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Hardware Implementation of High-performance Classifiers for Edge Gateway of Smart Automobile 1

Hardware Implementation of High-performance Classifiers for ...

1st International Conference on the Paradigm Shifts in communication, Embedded Systems, Machine Learning and Signal Processing, PCEMS 2022

作者： Gaikwad, Nikhil B. Khare, Smith K. Satpute, Nitin Keskar, Avinash G. Indian Institute of Information Technology Design and Manufacturing Electronics and Communication Discipline Jabalpur India Electronics and Communication Department Nagpur India Aarhus University Department of Electrical and Computer Engineering Denmark

ISBN: (纸本)9781665459044

Fog computing is a key solution for internet of things (IoT) applications, which demands operational security, real-time and power efficient intelligent responses, and low bandwidth usage. This paper introduces a novel idea related to an hardware implementation of High-performance classifiers for real-time and low power sensor data analytic on the intelligent edge gateway running on smart automobile. The high-performance classifiers uses an artificial neural network (ANN) to extract conclusive inferences from the raw automotive sensors information. The multiple classifiers are embedded into a re-configurable ANN hardware deign i.e. intellectual property core (IP core) which implemented and tested using field-programmable gate array fabric. In addition, this work studies the effect of the IP cores on the performance of the edge gateway. The implementation of fog/edge computing enables throughput reduction of 96.78% to 98.75% compared with the traditional gateway. The hardware design of the high-performance classifiers IP core requires only 31μ s and power consumption of 124mW for classification. The concept of re-configurable ANN model reduce about 41% to 93% of hardware resources requirement that contributing to reduced system power and cost. © 2022 IEEE.

关键词： Neural networks

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Perioperative Anesthesia Data: Visualization, Effects, Analysis with Artificial Intelligence 4

Perioperative Anesthesia Data: Visualization, Effects, Analy...

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4th IEEE International Conference on Electronic communications, Internet of Things and Big Data, ICEIB 2024

作者： Liu, Xiaoxiao McGrath, Sean Flanagan, Colin Lei, Yiming Zeng, Liaoyuan Electronic and Computer Engineering University of Limerick Limerick Ireland School of Mathematics and Information HeBei University HeBei China Mobile Digital Hospital System School of Electronics Peking University Ministry of Education Engineering Research Center Beijing China University of Electronic Science and Technology of China Department of Information and Communication Engineering Chengdu China

ISBN: (纸本)9798350360721

A comprehensive understanding of physiological data is essential for anesthesiologists to monitor and maintain the health of surgical patients. Understanding anesthetic signals is essential to improve over the past ten decades to comprehend a variety of diseases, ensure the safety of patients, and accelerate their recovery. We identified and summarized four themes of artificial intelligence (AI) research in anesthesia: applications of AI in anesthesiology, signals monitored in different anesthesia stages, commonly monitored signs, and data standardization. The effect of various AI technologies on data analysis was studied, and the types, functions, sensors, characteristics, and intelligent analysis of physiological signals monitored during anesthesia are presented in this article. AI influences anesthesia practices in many ways, including preoperative data evaluation, anesthetic depth monitoring, and postoperative event prediction. © 2024 IEEE.

关键词： Machine learning

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Stationary Wavelet-Oriented Luminance Enhancement Approach for Brain Tumor Detection with Multi-modality Images 11th

Stationary Wavelet-Oriented Luminance Enhancement Approach f...

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11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Ahilan, A. Anlin Sahaya Tinu, M. Jasmine Gnana Malar, A. Muthu Kumar, B. Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Electronics and Communication Engineering Anna University Tamil Nadu Chennai600025 India Department of Computer Science and Engineering School of Computing and Information Technology REVA University Karnataka Kattigenahalli560064 India

