Journal of Science Engineering Technology and Management Science

Monkeypox is a contagious viral disease that primarily affects the skin and can spread rapidly if not identified at an early stage. Accurate and timely diagnosis is therefore essential to support effective treatment and prevent further transmission. This study proposes an AI-Driven Monkeypox Recognition Using Enhanced VGG16 and Custom Convolutional Networks for automatic classification of skin lesion images. The proposed system utilizes image preprocessing techniques such as resizing, normalization, and dataset partitioning to improve data quality before model training. An enhanced VGG16 model based on transfer learning is employed to extract high-level visual features, while a custom convolutional neural network is developed to learn disease-specific image patterns. The performance of both models is evaluated using standard metrics, including accuracy, precision, recall, F1-score, and confusion matrix analysis. Experimental results indicate that the custom CNN model achieves superior classification performance compared with the enhanced VGG16 model, providing reliable detection of Monkeypox and normal skin conditions. The developed prediction system enables users to upload skin lesion images and receive rapid diagnostic results, making it a practical decision-support tool for healthcare professionals. Overall, the proposed framework demonstrates the effectiveness of deep learning in improving automated Monkeypox recognition and supporting intelligent medical image analysis.

Keywords :

Author : Jyothi Prakash Arya, M. Anusha

Title : AI-Driven Monkeypox Recognition Using Enhanced VGG16 and Custom Convolutional Networks

Volume/Issue : 2026;03(07)

Page No : 943-949

Borewell accidents involving children remain a serious safety concern, particularly in rural areas where unused borewells are often left uncovered. Delays in locating and rescuing trapped victims can significantly reduce the chances of survival, highlighting the need for an intelligent rescue support system. This research proposes a Smart AI Framework for Real-Time Borewell Child Detection and Rescue Operations that combines wireless monitoring, artificial intelligence, and embedded technologies to improve emergency response. The system uses a Raspberry Pi with a camera module to continuously monitor the child inside the borewell. An enhanced VGG-based model analyzes facial expressions to identify signs of fear or distress, while a VGGish-based audio model detects voice responses to determine whether the child is conscious. A temperature sensor monitors the child's physical condition, and all collected information is transmitted through a wireless network to a web-based interface for real-time observation by rescue teams. The framework enables continuous monitoring of the child's status and supports faster decision-making during rescue operations. By integrating AI-driven monitoring with wireless communication, the proposed system offers a practical, reliable, and efficient approach for improving child safety and assisting rescue personnel during borewell emergencies.

Keywords :

Author : Chiluka Tulasi, M. Anusha

Title : Smart AI Framework for Real-Time Borewell Child Detection and Rescue Operations

Volume/Issue : 2026;03(07)

Page No : 950-956

Adaptive Intelligence for Early Thyroid Disease Detection and Classification presents an intelligent decision-support system designed to assist in the early identification of thyroid disorders using machine learning techniques. The proposed application provides separate modules for administrators and users, enabling efficient dataset management, model training, and disease prediction through a userfriendly interface. The thyroid dataset contains multiple clinical attributes associated with three diagnostic categories: Normal, Hyperthyroid, and Hypothyroid. Before model development, the data undergoes preprocessing, including cleaning, label encoding, and feature transformation to ensure compatibility with machine learning algorithms. Several classification models, including Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, and Multi-Layer Perceptron, are trained and evaluated using performance measures such as accuracy, precision, recall, and F1-score. Comparative analysis identifies Random Forest and Decision Tree as the most effective models for thyroid disease classification. The system also allows users to predict thyroid conditions by entering clinical values or uploading test data. Developed using Python, Django, and MySQL, the proposed framework simplifies disease assessment, supports early diagnosis, reduces manual effort, and provides a reliable platform for intelligent thyroid disease detection.

Keywords :

Author : B Sai Lokesh, M. Anusha

Title : Adaptive Intelligence for Early Thyroid Disease Detection and Classification

Volume/Issue : 2026;03(07)

Page No : 957-963

Financial fraud has become one of the major challenges faced by banking and digital payment systems due to the increasing volume of online transactions. Detecting fraudulent activities at an early stage is essential for minimizing financial losses and improving the security of banking services. This paper presents a machine learning-based fraud detection system that identifies suspicious banking transactions by analyzing historical transaction records. Before model training, the collected dataset undergoes preprocessing to eliminate missing values, remove non-numeric attributes, and transform the data into a suitable format for classification. Several supervised learning algorithms, including Logistic Regression, Naïve Bayes, Support Vector Machine (SVM), Decision Tree, AdaBoost, Multi-Layer Perceptron (MLP), Deep Neural Network (DNN), and Random Forest, are implemented and evaluated using performance measures such as accuracy, precision, recall, and F1-score. Experimental analysis shows that the Random Forest classifier outperforms the remaining models by providing the highest classification accuracy and more reliable fraud prediction. The developed application offers an interactive interface for dataset preprocessing, model training, algorithm comparison, and fraud prediction, enabling users to analyze transaction data efficiently. The comparative evaluation demonstrates that ensemble learning techniques can significantly improve the detection of fraudulent banking transactions while reducing false classifications. The proposed framework provides an effective, scalable, and practical solution for enhancing fraud detection capabilities in modern banking systems and supporting secure digital financial transactions.

