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12th International Conference on computer engineering and Networks, CENet 2022

作者： Zhu, Tao Xu, Yin-qing Guo, Hao-qi Wang, Yi-bo Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing China Engineering Research Center of Electronic Data Forensics Analysis Jiangsu Province Nanjing China Key Laboratory of Digital Forensics Department of Public Security of Jiangsu Province Nanjing210031 China

ISBN: (纸本)9789811969003

The 5G mobile network is a kind of critical information infrastructure for future Internet of Things. Due to its rapid development, the planning and deployment of 5G network base stations is a more urgent and meaningful problem than ever before from the aspect of optimization. Because the purpose of establishing a 5G base station is to make its wireless signal cover as many areas as possible, a 0–1 programming model with two optimal objectives is proposed for 5G base stations based on the cover set. Then after analyzing the difficulties in solving the problem expressed by above model, employing the idea of divide and conquer and the strategies of parallelization, a scheme based on Binary Particle Swarm Optimization (BPSO) algorithm is proposed to solve this problem. In the first step of the scheme, the partition processing based on coordinate position is considered by the way of dividing the target area into many smaller sub-areas with the algorithm of Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH). The next step is to use BPSO algorithm improved by sparse particles initialization and a suppressor factor to solve each small sub-area with the parallel computing and the memory mapping technology. The proposed approach has been tested in several experiments and the experimental results demonstrate its effectiveness. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Base stations

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AI-driven Dynamic Workload Balancing for Real-time Applications on Cloud Infrastructure

AI-driven Dynamic Workload Balancing for Real-time Applicati...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Madhusudhan Dasari Sreeramulu Abdul Sajid Mohammed Dinesh Kalla Nagesh Boddapati Yuvaraj Natarajan Department of Cyber Security American Express USA Department of School of Computer and Information Sciences University of the Cumberlands Kentucky USA Department of Computer Science Colorado Technical University Charlotte NC USA Department of Computer Science Microsoft Charlotte NC USA Department of Computer Science Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The specification provides a new, resource-efficient method of Dynamic Workload Balancing in AI-driven Real-time Applications over Cloud Infrastructure. The real-time application keeps processing data at high speeds, and it is too difficult to get or make this type of arrangement using our traditional cloud setups as the system employs artificial intelligence methods responsively reallocate resources between virtual machines in accordion with demanded quality of services slabs in behalf-to-capacity ratio. It is scheduling jobs to resources efficiently and delivering results in a timely fashion, which is necessary for real-time applications like video processing or stock trading. It uses machine learning models based on historical data to predict the resources required for upcoming tasks. These predictions are subsequently utilized to smartly assign resources across VMs, factoring in network latencies and inter-dependence. It allows the system to modify queues in real-time according to changes in workload patterns, performing a dynamic load balancing.

关键词： Cloud computing Quality of service Streaming media Dynamic scheduling Real-time systems Virtual machines Time factors Servers Slabs Load modeling

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Image Classification and Analysis Using Deep Learning: Trends, Insights, and Applications of Convolutional Neural Networks

Image Classification and Analysis Using Deep Learning: Trend...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Smita Rukhande Nilakshi Jain Vineet Kumar Department of Information Technology Shah and Anchor Kutchhi Engineering College Mumbai India Department of Cyber Security Shah and Anchor Kutchhi Engineering College Mumbai India Cyberpeace Foundation Delhi India

ISBN: (数字)9798331598488

ISBN: (纸本)9798331598495

Dental imaging plays a crucial role in various healthcare applications such as dental planning, disease treatment, and post-mortem human identification in case of a disaster scenario. This paper provides a comprehensive study of the diverse applications of the various dental imaging techniques for the different applications. It focuses on the methodology, application, dataset, merits, demerits, and system evaluation metrics. Further, it provides the implementation of dental radiograph classifications using a deep convolution neural network (DCNN) to depict the texture and shape attributes of dental radiographs. The effectiveness of the DCNN-based dental radiograph classification is estimated on the dental radiography dataset (DRD) based on recall, precision, F1-score, negative predictive value (NPV), selectivity, and accuracy on the dental radiograph dataset. The DCNN provides improved overall accuracy of 91%, recall of 90.7%, selectivity of 91.3%, precision of 91.3%, F1-score of 91.04%, and NPV of 90.7%.

