International Journal of Research in Engineering, Science and Management
Call for Papers Publish in Multidisciplinary Journal Volume 9, Issue 4, April 2026 7000+ Articles | 10,000+ Authors | 30+ Countries Publication Process Submit Manuscript ISSN (Online): 2581-5792 Publication Frequency: Monthly Subject/Scope: Multidisciplinary Areas Publication Format: Online CrossRef DOI is assigned to all published articles Free e-certificates for all authors Fast Publication: Within 3 Days Article Publication Fee (APC): ₹2000 (India), $52 (Africa), $59 (Others) International Journal of Research in Engineering, Science and Management, is an open-access International Journal for Paper Publication. To publish a paper, the authors can submit research papers, case studies, survey papers, academic project works, scholarly articles, academic articles, original or extended versions of previously published papers in conferences, and basic advances from all the areas of academic disciplines. With a broad scope, the journal is focused on multidisciplinary research, from the viewpoints in the advancement of research. The journal welcomes articles from all the academic disciplines. The journal aims to provide a platform for authors to publish research work online. Students at graduate and post-graduate levels are welcome to publish project papers. Academicians, and researchers, who carry out research can submit and publish research work in the IJRESM journal. The authors who wish to publish a paper in IJRESM, are advised to read the publication process and submit a paper for publication.
Rubberised Concrete as a Climate-Mitigation Strategy: Integrating AI Prediction and Atmospheric Dispersion Modelling to Assess Urban Air-Quality Impacts
Cement production is responsible for about 7-8% of global carbon dioxide (CO2) emissions; while over 1-1.6 billion tyres come to the end of their life every year. This study examines the environmental aspects of using pulverised waste tyre rubber (PWTR) as part of a concrete mix. An integrated modelling framework between global estimation of the concrete demand, the tyre utilisation modelling, the carbon footprint modelling, the atmospheric dispersion modelling, and Support Vector Machine (SVM) prediction has been established. Results show that when small percentages of the cement and sand are replaced with PWTR the compressive strength is preserved at around 24 MPa with the CO2 emissions reduced by about 9.59 kg CO2 per m3 of concrete. In the example of a 777.5 km infrastructure trench project, the results of this analysis estimate a recycling of about 5.3 million tyres, a saving of 4 353.76 metric tons of CO2 and saving of 473.79 TJ of energy. Long-term simulations reveal cumulative global CO2 savings under adoption scenarios resulting in measurable reductions in projected global temperature increase. These results show that rubberised concrete can play its role in the utilisation of waste tyres and mitigation of emissions in infrastructure construction.
Copyright (c) 2026 Dominic Wambugu Mwaniki https://creativecommons.org/licenses/by/4.0
Teachers’ Instructional Competence and Classroom Learning Environment on English Language Learning Success: A Regression Analysis
The English performance of learners in the Philippines has continued to fall below proficiency standards, as reflected in national and international assessments showing persistent challenges in reading, writing, and comprehension. This study investigated the relationship and impact of teachers’ instructional competence and the classroom learning environment on English language learning success among Grade 9 students in six public secondary and integrated schools in New Bataan District, Davao de Oro, for the school year 2024–2025. Using a quantitative descriptive-correlational design, 166 respondents completed adapted questionnaires measuring teachers’ instructional competence (mastery of subject matter, teaching skills, classroom management, and assessment skills) and the classroom learning environment (content, teacher and student roles, atmosphere, and evaluation). Students’ first-quarter English test scores served as indicators of learning success. Data were analyzed using mean, standard deviation, Pearson r, and multiple regression. Results revealed that both teachers’ instructional competence and the classroom environment were rated as “Agree” and moderately evident. However, students’ English performance was rated “nearly proficient,” suggesting that favorable instructional and environmental conditions alone did not ensure full proficiency. Statistically no significant relationships or impacts were found between the independent variables and students’ English performance, implying that other factors such as motivation, home language use, and socio-economic conditions may play a greater role. These findings highlighted the need for comprehensive interventions beyond classroom instruction, including enrichment programs, community involvement, and authentic language-use opportunities, to improve learners’ proficiency and confidence in English.
Copyright (c) 2026 Mary Grace B. Adaro, Dhan Timothy M. Ibojo https://creativecommons.org/licenses/by/4.0
Automatic Number Plate Recognition Using Raspberry Pi
Automatic Number Plate Recognition (ANPR) is an important technology used for automatically detecting and reading vehicle number plates using image processing techniques. It plays a major role in modern traffic management systems, parking automation, and security applications. In this project, a low-cost and efficient ANPR system is developed using Raspberry Pi 4 Model B+ along with server integration for data storage and remote access. The system captures vehicle images using a Raspberry Pi Camera Module and processes them using OpenCV. Image preprocessing techniques such as grayscale conversion, edge detection, and contour detection are used to identify the number plate region. After detection, Optical Character Recognition (OCR) is applied using Tesseract to extract the vehicle number. The extracted data is displayed on a TFT display (ST7789) and also sent to a server using HTTP requests. A server-based system is implemented using FastAPI, which stores detected number plates along with timestamps and images. This allows remote monitoring and data management through a web interface. The system works in real-time and provides good accuracy under proper lighting conditions. The main advantage of this system is its low cost, portability, and ability to integrate with cloud or local servers. It is suitable for applications such as parking systems, toll collection, and security surveillance, especially in small-scale environments.