Keywords :

Author : Posam Srilakshmi, Dr. G. Purna Chandar Rao

Title : Adaptive Machine Learning Approach for Banking Transaction Fraud Identification

Volume/Issue : 2026;03(07)

Page No : 964-970

The rapid growth of online banking, digital payment platforms, and e-commerce services has led to a significant increase in electronic financial transactions, making fraud detection an important challenge for financial institutions. Fraudulent activities not only result in financial losses but also reduce customer confidence in digital payment systems. This work presents an intelligent fraud detection framework that analyzes transaction records to identify suspicious activities using machine learning techniques. Initially, the transaction dataset is preprocessed by handling missing values, converting categorical information into numerical form, normalizing the data, and eliminating inconsistencies to improve data quality. The processed data is further analyzed through process mining to capture user transaction behavior and distinguish normal activities from anomalous patterns. The extracted behavioral information is then utilized for training machine learning models, including Support Vector Machine (SVM) and Random Forest. The performance of both models is evaluated using standard classification metrics such as accuracy, precision, recall, and F1-score. Experimental results indicate that the Random Forest classifier achieves better prediction performance by accurately distinguishing fraudulent transactions from legitimate ones. The developed system also provides a user-friendly interface for dataset processing, behavioral analysis, model evaluation, and fraud prediction, allowing users to monitor transaction activities efficiently. The proposed framework offers a practical and scalable solution for strengthening fraud detection in modern financial systems by combining behavioral analysis with machine learning, thereby improving transaction security and reducing the risk of financial fraud.

Keywords :

Author : Satya Narayan Behera, Dr. G. Purna Chandar Rao

Title : A Secure Machine Learning Framework for Behavioral Fraud Analysis in E-Commerce Transactions

Volume/Issue : 2026;03(07)

Page No : 971-977

Oral cancer is one of the leading causes of cancer-related deaths worldwide, mainly because many cases are diagnosed at an advanced stage. Early identification of pre-cancerous lesions can significantly improve treatment outcomes and patient survival. This work presents an automated oral cancer classification system using white light images and the Light Gradient Boosting Machine (LightGBM) algorithm. Initially, oral cavity images are preprocessed to improve image quality and remove unwanted variations. Relevant colour and texture features are then extracted to capture the visual characteristics of normal and abnormal tissues. These features are provided to the LightGBM classifier, which efficiently distinguishes between different oral lesion categories. The proposed approach offers fast training, reduced computational complexity, and high classification accuracy compared to conventional methods. By minimizing human intervention and supporting early diagnosis, the system can assist healthcare professionals in making reliable clinical decisions. This computer-aided approach has the potential to improve oral cancer screening and contribute to timely treatment and better patient outcomes.

Keywords :

Author : M. Madhu Chary, Dr. G. Purna Chandar Rao

Title : A Robust LightGBM Model for Classifying Oral Cancer and PreCancerous Lesions from White Light Images

Volume/Issue : 2026;03(07)

Page No : 978-985

Smart Driving Style Analytics Through Multi-Stage CNN-LSTM and BiLSTM Integration presents an intelligent deep learning framework for recognizing driving behavior using vehicle operation and dynamic driving data. The proposed system is designed to improve driving style classification by combining the feature extraction capability of Convolutional Neural Networks (CNN), the temporal learning strength of Long Short-Term Memory (LSTM), and the contextual sequence modeling ability of Bidirectional LSTM (BiLSTM). Initially, driving signals such as accelerator pedal position, brake usage, steering information, and vehicle dynamics are collected and preprocessed to remove inconsistencies and prepare the data for model training. The multi-stage architecture learns both spatial and sequential driving patterns, enabling accurate identification of different driving styles. Model performance is evaluated using standard classification metrics, including accuracy, precision, recall, and F1-score, along with comparative analysis against conventional approaches. Experimental results indicate that the hybrid CNN-LSTMBiLSTM framework provides higher recognition accuracy, improved temporal feature learning, and better generalization than individual deep learning models. The proposed system can support intelligent transportation, personalized driver assistance, and adaptive Advanced Driver Assistance Systems (ADAS) by providing reliable real-time driving style analysis and enhancing overall road safety.

