Enhancing Glaucoma Detection in Fundus Images: A ResNet based Segmentation and Advanced ML Algorithms with Duck Pack Optimizer
Catboost; Duck Pack Optimizer; Fundus Images; Glaucoma Detection; Lightgbm; Resnet Based Segmentation; Xgboost
Untreated glaucoma, a chronic eye illness, can cause irreversible vision loss if not caught early. The condition begins with abnormalities in the eye's drainage flow, leading to a rise in intraocular pressure. As the disease progresses, the optic nerve head deteriorates, resulting in vision loss. Ophthalmologists need extensive training and expertise to interpret findings accurately during medical follow-ups to examine the retina. To address this challenge, deep learning-based algorithms have been developed to screen for and diagnose glaucoma using images of the optic nerve, retinal structures, and retinal fundus. This research explores the use of classification and segmentation algorithms based on ResNet to identify glaucoma in fundus images. We fine-tuned the classifier using the DuckPack optimizer and employed XGBoost, LightGBM, and CatBoost algorithms for classification. The results were promising. The segmentation model based on ResNet effectively extracted features, aiding the classification models in accurately identifying glaucoma. All three algorithms performed admirably, though further fine-tuning is needed to determine the best one. Enhancing the model's performance was straightforward after using the DuckPack optimizer for fine-tuning. This study highlights the promising applications of deep learning and sophisticated machine learning algorithms in glaucoma detection. Its findings could inform the development of future diagnostic tools. The Author(s) 2025.
Manjunath, Chinthakunta; Sasi, Archana; Chowdary Ch, Smitha; Sharon Roji Priya, C.; Naick, B. Raveendra; Macherla, Harshini; Lella, Kranthi Kumar
International Research Journal of Multidisciplinary Technovation;Volume;7;Issue;2;pp.108-120
https://doi.org/10.54392/irjmt2529 https://www.scopus.com/pages/publications/105001519610?origin=resultslist
Manjunath C., Department of Computer Science and Engineering (AI & ML), Vidyavardhaka College of Engineering, Mysore, India; Sasi A., Department of CSE, Faculty of Engineering and Technology, Jain University, Kannagapura Rd, Karnataka, Bengaluru, 562112, India; Chowdary Ch S., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, 522302, India; Sharon Roji Priya C., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Naick B.R., Department of CSE (AI&ML), School of Computing, Mohan Babu University, Tirupati, India; Macherla H., Department of Information Technology, MLR Institute of Technology, Hyderabad, 500043, India; Lella K.K., School of Computer Science and Engineering, VIT-AP University, Vijayawada, 522237, India
Enhancing Greedy Web Proxy caching using Weighted Random Indexing based Data Mining Classifier
Clustering; Data Mining; GD? ; GDS; GDSF; Proxy; Random indexing
Web Proxy caching system is an intermediary between the Web users and servers that try to alleviate the loads on the origin servers by caching particular Web objects and behaves as the proxy for the server and services the requests that are made to the servers. In this paper, the performance of a Proxy system is measured by the number of hits at the Proxy. Higher number of hits at the Proxy server reflects the effectiveness of the Proxy system. The number of hits is determined by the replacement policies chosen by the Proxy systems. Traditional replacement policies that are based on time and size are reactive and do not consider the events that will possibly happen in the future. The performance of the web proxy caching system is improved by adapting Data Mining Classifier model based on Web User clustering and Weighted Random Indexing Methods. The outcome of the paper are proactive strategies that augment the traditional replacement policies such as GDS, GDSF, GD? which uses the Data Mining techniques. 2019
Pernabas J.B.; Fidele S.F.; Vaithinathan K.K.
Egyptian Informatics Journal, Vol-20, No. 2, pp. 117-130.
https://doi.org/10.1016/j.eij.2019.01.001 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059870146&doi=10.1016%2fj.eij.2019.01.001&partnerID=40&md5=b48ca6836536b0dc9e0fe032db032972
Pernabas J.B., Department of Computer Science and Engineering, Faculty of Engineering, CHRIST(Deemed To be University), Kengeri Campus, Kanmanike, Bangalore, 560074, Karnataka, India; Fidele S.F., Department of Computer Science and Engineering, Pondicherry Engineering College, ECR, Pillaichavady, Pondicherry, 605014, India; Vaithinathan K.K., Department of Computer Engineering, Karaikal Polytechnic College, Varichikudy, Karaikal-609609, India
Enhancing greedy web proxy caching using weighted random indexing based data mining classifier /
Egyptian Informatics Journal, Vol.20, Issue 2, pp. 117-130, ISSN No. 1110-8665.