ISBN: (纸本)9789819967018

A brain tumor is an abnormal growth of cells in the brain. However, manually detecting brain tumors is hard because it is hard to find erratically shaped tumors with only one modality and time-consuming. In this work, a novel stationary wavelet-oriented luminance enhancement (SOLE) approach to denoise the multi-modal images. Initially, medical images like MRI, CT, and PET are gathered from publicly available datasets. These multi-modality images are divided into low and high-frequency sub-images using stationary wavelet transform (SWT), which has the advantage of preserving temporal features so that information loss can be stopped. Then the low-frequency and high-frequency images are processed with distribution and denoising modules to remove the noise, respectively. The approximation coefficient is pre-processed using multi-scale retinex with gamma correction for efficaciously retrieving the noise-free image. Consequently, the remaining coefficients are pre-processed using a multi-scale Gaussian bilateral filter, and tracking wavelet denoising (TWD) algorithm in the denoising module dynamically enhanced the color detail information without human intervention so that observed image contrast and visibility are well preserved. Lastly, noise-free image is reconstructed from sub-enhanced images using Inverse-SWT to detect brain tumors. Experimental results show that the proposed algorithm has a mean error rate of 0.03 compared to the other filters. The proposed SOLE technique achieves a less running time of 0.97 s, whereas other existing techniques such as K-SVD, DRAN, 2-stage CNN, and AMF-AWF achieve the running time of 6.9, 1.63, 1.52, and 8.75 s. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Luminance

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Exploring the Potential Benefits of Machine Learning for Optimizing Real-Time Data Analysis 3

Exploring the Potential Benefits of Machine Learning for Opt...

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3rd International Conference on Smart Generation Computing, communication and Networking, SMART GENCON 2023

作者： Kumar, Ravindra Sakhare, Nitin N. Thangarasu, N. Senthilkumar, K.K. Wawage, Pawan S. Rengarajan, A. Vivekananda Global University Department of Civil Engineering Jaipur India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India

ISBN: (纸本)9798350319125

This studies paper examines the capability blessings of implementing device getting to know algorithms to optimize real-time information evaluation. The subjects mentioned consist of techniques for predicting traits in actual-time information, the ability of gadget studying algorithms to discover anomalies and outliers in datasets, and how using records mining and herbal language processing (NLP) can improve the accuracy of real-time facts insights. It also evaluates the demanding situations that can arise while applying machine gaining knowledge of algorithms to actual-time statistics, along with scalability problems and overfitting of records. In end, the authors argue that system studying has notable potential to enhance the accuracy and timing of actual-time facts analysis, and that they provide steerage on choosing and enforcing appropriate gadget getting to know algorithms. © 2023 IEEE.

关键词： Machine learning

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Machine Learning-Based Brain Disease Classification Using EEG and MEG Signals 11th

Machine Learning-Based Brain Disease Classification Using EE...

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11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Ahilan, A. Angel Sajani, J. Jasmine Gnana Malar, A. Muthu Kumar, B. Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Electronics and Communication Engineering Anna University Tamil Nadu Chennai600025 India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Computer Science and Engineering School of Computing and Information Technology REVA University Karnataka Kattigenahalli560064 India

ISBN: (纸本)9789819967018

Electroencephalography (EEG) and Magneto-encephalography (MEG) are important tools for assessing brain activity that are being developed scientifically. EEG enables better clinical and healthcare services to meet the rising need for the early diagnosis of brain disease at cheap prices. In this paper, EEG and MEG signals are used as an input to detect brain cancer and strokes in its early stages. A single-trial channel data (STD), averaged channel data (ACT), and time–frequency data organisation of the channel data is required for the EEG/MEG signal projection from the channel to the source space (TFD). The signals are pre-processed using discrete wavelet transform to adaptive time–frequency resolution of analysis on nonstationary signals. Then, the signals are given as an input to the Fast Fourier Transform to get subsets of typical in-class "invariant" coefficients from wavelet coefficients (time–frequency information). Finally, the multi-class SVM is employed for classifying normal, cancer, and stroke cases using EEG and MEG signals. A quantitative analysis of the proposed method is conducted using parameters like accuracy, specificity, and precision. The proposed Dual signal classification model achieved higher accuracy is 92.59%. According to the proposed Dual signal classification model, Bootstrap models, SVM, XGBoost, and Fast Fourier Transform improve the overall accuracy by 4.61%, 0.16%, 15.77%, and 9.63%, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Discrete wavelet transforms

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Enhancing Image Segmentation in the Marine Atmosphere for Time-Sensitive Contextualisation using Convolutional Long Short-Term Memory 2

Enhancing Image Segmentation in the Marine Atmosphere for Ti...

