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VOLUME 14, ISSUE 10, OCTOBER 2026
DEVELOPMENT AND MULTI‑CROP EFFICACY EVALUATION OF FORTIFIED BIO‑LIQUID FERTILIZER FROM AGRO‑WASTE
S. Sahabi*, A. Yakubu, A. Magaji, A.L Mustapha, D.S. Garba and B. B. Lailaba
Indian Sign Language to Text/Speech Translation
Miss. Nandini J. Kakde, Asst. Prof. Arsalan A. Shaikh
Geo-Location Based Attendance Tracking Mobile Application
Miss. Shivani Mahendra Dutte, Asst. Prof. Ms.Sapana A. Fegade
A STUDY ON AWARENESS AND ADOPTION OF ARTIFICIAL INTELLIGENCE (AI) TOOLS AMONG POSTGRADUATE STUDENTS
Miss. Priya Kulkarni, Asst. Prof. Arsalan A. Shaikh
Conversational Chatbot Builder: Smarter Virtual Assistants with Domain-Specified AI
Mr. Sujit Sachin Shinde, Asst. Prof. Pratiksha R. Lad
ASSURED CONTRACT FARMING SYSTEM FOR STABLE MARKET ACCESS
Miss. Vishakha P. Mahajan, Asst. Prof. Chetana M. Kawale
Distributional Properties of Nigerian Macroeconomic Indicators, 2008–2024: Less Non-Normal Than Assumed
Ofierohor Ufuoma Earnest
Automatic Road Extraction and Alert Generation from Satellite Imagery Using Deep Convolutional Neural Networks
Mr. Pushparaj Girase, Asst. Prof. Arsalan Shaikh
Design and Implementation of Smart EV Tractor for Sustainable Farming
Janardhan L K, Lokanathan M S, Yashoda D S
Intelligent E-commerce Product Recommendation System using Machine Learning.
Mr. Kuldip Dilip More, Asst. Prof. Chetana M. Kawale
A Survey on Recent Research development on Design and Development of Smart Gloves for Real-Time CPR Monitoring Using ESP32 and MPU6050
Anushri. B. R, Keerthana. R. T, Pallavi. K. B, Rekha. S. R, Kaviyarasi. D
LITERATURE REVIEW ON AUTONOMOUS AIRBORNE COMMUNICATION NETWORKS FOR DATA EXCHANGE
Mrs. G. Soujanya, Arun A, Athish Prasad Raj, Madhan K, Vishwanath A V
Emerging IT Skill Demand Analysis and Skill Trend Prediction Using Job Posting Data and Machine Learning
Dhanya Sri G, Bhuvaneswari K, Kavya M
MentorConnect: A Digital Platform for Structured Mentor–Mentee Interaction
Dhanusri P, Varun Kumar D.
A Machine Learning-Based Multi-Objective Framework for Optimizing Fruit-Peel Waste- Derived Bio-Pesticide Formulations
Mithra R, Harini L
Smart Tracking Basket
Mahendrachari, H M Lavanya, Harshal Vijay Patil, Harshitha K, Lekha G N
An ARDUINO-BASED IoT SMART FARM MODEL for AUTOMATED IRRIGATION and FLOOD-RESPONSIVE DRAINAGE with CLOUD MONITORING
Enyinnaya Pascal Eni, Kenneth A. Akpado, Scholastica Ukamaka Nnebe
Does Risk Management Improve Portfolio Outcomes in Nigeria? A Historical Comparison of Equity, Deposit and Volatility-Managed Allocations.
Ofierohor Ufuoma Earnest
AGROINFORMATIX: THE DIGITAL TRANSFORMATION OF THE FARM. DEVELOPMENT AND OPERATION OF AN APPLICATION THROUGH NO-CODE PROGRAMMING IN VOCATIONAL EDUCATION
Klonis Christos, Dr Klonis Apostolos
Abstract
Agentic AI for Intelligent 6G Network Management and Resource Optimization
Rudra Narayan Dash
Abstract: With regards to network intelligence, resource optimization, ultra low-latency, and autonomous service management, rapid development of sixth generation (6G) wireless communication technologies brings new challenges. The future 6G networks will be dynamic, heterogeneous, and geographically dispersed, being impossible to manage using conventional network management techniques. The concept of intelligent network orchestration can experience a revolution because of the advent of Agentic Artificial Intelligence (Agentic AI) that has such features as autonomous reasoning, planning, collaboration, and adaptation. In order to perform intelligent management and optimization of 6G wireless networks, an Agentic AI framework consisting of multiple interacting AI agents for handling fault recovery, orchestration, monitoring, forecasting, and dynamic resource allocation is proposed in the paper. The proposed solution constantly assesses network status and optimizes energy efficiency, spectrum efficiency, Quality of Experience (QoE), and Quality of Service (QoS). The simulation-based evaluation shows improved latency, throughput, network reliability, and resource efficiency as compared to traditional AI-based approaches. Scalable, robust, and self-optimizing 6G ecosystems are enabled by the proposed architecture for next generation autonomous wireless networks.
