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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Sun, Zhe Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9798350337020

Edge computing is a new architectural model that aims to offer computing, storage, and networking resources to support Internet of Things. Its primary strategy involves transferring computational tasks to the edge of network, which is closer to end-users. This paradigm facilitates offloading of computation, resulting in reduced latency and improved system performance. However, nodes located at the network edge have restricted energy and resources. As a result, running tasks entirely at the edge leads to higher energy consumption. This work proposes a novel three-tier offloading framework comprising of multiple mobile vehicles (MVs), a base station (BS), and a cloud data center (CDC). It jointly optimizes offloading rates of tasks, CPU computation rates of MVs, BS, and CDC, and the allocation of wireless bandwidth resources at MVs during partial computation offloading of tasks. It also considers limits of maximum computational resources and maximum delay of task execution. To further reduce the total system energy consumption, this work actively caches execution codes of tasks in MEC servers to reduce data transmission energy of MVs, which minimizes the total system energy consumption. This work develops a mixed integer nonlinear program and designs a mixed meta-heuristic algorithm with a multi-strategy adaptive particle swarm optimizer. Simulation results demonstrate that it outperforms various state-of-the-art algorithms by achieving lower energy consumption in fewer iterations. © 2023 IEEE.

关键词： cloud computing Computation offloading particle swarm optimization vehicular edge computing

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Design and Investigate the Deep Learning Models for White Blood Cell Classification 3

Design and Investigate the Deep Learning Models for White Bl...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Gaikwad, Prajwal. S. Deshmukh, Rakesh K. Pise, Priya Bhopale, Shrikant D. Kalinga University Raipur India AISSMS's Institute of Information Technology Dept. of Computer Engineering Pune India Artificial Intelligence and Data Science Indira College of Engineering and Management Pune India Dr. D.Y. Patil Pratisthan's Colleg of Engineering Salokhenagar Kolhapur India

ISBN: (纸本)9798350381931

For the purpose of illness prediction, the objective of this study is to develop automated models for the categorization of white blood cells (WBCs). In the human immune system, white blood cells (WBCs) function to combat infections and shield the body from external substances. Eosinophils, neutrophils, monocytes, basophils, and lymphocytes are the components that make up these cells;each of these cell types accounts for a different proportion and is responsible for a distinct set of functions overall. The clinical laboratory technique for counting the specific types of white blood cells (WBCs) has long been an essential component of a testing procedure known as a complete blood count (CBC), which is used to aid in the monitoring of individuals' health. However, such manual processes are time-consuming and error-prone for disease detection. The recently proposed machine and deep learning-based solutions for the blood cell classification delivered better results, however, suffered from challenges as well. These challenges motivate us to propose a novel AI-based approach for automatic WBCs classification in this research work. © 2024 IEEE.

关键词： Cells

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Cost-Minimized Partial Computation Offloading in Cloud-Assisted Mobile Edge Computing Systems

Cost-Minimized Partial Computation Offloading in Cloud-Assis...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Nowadays, smart mobile devices (SMDs) support various computation-intensive and delay-sensitive applications, e.g., online games, and figure compression. However, SMDs have limited computing resources and battery energy and cannot execute all tasks of the above applications in a real-time manner. Cloud computing provides enormous computing resources and energy that can easily execute tasks offloaded from SMDs. However, could data centers (CDCs) are often located in remote sites, which leads to long transmission time. Small base stations (SBSs) offer high-bandwidth and low-latency services for SMDs, which solves the problem of cloud computing. However, it becomes a challenge to achieve the lowest cost in such a heterogeneous architecture including multiple SMDs, SBSs, and the CDC while meeting delay requirements of tasks. This work proposes a cost-minimized computation offloading strategy to minimize the total cost of the system. A constrained optimization problem is first formulated based on the hybrid architecture. Afterward, a two-stage optimization algorithm called a Lévy flights and Simulated Annealing-based Grey wolf optimizer (LSAG) is developed to optimize the total cost of the system. In the first stage, the optimal edge selection policy is determined given multiple available SBSs. In the second stage, task offloading and resource allocation among SMDs, SBSs, and the cloud are determined. Experiments with real-life tasks prove that LSAG significantly achieves lower cost with faster convergence speed than state-of-the-art peers. © 2023 IEEE.

