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4th international conference on Recent Innovations in Computing, ICRIC 2021

4th International Conference on Recent Innovations in Comput...

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4th international conference on Recent Innovations in Computing, ICRIC 2021

ISBN: (纸本)9789811688911

the proceedings contain 45 papers. the special focus in this conference is on Recent Innovations in Computing. the topics include: learning-Based Techniques for Assessing Zero-Day Attacks and Vulnerabilities in IoT;a Review of Credit Card Fraud Detection Techniques;an Intensive Literature Review on machine Translation Using Neural Approach;cyber Security and IoT: Attacks and Security Countermeasures;scope and Challenges of Blockchain Technology;web Usage Mining—Process, Tools and Practices;e-Health Systems for Mother and Child Care Domain: A Systematic Review;hierarchical Clustering Based Characterization of Protein database Using Molecular Dynamic Simulation;model to Predict the Crop Cost Using machine learning and Read dataset;a Survey on Scalable Resource Allocation in Cloud Computing;computational Intelligence Techniques for Big data Analytics: A Contemplative Perspective;recent Techniques for Sentiment Analysis on Medical data to Predict Depression: A Review;deep learning in IoT and Edge/Fog Integrated Environments: A Review;challenges in recognition of Kashmiri Script;plantaeK: A Leaf database of Native Plants of Jammu and Kashmir;plummeting Makespan by Proficient Workflow Scheduling in Cloud;web Recommendation by Exploiting User Profile and Collaborative Filtering;Feature Extraction from Radiology Images for Visual Question Answering System Using CNN and BiLSTM Model;superpixels-Guided Background Modeling Approach for Foreground Detection;a Review on Leaf-Based Plant Disease Detection Systems Using machine learning;a Review on machine learning Techniques for the Diagnosis of Cancer;Usability Improvements in E-Governance Application with Simple & Usable Interface Using Citizen-Satisfaction-Model-of-Performance-Related-Pay (CSMPRP);License Plate Detection and recognition Using OpenCV–Python;corpus Creation for Anaphora Resolution in Punjabi Language.

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Exploring the Consistency of Positive and Negative Images In Classification Networks 4

Exploring The Consistency of Positive and Negative Images In...

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4th IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Hu, Bibo Zuo, Wentao Liu, Zhongling Maalla, Allam Guangzhou College of Technology and Business School of Engineering Guangzhou510850 China

ISBN: (纸本)9798350363586

After converting the original sample pictures (positive pictures) to negative images, the retained objects' critical category information can be classified by humans. Still, the general neural network models are weak in recognizing negative images. In artificial intelligence, researchers are more interested in the performance of the models on the original images, and there is little research on negative image samples. To explore the effect of negative image features on model learning, using the visualization of feature maps and comparison of recognition ability in different scenes, three models trained by positive samples set, negative samples set, and mixed samples set are investigated for three image datasets: MNIST, CIFAR10, and ImageNet. the positive and negative samples were found to be consistent in the feature space of the model, enabling the network to fit both positive and negative images. From the accuracy point of view, the addition of negative samples adjusts the deep feature space of the network, resulting in uniform confidence output of positive and negative images for each class and a more compact distribution of samples within categories. From the perspective of adversarial robustness, models that learn positive and negative picture features also show symmetry in adversarial perturbations;moreover, models trained with negative samples can resist transfer attacks of general models to a certain extent. © 2024 IEEE.

关键词： Adversarial machine learning

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4th international conference on Recent Innovations in Computing, ICRIC 2021

4th International Conference on Recent Innovations in Comput...

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4th international conference on Recent Innovations in Computing, ICRIC 2021

ISBN: (纸本)9789811682476

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A Review of the Various machine learning Algorithms for Cloud Computing 4

A Review of the Various Machine Learning Algorithms for Clou...

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4th international conference on Advanced Science and Engineering, ICOASE 2022

作者： Amanuel, Sandy Victor Ahmed, Ibrahim M. Technical College of Informatic Akre Duhok Polytechnic University Information Technology Department Duhok Iraq College of Science University of Mosul Mosul Iraq

ISBN: (纸本)9781665472227

Cloud computing (CC) provides network services on request, especially data storage and processing capacity, without users' specific and direct management. CC recently became a collection of public and private data centers that provide the customer with a shared Internet network. Edge Computing is an emerging computing and knowledge storage model that puts end-users closer together to increase reaction times and save communication power. However, CC and edge computing face protection issues, including customer risk and corporate recognition, that hinder the swift implementation of computing modelling. One solution to this problem, because of its complexity and severity, is machine learning (ML) which consists of researching computational algorithms and naturally advancing knowledge. the problem and solution issues are raised by the overview article that analyses CC safety risks, problems, and solutions that use one or more ML algorithms. Study various ML algorithms, such as controlled, unmonitored, semi-supervised, and enforced training, to solve cloud protection problems. the paper assesses each technique's efficiency based on its characteristics, advantages, and drawbacks. In addition, it will have potential study guidance on safeguarding CC usage and applications. © 2022 IEEE.

