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Enhanced Modified Contrast Enhancement and Exposure Fusion: a visionary dehazing approach for clarity in hazy imagery

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Multimedia Tools and Applications 2025年 1-24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 Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

In the field of image processing, haze is a type of atmospheric scattering that reduces contrast and clarity of images, frequently masking small details and far-off objects. It is mostly caused by microscopic airborne particles like smoke, dust, and water droplets that scatter incoming light. The scattered light, producing a hazy image, diminishes the direct light that reaches the camera sensor. Many image processing methods have been presened to solve this problem and lessen the impacts of haze. Usually, these methods work by determining how much haze is there in an image, and then using this information to get the image back to its assumed previous quality. In this work, we present the proposed Modified Contrast Enhancement and Exposure Fusion (MCEEF) technique. The MCEEF dehazing technique falls under the umbrella of enhancement-based dehazing techniques. In this technique, hazy frames or images undergo sharpening through a Smoothing-Sharpening Image Filter (SSIF) designed to accentuate the disparity between the haze and objects in images. Subsequently, Gamma Correction (GC) and Color Preserving Adaptive Histogram Equalization (CP-AHE) enhance the sharpened hazy images by augmenting their contrast. Dehazed images are then obtained by fusing the results of the CP-AHE- and GC-enhanced images. The MCEEF dehazing technique is aided with enhanced hazy images or frames as input for the dehazing process. Moreover, essential enhancement tools precede the MCEEF dehazing technique. These tools include homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE), which are instrumental in controlling the dynamic range before the dehazing phase. In the proposed approach, a hazy image or frame is initially subjected to homomorphic processing, followed by the application of CLAHE, and finally, the MCEEF dehazing technique is employed. To demonstrate the effectiveness of the proposed approach, we apply it on both visible and Near Infrared (NIR) frames. We

关键词： Demulsification

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Efficient Optimization of Energy Consumption at Home through Machine Learning 5

Efficient Optimization of Energy Consumption at Home through...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Sharada Prasad, N. Lakshmi, Shree M.S. Manjunatha, S. Vekariya, Vipul Benal, Ganga D. Patil, Harshal RNS Institute of Technology Department of Electrical and Electronics Engineering Karnataka Bangalore560098 India Cambridge Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560036 India Parul University Faculty of Engineering and Technology Parul Institute of Engineering and Technology Department of Computer Science and Engineering Gujarat Waghodia391760 India Symbiosis Institute of Technology Maharashtra Pune412115 India

ISBN: (纸本)9798350395235

In recent years, the imperative need for efficient energy utilization in residential buildings has become increasingly evident due to the unwarranted wastage of electrical energy. This has spurred significant interest in optimizing energy consumption while maintaining user comfort. Accurate energy prediction is a crucial component of this optimization. This study focus on minimizing energy consumption by accurately predicting it through advanced Machine Learning (ML) models and optimization techniques. The study begins with the collection of energy data from a reliable source, Kaggle, comprising 29 features. To streamline the dataset, unnecessary features are discarded, and data normalization is performed to ensure consistency and reliability. Subsequently, ML models, specifically Long Short-Term Memory (LSTM), are designed and optimized through the Genetic Algorithm (GA) and Grey Wolf Optimization (GWO) to fine-tune hyperparameters. The prediction results are evaluated using error values to assess the accuracy and reliability of the models. Notably, our findings indicate that the GWO-LSTM model outperforms the others, exhibiting minimal errors and therefore showcasing its superior predictive capabilities. Accurate energy prediction not only serves as a valuable tool in its own right but also plays a pivotal role in enabling proactive energy management. By accurately forecasting future energy requirements, it becomes possible to optimize energy consumption further. Such precision paves the way for intelligent scheduling of home appliances, which, in turn, leads to significant reductions in energy consumption. © 2024 IEEE.

关键词： Energy utilization

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Hyperbolic Pattern Detection in Ground Penetrating Radar Images Using Faster R-CNN 20

Hyperbolic Pattern Detection in Ground Penetrating Radar Ima...

