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3rd international conference on Innovations in Computer Science and Software Engineering, ICONICS 2022

3rd International Conference on Innovations in Computer Scie...

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3rd international conference on Innovations in Computer Science and Software Engineering, ICONICS 2022

ISBN: (纸本)9781665461412

The proceedings contain 28 papers. The topics discussed include: experimentation for decentralized resource-based multi-pool mining in Ethereum blockchain;performance enhancement in agriculture sector based on image processing;frameworks, applications and challenges in streaming big data analytics: a review;prevention and safety strategies for road accidents by using evolutionary algorithms;intrusion detection using deep learning techniques;analyzing applicant’s pre-admission data and predicting applicant’s performance in pre-admission test using data mining techniques;quantum computing and machine learning algorithms - a review;real-time violence detection using deep learning techniques;a study on the e-commerce trends using data analysis;and a systematic review on the motivations of cyber-criminals and their attacking policies.

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Research on Medical Image Classification for Oral Cancer 3rd

Research on Medical Image Classification for Oral Cancer

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Wang, Kunkun Shi, Linjun Gu, Chaochen Mo, Fan Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Oral Mucosal Diseases Ninth People’s Hospital Shanghai Jiaotong University School of Medicine Shanghai China Shanghai Key Laboratory of Stomatology and Shanghai Research Institution of Stomotology National Clinical Research Center of Stomatology Shanghai China

ISBN: (纸本)9789819624676

In recent years, deep learning has made significant progress in medical imaging, deepening the crossover between the medical and industrial fields. However, not all medical images are suitable for deep learning neural networks;simultaneously, some medical images are difficult to collect and preprocess. In this paper, we reconstruct the image information collected by dental clinicians over many years, normalizing it with diagnostic results as the gold standard. On this basis, we explore the applicability and potential of deep learning on complex medical images. Further, we compare the performance of multiple models on this dataset and visualize the learning outcomes, demonstrating that deep learning networks exhibit good learning capabilities on certain images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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An Explainable AI Framework for Treatment Failure Model for Oncology Patients 3rd

An Explainable AI Framework for Treatment Failure Model for ...

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21st Artificial Intelligence in Medicine (AIME) conference / 3rd international Workshop on Explainable Artificial Intelligence in Healthcare (XAI-Healthcare) / 1st international Workshop on Process mining Applications for Healthcare (PM4H)

作者： Zaidi, Syed Hamail Hussain Hashmat, Bilal Farooq, Muddassar CureMD Res 80 Pine St 21St Floor New York NY 10005 USA

ISBN: (纸本)9783031543029;9783031543036

The black box nature of current AI models has raised serious concerns about accountability, bias and trust in the models that might undermine their relevance and usefulness in the field of medicine where human lives are at risk. AI in medicine has the ability to derive meaningful inferences from real world data - an emerging school of thought namely Real World Evidence (RWE) studies - that can assist medical practitioners to improve evidence based quality of care. In the field of oncology, the accuracy and performance of inference models are as important as clinically relevant and sound explanations of the inference. In this paper, we present an Explainable AI (XAI) framework for our AI model that predicts the suitability of a chemotherapy treatment at the time of its prescription based on RWE. The framework provides explanations both for a specific patient and also for the model. It provides explanations like feature analysis, counterfactual, and top risk factors that contribute to a treatment failure. As a result, the framework adds an explainability layer between treatment failure predictive model and oncologists, thereby enabling evidence based assistance to oncologists in designing chemotherapy plans.

关键词： Explainability machine learning Model Agnostic Oncology

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A Comprehensive Assessment on Predictive Analytics using data mining Techniques 3

A Comprehensive Assessment on Predictive Analytics using Dat...

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3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Vaidehi, B. Arunesh, K. Sri S.Ramasamy Naidu Memorial College Department of Computer Science Sattur India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Educational data mining is a fast emerging multidisciplinary field that reveals unexpected patterns in educational organizations. Considering the issues and demanding situations faced through instructional establishments in studying people overall performance and enhancing their instructional management, the diverse automated studying strategies have been examined. In this work, more Research contribution were reviewed and evaluated in revealing alternative ways for evaluating students' educational attainment to improve quality and support students in attaining remarkable achievements in their academics. The most typical applications of educational data mining encompass: enhancing the study process, intensifying course completion, assisting students in curriculum selection, student profiling, classifying factors that predispose to dropout, student targeting, curriculum planning, predicting student performance, and as a support for decision-making at the time of student enrolment. Predictive analytics is a machine learning and data mining method that is commonly utilized in educational data mining, to foresee expected future trends, events, and behaviors based on prior data. As a result of data mining algorithms, academic establishments will be able to make people academic outcomes accessible in aims to enhance the quality of learning they deliver. © 2022 IEEE.