关键词： cloud computing computation offloading intelligent optimization algorithms Lévy flights Mobile edge computing

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Network Anomaly Detection with Stacked Sparse Shrink Autoencoders and Improved XGBoost

Network Anomaly Detection with Stacked Sparse Shrink Autoenc...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Guan, Ziyue Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Efficient and accurate identification of network anomalies is of great significance to the construction of network security systems in the information age. It is highly challenging to accurately detect abnormal behaviors in the increasing network data. Currently, classification methods based on feature extraction of autoencoders have been proven to be suitable for network anomaly detection. However, traditional detection models with autoencoders have poor detection accuracy in the face of massive network features. In addition, the hyperparameter optimization of their models cannot be effectively solved. For network anomaly detection, this work proposes a new network anomaly detection method named SAXP, which integrates Stacked sparse shrink Autoencoders and a XGBoost model based on genetic simulated annealing Particle swarm optimization (GSPSO). Specifically, features extracted by stacked sparse shrink autoencoders are introduced into the XG Boost model for classification, and GSPSO is used to optimize the hyperparameters of XGBoost. Experimental results based on two real-life data sets demonstrate that the proposed SAXP achieves higher recognition accuracy than several state-of-the-art algorithms. © 2023 IEEE.

关键词： autoencoders feature extraction Network anomaly detection particle swarm optimization XGBoost

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Task and Process Capturing Toolkit using GUI Automation 4

Task and Process Capturing Toolkit using GUI Automation

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Perera, Ravindu Lakshan Bellanthudawa, Hiruni Piyumika Hevavitharana, Nipun Dimantha Ariyasinghe, Kavindu Malshan Wickramarathne, Jagath Perera, Jeewaka Sri Lanka Institute of Information Technology Dept.Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665468350

Automation is one of the best ways to make easier everyone's day-to-day life. When considering the industry, if someone could be able to automate the mechanism of solving computer software issues, that will be helpful to every company. Because most companies are facing difficulties when it comes to giving IT support. Some companies are suffering from a lack of IT supporters because they have to solve the same problem on different computers at the same time. This most likely reduces the efficiency of the company and affects its performance as well. Without the proper knowledge of technology, employees tend to get a lot of technical issues while working with the technology. Here comes the opportunity to automate the IT-related problem-solving mechanism. That will save a lot of time and increase the company's efficiency as well. While carrying out the background research we thought, by automating the IT-related problem it will reduce the allocation of human power and that will directly affect company efficiency. The research team came up with the idea of creating a software product that can export an executable file by capturing selected tasks of the user's screen and its' relevant processes. Task and process capturing toolkit is a software product called the 'ClickMe toolkit'. It can capture relevant processes that are happening in any computer which runs on Windows 10 platform, then make a script called 'ClickMe script', and convert it into an executable file. It can be run on any Windows 10 Platform. © 2022 IEEE.

关键词： Graphical user interfaces

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CONNECTED: A Knowledge Graph-Driven Platform for Clinical Data Harmonization and Personalized Digital Twin-Based Healthcare

CONNECTED: A Knowledge Graph-Driven Platform for Clinical Da...

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IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Alberto Marfoglia Christian D’Errico Filippo Nardini Sabato Mellone Antonella Carbonaro Dept. of Computer Science and Engineering University of Bologna Bologna Italy Dept. of Industrial Engineering University of Bologna Bologna Italy Dept. of Electrical Electronic and Information Engineering "Guglielmo Marconi" University of Bologna Bologna Italy

ISBN: (数字)9798331535537

ISBN: (纸本)9798331535544

The digital healthcare innovation surge requires frameworks integrating heterogeneous clinical data to support real-time, personalized care. However, existing solutions often face interoperability, scalability, and adaptability limitations, restricting their utility in predictive, precision medicine. This paper introduces CONNECTED (COmpreheNsive and staNdardized hEalth-Care plaTforms to collEct and harmonize clinical Data), a multi-layer, microservices-based platform designed to facilitate Digital Twins (DTs) for patient monitoring and personalized treatment. The solution’s core innovation lies in using knowledge graphs, which harmonize diverse clinical data sources and link patient information to AI models through automated, manifest-driven interfaces. This approach enables adaptive, patient-specific simulations by integrating real-time and historical data. We validate CONNECTED by implementing a stroke risk classifier, demonstrating the platform’s potential to provide patient-specific predictions supporting early intervention strategies. CONNECTED thus offers a scalable, flexible foundation for precision medicine, equipping clinicians with actionable insights across varied clinical applications.

关键词： Pervasive computing Technological innovation Adaptation models Precision medicine Conferences Knowledge graphs Data models Real-time systems Digital twins Surges

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Learning Priors of Human Motion With Vision Transformers

arXiv

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arXiv 2025年

作者： Falqueto, Placido Sanfeliu, Alberto Palopoli, Luigi Fontanelli, Daniele Dept. of Information Engineering and Computer Science University of Trento Italy Universitat Politècnica de Catalunya Barcelona Spain Dept. of Industrial Engineering University of Trento Italy

A clear understanding of where humans move in a scenario, their usual paths and speeds, and where they stop, is very important for different applications, such as mobility studies in urban areas or robot navigation tasks within human-populated environments. We propose in this article, a neural architecture based on Vision Transformers (ViTs) to provide this information. This solution can arguably capture spatial correlations more effectively than Convolutional Neural Networks (CNNs). In the paper, we describe the methodology and proposed neural architecture and show the experiments’ results with a standard dataset. We show that the proposed ViT architecture improves the metrics compared to a method based on a CNN. © 2025, CC BY.