关键词： Deep learning Support vector machines Measurement Accuracy Shape Neural networks Nearest neighbor methods Dentistry Planning Medical diagnosis

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Energy-Efficient Routing Algorithm Based on Multipath Routing in Large-Scale Networks

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computers, Materials & Continua 2021年第8期68卷 2029-2039页

作者： Haijun Geng Qidong Zhang Jiangyuan Yao Wei Wang Zikun Jin Han Zhang Yangyang Zhang School of Computer and Information Technology Shanxi UniversityShanxi030006China School of Computer Science and Cyberspace Security Hainan UniversityHaikou570228China School of Cyber Space and Technology Beihang UniversityBeijing100191China College of Engineering Northeastern University Boston02115MAUSA

A reduction in network energy consumption and the establishment of green networks have become key scientific problems in academic and industrial *** energy efficiency schemes are based on a known traffic matrix,and acquiring a real-time traffic matrix in current complex networks is ***,this research investigates how to reduce network energy consumption without a real-time traffic *** particular,this paper proposes an intra-domain energy-efficient routing scheme based on multipath *** analyzes the relationship between routing availability and energy-efficient routing and integrates the two mechanisms to satisfy the requirements of availability and energy *** main research focus is as follows:(1)A link criticality model is evaluated to quantitatively measure the importance of links in a network.(2)On the basis of the link criticality model,this paper analyzes an energy-efficient routing technology based on multipath routing to achieve the goals of availability and energy efficiency simultaneously.(3)An energy-efficient routing algorithm based on multipath routing in large-scale networks is proposed.(4)The proposed method does not require a real-time traffic matrix in the network and is thus easy to apply in practice.(5)The proposed algorithm is verified in several network *** results show that the algorithm can not only reduce network energy consumption but can also ensure routing availability.

关键词： Energy-efficient routing multipath routing link criticality model energy-saving ratio large-scale network

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SE-PifPaf: A Multi-Person Human Pose Estimation Methond for Complex Scenes

SE-PifPaf: A Multi-Person Human Pose Estimation Methond for ...

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2023 International Conference on Image Processing and computer Vision, IPCV 2023

作者： Wang, Tianbao Meng, Caixia Shi, Lei Gao, Yufei Wang, Qingxian Wei, Lin School of Cyber Science and Engineering Zhengzhou University Zhengzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Railway Police College Department of Image and Network Investigation Technology Zhengzhou China

ISBN: (纸本)9781665473941

Images in complex scenes are prone to cross-obscuration of people, which brings great challenges to multi-person human pose estimation. Previous multi-person human pose estimation algorithms suffer from limited accuracy and human skeleton loss in complex scenes. To solve the above problems, a novel pose estimation optimization algorithm SE-PifPaf is proposed. Firstly, the grouped convolution network is employed to mine semantic features relevant to the human pose estimation task. Secondly, the attention mechanism is applied to realize the interaction among channels, obtaining the weight of each channel on the pose estimation task in complex scenes. Experimental results show that SE-PifPaf achieves an accuracy of 72.3% on COCO 2017 dataset. More importantly, it improves the accuracy of pose estimation by 1.1% in complex scenes on CrowdPose dataset. © 2023 IEEE.

关键词： Convolution

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Classification of benign and malignant breast lesions in mammograms using dense-unified multiscale attention network and data-efficient image transformers

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The European Physical Journal Special Topics 2025年 1-19页

作者： Paavankumar, S. Karthik, R. Idayachandiran, Gangatharan Sri, Pulaparthi Penchala Deepthi Illakiya, T. School of Computer Science Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Chennai India

Breast cancer is a common and a serious health problem and it is the major cause of morbidity and mortality for women. Early detection of the disease is particularly challenging because abnormalities such as masses and microcalcifications exhibit subtle and diverse characteristics that are often difficult to identify in mammograms. In recent years, advancement in artificial intelligence, particularly deep learning (DL), has shown to improve diagnostic accuracy and early-stage tumor detection. This study aims to improve performance of DL models by considering both masses and microcalcifications in the proposed work to classify breast cancer abnormalities. The proposed work introduces a novel dual-track network that employs a combination of dense-unified multiscale attention fusion (UMAF) track and data-efficient image transformer (DeiT). The DeiT track processes the entire image simultaneously using patch embeddings, enabling them to capture multiscale representations and dependencies across the entire image. Simultaneously, the Dense-UMAF track focuses on extracting localized features while utilizing connectivity of DenseNet architecture to enable effective feature reuse. This approach generates relevant input features through residual connections of varying lengths, thereby effectively addressing the vanishing gradient problem. The UMAF improves feature extraction by capturing multiscale information, resulting in a better representation of the input data. This dual-track architecture is specifically designed to capture the characteristics of mass and calcification abnormalities in mammograms, which display both localized features and global contextual patterns. The proposed network was evaluated on the Curated Breast Imaging Subset of Digital Database for Screening Mammography dataset, obtaining a classification accuracy of 88.69%.

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A Study on Classification of Mobile Application Network Traffic on MIRAGE-COVID-CCMA-2022 Dataset via Machine/Deep Learning and Feature Selection Methods

A Study on Classification of Mobile Application Network Traf...