关键词： Cybersecurity

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Automatic Inverse Synthetic Aperture Radar Ship Targets recognition using Simple Multiple Kernel learning Automatic ISAR Ship Targets recognition using Simple-MKL 24

Automatic Inverse Synthetic Aperture Radar Ship Targets Reco...

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4th international conference on Big data, Artificial Intelligence and Risk Management, ICBAR 2024

作者： Wu, Yuan Su, Fulin Li, Xue Zhu, Peipei Xie, Xunwei National Key Laboratory of Complex Aviation System Simulation Southwest China Institute of Electronic Technology Sichuan Chengdu China School of Electronics and Information Engineering Harbin Institute of Technology Heilongjiang Harbin China

ISBN: (纸本)9798400709753

In radar automatic target recognition (RATR), inverse synthetic aperture radar (ISAR) image recognition shows its advantages. Due to the limited sample size of ISAR images, support vector machine (SVM), known for its robustness in small sample classification, is often used for ISAR image recognition. For ISAR images of different targets, the single kernel SVM algorithm might lose its robustness. therefore, this paper applies multiple kernel learning (MKL) to ISAR ship target recognition. the process begins with the preprocessing of the ISAR images to suppress Gaussian white noise. then, principal component analysis (PCA) is employed to extract features from the ISAR images. Finally, the Simple-MKL method is used to recognize the samples. Experiments based on simulation data indicate that the method used in this paper improves the accuracy compared to other single-kernel SVM algorithms with different kernel functions. © 2024 Copyright held by the owner/author(s).

关键词： Inverse Synthetic Aperture Radar (ISAR) Radar Automatic Target recognition (RATR) Simple Multiple Kernel learning (SimpleMKL) Support Vector machine (SVM)

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Node Classification in CAD data: A Graph learning Approach for Machining Feature Localization 4

Node Classification in CAD Data: A Graph Learning Approach f...

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4th international conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Bohm, Stefan Andreas Neumayer, Martin Song, Rui Rib, Fabian Knoll, Alois Technical University of Munich Chair of Robotics Artificial Intelligence and Real-Time Systems Munich Germany Rosenheim Technical University of Applied Sciences Research and Development Department Rosenheim Germany Fraunhofer Institute for Transportation and Infrastructure Systems Ingolstadt Germany Rosenheim Technical University of Applied Sciences Faculty of Engineering Sciences Rosenheim Germany

ISBN: (纸本)9798350391183

pattern recognition is becoming increasingly important in many sectors, such as the manufacturing industry. With rising customer demands and advancing technology, product data, often represented as 3D meshes, is becoming highly complex. therefore, intelligent approaches are needed to recognize and localize features in 3D meshes to determine complex manufacturing processes. However, previous approaches solely work on voxel-, pixel-or. STEP-based formats and not directly on mesh-based formats such as STL data. To overcome these issues, we present a configurable data generator and customize graph neural network architectures for the problem of localizing machining features directly in 3D meshes. After comparing the graph neural networks against each other, the best resulting network is compared to an existing state-of-the-Art approach, i.e., SsdNet. Our results indicate that the best-performing graph neural network exhibits superior classification accuracies on meshes with few machining features compared to SsdNet with an average F1 score of 80%. While the accuracy slightly falls behind SsdNet with more features, the graph neural network shows substantially better runtime performance results, which are, on average, 70 times faster. © 2024 IEEE.

关键词： Graph neural networks

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Tool Wear State recognition Based on Multi-source Feature Fusion and Deep learning

Tool Wear State Recognition Based on Multi-source Feature Fu...

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machine learning and Intelligent Systems Engineering (MLISE), international conference on

作者： Ning Song Yuna Yu Tongtongn Han Guihua Xie Desheng Mo Na Li Qilu Institute of Technology Jinan China

ISBN: (数字)9798350375077

ISBN: (纸本)9798350375084

Aiming at the problems of single feature dimension and low accuracy in condition monitoring only from vibration characteristics and tool wear characteristics. In this paper, a tool wear state recognition method based on multi-source feature fusion and deep learning is proposed. the main innovation of our work lies in two aspects: firstly, the tool wear data is processed by multi-source feature fusion method, which effectively fuses multiple sensor signals and improves the efficiency and accuracy of data processing. Secondly, we optimize GRU (Gated Recursive Unit) model by combining whale algorithm for parameter optimization and add XGBoost to improve the prediction performance and robustness. Different milling wear experimental data sets are used to verify the recognition performance of the training model. the experimental results show that compared with the traditional methods, our proposed method has obvious advantages in tool wear state recognition.

