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Efficient Saliency Map Detection for Low-Light Images Based on Image Gradient

IEEE Transactions on Circuits and Systems for Video Technology 2024年第2期34卷 852-865页

作者： Lin, Chun-Yi Haq, Muhamad Amirul Chen, Jiun-Han Ruan, Shanq-Jang Naroska, Edwin National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan Hochschule Niederrhein University of Applied Science Faculty of Electrical Engineering and Computer Science Krefeld47805 Germany

Recently, deep learning has been widely employed across various domains. The Convolution Neural Network (CNN), a popular deep learning algorithm, has been successfully utilized in object recognition tasks, such as face recognition, vehicle recognition, and license plate recognition. However, conventional methods for object recognition may not be appropriate for low-light image recognition due to information loss in the dark regions and unexpected noise that can impair object quality. Therefore, the development of specialized techniques for low-light image enhancement has become a major research focus for object detection. This paper proposed a gradient-based saliency map detection method with an improved ResNet architecture that outperforms previous works in detecting multiple or large objects. Additionally, the proposed method enhances images with the object as the center and emphasizes foreground-background differences. Compared with previous works, this paper achieves1.28× improvements in the parameters and 1.32× faster inference speed than the original ResNet architecture. © 1991-2012 IEEE.

关键词： Neural networks

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computer Python Network Security Monitoring System Based on Intelligent Algorithms 4

Computer Python Network Security Monitoring System Based on ...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Na, Ren Xiang, Rui Ordos Institute of Applied Technology Department of Mathematics and Computer Engineering Kangbashi Ordos China

ISBN: (纸本)9798350352931

In response to the increasingly complex and diverse network security threats, a network security monitoring system is introduced that combines intelligent algorithms and Python software to achieve efficient and accurate real-time security monitoring and protection. Firstly, the system collects network traffic and log data through libraries such as Scapy and Pyshark, and performs preprocessing. Then, using Python's machine learning library Scikit learn, features are extracted, including traffic patterns, protocol types, source IP (Internet Protocol) addresses, etc., to construct a classification model. Next, based on the characteristics of network attacks, the random forest algorithm in supervised learning is selected to train the annotated attack samples. The false alarm rate of the system is 3.1%, indicating that when detecting the spread of malicious software, the system occasionally misjudges normal file transfers as malicious behavior. The system has significantly improved the efficiency and accuracy of network security monitoring through the application of intelligent algorithms, and has broad application prospects. © 2024 IEEE.

关键词： Random forests

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Maintenance Optimization of Dormant Systems Submitted to Failure Based PM and Imperfect CM.

Maintenance Optimization of Dormant Systems Submitted to Fai...

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2023 International Conference on Information and Digital Technologies, IDT 2023

作者： Bris, Radim Jahoda, Pavel Vsb - Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Applied Mathematics Czech Republic

ISBN: (纸本)9798350305869

This paper deals with the exploration of a system which acts on demand in case of emergency matter. To verify functionality, each component of the system must be regularly inspected with a specified inspection interval and tested to find latent failures. A failure-based preventive maintenance (PM) is considered, in context with the imperfect corrective maintenance model. This means that PM brings full renew which is realized after a beforehand setting the number of failures. If a failure occurs, it is detected during the first follow-up inspection and the reparation process starts. The imperfect corrective maintenance model is intended where each repair deteriorates the system life-time whose probability distribution is gradually altered through rising failure rate. Reliability mathematics for unavailability analysis is briefly demonstrated. The new renewal process model, including the failure-based PM, has been termed as a real ageing process. The imperfect corrective maintenance as well as increasing length of the inspection interval result in unwanted increase of system unavailability function which can be eliminated as well as optimized by the properly selected failure-based PM applied to all system components. If the PM is fixed due to cost, inspection intervals of system components can be optimized to keep the maximal value of the unavailability function in its original level. Sensitivity analysis applied to the length of the optimal inspection intervals with regard to gradually changing PM is demonstrated. This optimization process is demonstrated on a four-component system adopted from references. © 2023 IEEE.

