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Review of dehazing techniques: challenges and future trends

Multimedia Tools and Applications 2025年第3期84卷 1103-1131页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri

关键词： Generative adversarial networks

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A modified grey wolf optimizer with multi-solution crossover integration algorithm for feature selection

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Journal of Ambient Intelligence and Humanized Computing 2025年第1期16卷 329-345页

作者： Ihsan, Muhammad Din, Fakhrud Zamli, Kamal Z. Ghadi, Yazeed Yasin Alahmadi, Tahani Jaser Innab, Nisreen Faculty of IT Department of Computer Science and IT University of Malakand Khyber Pakhtunkhwa Chakdara18800 Pakistan Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pahang Pekan26600 Malaysia Department of computer science and software engineering Al Ain University Abu Dhabi12555 United Arab Emirates Department of Information SystemsCollege of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh84428 Saudi Arabia Department of Computer Science and Information SystemsCollege of Applied Sciences Al-Maarefa University Diriyah Riyadh13713 Saudi Arabia

Feature selection helps eradicate redundant features which is essential to mitigate the curse of dimensionality when a machine-learning model deals with high-dimensional datasets. Grey Wolf Optimizer (GWO) is a swarm-based algorithm that simulates the wolves’ hunting behavior. Although very efficient, GWO faces some limitations which may cause premature convergence and/or local optima trapping. Moreover, GWO relies mainly on the three best wolves, limiting its potential for diverse exploration and exploitation. This work proposes an improved version of GWO namely, a modified grey wolf optimizer with multi-solution crossover integration (MGWO-MCI) algorithm. MGWO-MCI algorithm incorporates a multi-solution strategy that evolves new potential solutions in the optimization process. A crossover operation is performed between the new wolves and the existing hierarchy, reforming the position-updating process. MGWO-MCI utilizes this position-updating process using two different approaches. The first approach named MGWO-MCI-I expands the additional wolves’ role to both exploration and exploitation whereas the second approach named MGWO-MCI-II incorporates their role to exploration only. These approaches are evaluated and tested using 18 datasets and an Intrusion detection dataset NSL-KDD for feature selection. Statistically, the results are analyzed through the Wilcoxon test, which shows the superiority of MGWO-MCI-II. MGWO-MCI-II outperforms others with an accuracy of 98.6% on NSL-KDD and achieves 55.5% overall best outcomes on other datasets. Moreover, the MGWO-MCI was evaluated on two constrained optimization problems, the pressure vessel and welded beam design validating its effectiveness and adaptability in solving different optimization problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Constrained optimization

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Deep Learning Approach for Incongruous Data: Dataset Matters 23

Deep Learning Approach for Incongruous Data: Dataset Matters

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Torabinejad, Tina Sood, Kanika College of Engineering and Computer Science California State University FullertonCA92831 United States California State University Faculty of College of Engineering and Computer Science FullertonCA92831 United States

ISBN: (纸本)9798331528041

Today, humans seek opportunities to grow professionally and improve their mental health. As a result, anxiety and depression are now prominent to the point that people often accept them as part of their usual regular life routine. According to the World Health Organization (WHO), more than 300 million people around the world are suffering from depression and anxiety disorder. Therefore, being able to detect risk factors that lead to depression can be beneficial to the healthcare system. We use machine learning techniques to detect depression risk factors using a medical survey dataset, the Behavioral Risk Factor Surveillance System (BRFSS), available from the Centers for Disease Control and Prevention (CDC). This survey is obtained explicitly through telephone calls from adults 18 years and older who live in the United States of America. Oversampling and undersampling techniques and eight machine learning techniques are used to evaluate further and examine the data set. Among all, the SMOTE oversampling techniques show enhanced performance over the undersampling and no sampling techniques, and the deep neural network generates the best outcome compared to other techniques. © 2024 IEEE.

关键词： Disease control

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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Big Data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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Prototype Proposal of an 18 kW Non-Isolated Bidirectional Converter for Battery Energy Storage System 22

Prototype Proposal of an 18 kW Non-Isolated Bidirectional Co...

