An Algorithm for Cybersecurity Threats Detection in the Internet of Things using Deep Learning Approach
Bidirectional Recurrent Neural Networks; CNN; Cyber Security; Internet of Things; Long Short-Term Memory
We perform research to develop a combined deep learning algorithm that enhances security threat detection within the Internet of Things networks. The resource variations across IoT devices create obstacles for Traditional Intrusion Detection Systems (IDSs) regarding their scalability and adaptability elements. This study explores the application of Bidirectional Recurrent Neural Networks and Long Short-Term Memory networks, which are trained on Traffic data records from NSL-KDD, a widely recognized benchmark dataset. It's a secondary dataset. This dataset is preprocessed and features are engineered to be optimized for sequential pattern recognition and handling of long-term dependency. Experimental results validate the achievement of a cross-validation accuracy of 93.40%, F1 is 91.62% and precision is 90.42%, which is greater than the individual models, such as CNN, BiRNN, or LSTM. The stacking Models Bi-RNN sequential learning and LSTM dependency retention makes the system perform better at threat classification along with elevated detection accuracy for IoT-related security issues like DoS, Probe, R2L, and U2R. The consistent performance of the model through this validation split provides evidence that the system can effectively handle IoT cybersecurity threats. 2025 IEEE.
Mahnot, Saksham; George, Jossy P; Paul Alapatt, Bosco
Proceedings of 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2025;pp.1464-1471
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICICV64824.2025.11085461 https://www.scopus.com/pages/publications/105013074321?origin=resultslist
Mahnot S., CHRIST University, India; George J.P., CHRIST University, India; Paul Alapatt B., CHRIST University, India
Building Smarter Systems with Advanced Computational Techniques
Biological data analysis; Diabetes; Firefly Pearson's Correlation Coefficient; Ladybug Beetle Optimization; Leukemia; Modified Z-Score; Twin Boosted Adaptive Support Vector Machine; Universum Learning
The biological data analysis is a key approach that uses the genetic, transcriptomics, proteomics, metabolomics, or clinical data to discover diseases. Diabetes and leukemia are two independent medical disorders, but research has found that people with type 2 diabetes have a 20% higher chance of developing blood malignancies such as acute leukemia, showing a link between the two. Early identification of these disorders by studying biological datasets is critical for providing prognostic information. However, the class imbalance and high dimensionality problems in Machine Learning (ML)based techniques have often degraded effective analysis of clinical and genomic datasets for disease detection. This paper focuses on developing an efficient clinical decision support system using advanced metaheuristic and ML algorithms to solve class imbalance and high dimensionality problems. The first stage of the proposed approach utilizes an optional data augmentation and another pre-processing method for outlier detection and removal using Modified Z-Score (MZS) based on the Median Absolute Deviation (MAD) metric. Then, the optimal features/genes are selected using a hybrid Firefly Pearson's Correlation Coefficient (FPCC)-based Feature/Gene Selection method to reduce the higher feature dimensionality problem. Once the features/genes are selected, the proposed Ladybug Beetle Optimized Universum Learning-based Twin Boosted Adaptive Support Vector Machine (LBO-ULTBASVM) classifier detects the disease with reduced model complexity and error rates. LBO-ULTBASVM is developed by improving the Twin Support Vector Machine (TSVM) classifier by integrating the Universum Learning, Ladybug Beetle Optimization (LBO), and XGBoost for solving the class imbalance problem, reducing training time and improving disease accuracy. Experiments are conducted using PIMA Indians Diabetes and GSE9476 Leukemia datasets and the outcomes indicated that the LBO-ULTBASVM-based model increases the diabetes and leukemia detection accuracy with reduced model complexity and processing time. 2025 IEEE.
Sivakumar, R.; Kirubanand, V.B.; Ganesan, V.; Sivaraman, M.; Kumarachelvan, A.; Ulaganathan, G.
Proceedings of 8th International Conference on Inventive Computation Technologies, ICICT 2025;pp.772-777
https://doi.org/10.1109/ICICT64420.2025.11004665 https://www.scopus.com/pages/publications/105007426760?origin=resultslist
Sivakumar R., CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Kirubanand V.B., CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Ganesan V., Department of Computer Science, Dr.G.R.Damodaran College of Science, Tamilnadu, Coimbatore, India; Sivaraman M., Department of Computer Science, Karpagam Academy of Higher Education, Tamilnadu, Coimbatore, India; Kumarachelvan A., Department of Library and Information Science, Dr. SNS Rajalakshmi College of Arts and Science (Autonomous), Tamilnadu, Coimbatore, India; Ulaganathan G., Department of Library and Information Science, Dr. SNS Rajalakshmi College of Arts and Science (Autonomous), Tamilnadu, Coimbatore, India
Blockchain Technology for Mitigating Copyright Infringement on OTT Platforms through the PRISMA Model
Blockchain technology; copyright infringement; OTT Platforms; PRISMA model; techno-legal solutions
Copyright infringement has been a rampant issue on OTT (Over-the-Top) platforms and post-COVID-19 pandemic. Continuous infringement, illegal streaming, and piracy have contributed to 87 billion INR (Indian rupees) in the piracy economy through OTT platforms. Copyright and its protection regime need upscaling through Blockchain technology as a highly monetized and important asset. This paper identifies key gaps in the ongoing research on applying Blockchain technology to mitigate copyright infringement on OTT platforms through the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) model. It creates a techno-legal framework to enhance this application and make it more compliant with the existing copyright laws. The study further showcases the absence of legal implications in the existing studies surrounding Blockchain technology, making them a base for the proposed framework. The proposed framework can be further used to create a robust copyright portfolio for OTT platforms while testing their relevant advancements in Blockchain technology in line with legal and commercial necessities. 2025 IEEE.