Julain Benadit Pernabas, Sagayaraj Francis Fidele and Krishna Kumar Vaithinathan
Enhancing Healthcare Ecosystems Through the Integration of IoT for Patient-Centric Solutions
Healthcare; Interoperability; IoT; Patient-centric; Wearable sensors
The Internet of Things (IoT) is a newly implemented technology in the field of healthcare and can enhance patient-centrical care and efficiency in the healthcare field. IoT may be used to assist in delivering real-time health data and predictive diagnostics and custom care plans by interconnecting medical equipment, sensors, and information systems. This paper will discuss how the IoT technologies, particularly wearable sensors, cloud-based analytics and smart health architectures are changing the way healthcare is delivered. The article highlights how the data merge on the utilization of clinical and non-clinical sources to aid in remote patient monitoring, resources use optimization, and positive patient outcomes. It also identifies the concerns of the implementation of IoT such as the security risks, data privacy and the failure to connect the devices and systems. To address them, the paper discusses the new structures that integrate blockchain and artificial intelligence and ensure safe implementation of data management and heightened clinical decision-making. The results of various works of 2015-2020 have revealed that IoT applications and patent health care-related tendencies are growing, which implies that the shift towards interconnected and intelligent ecosystems is rapid. The consequences of this digital transformation are not confined to the hospital sphere only, as it is extended to homecare, telemedicine, and state population management. Lastly, IoT will allow healthcare stakeholders to shift their healthcare systems to patient-centered, rather than hospital-centered systems, in which a focus on accessibility, efficiency, and personalization would be placed on healthcare provision. 2025 IEEE.
Singh, Ramendra; Kshirsagar, Pravin R.; Tak, Tan Kuan; Upreti, Kamal; Gora-Blaszczykowska, Agnieszka; Radhakrishnan, G.V.
2025 IEEE 5th International Conference on ICT in Business Industry and Government, ICTBIG 2025;
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICTBIG68706.2025.11323576 https://www.scopus.com/pages/publications/105032888071?origin=resultslist
Singh R., Raj Kumar Goel Institute of Technology, Ghaziabad, India; Kshirsagar P.R., J D College of Engg & Mgmt, Nagpur, India; Tak T.K., Sinagpore Institute of Technology, Singapore; Upreti K., Christ University, Ghaziabad, India; Gora-Blaszczykowska A., War Studies University, Warsaw, Poland; Radhakrishnan G.V., KIIT, Bhubaneswar, India
ENHANCING HEALTHCARE SECURITY WITH BLOCKCHAIN-POWERED SMART CONTRACTS
Blockchain; Data security; Digital transformation; Healthcare system; Smart Contract
The rationale behind this research stems from the increasing frequency of data breaches in healthcare and the inadequacy of centralized systems to ensure privacy, interoperability, and regulatory compliance. The Present study emphasizes the importance of applying security in healthcare. This model was prepared by utilizing Smart Contracts. It has been noted that there are some emerging concerns about data security and privacy as well as interoperability within healthcare organizations. The focus of a research paper is on the deployment of Smart Contracts along with blockchain technologies. The fundamental vision is to improve healthcare infrastructures security. Blockchain is transforming healthcare systems for the better by eliminating inefficiencies caused by fraud and outdated technologies, allowing for the efficient, transparent, and secure issuance of Smart Contracts. The challenges of confidentiality, data security, and access to relevant patient information for medical professionals have been a problem in the healthcare sector. Most of the existing EHR systems do not have adequate mechanisms for enforcing security access controls, which hampers cooperation between healthcare institutions. These security concerns pose risks for patients privacy and cripple the adoption of modern information technology within the health sector. Simulation works shows that Transaction processing time in case of proposed model is below 1.5 second where as it is 2.5 in case of conventional model. Security breach probability of proposed model has been reduced to 0.05 that was 0.35 in case of conventional model. Data integrity verification time in case of proposed model is below 1.0 that is above 1.75 in case of conventional model. While with the existing Electronic Health Record (EHR) systems face limitations in security, privacy enforcement, and interoperability, this study addresses the lack of automated, decentralized access control mechanisms. It proposes a blockchain-powered Smart Contract model to fill these gaps and enhance healthcare data governance and trust. Little Lion Scientific
Shaikh, Naim; Mamatha, G.; Kumari, Kukati Aruna; Giridhar, M.S.; Mishra, Krishna Nand; Pankajam, A.; Ganesh Kumar, R.; Gupta, Swati
Journal of Theoretical and Applied Information Technology;Volume;103;Issue;19;pp.8045-8061
https://www.scopus.com/pages/publications/105025669366?origin=resultslist
Shaikh N., Dr., Global Business School and Research Centre, Dr. D. Y. Patil Vidyapeeth, Maharashtra, Pune, India; Mamatha G., Dr., Department of Management Studies, Sri Siddhartha Institute of Business Management, Karnataka, Tumkur, India; Kumari K.A., Department of Electronics and Communication Engineering, Prasad V Potluri Siddhartha Institute of Technology, Andhra Pradesh, Vijaywada, India; Giridhar M.S., Dr., Department of EEE, Lakireddy Bali Reddy College of Engineering, Mylavaram, JNTUK Kakinada, Andhra Pradesh, India; Mishra K.N., Dr., Department of Computer Science And Engineering, Ambalika Institute of Management & Technology, Mohanlalganj, Uttar Pradesh, Lucknow, 226301, India; Pankajam A., Dr., Department of Business Administration, Avinashilingam Institute for Home Science and Higher Education for Women, Tamil Nadu, Coimbatore, 641043, India; Ganesh Kumar R., Dr., Department of Computer Science and Engineering, CHRIST (Deemed to be University), School of Engineering and Technology, Kengeri Campus, Kanminike, Karnataka, Bangalore, 560074, India; Gupta S., Department of Computer Science and Engineering, Vaish College of Engineering, Haryana, Rohtak, India
