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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4087-4100页

作者： Teimoori, Zeinab Yassine, Abdulsalam Lu, Chaoru the Department of Electrical and Computer Engineering Lakehead University Thunder BayONP7B 5E1 Canada the Department of Software Engineering Lakehead University Thunder BayONP7B 5E1 Canada the Department of Built Environment Oslo Metropolitan University Oslo0166 Norway

—Implementation of advanced intelligent deep learning techniques for electric vehicles (EVs) energy consumption analysis is obstructed by two main subjects. First, the problem of finding a very similar collection of datasets to the actual EVs energy usage in terms of feature space and data distribution. Second, training a retrained model from scratch requires a massive amount of computational power;however, this does not guarantee to catch rare events included in datasets. To mitigate the aforementioned concerns, this article aims to present a model based on deep transfer learning (DTL) between domain-variant datasets, to reduce the need for the existence of a vast amount of EVs data, including driving characteristics and patterns. Also, this model applies a distributed cooperative learning approach to identify highly correlated energy consumption parameters by building the model on previously acquired knowledge from preceding learning phases in order to enhance the artificial intelligence (AI) accuracy level of the proposed energy management system. Impact Statement—The drastic growth in the conventional transportation system raises serious air pollution concerns. Eco-friendly vehicles, in contrast, have been introduced as an alternative to alleviate such environmental issues. The aforementioned switch requires proper infrastructure to increase the public’s interest before mass production. Ensuring EV owners’ satisfaction by increasing the quality of experience and desecrating range anxiety is the primary goal for EV producers. One attempt to reach this goal is to provide a precise remaining battery level for EVs based on users’ driving destinations and behaviors. However, achieving accurate battery estimation leveraging AI is limited to the amount of historical data. We, therefore, consider introducing an estimation model which trains insufficient EV data history by transferring previously trained knowledge to increase the precision of remaining battery level pred

关键词： Energy utilization

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Gland Segmentation in H&E Histopathological Images using U-Net with Attention Module 12

Gland Segmentation in H&E Histopathological Images using U-N...

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12th International Conference on Image Processing Theory, Tools and Applications, IPTA 2023

作者： Yildirim, Zeynep Samet, Refik Hancer, Emrah Nemati, Nooshin Mali, Mohamed Traore Ankara University Department of Computer Engineering Ankara06830 Turkey Mehmet Akif Ersoy University Department of Software Engineering Burdur15030 Turkey Victoria University of Wellington School of Engineering and Computer Science Wellington6140 New Zealand

ISBN: (纸本)9798350325416

Gland segmentation refers to the process of identifying and delineating glandular regions within histopathology images. However, gland segmentation in histopathology images is a challenging task due to several factors such as shape variations and overlapping structures. Despite numerous research efforts dedicated to addressing this challenge, it remains an unresolved problem. To address this issue, we introduce a deep gland segmentation method by designing an enhanced U-Net variant combined with an Attention module. By incorporating the Attention module into the U-Net architecture, the model becomes more adept at capturing contextual information and fine details within the histopathology images. To verify the effectiveness of the introduced method, we compare it with well-known semantic segmentation architectures on the GLAS and GRAG datasets. According to the results, our approach demonstrates a high success rate in gland segmentation, effectively competing with existing state-of-the-art methods. © 2023 IEEE.

关键词： Semantic Segmentation

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Emulator-Based Configuration on QoS Measurement: A Case Study of DRL Actions on Mininet and RYU Testbed 15th

Emulator-Based Configuration on QoS Measurement: A Case Stud...

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15th International Conference on computer Science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Kang, Seungwoo Tam, Prohim Kim, Seokhoon Department of Software Convergence Soonchunhyang University Chungcheongnam-do Asan-si31538 Korea Republic of Department of Computer Software Engineering Soonchunhyang University Chungcheongnam-do Asan-si31538 Korea Republic of

ISBN: (纸本)9789819724468

Deep reinforcement learning (DRL) has found widespread applications in the realm of communication and networking, addressing a multitude of service scenarios. In this study, we offer a detailed case study involving DRL action configuration on the Mininet and RYU testbeds. The working flow guides the dynamic adjustment of software-defined networking (SDN) flows, resource allocation, and NFV service scaling to enhance network elasticity and virtualization. Our experiments are conducted in an emulator testbed by leveraging the networking tools such as Mininet, Mini-NFV, and the RYU SDN controller. The results are captured to illustrate that our proposed approach significantly enhances Quality of Service (QoS) satisfaction, covering key aspects such as latency, throughput, packet loss, and resource utilization. Our study also includes a comparison with established baseline configurations, namely Fixed QoS and Reactive QoS, further illustrating the dynamic provisioning through DRL. The testbed configuration holds the applicability for responsive and reliable network management in core control domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Quality of service

