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Artificial Intelligence and machine learning in Human Resource Management and Market Research for Enhanced Effectiveness and Organizational Benefits 4

Artificial Intelligence and Machine Learning in Human Resour...

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

作者： Indoria, Devadutta Narendra Kiran, P.B. Kumar, Anuj Goel, Manjusha Shelke, Nitin Arvind Singh, Jagendra Vikram Dev University Department of Commerce Jeypore India Mittal School of Business Lovely Professional University Phagwara India Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India Raj Kumar Goel Institute of Technology Department of Management Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350306118

this study investigates the substantial implications of cutting-edge technology on forecasting market demand, improving safety, and human resource management (HRM). the study simplifies the candidate selection method by applying Long Short-Term Memory (LSTM) models to automate application screening. the use of Arduino micro controllers in conjunction with temperature, fire, and smoke sensors increases real-time reactions to potential dangers for improving safety in resource management. Artificial neural networks (ANN) are used to forecast market demand based on data from automotive sales from 2016 to 2023. the outcomes demonstrate how effective each technique was. LSTM-driven applicant screening demonstrates the capacity to boost HRM by accelerating the recruiting process. the integration of Arduino controllers demonstrates rapid and precise risk recognition, enhancing industrial safety. When it comes to forecasting automotive sales for 2022, the ANN model, in particular, predicts market demand with astonishing precision, achieving a 100% accuracy rate. this implies that it is capable of strategic planning and resource management. these findings underscore the critical role that AI-driven systems play in improving organisational performance, security, and decision-making, as well as the revolutionary potential of AI -driven systems across a wide range of industries. By acting as a sobering reminder of the dynamic interplay that happens between industry and technology, the research lays the path for a more safe and successful future. © 2023 IEEE.

关键词： Human resource management

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Dynamic On-Demand machine Provisioning and Continuous Resource Management 4

Dynamic On-Demand Machine Provisioning and Continuous Resour...

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

作者： Chatterjee, Arindam Dubey, Priya Nigam, Ashutosh Adobe Systems India

ISBN: (纸本)9798350306118

machine provisioning is an industry wide common and critical problem when there are vast number of machines that needs to be provisioned per day. Due to the complexity and resource constraints, machine provisioning needs to be completely optimized for effective utilization of resources. this paper presents an automated system for on-demand machine provisioning and continuous resource management that aims to optimize provisioning speed and utilization through dynamic allocation. A conversational AI chatbot with natural language processing allows users to request machines. the chatbot interacts with a Smart Agent that allocates optimal machines based on inventory, usage metrics, and request history. In this work, we automate the process of machine provisioning as per the user demand. Our automation scripts or playbooks provisions software based on configuration files. Furthermore, we propose machine Utilization-Aware Alert Manager for machine monitoring and effective utilization of resources. the monitoring and alerting agent monitors resource usage while alerts optimize allocation by detecting idle machines. the solution has been successfully applied in our organization and has brought practical benefits in real-world applications. the system uniquely incorporates predictive analytics models trained on historical usage data to forecast demand and guide dynamic utilization of resource allocations. It optimizes provisioning across existing infrastructure by analyzing current utilization and capacity metrics to enable inventory-Aware on-demand allocation. © 2023 IEEE.

关键词： machine learning

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Lifestyle Classification Amidst Psychological Impact of COVID: A machine learning Approach 7

Lifestyle Classification Amidst Psychological Impact of COVI...

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7th international conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Agrawal, Alok Kumar Ahmed, Bilal Jakhar, Yogesh Kumar Bajaj, Karan Kumar, Girish Kumar, Mukesh Chitkara University School of Engineering & Technology Chitkara University Himachal Pradesh India School of Computer Application Lovely Professional University India University of Engineering & Management Department of Computer Application Jaipur India

ISBN: (纸本)9798350340600

the COVID pandemic has caused significant social changes, affecting not just physical health but also individuals' psychological well-being. this study investigates the complex interaction between lifestyle choices and the psychological impact of the pandemic, using a machine learning (ML) technique to categorise and assess distinct lifestyle patterns in the context of COVID. In this study, a significant data from a variety of sources, including questionnaires, social media, and healthcare records, to get insight into how the pandemic has affected lifestyle characteristics such as exercise, food, social connections, work habits, and leisure activities. this study has analysed the psychological effects of these lifestyle changes, including stress, anxiety, sadness, and overall mental health. this research article implements the basic classification models like (Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, and XGBoost), which can classify various lifestyle changes among individuals based on their pandemic-related behaviours. the impact of these lifestyle changes on mental health outcomes is investigated, offering light on the intricate interplay between personal decisions and psychological well-being during times of crisis. © 2023 IEEE.

关键词： machine learning

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Intelligent Parking Big Data Cloud Platform Algorithm Research and application 6

Intelligent Parking Big Data Cloud Platform Algorithm Resear...