关键词： data mining

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A Sample-Quality-Based Imitation Approach for Offline Reinforcement learning 3rd

A Sample-Quality-Based Imitation Approach for Offline Reinfo...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Li, You Liu, Juntao Zeng, Cheng Tang, Hainan Gong, Hui Wuhan Digital Engineering Institute Wuhan430205 China Hubei University Wuhan430062 China

ISBN: (纸本)9789819624676

The objective of offline reinforcement learning is to derive an optimal policy using a static dataset. Due to the estimation errors of out-of-distribution (OOD) action value, most offline reinforcement learning algorithms constrain the policy within the actions in the dataset. TD3 + BC is one of these methods to alleviate OOD problem. In TD3 + BC, the agent employs behavior cloning to align its policy closely with that found in the dataset. Although TD3 + BC performances well in some environments, it is too conservative. TD3 + BC simply imitates the existing policy in the dataset. It disregards the quality of the state-action pairs within the dataset. This leads to a failure to learn good policies. Therefore, in this paper, a sample quality based imitation approach for offline reinforcement learning (SQIRL) is proposed. In the proposed method, the quality of each training sample in the dataset is evaluated, and then the high-quality samples are imitated. Therefore, the proposed method not only alleviates the OOD problem as original TD3 + BC, but also learns a better policy. This algorithm is compared with the existing methods, such as imitation learning methods, policy regularization methods, uncertainty penalty methods, and policy constraints methods. It is found that this algorithm outperforms the comparison methods in the MuJoCo environment. It is important to highlight that this approach is especially effective in scenarios where the overall data quality is low. For example, the average normalized rewards of the proposed algorithm are 200%, 181% higher than those of TD3 + BC in the Random dataset of halfcheetah and hopper environment. Meanwhile, in the Full-Replay dataset of halfcheetah, hopper and walker2d environments, compared with the TD3 + BC, the average normalized rewards obtained by the proposed method increase by 81.4%,157%, and 21.8%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Fault Diagnosis in Micro Grid Using Wavelet Transform in Combination with machine learning 3

Fault Diagnosis in Micro Grid Using Wavelet Transform in Com...

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3rd Odisha international conference on Electrical Power Engineering, Communication and Computing Technology, ODICON 2024

作者： Saiprakash, Chidurala Kumar Joga, S Ramana Sowmya, Bera Krishna, Teki Vamsee Narayana, V. Lakshmi Manoj, Vasupalli Sr University Department of Electrical and Electronics Engineering Warangal India Dadi Institute of Engineering and Technology Eee Department Anakapalle India Department of EEE Tadepalligudem India Dadi Institute of Engineering and Technology Bs&h Department Anakapalle India Gmrit Eee Department Andhra Pradesh Rajam India

ISBN: (纸本)9798350354379

Microgrids, which are defined by their decentralized methods of generating and distributing electricity, have a vital role in improving the resilience and sustainability of energy systems. Ensuring the stability and efficiency of micro grids relies heavily on the identification of faults. Conventional fault detection techniques often lack the necessary sophistication to effectively manage the intricacy and variety of contemporary micro grid systems. This paper focuses on the issue of fault identification in micro grids. It presents a new method that combines wavelet transform and data mining approaches to tackle this difficulty. The goal is to provide a reliable approach that can accurately detect and categorize defects, resulting in reduced downtime and enhanced system performance. The wavelet transform allows for the analysis of signals by breaking them down into several frequency components, thereby offering a comprehensive depiction of the dynamics of a system. The suggested technique provides various benefits over existing fault detection methods, which often depend on unsophisticated threshold-based approaches. As a consequence, there is an enhancement in precision and dependability, which allows for prompt detection and resolution of errors, while reducing interruptions in the provision of energy. Moreover, the use of sophisticated data analytics improves the capability to distinguish between typical operational circumstances and distinct fault situations, hence minimizing inaccurate alerts and enhancing the efficiency of the system. The effectiveness of the suggested technique in improving the accuracy of fault detection and assuring the dependable functioning of micro grids is confirmed via simulation results. © 2024 IEEE.