Keywords :

Author : Ankitha Bai, Dr. G. Purna Chandar Rao

Title : Smart Driving Style Analytics Through Multi-Stage CNNLSTM and BiLSTM Integration

Volume/Issue : 2026;03(07)

Page No : 986-994

Academic success is influenced by several factors, including students’ learning habits, attendance, participation, and overall academic engagement. This project presents a machine learningbased approach to predict student academic performance using historical educational data. The dataset is first preprocessed by removing inconsistencies, handling missing values, and preparing the features for model training. Multiple machine learning algorithms, including Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), Logistic Regression, Gradient Boosting, and XGBoost, are trained and evaluated using performance measures such as accuracy, precision, recall, and F1-score. The experimental results indicate that XGBoost achieves the best prediction accuracy compared to the other models. The proposed system also provides a simple graphical user interface that allows users to load datasets, train models, predict student performance, and visualize results through graphs. This intelligent prediction system can help educators identify students who may require additional academic support, enabling timely interventions and improving overall educational outcomes.

Keywords :

Author : Annadevara Monika sai, Dr. G. Purna Chandar Rao

Title : Intelligent Student Academic Performance Prediction Using Machine Learning

Volume/Issue : 2026;03(07)

Page No : 995-1003

Diabetic retinopathy is one of the leading causes of vision impairment among people with diabetes, making early diagnosis essential for preventing permanent blindness. Manual examination of retinal fundus images is time-consuming and depends heavily on the expertise of ophthalmologists. This study presents a lesion-guided hybrid deep learning framework for the automatic detection of diabetic retinopathy from retinal images. The proposed approach combines the feature extraction capability of Convolutional Neural Networks (CNN) with the discriminative strength of a hybrid deep learning architecture to identify retinal lesions such as microaneurysms, hemorrhages, and exudates. Before training, the retinal images are preprocessed and enhanced to improve image quality and reduce noise. Data augmentation is applied to address class imbalance and improve the model's generalization ability. Experimental results demonstrate that the proposed framework achieves higher classification accuracy, precision, recall, and F1-score compared with conventional deep learning models. The developed system offers a reliable and efficient computer-aided solution for early diabetic retinopathy screening, supporting ophthalmologists in making faster and more accurate clinical decisions.

Keywords :

Author : Nayak Badrinath, Dr. G. Purna Chandar Rao

Title : Lesion-Guided Hybrid Deep Learning Framework for Accurate Diabetic Retinopathy Detection

Volume/Issue : 2026;03(07)

Page No : 1004-1012

The Eye Gaze-Based Cursor Control System using OpenCV is developed to provide a handsfree method of controlling the computer cursor through eye movements. The main objective of this project is to assist individuals with physical disabilities who find it difficult to use a conventional mouse or keyboard. The system uses a standard webcam to capture real-time video and detect the user's face and eye region. By tracking the position of the pupil with the help of OpenCV, Dlib, and Python libraries, the cursor moves according to the direction of the user's eye movement. The implementation is simple, costeffective, and does not require any specialized hardware or wearable sensors. This approach improves accessibility while offering a natural and convenient way of interacting with a computer. The project demonstrates that computer vision techniques can be effectively used to build user-friendly humancomputer interaction systems, making digital technology more accessible to people with motor impairments and enhancing their independence.

Keywords :

Author : Vamshidhar Reddy, Dr. G. Purna Chandar Rao

Title : OpenCV-Based Eye Movement Detection for Cursor Navigation

Volume/Issue : 2026;03(07)

Page No : 1013-1018

Artificial intelligence (AI) has emerged as a transformative force in the banking industry by improving operational efficiency, enhancing customer engagement, strengthening risk management, and supporting data-driven decision-making (Dwivedi et al., 2023). At the same time, sustainable finance has become a strategic priority for Indian banks due to increasing environmental, social, and governance (ESG) expectations, climate-related financial risks, and regulatory initiatives promoting responsible banking (United Nations Environment Programme Finance Initiative [UNEP FI], 2023). Although public and private sector banks have accelerated their digital transformation, empirical evidence comparing their AI-enabled sustainable finance practices remains limited (OECD, 2024). This study investigates the influence of AI adoption on sustainable finance transformation by comparing selected state-owned public sector and private sector banks in India. A quantitative research design is proposed using a structured questionnaire administered to 300 banking professionals selected through stratified random sampling from major public and private sector banks. Secondary information will be obtained from annual reports, sustainability reports, Reserve Bank of India publications, and ESG disclosures. The collected data will be analysed using descriptive statistics, Cronbach's alpha, exploratory factor analysis, correlation analysis, multiple regression, and structural equation modelling to examine the relationships among AI adoption, digital transformation, and sustainable finance performance. The findings are expected to indicate that AI adoption significantly improves sustainable finance practices by enhancing green lending, ESG integration, fraud detection, operational efficiency, and financial inclusion. The study is also anticipated to reveal that private sector banks demonstrate comparatively higher AI maturity and sustainability performance than public sector banks because of greater technological investment and organisational agility. The research contributes to the emerging literature on AI-driven sustainable banking and offers practical recommendations for bank executives, policymakers, and regulators to strengthen responsible digital transformation and achieve long-term sustainable financial development within the Indian banking sector.

