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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Roselinkiruba, R. Jude Moses Anto devakanth, J. Sharmila, l. Vasumathy, M. Misba, M. Saranya Jothi, C. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai Avadi India Madanapalle Institute of Technology and Science Department of Computer Applications Andra Pradesh India Department of Information Technology Kingston Engineering College Vellore India

ISBN: (纸本)9798331515720

Steganography is the method that convert data into bits to make it suitable for hiding. While embedding data the factors such as stego image quality and distortion which cannot be observed by the human eyes. The aim of this study is to increase the stego image with good quality. Initially, the image pixels are separated into different bit planes then it is compared with the secret data bit planes using Fruitfly Optimization Improved Hybridized Algorithm Pixel Similarity (FOIS-PS). For that similar pixel Fitness Function (FF) is applied based on the three important criteria such as edge, entropy and correlation. Finally, the data is embedded in the lSB bits based on their Embedding Capacity (EC). Using this method, the size of the input is reduced. Then, the data is embedded based on difference of pixels within each group of similar pixels. Evaluation metrics shows the quality of the stego image is boosted and also reduces the processing time. © 2024 IEEE.

关键词： Image quality

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detection of Medicinal Plants Using Machine learning 1

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Ravikumar, S. Eugene Berna, I. Babu, R. Arockia Raj, Y. Vijay, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (数字)9789819789467

ISBN: (纸本)9789819789450

An exciting new direction in plant categorization and identification is the use of machine learning techniques for medicinal plant detection. Using a variety of photo datasets containing medicinal plants, this technique trains models using ANNs. The Ml model learns the complex traits that distinguish various plant species by using data from pre-trained models or by developing bespoke architectures. Thanks to the trained model’s precision in visually recognizing therapeutic plants, everyone from researchers to ecologists can reap the benefits. Methods for medicinal plant identification that are both accurate and objective are crucial to biodiversity research and attempts to safeguard resources. Considerations of ethics, model calibration, and data curation should be incorporated into the construction of such systems. Gather any and all images of plants used for medical purposes. Various viewpoints, lighting scenarios, and plant types will enhance the set. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2025.

关键词： Plant diseases

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A Federated learning Paradigm for Early Alzheimer's detection using MRI Imaging 5

A Federated Learning Paradigm for Early Alzheimer's Detectio...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Naga Ramesh, Janjhyam Venkata Poonkuzhali, P. Krishna, S. Rama Ranjidha, P. Farithkhan, A. Nayaki, A. Periya Graphic Era Hill University Graphic Era Deemed to Be University Department of Cse Dehradun India R.M.D. Engineering College Department of Ece Kavaraipettai India Department of Computer Science & Engineering GSoT Hyderabad502329 India Panimalar Engineering College Department of Ai &ds India Vel Tech Rangarajan Dr.sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Velammal College of Engineering and Technology Department of It Madurai India

ISBN: (纸本)9798350379945

Alzheimer's disease is a progressive neurovascular disorder that results in loss of cognitive function and memory impairment. detection of this infection earlier can help reduce the threat of this infection to a minimum, by providing required intervention and management. In this contribution, we present a novel model of Hybrid Xception-GhostNet Classification (HXGC) in combination with federated learning for classifying the progression of Alzheimer's disease (Ad) across MRI images in the Alzheimer's MRI Preprocessed dataset (AMP dataset). The HXGC architecture, which combines the excellent feature extraction of Xception with the lightweight GhostNet, exhibits strong and lightweight features. Collaborative model training using federated learning across multiple healthcare institutions and kept patient data privacy The HXGC model was trained locally on each center's portion of the dataset, and these locally trained models were then combined into a global model using federated averaging on the central server. The HXGC model achieved a top accuracy of 97.5% on the training set, 97.0% on the validation set, and 97.5% on the test set, which significantly exceeded other models in both accuracy rates and time cost. At a glance, the study demonstrates the potential of advanced neural network architectures and federated learning for early detection and diagnosis of Alzheimer's disease. © 2024 IEEE.

关键词： Alzheimer's disease AMP dataset data privacy early detection federated learning healthcare Hybrid Xception-GhostNet Classification (HXGC) machine learning MRI imaging neural network

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Machine learning Based Sentiment Analysis and Swarm Intelligence

Machine Learning Based Sentiment Analysis and Swarm Intellig...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Patra, Raj Kumar Patil, Bassamma Kumar, T. Sathish Shivakanth, Gandla Manjula, M.B. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India H. K. E. Society's S. L. N. College of Engineering Department of Electronics & Communication Engg Raichur India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana India K L University Department of Computer Science & Engineering Vadeeshwaram Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