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A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

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computer Modeling in engineering & Sciences 2023年第5期135卷 1209-1228页

作者： Chuan Wang Ruoyu Zhu Yi Jiang Weili Liu Sang-Woon Jeon Lin Sun Hua Wang College of Software Henan Normal UniversityXinxiang453007China College of Computer and Information Engineering Henan Normal UniversityXinxiang453007China School of Computer Science and Engineering South China University of TechnologyGuangzhou510006China School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou510665China Department of Military Information Engineering Hanyang UniversityAnsan15588South Korea College of Engineering and Science Institute for Sustainable Industries and Liveable CitiesVictoria UniversityMelbourneVIC 8001Australia

The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to *** paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP *** work is novel and contributes to three aspects:problemmodel,optimization framework,and ***,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over ***,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic *** framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous *** way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme *** the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational ***,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search *** evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic *** experimental results show that ACO with 2-opt can solve the DTSPs effectively.

关键词： Dynamic traveling salesman problem(DTSP) offline optimization and online application ant colony optimization(ACO) two-optimization(2-opt)strategy

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Smart Scheduling in Cloudlet Federation

Smart Scheduling in Cloudlet Federation

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2023 International Conference on Energy, Power, Environment, Control, and Computing, ICEPECC 2023

作者： Asim, Sherrjel Nayyer, Muhammad Ziad Ahmed, Usama Faheem, Muhammad GIFT University Department of Computer Science Gujranwala Pakistan Government College University Department of Software Engineering Faisalabad Pakistan

ISBN: (纸本)9781665476010

Rapidly increasing data requires a lot of time to execute different tasks by the systems at Cloudlet Federation (CF). The demand of today's computing is to exploit resource utilization up to maximum. Efficient resource allocation and scheduling of tasks are the significant characteristics of Cloudlet Federation (CF). Different tasks have diverse execution time. Some tasks require greater execution time whereas some need just a couple of seconds for their execution. Scheduling offers a feasible solution for handling these tasks with the help of different scheduling algorithms. To address task scheduling issue, this paper proposes a scheduling technique named Least Busy Time Scheduling (LBTS) for smart scheduling of tasks in Cloudlet Federation (CF). The proposed technique assists the scheduler to assign task to a system considering the parameter of least busy time to reduce waiting time and optimize the overall performance of the system. © 2023 IEEE.

关键词： Scheduling algorithms

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Fault Detection and Mitigation in Vehicle Platooning

Fault Detection and Mitigation in Vehicle Platooning

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Ahmed, Najeebuddin Naser, Hassan Ameli, Amir Lakehead University Faculty of Engineering Thunder Bay Canada Lakehead University Department of Software Engineering Thunder Bay Canada Lakehead University Department of Electrical and Computer Engineering Thunder Bay Canada

ISBN: (纸本)9798350304602

This paper addresses the challenges posed by faults in the complex systems of autonomous vehicles within vehicle platoons. It presents a state-space model tailored for vehicle platoons, incorporating an Unknown Input Observer (UIO) to estimate internal states for each vehicle. By monitoring discrepancies between measured and estimated states, the framework effectively detects faults affecting a vehicle's position, velocity, and acceleration, often stemming from malfunctions in its control and navigation components. The paper also introduces fault detection and identification UIOs to pinpoint faulty parameters and estimate associated fault inputs. To validate its effectiveness, the proposed method undergoes MATLAB simulations across diverse scenarios, confirming its capability to mitigate faults within the vehicle platoon. © 2023 IEEE.

关键词： Autonomous vehicles

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Optimal Resource Allocation Method for Device-to-Device Communication in 5G Networks

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computers, Materials & Continua 2022年第4期71卷 1-15页

作者： Fahd N.Al-Wesabi Imran Khan Saleem Latteef Mohammed Huda Farooq Jameel Mohammad Alamgeer Ali M.Al-Sharafi Byung Seo Kim Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Electrical Engineering University of Engineering and TechnologyPeshawarPakistan Department of Medical Instrumentation Techniques Engineering Electrical Engineering Technical CollegeMiddle Technical UniversityBaghdad10013Iraq Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA Department of Software and Communications Engineering Hongik UniversityKorea

With the rapid development of the next-generation mobile network,the number of terminal devices and applications is growing ***,how to obtain a higher data rate,wider network coverage and higher resource utilization in the limited spectrum resources has become the common research goal of ***-to-Device(D2D)communication technology and other frontier communication technologies have ***-to-Device communication technology is the technology that devices in proximity can communicate directly in cellular *** has become one of the key technologies of the fifth-generation mobile communications system(5G).D2D communication technology which is introduced into cellular networks can effectively improve spectrum utilization,enhance network coverage,reduce transmission delay and improve system throughput,but it would also bring complicated and various interferences due to reusing cellular resources at the same *** resource management is one of the most challenging and importing issues to give full play to the advantages of D2D *** resource allocation is an important factor that needs to be addressed in D2D ***,this paper proposes an optimization method based on the game-matching *** main idea is to model the optimization problem of the quality-of-experience based on user fairness and solve it through game-matching *** results show that the proposed algorithm effectively improved the resource allocation and utilization as compared with existing algorithms.