Chhangani, Deepika; Chakraborty, Avishek
Proceedings - 2025 2nd International Conference on Intelligent Computing and Robotics, ICICR 2025;pp.1200-1207
https://doi.org/10.1109/ICICR65456.2025.00213 https://www.scopus.com/pages/publications/105018737362?origin=resultslist
Chhangani D., Christ University, School of Law, Bangalore, India; Chakraborty A., Christ University, School of Law, Bangalore, India
Artificial Intelligence in Detecting and Mitigating Online Child Sexual Abuse: Approaches and Solutions
Artificial Intelligence; Data Privacy; Online Child Sexual Abuse; Security and PRISMA
Previous research papers have discussed whether Artificial Intelligence (AI) -based tools like Chat Bots, Law-U Model, and Sweetie. 20 have the potential to mitigate online child sexual abuse. The literature review indicates that AI tools promise good intervention and prevention strategies for several tech-based companies like Google and Microsoft. However, there is a lack of systematic study on AI tools' potential uses, limitations, and legal risks. This paper conducts a systematic literature review to explore the uses and limitations of AI-based interventions in combating online child sexual abuse. It explores the legal and ethical risks of deploying such technological innovations from the viewpoint of data protection, privacy, and security. The authors use the PRISMA technique and thematically answer the research questions. Data are collected from reliable sources such as Statista and the World Health Organisation. The findings of this paper highlight the potential uses of AI for law agencies, forensic experts, victims, and technology companies. The research reports the absence of a sufficient legal framework for the governance and accountability of AI tools. The findings further indicate the need for clarification in the law regarding the legal status of AI tools like Sweetie 2.0. Lastly, this paper offers a framework for harmonizing AI usage with human rights standards. 2025 IEEE.
Proceedings - 2025 2nd International Conference on Intelligent Computing and Robotics, ICICR 2025;pp.464-471
https://doi.org/10.1109/ICICR65456.2025.00087 https://www.scopus.com/pages/publications/105018744795?origin=resultslist
Ali K.A.R., Christ University, Bengaluru, India; Somu C.S., Christ University, Bengaluru, India
Cooperative Social Entrepreneurship Among Rural Artisans: A YouTube-Based NLP Analysis of Environmental Practices and Livelihood Outcome
Cooperative social entrepreneurship; HandiCrafts; Karnataka; Rural Artisans
This paper presents a novel data-driven study of rural artisans in Karnataka by leveraging YouTube video transcripts and natural language processing (NLP) to examine how cooperative social entrepreneurship (CSE) relates to environmental practices and livelihood outcomes. CSE initiatives in India typically rely on primary surveys to understand how artisanal groups adopt eco-friendly practices and how this affects their livelihoods, but such data are costly to collect and difficult to scale. We investigate whether publicly available video narratives can serve as a scalable secondary data source for studying CSE among rural artisans. We compile a corpus of YouTube videos on banana-fibre craft, Anegundi/Hampi artisan collectives, and Karnataka handicrafts. Audio is transcribed using an automatic speech recognition pipeline, and the resulting bilingual/multilingual text (English-Hindi-Kannada) is processed with a rule-based NLP tagger to identify three constructs central to our CSE perspective: (i) artisan and community references (CSE signals), (ii) environmental practices (e.g., "banana waste to fibre,""eco-friendly,""sustainable"), and (iii) livelihood/product mentions (e.g., baskets, mats, runners) as observable proxies for livelihood outcomes. On top of this, we apply text mining techniques topic modeling, sentiment analysis, and supervised classification with fine-tuned transformer models (BERT) to classify transcript segments (e.g., environmental focus vs livelihood focus) and extract key thematic topics. Experimental results show that our BERT-based classifier achieves over 90% accuracy, substantially outperforming traditional baselines such as TF-IDF+SVM and LSTM. The videos frequently encode both CSE signals and explicit environmental practices, and a non-trivial subset articulates marketable products, suggesting that platform narratives can partially capture the CSE-environment-outcome chain without questionnaires. However, explicit statements about market, seasonality, or constraint variables remain sparse, revealing limitations of video-based secondary data. The study contributes methodologically by integrating digital media analytics into rural development research, offers complexity and performance analysis of the employed algorithms, and stresses reproducibility through transparent documentation of data sources, model architectures, training configurations, and evaluation metrics. 2025 IEEE.