Keywords :

Author : N.Ravi Vardhan, Dr. E. Nirupama

Title : ARTIFICIAL INTELLIGENCE ADOPTION AND SUSTAINABLE FINANCE TRANSFORMATION: A COMPARATIVE ANALYSIS OF STATE PUBLIC SECTOR AND PRIVATE SECTOR BANKS IN INDIA

Volume/Issue : 2026;03(07)

Page No : 1019-1026

This study presents an intelligent weapon detection system using the YOLO (You Only Look Once) deep learning model to automatically identify dangerous weapons such as guns, rifles, and knives in images and video streams. The system aims to enhance public safety by enabling real-time surveillance and early threat detection. A dataset containing various weapon images is used to train the model so that it can accurately classify and localize different types of weapons. The YOLO architecture performs object detection in a single stage, allowing faster processing compared to traditional multi-stage detection methods. During training, the model learns distinctive visual features of weapons to differentiate them from normal objects. The trained model is then integrated with a monitoring system capable of analyzing live camera feeds. When a weapon such as a gun, rifle, or knife is detected, the system generates an alert for security personnel. This approach helps reduce manual monitoring efforts and improves response time in critical situations. Experimental results show that the YOLO-based model achieves high detection accuracy with low latency. Therefore, the proposed system provides an effective solution for automated weapon detection in smart surveillance applications.

Keywords : Weapon Detection, Deep Learning, Computer Vision, YOLOv8, Public Safety

Author : 1M. Eswari, 2B. Kondamma, 3K. Naga Mounika, 4K. Jeevan Sai Poojitha, 5M. Suseela

Title : Weapon Detection Using Deep Learning

Volume/Issue : 2026;03(07)

Page No : 1027-1035

ThreatSentinelGPT is an intelligent real-time AI-based security framework designed to detect, analyze, and respond to security threats in both cybersecurity and physical surveillance environments. The system integrates machine learning, deep learning, and Generative Pre-trained Transformer (GPT) models to monitor network traffic, system logs, CCTV video streams, and IoT sensor data. By combining anomaly detection, computer vision, and natural language processing, the framework identifies suspicious activities and generates automated alerts with contextual explanations. The system ensures scalability, low latency, and reliability through real-time processing and role-based access control. ThreatSentinelGPT enhances situational awareness, minimizes false positives, and reduces response time, making it suitable for smart cities, enterprises, and critical infrastructure protection.

Keywords : ThreatSentinelGPT, Cybersecurity, Physical Surveillance, Real-Time Threat Detection, Artificial Intelligence, Automated Alerts

Author : 1Dr. P. P. Sadhu Naik, 2K. Harsha Vardhan Reddy, 3Ch. Venkateswarula Reddy, 4K. Venkatesh, 5Sk. Bhasha, 6Sk. Umair Baji

Title : Threat Sentine Gpt Real Time AI Framework for Detecting Security Threats and Generating Automated Alerts in Cyber Security and Physical Surveillance System

Volume/Issue : 2026;03(07)

Page No : 1036-1041

Agriculture plays a vital role in the economy and food security of many countries. Paddy is one of the most important staple crops cultivated worldwide. Accurate crop detection and appropriate soil recommendation are essential for improving yield and reducing losses. Traditional farming practices rely heavily on farmer experience and manual observation. These methods may lead to inefficient resource usage and reduced productivity. Artificial Intelligence provides advanced solutions for smart agriculture. This project focuses on paddy crop detection and soil recommendation using AI techniques. Image processing and machine learning models are used to detect paddy crop conditions. Soil parameters such as moisture, pH, and nutrient content are analyzed. The system recommends suitable soil treatments and fertilizers. Data is collected from sensors and crop images. AI models process this data for accurate prediction. The system supports early detection of crop issues. It helps farmers make informed decisions. Automation reduces manual effort. The approach improves crop yield and soil health. The system is scalable and costeffective. It promotes sustainable farming practices. The proposed solution demonstrates the effectiveness of AI in agriculture.

