Authors: Bhargavi Jangam, Yagnavi Rajula, Nivedhika Poloju, Aravind Kumar Kurakula

Abstract: Planning safe evacuation during disasters is extremely important, yet traditional methods are oftenrigid, expensive, and difficult to update. In this paper, we present a Digital Twin–based Disaster Evacuation Simulation System that creates a virtual version of real-world environments such as buildings. The system uses agent-based simulation implemented in Python along with real-time visualization to model how people move during emergencies like fires, floods, or earthquakes. It helps in understanding how congestion forms and how evacuation routes are used under different conditions. By testing multiple scenarios in a virtual setup, the system makes it easier to identify bottlenecks and improve evacuation strategies. Overall, this approach offers a safer and more cost-effective alternative to physical drills and supports better planning for emergency situations.

DOI: https://doi.org/10.5281/zenodo.19443813

 

Published by: vikaspatanker

Authors: Amizur Nachshoni

Abstract: This mixed-methods study examines how technology-enhanced learning (TEL) influences student motivation among 11th and 12th-grade students at Golda Meir High School in Ness Ziona, Israel. The research utilized a convergent parallel design to collect both quantitative survey data (n=43) and qualitative open-ended responses from students engaging with Classoos, Google Classroom, Kahoot, and Padlet. Quantitative results demonstrated strong positive trends, with 88.6% of students agreeing or strongly agreeing that technology increases motivation and 91.4% reporting enhanced interactivity. However, 45.7% acknowledged technology-related distractions. Thematic analysis of qualitative data revealed four primary themes: (1) Increased Engagement Through Interactivity and Choice; (2) Autonomy and Access Support Self-Directed Learning; (3) Collaboration and Social Learning Enhance Connection; and (4) Technical and Pedagogical Barriers as Demotivators. The findings suggest that a strategic blend of interactive, collaborative, and autonomy-supportive technology can significantly enhance student motivation when implemented with attention to pedagogical integration and digital distraction management. This study contributes to the understanding of TEL in Israeli secondary education and provides practical implications for educators seeking to optimize technology integration for motivational benefits.

DOI: https://doi.org/10.5281/zenodo.19706653

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Authors: O.B Ayoko

Abstract: Blockchain technology is reshaping how electricity can be produced, traded, and governed, offering new possibilities for countries grappling with unreliable grids and persistent supply gaps. This paper investigates the emergence of blockchain‑enabled peer‑to‑peer (P2P) energy trading, using Nigeria as a lens to explore how decentralized digital infrastructure could redefine participation in electricity markets. Drawing on parallels with the rapid digitalization of financial services, the study examines how distributed ledger systems can support direct energy exchange between prosumers, shift utilities toward roles as market custodians, and improve system trust through transparent, tamper‑proof transaction records. The analysis evaluates regulatory readiness, technical prerequisites, and socioeconomic impacts within Nigeria’s evolving energy ecosystem, where chronic shortages and grid instability create both urgency and opportunity for alternative market models. The findings highlight the potential for P2P trading to accelerate energy access, stimulate local investment, and catalyse a more resilient, consumer‑centric electricity sector.

DOI:

 

 

Published by: vikaspatanker

Authors: Mrs.Ch.Sowjanya, Kadari Jagadeeswara Veerraju, Yerra Pallavi Rani, Ganni Sameera, Bobbili Lakshmi, Thumu Jayanth

Abstract: Cloud computing has transformed the way organizations store data, deploy applications, and manage digital infrastructure. Its scalability, flexibility, and cost efficiency have made it an essential technology for modern businesses. However, as cloud environments grow in size and complexity, they also become more vulnerable to various cybersecurity threats. Issues such as misconfigurations, insecure APIs, weak authentication mechanisms, and unauthorized access can expose cloud systems to serious security risks. Traditional security mechanisms such as firewalls and rule-based intrusion detection systems often struggle to detect new or evolving threats in dynamic cloud environments.To address these challenges, this work explores the use of machine learning techniques to improve cloud security by predicting and detecting vulnerabilities in distributed systems. The proposed approach analyses security-related data such as system logs, network traffic patterns, and vulnerability reports to identify abnormal behaviour and potential threats. Multiple machine learning algorithms, including Decision Tree, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and Isolation Forest, are evaluated to determine their effectiveness in detecting security vulnerabilities.The experimental results indicate that ensemble models, particularly Random Forest, provide higher accuracy and better detection capability compared to other algorithms. Machine learning-based security systems can analyse large volumes of data in real time, identify suspicious patterns, and respond to potential threats more quickly than traditional security approaches.By integrating machine learning into cloud security frameworks, organizations can build more proactive and intelligent defence systems capable of adapting to evolving cyber threats. The proposed approach enhances vulnerability detection, reduces response time to security incidents, and supports the development of more resilient and secure cloud infrastructures.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.174

