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Development of Healthcare Model Using AR-DVAE with Mayfly-MLP-BPN for Parkinson's Disease Detection

Development of Healthcare Model Using AR-DVAE with Mayfly-ML...

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2023 International Conference on Data Science and Network Security, ICDSNS 2023

作者： Murthy, G. N. Keshava Pareek, Piyush Kumar Manjunath, T.N. Shwetha, N. Deepak, R. Rashmi, P. Siddaganga Institute of Technology Department of Electronics and Instrumentation Engineering Tumakuru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India East West Institute of Technology Department of Computer Science and Engineering Bengaluru India Bgs Institute of Technology Adichunchanagiri University Department of Electronics and Communication Engineering B G Nagara India Inventuriz Labs Private Limited Bengaluru India

ISBN: (纸本)9798350301595

Research on reliable biomarkers that permit the creation of medical-decision support technologies is currently a major focus in healthcare biometrics. These gadgets aid in the analysis and nursing of conditions like Parkinson's disease (PD). If PD patients' vocal alterations could be detected, this would allow for early diagnosis and treatment, thereby preventing the onset of physically disabling symptoms. More recent research has centred on the suggestion of new automatic and objective tools to aid in diagnosis and severity assessment. In this study, we provide a dual approach to detecting PD. The first section extracts features mechanically with the help of an AR-DVAE model (autoregressive deep variational autoencoder). Lastly, the Mayfly Algorithm is used to maximise the bias weight in a modified multilayer perceptron utilising a backpropagation network (MLP-BPN). To conduct this research, we used the publicly available PD Using Digitized Graphics Tablet dataset. In comparison to other models, the suggested model performed better across the board, as shown by the validation analysis. © 2023 IEEE.

关键词： Decision support systems

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A Review on the Developments in the Field of AI-Based Gait Analysis

A Review on the Developments in the Field of AI-Based Gait A...

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artificial intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Vidhi Wankhede Prateek Verma Shailesh Gahane Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Faculty of Sciences and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The examination of human gait is essential for comprehending motor function, the evolution of diseases, and the effectiveness of rehabilitation in different clinical and research environments. artificial intelligence (AI) advancements have recently improved gait analysis methodologies, resulting in increased accuracy, efficiency, and therapeutic effectiveness. This review article compiles the existing literature on AI-based gait analysis. The review emphasizes the role of AI in gait analysis, namely via the use of vision-based approaches, wearable sensor technologies, markerless motion capture systems, and inertial sensor-based methodologies. AI has shown potential in identifying musculoskeletal problems including osteopenia and sarcopenia, forecasting the start of Parkinson's disease, assessing the effects of post-stroke rehabilitation, and diagnosing neurological conditions. In addition, the integration of AI with new technologies such as augmented reality (AR), smart sensors, and wearable devices presents intriguing opportunities to improve gait rehabilitation, enable individualized therapies, and facilitate remote patient monitoring. Although there have been significant breakthroughs, there are still obstacles in creating AI models that can be easily understood for clinical decision-making, establishing standardized procedures for collecting data, and verifying AI algorithms across various populations. This study underlines the transformational potential of AI-based gait analysis in customized healthcare, rehabilitation techniques, and clinical results. In order to fully harness the potential of AI in enhancing human health and well-being, it is imperative to do more research and foster multidisciplinary cooperation.

关键词： Patient monitoring Musculoskeletal system Reviews Parkinson's disease Medical treatment Motion capture artificial intelligence Wearable devices Gait recognition Wearable sensors

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Hyper Spectral Spatial Image Segmentation Using machine learning Algorithms 1

Hyper Spectral Spatial Image Segmentation Using Machine Lear...

