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International Conference on Signal Processing and Communication Systems, ICSPCS

作者： Irfan Ahmad Abubakar Sharif Aneeka Azmat Muhammad Usman Jamil Maynooth International Engineering College Faculty of Science and Engineering Maynooth University Ireland Maynooth International Engineering College Faculty of Science and Engineering Maynooth University Fuzhou James Watt School of Engineering University of Glasgow United Kingdom College of Electrical Engineering and Computer Science Institute of Information Systems and Application National Tsinghua University Hsinchu Taiwan School of Mechanical and Electrical Engineering Chuzhou University Chuzhou China

ISBN: (数字)9798350389630

ISBN: (纸本)9798350389647

Assistive mobile applications play a pivotal role for visually impaired individuals worldwide. These applications often face challenges in currency recognition due to varying perspectives, inconsistent illumination, and background clutter. This issue is especially pressing in developing countries like Thailand, where there is a notable gap in robust currency recognition systems, particularly for the new Thai currency notes. This study employs a deep learning approach using a convolutional neural network (CNN) to automate the recognition of the new Thai currency notes. Using transfer learning, we fine-tuned the CNN using the Xception model, renowned for its depth-wise separable convolution. The network trained on a meticulously curated dataset comprising 3600 images (without data augmentation) of five different denominations of the new Thai currency (20, 50, 100, 500, and 1000 baht) notes, captured under various conditions. The resulting model achieved an average training accuracy of 99.5% and a validation accuracy of 99.8%. Given its robustness and high accuracy, the model can be integrated into an Android application. Such an application would offer a user-friendly and reliable tool for visually impaired individuals to effortlessly identify the new Thai currency notes in their everyday transactions.

关键词： Deep learning Training Accuracy Face recognition Transfer learning Pressing Robustness Mobile applications Convolutional neural networks Currencies

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Edit-History Vis: An Interactive Visual Exploration and Analysis on Wikipedia Edit History

Edit-History Vis: An Interactive Visual Exploration and Anal...

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Yuhan Guo Qin Han Yuke Lou Yiming Wang Can Liu Xiaoru Yuan Key Laboratory of Machine Perception (Ministry of Education) and School of Intelligence Science and Technology Peking University China School of Electronic Engineering and Computer Science Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China

We propose Edit-History Vis, a visual analytics system designed to facilitate interactive exploration on Wikipedia edit history at a fine-grained level. The examination of detailed changes in Wikipedia articles is crucial for understanding how authors’ perspectives vary and conflict during the collaborative editing process. However, it is challenging to reveal the details while preserving the heterogeneous attributes of revisions, namely the time, content, and editor. The Edit-History Vis system integrates editor and textual changes of revisions by utilizing a force-directed revision graph that groups revisions based on standpoints. Through this revision graph, users can identify and analyze editing events such as edit wars, vandalism, repair, and normal updates. The effectiveness of the system in analyzing the edit history is validated through a qualitative comparison with prior work and a quantitative rating from a user study.

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Expression of Concern for: Utilizing Machine Learning to Develop an Improved Real-Time Data Analysis System

Expression of Concern for: Utilizing Machine Learning to Dev...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Harsimrat Khandari Girija Shankar Sahoo Raja Praveen K N Ritu Shree Umamaheswari B Yashwant V Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

With the constantly growing volume of facts being produced, conventional data evaluation structures are regularly insufficient in terms of presenting an correct overview of the most modern-day data. The utilization of system mastering provides the potential to create an improved real-time records evaluation device this is more able to making experience of and knowledge the records. The development of an progressed actual-time statistics evaluation device utilizing system studying can hugely enhance statistics analysis techniques by means of integrating relevant data and structured analyses of the identical. this will include strategies inclusive of natural language processing and clustering algorithms to perceive trends, sentiment evaluation to understand customer feedback, and predictive analytics to better perceive areas for improvement. Moreover, the realtime statistics analysis system can be further enhanced by means of incorporating AI to make extra correct predictions, as well as reinforcement studying to evolve present models as new statistics is integrated. this would allow the system to learn more efficaciously and modify its algorithms to continually enhance its overall performance. Universal, by using machine studying to develop an advanced actual-time statistics evaluation system, extra correct and comprehensive insights can be generated from information, and groups can use this facts to better tell their choice making. moreover, temporal records evaluation can be advanced with faster response times as a result of the device being able to process records

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Utilizing Machine Learning to Develop an Improved Real-Time Data Analysis System

Utilizing Machine Learning to Develop an Improved Real-Time ...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

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Deep Learning Techniques for Advanced Large-Scale Data Processing