Enhancing Heritage and Cultural Education through Immersive Audio-Visual Techniques
Augmented reality, 3D soundscapes; Cultural heritage; Immersive media; Interactive learning, 6G technology; Virtual reality
Growth in immersive media technologies offers unique opportunities for improved learning experiences. This is important in understanding and preserving heritage and cultural elements. The establishment of hybrid classrooms and the adoption of technologies like virtual reality (VR), augmented reality (AR), and artificial intelligence (AI) make the learning experience more immersive and interactive. These experiences can lessen the differences between theoretical and emotional connections which also help learning to be more impactful and effective. This chapter examines how immersive technologies such as audio-visual technology can change the heritage and cultural education of people across diverse ages. The chapter discussed both 3D soundscapes and visual imagery in relation to VR/AR applications and how well these elements are integrated into a unified whole, which provides some interesting immersive cultural heritage education experiences. This study also analyzes use cases such as virtual museum tours with immersive audio-embedded recreations of historical environments, AR-enhanced archaeological site visits supported by ambient sounds, and interactive cultural heritage simulations guided by spatial audio. It further explores successful research integrating 3D soundscapes into AR/VR use cases. These applications highlight potential VR/AR educational contexts where audio-visual enhancement provides multisensory learning that encourages engagement and cognition surrounding heritage and cultural elements. The study also covers the benefits of this approach like increased public engagement with cultural sites and artifacts, improved retention of historical information, and enhanced accessibility to cultural experiences. Ultra-low latency, higher connectivity, and high-speed data transfer would mean that the quality and accessibility offered by 6G will be on another level altogether. On the other hand, AI can bring immersive experiences along with customization, immediate feedback, and interactions in virtual worlds. The chapter further focuses on the opportunities provided by emerging technologies such as 6G and AI tools in improving understanding of culture/heritage preservation and cultures. The chapter ends with the user applicability of audio-visual enhanced VR/ AR in cultural and heritage education. The study suggests continued research and collaboration between various stakeholders to develop better methods to preserve and disseminate the rich heritage and cultural values. Integrating evolving technologies can be an aid to reducing many of the existing limitations by providing better realistic, interactive, and accessible cultural educational tools. 2026 Scrivener Publishing LLC. All rights reserved.
Paul, Ajith; Kunnumpurath, Biju; Balakrishnan, C.; Ramachandran, Selvakumar; Suseelan, Anitha
Virtual Reality and Augmented Reality with 6G Communication;pp.245-269
https://doi.org/10.1002/9781394336081.ch10 https://www.scopus.com/pages/publications/105025495912?origin=resultslist
Paul A., Department of Media Studies, Christ University, Bengaluru, India; Kunnumpurath B., Department of Media Studies, Christ University, Bengaluru, India; Balakrishnan C., Department of Computer Science, Christ University, Bengaluru, India; Ramachandran S., Department of Continuing Education, University of Oxford, Oxford, Oxfordshire, United Kingdom; Suseelan A., School of Architecture, Christ University, Bengaluru, India
ENHANCING home security through visual CRYPTOGRAPHY
Biometric authentication; CVC, Siamese network; Security; Visual cryptography
Home security systems in the recent times have gained greater importance due to increasing threat in the society. Biometrics deals with automated approaches of recognizing a user or verifying the user identity based on behavioral or physiological features. Visual cryptography is a scheme of secret sharing where a secret image is encrypted into shares which disclose no data independently about the original secret image. As the template of biometric are stored in centralized database due to the threats of security the template of biometric may be changed by attacker. If the template of biometric is changed then the authorized user will not be permitted to access the resource. To manage this problem the schemes of visual cryptography can be used to secure the face recognition. Visual cryptography offers huge ways for supporting such needs of security as well as additional authentication layer. To manage this problem the visual cryptography schemes can be used to secure digital biometric information privacy. In this approach the face or private image is dithered in two varied host images that is sheets and are stored in separate servers of data so as to assure that the original image can get extracted only by accessing both sheets together at a time and a single sheet will not be capable to show any data of private image. The main aim of the study is to propose an algorithm which is a combination of CVC and Siamese network. This research implements visual cryptography for face images in a biometric application. The Siamese network is essential to solve one shot learning by representation of learning feature that are compared to verification tasks. In this research face authentication helps in accomplishing robustness by locating face image from an n input image. This research explores the availability of using visual cryptography for securing the privacy to biometric data. The results of the proposed approach provide an accuracy of 93% which is found to be superior when compared with that of the approaches that are already in practice. 2020
