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7th International Conference on Smart City Applications, SCA 2022

作者： Hankar, Mustapha Birjali, Marouane Beni-Hssane, Abderrahim LAROSERI Laboratory Computer Science Department Faculty of Sciences University of Chouaib Doukkali El Jadida Morocco

ISBN: (纸本)9783031268519

Due to the increased prices of new cars nowadays, many customers prefer to buy a used car. Several online websites are made to offer their third-party services to contact used car sellers with the potential buyers. However, many customers who log into these websites in order to buy or sell a used car don’t have prior knowledge about its pricing value. The price of used cars often varies based on many features like production year, mileage, fuel type, fiscal power, mark, etc. In this paper, we designed a machine learning (ML) system that enables both potential buyers and sellers of used cars to estimate their car price before making a decision to buy or sell. In all tested ML models, gradient boosting regressor showed a high R-squared score (82%), a low root mean square error (RMSE), and a low mean absolute percentage error (MAPE). The system is finally deployed in a web application for a further usage of end users. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Evidential uncertainty quantification with multiple deep learning architectures for spatiotemporal drought forecasting

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Neural Computing and Applications 2025年第15期37卷 8773-8797页

作者： Ferchichi, Ahlem Chihaoui, Mejda Toujani, Radhia Ferchichi, Aya Hendaoui, Fatma Computer Science Department Applied College University of Ha’il Hail55424 Saudi Arabia RIADI-GDL Laboratory ENSI University of Manouba Manouba2010 Tunisia BESTMOD Department Higher Institute of management University of Tunis Bardo 2000 Tunisia

Deep learning approaches are increasingly being applied to forecasting challenges, but many current methods rely on advanced neural networks that provide only point estimates, without adequately addressing the epistemic and aleatoric uncertainties associated with predictions. Accurately capturing and quantifying both types of uncertainty is essential for assessing the confidence level of model outputs, especially in decision-sensitive contexts. Traditional techniques such as quantile regression, Bayesian neural networks, and Monte Carlo dropout tend to be either computationally intensive or prone to inaccuracies. In this paper, we propose an evidential deep learning (EDL)-based approach for drought forecasting that integrates uncertainty quantification. This method characterizes both aleatoric and epistemic uncertainties, offering a more comprehensive understanding of predictive reliability in relation to drought events. By leveraging the Dirichlet distribution, the model interprets uncertainties as evidence values associated with the neural network outputs. The proposed EDL approach is evaluated across three deep learning architectures-CNN, LSTM, and GAN-applied to datasets from the Horn of Africa and Southwestern Europe. Experimental results show that the EDL method outperforms traditional probabilistic and deterministic models, emphasizing the critical role of uncertainty quantification in enhancing forecasting accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Drought

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Rice Cultivation and Its Disease Classification in Precision Agriculture

Rice Cultivation and Its Disease Classification in Precision...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Kumar, Amit Bhowmik, Biswajit National Institute of Technology Brics Laboratory Department of Computer Science and Engineering Karnataka Bharat Mangalore India

ISBN: (纸本)9798350322309

Bharat is a cultivation-based country that cultivates various crops, including rice, in colossal capacity. Rice is one of the daily used crops across the country and other parts of the globe. Rice is cultivated in almost every state in Bharat. However, rice plant diseases severely degrade the quality and quantity of the crop. Rice plants can be affected by different conditions, e.g., foot rot, sheath blight, etc., causing a loss in the farming output. Various steps, including academic research, are practiced to detect rice diseases in their early stage to minimize the loss and increase the productivity of the agriculture sector. The schemes involve traditional and technology-based innovative solutions like agricultural cyber-physical systems (ACPS) or precision agriculture. This paper provides a state-of-the-art on rice crop cultivation, its disease classifications, etc. The paper depicts the importance of rice in daily life and how it plays a vital role in health. Later, this paper provides statistics on rice production in Bharat, followed by the export of rice to different countries. Subsequently, we offer a detailed analysis of rice cultivation, various diseases that can occur in rice plants and other prevention mechanisms for these diseases in the early stages of their life cycle. Finally, recent developments in precision agriculture are provided. © 2023 IEEE.

关键词： Precision agriculture

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Real-Time Implementation of Quadrotor UAV Control System Based on a Deep Reinforcement Learning Approach

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computers, Materials & Continua 2024年第12期81卷 4757-4786页

作者： Taha Yacine Trad Kheireddine Choutri Mohand Lagha Souham Meshoul Fouad Khenfri Raouf Fareh Hadil Shaiba Aeronautical Sciences Laboratory Aeronautical and Spatial Studies InstituteBlida 1 UniversityBlida0900Algeria Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Energy and Embedded Systems for Transportation Research Department ESTACA-LABMontigny-Le-Bretonneux78066France Department of Electrical Engineering University of SharjahSharjah27272United Arab Emirates Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural ***,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in *** strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control *** paper investigates a Reinforcement Learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental *** address the challenge of RL hyper-parameter tuning,a new framework is introduced that combines Simulated Annealing(SA)with a reinforcement learning algorithm,specifically Simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(SA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and Sliding Mode Control(SMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control was successfully implemented on a Parrot Mambo mini *** results showcase the potential of the proposed SA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.