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1st International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems, iTech SECOM 2023

作者： Saravanan, G. Suresh, A. Jagannathan, J. Thanga Mariappan, L. Majeeth, Najeem Dheen Abdul Kumar, Nithish Veltech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu India Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamilnadu Vellore India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Tamilnadu India

ISBN: (纸本)9798350371185

In recent years, there has been a notable surge in the development of methods dedicated to classifying hyper spectral image samples. However, the inherent high dimensionality of hyper spectral data presents significant challenges for traditional supervised classification algorithms. Prior studies have attempted to address these challenges by incorporating active learning methods for hyper spectral data segmentation. Nevertheless, a crucial drawback of active learning lies in its dependence on labeled data samples, leading to less improved classification results for unlabeled data samples. In response to this limitation, our work introduces Supervised Active learning (SAL) techniques that harness both spectral and spatial information. The key innovation of our research lies in the introduction of a novel hyper spectral image segmentation approach based on the k-dependence Bayesian (KDB) model. We estimate the maximizer of the posterior marginal using Symmetric Iterative Proportional Fitting (SIPF) within this KDB model. By implementing the KDB model for hyper spectral image segmentation, our goal is to enhance the classification accuracy of our SAL classifiers. The SAL framework integrates shape prior probability estimation, class density results from Improved Multi-Level Logistic (IMLL) methods, and the maximization of the posterior marginal estimated through Symmetric Iterative Proportional Fitting (SIPF). We evaluate the accuracy of our proposed MPM-SIPF-SAL system using the real ROSIS Hyper Spectral Dataset, demonstrating that our system outperforms existing methods in terms of classification accuracy. © 2023 IEEE.

关键词： Image segmentation

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Feature Selection Based Naïve Bayes Algorithm for Twitter Sentiment Analysis 4

Feature Selection Based Naïve Bayes Algorithm for Twitter S...

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4th International Conference on Emerging Research in Electronics, Computer Science and technology, ICERECT 2022

作者： Aruna, T.M. Asha, K. Divyaraj, G.N. Pareek, Piyush Kumar Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India REVA University Department of Computer Science and Engineering Bengaluru India Sri Venkateshwara College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665456357

A intriguing areas of study now is Twitter sentiment investigation. It fuses the data mining methods used to create such systems with natural language processing techniques. The majority of currently available methods for analyzing Twitter sentiment do not do well when presented with messages that are both brief and ambiguous, since this is the only kind of information they take into account. Better classification outcomes are guaranteed if the right collection of features is used to identify emotion in online textual material. The computational difficulty of doing optimal feature selection drives the need for the development of creative approaches to enhancing classifier performance. In this work, an effective method for analyzing Twitter user sentiment was presented. A machine learning model was developed by the suggested method to identify good and negative tweets. Characteristics are extracted after pre-processing, and it's possible that include irrelevant features can lower classification accuracy. For this reason, the model implemented the Vortex Search Algorithm (VSA) to choose the best characteristics and discard the rest. The final tweets categorization is done using the Naive Bayes (NB) algorithm. A total of four Twitter datasets are used for the studies, each measuring a unique set of parameters and made accessible to the public. Marketing, detecting political polarization, and product reviews are just a few of the many areas that may benefit from the suggested system's ability to gauge user sentiment based on tweets. © 2022 IEEE.

关键词： Sentiment analysis

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Peli-Remo Optimization Algorithm for Reliable routing in MANET: K-means node clustering process and CNN-based Energy Prediction

Peli-Remo Optimization Algorithm for Reliable routing in MAN...

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

作者： Manjula A. Biradar Sujata Mallapure Department of Computer Science and Engineering Sharnbasava University Sharan Nagar Kalaburagi Karnataka India Department of Artificial Intelligence and Machine Learning Faculty of Engineering & Technology (Exclusively for Women) Sharnbasava University Sharan Nagar Kalaburagi Karnataka India

MANET, an ad hoc wireless network is made up of several nodes connected, governed by certain rules for packet movement from one node point to another. The structure of communication may be designed using a variety of protocols. For an ad hoc network, routing protocols are used to manage the connections. To improve communication, this article suggests an effective MANET cluster-based routing paradigm for multimedia communication. The clustering process is started using the K-means clustering method. The Deep Convolutional Neural Network (DCNN) model will first predict the node energy. The cluster head will be chosen using the Hybrid Remora with Pelicon Optimization technique (HRPOA), which integrates the Remora optimization technique and the Pelican optimization algorithm. The HRPOA model's channel quality for round 500 is around 1.56, whereas the conventional techniques obtained the lowest channel capacity, with BWO=~0.5, GWO=~0.78, BA=~0.98, POA=~0.57, and ROA=~0.78, in that order.