Copyright (c) 2026 Faiz Muzaffar Ansari, Kartik Anil Jakhere, Prathamesh Avinash Palande, Huzaifa Shakeel Ansari, Madhuri Pramod Rodge https://creativecommons.org/licenses/by/4.0
Smart Parking System with Real Time Slot Allocation
The rapid growth of urbanization and the increasing number of vehicles on the road have led to significant challenges in managing parking spaces efficiently. Traditional parking systems often cause traffic congestion, fuel wastage, environmental pollution and driver frustration due to the lack of real-time information about available parking spots. To address these issues, this project proposes a Smart Parking System for Real-Time Spot Allocation, which utilizes modern technologies such as the Internet of Things (IoT), cloud computing, mobile applications, and real-time data processing to optimize parking management. The proposed system is designed to monitor parking slot availability in real time using sensors installed in individual parking spaces. These sensors detect the presence or absence of vehicles and transmit the data to a centralized cloud server through a wireless communication network. The collected data is continuously updated and processed to determine available parking slots instantly. Users can access this information through a dedicated mobile application or web interface, where they can view nearby parking areas, check slot availability, reserve a spot in advance and receive navigation guidance to the allocated space.
Copyright (c) 2026 C. Radhiya Devi, S. Sukrithi https://creativecommons.org/licenses/by/4.0
The Influence of Learning Outcomes on Student Performance in Universities: A Meta-Analytic Review
This meta-analysis synthesizes empirical research examining the relationship between clearly defined learning outcomes (LOs) and student academic performance in higher education. A systematic review of 32 quantitative and mixed-methods studies published between 2000 and 2023 reveals that well-implemented learning outcomes positively influence student performance, with a weighted average effect size of Cohen's d = 0.42 (moderate effect). The mechanisms underlying this relationship include reduced cognitive load, enhanced self-regulated learning, improved metacognitive awareness, and better alignment between teaching activities and assessments. Moderating factors include disciplinary differences, implementation quality, and student awareness of outcomes. While structured disciplines (sciences, engineering) show stronger effects, humanities contexts demonstrate more variable results. The analysis highlights that learning outcomes function effectively when integrated meaningfully into pedagogy rather than treated as bureaucratic requirements. Implications for curriculum design, faculty development, and quality assurance are discussed, alongside recommendations for future research on longitudinal outcomes and cross-cultural effectiveness.
Copyright (c) 2026 Judith Bijurenda Asiimwe https://creativecommons.org/licenses/by/4.0
Entrepreneurial Competencies Among Students of College of Business Administration and Management (CBAM) at Sulu State College
The research study entitled “Entrepreneurial Competence among Students of College of Business Administration and Management (CBAM) at Sulu State College” answered the following questions:1. What is the demographic profile of student-respondents in terms of gender, age, parent’s average monthly income, parent’s educational attainment, and year level? 2. What is the level of entrepreneurial competencies of among students of CBAM at Sulu State College in context of opportunity competencies, relationship competencies, conceptual competencies, organizing competencies, strategic competencies, and commitment competencies. 3. Is there a significant difference in the level of entrepreneurial competencies among students of CBAM at Sulu State College when data are classified according to demographic profile? 4. Is there a significant correlation among the levels subsumed under level of entrepreneurial competencies among students of CBAM at Sulu State College? A descriptive research design method was employed in this study. One hundred (100) students of CBAM at Sulu State College were the main source of data which were quantified to answer the research questions in this study. This study was conducted at the CBAM at Sulu State College during the Academic Year 2024-2025. A non-probability sampling design through purposive sampling method was employed in this study. A survey questionnaire was the main instrument employed to gather data on the level of entrepreneurial competencies among students of CBAM at Sulu State College. Competencies questionnaire was adapted and patterned with slight medication from Phelan, Chris (2014) Model of Competency Cluster derived from Man, Lau and Chan (2002) entrepreneurial skills and competencies. The research instrument used in this study consists of two parts. Part I of the questionnaire focused on obtaining the demographic profile of the students of CBAM at Sulu State College. Part II was geared towards obtaining data on the level of entrepreneurial competencies of CBAM students. For research problem number one 1, frequency counts and percentage was employed to determine the profile of student-respondents. For research problem number two 2, mean and standard deviation was employed to determine the level of entrepreneurial competencies. For research problem number 3, t-test for independent samples were employed to determine the significant differences in the level of entrepreneurial competencies when data are grouped according to gender; and One-way Analysis of Variance (ANOVA) was employed to determine the significant differences when data are grouped according to age, parent’s educational attainment, parent’s average monthly income, and year level. For research question number 4, Pearson Product Moment Correlation Coefficient (Pearson r) was employed to determine the degree of correlation among the sub-categories subsumed under the level of entrepreneurial competencies. The female respondents are more than the male respondents, either 20 years old and below or between 21 – 22 years old, majority of parents earn P10,000.00 and below each month, one – third finished Bachelor’s Degree /Vocational, and equal number of respondents were taken from each year level. The respondents indicated often on the level of competencies among CBAM students at Sulu State College in the context of opportunity competencies, relationship competencies, conceptual competencies, organizing competencies, strategic competencies, and commitment competencies. The null hypothesis is accepted in terms of gender, age, parent’s average monthly income, parent’s educational attainment, and year level. There is a very high correlation among the levels subsumed under the level of entrepreneurial competencies among CBAM students at Sulu State College.
