Journal of Science Engineering Technology and Management Science

Plant diseases pose a significant threat to agricultural productivity and food security, making early and accurate detection essential. Traditional image processing and machine learning techniques often struggle to handle variations in lighting, background noise, and complex disease patterns. To address these challenges, this paper proposes a hybrid plant disease prediction system that integrates quantum-inspired image processing with deep neural networks. The proposed approach utilizes angle-based quantum encoding to transform leaf images into quantum-like representations, enabling efficient and discriminative feature extraction. Statistical measurements derived from these representations are combined with color and texture features to form a robust feature set. These features are then fed into a deep neural network model for multi-class disease classification. The system is designed to be scalable and robust, capable of handling diverse plant disease categories under varying environmental conditions. Experimental results demonstrate that the proposed method significantly improves classification accuracy and reliability compared to conventional approaches. The developed framework can be effectively used in real-world agricultural decision support systems for early disease diagnosis and crop management.

Keywords : Plant Disease Detection, Quantum-Inspired Image Processing, Deep Neural Networks (DNN), Feature Extraction, Image Classification, Agricultural AI, Leaf Image Analysis, Machine Learning, Pattern Recognition, Smart Farming

Author : T.S.R.Krishna Prasad,Kollati Ribka,J Venkata Guru Prasad,Jyothi Ayyappa Prasanna Kumar,Madugula Ajay Kumar

Title : Plant disease detection using quantum image processing

Volume/Issue : 2026;03(04)

Page No : 688-697

Chronic Heart Failure (CHF) is a serious cardiovascular condition associated with high morbidity and mortality worldwide. Early and accurate detection of CHF can significantly enhance patient outcomes and reduce healthcare costs. Traditional diagnostic methods, including echocardiography and clinical assessment, are often expensive and require specialized expertise. This project proposes a machine learning-based system to classify CHF using heart pulsation data derived from electrocardiograms (ECG) and photoplethysmography (PPG) signals. Signal preprocessing enhances data quality by removing noise and artifacts. Time-domain and frequency-domain features are extracted to characterize pulsation patterns. Multiple machine learning classifiers such as Support Vector Machines (SVM), Random Forests, and Artificial Neural Networks (ANN) are trained on labeled datasets. Feature selection reduces dimensionality and improves model performance. The system identifies CHF with high accuracy, precision, recall, and F1-score. Cross-validation ensures model robustness. The model supports real-time prediction capabilities for wearable devices. Visualization dashboards provide insight into detected patterns. Automated alerts notify healthcare professionals of suspected CHF cases. The approach reduces dependence on costly clinical diagnostics. The system contributes to accessible and scalable CHF screening. Ethical data handling and privacy compliance are maintained.

Keywords : Chronic Heart Failure (CHF) Machine Learning Classification Heart Pulsation Signals ECG/PPG Feature Extraction

Author : 1Kolli Sivalakshmi, 2Pothireddy Susanna, 3Pujari Somnath, 4Sadhu Deepthi, 5Ravi Mani Deepthi, 6Mrs. L Mounika,

Title : Utilizing Machine Learning for the Identification of Chronic Heart Failure (CHF) from Heart Pulsations

Volume/Issue : 2026;03(04)

Page No : 698-703

Accurate discrimination between natural earthquakes and artificial explosions is critical for seismic monitoring, disaster management, and national security. Traditional seismic analysis techniques rely heavily on handcrafted features and expert interpretation, which can be time-consuming and error-prone. With the advancement of deep learning, automated seismic signal classification has gained significant attention. This project proposes a Deep Convolutional Neural Network (DCNN)-based approach to classify seismic events as earthquakes or explosions. Seismic waveform data are transformed into time-frequency representations such as spectrograms for effective feature learning. The CNN model automatically extracts discriminative spatial and temporal features from seismic signals. Data preprocessing enhances signal quality and reduces noise interference. The trained model achieves high classification accuracy without manual feature engineering. Performance is evaluated using accuracy, precision, recall, and F1-score metrics. The system supports rapid and reliable seismic event classification. Explainability tools provide insight into model decisions. The proposed framework improves detection reliability in real-time seismic monitoring systems. This approach enhances both scientific research and security surveillance. Overall, the system offers a scalable and intelligent solution for seismic event classification.