Social networking platforms, online news outlets, and weblog hosting services continue to expand, and with them come an increasing number of user-generated content contributions such product evaluations, comments on recent articles, and more. Products, movies, shopping sites, and review sites are common areas for customer feedback. The sheer volume and rate of growth of material that expresses opinions is becoming a burden on manufacturers who must manually categorise this data. Also, the perspective on entities at the level of aspects is expected by the public. It is for this reason that an automated sentiment analyzer must be built, one that can detect the bipolar and multipolar sentiment polarity of documents and/or aspects. People's ability to voice their opinions openly in public has greatly increased with the advent of various social networking apps. As a result, this helps to further the field of automated emotional analysis by providing a wealth of data on which to base analyses of people's feelings. User review categorization and analysis has emerged as an important part of sentiment analysis in recent years. Opinion mining is used to determine the degree of positivity or negativity in each user review posted on a social network. Numbers, star ratings, and descriptive text are the three polarity indications in a review. The sentiments of the public have been analysed using a wide variety of machine learning methods, but these methods often fall short in key areas such as classification accuracy, precision, recall, and F-measure due to pre-existing classification problems such as the two-class problem, overfitting, and parallel processing. The primary goal of the study is to create a fully automated system that can analyse a massive dataset of movie reviews using aspect-based SA or OM. We use natural language processing to tally up the good, bad, and ugly reviews. The research enhances advertising efforts and guides customers to the most suitable products. I

关键词： Sentiment analysis

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A Framework for Hyperspectral Image Classification 2

A Framework for Hyperspectral Image Classification

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Phaneendra Kumar, B.l.N. Vaddi, Radhesyam Manoharan, Prabukumar K L Deemed to Be University Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Velagapudi Ramakrishna Siddhartha Engineering College Department of Inforamtion Technology Vijayawada India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350325799

The high volume and replication of data in hyperspectral images results in improper classification. This paper addresses a non-linear spectral feature extraction using local linear Embeddings in which graph-based structures are utilized. The representative features are then trained for smoothed features using a spatial filter. The extracted spatial features are then trained for classification using a nonlinear version of Support Vector Machine. The proposed model is tested on two datasets: Indian Pines & Pavia. Proposed approach is tested with well-known dimensionality reduction techniques. The reported Overall Accuracy is 92.33% & 98.21% on Indian Pines & Pavia respectively and outperformed well. The algorithm is used for hyperspectral change detection. © 2023 IEEE.

关键词： Support vector machines

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FraudGuard Pro: An Ensemble-Based Feature Selection Algorithm for Credit Card Fraud detection 15

FraudGuard Pro: An Ensemble-Based Feature Selection Algorith...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bhardwaj, Shashank Baskar, S. Marakala, Vijaya Prasannan Gangodkar, durgaprasad dhole, Sumedh Vithalrao Dr. A.P.J. Abdul Kalam Technical University Department of Computer Application Lucknow India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of EE Tamil Nadu Chennai India University of Bisha College of Medicine Saudi Arabia Karpagam College of Engineering Department of Civil Engineering Tamilnadu Coimbatore India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India College of Engineering Department of Electronics & Communication Engineering Maharashtra Pune India

ISBN: (纸本)9798350370249

In recent years, the exponential growth in data volume and sample sizes across various domains, including finance and online transactions, has presented significant challenges for machine learning and data mining algorithms. High-dimensional and imbalanced datasets, often tainted with noise, have impeded the performance and interpretability of fraud detection models. Feature selection has emerged as a pivotal technique to address these challenges by identifying the most relevant features while discarding redundant or irrelevant *** research explores the landscape of feature selection methods and confronts the existing challenges in credit card fraud detection. We propose the 'FraudGuard Pro Ensemble Feature Selection Algorithm,' designed specifically for credit card fraud detection tasks. Notably, our proposed model achieves an impressive accuracy rate of 99%, outperforming traditional models such as logistic Regression (95%), Random Forest (98%), and Support Vector Machine (94%). This substantial improvement underscores the effectiveness of our algorithm in mitigating fraud while minimizing false positives, offering a promising solution for enhanced security in online transactions. © 2024 IEEE.

关键词： Support vector machines

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Rapid Colorimetric and Artificial Intelligence-Based Methods for determining the Microbial Quality of Raw Milk, Processed Milk, and Milk Products

Rapid Colorimetric and Artificial Intelligence-Based Methods...

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International Conference on Inventive Computation Technologies (ICICT)

作者： S. Muthusundari Gomasani Sreeja GM HemaSree K HemaPriya Department of Computer Science And Engineering R.M.D Engineering College Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Besides maintaining the microbial quality of milk anddairy foods for food safety and public health, contamination detection by traditional methods has been slow and labor-intensive. Fast and efficient alternative methods are therefore urgently needed. This paper discusses the application of the advanced colorimetric and artificial intelligence (AI)-based approaches of assessing microbial quality in raw milk, processed milk, and different dairy products. Meanwhile, AI models such as machine and deep learning approaches analyze spectral data, images, and sensor outputs to develop accuracy and predictive capability. Therefore, the integrated approaches provide real-time, non-invasive, and economically effective means for microbial assessment, hence an advantage over conventional techniques. Experiments have illustrated that these methods can very efficiently detect microbial contamination to high confidence levels. This technique has a lot of potential in improving quality control among dairy industries for consumer safety and efficient supply chain management.