关键词： Deep reinforcement learning hyper-parameters optimization path following quadrotor twin delayed deep deterministic policy gradient and simulated annealing

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Exploring Invariance Matters for Domain Generalization

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IEEE Transactions on Image Processing 2025年 34卷 3336-3351页

作者： Wang, Shanshan He, Houmeng Yang, Xun Liu, Zhipu Zhong, Yuanhong Zhang, Xingyi Wang, Meng Anhui University National Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology Institutes of Physical Science and Information Technology Hefei230601 China University of Science and Technology of China School of Information Science and Technology Department of Electronic Engineering and Information Science Hefei230026 China Chongqing University of Technology School of Computer Science and Engineering Chongqing400054 China Chongqing University School of Microelectronics and Communication Engineering Chongqing400030 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230009 China

Domain generalization (DG) aims to solve the problem of significant performance degradation when target domain data collected from the Out-Of-Distribution (O.O.D). Previous efforts try to exploit invariant features in the source domain through CNN networks. However, inspired by causal mechanisms, we find that the complex spurious-invariant information is still hidden in this view invariant features, and the impact of domain and class discrepancies on extracting invariance has not been effectively mitigated. To alleviate these issues, we propose a self-weighted multi-view mining invariance domain generalization framework (SMIDG). On the one hand, to make up for the insufficiency of traditional single-view convolutional feature extraction networks, we propose to mine features from another frequency view and use the self-adaptive adversarial masks to eliminate some spurious correlations, ensuring causal invariance in the coarse-grained generalization. However, due to inconsistencies in discriminative information between interdomain and intra-domain samples, as well as inter-class and intra-class samples, the coarse-grained elimination of spurious associations does not fully resolve this issue. On the other hand, we also consider the fine-grained generalization from two aspects. Firstly, to better tackle the domain discrepancies, we propose a novel progressive contrastive learning strategy that learns the underlying specific features of samples while gradually mitigating domain discrepancies, thereby ensuring domain invariance in fine-grained generalization. Secondly, due to the issue of feature inconsistency, we adopt a self-adaptive hard sample mining method with information gain to ensure that the model pays more attention on hard disentangled samples, thus maintaining feature invariance. Extensive experiments on five benchmark datasets demonstrate that our method outperforms state-of-the-art approaches. © 1992-2012 IEEE.

关键词： Contrastive learning Feature extraction Frequency-domain analysis Training Data models Data mining Correlation Data augmentation Convolutional neural networks computer science

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Generating prediction clustering graph network analysis data from masked time series with GANs

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Neural Computing and Applications 2025年第14期37卷 8699-8729页

作者： Toujani, Radhia Hendaoui, Fatma Ferchichi, Ahlem Chihaoui, Mejda Ferchichi, Aya Bakari, Wided Computer Science Department Applied College University of Ha’il Hail55424 Saudi Arabia BESTMOD Department Higher Institute of management University of Tunis Bardo 2000 Tunisia RIADI-GDL Laboratory ENSI University of Manouba Manouba2010 Tunisia

Research in social community mining from social media faces challenges due to the constantly evolving nature of online networks. Deep learning methods often struggle with noisy data caused by sudden changes and fail to capture essential temporal information for community evolution. Additionally, data security concerns are frequently overlooked. This paper presents a novel probabilistic generative adversarial network (GAN) model designed for dynamic social community detection, addressing challenges posed by the evolving nature of networks and noisy data. Our approach integrates a hybrid probabilistic convolutional neural network (PCNN) and gated recurrent unit (GRU) architecture, capturing temporal community dynamics while preserving data confidentiality through deep generative masking techniques. The model generates synthetic time series data that effectively mimics real data while safeguarding sensitive information and improving forecasting accuracy. Experimental results demonstrate the model’s superior performance in terms of RMSE, MAPE, CE, CRPS*, and ARR, outperforming traditional deep learning models and providing valuable insights for decision-making in spatiotemporal community detection. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Generative adversarial networks

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Games for Artificial Intelligence Research: A Review and Perspectives

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 5949-5968页

作者： Hu, Chengpeng Zhao, Yunlong Wang, Ziqi Du, Haocheng Liu, Jialin Research Institute of Trustworthy Autonomous System Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China LightSpeed Studios Tencent Shenzhen518057 China

Games have been the perfect test beds for artificial intelligence (AI) research for the characteristics that widely exist in real-world scenarios. Learning and optimization, decision-making in dynamic and uncertain environments, game theory, planning and scheduling, design, and education are common research areas shared between games and real-world problems. Numerous open-source games or game-based environments have been implemented to study AI. In addition to single- or multiplayer, collaborative, and adversarial games, there has also been growing interest in implementing platforms for creative design in recent years. Those platforms provide ideal benchmarks for exploring and comparing AI ideas and techniques. This article reviews the games and game-based platforms for AI research, provides guidance on matching particular types of AI with suitable games for testing and matching particular needs in games with suitable AI techniques, discusses the research trend induced by the evolution of those games and platforms, and gives an outlook. © 2024 IEEE.