Keywords: Agentic AI, 6G Networks, Network Intelligence, Resource Optimization, Multi-Agent Systems, Autonomous Networking, QoS, QoE, Deep Reinforcement Learning, Edge Intelligence
Keywords: Agentic AI, 6G Networks, Network Intelligence, Resource Optimization, Multi-Agent Systems, Autonomous Networking, QoS, QoE, Deep Reinforcement Learning, Edge Intelligence
Abstract
Agentic AI for Healthcare Fraud Detection and Claims Verification
Alisha Panda
Abstract: Due to the reasons like insurance frauds, duplicate billing, identity theft, and professional misconduct, fraud in the healthcare sector leads to billions of losses. Fraud detection mechanisms that use static parameters cannot detect the highly advanced fraud patterns. Due to the inclusion of the autonomous reasoning, constant surveillance, adaptation, and collaborative decisions, Agentic Artificial Intelligence increases the fraud detection process and claims verification. In this research work, we provide an Agentic AI framework which includes intelligent agents related to policy compliance, provider verification, anomaly detection, claims verification, and automated decision-making. Several AI agents can cooperate with each other in order to detect the suspicious health care transaction process without generating too many false positives through machine learning, knowledge graph, explainable AI, and reinforcement learning. The proposed approach will be beneficial in increasing the performance and fraud prevention in the healthcare ecosystems. Simulation results show that the proposed framework outperforms existing fraud detection systems in terms of accuracy, processing time, and claim verification process. The proposed technique will be helpful in designing future intelligent healthcare infrastructures with safe and reliable health insurance management.
Keywords: Agentic AI, Healthcare Fraud Detection, Claims Verification, Medical Insurance, Explainable AI, Multi- Agent Systems, Machine Learning, Healthcare Analytics.
Keywords: Agentic AI, Healthcare Fraud Detection, Claims Verification, Medical Insurance, Explainable AI, Multi- Agent Systems, Machine Learning, Healthcare Analytics.
Abstract
DEVELOPMENT AND MULTI‑CROP EFFICACY EVALUATION OF FORTIFIED BIO‑LIQUID FERTILIZER FROM AGRO‑WASTE
S. Sahabi*, A. Yakubu, A. Magaji, A.L Mustapha, D.S. Garba and B. B. Lailaba
DOI: 10.17148/IJIREEICE.2026.141001
Abstract: This study developed a fortified bio‑liquid fertilizer from poultry manure and lignocellulosic biomass through anaerobic co‑digestion under mesophilic conditions (35–37 °C) for 14 days, followed by mineral fortification with urea, single superphosphate (SSP), and muriate of potash (MOP) to achieve an NPK 12:6:8 formulation. Physicochemical analyses confirmed improved nutrient balance, stability, and sustained release compared with unfortified bio‑liquid. Nitrogen release reached ~90% by day 20, phosphorus ~70%, and potassium ~50%, supporting gradual nutrient availability. The fortified product demonstrated potential as a cost‑effective and eco‑friendly alternative to synthetic NPK 15:15:15, while retaining organic carbon for soil health. These findings highlight agro‑waste valorization as a viable pathway for sustainable fertilizer production, aligning with circular economy principles and agricultural intensification.
Keywords: Agro-waste valorization, Fortified Bio-liquid Fertilizer, Anaerobic digestion, Circular Economy
Keywords: Agro-waste valorization, Fortified Bio-liquid Fertilizer, Anaerobic digestion, Circular Economy
Abstract
Indian Sign Language to Text/Speech Translation
Miss. Nandini J. Kakde, Asst. Prof. Arsalan A. Shaikh
DOI: 10.17148/IJIREEICE.2026.141002
Abstract: Indian Sign Language (ISL) is an important visual language used by deaf and hard-of-hearing people in India for communication. However, many people who do not understand sign language face difficulties in communicating with ISL users. In many situations, communication depends on another person who understands sign language, which may not always be available. Therefore, there is a need for a system that can automatically recognize Indian Sign Language gestures and convert them into understandable text and speech.
This research proposes an Indian Sign Language Text/Speech Translation System to make communication between ISL users and non-sign-language users easier and more accessible. The system provides a digital method to capture ISL gestures through a camera, process the input, identify the signs, and convert the recognized gestures into text. The generated text can then be converted into speech using a text-to-speech component. The proposed system can use computer vision, image processing, machine learning or deep learning techniques, and natural language processing for recognizing and translating ISL gestures. The system can support real-time communication and reduce the need for manual interpretation in basic communication situations. It can also provide a foundation for future development of multilingual translation and improved recognition of continuous signs.
Keywords: Indian Sign Language, ISL Translation, Sign Language Recognition, Text Translation, Speech Generation, Computer Vision, Deep Learning, Natural Language Processing.
