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Enhancing Glioma Brain Tumor Detection from mRI using Deep Learning techniques

Enhancing Glioma Brain Tumor Detection from MRI using Deep L...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： S Sankara Narayanan L C meena K Chidambara Thanu P Chandrasekar Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India RMD Engineering College Chennai Department of Computer Science and Business systems R.M.K Engineering College Kavaraipettai Tamil Nadu Department of ECE Siddartha Institute of Science and Tech Puttur Andhra Pradesh

Brain tumor classification from mRI image is one of the important areas in biomedical research. However, the interpretation of these images is subjective, and automated detection methods can improve diagnostic accuracy. This study proposes a hybrid model that combines deep learning, machine learning, and traditional image processing techniques to detect brain tumors from mRI images. The proposed model is based on a convolutional neural network (CNN) algorithm and a machine learning model. The first stage is employing the histogram equalisation approach to enhance the image. In the second stage, features of the grey level co-occurrence matrix are extracted from the improved image. Convolutional neural networks are utilised in the third stage to categorise as normal or abnormal. The abnormal brain tumor is segmented using a morphological segmentation algorithm. This study suggests a number of performance criteria, such as accuracy, sensitivity, and specificity, to evaluate the efficacy of the proposeed method. Our proposes algorithm demonstrates superior performance compared to existing methods regarding accuracy, sensitivity, and specificity.

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Advancements in Artificial Intelligence for Better Image Analysis, Recognition, and Interpretation in the Field of Image Processing

Advancements in Artificial Intelligence for Better Image Ana...

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Communications and Information technologies (GCCIT), Global Conference on

作者： V. Saravanan A. Adaikkammai G. Sasikala Iyswariya A A. Babisha m. Latha Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Computer Science and Business Systems R.M.D. Engineering College Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of ECE R.M.K. Engineering College Chennai Department of Artificial Intelligence and Data Science Panimalar Engineering College Department of IT SRM Institute of Science and Technology

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The rapid advancement of artificial intelligence (AI) has brought about a significant revolution in image processing. This revolutionary technology has enabled the analysis, recognition, and interpretation of images in ways that were previously inconceivable. An investigation into the most recent developments in artificial intelligence techniques that have improved the efficiency and precision of image processing tasks is presented in this study. We survey the emergence of transformer architectures and the integration of deep learning models, namely Convolutional Neural Networks (CNNs), to explore how these technologies have transformed the understanding and application of images in several industries. An overview of existing image processing methods and the limits of those approaches is presented at the beginning of our research. This highlights the necessity of more advanced AI-driven solutions. After that, we examine the advancements that have been made in artificial intelligence, such as the incorporation of attention processes, the development of more effective CNN architectures, and the utilisation of generative adversarial networks (GANs) for the purpose of picture synthesis and augmentation. The purpose of this research is to investigate the impact that artificial intelligence has on particular applications of image processing, such as autonomous car navigation systems, facial recognition, medical imaging, and satellite images analysis. Specifically, we focus on the enhancements in diagnostic accuracy, environmental monitoring, security, and safety that arose as a result of the adoption of artificial intelligence in these sectors through the use of case studies. Furthermore, we discuss the issues that are associated with artificial intelligence in image processing. These challenges include the necessity for explainable AI models, the requirement for computational resources, and the protection of data privacy. A number of potential solutions to these problems

关键词： Industries Image recognition Image analysis Transformers Satellite navigation systems Generative adversarial networks Satellite images Convolutional neural networks Security Artificial intelligence

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Big Data Analytics for Smart meter Data in Power Systems

Big Data Analytics for Smart Meter Data in Power Systems

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International Conference on science technology engineering and management (ICONSTEm)

