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1. |
Record Nr. |
UNINA9910464635303321 |
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Titolo |
Human resource management practices in Chinese organisations / / guest editors, Professor Song Lin and Professor David Lamond |
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Pubbl/distr/stampa |
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[Bradford, England] : , : Emerald, , 2014 |
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©2014 |
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ISBN |
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Descrizione fisica |
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1 online resource (169 p.) |
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Collana |
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Chinese Management Studies, , 1750-614X ; ; Volume 8, Number 1 |
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Disciplina |
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Soggetti |
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Personnel management - Latin America |
Personnel management |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters. |
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Nota di contenuto |
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Cover; EDITORIAL ADVISORY BOARD; Human resource management practices in Chinese organisations; Personality traits and simultaneous reciprocal influences between job performance and job satisfaction; Relative leader-member exchange and employee voice; Institutional influence, cognition and competence of top managersand innovative firms; Occupational commitment, industrial relations and turnover intention; Relationship between employees' performance and social network structure; Leadership, work stress and employee behavior |
Psychological ownership, organization-based self-esteem and positive organizational behaviorsExamining the effect of individualism and collectivism on knowledge sharing intention |
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Sommario/riassunto |
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This special issue of Chinese Management Studies focuses attention on a central activity of Chinese organisations - managing people. Our aim in doing so is to support efforts to move beyond HRM research in China as a subset of international or comparative HRM research and promote indigenous approaches to research in China. The issue opens with Yang and Hwang's (2014) exploration of the relationships among three important variables in the field of industrial psychology - personality traits, job performance, and job satisfaction. Utilising sample data from 360 respondents in 31 Taiwanese financi |
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2. |
Record Nr. |
UNISA996483159303316 |
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Titolo |
Advances in computing and data sciences : 6th International Conference, ICACDS 2022, Kurnool, India, April 22-23, 2022, Revised selected papers. Part I / / Mayank Singh [and four others] editors |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2022] |
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©2022 |
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ISBN |
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Descrizione fisica |
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1 online resource (448 pages) |
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Collana |
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Communications in computer and information science ; ; 1613 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Computer science |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Hardware Description Language Enhancements for High Level Synthesis of Hardware Accelerators -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Syntax Identification and Code Extraction -- 3.2 Syntax Transformation -- 3.3 Code Replacement -- 4 Implementation -- 4.1 Keyword Extractor -- 4.2 Argument Extractor -- 4.3 Syntax Transformation -- 5 Results -- 6 Conclusion and Future Work -- References -- CrDrcnn: Design and Development of Crow Optimization-Based Deep Recurrent Neural Network for Software Defect Prediction -- 1 Introduction -- 2 Related Works -- 2.1 Challenges -- 2.2 Feature Selection Using Wrapper Algorithm -- 3 Proposed Software Defect Prediction Model Using the Optimized Deep NN Classifier: -- 3.1 Software Defect Prediction Using Proposed Crow Optimization-Based DNN for Software Defect Prediction -- 4 Results and Discussion -- 4.1 Experimental Setup: -- 4.2 Performance Metrics -- 4.3 Comparative Analysis -- 4.4 Comparative Discussion -- 5 Conclusion -- References -- Text Sentiment Analysis Using the Bald Eagle-Based Bidirectional Long Short-Term Memory -- 1 Introduction -- 2 Motivation -- 3 Proposed Model for Sentiment Analysis Using the Textual Data -- 3.1 Data Pre-processing: -- 3.2 Feature Extraction -- 3.3 Textual Sentimental Analysis Using the Proposed Bald Eagle- |
