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Stacking-Based Transfer Learning for Optimized Employee Management and Retention in HR Systems

Stacking-Based Transfer Learning for Optimized Employee Mana...

引用

automation and Computation (AUTOCOM), international conference on

作者： K. Krishna Rao R. Shakthivel B Mohan Krishna Bommireddy Poojitha Parimi Jayanth Sai Chitti Sai Lakshman Koneru Lakshmaiah Education Foundation KL Business School Vaddeswaram A.P India Department of Mathematics Sona College of Technology Salem Tamil Nadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Background: For human resources departments trying to ensure organisational stability and lower turnover costs, employee retention is a major challenge. In terms of employee management, traditional approaches often lack the capacity to correctly mix insights from several sources, so limiting the predictive power of these approaches. Methodology: This work proposes a Stacking-Based Transfer Learning (TL) model to support bettering of employee retention policies. This model aggregates domain-specific data and pre-trained models to offer the most exact projections. The approach extracts and combines textual and numerical insights from HR data by means of a hierarchical stacking ensemble of deep learning models, which consists of BERT and ResNet. Tests carried out on a dataset of employee retention easily accessible to the general public reveal the efficiency of the model. Findings and Result: With 92.5%, the suggested method's accuracy exceeded those of other now in use methods including Random Forest (88.3%) and Support Vector Machine (85.7%). Moreover, by 17% over baseline models, the Stacking-based TL model lowers the prediction error linked with employee turnover rates. These findings indicate that HR analytics has great potential to include innovative learning strategies into order to increase employee satisfaction and retention rates respectively.

关键词： Support vector machines Accuracy Transfer learning Stacking Stability criteria Predictive models Data models Numerical models Computational efficiency Random forests

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NPC Behavior in Games Using Unity ML-Agents: A Reinforcement Learning Approach

NPC Behavior in Games Using Unity ML-Agents: A Reinforcement...

引用

automation and Computation (AUTOCOM), international conference on

作者： Revat Meshram Sachith Krishna Omkaresh Kulkarni Rutuja Rajendra Patil Gagandeep Kaur Shruti Maheshwari Dept. of Computer Science Engineering (Artificial Intelligence and Machine Learning) Vishwakarma Institute of Information Technology Pune India Symbiosis Institute of Technology Pune Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Machine learning is having a significant impact on video games, from speeding up the development of new games to the development of new methods in gaming. This study discusses the impact of using ML models in the development and improvement of video games. Examples cited in this paper relate to the ways in which NPCs behave. With the aid of Unity's ML-Agents toolkit, developers can create NPCs that respond to different strategies in real time. This makes the games more exciting and complex. The paper also outlines the possibilities of single-player games, such as first-person shooters and a role-playing game, to notice NPC behavior during the gameplay. In these gaming styles, RL frameworks can be applied to NPCs to enable them to learn and adapt to the environment to create an interactive experience for the player. The conclusions put an emphasis on the evolution of video games with the advent of ML tools; as predicted, NPC behavior becomes more intuitive and less expected.

关键词： Industries Procedural generation automation Role playing games Games Reinforcement learning Real-time systems

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Semi-supervised Video Anomaly Detection With Compact Deformable 3D Convolution

Semi-supervised Video Anomaly Detection With Compact Deforma...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shibo Gao Peipei Yang Linlin Huang Beijing Jiaotong University State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Semi-supervised video anomaly detection (SVAD) is a challenging computer vision task due to the diversity, randomness, and rarity of abnormal events in videos. A series of important SVAD methods follow the frame prediction strategy, where the model is trained only on normal videos and thus frames with higher prediction error are regarded as anomalies. However, these methods often suffer from inadequate sensitivity to anomalies because the prediction error of anomalous frames is insufficiently distinguishable from the error of normal frames. In this paper, we propose a compact deformable 3D convolution (CD3D) for feature extraction in SVAD models, which effectively enhances the discriminating ability between normal and anomalous frames. In CD3D, the offsets of sampling locations are predicted by applying a set of extra separable 3D convolutions to multiple frames, so that the context information is better utilized with reduced computational costs. Our proposed method achieves the best performance among the methods without resorting to additional supervised information. It can also be conveniently applied to methods that utilize extra supervised information, which further enhances their performances.

关键词： Deformable models Solid modeling Adaptation models Three-dimensional displays Convolution Shape Computational modeling Predictive models Speech processing Anomaly detection

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Campus Placement Analysis: Predictive Insights with Machine Learning models

Campus Placement Analysis: Predictive Insights with Machine ...