Keywords : Convolutional Neural Networks, Deep Learning, soil suggestion, paddy crop colour images, Computer Vision,AI

Author : 1 I. Subhashini, 2M. Praveen Raj,3Harikrishna,4Chandhu,5Ch. Mohan Reddy,6 SK. Hardif

Title : Smart Paddy Identification and Soil Suitability Prediction Using Deep Learning Techniques

Volume/Issue : 2026;03(07)

Page No : 1042-1048

Satellite image-based weather prediction using deep learning to make climate monitoring and forecasting more accurate. Traditional weather models rely on physical and numerical models, which are costly to run and not always reliable during extreme weather. The new data-driven approach learns atmospheric patterns directly from large amounts of satellite images. Convolutional Neural Networks help identify features like cloud patterns and weather structures.CNN and LSTM models are used to track changes in the atmosphere over time. The system is trained on both historical and current satellite data to make predictions more reliable. It helps forecast rainfall, cloud movement, and temperature changes. This deep learning method cuts down on computational costs compared to old models. It also works better when the weather changes quickly. Overall, the system helps with early warning, disaster response, and building long-term climate resilience.

Keywords : Satellite images, Weather prediction, Deep Learning, CNN, Climate Monitoring, Forecasting System.

Author : 1 I. Subhashini,2S. Anjali,3K. Siri Nayana,4M. Manasa,5G. Kavitha

Title : Satellite Image-Based Weather Prediction and Analysis Using Deep Learning for Climate Monitoring and Forecasting

Volume/Issue : 2026;03(07)

Page No : 1049-1054

In this project, we will develop a Phishing Website Detection System using Machine Learning to identify and classify malicious websites. The system will analyze various URL and webpage features, such as domain patterns and security indicators, to determine whether a website is legitimate or phishing. We will collect and preprocess real-world data, extract relevant features, and apply supervised machine learning algorithms like Random Forest, SVM, or Logistic Regression to train the detection model. The trained model will be evaluated using standard performance metrics to ensure accuracy and reliability. Finally, the model will be integrated into a webbased application that allows users to enter URLs and receive instant phishing predictions. This project aims to enhance online security, reduce cyber fraud, and provide an automated and efficient solution for phishing detection.

Keywords : Supervised learning algorithms, Random Forest, Support Vector Machines, and Neural Networks, Web Link Threat Detection

Author : 1G. Ramanaiah, 2G. Raghu Kumar,3C. Kiran Kumar,4P. Tejesh,5B. Vijendra,6K. Siva

Title : Phishing Website Detection Using Machine Learning

Volume/Issue : 2026;03(07)

Page No : 1055-1061

OTP-Based Cardless Transaction System using Face Recognition for Secure Financial Operations allows users to perform ATM transactions without a physical card by using two-factor authentication, one-time-password (OTP) authentication with AI-based facial recognition to ensure multi factor verification of users during financial operations. Traditional card-based transaction systems are vulnerable to security threats such as card theft, skimming, duplication and pin leakage. Users may also forget or lose their cards. Therefore, there is a need for a secure, reliable, and cardless transaction system that ensures strong user authentication while maintaining ease of use. In this project Python, Opencv, Mysql, Technologies are used and face detection, recognition models are used. It is providing a secure and convenient method for financial transactions without using physical cards.

Keywords : Python, OpenCV, Flask, MYSQL

Author : 1M. Vani, 2B. Sowmya,3B. Blessy,4B. Premila Rani,5R. Sheeba

Title : OTP-Based Cardless Transaction System Using Face Recognition for Secure Financial Operations

Volume/Issue : 2026;03(07)

Page No : 1062-1069

As one of the most stressed structural subsystems in an airplane, the landing gear must absorb the impact of landing, support the whole airframe's weight on the ground, and transfer the stresses of taxiing, braking, and turning—all within a small and weight-limited space. A twin-wheel main landing gear leg with an oleo-pneumatic strut (cylinder and piston), upper and lower drag/side-stay arms, a central pivot joint, an axle, twin tyres, and wheel-retention bolts is illustrated in this paper through three-dimensional solid modeling and static-structural finite element analysis. The nine-body assembly was imported into ANSYS Workbench after being modeled in CATIA V5. Titanium Alloy, Structural Steel, and Aluminium Alloy properties were assigned to the various bodies. At the mating interfaces, forty-eight bonded contact regions were defined. The geometry was then discretized into a tetrahedral mesh with 14,793 nodes and 11,140 elements. An upper strut trunnion was used as a permanent support, and the axle/wheel contact was subjected to a static ground reaction force of about 1000 N. The pivot-joint and stay-arm clevis fillets experience a concentration of approximately 2.10 MPa equivalent (von Mises) stress, a maximum equivalent elastic strain of 3.13 × 10⁻⁵ mm/mm, and a maximum total deformation of 0.0277 mm. This corresponds to a static factor of safety of about 119 against the lowest material yield strength (250 MPa, Structural Steel). Using a simplified reference load instead of certificated FAR/CS ground-load factors and a fully bonded representation of sliding and rotating joints in the physical assembly, this paper analyzes the limitations of the baseline result. It then identifies the extensions—certified load cases, non-bonded contact, and dynamic drop-test simulation—needed to advance the model toward a certification-representative structural substantiation.

