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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ouaddi, Charaf Benaddi, Lamya Souha, Adnane Naimi, Lahbib Bouziane, El Mahi Jakimi, Abdeslam Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

The development of Decision Support Systems (DSS) for several companies operating in sectors such as tourism, healthcare, or others, presents significant challenges due to the nature of their multi-component architecture. A typical DSS consists of a data component, a model component incorporating machine learning and deep learning techniques, and a user interface component, each one requires diverse technologies. This complexity increases development time and effort, hindering the efficient design of DSS tailored to specific domains like tourism, where real-time decision-making is essential. One promising approach to overcome these challenges is the use of Domain-Specific Languages (DSLs). DSLs allow developers to model and generate code more efficiently by providing high-level abstractions suited to the domain. By leveraging DSLs, it is possible to simplify the design process and automate the generation of code or parts of the DSS, thereby reducing the overall complexity and time required for development. This paper proposes a metamodel for designing DSS, specifically for the Smart Tourism sector. The metamodel serves as a foundation for the creation of DSL. The goal is to provide a solution to facilitate the development of DSS for the demands of tourism. The paper outlines the key concepts, approach, and results, presenting the first element of the proposed DSL which is the metamodel capturing the essential components and relationships for DSS in Smart Tourism. © 2024 IEEE.

关键词： Digital subscriber lines

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Generative Adversarial Networks (GANs) for Image Synthesis and Augmentation 4

Generative Adversarial Networks (GANs) for Image Synthesis a...

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4th IEEE International Conference on Electronic technology, Communication and Information, ICETCI 2024

作者： Wen, Yuxin Mei, Zhenhao Qi, Chuyue Xinjiang University Department of Software Engineering Urumqi China Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350361643

In the realm of medical imaging, a scarcity of reliable, sizable datasets for training supervised deep learning models persists. One solution involves leveraging Generative Adversarial Networks (GANs) to fabricate synthetic deepfake images, bridging this gap and enriching available data. Deepfake technology seamlessly transfers crucial features from source to target images or videos, mimicking the source with remarkable fidelity. Over the past decade, propelled by cutting-edge deep learning techniques and vast datasets, medical image processing has made monumental strides. Supervised deep learning models now achieve superhuman performance across diverse medical imaging applications. Synthetic deepfake images, produced through techniques like GANs, serve various purposes, including dataset balancing, modal translation, and expansion. To efficiently augment training data for deep learning, a hierarchical GAN (HGAN) framework is proposed, focusing on generating high-quality knee images. HGAN integrates spectral normalization in discrimination and pixel normalization in generation, enhancing training stability. Evaluation metrics such as AM Score and Mode Score are employed to benchmark HGAN against alternatives like PGGAN, reflecting its efficacy in advancing medical imaging research and application. © 2024 IEEE.

关键词： Medical imaging

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Two-Stage Lesion Detection Approach Based on Dimension-Decomposition and 3D Context

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Tsinghua Science and technology 2022年第1期27卷 103-113页

作者： Jiacheng Jiao Haiwei Pan Chunling Chen Tao Jin Yang Dong Jingyi Chen the College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China the School of Software Tsinghua UniversityBeijng 100084China

Lesion detection in Computed Tomography(CT) images is a challenging task in the field of computer-aided *** important issue is to locate the area of lesion *** a branch of Convolutional Neural Networks(CNNs),3D Context-Enhanced(3DCE) frameworks are designed to detect lesions on CT *** False Positives(FPs) detected in 3DCE frameworks are usually caused by inaccurate region proposals,which slow down the inference *** solve the above problems,a new method is proposed,a dimension-decomposition region proposal network is integrated into 3DCE framework to improve the location accuracy in lesion *** the restriction of "anchors" on ratios and scales,anchors are decomposed to independent "anchor strings".Anchor segments are dynamically combined in accordance with probability,and anchor strings with different lengths dynamically compose bounding *** show that the accurate region proposals generated by our model promote the sensitivity of FPs and spend less inference time compared with the current methods.

关键词： lesion detection Computed Tomography(CT) dimension-decomposition 3D context computer-aided diagnosis

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Robot positioning and navigation technology is based on Integration of the Global Navigation Satellite System and real-time kinematics

Robot positioning and navigation technology is based on Inte...

