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A novel end-to-end approach for epileptic seizure classification from scalp EEG data using deep learning technique

International Journal of Information Technology (Singapore) 2023年第8期15卷 4223-4231页

作者： Kumar, Puranam Revanth Shilpa, B. Jha, Rajesh Kumar Mohanty, Sachi Nandan Department of Electronics and Communication Engineering Faculty of Science and Technology (IcfaiTech) ICFAI Foundation for Higher Education Hyderabad India School of Computer Science and Engineering (SCOPE) VIT-AP University Andhra Pradesh Amaravathi India

Early detection and proper treatment of epilepsy seizure is essential and meaningful to those who suffer from this disease. Symptoms of seizures are confusion, abnormal gazing, and obsessive hand movements. Neurological exams, blood testing, cognitive tests, and neuroimaging modalities are some of the techniques used to detect epileptic seizure activity in patients. Among them, neuroimaging techniques have attracted a lot of interest from doctors in the field. The use of computer-aided diagnostic systems (CADS) based on deep learning (DL) and neuroimaging modalities can help quickly and accurately diagnose epileptic seizures. This research introduces a novel seizure detection approach based on a bidirectional long short-term memory (Bi-LSTM) network. As a result, Bi-LSTM preserves the nonstationary nature of electroencephalogram (EEG) data while decreasing processing costs by employing local mean decomposition (LMD) and statistical feature extraction algorithms. Two distinct LSTM networks with opposing propagation directions are combined in the deep architecture. So, the deep model can use information from both before and after the current time of analysis to decide the output state. On a long-term scalp EEG database, an average accuracy of 97%, sensitivity of 95.70%, specificity of 93.90%, and G-Mean of 94.80% were achieved, with an anticipation time of 10 min. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Automatic seizure detection Bi-LSTM Deep learning EEG Data Epilepsy Local Mean Decomposition (LMD)

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Augmented Reality for Public Engagement in Sustainable City Planning: Cloud and Machine Learning Integration

Augmented Reality for Public Engagement in Sustainable City ...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Sabitha, R. Gowriswari, S. Yuvaraj, S. Gayathri Devi, M. Babuji, R. Murugan, S. Rajalakshmi Engineering College Department Of Computer Science And Engineering Tamil Nadu Chennai India Sri Sairam Engineering College Department Of Instrumentation And Communication Engineering Tamil Nadu Chennai India Srm Institute Of Science And Technology Faculty Of Engineering And Technology Department Of Electronics And Communication Engineering Kattankulathur Tamil Nadu Chennai India Department Of Biomedical Engineering Tamil Nadu Rasipuram India R.M.K. Engineering College Department Of Electronics And Communication Engineering Tamil Nadu Chennai 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)9798350371567

This paper explores how the convergence of augmented reality (AR), cloud computing, and machine learning might transform public engagement in sustainable city planning in today's dynamic urban environment. Citizens may experience proposed urban developments in unprecedented detail, including their possible social, environmental, and infrastructural impacts, due to the use of augmented reality. The potential for AR, cloud computing, and Support Vector Machines (SVM) to dramatically increase citizen participation in green urban design is investigated. With AR, individuals may explore potential new developments in virtual cityscapes. Intricate linkages within the dynamic dataset created during public participation are captured by the system's usage of SVM to evaluate and understand user comments. The ability of SVM to deal with non-linear patterns is crucial to gleaning useful insights from stakeholders' complex and varied contributions. In order to ensure that all parties involved in a system have access to the most up-to-date information, cloud computing plays a crucial role by enabling real-time collaboration and changes to the AR models This all-encompassing method not only improves the effectiveness of eco-friendly city planning, but also encourages openness and participation in policymaking. The suggested system represents a paradigm shift, demonstrating that innovative technology may be combined with informed citizenry to produce ecologically responsible and socially dynamic cities. © 2024 IEEE.