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6th international conference on computer Information Science and application Technology, CISAT 2023

作者： Yan, Xiaobo Zou, Xue Cai, Shan Wang, Lin Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province Guizhou Minzu University Guiyang China College of Date Science and Information Engineering Guizhou Minzu University Guiyang China

ISBN: (纸本)9781510668546

With the rapid development of the domestic economy and the increasing living standards of the people, the ownership of private cars has increased explosively. Currently, urban traffic congestion and parking difficulty have gradually become a hot topic of research from all walks of life. Parking difficulty is mainly reflected on the following aspects: first, the existing parking spaces in cities are not effectively utilized;the second reason is that drivers cannot grasp relevant information about parking spaces near the ground in real time. this article conducts in-depth research on the issue of parking difficulty. Based on the existing security monitoring systems in parking lots, real-time detection of parking spaces status is conducted based on computer vision, machine learning, and other methods. By building a comprehensive, synthesize, and multi-dimensional parking resources dynamic sensing system, a smart parking big data cloud platform is constructed to achieve the effect of intelligent indoor parking lots management. © 2023 SPIE.

关键词： machine learning

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Visual Patterns in an Interactive App for Analysis Based on Control Charts and SHAP Values

Visual Patterns in an Interactive App for Analysis Based on ...

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26th European conference on Artificial Intelligence (ECAI)

作者： Grabska-Gradzinska, Iwona Szelazek, Maciej Bobek, Szymon Nalepa, Grzegorz J. Jagiellonian Univ Fac Phys Astron & Appl Comp Sci Krakow Poland AGH Univ Sci & Technol Fac Appl Comp Sci Krakow Poland

ISBN: (纸本)9783031504846;9783031504853

the aim of this paper is to describe the tool for combining the information of control charts, widely used for statistical quality control with the information obtained from the process of explanation the classification decision made using the machine learning model. Control charts are used to monitor production and show deviations from normal behavior, especially when upper and lower control limits of measured parameters are defined. Analysis of SHAP explanations allowed us to show some dependencies of classification process. Our application, which allows the user to compare the information taken from both control charts and SHAP plots, helps the user discover anomalies, especially dependencies between exceeding the limits of controlled parameters and the relevance of these values in SHAP calculations.

关键词： control charts Explainable machine learning SHAP values

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Automatic detection of green litchi based on YOLOV3, YOLOV4, YOLOV5-S and YOLOV5-X deep learning algorithm 4

Automatic detection of green litchi based on YOLOV3, YOLOV4,...

引用

4th international conference on computer Engineering and application, ICCEA 2023

作者： Yang, Maoling Wang, Chenglin College of Intelligent Manufacturing Engineering Chongqing University of Arts and Sciences Chongqing China Kunming University of Science and Technology Faculty of Modern Agricultural Engineering Kunming China

ISBN: (纸本)9798350347548

the accurate detection of green litchi is one of the important means of litchi plantation, and plays an important role in disease and insect protection and yield estimation. Selecting models that already exist on the market is not only convenient and fast, but also saves costs. In this experiment, YOLOV3, YOLOV 4, YOLOV5-S and YOLOV5-X of YOLO series were used to detect green litchi in the natural environment, and the most suitable model for detection was obtained through comparison and analysis. the experimental results show that the model detection effect of YOLOV5 is the best, and the detection speed of YOLOV5-S model is the fastest. the average accuracy of YOLOV5-X is 0.998, the average accuracy of FPS is 11.13, the average accuracy of YOLOV5-S is 0.993, and the average accuracy of FPS is 47.16. To sum up, YOLOV5-S model is more suitable for detecting green litchi in natural environment because of its fast speed and relatively high accuracy. © 2023 IEEE.

关键词： learning algorithms

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Towards Fluent Expression: the Impact of Deep learning in Grammar Correction 4

Towards Fluent Expression: The Impact of Deep Learning in Gr...

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

作者： Toleti, Jayant Amirthavarshini, V. Natarajan, B. Elakkiya, R. Amrita Vishwa Vidyapeetham Chennai India Bits Pilani XPERT Research Group Department Of Computer Science Dubai Campus Dubai United Arab Emirates

ISBN: (纸本)9798350384628

Grammatical Error Correction is a crucial task in Natural Language Processing. this paper discusses the evolution of grammar error correction models from early rule-based approaches to advanced deep learning-based methodologies. this work utilizes the NAIST Lang-8 dataset, a valuable dataset offering parallel data, facilitating more straightforward evaluation and eliminating the need for additional data creation. this research explores Deep learning frameworks like encoder-decoders, attention mechanisms, and new-age transformer models. the performance of each model is then measured with the help of a standard metric, the GLEU score. After extensive experimentation, it has been observed that the T5 model has outperformed other models by a significant margin with a GLEU Score of 0.855. the outstanding performance is primarily due to its pre-trained nature and fine-tuning parameters. this research highlights deep learning models' pivotal role in advancing grammar error correction. © 2023 IEEE.