关键词： Deep learning Vibrations Training Technological innovation Accuracy machine learning algorithms Whales

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Design and Evaluation of a Transfer learning Model for Ailment recognition in Plant Images

Design and Evaluation of a Transfer Learning Model for Ailme...

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data Engineering and Communication Systems (ICDECS), international conference on

作者： R.Raja Subramanian Marni Srija Gujula Rohini Reddy Mangu Sai Sree Krishna Bathula Lakshmi Prasanna Department of Computer Science and Engineering Kalasalingam Academy of Research and Education

the agriculture sector is still working to mitigate the detrimental effects of plant diseases on crop production and food security. Accurate and prompt disease diagnosis is essential to preventing extensive crop damage. Deep learning, a subfield of machine learning, has generated a great deal of interest and has astounding potential for automating the identification of plant diseases from images. this article presents a comprehensive overview of the advancements, strategies, and potential applications of CNN for the diagnosis of plant diseases.. the paper covers important facets of the deep learning-based methodology, such as dataset curation and collection, picture preprocessing methods, architectural decisions, and training methods. the effectiveness of CNN models for detecting plant diseases is discussed in this article, with a special emphasis on convolutional neural networks (CNNs) as playing a key role.

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Robot Debater: Debate-styled Text Auto-generation System Based on Large Foundation Language Models

Robot Debater: Debate-styled Text Auto-generation System Bas...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Yu Zhu Yijun Ling Xufeng Ling Jie Yang Shanghai Library (Institute of Scientific and Technical Information of Shanghai) Shanghai China Shanghai Xiangming High School Shanghai China School of Artificial Intelligence Shanghai Normal University Tianhua College Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

We use a large foundation language model, which is fine-tuned with debate corpora, to develop a robot debater application. To address the limitations of requiring immense computational power in large base language models, this study takes advantage of the Low Rank Adaption characteristic prevalent in domain expert knowledge. By applying Low Rank Adaption technology and fine-tuning with a dedicated dataset, the computational load is reduced to just one-thousandth of what is needed for a large language model, greatly expanding the application scenarios of robot debaters using large language models. In view of the characteristics of debate competitions, this model can preset a variety of debate scenarios and supports personalized debate processes. It employs intelligent voice recognition technology combined with a multi-channel voice input method, allowing for precise localization of different human debaters and improving the accuracy of voice input recognition. the system can support multiple large-scale language generation models and utilize various different voice broadcasting systems, including male and female voice styles, as well as a range of voice emotions. this model can be applied to debate competitions held in universities, high schools, and various industries. It can support human-machine debates as well as machine-to-machine debates.

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Identification of Alzheimer’s Disease Associated Genes through Explicable Deep learning and Bioinformatic

Identification of Alzheimer’s Disease Associated Genes thro...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Xiangrong Sun Wei Shu Yuxin Zhang Xiansheng Huang Juxia Liu Yuan Liu Tiejun Yang Department of intelligent medicine College of intelligent medicine and biotechnology Key Laboratory of medical biotechnology and translational medicine of Guangxi education department Guilin Medical University Guilin China Department of intelligent medicine College of intelligent medicine and biotechnology Guilin Medical University Guilin China

We utilized interpretable deep learning to identify key genes and potential biomarkers associated with Alzheimer’s disease (AD). AD-related gene expression data from GEO datasets and Alzdata were collected. To create a training set, we performed differential expression analysis to delete low-expressed genes. To explore disease-related genes and potential biomarkers, we employed a pathway-centric deep learning network, which integrated gene expression data with established biological pathways, building upon the foundation of PASNet. Our study employed a pathway-related deep learning model based on PASNet combined with bioinformatic analysis for AD risk prediction, and achieved a good performance (AUC = 0.82, F1 score = 0.73). the model accurately classified AD patients based on gene expression data from the brain tissue while providing interpretability deep neural networks. Potential biomarkers (DYNC1I1, DNAJC1, SCRIB, TFEB, MAPKAPK3) and biological pathways (Senescence-Associated Secretory Phenotype, Transcriptional activity of SMAD2/SMAD3:SMAD4 heterotrimer, Toll-like Receptor Cascades) associated with AD were identified. We have employed a combination of bioinformatics analysis and deep learning models to identify a series of candidate genes associated with the diagnosis of Alzheimer’s disease (AD). Moreover, we have validated their potential as biomarkers through the utilization of external datasets, and finally focus on the five genes DYNC1I1, DNAJC1, SCRIB, TFEB, and MAPKAPK3.

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