Copyright (c) 2026 Merry-Rose L. Sibayan https://creativecommons.org/licenses/by/4.0
WhatsApp is a well-known social networking platform with more than 2 billion members. WhatsApp is a vital communication tool in people's lives. They use WhatsApp to send text messages expressing their emotions. Since WhatsApp is available in more than 180 countries, code-switching is frequent. Additionally, as emojis have grown more widely used and prevalent, they are now essential for sentiment research. This paper discusses our method for transforming unstructured WhatsApp messages into a structured format that can be used for sentiment analysis using a variety of data mining techniques. We go over how we handle codemixing, the many emojis and the feelings they represent, and how to use this method to conduct basic analysis.
Copyright (c) 2026 Eshan Bhuse, Om Doshi, Sahil Gavande, Anuj Tawari https://creativecommons.org/licenses/by/4.0
A Neuro-Evolutionary Framework for Autonomous Vehicle Control Using Genetic Algorithms and Neural Networks
Autonomous vehicle development demands reliable control strategies that can operate under dynamic and safety-critical conditions. Simulation-based evaluation plays a vital role by enabling controlled, repeatable testing without the risks of real-world deployment. This paper presents a custom-built self-driving car simulation framework that integrates feedforward neural networks with genetic algorithms for autonomous control optimization. Implemented entirely in JavaScript without external dependencies, the framework evolves steering and acceleration policies through neuro-evolution using sensor-based perception. A lightweight sensor configuration and multi-objective fitness evaluation enable smooth navigation and effective collision avoidance across complex track layouts. Experimental results demonstrate a navigation success rate of 91.7 percent and a collision avoidance accuracy of 96.8 percent after 47 generations, while achieving faster convergence than gradient-based training methods. The proposed framework offers a flexible and computationally efficient platform for autonomous vehicle research and rapid prototyping.
Copyright (c) 2026 Jay Nadkarni, Uthkrisht Narayan, Pranav Sati, Nikahat Mulla, Aparna Halbe, Varsha Hole https://creativecommons.org/licenses/by/4.0
Machine Learning Based Prediction of Energy Generation in Plant–Microbial Fuel Cells Using Environmental and Soil Data
Plant–Microbial Fuel Cells (P-MFCs) are emerging as sustainable, bio electrochemical systems that harness energy from the interaction between plant root exudates and microbial communities in soil. Predicting energy generation from P-MFCs is challenging due to the influence of multiple environmental factors (temperature, moisture, pH, soil nutrients, etc.). This study proposes a machine learning (ML) framework that predicts electrical output in P-MFCs using environmental and soil sensor data. We evaluated multiple ML models including Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting Machines (GBM) using data collected from field trials and laboratory experiments. Results show that ensemble methods (RF and GBM) achieve strong predictive accuracy (R² > 0.92) across varied environmental scenarios, outperforming SVR. Feature importance analysis reveals soil moisture and pH as key predictors of power output. The proposed model enables real time prediction and optimization of P-MFC energy generation, offering a path toward smarter bioenergy harvesting from agricultural ecosystems.
Copyright (c) 2026 Praveen Kumar, Rajendra Singh https://creativecommons.org/licenses/by/4.0
Impact of Psychosocial Attributes and Instructional Characteristics of Students in Mathematics
This study examined the relationship and impact of psychosocial attributes and instructional characteristics on the mathematics performance of Grade 9 students in six public secondary and integrated schools in New Bataan District, Davao de Oro, for the school year 2025–2026. Using a quantitative descriptive-correlational design, 166 respondents completed two adapted questionnaires measuring psychosocial attributes (academic self-efficacy, academic motivation, locus of control, mathematics anxiety, and peer influence) and instructional characteristics (knowledge of mathematics content, communication ability, use of appropriate teaching strategies, and classroom management skills). Data were analyzed using mean, standard deviation, Pearson r, and multiple regression. Results showed that both psychosocial attributes and instructional characteristics were often observed, indicating moderate student engagement and satisfactory teaching practices. Students’ mathematics performance was nearly proficient, suggesting that adequate teaching and classroom conditions alone did not ensure full proficiency. No significant relationship was found between overall psychosocial attributes or instructional characteristics and mathematics performance; however, academic self-efficacy emerged as the only significant predictor. These findings implied that strengthening students’ confidence in their abilities was more critical to improving performance than teaching methods alone, highlighting the need for interventions such as motivational activities, peer tutoring, and confidence-building strategies.