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International Conference on Emerging Electronic and Automation technology 2022, ICEEAT 2022

作者： Geng, Junzuo Mokhtarzadeh, Amir Ali Fei, Hongyan Gao, Han Ding, Nan Kong, Rongshuang Zhou, Jiayu Huaiyin Institute of Technology School of Computer and Software Engineering 223003 China Jiangsu Foreign Expert WorhShop Jiangsu Huaian223003 China Nanjing Institute of Technology School of Computer and Software Engineering 211167 China

This paper provides a new robot positioning and navigation technique that combines GNSS-RTK and visual SLAM to address the shortcomings of existing single robot positioning and navigation techniques, such as limited adaptation to scenarios and susceptibility to environmental influences. Real-time kinematics (RTK) is a real-time dynamic observation technique based on carrier phase observations, which can provide real-time 3D positioning results of the measurement site in a specified coordinate system, and can be positioned with centimetre-level accuracy, which is suitable for It is suitable for outdoor positioning of mobile robots. In this paper, a dataset is studied to compare and analyse the advantages and disadvantages of different SLAMs in order to determine the most suitable robot positioning and navigation technique for autonomous indoor and outdoor mobility. These sensors were well calibrated and the composition was applied to the RK3588. The results show good performance in indoor-outdoor switching scenarios, obtaining a mapping technique that cannot be achieved with a single visual SLAM, effectively improving the ability to adapt to multiple scenarios and enhancing the operational range of the transportable robot. © Published under licence by IOP Publishing Ltd.

关键词： Kinematics

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Performance Analysis of Machine Learning Based On Optimized Feature Selection for Type II Diabetes Mellitus

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Multimedia Tools and Applications 2025年第8期84卷 4945-4964页

作者： Bhat, Salliah Shafi Ansari, Gufran Ahmad Ansari, Mohd Dilshad B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai48 India Department of Computer Science and Software Engineering University of Hail Hail55424 Saudi Arabia Department of Computer Science & Engineering SRM University Delhi-NCR Harayana Sonepat131029 India

Type 2 Diabetes Mellitus (T2DM) is a common chronic illness caused by variations in the secretion of insulin. T2DM will be treated early with the help of an early diagnosis reducing the risk of early death and controlling is course of illness. In this Paper Machine Learning Algorithms (MLA) for T2DM diagnosis are proposed. The major objective of this research is to assess use of MLA and its methods to forecast different illnesses at an initial stage. In this research, we examine most recent proposed strategies with some limitations and potential improvements for future work. The data is collected from Kaggle (University of California) *. Researchers highlight how important is to identify important features that enhance outcomes suggested by recent methods. Six MLA were used in this paper such as Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbour (KNN) were applied in Jupiter Notebook. Feature sets are selected using three feature selection methodologies such as information gain-based selection, correlation-based selection and sequential feature selection. On these feature subsets a variety of MLA are applied and optimal feature subset is chosen based on their performance. Finally a DT is suggested as the top model among the top six performances. The suggested method provides an improved performance label with an average accuracy of 96.10%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature Selection

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Steel surface defect detection based on Improved YOLOX Algorithm

Steel surface defect detection based on Improved YOLOX Algor...

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International Conference on Emerging Electronic and Automation technology 2022, ICEEAT 2022

作者： Li, Guohao Mokhtarzadeh, Amir Ali School of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu Huaian223003 China

As one of the very common industrial products, strip steel plays a very important role in various fields. Steel production has received extensive attention. In industrial production, scratches are unavoidable due to numerous forces beyond human control. Cracks and other flaws appear, and their presence has a direct impact on the product's quality. Therefore, it is of great significance to develop efficient and accurate strip surface defect detection. In this study, the most widely used metal material strip steel is selected as the research object, and its surface defect detection problem is studied accordingly. The activation function and loss function are improved on the basis of the YOLOX model. Resolve the problem of overexposure and content distortion of the image caused by the high reflecting characteristics of the strip, which degrades the image quality, and the embedding of the sub-attention mechanism to enhance the regional characteristics of hidden small targets. The experimental results show that the mAP of the improved model for strip surfaces defect detection is 80.7%, which is 4.1% higher than that of the original YOLOX algorithm, and the improved model can reduce the false detection and missed detection rate of defects. © Published under licence by IOP Publishing Ltd.

关键词： Strip metal

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Fortifying Healthcare Data Security in the Cloud:A Comprehensive Examination of the EPM-KEA Encryption Protocol

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computers, Materials & Continua 2024年第5期79卷 3397-3416页

作者： Umi Salma Basha Shashi Kant Gupta Wedad Alawad SeongKi Kim Salil Bharany Computer Science and Information Technology Jazan UniversityJazanSaudi Arabia CSE Eudoxia Research UniversityNew CastleUSA Department of Information Technology College of Computer ScienceQassim UniversityBuraydahSaudi Arabia National Center of Excellence in Software Sangmyung UniversitySeoulKorea Department of Computer Science and Engineering Lovely Professional UniversityPhagwaraPunjabIndia