关键词： D2D communication resource allocation optimization networks

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Adapt PointFormer: 3D Point Cloud Analysis via Adapting 2D Visual Transformers 27

Adapt PointFormer: 3D Point Cloud Analysis via Adapting 2D V...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Li, Mengke Li, Da Yang, Guoqing Cheung, Yiu-Ming Huang, Hui Shenzhen China VCC College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University Hong Kong

ISBN: (纸本)9781643685489

Pre-trained large-scale models have exhibited remarkable efficacy in computer vision, particularly for 2D image analysis. However, when it comes to 3D point clouds, the constrained accessibility of data, in contrast to the vast repositories of images, poses a challenge for the development of 3D pre-trained models. This paper therefore attempts to directly leverage pre-trained models with 2D prior knowledge to accomplish the tasks for 3D point cloud analysis. Accordingly, we propose the Adaptive PointFormer (APF), which fine-tunes pre-trained 2D models with only a modest number of parameters to directly process point clouds, obviating the need for mapping to images. Specifically, we convert raw point clouds into point embeddings for aligning dimensions with image tokens. Given the inherent disorder in point clouds, in contrast to the structured nature of images, we then sequence the point embeddings to optimize the utilization of 2D attention priors. To calibrate attention across 3D and 2D domains and reduce computational overhead, a trainable PointFormer with a limited number of parameters is subsequently concatenated to a frozen pre-trained image model. Extensive experiments on various benchmarks demonstrate the effectiveness of the proposed APF. The source code and more details are available at https://***/research/2024/PointFormer. © 2024 The Authors.

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Resource Availability Forecasting for Federated Clouds

Resource Availability Forecasting for Federated Clouds

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2023 International Conference on Energy, Power, Environment, Control, and Computing, ICEPECC 2023

作者： Mursal, Muhammad Hassan Ahmed, Usama Nayyer, Muhammad Ziad Government College University Department of Software Engineering Faisalabad Pakistan GIFT University Department of Computer Science Gujranwala Pakistan

ISBN: (纸本)9781665476010

Cloud federation has enabled organizations to adopt collaborative services for sharing data and workloads across various platforms. Induction of federation members may require some verifications and predictions related to capacity and capability of these members for compensating such types of workloads. However, the nature of federated services require stringent methods to keep track of dynamically forming resource clusters for forecasting their behavior. Recent literature has mostly focused on the applicability of forecasting algorithms based on static datasets with little or no applicability to real time scenarios. Proposed research has utilized a real world application of Clouds4Coordination (C4C) federation system. A resource forecasting strategy using two well-known algorithms, namely Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) has been proposed for collaborative clouds used in Architecture, engineering and Construction (AEC) industry. The results have shown that no single algorithm is sufficient enough to deal with dynamic scenarios of cloud federation. Moreover, the selection of algorithm is highly dependent upon the type and duration of prediction required i.e. short term or long term as required by the user. © 2023 IEEE.

关键词： Forecasting

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Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model

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computer Systems Science & engineering 2023年第8期46卷 2247-2262页

作者： N.Sharmili Saud Yonbawi Sultan Alahmari E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa Computer Science and Engineering Department Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnamAndhra PradeshIndia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Facial expression recognition(FER)remains a hot research area among computer vision researchers and still becomes a challenge because of high intraclass *** techniques for this problem depend on hand-crafted features,namely,LBP,SIFT,and HOG,along with that a classifier trained on a database of videos or *** execute perform well on image datasets captured in a controlled condition;however not perform well in the more challenging dataset,which has partial faces and image ***,many studies presented an endwise structure for facial expression recognition by utilizing DL ***,this study develops an earthworm optimization with an improved SqueezeNet-based FER(EWOISN-FER)*** presented EWOISN-FER model primarily applies the contrast-limited adaptive histogram equalization(CLAHE)technique as a pre-processing *** addition,the improved SqueezeNet model is exploited to derive an optimal set of feature vectors,and the hyperparameter tuning process is performed by the stochastic gradient boosting(SGB)***,EWO with sparse autoencoder(SAE)is employed for the FER process,and the EWO algorithm appropriately chooses the SAE ***-ranging experimental analysis is carried out to examine the performance of the proposed *** experimental outcomes indicate the supremacy of the presented EWOISN-FER technique.

关键词： Facial expression recognition deep learning computer vision earthworm optimization hyperparameter optimization

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