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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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2023 4th international conference for Emerging Technology, INCET 2023

2023 4th International Conference for Emerging Technology, I...

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4th international conference for Emerging Technology, INCET 2023

ISBN: (纸本)9798350335750

the proceedings contain 411 papers. the topics discussed include: artificial intelligence energy efficiency in low power applications;deep CNN: classification of known classes and unknown classes based on Softmax prediction probabilities;an efficient decision support system for stock market prediction using technical indicators as features by machine learning approaches;synopsis creation for research paper using text summarization models;smart intelligent system that can code like a human being;network intrusion detection system using reinforcement learning;simulation and analysis of a mems based micromirror system for deployment in optical beam steering systems used in experimental quantum computing;automated breast density assessment using image processing techniques;language agnostic program conformance analysis;power saving in multi-hop device to device communication;securing data storage in cloud after migration using immutable data dispersion;mental health analysis using deep learning of social media data gathered using chrome extension;indoor violence detection using lightweight transformer model;indexing and segmentation of video contents: a review;and diabetes prediction using logistic regression and rule extraction from decision tree and random forest classifiers.

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Domain Adaptation in Color-Shifted Datasets through Adversarial learning Balancing 4

Domain Adaptation in Color-Shifted Datasets Through Adversar...

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4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Hidayaturrahman Trisetyarso, Agung Kartowisastro, Iman Herwidiana Budiharto, Widodo School of Computer Science Bina Nusatara University Mathematics Department Jakarta11480 Indonesia Bina Nusatara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Computer Engineering Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Due to the disparity in the levels of difficulty presented by the several tasks, doing domain adaptation in an adversarial way may result in an imbalanced learning process. In the MNIST dataset, this phenomenon also manifests itself in the form of domain adaptation for color-shifted distribution. In this particular situation, the domain classifier has a higher tendency to fit more quickly, but the category classifier fits quite poorly in the learning process. In order to address this problem, a new hyper-parameter has been added to the loss function in order to strike a compromise between the learning speed of the domain and the categorical classifier. By using this technique, the categorical classifier may better match the data while still maintaining the same level of performance as the domain classifier. In order to determine whether or not making use of this hyper-parameter is useful, the phenomena in question is examined using three distinct color-shifted settings. Following the evaluations, it was discovered that the newly introduced hyper-parameter is capable of coping with imbalanced learning while simultaneously engaging in domain adaptation. © 2023 IEEE.

关键词： Color

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A Heterogeneous Network Model Based Analysis and Detection of RNA Enabling Convolutional Neural Network 4

A Heterogeneous Network Model Based Analysis and Detection o...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Priyatharsini, G. Soniya Aruna, M. Myneni, Madhu Bala Kumar, P. Kiran Anil Kumar, D.B. Singh, Vijay Dr. M.G.R. Educational and Research Institute Department of Computer Science and Engineering Tamil Nadu Chennai600095 India Nitte Meenakshi Institute of Technology Department of Electrical and Electronics Engineering Bangalore India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Cvr College of Engineering Department of Computer Science and Engineering Telangana Ibrahimpatnam India School of Electrical and Electronic Engineering Reva University Karnataka Bengaluru India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350300888

RNA is an important target in the research of genetic diseases and disorders since it is essential for the process of gene expression and control. Convolutional neural networks (CNNs) and other machine learning-based methods for analyzing RNA sequences have recently attracted more attention. But reliable analysis and detection are significantly hampered by the enormous variability of RNA sequences. In this research, a brand-new heterogeneous network model for CNN-based RNA analysis and detection is provided. this model accounts for the wide range of RNA sequence properties, such as length fluctuation, secondary structure, and chemical changes. A unified model that incorporates these features is created by combining 1D and 2D convolutional layers. Multiple datasets, including synthetic and real-world datasets, were used to assess proposed model's performance. the outcomes show that the model performs better in terms of accuracy, sensitivity, and specificity than current state-of-the-art approaches. Furthermore, the model's performance is greatly enhanced with the addition of 2D convolutional layers and attention processes, according to rigorous studies to assess the contributions of each component of the model. Overall, the heterogeneous network model offers a reliable and precise method for analyzing and identifying RNA sequences, which has significant ramifications for comprehending hereditary illnesses and creating targeted therapies. © 2023 IEEE.

