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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"

共 4173 条记录，以下是411-420 订阅

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Towards Equitable AI in HR: Designing a Fair, Reliable, and Transparent Human Resource Management application 4th

Towards Equitable AI in HR: Designing a Fair, Reliable, and ...

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4th international conference on Deep learning theory and applications (DeLTA)

作者： Danner, Michael Hadzic, Bakir Weber, thomas Zhu, Xinjuan Raetsch, Matthias Univ Surrey CVSSP Guildford Surrey England Reutlingen Univ ViSiR Reutlingen Germany Xian Polytech Univ Xian Shaanxi Peoples R China

ISBN: (纸本)9783031390586;9783031390593

the aim of this work is the development of artificial intelligence ( AI) application to support the recruiting process that elevates the domain of human resource management by advancing its capabilities and effectiveness. this affects recruiting processes and includes solutions for active sourcing, i.e. active recruitment, pre-sorting, evaluating structured video interviews and discovering internal training potential. this work highlights four novel approaches to ethical machine learning. the first is precise machine learning for ethically relevant properties in image recognition, which focuses on accurately detecting and analysing these properties. the second is the detection of bias in training data, allowing for the identification and removal of distortions that could skew results. the third is minimising bias, which involves actively working to reduce bias in machine learning models. Finally, an unsupervised architecture is introduced that can learn fair results even without ground truth data. Together, these approaches represent important steps forward in creating ethical and unbiased machine learning systems.

关键词： Human resource management

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Parallel Implementation of Random Forest using MPI 4

Parallel Implementation of Random Forest using MPI

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4th international conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Allam, Zyad Atef Shajera, Fatehan Tael Majed, Reem Jamal Alqaddoumi, Abdulla University of Bahrain Sakhir Bahrain University of Bahrain Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9798350369786

computer vision is a quickly rising field in machine learning. It has many use cases and implementations across various industries. Powerful classifiers can be trained to recognize visual patterns and detect people and objects. However, since these models use images as inputs, they often end up being trained with very complex architectures, with a high number of epochs and very large data sizes. this often results in very long training times, especially for weaker machines. this study investigated the use of parallel processing to speed up machine learning model training. Message-passing Interface (MPI) is used to parallelize the training of Random Forest, achieving speed-ups of up to 5 and an efficiency of up to 1.1. the study concludes that when dealing with big and complex data and building complex models, parallel processing can significantly reduce the training time of the model with minimal loss in accuracy. © 2023 IEEE.

关键词： Adversarial machine learning

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4th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

4th EAI International Conference on Cognitive Computing and ...

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4th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

ISBN: (纸本)9783031488870

the proceedings contain 39 papers. the special focus in this conference is on Cognitive Computing and Cyber Physical Systems. the topics include: Drug Recommendations Using a Reviews and Sentiment Analysis by RNN;optimizing Real Estate Prediction - A Comparative Analysis of Ensemble and Regression Models;estimation of Power Consumption Prediction of Electricity Using machine learning;medical Plants Identification Using Leaves Based on Convolutional Neural Networks;An Efficient Real-Time NIDS Using machine learning Methods;Maixdock Based Driver Drowsiness Detection System Using CNN;A Novel Approach to Visualize Arrhythmia Classification Using 1D CNN;exploring machine learning Models for Solar Energy Output Forecasting;the Survival Analysis of Mental Fatigue Utilizing the Estimator of Kaplan-Meier and Nelson-Aalen;an Optimized Ensemble machine learning Framework for Multi-class Classification of Date Fruits by Integrating Feature Selection Techniques;EEMS - Examining the Environment of the Job Metaverse Scheduling for Data Security;text Analysis Based Human Resource Productivity Profiling;a Novel Technique for Analyzing the Sentiment of Social Media Posts Using Deep learning Techniques;comparative Analysis of Pretrained Models for Speech Enhancement in Noisy Environments;Use of Improved Generative Adversarial Network (GAN) Under Insufficient Data;unraveling the Techniques for Speaker Diarization;LUT-Based Area-Optimized Accurate Multiplier Design for Signal Processing applications;speaker Recognition Using Convolutional Autoencoder in Mismatch Condition with Small Dataset in Noisy Background;face Emotion Recognition Based on Images Using the Haar-Cascade Front End Approach;textRank – Based Keyword Extraction for Constructing a Domain-Specific Dictionary;harmonizing Insights: Python-Based Data Analysis of Spotify's Musical Tapestry;Enlighten GAN for Super-Resolution Images from Surveillance Car.

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Iproceedings of the 4th international conference on Artificial Intelligence and computer Engineering, ICAICE 2023

IProceedings of the 4th International Conference on Artifici...