Copyright (c) 2026 Marry Cris Q. Tumimang, Romulo G. Doronio https://creativecommons.org/licenses/by/4.0
The project aims to revolutionize package sorting in local non-metropolitan post offices by introducing an Automatic Package Segregation Machine (APSM) that utilizes image processing technology. Traditional sorting methods are labor-intensive and time-consuming, leading to inefficiencies in package handling. The APSM offers a user-friendly solution by automatically sorting packages based on addresses printed on them, thereby reducing manual effort and time consumption. Unlike bulky machines used in metropolitan cities, the APSM is designed to be space-efficient, making it suitable for regional non-metropolitan post offices. Additionally, a web-based platform will be developed to monitor sorting in real-time and maintain a database of sorted packages, providing enhanced efficiency and transparency in postal operations. Through the implementation of the APSM and accompanying web platform, the project aims to streamline package sorting processes, enhance operational efficiency, and modernize postal services in small towns and cities where alternative sorting methods are limited.
Copyright (c) 2026 Satyam Tiwari, Rudra Talekar, Pranav Raina, Najib Ghatte https://creativecommons.org/licenses/by/4.0
WaveSplit: A Multi-Stage Framework for Audio Enhancement and Audio Denoising – Combining Deep Learning with Psychoacoustic Principles and Adaptive Noise Processing
In the field of audio processing, noise interference poses a significant challenge, affecting speech intelligibility and communication quality across multiple domains. Current audio denoising methods often struggle with the delicate balance be- tween noise removal and speech preservation. This paper presents WaveSplit, a novel multi-stage framework for audio enhancement and denoising that addresses these limitations by combining deep learning techniques with psychoacoustic principles and adaptive noise processing. Building upon the CleanUNet architecture, our approach introduces several innovative components: adaptive SNR-based processing, harmonic enhancement that preserves critical speech components, vocal clarity enhancement, and perceptual processing leveraging human hearing characteristics. Evaluations demonstrate that our framework achieves superior performance compared to baseline models, with significant improvements in SNR (76.36 dB compared to 7.20-8.10 dB in baseline models), PESQ scores (1.05 improvement versus 0.77- 0.91), and STOI metrics (0.15 versus 0.09-0.13) while reducing the “robotic” artifacts common in traditional methods. This research has significant implications for applications including telecommunications, hearing assistive technologies, content production, and speech recognition systems. By addressing both objective quality metrics and perceptual factors, WaveSplit represents an advancement toward more effective, natural-sounding audio enhancement solutions for real-world environments.
Copyright (c) 2026 Parth Gandhi, Kevin Doshi, Anand Godbole https://creativecommons.org/licenses/by/4.0
IoT Driven Battery Health Monitoring Framework for ICE Two Wheelers with WhatsApp Based User Interaction
India’s two-wheeler ecosystem remains heavily dependent on Internal Combustion Engine (ICE) vehicles. Unlike electric vehicles, ICE batteries operate without intelligent monitoring systems, leaving users unaware of battery condition until failure occurs. This research presents a conceptual Internet of Things (IoT) framework designed to introduce real-time battery health visibility to ICE two-wheelers using affordable electronic components and cloud connectivity. The proposed system measures voltage, current, and temperature parameters through sensors connected to an ESP32 microcontroller. Battery data is transmitted to cloud servers and communicated to riders through WhatsApp Business API in the form of alerts and maintenance reminders. In addition to the technical framework, this study introduces an integrated business model that focuses on delivery riders, motorcycle enthusiasts, and garage mechanics. Primary insights were obtained through field interactions with mechanics and battery retailers in West Bengal. The framework demonstrates how low-cost IoT infrastructure can reduce unexpected battery failures while strengthening relationships between riders, service providers, and manufacturers. This study offers a practical approach for modernizing conventional ICE battery systems in emerging mobility markets.
Copyright (c) 2026 Moubani Mukherjee https://creativecommons.org/licenses/by/4.0
Stock Market Prediction and Education Platform Using Machine Learning
The ability to predict stock market trends accurately is highly valuable for investors and traders. This paper presents an integrated machine learning-based system that predicts stock prices using historical data and news sentiment analysis. The platform leverages techniques like sentiment analysis, Named Entity Recognition (NER), and deep learning models such as Bidirectional Long Short-Term Memory (Bi-LSTM). Along with predictions, the platform offers educational resources to enhance financial literacy, making it an ideal tool for novice investors seeking both knowledge and actionable market insights.
Copyright (c) 2026 Kunal Bhatia, Siddhesh Kirdat, Abhimanyu Kapoor, Anuj Tawari https://creativecommons.org/licenses/by/4.0
Disruption of Small World Properties in Functional Brain Networks of Alzheimer’s Disease
Alzheimer’s disease (AD) is a progressive neuro degenerative disorder characterized by cognitive decline and wide spread alterations in brain connectivity. In recent years, graph theory has emerged as a powerful framework for modeling functional brain networks and quantifying their topological properties. In this study we investigate disruptions in small world organizations of functional brain networks in Alzheimer’s disease using graph theoretic measures. Functional connectivity networks are constructed from resting state brain regions and edges denote pairwise functional interactions. Key network metrics including clustering coefficient, characteristic path length and global efficiency are computed and compared between Alzheimer’s patients and health control subjects. The results reveal a significant reduction in local clustering increased path length and decreased global efficiency in AD networks indicating impaired balance between functional segregation and integration. Furthermore, small worldness analysis demonstrates a clear breakdown of optimal network organization in Alzheimer’s disease. These findings provide quantitative evidence of functional brain network disorganization in AD and highlight graph theoretic measures as potential biomarkers for neuro degenerative disorders.
