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Accurate and Optimized Labelling of Fashion Products through Attention Based Spiking Neural Networks 10

Accurate and Optimized Labelling of Fashion Products Through...

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10th international conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vithyatharshana, N. thrinayani, Yedhoti Kulkarni, Yogeshwar Nitin Singh, Rimjhim Padam Kanchan, Sneha Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering and Computer science Malaysia

ISBN: (纸本)9798350384369

Automation of garment classification has become the backbone of e-commerce, influencing product search, personalization, and customer experience. this paper uses the Fashion MNIST dataset, a rich collection of clothing images to investigate the potential of Spiking Neural Networks (SNNs) for accurate and efficient classification. SNNs are an excellent choice for widespread usage and real-world integration due to their low power consumption and biomimicry. With the intention of revolutionizing product search, personalization, and customer experience, SNNs are made to address style differences, perspectives, and illumination. In order to fully utilize Fashion MNIST, this paper suggests strategic improvements to the SNN architecture. By addressing key challenges and introducing novel optimizations, our research contributes to a more robust and intelligent future for automated fashion product labelling. An SNN model with 93.6% accuracy has been developed as the base model for all the applications for the same. this data-driven approach leads the way to flawless product searches, highly customized recommendations, and a more intelligent fashion technology in future. © 2024 IEEE.

关键词： Deep learning

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learning-Based Collaborative Service Placement for AI-oriented Mobile Edge Computing 11

Learning-Based Collaborative Service Placement for AI-orient...

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11th international conference on Advanced Cloud and Big data, CBD 2023

作者： Li, Wei Li, Miao Liu, Yuezhao Liu, Jiong Cui, Lanlan Nanchang University School of Information Engineering Nanchang China National University of Defense Technology School of Information and Communication Wuhan China Xi'An University of Technology School of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798350345346

Next generation mobile communication technology (6G) intelligent endogenous network deeply integrates network connectivity with artificial intelligence (AI) elements. It is urgent to push AI services to the edge of the network. How to deploy computing intensive AI models in edge devices with limited resources, and consider the collaborative training and inference between edge-edge or edge-cloud is a challenge. the partition of complex edge AI models into small subtasks and the collaborative executions among the edge nodes can efficiently solve this problem. therefore, in this paper, exploiting the dispersive resource across edge nodes and devices, we propose a collaborative service placement strategy efficiently deploying all the sub-services among edge nodes and considering the service migration due to the user's mobility. Moreover, we will explore the trade-offs between different metrics, i.e., accuracy, latency and migration cost, helping to improve the efficiency of deploying AI application at edge. To overcome the unknown system dynamics, we formulate the problem as a combinatorial contextual Multi-armed Bandit (MAB) problem. then based on MAB theory and greedy method, we give a online learning algorithm to cope with the system dynamics. theoretical analysis and evaluations demonstrate the proposed algorithm can effectively learn and adapt to the dynamic network environment and user demands, reduce the latency and migration cost while satisfying the minimal service requirement. © 2023 IEEE.

关键词： Computation theory

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Comprehensive Performance Assessment of Machine learning Techniques in NIFTY-50 Stock Index Prediction 11

Comprehensive Performance Assessment of Machine Learning Tec...

引用

11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Samantaray, Subham Mohapatra, Subhashree Mishra, Manohar Computer Science and Engineering Iter Bhubaneswar India

ISBN: (纸本)9798350350357

In the ever-changing landscape of global finance, the precise forecasting of stock indices has emerged as a pivotal concern for both investors and financial analysts. this study explores the effectiveness of multiple machine learning (ML) based techniques, including Linear Regression (LR), Support Vector Regression (SVR), Adaboost, Random Forrest (RF) and Feedforward Neural Network (FNN), implemented within the MATLAB framework. MATLAB, a versatile and powerful platform, is employed for deploying and evaluating predictive models. Adaboost, known for its ensemble learning approach, iteratively combines weak learners to construct a robust prediction model. Simultaneously, model optimization is achieved through techniques such as grid search. the study utilizes several technical indicators calculated from the historical NIFTY-50 index data to train and validate the predictive models. Performance metrics such as RMSE, MAE, MAPE, R2 errors are employed to evaluate the efficacy of the algorithms. the research results contribute to the growing knowledge base in financial prediction, showcasing the practical application of ML techniques within the MATLAB Software framework. the results not only provide a basis for improved predictions in the NIFTY-50 index but also offer valuable insights for future research in the domain of algorithmic trading and financial market analysis. © 2024 IEEE.

关键词： Adaptive boosting

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Analysis of Eye Disease classification by Fundus Images using Different Machine/Deep/Transfer learning Techniques 4

Analysis of Eye Disease classification by Fundus Images usin...