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20th International Conference on Electrical engineering/electronics, computer, Telecommunications and Information technology, ECTI-CON 2023

作者： Srimuk, Pachara Boonpoonga, Akkarat Kaemarungsi, Kamol Athikulwongse, Krit Torrungrueng, Danai Chantasen, Nattawat King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand National Science and Technology Development Agency National Electronics and Computer Technology Center Pathumthani Thailand Department of Teacher Training in Electrical Engineering Bangkok Thailand Rajamangala University of Technology Phra Nakhon Faculty of Engineering Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350310467

This study presents the application of Faster RCNN, a popular Region Based Convolutional Neural Network, for detecting hyperbolic patterns in Ground Penetrating Radar (GPR) images. GPR is an important tool for subsurface imaging in various fields such as geology, archaeology, and engineering. However, the analysis of GPR images can be challenging due to noise, small objects, and variations in object sizes. To evaluate the performance of the proposed method, 369 simulated GPR B-scan images were generated using GprMax simulation software. These images included single, double, and triple hyperbolic patterns. The results showed that preprocessing improved the detection accuracy and led to higher Intersection over Union (IoU) scores. The experimental results demonstrate that Faster R-CNN is an effective tool for hyperbolic pattern detection in GPR images and provides a promising direction for future research in the field. © 2023 IEEE.

关键词： Ground penetrating radar systems

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Analysis of BrainMRI: AI-Assisted Healthcare Framework for the Smart Cities

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Intelligent Automation & Soft Computing 2023年第2期35卷 1843-1856页

作者： Walid El-Shafai Randa Ali Ahmed Sedik Taha El-Sayed Taha Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of ROBOTICS and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The use of intelligent machines to work and react like humans is vital in emerging smart ***-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare *** AI(Artiﬁcial Intelligence)driven healthcare applications,tumor detection is one of the contemporary researchﬁelds that have become attractive to *** are several modalities of imaging performed on the brain for the pur-pose of tumor *** paper offers a deep learning approach for detecting brain tumors from MR(Magnetic Resonance)images based on changes in the division of the training and testing data and the structure of the CNN(Convolu-tional Neural Network)*** proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy,including about 4700 MRI images of ten brain tumor cases with both normal and abnormal *** dataset is divided into test,and train subsets with a ratio of the training set to the validation set of 70:*** main contribution of this paper is introdu-cing an optimum deep learning structure of CNN *** simulation results are obtained for 50 epochs in the training *** simulation results reveal that the optimum CNN architecture consists of four layers.

关键词： Healthcare smart cities clinical automation CNN machine learning brain tumor medical diagnosis

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An Attention Mechanism Approach for Natural Color Retrieval for Plant Monitoring 20

An Attention Mechanism Approach for Natural Color Retrieval ...

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20th International Conference on Electrical engineering/electronics, computer, Telecommunications and Information technology, ECTI-CON 2023

作者： Chadavadh, Yanatorn Kasetkasem, Teerasit Patrapornnant, Teera Parittotakapron, Sirichai Isshiki, Tsuyoshi Kasetsart University Faculty of Engineering Department of Electrical Engineering Bangkok Thailand National Electronics and Computer-Technology Center Pathunthani Thailand Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9798350310467

Even though agriculture practices have been continuously developed with the support of modern technologies, many more improvements can be made to enhance agricultural technologies and businesses. One such technology is the use of specific light color combinations to optimize the growth rate of plants. One obvious drawback is that plants' color will change according to the light color combinations. The light color can fool human eyes and may cause errors when monitoring for plant anomalies. Color correction methods should be applied to help restore the natural plant color with the white light source from the unnaturally colored plant images. Our color correction method uses an application of self-dot-product attention, multi-head attention, and channel attention combined with a U-Net-based model. This proposed method performs the color correction with the input image in the RGB color space in two steps. First, a global transformation network provides the global function that maps the input RGB color vectors from every pixel and produces the corrected RGB color vectors. The global mapping function is the same for all pixels in the image. Next, a local transformation network attempts to correct the local color distortions such as light the flickering of LED light due to the AC power supplier. © 2023 IEEE.

关键词： Deep learning

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An empirical intelligent water irrigation system using soft computing and IoT

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International Journal of Information technology (Singapore) 2024年第6期16卷 3983-3989页

作者： Patel, Warish Sangeetha, Y. Gopagoni, Deepa Rani Mallik, D. M. Arvind Agrawal, Rakshal Mishra, Saket Sunil, M.P. Kolandaisamy, Raenu Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat India Department of Information Technology VR Siddhartha Engineering College Andhra Pradesh Vijayawada India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India PES Institute of Technology and Management Karnataka Shivamogga India Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India Centre of Interdisciplinary Research in Business and Technology Chitkara Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka Bengaluru India Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia

