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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Mohammed, Faris E. Zghal, Nadia Smaoui Aissa, Dalinda Ben El-Gayar, M.M. Tunis University Microwave Electronics Research Laboratory Faculty of Science Tunisia Enis Control and Energy Management Research Laboratory Sfax Tunisia New Mansoura University Faculty of Computer Science and Engineering Department of Computer Science Egypt

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Breast cancer is one of the most important diseases that lead to death, according to the reports of the World Health Organization. Reports also indicated that breast cancer affects women more than men. Late or wrong diagnosis leads to a deterioration in the patient's condition and may lead to death. Therefore, automated multiple classifications using machine learning of breast cancer from histopathological images plays a major role in the early diagnosis of the disease. Multiple classifications of breast cancer is the identification of tumors such as Carcinoma, Fibroadenoma, Cysts, etc. However, this type of classification faces many challenges such as extracting subtle differences between the binary classes of tumors and whether they are benign or malignant, strong coherence of tumor cells and widespread color heterogeneity. Therefore, we propose in this paper the use of a sophisticated model that works on semantic segmentation and extracting distinctive patterns and classifying them using a deep neural network. The structured deep learning model performed impressively (average accuracy of 98.7%) on a large-scale data set, demonstrating the power of our method in providing an effective tool for classifying multiple breast cancers in clinical settings. © 2022 IEEE.

关键词： Semantic Segmentation

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Developments in Natural Language Processing: Applications and Challenges 10

Developments in Natural Language Processing: Applications an...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Sunil, G. Tagesse Takore, Tamirat Ravuri, Praseeda Nagpal, Amandeep Lourens, Melanie Babu, K. Ganapathi School of Cs & Ai Sr University Telangana Warangal India Wachemo University Department of Electrical and Computer Engineering Ethiopia Oregon State University Computer Science Engineer CorvallisOR97331 United States Lovely Professional University Phagwara India Durban University of Technology Deputy Dean Faculty of Management Sciences South Africa Computer Science and Engineering St. Martin's Engineering College Telangana Secunderabad500100 India

ISBN: (纸本)9798350382471

Natural language is any language that a person uses or speaks, such as Hindi, English, French, Marathi, Bengali, Gujrati, and so *** can better comprehend, interpret, and manipulate human language with the help of the area of called NLP. NLP is dependent on a number of academic disciplines, notably software engineering and computational languages, in order to bridge the gap among human communication and machine understanding. Natural language processing (NLP), which can represent and interpret human language computer, has attracted a lot of attention *** applications have grown to include, among other things, machine translation, spam email detection, information extraction, summarization, and question answering. The article breaks its topic into four sections: a discussion of different NLP levels and NLG components comes first, then the history and evolution of NLP, the state of the art, a rundown of all the different NLP applications, and a discussion of recent advancements and difficulties. © 2023 IEEE.

关键词： application challenges development NLP techniques

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Dynamic AI-Driven Network Slicing with O-RAN for Continuous Connectivity in Connected Vehicles and Onboard Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 720-733页

作者： Shah, Syed Danial Ali Bashir, Ali Kashif Al-Otaibi, Yasser D. Dabel, Maryam M. Al Ali, Farman University of Leeds School of Electronic and Electrical Engineering LeedsLS2 9JT United Kingdom Manchester Metropolitan University Department of Computing and Mathematics M15 6BH United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Jeddah21589 Saudi Arabia University of Hafr Al Department of Computer Science and Engineering Batin Saudi Arabia Sungkyunkwan University School of Convergence Seoul Korea Republic of