This research proposes an Indian Sign Language Text/Speech Translation System to make communication between ISL users and non-sign-language users easier and more accessible. The system provides a digital method to capture ISL gestures through a camera, process the input, identify the signs, and convert the recognized gestures into text. The generated text can then be converted into speech using a text-to-speech component. The proposed system can use computer vision, image processing, machine learning or deep learning techniques, and natural language processing for recognizing and translating ISL gestures. The system can support real-time communication and reduce the need for manual interpretation in basic communication situations. It can also provide a foundation for future development of multilingual translation and improved recognition of continuous signs.
Keywords: Indian Sign Language, ISL Translation, Sign Language Recognition, Text Translation, Speech Generation, Computer Vision, Deep Learning, Natural Language Processing.
Abstract
Geo-Location Based Attendance Tracking Mobile Application
Miss. Shivani Mahendra Dutte, Asst. Prof. Ms.Sapana A. Fegade
DOI: 10.17148/IJIREEICE.2026.141003
Abstract: The rapid growth of smart phones, GPS technology, and location-based service as created new opportunities. or automating attendance management. Traditional attendance systems commonly depend on manual registers or conventional digital methods, which can require considerable time and may allow inaccurate or proxy attendance. This research presents a Geo-Location Based Attendance Tracking Mobile Application designed to record attendance by verifying the user's current geographical location.
The proposed system uses GPS-based location detection and a predefined geographical boundary, or geo-fence, to determine whether auseris present within an authorized attendance area. When the user's location satisfies the defined criteria, the application records attendance along with date and time. The system is intended to provide a simple, automated, anduser- friendly approach to attendance tracking while reducing manual work.
The study focuses on system design, location verification, attendance recording, and application evaluation. The proposed application can be useful in educational institutions and organizations where attendance needs to be associated with physical presence at a specified location. Future extensions may include face verification, cloud synchronization, analytics, and stronger location-spoofing protection.
Keywords: Geo-Location, GPS, Attendance Tracking, Mobile Application, Geo-Fencing, Location-Based Services, Automated Attendance.
The proposed system uses GPS-based location detection and a predefined geographical boundary, or geo-fence, to determine whether auseris present within an authorized attendance area. When the user's location satisfies the defined criteria, the application records attendance along with date and time. The system is intended to provide a simple, automated, anduser- friendly approach to attendance tracking while reducing manual work.
The study focuses on system design, location verification, attendance recording, and application evaluation. The proposed application can be useful in educational institutions and organizations where attendance needs to be associated with physical presence at a specified location. Future extensions may include face verification, cloud synchronization, analytics, and stronger location-spoofing protection.
Keywords: Geo-Location, GPS, Attendance Tracking, Mobile Application, Geo-Fencing, Location-Based Services, Automated Attendance.
Abstract
A STUDY ON AWARENESS AND ADOPTION OF ARTIFICIAL INTELLIGENCE (AI) TOOLS AMONG POSTGRADUATE STUDENTS
Miss. Priya Kulkarni, Asst. Prof. Arsalan A. Shaikh
DOI: 10.17148/IJIREEICE.2026.141004
Abstract: Artificial Intelligence (AI) has become an important part of modern higher education. Postgraduate students increasingly encounter AI-based tools for searching information, understanding difficult concepts, writing and improving text, coding, summarising content, preparing presentations, generating ideas, and supporting research. This research studies the awareness and adoption of AI tools among postgraduate students, with emphasis on knowledge of AI tools, frequency of use, academic purposes, perceived benefits, challenges, trust, privacy, accuracy, academic integrity, and responsible use. The study follows a descriptive quantitative survey design using a structured questionnaire. The target population is postgraduate students, and the proposed data collection method is a questionnaire-based survey using convenience or purposive sampling. The literature review includes studies on student use of Generative AI, technology acceptance, trust, and learning outcomes. The paper presents a research framework, questionnaire, proposed analysis plan, and responsible-use recommendations. Numerical findings are intentionally not fabricated; final percentages and statistical results should be inserted after actual questionnaire data are collected.
Keywords: Artificial Intelligence, Generative AI, AI Awareness, AI Adoption, Postgraduate Students, Higher Education, AI Literacy, Academic Integrity.
Keywords: Artificial Intelligence, Generative AI, AI Awareness, AI Adoption, Postgraduate Students, Higher Education, AI Literacy, Academic Integrity.
Abstract
Conversational Chatbot Builder: Smarter Virtual Assistants with Domain-Specified AI
Mr. Sujit Sachin Shinde, Asst. Prof. Pratiksha R. Lad
DOI: 10.17148/IJIREEICE.2026.141005
Abstract: The enterprise software landscape has experienced a monumental shift towards natural language interfaces, driven largely by the advent of Large Language Models (LLMs). However, generic LLMs frequently suffer from domain hallucination, data privacy vulnerabilities, and an inability to interface with proprietary corporate knowledge. This research presents the architecture and implementation of a Conversational Chatbot Builder designed specifically to construct smarter, domain-specified virtual assistants. The proposed system employs a Retrieval-Augmented Generation (RAG) framework, seamlessly integrating pre-trained foundation models with specialized vector databases. By transforming institutional datasets into high-dimensional semantic embeddings, the system grounds the virtual assistant's responses entirely in verified, contextually relevant data. The platform abstracts the complexities of NLP orchestration, offering a dynamic pipeline for query ingestion, intent classification, semantic search, and prompt injection. Furthermore, the architecture implements rigorous security guardrails, including real-time Personally Identifiable Information (PII) redaction and strict Role-Based Access Control (RBAC). Extensive empirical evaluations demonstrate that this domain-specified approach significantly reduces response latency compared to heavy generalized inference, while entirely mitigating factual hallucinations. The resulting conversational interface achieves human-like fluency combined with deterministic accuracy, representing a paradigm shift in how organizations deploy secure, intelligent virtual agents.