Keywords : Seismic Signal Classification Deep Convolutional Neural Networks (DCNN) Earthquake Detection Explosion Identification Time-Frequency Analysis

Author : 1Pulivarthi Raghuvardhan, 2Karrothu Chaitanya Kumar, 3Gummadi Praveen, 4Y Srinu, 5Mrs.B Roja Lakshmi

Title : Using Deep Convolutional Neural Networks for Earthquake and Explosion Classification

Volume/Issue : 2026;03(04)

Page No : 704-708

Secure file sharing is a critical requirement in modern digital ecosystems, especially with increasing concerns about data privacy, integrity, and unauthorized access. Traditional centralized storage systems often suffer from single-point failures, data tampering, and privacy vulnerabilities. This project proposes a decentralized secure file sharing system leveraging Blockchain and InterPlanetary File System (IPFS). Blockchain ensures immutability, transparency, and tamper-evident audit trails for file transactions. IPFS provides distributed, content-addressed storage that eliminates reliance on centralized servers. Users can upload, share, and retrieve files securely using cryptographic hashes. Smart contracts govern access control, permission updates, and file metadata management on the blockchain. File content itself is stored on IPFS, while its reference hash is recorded onchain for verification. End-to-end encryption ensures confidentiality of sensitive data. Consensus mechanisms prevent unauthorized modifications. The system supports user authentication and role-based access control. Decentralized storage enhances fault tolerance and availability. Real-time tracking of file changes prevents data tampering. Auditable logs enable forensic investigations. Scalability is achieved through distributed networks. The architecture supports interoperability with existing storage solutions. This approach strengthens data security, privacy, and trust for collaborative environments. Overall, blockchain and IPFS jointly provide a scalable and secure file sharing framework for modern applications.

Keywords : Secure File Sharing Blockchain Technology Smart Contracts IPFS (InterPlanetary File System) Decentralized Storage

Author : 1Majji Pavan Kumar, 2Rebba Surya, 3Gogula Ajay, 4Sake Sravanthi, 5Mr.V.V.B. Chari

Title : Secure File Sharing with Blockchain And IPFS

Volume/Issue : 2026;03(04)

Page No : 709-715

The Online Voting System using Blockchain is an innovative approach aimed at enhancing the transparency, security, and reliability of electoral processes. Traditional voting systems often suffer from issues such as vote manipulation, lack of transparency, and centralized control. This project introduces a decentralized voting mechanism using blockchain technology to eliminate these challenges. Blockchain ensures that all votes are recorded in a secure, immutable ledger, making it nearly impossible to alter or tamper with voting data. Each vote is encrypted and stored as a block, ensuring voter anonymity and data integrity. Smart contracts are utilized to automate vote validation and counting processes, reducing human intervention and errors. The system provides real-time access to election results while maintaining strict security protocols. Voters can participate remotely using secure authentication methods, increasing accessibility and voter turnout. The decentralized nature of blockchain removes the dependency on a central authority, enhancing trust among users. Additionally, the system prevents double voting and unauthorized access through cryptographic techniques. The implementation of this system can significantly improve election efficiency and fairness. It is scalable and adaptable for various types of elections, including governmental and organizational voting. Overall, this solution aims to modernize voting systems by leveraging cutting-edge technology. The proposed system ensures transparency, accountability, and accuracy

Keywords :

Author : 1B. Naga Jagadish, 2B. Rajani Kumari, 3D. Naga Ratnam, 4R. Shanmukha Rao, 5Mr. G Koteswara Rao

Title : ONLINE VOTING SYSTEM USING BLOCK CHAIN

Volume/Issue : 2026;03(04)

Page No : 716-723

Hypertension is one of the most common chronic health conditions and a major risk factor for cardiovascular diseases worldwide. Early detection and continuous monitoring are essential to prevent severe complications such as stroke, heart attack, and kidney failure. Traditional diagnosis relies on periodic blood pressure measurements and clinical assessment, which may miss early warning signs. This project proposes a machine learning-based healthcare system for the prediction of hypertension using patient health data. The system analyzes clinical parameters such as age, body mass index (BMI), blood pressure readings, cholesterol levels, lifestyle factors, and family history. Data preprocessing ensures quality and consistency of medical records. Multiple machine learning algorithms are trained to classify individuals as hypertensive or non-hypertensive. Feature selection improves model accuracy and efficiency. The system provides early risk prediction and decision support for healthcare professionals. Performance is evaluated using accuracy, precision, recall, and F1-score. Automated alerts notify users of potential hypertension risks. The model supports preventive healthcare and personalized treatment planning. Secure data handling ensures patient privacy. The proposed approach enhances accessibility, accuracy, and efficiency in hypertension management.

