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6th International Conference on Advanced Aspects of Software Engineering, ICAASE 2024

作者： Ferhat, Aridj Zitouni, Farouq Limanc, Abdelhadi Lakbichi, Rihab Harous, Saad Department of Computer Science and Information Technology Laboratory of Artificial Intelligence and Information Technology Kasdi Merbah University Ouargla Algeria College of Computing and Informatics Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798331522810

This paper introduces OBL-AHO, an enhanced Archerfish Hunting Optimizer (AHO) variant that integrates Opposition-Based Learning (OBL) to improve AHO's exploratory capabilities. By leveraging opposite solutions alongside newly generated ones, OBL-AHO broadens the search space, effectively reducing the risk of local optima entrapment-A common challenge in metaheuristic algorithms. The performance of OBL-AHO is rigorously compared with the original AHO and several prominent Metaheuristic Algorithms (MAs), including TLBO (Teaching-Learning-Based Optimization), PSO (Particle Swarm Optimization), GWO (Grey Wolf Optimizer), OOBO (OnE-To-One Based Optimizer), SSA (Salp Swarm Algorithm). Evaluations were conducted using test functions taken from CEC 2022, and the outcomes were statistically analyzed using the Friedman test. The findings demonstrate that OBL-AHO significantly improves exploration while maintaining competitive exploitation relative to its opponents. © 2024 IEEE.

关键词： Contrastive Learning

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Comparative Analysis of Four Metaheuristic Algorithms for Estimating Parameters in Solar Photovoltaic Models 6

Comparative Analysis of Four Metaheuristic Algorithms for Es...

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6th International Conference on Advanced Aspects of Software Engineering, ICAASE 2024

作者： Lakbichi, Rihab Zitouni, Farouq Limane, Abdelhadi Ferhat, Aridj Harous, Saad Department of Computer Science and Information Technology Laboratory of Artificial Intelligence and Information Technology Kasdi Merbah University Ouargla Algeria College of Computing and Informatics University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331522810

In the last decade, Metaheuristic Algorithms (MAs) have been widely employed to estimate parameters in photovoltaic (PV) models to maximize solar energy efficiency. This paper presents a comparative study of the performance of four novel and efficient MAs applied to solve the parameter extraction problem in three ubiquitous PV models: The Single-Diode Model (SDM), the Double-Diode Model (DDM), and the Photovoltaic Module Model (PMM). The primary objective of this study is to evaluate and compare the efficiency of these MAs and identify the most effective one based on the Root Mean Square Error (RMSE) between the simulated and the actual data. This work compares the performance of the Red-Tailed Hawk Optimizer (RTH), the One-To-One Based Optimizer (OOBO), the Sand Cat Swarm Optimization (SCSO), and the Archerfish Hunting Optimizer (AHO). The numerical results demonstrate that RTH significantly outperforms its counterparts in estimating parameters for the examined models. This exceptional performance underscores RTH's efficiency and effectiveness as a powerful tool for parameter estimation in PV models. Given these promising results, RTH stands out as a preferred method and holds the potential for further refinement and enhancement in the future. © 2024 IEEE.

关键词： Mean square error

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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

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Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis Intelligent prediction Improved CNN

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Optimizing Wireless Sensor Network Lifespan Through Weighted Fitness Function in K-Means Clustering and Cluster Head Selection 8

Optimizing Wireless Sensor Network Lifespan Through Weighted...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Fatima, Harrouz Mohammed, Kaddi Mohammed, Omari University of Adrar Lddi Laboratory Mathematics and Computer Science Department Adrar Algeria School of Engineering American University of Ras Al Khaimah Computer Science and Engineering Department Ras Al Khaimah United Arab Emirates

ISBN: (纸本)9798350354423

One of the primary challenges in Wireless Sensor Networks (WSNs) is the efficient management of energy con sumption and maintaining robust network connectivity over time. To address this issue, we propose a model that initially employs k-means clustering to form clusters and subsequently selects cluster heads in each round based on three critical factors: remaining energy, distance to the base station, and distance to the cluster center. By leveraging k-means clustering at the beginning, optimal cluster formation is achieved, ensuring balanced initial energy distribution. In subsequent rounds, dynamic selection of cluster heads is performed using weighted weights for each parameter to enhance network performance. Various weight values were tested to determine the most effective configuration. Our approach effectively balances energy consumption among nodes, extends network lifetime, and maintains strong connectivity, thereby addressing key challenges in the deployment and operation of WSNs. © 2024 IEEE.

关键词： K-means clustering

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Real-Time Human Action Representation Based on 2D Skeleton Joints 6

Real-Time Human Action Representation Based on 2D Skeleton J...

