Journal of Science Engineering Technology and Management Science

This research presents the development of an Internet of Things (IoT)-integrated automation framework designed to optimize operational efficiency and animal welfare in dairy cattle farms. The proposed system utilizes the ESP32 microcontroller as the primary processing and communication gateway to synchronize a network of sensors and actuators. Key hardware components include a Real-Time Clock (RTC) for precise task scheduling, DHT22 sensors for microclimatic monitoring, and an I2C LCD for localized status reporting. To mitigate the inconsistencies of manual labor, the system automates critical livestock care through motor-driven feed trays and controlled irrigation via water pumps. Furthermore, environmental stability is maintained through automated foggers and DC fans that activate based on real-time atmospheric thresholds. Leveraging the ESP32’s dual-core architecture and integrated Wi-Fi, the system enables seamless data transmission to a web-based dashboard, allowing farmers to remotely monitor livestock conditions and configure management parameters. Results indicate that this scalable, cost-effective solution significantly reduces manual intervention, minimizes resource waste, and ensures a stress-free environment for cattle. This intelligent farming model provides a viable technological path for small-to-medium-scale dairy enterprises to transition toward precision agriculture.

Keywords : Precision Livestock Farming (PLF), ESP32 Microcontroller, Internet of Things (IoT), Smart Dairy Automation, Climate Control, Sustainable Agriculture

Author : B. Suresh Kumar, B. Bharath Kumar Reddy, K. Yaswanth Kumar Reddy, Dabbugodala Mahesh, Kampa V Balu Mahendra

Title : Design and Implementation of an IoT-Based Autonomous Environment and Nutrition Management System for Precision Dairy Farming

Volume/Issue : 2026;03(04)

Page No : 1-7

Urban traffic congestion remains a primary impediment to emergency medical services, where critical delays in ambulance transit times directly correlate to increased mortality rates. Research indicates that a one-minute reduction in response time can improve patient survival outcomes by 7% to 10%. This paper proposes a decentralized Emergency Vehicle Preemption (EVP) system designed to establish an instantaneous “Smart Green Corridor” using a heterogeneous microcontroller framework. The system architecture utilizes an ESP32 microcontroller at the transmitter (TX) node, integrated within the emergency vehicle, and an Arduino Uno at the receiver (RX) node to manage the Traffic Signal Control (TSC) logic. Wireless communication is established via the Zigbee (IEEE 802.15.4) protocol, providing a low-power, high-reliability data link that operates effectively in high-interference urban environments. The TX unit, powered by the dual-core processing of the ESP32, transmits road-specific priority signals which are intercepted by the RX unit’s Arduino Uno. Upon signal reception, the TSC executes a preemptive interrupt algorithm that overrides the standard four-phase traffic cycle, immediately transitioning the requested lane to a "Green" state while holding conflicting manoeuvres at "Red." Experimental validation of the prototype demonstrates a significant reduction in the Average Waiting Time (AWT) for ambulances, achieving near-zero delay at signalled intersections. By utilizing a costeffective combination of ESP32 and Arduino Uno, this research contributes a scalable, low-latency solution for intelligent transportation systems that operates independently of centralized cloud processing or line-of-sight dependent infrared sensors

Keywords : Emergency Vehicle Preemption (EVP); Zigbee Communication; Smart Traffic Control; Green Corridor; Intelligent Transportation Systems (ITS); Arduino Microcontroller

Author : M. Suhasini, Kommi Jyothi, Koduru Chandra Lekha, Amari Sindhu, Gadde Suhitha

Title : Design and Implementation of an Intelligent Green Corridor System for Emergency Vehicle Preemption

Volume/Issue : 2026;03(04)

Page No : 8-15

Traditional information dissemination in educational institutions relies heavily on manual, paper-based notice boards, which are prone to delays, high maintenance costs, and physical constraints. This paper proposes a centralized, real-time IoT-based Campus Display Board (ICDB) system powered by the high-performance Raspberry Pi 5. The proposed architecture integrates a web-based administrative server with distributed edge nodes via a secure IoT communication layer. Utilizing a Flask-driven backend and a dynamic HTML5/CSS3 frontend, authorized administrators can remotely broadcast multimedia content—including high-definition video, static images, and emergency alerts—from any location. The Raspberry Pi 5 serves as the core processing unit, leveraging its quad-core capabilities and improved networking stack to ensure lag-free rendering and continuous synchronization with the server via a polling-based manifest protocol. Experimental results indicate that the system significantly reduces the latency between announcement creation and public display compared to conventional methods. Furthermore, the modular design promotes sustainability by eliminating paper waste and offers a scalable infrastructure for future smart-campus integrations, such as AI-driven content scheduling and automated cloud synchronization. This research demonstrates a cost-effective, energyefficient, and user-friendly solution for modernizing institutional communication and enhancing administrative transparency in a digitally driven academic environment.