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4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Sharma, Himanshu Wasim, Javed Sharma, Pankaj Mangalayatan University Computer Science & Engineering Uttar Pradesh India Mangalayatan University Department of Computer Engineering and Applications Uttar Pradesh India Eshan College of Engineering Computer Science & Engineering Mathura India

ISBN: (纸本)9798350307757

the fundus image is a crucial diagnostic tool for diseases like diabetic retinopathy (DR) because it catches a retina, a back of an eye. this issue is most prevalent among diabetics and a leading contributor to blindness, particularly in young adults and those engaged in the workforce. Detecting DR in its early stages is crucial in reducing the likelihood of blindness in patients. An ophthalmologist performed DR screening and looked for lesions on the fundus imaging. intelligent systems have the ability to make early identification of DR more efficient, especially with the recent advancements in machine learning in healthcare. this study compares and contrasts many methods for detecting DR in fundus images. this study lays out methods for improving upon pre-trained architectures such that they can correctly classify fundus images, allowing for early detection and prediction of diabetes. A concatenate model is presented in this comparative study as a method for classifying fundus images using ML and deep/transfer learning. Various model architectures are used, including SVM, Gradient Boosting, Efficient Net, Efficient-Net B4 and B7, MLP, CNN, ResNet, VGG-19, and DNN, among others. the models' accuracy, F1-score, recall, and precision are measured in experimental findings that compare them. Our strategies outperform a single model with results ranging from 84% to 96% accuracy, respectively. they found that our ML- DL-TL methods work well for automated DR classification employing fundus image data in this experiment. © 2024 IEEE.

关键词： intelligent systems

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Comparison of Commercial and Open Source ETL Tools 11

Comparison of Commercial and Open Source ETL Tools

引用

11th IEEE international conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2024

作者： Szilveszter, Milan Molnar, Gyorgy Nagy, Eniko Óbuda University Budapest Hungary Óbuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Széchenyi University Apáczai Csere János Faculty of Humanities Education and Social Sciences Budapest Hungary Óbuda University Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty Budapest Hungary

ISBN: (纸本)9798350350142

the extraction, transformation, and loading (ETL) process is a critical component in data integration. this article conducts a comprehensive comparative analysis between commercial and open source ETL tools, shedding light on their respective attributes, benefits, and drawbacks. the objective is to facilitate data professionals and organizations in making well-informed decisions when selecting ETL tools tailored to their specific requirements. this comparison meticulously assesses both commercial and open source ETL tools, scrutinizing aspects including functionality, performance, scalability, user-friendliness, and extensibility. Furthermore, it considers the ecosystem encompassing these tools, such as available connectors, plugins, and contributions from the community. Real-world case studies and practical user experiences are analyzed to furnish actionable insights into the strengths and limitations of each ETL tool category. Ultimately, the choice between commercial and open source ETL tools hinges on project-specific requisites, financial constraints, and the degree of customization sought. the survey shows that open-source tools can compete with those intended for the commercial market, especially for smaller companies or individual users. © 2024 IEEE.

关键词： data warehouses

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REVISITING GRAPH ADVERSARIAL ATTACK AND DEFENSE FROM A data DISTRIBUTION PERSPECTIVE 11

REVISITING GRAPH ADVERSARIAL ATTACK AND DEFENSE FROM A DATA ...

引用

11th international conference on learning Representations, ICLR 2023

作者： Li, Kuan Liu, Yang Ao, Xiang He, Qing Key Lab of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China

Recent studies have shown that structural perturbations are significantly effective in degrading the accuracy of Graph Neural Networks (GNNs) in the semi-supervised node classification (SSNC) task. However, the reasons for the destructive nature of gradient-based methods have not been explored in-depth. In this work, we discover an interesting phenomenon: the adversarial edges are not uniformly distributed on the graph, and a majority of perturbations are generated around the training nodes in poisoning attacks. Combined with this phenomenon, we provide an explanation for the effectiveness of the gradient-based attack method from a data distribution perspective and revisit both poisoning attack and evasion attack in SSNC. From this new perspective, we empirically and theoretically discuss some other attack tendencies. Based on the analysis, we provide nine practical tips on both attack and defense and meanwhile leverage them to improve existing attack and defense methods. Moreover, we design a fast attack method and a self-training defense method, which outperform the state-of-the-art methods and can effectively scale to large graphs like ogbn-arxiv. We validate our claims through extensive experiments on four benchmark datasets. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Graph neural networks

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Application of Startup Success Prediction Models and Business Document Extraction Using Large Language Models to Enhance Due Diligence Efficiency 11

Application of Startup Success Prediction Models and Busines...

