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Machine Learning Techniques in Epileptic Seizure Detection: A Comprehensive Review

Machine Learning Techniques in Epileptic Seizure Detection: ...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Vishal Thakare Rekha Ranawat Dept. of computer Science and Engineering SAGE University Indore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The neurological disorder known as epilepsy is defined by its recurrent, spontaneous episodes of seizures impacting millions of people globally. The timely and accurate detection of epileptic seizures is necessary for a successful administration and ensuring patient safety. In recent times, techniques using machine learning have emerged as promising solutions to automate the detection of seizures. This review paper presents a thorough overview of the latest advancements in the identification of epileptic seizures through the application of machine learning, encompassing data sources, extraction of features, application of classification algorithms, use of performance evaluation metrics, addressing various challenges, and future directions. In addition, we put forth a novel methodology in order to enhance seizure detection accuracy by integrating the models of deep learning. Our approach uses multiple modalities for seizure prediction with cross-model training to mitigate data scarcity. Through a rigorous analysis of existing literature and our proposed methodology, this paper provides insightful information for practitioners and researchers looking into the detection of epileptic seizures.

关键词： Deep learning Training Accuracy Reviews Soft sensors computer architecture Feature extraction Electroencephalography Safety Long short term memory

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DevOps Practices Into Machine Learning

DevOps Practices Into Machine Learning

引用

Intelligent Systems, Smart and Green Technologies (ICISSGT), IEEE International Conference on

作者： Samridhi Jain Puneet Kumar dept. of Computer Science & Engineering Chandigarh University Mohali India

ISBN: (数字)9798350304329

ISBN: (纸本)9798350304336

All industrial machine learning (ML) projects have their ultimate objective to quickly develop and deploy ML solutions. However, a lot of machine learning projects are failing, and never reach production. In order to address these concerns, this paper aims to investigate how manual ML processes can be automated and operationalised so that more ML concept can be put into production. The advantages of DevOps automation and machine learning capabilities are now being combined by industries. MLOps is the name of this combination. Our analysis found that while there are some similar aspects in defining MLOps, still there is a lack of standardisation in the term, which can occasionally lead to misunderstandings and uncertainty. We carried out the research, which includes a review of the literature and an evaluation of the MLOps tools, to fill this gap. These investigations have led us to define MLOps and identify unresolved issues in the field. In addition to this, we offer a workflow-based summary of all essential components. Finally, this paper gives guidance for applying DevOps practises to machine learning for researchers and practitioners.

关键词： Industries DevOps Uncertainty Reviews Pipelines Machine learning Production Delays Intelligent systems Testing

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Leveraging Machine Learning to Enhance Crime Prediction and Community Safety

Leveraging Machine Learning to Enhance Crime Prediction and ...

引用

International Conference on ent Computing and Control Systems (ICICCS)

作者： S. Jancy Gunda Venkata Vamsi Krishna Gurrampati Ajith Reddy Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Understanding crime patterns is essential for effectively addressing criminal behavior. The study analyzes crime data from Indore, publicly available on the Indore Police website, to generate predictions for the types of crimes likely to occur at specific times and locations within the city. The study employs AI and machine learning algorithms to investigate their efficacy in crime prediction, as these technologies have demonstrated success in audio and visual-based crime detection. Such systems have challenges demonstrating their dependability to legal authorities, mainly when employed for event prevention objectives. The research addresses classification issues by categorizing diverse crime types into generalized classifications, enhancing classification systems' significance and practical utility. This study evaluates various machine learning classifiers to identify the optimal classification approach and demonstrates the impact of our results. The study illustrates the capabilities of an AI-driven crime prediction system and offers ideas for improving crime prevention strategies through future research.

关键词： Machine learning algorithms Law enforcement Computational modeling Scalability Prevention and mitigation Urban areas Predictive models Prediction algorithms Data models Computational efficiency

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Detecting Digital Financial Transaction Frauds Using CNN Model

Detecting Digital Financial Transaction Frauds Using CNN Mod...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Chadalavada Ester Rani V. Purushothama Raju Dept. of Computer Science & Engineering Shri Vishnu Engineering College for Women(A) Bhimavaram India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

The Unified Payments Interface has become increasingly popular as a convenient and user-friendly digital payment platform. However, the surge in Digital Transaction has also resulted in a rise in fraudulent activities, presenting a significant security challenge. This paper introduces a novel approach to detecting fraudulent Digital Transaction by leveraging convolutional neural networks (CNN). By incorporating the CNN model, this study aims to offer a cost-effective solution for identifying Digital Financial fraud. The CNN's ability to analyse patterns in data makes it particularly suitable for fraud detection in Digital Financial Transactions. We evaluate our proposed methodology using a comprehensive dataset that includes both fraudulent and genuine transactions. Our results demonstrate a significant improvement in fraud detection accuracy and efficiency compared to existing methods. This research contributes to the advancement of secure digital payment systems by presenting an innovative approach to detect fraud transactions. Financial institutions and payment service providers can utilize this methodology to enhance the security of Digital Financial Transactions and protect users from fraudulent activities.