Davis, Antony; Lakshmypriya, K.; Basha, Md Shaik Amzad; Daphne, Simita
Proceedings of 6th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2025;pp.1766-1771
https://doi.org/10.1109/ICICNIS66685.2025.11315777 https://www.scopus.com/pages/publications/105032740157?origin=resultslist
Davis A., CHRIST (Deemed to be University), Christ Academy Institute for Advanced Studies, School of Business & Management, Bangalore, India; Lakshmypriya K., CHRIST (Deemed to be University), School of Business & Management, Bangalore, India; Basha M.S.A., Gandhi Institute of Technology and Management ((Deemed to be University), Christ Academy Institute for Advanced Studies, GITAM School of Business, Bangalore, India; Daphne S., Christ Academy Institute for Advanced Studies, Department of Management Studies, Bangalore, India
Beyond Transcripts: A Learner-Centred Review for Closing the Graduate Skills Gap
Authentic assessment; Digital skills; Employability; Experiential learning; Graduate skills gap; Higher education reform; Learner-centred pedagogy
The majority of the university graduates leave their courses with high grades, but they usually do not have the necessary skills needed in the working environments including teamwork, problem-solving and digital skills. This disconnect between higher education training and the needs of the industry is what is referred to as the graduate skills gap. The article consists of a literature review from 2020-2025 to explore how learner-centred pedagogy can be used to reduce this gap. The results have shown that project-based learning, real-life assessment, internship and micro-credential equip students better than conventional exams. Employers prefer technical and soft skills to academic performance, but most universities are facing problems with stiff curricula and lack of faculty training. This review proposes the incorporation of practice projects, industry partnership, and online skill records to fill the gap. These are some of the strategies that can be used to equip the students with the competencies needed in the current dynamically changing labour market. 2025 IEEE.
Khan, Hasib Mohammad; Singh, Monisha; Lakshmi, R.
Proceedings of 6th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2025;pp.1509-1515
https://doi.org/10.1109/ICICNIS66685.2025.11315761 https://www.scopus.com/pages/publications/105032736729?origin=resultslist
Khan H.M., Christ University, Dept. of Statistics and Data Science, Bangalore, India; Singh M., Christ University, Dept. of Statistics and Data Science, Bangalore, India; Lakshmi R., Christ University, Dept. of Statistics and Data Science, Bangalore, India
Deep Learning for Mental Health: Attention-Driven Multilayer CNN for Audio Depression Detection
attention mechanism; audio depression detection; deep learning; mental health screening; multi-layer convolutional neural networks (CNN)
Depressive Disorder is a common mental health problem that affects millions of people around the world. This study proposes a Self-attention based Multi-layer Convolutional Neural Network (CNN) model to perform enhanced depression detection from audio modality. The model employs a diverse array of filters, kernel sizes, and pooling strategies across multiple CNN layers to capture local features, while the attention mechanism prioritizes emotionally salient parts of the speech signal, such as regions of low energy and lengthened pauses by assigning higher weights. Measured against the RAVDESS and TESS emotional speech datasets, the method attains an F1 score of 0.81, an accuracy of 83% and ROC-AUC of 0.96 when using attention, beating the baseline CNN model, F1 score of 0.77 and 83% accuracy without attention. The results demonstrate the effectiveness of attention-enhanced architectures in detecting depressive cues from speech and support the feasibility of developing real-world, speech-based mental health screening tools. 2025 IEEE.
Joy, Salna; Krishna, B.V. Santhosh; Rajesh, Siddharth; Neethu, P.S.
International Conference on Intelligent Communication Networks and Computational Techniques, ICICNCT 2025;
https://doi.org/10.1109/ICICNCT66124.2025.11233143 https://www.scopus.com/pages/publications/105030492475?origin=resultslist
Joy S., New Horizon College of Engineering, Department of Electronics and Communication Engineering, Bengaluru, India; Krishna B.V.S., New Horizon College of Engineering, Department of Electronics and Communication Engineering, Bengaluru, India; Rajesh S., New Horizon College of Engineering, Department of Electronics and Communication Engineering, Bengaluru, India; Neethu P.S., School of Engineering and Technology, Christ University, Department of Aiml and Data Science, Bengaluru, India
Hybrid Semantic Evaluation of Student Answers Using Rule Matching and BERT Embeddings
AI-based Assessment; BERT; Hybrid model; Natural Language Processing; Text similarity
Accurate evaluation of student answers in online and traditional assessments is critical in education. In recent years, various text similarity-based methods have been proposed. However, there are certain challenges, such as the semantic and structural understanding of text. Thus, this paper uses the BERT model to present a hybrid evaluation framework that combines rule-based similarity techniques with deep semantic knowledge. The rule-based component utilizes predefined linguistic and domain-specific rules to ensure interpretability. At the same time, BERT-based similarity captures the semantic similarity and the paraphrased answers. Experimentation has been carried out on benchmark datasets, with the proposed hybrid model and human experts on manual evaluation. The performance comparison demonstrates that the hybrid model has performed significantly better than the traditional machine learning approaches in terms of accuracy and fairness of scoring. The proposed hybrid model is also compatible with deployment in educational platforms as it provides suitable feedback to learners. 2025 IEEE.