作者： E. Afreen Banu Lavanya R R. Sandhiya Bondu Vijayakumar K. Kavitha Chirag Vibhakar Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai Tamil Nadu India Department of Informtion Technology Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Tamil Nadu India Department of Computer Science and Engineering R M K College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Electrical & Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Electrical & Electronics Engineering Annamalai University Tamil Nadu India Department of Electrical Engineering Gujarat Power Engineering & Research Institute Gujarat Technological University Ahmedabad Gujarat India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The integration of big data analytics in power systems has surfaced as a promising approach to influence smart cadence data for perfecting functional effectiveness, enhancing grid trustability, and enabling demand- side operation. This paper explores the operation of big data analytics ways to reuse, dissect, and excerpt precious perceptivity from smart cadence data in power systems. By employing the vast quantum of data generated by smart measures, serviceability can gain a deeper understanding of client energy consumption patterns, identify anomalies, and optimize grid operations in real- time. The paper discusses colorful big data analytics methodologies, including machine literacy algorithms, data mining ways, and prophetic analytics models, applied to smart cadence data for cargo soothsaying, fault discovery, and energy optimization. Through a comprehensive review of being literature and case studies, this exploration elucidates the implicit benefits, challenges, and unborn directions of big data analytics for smart cadence data in power systems, offering precious perceptivity for experimenters, interpreters, and policymakers in the energy sector.

关键词： Reviews Energy measurement Big Data Gain measurement Time measurement Smart meters Real-time systems

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A Novel Framework in Scheduling Packets for Energy-Efficient Bandwidth Allocation in Wireless Networks

A Novel Framework in Scheduling Packets for Energy-Efficient...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： E Sivajothi J. Jayaudhaya S. Santhiya Naresh Kumar Thapa S. Kamatchi N. Bala Sundara Ganapathy Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Electronics and Communication Engineering R.M. D. Engineering College Tamil Nadu India Department of Computer Science and Engineering M. Kumarasamy College of Engineerin Tamil Nadu India Department of Electronics and Communication Engineering R.M.K Engineering College Tamil Nadu India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamil Nadu India Department of Information Technology Panimalar Engineering College Tamil Nadu India

The research work provides a brand-new technique for distributing bandwidth in wireless networks that is also energy-efficient. To begin, look into how channel allocations, which ultimately determine the transmission rate for mobile terminals, affect the fundamental relationship that exists between energy use and transmission rates. The Fatality Selection Algorithm (FSA) and the Receiver Selection Algorithm (RSA) are two methodologies that control connection admittance to reduce the amount of energy used by each individual terminal. Furthermore, provide a corrective strategy for statistically satisfying the quality of service (QoS) criteria throughout the resource allocation process. Throughput, call blockage probability, and the energy consumption rate of each successfully sent bit are used to evaluate the efficiency of the recommended solutions. An extensive investigation into analysis and simulation is carried out in the case of Poisson and self-similar.

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Improving Wart Treatment Efficacy Prediction with Immunotherapy and Cryotherapy Using SVm, RF, CVm, k-NN, mLP, and BLR Algorithms in machine Learning

Improving Wart Treatment Efficacy Prediction with Immunother...

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Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International Conference on

作者： R Sanmugasundaram Parthasarathy Ramadass m.Kiruthiga Devi Unnamalai K Sannasi Rajan. m. V Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information and Technology Dr. M.G.R. Educational and Research Institute Chennai India Department of Computer Science Anna Adarsh College for Women Sidani Innovative Management Services Dubai

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

Warts, characterized by small, rough, benign tumors induced by human papillomavirus (HPV), pose a challenge in treatment prediction due to variations in patient response and disease characteristics. While a machine learning-based expert prediction system and associated rules have been proposed, their success remains unsatisfactory and lacks interpretability for medical professionals. In this study, we employ a decision tree-based approach to delineate rules for predicting wart treatment success. Our findings reveal success rates ranging from 90% to 95% across different treatment methods, surpassing previous reports. Additionally, the decision tree rules generated can be converted into visual representations, enabling intuitive interpretation of treatment success rates in relation to patient age and disease duration. This method offers a straightforward and more precise interpretation of rules for medical experts, facilitating the prediction of treatment success as a percentage.

关键词： Fuzzy logic Visualization Accuracy Computational modeling Immunotherapy Predictive models Decision trees

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Automated Epilepsy Detection System Based On Tertiary Wavelet model(TWm) techniques

Automated Epilepsy Detection System Based On Tertiary Wavele...

