Miao Liu, Lei Feng, Yexun Yuan, Ye Liu, Peng Geng^*

Journal on Artificial Intelligence, Vol.5, pp. 181-193, 2023, DOI:10.32604/jai.2023.046066

Abstract This article aims to address the clustering effect caused by unorganized charging of electric vehicles by adopting a two-tier recommendation method. The electric vehicles (EVs) are classified into high-level alerts and general alerts based on their state of charge (SOC). EVs with high-level alerts have the most urgent charging needs, so the distance to charging stations is set as the highest priority for recommendations. For users with general alerts, a comprehensive EV charging station recommendation model is proposed, taking into account factors such as charging price, charging time, charging station preference, and distance to the charging station. Using real data… More >

Yuting Zhu¹, Yanfang Fu^2,*, Yang Ce³, Pan Deng¹, Jianpeng Zhu¹, Huankun Su¹

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3867-3894, 2023, DOI:10.32604/cmc.2023.046772

Abstract In light of the escalating demand and intricacy of services in contemporary terrestrial, maritime, and aerial combat operations, there is a compelling need for enhanced service quality and efficiency in airborne cluster communication networks. Software-Defined Networking (SDN) proffers a viable solution for the multifaceted task of cooperative communication transmission and management across different operational domains within complex combat contexts, due to its intrinsic ability to flexibly allocate and centrally administer network resources. This study pivots around the optimization of SDN controller deployment within airborne data link clusters. A collaborative multi-controller architecture predicated on airborne data link clusters is thus proposed.… More >

Tuo Ma, Zhiwei Li, Qingjiang Li^*, Haijun Liu, Zhongjin Zhao, Yinan Wang

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3237-3263, 2023, DOI:10.32604/cmc.2023.045948

Abstract With the continuous development of deep learning, Deep Convolutional Neural Network (DCNN) has attracted wide attention in the industry due to its high accuracy in image classification. Compared with other DCNN hardware deployment platforms, Field Programmable Gate Array (FPGA) has the advantages of being programmable, low power consumption, parallelism, and low cost. However, the enormous amount of calculation of DCNN and the limited logic capacity of FPGA restrict the energy efficiency of the DCNN accelerator. The traditional sequential sliding window method can improve the throughput of the DCNN accelerator by data multiplexing, but this method’s data multiplexing rate is low… More >

Chaoqian He^1,2, Runfang Hao^1,2,*, Kun Yang^1,2, Zhongyun Yuan^1,2, Shengbo Sang^1,2, Xiaorui Wang^1,2

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3423-3442, 2023, DOI:10.32604/cmc.2023.045718

Abstract Intelligent fault diagnosis in modern mechanical equipment maintenance is increasingly adopting deep learning technology. However, conventional bearing fault diagnosis models often suffer from low accuracy and unstable performance in noisy environments due to their reliance on a single input data. Therefore, this paper proposes a dual-channel convolutional neural network (DDCNN) model that leverages dual data inputs. The DDCNN model introduces two key improvements. Firstly, one of the channels substitutes its convolution with a larger kernel, simplifying the structure while addressing the lack of global information and shallow features. Secondly, the feature layer combines data from different sensors based on their… More >

Wenlong Zhu^1,2,*, Yu Miao¹, Shuangshuang Yang³, Zuozheng Lian^1,2, Lianhe Cui¹

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3095-3117, 2023, DOI:10.32604/cmc.2023.045646

Abstract Influence Maximization (IM) aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes. However, most existing studies on the IM problem focus on static social network features, while neglecting the features of temporal social networks. To bridge this gap, we focus on node features reflected by their historical interaction behavior in temporal social networks, i.e., interaction attributes and self-similarity, and incorporate them into the influence maximization algorithm and information propagation model. Firstly, we propose a node feature-aware voting… More >

Xiulai Li^1,2,3,4, Jieren Cheng^1,3,*, Chengchun Ruan^1,3, Bin Zhang^1,3, Xiangyan Tang^1,3, Mengzhe Sun⁵

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3265-3288, 2023, DOI:10.32604/cmc.2023.045588

Abstract With the rising adoption of blockchain technology due to its decentralized, secure, and transparent features, ensuring its resilience against network threats, especially Distributed Denial of Service (DDoS) attacks, is crucial. This research addresses the vulnerability of blockchain systems to DDoS assaults, which undermine their core decentralized characteristics, posing threats to their security and reliability. We have devised a novel adaptive integration technique for the detection and identification of varied DDoS attacks. To ensure the robustness and validity of our approach, a dataset amalgamating multiple DDoS attacks was derived from the CIC-DDoS2019 dataset. Using this, our methodology was applied to detect… More >

Jiamin Xiang¹, Ying Zhang¹, Xiaohua Cao^1,*, Zhigang Zhou²

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3443-3466, 2023, DOI:10.32604/cmc.2023.045120

Abstract This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles (AGVs) under the composite operation mode. The multi-objective model aims to minimize the maximum completion time, the total distance covered by AGVs, and the distance traveled while empty-loaded. The improved hybrid algorithm combines the improved genetic algorithm (GA) and the simulated annealing algorithm (SA) to strengthen the local search ability of the algorithm and improve the stability of the calculation results. Based on the characteristics of the composite operation mode, the authors introduce the combined coding and parallel decoding… More >

Abdulwahab Alazeb^*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2995-3014, 2023, DOI:10.32604/cmc.2023.043173

Abstract Data protection in databases is critical for any organization, as unauthorized access or manipulation can have severe negative consequences. Intrusion detection systems are essential for keeping databases secure. Advancements in technology will lead to significant changes in the medical field, improving healthcare services through real-time information sharing. However, reliability and consistency still need to be solved. Safeguards against cyber-attacks are necessary due to the risk of unauthorized access to sensitive information and potential data corruption. Disruptions to data items can propagate throughout the database, making it crucial to reverse fraudulent transactions without delay, especially in the healthcare industry, where real-time… More >

Shi Qiu¹, Pengchang Zhang^1,*, Xingjia Tang², Zimu Zeng¹, Miao Zhang¹, Bingliang Hu^1,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3783-3800, 2023, DOI:10.32604/cmc.2023.042074

Abstract Sanxingdui cultural relics are the precious cultural heritage of humanity with high values of history, science, culture, art and research. However, mainstream analytical methods are contacting and detrimental, which is unfavorable to the protection of cultural relics. This paper improves the accuracy of the extraction, location, and analysis of artifacts using hyperspectral methods. To improve the accuracy of cultural relic mining, positioning, and analysis, the segmentation algorithm of Sanxingdui cultural relics based on the spatial spectrum integrated network is proposed with the support of hyperspectral techniques. Firstly, region stitching algorithm based on the relative position of hyper spectrally collected data… More >

Mohammad Mazyad Hazzazi¹, Muhammad Sajjad², Zaid Bassfar³, Tariq Shah^2,*, Ashwag Albakri⁴

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3659-3675, 2023, DOI:10.32604/cmc.2023.039013

Abstract In block ciphers, the nonlinear components, also known as substitution boxes (S-boxes), are used with the purpose to induce confusion in cryptosystems. For the last decade, most of the work on designing S-boxes over the points of elliptic curves, chaotic maps, and Gaussian integers has been published. The main purpose of these studies is to hide data and improve the security levels of crypto algorithms. In this work, we design pair of nonlinear components of a block cipher over the residue class of Eisenstein integers (EI). The fascinating features of this structure provide S-boxes pair at a time by fixing… More >