Keywords : Hypertension Prediction Machine Learning in Healthcare Predictive Analytics Chronic Disease Monitoring Health Risk Assessment

Author : 1Shaik Mubhashira, 2Rasipogula Swapna, 3Shaik Sadhik, 4T V Sai Charan,5Gunja Narendra, 6Mr.V.V.B. Chari

Title : Prediction of Hypertension & Healthcare System using ML

Volume/Issue : 2026;03(04)

Page No : 724-729

The rapid growth of online payment systems has significantly increased the risk of financial fraud. Fraudulent transactions cause substantial financial losses to individuals, banks, and e-commerce platforms. A major challenge in fraud detection is the highly imbalanced nature of transaction datasets, where fraudulent cases are rare compared to legitimate ones. Traditional machine learning models tend to be biased toward majority classes, resulting in poor fraud detection rates. This project proposes an Online Fraud Payment Detection system using balanced machine learning algorithms to address class imbalance. Data balancing techniques such as SMOTE and undersampling are applied to improve model performance. Multiple machine learning classifiers are trained and evaluated on balanced datasets. Features related to transaction amount, location, time, and user behavior are analyzed. The system accurately identifies fraudulent transactions in real time. Performance is evaluated using precision, recall, F1-score, and ROC-AUC metrics. The model minimizes false negatives, which are critical in fraud detection. Automated alerts notify stakeholders of suspicious activities. The system improves decision-making for financial institutions. Scalability ensures handling of high-volume transactions. Security and data privacy are maintained throughout the process. The proposed approach enhances fraud detection accuracy and reliability. Overall, the system provides a robust and efficient solution for online payment fraud prevention.

Keywords : Online Payment Fraud Machine Learning Class Imbalance SMOTE Fraud Detection Systems

Author : 1Shaik Siddhik, 2Shaik Mohith, 3Gunja Sirisha, 4Yadagiri Vasantha Laxmi, 5Mr.N. Anjaneyulu

Title : Online Fraud Payment Detection using Balanced ML Algorithms

Volume/Issue : 2026;03(04)

Page No : 730-735

In recent years, the growing urgency to address environmental challenges has brought green finance to the forefront of academic and policy discussions. Against this backdrop, the present study examines how financial considerations and sustainability imperatives are shaping investment behaviour in the renewable energy sector. The paper seeks to understand not only the motivations driving investors and firms toward green investments but also the practical constraints that continue to influence their decisions. Drawing on both primary insights and secondary evidence, the study adopts an analytical approach to explore the role of environmental awareness, regulatory support, and financial incentives in guiding investment choices. The findings suggest that while there is a clear and increasing shift toward sustainability-oriented investments, decision-making remains strongly influenced by perceived risk, cost structures, and policy stability. Investors appear more inclined toward renewable energy projects when supported by transparent regulations, fiscal incentives, and long-term viability signals. At the same time, the study highlights those barriers such as high initial capital requirements, information asymmetry, and uncertainty in policy implementation continue to limit the full potential of green finance. These challenges underline the need for a more integrated framework where financial markets, regulatory bodies, and corporate strategies align toward sustainability goals. The paper concludes that strengthening green finance mechanisms and improving investor confidence are critical to accelerating the transition toward a sustainable energy future. It offers practical implications for policymakers and financial institutions to design more effective instruments that can bridge the gap between sustainability ambitions and investment realities.

Keywords : Green Finance, Corporate Sustainability, Renewable Energy, Investment Behaviour, Sustainable Investment

Author : P. Shirisha, Kavali Rudransh, Vadlakonda Kavitha

Title : GREEN FINANCE AND CORPORATE SUSTAINABILITY: INVESTMENT BEHAVIOUR IN THE RENEWABLE ENERGY SECTOR

Volume/Issue : 2026;03(04)

Page No : 736-745

The rapid growth of startup ecosystems has created a strong need for reliable methods to evaluate the potential success of newly established companies. Traditional investment decisions often rely on subjective judgment, limited historical analysis, and manual evaluation, which may lead to inaccurate predictions and high financial risk. This research proposes an efficient and novel machine learning–based approach for predicting startup company success rates using advanced ML paradigms. The proposed system integrates multiple data sources such as financial metrics, founder experience, market trends, funding history, product innovation, and customer engagement indicators to build a comprehensive predictive model. The methodology involves data preprocessing, feature engineering, and the application of supervised learning algorithms including Random Forest, Support Vector Machine, Gradient Boosting, and Neural Networks. Ensemble learning techniques are employed to improve prediction accuracy and reduce model bias. The system utilizes historical startup datasets to train and validate models, enabling identification of critical success factors influencing startup growth and sustainability. Performance evaluation is conducted using accuracy, precision, recall, and F1-score metrics to ensure robust model performance. Experimental results demonstrate that the proposed hybrid ML framework significantly improves prediction reliability compared to traditional statistical methods. The model assists investors, entrepreneurs, and policymakers in making datadriven decisions by providing early insights into startup viability. Overall, the proposed approach enhances risk assessment, optimizes investment strategies, and contributes to the development of intelligent decision-support systems within entrepreneurial ecosystems.

