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International Conference on Advanced Cloud and Big Data (CBD)

作者： Jingjing Wan Ling Wang Wenjie Zhu Bolun Chen Department of Gastroenterology The Second People's Hospital of Huai'an Huaian China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Early detection of colorectal polyps is crucial for preventing colorectal cancer. Although endoscopy is the current standard diagnostic method, it still faces challenges in terms of accuracy, efficiency, and patient comfort. To address these issues, this paper proposes a colorectal polyp detection model named DM-Net. This model utilizes the Dual Feature Integration Block (DFIB) to integrate channel and spatial features, enhancing feature extraction efficiency. Additionally, the model incorporates the Multi-Layer Path Aggregation Network (MLPAN) to handle the multi-scale variations of polyps. Experimental results demon-strate that DM-Net significantly outperforms existing methods in terms of accuracy and efficiency, offering high clinical applica-bility.

关键词： YOLO Accuracy Endoscopes Colorectal cancer Big Data Feature extraction Real-time systems Standards Faces

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Design and Simulation of a Network Infrastructure for a Multi-Branch Organization Using Cisco Packet Tracer 25

Design and Simulation of a Network Infrastructure for a Mult...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Aljamal, Osama S. Aljamal, Mahmoud Alsarhan, Ayoub Aljaidi, Mohammad Okour, Saif Al-Aiash, Issa Al-Qerem, Ahmad Daoud, Essam Al Elrashidi, Ali Al Hussein Technical University Department of Computer Science Amman Jordan Irbid National University Faculty of Science and Information Technology Department of Cybersecurity Irbid21110 Jordan The Hashemite University Department of Information Technology Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan Irbid National University Department of Software Engineering Irbid Jordan Zarqa University Department of Cybersecurity Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Designing and simulating a network infrastructure for multi-branch organizations is essential for ensuring seamless connectivity and efficient operations. This paper addresses the challenges faced by organizations in maintaining consistent and reliable network connectivity across multiple locations, including managing network traffic, ensuring security, and optimizing resource allocation while keeping costs under control. The study presents a comprehensive methodology involving the conceptualization of network ideas, designing the network architecture, adding necessary devices and tools, configuring these devices according to the network topology, and troubleshooting to ensure optimal performance. Key elements such as VLANs, routers, switches, and routing protocols like RIP, OSPF, and EIGRP are incorporated, with the entire design and simulation conducted using Cisco Packet Tracer. The findings demonstrate that full connectivity between headquarters and branch sites can be achieved within a budget of $40,000. This paper provides a practical guide for implementing similar network projects, emphasizing the importance of detailed configurations and troubleshooting steps, and recommends adopting this methodology to enhance network infrastructure efficiently and cost-effectively. © 2024 IEEE.

关键词： Budget control

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately. © 2023 Elsevier B.V.. All rights reserved.

关键词： computer vision

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Electrocoagulation-based AZO DYE (P4R) Removal Rate Prediction Model using Deep Learning

Electrocoagulation-based AZO DYE (P4R) Removal Rate Predicti...

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2023 International Symposium on Green Technologies and Applications, ISGTA 2023

作者： Akoulih, Meryem Tigani, Smail Byoud, Fouzia Rharib, Meryem El Saadane, Rachid Pierre, Samuel Chehri, Abdellah Ghachtouli, Sanae El IME Lab Faculty of Science Hassan 2 University Casablanca20250 Morocco Research and Development Unit Accsellium LLC Fez Morocco Electrical Engineering Department SIRC-LaGeS Hassania School of Public Labors Casablanca20250 Morocco Department of Computer and Software Engineering Polytechnique Montréal MontréalQC Canada Department of Mathematics and Computer Science Royal Military College of Canada KingstonON11 K7K 7B4 Canada