引用

11th international conference on Advanced Informatics: Concept, theory and Application, ICAICTA 2024

作者： Samudra, Vito Christian Satya, Dicky Prima School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798331520311

Startups face extreme uncertainty and high failure rates, posing challenges for investors in identifying promising ventures. this research, based on a case study and interviews at a prominent Indonesian corporate venture capital firm, explores the due diligence process, typically taking 4-6 weeks depending on data completeness. Using Large Language Model (LLM) and Machine learning (ML) technologies developed with the Team data Science Process (TDSP) methodology, the research aims to enhance due diligence efficiency. Key development steps include data integration, ML model creation for startup success classification, and the integration of OpenAI's GPT -4 and Google Search APIs for comprehensive business analysis. the system's dashboard offers features such as pitch deck, financial, market trends, competitor, and founding team analyses, along with startup success prediction using the XGBoost model. this model, deployed via Flask, demonstrated consistent results through cross-validation. Customer acceptance testing, conducted with eight experienced startup investors, yielded a high satisfaction rate of 4.50 out of 5.00, indicating strong approval of the system's effectiveness. © 2024 IEEE.

关键词： Acceptance tests

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Developing A Machine learning-Based Computer Vision Model for External Defect Detection in Camera Components 11

Developing A Machine Learning-Based Computer Vision Model fo...

引用

11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Duong, Tan Van Tran, Hoai Nam Dao, Duc Khanh Dang, Van Hieu Phan, Hoang-Anh Dang, Bao Lam thi thanh, Van Nguyen VNU University of Engineering and Technology Viet Nam Hanoi Unversity of Science and Technology Viet Nam

ISBN: (纸本)9798350355314

In this article, through exploring the operational procedures of domestic component manufacturers, specifically the production process of cameras for technological products, it is evident that post-processing product defect control poses a significant challenge. this process requires intervention from experienced personnel to conduct manual inspections, which has led to decreased productivity and diminished reliability. Based on this scenario, this paper proposes a computer vision model to detect visual defects in camera components. Machine learning and deep learning will be the primary technologies applied to overcome this challenge. the proposed method is evaluated in terms of accuracy and high performance. However, the model still requires further architectural adjustments and increased training data to achieve optimal refinement. the gained results will significantly enhance the effective of industrial camera production. © 2024 IEEE.

关键词： Machine vision

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Empowering Farmers: Federated learning CNN for Accurate Strawberry Leaf Disease 11

Empowering Farmers: Federated Learning CNN for Accurate Stra...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： thapliyal, Nitin Kukreja, Vinay Garg, Navin Mehta, Shiva Anand, John Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India Iimt College of Engineering Uttar Pradesh Greater Noida201310 India

ISBN: (纸本)9798350350357

the project is directed to accurately recognize strawberry leaf disease at different degrees of severity by employing federated learning with the Convolutional Neural Network (CNN). A dataset with four types of clients' leaf pictures was used in the research, each of which was from a different region and used different farming techniques. the trials prove the viability of the decision flash process, which extracts key points from the separate client's data set, thus generating global knowledge. Among the federated averaging methods, choosing macro, micro, and weighted averages of precision, recall, F1-score, support, and accuracy scores across the local datasets is most likely to be considered during the process. the macro-average ranges among all samples between 96.27 and 96.92 percent, while the micro-average accuracy varies from 96.25 to 96.90 percent. the weighted average of accuracy varied from 95.49% to 96.91 % for most cases. Highlights are data about the applicability of this model to generalize the classification task of different strawberry leaf ***, the study tackles the issue of what information from a local level can be aggregated to extract global insights when the federated averaging method is used. the findings indicate a robust and trustworthy illness classification in regions with different geographical settings. Generally, this study provides a scalable and less time-consuming approach for diagnosing common leaf diseases in strawberries through federatedlearning. © 2024 IEEE.

关键词： Diagnosis

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CUTS: NEURAL CAUSAL DISCOVERY FROM IRREGULAR TIME-SERIES data 11

CUTS: NEURAL CAUSAL DISCOVERY FROM IRREGULAR TIME-SERIES DAT...

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11th international conference on learning Representations, ICLR 2023

作者： Cheng, Yuxiao Yang, Runzhao Xiao, Tingxiong Li, Zongren Suo, Jinli He, Kunlun Dai, Qionghai Department of Automation Tsinghua University China China Chinese PLA General Hospital China

Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and high compatibility with emerging deep neural networks. However, most existing methods assume structured input data and degenerate greatly when encountering data with randomly missing entries or non-uniform sampling frequencies, which hampers their applications in real scenarios. To address this issue, here we present CUTS, a neural Granger causal discovery algorithm to jointly impute unobserved data points and build causal graphs, via plugging in two mutually boosting modules in an iterative framework: (i) Latent data prediction stage: designs a Delayed Supervision Graph Neural Network (DSGNN) to hallucinate and register irregular data which might be of high dimension and with complex distribution;(ii) Causal graph fitting stage: builds a causal adjacency matrix with imputed data under sparse penalty. Experiments show that CUTS effectively infers causal graphs from irregular time-series data, with significantly superior performance to existing methods. Our approach constitutes a promising step towards applying causal discovery to real applications with non-ideal observations. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Deep neural networks

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