关键词： Industries deep learning dairy products Supply chain management Biological system modeling Quality control Predictive models Food safety Real-time systems Contamination

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A High-Gain dC-dC Boost Converter for Solar Microgrid Systems Using a Switching Inductor and a Capacitor 1

A High-Gain DC-DC Boost Converter for Solar Microgrid System...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Prasanth, S. Priyadarsini, S. lakshmi, R. Sneha, G. Ajitha, S.S. Saravanakumar, R. Narendra, Konnaiah Gari Unnamalai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Kovilpatti India PSR Engineering College Department of Computer Science and Engineering Tamil Nadu Sivakasi India Siddharth Institute of Engineering and Technology Department of Elecrical and Electroncis Engineering Puttur India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Iconix Software Solution Tamil Nadu Tirunelveli India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electonics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331518578

This paper outlines the design and implementation of a solar microgrid-specific high-gain dC-dC booster converter that makes use of a variable inductor and capacitors. To improve the suitability of photovoltaic (PV) panels for incorporation into microgrids, the proposed converter efficiently increases their low-voltage outputs. Conventional boost converters can't offer that kind of voltage increase because they lose too much performance and put too much strain on their internal components. The converter increases the voltage gain dramatically while maintaining a high degree of efficiency thanks to a switching inductor-capacitor network. The main breakthrough is an inventive multi-stage energy transfer and storage device that cleverly employs a switching inductor and capacitor. This configuration increases the output voltage while decreasing the voltage and current ripples, which in turn reduces the amount of electromagnetic interference (EMI) and increases the life of the components. Thorough assessments of the converter's working via theoretical modeling and simulation studies confirm its performance across several load and input situations common to solar microgrids. The prototype's testing findings show a significant voltage increase and a peak efficiency of over 95% when compared to traditional boost converters. Moreover, the converter's reliable performance over a wide range of voltage sources and workload scenarios proves its suitability for practical solar applications. As a result, the proposed high-gain boost converter will greatly improve the efficiency and reliability of solar microgrids, leading to a rise in the use of renewable energy sources in dGS. © 2025 IEEE.

关键词： Microgrids

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Extended Kalman Filter Algorithm to detect False data Injection in Wireless Sensor Network 2

Extended Kalman Filter Algorithm to Detect False Data Inject...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Babuji, R. Ganesan, J. Jeya lekha, d. Karthikeyan, M. Kalai Selvi, d.M. Rmk Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350325799

Wireless Sensor Networks (WSN) contains spatially distributed sensor nodes that collaborate with each other. However, the WSN is susceptible since the wireless medium is unpredictable. Several conventional approaches are used to distinguish the abnormal in the WSN, and all have difficulties. An Extended Kalman Filter Algorithm (EKFA) to detect false inject data in WSN is proposed in this paper. It addresses two issues such as abnormality recognition and obstacle detection. It also distinguishes false insert data. Particularly, it observes neighbors' behaviors and then applying EKFA to forecast their future states. The node behavior is observed by incorporating abnormal node examination and system examination elements. The Path Optimization Method (POM) is a shortest route that is used to discover the obstacle in the WSN. The EKFA mechanism minimizes the forwarder count and delay by applying POM. Simulation results demonstrate that the EKFA approach enhances abnormal node detection and minimizes the false negative ratio. © 2023 IEEE.

关键词： Extended Kalman filters

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Virtual Frontiers Navigating the Impact and Potential of AR and VR 2

Virtual Frontiers Navigating the Impact and Potential of AR ...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Varun, C.M. Kumar, R.P. Anto Gokila, E. Gayathri, S. Nalini, M. Siva Subramanian, R. R.M.K. Engineering College Dept of Computer Science and Business Systems Kavaraipettai India St. Xavier's Catholic College of Engineering Dept: of Cse India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of science and technology and technology India S.A Engineering College Dept of Cse Chennai600077 India S.A Engineering College Dept of Cse India Rmk College of Engg and Tech Dept of Cse India

ISBN: (纸本)9798350368413

This research study aims to investigate the two rapidly growing fields of Augmented Reality and Virtual Reality from their technical aspect, possible uses, issues, and evolution. AR and VR are innovations that have experienced a massive advancement and are prevalent across the entertainment industry and the learning process, healthcare, industries, and more. The study defines the basic concepts and the physical components that form the basis of AR and VR. It then goes over specific examples, using AR in the context of interactive education and its social impact as well as in operations such as surgeries and in factory maintenance while demonstrating the applications of VR in gaming, education and as a therapy tool. A fundamental question of the examined technologies, which is primarily discussed in the paper, includes the distinct challenges it encountered the hardware constraints of the solutions, the problems related to UX and UI, and the privacy-related concerns. Moreover, it covers the morality and social impact of the integration of AR and VR into society. Finally, examining the future directions of the survey, it is revealed that possible technologies and research areas for the next generation of AR and VR applications are discussed. Consequently, this overview seeks to present useful recommendations regarding the current status of four AR and VR technologies and their possible future developments. © 2024 IEEE.

关键词： Virtual environments

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