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Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), international conference on

作者： Sathyendra Kumar V Ramya Mahitha Palle Prathyusha Budarapu Reddy Sudhan Reddy Pullaganti Siva Karthik Reddy Moyilla Department of CSE Annamacharya University Rajampet India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The system is designed using machine learning to predict the placement of students which is being trained on the historical features such as academic achievements, demographics and extracurricular activities. The model is designed to help educational institutions and students gauge placement likelihood by utilizing attributes like academic performance, field of study, work experience, and specialization. As a result, the system implements multiple data preprocessing methods, such as missing data imputation, and label encoding for the purpose of converting categorical variables to numerical format. Utilizing performance assessment metrics such as precision and recall, a decision tree classifier is trained to predict whether or not a student will be placed in a company. In addition, the system contains visualization tools which help to illustrate the relationship among factors such as specialization and salary and gives a holistic view of how placement is affected by the different attributes. Streamlit is used to construct a nice interface and users are able to upload new data for prediction and analysis in a real time setting. Aiming to fuse both data-driven approach and an easy user interface, this paper is prepared to assist in placement forecasts thus aiding career guidance counsellors and students to use the available job opportunities more effectively. The system could, however, be developed further by including parameters relevant to the industry so that it can be applicable to a number of fields and professions.

关键词： Analytical models Accuracy Engineering profession Computational modeling Data preprocessing Predictive models Data models Telecommunication computing Decision trees Remuneration

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Evolution-based Deployment with Efficient Routing and Crow Search Optimization in Internet of Things Application

Evolution-based Deployment with Efficient Routing and Crow S...

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international conference on ent Computing and Control Systems (ICICCS)

作者： C. Kalaivanan Ayman Amer Maria Rubiston. M M. Vennila Jayant Giri M. Dinesh Department of EEE Sona College of Technology Salem Department of Energy Engineer Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of ECE S.A. Engineering College Chennai Department of of Robotics and Automation Karpaga Vinayaga College of Engineering and Technology Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Division of Research and Development Lovely Professional University Phagwara India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

IoT technologies to bolster residential security, safety, and sustainability. By leveraging facial recognition for door access, enhancing fire safety monitoring, and integrating energy harvesting, it aims to create more resilient and eco-friendly residential environments. These innovations offer comprehensive solutions to address evolving challenges and optimize residential living. An ED Model coupled with Crow Search Optimization (CSO) for enhancing the efficiency and reliability of IoT applications. The ED model integrates static routing mechanisms with efficient protocols like AOMDV, ensuring robust connectivity and energy-efficient operation in diverse environments. Leveraging CSO, inspired by collective crow intelligence, enhances network performance by dynamically adapting to changing conditions. Through simulations and comprehensive analysis, the proposed approach demonstrates superior performance compared to existing models, achieving high data delivery ratio, network throughput, and energy efficiency. This integrated framework offers a sustainable solution for resilient IoT deployments, addressing evolving challenges and optimizing residential living environments.

关键词： Adaptation models Analytical models Routing Throughput Energy efficiency Data models Internet of Things Reliability Sustainable development Optimization

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Analysis of Noise Interference Prediction methods of Electric Drive System Under Different Conditions

Analysis of Noise Interference Prediction Methods of Electri...

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Smart Grid and Artificial Intelligence (SGAI), international conference on

作者： Jianben Liu Yan Liu Zhiwei Chen State Key Laboratory of Power Grid Environmental Protection China Electric Power Research Institute Wuhan China School of Electrical and Automation Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

Since the conduction and radiation noise of the motor drive system of electric vehicles can affect such as the vehicle communication antenna and other on-board electronic equipment, countries around the world have limited the electromagnetic interference noise level of electric vehicles (such as CISPR25). Based on the finite element simulation of electromagnetic field, two methods for predicting the radiated noise of the machine table under different conditions are analyzed and compared in this paper. The main difference between the two methods is that the common mode current return path is different under different environmental conditions.

关键词： Inductance Solid modeling Motor drives Noise Electromagnetic interference Predictive models Electric vehicles Capacitance Smart grids Power generation

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Early Detection of Melanoma Through Deep Learning-Based Skin Lesion Classification Using VGG16 and InceptionV3

Early Detection of Melanoma Through Deep Learning-Based Skin...

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automation and Computation (AUTOCOM), international conference on

作者： Rajeev Kumar Bhaskar Balasubramaniam Kumaraswamy Department of CS & IT Kalinga University Raipur India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

This work aims at investigating classification of skin lesion in order to detect melanoma using deep learning models such as VGG16 and InceptionV3. Skin cancer especially the melanoma needs proper identification for cure to be administered on the affected tissues. The research which underpins this application makes use of convolutional neural networks (CNNs) in an effort to decide whether skin lesions are benign or malignant. In the present study, to assess the performance of the suggested models, the dermoscopic image data set in the public domain was used for training VGG16 and InceptionV3 models. The experimental evaluation demonstrated that, in comparison to the VGG16 network and other relevant algorithms, including ResNet50 and DenseNet121, InceptionV3 provides higher accuracy, precision, recall, F1-score, as well as the ROC-AUC value. InceptionV3 delivered 91.5 % classification accuracy and 92.1 % F1 score representing the enhanced melanoma diagnosis capability of the algorithm with fewer statistical errors between the true positive and true negative cases. The broad fractal dimension analysis feature of the proposed model means that variations in lesion size and texture do not pose a challenge to it. The deep learning models take more time in training than the traditional machine learning methods, although their outcomes justify their employment in clinical practices. The study identifies InceptionV3 as a viable tool for analysis of melanoma images with a view to assist the doctors in early identification of the disease hence enhancing the generality of patients prognosis.