Copyright (c) 2026 Renuka Lakshmi Avvari https://creativecommons.org/licenses/by/4.0
Managing Strategic Uncertainty in Emerging Economies: A 4A Framework for Entrepreneurial Strategy in BRICS SMEs
Small and medium-sized enterprises (SMEs) operating in emerging economies face persistent and multidimensional strategic uncertainty arising from regulatory volatility, institutional heterogeneity, market turbulence, and chronic resource constraints [6], [7], [25]. In BRICS economies - Brazil, Russia, India, China, and South Africa these uncertainties fundamentally shape entrepreneurial decision-making, strategic behavior, and firm survival. While prior research has examined entrepreneurial orientation, innovation capability, dynamic capabilities, organizational agility, and firm performance in emerging markets [1], [2], [4], [15], the literature remains fragmented and conceptually siloed. Most studies examine isolated strategic dimensions without integrating how strategic intent is formed, how actions are executed, how learning and adaptation occur, and how outcomes are evaluated over time. This paper develops a comprehensive 4A framework - Aim, Action, Adaptation, and Accountability to synthesize and systematize the literature on entrepreneurial strategy under uncertainty in BRICS SMEs. Drawing on strategic management, entrepreneurship, dynamic capabilities, organizational learning, and performance measurement research, the study conceptualizes strategy as an iterative, learning-based process rather than a linear planning exercise [1], [2], [10]. The framework emphasizes feedback loops, capability alignment, and strategic renewal as central mechanisms through which SMEs navigate uncertainty in resource-constrained environments. The paper contributes theoretically by integrating dispersed research streams into a coherent process-oriented framework tailored to emerging economies and contributes practically by offering entrepreneurs and policymakers a clear roadmap for managing uncertainty using simple, actionable mechanisms. The study also discusses limitations and outlines a detailed agenda for future empirical research across BRICS contexts.
Copyright (c) 2026 A. Kapit, Neeraj Kumar Kushwaha https://creativecommons.org/licenses/by/4.0
Advanced Camouflaged Target Detection with Quadrapod Drone – A Fusion of Air and Land Technology
In order to identify camouflaged objects in challenging situations, we describe a unique system that combines air and land technologies. The platform, known as a quadrapod drone, combines quadrapod flying with legs to conduct intimate inspection at ground level as well as aerial environment. The drone is about being able to fly and walk; it’s about using both abilities to get best possible view of the environments. This fusion of locomotion modes enables the systems to take position itself for optimal target detection in the environments. Data from the sensors are processed by enhanced YOLOv8 and deep learning framework, which has been gets for the challenges of Camouflaged Object Detection. Our detection process combines a hierarchical sensor-fusion module for air- ground collaboration, multiple imaging, and a deep camouflaged object detection backbone. Using a bespoke dataset, we assess the method in mixed-terrain settings and demonstrate enhanced detection recall. The benefits of using an air-land hybrid to the object detection problem are demonstrated by an experimental evaluation, perceptual algorithms, hardware design, and data-fusion approach. The paper presents complete system architecture, details the camouflaged detection framework and outlines a methodology for its future validation. A robot like this has the potential to make real difference in the world, whether it’s making military missions safer, helping find survivors after a disaster or allowing scientists to study hard to find animals in the wild.
Copyright (c) 2026 Ashish A. Desai, Shravan R. Neje, Shridhar V. Sutar, Alankar U. Patil, Prathamesh B. Tambekar https://creativecommons.org/licenses/by/4.0
Modeling and Virtual Prototyping of a Human Mannequin Equipped with a Multifunctional Solar Backpack
This paper aims to model a 3D mannequin wearing a novel multifunctional electronic-textile backpack. To achieve this goal, a human mannequin is initially created using MakeHuman software involving innovative anthropometric databases. Then, the multi-propose virtual 3D backpack is equipped a mini solar panel, a battery charging circuit, a battery, two microcontrollers (Arduino Uno and ESP32 WROOM-32), DC voltage regulator modules (XL6009 IC), geolocation devices, weight controller sensor, MPU6050 sensor, a testing Smartphone with software application developed via Thunkable. etc. Finally, the overall platform is implemented and well visualized under Blender software, as an innovative virtual 3D studio for multifunctional solar Backpack.
