Daily Archives: April 6, 2026

Authors: Dr. A.Avinash, Dosapathni Durga Venkata Lakshmi, Rayudu Dona Nikhila, Rayudu Dona Nikhila, Dulla Lokesh Veera Sai Nandan

Abstract: Efficient energy management has become increasingly important due to the growing demand for electricity, rising energy costs, and the need to reduce environmental impact. Accurate prediction of household energy consumption can help individuals and energy providers optimize energy usage, improve resource planning, and promote sustainable living. Traditional statistical forecasting methods often struggle to capture complex consumption patterns present in real-world energy datasets. With the advancement of artificial intelligence, machine learning techniques have shown strong potential for analysing energy consumption data and producing more accurate predictions. This study proposes a machine learning–based framework for predicting household energy consumption using historical electricity usage data. The system analyses various factors such as electrical current, voltage, frequency, and previous energy consumption values to forecast future energy usage. Multiple machine learning and deep learning models, including Linear Regression, Random Forest Regressor, LightGBM, XGBoost, CatBoost, LSTM, and BiLSTM, are implemented and evaluated to identify the most effective model for energy consumption prediction. In addition to prediction accuracy, the proposed framework integrates Explainable Artificial Intelligence (XAI) techniques to improve transparency and interpretability of model predictions. Explainability methods such as Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) are used to analyse the importance of different input features and understand how they influence the prediction results. Experimental results demonstrate that gradient boosting–based models provide highly accurate predictions, while XAI techniques help reveal the key factors that influence energy consumption patterns. The proposed system provides both accurate forecasting and interpretable insights, enabling users to better understand their energy usage behaviour. Such intelligent systems can support energy-efficient decision making, contribute to smart home energy management, and assist in the development of sustainable energy solutions.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.171

 

Published by: vikaspatanker

Authors: Mrs.KanakaTulasi P.Reddi, Jittuka Harsha Dinni Sri, Mohan Sai Krishna Bhuvanasi, Adipudi Naga Sai Sri Sowmya, Koruprolu Gowtham

Abstract: The rapid increase in carbon dioxide (CO₂) emissions has become a major environmental concern due to its significant contribution to global warming and climate change. Accurate prediction of CO₂ emissions is essential for developing effective environmental policies and implementing sustainable strategies to reduce greenhouse gas emissions. Traditional statistical forecasting methods often struggle to capture complex relationships between multiple environmental and industrial factors that influence carbon emissions. In recent years, machine learning techniques have emerged as powerful tools for analysing environmental data and improving prediction accuracy.This study presents a machine learning–based framework for forecasting CO₂ emissions using historical environmental and fuel consumption data. The proposed system analyses various factors such as fuel consumption patterns, vehicle characteristics, engine size, and other related attributes to estimate future carbon emissions. Several machine learning regression algorithms, including Linear Regression, Gaussian Process Regression, Multilayer Perceptron (MLP), and Sequential Minimal Optimization for Regression (SMOreg), are implemented and evaluated to determine the most accurate prediction model.The dataset used in this research is obtained from a publicly available environmental dataset and undergoes preprocessing steps such as data cleaning, normalization, and outlier detection to improve model performance. The trained models are evaluated using performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), and correlation coefficient.Experimental results indicate that machine learning algorithms can effectively predict CO₂ emissions, with SMOreg demonstrating superior performance compared to other models in terms of prediction accuracy and error reduction. The proposed framework can assist environmental researchers and policymakers in understanding emission trends and making informed decisions for climate change mitigation.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.170

 

Published by: vikaspatanker

Authors: Dr. Manjula Devarakonda Venkata, Jagilinki Hemanjali, Datla Siva Rama Raju, Karri Kalyana Sri Madhuri, Kamireddy Sri Siva Sarojaditya, Mohammad Chisty Madeena Sharieff