Keywords: Conversational AI, Chatbots, Retrieval-Augmented Generation, RAG, Natural Language Processing, LLMs, Vector Databases, Domain-Specific AI.
Keywords: Conversational AI, Chatbots, Retrieval-Augmented Generation, RAG, Natural Language Processing, LLMs, Vector Databases, Domain-Specific AI.
Abstract
ASSURED CONTRACT FARMING SYSTEM FOR STABLE MARKET ACCESS
Miss. Vishakha P. Mahajan, Asst. Prof. Chetana M. Kawale
DOI: 10.17148/IJIREEICE.2026.141006
Abstract: Agriculture is an important source of livelihood and income for rural communities, yet farmers often face uncertainty while selling their produce. Price fluctuations, limited access to reliable buyers, weak bargaining positions, inadequate market information, quality requirements, and dependence on intermediaries can affect the stability of agricultural marketing. Contract farming provides a structured arrangement in which farmers and buyers agree on production and marketing conditions before or during production. This research studies an Assured Contract Farming System for Stable Market Access and proposes a technology-supported approach for improving transparency,
based research design. Relevant research papers, systematic reviews and published studies on contract farming, farmer participation, income, risk management and market access are reviewed. Existing practices are analysed to identify farmer and buyer requirements, implementation challenges and opportunities for digital support. Based on the literature findings, a conceptual framework is proposed in which farmer registration, buyer requirements, contract creation, production monitoring, quality verification, delivery, payment records and alerts are connected through a structured system. The expected contribution is a practical conceptual model that can support more reliable market relationships and reduce uncertainty in agricultural marketing.
Keywords: Contract Farming, Market Access, Farmers, Buyers, Digital Agriculture, Risk Management, Transparency, Stable Market Access.
based research design. Relevant research papers, systematic reviews and published studies on contract farming, farmer participation, income, risk management and market access are reviewed. Existing practices are analysed to identify farmer and buyer requirements, implementation challenges and opportunities for digital support. Based on the literature findings, a conceptual framework is proposed in which farmer registration, buyer requirements, contract creation, production monitoring, quality verification, delivery, payment records and alerts are connected through a structured system. The expected contribution is a practical conceptual model that can support more reliable market relationships and reduce uncertainty in agricultural marketing.
Keywords: Contract Farming, Market Access, Farmers, Buyers, Digital Agriculture, Risk Management, Transparency, Stable Market Access.
Abstract
Distributional Properties of Nigerian Macroeconomic Indicators, 2008–2024: Less Non-Normal Than Assumed
Ofierohor Ufuoma Earnest
DOI: 10.17148/IJIREEICE.2026.141007
Abstract: Regression inference and many machine-learning loss functions assume, or at least work best with, errors that are not extremely fat-tailed or asymmetric. Emerging-market series are widely believed to violate this, and the belief is rarely tested carefully. This paper documents the distributional properties of ten Nigerian macroeconomic indicators over 67 quarters from 2008Q2 to 2024Q4, and finds a more mixed picture than the folklore suggests. Three of the ten do not reject normality at the 5% level: FDI itself (Jarque-Bera 2.11, p = 0.348), GDP (0.24, p = 0.885) and oil price (3.63, p = 0.163). Seven do reject, with exchange-rate volatility the most extreme (skewness 3.21, excess kurtosis 10.14). The result for FDI matters most, because FDI is the dependent variable in most Nigerian FDI research and is routinely assumed to be severely non-normal. An earlier version of this analysis, run on a longer sample in which everything before 2008 was interpolated from annual figures, found all ten variables rejecting normality and reported FDI with excess kurtosis of 7.95. On genuine data FDI has excess kurtosis of -0.17. The apparent fat tails were an artefact of splicing a smoothed series onto a noisy one.
Keywords: Non-Normality; Skewness; Kurtosis; Jarque-Bera; Nigerian Macroeconomic Data.
Keywords: Non-Normality; Skewness; Kurtosis; Jarque-Bera; Nigerian Macroeconomic Data.
