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Artificial Intelligence, Networking and Information technology (AINIT), International Seminar on

作者： Ying Yuan Xin Yi Junbin ShangGuan School of Computer & Information Engineering Xiamen University of Technology Institute of Space Information Technology Xiamen University of Technology Xiamen China Software department Bozhongtuxin (Xiamen) Information Technology Co. Ltd Xiamen China

With the continuous improvement of social informationization, the digitalization of corporate construction is also developing comprehensively. Data, as a crucial source for creating wealth and competitive advantage, is undoubtedly the most significant asset for all enterprises. However, many companies face the dilemma of insufficient data granularity and configuration flexibility, which undermines the security and availability of their data assets. Therefore, this paper proposes an Api-based data permission control model (ADPC) based on the theoretical foundation of RBAC in the background of data application in the enterprise environment. Firstly, the model maps data objects to different levels of environmental variables in the corporate department structure, then flexibly authorizes and controls permissions through API configuration, and finally verifies its security with SQL AST. The ADPC model was implemented using the Spring Boot, Spring Security and MyBatis framework and was applied in a real enterprise environment. Through this approach, the ADPC model effectively addresses the limitations of traditional data permission control mechanisms in the enterprise application environment.

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Diagnosing Spinal Abnormalities Using Machine Learning: A Data-Driven Approach

Diagnosing Spinal Abnormalities Using Machine Learning: A Da...

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Artificial Intelligence (ICAI), International Conference on

作者： Zia Ul Islam Nasir Khalil Khan Momna Asghar School of Interdeciplanary Engineering & Sciences(SINES) National University of sciences & Technology (NUST) Isamabad Pakistan Department of Computer Software Engineering & Computer Science Pak-Austria Institute of Applied Science & Technology Haripur Pakistan Department of Electrical Engineering University of Azad Jammu & Kashmir Muzaffarabad A.K

Low back pain is a predominant condition which can affects people from different diaspora. The goal of this work is to use machine learning approach to forecast spinal abnormalities. Extratreesclassifier is utilized as a data preprocessing stage to choose the dataset's most prominent features. On a dataset of 310 samples, spinal anomalies are diagnosed using machine learning algorithms like the Support Vector Machine (SVM) and the multilayer perceptron (MLP). The purpose of this study is to determine the most crucial factors that produce backbone abnormalities and to predict them using supervised machine learning techniques. The classification of normal and abnormal spinal patients is investigated in terms of various aspects, including testing and training accuracy, precision, and recall. The observed accuracies for SVM and MLP with 80% training data are 92% and 90%, respectively. The result shows that these models can achieve high accuracy in predicting spinal abnormalities, with the SVM model performing the better. The result suggest that this approach has the potential to significantly improve the efficiency and accuracy of spinal abnormality diagnosis, leading to better patient outcomes.

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Tri-PANet: Triplanar Ensemble Network with Parallelly Aggregating Global and Local Information for Brain Tumor Segmentation

Tri-PANet: Triplanar Ensemble Network with Parallelly Aggreg...

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Machine Vision, Image Processing and Imaging technology (MVIPIT), International Conference on

作者： Li Fang Zhou Jun Li School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350306545

ISBN: (纸本)9798350306552

Accurate segmentation of brain tumor images holds great importance in the clinical diagnosis and effective treatment of brain tumors diseases. Nonetheless, tumors exhibit extensive variations in their characteristics, both in terms of their visual appearance and their location within brain MR images. This poses a significant challenge in achieving robust tumor segmentation. Furthermore, sample labeling bias problem also limits the segmentation accuracy of current methods. In this work, we propose a innovation approach named Tri-PANet for brain tumor segmentation. On the one hand, Tri-PANet learns both local features and global dependencies of images to better obtain shape and location information from a global perspective. On the other hand, Tri-PANet constrains tumor regions that are prone to mislabeling by a triplanar ensemble framework, with an independent tumour core prediction module. We evaluate our method using publicly accessible brain tumor segmentation datasets BraTS2019 and BraTS2020. The experimental results demonstrate that our propose model effective segmentation the tumor region and consistently outperform state-of-the-art methods.

关键词： Image segmentation Visualization Technological innovation Accuracy Shape Imaging Object segmentation

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A novel three-phase primary-side control topology for high-power IPT system

A novel three-phase primary-side control topology for high-p...