A new era of data access and management has begun with the use of cloud computing in the healthcare *** the efficiency and scalability that the cloud provides, the security of private patient data is still a majorconcern. Encryption, network security, and adherence to data protection laws are key to ensuring the confidentialityand integrity of healthcare data in the cloud. The computational overhead of encryption technologies could leadto delays in data access and processing rates. To address these challenges, we introduced the Enhanced ParallelMulti-Key Encryption Algorithm (EPM-KEA), aiming to bolster healthcare data security and facilitate the securestorage of critical patient records in the cloud. The data was gathered from two categories Authorization forHospital Admission (AIH) and Authorization for High Complexity *** use Z-score normalization forpreprocessing. The primary goal of implementing encryption techniques is to secure and store massive amountsof data on the cloud. It is feasible that cloud storage alternatives for protecting healthcare data will become morewidely available if security issues can be successfully fixed. As a result of our analysis using specific parametersincluding Execution time (42%), Encryption time (45%), Decryption time (40%), Security level (97%), and Energyconsumption (53%), the system demonstrated favorable performance when compared to the traditional *** suggests that by addressing these security concerns, there is the potential for broader accessibility to cloudstorage solutions for safeguarding healthcare data.

关键词： Cloud computing healthcare data security enhanced parallel multi-key encryption algorithm(EPM-KEA)

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ma, Wenqiang Hau, Yaqing Yu, Hua Tong, Xiangrong Zhu, Zexuan Zhang, Qiang School of Computer Science and Technology Dalian University of Technology Dalian116024 China School of Computer and Control Engineering Yantai University Yantai264005 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9798350308365

Deep neural networks are powerful and popular learning models;however, recent studies have shown that deep neural network-based policies are susceptible to deception by adversarial attacks. A minimalistic attack is a specialized form of adversarial attack that aims to accomplish successful attacks at the lowest possible cost. Recently, transfer optimization algorithms have been applied to deceive previously trained policies by acquiring knowledge from previously solved tasks. Experiments indicate that the transfer optimization algorithms perform well compared to traditional optimization algorithms. However, current transfer algorithms for addressing minimalistic attacks not only select a single source task for knowledge transfer but also tend to overly rely on identified appropriate source tasks. To address this issue, this paper introduces a similar locality based transfer evolutionary optimization algorithm. It can adaptively select multiple source tasks and extract valuable knowledge from these source tasks. Moreover, by leveraging the concept of similar locality, the algorithm alleviates its excessive dependence on familiar tasks, thereby providing fresh knowledge for the optimization of the target task. On this basis, the algorithm can mine more valuable knowledge from the large source task space to achieve a successful attack in a shorter period. The algorithm is tested on three Atari games-BeamRider, Qbert, and Seaquest-demonstrating its ability and potential to outperform other transfer optimization algorithms currently available in solving this problem. © 2024 IEEE.

关键词： Optimization algorithms

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Optimize the application of attention mechanism in complex labels text detection algorithms 2

Optimize the application of attention mechanism in complex l...

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2nd International Symposium on computer Applications and Information Systems, ISCAIS 2023

作者： Gao, Yingning Liu, Weisheng School of Computer and Software Engineering Liaoning University of Science and Technology Liaoning Anshan China

ISBN: (纸本)9781510666665

The label is used to mark the object information or classification of the logo, complex label then contains more layout information than ordinary labels. These include Chinese and English characters, pictures, symbols and barcodes. When text detection is performed in complex labels, the detection capability of the algorithm is reduced and is prone to feature overlap resulting in missed and false detection. In this paper, the original YOLOv5 algorithm is optimized by an improved channel attention mechanism to form the new algorithm SA-YOLOv5. The experimental results show that the SAYOLOv5 model can effectively improve detection efficiency and solve the problem of false and missed detections. The average detection accuracy mAP value is 92.24%, which is 0.68 percentage points higher than the original algorithm, proving the effectiveness of the algorithm. © 2023 SPIE.

关键词： Classification (of information)

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Research on Obstacle Avoidance Algorithm of UAVs Reinforcement Learning based on Vision 5

Research on Obstacle Avoidance Algorithm of UAVs Reinforceme...

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5th International Conference on Frontiers technology of Information and computer, ICFTIC 2023

作者： Gao, Yuan Shi, Tianwei Xu, Teng Ding, Jianbang Jing, Rongchen University of Science and Technology Liaoning School of Computer Science and Software Engineering Anshan China

ISBN: (纸本)9798350309034

In recent years, the combination of deep reinforcement learning and unmanned aerial vehicle (UAV) to achieve autonomous flight has been a hot research field. In this paper, an obstacle avoidance navigation algorithm (PA-SAC) based on priority experience buffer pool (PEBP), attention, and Soft-Actor-Critic (SAC) is proposed to solve the continuous space obstacle avoidance navigation problem of UAVs by using a deep reinforcement learning algorithm. In the two simulation experiments, the success rate of the PA-SAC algorithm was 95.6% in the known environment and 73% in the unknown environment. These results demonstrate that the PA-SAC algorithm can achieve autonomous obstacle avoidance for UAVs with deep image input. © 2023 IEEE.

关键词： Attention component Obstacle Avoidance PEBP UAVs

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