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Toward Optimal Periodic Crowd Tracking via Unmanned Aerial Vehicle

computer Modeling in engineering & sciences 2023年第10期137卷 233-263页

作者： Khalil Chebil Skander Htiouech Mahdi Khemakhem Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj11942Saudi Arabia Data Engineering and Semantics Research Unit Faculty of Sciences of SfaxUniversity of SfaxSfaxTunisia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Mathematics and Business Intelligence College of Electronics and Telecommunications Engineering of SfaxUniversity of SfaxSfaxTunisia

Crowd management and analysis(CMA)systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles(UAVs)*** tracking using UAVs is among the most important services provided by a *** this paper,we studied the periodic crowd-tracking(PCT)*** consists in usingUAVs to follow-up crowds,during the life-cycle of an open crowded area(OCA).Two criteria were considered for this *** first is related to the CMA initial investment,while the second is to guarantee the quality of service(QoS).The existing works focus on very specified assumptions that are highly committed to CMAs applications *** study outlined a new binary linear programming(BLP)model to optimally solve the PCT motivated by a real-world application study taking into consideration the high level of *** closely approach different real-world contexts,we carefully defined and investigated a set of parameters related to the OCA characteristics,behaviors,and theCMAinitial infrastructure investment(e.g.,UAVs,charging stations(CSs)).In order to periodically update theUAVs/crowds andUAVs/CSs assignments,the proposed BLP was integrated into a linear algorithm called PCTs *** main objective was to study the PCT problem fromboth theoretical and numerical *** prove the PCTs solver effectiveness,we generated a diversified set of PCTs instances with different scenarios for simulation *** empirical results analysis enabled us to validate the BLPmodel and the PCTs solver,and to point out a set of new challenges for future research directions.

关键词： Unmanned aerial vehicles periodic crowd-tracking problem open crowded area optimization binary linear programming crowd management and analysis system

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SDN-Enabled Content Dissemination Scheme for the Internet of Vehicles

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computers, Materials & Continua 2023年第5期75卷 2383-2396页

作者： Abida Sharif Muhammad Imran Sharif Muhammad Attique Khan Nisar Ali Abdullah Alqahtani Majed Alhaisoni Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadPakistan Department of Computer Science HITEC University TaxilaPakistan College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

The content-centric networking(CCN)architecture allows access to the content through name,instead of the physical location where the content is stored,which makes it a more robust and flexible content-based ***,in CCN,the broadcast nature of vehicles on the Internet of Vehicles(IoV)results in latency and network *** IoVbased content distribution is an emerging concept in which all the vehicles are connected via the *** to the high mobility of vehicles,however,IoV applications have different network requirements that differ from those of many other networks,posing new *** this,a novel strategy mediator framework is presented in this paper for managing the network resources ***-defined network(SDN)controller is deployed for improving the routing flexibility and facilitating in the interinteroperability of heterogeneous devices within the *** to the limited memory of edge devices,the delectable bloom filters are used for caching and ***,the proposed scheme is compared with the existing variants for validating its effectiveness.

关键词： Internet of vehicles(IOV) content dissemination multi mediator data traffic management

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Solving dynamic optimization problems using parent–child multi-swarm clustered memory (PCSCM) algorithm

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Neural Computing and Applications 2024年第31期36卷 19549-19583页

作者： Mohammadpour, Majid Mostafavi, Seyedakbar Mirjalili, Seyedali Department of Computer Engineering Yazd University Yazd Iran Center for Artificial Intelligence Research and Optimization Torrens University Australia Brisbane Australia Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Czech Republic

The Particle Swarm Optimization (PSO) algorithm faces several inherent challenges when applied to dynamic and large-scale optimization problems. These challenges encompass the issues of outdated particle memory, inadequate scalability in high-dimensional search spaces, the incapability to detect environmental changes, a continual trade-off between exploration and exploitation, and the potential loss of population diversity within the problem space. To address these challenges, we propose a novel hybrid PSO algorithm, denoted as Parent–Child Multi-Swarm Clustered Memory (PCSCM). PCSCM is explicitly designed to leverage an enhanced memory system, capable of mitigating the issue of outdated particle memory after convergence, and efficiently adapting to changing environmental conditions. This innovative memory system retains and retrieves promising solutions from the past when environmental alterations occur. Additionally, PCSCM introduces clustering mechanisms for particles within each swarm, aimed at augmenting diversity within the problem space. This clustering strategy substantially bolsters the algorithm’s performance in tracking evolving optimal solutions and positively contributes to its scalability. Crucially, the clustering approach is implemented not only for the main population but also for stored solutions in memory, which collectively strike a balance between exploration and exploitation. In the proposed method, particle swarms are divided into parent and child swarms, with parent swarms dedicated to preserving diversity;while, child swarms focus on identifying local solutions. These clustering and memory strategies are consistently applied within each sub-swarm to effectively address the challenges posed by high-dimensional search spaces. In addition to addressing challenges related to dynamic optimization, our proposed Parent–Child Multi-Swarm Clustered Memory (PCSCM) algorithm introduces an innovative mechanism for detecting environmental changes. This n