Beri, Nimisha; Basha, J. Jeelan; Thilagam, M.; Suganthi, J.; Santhiya, C.; Hemaswathi, S.
https://doi.org/10.1109/ICICNCT66124.2025.11232975 https://www.scopus.com/pages/publications/105030446877?origin=resultslist
Beri N., School of Education, Lovely Professional University, Phagwara, India; Basha J.J., School of Management Studies, Reva University, Bengaluru, India; Thilagam M., Sri S.Ramasamy Naidu Memorial College, Department of Computer Science, Sattur, India; Suganthi J., Christ Deemed to be university, Department of Computer Science, Bengaluru, India; Santhiya C., Thiagarajar College of Engg., Department of Computer Sci. and Engg, Madurai, India; Hemaswathi S., Kalasalingam Academy of Research and Educ., Department of Information Technology, Tamil Nadu, India
Measuring Critical Thinking Skills with the R-BiLSTM-C Model using a Logical Approach
bidirectional long short-term memory (BiLSTM); critical thinking; enhanced thinking skills
Critical thinking is essential for making informed judgements; it necessitates careful evaluation of pertinent evidence and the application of reasoning. While some individuals possess a more constrained perspective on critical thinking, this elucidation encompasses the predominant views held by the majority. Note-taking, formulating enquiries, and designing experiments exemplify practical actions that may be applied to other creative pursuits, rendering it a valuable skill across diverse domains. This work employed a systematic approach for data preparation, model training, and feature extraction. The primary phase in training unified R-BiLSTM-C models was identified as feature extraction. Standardisation and normalisation, two crucial preprocessing techniques, were employed to ensure uniform and dependable data handling. Furthermore, as the quantity of dependent observations escalated, the study evaluated the efficacy of Reduced Kernel PCA. The proposed solution achieved a 96.82% accuracy rate, surpassing advanced techniques such as CNN and BiLSTM. The findings indicate that the systematic approach enhances the model's performance. A systematic approach is essential for enhancing precision, and analytical reasoning skills are crucial for developing effective machine learning models. The study reinforces the significance of critical thinking in effective decision-making and problem resolution. 2025 IEEE.
Jadhav, Shital; Kumar, M. Dhiliphan; Senthil Kumar, M.K.; Mohan, S.; Basha, H. Anwer; Banerjee, Devleena
2025 International Conference on Intelligent Computing and Knowledge Extraction, ICICKE 2025;
https://doi.org/10.1109/ICICKE65317.2025.11136721 https://www.scopus.com/pages/publications/105022246409?origin=resultslist
Jadhav S., G H Raisoni Institute of Engineering and Business Management, Department of Computer Science and Engineering, Jalgaon, India; Kumar M.D., Stella Mary's College of Engineering, Department of Management Studies, Kanniyakumari, India; Senthil Kumar M.K., Sri Ramakrishna College of Arts & Science, Department of Commerce, Coimbatore, India; Mohan S., Kalasalingam Academy of Research and Education, Department of English, Virudhunagar, India; Basha H.A., Raak Arts and Science College, Perambai, India; Banerjee D., Christ University, Bangalore, India
An Intelligent Method for Fraud Detection in Digital Payments based on SVR with GC-RF Approach
digital transformation; fraud detection (FD); grey catastrophe point (GC); support vector regression
The use of automated algorithms to detect fraud on electronic payment networks is challenging. Digital payment systems and their users are vulnerable to cybercriminals who take advantage of security holes or users' negligence to steal passwords, perpetrate fraud, launder money, and carry out other malicious acts. Conventional methods of fraud detection are challenging to execute because of the difficulties of acquiring massive volumes of manually annotated data. It is tough to notice new trends because fraudsters are often changing their techniques. Feature extraction, model training, and data preprocessing were the main areas of emphasis in this systematic research. Data pretreatment encompassed tasks such as acquiring training sample data, cleaning, converting, integrating, and altering the data. Feature extraction is the backbone of SVR-GC-RF model training; it takes all the data in a dataset and turns it into features. The suggested method outperformed SVR and RF in terms of accuracy by 95.23 percent. The importance of hierarchical fraud detection in online payment systems is highlighted in this paper. Through the use of effective feature extraction and model training, the study enhances fraud detection. Methods for detecting fraud need to change if they are to keep up with the criminals. 2025 IEEE.
Adhisekar, Kamalakkannan; Mulla, Gulnar Sadat; Thirumalaikumari, T.; Vanitha, P.; Kamepalli, Sujatha; Acharjee, Purnendu Bikash
https://doi.org/10.1109/ICICKE65317.2025.11136358 https://www.scopus.com/pages/publications/105022297396?origin=resultslist
Adhisekar K., Department of Corporate Secretaryship & Accounting and Finance, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, India; Mulla G.S., University of Technology Bahrain Salmabad, Bahrain; Thirumalaikumari T., Department of Computer Science, Saveetha College of Liberal Arts and Sciences, Saveetha Institute of Medical and Technical Science, Chennai, India; Vanitha P., Department of Management Studies, M. Kumarasamy College of Engineering, Karur, India; Kamepalli S., Department of Information Technology, Vignan's Foundation for Science Technology and Research, Guntur, India; Acharjee P.B., Department of Computer Science, CHRIST University, Bangalore, India
Factors Affecting Predicting Teacher Evaluation in Higher Educational Institutions Among Faculty Members based on SA-BiLSTM
educational data mining (EDM); higher educational institutions (HEI); mutual information (MI); self-cure network (SCN)
To keep up with good teaching standards and pedagogical improvements, it is vital to predict assessment of higher education teachers. Based on principles of pragmatism, psychology, and pedagogy, this research should contribute to the development of diagnostic procedures and training standards that may be used in many educational settings. Because of the three-tiered speciality training architecture used to prepare teachers in master's degree programs in education in Ukraine, institutions there require a differentiated approach. Model training, feature extraction, and preprocessing are all parts of the proposed methodology. While translating, cleaning, reducing, and normalising data is part of data preparation, feature selection uses correlation and mutual information criteria to establish the significance of variables. Following feature selection using information gain, the SA-BiLSTM model begins training. Different institutional features were associated with unique approaches to teacher preparation, according to a comparison of active empathy and the growth of pedagogical reflection. The SA-BiLSTM model outperforms SA and BiLSTM when it comes to predicting teacher ratings. The results show how important it is to have different levels of individualised strategies for preparing teachers. Institutions can improve training and instruction with the support of the predictive approach, which better evaluates teachers. 2025 IEEE.