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4th international conference on Artificial Intelligence and computer Engineering, ICAICE 2023

ISBN: (纸本)9798400708831

the proceedings contain 200 papers. the topics discussed include: heat-aware graph data placement strategy for NVM;parameters estimation of photovoltaic models via an improved differential evolution algorithm;study on the extraction of law enforcement relationships in administrative law enforcement instrument data;online algorithm for exploring a grid polygon with two robots;research on gesture recognition method by improving dung beetle algorithm to optimize BP neural network;an improved algorithm for frequent sequence pattern mining based on PrefixSpan-ComplexPrefixSpan;machine learning-based research on reserve prediction of natural-gas-hydrates;enhancing coal mine safety monitoring algorithm using graph computing techniques;reverse distillation support vector data description for unsupervised anomaly detection;and few-shot object counting model based on self-support matching and attention mechanism.

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4th international conference on Communication Technologies, ComTech 2023

4th International Conference on Communication Technologies, ...

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4th international conference on Communication Technologies, ComTech 2023

ISBN: (纸本)9798350337211

the proceedings contain 23 papers. the topics discussed include: efficient, immutable and privacy preserving e-healthcare systems using blockchain;an untraceable monitoring for patients using wireless medical sensor networks;angle estimation using weighted beam pattern in frequency diverse array radar;co-clustering based hybrid collaborative filtering model;mosquitoes species classification using acoustic features of wing beats;detection and analysis of mental health illness using social media;reduce emergency response time using machine learning technique;protection mechanism against software supply chain attacks through blockchain;electricity theft detection via deep learning;data centers sustainability: approaches to green data centers;a comparative analysis of implementation of privacy by design principles on different blockchain platforms;and machine learning based encrypted content type identification.

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A Key Factor in Traffic Management - Vehicle Speed Prediction Using machine learning 4

A Key Factor in Traffic Management - Vehicle Speed Predictio...

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

作者： Maurya, Ayush Dwivedi, Ashish Pratap, Saurabh Dubey, Suchi Zhou, Fuli Shukla, Vinod Kumar Shambhunath Institute of Engineering and Technology Department of Computer Science and Engineering U.P Prayagraj India Jindal Global Business School O.P. Jindal Global University Sonipat India Department of Mechanical Engineering Varanasi India Manipal Academy of Higher Education Dubai United Arab Emirates College of Economics and Management Zhengzhou University of Light Industry Zhengzhou450001 China Amity University Dubai Department of Engineering and Architecture United Arab Emirates

ISBN: (纸本)9798350393248

the analysis of transportation data is considered as an essential application for machine learning (ML) based-intelligent transportation design and control especially for safety and improving energy management. there is a requirement to identify efficient methods for optimizing transportation operations and enhance traffic management. this study adopts exploratory data analysis to analyse transportation data including year, month, vehicle type, time period, area type, functional classification. Predictive analytics is used to predict vehicle speed as dependent variable. the study adopts four supervised ML models such as Multiple Linear Regression (MLR), Random Forest Regression (RFR), Decision Tree (DT) and Artificial Neural Network (ANN) approach for analysing the speed prediction factors. Speed prediction assists as a foundation of advanced traffic management systems. the results from the findings suggest that ANN and RFR are the commendable model for performance analytics. © 2023 IEEE.

关键词： machine learning

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Comparative Analysis on machine learning Models for Image Captions Generation 4

Comparative Analysis on Machine Learning Models for Image Ca...

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

作者： Mitta, Anirudh B Hegde, Ajay H Shreegagana, Beluvigi Ullah, Mohammad Rooh Asha Rani, K.P. Gowrishankar, S. Dr. Ambedkar Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350300888

this research paper conducts a comparative analysis on five distinct machine learning models employed for generating signature images using shared data and multiple evaluation metrics. the primary aim is to evaluate the effectiveness of these models and identify their strengths and weaknesses. the assessment of the generated image quality is performed through various criteria including BLEU, ROUGE, METEOR, and CIDEr. the results reveal that each model exhibits varying performance across different metrics, with no single model outperforming the others. However, certain models demonstrate higher scores on specific metrics, indicating their potential suitability for particular applications. the study emphasizes the importance of employing multiple evaluation metrics to accurately assess the quality of generated labels. It underscores the effectiveness of different machine learning models in this task. For instance, the CIDEr metric proves valuable when multiple tags are required for an image, as it considers tag diversity. Conversely, metrics like BLEU and ROUGE are more suitable for applications that prioritize clear and precise text by evaluating the clarity and overlap between generated and reference labels. this research underscores the significance of considering multiple evaluation methods to ensure the accurate assessment of label quality. the findings have practical implications for the development of efficient sign-making systems, providing guidance on the selection of appropriate models and measurements tailored to specific applications. © 2023 IEEE.