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Based Deep BiLSTM Classifier -- 3.4 Architecture of BiLSTM Classifier -- 4 Results and Discussion -- 4.1 Dataset Description -- 4.2 Parameter Metrics -- 4.3 Experimental Setup -- 4.4 Comparative Methods -- 4.5 Comparative Analysis of Bald Eagle Based BiLSTM Classifier -- 5 Conclusion -- References -- Comparison of Multiple Machine Learning Approaches and Sentiment Analysis in Detection of Spam -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 4 Result Analysis. |
5 Conclusion -- References -- A Voice Assisted Chatbot Framework for Real-Time Implementation in Medical Care -- 1 Introduction -- 2 Background and Related Works -- 3 Methodology -- 4 Algorithm and Results -- 5 Conclusion -- References -- Robust Vehicle Detection for Highway Monitoring Using Histogram of Oriented Gradients and Reduced Support Vector Machine -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Feature Extraction -- 3.2 Detection -- 3.3 Classification -- 4 Results and Discussion -- 5 Conclusion -- References -- A Secure Framework Based on Nature-Inspired Optimization for Vehicle Routing -- 1 Introduction -- 2 State of the Art -- 3 Proposed Framework -- 3.1 Registration Process -- 3.2 Communication Process -- 3.3 Proposed Algorithm for Vehicle Routing -- 4 Performance Evaluation -- 5 Conclusion -- References -- Detection of Bangla Hate Comments and Cyberbullying in Social Media Using NLP and Transformer Models -- 1 Introduction -- 2 Related Works -- 3 Dataset -- 4 Methodology -- 4.1 Preprocessing -- 4.2 Transformer Models -- 5 Result and Analysis -- 6 Conclusion -- References -- A Modified Pyramid Scale Network for Crowd Counting -- 1 Introduction -- 2 Related Works -- 3 The Proposed Method -- 3.1 Modified PSNet -- 3.2 Modified Pyramid Scale Module -- 3.3 Implementation Details -- 4 Experiments and Discussion -- 4.1 Evaluation Metrics -- 4.2 ShanghaiTech dataset -- 4.3 UCFCC50 -- 5 Conclusions and Future Scope -- References -- Driving Impact in Claims Denial Management Using Artificial Intelligence -- 1 Introduction -- 2 Literature Review -- 3 Data Summary -- 4 Methodology -- 4.1 Ground Truth -- 4.2 Data Preparation -- 4.3 Feature Selection -- 4.4 Model Development -- 4.5 Explainability Using SHAP -- 4.6 Joint Probability -- 5 Results and Discussion -- 5.1 Stratification -- 5.2 Strengths -- 6 Conclusion -- References. |
Identification of Landslide Vulnerability Zones and Triggering Factors Using Deep Neural Networks - An Experimental Analysis -- 1 Introduction -- 1.1 Study Area -- 1.2 Types of Landslides in Kerala -- 2 Related Work -- 3 Proposed Work -- 3.1 Spatial Database -- 3.2 Deep Neural Networks -- 4 Experimental Setup -- 5 Experimental Results -- 6 Conclusion -- References -- Classifying Offensive Speech of Bangla Text and Analysis Using Explainable AI -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Methodology -- 4.1 Convolutional Neural Network (CNN) -- 4.2 Long Short Term Memory (LSTM) -- 4.3 Support Vector Machine (SVM) -- 5 Result and Analysis -- 5.1 Classifiers and Accuracy -- 5.2 Explainable AI -- 6 Conclusion and Future Work -- 6.1 Conclusion -- 6.2 Future Work -- References -- Android Malware Detection Using Hybrid Meta-heuristic Feature Selection and Ensemble Learning Techniques -- 1 Introduction -- 2 Related Work -- 3 The proposed Android Malware Detection using hybrid meta-heuristic feature selection and Ensemble Learning Techniques -- 3.1 Feature Selection Process -- 3.2 Ensemble Learning-based Malware Detection -- 4 Performance Evaluation and Discussion -- 4.1 Without Feature Selection -- 4.2 With Feature Selection -- 5 Conclusion -- References -- Scoring Scheme to Determine the Sensitive Information Level in Surface Web and Dark Web -- 1 Introduction -- |