The rise of connected and autonomous vehicles signifies an era of intelligent transportation systems, where robust and continued network connectivity is essential for critical applications and enhanced in-vehicle Consumer Electronics (CE) experiences. Slicing at the network's edge offers tailored and dedicated logical networks for diverse and low-latency vehicular demands, including Advanced Driver Assistance Systems (ADAS) and in-car infotainment. However, seamless migration of network slices as vehicles traverse coverage areas of different network operators presents formidable challenges, such as ensuring continuous connectivity and uninterrupted service for both safety-critical systems and consumer-oriented services. In this paper, we introduced dynamic network slicing for continuous connectivity in connected vehicles and onboard CE using the Open Radio Access Network (O-RAN) framework in a highly dynamic and mobile environment. We implemented an xAPP within O-RAN that enables Deep Reinforcement Learning (DRL) agent to learn optimal policies through interaction with the network, guiding intelligent decisions on slice migration, resource allocation, and handover optimization. We conducted simulations and evaluations to demonstrate the effectiveness of the proposed xAPP in maintaining optimal Quality of Service (QoS), ensuring efficient RAN resource utilization, minimizing service interruptions, and prioritizing safety-critical slices, all while supporting seamless operation of CE within vehicles during mobility. © 1975-2011 IEEE.

关键词： Advanced driver assistance systems

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ML Techniques for Employee Performance Prediction

ML Techniques for Employee Performance Prediction

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Kumar, Brajesh Agrawal, Prateek Uike, Dipesh Lourens, Melanie Bhati, Indira Peter, M. Chandrakumar Dr. Apj Abdul Kalam Women's Institute of Technology Department of Computer Science & Engineering Bihar Darbhanga India India Dr. Ambedkar Institute of management studies and research Maharashtra Nagpur India Durban University of Technology Faculty of Management Sciences South Africa Department of Management India PeriyarManiammai Institute of Science and Technology Department of Software Engineering Tamilnadu Thanjavur India

ISBN: (纸本)9798331543617

When it comes to maximizing the effectiveness of a business and promoting professional growth, employee performance prediction is an extremely important factor. This research article investigates the use of machine learning (ML) approaches to forecast employee performance, with a particular emphasis on the incorporation of different algorithms from different sources to improve accuracy and dependability. Methods that have been used for a long time, such as linear regression and decision trees, are evaluated alongside more recent approaches, such as ensemble methods and deep learning. To refine the prediction models, our research makes use of approaches such as feature selection and dimensionality reduction. These techniques are utilized by utilizing previous performance data, demographic information, and behavioral indicators. It has been demonstrated through the analysis that sophisticated machine learning techniques, in particular ensemble and deep learning models, are superior to traditional methods when it comes to anticipating employee performance. Our findings offer HR departments concrete insights that can be used to adopt data-driven methods for performance management, which will eventually contribute to improved organizational outcomes and increased employee satisfaction. This paper offers opportunities for future development in predictive analytics within the field of human resources and emphasizes the potential of machine learning to alter performance evaluation systems. © 2024 IEEE.

关键词： Predictive analytics

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Capacity Maximization for V2I-VLC System with Angular Diversity Receiver 3

Capacity Maximization for V2I-VLC System with Angular Divers...

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Yahia, Selma Meraihi, Yassine Cherif, Amar Ramdane Eldeeb, Hossien B. Muhaidat, Sami Ho, Tu Dac University of m'Hamed Bougara Boumerdes List Laboratory Boumerdes35000 Algeria University of Versailles St-Quentin-en-Yvelines Lisv Laboratory Velizy78140 France Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates UiT Arctic University of Norway Faculty of Engineering Science and Technology Department of Electrical Engineering Norway

ISBN: (纸本)9798350333732

This paper investigates the effectiveness of angle diversity technology-based vehicle-to-infrastructure (V2I) visible light communication (VLC) system designed for a multi-lane road scenario. Cars use their headlights to transmit signals to a traffic light pole, which is equipped with an angle diversity receiver (ADR) comprising three photodetectors (PD) arranged in different directions to improve signal reception from various directions. We utilize an advanced ray tracing channel modeling approach and investigate the impact of the number of PDs and the elevation angles on the received power. Additionally, we conduct a comprehensive performance analysis in terms of capacity, considering different car positions along the road. The results demonstrate that the V2I-VLC system with ADR achieves a channel capacity of over 2.5 Mb/s at a transmission distance of 50 meters, highlighting its potential to enhance V2I-VLC connectivity. © 2023 IEEE.