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22nd International Symposium on Power Electronics, Ee 2023

作者： Brandis, Andrej Knol, Kristian Pelin, Denis Topic, Danijel Computer Science and Information Technology Osijek Faculty of Electricity Engineering Department of Electromechanical Engineering Osijek Croatia Computer Science and Information Technology Osijek Faculty of Electrical Engineering Department of Power Engineering Osijek Croatia

ISBN: (纸本)9798350343175

In this paper, a brief overview of converter topologies used in stationary battery energy storage systems is given. A simulation model of converter was developed in MATLAB Simulink. A simulation is conducted to provide a preliminary understanding of the converter's bidirectional nature. The results indicate that the state of charge (SOC) increases during battery charging and decreases during discharging. The topology of non-isolated two-stage bidirectional converter is presented for proposed real-world prototype, including the high-level electrical schematic and mechanical layout. An 18-kW prototype converter for stationary battery energy storage systems was developed and presented. The converter housing with all installed ancillary components for proper operation is also described. At the time of writing this paper, the actual converter is still in the development phase. © 2023 IEEE.

关键词： Charging (batteries)

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An ensemble deep learning model for cyber threat hunting in industrial internet of things

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Digital Communications and Networks 2023年第1期9卷 101-110页

作者： Abbas Yazdinejad Mostafa Kazemi Reza M.Parizi Ali Dehghantanha Hadis Karimipour Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical Engineering Faculty of EngineeringShahed UniversityTehranIran College of Computing and Software Engineering Kennesaw State UniversityGAUSA School of Engineering University of GuelphOntarioCanada

By the emergence of the fourth industrial revolution,interconnected devices and sensors generate large-scale,dynamic,and inharmonious data in Industrial Internet of Things(IIoT)*** vast heterogeneous data increase the challenges of security risks and data analysis *** IIoT grows,cyber-attacks become more diverse and complex,making existing anomaly detection models less effective to *** this paper,an ensemble deep learning model that uses the benefits of the Long Short-Term Memory(LSTM)and the AutoEncoder(AE)architecture to identify out-of-norm activities for cyber threat hunting in IIoT is *** this model,the LSTM is applied to create a model on normal time series of data(past and present data)to learn normal data patterns and the important features of data are identified by AE to reduce data *** addition,the imbalanced nature of IIoT datasets has not been considered in most of the previous literature,affecting low accuracy and *** solve this problem,the proposed model extracts new balanced data from the imbalanced datasets,and these new balanced data are fed into the deep LSTM AE anomaly detection *** this paper,the proposed model is evaluated on two real IIoT datasets-Gas Pipeline(GP)and Secure Water Treatment(SWaT)that are imbalanced and consist of long-term and short-term dependency on *** results are compared with conventional machine learning classifiers,Random Forest(RF),Multi-Layer Perceptron(MLP),Decision Tree(DT),and Super Vector Machines(SVM),in which higher performance in terms of accuracy is obtained,99.3%and 99.7%based on GP and SWaT datasets,***,the proposed ensemble model is compared with advanced related models,including Stacked Auto-Encoders(SAE),Naive Bayes(NB),Projective Adaptive Resonance Theory(PART),Convolutional Auto-Encoder(C-AE),and Package Signatures(PS)based LSTM(PS-LSTM)model.

关键词： Internet of things IIoT Anomaly detection Ensemble deep learning Neural networks LSTM

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Statistical Testing & TOPSIS Analysis of Acute Sinusitis Interventions

Statistical Testing & TOPSIS Analysis of Acute Sinusitis Int...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Pisal, Tejas Kumar, Hemant Kumar, Praveen Gote, Pradanyawant Faculty of Engineering & Technology Department of Computer Science & Design Wardha Maharashtra Sawangi442001 India Faculty of Engineering & Technology Department of Basic Sciences and Humanities Wardha Maharashtra Sawangi442001 India Faculty of Engineering & Technology Department of Computer Science & Medical Engineering Wardha Maharashtra Sawangi442001 India

ISBN: (纸本)9798350361537

Acute sinusitis, often occurring as a consequence of recovering from a cold, presents a significant health concern due to its impact on the interconnected hollow spaces located behind the cheekbones, forehead, and nose. These sinus passages play a crucial role in the respiratory process by facilitating the smooth flow of air towards the lungs. Given the discomfort and potential complications associated with acute sinusitis, there is a pressing need for effective pharmaceutical interventions. This paper embarks on a comprehensive analysis of such interventions, leveraging real-world data subjected to rigorous examination through the Kruskal-Wallis statistical testing and the TOPSIS decision-making method. Our study is dedicated to discerning the most effective drug for acute sinusitis, considering a spectrum of factors including efficacy, ease of use, user satisfaction, and cost-effectiveness. By synthesizing empirical evidence and statistical analyses, we aim to provide valuable insights that can inform clinical decision-making and optimize patient care in the management of acute sinusitis. This research holds promise in guiding healthcare providers towards evidence-based prescribing practices, enhancing treatment outcomes, and ultimately improving the quality of life for individuals suffering from acute sinusitis. © 2024 IEEE.