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Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： Goldi Soni Vandana Roy Praveen Sharma Prashant Kumar Shukla Sourabh Sahu C. Raja Amity University Chhattisgarh India DoEC Gyan Ganga Institue of Technology and Sciences Jabalpur M.P. India Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P. India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India Department of Electronics and Communication Engineering Gyan Ganga Institute of Technology and Sciences Jabalpur M.P India ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

A computerized method for classifying epilepsy from electroencephalogram (EEG) data using a Tertiary Wavelet model (TWm) as a feature extractor is detailed. Before extracting features such as PFD, HFD, and Approximate Entropy, the feature set is standardized. Then, the initial information is divided into five sub bands—gamma, beta, alpha, theta, and delta—in the suggested method for EEG information. We then use the Kruskal-Walli’s test to identify the traits that vary substantially across the various groups. Afterwards, the notable characteristics are fed into well-known machine learning algorithms as XGBoost, DT, KNN, SVm, and RF. The outcomes show that the Random Forest classifier (with a standard Sen of $\mathbf{9 9. 7 5 7 5 \%}$, Spe of $\mathbf{9 9. 9 1 \%}$, and Acc of $\mathbf{9 9. 1} \%$ in melanoma detection) is the best classification. By classifying the signals into seizure and non-seizure categories, the Bonn University EEG Databases are utilized provides the basis for the system’s overall evaluation. According to the results shown in the picture below, the suggested model outperforms the prior approaches. In epilepsy, this demonstrates that TWm improves both the calculation speed and accuracy of the discriminant in seizure detection.

关键词： Support vector machines Accuracy Sensitivity Computational modeling Epilepsy Feature extraction Brain modeling Electroencephalography Entropy Classification tree analysis

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Exploring Consumer Behavior in E-Commerce with AI Personalization and market Trends Analysis models

Exploring Consumer Behavior in E-Commerce with AI Personaliz...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Gaurav Kumar G. Devayani C Saraswathi R. melba Kani Sandeep Dongre m. S. mohamed mallick School of Computer Application Lovely Professional University Phagwara India Department of Computer Science and Business Systems M.Kumarasamy College of Engineering Karur India School of Management Studies Bannari Amman Institute of Technology Erode India Master of Business Administration Mar Ephraem college of Engineering and Technology Kanyakumari India Symbiosis Institute of Business Management (SIBM) Constituent of Symbiosis International (Deemed University) Nagpur India Department of Computer Science Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology (Deemed to be University) Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Online retailers must monitor and respond to changing consumer trends to stay competitive. AI-driven customization's revolutionary impact on market trends is the focus of this study on online buying and customer behavior. As AI advances, e-commerce platforms will be able to customize user experiences. Preprocessing, feature extraction, and model training comprise the AI-driven personalization process. Customer data from online marketplaces is collected first. It manages missing values, balance the data, encode categorical variables, scale the features, and minimize the dimensions as part of our thorough data preprocessing to ensure fair and applicable results. To trained the algorithms with CNN and Convolutional LSTm Based on key criteria, the AI model beat LSTm and CNN. It provides personalized insights and recommendations with an average accuracy of 92.03%. Personalized experiences and product suggestions enabled by AI-powered personalization have transformed e-commerce. It's important to use smart algorithms to engage customers and examine large databases. This proposed approach to shows that AI can transform online shopping and improve customer experiences.

关键词： Consumer behavior Accuracy Data preprocessing Transforms market research Electronic commerce Convolutional neural networks Integrated circuit modeling Artificial intelligence Long short term memory

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Enhancing Speech Recognition for Hearing-Impaired Individuals Using Deep Learning models

Enhancing Speech Recognition for Hearing-Impaired Individual...

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International Conference on Circuit, Power and Computing technologies (ICCPCT)

作者： K. Kalaiarasi Winson medidhi Niresh Kumar S S. Thenappan R. Sathiyaseelan mohana Sundaram K Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of CSE(Data Science) Prasad V. Potluri Siddhartha Institute of Technology Kanuru Vijayawada Andhra Pradesh India Department of CSE St. Joseph's College of Engineering OMR Chennai Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of Computer Science Engineering Arunai Engineering College Thiruvannamalai Tamil Nadu India Department of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai Thiruvallur India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