关键词： Visible light communication

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Sorting of Fresh and Damaged Apple Fruits using Machine Learning Approach

Sorting of Fresh and Damaged Apple Fruits using Machine Lear...

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Emerging Technology (INCET), International Conference for

作者： Ujwala Ghodeswar Chetan Puri Sharvari Shingade Teena Waware Ajinkya Ladhe Tejas Durge Department of Electronics Engineering Yeshwantrao chavan College of Engineering Nagpur India Department of Computer Science & Design Faculty of Engineeering & Technology Wardha India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

The research investigates how machine learning algorithms and Internet of Things (IoT) technologies might be combined to solve the crucial problem of separating fresh fruits from damaged apple fruits. Using Internet of Things (IoT) devices with sensors, the system collects extensive data on important characteristics, such as real-time apple picture analysis and available apple database. Convolutional neural networks (CNNs) and YOLO are two examples of sophisticated machine learning models that are used with this data to accurately classify the apples into different groups according to their quality. This initiative fosters enhanced efficiency, production, and profitability in the agricultural supply chain while providing a sustainable solution to the problems connected with fruit sorting. It represents a synergistic convergence of IoT, machine learning, and agriculture.

关键词： YOLO Training Machine learning algorithms Databases Supply chains Machine learning Production

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The rise of mobile development: a comparison between Ionic and Flutter 14

The rise of mobile development: a comparison between Ionic a...

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14th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2022

作者： Novac, Ovidiu Constantin Novac, Cornelia Mihaela Ciora, Bogdan Gordan, Cornelia Emilia Gordan, Mircea Ioan Bujdoso, Gyongyi University of Oradea Electrical Engineering and Information Technology Faculty Department of Computers and Information Technology Oradea Romania University of Oradea Electrical Electrical Engineering and Information Technology Faculty Department of Electrical Engineering Oradea Romania University of Oradea Management in Information Technology Engineering and Information Technology Faculty Oradea Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of of Electronics and Telecommunications Oradea Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of Electrical Engineering Oradea Romania University of Debrecen Faculty of Informatics Department of Computer Science and Library and Information Science Debrecen Hungary

ISBN: (数字)9781665495356

ISBN: (纸本)9781665495356

A dive into the world of mobile development frameworks. An overview regarding the mobile technologies, in general but also a close look at two major mobile development frameworks, which are Ionic and Flutter. They are being analyzed at an individual level and furthermore, a comparison between these two technologies is taking place. © 2022 IEEE.

关键词： computers Games Software Marine vehicles Artificial intelligence

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Empirical Evaluation of Stock Market Prediction System using Intelligent Learning Scheme with Data Processing Logic 5

Empirical Evaluation of Stock Market Prediction System using...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Prabakar, S. Kumar, Ankit Jayakarthik, R. Venkatesh, D. Pratheeba, R.S. Khan, Bhola Isbr Business School Finance and Accounting Bangalore India Faculty of Science Swami Vivekanand Subharti University Department of Computer Application Meerut India Saveetha College of Liberal Arts and Sciences Simats Dept of Computer Science India Commerce School of Commerce and Management Mohan Babu University Tirupati India Velammal Institute of Technology Department of Computer Science and Engineering Panchetti601204 India Mjp Rohilkhand University Department of Regional Economics Bareilly India