Ravi Kumar, P.; Acharjee, Purnendu Bikash; Kar, Rohit; Alam, Mohammad Shabbir; Santhoshkumar, M.P.; Sethumadhavan, R.
https://doi.org/10.1109/ICICKE65317.2025.11136199 https://www.scopus.com/pages/publications/105022260221?origin=resultslist
Ravi Kumar P., KPR Institute of Engineering and Technology, Department of Electrical and Electronics Engineering, Coimbatore, India; Acharjee P.B., CHRIST University, Department of Computer Science, Bangalore, India; Kar R., St. Claret College, Department of Management, Bangalore, India; Alam M.S., Jazan University, College of Computer Science and Information Technology, Department of Computer Science, Jizan, Saudi Arabia; Santhoshkumar M.P., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Computer Science, Chennai, India; Sethumadhavan R., Dayananda Sagar College of Engineering, Department of Master of Business Administration, Bangalore, India
Quantum-Inspired Genetic Algorithms for Secure and Scalable Cloud-Based Decision Support Systems
Anomaly Detection; Cloud Security; Cloud-Based Decision Support Systems; Quantum-Inspired Genetic Algorithm; Resource Optimization
Cloud-based DSS are critical for data-intensive decision-making but tremendously challenged by issues of scalability, security, and the optimization of resources. In general, optimization approaches such as GA, PSO, and ACO treat the problems of allocation and security enhancement of cloud resources very inadequately. Hence, the present work addresses developing a QIGA-based optimization framework for the performance optimization of cloud DSS. At its core, it utilizes quantum-like principles, such as superposition and probabilistic search, for resource optimization with respect to stability, security, and rapid convergence. Therefore, this underpinning framework comprises resource optimization, anomalous detection, and quantum-independent encryption through QIGA, which enhances the data security along with computational efficiency. The experimental results depict the performance efficiency of QIGA since its execution time, CPU, memory utilization requirements, and energy consumption are less while the task completion rate is higher and the security vulnerabilities are reduced in comparison to traditional optimization techniques. QIGA-based anomaly detection improves accuracy at the expense of response time, while its quantum-inspired encryption provides the best cryptographic security. Therefore, these results verify that QIGA is an efficient secured methodology for scalable cloud-controlled DSS, hence being a potential candidate for decision-making optimization in highly dynamic cloud environments. 2025 IEEE.
Dsouza, Mithun; Pradhan, Rahul; Kaur, Chamandeep; Christal Mary, S.Suma; Preshiya, Jyothi; Ponni Valavan, M.
2025 2nd International Conference on Integration of Computational Intelligent System, ICICIS 2025;
https://doi.org/10.1109/ICICIS65613.2025.11371081 https://www.scopus.com/pages/publications/105034708047?origin=resultslist
Dsouza M., CHRIST (Deemed to Be University), Department of Computer Science, Karnataka, Bangalore, India; Pradhan R., GLA University, Department of Computer Engineering and Applications, Mathura, India; Kaur C., Jazan University, Computer Science & Information Technology Department, Jizan, Saudi Arabia; Christal Mary S.S., Chennai Institute of Technology, Department of AI & DS, Chennai, India; Preshiya J., Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Department of ECE, Saveetha University, Tamil Nadu, Chennai, India; Ponni Valavan M., K.Ramakrishnan College of Engineering, Department of AI & DS, Tamil Nadu, Trichy, India
Crop Disease and Pest Management in Agriculture via UAV Remote Sensing and Advanced Machine Learning Models
crop diseases and pests; proximal policy optimization (PPO); remote sensing (RS)
Pests and diseases greatly reduce crop quality and yield; therefore, IA relies on effective pest and disease control. UAVs have become a crucial remote sensing (RS) tool for agricultural process monitoring and management. This study will examine major advances in this field using bibliometric methodologies including author co-occurrence and keyword co-contribution studies. The suggested technique involves preprocessing, feature extraction, and model training. Data quality improves with preprocessing. UAV images are used for feature extraction, focusing on canopy structure and height. PPO is trained the prediction model. Compared to ultramodern GANs and LSTM networks, the recommended model wins. The model consistently outperforms competitors with 91.17 percent accuracy. The study suggests employing UAVs in smart farming to reduce pests and diseases. The suggested model's accuracy and reliability improve crop quality and production by solving agricultural monitoring and management problems. 2025 IEEE.