Keywords : Internet of Things (IoT), Raspberry Pi 5, Digital Signage, Flask Framework, Smart Campus, Real-time Communication.

Author : G. Tejaswi, Mopuru Rajesh, Veeramreddy Pratap Reddy, Balli Sarath Chandra, Nanda Bhaskar Rao

Title : IoT-Enabled Campus Digital Signage Framework using Flask-Based Edge Computing

Volume/Issue : 2026;03(04)

Page No : 16-24

In advanced IC manufacturing, yield losses contribute to nearly 20–30% of total fabrication cost, with variations in die size and transistor scaling responsible for more than 60% of yield degradation, while modern fabs generate terabytes of process data per production cycle, making efficient prediction essential. Existing manual and traditional yield prediction approaches rely on low-level statistical analysis and expert-driven rule formulation, resulting in high time consumption, poor scalability, limited ability to model non-linear interactions, and reduced reliability at advanced technology nodes. To address these limitations, this work proposes a Quantum Neural Network–based yield prediction framework for accurate estimation of die size and transistor scaling, where the Integrated Chip (IC) manufacturing dataset is first subjected to comprehensive data preprocessing including normalization, noise reduction, and missing-value handling, followed by Exploratory Data Analysis (EDA) to identify key statistical trends and process correlations. For performance comparison, existing models such as the Restricted Boltzmann Machine (RBM) Regressor, Gradient Boosting Regressor (GBR), and Extreme Gradient Boosting (XGB) Regressor are implemented to evaluate classical learning capabilities. The proposed system integrates a Variational Quantum Neural Network (VQNN) for advanced quantum feature extraction, leveraging parameterized quantum circuits to capture highdimensional, non-linear, and entanglement-based relationships among manufacturing parameters, which are difficult to model using classical methods. These quantum-enhanced features are then processed using an Ensemble Oblique Trees (EOT) regressor to achieve robust and highly accurate yield prediction, significantly improving prediction accuracy, adaptability, and computational efficiency, thereby enabling early-stage yield optimization and cost reduction in next-generation IC manufacturing

Keywords : Quantum Neural Network, IC Manufacturing Yield Prediction, Variational Quantum Neural Network, Ensemble Oblique Trees, Transistor Scaling, Die Size Optimization

Author : G. Kiran Kumar, Nandimandalam Sivarama Raju, Mohammad Nawaz, Nayudu Pradeep

Title : Variational Quantum Circuit Regressors for Joint Optimization of Transistor Scaling and Die Footprint in Advanced IC Manufacturing

Volume/Issue : 2026;03(04)

Page No : 25-36

Defect classification in industrial components is critical to ensuring product quality, operational safety, and cost efficiency, as manufacturing industries face increasing defect rates due to highspeed production and complex processes. Studies indicate that a significant percentage of industrial failures originate from undetected surface and structural defects, leading to rework, downtime, and financial loss. This research is motivated by the need for accurate and automated defect detection in machinery parts, painted surfaces, and welded joints, including subtypes such as cracks, corrosion, paint blisters, scratches, porosity, lack of fusion, and weld spatter. The expected outcome is a robust classification system capable of reliably identifying defect categories and subcategories across these domains. Traditional defect inspection relies heavily on manual visual inspection, which is time-consuming, subjective, and prone to human error. Such manual systems struggle with consistency, scalability, and real-time deployment in modern industrial environments. In this work, RGB images of industrial components are utilized, followed by image preprocessing techniques including resizing and normalization to enhance feature consistency and model performance. Existing machine learning approaches such as K-Nearest Neighbours (KNN) and Decision Tree Classifier (DTC) are reviewed as baseline models for defect classification. The proposed approach employs Convolutional Neural Network (CNN)-based feature extraction combined with Logistic Regression (LR) for efficient and accurate classification. The system outputs precise defect classification results for machinery, paint, and welding components, including their respective defect subtypes, demonstrating improved accuracy and reliability over traditional methods.

Keywords : Industrial Defect Classification, Automated Defect Detection, Machine Learning, Quality Inspection, Predictive Maintenance

Author : P. C. N. Lalithya, Sreemanthula V Sai Chethana, Madireddy Dharani, Panthangi Manasa

Title : Deep Feature Fusion for Hierarchical Defect Classification in Industrial Inspection Systems

Volume/Issue : 2026;03(04)

Page No : 37-46

In modern era, construction of high-rise buildings is rapidly increasing throughout the world. Due to decrease of available free land and due to wide spread urban area, the architects and the engineers have started developing cities vertically. Recently, the dia grid structural system has been widely used for tall buildings due to structural efficiency and aesthetic potential provided by triangulation of the system. Compared to the conventional frame buildings having exterior vertical column, dia grid structure resists the lateral loads more efficiently due to presence of inclined columns. In dia grid system the lateral loads are resisted by the axial action of the inclined columns that are placed at the exterior periphery of the buildings. Seismic isolation and energy dissipating systems present an effective way to common seismic design for improving the seismic performance of structures. These techniques reduce the seismic forces by changing the stiffness and damping in the structures, whereas conventional seismic design is required for an additional strength and ductility to resist seismic forces. In the present study a 21 storied dia grid building is analyzed with in different seismic zones by using ETABS software . The results like story drift, story shear, story bending, time period, model stiffness are compared for different seismic zones as response spectrum analysis.