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Information security and Cryptology (ISCTURKEY), International Conference on

作者： İmren Daşdemir Yaşar Aysun Coşkun Department of Information Security Engineering Gazi University Ankara Turkey Department of Computer Engineering Faculty of Technology Gazi University Ankara Turkey

ISBN: (数字)9798331533649

ISBN: (纸本)9798331533656

In recent years, the use of mobile internet has become widespread rapidly with the introduction of smartphones. The increasing weight of mobile network traffic in the overall network traffic has made mobile network traffic classification a new and important topic of study. The study utilizes MIRAGE-COVID-CCMA-2022 dataset for the classification of mobile application network traffic. The MIRAGE-COVID-CCMA-2022 dataset contains the network traffic of nine communication and collaboration mobile applications including Discord, GotoMeeting, Meet, Messenger, Skype, Slack, Teams, Webex, and Zoom. Experiments were conducted using four machine learning classifiers (Support Vector Machine, k-Nearest Neighbor, Adaptive Boosting, and Random Forest), four deep learning classifiers (Convolutional Neural Networks, Long Short-Term Memory, Bidirectional Long Short-Term Memory, and Gated Recurrent Units), and three feature selection methods (Minimum Redundancy Maximum Relevance, k-Nearest Neighbor, and Random Forest). The results of the study show that an overall accuracy of over 95% for the MIRAGE-COVID-CCMA-2022 dataset with a nine-class classification. The study concludes that the most appropriate classification structure is to select the features using the Minimum Redundancy Maximum Relevance method and classify the features with the Random Forest classifier. Also, this classification structure stands out as one of the classification structures with the fastest running time.

关键词： Support vector machines Redundancy Telecommunication traffic Nearest neighbor methods Logic gates Feature extraction Mobile applications Random forests Long short term memory Smart phones

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A Novel Framework for Scalable Video Transmission Based on the Outage Probability Analysis of MIMO Coding

A Novel Framework for Scalable Video Transmission Based on t...

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Information Communication Technologies Conference (ICTC)

作者： Yongjian Zhang Dongyu Wang Zhiqiang Zhang Shiguo Wang School of Cyber Science and Engineering University of International Relations Beijing China Department of Electronic Engineering North China University of Technology Beijing China Department of Cyber Security Shanxi Police College Taiyuan China Computer and Communication Engineering Institute Changsha University of Science and Technology Changsha China

This work presents a radical approach for space-time codings, which could realize the goal of unequal error protection (UEP) transmitting for scalable video. This analysis leads us to develop a hybrid coding framework that combines vertical bell-labs layered space-time (V-BLAST) to ensure high-data-rate for the less crucial enhancement layer (EL) and Alamouti coding to ensure high reliability for the base layer (BL). According to those simulation findings, the suggested hybrid delivery method performs substantially better based on peak signal-to-noise ratio than either a pure special multiplexing or a pure spatial diversity delivery scheme.

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Deep Reinforcement Learning-Based Framework for Enhancing cybersecurity

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第3期19卷 170-190页

作者： Al-Nawashi, Malek M. Al-Hazaimeh, Obaida M. Tahat, Nedal M. Gharaibeh, Nasr Abu-Ain, Waleed A. Abu-Ain, Tarik Al-Huson University College Al-Balqa Applied University Irbid Jordan Faculty of Information Technology Department of Information Security and Cybersecurity Philadelphia University Amman Jordan Faculty of Science The Hashemite University Zarqa Jordan College of Science and Computer Engineering Taibah University Yanbu Saudi Arabia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The detection of cyberattacks has been increasingly emphasized in recent years, focusing on both infrastructure and people. Conventional security measures such as intrusion detection, firewalls, and encryption are insufficient in protecting cyber systems against growing and changing threats. In order to address this problem, scholars have explored reinforcement learning (i.e., RL) as a potential solution for intricate cybersecurity decision-making difficulties. Nevertheless, the use of RL faces several obstacles, including dynamic attack scenarios, insufficient training data, and the challenge of replicating real-world complexities. This study presents a novel framework that uses deep reinforcement learning (i.e., DRL) to simulate harmful cyberattacks and improve cybersecurity. This study presents an agent-based framework that is capable of ongoing learning and adaptation in a dynamic network security environment. The agent determines the optimal course of action by considering the current state of the network and the rewards it receives for its decisions. The CIC-IDS-2018 database, constructed using Python 3.7 programming, was used. The conducted studies yielded outstanding results, with a detection accuracy of 98.82% achieved for the CIC-IDS-2018 database in cyber-attack classification. © 2025 by the authors of this article. Published under CC-BY.

关键词： cybersecurity

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Correction to: Color image encryption based on novel kolam scrambling and modified 2D logistic cascade map (2D LCM)

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The Journal of Supercomputing 2023年第3期80卷 4420-4422页

作者： Mathivanan, P. Maran, Ponnambalam Department of Computer Science and Engineering (Cyber Security) Amrita Vishwa Vidyapeetham Chennai Campus Vengal India Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Chennai Campus Vengal India

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