Abstract: Cryptocurrency markets are known for their high volatility and complex price dynamics, which make accurate prediction and analysis extremely challenging. Traditional financial forecasting models and classical machine learning algorithms often struggle to capture the nonlinear and rapidly changing patterns present in cryptocurrency datasets. In recent years, advancements in artificial intelligence and quantum computing have opened new possibilities for analyzing complex financial data and improving prediction accuracy.This study proposes a quantum computing–based framework for cryptocurrency market prediction by integrating quantum machine learning techniques with financial time-series analysis. The proposed model utilizes quantum computing concepts such as quantum feature mapping, variational quantum circuits, and quantum recurrent neural networks to analyze cryptocurrency market data. Historical datasets containing information about cryptocurrency prices, trading volume, and market capitalization are used to train and evaluate the model.The proposed system aims to identify hidden patterns in cryptocurrency market trends and generate accurate predictions for future price movements and market volatility. The performance of the quantum-based model is compared with classical deep learning models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. Experimental results indicate that the quantum machine learning approach achieves improved prediction accuracy and lower forecasting error compared to traditional deep learning models.By leveraging the computational advantages of quantum computing, the proposed framework provides a powerful approach for analyzing highly complex financial datasets. The results demonstrate that quantum machine learning techniques have the potential to significantly enhance cryptocurrency market analysis, enabling more accurate forecasting and better decision-making for investors and financial analysts.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.169

 

Published by: vikaspatanker

Authors: Dr.S.Suresh, Namala Sireesha, Shaik Davud, Tirumani Bhanu Shankar Satyanarayana, Kada Rama Satya Pavan, Kala Tirumala Venkata Sai Teja

Abstract: The rapid expansion of social media platforms and online communication systems has significantly increased the amount of user-generated content on the internet. While these platforms enable people to share ideas and communicate freely, they also expose users to harmful content such as hate speech, offensive language, cyberbullying, and abusive comments. Toxic comments not only affect healthy online discussions but also create negative psychological and social impacts on individuals. Therefore, developing automated systems capable of detecting and filtering toxic comments has become an important research problem in natural language processing and online content moderation. This study presents an intelligent framework for detecting toxic comments using machine learning and natural language processing techniques. The proposed system analyses textual data collected from online platforms and classifies comments into toxic and non-toxic categories. Various preprocessing techniques such as tokenization, stop-word removal, text normalization, and lemmatization are applied to clean and prepare the dataset for model training. Feature extraction methods including Term Frequency–Inverse Document Frequency (TF-IDF) and word embedding techniques are used to transform textual data into numerical representations suitable for machine learning models. Several machine learning and deep learning algorithms, including Naive Bayes, Support Vector Machines (SVM), Logistic Regression, and Convolutional Neural Networks (CNN), are implemented and compared to determine the most effective model for toxic comment classification. The models are evaluated using standard performance metrics such as accuracy, precision, recall, and F1-score. Experimental results indicate that deep learning models, particularly CNN-based architectures, achieve higher classification accuracy and better performance in detecting complex toxic language patterns. The proposed system can assist online platforms in automatically identifying harmful content and maintaining safer digital communication environments. By integrating machine learning techniques with advanced natural language processing methods, the framework contributes to improving online content moderation and promoting respectful interactions in digital communities.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.168

 

Published by: vikaspatanker

Authors: Dr.K.ChandraSekhar, Sathi Sudharshan Reddy, Anakapalli Bhargavi, Ulli Sri Satyasai Ramcharan Teja, Gubbala Y V Ganesh Kumar, Kakara Vivek

