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data-Driven Approaches for Statistical Analysis in Human Resource Management: A Comprehensive Review and Future Directions

Data-Driven Approaches for Statistical Analysis in Human Res...

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Advance Computing and Innovative Technologies in engineering (ICACITE), international conference on

作者： G. Radha Krishna Murthy Sammaiah Buhukya Anubhuti Dhyani Anjana Prusty Shakira Mukhtar Melanie Lourens Department of Management COBE Wollega University Nekemte Ethiopia Department of MBA Megha Institute of Engineering and Technology For Women JNTU Hyderabad Uttaranchal Institute of Technology Uttaranchal University School of Sciences SR University Waranagl Telanagana Department of Management Studies Islamic University of Science and Technology Deputy Dean Faculty of Management Sciences Durban University of Technology South Africa

This paper provides a comprehensive review of the use of statistical analysis in human resource management (HRM). It starts by exploring the various ways in which statistical analysis has been applied in HRM, including personnel selection, performance appraisal, and compensation. The paper then discusses the limitations and challenges of using statistical analysis in HRM, such as small sample sizes, lack of relevant data, and ethical concerns. The paper concludes with a discussion of future directions for the use of statistical analysis in HRM, including the potential benefits of big data and machine learning, as well as the need for further research in this area. The paper provides insights for HR practitioners and researchers in helping organizations to better understand and use statistical analysis in HRM.

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Misinformation Detection Using a Hybrid Deep learning Model with Transformer Based Pre-Trained ALBERT Model

Misinformation Detection Using a Hybrid Deep Learning Model ...

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data, Decision and Systems (ICDDS), IEEE international conference on

ISBN: (数字)9798350363890

ISBN: (纸本)9798350363906

Rise of social media platforms has made the spread of information easier. This also raises the issue of spreading misinformation which creates serious problems for society and governments and has a negative impact on many facets of daily life. One of the difficult problems in natural language processing is misinformation detection, which involves separating misinformation from genuine (or truthful) information. This study proposes a transformer-based pretrained Albert model and integrated deep learning model which provides enhanced feature learning on dataset collected from different sources to provide a solution to the problem of misinformation detection. The dataset used for the task is a combination of Liar, genuine and misleading information which leads to Combined Corpus dataset. The deep learning model used are Bi-directional Gated Recurrent Unit (Bi-GRU) and a Convolutional Neural Network (CNN) to provide better feature engineering to categorize genuine and misinformation. A base Albert model has also been trained on the Combined Corpus (CC) dataset to evaluate the effect of enhanced feature learning using Bi-GRU and CNN with Albert. The results obtained reflect 1.30% and 0.76% increment in accuracy on Combined Corpus (CC) dataset from the base Albert model.

关键词： Representation learning Deep learning Social networking (online) Semantics Transformers data models Natural language processing Convolutional neural networks Fake news Context modeling

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Automated Garbage Waste Management using Deep learning for sustainability

Automated Garbage Waste Management using Deep Learning for s...

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

作者： Kavitha R Yazhini S Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

As smart cities evolve, waste management constitutes one of the major primary elements of it. Recycling can be made more affordable in the vital phase of garbage sorting process. However, categorizing garbage manually involves an enormous amount of effort and time. A machine vision technique is presented in this investigation to autonomously recognize and divide garbage into six categories—plastic, paper goods, glass, metallic materials, cardboard, and trash. There are over 2509 pictures in the deep learning structure dataset. Garbage datasets are also additionally collected and tested in real time employing mobile devices. The model is constructed using CNN-2D and Yolo Tiny V3, deep convolution neural network-based methods. Furthermore, dropout and data augmentation have been employed to enhance accuracy. The results indicate that the proposed Yolo Tiny V3 approach operates effectively in garbage classification, obtaining greatest likelihood distributions for every garbage class that was categorized.

关键词： Waste management Training Deep learning Waste materials Accuracy Smart cities Glass Telecommunication computing Convolutional neural networks Testing

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Hybridization of Sparrow Search Algorithm and Sine Cosine Algorithm for Epileptic Seizure Detection

Hybridization of Sparrow Search Algorithm and Sine Cosine Al...