Punitha, A.; Jayamangala, S.; Joel Josephson, P.; Bharathi, K.; Sindhu, V.; Singh, Kamlesh
3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025;
https://doi.org/10.1109/ICICACS65178.2025.10968470 https://www.scopus.com/pages/publications/105004741365?origin=resultslist
Punitha A., Chettinad College of Engineering and Technology, Department of Computer Science and Engineering, Karur, India; Jayamangala S., Santhiram Engineering College Nandyal, Department of Electronics and Communication Engineering, Nandyal, India; Joel Josephson P., Malla Reddy Engineering College, Department of Electronics and Communication Engineering, Dhulapally, Medchal, India; Bharathi K., Erode Sengunthar Engineering College, Department of Agricultural Engineering, Erode, India; Sindhu V., Christ University, Department of Computer Science, Bangalore, India; Singh K., School of Management Sciences, Department of Mechanical Engineering, Uttar Pradesh, India
Enhancing Human Resource Management Practices in Marketing Companies Using Dual Graph Attention Networks
human resource management (HRM); marketing strategy; multi-granularity gated graph attention network (MGGAN)
Marketing organization features and strategy implementation have been studied for over 30 years. These include organizational structure, culture, leadership, and processes. HR regulations can motivate marketing professionals to support group and individual goals when correctly implemented, but this part of HR has gotten little attention. Model preparation, feature extraction, and training comprise the suggestive technique. It reviewed data quality, evaluated dataset structure, and described data types during pre-processing. Principal component analysis (PCA) ranked and evaluated decision-making units to reduce dimension. Model training used MGGAN. In comparison to GAN and CNN, the proposed model performed well. With an average accuracy rate of 94.36%, it surpassed earlier approaches and captured all dataset peculiarities. MGGAN modeling can increase predictive performance, and marketing organizations should integrate HR regulations, according to this study. This study opens up new organizational analysis and strategy execution methods. 2025 IEEE.
Roopalatha, N.; Dhanalakshmi, K.; Babu, G.N.P.V.; Krishna, P. Vamsi; Perada, Anvesh; Mulagapati, Harun Kumar
https://doi.org/10.1109/ICICACS65178.2025.10968426 https://www.scopus.com/pages/publications/105004739804?origin=resultslist
Roopalatha N., School of Business, Gitam University, Hyderabad, India; Dhanalakshmi K., School of Business and Management, Christ University, Bangalore, India; Babu G.N.P.V., School of Business and Management, Christ University, Bangalore, India; Krishna P.V., School of Management and Commerce, Malla Reddy University, Hyderabad, India; Perada A., Drexel University, Department of Electrical and Computer Engineering, Philadelphia, United States; Mulagapati H.K., Srkr Engineering College, Department of Engineering Mathematics and Humanities, Chinna Amiram, India
User Behavior Prediction with Deep Learning: An Evaluation of CNN, LSTM, RNN, and Hybrid Models
convolutional neural network (CNN); deep learning; hybrid CNN-LSTM model; long short-term memory (LSTM); recurrent neural network (RNN); user behavior prediction
In this work, with a novel Hybrid CNN LSTM model, we present an in depth comparison and evaluation of various deep learning models (CNN, LSTM, RNN) on predicting user behaviour. To compare our proposed models we conducted detailed experimentation and rigorous performance comparisons using aforementioned metrics: accuracy, precision, recall, F1-score. The findings show that our RNN model performed incredibly well, reporting an accuracy of 99.86%. These results illustrate the power of the model in generalizing, and in capturing sequential dependencies in the user behaviour. On the other hand, the CNN model also performed very robustly, achieving 97.86% accuracy showing that it has powerful ability to extract spatial features. However, the LSTM model got decent results, with an accuracy of 87.14%. However, the Hybrid CNN-LSTM model's accuracy was for 80.29% which was up to the Hybrid CNN as mentioned above, and hence had lagged behind and has scope for improvement. Our proposed approach has provable advantages over existing methods, most notably, by using the RNN model outperforming the existing method. The RNN is able to detect sequential patterns more effectively than traditional techniques and show its ability to learn deeply to better predict user behaviour. Finally, we also hope that this investigation highlights two important directions for future RNN research: first, the promise of RNNs, and second, promising avenues for future research, such as refining hybrid architectures to yield improved performance. 2025 IEEE.