Keywords :

Author : Kummari Anil Kumar, Mr. D. Prabhakar, Dr. B. Sandhya Rani

Title : Development and Static Structural Finite Element Evaluation of a Main Landing Gear Assembly for Multi-Body Aircraft

Volume/Issue : 2026;03(07)

Page No : 1070-1076

Because it is cantilevered from the fuselage tailcone, the vertical fin (also known as a vertical stabilizer) must be as lightweight as possible while nevertheless being large enough to support significant bending and shear loads in order to maintain the aircraft's directional (yaw) stability. Comparing the structural performance of three candidate materials structural steel, E-glass fiber, and carbon fiber (230 GPa grade) under an identical static side-load case, this paper presents the design, 3D solid modeling, and finite element structural analysis of a representative fin blade assembly for a cargo aeroplane. Under a uniformly distributed 0.5 MPa pressure load and with a fixed-support boundary condition at the root fitting, the fin geometry—which includes a primary fin box, spar, and lower ventral fairing—was modeled in CATIA V5 and studied in ANSYS Workbench. Total deformation was 0.471 mm and peak von Mises stress was 48.16 MPa for structural steel, while mass was highest for E-glass fiber at about one-third the density of steel. The highest deformation was 4.066 mm for carbon fiber, which was modeled using dry, fibers-only orthotropic properties and had an unfavourable fiber orientation relative to the fin load path, as well as pronounced transverse compliance. By all accounts, the three materials did not deviate from their elastic limit values. The findings suggest that E-glass fiber provides a safe alternative to steel in terms of weight reduction when using the simplified material models that were considered. However, it is anticipated that a carbon fiber reinforced polymer structure that is correctly laminated and optimized for weight reduction will offer the most significant weight savings, pending additional orthotropic laminate analysis

Keywords :

Author : Perugu Anil Kumar, Dr. P. Ravikanth Raju, Dr. B. Sandhya Rani

Title : Building a Cargo Aircraft's Vertical Fin Blade with Structural Steel, E-Glass, and Carbon Fiber: A Finite Element Comparison and Design Study

Volume/Issue : 2026;03(07)

Page No : 1077-1085

To guarantee sufficient strength, stability, and serviceability under applied loading circumstances, structural analysis is a crucial step in the design process of reinforced concrete (RC) structures. For more precise and time-saving structural analyses, modern structural engineering practices rely heavily on finite element-based software. A G+5 reinforced concrete structure was analyzed structurally under dead and live loads using STAAD in this research.Professional Connect Edition. In compliance with IS 456:2000 and IS 875 (Part 1 and Part 2), a three-dimensional analytical model was created by specifying the building's geometry, materials, sections of members, support conditions, and loads. A total of 558 frame members, 144 slab plate elements, and 245 joints make up the structure. The technique of matrix stiffness was used to conduct linear static analysis, and self-weight and uniformly distributed live loads were used to provide gravity loading. Measurements of axial forces, bending moments, shear moments, nodal displacements, and support responses were used to assess the structural response. The analytical model was shown to be reliable via validation procedures that included checking the geometry, member connection, and static equilibrium. Under the specified loading circumstances, the findings show that the structure is strong and serviceable, proving that STAAD was successful.Pro as a trustworthy instrument for the evaluation of structures with reinforced concrete frames.

Keywords :

Author : Malothu Veeranna, Mrs. P. Sushmitha, Dr. B. Sharath Chandra

Title : BUILDING STRUCTURAL ANALYSIS WITH DEAD AND LIVE LOADS IN STAAD.PRO FOR A G+5 REINFORCED CONCRETE STRUCTURE

Volume/Issue : 2026;03(07)

Page No : 1086-1091

The rapid growth of urban infrastructure has increased the demand for safe, economical, and efficient reinforced concrete RC construction systems. The evaluation of the behavior of complicated building frames and the assurance of conformity with design requirements are now facilitated by structural analysis and design software. A G+3 reinforced concrete residential structure was modeled, analyzed, and designed in three dimensions using STAAD in this work.Elite V8i. Elements of the structural system that adhere to the limit state requirements of IS 456:2000 include reinforced concrete beams, columns, and slabs. With the use of frame and plate components, an exhaustive analytical model was created, which included the characteristics of M30 grade concrete and Fe500 reinforcing steel. In accordance with the applicable criteria of the Indian Standard, the structure was evaluated under gravity loading circumstances, which included dead load, live load, and factored load combinations. The structural response, nodal displacements, and member forces were all evaluated using linear elastic analysis. Section of STAAD that deals with concrete design.Beams, columns, and slab reinforcement needs were subsequently obtained using Pro. It was determined from the design that all structural parts met the strength and serviceability standards set forth by IS 456:2000. The structural sufficiency was confirmed by evaluating the maximum values of beam bending moment, shear force, column axial load, and displacement. Using STAAD, we optimized the reinforcement details.The entire amount of concrete used was about 84 m³, and 6.84 tons of reinforcing steel was produced by Pro. A strong agreement was seen when comparing software findings with reduced human calculations, indicating that the numerical model could be relied upon. The results show that STAAD is effective.While demonstrating the significance of engineering judgment and code-based verification in understanding softwaregenerated findings, Pro V8i is used as a computational tool for structural analysis and design of low-rise reinforced concrete structures.