关键词： Support vector machines

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An Efficient ResNetSE Architecture for Smoking Activity Recognition from Smartwatch

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Intelligent Automation & Soft Computing 2023年第1期35卷 1245-1259页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering Image Information and Intelligence LaboratoryFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication Technology University of PhayaoPhayao56000Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok 10800Thailand

Smoking is a major cause of cancer,heart disease and other afflictions that lead to early *** effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment *** activities often accompany other activities such as drinking or ***,smoking activity recognition can be a challenging topic in human activity recognition(HAR).A deep learning framework for smoking activity recognition(SAR)employing smartwatch sensors was proposed together with a deep residual network combined with squeeze-and-excitation modules(ResNetSE)to increase the effectiveness of the SAR *** proposed model was tested against basic convolutional neural networks(CNNs)and recurrent neural networks(LSTM,BiLSTM,GRU and BiGRU)to recognize smoking and other similar activities such as drinking,eating and walking using the UT-Smoke *** different scenarios were investigated for their recognition performances using standard HAR metrics(accuracy,F1-score and the area under the ROC curve).Our proposed ResNetSE outperformed the other basic deep learning networks,with maximum accuracy of 98.63%.

关键词： Smoking activity recognition deep residual network smartwatch sensors deep learning

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Industrial Automation Information Analogy for Smart Grid Security

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computers, Materials & Continua 2022年第5期71卷 3985-3999页

作者： Muhammad Asif Ishfaq Ali Shahbaz Ahmad Azeem Irshad Akber Abid Gardezi Fawaz Alassery Habib Hamam Muhammad Shafiq Department of Computer Science National Textile UniversityFaisalabad37610PunjabPakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabadPakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Industrial automation or assembly automation is a strictly monitored environment,in which changes occur at a good *** are many types of entities in the focusing environment,and the data generated by these devices is *** addition,because the robustness is achieved by sensing redundant data,the data becomes *** data generating device,whether it is a sensing device or a physical device,streams the data to a higher-level deception device for calculation,so that it can be driven and configured according to the updated *** the emergence of the Industry 4.0 concept that includes a variety of automation technologies,various data is generated through numerous ***,the data generated for industrial automation requires unique Information Architecture(IA).IA should be able to satisfy hard real-time constraints to spontaneously change the environment and the instantaneous configuration of all *** understand its applicability,we used an example smart grid *** smart grid system needs an IA to fulfill the communication requirements to report the hard real-time changes in the power immediately following the *** addition,in a smart grid system,it needs to report changes on either side of the system,i.e.,consumers and suppliers configure and reconfigure the system according to the *** this article,we propose an analogy of a physical phenomenon.A point charge is used as a data generating device,the streamline of electric flux is used as a data flow,and the charge distribution on a closed surface is used as a ***,the intensity changes are used in the physical process,e.g.,the smart *** analogy is explained by metaphors,and the structural mapping framework is used for its theoretical *** proposed analogy provides a theoretical basis for the development of such information architectures that can represent data flows,definition changes(deterministic and non-deterministic),events,and i

关键词： Industry 4.0 information architecture industrial automation

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Cyber Twin-Enabled Real-Time AI Orchestration for Advanced IoT Security and Dynamic Software Optimization 4

Cyber Twin-Enabled Real-Time AI Orchestration for Advanced I...

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4th IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Sunkara, Krishna Chaitanya Barmola, Pareshwar Prasad Ravi, K.S. Shukla, Vishal Soni, Umang Sapatnekar, Amol Oracle Cloud Engineering Data Center Design & Modeling NC United States Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Vidyavardhaka College of Engineering Department of Mechanical Engineering Mysuru India Ramdeobaba University Department of Mechanical Engineering Nagpur India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electronics & Communication Engineering Gujarat India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The rapid expansion of Internet of Things (IoT) ecosystems has increased the complexity and vulnerability of software systems, making them more susceptible to security breaches and performance inefficiencies. Traditional security and optimization techniques struggle to keep up with the dynamic nature of IoT networks. This calls for the development of advanced frameworks that can adapt in real-time to evolving threats and operational requirements. This paper introduces Cyber Twins, an AI-driven framework designed for real-time security and software optimization in IoT environments. Cyber Twins leverage artificial intelligence and digital twin technology to create dynamic cyber representations of physical IoT devices and software processes. The framework integrates reinforcement learning for adaptive optimization, convolutional neural networks (CNNs) for anomaly detection, and graph neural networks (GNNs) for real-time threat analysis. By continuously synchronizing with live IoT data, Cyber Twins enable proactive threat detection and autonomous system optimization, ensuring a secure and high-performing IoT ecosystem. The proposed framework combines Federated Learning (FL) for distributed intelligence and Reinforcement Learning (RL) for adaptive decision-making. Key advancements include (1) a 35% improvement in intrusion detection accuracy compared to traditional methods, (2) a 40% reduction in false positive rates, and (3) a 25% increase in dynamic software adaptation speed. Experiments conducted on a simulated IoT environment with 1,000 devices demonstrated scalability and resilience under varying network conditions. Additionally, the framework achieved a 20% decrease in latency during AI model updates and a 30% reduction in computational overhead, ensuring efficient resource utilization. © 2024 IEEE.