ISBN: (纸本)9798350385649

The Stock Market Prediction System using Intelligent Learning Scheme (SMS-ILS) presented in this study integrates advanced methodologies to enhance the accuracy and reliability of stock market predictions. The system encompasses a preprocessing phase, addressing missing data and outliers, followed by feature extraction utilizing Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) to capture intricate temporal dependencies. A Deep Belief Network (DBN) is employed for classification, categorizing stocks based on learned representations from historical and technical indicators. Grey Wolf Optimization (GWO) optimizes the system's parameters, ensuring adaptability to dynamic market conditions. Notably, the system achieves an impressive accuracy of 97%, emphasizing its efficacy in making correct predictions. This multi-model approach, integrating LSTM, RNN, DBN, and GWO, provides a comprehensive and adaptive framework for navigating the complexities of stock market prediction. The study emphasizes the importance of choosing metrics aligned with specific goals and conducting thorough analysis to ensure the robustness of the evaluation results in the challenging and dynamic context of financial markets. © 2024 IEEE.

关键词： Long short-term memory

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Three-dimensional oscillation of an acoustic microbubble between two rigid curved plates

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Journal of Hydrodynamics 2021年第5期33卷 1019-1034页

作者： Kawa M.A.Manmi Imad A.Aziz Arun Arjunan Rostam K.Saeed Abdolrahman Dadvand Department ofMathematics College of ScienceSalahaddin University-ErbilKurdistan RegionIraq Department of Information Technology College of Engineering and Computer ScienceLebanese French UniversityKurdistan RegionIraq Faculty of Science and Engineering University of WolverhamptonWolverhamptonUK Faculty of Mechanical Engineering Urmia University of TechnologyUrmiaIran Karlsruhe Institute of Technology(KIT) Institute for Chemical Technology and Polymer ChemistryKarlsruheGermany

Understanding the near boundary acoustic oscillation of microbubbles is critical for the effective design of ultrasonic biomedical devices and surface cleaning ***,this study investigates the three-dimensional microbubble oscillation between two curved rigid plates experiencing a planar acoustic field using boundary integral method(BIM).The numerical model is validated via comparison with the nonlinear oscillation of the bubble governed by the modified Rayleigh-Plesset equation and with the axisymmetric model for an acoustic microbubble in infinite fluid ***,the influence of the wave direction and horizontal standoff distance(h)on the bubble dynamics(including jet velocity,jet direction,centroid movement,total energy,and Kelvin impulse)were *** was concluded that the jet velocity,the maximum radius and the total energy of the bubble are not significantly influenced by the wave direction,while the jet direction and the high-pressure region depend strongly on *** importantly,it was found that the jet velocity and the high-pressure region around the jet in acoustic bubble are drastically larger than their counterparts in the gas bubble.

关键词： Acoustic microbubble bubble dynamics boundary integral method(BIM) two curved plates potential flow

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Towards a Conceptual Model for Assessing User Satisfaction in Metaverse-Based Learning 11

Towards a Conceptual Model for Assessing User Satisfaction i...

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11th International Conference on Information Technology, ICIT 2023

作者： Alhalaybeh, Awf Althunibat, Ahmad Al-Mahadeen, Bassam M. Habashneh, Khaldoun Khalil Daradkeh, Ali Mohammad Software Engineering Al-Zaytoonah University of Jordan Madaba Jordan Software Engineering Al-Zaytoonah University of Jordan Amman Jordan Mu'Tah University Department of Computer Science Alkarak Jordan Faculty of Arts Amman Jordan Amman Jordan

ISBN: (纸本)9798350320060

this paper proposes a conceptual model for assessing user satisfaction in metaverse-based learning, a novel field that utilizes virtual reality technology to create an immersive and interactive learning experience. Despite the growing popularity of metaverse-based learning, there is a lack of research on how to evaluate user satisfaction in this type of learning environment. The proposed model includes four factors: Perceived Trialability, Perceived Observability, User Compatibility, and Perceived Complexity, each of which can be measured using statistical methods. The study aims to address the research gap in evaluating user satisfaction in metaverse-based learning and provides a model for educators and researchers to assess and improve user satisfaction in this innovative learning environment. The findings of this study contribute to the growing body of literature on metaverse-based learning and provide insights into the unique challenges associated with implementing this technology in educational settings. © 2023 IEEE.

关键词： Learning systems

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