Microprocessors and Microsystems, Vol-80
https://doi.org/10.1016/j.micpro.2020.103355 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094858951&doi=10.1016%2fj.micpro.2020.103355&partnerID=40&md5=e8237fec0cbb8129e98292c3f3f9524c
Mohan J., Research Scholar, Department of Computer Science, Bharathiar University, Coimbatore, India; R D.R., Associate Professor, Department of Computer Science, CHRIST(Deemed to be University), Bengaluru, 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
3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025;
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
Enhancing Human Resource Management With Fuzzy Logic and Neural Networks for Personalized Performance Management
Artificial Intelligence in HR; Data-Driven HRM; Decision Support; Employee Performance Prediction; Explainable AI; Fuzzy Logic; HR Analytics; Human Resource Management; Hybrid Model; Interpretability; Machine Learning; Neural Networks; Performance Metrics; Personalized Performance Evaluation; Rule-Based Systems
Human resource management (HRM) encounters that is making more challenging some exact, fair and person related performance appraisals on a large scale. Traditional methods often fail to capture the richness of human behaviour and tend to be opaque to interpretation. The proposed study contributes a new hybrid approach of Fuzzy Logic and Neural Network for enhanced personalized performance management. The facts are presented qualitatively by the fuzzy inference system in linguistic terms while for numerical features, the neural network analyses such that it can find complex relationship patterns. This methodology ensures the simplicity and high predictability. The model trained on Kaggle dataset achieved an accuracy of 94.7%, F1-score of 0.942, precision of 0.945, recall of 0.940 and AUC-ROC of. 976 which were higher compared to baseline approaches like Logistic Regression and Decision Trees respectively. The solution helps HR professionals make sense of relevant information into employee performance and developmental needs, which are highlighted in real time. The results suggest that combining rule-based reasoning and machine learning enhances personalisation and offers more transparent human resources practices. This study provides a foundation for the next generation of intelligent HRM systems enabling adaptive decision support in various organizational settings. 2026 IEEE.
Rani, Mary Benitta; Praveena, F.; Talamala, Suresh; Paul, Gaddam Rahul; Vasanti, G.; Praveena, S.
CCIC 2026 - Contemporary Computing Innovations Conference 2026;
https://doi.org/10.1109/CCIC68129.2026.11486118 https://www.scopus.com/pages/publications/105038712434?origin=resultslist
Rani M.B., College of Administrative and Financial Sciences, University of Technology, Bahrain; Praveena F., Mar Ephraem College of Engineering and Technology, Department of Master of Business Administration, Tamil Nadu, Kanyakumari, India; Talamala S., School of Business, Aditya University, Surampalem, Andhra Pradesh, Kakinada, India; Paul G.R., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Vasanti G., Aditya Institute of Technology and Management (Autonomous), Department of Basic Science and Humanities, Tekkali, Andhra Pradesh, Srikakulam, India; Praveena S., St. Joseph's College of Engineering, Department of Master of Business Administration, Tamil Nadu, Chennai, India
Enhancing Human-Computer Interaction with a Low-Cost Air Mouse and Sign Language Recognition System
Air Mouse; CNN; Hand Gesture; Mediapipe; Proton Assistant; Sign Language
The purpose of this study is to investigate the development of assistive technologies that are designed to empower people with disabilities by increasing their level of freedom and accessibility. Voice assistants, air mice, and software that recognizes sign language are some of the topics that are specifically covered in this. Those who have impaired fine motor skills can benefit from using air mice since they allow controls to be made by hand gestures. Using machine learning algorithms, sign language recognition software is able to decipher signs with an accuracy rate of over 90 percent, making it easier for people who are deaf or hard of hearing to communicate themselves. By relying solely on vocal instructions, voice assistants like Alexa make it possible to control devices without using your hands. Not only do these technologies have the potential to be revolutionary, but they also confront obstacles in terms of improving identification accuracy and integrating them into common gadgets. In this study, the development and impact of voice assistants, sign language software, and air mice are discussed. More specifically, the paper highlights the potential for these technologies to help millions of people with disabilities all over the world. Additionally, it examines potential enhancements that could be made to these technologies in the future in order to further improve accessibility and inclusivity. This research integrates computer vision and machine learning to create a multimodal system blending air mouse functionality with real-time sign language translation. Achieving 95% accuracy in gesture recognition for air mouse control and 98% accuracy in sign language letter classification using a basic webcam, the system promotes accessible interaction without specialized hardware. Despite limitations in vocabulary and lighting sensitivity, future efforts aim to broaden data training and explore mobile deployment. These advancements hold promise for enhancing natural human-computer interaction, particularly for users with disabilities, by enabling intuitive, hands-free control and communication. 2024 IEEE.