关键词： Artificial intelligence

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An Asynchronous Robust Federated learning Approach for data Value in IoT 3rd

An Asynchronous Robust Federated Learning Approach for Data ...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Liang, Hua Jin, Min Yan, Hua Han, Shihai Li, Wei Yang, Zhigang Wang, Zhuotong State Grid Chongqing Electric Power Company Electric Power Research Institute Chongqing401123 China Digitization Department State Grid Chongqing Electric Power Company Chongqing400015 China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing Chongqing400065 China School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing Chongqing400065 China

ISBN: (纸本)9789819624676

The heterogeneity of IoT devices, resource bottlenecks, and security risks present challenges to the robustness and privacy of federated learning. The difference in the value of IoT data further weakens the robustness of the model. Additionally, attack methods such as Byzantine attacks and privacy inference attacks escalate the risks and vulnerabilities of federated learning models. To address these issues, we propose an asynchronous robust federated learning method based on data value. Firstly, an asynchronous grouping robust aggregation (AGRA) algorithm is designed and combined with a cosine anomaly filter to eliminate abnormal gradients. Simultaneously, an inter-group delay aggregation strategy oriented towards data value is proposed. Secondly, an efficient and robust aggregation protocol supporting asynchronous grouping is constructed to significantly reduce communication overhead while safeguarding data privacy. Finally, through data grading and secure storage adjustment using differential privacy, high-value data can be maximized, thereby enhancing the efficiency and performance of federated learning while protecting privacy. Results show that the algorithm can significantly improve accuracy by 0.9% to 65.8% in the presence of stragglers, while also reducing communication overhead by 4.2% to 5.4%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Deep learning-based Sentiment Analysis of Movie Reviews 3

Deep Learning-based Sentiment Analysis of Movie Reviews

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3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Lou, Yuqi School of Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9781510664814

Sentiment analysis, sometimes regarded as sentiment classification or opinion mining, can be utilized to assess public perceptions of certain products, occasions, people or ideas. There is a wide range of applications for sentiment analysis, including detecting the overall sentiment of text documents, identifying positive and negative opinions expressed in texts, and predicting the future stock market trend based on social media sentiment. In this study, the convolutional neural networks are employed to categorize the sentiment of English movie reviews, i.e., the CNNs are utilized to detect whether a review is positive or negative. Two text representation techniques, the bag-of-words model and TF-IDF, are applied to convert the movie reviews into numerical representation before transmitting the processed data to the CNN model. According to the model's prediction results on the testing set after training, the text processed with the bag-of-word model achieves a higher classification accuracy of 84.84% in the proposed CNN framework, while the TF-IDF for text processing has better precision of 88.96%. © 2023 SPIE.

关键词： Sentiment analysis

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machine learning Techniques For Software Component Reusability 3

Machine Learning Techniques For Software Component Reusabili...

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3rd international conference for Emerging Technology, INCET 2022

作者： Khan, Feroz Lingala, Gowtham Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Computer Science and Engineering Andhra Pradesh Guntur522502 India

ISBN: (纸本)9781665494991

Software reuse has considerably reduced the products time to market with increased reliability and productivity. Widespread use of software reuse, agile software development, User stories and component-based development methods can be observed nowadays. To reap the advantages of software reuse developers can access it from the repositories. The repositories are generated through different techniques used in data mining. In order to predict the reusability of a component machine learning techniques is used. This article provides a method of integration of agile, user stories and component-based developments methods using artificial neural network. The factors that determine the reusability of software are studied and corresponding software metrics are input to the proposed classifier. The metrics used for the study are Cyclomatic Complexity (CC), Design Structure Quality Index (DSQI), Cohesion, weighted method per class (WMP), Bugs, Coupling. Depth Inheritance Tree (DIT), Number of children (NOC). The classifier was tested with some standard classifiers namely Random Forest, Logit Boost, J48 and ESRT used in machine learning and the accuracy achieved is 91.8%. © 2022 IEEE.

关键词： Computer software reusability

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A Review of Multi-Label Feature Selection 3

A Review of Multi-Label Feature Selection

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3rd international conference on Computer, Big data and Artificial Intelligence, ICCBDAI 2022

作者： Wang, Xiujuan Zhou, Yuchen Beijing University of Technology Faculty of Information and Technology Beijing100020 China Beijing University of Technology Beijing-Dublin International Technology Beijing100020 China

Feature selection has become a vital issue in data mining and machine learning. But some challenges have been outstanding when trying to improve the performance of feature selection, such as small sample, uncertain classes, complex features, complementation and redundancy between each feature. In this paper, firstly the background of feature selection is introduced. Then we have presented a new perspective to analyze multi-label feature selection and provided typical papers on different classifications. To further analyze these algorithms, evaluation criterion on results of multi-label feature selection is summarized. Finally, some reflects on research directions, future works and conclusions are organized. © Published under licence by IOP Publishing Ltd.

关键词： Feature Selection

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