Keywords :

Author : Mohd Shoyebuddin, Mrs. K. Renuka, Dr. B. Sharath Chandra

Title : USING STAAD, WE ANALYZED AND DESIGNED A G+3 REINFORCED CONCRETE RESIDENTIAL BUILDING.AS TO THE REQUIREMENTS OF IS 456:2000, PRO V8I

Volume/Issue : 2026;03(07)

Page No : 1092-1102

With India's major cities growing at a dizzying rate, there's a greater need than ever before for secure, functional, and cost-effective multi-story housing. The strength, longevity, and appropriateness of reinforced concrete (RC) framed construction for medium- and high-rise buildings make them a popular choice for such projects. Partial safety factors are a part of conventional structural design methods that adhere to Indian Standard codes. However, these deterministic approaches don't exactly measure the likelihood of failure due to unknown material properties, structural dimensions, and applied loads. Ground plus six stories plus roof (G+6+R) reinforced concrete residential building in Hyderabad is the subject of this study's analysis, design, and reliability evaluation. Using STAAD.Pro CONNECT Edition, the structure has been rendered as a threedimensional space frame. All structural parts have been reinforced with M40 grade concrete and Fe500 grade steel. The building is situated on hard rocky strata with a safe bearing capacity of 180 kN/m² and has a uniform floor height of 3.5 m. The plan area of the construction is 25 m × 16 m. Following the requirements of IS 875 and IS 1893, the structural model—which contains 553 members and 240 nodes—has been examined under various load circumstances, including dead load, superimposed dead load, live load, wind load, and seismic stress. The member forces, lateral displacements, and storey drift responses were obtained by linear static analysis after forty-five combinations of ultimate and serviceability loads were created. Beams, columns, and isolated footings were reinforced concrete designed utilizing the STAAD RCDC module in accordance with the criteria of IS 456 and IS 13920. To evaluate the intended structure's safety margin, a probabilistic reliability evaluation was conducted in addition to traditional analysis and design. Uncertainties in material strength, member characteristics, and loading circumstances were taken into account while formulating limit state functions for flexure, shear, and axial-flexure interaction. Calculations for the Hasofer-Lind reliability index and failure probability were performed using Monte Carlo Simulation using the First Order Reliability Method (FORM). Critical structural components and significant sources of uncertainty were identified via the development of reliability indices, fragility curves, and sensitivity analysis. The findings show that the examined building is strong enough and functional enough to meet the standards set forth by Indian Standard regulations. In general, RC structures aim for a dependability level of 3.3–3.9, and the obtained reliability indices are within this acceptable range. Research shows that code-based designs have a good margin of safety, and it stresses the need for reliability-based assessment to learn how uncertainties affect structural performance. Staad is a keyword.Structural reliability, Monte Carlo simulation, FORM, reliability index, fragility curve, IS 456; IS 1893; IS 875; Hyderabad; Reinforced concrete frame; multi-story structure; Seismic analysis; Wind analysis.

Keywords :

Author : Bagari Lokesh, Dr. B. Sharath Chandra

Title : USING STAAD, WE DESIGNED AND ANALYZED THE RELIABILITY OF A MULTI-STORY RESIDENTIAL BUILDING IN HYDERABAD.VERSION PRO WITH CONNECT

Volume/Issue : 2026;03(07)

