75°C for 4 hours, and stored for chemical analysis. Some were cultivated by scrapping mature mushroom seeds into substrates from their natural habitats and refuse dumps. After 14 days of cultivation, mushrooms were harvested, cleaned, and dried for further analysis. The chemical analysis re-vealed that moisture content ranged from 81.79% to 97.84%, with Amanita phalaoides showing the highest. Dry matter ranged from 2.63% to 18.36%, indicating high roughage content. Crude protein ranged from 8.16% to 24.67%, comparable to seeds and legumes. Ash content ranged from 3.26% to 14.33%, indicating high mineral presence, while lipids were low (1.00% to 6.68%), making the mushrooms suitable for diabetic and heart disease diets. Carbohydrates were between 32.00% and 35.40%. Vitamin C levels were low (0.01-0.37 mg/100g). Heavy metal concentrations like Na, K, Ca, Mg, and Fe were within WHO guidelines, while trace metals such as Cd, Co, Cu, and Zn showed significant differences between wild and cultivated mushrooms. Bioaccumulation factors for metals were higher than acceptable limits, particularly for Cd and Ni, indicating potential risks from polluted substrates
DOI: https://doi.org/10.5281/zenodo.18784218
AI-Based Real-Time Parkinson’s Dis-easse Detection and Monitoring System Using Voice Analysis
Authors: Kanimozhi S, Hemalatha S, Devasri M, Kanisha S
Abstract: Parkinson’s disease (PD) is a progressive neurodegenerative disorder in which speech impair-ment appears at an early stage, making voice analysis a reliable and non-invasive biomarker for diagnosis and monitoring. However, many existing speech-based systems rely on manually engi-neered acoustic features and focus primarily on disease detection without providing objective severity assessment or continuous monitoring. This project presents an end-to-end AI-based real-time Parkinson’s disease detection and monitoring system using voice analysis. Speech signals are transformed into log-Mel spectrogram representations to preserve spectral and temporal char-acteristics and are analyzed using a Conformer-based deep learning architecture that integrates convolutional layers with self-attention mechanisms. A multi-task learning strategy is employed to simultaneously identify Parkinson’s disease and estimate its severity level. To improve robust-ness, data augmentation techniques are applied to handle noise and speaker variability. The sys-tem further incorporates an AI-driven recommendation module that provides personalized health suggestions based on the predicted results. A secure cloud- connected dashboard enables real-time storage, visualization, and tracking of patient data over time. Additional modules, including speech emotion analysis, smart alerts via email and SMS, automated PDF report generation, mobile application integration, and data encryption, enhance usability, safety, and accessibility. The proposed system supports early detection, objective disease monitoring, and scalable de-ployment in clinical and remote healthcare environments.
IOT-Enabled Smart Onion Preservation System For Post – Harvest Loss Reduction
Authors: P. Akilan, T.Ananth Kumar, K.Jayasurya, Dr.T.Sengolrajan
Abstract: Post-harvest onion losses due to inadequate storage conditions continue to pose a major challenge in the agricultural sector, particularly for small and marginal farmers. Improper ventilation, excess humidity, and temperature fluctuations often lead to sprouting, rotting, and significant economic losses. To address this issue an IoT-based Smart Onion Preservation System has been developed to maintain optimal environmental conditions within the storage unit. The system continuously monitors temperature and humidity levels with help of DHT11 Sensor and placed inside the storage chamber. Based on real-time data the system automatically controls with ESP32 Micro-controller to automate 60 W incandescent bulb to regulate airflow and maintain suitable tempera-ture levels. The exhaust fan helps remove excess moisture and heat while the bulb provides con-trolled warmth when required. Additionally, natural materials such as river sand, charcoal powder and dry coconut leaf fibers are incorporated to enhance moisture absorption, improve air quality and reduce odor. This integration of IoT technology with eco-friendly materials ensures energy efficiency, low operational cost and reliable performance. The system is compact, affordable and particularly suitable for small-scale onion storage appl cations in rural areas.
An Iot Based Electric Trolley System with Integrated on-off and Directional Drive Control
Authors: P. Jegan, M. Ishaq, E. Saran, C.Gowrishankar
Abstract: Material handling in industries, warehouses, hospitals, and shopping complexes requires efficient and user-friendly transportation systems. Conventional trolleys require manual effort and lack intelligent control features. T AN IoT BASED o overcome these limitations, an IoT-Based Elec-tric Trolley System with Integrated ON–OFF and Directional Drive Control has been developed. The proposed system uses a Raspberry Pi Pico as the main control unit, integrated with an HC-05 Bluetooth Module for wireless communication. The trolley can be controlled remotely via a smartphone using Bluetooth connectivity. A two-channel relay module and single-channel relay module are used to control motor switching and direction. The motor driver circuit enables for-ward and reverse motion control of the trolley. The system provides efficient ON/OFF switching, directional control, reduced manual effort, and improved operational convenience. It is cost-effective, energy-efficient, and suitable for small-scale industrial and domestic applications.
Study Of Linear Programming Problem (LPP) And Its Practical Applications
Authors: Dr Vinit Kumar Sharma, Madhav Kalra
Abstract: Linear Programming Problem (LPP) is one of the most important optimization techniques in operations research and applied mathematics. It is used to determine the best possible outcome under a set of linear constraints. LPP plays a significant role in decision-making, resource alloca-tion, production planning, transportation, finance, agriculture, healthcare, and industrial manage-ment. The purpose of this research paper is to study the fundamental concepts of linear program-ming, its mathematical formulation, methods of solution, and practical applications in different sectors. The study also highlights advantages, limitations, and future prospects of LPP in modern computational and industrial systems.
DOI: http://doi.org/10.5281/zenodo.20504551