关键词： machine learning

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machine learning and Data Privacy in Human Resource Management 4

Machine Learning and Data Privacy in Human Resource Manageme...

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4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Manoharan, Geetha Jalaja, V. Sathe, Manoj A. Neetika Lourens, Melanie Suresh, K. School of Business Sr University Telangana Warangal India Reva University Bengaluru560064 India Pes Modern Institute of Business Studies Computer Science Pune India School of Management Ims Ghaziabad India Durban University of Technology South Africa Sri Venkateswara College of Engineering TamilNadu Chennai India

ISBN: (纸本)9798350393248

the incorporation of machine learning (ML) for recruiting has been one of the primary subjects in human resources because ML software investigated the first recruitment software and discovered that utilising technology enhances their productivity at work, speed, and makes the method simpler. In addition to developing and testing a prototype of the system for the activities it is intended to accomplish, this study intends to highlight the significance of employing Al in the recruitment process for the Indian Police Academy. this article has three goals: to evaluate the necessity for ML in the organization's hiring procedures, to evaluate the degrees of adoption of this technology, and, ultimately, to look at the incidence of complaints during such important organisational activities. Additionally, in order to increase the validity and trustworthiness of the study's findings, a survey research design is used, as well as a triangulation of qualitative and quantitative approaches. © 2023 IEEE.

关键词： Human resource management

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Financial distress Prediction based on deep learning model 4

Financial distress Prediction based on deep learning model

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4th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Sun, Yongliang Dongying Vocational Institute Dongying257091 China

In order to better solve the problem that the traditional prediction model can not effectively describe the causes of enterprise financial crisis, this article uses deep learning model to further improve the effectiveness of enterprise financial distress prediction. To this end, this article selects A-share (non-financial industry) listed companies in Shanghai and Shenzhen from 2001 to 2018 as ST sample companies, the financial distress prediction model of listed companies including 23 financial indicators based on deep learning was constructed, and the BP algorithm was used to test and verify the model. the prediction accuracy of the model is 78.33%, and the accuracy of predicting ST company is 82%, which verifies the feasibility of the model in predicting financial distress. © 2024 the Authors.

关键词： Prediction models

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Social Recommendation Algorithm Based on Fusion Graph Convolutional Neural Network and Gated Recurrent Unit Network 4

Social Recommendation Algorithm Based on Fusion Graph Convol...

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4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Ding, Di Wang, Hui Li, Zhiwen Huaxin Consulting Design & Research Institute Group Customer Department Hangzhou China China Ordnance Industry Computer Application Technology Institute Internet and Information Technology Department Beijing China

ISBN: (纸本)9798350373233

this paper proposes a fusion model named FGCN-GRU (Fusion Graph Convolutional Neural network and Gated Recurrent Unit network) to predict users' ratings on items. Firstly, FGCN-GRU constructs a new graph convolutional neural network by regarding users' ratings on items as relative ratings and introduces the GRU network by considering the fact that the user's interest in items will change over time. then the improved graph convolutional neural network, GRU network and users' social network are used to model users, items, and social relations respectively. the experimental results on the datasets of Ciao and Epinions show that the recommendation performance of the proposed model is better. © 2023 IEEE.

关键词： Convolutional neural networks

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machine learning-Based Cardiovascular Disease Prediction 1

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4th international conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Kumar, Sudhanshu Yadav, Himanshu Kaur, Mandeep Goyal, Mayank Kumar Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (数字)9789819736904

ISBN: (纸本)9789819736898

In contemporary times, cardiovascular diseases have emerged as a significant and critical cause of mortality for individuals. therefore, it would be a boon for human existence that there exists a technology with high accuracy capable of detecting these diseases at an earlier phase but it has emerged as a challenge for data processing techniques. It has been surveyed that machine learning algorithms are capable of analysing, predicting and generating judgements. Due to advancements in IoT a variety of sensors had been developed that had contributed towards the development of better ML methods. In few articles it is observed that cardiovascular diseases are anticipated by examining the symptoms. therefore, using machine learning techniques, a new and better solution in this domain is proposed that can predict cardiovascular diseases with better accuracy. the proposed article examines the probability of a cardiovascular disease in humans and classifies patient risk levels using various data mining techniques. this article brings out the analysis of various machine learning-based algorithms that are implemented to predict cardiovascular diseases. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Medical Imaging Analysis Using machine learning By Design thinking Approach 4

Medical Imaging Analysis Using Machine Learning By Design Th...