Abstract
Automatic Road Extraction and Alert Generation from Satellite Imagery Using Deep Convolutional Neural Networks
Mr. Pushparaj Girase, Asst. Prof. Arsalan Shaikh
DOI: 10.17148/IJIREEICE.2026.141009
Abstract: The rapid urbanization and infrastructural development globally necessitate accurate, up-to-date cartographic data. Traditional road extraction methods relying on manual digitization of satellite imagery are time- consuming, expensive, and prone to human error. This research paper presents a fully automated system for road extraction and alert generation designed to detect newly constructed roads in high-resolution satellite imagery using deep Convolutional Neural Networks (CNNs). The proposed architecture leverages a modified U-Net model with a ResNet backbone to perform pixel-wise semantic segmentation of road networks. Extracted binary masks are subjected to morphological post-processing and vectorization to generate GeoJSON representations. A spatial intersection engine then cross-references these newly vectorized roads against existing baseline databases (such as OpenStreetMap or proprietary governmental GIS databases). Upon detecting unregistered or novel infrastructural additions, the system triggers automated webhook alerts and dashboard notifications to relevant authorities. Extensive evaluation on the Massachusetts Roads Dataset and custom temporal satellite data demonstrates that our model achieves a Dice Coefficient of 0.89 and an Intersection over Union (IoU) of 0.81, significantly outperforming legacy threshold-based GIS algorithms. Furthermore, the implemented event-driven architecture ensures low-latency alert distribution, enabling near real-time urban monitoring. This paper details the mathematical loss functions, topological vectorization constraints, and secure communication protocols that comprise the system.
Keywords: Deep Learning, Road Extraction, U-Net, Semantic Segmentation, Remote Sensing, GIS, Alert Systems, Webhooks.
Keywords: Deep Learning, Road Extraction, U-Net, Semantic Segmentation, Remote Sensing, GIS, Alert Systems, Webhooks.
Abstract
Design and Implementation of Smart EV Tractor for Sustainable Farming
Janardhan L K, Lokanathan M S, Yashoda D S
DOI: 10.17148/IJIREEICE.2026.141010
Abstract: Existing farming practices rely mainly on diesel-powered tractors, resulting in high operational costs, health impacts from pollution, carbon emissions, and a dependence on fossil fuels.even though the Electric tractors are used it is having limited battery range, high upfront costs, lack of charging infrastructure,This slow down the widespread adoption of electric tractors in remote areas and as well as rural areas. These technical and socioeconomic obstacles must be considered and design Smart Electric Vehicle (EV) Tractor for sustainable agricultural practices. The primary objective of this research is to design a model a zero-emission electric tractor tailored for sustainable farming, aiming to reduce carbon footprints with-same endurance.this study encompasses the integration of a high-torque electric power-train, a modular battery architecture, and an Io T-driven sensor framework designed for real-time field telemetry, automated path planning, and remote monitoring.Comparative field operational trials were conducted against standard diesel/ petrol tractors across typical farming operations, including tillage, seeding, and crop transport.smart electric tractors offer a viable, cost-effective, and environmentally sustainable compared to traditional internal combustion engine tractors. the integration of autonomous navigation and real-time soil sensing reduced field overlap Smart electric tractors serve as a superior alternative to traditional diesel tractors. By eliminating diesel exhaust and also minimize environmental pollution while significantly lowering operational expenses. Integrating electric power with advanced precision technology allows farmers to optimize resource use and substantially cut down overall farming costs
Keywords: Smart EV Tractor, Sustainable Farming, Photovoltaic Solar Charging, Internet of Things (IoT), Embedded Microcontrollers, Precision Agriculture, Motor Drivers, Zero-Emission Machinery.
Keywords: Smart EV Tractor, Sustainable Farming, Photovoltaic Solar Charging, Internet of Things (IoT), Embedded Microcontrollers, Precision Agriculture, Motor Drivers, Zero-Emission Machinery.
Abstract
Intelligent E-commerce Product Recommendation System using Machine Learning.
Mr. Kuldip Dilip More, Asst. Prof. Chetana M. Kawale
DOI: 10.17148/IJIREEICE.2026.141011
Abstract: Today, online shopping websites have millions of products. When a customer buys a main product, like a laptop or a smartphone, they often need accessories like a mouse, a bag, or a keyboard. However, finding accessories that are both affordable (within their budget) and technically compatible (matching the exact features) is very confusing and takes a lot of time. Traditional recommendation systems fail here because they only show items based on "what other people bought". They do not check if the accessory fits the customer's personal budget or if it actually connects with their specific device. To solve this real-world problem, this project introduces the "Intelligent E-commerce Product Recommendation System using Machine Learning". This system acts like a smart digital shopkeeper. Instead of showing random popular products, it uses Machine Learning (ML) algorithms to intelligently analyze two main things: the technical specifications of the selected primary product and the exact budget limit set by the user. By matching these details, the system filters the database and suggests only the most perfect, compatible, and pocket-friendly accessories. The project is built with a strong technical foundation, utilizing a relational database structure to safely store user and product details. To plan the software architecture perfectly, comprehensive UML models have been designed, including Use Case, Sequence, ER, Activity, Class, and Deployment diagrams, ensuring the web application is robust and logical. In conclusion, this intelligent system completely upgrades the online shopping experience. For the customers, it saves their valuable search time and provides highly personalized, cost-effective suggestions. For the e-commerce platform owners, it acts as a powerful tool to increase their daily sales and profit (cross-selling) by automatically showing the right product to the right user at the right time.