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Wireless Power Week (WPW)

作者： Zhihao He Duleepa Thrimawithana Martin Neuburger Grant Covic Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Faculty of Mobility and Technology Esslingen University of Applied Sciences Goppingen Germany

There is a recognised need for developing multiphase inductive power transmission (IPT) systems to shorten the charging time in the wireless charging field. Researchers have not treated the converter needed to drive the multi-phase coils in much detail. This paper proposes a three-phase integrated boost multilevel converter (TP-IBMC) based on a multi-phase IPT system. There are advantages of multi-level converters and the boost function in TP-IBMC. Using a digitized modulation scheme, it can output a square wave voltage with variable amplitude to regulate the power in the primary. The TP-IBMC includes three bridge arms (a, b, c), each of which contains a DC inductor to achieve energy storage and multiple sub-modules to realize variable levels. The number of sub-modules can be increased or decreased. This paper establishes a three-phase IPT model considering the mutual inductance between phases and discusses the capacitor voltage balancing strategy. A primary-side control IPT simulation model compliant with the SAE J2954 WPT 3 standard is built to verify that TP-IBMC can regulate output power through a digitised modulation scheme, under the fluctuations of coupling factor (i.e., 0.14 - 0.31) and battery voltage (i.e., 280 V - 420 V).

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G2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns

arXiv

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arXiv 2023年

作者： Zhang, Qin Shi, Zelin Zhang, Xiaolin Chen, Xiaojun Fournier-Viger, Philippe Pan, Shirui Big Data Institute College of Computer Science and Software Engineering Shenzhen University China School of Information and Communication Technology Griffith University Queensland Australia

Node classification is the task of predicting the labels of unlabeled nodes in a graph. State-of-the-art methods based on graph neural networks achieve excellent performance when all labels are available during training. But in real-life, models are often applied on data with new classes, which can lead to massive misclassification and thus significantly degrade performance. Hence, developing open-set classification methods is crucial to determine if a given sample belongs to a known class. Existing methods for open-set node classification generally use transductive learning with part or all of the features of real unseen class nodes to help with open-set classification. In this paper, we propose a novel generative open-set node classification method, i.e., G2Pxy, which follows a stricter inductive learning setting where no information about unknown classes is available during training and validation. Two kinds of proxy unknown nodes, inter-class unknown proxies and external unknown proxies are generated via mixup to efficiently anticipate the distribution of novel classes. Using the generated proxies, a closed-set classifier can be transformed into an open-set one, by augmenting it with an extra proxy classifier. Under the constraints of both cross entropy loss and complement entropy loss, G2Pxy achieves superior effectiveness for unknown class detection and known class classification, which is validated by experiments on benchmark graph datasets. Moreover, G2Pxy does not have specific requirement on the GNN architecture and shows good generalizations. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Mohsen Tajdinian Behzad Behdani Harold R. Chamorro Vijay K. Sood Hitachi Energy Research Center Hitachi Energy Västerås Sweden Department of Electrical Sustainable Energy Delft University of Technology Delft The Netherlands School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University Ontario Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Failures of Induction Motors (IMs) can lead to unscheduled downtime and interruption in industry processes. This paper concentrates on the detection of the stator’s inter-turn faults which are one of the most frequent causes of failures in IMs. The proposed detection method is based on a similarity index that uses the current waveform. To be more specific, the proposed algorithm presents a full-cycle sliding-window-based index based on cosine similarity that only uses current signals for detection of the stator’s inter-turn faults. The proposed index cuts the phase difference before/after the disturbance and, as a result, it only depends on the size variations of the current waveform. The proposed method is technically unaffected by non-fault transient conditions including voltage imbalance, voltage sag, voltage swell, and heavy load changes. The performance of the proposed method is validated with numerous simulated scenarios and has good accuracy and speed of convergence.

关键词： Industries Voltage fluctuations Induction motors Accuracy Fault detection Power quality Stators

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A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

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computers, Materials & Continua 2022年第1期70卷 1617-1630页

作者： Khadija Manzoor Fiaz Majeed Ansar Siddique Talha Meraj Hafiz Tayyab Rauf Mohammed A.El-Meligy Mohamed Sharaf Abd Elatty E.Abd Elgawad Department of Information Technology University of GujratGujrat50700Pakistan Department of Software Engineering University of GujratGujrat50700Pakistan Department of Computer Science COMSATS University Islamabad-Wah CampusWah Cantt47040Pakistan Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke-on-TrentUK Advanced Manufacturing Institute King Saud UniversityRiyadh11421Saudi Arabia Department of Industrial Engineering College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

Skin diseases effectively influence all parts of *** and accurate detection of skin cancer is necessary to avoid significant *** manual detection of skin diseases by dermatologists leads to misclassification due to the same intensity and color ***,an automated system to identify these skin diseases is *** studies on skin disease classification using different techniques have been ***,previous techniques failed to identify multi-class skin disease images due to their similar *** the proposed study,a computer-aided framework for automatic skin disease detection is *** the proposed research,we collected and normalized the datasets from two databases(ISIC archive,Mendeley)based on six Basal Cell Carcinoma(BCC),Actinic Keratosis(AK),Seborrheic Keratosis(SK),Nevus(N),Squamous Cell Carcinoma(SCC),and Melanoma(M)common skin ***,segmentation is performed using deep Convolutional Neural Networks(CNN).Furthermore,three types of features are extracted from segmented skin lesions:ABCD rule,GLCM,and in-depth *** transfer learning is used for deep feature extraction,while a support vector machine(SVM)is used for *** results show that SVM outperformed other studies in terms of accuracy,as AK disease achieved 100%accuracy,BCC 92.7%,M 95.1%,N 97.8%,SK 93.1%,SCC 91.4%with a global accuracy of 95.4%.