关键词： Machine vision

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Energy Efficient Trust Routing Based on Mud Ring Optimization in Wireless Sensor Network

SSRN

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SSRN 2024年

作者： Maradona, N. Jaya, T. Dept. Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai India Dept. Information and Communication Engineering Saveetha Engineering College Tamil Nadu Chennai India

One of the most challenging tasks in constructing a routing model for a Wireless Sensor Network (WSN) is ensuring energy efficiency. Various energy-efficient routing strategies are developed to guide data packets across Cluster Heads (CH) along secure paths. However, within the context of a WSN, achieving both network longevity and substantial scalability proves to be a formidable challenge. Traditional trust-based routing solutions are inadequate in offering security against attacks, and as a result, trust management remains a significant obstacle in routing. To address the need for routing data packets securely and efficiently to their intended receivers, a novel routing model is devised. This model employs the Trust Routing based on Mud Ring Optimization (TR-MRO) algorithm. By incorporating the principles of the TR-MRO algorithm, which takes into account recent trust, direct trust, indirect trust, and probability requirements, the proposed model guides the routing process through CHs. Through the TR-MRO algorithm, the most optimal and secure path for data transfer is determined based on fitness considerations. The performance of the proposed network is evaluated in terms of several key metrics, including network lifetime (3050 rounds), delay (0.03s), throughput (0.93kbps), trust (0.9), and packet delivery ratio (0.98). The evaluation is conducted in comparison to existing models, highlighting the effectiveness of the proposed approach. © 2024, The Authors. All rights reserved.

关键词： Wireless sensor networks

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Latency-Minimized Computation Offloading in Vehicle Fog Computing with Improved Whale Optimization Algorithm

Latency-Minimized Computation Offloading in Vehicle Fog Comp...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Xue, Xiangdong Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Fog computing provides lower latency and higher bandwidth compared to cloud computing and is widely used in Internet of Vehicles (IoV). Vehicles cannot compute all tasks locally due to their limited computing power and battery capacity. Thus, it is a useful way to offload some tasks of vehicles to other resource-rich servers. However, due to the high mobility of vehicles, there may be a failure of returning computing results. Thus, it is a challenge to minimize the latency of tasks while meeting the constraint of energy consumption. This work proposes a vehicle-fog offloading system that offloads tasks to fog servers or idle vehicles which is a probabilistic offloading problem. So this work proposes an improved optimization algorithm called an adaptive Lévy flight-based Whale optimization algorithm with Hierarchical learning (LWH) to solve this problem. Simulation experiments show that LWH has strong global search capability and outperforms its five typical and widely used algorithms. © 2023 IEEE.

关键词： computation offloading fog computing Lévy flight Vehicular networks whale optimization algorithm

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Web Traffic Anomaly Detection Using a Hybrid Spatio-temporal Neural Network

Web Traffic Anomaly Detection Using a Hybrid Spatio-temporal...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Xu, Lifeng Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China School of Engineering Dept. of Computer Science in Lyle Southern Methodist University DallasTX United States

ISBN: (纸本)9798350337020

Nowadays, rapid development of Internet has brought a sharp increase in traffic data. Abnormal traffic haS serious impact on network security. Traffic anomaly detection can be achieved by extracting characteristics of network traffic to detect anomalous intrusions, and therefore, anomaly detection algorithms are of great significance to maintenance of network security. This work proposes a hybrid spatio-temporal neural network with attention named CTGA to effectively identify anomalous traffic. CTGA combines a Convolutional neural network (CNN), a Temporal convolutional network (TCN), a bidirectional Gated recurrent unit network (BiGRU), and a self-Attention mechanism. It automatically extracts temporal and spatial features of sequences from raw data by sliding window preprocessing followed by CNN, TCN, BiGRU, and the self-attention mechanism to detect anomalous data. CNN is used to extract spatial features of time sequences and reduce the loss of spatial information. In the sequence, TCN obtains short-term features. Long-term dependencies in the data are captured by BiGRU, and the self-attention mechanism obtains important information in the sequence. Finally, experiments with the real-life Yahoo S5 dataset prove that CTGA outperforms other approaches substantially. © 2023 IEEE.

关键词： Anomaly detection bidirectional gated recurrent unit convolutional neural network self-attention temporal convolutional network

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