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6th International Conference on Networking and Advanced Systems, ICNAS 2023

作者： Adil, Bouguerra Mounine, Hemam Sofiane Ouassila, Hioual Abbes Laghrour University ICOSI Laboratory of Khenchela Department of Mathematics and Computer Science Khenchela Algeria Abbes Laghrour University LIRE Laboratory of Constantine 2 Department of Mathematics and Computer Science Khenchela Algeria

ISBN: (纸本)9798350319170

We present a new approach to capture human actions by using 2D skeletal joints as the foundation for the real-time representation. Our approach combines three distinct types of information, namely: (1) motion detection to identify salient regions within the action. This allows us to compute joint contribution ratios and save processing time by excluding still joints and focusing on the main joints involved in the action;(2) utilization of a predefined map for joints trajectory shapes, which encodes the temporal information and avoids noisy data using the map;and (3) incorporation of a direction map that captures the movement of the joints, in the spatial space. By integrating these elements, we have devised a comprehensive representation capable of discerning even highly similar actions. To evaluate the effectiveness of our proposed representation, we conducted experiments on the UTD-MHAD dataset [25], which encompasses a diverse range of 27 actions performed by 8 subjects (4 females and 4 males), each with 4 repetitions. The evaluation results exhibit a notable dissimilarity within the same action category (intra-class) and a significant similarity across different action categories (inter-class). Specifically, our approach achieved a commendable score of 94.81 on the UTD-MHAD dataset, thus demonstrating its efficacy and robustness. © 2023 IEEE.

关键词： Musculoskeletal system

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Impact Machine Learning Classification And Technical Indicators Forecast The Direction Of Bitcoin 14

Impact Machine Learning Classification And Technical Indicat...

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14th International Conference on Intelligent Systems: Theories and Applications, SITA 2023

作者： El Badaoui, Mohamed Raouyane, Brahim El Moumen, Samira Bellafkih, Mostafa Faculty of Sciences*Ain Chock N&idp Team Department of Mathematics and Computer Science Morocco Department of Mathematics and Computer Science Raiss Laboratory Rabat Morocco

ISBN: (纸本)9798350308211

This study assesses the effectiveness of machine learning (ML) models in predicting fluctuations in the direction of Bitcoin prices using conventional technical indicators. Given the volatile nature of Bitcoin markets, achieving precise predictions is of utmost importance. The study improves machine learning models including logistic regression (LR), decision trees (DT), random forests (RF), support vector machines (SVM), Xgboost, and artificial neural networks (ANN) by integrating established technical indicators. By employing Optuna for hyperparameter optimization and the 'Time Series Split' technique for cross-validation, the study optimizes the models for time series data. The results demonstrate that ML models, when integrated with technical indicators, significantly outperform predictions based solely on those indicators. Evaluation metrics include accuracy, recall, and the F1 score. Through the incorporation of signal indicators, ML models offer a robust approach to predicting changes in Bitcoin prices. The proposed ML approach achieves a test accuracy exceeding 65.15%, highlighting the potential synergy between traditional financial prediction tools and contemporary technology. This study underscores the potential effectiveness of combining ML models with technical indicators for forecasting Bitcoin price movements. © 2023 IEEE.

关键词： Forecasting

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Clustering in Wireless Sensor Networks Using Fuzzy Logic for Clusterhead Election: Review 1

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Harrouz, Fatima Omari, Mohammed Kaddi, Mohammed LDDI Laboratory Mathematics and Computer Science Department University of Adrar Adrar Algeria Computer Science and Engineering Department School of Engineering American University of Ras Al Khaimah Ras Al-Khaimah United Arab Emirates

ISBN: (数字)9789819786664

ISBN: (纸本)9789819786657

Wireless sensor networks refer to a collection of sensors strategically deployed in specific areas for purposes such as environmental monitoring, surveillance, military applications, and various other uses. These sensor nodes are responsible for detecting information and transmitting it to a central base station. The sensors are generally battery operated and cannot be rechargeable. Since monitoring and data transmission processes consume a significant amount of energy, achieving energy efficiency is a primary concern. Therefore, clustering has become the most popular approach to improving energy efficiency. This approach consists of dividing the network into clusters, each characterized by a cluster head sensor and several member sensors. The cluster head assumes the responsibility of collecting data from member sensors within its cluster, aggregating this data and then transmitting it to the central base station. The central aspect of this clustering approach lies in the selection of the optimal cluster head, as it significantly influences the performance and lifetime of the network. To address this challenge, various protocols have been introduced using fuzzy logic, which has become one of the most widely used methodologies in this context due to its ability to handle uncertain and ambiguous inputs, closely mimicking human reasoning processes. This survey paper aims to provide a comprehensive overview of clustering techniques that use fuzzy logic for selection of cluster heads, using a range of inputs such as residual energy levels, distance to node receiver, network density, and other relevant criteria. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sensor nodes

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Polylogarithmic Approximations for Robust s-t Path 51

Polylogarithmic Approximations for Robust s-t Path

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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Li, Shi Xu, Chenyang Zhang, Ruilong Department of Computer Science and Technology Nanjing University Jiangsu China Shanghai Key Laboratory of Trustworthy Computing East China Normal University China Department of Computer Science City University of Hong Kong Hong Kong

