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Japan-Egypt International Conference on electronics, Communications and computers (JEC-ECC)

作者： Mohamed W. Nomeir Bassem Mokhtar Department of Electrical Engineering Faculty of Engineering Alexandria University Alexandria Egypt Computer Science and Engineering Department School of Electronics Communications and Computer Engineering Egypt-Japan University of Science and Technology Alexandria Egypt

ISBN: (纸本)9781665464642

Improving computer networks performance is a tremendous need in our life for meeting ever-changing quality of service (QoS) requirements of interesting applications. We are searching continuously on how to increase the performance of computer networks. One of the most important factors that aid in determining network performance is the available and accessible network information. In this paper, we deal with this problem in an information theoretic framework. Our approach is different than most recent papers in this field, where we use predefined measures that normally work on any computer network and we apply it to a general network model. We examine these measures on a specific model that is more comprehensive and more applicable than previous models discussed in other papers. Also, one of the important reasons in our extended study is to investigate the shortcomings in adopting these measures and how they appear in the results. We discuss briefly how to enhance these measures to be compatible with real networks and to give more precise and efficient results. Our analytical study of the developed model shows that obtained results can be used to guide future network designs since our model can be easily manipulated to get many real network models.

关键词： Analytical models Computational modeling Quality of service Distortion computer networks Information theory

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Speech Emotion Recognition: A Brief Review of Multi-modal Multi-task Learning Approaches

Speech Emotion Recognition: A Brief Review of Multi-modal Mu...

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International Conference on Advanced engineering Theory and Applications, AETA 2022

作者： Pham, Nhat Truong Tran, Anh-Tuan Pham, Bich Ngoc Hong Dang-Ngoc, Hanh Nguyen, Sy Dzung Dang, Duc Ngoc Minh Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ho Chi Minh City Open University Ho Chi Minh City Viet Nam Department of Telecommunications Engineering Ho Chi Minh City University of Technology Ho Chi Minh City Viet Nam Laboratory for Computational Mechatronics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical–Electrical and Computer Engineering Van Lang University Ho Chi Minh City Viet Nam Computing Fundamental Department FPT University Ho Chi Minh Viet Nam

ISBN: (纸本)9789819987023

Speech emotion recognition (SER) has become an attention-grabbing topic in recent years thanks to the development of deep learning in the field of speech processing. However, it is difficult to recognize an accurate emotional state from only speech signals. Therefore, researchers have investigated multi-modalities such as speech, visual, and text inputs to improve the emotional recognition rate of speech. In addition, to enhance the generalized deep learning models for SER, multi-task learning (MTL) strategies have also been applied in the past decade. In this paper, a brief and comprehensive review of multi-modal multi-task learning (3MTL) approaches for recognizing emotional states from speech signals is presented, including multi-modal SER, multi-task learning SER, and multi-modal multi-task learning SER. This paper also discusses about some problems that still need to be solved in 3MTL SER and gives some suggestions for the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Emotion Recognition

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Efficient Deep Post-Decision State Learning for Privacy-Conscious Offloading in MEC-Enabled 6G Networks

Efficient Deep Post-Decision State Learning for Privacy-Cons...

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Emerging Technologies for Dependable Internet of Things (ICETI), International Conference on

作者： Mamoon M. Saeed Rania A. Mokhtar Mohammed S. Elbasheir Elmustafa Sayed Ali Zeinab E. Ahmed Mayada A. Ahmed Department of Communications and Electronics Engineering University of Modern Sciences (UMS) Yemen Faculty of Engineering School of Elecs Eng. Sudan University of Science and Technology Khartoum Sudan School of Electronic Engineering Sudan University of Science and Technolog Khartoum Sudan Department of Electrical & Electronic Engineering Faculty of Engineering Red Sea University Sudan Department of Electrical and Computer International Islamic University Malaysia

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

With the growing demand for data-intensive applications, offloading tasks to multi-access edge computing (MEC) servers has become essential. However, privacy concerns arise as sensitive data is transmitted to remote servers. To address this, the proposed method uses deep learning techniques to intelligently analyze and classify data packets, ensuring that only non-sensitive information is offloaded. This paper proposes a novel approach called a deep post-decision state (PDS) learning privacy-concerned offloading in MEC-enabled 6G Environments. A Post-Decision State (PDS) approach that selectively selects data packets while maintaining privacy is used to train the deep learning model. The suggested method successfully balances the trade-off between increasing system performance and safeguarding user privacy to achieve privacy-aware offloading in MEC-enabled 6G networks, as shown by the experimental findings. Also, it performs noticeably better than the traditional DQN, according to simulation data.