This article presents a study on the effectiveness of electrocoagulation (EC) for the removal of azo dyes from wastewater. The analysis was performed using a combination of statistical methods, including density estimation, correlation analysis, and deep learning for electrocoagulation performance prediction. The results showed that electrocoagulation was able to effectively remove azo dyes from the wastewater, considering the energy consumption and the mass of flocs being important factors in the process. Deep Learning (DL) is used to build our predictive model using the datasets collected during the experimentation stage. Overall, the findings suggest that electrocoagulation is a promising technique for the treatment of wastewater containing azo dyes, and that the use of statistical and machine learning methods can aid in the optimization of the process. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Azo dyes

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A neural networks approach for designing compact all-optical photonic crystal based and logic gate

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Journal of Optical Communications 2024年第s1期45卷 s2627-s2636页

作者： Parandin, Fariborz Yahya, Salah I. Rezaeenia, Mehdi Askarian, Asghar Roshani, Saeed Roshani, Sobhan Ghadi, Yazeed Yasin Jamshidi, Mohammad Rezaee, Sahar Department of Electrical Engineering Kermanshah Branch Islamic Azad University Kermanshah Iran Department of Communication and Computer Engineering Cihan University Erbil Iraq Department of Software Engineering Faculty of Engineering Koya University KoyaKOY45 Iraq Department of Electrical Engineering Arak Branch Islamic Azad University Arak Iran Software Engineering and Computer Science Department Al Ain University Al Ain United Arab Emirates Hong Kong Department of Physics Kermanshah Branch Islamic Azad University Kermanshah Iran

This paper introduces a new method for creating an all-optical AND gate by utilizing a two-dimensional photonic crystal configuration for the first time. This gate design is intended for applications in optical computing and all-optical logic, offering the potential for rapid computation and parallel processing. The described gate is characterized by its compact dimensions and comprises two inputs and a single output. The high and low logic states are defined based on power values, where logic 0 corresponds to low power and logic 1 corresponds to high power emitted from the light source. To enhance the design process, artificial neural networks (ANNs) are utilized. ANNs offer a powerful tool for optimizing and fine-tuning the photonic crystal structure parameters to achieve the desired logic functionality. With the help of the applied ANNs, the design process is eased and high performance is achieved for the proposed photonic crystal structure. By integrating ANNs into the design process, this research opens up new possibilities for advancing the field of photonic logic circuits. Combining photonic crystals and ANN optimization provides a powerful approach to designing complex and efficient optical computing systems. The results show that the obtained power values are high for 1 logic state and low for the 0 logic state, which verifies the AND gate accuracy table. The achieved accurate results verify the validity of the proposed approach for achieving precise and reliable all-optical logic operations. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Photonic crystals

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Discrete RKZD(Reciprocal-Kind Zhang Dynamics) Algorithm Solving Time-Independent Linear Matrix-Vector Equation(TILMVE) Simplified from Time-Dependent Situation

Discrete RKZD(Reciprocal-Kind Zhang Dynamics) Algorithm Solv...

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第42届中国控制会议

作者： Yunong Zhang Ji Lu Mingzhi Mao Shuai Li School of Computer Science and Engineering Sun Yat-sen University School of Software Engineering Sun Yat-sen University Faculty of Information Technology and Electrical Engineering University of Oulu

We present a continuous reciprocal-kind Zhang dynamics(RKZD) model for solving the time-dependent linear matrixvector equation. On the basis of the model, we deduce its simplified form for solving the time-independent linear matrix-vector equation(TILMVE). Subsequently, for more efficient computation and easier implementation in digital hardware, we utilize Euler forward difference formula(EFDF) to discretize the continuous RKZD model, resulting in a discrete RKZD ***, numerical experimental results attest to the feasibility and high effectiveness of the discrete RKZD algorithm for solving TILMVE. Comparisons with the discrete gradient neural network(or termed discrete gradient dynamics), Jacobi iteration, as well as Gauss-Seidel iteration highlight the superior convergence properties of the discrete RKZD algorithm.

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Leveraging Digital Twins and Demand Side Recommender Chatbot for Optimizing Smart Grid Energy Efficiency

Leveraging Digital Twins and Demand Side Recommender Chatbot...