Keywords :

Author : Mr. L. N. V. Rao1 , KONDETI ANAND PAL2 ,RAVULAKOLLU LAKSHMI NARASAMMA3 ,METHUKUMILLI DIVYA4 ,KUNUKU SRI LAKSHMI5

Title : TRANSFORMING HAND GESTURE INTO WORDS A NOVEL APPROCH FOR ASSISTIVE COMMUNICATIONS USING ML TECHNIQUES

Volume/Issue : 2026;03(04)

Page No : 746-754

The rapid growth of online payment systems and digital transactions has significantly increased the risk of financial fraud, making fraud detection an essential component of modern financial security. Online fraud payment detection aims to identify suspicious or unauthorized transactions in real time to prevent financial losses for both users and financial institutions. However, one of the major challenges in fraud detection is the imbalance in transaction datasets, where fraudulent transactions represent only a small portion compared to legitimate ones. Traditional machine learning models often fail to accurately detect fraud due to this class imbalance problem. This study proposes an online fraud payment detection system using balanced machine learning algorithms to improve detection accuracy and reliability. The proposed approach applies data balancing techniques such as oversampling, under sampling, and synthetic data generation to ensure equal representation of fraudulent and non-fraudulent transactions during model training. Balanced algorithms including Random Forest, Support Vector Machine, Logistic Regression, and Gradient Boosting are utilized to enhance classification performance. Feature engineering and preprocessing techniques are also employed to extract meaningful transaction patterns and reduce noise in the dataset. The system evaluates performance using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC, which are more suitable for imbalanced datasets. Experimental results demonstrate that balanced machine learning models significantly improve fraud detection rates while minimizing false positives. The proposed framework provides a scalable and efficient solution for secure online payment environments, enabling financial organizations to detect fraudulent activities proactively and enhance customer trust.

Keywords :

Author : Mr.V. SUDHAKAR1 , MORAMPUDI JOSHNITHA2 ,PILLI NAGA VIJAYA LAKSHMI3 , MUDDUNENI KOMALI NAGA SRI SAI KEERTIKA4 , MADDALI ROHITH BABU5

Title : ONLINE FRAUD PAYMENT DETECTIONUSING BALANCED ML ALGORITHMS

Volume/Issue : 2026;03(04)

Page No : 755-763

Stock price prediction has become an important research area in financial analytics due to the increasing availability of historical market data and advancements in machine learning techniques. The objective of this project is to develop an intelligent system that analyzes past stock market trends and predicts future price movements with improved accuracy. The proposed approach utilizes data preprocessing, feature engineering, and predictive modeling algorithms such as Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) networks to capture both statistical patterns and temporal dependencies in stock data. Historical price data, including open, high, low, close, and trading volume, are used as input features to train the model. The system aims to assist investors and financial analysts in making informed decisions by providing trend forecasts and risk insights. Performance evaluation is carried out using metrics such as Mean Squared Error (MSE) and accuracy comparisons across different models. The results demonstrate that machine learning–based methods can effectively identify hidden patterns in financial markets, offering a scalable and data-driven solution for stock price prediction while reducing manual analysis effort.

Keywords :

Author : Venkata Ratnam Ganji1 , A. Jyotsna Sri2 , J.Lavanya3 , K. Hima Sankar4 , G.Navya5

Title : STOCK PRICE PREDICTION USING MACHINE LEARNING TECHNIQUES

Volume/Issue : 2026;03(04)

Page No : 764-774

Effective communication is a fundamental challenge for individuals with speech or hearing impairments. Traditional communication aids often require manual input or are limited in scope. This project proposes a novel machine learningbased approach to transform hand gestures into textual words, enabling real-time assistive communication. The system uses computer vision techniques to capture hand gestures and employs machine learning algorithms, including Convolutional Neural Networks (CNNs) and LSTM networks, to recognize and translate gestures into corresponding words. The approach focuses on accuracy, speed, and adaptability, allowing users to communicate efficiently without prior extensive training. Experimental results demonstrate that the system can accurately interpret diverse hand gestures and convert them into readable text, providing a practical and user-friendly assistive tool for individuals with communication difficulties.

Keywords :

Author : Mr. L.N.V. Rao1 , UMMADISETTI YASASRI2 , PARASA NARESH 3 , MERUGUMALA RAVI KIRAN4 , THOTA THARUN PRUDHVI5

Title : TRANSFORMING HAND GESTURE INTO WORDS A NOVEL APPROCH FOR ASSISTIVE COMMUNICATIONS USING ML TECHNIQUES

Volume/Issue : 2026;03(04)

Page No : 775-784