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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Amrita Kumar, Rajiv Joshi, Shivani Raj, Daksh Centre for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Sharda University U.P. Greater Noida India G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Nvidia Ai Learning & Research Center U.P. Greater Noida India G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering U.P. Greater Noida India Galgotias College of Engineering and Technology Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9798350371055

With the increasing popularity of live streaming platforms, there is an imperative need for robust methods for identifying individuals in real-time. The proposed system utilizes Convolutional Neural Networks (CNN) computer vision algorithms to identify faces in live streams and match them against a pre-existing database of known individuals. Additionally, the system employs deep learning and supervised learning techniques along with various types of CNN to achieve robust facial recognition in diverse lighting and environmental conditions. The system's accuracy is evaluated using various performance metrics, including precision, recall, and F1-score. The results demonstrate that the proposed system achieves high accuracy in person identification during real-time live streams. This system holds potential applications in a variety of domains, including security, entertainment, and social media. © 2024 IEEE.

关键词： computer vision Convolutional neural networks (CNN) deep learning F1-score Live streaming precision recall supervised learning

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Social-Aware Group Display Configuration in VR Conference 38

Social-Aware Group Display Configuration in VR Conference

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Hsu, Bay-Yuan Shen, Chih-Ya Yuan, Hao Shan Lee, Wang-Chien Yang, De-Nian Department of Industrial Engineering and Engineering Management National Tsing Hua University China Department of Computer Science National Tsing Hua University China Institute of Information Systems and Applications National Tsing Hua University China Department of Computer Science and Engineering Pennsylvania State University United States Institute of Information Science Academia Sinica Taiwan Research Center of Information Technology Innovation Academia Sinica Taiwan

ISBN: (纸本)1577358872

Virtual Reality (VR) has emerged due to advancements in hardware and computer graphics. During the pandemic, conferences and exhibitions leveraging VR have gained attention. However, large-scale VR conferences, face a significant problem not yet studied in the literature - displaying too many irrelevant users on the screen which may negatively impact the user experience. To address this issue, we formulate a new research problem, Social-Aware VR Conference Group Display Configuration (SVGD). Accordingly, we design the Social Utility-Aware VR Conference Group Formation (SVC) algorithm, which is a 2-approximation algorithm to SVGD. SVC iteratively selects either the P-Configuration or S-Configuration based on their effective ratios. This ensures that in each iteration, SVC identifies and chooses the solution with the highest current effectiveness. Experiments on real metaverse datasets show that the proposed SVC outperforms 11 baselines by 75% in terms of solution quality. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Virtual reality

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An Immune-Inspired Approach with Interval Allocation in Solving Multimodal Multi-Objective Optimization Problems with Local Pareto Sets

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computers, Materials & Continua 2024年第6期79卷 4237-4257页

作者： Weiwei Zhang Jiaqiang Li Chao Wang Meng Li Zhi Rao College of Computer Science and Technology Zhengzhou University of Light IndustryZhengzhou450001China Technology Research and Development Center Jilin Tobacco Industry Company LimitedChangchun130031China College of Tobacco Science and Engineering Zhengzhou University of Light IndustryZhengzhou450001China Raw Material Department Hongyun Honghe Tobacco(Group)CompanyKunming650202China

In practical engineering,multi-objective optimization often encounters situations where multiple Pareto sets(PS)in the decision space correspond to the same Pareto front(PF)in the objective space,known as Multi-Modal Multi-Objective Optimization Problems(MMOP).Locating multiple equivalent global PSs poses a significant challenge in real-world applications,especially considering the existence of local *** identifying and locating both global and local PSs is a major *** tackle this issue,we introduce an immune-inspired reproduction strategy designed to produce more offspring in less crowded,promising regions and regulate the number of offspring in areas that have been thoroughly *** approach achieves a balanced trade-off between exploration and ***,we present an interval allocation strategy that adaptively assigns fitness levels to each *** strategy ensures a broader survival margin for solutions in their initial stages and progressively amplifies the differences in individual fitness values as the population matures,thus fostering better population ***,we incorporate a multi-population mechanism that precisely manages each subpopulation through the interval allocation strategy,ensuring the preservation of both global and local *** results on 21 test problems,encompassing both global and local PSs,are compared with eight state-of-the-art multimodal multi-objective optimization *** results demonstrate the effectiveness of our proposed algorithm in simultaneously identifying global Pareto sets and locally high-quality PSs.