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2023 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2023

作者： Onile, Abiodun E. Belikov, Juri Petlenkov, Eduard Levron, Yoash Tallinn University of Technology Department of Software Science Akadeemia tee 15a Tallinn12618 Estonia Tallinn University of Technology Department of Computer Systems Akadeemia tee 15a Tallinn12618 Estonia Technion - Israel Institute of Technology Faculty of Electrical & Computer Engineering Haifa3200003 Israel

ISBN: (纸本)9798350327748

Electricity consumers often face the challenge of selecting an optimal plan for saving energy. Strategic energy management and monitoring plays a key role in overcoming these challenges. Developments around Industry 5.0 powered smart grid proffers adequate solutions which allows end-consumers to monitor their energy performance towards effecting demand side recommendation services. Specific problems where end-users are likely to ignore recommended advice exists, thereby contributing to widening 'knowledge-action gap'. An ensemble of hybrid digital twins (DT) asset modelling based on ordinary differential equation (ODE) physics engine and data driven recurrent neural network (RNN) prediction approach alongside PageRank based asset behavior scoring algorithm deployed for demand side recommender and generative pre-trained transformers (GPT) based conversational chatbot technology show effectiveness in engaging and extending end-consumers interests in recommended advice. The novelty of the study lies in extending current scope of demand side recommender scheme via conversational chatbot interface for DT of electricity grid assets that better engage and monitors end-user's energy behavior while offering appropriate energy efficiency advice towards achieving energy conservation goals of smart grid consumers. Extensive experiments, including evaluation of end-user studies, revealed the effectiveness of proposed approach in terms of improved recommendation quality and user engagement towards net electricity demand reduction. © 2023 IEEE.

关键词： Energy efficiency

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Applications of Digital Twins for Demand Side Recommendation Scheme with Consumer Comfort Constraints

Applications of Digital Twins for Demand Side Recommendation...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Onile, Abiodun E. Belikov, Juri Petlenkov, Eduard Levron, Yoash Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia Faculty of Electrical and Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel

ISBN: (纸本)9798350396782

The next evolution of traditional energy systems towards smart grid will require end-consumers to actively participate and make informed decisions regarding their energy usage. Industry 4.0 facilitates such progress by allowing more advanced analytics and creating means for end-consumers and distributed grid assets to be modelled as their Digital twins (DT) equivalents, paving the way for asset-level analytics. Note-worthily, consumers’ comfort is crucial towards promotion of easy adoption of such models from consumers’ perspectives. This study presents the application of hybrid DT and multiagent reinforcement learning models for real-time estimation of end-consumers future energy behaviors while generating actionable recommendation feedback for improving their energy efficiency and enhancing end-user comfort. © 2023 IEEE European Union.

关键词： Consumer comfort Demand side recommender system Distributed power systems Hybrid digital twins Industry 5.0

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Comparison of IDS in IoT, Mobile and Cloud based Systems

Comparison of IDS in IoT, Mobile and Cloud based Systems

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Görkem Gök Aytuğ Boyacı Mustafa Ulaş Department of Software Engineering Faculty of Technology Fırat University Elazığ Türkiye Department of Computer Engineering Air Force Academy National Defence University İstanbul Türkiye Department of Artificial Intelligence and Data Science Engineering Faculty Of Engineering Fırat University Elazığ Türkiye

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

This study is meant to research the evolution of intrusion detection and network monitoring within computer, cloud-based systems, IIoT, and mobile environments. The source has outlined the novel technologies in IDS, from “Fast Learning Network” to hybrid classifiers aiming at protecting cloud computing. The findings highlight innovation IDS technologies that provide flexible and peculiar solutions to ward off potential cyber threats. In addition, it presented with the use of Nagios, NConf, the state of the art in network traffic monitoring, and deep learning as tools for advanced management and network protection. Besides, the research also reviewed concepts of Big Data Analytics and Federated Learning in bettering direct integration and anomaly detection in IIoT. A good comparison of the two would show that recent technological advancements are much needed for network efficiency and its cybersecurity. Based on the findings, the scope of networked system security develops dynamically amid current challenges.

关键词： Deep learning Performance evaluation Cloud computing Technological innovation Federated learning computer hacking Intrusion detection

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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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