Copyright (c) 2026 Ninon Rosine Nkoulou Nkoulou, Jean Mbihi, Salome Essiane Ndjakomo https://creativecommons.org/licenses/by/4.0
Physiographic Characterization of the Tshopo River and Hydroelectric Dam Using Geomatics and Remote Sensing Tools
The Tshopo River watershed offers significant potential in terms of both energy and navigability. For example, hydroelectric developments have been underway since 1955, and the Kisangani River is supplied with water from this dam. A 2m x 2m spectral band was used to approximate reality, and a 30m x 30m pixel was used to visualize drainage densities and the shape (sinuation) of the watercourses. Maps presenting the study area, the length of the main watercourse, and the boundaries and watercourses were also produced using ArcGIS 10.8 and HEC-HMS 6.4.1 software, which were essential for carrying out this study. We were able to extract 839 sub-watersheds with physiographic characteristics (geometric, topographic/hypsometric, and hydrographic features) with a total area of 19,306–19,317.31 km², a perimeter ranging from 1,030.1 to 1,142.04 km, and a main channel length of 561.7361 km. The Tshopo River has a low drainage density (i.e., less than 1). Our Tshopo River watershed is very elongated, as the gravel coefficient varies from 2.1 to 2.3 depending on the pixel size and spatial resolution; the steepest slopes are concentrated in the south-central part of the Tshopo watershed, with values ranging from 7 to 66 degrees, while the gentler slopes are much more concentrated around the dam (outlet) to the west. The vegetation cover (less dense and dense forest occupies more than 90%. And 10 altitudes classes according to their areas have been identified ranging from 455-506 m and 916-967 m with Time of Concentration (Tc) of 3766.8 minutes.
Copyright (c) 2026 Mokili Enasi Emmanuel, Sansabana Kimvuka Herve, Mokili Enasi Josue, Wabwandi Wabwandi Albert, Isige Getombo Jean Pierre, Lilombi Lokanga Augustin, Misilu Mia Nsokimieno Eric, Kabasele Yenga Yenga Albert https://creativecommons.org/licenses/by/4.0
Comparative Analysis of Machine Learning and Deep Learning Algorithms for Early Detection of Hardware Vulnerabilities in Hardware Security Systems
Early detection of hardware vulnerabilities is critical to ensuring the security and reliability of electronic systems. This study investigates and compares various machine learning and deep learning algorithms for their effectiveness in identifying hardware vulnerabilities during the early stages of the development lifecycle. The analysis focuses on algorithms commonly used in hardware security applications, including anomaly detection, classification, and pattern recognition, to detect issues such as hardware Trojans, side-channel attacks, and other security threats. Key performance metrics such as accuracy, detection speed, computational efficiency, and robustness against adversarial scenarios are evaluated. Furthermore, the study highlights the trade-offs between machine learning and deep learning approaches, considering their scalability and deployment feasibility in resource-constrained environments. The findings aim to provide insights into selecting the most suitable algorithm for early vulnerability detection, contributing to the advancement of secure hardware design and the mitigation of potential risks in critical systems.
Copyright (c) 2026 Milind Paraye, Abhishek Ratnu, Shubham Sinha, Pratik Waghmode https://creativecommons.org/licenses/by/4.0
AI-Driven Employability Prediction: Integrating Machine Learning and Educational Analytics for Student Placements
This paper presents an Artificial Intelligence (AI) and Machine Learning (ML)–based framework for predicting student employability outcomes in higher education. The study aims to bridge the gap between academic learning and industry requirements by analyzing multiple features such as academic performance, technical skills, internships, and leadership experience. Using supervised learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), and XGBoost (XGB), the system forecasts suitable job roles and potential recruiting companies. Among these models, XGBoost achieved the highest performance, with an accuracy of 87.3% and an F1-score of 86.2%. The results demonstrate that combining technical proficiency with behavioral and academic features provides superior predictive capability. The proposed framework contributes to educational data analytics by enabling data-driven career guidance, curriculum design, and employability enhancement. This approach can assist institutions in aligning student competencies with evolving market needs, fostering better preparedness and placement efficiency in business and engineering education.
Copyright (c) 2026 Basuri Bhujade, Anmol Khy, Surekha Dholay https://creativecommons.org/licenses/by/4.0
Comparative Analysis of TF-IDF and LLM-Based Text Corpus Generation for Course Recommendation Systems
Course recommendation systems have become increasingly critical in addressing the complex educational challenges of personalized learning pathways. This research intro- duces a novel approach to text corpus generation that leverages advanced natural language processing techniques to enhance course recommendation accuracy. Traditional keyword extraction methods, particularly TF-IDF, often struggle to capture nuanced semantic relationships within academic content. Our study proposes an innovative methodology that combines traditional statistical methods for large language model (LLM) keyword extraction to generate a comprehensive, multidimensional course corpus. The proposed framework segments college databases across semesters and subjects, generating a three-dimensional keyword representation that captures the intricate relationships between academic content. By comparing traditional TF-IDF keyword extraction with LLM-based semantic keyword generation, we demonstrate significant improvements in recommendation relevance and precision. Experimental results reveal that LLM-based approaches substantially outperform traditional statistical methods in capturing contextual and semantic nuances of academic content.