关键词： Federated learning

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Pre-Impact and Impact Fall Detection Based on a Multimodal Sensor Using a Deep Residual Network

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Intelligent Automation & Soft Computing 2023年第6期36卷 3371-3385页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayaoThailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok 10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North BangkokBangkok 10800Thailand

Falls are the contributing factor to both fatal and nonfatal injuries in the ***,pre-impact fall detection,which identifies a fall before the body collides with the floor,would be ***,researchers have turned their attention from post-impact fall detection to pre-impact fall ***-impact fall detection solutions typically use either a threshold-based or machine learning-based approach,although the threshold value would be difficult to accu-rately determine in threshold-based ***,while additional features could sometimes assist in categorizing falls and non-falls more precisely,the esti-mated determination of the significant features would be too time-intensive,thus using a significant portion of the algorithm’s operating *** this work,we developed a deep residual network with aggregation transformation called FDSNeXt for a pre-impact fall detection approach employing wearable inertial *** proposed network was introduced to address the limitations of fea-ture extraction,threshold definition,and algorithm *** training on a large-scale motion dataset,the KFall dataset,and straightforward evaluation with standard metrics,the proposed approach identified pre-impact and impact falls with high accuracy of 91.87 and 92.52%,*** addition,we have inves-tigated fall detection’s performances of three state-of-the-art deep learning models such as a convolutional neural network(CNN),a long short-term memory neural network(LSTM),and a hybrid model(CNN-LSTM).The experimental results showed that the proposed FDSNeXt model outperformed these deep learning models(CNN,LSTM,and CNN-LSTM)with significant improvements.

关键词： Pre-impact fall detection deep learning wearable sensor deep residual network

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Image Enhancement of Chest X-Ray Quality Using Nonlinear and Spatial Filter for Noise Reduction 3rd

Image Enhancement of Chest X-Ray Quality Using Nonlinear and...

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3rd International Conference on Signal and Data Processing, ICSDP 2023

作者： Bhargava, Ritu Singh, Surbhi Bhargava, Neeraj Rathore, Pramod Singh Panwar, Avinash Ajmer India MDS University Ajmer India Professor School of Engineering and System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur Jaipur India Mohanlal Sukhadia University Udaipur India

ISBN: (纸本)9789819795772

This paper primarily deals with spatial domain filtering methods as a means of enhancing image quality. Images often suffer from various types of noise, such as Gaussian noise, speckle noise, and salt-and-pepper noise, which can obscure vital information within the image. To mitigate these noise artefacts, this study seeks spatial domain techniques, encompassing both linear and nonlinear spatial filtering methods. Furthermore, the paper explores the practical application of convolutional neural networks to enhance the quality of chest X-ray images using spatial filtering techniques for noise reduction. The experimentation involves two distinct chest X-ray images, serving as the basis for all computational assessments. Ultimately, the outcomes of this investigation are illustrated through graphical representations, detailing the processing time elapsed for both chest X-ray images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Salt and pepper noise

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A Rule-Based Deep Learning Method for Predicting Price of Used Cars 4th

A Rule-Based Deep Learning Method for Predicting Price of Us...