Abstract: Heart disease remains one of the leading causes of death worldwide, making early detection and accurate diagnosis essential for improving patient outcomes. Traditional diagnostic approaches often rely on clinical examinations and expensive medical tests, which may not always be accessible in all healthcare environments. In this research, we explore the use of machine learning techniques to develop an intelligent system for predicting the presence of heart disease using clinical parameters such as age, gender, blood pressure, cholesterol level, and heart rate. The dataset used in this study contains labelled medical records that are pre-processed, balanced, and divided into training and testing sets to ensure reliable model evaluation. Several supervised machine learning algorithms, including Logistic Regression, Support Vector Machines, Naïve Bayes, Decision Trees, K-Nearest Neighbors, and Linear Discriminant Analysis, are implemented and compared to identify the most effective model for heart disease diagnosis. Feature selection techniques are applied to determine the most influential clinical attributes contributing to disease prediction. To evaluate model performance, we employ a 5-fold cross-validation approach along with evaluation metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve (AUC).Experimental results demonstrate that the Logistic Regression and Linear Discriminant models achieve the highest prediction accuracy, showing strong capability in identifying heart disease risk from clinical data. In addition, the integration of optimized feature selection methods improves the overall diagnostic performance while reducing computational complexity. The proposed machine learning framework provides an effective and scalable approach for supporting early heart disease detection and assisting healthcare professionals in clinical decision-making.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.167

 

Published by: vikaspatanker

Authors: Mr.G.Vijay Kumar, Pathi Krishna Kanth, Srikakolapu Chandi Mohana Manjusha, Palacharla Vidhatri, Makineedi Hari Gangadhar Satya Sairam, Bathula James

Abstract: Water pollution has become a major environmental concern due to the increasing discharge of industrial and domestic contaminants into wastewater systems. Continuous monitoring of wastewater quality is essential to detect harmful pollutants and prevent environmental damage. This study proposes an intelligent wastewater pollution detection system that integrates low-cost multisensor technology with deep learning techniques. The system collects environmental data using multiple sensors capable of measuring chemical characteristics present in wastewater. The acquired sensor data is pre-processed and transformed into structured textual representations, enabling advanced machine learning models to analyse patterns associated with different pollutants. A deep learning model based on transformer architecture is then employed to classify and identify contaminants present in the wastewater. The proposed approach improves detection accuracy while maintaining computational efficiency. Experimental evaluation demonstrates that the system achieves higher classification performance compared to conventional machine learning methods. The developed framework provides a cost-effective and scalable solution for real-time wastewater monitoring and environmental protection. Future improvements may include integration with IoT-based monitoring platforms and deployment in large-scale environmental monitoring systems.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.166

 

Published by: vikaspatanker

Authors: Mrs.P.Lakshmi Satya, Dadala Aksha, Pandrangi Sri Venkata Arya, Akula Raja, Pithani Hemalatha, Thota Venkata Subha Santosh

Abstract: Rice quality assessment plays a crucial role in the food industry as it directly affects consumer satisfaction, market value, and food safety. Traditional rice inspection methods rely mainly on manual observation and mechanical tools, which are time-consuming, labour-intensive, and prone to human error. To address these limitations, this study proposes an intelligent computer vision framework for automated rice quality assessment using deep learning and explainable artificial intelligence techniques. The system captures high-resolution images of rice grains and applies image preprocessing techniques such as grayscale conversion, edge detection, and segmentation to extract important visual features. Deep learning models, including VGG16 and ResNet50, are used to learn complex feature representations and classify rice grains based on their physical attributes such as size, shape, texture, and colour. To improve transparency and interpretability of the model predictions, Explainable AI (XAI) techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM) are integrated into the framework. Experimental results demonstrate that the proposed approach significantly improves classification accuracy and reliability compared to traditional inspection methods. The developed system provides an efficient, scalable, and automated solution for rice quality evaluation in agricultural and food processing industries.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.165

 

Published by: vikaspatanker

Authors: Ms.A.Harini, Battina Sai Mounika, Kondeti Sushan Niharika, Mummidi Rajesh, Sodasani Hari Veera Narasimha Manikanta, Kalla Vinod