This study examines the constraints of current methodologies, specifically in diverse acoustic environments, to surmount the obstacles associated with speech recognition for individuals with hearing impairments. Conventional processes frequently experience reduced accuracy and utility as a result of their incapability to dynamically adjust to evolving circumstances. This study presents an innovative approach that surpasses these constraints through the utilization of a transformer-based model. Through the implementation of sophisticated data augmentation techniques, the proposed model demonstrates exceptional performance across various acoustic environments, notwithstanding obstacles such as speaker adjustments and background noise. One notable characteristic of this methodology is its transformer architecture, which aptly captures subtle speech inflections by employing self-attention processes. Additionally, through independent learning, the model improves its ability to address the unique challenges associated with hearing-impaired speech. Integrating these components, the model achieves superior accuracy and adaptability compared to current technologies.

关键词： Adaptation models Accuracy Speech recognition Auditory system Artificial neural networks Speech enhancement Transformers

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Application of machine Learning Algorithms for Fault Detection and Diagnosis in Power Systems

Application of Machine Learning Algorithms for Fault Detecti...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： m P Haripriya R Durai Vasanth m. Suresh Anand Vikas Vitthal Kulkarni S Farook K.R. Senthil Kumar Department of Computer Science Kristu Jayanti College Bangalore Karnataka India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Computing Technologies School of Computing SRM Institute of Science & Technology Kattankulathur Tamil Nadu India Department of Electrical Engineering AISSMS College of Engineering Pune India Department of Electrical and Electronics Engineering School of Engineering Mohan Babu University Tirupathi Andhar Pradesh India Department of Mechanical Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The operation of machine literacy algorithms for disfigurement identification and opinion within power systems is presented in this exploration composition. In light of the added complexity of moment’s power grids, it’s of the utmost significance to develop fault discovery and opinion styles that are both effective and effective. As a result of their capacity to assay big datasets and honor patterns, machine literacy ways present several openings that hold great pledges. Several different machine learning algorithms, including as neural networks, decision trees, support vector machines, and clustering approaches, are delved in this study. The purpose of the disquisition is to estimate the utility of these algorithms in relating and diagnosing blights in power systems. Through the use of empirical evaluation and case studies, the exploration reveals that these algorithms are applicable in the process of perfecting the trustability and effectiveness of power system operations. This exploration donates to the advancement of fault identification and opinion approaches in power engineering, hence paving the way for further exploration.

关键词： Support vector machines Fault diagnosis Power engineering machine learning algorithms Fault detection Neural networks Clustering algorithms

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A Comprehensive Analysis on Co-Saliency Detection on Learning Approaches

A Comprehensive Analysis on Co-Saliency Detection on Learnin...

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Innovative Practices in technology and management (ICIPTm), International Conference on

作者： Anurag Vijay Agrawal Tadi Satya Kumari m. Soniya S. meenakshi R. Jegadeesan S. Rehan Ahmad Department of Electronics and Communication Engineering ICT Academy Indian Institute of Technology Uttarakhand India Department of Computer Science and Engineering Aditya College of Engineering Andhra Pradesh India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai Tamil Nadu India Department of Science and Humanities (General Engineering) R. M. K. Engineering College Tamil Nadu India Department of Computer Science and Engineering Jyothishmathi Institute of Technology & Science Telangana India Department of Zoology Hiralal Mazumdar Memorial College for Women Kolkata India

Co-saliency recognition is a fast-growing field in computer vision. Co-saliency detection, a unique field of optical sensitivity, relates to the identification of shared and salient backgrounds from two or even more pertinent images, and it is broadly applicable to a variety of computer vision tasks. Various co-saliency detection techniques are composed of three modules: extracting image characteristics, examining informative signals or elements, and creating effective computer foundations to construct co-saliency. Even though several strategies have been created, there hasn't yet been a thorough analysis and assessment of co-saliency prediction methods in the literature. This work intends to provide a thorough analysis of the foundations, difficulties, and implications of co-saliency detecting. An overview is offered based on the connected computer vision works, investigates the detection history, outline and classify the important algorithms and address certain unresolved difficulties, explain the possible co-saliency identification applications as point out certain unsolved obstacles and interesting future appears to work.

关键词： computer vision Sensitivity Prediction methods Prediction algorithms Classification algorithms Optical sensors History Task analysis Saliency detection

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