Keywords: Machine Learning, E-commerce, Product Recommendation System, Cross-selling, Collaborative Filtering, Content-Based Filtering, Personalization.
Keywords: Machine Learning, E-commerce, Product Recommendation System, Cross-selling, Collaborative Filtering, Content-Based Filtering, Personalization.
Abstract
A Survey on Recent Research development on Design and Development of Smart Gloves for Real-Time CPR Monitoring Using ESP32 and MPU6050
Anushri. B. R, Keerthana. R. T, Pallavi. K. B, Rekha. S. R, Kaviyarasi. D
DOI: 10.17148/IJIREEICE.2026.141012
Abstract: High-quality cardiopulmonary resuscitation (CPR) requires appropriate chest-compression rate, depth, recoil and minimal interruption. Wearable sensing can provide immediate feedback without requiring bulky external instrumentation. This literature review investigates recent IEEE and closely related research on smart gloves, CPR feedback, inertial measurement units (IMUs), wearable healthcare sensing, edge processing and intelligent CPR assessment, with emphasis on an ESP32 and MPU6050 implementation. A 2024 IEEE Access smart-glove study demonstrated real-time monitoring using e-textile pressure sensors and IMUs, including compression depth, rate, recoil and interruptions. More recent 2025–2026 research has expanded toward wearable feedback, multimodal AI, computer vision, robust IMU processing and edge intelligence. The review identifies a research opportunity for a low-cost ESP32–MPU6050 smart glove that performs real-time compression detection and feedback while explicitly addressing calibration, orientation, drift, motion artifacts, recoil and external validation. A modular extension using a force/pressure sensor is recommended to strengthen depth estimation. The paper proposes a research methodology, gap-to-solution matrix, experimental plan, evaluation metrics and future roadmap including TinyML, multimodal fusion, BLE/mobile analytics and energy-aware operation.
Keywords: CPR monitoring, smart glove, ESP32, MPU6050, IMU, wearable healthcare, chest compression, real-time feedback, edge AI, TinyML.
Keywords: CPR monitoring, smart glove, ESP32, MPU6050, IMU, wearable healthcare, chest compression, real-time feedback, edge AI, TinyML.
Abstract
LITERATURE REVIEW ON AUTONOMOUS AIRBORNE COMMUNICATION NETWORKS FOR DATA EXCHANGE
Mrs. G. Soujanya, Arun A, Athish Prasad Raj, Madhan K, Vishwanath A V
DOI: 10.17148/IJIREEICE.2026.141014
Abstract: Flying ad hoc networks (FANETs) enable unmanned aerial vehicles (UAVs) to exchange information without relying exclusively on fixed infrastructure. The literature identifies high mobility, rapidly changing topology, intermittent wireless links, routing overhead, energy constraints, reliability, and security as major airborne-network issues. This review synthesizes the literature supplied with the project together with additional verified studies on FANET routing, collaborative UAV communication, UAV-assisted mesh relaying, communication standardization, packet reliability, and recent routing taxonomies. The review compares architectural focus, routing strategy, reliability mechanisms, validation approach, and practical limitations. A recurring gap is identified between algorithms or simulation-based studies and lightweight embedded implementation. This gap is directly relevant to practical airborne communication systems using multi-hop forwarding, acknowledgement/retransmission, dynamic rerouting, node-failure handling, and network monitoring. The review establishes the research background and provides a basis for subsequent prototype validation.
Keywords: Airborne networks, dynamic routing, FANET, multi-hop networking, packet reliability, routing protocols, UAV communication, wireless mesh networks.
Keywords: Airborne networks, dynamic routing, FANET, multi-hop networking, packet reliability, routing protocols, UAV communication, wireless mesh networks.
Abstract
Emerging IT Skill Demand Analysis and Skill Trend Prediction Using Job Posting Data and Machine Learning
Dhanya Sri G, Bhuvaneswari K, Kavya M
DOI: 10.17148/IJIREEICE.2026.141015
Abstract: Online job advertisements describe the skills employers request, but this information is embedded in unstructured text. This paper presents a data analytics framework that cleans job-posting records, extracts IT skills with a documented dictionary, measures demand as the share of unique postings mentioning each skill, identifies emerging skills from changes in that share, and tests whether machine learning can forecast next-month demand better than simple baselines. The framework is implemented and validated on a simulated corpus of 11,820 postings spanning 36 months (January 2023 to December 2025) with 30 skills. Rule-based extraction with context checks removed all 269 false matches produced by naive keyword matching on this corpus. In rolling one-step-ahead forecasting over a nine-month test period, a damped-trend exponential smoothing model gave the lowest error (MAE 1.49 percentage points), 18.0% lower than the naive baseline, while a Random Forest on lag features did not outperform a moving average. Because the corpus is simulated, the numerical rankings and trends demonstrate the method only and are not measurements of the real labour market; the same code is intended to be re-run on a documented public job-posting dataset.
