Concurrency and Computation

doi: 10.1002/cpe.7803pmid: N/A

Yadav, Kumar Sachin; Keshri, Ajit Kumar

doi: 10.1002/cpe.8270pmid: N/A

Securing an e‐commerce system using epidemic mathematical modeling with neural networks involves adapting epidemiological principles to combat the spread of misinformation. Just like how epidemiologists track the spread of diseases through populations, we can track the dissemination of fake news through online platforms. By modeling how fake news spreads, we gain insights into its propagation patterns, enabling us to develop more effective countermeasures. Neural networks, with their ability to learn from data, play a crucial role in this process by analyzing vast amounts of information to identify and mitigate the impact of fake news. One potential disadvantage of using epidemic mathematical modeling with neural networks to secure e‐commerce systems is the complexity of the approach. The epidemic‐based recurrent long short‐term memory (E‐RLSTM) technique addresses the complexity and evolving nature of fake news propagation by leveraging the strengths of recurrent neural networks (RNNs), specifically long short‐term memory (LSTM) units, within an epidemic modeling framework. One advantage of using epidemic mathematical modeling with neural networks to secure e‐commerce systems is its proactive nature. One significant finding in employing this approach is the ability to uncover hidden connections and correlations within the data. E‐RLSTM stands out by capturing temporal dynamics and integrating epidemic parameters into its LSTM architecture, ensuring robustness and adaptability in detecting and combating fake news within e‐commerce systems, outperforming other techniques in accuracy and performance. Description of the NSL‐KDD dataset offers easy access to a valuable repository for benchmarking cyber security. Contained within are more than 120,000 authentic samples of cyber‐attacks across 41 distinct categories, providing an excellent environment for testing intrusion detection systems.

Kakugawa, Hirotsugu; Kamei, Sayaka; Shibata, Masahiro; Ooshita, Fukuhito

doi: 10.1002/cpe.8281pmid: N/A

Fault‐tolerance and self‐organization are critical properties in modern distributed systems. Self‐stabilization is a class of fault‐tolerant distributed algorithms which has the ability to recover from any kind and any finite number of transient faults and topology changes. In this article, we propose a self‐stabilizing distributed algorithm for the 1‐MIS problem under the unfair central daemon assuming the distance‐3 model. Here, in the distance‐3 model, each process can refer to the values of local variables of processes within three hops. Intuitively speaking, the 1‐MIS problem is a variant of the maximal independent set (MIS) problem with improved local optimizations. The time complexity (convergence time) of our algorithm is O(n)$$ O(n) $$ steps and the space complexity is O(logn)$$ O\left(\log n\right) $$ bits, where n$$ n $$ is the number of processes. Finally, we extend the notion of 1‐MIS to p$$ p $$‐MIS for each nonnegative integer p$$ p $$, and compare the set sizes of p$$ p $$‐MIS (p=0,1,2,…$$ p=0,1,2,\dots $$) and the maximum independent set.

Qingmiao, Zhang; Dinghua, Zhang

doi: 10.1002/cpe.8241pmid: N/A

Nowadays, intelligent transportation systems pay a lot of attention to autonomous vehicles it is believed that an autonomous vehicle improves mobility, comfort, safety, and energy efficiency. Making decisions is essential for the development of autonomous vehicles since these algorithms must be able to manage dynamic and complex urban crossings. In this research an optimal deep BiLSTM‐GAN classifier to detect the movement of smart vehicles, initially the preprocessing stage is performed to decrease noise in the received data after that the essential regions are next be extracted in the region of interest (ROI) to make the right decision. The extracted data are forwarded to the GAN for road segmentation as well as the optimized deep BiLSTM classifier, which recognizes the traffic sign, simultaneously making it possible to do a modified Hough line‐based maneuver prediction using the segmented information from the roads. Finally, the GAN determines the lane, and the BiLSTM predicts the traffic sign. The K‐nearest neighbor (KNN)‐based autonomous vehicle movement controllers are used to make the decision based on the predicted traffic sign and information about the lane. The proposed HSO algorithm was developed as the outcome of the common fusion of hawk and swarm optimization. Based on lane detecting achievements, at training percentage (TP) 90, the accuracy is 91.75%, Peak signal‐to‐noise ratio (PSNR) is 64.84%, mean square error (MSE) is 28.78, and mean absolute error (MAE) is 20.20, respectively, similarly based on the traffic sign prediction achievements at TP 90, the accuracy is 93.71%, sensitivity is 95.15%, specificity is 93.91%, and MSE is 28.78%, respectively.