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4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Devi, N. Ramya Revathi, K. Lekhaa, T.R. Aravindhan, K. Kavinkumar, K. Sarumathi, U. Sns College Of Engineering Department Of Information Technology Tamil Nadu Coimbatore India Reva University Department Of Computing And Information Technology Bangalore India Sns College Of Engineering Department Of Artificial Intelligence And Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384628

Breast cancer is a significant global health concern and a leading cause of cancer-related morbidity and mortality among women. Early detection and accurate prediction of breast cancer are crucial for improving outcomes and reducing the impact of this disease. the development of predictive models and risk assessment tools has become an important area of research, as they can help healthcare professionals identify individuals at a higher risk of developing breast cancer and tailor screening and prevention strategies accordingly. this introduction provides an overview of breast cancer prediction, highlighting the importance of the topic and the motivation behind predictive models. It sets the stage for a more comprehensive exploration of the subject and the various methods, technologies, and factors involved in breast cancer prediction. © 2023 IEEE.

关键词： machine learning

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machine learning Model to Detect Chronic Leukemia in Microscopic Blood Smear Images 1

Machine Learning Model to Detect Chronic Leukemia in Microsc...

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1st IEEE international conference on Contemporary Computing and Communications, InC4 2023

作者： Patil, Ashwini P. Hiremath, Manjunath Department of Computer Science Bengaluru India CMR Institute of Technology Department of Computer Application Bengaluru India

ISBN: (纸本)9798350335774

Chronic leukemia is a slow-progressing form of disease, If not diagnosed on time can progress and increase the risk of life-threatening complications. It is essential to develop a fully automated system to recognize and categorize type of leukemia for proper evaluation and treatment. this paper aims to provide a machine learning model to identify and classify chronic lymphocytic leukemia, chronic myeloid Leukemia and healthy cells. Digital microscopic blood smear images were automatically cropped into single nucleus and segmented using watershed algorithm. Grey level co-occurrence matrix (GLCM) and geometrical features were extracted from the segmented nucleus images and random forest algorithm is used to classify chronic leukemia and healthy cells. this prognosis aids pathologists and physicians in identifying leukemic patients early and selecting the most effective course of action. © 2023 IEEE.

关键词： machine learning

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Automated Mango Type Classification Using CNN-SVM Ensemble: A Deep learning Approach for Quality Control and Market Analysis in the Mango Industry 4

Automated Mango Type Classification Using CNN-SVM Ensemble: ...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Yashu Kukreja, Vinay Madan, Karun Chitkara University Institute of Engineering and Technology Chitkara University Punjab India MTSM College for Women Punjab Ludhiana India

ISBN: (纸本)9798350306118

In this paper, a mango-type detection model is proposed that combines feature extraction from a convolutional neural network (CNN) with classification from a support vector machine (SVM). Alphonso, Kent, Ataulfo, Keitt, and Francis are only a few of the five main mango varieties included in the dataset of labelled photos. the CNN automatically recovers complex patterns and distinctive traits from unprocessed images, facilitating efficient feature learning. the SVM classifier then classifies the various mango varieties using these features. Fl-scores ranging from 83.16% to 85.99% in the evaluation results demonstrate great accuracy. the best precision and recall are displayed by Alphonso and Kent, while Ataulfo has the highest Fl score. the Keitt mango variety, however, poses difficulties because recall is comparatively poorer. this CNN-SVM model provides a dependable method for automatically classifying mango types, enhancing quality assurance and market research for the mango sector. To further improve the model's accuracy, future research may look into more sophisticated CNN architectures and transfer learning methods. Overall, this study emphasises how machine learning and deep learning algorithms have the potential to maximise agricultural productivity. © 2023 IEEE.

关键词： Support vector machines

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