Proceedings of International Conference on Circuit Power and Computing Technologies, ICCPCT 2024, pp. 93-98.
https://doi.org/10.1109/ICCPCT61902.2024.10673196 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205577953&doi=10.1109%2fICCPCT61902.2024.10673196&partnerID=40&md5=9bb0cbeafa7075e3ac8caee0c543e82f
Santhan H., CHRIST (Deemed to Be University), Computer Science, Bangalore, India; Sudhakar T., CHRIST (Deemed to Be University), Computer Science, Bangalore, India; Joy H.K., CHRIST (Deemed to Be University), Computer Science, Bangalore, India
Enhancing Image Classification Performance through Hybrid Self-Supervised Learning Strategies
Computer Vision; Contrastive Clustering; Hybrid SSL; Image classification; Multi-Modal; Self-Supervised
Image classification is a cornerstone of computer vision, with the applications spanning healthcare, autonomous driving and security. The dependence on large labeled datasets for supervised learning poses significant challenges, particularly in specialized fields where the labeled data is scarce and expensive to obtain. Self-supervised learning (SSL) has emerged as a promising paradigm, enabling models to learn useful representations from unlabelled data by designing pretext tasks that generate pseudo-labels. SSL faces limitations in handling complex data distributions and achieving robust generalization. This paper explores hybrid self-supervised learning strategies that combine multiple SSL techniques, such as contrastive learning, masked image modeling, and clustering, to enhance image classification performance and reduce dependence on labeled data. This study proposes a comprehensive framework that integrates data augmentation, feature extraction, and hybrid learning mechanisms, evaluated on the CIFAR-100 dataset. The experimental results demonstrate that hybrid SSL approaches achieve significant improvements in performance. The combination of SimCLR and masked image modeling (MAE) achieves a Top-1 accuracy of 77.8% on the clean test set and 71.4% on the domain-shifted set, and self-distillation with contrastive learning (DINO) achieves the highest Top-1 accuracy of 78.4% on the clean test set and 72.1% on the domain-shifted set. Advanced data augmentation techniques, such as CutMix and RandAugment, additionally enhance model robustness, with SwAV (contrastive clustering) achieving 76.5% Top-1 accuracy on the clean test set and 70.1% on the domain-shifted set. The findings highlight the effectiveness of hybrid SSL methods in addressing the challenges of limited labelled data, offering valuable insights for future research and applications in image classification. 2025 Seventh Sense Research Group.
Deepa, S.; Sheetal; Alli, A.; Siddalingappa, Rashmi
SSRG International Journal of Electronics and Communication Engineering;Volume;12;Issue;7;pp.90-101
https://doi.org/10.14445/23488549/IJECE-V12I7P108 https://www.scopus.com/pages/publications/105012894771?origin=resultslist
Deepa S., Department of Computer Science, Christ University, Bengaluru, India; Sheetal, Department of Computer Applications, Presidency College, Bengaluru, India; Alli A., Department of Computer Applications, Presidency College, Bengaluru, India; Siddalingappa R., Department of Computer Science, Christ University, Bengaluru, India
Enhancing image compression through a novel Structural Fidelity Weighted Ensemble (SFWE) model
With the explosion of digital images across multiple sectors like social media, health care, medical imaging, and remote sensing, there is a demand to optimise the storage and transmission of images. In this paper, a novel Structural Fidelity Weighted Ensemble model is proposed to dynamically adjust the weights between SVD and PCA outputs to enhance the quality of reconstructed images.Unlike traditional static fusion techniques, the proposed SFWE deploys a fast bounded scalar optimization strategy so as to dynamically estimate the optimal fusion weights thereby ensuring non-negativity and simplex constraints while significantly reducing computational overhead compared to Sequential Quadratic Programming(SQP) or constrained gradient descent methods.Validation was done across multiple benchmarks datasets namely, USC-SIPI Sequences (grayscale TIFF), Kodak, BSDS500, DRIVE (Digital Retinal Images for Vessel Extraction), and ISPRS Potsdam which cover natural, medical, and remote-sensing images. Per-image processing, runtime measurement, and compressed ratio (CR) were produced automatically by the provided evaluation pipeline;The SFWE method provides greater image quality and structural fidelity across diverse datasets, attaining a PSNR of 40 dB and SSIM of 0.95, outperforming existing approaches such as Discrete Cosine Transform (DCT), Wavelet Transform, Singular Value Decomposition (SVD), and Principal Component Analysis and JPEG2000 + CNN models. In addition, it also maintains a good compression ratio leading to an effective balance between the reduction in file size as well as visual quality of the images, which confirms enhanced structural preservation across diverse image types. To implement a novel ensemble model (SFWE) that optimally balances the outputs of SVD and PCA for doing effective image compression. To achieve a higher SSIM (0.95) and good PSNR (40 dB) compared to compression techniques such as DCT, Wavelet, SVD, PCA, and JPEG2000 + CNN. To ensure adaptive high-quality reconstruction across multiple datasets, demonstrating its suitability for diverse image-intensive applications. 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/