关键词： Deep learning Training Accuracy Melanoma Medical services Skin Data models Classification algorithms Lesions Medical diagnostic imaging

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Visual Question Answering: In-Depth Analysis of methods, Datasets, and Emerging Trends

Visual Question Answering: In-Depth Analysis of Methods, Dat...

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automation and Computation (AUTOCOM), international conference on

作者： Pooja Singh Munish Mehta Gourav Bathla M. Batumalay Department of CAE GLA University Mathura India School of Mech. Engg Lovely Professional University Jalandhar India Faculty of IT INTI University Nilai Malaysia

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Visual Question Answering (VQA) is a multi-modal technique that merges computer vision and natural language processing to enable systems to answer questions about images. Recently, several improvements have been made to VQA, driven by its wide applications. In this paper, we analyze and compare key studies, focusing on traditional and modern approaches to VQA. Traditional techniques mostly depend on Convolution Neural Networks (CNN), which extract visual features, while Recurrent Neural Networks (RNN) are used for text-based features. Further, in advanced models transformers, self-attention, and pre-trained CNN models are applied for better accuracy. In this paper, traditional and modern techniques are examined in detail for a better understanding of future researchers. Further, popular VQA datasets like VizWiz, COCO-QA, etc. are discussed for their strengths and limitations. In spite of improvements, VQA system faces issues like handling complex reasoning and achieving generalization among various domains. These open research problems and issues are presented in this paper.

关键词： Training Visualization Computer vision Recurrent neural networks Transformers Market research Feature extraction Question answering (information retrieval) Cognition Long short term memory

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Multi-Time Scale Simulation of Integrated Energy System Based on Finite Difference Approximation

Multi-Time Scale Simulation of Integrated Energy System Base...

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Electrical automation and Artificial Intelligence (ICEAAI), international conference on

作者： Wentao Yang Aochen Jin Hao Sun Wenbin Yang Guangxi University College of Electrical Engineering Nanning China The R&D Department II China Mobile Industry Research Institute Chengdu China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

The time scale span of energy transmission in the electric gas thermal integrated energy system is enormous, and the mathematical modeling of dynamic processes in multi-energy flow systems at different time scales is very complex. The simulation efficiency and accuracy of multi-energy flow systems are difficult to balance, and the accurate characterization of system response faces great challenges. In response to the above issues, this paper proposes a multi-time scale simulation method of integrated energy systems based on finite difference approximation. Firstly, the partial differential equations of gas and heat pipelines are discretized using the finite difference approximation theory, and a dynamic model involving time delay in gas and heat pipelines is established. Secondly, according to the breadth-first traversal algorithm, network constraints are met to connect coupled devices and networks. Finally, the simulation of the real operating state of the system when encountering small disturbances or faults verifies that the dynamic model proposed in this paper can significantly reduce the complexity and computational volume of the model, and improve simulation efficiency while ensuring accuracy.

关键词： Heating systems Solid modeling Analytical models Accuracy Computational modeling Pipelines Mathematical models Computational efficiency Complexity theory Finite difference methods

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Towards Designing a Medication Assistive Intelligent System with Automated Insulin-dose Prediction for Elderlies

Towards Designing a Medication Assistive Intelligent System ...

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international conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Abrar Ahmed Minhazul Islam Mahi M. Akhtaruzzaman Jamal Uddin Tanvin Fahim Shahryer Sachitra Halder Muhammad Towfiqur Rahman Md Shofiqul Islam Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh Department of Computer Science & Engineering University Asia Pacific (UAP) Dhaka Bangladesh Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Geelong Victoria

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Medication management poses significant challenges for many patients, particularly the elderlies, who often struggle with keeping track of their medication schedules and taking the correct dosages. To address this issue, this study aims to design an AI-integrated Medication Management and Assistive Unit (AMMAU) for elderlies with some important features like, schedule reminder, medicine recognition and count, early indication of shortage, sorted slots, and so on. Automated insulin-dose prediction and alert system makes the system unique and more demanding at these current scenarios. For this study, a suitable machine learning model is designed, analyzed, verified, and embedded in the proposed system so that the elderly diabetic patients can get alert, further compare with the current insulin-doses. Though, the proposed system is currently focusing on only the Basal insulin doses prediction, the system will definitely reduce the risks associated with wrong management of medication by degrading the chances of missed doses or taking wrong pills for elderlies at home.

关键词： Training Schedules Analytical models Prototypes Machine learning Predictive models Internet of Things Older adults Insulin Physical design

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