关键词： Chaos theory

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An Energy-Aware Resource Management Strategy Based on Spark and YARN in Heterogeneous Environments

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第2期8卷 635-644页

作者： Shabestari, Fatemeh Jafari Navimipour, Nima Islamic Azad University Sofian Branch Department of Computer Engineering Sofian Iran Kadir Has University Department of Computer Engineering Istanbul34083 Turkey National Yunlin University of Science and Technology Future Technology Research Center Douliou64002 Taiwan

Apache Spark is a popular framework for processing big data. Running Spark on Hadoop YARN allows it to schedule Spark workloads alongside other data-processing frameworks on Hadoop. When an application is deployed in a YARN cluster, its resources are given without considering energy efficiency. Furthermore, there is no way to enforce any user-specified deadline constraints. To address these issues, we propose a new deadline-aware resource management system and a scheduling algorithm to minimize the total energy consumption in Spark on YARN for heterogeneous clusters. First, a deadline-aware energy-efficient model for the considered problem is proposed. Then, using a locality-aware method, executors are assigned to applications. This algorithm sorts the nodes based on the performance per watt (PPW) metric, the number of application data blocks on nodes, and the rack locality. It also offers three ways to choose executors from different machines: greedy, random, and Pareto-based. Finally, the proposed heuristic task scheduler schedules tasks on executors to minimize total energy and tardiness. We evaluated the performance of the suggested algorithm regarding energy efficiency and satisfying the Service Level Agreement (SLA). The results showed that the method outperforms the popular algorithms regarding energy consumption and meeting deadlines. © 2017 IEEE.

关键词： Energy utilization

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Semi‑supervised contour‑driven broad learning system for autonomous segmentation of concealed prohibited baggage items

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 1-18页

作者： Divya Velayudhan Abdelfatah Ahmed Taimur Hassan Muhammad Owais Neha Gour Mohammed Bennamoun Ernesto Damiani Naoufel Werghi Department of Electrical Engineering and Computer Sciences Center for Cyber‑Physical SystemsKhalifa University of Science and TechnologyAbu Dhabi 127788United Arab Emirates Department of Electrical Computer and Biomedical EngineeringAbu Dhabi UniversityAbu Dhabi 59911United Arab Emirates Department of Computer Science and Software Engineering the University of Western AustraliaPerthWA 6009Australia

With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation *** X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger *** address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation ***,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset ***,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as *** research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage *** proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage *** specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage *** multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign *** contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation *** proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,***,the lim

关键词： Baggage X-ray imagery Broad learning systems Threat detection Threat segmentation

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Edge artificial intelligence for big data: a systematic review

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Neural Computing and Applications 2024年第19期36卷 11461-11494页

作者： Hemmati, Atefeh Raoufi, Parisa Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan

Edge computing, artificial intelligence (AI), and machine learning (ML) concepts have become increasingly prevalent in Internet of Things (IoT) applications. As the number of IoT devices continues to grow, relying solely on cloud computing for real-time data processing and analysis is proving to be more challenging. The synergy between edge computing and AI is particularly intriguing due to AI's reliance on rapid data processing, a capability facilitated by edge computing. Edge AI represents a significant paradigm shift, leveraging AI within edge computing frameworks to reduce reliance on internet connections and mitigate data latency issues. This approach accelerates data processing, supporting use cases that demand real-time inference. Additionally, as cloud storage costs continue to rise, the feasibility of streaming and storing large volumes of data comes into question. Edge AI offers a compelling solution by performing big data analytics closer to the end device where edge computing is deployed. This paper presents a systematic literature review (SLR) of 85 articles published between 2018 and 2023 within Edge AI. The study provides a comprehensive examination of the analysis of measurement environments and assesses factors applied to Edge AI for big data. It offers taxonomies specific to Edge AI within the big data domain, presents case studies, and outlines the challenges and open issues inherent in Edge AI for big data. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Internet of things