Deep Learning Techniques for Advanced Large-Scale Data Proce...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Mp Sunil Jyoti Shekhawat T. Vanaja Vazeer Ali Mohammed Shailesh J. Thaware Vaishali Singh Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer & Information System Lewis University Illinois USA Department of Computer Engineering Vishwakarma Institute of Technology Pune INDIA Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Deep getting to know techniques have become increasingly more popular due to their potential to improve huge-scale records processing performance. Leveraging specialized hardware which includes GPUs, deep getting to know algorithms can attain mind-blowing pace will increase compared to traditional algorithms. further, deep gaining knowledge of fashions can discover complicated non-linear relationships in big datasets, permitting greater accurate overall performance and less fake predictions. Latest advances in deep mastering have enabled it to be implemented to remedy a plethora of information processing duties, from photo recognition to natural language processing. CNNs, RNNs, and GANs are a number of the maximum famous deep gaining knowledge of fashions used in tackling complex data processing obligations. agencies consisting of Google, fb, and Amazon use these sophisticated frameworks to build predictive models that help improve numerous programs. Deep getting to know strategies offer exquisite ability to take massive-scale information processing to the following stage. corporations are increasingly more becoming privy to the emerging opportunities, and actively investing in deep mastering to liberate the untapped strength of their information.

关键词： Deep learning Accuracy Scalability Information processing Predictive models Data processing Prediction algorithms Hardware Natural language processing Optimization

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FedHiDiC: High-Dimensional Heterogeneous Data Condensation and Distillation Contrastive Learning in Federated Learning

FedHiDiC: High-Dimensional Heterogeneous Data Condensation a...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ming Jiang Yun Li Feng Zhang Biao Guo Ping Qin Shuo Zhang Yao Lu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin Guangxi China Metaverse Application Engineering Center Nanning Guangxi China Guangxi Institute of Digital Technology Nanning Guangxi China School of Information Engineering Guangxi Polytechnic of Construction Nanning China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In response to key challenges in federated learning, particularly those concerning the heterogeneity of locally distributed data which engenders client drift and the problematic occurrence of dimensionality collapse, we propose FedHiDiC: high-dimensional heterogeneous data condensation and distillation contrastive learning. A key idea of FedHiDiC is to integrate mean squared error, standard deviation, and covariance as regularization terms in local training to control parameter norm growth and bolster the capacity to handle high-dimensional, heterogeneous data. Another key idea is to utilize the similarity of representations between models to correct for local training, which reinforces intra-class compactness and inter-class distinction and reduces model variability through knowledge distillation from the global to local models, combined with contrastive strategies. Our experiments on CIFAR10 and CIFAR100 datasets show that FedHiDiC achieves significant performance improvements, surpassing FedAvg, FedProx, and MOON by 2.9% to 4.0% in accuracy, which confirms the effectiveness of FedHiDiC in tackling heterogeneity challenges.

关键词： Training Accuracy Federated learning Moon Distributed databases Contrastive learning Data models Complexity theory Standards Cybernetics

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Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation

arXiv

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arXiv 2023年

作者： Chen, Ziyang Pan, Yongsheng Ye, Yiwen Cui, Hengfei Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China School of Biomedical and Engineering ShanghaiTech University Shanghai201210 China

Although recent years have witnessed the great success of convolutional neural networks (CNNs) in medical image segmentation, the domain shift issue caused by the highly variable image quality of medical images hinders the deployment of CNNs in real-world clinical applications. Domain generalization (DG) methods aim to address this issue by training a robust model on the source domain, which has a strong generalization ability. Previously, many DG methods based on feature-space domain randomization have been proposed, which, however, suffer from the limited and unordered search space of feature styles. In this paper, we propose a multi-source DG method called Treasure in Distribution (TriD), which constructs an unprecedented search space to obtain the model with strong robustness by randomly sampling from a uniform distribution. To learn the domain-invariant representations explicitly, we further devise a style-mixing strategy in our TriD, which mixes the feature styles by randomly mixing the augmented and original statistics along the channel wise and can be extended to other DG methods. Extensive experiments on two medical segmentation tasks with different modalities demonstrate that our TriD achieves superior generalization performance on unseen target-domain data. Code is available at https://***/Chen-Ziyang/TriD. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Deep-sea Nodule Mineral Image Segmentation Algorithm Based on Pix2PixHD

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computers, Materials & Continua 2022年第10期73卷 1449-1462页

作者： Wei Song Haolin Wang Xinping Zhang Jianxin Xia Tongmu Liu Yuxi Shi School of Information and Engineering Minzu University of ChinaBeijing100081China Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural ResourceGuangzhou510300China National Language Resource Monitoring&Research Center of Minority Languages Beijing100081China School of Ocean Science China University of GeosciencesBeijing100191China Department of Buoy Engineering South China Sea Marine Survey and Technology CenterGuangzhou510300China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNew Jersey07102USA