Page No : 1103-1115

To ensure that reinforced concrete (RC) structures are safe, cost-effective, and efficient, structural optimization and analysis are crucial design procedures. This study offers the results of an integrated numerical examination of a reinforced concrete framed structure with a low-rise structure utilizing ANSYS Workbench and STAAD.Pro V8i. It all started with a STAAD model of a G+2 reinforced concrete frame in three dimensions.To test how a structure reacts to gravity loads, experts use beam-column elements. In accordance with the applicable rules of the Indian Standard, dead load, live load, and factored load combinations were considered. The findings of the bending moment, shear force, axial force, displacement, and support response were used to evaluate the structural behavior. How precise the STAAD modeling is.We compared the findings produced by the program with those from simpler human calculations grounded on traditional structural analysis methods in order to confirm the accuracy of the Pro model. Departures within an acceptable range of ±12% were observed in the comparison, indicating that analytical and computational approaches were in excellent agreement. Additional analysis was conducted on the verified structural model using ANSYS Workbench's finite element method. To examine the distribution of stresses, the behavior of deformation, and the important structural areas under combined gravity and equivalent seismic loading, a mixed-element model was created. This model includes shell elements for slabs and line elements for frame members. According to ANSYS, the area where the roof slab was subjected to the most deformation was also the location where critical stress concentrations formed, close to the bases of the columns supporting the bottom floor and the areas where the slab and columns were connected. Parametric optimization was then used to decrease structural mass while still meeting the criteria for stress, drift, and stiffness by adjusting the thickness of the slab and the size of the columns. Optimal layout reduced structural mass by around 18% compared to original design while maintaining structural safety. This research shows that STAAD has supplementary benefits.Pro for realistic structural design and ANSYS Workbench for sophisticated finite element analysis and optimization of RC buildings.

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Author : Katakam Maneendra, Mrs. P. Sushmitha, Dr. B. Sharath Chandra

Title : A REINFORCED CONCRETE BUILDING'S STAAD-BASED STRUCTURAL ANALYSIS, FINITE ELEMENT ANALYSIS, AND OPTIMIZATION!WORKBENCH AND ANSYS PRO

Volume/Issue : 2026;03(07)

Page No : 1116-1125

Efficient structural design techniques are necessary for reinforced concrete framed structures in seismic areas to provide sufficient strength, stiffness, and economy. Traditional methods of design often use conservative member size, which may lead to wasteful material usage with little to no improvement in structural performance. A two-story framed structure was the subject of a finite element-based optimization study conducted using ANSYS Workbench in this research. Surface bodies were used for slab components and line bodies for structural elements in the development of a three-dimensional structural model. The model was then subjected to finite element discretization and static structural analysis under combined gravity and equivalent seismic loading conditions. Using total deformation, directed deformation, and equivalent stress distribution, the structural reaction was analyzed. Seismic loading deformation patterns and areas of significant stress concentration were uncovered by the research. Slab thickness and column cross-sectional dimensions were varied using the validated finite element model for structural optimization. The goal was to minimize structural mass while satisfying strength, drift, and frequency constraints, as outlined in IS 456:2000 and IS 1893 (Part 1):2016 provisions. In order to find the most efficient structural arrangement, we used an optimization method based on response surfaces to compare several design options. Minimizing structural mass without sacrificing dynamic performance, inter-storey drift limitations, or tolerable stress levels was the goal of the optimized model. This research provides a realistic foundation for cost-effective and code-compliant structural design by showing that optimization based on finite elements may significantly increase the material efficiency of seismicresistant low-rise buildings.

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Author : JUNUTHULA JANARDHAN REDDY, Dr. B. SHARATH CHANDRA

Title : A TWO-STORY BUILDING'S STRUCTURAL MEMBER OPTIMIZATION FOR IMPROVED SEISMIC PERFORMANCE VIA THE USE OF FINITE ELEMENT ANALYSIS

Volume/Issue : 2026;03(07)

Page No : 1126-1133

English language education occupies an important position in the Indian education system, particularly in states such as Odisha, where English is associated with higher education, employment opportunities, social mobility, and access to global knowledge. However, the teaching and learning of English in Odisha is influenced by several contextual factors, including learners' linguistic backgrounds, rural–urban differences, availability of educational resources, classroom practices, teacher competence, and students' attitudes toward English. This study examines learners' perceptions of English language education and the classroom realities experienced by students in Odisha. A descriptive survey design was adopted, using a structured questionnaire and classroom observations as the principal tools of data collection. A hypothetical sample of 300 secondary and higher-secondary learners from selected schools representing urban, semi-urban, and rural settings was considered for analysis. The findings indicate that most learners recognize English as important for higher education and employment, but many experience difficulties in speaking, vocabulary, pronunciation, and spontaneous communication. Teacher-centred instruction, examination-oriented learning, limited opportunities for oral communication, and inadequate access to digital resources were identified as major classroom challenges. The study also indicates that learners generally have a positive attitude toward English but require more interactive, learner-centred, and communicative teaching practices. The study recommends strengthening teacher training, improving digital and language-learning resources, promoting multilingual pedagogies, and creating greater opportunities for authentic English communication.

Keywords : English language education, Odisha, learners' perceptions, classroom realities, language learning, communicative competence, multilingualism, English teaching

Author : Susanta Sahu,Dr. Sudipta Roy Chowdhury,Dr. P. Rajya Laxmi

Title : ENGLISH LANGUAGE EDUCATION IN ODISHA: LEARNERS' PERCEPTIONS AND CLASSROOM REALITIES

Volume/Issue : 2026;03(07)

Page No : 1134-1142