Sreedevi, S.; Rajashekar; Ayesha, Samreen; Basha, Md Shaik Amzad; Prathibha, L.
https://doi.org/10.1109/ICICACS65178.2025.10967848 https://www.scopus.com/pages/publications/105004738556?origin=resultslist
Sreedevi S., Sri Venkateswara College of Engineering, Department of Management Studies, Tirupati, India; Rajashekar, Rajeev Gandhi Memorial College of Engineering and Technology, Department of Management studies, Nandyal, India; Ayesha S., Christ University, Department of Professional Studies, Bangalore, India; Basha M.S.A., Gitam School of Business, Gitam (Deemed to be University), Hyderabad, India; Prathibha L., Ashoka Women Engineering College (Autonomous), Kurnool, India
A Computing Assisted Test Method in Healthcare Industry Using Artificial Intelligence
Algorithms; Analysis; Breast Cancer; Data; Machine Learning
New and improved methods of diagnosis are needed because breast cancer is still the leading cancer-related killer worldwide. Updates to the methods used to categorize breast cancer have emerged because of recent advances in DL and ML. This research goal in conducting this research is to bring together existing breast cancer diagnostic and classification methods that make use of deep learning and machine learning techniques. Early detection and accurate prediction of breast cancer are crucial for improving outcomes and reducing the impact of this disease. The creation of prediction models and tools to assess risk has become an important field of research as it can assist healthcare workers in identifying individuals who are more likely to acquire breast cancer and adapting screening and preventative programs accordingly. This introduction provides an overview of breast cancer prediction, highlighting the importance of the topic and the motivation behind predictive models. It sets the stage for a more in-depth exploration of the subject and the various technologies, methods and factors involved in breast cancer prediction. 2025 IEEE.
Karthick, K.K.; Ganesan, V.; Sivakumar, R.; Sivaraman, M.; Natarajan, Arul Kumar; Shahina, A. Srimathi
Proceedings - 1st International Conference on Frontier Technologies and Solutions, ICFTS 2025;
https://doi.org/10.1109/ICFTS62006.2025.11031857 https://www.scopus.com/pages/publications/105010828041?origin=resultslist
Karthick K.K., Saveetha Engineering College, Tamilnadu, Chennai, India; Ganesan V., Dr.G.R.Damodaran College of Science, Tamilnadu, Coimbatore, India; Sivakumar R., Christ (Deemed to be University, Karnataka, Bengaluru, India; Sivaraman M., Dr. Sns Rajalakshmi College of Arts and Science, Tamilnadu, Coimbatore, India; Natarajan A.K., University of Technology and Applied Sciences (UTAS), Oman; Shahina A.S., Dr.G.R.Damodaran College of Science, Tamilnadu, Coimbatore, India
Scalable Quantum Computing Approaches for Next-Generation Edge and IoT Devices
Edge computing; Internet of Things (Io'T); Post-quantum security; Quantum computing; Scalable architectures
The fast development of Internet of Things (IoT) and edge devices requires secure energy efficient and scalable solutions to computing in highly resource-constrained environments. In the post-quantum future, classical cryptographic and computational algorithms will be restricted in the ability to fulfil stringent performance and security requirements, such as high latency, bandwidth, and security. Such issues might be solved by the framework proposed in this paper which is built upon the ideas of lightweight quantum algorithms, quantum-inspired optimization, and post-quantum security primitives in the form of a scalable quantum-assisted hybrid architecture. the architecture is heterogeneous, supporting a variety of deployment types in the Internet of Things, with modular quantum co-processors and cloud-edge interconnection making them deployable. Experimental measurements are showing 32 per cent less encryption decryption latency than RSA/ECC and PQC baselines, as well as enhanced fault tolerance in noisy settings and scalability in the thousands of devices. These outcomes underscore the technical contributions achieved by the proposed architecture as well as the practical viability of the proposed architecture in next generation IoT systems. The bridge between theoretical advancements and deployment-oriented design is an essential contribution that the study provides in guaranteeing the security, intelligent, and sustainability of the IoT ecosystems based on scalable quantum approaches. 2025 IEEE.
Prasant, P.; Rao, M Nagabhushana; Yuvaraju, Chinnam; Jeshurun, Subramania Bala; Rengaraju, Perumalraja; Sravani, Mekala Guru
2025 International Conference on Future Technologies, ICFT 2025;
https://doi.org/10.1109/ICFT66708.2025.11336574 https://www.scopus.com/pages/publications/105033683925?origin=resultslist
Prasant P., SRM University, Department of Computer Science & Engineering, U.P, Ghaziabad, India; Rao M.N., School of Computer Science & Technology, Malla Reddy (MR) Deemed to Be University, Department of Computer Science & Engineering, Telangana, India; Yuvaraju C., School of Computer Science & Technology, Malla Reddy (MR) Deemed to Be University, Computer Science & Engineering, Telangana, India; Jeshurun S.B., School of Business and Management, CHRIST (Deemed to Be University), Bangalore, India; Rengaraju P., SRM TRP Engineering College, Department of Artificial Intelligence and Data Science, Trichy, India; Sravani M.G., Amity Institute of Information Technology, Amity University, Bengaluru, India
Quantum Machine Learning Models for Enhancing Big Data Analytics
Big Data Analytics; High-Dimensional Data; Hybrid Quantum-Classical Models; Pattern Recognition; Predictive Analytics; Quantum Computing; Quantum Machine Learning; Real-Time Decision Making
The blistering growth of information in contemporary business is a great challenge to the traditional analytics in the context of speed, accuracy, and scalability. The Quantum Machine Learning (QML) has the chance to provide a ground-breaking solution, based on quantum superposition, entanglement execution to speed up a computational process and increase predictability. The current work proposed a Hybrid Quantum Classical Framework (HQCF) which is a combination of quantum algorithms and conventional machine learning to solve high-dimensional big data analytics. The proposed system shows huge performance improvements over classical foundations - attaining up to 10-percent higher prediction error, and cutting training costs by a factor of 47 percent in various fields such as finance, healthcare, and internet of things sensor information. The hybrid structure features high scalability as well which means that it can process datasets that are up to six million samples and thus it has a high level of scalability and strength. Such quantitative indicators indicate that quantum-enhanced analytic is technologically advanced or progressive to enhance computational-efficiency, generalization-of-models, and real-time ability to make a decision, with large-scale data settings. 2025 IEEE.