Deep-sea mineral image segmentation plays an important role in deep-sea mining and underwater mineral resource monitoring and *** application of artificial intelligence technology to deep-sea mining projects can effectively improve the quality and efficiency of *** existing deep learning-based underwater image segmentation algorithms have problems such as the accuracy rate is not high enough and the running time is slightly *** order to improve the segmentation performance of underwater mineral images,this paper uses the Pix2PixHD(Pixel to Pixel High Definition)algorithm based on Conditional Generative Adversarial Network(CGAN)to segment deep-sea mineral *** model uses a coarse-to-fine generator composed of a global generation network and two local enhancement networks,and multiple multi-scale discriminators with same network structures but different input pictures to generate highquality *** test results on the deep-sea mineral datasets show that the Pix2PixHD algorithm can identify more target minerals under certain other *** evaluation index shows that the Pix2PixHD algorithm effectively improves the accuracy rate and the recall rate of deep-sea mineral image segmentation compared with the CGAN algorithm and the U-Net *** is important for expanding the application of deep learning techniques in the field of deep-sea exploration and mining.

关键词： Deep-sea mineral image segmentation generative adversarial network

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Enhancing Cloud Data Security with Machine Learning Through the Analysis of Random Forest, Deep Neural Network, and Q-Learning Approaches

Enhancing Cloud Data Security with Machine Learning Through ...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Sreedevi Kadiyala G Sujatha Afreen Fatima Mohammed Lucky Gupta Praveen Kumar Harishchander Anandaram Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Hyderabad India Department of Computer Science and Engineering Kommuri Pratap Reddy Institute of Technology Hyderabad India Department of CSE-DS CVR College of Engineering Hyderabad India Department of Management Studies Raj Kumar Goel Institute of Technology Ghaziabad India Department of Computer Science & Application Patna Womens College Patna India Department of Artificial Intelligence Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

This research outlines the cloud data security when using machine learning techniques - Random Forest, Deep Neural Networks, and Q-Learning to prevent unauthorized data transfers and leaks. The major findings point to the fact that DNN showed a higher level of prediction capabilities in comparison with Random Forest - 95% of overall accuracy as opposed to 92 %. It is particularly important to consider AVC-ROC of Random Forest, which is 0.96, making it the most reliable model. However, Q-Learning appears to be less accurate with 88% yet more effective when it comes to a cumulative reward and a policy optimization - features that are vital for a changing environment of cloud servers. The findings of the research make a significant contribution to the field of cloud data security, showing the effectiveness of advanced machine learning models in terms of identifying and mitigating security breaches. This, in turn, creates the opportunity of implementation of these techniques into security frameworks for enhancing the resilience and efficiency of the latter. The recommendations for further research lie in the area of hybrid models creation, in particular, the models that would be able to utilize the positive sides of all three techniques. Moreover, the results would be more generalized with a significantly larger dataset comprising diverse cloud environments and threat situations. Finally, the investigation of the models described in a real-time setting and large cloud-based systems may be suggested for further research, given that these characteristics are essential for an effective practical deployment helping resist emerging threats.

关键词： Training Q-learning Accuracy Data security Artificial neural networks Resists Data transfer Data models Servers Random forests

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Breast Cancer Classification in Mammograms Using Support Vector Machines (SVMs) and Histogram of Oriented Gradients (HOG)

Breast Cancer Classification in Mammograms Using Support Vec...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Choudhry, Rashmi Sivadharshini, B. Dua, Madhav Nirmala, V. Husain, Saif O. Arunkumar, R.S. Ies University Department of Information Technology M.P. Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Karpagam Academy of Higher Education Coimbatore Faculty of Engineering Department of Science and Humanities Tamilnadu India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Saveetha School of Engineering Department of Cse Chennai India

ISBN: (纸本)9798350390759

This work focuses on breast cancer classification in mammograms using SVMs classifier and histogram of oriented gradients features. The envisioned system is designed in such a way to enhance the diagnostic precision through integration of the virtues of both SVMs and HOG in identifying discriminative characteristics of breast tissue. Special emphasis was given on comparing and testing the performance of the intended model with other models on a public mammogram dataset containing both malignant and benign cases;the optimized SVM model provided an accuracy of 92%. 5%, sensitivity of 91. 0%, specificity of 93. 74 (fig 5) and 8%, and an AUC-ROC of 0. 95. This study shows that the method is efficient in discriminating between the malignant and benign first axillary lymph node tissue. The developed CAD system was found to possess the prospect of becoming a valuable support system for radiologists in the early detection of breast cancer. © 2024 IEEE.

关键词： Lung cancer

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