关键词： Training Costs Accuracy Estimation Surge protection Fraud Convolutional neural networks Security Usability Surges

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Outage Management of Power Distribution Systems with Electricity-Dependent Medically-Vulnerable Critical Loads 59

Outage Management of Power Distribution Systems with Electri...

引用

59th IEEE/IAS Industrial and Commercial Power Systems Technical Conference, I and CPS 2023

作者： Rostamzadeh, Mehdi Kapourchali, Mohammad Heidari Zhao, Long Aravinthan, Visvakumar Wichita State University Department of Electrical and Computer Engineering WichitaKS United States University of Alaska Anchorage Department of Electrical Engineering AnchorageAK United States South Dakota School of Mines and Technology Dept of Electrical Engineering and Computer Science Rapid CitySD United States

ISBN: (纸本)9798350396515

By deploying mobile power sources and using their network topology effectively, electric utilities can enhance resilience during power outages. This paper develops an outage management approach for power distribution systems in which loads equipped with electricity-dependent medical devices are considered as critical loads. Load criticality is determined by the distance of the load from shelters, the type of medical device, the socio-economic status of the customers, and the average age of the customers at medically vulnerable load points. The optimization model based on mixed-integer linear programming optimizes network reconfiguration, the installation of mobile power sources, and the curtailing of minimum critical loads. The model includes the constraints for a three-phase network configuration for practical grid representation. Optimal restoration schemes are obtained by maximizing restored weighted loads during restoration. In order to deal with uncertainty and imprecise judgments, a fuzzy modification of the analytical hierarchical process (FAHP) is used to prioritize the loads during restoration. The proposed method is tested on the modified IEEE 34-bus distribution test system under several scenarios. The outcome of this study can provide a strategy for power distribution grid operators to manage outages in the presence of loads with electricity-dependent medical devices. © 2023 IEEE.

关键词： Restoration

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Integrating Fuzzy Logic with Supply Chain Management

Integrating Fuzzy Logic with Supply Chain Management

引用

Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Afrah Fathima. S Muqaddam Aaqil Sheriff Addepalli Raja Vyshnavi Jananee V Dept of Computer Science Rajalakshmi Engineering College

Supply chain management concepts are of great importance in the current era. It is also famously quoted that "Whoever controls the spice, controls the whole world". Transportation of goods from one place to another and at the right time is very essential. The two major Parties playing huge roles in the whole concept are the suppliers and the manufacturers. The supplier is the one who is responsible for supplying raw materials to the manufacturer. The manufacturer has to hunt down for suppliers which is a tedious task. The success of the supply chain as a whole has a significant impact on the success of a manufacturing firm. The demand for specific goods is determined by all supply chain participants, who subsequently supply the raw materials needed to make those goods and complete consumer orders. The chain as a whole could be in danger if one link breaks or becomes weak. On the other hand Artificial Intelligence plays a very important role in automating and taking right decisions. Without the role of Artificial Intelligence(Fuzzy Logic) our lives could be unpredictable on really different levels. Primarily the type of Intelligence which is portrayed by machines in contrast to that of human beings. The Data that the AI model has could be of great significance to SCM specialists. These two concepts can be put into use and certain major problems that SCM specialists face can be solved with the help of Artificial Intelligence.

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Reconstruction of Elasticity Estimates in Shear Wave Elastography through Relaxed Frame Rate Acquisition Combined with Radial Basis Function Interpolation

Reconstruction of Elasticity Estimates in Shear Wave Elastog...

引用

European Signal Processing Conference (EUSIPCO)

作者： Sajjad Afrakhteh Libertario Demi Dept. of Information Engineering and Computer Science University of Trento Italy

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Over the past decades, ultrasound elasticity imaging has become indispensable for assessing tissue stiffness. Achieving a high frame rate acquisition in shear wave elastography (SWE) is critical for accurately capturing the transient propagation of shear waves. However, attaining exceptionally high frame rates presents challenges. This study aims to mitigate the need for such high frame rates in precise SWE assessments. To accomplish this, we propose a lower frame rate acquisition coupled with radial basis function (RBF) interpolation. We applied this technique to an experimental dataset and found that it performed precisely in estimating shear wave speed (gSWS). Specifically, the structural similarity index (SSIM) between the acoustic radiation force (ARF) reconstructed images of the proposed low frame rate and original high frame rate strategy exceeds 96%, and even at a down-sampling (DS) rate of 4, the reconstruction errors in gSWS, phase velocity, and dispersion remain below 5%.