Liu, Yibo; Zhang, Xuyan; Wu, Chaoqun; Yang, Minghui

doi: 10.1002/cpe.8245pmid: N/A

Obstacle avoidance planning is the primary element in ensuring safe robot applications such as welding, assembly, and drilling. The states in the configuration space (C‐space) provide the pose information of any part of the manipulator and are preferentially considered in motion planning. However, it is difficult to express the environmental information directly in the high dimensional C‐space, limiting the application of C‐space obstacle avoidance planning. This paper proposes a singular manifold splitting C‐space method and designs a compatible obstacle avoidance strategy. The specific method is as follows: first, according to the specific structure of industrial robots, arm‐wrist separation obstacle avoidance planning is proposed to fix the robot as a 3R manipulator to reduce the dimension of C‐space. Next, the C‐space is segmented according to the singular manifolds, and the unique domain is delineated to complete the streamlining of the volume of the C‐space. Then, with the help of the point cloud, the obstacles are enveloped and mapped to the unique domain to construct the pseudo‐obstacle map. Industrial robots' obstacle avoidance planning is completed based on the pseudo‐obstacle map combined with an improved Rapidly‐Exploring Random Trees (RRT) algorithm. This method dramatically improves the efficiency of obstacle avoidance planning in the C‐space and avoids the effect of singularities on industrial robots. Finally, the effectiveness of the method is verified by physical experiments.

Singh, Shubham; Mishra, Amit Kumar; Arjaria, Siddhartha Kumar; Bhatt, Chinmay; Pandey, Daya Shankar; Yadav, Ritesh Kumar

doi: 10.1002/cpe.8275pmid: N/A

Cloud computing is commonly utilized in remote contexts to handle user demands for resources and services. Each assignment has unique processing needs that are determined by the time it takes to complete. However, if load balancing is not properly managed, the effectiveness of resources may suffer dramatically. Consequently, cloud service providers have to emphasize rapid and precise load balancing as well as proper resource supply. This paper proposes a novel enhanced deep network‐based load predictor and load balancing in cloud‐fog services. In prior, the workload is predicted using a deep network called Multiple Layers Assisted in LSTM (MLA‐LSTM) model that considers the capacity of virtual machine (VM) and task as input and predicts the target label as underload, overload and equally balanced. According to this prediction, the optimal load balancing is performed through a hybrid optimization named Osprey Assisted Pelican Optimization Algorithm (OAPOA) while taking into account several parameters such as makespan, execution cost, resource consumption, and server load. Additionally, a process known as load migration is carried out, in which machines with overload tasks are assigned to machines with underload tasks. This migration is applied optimally via the OAPOA strategy under the consideration of constraints including migration cost and migration efficiency.

V, Charles Prabu; Perumal, Pandiaraja

doi: 10.1002/cpe.8232pmid: N/A

Anomaly event recognition and identification has a crucial part in several areas, particularly in night vision environments. Conventional techniques of event recognition are hugely based upon data extracted from certain images for classification purposes. This needs users to select suitable features to establish the feature depictions for actual images per definite situations. Manual feature selection is laborious as well as heuristic tasks and the features obtained in this manner generally have worse robustness. Here, a Faster Region‐based Convolutional fused Social Generative Adversarial Network (FRC‐SGAN) is designed for anomaly event recognition in a night vision environment. At the cloud, key frame extraction, pre‐processing, feature extraction, human detection (HD) and anomalous event recognition are carried out. Initially, input video from the database is subjected to perform pre‐processing. The visibility enhancement is utilized for pre‐processing. Thereafter, features like ResNet features, texture features and statistical features are extracted. Then, HD is accomplished by DeepJoint segmentation with chord distance. Finally, anomalous detection is done by FRC‐SGAN that is the incorporation of Fast Regional Convolutional Neural Network (FR‐CNN) and Social Generative Adversarial Network (SGAN). In addition, FRC‐SGAN acquired 90.8% of accuracy, 89.7% of precision, and 89.2% of recall.