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1.1 Personal Information and Sensitive Information -- 1.2 Pastes -- 2 Background -- 2.1 Personal Information -- 2.2 Sensitive Information -- 2.3 Sensitive Personal Information -- 2.4 Types of Sensitive Information -- 3 Implementation -- 4 Results -- 5 Conclusion -- References -- Video Descriptor Using Attention Mechanism -- 1 Introduction -- 2 Literature Survey -- 3 Proposed System -- 3.1 Encoder -- 3.2 Decoder -- 3.3 NLP -- 4 Training Procedure -- 5 Results -- 6 Conclusion -- References. |
Informative Software Defect Data Generation and Prediction: INF-SMOTE -- 1 Introduction -- 2 Literature Review -- 3 Proposed Method -- 3.1 Elimination Uninformative Samples -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Metric -- 4.3 Experimental Results -- 5 Conclusion and Future Work -- References -- 2D-CNN Model for Classification of Neural Activity Using Task-Based fMRI -- 1 Introduction -- 1.1 Deep Neural Network Framework -- 2 Related Work -- 2.1 Neural Activity -- 2.2 Deep Learning Approaches -- 3 Materials and Methods -- 3.1 Dataset Analysis -- 3.2 Methodology -- 3.3 Functional Connectivity Analysis -- 4 Proposed Network Architecture -- 5 Experimental Results -- 5.1 Prediction Accuracy of the Proposed 2D-CNN Model -- 5.2 Evaluation of Model Performance -- 5.3 Classification of Voxel Response fMRI Images -- 6 Conclusion -- References -- Real-Time Multi-task Network for Autonomous Driving -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Backbone Network -- 3.2 Neck Network -- 3.3 Detection Head -- 3.4 Segmentation Head -- 3.5 Loss Function -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Results -- 5 Conclusion -- References -- Cardiovascular Disease Classification Based on Machine Learning Algorithms Using GridSearchCV, Cross Validation and Stacked Ensemble Methods -- 1 Introduction -- 2 Literature Survey -- 3 Research Methodology -- 3.1 Proposed Approach -- 3.2 Cross Validation, Stacked Ensemble, GridSearchCV Method -- 4 Experimental Design -- 4.1 Dataset Description -- 4.2 Data Pre-processing -- 4.3 Baseline Models -- 4.4 Evaluation Classifier Performance -- 4.5 Implementation Specifics -- 5 Experiment Results -- 5.1 Accuracy -- 5.2 AUC, ROC, Precision and Recall -- 5.3 KS Statistics -- 5.4 Cumulative Gain and Lift Curve -- 5.5 Learning Curve and Calibration Curve -- 5.6 Cross Validation Score. |
6 Conclusion and Future Work -- References -- Human Emotion Recognition from Body Posture with Machine Learning Techniques -- 1 Introduction -- 2 Prior Research -- 3 Proposed Approach -- 4 Emotion Classification Using Machine Learning Models -- 5 Experimental Results and Discussion -- 5.1 GEMEP Dataset -- 5.2 Performance Metrics -- 5.3 Results and Discussion -- 6 Conclusion -- References -- A Body Area Network Approach for Stroke-Related Disease Diagnosis Using Artificial Intelligence with Deep Learning Techniques -- 1 Introduction -- 2 Related Works -- 3 Research Methodology -- 3.1 Data Preprocessing -- 3.2 Proposed Model -- 4 Deep Learning Models -- 4.1 Convolutional Neural Network (CNN) -- 4.2 Radial Basic Function(RBF) -- 4.3 Multilayer Perceptron (MLPs) -- 4.4 Deep Belief Networks (DBNs) -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Evaluation Metrics -- 5.3 Mathematical Model -- 6 Results and Discussion -- 6.1 Accuracy Results Based on Dataset -- 6.2 Accuracy Results Based on Deep Learning Models -- 6.3 Accuracy Results Based Training Data Set -- 7 Conclusion -- References -- Accelerating the Performance of Sequence Classification Using GPU Based Ensemble Learning with Extreme Gradient Boosting -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Result and Discussion -- References -- Automated Vehicle Number |
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Recognition Scheme Using Neural Networks -- 1 Introduction -- 2 Lıterature Survey -- 3 System Model -- 4 Experimental Results -- 5 Conclusion -- References -- Noise Prediction Using LIDAR 3D Point Data - Determination of Terrain Parameters for Modelling -- 1 Introduction -- 2 Literature Review -- 3 Research Methodology -- 4 Result and Discussion -- 4.1 LIDAR Data Acquisition -- 4.2 Building Extraction -- 4.3 Building Corner Estimation -- 4.4 Path Determination -- 4.5 Formulation for Determination of Terrain parameters. |
5 Determination of Terrain Parameter and Noise Mapping. |
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