关键词： Interpolation Ultrasonic imaging Force Imaging Elasticity Indexes Elastography Transient analysis Image reconstruction Dispersion

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Hybrid Deep Learning Model for Open-Set Seed Classification Using Hyperspectral and RGB Imaging

Hybrid Deep Learning Model for Open-Set Seed Classification ...

引用

Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Kishore Jawahar G. Muthupandi Shopika S Pavithra S Dept of Computer Science and Engineering Chennai Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

The classification of seed varieties is important to maintain the productivity of agriculture and check fraudulence and the control of quality in seed businesses. The earlier approaches for the seed classification conventional techniques are tedious and rudimentary, besides being to an extent, arbitrary and though permissible they have low scalability, especially to big data or even as real-time solutions. This paper is a suggestion of using hybrid deep learning framework open to hyperspectral imaging with HSI and RGB Imaging. To this end, the newly proposed Dual Deep Support Vector Data Description model and a vision transformer-based architecture are used for our classification task of known seed types along with the rejection of unknown seeds. The framework uses incremental learning techniques, and spatial-spectral features as well as attention-based bands to enhance the scalability of the technique and its performance. Comparing the results of the present work with previous techniques, it can be concluded that the proposed methods of hybrid imaging and improved deep learning model may help to solve the problems of classic, as well as open-set recognition in agriculture. Future studies could apply this approach to other crops with the goal to pursue precision agriculture and improve world’s food safety.

关键词： Deep learning Precision agriculture Incremental learning Accuracy Scalability Imaging Transformers Vectors Seeds (agriculture) Hyperspectral imaging

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Enhancing Human Action Recognition in Videos through Dense-Level Features Extraction and Optimized Long Short-Term Memory

Enhancing Human Action Recognition in Videos through Dense-L...

引用

Electronics, Communications, and Control engineering (ICECC), International Conference on

作者： Najmul Hassan Abu Saleh Musa Miah Jungpil Shin Dept. of computer science and engineering The University of Aizu Aizuwakamatsu Japan

ISBN: (数字)9798350367195

ISBN: (纸本)9798350367546

Human action recognition (HAR) in videos is a field currently receiving considerable attention in computer vision and pattern recognition. While numerous researchers have been working on developing a HAR system, they still face challenges in achieving satisfactory performance in video-based activity recognition systems. Video-based action recognition poses a significant research challenge due to temporal feature dependency. Currently, there is a demand to develop dynamic HAR systems that can deliver high-performance accuracy with generalizability. In order to address these shortcomings, we propose a dynamic HAR system by leveraging a deep learning (DL) based temporal feature extraction approach. In this process, we initially use the DL-based DenseNet121 model to extract frame-level dense features. Subsequently, we feed these features into an optimized Long Short-Term Memory (LSTM) network to learn dependencies and process data for optimal predictions. Furthermore, during the testing phase, an iterative fine-tuning procedure is incorporated to update the high parameters of the trained system, aiming to achieve optimal results. Experiments conducted on a benchmark dataset demonstrate superior performance in terms of both accuracy and losses compared to existing methods.

关键词： Deep learning Human computer interaction Accuracy Face recognition Feature extraction Human activity recognition Iterative methods

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A CNN Based Underwater Aquatic Exploring System for Real Time Identification of Fish Species

A CNN Based Underwater Aquatic Exploring System for Real Tim...

引用

International Conference on computer and Communications Technologies (ICCCT)

作者： Ashok P Bhoomika V Janani A Lavanya S Dept of Computer Science and Engineering Sri Sairam Institute of Technology Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

The application of machine learning models is increasing rapidly, particularly solving challenges within underwater environments. This research paper introduces a machine learning model designed for the detection of underwater species and garbage waste in underwater *** systems are lacking due to the inadequate dataset real time classification and misclassifications of images. These systems typically identify only a small number of species because of the difficulties associated with varying underwater light conditions. This research proposes a new approach by utilizing the YOLO algorithm for the detection and classification of underwater images. The system functions by capturing underwater images, which are then preprocessed to improve the features. The dataset consists of 600 images, with 7 different classes and splitted into training and testing. The proposed method detects and classifies underwater objects with an overall precision of 95% to 97%. This research helps marine researchers and students in identifying underwater species and helps to improve underwater environments.

关键词： YOLO Training Waste materials Image processing Machine learning Fish Real-time systems Communications technology Classification algorithms Testing

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