I, Priya Stella Mary; Siddalingappa, Rashmi; M, Vinay; S, Deepa; P, Margaret Savitha; M, Kannan
MethodsX;Volume;15;Issue;;Article No.;103695;
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.1016/j.mex.2025.103695 https://www.scopus.com/pages/publications/105023898486?origin=resultslist
I P.S.M., Department of Computer Science, CHRIST University, Yeshwanthpur, Bangalore, India; Siddalingappa R., Department of Computer Science and Data Science, York St John University, London, E14 2BA, United Kingdom; M V., Department of Computer Science, CHRIST University, Yeshwanthpur, Bangalore, India; S D., Department of Computer Science, CHRIST University, Yeshwanthpur, Bangalore, India; P M.S., Department of Computer Science, CHRIST University, Yeshwanthpur, Bangalore, India; M K., Department of Computer Science, CHRIST University, Yeshwanthpur, Bangalore, India
Enhancing Industrial Equipment Reliability: Advanced Predictive Maintenance Strategies Using Data Analytics and Machine Learning
In today's dynamic industrial landscape, optimizing machinery performance and minimizing downtime are paramount for sustained operational excellence. This paper presents advanced predictive maintenance strategies, with a focus on leveraging machine learning and data analytics to enhance the reliability and efficiency of industrial equipment. The study explores the key components of predictive maintenance, including data collection, condition monitoring, predictive models, failure prediction, optimized maintenance scheduling and the extension of equipment longevity. The paper discusses how predictive maintenance aligns with modern industrial paradigms. The study evaluated the performance of five popular forecasting models like Random Forest, Linear Regression, Exponential Smoothing, ARIMA, and LSTM, to estimate maintenance for industrial equipment. The effectiveness of each model was evaluated using a number of performance metrics. The percentage of the variation in the real data that the model can explain is shown by the R-squared number. The lowest MSE, RMSE, and greatest R-squared values indicate a model's accuracy. The study highlights practical implications across diverse industries, showcasing the transformative impact of predictive maintenance on minimizing unplanned downtime, reducing maintenance costs, and maximizing the lifespan of critical machinery. When it comes to predictive maintenance for industrial machinery, the LSTM model has been shown to be the most accurate and efficient model with the highest R-squared value, indicating a better fit and higher predictive ability. As technology continues to evolve, the paper discusses future directions, including the integration of artificial intelligence and advanced analytics, and emphasizes the importance of continuous improvement in refining predictive maintenance strategies for the evolving needs of industries worldwide. 2024 IEEE.
Vincent M.; Thomas S.; Suresh S.; Prathap B.R.
Proceedings of InC4 2024 - 2024 IEEE International Conference on Contemporary Computing and Communications
Vincent M., CHRIST Deemed to be University, Department of Computer Science and Engineering, Bengaluru, India; Thomas S., CHRIST Deemed to be University, Department of Computer Science and Engineering, Bengaluru, India; Suresh S., CHRIST Deemed to be University, Department of Computer Science and Engineering, Bengaluru, India; Prathap B.R., CHRIST Deemed to be University, Department of Computer Science and Engineering, Bengaluru, India
Enhancing instructional effectiveness using the metaverse: An empirical analysis of the role of attitude and experience of participants
The Metaverse has been gaining importance, with businesses looking to adopt the same for processes rangingfrom onboarding to customer experience. The current study has been conducted to evaluate the impact of learner characteristics on motivation to participate in metaverse-based training programs across various organizations. Based on literature and theory, two main characteristics were identified: attitude towards the metaverse and experience with the technology. Data for the study was collected using a structured questionnaire and 103 responses were collected from employees belonging to various organizations in India. The analysis and interpretation of the data was done using statistical techniques through the tool of SPSS. The study found out that both the learner characteristics have a strong positive relationship with each other, and attitude towards metaverse has a stronger relationship with learner motivation than the experience of use. The findings suggest organizations focus more on the manner in which they should introduce metaverse at the workplaces and need to keep the employee attitude towards any kind of change; more of a technological change in mind when they are strategizing to implement metaverse-based training programs. 2024, IGI Global. All rights reserved.