关键词： Decision making

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Wheat Pest Identification Based on Deep Learning Techniques 7

Wheat Pest Identification Based on Deep Learning Techniques

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7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Li, Cheng Chen, Siyi Ma, Yueqi Song, Mengjie Tian, Xiaolin Cui, Hanwen Macau University of Science and Technology School of Computer Science and Engineering Faculty of Innovation Engineering China China Agricultural University School of Plant Protection Beijing China Zhuhai College of Science and Technology School of Computer Science Zhuhai China

ISBN: (纸本)9798350352009

In recent years, effective pest management has become of cross-application of artificial intelligence and big data technologies in agriculture and environment. Nowadays, plant pests have just become a serious problem, which affects the ecological health as well as the growth of crops. In order to further better target traditional crop wheat for pest management, this study utilizes advanced deep learning techniques to accurately detect and categorize common wheat pests ('wireworm', 'Beet spot flies', and 'Cicadellidae'). Through the comparative analysis with the baseline model ResNet-18 model, this study also introduces the more advanced models SE-RegNetY and SEInception-ResNet-V3 models for pest identification in previous studies. Notably, among the three models applied in this study, the SE-Inception-ResNet-V3 model showed excellent results, achieving a significant average overall accuracy of 81.50% and kappa coefficient of 72.24%, which were 10.3% and nearly 14.8% higher than the results of the benchmark ResNet-18 model, respectively. This study marks a further application of deep learning and artificial intelligence in agricultural pest management. By improving the accuracy of agricultural pest identification and classification, it provides the basis and foundation for more sustainable and environmentally friendly pest control methods. © 2024 IEEE.

关键词： Agriculture Deep Learning Techniques Image Processing Pest Identification Wheat

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An Attention-Based Model for Clinical Time Series Prediction: Enhancing ICU Readmission Prediction 14

An Attention-Based Model for Clinical Time Series Prediction...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Madinei, Hananeh Sadat Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

ISBN: (纸本)9798331511272

— In recent years, time series prediction has become a highly interesting topic in various applied areas, including clinical time series analysis. Hospitals and other clinical healthcare systems collect Electronic Health Records. These records contain important information that needs to be mined. This information provides a solid basis for predicting hospital readmission, helping to improve healthcare, enhance patient outcomes, and optimize hospital resources. This study introduces a novel deep learning attention-based approach for predicting 30-day ICU readmissions using clinical time series information extracted from the MIMIC III dataset. This approach incorporates time-aware positional encoding into the attention mechanism to enhance predictive performance by capturing the temporal representation of data and considering the temporal order of clinical time series data. This model is compared with cutting-edge models. The significant enhancements in the model performance achieved by the proposed method highlight the effectiveness of incorporating time-aware positional encoding in capturing temporal dependencies and improving prediction outcomes. These results underscore the potential of advanced attention mechanisms in clinical time series analysis. ©2024 IEEE.

关键词： Electronic health record

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Recommender Systems for Students' Performance in Higher Educations: A Review

Recommender Systems for Students' Performance in Higher Educ...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Agustri, Suzan Ermatita, Ermatita Rini, Dian Palupi Universitas Sriwijaya Faculty of Engineering Palembang Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9798350307306

Student success is a primary strategic goal for most higher education institutions. With budget cuts and rising operating costs, higher education institutions are focusing more on retaining student enrollment in their programs while preserving educational quality. This motivates academics to develop the best way for predicting students' future success based on Recommender System, which aids students in their academic achievement. As a result, the primary goal of this study is to identify the most often investigated elements influencing student performance. In this research, 15 research articles were critically reviewed and analyzed. This review found that from the small amount of research, most studies use machine learning algorithms to find solutions to the problems. However, this article will look to find a method that may prove to be more effective. Finally, this article contributes to students' academic achievement via a recommender system and identifies the most successful machine learning methods. © 2023 IEEE.

关键词： Students

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