关键词： machine learning

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Tobacco Quality Prediction in Tobacco machine Drying Based on Data-Driven Deep Reinforcement learning 4

Tobacco Quality Prediction in Tobacco Machine Drying Based o...

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4th international conference on machine learning and computer application, ICMLCA 2023

作者： Dai, Shuang Zhou, Zhengping Fan, Jiawei Guo, Huancheng Lanzhou Cigarette Factory Silk Workshop Gansu Tobacco Industry Co. Ltd China

ISBN: (纸本)9798400709449

In order to enhance the quality of cigarettes and achieve predictive assessment of the roasting quality in the roasting process, a data-driven approach for predicting roasting quality is proposed. Initially, the random forest algorithm is employed for feature selection and to obtain the influence coefficients of each process parameter on the roasting quality. Subsequently, based on the deep reinforcement learning algorithm, the prediction of roasting quality in the roasting machine is modeled as a guessing game. Starting from the elements of the reinforcement learning algorithm, a prediction model for roasting quality is established, and a deep neural network is used to approximate the decision network for finding the optimal strategy. this approach realizes roasting quality prediction using the deep reinforcement learning algorithm, and investigates the impact of the distribution of quality types in different sample data on the accuracy of roasting quality prediction. the feasibility and universality of this method are demonstrated through experimentation. Based on the prediction results and combined with the influence coefficients of each process parameter on roasting quality, a basis is provided for the control optimization of the roasting process in the roasting machine. © 2023 ACM.

关键词： Tobacco

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Comparative Analysis on Medical Image Prediction of Breast Cancer Disease using Various machine learning Algorithms 4

Comparative Analysis on Medical Image Prediction of Breast C...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Gurusamy, Ravikumar Rajmohan, V. Sengottaiyan, N. Kalyanasundaram, P. Ramesh, S.M. M.P.Nachimuthu M.Jaganathan Engineering College Department of Ece Erode India Simats School of Engineering Chennai India Jain Global Campus Computer Science Faculty of Engineering and Technology Jakkasandra Kanakapura India Ksr Institute for Engineering and Technology Department of It Tiruchengode637215 India Kpr Institute of Engineering and Technology Department of Ece Coimbatore641407 India

ISBN: (纸本)9798350300093

Breast Cancer disease is the utmost characterized heterogeneous illnesses consisting of various types. Apart from lung cancer, Breast cancer is spreading widely everywhere. this research work confines to accurately analyzing the benign cells and the defective malignant cells by data mining technique like Support Vector machine (SVM). To have the comparative study, a total number of 659 sample are drawn from the UCI machine learning laboratory. the G power calculation with a confidence interval of 0.8 using maximum level of acceptable error rate of 0.5 is used for this analysis. Support Vector machine offer better prediction in terms of F1 score, precision and recall as 100%, 92%, 97% for benign cells 94%, 100%, 97% for malignant cells respectively. the significance value is arrived as 0.36 for this proposed system. the SVM appears to have better results in finding the benign and malignant cells diagnosis using Wisconsin Dataset. © 2023 IEEE.

关键词： Support vector machines

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Prediction of Student Performance by Using machine learning Techniques 4

Prediction of Student Performance by Using Machine Learning ...

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4th international conference on Artificial Intelligence, Robotics and Control, AIRC 2023

作者： Ali, Donia Ashraf Aborizka, Mohamed Dahroug, Ahmed College of Computing and Information Technology Cairo Egypt Arab Academy for Science Technology and Maritime Transport Department Information System Egypt Arab Academy for Science Technology and Maritime Transport Department Computer Science Egypt

ISBN: (纸本)9798350348248

Each person's learning process is distinct and diversified. therefore, using the right teaching techniques is crucial to improving student performance and academic achievement. Numerous educational institutions, including schools and universities, make an effort to anticipate their students' academic performance in order to improve the effectiveness of the educational systems. In educational technology and the recent learning systems, discovering useful hidden patterns in learner data is significantly valuable. In this paper, several machine learning classifiers were applied such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Support Vector machine (SVM), and Naive Bayes (NB), to predict the student's performance. Furthermore, a hybrid model was used to reduce data dimensionality and improve the overall performance of the classifiers used. A real dataset obtained from the UCI machine learning repository is used to evaluate the proposed hybrid model. the reported results show the performance of CNN-RNN outperforms other methods with an accuracy equal to 89.1%. © 2023 IEEE.

关键词： Recurrent neural networks

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