Exploring the Use of Metaverse in Business and Education, pp. 256-274.
Nair S., School of Business and Management, CHRIST University, India; Tare T., Incture Technologies, India
Enhancing Investment Advisory with Machine Learning for a New Era in Financial Services
Explainable AI (XAI); Financial services; Investment advisory; Machine learning; Personalized finance
The current financial service environment, where the volatility of markets and the need to offer flexible solutions is growing, is starting to challenge the traditional investment advisory models. This paper implements a new framework, which incorporates the most advanced methods of machine learning, to make investment advising a process driven by real data. This is unlike the current models which are overly dependent on historical trends or fixed risk profiles, our system allows us to use real time behavior analytics, sentiment analysis and dynamic portfolio optimization to give hyper personalized investment recommendations. The framework feeds the ensemble learning, attention-based neural networks, explainable AI (XAI) to make sure the transparency, regulatory, and investor trust. The innovation in particular is based on the constant interaction between client and adjustment of the model in terms of a ready and sensitive advisory intervention. The study will not only improve the relevance and precision of financial advice, but will with its informed automation of advisor-client relationship led to a redefinition of the advisor-client relationship. The insights guide to a world of advisory services where ML and machine learning complement strategic decision-making with unheard levels of specificity and individuality. 2025 IEEE.
Kafila; Wale, Swati Khanderao; Jain Jacob, M.; Sidhu, Kawerinder Singh; Kulshrestha, Nitin; Dhoke, Satish Manikrao
Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.217-222
https://doi.org/10.1109/SISIMPACT67725.2025.11439233 https://www.scopus.com/pages/publications/105037466894?origin=resultslist
Kafila, School of Business, SR University, Telangana, Warangal, India; Wale S.K., Shree Ramchandra College of Engineering Lonikand, Artificial Intelligence and Data Science, Pune, India; Jain Jacob M., SRM Institute of Science and Technology, Faculty of Management (MBA), Tamilnadu, India; Sidhu K.S., Uttaranchal Institute of Management, Uttaranchal University, Uttarakhand, Dehradun, India; Kulshrestha N., Christ Deemed to Be University, India; Dhoke S.M., Moreshwar Arts Science and Commerce College, Department of Commerce, Maharashtra, India
Enhancing IoT Security Through Deep Learning-Based Intrusion Detection
Adaptive Moment Estimation; Edge Computing; Gated Recurrent Units; Intrusion Detection; IoT-23
The Internet of Things (IoT) has revolutionized the way we interact with technology by connecting everyday devices to the internet. However, this increased connectivity also poses new security challenges, as IoT devices are often vulnerable to intrusion and malicious attacks. In this paper, we propose a deep learning-based intrusion detection system for enhancing IoT security. The proposed work has been experimented on IoT-23 dataset taken from Zenodo. The proposed work has been tested with 10 machine learning classifiers and two deep learning models without feature selection and with feature selection. From the results it can be inferred that the proposed work performs well with feature selection and in deep learning model named as Gated Recurrent Units (GRU) and the GRU is tested with various optimizers namely Follow-the-Regularized-Leader (Ftrl), Adaptive Delta (Adadelta), Adaptive Gradient Algorithm (Adagrad), Root Mean Squared Propagation (RmsProp), Stochastic Gradient Descent (SGD), Nesterov-Accelerated Adaptive Moment Estimation (Nadam), Adaptive Moment Estimation (Adam). Each evaluation is done with the consideration of highest performance metric with low running time. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Communications in Computer and Information Science, Vol-1823 CCIS, pp. 95-105.