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2nd International Conference on Advances in Computation, communication and information Technology, ICAICCIT 2024

作者： Reethika, A. Rajeshwari, R. Vijayalakshmi, S. Malathy, S. Gnanasaravanan, S. Suresh Kumar, M. Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Information Technolog KPR Institute of Engineering and Technology Coimbatore India Department of Biomedical Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Information Technology Sri Ranganathar Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798331541217

The work investigates autonomous surface vehicle obstacle avoidance and detection using computer vision techniques. It recognizes boundaries in marine environments by using the temporal context from video feeds from unmanned surface vehicles. The study shows how successful LSTM cells are in the CNN topologies that are currently in use, emphasizing how they might perform better. Nevertheless, datasets and network training techniques are not perfect. The work also uses a benchmarking approach to compare new network configurations. © 2024 IEEE.

关键词： Image segmentation

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Risk Management in Robotic Process Automation 2

Risk Management in Robotic Process Automation

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Yendluri, Dileep Kumar Thatikonda, Ramya Thatikonda, Reshmi Ponnala, Jainath Kempanna, M. Bhuvanesh, A. University of the Cumberlands Department of Computer Science Williamsburg United States University of the Cumberlands Department of Information Technology Williamsburg United States University of Central Missouri Department of Computer Information Systems Williamsburg United States Jawaharlal Technological University Department of Electronics and Communication Engineering Hyderbad India Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Bangalore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350360523

The paper investigates the nuances of RPA risk management, detailing the difficulties, tendencies, and approaches organizations must adopt to integrate RPA into their operations successfully. From automation to optimization, RPA's impact on industry workflow is revolutionary. However, RPA introduces a spectrum of technical, operational, business, and compliance risks alongside its benefits. Amidst the intricate landscape of RPA, this paper sheds light on key risks. It offers practical strategies for tackling them, enabling organizations to confidently unlock automation's transformative power. Trends like advanced analytics fusion, AI-driven risk analysis, and blockchain utilization will likely influence future RPA risk management. In addition, the paper discusses evolving challenges, including ethical issues, breakneck technology evolution, and digital security dangers. Using an in-depth examination of RPA risk management, this paper prepares organizations for the ever-changing automation environment with informed decisions and proactive measures. © 2024 IEEE.

关键词： Risk assessment

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Comparison of *** and Spring Boot in Web Development 15

Comparison of *** and Spring Boot in Web Development

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15th International Conference on electronics, computers and Artificial Intelligence, ECAI 2023

作者： Novac, Ovidiu Constantin Ghiurau, David Novac, Mihaela Cornelia Gordan, Cornelia Emilia Oproescu, Mihai Bujdoso, Gyongyi Mihalca, Vlad Ovidiu University of Oradea Electrical Engineering and Information Technology Faculty Department of Computers and Information Technology Oradea Romania University of Oradea Management in Information Technology Electrical Engineering and Information Technology Faculty Oradea Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of Electrical Engineering Oradea Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of Electronics and Telecommunications Oradea Romania University of Pitesti Faculty of Electronics Communication and Computer Science Department of Electronics Computers and Electrical Engineering Pitesti Romania University of Debrecen Faculty of Informatics Department of Computer Science and Library and Information Science Debrecen Hungary School of Engineering Sciences University of Oradea Electrical Engineering and Information Technology Faculty Oradea Romania

ISBN: (纸本)9798350321388

In this paper we present two types of server implementation, we will look at the advantages and disadvantages of each technology. *** server application, built with ***, a single threaded server which will treat the requests in an asynchronous manner, with a non-blocking principle, whilst Spring Boot is a more robust and more common approach of a server technology with the benefits of having a strongly typed architecture and multiple threads available. These types of application will behave the same, making almost impossible to tell the difference between a *** and a Spring Boot web server. © 2023 IEEE.