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Electrical Power engineering, Communication and Computing Technology(ODICON), Odisha international conference on

作者： Sumant Kumar Mohapatra Kaliprasanna Swain Puspanjali Mallik Sabita Mali Farida A. Ali Trident Academy of Technology Bhubaneswar Odisha Dept. of Computer Science Shailabala Women's Autonomous College Cuttack India Faculty of Engineering ITER SOA University Bhubaneswar India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

An imbalance in electrical signal flow among neurons causes epilepsy, a complex brain disease that affects other parts of the body and results in seizures. Researchers and neurologists have put their efforts into devising powerful electroencephalogram devices to address the importance of health issues. In this context, machine learning algorithms are used to accurately detect the presence of a seizure signal. This experiment uses the hybridization of two meta-heuristic optimization algorithms: the sparrow search algorithm and the sine cosine algorithm. To conduct the research, this experiment uses the publicly available CHB-MIT EEG dataset, and for feature extraction, it uses the discrete wavelet transform (DWT). ANOVA We use feature selection to identify similar groups. This study uses three classifiers: support vector machines (SVM), Adaptive Neuro Fuzzy Inference System (ANFIS), and Extreme learning ANFIS (ELANFIS). We got 99.8% accuracy, 98.35% sensitivity, 99.02% specificity, 98% positive predictive value, and 1 for area-under-curve for SSA-SCA-DWT-ELANFIS. This shows that it works better than SSA-SCA-DWT-SVM, SSA-SCA-DWT-ANFIS, and other methods that are already out there. In the future, we can integrate this study with the Internet of Things (IoT).

关键词： Support vector machines machine learning algorithms Accuracy Transforms Prediction algorithms Feature extraction Electroencephalography Discrete wavelet transforms Classification algorithms Internet of Things

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An Improved Model for Gene Expression data Analysis Using Combined Feature Reduction and ELM Approaches

An Improved Model for Gene Expression Data Analysis Using Co...

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Electrical Power engineering, Communication and Computing Technology(ODICON), Odisha international conference on

作者： Jogeswar Tripathy Rasmita Dash Binod Kumar Pattanayak Debaniranjan Mohapatra Nrusingha Tripathy Subrat Kumar Nayak Department of CSE ITER Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar Odisha India Department of ECE ITER Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar Odisha India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

On a worldwide scale, the second leading cause of death is cancer. To combat cancer, a variety of approaches have been proposed. It is strongly predicted that gene expression data would be used to help in the development of better cancer detection. On the other hand, gene expression data is dimensionally high, has a small sample size in comparison to the data dimension, and many genes are redundant and also noisy by nature. As a result, rather than using all genes to classify gene expression data, it would be wiser to use a subset of genes. Thus, in this research work, the proposed model is designed considering ranked-based filter sequence (FS) approaches for significant feature extraction and Extreme learning machine (ELM) for data classification. This analysis is carried out over four gene expression data. As FS approaches are highly successful for high dimension data, significant features are extracted using 4-stage feature reduction strategy applying RF approach. Thereafter the reduced data is applied for classification. ELM is a learning algorithm for a Single-Layer Feed-Forward Network (SLFN) that converges much faster than traditional methods and produces promising results. Thus, due to its superior approximation capability and generalization, ELM has equipped the interest of numerous researchers in recent years. In this analysis, considering different types of activation functions and considering a collection of network architecture, four numbers of ELM models are designed and used for classification purposes with four different activation functions. From all the experimental results it is found that IIELM (CM4) outperforms over all the model.

关键词： Radio frequency Analytical models Power engineering Computational modeling Neural networks Feature extraction data models Gene expression Noise measurement Cancer

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Research on Lightweight Unsafe State Recognition Algorithm Based on Dual-teacher Distillation Network

Research on Lightweight Unsafe State Recognition Algorithm B...

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Cloud Computing, Big data Applications and Software engineering (CBASE), international conference on

作者： Hao Feng Haochen Zhang Chaoyu Zhang Peng Hu Jiangzhe Feng Long Yuan (Beijing) New Energy Engineering Technology Co. Ltd. Beijing China National Energy and Wind Power Operation Technology R&D (Experimental) Center Xi'an China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

This research is dedicated to the development of lightweight unsafe state recognition algorithms for new energy stations to optimize safety monitoring and real-time risk assessment. Given that traditional deep learning models are computationally redundant, have large parameters, and are not suitable for resource-constrained environments, we have constructed a lightweight CNN model by innovatively integrating integrating multi-scale modules and knowledge distillation techniques. The model accurately locates anomalies through multi-scale feature modules, and adopts a dual-teacher distillation strategy to reduce the number of parameters and storage requirements, ensuring recognition accuracy while realizing a lightweight model. This algorithm not only meets the needs of edge computing, but also follows the principle of data confidentiality, providing an efficient solution for the safety monitoring of new energy stations.

关键词： Training data privacy Computational modeling data models Classification algorithms Safety Computational efficiency Risk management Monitoring Software engineering

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Smart Crop Cultivation: Harnessing machine and Deep learning for Informed Agricultural Practices

Smart Crop Cultivation: Harnessing Machine and Deep Learning...