Springer Science and Business Media Deutschland GmbH
Jyotsna A., Department of Computer Science and Engineering, Christ (Deemed to Be University), Bangalore, India; Mary Anita E.A., Department of Computer Science and Engineering, Christ (Deemed to Be University), Bangalore, India
Enhancing IoT Security Through Multilayer Unsupervised Learning and Hybrid Models
Anomaly Detection; Attack Detection; Cybersecurity; Deep Learning; Internet of Things (IoT); Intrusion Detection; Machine Learning; Security; Smart Home Devices; Unsupervised Learning
This research addresses the challenge of limited unsupervised learning in current IoT security research, which heavily relies on labelled datasets, hindering the detection of unknown threats. To overcome this constraint, the study proposes a sophisticated methodology integrating K-means clustering, autoencoders, and a hybrid model (combining both). The aim is to enhance detection capabilities without being reliant on prior labelled data. Emphasizing the need to go beyond traditional models, the research underscores the significance of incorporating a diverse range of smart home IoT devices to gain comprehensive insights. Tests conducted on the N-BaIoT dataset, which incorporates authentic traffic data from nine commercial IoT devices afflicted with Mirai and BASH-LITE infections, demonstrate the effectiveness of the suggested models. K-means clustering demonstrates excellence in precision, recall, and F1-scores, particularly in Doorbell and Thermostat categories. The Hybrid model consistently achieves high precision and recall metrics across various device categories by leveraging the strengths of both Kmeans and autoencoder techniques. Notably, the Autoencoder model stands out for its exceptional ability to achieve a perfect 100% detection rate for anomalies across all devices. This study highlights the robust performance of the proposed unsupervised learning models, emphasizing their strengths and potential areas for refinement in enhancing IoT network security. 2024 IEEE.
TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
Vetal A., Christ (Deemed to Be) University, Department of Data Science, Pune, India; Lekha J., Christ (Deemed to Be) University, Department of Data Science, Pune, India; Sweety, Christ (Deemed to Be) University, Department of Data Science, Pune, India
Enhancing Kannada Handwritten Text Processing: A Deep Learning Approach to Optimized Recognition and Segmentation
Kannada Handwritten Characters; Recognition; Segmentation; Text Processing
Digitalization ensures that information is available in diverse regional languages, empowering more cultures and perspectives to be heard and understood. One of the regional languages considered for empowering information access is the Handwritten Kannada document. Extracting text from these documents requires overcoming several obstacles, such as deciphering diverse handwriting styles, accommodating inconsistencies in character size, and the presence of multiple touches between characters. The present paper explored recognizing and segmenting Kannada handwritten characters using a deep learning model, specifically YOLOv8. While YOLOv8 is primarily known for real-time object detection, the paper suggests its potential for character detection tasks. The model achieved a promising mean Average Precision (mAP) of 96.8% at a threshold of 0.5 on a hybrid dataset consisting of 2476 images and 95.0% on character segmentation. This experiment adds to the growing body of research exploring YOLOv8s capabilities beyond traditional real-time object detection and instance segmentation. 2025 The Authors. Published by Elsevier B.V.
Procedia Computer Science;Volume;260;pp.439-446
https://doi.org/10.1016/j.procs.2025.03.221 https://www.scopus.com/pages/publications/105008869372?origin=resultslist
Malini M., School of CSA, REVA University, Bengaluru, India; Hemanth K.S., Christ University, Bengaluru, India
Enhancing Kubernetes Auto-Scaling: Leveraging Metrics for Improved Workload Performance
autoscaling; cluster autoscaler; container scaling; hpa; kubernetes; performance; scaling; vpa
Kubernetes is an open-source production-grade container orchestration platform, that can enable high availability and scalability for various types of workloads. Maximizing the performance and reducing the cost are two major challenges modern applications encounter. To achieve this, resource management and proactively deploying resources to meet specific application requirements becomes utmost important. Adopting Kubernetes auto-scaler to fit one's needs are important to maximize the performance. This study aims to perform a comprehensive analysis of Kubernetes auto-scaling policies. This paper also lists out the various parameters considered for auto-scaling, and prediction methods used to efficiently handle resource requirements of applications. The research findings reveal a scarcity in the existing work regarding the variety of workload based auto-scaling and custom metrics. This paper provides a concise overview of a forthcoming research endeavor that explores the utilization of custom metrics in conjunction with auto-scaling. 2023 IEEE.
2023 Global Conference on Information Technologies and Communications, GCITC 2023
Joyce J.E., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India; Sebastian S., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India
Enhancing language teaching materials through artificial intelligence: Opportunities and challenges
Incorporating artificial intelligence (AI) into language education signifies a paradigm shift that promotes originality and inclusiveness. The partnership between AI developers and educators effectively tackles obstacles and establishes a foundation for continuous progress. Anticipating the future, the progression of AI holds the potential to deliver intricate customization, customizing educational encounters to suit the unique requirements of each individual. Responsible incorporation of AI into teaching methodologies transforms them into a collaborative model that empowers educators to engage in individualized interactions. Ethics remain of the utmost importance, encompassing bias mitigation and privacy. In essence, the integration of AI into language education signifies an impending era in which the combined powers of technology and human proficiency foster the development of capable individuals who are prepared to navigate an interconnected, digitally globalized society. 2024, IGI Global. All rights reserved.
AI in Language Teaching, Learning, and Assessment, pp. 22-42.
Singha S., Kristu Jayanti College (Autonomous), India; Singha R., Christ University, India; Jasmine E., Indian Institute of Psychology and Research, India