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IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

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computers, Materials & Continua 2024年第2期78卷 2245-2267页

作者： Ashraf S.Mashaleh Noor Farizah Binti Ibrahim Mohammad Alauthman Mohammad Almseidin Amjad Gawanmeh School of Computer Sciences Universiti Sains MalaysiaPenangMalaysia Department Computer Center Al-Balqa Applied UniversitySaltJordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan Department of Computer Science Tafila Technical UniversityTafilaJordan College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic *** IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection *** paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT *** logic addresses IoT threat uncertainties and ambiguities *** component settings are optimized using PSO to improve *** methodology allows for more complex thinking by transitioning from binary to continuous *** of expert inputs,PSO data-driven tunes rules and membership *** study presents a complete IoT botnet risk assessment *** methodology helps security teams allocate resources by categorizing threats as high,medium,or low *** study shows how CICIoT2023 can assess cyber *** research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

关键词： IoT botnet detection risk assessment fuzzy logic particle swarm optimization(PSO) cybersecurity interconnected devices

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Deep Learning-Based Mask Identification System Using ResNet Transfer Learning Architecture

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computer Systems Science & engineering 2024年第2期48卷 341-362页

作者： Arpit Jain Nageswara Rao Moparthi A.Swathi Yogesh Kumar Sharma Nitin Mittal Ahmed Alhussen Zamil S.Alzamil MohdAnul Haq Department of Computer Science and Engineering KoneruLakshmaiah Education FoundationVaddeswaram522302India Department of Computer Science and Engineering Sreyas Institute of Engineering and TechnologyHyderabad500068India University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Recently,the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the *** Health Organization(WHO)and many others advised controlling Corona Virus Disease in *** limited treatment resources,medical resources,and unawareness of immunity is an essential horizon to *** all resources,wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)*** countries made masks mandatory to prevent *** such enforcement,automatic and effective face detection systems are *** study presents a face mask identification approach for static photos and real-time movies that distinguishes between images with and without *** contribute to society,we worked on mask detection of an individual to adhere to the rule and provide awareness to the public or *** paper aims to get detection accuracy using transfer learning from Residual Neural Network 50(ResNet-50)architecture and works on detection *** experiment is tested with other popular pre-trained models such as Deep Convolutional Neural Networks(AlexNet),Residual Neural Networks(ResNet),and Visual Geometry Group Networks(VGG-Net)advanced *** proposed system generates an accuracy of 98.4%when modeled using Residual Neural Network 50(ResNet-50).Also,the precision and recall values are proved as better when compared to the existing *** outstanding work also can be used in video surveillance applications.

关键词： Transfer learning depth analysis convolutional neural networks(CNN) COVID-19

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WavEnhancer: Unifying Wavelet and Transformer for Image Enhancement

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Journal of computer Science & Technology 2024年第2期39卷 336-345页

作者：李梓诺陈绪行郭淑娜王书强潘治文 Department of Computer and Information Science University of MacaoMacao 999078China Research Center for Biomedical Information Technology Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Image enhancement is a widely used technique in digital image processing that aims to improve image aesthetics and visual quality. However, traditional methods of enhancement based on pixel-level or global-level modifications have limited effectiveness. Recently, as learning-based techniques gain popularity, various studies are now focusing on utilizing networks for image enhancement. However, these techniques often fail to optimize image frequency domains. This study addresses this gap by introducing a transformer-based model for improving images in the wavelet domain. The proposed model refines various frequency bands of an image and prioritizes local details and high-level features. Consequently, the proposed technique produces superior enhancement results. The proposed model’s performance was assessed through comprehensive benchmark evaluations, and the results suggest it outperforms the state-of-the-art techniques.

关键词： transformer wavelet transform image enhancement

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A Parallel Color Image Encryption Algorithm Based on a 2-D Logistic-Rulkov Neuron Map

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IEEE Internet of Things Journal 2025年第11期12卷 18115-18124页

作者： Gao, Suo Zhang, Zheyi Iu, Herbert Ho-Ching Ding, Siqi Mou, Jun Erkan, Uğur Toktas, Abdurrahim Li, Qi Wang, Chunpeng Cao, Yinghong Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering PerthWA6009 Australia Dalian University of Technology School of Mathematical Sciences Dalian116024 China Karamanoglu Mehmetbey University Faculty of Engineering Department of Computer Engineering Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This article aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2-D logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multitimescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a 2-D framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%. © 2014 IEEE.

关键词： Encryption algorithms

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