关键词： Multimodal multi-objective optimization problem local PSs immune-inspired reproduction

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Prioritizing Network-On-Chip Routers for Countermeasure Techniques against Flooding Denial-of-Service Attacks:A Fuzzy Multi-Criteria Decision-Making Approach

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computer Modeling in engineering & sciences 2025年第3期142卷 2661-2689页

作者： Ahmed Abbas Jasim Al-Hchaimi Yousif Raad Muhsen Wisam Hazim Gwad Entisar Soliman Alkayal Riyadh Rahef Nuiaa Al Ogaili Zaid Abdi Alkareem Alyasseri Alhamzah Alnoor Department of Electromechanical Systems Engineering Thi-Qar Technical CollegeSouthern Technical UniversityBasra61001Iraq Department of Civil Engineering Wasit UniversityAl Kut52001WasitIraq Technical Engineering College Al-Ayen UniversityThi-Qar64001Iraq Department of Artificial Intelligence Engineering College of EngineeringAlnoor UniversityNinavah41012Iraq Information Technology Department Faculty of Computing and Information Technology at Rabigh King Abdulaziz UniversityJeddah22230Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyWasit UniversityAl kut52001WasitIraq Information Technology Research and Development Center University of KufaNajaf54001Iraq College of Engineering University of Warith Al-AnbiyaaKarbala56001Iraq Management Department College of Business Administration(COBA)A’Sharqiyah University(ASU)Ibra400Oman

The implementation of Countermeasure Techniques(CTs)in the context of Network-On-Chip(NoC)based Multiprocessor System-On-Chip(MPSoC)routers against the Flooding Denial-of-Service Attack(F-DoSA)falls under Multi-Criteria Decision-Making(MCDM)due to the three main concerns,called:traffic variations,multiple evaluation criteria-based traffic features,and prioritization NoC routers as an *** this study,we propose a comprehensive evaluation of various NoC traffic features to identify the most efficient routers under the F-DoSA ***,an MCDM approach is essential to address these emerging *** the recent MCDM approach has some issues,such as uncertainty,this study utilizes Fuzzy-Weighted Zero-Inconsistency(FWZIC)to estimate the criteria weight values and Fuzzy Decision by Opinion Score Method(FDOSM)for ranking the routers with fuzzy Single-valued Neutrosophic under names(SvN-FWZIC and SvN-FDOSM)to overcome the *** results obtained by using the SvN-FWZIC method indicate that the Max packet count has the highest importance among the evaluated criteria,with a weighted score of *** contrast,the Hop count is identified as the least significant criterion,with a weighted score of *** remaining criteria fall within a range of intermediate importance,with enqueue time scoring 0.1845,packet count decremented and traversal index scoring 0.1262,packet count incremented scoring 0.1124,and packet count index scoring *** terms of ranking,SvN-FDOSM has two approaches:individual and *** the individual and group ranking processes show that(Router 4)is the most effective router,while(Router 3)is the lowest router under *** sensitivity analysis provides a high stability in ranking among all 10 *** approach offers essential feedback in making proper decisions in the design of countermeasure techniques in the domain of NoC-based MPSoC.

关键词： NoC-based MPSoC security flooding DoS attack MCDM FDOSM FWZIC fuzzy set

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Boosting Monolayer Transition Metal Dichalcogenides Growth by Hydrogen-Free Ramping during Chemical Vapor Deposition

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Nano Letters 2024年第27期24卷 8277-8286页

作者： Liu, Hongwei Zhang, Tianyi Wu, Peng Lee, Hae Won Liu, Zhenjing Tang, Tsz Wing Tang, Shin-Yi Kang, Ting Park, Ji-Hoon Wang, Jun Zhang, Kenan Zheng, Xudong Peng, Yu-Ren Chueh, Yu-Lun Liu, Yuan Palacios, Tomás Kong, Jing Luo, Zhengtang Department of Chemical and Biological Engineering Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology William Mong Institute of Nano Science and Technology and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction The Hong Kong University of Science and Technology Kowloon999077 Hong Kong Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Materials Science and Engineering National Tsing-Hua University Hsinchu30013 Taiwan Department of Materials Science and Engineering Korea University Seoul02841 Korea Republic of Key Laboratory for Micro-Nano Optoelectronic Devices of Ministry of Education School of Physics and Electronics Hunan University Changsha410082 China