关键词： Preventive maintenance

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Transfer learning in breast mass detection and classification

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Journal of Ambient Intelligence and Humanized Computing 2024年第10期15卷 3587-3602页

作者： Ryspayeva, Marya Bria, Alessandro Marrocco, Claudio Tortorella, Francesco Molinara, Mario Department of Electrical and Information Engineering University of Cassino and Southern Lazio Viale dell’Università Cassino03043 Italy Department of Computer Engineering Astana IT University Mangilik El block C.1 Astana010000 Kazakhstan Department of Electrical Information Engineering and Applied Mathematics University of Salerno Via Giovanni Paolo II 132 Fisciano84084 Italy

Covid-19 infection influenced the screening test rate of breast cancer worldwide due to the quarantine measures, routine procedures reduction, and delay of early diagnosis, causing high mortality risk and severity of the disease. X-ray mammography is the gold standard for diagnosing early signs of breast cancer, and Artificial Intelligence enables the detection of suspicious lesions and classifying them in terms of malignancy. This paper aimed to investigate mass detection and classification in a large-scale OPTIMAM dataset with 6000 cases and extracted 3524 images with masses in the mammograms of the Hologic manufacturer. The methodology of the detection step is to train the RetinaNet architecture of ResNet50, ResNet101, and ResNet152 backbones with three types of initializations by ImageNet and COCO weights and from scratch. The dataset was pre-processed to generate two types of input with entire mammograms and patches, which are stated as the first and the second approaches. The results show that in the first approach, RetinaNet of ResNet50 backbone with ImageNet and COCO weights and ResNet152 with the same weights performed 0.91 True Positive Rate at 0.78 False Positive Per Image, respectively. In contrast, in the second approach, ResNet152 with ImageNet weights reached 0.88 TPR at 0.78 FPPI. In the classification step, the Transfer Learning approach was applied with fine-tuning by adding L2-regularization and class weights to balance class distribution in the datasets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Mammography

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A transformer model for boundary detection in continuous sign language

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Multimedia Tools and Applications 2024年第42期83卷 89931-89948页

作者： Rastgoo, Razieh Kiani, Kourosh Escalera, Sergio Electrical and Computer Engineering Faculty Semnan University Semnan*** Iran Sergio Escalera Department of Mathematics and Informatics Universität de Barcelona and Computer Vision Center Barcelona Spain

Sign Language Recognition (SLR) has garnered significant attention from researchers in recent years, particularly the intricate domain of Continuous Sign Language Recognition (CSLR), which presents heightened complexity compared to Isolated Sign Language Recognition (ISLR). One of the prominent challenges in CSLR pertains to accurately detecting the boundaries of isolated signs within a continuous video stream. Additionally, the reliance on handcrafted features in existing models poses a challenge to achieving optimal accuracy. To surmount these challenges, we propose a novel approach utilizing a Transformer-based model. Unlike traditional models, our approach focuses on enhancing accuracy while eliminating the need for handcrafted features. The Transformer model is employed for both ISLR and CSLR. The training process involves using isolated sign videos, where hand keypoint features extracted from the input video are enriched using the Transformer model. Subsequently, these enriched features are forwarded to the final classification layer. The trained model, coupled with a post-processing method, is then applied to detect isolated sign boundaries within continuous sign videos. The evaluation of our model is conducted on two distinct datasets, including both continuous signs and their corresponding isolated signs, demonstrates promising results. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Gesture recognition

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Electricity demand forecasting at distribution and household levels using explainable causal graph neural network

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Energy and AI 2024年第2期16卷 385-395页

作者： Amir Miraki Pekka Parviainen Reza Arghandeh Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergenNorway Department of Informatics University of BergenBergenNorway

Forecasting electricity demand is an essential part of the smart grid to ensure a stable and reliable power grid. With the increasing integration of renewable energy resources into the grid, forecasting the demand for electricity is critical at all levels, from the distribution to the household. Most existing forecasting methods, however, can be considered black-box models as a result of deep digitalization enablers, such as deep neural networks, which remain difficult to interpret by humans. Moreover, capture of the inter-dependencies among variables presents a significant challenge for multivariate time series forecasting. In this paper we propose eXplainable Causal Graph Neural Network (X-CGNN) for multivariate electricity demand forecasting that overcomes these limitations. As part of this method, we have intrinsic and global explanations based on causal inferences as well as local explanations based on post-hoc analyses. We have performed extensive validation on two real-world electricity demand datasets from both the household and distribution levels to demonstrate that our proposed method achieves state-of-the-art performance.