关键词： 6G mobile communication Deep learning Performance evaluation Privacy Data privacy Multi-access edge computing System performance Servers Internet of Things Energy harvesting

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Expression of Concern for: Utilizing Machine Learning Algorithms to Optimize Real-Time Data Analysis

Expression of Concern for: Utilizing Machine Learning Algori...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Vaishali Singh Eesha Gupta D Little Femilin Jana Gopal B. Deshmukh Abhishek Singla K. Gopala Krishna Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Bangalore Karnataka India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

This paper offers a way for optimizing actual-time records analysis the usage of a variety of machine gaining knowledge of algorithms. The algorithms paintings by creating predictive fashions which extract useful information from datasets, follow appropriate preprocessing techniques to make sure the high-quality of the records, and model the information to deduce meaningful capabilities from the data. The predictive models are then used to discover applicable developments, find patterns, and summarize the data for clean analysis. specifically, this paper outlines the technique of using supervised and unsupervised learning algorithms, consisting of Random wooded area, Naïve Bayes, and k-approach clustering, to extract meaningful styles and relationships from facts points in real-time. Additionally, the paper discusses wonderful capacity packages of the proposed technique, including fraud detection and prediction of stock costs. Such capability programs demonstrate the fee of actual-time records analysis for organizations and organizations.

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Switching Activity Reduction in Graphene PN Junction Circuits using Circuit Re-structuring

Switching Activity Reduction in Graphene PN Junction Circuit...

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International Symposium on Devices, Circuits and Systems (ISDCS)

作者： Subrata Das Arighna Deb Petr Fiser Dept. of Computer Science & Engineering Sister Nivedita University Kolkata India School of Electronics Engineering KIIT DU Bhubaneswar India Faculty of Information Technology Czech Technical University Prague Czech Republic

Graphene PN Junction (GPNJ) logic circuits received significant attention from the researchers thanks to the availability of electrostatically doped graphene PN Junction (GPNJ) device- a promising one for designing low-power, high-speed circuits. Several design approaches for GPNJ logic circuits realizing important arithmetic and arbitrary Boolean functions exist in literature. However, no detailed evaluations of the resulting circuit complexities with respect to relevant cost metrics are reported. It remains open, how do we synthesize GPNJ logic circuits with minimal switching activity, an essential cost metric influencing the dynamic power dissipation in the resulting circuits. In this paper, we introduce a synthesis approach for GPNJ logic circuits that interconnects a set of GPNJ devices in parallel. Such parallel circuit structures ensure the reduction of switching activity in the resulting GPNJ logic circuits. To the best of our knowledge, so far only one research work discussed the computation of switching activity of GPNJ logic circuits. Experimental evaluations confirm that an average 67.35% reduction in switching activity can be achieved using the proposed approach over the existing one.

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Utilizing Machine Learning Algorithms to Optimize Real-Time Data Analysis

Utilizing Machine Learning Algorithms to Optimize Real-Time ...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

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Cloud-Based Oxygen Management System with Seamless HTTP Protocol Integration

Cloud-Based Oxygen Management System with Seamless HTTP Prot...

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Innovative Practices in technology and Management (ICIPTM), International Conference on

作者： S. Hemalatha Jayabharathi Ramasamy K. Selvi P. Anandan R. Meenakshi S. P. Maniraj Department of Artificial Intelligence and Data Science Tagore Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Faculty of Management Studies S. A. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

In this study, an efficient system to improve the oxygen supply in hospitals and other healthcare institutions is proposed. It uses Internet of things (IoT) devices and cloud based data transmission and processing system. The controller unit collects the oxygen sensor data for the analysis and uses HTTP protocols to send and receive data with cloud computing in HTTP website server. The cloud is data storage, processes and real-time monitoring, analysis and control system. It has a user interface with viewed administrators and medical professionals. The interface provides real-time data regarding oxygen supply, consumption patterns, and possible concerns. Authorized workers can access and manage the system via the display, allowing for timely adjustments to oxygen supply settings, optimizations to refill schedules, and responses to emergency circumstances. It's adaptable, flexible, and resilient, which are benefits. There will be less need to worry about oxygen shortages and interruptions, and healthcare institutions of all sizes can benefit from this. The cloud's remote access and control capabilities provide continuous functioning and rapid reaction times. It's an effective solution for hospitals and other healthcare facilities that need to control their oxygen supplies. With its cloud-based design, the solution increases productivity, improves patient safety, and simplifies oxygen management.

关键词： Cloud computing Hospitals Process control User interfaces Control systems HTTP Real-time systems

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IoT Based Alcohol Detection and Vehicle Control System

IoT Based Alcohol Detection and Vehicle Control System

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： I. Sapthami V V Narasimha Raju V. Vaithianathan P. Chinnasamy G Kumaran Ramesh Kumar Ayyasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Marri Laxman Reddy Institute of Technology and Management Dundigal Hyderabad Telangana India Department of Electronics and Communication Engineering Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Chennai Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputtur TamilNadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