Copyright (c) 2026 Deepanshu Aggarwal, Neerja Doshi, Sanskar Kamble, Sudhir Dhage https://creativecommons.org/licenses/by/4.0
IoT-Based Smart Egg Incubation System for Improved Hatchability in Small-Scale Poultry Farming
This paper presents the design and experimental evaluation of an Internet of Things (IoT)-based smart egg incubation system developed for small-scale poultry farming applications. Conventional manual incubation practices suffer from inconsistent temperature and humidity regulation, leading to low hatch rates typically limited to 50–60% and requiring intensive manual labor. The proposed system automates the incubation process using an ESP8266 microcontroller integrated with DHT22 temperature and humidity sensors. Critical incubation conditions of 37.5°C ± 0.3°C (after calibration) and relative humidity between 55–70% are maintained throughout the 21-day incubation cycle. The system includes relay-controlled heating elements, automated egg turning using servo motors, and real-time monitoring through an OLED display. Remote access and monitoring are enabled via Wi-Fi using cloud platforms such as Blynk and ThingSpeak. Experimental evaluation conducted over five complete incubation cycles demonstrated stable temperature control within ±0.3°C and humidity regulation within ±3%. The system achieved an average hatch rate of 92%, representing a significant improvement over manual incubation methods. Energy consumption was measured at approximately 0.15 kWh per day, while manual labor requirements were reduced by nearly 80%. With total hardware costs below $60, the proposed system offers a reliable, energy-efficient, and affordable solution for improving hatchability and productivity in resource-constrained poultry farming environments.
Copyright (c) 2026 Aditya Pawar, Malhar Patki, Omkar Shirse, V. S. Jadhav https://creativecommons.org/licenses/by/4.0
Credit Card Fraud Detection: Use of Artificial Intelligence
According to Minchin (2025) fraud is UK’s biggest financial threats, which includes over 2 million cases reported between January and July in 2025 alone. This sums up to over £620 million lost just in the first half of 2025. With regards to its origins, over 70% of fraud incidents starts online, and about 17% over a simple telephone call. However, banks and financial institutions introduce new tactics to reduce fraud incidents daily. Minchin (2025) also explains that UK banks were able to reimburse 98% of victims caught up in fraud incidents where criminals had gained access to their accounts or cards and had stolen money. Whereas, through authorized push payments methods, or APP for short, which ideally means that the victims were on the phone with criminals who had tricked then into sending them money through bank transfers. Banks were able to identify the genuineness of these cases as was able to return money of approximately 62% of such victims. On this paper, fraud and scams pertaining to credit cards are explained and how artificial intelligence and machine learning models, and even generative AI, can be used to identify and prevent such fraudulent activities are discussed.
Copyright (c) 2026 Minul Mindula Subasinghe https://creativecommons.org/licenses/by/4.0
A Risk-Governance and Exposure-Monitoring Framework for Synthetic Food Dyes in Pediatric Diets in the United States: Decision Indicators, Labeling Salience, and Post-Market Surveillance
Purpose: Synthetic food dyes remain pervasive in the U.S. processed-food supply, with exposure concentrated among children and shaped by product formulation, purchasing patterns, and labeling practices. While clinical and toxicological literature increasingly emphasizes heterogeneous susceptibility, regulatory decision-making still faces operational challenges: translating complex evidence into proportionate, implementable risk-management actions. This study proposes a regulatory-science framework that integrates exposure monitoring, decision indicators, and post-market surveillance to strengthen governance for pediatric-relevant dye exposure. Methodology: A conceptual framework was developed using a structured narrative synthesis of (i) clinical evidence on neurobehavioral sensitivity in subgroups, (ii) mechanistic/toxicological plausibility (including oxidative stress and inflammatory signaling), and (iii) regulatory documentation and exposure-context materials from major authorities. The framework was built through an iterative design-science logic: identification of governance gaps; definition of decision indicators; specification of operational levers (benchmark reassessment, labeling as risk communication, surveillance triggers); and articulation of an implementation pathway suitable for routine oversight. Findings: The proposed framework addresses three recurring governance gaps: (1) limited integration of contemporary pediatric exposure patterns into decision thresholds; (2) insufficient operational treatment of heterogeneity and susceptible subgroups; and (3) underutilization of labeling salience and surveillance loops as low-burden risk-management tools. The framework provides a practical approach for tiering exposure contexts, specifying triggers for reassessment, and strengthening post-market monitoring without requiring categorical hazard assumptions. Practical Implications: A proportionate governance model can reduce preventable risk in high-exposure pediatric contexts by improving transparency, enabling caregiver-level exposure management, and supporting periodic reassessment anchored in real-world consumption. The framework is designed to be implementable through existing regulatory infrastructure and adaptable to evolving evidence. Originality: This paper contributes a decision-oriented, implementable governance framework that translates multidisciplinary evidence into operational oversight mechanisms, emphasizing pediatric exposure concentration, susceptibility, and surveillance-driven iteration.