Keywords : diagrid, ETABS, Seismic isolation, story drift, story shear, story bending, time period, model stiffness.

Author : P SUBHASRI1 , KONDEPOGU ABHINESH2 , SHAIK ARSHAD3 , POLUDASU DEEPAK KUMAR4

Title : SEISMIC LOAD ANALYSIS AND WIND LOAD ANALYSIS OF 25 STOREY BUILDING COMPARATIVE ANALYSIS OF G+20 DIA GRID BUILDING IN DIFFERENT SESIMIC ZONES BY USING ETABS

Volume/Issue : 2026;03(04)

Page No : 47-54

Recent studies report that unplanned machine failures account for nearly 20–30% of total production downtime in smart manufacturing environments. With Industry 4.0 enabling continuous sensor-based monitoring, industries now generate massive volumes of operational data, yet effective fault analysis and decision-making remain a major challenge. Conventional manual fault analysis systems suffer from critical limitations, including heavy dependence on human expertise and periodic inspections, which often result in delayed fault detection and inconsistent decision-making. In this study, a comprehensive data set is utilized containing parameters such as timestamp, machine id, temperature, vibration level, power consumption, pressure, material flow rate, cycle time, error rate, downtime, maintenance flag, efficiency score, and production status. The data undergoes systematic preprocessing, including noise handling, normalization, missing-value treatment, and feature alignment, followed by Exploratory Data Analysis (EDA) to understand operational patterns, fault correlations, and feature importance. Existing machine learning models such as AdaBoost-CART, XGBoost-CART, and Passive-Aggressive (PA)- CART are implemented as baseline methods. To overcome the limitations of fixed-feature learning and manual feature engineering, a proposed Neural Architecture Search (NAS)-based feature extraction framework integrated with a Greedy Rule Forest (GRF)-CART model is introduced. The NAS component automatically learns optimal feature representations from complex sensor interactions, while the GRF-CART enhances interpretability and decision robustness. The proposed framework performs classification tasks to predict downtime occurrence, maintenance requirement (maintenance flag), and production status, along with a regression task to accurately estimate the efficiency score. Experimental results demonstrate that the proposed NAS–GRF–CART approach significantly improves fault prediction accuracy, reduces false maintenance alerts, and provides reliable efficiency assessment, making it well-suited for intelligent, data-driven maintenance strategies in Industry 4.0 environments

Keywords : Industry 4.0, Neural Architecture Search (NAS), Greedy Rule Forest (GRF)-CART, Predictive Maintenance, Feature Representation Learning.

Author : K. Sravan Kumar, Naguru Navya, Surayapalem V Naga Dharmika, Perumalla Vyshnavi

Title : Data-Driven Fault Intelligence and System Optimization in Modern Industrial Environments

Volume/Issue : 2026;03(04)

Page No : 55-68

This research presents the design and implementation of an intelligent, multi-purpose robotic system developed to enhance public health and workplace safety through automated sanitization and hazard detection. Built on the ESP32 microcontroller architecture, the robot integrates ultrasonic-based obstacle avoidance with specialized chemical sensing for fire and gas leak detection. Unlike static sanitization units, this mobile platform utilizes a high-pressure pumping system for disinfectant dispersal, controlled via a real-time Internet of Things (IoT) interface. The system employs a Wi-Fienabled communication protocol to transmit environmental data including obstacle proximity, flammable gas concentrations, and thermal anomalies to a centralized PHP-based cloud server. Experimental results demonstrate a significant reduction in human exposure to hazardous environments, as the robot can be remotely maneuverer via Serial-over-Wi-Fi commands while providing instantaneous feedback through a Liquid Crystal Display (LCD) and a remote web dashboard. The study concludes that the integration of low-cost, high-efficiency sensors with IoT logging provides a scalable solution for maintaining hygiene standards in hospitals, laboratories, and manufacturing hubs, effectively bridging the gap between manual labor and fully autonomous industrial safety protocols.

Keywords : Internet of Things, ESP32 Microcontroller, Autonomous Sanitization, Hazard Detection, Industrial Automation.

Author : U. Penchalaiah, Shaik Saleha Nazini, Pralayakaveri Swathi, Singala Smana Sree

Title : IoT-Enabled Autonomous Robot for Real-Time Pathogen Sanitization and Hazard Mitigation in Industrial Environments

Volume/Issue : 2026;03(04)

Page No : 69-78