关键词： Reinforcement learning

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IoT-Enabled Smart Applications and Challenges 8

IoT-Enabled Smart Applications and Challenges

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Prathyusha, M.R. Bhowmik, Biswajit National Institute of Technology Karnataka Brics Laboratory Department of Computer Science and Engineering Mangalore575025 India

ISBN: (纸本)9798350396638

Significant advances in the development of information and communication technology lead to collaborating computational entities into cyber-physical systems (CPS). A CPS serves as the foundation for many latest technology, e.g., the internet of things (IoT). Both IoT and CPS technologies provide several advantages that make our daily lives more simple and smart. These technologies are employed for a wide range of smart applications like smart city, smart home, smart transport, smart healthcare, etc., that can improve business, security, and health-related experiences. This paper primarily explores various IoT-enabled smart applications and examines their necessity, characteristics, and related issues. The paper also highlights various challenges of these smart applications followed by the state-of-the-art to address them. Each smart application promotes a sustainable, healthier, and comfortable life, including their future in the smart world. © 2023 IEEE.

关键词： Cyber Physical System

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SmokerViT: A Transformer-Based Method for Smoker Recognition

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computers, Materials & Continua 2023年第10期77卷 403-424页

作者： Ali Khan Somaiya Khan Bilal Hassan Rizwan Khan Zhonglong Zheng College of Mathematics and Computer Science Zhejiang Normal UniversityJinhua321004China School of Electronics Engineering Beijing University of Posts and TelecommunicationsBeijing100876China Department of Electrical Engineering and Computer Science Khalifa University of Science and TechnologyAbu Dhabi127788United Arab Emirates Key Laboratory of Intelligent Education of Zhejiang Province Zhejiang Normal UniversityJinhua321004China

Smoking has an economic and environmental impact on society due to the toxic substances it *** Neural Networks(CNNs)need help describing low-level features and can miss important ***,accurate smoker detection is vital with minimum false *** answer the issue,the researchers of this paper have turned to a self-attention mechanism inspired by the ViT,which has displayed state-of-the-art performance in the classification *** effectively enforce the smoking prohibition in non-smoking locations,this work presents a Vision Transformer-inspired model called SmokerViT for detecting ***,this research utilizes a locally curated dataset of 1120 images evenly distributed among the two classes(Smoking and NotSmoking).Further,this research performs augmentations on the smoker detection dataset to have many images with various representations to overcome the dataset size *** convolutional operations used in most existing works,the proposed SmokerViT model employs a self-attention mechanism in the Transformer block,making it suitable for the smoker classification ***,this work integrates the multi-layer perceptron head block in the SmokerViT model,which contains dense layers with rectified linear activation and linear kernel regularizer with L2 for the recognition *** work presents an exhaustive analysis to prove the efficiency of the proposed SmokerViT *** performance of the proposed SmokerViT performance is evaluated and compared with the existing methods,where it achieves an overall classification accuracy of 97.77%,with 98.21%recall and 97.35%precision,outperforming the state-of-the-art deep learning models,including convolutional neural networks(CNNs)and other vision transformer-based models.

关键词： Smoker recognition SmokerViT deep learning transformer for vision

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A State of Art Review on Testing Open Multi-Agent Systems 3rd

A State of Art Review on Testing Open Multi-Agent Systems

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3rd International Conference on Novel and Intelligent Digital Systems, NiDS 2023

作者： Guassmi, Djaber Dehimi, Nour El Houda Derdour, Makhlouf Computer Science Department LIAOA Laboratory University of Oum El Bouaghi Oum El Bouaghi Algeria

ISBN: (纸本)9783031440960

Open Multi-Agent Systems (MASs) represent a paradigm in which multiple autonomous agents communicate and collaborate to achieve shared objectives in an open and dynamic environment. These systems require specific software engineering capabilities. In the realm of software validation, testing plays a crucial role, but it can be challenging to conceptualize and execute. Designing systems under the Open MAS paradigm poses even greater challenges due to characteristics such as autonomy, reactivity, pro-activity, and social abilities. This paper presents state-of-the-art research on testing Open MAS. The goal is to ultimately provide a new test method that addresses the unique characteristics of Open MAS, along with software and efficient test tools that facilitate the automation of testing activities for these systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multi agent systems

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