Keywords: IT skills, job postings, text mining, skill extraction, machine learning, demand forecasting.
Keywords: IT skills, job postings, text mining, skill extraction, machine learning, demand forecasting.
Abstract
MentorConnect: A Digital Platform for Structured Mentor–Mentee Interaction
Dhanusri P, Varun Kumar D.
DOI: 10.17148/IJIREEICE.2026.141016
Abstract: In academic institutions, Mentor systems help the students by connecting them with staff that support and motivate them. Proper communication and interaction about academic performance, feedback improves skills of students. Mentor Connect is a platform that allows mentors and students to interact through scheduling sessions, messages, and providing feedback. Mentor can add their availability slot time, which can be seen by their mentee, so they can easily see the mentor at that available time.
This research helps in tracking student progress in academic by updating their continuous assessment test and semester marks, the student also uploads their academic certificate which can be accessed by using valid institution email id so that the misusage can be highly avoided because of the identity validation process. With this, students obtain career advice, personal support, and timely help, while mentors can check their progress and solve issues. This system helps the student in both studies and personal growth, and also to face real-life challenges with confidence.
Keywords: Mentoring System, Mentor–Mentee Interaction, Academic Progress Tracking, Web-Based Application, Session Scheduling, Secure Authentication, Student Support System, Digital Mentorship, Higher Education, MERN Stack.
This research helps in tracking student progress in academic by updating their continuous assessment test and semester marks, the student also uploads their academic certificate which can be accessed by using valid institution email id so that the misusage can be highly avoided because of the identity validation process. With this, students obtain career advice, personal support, and timely help, while mentors can check their progress and solve issues. This system helps the student in both studies and personal growth, and also to face real-life challenges with confidence.
Keywords: Mentoring System, Mentor–Mentee Interaction, Academic Progress Tracking, Web-Based Application, Session Scheduling, Secure Authentication, Student Support System, Digital Mentorship, Higher Education, MERN Stack.
Abstract
A Machine Learning-Based Multi-Objective Framework for Optimizing Fruit-Peel Waste- Derived Bio-Pesticide Formulations
Mithra R, Harini L
DOI: 10.17148/IJIREEICE.2026.141017
Abstract: Fruit-processing waste can be a cheap source of biologically active plant compounds, but turning it into a workable bio-pesticide depends on choosing the right composition and operating conditions. This study presents a prediction-and-optimization workflow for 200 fruit-peel formulations, in which regression models estimate Mortality % and Repellency %. Five models were compared: Random Forest, Gradient Boosting, XGBoost, Support Vector Regression (SVR), and Extra Trees. In the saved five-model evaluation, which used the available five-predictor CSV, XGBoost gave the best held-out test results for Mortality % (R² = 0.854, MAE = 3.049, RMSE = 3.621), and Gradient Boosting did best for Repellency % (R² = 0.773, MAE = 3.631, RMSE = 4.795). These figures still need to be reconciled with the seven-predictor feature set described in the original manuscript before final submission. The earlier Pareto analysis kept 19 non-dominated formulations and picked FPF_177 as a computational screening candidate, though biological confirmation is still needed.
Keywords: fruit-peel waste; bio-pesticide; machine learning; XGBoost; Support Vector Regression; Extra Trees; Pareto optimization.
Keywords: fruit-peel waste; bio-pesticide; machine learning; XGBoost; Support Vector Regression; Extra Trees; Pareto optimization.
Abstract
Smart Tracking Basket
Mahendrachari, H M Lavanya, Harshal Vijay Patil, Harshitha K, Lekha G N
DOI: 10.17148/IJIREEICE.2026.141018
Abstract: The smart shopping cart integrates embedded electronics, sensors, and cloud technology to simplify shopping and billing. It uses barcode scanning and load cells for product identification and weight verification, while geared DC motors enable controlled movement. An OLED display provides real-time updates, and a web dashboard supports inventory management, order tracking, and billing validation. This system improves billing accuracy, reduces checkout delays, and enhances the overall shopping experience.
Keywords: Smart shopping, cloud technology.
Keywords: Smart shopping, cloud technology.