Kumar, M. Anand; Onyema, Edeh Michael; Sundaravadivazhagan, B.; Gupta, Manish; Shankar, Achyut; Gude, Venkataramaiah; Yamsani, Nagendar

doi: 10.1002/cpe.8256pmid: N/A

In order to make networks more adaptable and flexible, software‐defined networking (SDN) is an architecture that abstracts the many, easily distinct layers of a network. By enabling businesses and service providers to react swiftly to shifting business requirements, SDN aims to improve network control. SDN has become an important framework for Internet of Things (IoT) and 5G. Despite recent research endeavors focused on pinpointing constraints within SDN design components, various security attacks persist, including man‐in‐the‐middle attacks, host hijacking, ARP poisoning, and saturation attacks. Overcoming these limitations poses a challenge, necessitating robust security techniques to detect and counteract such attacks in SDN environments. This study is dedicated to developing a method for detecting and mitigating control plane attacks within Software Defined Network Environments utilizing Deep Learning Algorithms. The study presents a deep‐learning‐based approach to identifying malicious hosts within SDN networks, thus thwarting unauthorized access to the controller. Experimental results demonstrate the effectiveness of the proposed model in host classification, exhibiting high accuracy and performance compared to alternative approaches.

Godoy, William F.; Valero‐Lara, Pedro; Teranishi, Keita; Balaprakash, Prasanna; Vetter, Jeffrey S.

doi: 10.1002/cpe.8269pmid: N/A

We apply AI‐assisted large language model (LLM) capabilities of GPT‐3 targeting high‐performance computing (HPC) kernels for (i) code generation, and (ii) auto‐parallelization of serial code in C ++, Fortran, Python and Julia. Our scope includes the following fundamental numerical kernels: AXPY, GEMV, GEMM, SpMV, Jacobi Stencil, and CG, and language/programming models: (1) C++ (e.g., OpenMP [including offload], OpenACC, Kokkos, SyCL, CUDA, and HIP), (2) Fortran (e.g., OpenMP [including offload] and OpenACC), (3) Python (e.g., numpy, Numba, cuPy, and pyCUDA), and (4) Julia (e.g., Threads, CUDA.jl, AMDGPU.jl, and KernelAbstractions.jl). Kernel implementations are generated using GitHub Copilot capabilities powered by the GPT‐based OpenAI Codex available in Visual Studio Code given simple <kernel> + <programming model> + <optional hints> prompt variants. To quantify and compare the generated results, we propose a proficiency metric around the initial 10 suggestions given for each prompt. For auto‐parallelization, we use ChatGPT interactively giving simple prompts as in a dialogue with another human including simple “prompt engineering” follow ups. Results suggest that correct outputs for C++ correlate with the adoption and maturity of programming models. For example, OpenMP and CUDA score really high, whereas HIP is still lacking. We found that prompts from either a targeted language such as Fortran or the more general‐purpose Python can benefit from adding language keywords, while Julia prompts perform acceptably well for its Threads and CUDA.jl programming models. We expect to provide an initial quantifiable point of reference for code generation in each programming model using a state‐of‐the‐art LLM. Overall, understanding the convergence of LLMs, AI, and HPC is crucial due to its rapidly evolving nature and how it is redefining human‐computer interactions.

Deshmukh, Sushama A.; Kasar, Smita

doi: 10.1002/cpe.8277pmid: N/A

Blockchain (BC) technology has been incorporated into the infrastructure of different kinds of applications that require transparency, reliability, security, and traceability. However, the BC still has privacy issues because of the possibility of privacy leaks when using publicly accessible transaction information, even with the security features offered by BCs. Specifically, certain BCs are implementing security mechanisms to address data privacy to prevent privacy issues, facilitates attack‐resistant digital data sharing and storage platforms. Hence, this proposed review aims to give a comprehensive overview of BC technology, to shed light on security issues related to BC, and to emphasize the privacy requirements for existing applications. Many proposed BC applications in asset distribution, data security, the financial industry, the Internet of Things, the healthcare sector, and AI have been explored in this article. It presents necessary background knowledge about BC and privacy strategies for obtaining these security features as part of the evaluation. This survey is expected to assist readers in acquiring a complete understanding of BC security and privacy in terms of approaches, ideas, attributes, and systems. Subsequently, the review presents the findings of different BC works, illustrating several efforts that tackled privacy and security issues. Further, the review offers a positive strategy for the previously described integration of BC for security applications, emphasizing its possible significant gaps and potential future development to promote BC research in the future.