关键词： Node.Js non-blocking single-multi threaded Spring Boot web server

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A Novel Edge-Assisted IoT-ML-Based Smart Healthcare Framework for COVID-19

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computer Modeling in engineering & Sciences 2023年第12期137卷 2529-2565页

作者： Mahmood Hussain Mir Sanjay Jamwal Ummer Iqbal Abolfazl Mehbodniya Julian Webber Umar Hafiz Khan Department of Computer Sciences Baba Ghulam Shah Badshah UniversityRajouriJammu&KashmirIndia Department of Computer Sciences National Institute of Electronics and Information TechnologySrinagarJammu&KashmirIndia Department of Electronics and Communication Engineering Kuwait College of Science and TechnologyKuwait City20185145Kuwait Department of Geriatric Medicine Sher-i-Kashmir Institute of Medical SciencesSrinagarJammu&KashmirIndia

The lack of modern technology in healthcare has led to the death of thousands of lives worldwide due to COVID-19 since its *** Internet of Things(IoT)along with other technologies like Machine Learning can revolutionize the traditional healthcare *** of reactive healthcare systems,IoT technology combined with machine learning and edge computing can deliver proactive and preventive healthcare *** this study,a novel healthcare edge-assisted framework has been proposed to detect and prognosticate the COVID-19 suspects in the initial phases to stop the transmission of coronavirus *** proposed framework is based on edge computing to provide personalized healthcare facilities with minimal latency,short response time,and optimal energy *** this paper,the COVID-19 primary novel dataset has been used for experimental purposes employing various classification-based machine learning *** proposed models were validated using kcross-validation to ensure the consistency of *** on the experimental results,our proposed models have recorded good accuracies with highest of 97.767%by Support Vector *** to the findings of experiments,the proposed conceptual model will aid in the early detection and prediction of COVID-19 suspects,as well as continuous monitoring of the patient in order to provide emergency care in case of medical volatile situation.

关键词： COVID-19 edge computing framework Internet of Things(IoT) machine learning(ML) network symptoms

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Advancing Rose Disease Diagnosis: A Deep Learning Framework with EfficientNet-B7 8

Advancing Rose Disease Diagnosis: A Deep Learning Framework ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Poornima, S. Divya, R. Krishnan, R. Santhana Jegadeesan, S. Yamini, G. Rajkumar, G. Vinoth Sri Ramakrishna College of Arts and Science for Women Department of Computer Science Coimbatore India Psna College of Engineering and Technology Department of Information Technology Dindigul India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Information Technology Madurai India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of English Chennai India J.P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India

ISBN: (纸本)9798350376425

This study introduces an advanced deep learning framework for diagnosing rose diseases using EfficientNet B7, a high-performance model pre-trained on the ImageNet dataset. The research aims to automate the identification of four common rose diseases: Powdery Mildew, Black Spot, Downy Mildew, and Rust by applying transfer learning with EfficientNet B7. This approach is evaluated using two specialized datasets, the Rose Disease Dataset and the Rose Leaves Disease Detection Dataset. The framework's effectiveness is assessed by comparing its performance against other prominent deep learning models, including DenseNet, ResNet, and VGGNet. Performance metrics such as Accuracy, Precision, Recall, and F1 Score are used for this comparison. The results demonstrate that EfficientNet B7 outperforms DenseNet, ResNet, and VGGNet in terms of accuracy and reliability in disease classification. EfficientNet B7's efficiency and superior handling of complex feature representations contribute to its enhanced performance, making it a robust tool for rose disease diagnosis. This advanced framework offers significant improvements over existing models in terms of diagnostic accuracy and computational efficiency, providing a valuable resource for early detection and management of rose diseases. The results underscore the potential of leveraging state-of-the-art deep learning models to enhance agricultural practices and support more effective plant health management strategies. © 2024 IEEE.

关键词： Contrastive Learning

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