It is of the utmost importance to be aware of safety measures when operating a motor vehicle in the contemporary world, characterized by a fast-paced and hazardous environment. It has become necessary to develop innovative technology in order to improve road safety as a result of the alarmingly high number of incidents that can be linked to drivers who are under the influence of alcohol. As a solution to this significant problem, this study uses the capabilities offered by the Internet of Things (IoT) to develop an integrated system for detecting and controlling alcohol in commercial vehicles. This study aims to reduce the dangers connected with driving under the influence of alcohol by allowing for the seamless integration of cutting-edge alcohol detection technologies with real-time vehicle control mechanisms. The system makes use of specific sensors for the detection of alcohol. It interacts with the control mechanisms of the vehicle in order to restrict its functioning when alcohol is present. To make this possible, the hardware component of the system mounted in the vehicle uses the MQ3 sensor, also known as the alcohol sensor. This sensor is responsible for monitoring the alcohol molecules in the air around the driver to determine whether or not the driver is intoxicated. A buzzer, a DC motor, an LCD, a GPS, and a GSM are all components utilized by the system for data transfer. Additionally, the gear is powered by a 12-volt battery. As soon as the system determines that there is alcohol present in the vehicle, it immediately locks the accelerator. Using the GPS, the driver can obtain their location coordinates.

关键词： Refining Control systems Sensor systems Road safety Sensors Safety Internet of Things Reliability Vehicles Global Positioning System

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Explainable Artificial Intelligence-A New Step towards the Trust in Medical Diagnosis with AI Frameworks:A Review

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computer Modeling in engineering & Sciences 2022年第12期133卷 843-872页

作者： Nilkanth Mukund Deshpande Shilpa Gite Biswajeet Pradhan Mazen Ebraheem Assiri Department of Electronics&Telecommunication LavaleSymbiosis Institute of TechnologySymbiosis International(Deemed University)Pune412115India Electronics&Telecommunication Vilad GhatDr.Vithalrao Vikhe Patil College of EngineeringAhmednagarMaharashtra414111India Centre for AdvancedModelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering&ITUniversity of Technology SydneySydney2007Australia Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation Centre Universiti KebangsaanInstitute of Climate ChangeMalaysiaSelangor43600Malaysia Department of Computer Science LavaleSymbiosis Institute of TechnologySymbiosis International(Deemed University)Pune412115India Symbiosis Center for Applied Artificial Intelligence(SCAAI) LavaleSymbiosis International(Deemed University)Pune412115India

Machine learning(ML)has emerged as a critical enabling tool in the sciences and industry in recent ***’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks-thanks to advancements in technique,the availability of enormous databases,and improved computing *** learning models are at the forefront of this ***,because of their nested nonlinear structure,these strong models are termed as“black boxes,”as they provide no information about how they arrive at their *** a lack of transparencies may be unacceptable in many applications,such as the medical domain.A lot of emphasis has recently been paid to the development of methods for visualizing,explaining,and interpreting deep *** situation is substantially different in safety-critical *** lack of transparency of machine learning techniques may be limiting or even disqualifying issue in this ***,when single bad decisions can endanger human life and health(e.g.,autonomous driving,medical domain)or result in significant monetary losses(e.g.,algorithmic trading),depending on an unintelligible data-driven system may not be an *** lack of transparency is one reason why machine learning in sectors like health is more cautious than in the consumer,e-commerce,or entertainment *** is the term introduced in the preceding *** AImodel’s black box nature will become explainable with these *** in the medical domain,diagnosing a particular disease through AI techniques would be less adapted for commercial *** models’explainable natures will help them commercially in diagnosis decisions in the medical *** paper explores the different frameworks for the explainability of AI models in the medical *** available frameworks are compared with other parameters,and their suitability for medical fields is also discussed.

关键词： Medical imaging explainability artificial intelligence XAI

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Task Reverse Offloading with Deep Reinforcement Learning in Multi-Access Edge Computing

Task Reverse Offloading with Deep Reinforcement Learning in ...

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International Conference on computer and Communication engineering, ICCCE

作者： Mamoon M. Saeed Rashid A. Saeed Rania A. Mokhtar Othman O. Khalifa Zeinab E. Ahmed Mohammed Barakat Areeg Ali Elnaim Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif Saudi Arabia Department of Electrical and Computer Engineering International Islamic University Malaysia Malaysia Libyan Center for Engineering Research and Information Technology Bani Waleed Libya Electrical and Computer Engineering Dept. International Islamic University Malaysia Malaysia Computer Engineering Dept. University of Gezira Sudan School of Electronics Engineering Sudan University of Science and Technology Khartoum Sudan College of Engineering Alneelain University Khartoum Sudan

The Multi-access Edge Computing (MEC) technology’s quick development greatly benefits the Collaborative Mobile Infrastructure System (CMIS). To combine the data and produce tasks, crowd-sensing data will be transferred to the MEC server in CMIS. Nevertheless, if there are too many devices, it becomes extremely difficult for MEC to decide appropriately based on the data from the devices and infrastructure. This study builds a framework for reverse offloading that carefully balances the relationship between task completion time and user mobile energy consumption. Moreover, to decrease system use generally, an adaptive optimal reverse offloading method based on Deep Q-Network is created (DQN). The results of the simulations demonstrate that the suggested approach may successfully minimize energy consumption and work latency when compared to full local and fixed offloading techniques.

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