The controlled vapor-phase synthesis of two-dimensional (2D) transition metal dichalcogenides (TMDs) is essential for functional applications. While chemical vapor deposition (CVD) techniques have been successful for transition metal sulfides, extending these methods to selenides and tellurides often faces challenges due to uncertain roles of hydrogen (H2) in their synthesis. Using CVD growth of MoSe2 as an example, this study illustrates the role of a H2-free environment during temperature ramping in suppressing the reduction of MoO3, which promotes effective vaporization and selenization of the Mo precursor to form MoSe2 monolayers with excellent crystal quality. As-synthesized MoSe2 monolayer-based field-effect transistors show excellent carrier mobility of up to 20.9 cm2/(V·s) with an on-off ratio of 7 × 107. This approach can be extended to other TMDs, such as WSe2, MoTe2, and MoSe2/WSe2 in-plane heterostructures. Our work provides a rational and facile approach to reproducibly synthesize high-quality TMD monolayers, facilitating their translation from laboratory to manufacturing. © 2024 American Chemical Society.

关键词： Monolayers

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Bee Colony-Reptile Search Optimization Technique for Blood Cell Cancer Detection 17

Bee Colony-Reptile Search Optimization Technique for Blood C...

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17th International Conference on computer and Automation engineering, ICCAE 2025

作者： Hussein, Aziza I. Saleh, Mohammed Ali Aly, Rabab Hamed M. Energy and Technology Research Center College of Engineering Effat University Jeddah Saudi Arabia Helwan University Faculty of Engineering Computer and Systems Engineering Department Egypt Nahda University Faculty of Computer Science Computer Science Department Beni Suef Egypt The Higher Institute for Management and Information Technology Minya Egypt

ISBN: (纸本)9798331533816

The biosystem is a crucial system grounded in classification and detection, utilizing Artificial Intelligence (AI) approaches or metaheuristic techniques. Currently, cancer of the blood cells is among the deadliest cancers in the world. Acute lymphoblastic leukemia (ALL) is a cancer of blood cells that causes excessive proliferation of lymphocytes. It is extremely time-consuming and expensive to conduct diagnostic calculations. The number of platelets in a patient's blood is computed by a platelet count. A lacking number of platelets can indicate cancer, infection, or other health problems. A patient with too many platelets is at risk for blood strokes. A single drop of blood includes tens of thousands of platelets. The main goal of this paper is how to detect the features of blood cells and classify with predicting cancer type based on platelets analysis by using Bee Colony followed by Reptile Search Optimization (BCRSO) technique. According to the results, BCRSO algorithm performed better in terms of classification efficacy and accuracy rate than other algorithms. Based on simulation results, the proposed method is more effective than previously published research for classification optimization. © 2025 IEEE.

关键词： Ant Colony Optimization (A CO) Blood Cell Cancer Metaheuristic techniques Platelets tests Reptile Search Optimization (RSO)

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Frequency Response and Design Based on gm/IDof Amplifier in CNFET Technology 6

Frequency Response and Design Based on gm/IDof Amplifier in ...

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6th Iranian International Conference on microelectronics, IICM 2024

作者： Zanjani, S. Mohammadali Dolatshahi, Mehdi Dousti, Massoud Alaie, Zahra Jahangir Moshayedi, Ata Mehrabi, Arash Smart Microgrid Research Center Najafabad Branch Islamic Azad University Najafabad Iran Najafabad Branch Islamic Azad University Department of Electrical Engineering Najafabad Iran Science and Research Branch Islamic Azad University Department of Electrical and Computer Engineering Tehran Iran School of Information Engineering Jiangxi University of Science and Technology Jiangxi341000 China

ISBN: (纸本)9798331511371

The downscaling of Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) elements in the nanoscale, leads to the deviation of V-I characteristics compared with the ideal behavior. For instance, in the elements with short channels, Drain voltage considerably impacts the potential barrier in a way that the Source injects the carriers into the channel without any Gate role. This is due to the decrease in the height of the barrier by the Drain. An appropriate alternative for the MOS technology is the Carbon Nanotube Field Effect Transistors (CNFETs). The high mobility of carriers in the CNT results in their higher speed in CNFET transistors. Low power dissipation, nano dimension, low noise figure, the similarity of performance with the MOSFETs, and the ballistic transition of the carriers are their advantages. In this paper, together with the study of CNFET's structure and models, the impact of variations of input voltage, the number of nanotubes, pitches, and chirality on the current, threshold voltage, and V-I characteristics of a common source amplifier are studied. Moreover, it is observed that in the same specifications of the aspect ratio and bias, in the CNFET transistors, the gm/ID figure according to the normalized current in the Weak Inversion and Moderate regions is higher than the MOSFETs, shows that this technology as the fair choice can be an appropriate alternative for the MOSFETs. © 2024 IEEE.