Abstract
An ARDUINO-BASED IoT SMART FARM MODEL for AUTOMATED IRRIGATION and FLOOD-RESPONSIVE DRAINAGE with CLOUD MONITORING
Enyinnaya Pascal Eni, Kenneth A. Akpado, Scholastica Ukamaka Nnebe
DOI: 10.17148/IJIREEICE.2026.141019
Abstract: Climate variability, water scarcity, and flooding are persistent threats to smallholder agricultural productivity, and manual irrigation/drainage management is poorly suited to responding to these conditions in real time. This paper presents the design, implementation, and functional testing of a low-cost, Arduino Nano-based Internet of Things (IoT) smart farm model that jointly automates irrigation and flood-responsive drainage. The system integrates a thermistor, a resistive soil moisture sensor, and a water-level sensor to drive two independently actuated pumps (irrigation and drainage) through a relay/ULN2003 driver stage, with farm status relayed to a remote user via a SIM900 GPRS module to the ThingSpeak IoT platform. The control logic irrigates only when soil moisture is below a fixed threshold and ambient temperature falls within a defined range (0-35 degC), and drains only when both soil moisture and water level indicate flooding, deactivating once the water level sensor clears. Functional testing across four environmental scenarios (dry soil with conducive temperature, dry soil with non-conducive temperature, moist soil with no flood, and moist soil with flood) confirmed that the system correctly discriminates between these states and actuates the appropriate pump, with farm status correctly relayed to ThingSpeak. A full bill of engineering measurement and evaluation totalling ₦202,000 is reported. Consistent with a no-hallucination reporting standard, this paper does not claim a quantified before/after productivity improvement, since no parallel manual-baseline test bed was operated alongside the automated system; a controlled comparative field trial is identified as the necessary next step to substantiate any such claim.
Keywords: Internet of Things, smart farming, automated irrigation, flood monitoring, Arduino, ThingSpeak, precision agriculture.
Keywords: Internet of Things, smart farming, automated irrigation, flood monitoring, Arduino, ThingSpeak, precision agriculture.
Abstract
Does Risk Management Improve Portfolio Outcomes in Nigeria? A Historical Comparison of Equity, Deposit and Volatility-Managed Allocations.
Ofierohor Ufuoma Earnest
DOI: 10.17148/IJIREEICE.2026.141020
Abstract: This paper compares four simple ways for a Nigerian investor to divide savings between equities and bank deposits: all equities, all deposits, a fixed 50/50 mix rebalanced monthly, and a volatility-managed rule that holds less equity after volatile periods. Using monthly data on the Nigerian Exchange All-Share Index and the twelve-month deposit rate from the Central Bank of Nigeria Statistical Bulletin, the strategies are evaluated over February 2007 to December 2025 (227 months), with portfolio weights based only on past information. The volatility-managed rule, which targets 15 per cent annual volatility using the previous twelve months of returns, produced the highest compound growth (10.8 per cent a year, against 7.9 per cent for equities alone and 9.3 per cent for the 50/50 mix) and the best risk-adjusted return. Diversification of any kind cut the maximum drawdown from 70 per cent to around 40 per cent; the all-equity investor took until August 2023 to recover the peak of February 2008. A block bootstrap shows, however, that the growth advantage of the volatility rule over the 50/50 mix is not statistically reliable (95% interval −3.7 to +5.9 percentage points a year), and its performance is sensitive to the look-back window. Most strikingly, every strategy lost purchasing power. After adjusting for inflation, annual returns ranged from −3.2 per cent for the volatility-managed rule to −5.7 per cent for equities, and in US dollar terms all four lost money. Risk management changed how much Nigerian investors lost; it did not protect them from losing.
Keywords: asset allocation; volatility targeting; diversification; drawdown; real returns; Nigeria
JEL classification: G11; G12; G17
Keywords: asset allocation; volatility targeting; diversification; drawdown; real returns; Nigeria
JEL classification: G11; G12; G17
Abstract
AGROINFORMATIX: THE DIGITAL TRANSFORMATION OF THE FARM. DEVELOPMENT AND OPERATION OF AN APPLICATION THROUGH NO-CODE PROGRAMMING IN VOCATIONAL EDUCATION
Klonis Christos, Dr Klonis Apostolos
DOI: 10.17148/IJIREEICE.2026.141021
Abstract: This paper presents the innovative educational good practice “AGROINFORMATIX”, which was designed, developed and tested within the framework of the European Erasmus+ KA220-VET project, reference number 2023-1- RS01-KA220-VET-000154237. The main focus of this practice was the use of modern digital technologies to support agricultural and livestock production, while simultaneously developing the digital, vocational and collaborative skills of students in Vocational Education and Training (VET).
AGROINFORMATIX was designed as an enterprise-grade mobile application capable of operating in different environments and supporting real-world use cases in the agricultural and livestock sectors. It was developed by teachers and students from three European educational institutions: the Laboratory Centre of Serres in Greece, the Sonja Marinković agricultural school in Serbia, and the GRM Novo Mesto Biotechnology Centre in Slovenia.
Keywords: AGROINFORMATIX, No-Code Programming, Digital Transformation, Digital Agriculture, Vocational Education, Transnational Cooperation.
AGROINFORMATIX was designed as an enterprise-grade mobile application capable of operating in different environments and supporting real-world use cases in the agricultural and livestock sectors. It was developed by teachers and students from three European educational institutions: the Laboratory Centre of Serres in Greece, the Sonja Marinković agricultural school in Serbia, and the GRM Novo Mesto Biotechnology Centre in Slovenia.
Keywords: AGROINFORMATIX, No-Code Programming, Digital Transformation, Digital Agriculture, Vocational Education, Transnational Cooperation.