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4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Ayo, Femi Emmanuel Awotunde, Joseph Bamidele Misra, Sanjay Ajagbe, Sunday Adeola Mishra, Nishchol Department of Computer Science McPherson University Seriki-Sotayo Abeokuta Nigeria Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Computer Science and Communication Ostfold University College Halden Norway Department of Computer Engineering Ladoke Akintola University of Technology Ogbomoso Nigeria Rajiv Gandhi Technical University Bhopal India

ISBN: (纸本)9789819900466

The growth of internet used automobile shopping has been tremendous in recent years. It is important for sellers to correctly evaluate the price of the used cars. From the literature, classification is an approach that assists in data interpretation by analyzing data and providing a second opinion on data among other tasks most of the classifiers used for car price prediction suffered from lower accuracy ratios and higher error ratios. In this work, a hybrid feature selection model for predicting the price of used cars was developed. The developed model includes a CfsSubsetEval to determine the best feature subset, rule-based GA to search for the best solution, and a neural network for the final price prediction based on the selected features. The developed model was implemented in a Java programming language. The evaluation showed that the developed hybrid feature selection model achieved better performances with the correlation coefficient, mean absolute error and root mean square error of 0.9818, 1.1809, and 1.5643 respectively, compared to other related state-of-the-art algorithms. The results validated the efficiency of the developed model for used car price prediction. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Java programming language

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Wireless Liberty: Sensors Measuring Pressure and Temperature

Wireless Liberty: Sensors Measuring Pressure and Temperature

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2023 International Conference on Advances in Computation, communication and Information Technology, ICAICCIT 2023

作者： Chaudhary, Pavan Vanithalakshmi, M. Raja Praveen, K.N. Maharishi University of Information Technology School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

The continuous collection of physiological data, especially temperature and pressure, throughout the human body, using wireless, skin-adherent detectors based on NFC (near-field communication) technology, is presented in this research study. To better understand human health, forecast illnesses, and avoid problems like pressure ulcers, the article stresses the significance of monitoring skin temperature as well as pressure in diverse anatomical locations. This study proposes a novel NFC-based method for continuous physiological monitoring. It provides hitherto unattainable tracking capabilities for pressure and temperature throughout the human body and has the potential to significantly enhance healthcare results. In order to increase the radius and sensitivities of these sensors, future research will examine enlarged clinical investigations and developments in wireless technology. © 2023 IEEE.

关键词： Continuous monitoring and Health outcomes Healthcare NFC technology Physiological monitoring Pressure sensing element Pressure ulcers Skin temperature Temperature measurement Wireless sensors

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Convergence of Blockchain, AI and IoT for Effective Monitoring and Securing Smart City Infrastructure 2

Convergence of Blockchain, AI and IoT for Effective Monitori...

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2nd International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2023

作者： Agajo, James Imoru, Odunayo Aigbavboa, Clinton Mishra, Amit Onah, Fidelis Ijala, Abdullahi Danjuma Federal University of Technology Department of Computer Engineering Niger State Minna Nigeria University of Johannesburg Department of Electrical and Computer Engineering South Africa University of Johannesburg Faculty of Engineering and the Built Environment South Africa Department of Computer Science School of Computing and Applied Science Baze University Abuja Nigeria Veritas University Faculty of Engineering Abuja Nigeria Nigeria Communication Commission Abuja Nigeria

ISBN: (纸本)9798350358834

The convergence of block chain, artificial intelligence, and the internet of things (IoT) for effective monitoring and securing smart city infrastructure combines these three hot trends in technology and promises to advance the development of smart cities. Blockchain, AI, and IoT represent one of the most notable technological revolutions since the combination of computing and transaction processing systems, which is now having a positive impact. Due to the actions of cyber assault, the applications of these technologies will soon have such a significant impact on our society that it will be difficult to picture life without them. The Internet of Things Infrastructure is vulnerable to assaults since it is hosted on the cloud. Modern methods are thoroughly examined to talk about the advantages and difficulties in each domain. The combination of these three will change technology and advance a number of fields that will benefit academics and experts in industry, fostering improved service delivery and enhancing corporate structures. The convergence will change the game since it will address security and data privacy concerns. © 2023 IEEE.

关键词： Internet of things

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