关键词： Chirality

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An Improved Fire and Smoke Detection Method Based on YOLOv7 32

An Improved Fire and Smoke Detection Method Based on YOLOv7

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32nd International Conference on computer Communications and Networks, ICCCN 2023

作者： Lian, Jie Pan, Xinyu Guo, Jin Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University Shanghai China Shanghai Normal University Department of Computer Science Shanghai China Morgan State University Department of Computer Science MD United States

ISBN: (纸本)9798350336184

Fires can cause extensive damage to human life and property, highlighting the importance of accurate flame and smoke detection systems. However, current methods for detecting flame and smoke struggle to balance the demands of real-time processing and prediction accuracy, limiting their applicability in fire warning and detecting systems. In this paper, we propose a novel approach based on the YOLOv7 architecture for efficient and accurate detection of fire and smoke in images. Our approach incorporates partial convolutional layers into the E-ELAN module of the YOLOv7 network, enabling faster and more precise identification of fire and smoke. Furthermore, we introduce the Focal-EIoU loss function to address the issue of fluctuating model loss caused by low-quality samples. We validate our approach on a real-world dataset and report significant improvements in detection accuracy, with a mean average precision of 78.5 and an FPS increase to 63. These results demonstrate the effectiveness of our approach in enhancing the capability of fire and smoke detection systems. © 2023 IEEE.

关键词： Convolution

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Demand Forecasting of a Microgrid-Powered Electric Vehicle Charging Station Enabled by Emerging Technologies and Deep Recurrent Neural Networks

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computer Modeling in engineering & sciences 2025年第5期143卷 2237-2259页

作者： Sahbi Boubaker Adel Mellit Nejib Ghazouani Walid Meskine Mohamed Benghanem Habib Kraiem Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia The International Centre for Theoretical Physics Trieste UniversityTrieste34151Italy Faculty of Science and Technology University of JjelJijel18000Algeria Department of Civil Engineering College of EngineeringNorthern Border University Arar1321Saudi Arabia SAMATWAIQ for Drones Company Prince Sultan Roadeddah23621Saudi Arabia Physics Department Faculty of ScienceIslamic University of MadinahMadinah4235Saudi Arabia Department of Electrical Engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Center for Scientifc Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia

Electric vehicles(EVs)are gradually being deployed in the transportation *** they have a high impact on reducing greenhouse gas emissions,their penetration is challenged by their random energy demand and difficult scheduling of their optimal *** cope with these problems,this paper presents a novel approach for photovoltaic grid-connected microgrid EV charging station energy demand *** present study is part of a comprehensive framework involving emerging technologies such as drones and artificial intelligence designed to support the EVs’charging scheduling *** using predictive algorithms for solar generation and load demand estimation,this approach aimed at ensuring dynamic and efficient energy flow between the solar energy source,the grid and the electric *** main contribution of this paper lies in developing an intelligent approach based on deep recurrent neural networks to forecast the energy demand using only its previous ***,various forecasters based on Long Short-term Memory,Gated Recurrent Unit,and their bi-directional and stacked variants were investigated using a real dataset collected from an EV charging station located at Trieste University(Italy).The developed forecasters have been evaluated and compared according to different metrics,including R,RMSE,MAE,and *** found that the obtained R values for both PV power generation and energy demand ranged between 97%and 98%.These study findings can be used for reliable and efficient decision-making on the management side of the optimal scheduling of the charging operations.

关键词： Microgrid electric vehicles charging station forecasting deep recurrent neural networks energy management system

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Comparison of Attention Mechanisms in Machine Learning Models for Vehicle Routing Problems 4th

Comparison of Attention Mechanisms in Machine Learning Model...

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

作者： Krishna Munjuluri Vamsi, V.S. Telukuntla, Yashwanth Reddy Kumar, Parimi Sanath Gutjahr, Georg Department of Computer Science and Engineering Amritapuri India Department of Electronics and Communication Amritapuri India Center for Research in Analytics & Technologies for Education Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819900466

The Capacitated Vehicle Routing Problem (CVRP) is an important combinatorial optimization problem in which a fleet of vehicles must be routed to a set of customers. Many exact and heuristic methods have been proposed to solve this problem. Recently, efficient machine learning algorithms have been developed for solving the VRP. A major component of these algorithms is the Attention Mechanism which enables the vehicle to make more informed decisions on which customer to serve next. This paper compares various topologies for implementing such attention mechanisms when solving VRPs. Using simulations, it is found that Single-Head Attention works better with larger node instances, whereas Multi-Head Attention is superior for smaller node instances. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heuristic methods

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