关键词： Causal inference Electricity demand forecasting Explainable artificial intelligence(XAI) Graph neural network

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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

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Neutrosophic Sets and Systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

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computer Modeling in engineering & Sciences 2024年第3期138卷 2749-2767页

作者： Shimaa M.Amer Ashraf A.M.Khalaf Amr H.Hussein Salman A.Alqahtani Mostafa H.Dahshan Hossam M.Kassem Electronics and Communications Engineering Department Higher Institute of Engineering and TechnologyKafr ElsheikhEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMiniaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringHorus University EgyptNew DamiettaEgypt Department of Computer Engineering College of computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia School of Computing Mathematics and EngineeringCharles Sturt UniversityBathurstNSW2795Australia

Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to ***,it improves the array gain and directivity,increasing the detection range and angular resolution of radar *** study proposes two highly efficient SLL reduction *** techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,*** convolution process determines the element’s excitations while the GA optimizes the element *** M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,*** the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher *** the increased HPBWof the odd and even excitations,the element spacing is optimized using the ***,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the ***,for extreme SLL reduction,the DConv/GA is *** this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation *** provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.

关键词： Array synthesis convolution process genetic algorithm(GA) half power beamwidth(HPBW) linear antenna array(LAA) side lobe level(SLL) quality of service(QOS)

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A Comparative Assessment of eXplainable AI Tools in Predicting Hard Disk Drive Health 32

A Comparative Assessment of eXplainable AI Tools in Predicti...

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32nd Italian Symposium on Advanced Database Systems, SEBD 2024

作者： Amato, Flora Ferraro, Antonino Galli, Antonio La Gatta, Valerio Moscato, Francesco Moscato, Vincenzo Postiglione, Marco Sansone, Carlo Sperlì, Giancarlo Department of Electrical Engineering and Information Technology University of Naples Federico II Italy Department of Information Engineering Electrical Engineering and Applied Mathematics University of Salerno Italy

In addressing the challenge of optimizing maintenance operations in Industry 4.0, recent efforts have focused on predictive maintenance frameworks. However, the effectiveness of these frameworks, largely relying on complex deep learning models, is hindered by their lack of explainability. To address this, we employ eXplainable Artificial Intelligence (XAI) methodologies to make the decision-making process more understandable for humans. Our study, based on a previous work, specifically explores explanations for predictions made by a recurrent neural network-based model designed for a three-dimensional dataset, used to estimate the Remaining Useful Life (RUL) of Hard Disk Drives (HDDs). We compare the explanations provided by different XAI tools, emphasizing the utility of global and local explanations in supporting predictive maintenance tasks. Using the Backblaze Dataset and a Long Short-Term Memory (LSTM) prediction model, our developed explanation framework evaluates Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) tools. Results show that SHAP outperforms LIME across various metrics, establishing itself as a suitable and effective solution for HDD predictive maintenance applications. © 2024 Copyright for this paper by its authors.

关键词： Predictive maintenance

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A Novel 6G Scalable Blockchain Clustering-Based computer Vision Character Detection for Mobile Images

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computers, Materials & Continua 2024年第3期78卷 3041-3070页

作者： Yuejie Li Shijun Li Department of Mathematics and Computer Engineering Ordos Institute of TechnologyOrdos017000China School of Mathematics and Physics North China Electric Power UniversityBeijing102206China School of Electrical Information Hunan University of EngineeringXiangtan411104China

6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous *** tandem with these advancements,blockchain technology is leveraged to enhance computer vision applications’security,trustworthiness,and *** the widespread use of mobile devices equipped with cameras,the ability to capture and recognize Chinese characters in natural scenes has become increasingly *** can facilitate privacy-preserving mechanisms in applications where privacy is paramount,such as facial recognition or personal healthcare *** can control their visual data and grant or revoke access as *** Chinese characters from images can provide convenience in various aspects of people’s ***,traditional Chinese character text recognition methods often need higher accuracy,leading to recognition failures or incorrect character *** contrast,computer vision technologies have significantly improved image recognition *** paper proposed a Secure end-to-end recognition system(SE2ERS)for Chinese characters in natural scenes based on convolutional neural networks(CNN)using 6G *** proposed SE2ERS model uses the Weighted Hyperbolic Curve Cryptograph(WHCC)of the secure data transmission in the 6G network with the blockchain *** data transmission within the computer vision system,with a 6G gradient directional histogram(GDH),is employed for character *** the deployment of WHCC and GDH in the constructed SE2ERS model,secure communication is achieved for the data transmission with the 6G *** proposed SE2ERS compares the performance of traditional Chinese text recognition methods and data transmission environment with 6G *** results demonstrate that SE2ERS achieves an average recognition accuracy of 88%for simple Chinese characters,compared to 81.2%with traditional ***

关键词： 6G technology blockchain end-to-end recognition Chinese characters natural scene computer vision algorithms convolutional neural network

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