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Reseek on The Optimization Mould and Equation of E-Commerce Website Composition Based on Markov Mould 2

Reseek on The Optimization Mould and Equation of E-Commerce ...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Annie Vidhya, V. Suseela, D. Ganesh, S. Sankar Al-Jawahry, Hassan M. Murali, S. New Prince Shri Bhavani College Of Engineering And Technology Chennai India M.E.T Engineering College Nagercoil India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology India Malkajgiri DistrictTelangana State Medchal India College of technical engineering Islamic university Najaf Iraq Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798350305418

The quality of e-commerce website directly affects the competitiveness of enterprises in the electronic market environment, so how to improve and optimize the website has become a reseek topic that people are very concerned about. The quality of business websites directly affects the competitiveness of enterprises in the electronic market environment, so how to improve and optimize the composition of e-commerce websites has become a question that people are very concerned about. Since the optimization mould has many constraints and traits such as discreteness, it is very difficult to solve the above mould by using the existing graph principle equation. The Markov model, which is a statistical model with a double random path and has seen widespread use in the fields of speech recognition, creaturely sequence analysis, and management, serves as the foundation for this research. The Hidden Markov Model is built on a solid theoretical foundation consisting of probability and statistics, in addition to a robust mathematical framework. Its first applications were in the field of speech recognition and the analysis of creaturely sequences. Over the course of the last few days, individuals have started to investigate and investigate its applicability in the field of management. © 2023 IEEE.

关键词： e-commerce website equation Markov mould structural optimization mould

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Modified EfficientNetB3 Deep learning Model to Classify Colour Fundus Images of Eye Diseases 5

Modified EfficientNetB3 Deep Learning Model to Classify Colo...

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5th IEEE International Conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2023

作者： Sharma, Riya Gangrade, Jayesh Gangrade, Shweta Mishra, Ashish Kumar, Gautam Kumar Gunjan, Vinit Manipal University Jaipur Dept of Computer Science & Engineering Jaipur India Learning Manipal University Jaipur Dept. of Artificial Intelligence & Machine Jaipur India Manipal University Jaipur Dept of Information Technology Jaipur India Jaypee Institute of Information Technology Dept of Computer Science & Engineering Noida India CMR Institute of Technology Department of Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350338287

The only way to prevent blindness from eye problems is by early detection and prompt treatment. Although colour fundus photography (CFP) is useful for fundus inspection, there is a need for computer-assisted automated diagnosis tools due to the similarities between the early symptoms of many eye disorders. The suggested approach uses cutting-edge deep learning model to categorize images into several disease categories by learning distinguishing features from the input images. The high-resolution fundus photos from individuals with diabetic retinopathy (DR), glaucoma, cataract, and healthy eyes make up most of the dataset used for this research. The experimental findings show that the suggested system achieve 97% accuracy with modified efficientNetB3 model and surpasses current approaches for categorizing eye diseases. This approach may help doctors diagnose and treat eye conditions earlier, leading to better patient outcomes. © 2023 IEEE.

关键词： Color

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Exploring Cybersecurity Research Areas, Categories, and Limitations Utilizing an Interdisciplinary Approach

Exploring Cybersecurity Research Areas, Categories, and Limi...