Copyright (c) 2026 Jossineide Manuela da Silva Cordeiro https://creativecommons.org/licenses/by/4.0
Fundamental Properties of Absolute-Type Cesàro Sequence Space on Metric Spaces
Copyright (c) 2026 Adam Fikriansyah Akbar, Manuharawati Akbar https://creativecommons.org/licenses/by/4.0
From Models to Impact: A Systematic Review of Generative Artificial Intelligence in Education
Generative Artificial Intelligence (GenAI) is increasingly influencing educational systems by enabling personalized learning, automated content generation, adaptive assessment, and intelligent tutoring. Despite its rapid advancement and growing adoption, the effective and responsible integration of GenAI in education remains a challenge due to ethical concerns, limited empirical validation, and issues related to user trust. This paper presents a systematic review and analysis of existing literature to examine the pedagogical potential, ethical risks, and trust-related challenges associated with GenAI-enabled educational environments. It further analyzes global and Indian adoption trends to highlight domain-wise and regional variations in GenAI usage. By identifying key research gaps in current studies, the paper emphasizes the need for integrated, trust-aware, and ethically aligned approaches. The study contributes by synthesizing insights from prior research and proposing directions for the sustainable and responsible deployment of Generative AI in education.
Copyright (c) 2026 Prerna Agrawal https://creativecommons.org/licenses/by/4.0
Detection of Alzheimer’s Using Convolutional Neural Network
Alzheimer’s disease is a neurodegenerative disease that usually starts slow and progressively worsens. Early detection of Alzheimer’s can help us prevent deterioration of the tissue damage in the brain. In this paper, a detailed analysis on detection of Alzheimer’s disease using various Convolutional Neural Network (CNN) architectures in Machine Learning (ML) is shown. This disease causes people to suffer from memory loss and unusual brain tissue damages. Several ML models have been used by researchers for detection of Alzheimer’s. Analyzing Magnetic Resonance Imaging (MRI) scans is a very useful and common practice for the diagnosis of Alzheimer’s disease. ML is used to help the computers to train on the large and complicated datasets and detect the presence of Alzheimer’s disease in brain MRI. In today’s world Deep Learning techniques have successfully brought change in numerous fields including medical image analysis. We propose a deep CNN for Alzheimer’s disease using MRI scan data sets. Our model identifies Alzheimer’s disease in its different stages using different CNN architectures to obtain efficiency and accuracy for early-stage diagnosis of Alzheimer’s disease.
Copyright (c) 2026 Kolliboni Tejaswini, Balanagari Neha, Daram Ramya Sri, Indla Geethika Reddy https://creativecommons.org/licenses/by/4.0
Using Tiny-ML Models on Edge Devices to Improve Industrial Control Systems in Real Time
Tiny Machine Learning (Tiny-ML) lets machine learning models run on edge devices with very few resources. This opens up new possibilities for smart Industrial Control Systems (ICS) that don't need to be connected to the cloud all the time. Traditional Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acquisition (SCADA) systems predominantly depend on static, rule-based logic and face challenges in adapting to dynamic, data-intensive industrial settings that necessitate predictive maintenance, anomaly detection, and real-time process optimization. This paper examines the implementation of highly efficient Tiny-ML models on edge devices, including ARM-based micro-controllers and single-board computers, to attain low-latency inference, bandwidth optimization via local processing, and enhanced energy efficiency and security for industrial Internet of Things (IIoT) sensors. The paper talks about hardware and software limits, how to connect to old PLC/SCADA systems using industrial protocols, and gives an overview of contemporary industrial projects that show how edge AI is useful in modern workplaces.
Copyright (c) 2026 Kadari Achwak https://creativecommons.org/licenses/by/4.0
Emotional Intelligence Across Career Stages: A Study of Interpersonal Skills and Workplace Performance
In modern corporate settings, emotional intelligence (EI) has become a crucial skill affecting interpersonal effectiveness and professional performance. Technical proficiency is still crucial, but employees' capacity to identify, control, and react positively to emotions has a big impact on how they interact with coworkers and perform. This study looks at how emotional intelligence develops and shows up at various levels of professional experience, as well as how it helps people improve their interpersonal abilities. The study uses a mixed-methods approach, gathering primary data from 76 employees via a structured questionnaire. Employees with less than five years of experience (52 respondents) and those with more than five years of experience (24 respondents) were the two groups into which the respondents were divided. Initial emotional reactions, situational emotional intelligence, present emotional intelligence, and self-perceived emotional intelligence were all evaluated in the survey. To facilitate comparison analysis, qualitative replies were methodically transformed into quantitative scores using a predetermined scoring structure. The results show that emotional intelligence does not grow with experience in a linear fashion. While seasoned workers show emotional stability along with less situational flexibility, individuals in the early phases of their careers show more emotional adaptability, stronger situational emotional intelligence, and greater self-perceived emotional confidence. The findings show that learning orientation, exposure to the workplace, job demands, and ongoing reflection—rather than experience alone—all have an impact on the dynamic development of emotional intelligence. In order to maintain interpersonal effectiveness and job success, the study underscores the necessity of experience-specific emotional intelligence development interventions and stresses that emotional intelligence needs constant reinforcement throughout all career stages.
Copyright (c) 2025 Aditi Sharma, Anna Reji, Aayat Rehman https://creativecommons.org/licenses/by/4.0