关键词： Error correction

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9th international conference on machine learning, Optimization, and Data Science, LOD 2023

9th International Conference on Machine Learning, Optimizati...

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9th international conference on machine learning, Optimization, and Data Science, LOD 2023

ISBN: (纸本)9783031539688

the proceedings contain 34 papers. the special focus in this conference is on machine learning, Optimization, and Data Science. the topics include: Alternating Mixed-Integer Programming and Neural Network Training for Approximating Stochastic Two-Stage Problems;heaviest and Densest Subgraph Computation for Binary Classification. A Case Study;SMBOX: A Scalable and Efficient Method for Sequential Model-Based Parameter Optimization;accelerated Graph Integration with Approximation of Combining Parameters;improving Reinforcement learning Efficiency with Auxiliary Tasks in Non-visual Environments: A Comparison;a Hybrid Steady-State Genetic Algorithm for the Minimum Conflict Spanning Tree Problem;reinforcement learning for Multi-Neighborhood Local Search in Combinatorial Optimization;evaluation of Selected Autoencoders in the Context of End-User Experience Management;application of Multi-agent Reinforcement learning to the Dynamic Scheduling Problem in Manufacturing Systems;solving Mixed Influence Diagrams by Reinforcement learning;exploring Image Transformations with Diffusion Models: A Survey of applications and Implementation Code;multi-scale Heat Kernel Graph Network for Graph Classification;PROS-C: Accelerating Random Orthogonal Search for Global Optimization Using Crossover;a Multiclass Robust Twin Parametric Margin Support Vector machine with an application to Vehicles Emissions;LSTM Noise Robustness: A Case Study for Heavy Vehicles;ensemble Clustering for Boundary Detection in High-Dimensional Data;learning Graph Configuration Spaces with Graph Embedding in Engineering Domains;towards an Interpretable Functional Image-Based Classifier: Dimensionality Reduction of High-Density Diffuse Optical Tomography Data;on Ensemble learning for Mental Workload Classification;decision-Making over Compact Preference Structures;user-Like Bots for Cognitive Automation: A Survey;geolocation Risk Scores for Credit Scoring Models;On Channel Selection for EEG-Based Mental Workload Clas

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Brain Tumor Prediction from EEG Signal using machine learning Algorithm 4

Brain Tumor Prediction from EEG Signal using Machine Learnin...

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

作者： Jamunadevi, C. Bharanitharan, J. Deepa, S. John Paul Antony, T. Kongu Engineering College Perundurai Department of Ct_pg Tamil Nadu Erode638 060 India The American College Department of Computer Science Tamil Nadu Madurai India

ISBN: (纸本)9798350300093

Brain tumors are a type of cancerous growth that occurs in the brain tissue. It can develop in any part of the brain and can be either malignant or benign. Brain tumors can cause a range of symptoms such as headaches. seizures. difficulty speaking, vision problems, and cognitive impairment [1]. An early and precise diagnosis of a brain tumor is crucial for the effective administration of the remedy. Brain tumors can start outside the brain and spread there, or it can start inside the brain and grow there. Headaches, nausea, and balance issues are some of the indications and symptoms that the tumor can produce as it spreads because it puts pressure on the surrounding brain tissue and alters how it functions. Early tumor detection minimizes the need for surgery and other forms of treatment, improving the prognosis for many patients. the development of new technologies that improve neurosurgery's success rate and avoid problems is still ongoing today. Magnetic resonance imaging (MRr) is one of the most often utilized procedures for analyzing pictures of brain tumors [6]. EEG signal is used in the suggested method to forecast brain tumors. SVM is used by this system to forecast brain tumors. When compared to other methods, the accuracy rate of the SVM (support vector machine) approach was shown to be higher. © 2023 IEEE.

关键词： Support vector machines

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A Research of Spam Message Detection based on Ensemble learning 4

A Research of Spam Message Detection based on Ensemble Learn...

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

作者： Li, Tianfu Shao, Jiaxing Wang, Haoli College of Computer Science Sichuan University Chengdu China College of Public Administration Sichuan University Chengdu China

ISBN: (纸本)9798400708831

With the development of the network technology and the increase of active online users, the spam messages transmitted through the Internet are also growing rapidly. Not only do they potentially cause loss to the recipient, but they also waste network resources. therefore, it is very necessary to develop an efficient automatic classification model. However, traditional machine learning methods have limited capabilities, while the popular neural networks often encounter insufficient data. therefore, we proposed a three-layer stacking ensemble learning method to deal with medium-magnitude data and achieved awesome results in our spam detection task. More exactly, the accuracy is 97.8% and the F1 score is 0.913. Besides, we believe that the three-layer ensemble learning method we used to deal with this magnitude of data is also one of the novelty of our paper. © 2023 ACM.

关键词： learning systems

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