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IEEE UP Section Conference on Electrical Computer and Electronics (UPCON)

作者： Vinayak Mittal Mamta Martolia Payal Chhabra Arun Pratap Srivastav Department of Cyber Security College of Engineering Roorkee Artificial Intelligence and Machine Learning Krishna Institute of Engineering and Technology Ghaziabad Lloyd Institute of Engineering & Technology Greater Noida

Rapid technological advances have brought unprecedented levels of connectivity and security, but also new and complex cybersecurity challenges The purpose of this paper is to provide an in-depth insight into current cybersecurity research areas, divided into specific areas and defined boundaries in each Poetry, Cryptography, Law, Psychology, It also integrates ideas from other relevant fields In the face of increasing cyber threats, this paper provides a comprehensive insight into the multifaceted aspects of cybersecurity. Computer science, cryptography, law, psychology, research in related fields, applying central topic vision, studying current security research fields, qualifying on the side in human areas, establishing each class, major research boundaries and selection, technology, agricultural line, The conclusions broaden the light of the complex interpersonal contribute to smaller understandings of interdisciplinary cybersecurity, which build emphasizing the need for collaborative and adaptive solutions to meet the changing cyber environment

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Breast Cancer Classification Model Using Principal Component Analysis and Deep Neural Network 1

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World Conference on Information Systems for Business Management, ISBM 2022

作者： Sindhuja, M. Poonkuzhali, S. Vigneshwaran, P. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Tamil Nadu Thandalam Chennai602105 India Department of Computer Science and Engineering Rajalakshmi Engineering College Tamil Nadu Thandalam Chennai602105 India Department of Networking and Communications SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chengalpattu603203 India

ISBN: (数字)9789811974472

ISBN: (纸本)9789811974465

Life-threatening cancer is prevalent over the globe. According to statistics, most people are diagnosed with cancer in the later stages, even though cancer can be prevented and cured in early stages. The goal of this research is to diagnose a breast cancer is either benign or malignant, as well as to forecast the likelihood of a cancer recurrence even after a course of therapy has been completed. Despite the fact that many machine learning algorithms have resulted in strong predictions, the accuracy in the early stages of categorization is not up to the expected level. Deep learning (DL), a higher degree of machine learning, can forecast breast cancer types and recurrences. Classifiers were built using a deep neural network (DNN) that used Principal Component Analysis (PCA) to choose features. Different machine learning techniques are compared with the proposed system’s accuracy. Early-stage breast cancer prediction is more accurate with the DNN-based method. The clinical management system will benefit from the proposed system since it will aid in identification of cancer at an early stage and the subsequent provision of appropriate therapy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Dynamic Patient Triage Optimization in Healthcare Settings Using RNNs for Decision Support 10

Dynamic Patient Triage Optimization in Healthcare Settings U...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Ranganathan, Chitra Sabapathy Basavaraddi, Chethan Chandra S Saillaja, V. Pandey, Pramod Sundaramurthy, B. Murugan, S. Mphasis Corporation Tamil Nadu Chennai India Artificial Intelligence and Machine Learning R&D Don Bosco Institute of Technology VTU Karnataka Belagavi India SRM Institute of Science and Technology Faculty of Management Studies Tamil Nadu Chennai India Nagpur Campus Maharashtra Pune India Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350363104

The article presents a sophisticated healthcare system that uses recurrent neural networks (RNNs) to optimize real-time patient triage. The developed model integrates patient data from the Internet of Things (IoT), and it performs dynamic assessments of vital indicators like heart rate, blood pressure, and temperature to prioritize pre-operation care based on the urgency and severity of diseases. The RNN architecture considers the temporal connections included in the data, which enables a more sophisticated comprehension of the changing patient states. The training and evaluation of the model make use of a large dataset. In comparison to more conventional triage methods, the model displays considerable gains in both accuracy and efficiency. The system not only reduces the amount of time needed to respond and allocate resources, but it also improves the flexibility to react to shifting patient conditions. This research represents a significant step toward the development of intelligent decision support systems in the healthcare industry. It demonstrates the potential of more sophisticated machine learning approaches to transform patient care processes in environments with high stakes and a dynamic nature. © 2024 IEEE.

关键词： Recurrent neural networks

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