 

Published by: vikaspatanker

Authors: Dr.M.Radhika Mani, P Srinivasa Rama Harshitha, Vangala Vasudev, Sri Sai Vinay Vanaparthi, Gelam Jaya Shankar Krishna Mohan, Angadi Haribabu

Abstract: Agriculture plays a crucial role in ensuring food security and supporting the global economy. However, selecting the most suitable crop for a particular region remains a major challenge for many farmers due to variations in soil nutrients, climate conditions, and environmental factors. Incorrect crop selection can lead to reduced productivity, inefficient use of resources, and financial losses. With the increasing availability of agricultural data and advances in artificial intelligence, machine learning techniques have emerged as powerful tools for improving agricultural decision-making.This study presents an intelligent crop recommendation system that integrates machine learning and deep learning models to assist farmers in selecting the most suitable crop based on soil and environmental conditions. The proposed system analyses important agricultural parameters such as nitrogen (N), phosphorus (P), potassium (K), rainfall, soil pH, temperature, and humidity. These features are used to train predictive models that can recommend the optimal crop for cultivation.Several machine learning and deep learning algorithms, including Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Temporal Convolutional Networks (TCN), are implemented and evaluated. The models are trained using a publicly available agricultural dataset containing multiple crop types and environmental attributes. Performance evaluation is conducted using metrics such as accuracy, precision, recall, and F1-score to determine the most effective model.Experimental results demonstrate that ensemble and deep learning models achieve high prediction accuracy in recommending suitable crops. The system also includes a user-friendly interface that allows farmers to input soil and environmental parameters and receive crop recommendations in real time.The proposed approach contributes to the development of precision agriculture systems by supporting data-driven farming practices, improving crop productivity, and helping farmers make more informed agricultural decisions.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.173

 

Published by: vikaspatanker

Authors: Dr.K.ChandraSekhar, Villa Tejaswi, Vadakattu Lahari Malleswari, Chittavadagi Cristin Pratheek, Mandanakka Surya

Abstract: Brain tumours are one of the most serious neurological disorders that can significantly affect human health and quality of life. Early and accurate detection of brain tumours is essential for effective treatment and improved patient survival rates. Magnetic Resonance Imaging (MRI) is widely used by medical professionals to analyse brain structures and detect abnormalities. However, manual examination of MRI scans can be time-consuming and may lead to inconsistent results due to human interpretation. With recent advancements in artificial intelligence, deep learning techniques have shown great potential in assisting medical experts by automatically analysing medical images.This study presents an intelligent brain tumour detection and classification framework based on deep learning and transfer learning techniques. The proposed system utilizes pre-trained convolutional neural network models to extract meaningful features from MRI images and classify them into multiple tumour categories. Several deep learning architectures, including VGG16, InceptionV3, ResNet50, VGG19, InceptionResNetV2, and Xception, are implemented and evaluated for performance comparison. To improve classification accuracy, an ensemble learning approach is also explored by combining the predictions of the best-performing models.In addition to improving prediction accuracy, the system integrates Explainable Artificial Intelligence (XAI) techniques to provide visual explanations of the regions in MRI images that contribute to the model's predictions. This helps increase transparency and reliability, which are important for medical applications.Experimental results demonstrate that the ensemble-based deep learning model achieves higher accuracy compared to individual models while providing reliable tumour classification results. The proposed framework can assist healthcare professionals in detecting brain tumours more efficiently and may contribute to faster diagnosis and better treatment planning in clinical environments.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.172

 

Published by: vikaspatanker

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