Water loss from fruit and vegetable production is mostly caused by leakage in the transportation system and handling of land, as well as by using traditional processing techniques. In order to increase the wellness of plants, save power, and optimize consumption of water, information should be widely applied in designing intelligent irrigation systems, which is possible with the help of the Internet of Things (IoT). In the given paper, an IoT-based soft computing recommendation framework is proposed for performing irrigation in a smarter way. The system works in such a way that IoT devices are positioned in the field to gather information about crops and groundwater levels. The acquired data is then fed to a cloud repository, which makes use of soft computing techniques such as neural networks for analysing and providing effective recommendations to farmers on their smartphones. In this way, the framework is tuned and the farmers are also updated from time to time regarding judicious irrigation processes. Lastly, the performance of the proposed system is validated against traditional techniques by taking crop datasets into consideration. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Efficient water management Intelligent devices IoT Smart irrigation Soft computing

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An Effective Ensemble Learning Method for Fault Diagnosis of Photovoltaic Arrays 3rd

An Effective Ensemble Learning Method for Fault Diagnosis of...

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3rd International Conference on Electronic engineering and Renewable Energy, ICEERE 2022

作者： Mellit, Adel Boubaker, Sahbi Department of Electronics Faculty of Science and Technology University of Jijel Jijel Algeria Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (纸本)9789811962226

In this paper, a novel fault diagnosis method for photovoltaic (PV) arrays is proposed. The method combines three machine learning (ML) algorithms: the first one is an unsupervised ML algorithm (principal component analysis, ‘PCA’) used for features reduction;the second one is a kind of a recurrent neural networks (long short-term memory, ‘LSTM’) employed to predict the PV power;and the third one is a supervised ensemble learning categorical boosting algorithm (CatBoost), used mainly for fault classification improvement. Four types of faults (dust deposit on PV modules surface, partial shading effect, shunted diode and open-circuit diode) are examined in this study. Results showed the effectiveness of the proposed method in terms of detection and classification accuracy, 98.70% and 98.58% respectively, and demonstrate the superiority of this method over other investigated ML algorithms (e.g. decision tree, support vector machine, k-nearest neighbours, and random forest). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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Federated learning-based lightweight selective feature fusion and irregular-aware network for crack detection

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Optoelectronics Letters 2025年

作者： LIU Hui HE Tian CHENG Xu SHI Fan NIKOLI? Sa?a V. ZHANG Jianhua School of Computer Science and Engineering， Tianjin University of Technology Department of Electronics， Faculty of Electronic Engineering， University of Nis

Employing deep learning for pavement crack detection can significantly enhance accuracy， and federated learning can help to overcome the challenges of data silos and data security. This paper proposes a lightweight method to detect cracks. We use lightweight encoder modules to extract multi-scale features， and further feature fusion and modelling by Selective Fusion Blocks and Irregular-aware Blocks. Moreover， this method is the first to combine federated learning with crack detection， effectively resolving the tension between data privacy and data sharing in decentralised devices. Experiments were conducted to compare our proposed method with other crack detection methods on two publicly available datasets， including the original method without federated learning and five state-of-the-art methods combined with the same federated learning framework. The experiment proves that our model obtains comparable F1 score with minimal parameters and computational effort. For example， compared to the original method， the number of parameters and computation are reduced by 67.1% and 73.6%， respectively.

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Gauss Gradient and SURF Features for Landmine Detection from GPR Images

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computers, Materials & Continua 2022年第6期71卷 4457-4486页

作者： Fatma M.El-Ghamry Walid El-Shafai Mahmouad I.Abdalla Ghada M.El-Banby Abeer D.Algarni Moawad I.Dessouky Adel S.Elfishawy Fathi E.Abd El-Samie Naglaa F.Soliman Department of Electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazigSharqia44519Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Recently,ground-penetrating radar(GPR)has been extended as a well-known area to investigate the subsurface ***,its output has a low resolution,and it needs more processing for more *** paper presents two algorithms for landmine detection from GPR *** first algorithm depends on a multi-scale technique.A Gaussian kernel with a particular scale is convolved with the image,and after that,two gradients are estimated;horizontal and vertical ***,histogram and cumulative histogram are estimated for the overall gradient *** bin values on the cumulative histogram are used for discrimination between images with and without ***,a neural classifier is used to classify images with cumulative histograms as feature *** second algorithm is based on scale-space analysis with the number of speeded-up robust feature(SURF)points as the key parameter for *** addition,this paper presents a framework for size reduction of GPR images based on decimation for efficient *** further classification steps can be performed on images after *** sensitivity of classification accuracy to the interpolation process is studied in detail.

关键词： GPR images cumulative histogram gradient image neural classifier SURF

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