关键词： AlexNet CNN GLCM SVMs skin disease

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TrustFed: A Reliable Federated Learning Framework with Malicious-Attack Resistance

arXiv

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arXiv 2023年

作者： Su, Hang Zhou, Jianhong Niu, Xianhua Feng, Gang The School of Computer and Software Engineering Xihua University Chengdu China The National Key Lab on Communications University of Electronic Science and Technology of China Chengdu China

As a key technology in 6G research, federated learning (FL) enables collaborative learning among multiple clients while ensuring individual data privacy. However, malicious attackers among the participating clients can intentionally tamper with the training data or the trained model, compromising the accuracy and trustworthiness of the system. To address this issue, in this paper we propose a hierarchical audit based FL (HiAudit-FL) framework, with aim to enhance the reliability and security of the learning process. The hierarchical audit process includes two stages, namely model-audit and parameteraudit. In the model-audit stage, a low-overhead audit method is employed to identify suspicious clients. Subsequently, in the parameter-audit stage, a resource-consuming method is used to detect all malicious clients with higher accuracy among the suspicious ones. Specifically, we execute model audit method among partial clients for multiple rounds, which is modeled as a partial observation Markov decision process (POMDP) with aim to enhance the robustness and accountability of the decision-making in complex and uncertain environments. Meanwhile, we formulate the problem of identifying malicious attackers through a multi-round audit as an active sequential hypothesis testing problem and leverage a diffusion model-based AI-Enabled audit selection strategy (ASS) to decide which clients should be audited in each round. To accomplish efficient and effective audit selection, we design a DRL-ASS algorithm by incorporating the ASS in a deep reinforcement learning (DRL) framework. Our simulation results demonstrate that HiAudit-FL can effectively identify and handle potential malicious users accurately, with small system overhead. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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The Correlation Between Architecture Smells, Package Stability and Reusability: A Case Study

The Correlation Between Architecture Smells, Package Stabili...

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Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mrwan BenIdris Ehab A. Omar Elfallah Marwa Aslam Cheema Mohammed Salah Addin Alsma Tareq Ziad Kadiki Abdelfattah Elbarsha Dep. of Computer Science Faculty of Information Technology University of Benghazi Dept. of Software Engineering Faculty of Information Technology Libyan International Medical University Health Informatics Department Faculty of Public Health University of Benghazi

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. Architecture smells (ASs) can be used to identify any quality issues within the system. To further explore the relationships between ASs, stability, and reusability, Spearman correlation coefficients will be calculated. This paper seeks to examine the connection between architecture smells, software stability, and reusability. To do this, we utilized a dataset created by other researchers and conducted an empirical study of the associations between five types of architecture smells and software stability and reusability. This research is necessary as there is a lack of in-depth research on architecture smells and their relationship to software stability and reusability. We found a relationship between the presence of Feature Concentration in package and the stability and reusability of the package. Also, package's stability is affected by both Cyclic Dependency and Unstable Dependency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable Dependency and reusability. Finally, we found a slight negative correlation between package stability and reusability.

关键词： Correlation coefficient Correlation Codes Conferences computer architecture software systems software development management

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A Secured and Optimized Clustering Approch in Vehicular Ad-hoc Network

A Secured and Optimized Clustering Approch in Vehicular Ad-h...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Imran Khan Fazlullah Khan Asad Ali Bandar Alshawi Ryan Alturki Mohammad Wedyan Computer Science Department Abdul Wali Khan University Mardan KPK Pakistan Faculty of Science and Engineering School of Computer Science University of Nottingham Ningbo China Zhejiang China Department of Computer Science University of Engineering and Technology Peshawar KPK Pakistan Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Computer Sciences Faculty of Information Technology and Computer Sciences Yarmouk University (YU) Irbid Jordan

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Vehicular ad hoc networks (VANETs) take vital role in intelligent transportation systems, but they face challenges due to dynamic network topology, impacting communication efficiency, especially with increasing active nodes. Security and privacy concerns are paramount, necessitating authentication to ensure only legitimate vehicles access the network. Secure vehicular clustering offers a solution, yet the number of clusters affects network dependability and efficiency. To tackle this, we present a secure clustering mechanism using Bat optimization algorithm, mimicking echolocation behavior to optimize clusters. We introduce an authentication technique for cluster heads to admit approved members, enhancing vehicular authentication. Simulations using MATLAB evaluate our approach, showing improved performance in end-to-end delay, overhead, and cluster head count compared to similar methods.

关键词： Privacy Network topology Authentication Vehicular ad hoc networks Security Vehicle dynamics Optimization Intelligent transportation systems Faces Matlab

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