ISBN: (纸本)9783959773225

The paper revisits the Robust s-t Path problem, one of the most fundamental problems in robust optimization. In the problem, we are given a directed graph with n vertices and k distinct cost functions (scenarios) defined over edges, and aim to choose an s-t path such that the total cost of the path is always provable no matter which scenario is realized. Viewing each cost function as an agent, our goal is to find a fair s-t path, which minimizes the maximum cost among all agents. The problem is NP-hard to approximate within a factor of o(log k) unless NP ⊆ DTIME(npoly logn), and the best-known approximation ratio is Oe(√n), which is based on the natural flow linear program. A longstanding open question is whether we can achieve a polylogarithmic approximation for the problem;it remains open even if a quasi-polynomial running time is allowed. Our main result is a O(log n log k) approximation for the Robust s-t Path problem in quasipolynomial time, solving the open question in the quasi-polynomial time regime. The algorithm is built on a novel linear program formulation for a decision-tree-type structure, which enables us to overcome the Ω(√n) integrality gap for the natural flow LP. Furthermore, we show that for graphs with bounded treewidth, the quasi-polynomial running time can be improved to a polynomial. We hope our techniques can offer new insights into this problem and other related problems in robust optimization. © Shi Li, Chenyang Xu, and Ruilong Zhang.

关键词： Approximation algorithms

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A Feature-Driven Fixed-Ratio Lossy Compression Framework for Real-World Scientific Datasets 39

A Feature-Driven Fixed-Ratio Lossy Compression Framework for...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Rahman, Md Hasanur Di, Sheng Zhao, Kai Underwood, Robert Li, Guanpeng Cappello, Franck University of Iowa Computer Science Department IA United States Argonne National Laboratory Mathematics and Computer Science Division LemontIL United States University of Alabama at Birmingham Computer Science Department BirminghamAL United States

ISBN: (纸本)9798350322279

Today's scientific applications and advanced instruments are producing extremely large volumes of data everyday, so that error-controlled lossy compression has become a critical technique to the scientific data storage and management. Existing lossy scientific data compressors, however, are designed mainly based on error-control driven mechanism, which cannot be efficiently applied in the fixed-ratio use-case, where a desired compression ratio needs to be reached because of the restricted data processing/management resources such as limited memory/storage capacity and network bandwidth. To address this gap, we propose a low-cost compressor-agnostic feature-driven fixed-ratio lossy compression framework (FXRZ). The key contributions are three-fold. (1) We perform an in-depth analysis of the correlation between diverse data features and compression ratios based on a wide range of application datasets, which is a fundamental work for our framework. (2) We propose a series of optimization strategies that can enable the framework to reach a fairly high accuracy in identifying the expected error configuration with very low computational cost. (3) We comprehensively evaluate our framework using 4 state-of-the-art error-controlled lossy compressors on 10 different snapshots and simulation configuration-based real-world scientific datasets from 4 different applications across different domains. Our experiment shows that FXRZ outperforms the state-of-the-art related work by 108×. The experiments with 4,096 cores on a supercomputer show a performance gain of 1.18∼8.71× than the related work in overall parallel data dumping. © 2023 IEEE.

关键词： Machine learning

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Matching Gains with Pays: Effective and Fair Learning in Multi-Agent Public Goods Dilemmas 27

Matching Gains with Pays: Effective and Fair Learning in Mul...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Chen, Yitian Liu, Xuan Zhang, Shigeng Chen, Xinning Guo, Song School of Computer Science and Engineering Central South University China College of Computer Science and Electronic Engineering Hunan University China The Ministry of Education Key Laboratory of "Fusion Computing of Supercomputing and Artificial Intelligence" China Department of Computer Science and Engineering Hong Kong University of Science and Technology China

ISBN: (纸本)9781643685489

The training of multi-agent reinforcement learning (MARL) tasks with the public goods dilemma (PGD) is difficult because the selfish actions of individual agents for high personal rewards may reduce the collective utility of the whole group. Existing solutions to this problem, e.g., reward gifting or intrinsic rewards, although inducing cooperation among agents in small groups, cannot guarantee fairness among agents’ policies and fail to achieve optimal group utility in large-scale systems. In this paper, we propose F4PGD, an effective method to train large-scale MARL tasks with PGD in a decentralized manner, which is inspired by Adam’s equity theory that the match between a person’s payoff and his contribution is the key incentive for people to contribute to the common good. In F4PGD, a mechanism is designed to match an agent’s reward with its contribution, which suppresses agents from taking a free ride and meanwhile encourages well-learned agents to contribute to public goods. Experimental results show that F4PGD effectively learns optimal policies for the whole group and guarantees fairness among agents in several typical MARL tasks with PGD. © 2024 The Authors.

关键词： Adversarial machine learning

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