Abstract: Image classification has become a fundamental task in computer vision with applications in areas such as medical imaging, agriculture, environmental monitoring, and automated surveillance. Traditional machine learning techniques have achieved reasonable performance in classification tasks; however, they often struggle when dealing with high-dimensional and complex image datasets. Deep learning models, particularly Convolutional Neural Networks (CNNs), have significantly improved image classification performance by automatically learning hierarchical feature representations. Despite these advancements, classical deep learning models may still face challenges related to computational complexity and large-scale data processing.In recent years, quantum machine learning has emerged as a promising paradigm that combines principles of quantum computing with classical machine learning techniques to enhance computational efficiency and model performance. This study proposes a hybrid quantum–classical framework for image classification that integrates a deep residual network (ResNet-50) with a Quantum Support Vector Machine (QSVM). The ResNet-50 model is employed as a feature extraction mechanism to capture high-level visual representations from image data. The extracted features are then reduced in dimensionality using Principal Component Analysis (PCA) to simplify the feature space and improve computational efficiency.The reduced feature vectors are subsequently classified using a QSVM model that utilizes quantum feature maps to encode classical data into quantum states. Various quantum feature maps are explored to evaluate their impact on classification performance. Experimental results demonstrate that the hybrid quantum–classical approach achieves higher classification accuracy compared to conventional machine learning models such as Support Vector Machines and Random Forest classifiers. The proposed framework highlights the potential of combining classical deep learning architectures with quantum machine learning algorithms to address complex image classification challenges. This hybrid approach provides an efficient and scalable solution for advanced image analysis tasks and demonstrates the growing potential of quantum computing in artificial intelligence applications.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.164

 

Published by: vikaspatanker

Authors: Mr.KVV. SubbaRao, Neyigapula Jayakrishna, Meesala Venkata Sai Gnana Prakash, Pinninti Lakshmi Prasanna, Kallepalli Ramesh

Abstract: Traffic congestion has become a major challenge in urban transportation systems due to the increasing number of vehicles on roads. Conventional traffic signal systems generally operate on fixed timers, which often results in inefficient traffic management and unnecessary waiting time at intersections. To address this issue, an intelligent traffic control system based on image processing techniques is proposed. The system captures real-time traffic images using surveillance cameras and processes them to estimate vehicle density. The captured images undergo preprocessing operations such as grayscale conversion and noise reduction before applying the Canny edge detection algorithm to identify vehicle edges. The density of vehicles is determined by calculating the number of edge pixels in the processed image and comparing them with a reference image. In addition, the You Only Look Once (YOLO) object detection algorithm is used to identify emergency vehicles such as ambulances and provide them with priority signal allocation. Based on the estimated traffic density, the system dynamically adjusts traffic signal duration for each lane. The proposed approach improves traffic flow efficiency, reduces waiting time, and enhances emergency vehicle movement at intersections. This intelligent system can serve as a practical solution for modern smart city traffic management.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.163

 

Published by: vikaspatanker

Authors: Mrs.K.Sham Sri, Repuri P S S Chaitanya, Karibandi Manasa, Indraganti Sai Teja, Dulam Shiva, Kona Venkata Satya Sai Kumar

Abstract: The rapid growth of the Industrial Internet of Things (IIoT) has led to the continuous generation of large volumes of time-series data from sensors and industrial devices. These data streams are commonly stored and processed in cloud platforms to enable scalability, remote monitoring, and advanced analytics. However, storing sensitive industrial data in cloud environments introduces significant privacy and security risks, including unauthorized access and data breaches. To address these challenges, a secure data storage and sharing framework for time-series data streams in IIoT environments is proposed. The system employs a symmetric homomorphic encryption technique that enables analytics to be performed directly on encrypted data without revealing the original information. Additionally, the framework introduces fine-grained access control mechanisms that allow data owners to selectively share encrypted data streams with authorized third-party services. A verification mechanism based on message authentication ensures data integrity and authenticity during data processing and sharing. The proposed SmartCrypt-based approach enhances data confidentiality while maintaining efficient query processing and analytics capabilities. Experimental analysis demonstrates that the system improves query performance and throughput compared to existing encrypted data stream processing solutions, making it suitable for secure and scalable IIoT data management.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.162

Published by: vikaspatanker