Prasant, P.; Hariharan, R.; Sudha, E.; Jeshurun, Subramania Bala; Vedapradha, R.; Reddy, Paindla Vamshi Vardhan
https://doi.org/10.1109/ICFT66708.2025.11336505 https://www.scopus.com/pages/publications/105033653203?origin=resultslist
Prasant P., SRM University, Department of Computer Science & Engineering, U.P., Ghaziabad, India; Hariharan R., International Institute of Business Studies, Department of Management, Bangalore, India; Sudha E., CHRIST (Deemed to Be University), Department of Commerce, Bangalore, India; Jeshurun S.B., School of Business and Management, CHRIST (Deemed to Be University), Bangalore, India; Vedapradha R., School of Commerce, St. Joseph's College of Commerce, Bangalore, India; Reddy P.V.V., Vidya Jyothi Institute of Technology, Hyderabad, India
Quantum-Assisted Metaheuristics for Adaptive Resource Allocation in 6G Networks
6G networks; adaptive resource allocation; low latency; metaheuristics; network optimization; quantum computing; spectrum management
With 6G wireless communication systems, the level of demands is now ultra-low-latency, connectivity of devices in large numbers, and flexible spectrum utilization. To resolve these issues, in the current paper, the Quantum-Assisted Metaheuristic (QAM) framework is proposed that combines quantum-inspired operators and traditional metaheuristic methods of adaptive resource allocation in 6G networks. This framework uses quantum-enhanced exploration, dynamically tuned parameters and hybrid quantum-classical computing to trade-off scalability against efficiency, depending on the traffic and channel conditions. Oh! SIM Evaluating a simulated 6G environment, it proves that QAM can be used to improve spectral efficiency by up to 28 percent and the allocation latency by 31 percent over the state-of-the-art metaheuristics, and still treats users of varying densities fairly with a fairness index of 0.94. The results demonstrate the strength and extensiveness of the QAM, and makes it a viable solution to the efficient and intelligent management of resources in next generation wireless networks. 2025 IEEE.
Rao, M Nagabhushana; Paulin, M Sudha; Varghese, Juvitha; Rekha, N.; Rekha, M.D.; Gogineni, Saikiran
https://doi.org/10.1109/ICFT66708.2025.11336371 https://www.scopus.com/pages/publications/105033685667?origin=resultslist
Rao M.N., School of Computer Science & Technology, Malla Reddy (MR) Deemed to Be University, Dept. of CSE, Telangana, India; Paulin M.S., School of Business and Management, Christ University, Bangalore, India; Varghese J., School of Management, Kristu Jayanti College (Autonomous), Bangalore, India; Rekha N., School of Commerce, Finance and Accountancy, Christ (Deemed to Be) University, Bangalore, India; Rekha M.D., Department of Commerce, Christ (Deemed to Be University), Bangalore, India; Gogineni S., School of Computer Science & Engineering, Malla Reddy (MR) Deemed to Be University, Dept. of CSE, Telangana, India
A Comparative Study of Machine Learning Models for Predicting Earthquake Magnitudes
Earthquake Prediction; K-Nearest Neighbours; Machine Learning; Magnitude Prediction; Temporal and Geospatial Data
This study evaluates the effectiveness of machine learning models in predicting earthquake magnitudes, aiming to address the challenges posed by traditional seismological methods. By leveraging geospatial and temporal data, the research compares the performance of Random Forest (RF), Artificial Neural Network (ANN), and K-Nearest Neighbours (KNN) using Mean Absolute Error (MAE) as the primary metric. Random Forest demonstrated the lowest MAE of 0.3291, showcasing its ability to handle complex, non-linear patterns better than ANN (0.3660) and KNN (0.3758). The analysis highlights the importance of geospatial and temporal factors in improving prediction accuracy, offering insights into their predictive significance. While traditional methods struggle with high-dimensional data, this study eliminates these limitations by employing machine learning models capable of extracting meaningful patterns. These findings underscore the potential of ensemble methods like Random Forest for enhancing earthquake prediction systems. Future research will explore hybrid approaches and real-time data integration to further advance predictive accuracy in seismology. 2025 IEEE.
International Conference on Engineering, Technology and Management, ICETM 2025;
https://doi.org/10.1109/ICETM63734.2025.11051600 https://www.scopus.com/pages/publications/105012020590?origin=resultslist
Shaw A., Christ (Deemed to be University), Bangalore, Department of Computer Sciences, Delhi, India; Shoran P., Christ (Deemed to be University), Bangalore, Department of Computer Sciences, Delhi, India; Vidushi, Christ (Deemed to be University), Bangalore, Department of Computer Sciences, Delhi, India