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： Smit Meghani Jay Vele Kunal Madrecha Abhijeet Salunke Department of Computer Engineering Sardar Patel Institute of Technology Mumbai India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Amongst various occupations in India, farming is the most common. It boosts the Indian economy significantly and provides Indians with a wide range of job opportunities. The ideal crop for each type of soil is no longer chosen by farmers. This has an immediate effect on agricultural yields. It is causing serious financial losses for the farmers in the area. Cultivating a certain crop on a particular type of soil now involves several factors to take into account. The optimal crop to plant is determined by taking into account a number of soil specifications. This novel opinion mining method will consider the following parameters: temperature, air humidity, soil pH, nitrogen, potassium, phosphorus, and annual rainfall. The model aids in determining the kind of crop that has to be planted by taking into account the data collected from prior years. In order to support the mission, various algorithms are analyzed and evaluated on multiple fronts, including comparative research and analysis. In addition, the ideal fertilizer will be recommended depending on crop-related environmental factors, temperature, pH level, and soil moisture content.

关键词： Temperature dependence Sentiment analysis Temperature Rain Soil moisture Crops Phosphorus

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machine learning Techniques for Detecting DDOS Attacks

Machine Learning Techniques for Detecting DDOS Attacks

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Emerging Smart Technologies and Applications (eSmarTA), international conference on

作者： Mamoon M. Saeed Husam Nedhal Rashad Mohammed Othman Abdulkafi Hazaea Gazem Rashid A. Saeed Hossam Mohammed Ahmed Morei Aimen Ebdulkreem Thabet Eidah AbdulGuddoos S.A. Gaid Ameen Sami Ameen Saif Al-Uosfi Mohamed Galal Qasem Al-Madhagi Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Yemen Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia Dept. of Communication & Computer Engineering Faculty of Engineering & Information Technology Taiz University Taiz Yemen

The development witnessed by the world of science and technology and the emergence of the Internet, where cybersecurity has become one of the most important areas that are growing rapidly every day, and one of its goals is to maintain access to users at any time. Where it is noted that the biggest enemy for the availability is Distributed Denial of Service (DDoS) attack, which works to prevent access to the service and slow down the service. Through this paper, machine learning will be used to discover the DDoS attack and know its type to be aware of it and take the necessary measures for that. Where the CICDDoS2019 dataset will be used. After it is studied, analyzed, cleaned and the best algorithm was selected. Five classification algorithms (Random Forest, Decision Tree, SVM, Naive Bayes, and xgboost) were used to train and test the data from the datasets, and it can be said that of the five, Random Forest algorithms had the highest level of accuracy (99.95426%).

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An Identification of Guava Fruit Disease Using ML

An Identification of Guava Fruit Disease Using ML

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international conference on Advancement in Electrical and Electronic engineering (ICAEEE)

作者： Khandakar Rabbi Ahmed Towfika Salam Subarna Nandi Nazmul Hasan Omor Faruk Al Rashid Shafiun Miraz Md. Shafi Reza Farjana Rahman Miyan Research Institute International University of Business Agriculture and Technology Uttara Model Town Dhaka Bangladesh Dept. of Mechatronics Engineering Rajshahi University of Engineering and Technology (RUET) Kazla Rajshahi Bangladesh Dept. of Computer Science and Engineering Rajshahi University of Engineering and Technology (RUET) Rajshahi Bangladesh Dept. of Data Science London South Bank University 103 Borough Rd London Dept. of Computer Science and Engineering Varendra University Rajshahi Bangladesh Dept. of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Graduate School of Integrated Energy of AI Jeonbuk National University Jeonju North Jeolla South Korea

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

Fruits contain a lot of fiber. Fruits contain biologically active substances that improve our health. This study focuses on identifying and classifying guava fruit diseases. Guava disease has become a significant issue for guava production, threatening farmers’ socioeconomic growth. As a result of this phenomenon, an automated computer vision-based guava disease diagnosis system was developed that can detect malicious guava and direct it to early cure approaches, reducing relative economic loss. In light of this, we present in this study a guava illness detection and categorization recommendation approach based on convolutional neural networks (CNN). This paper employs transfer learning to classify images using our customized convolutional neural network (CNN) model architecture. The photographs in the data set were obtained from the Mendeley data website. The data set includes 306 images of healthy and unhealthy guava fruits and leaves. The four disease classes targeted by the data sets are dot, Canker, mummification, and Rust. We obtained an average accuracy of 74.72% for training and 34.74% for testing from the data set used.

关键词： Training Accuracy Transfer learning Production Computer architecture Optical fiber networks Convolutional neural networks

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Construction of English Chinese translation units and translation models based on deep learning algorithms

Construction of English Chinese translation units and transl...

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Advancement in Technology (ICONAT), international conference for

作者： Bing Wang Chengdu Neusoft University Chengdu Sichuan China

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

the mainly using computer methods and intelligent methods to make comprehensive judgments about the English translation process and content, so as to achieve better analysis and research. The role of translation units and models in the theoretical system of English Chinese translation is very important, but there are problems of poor timeliness and narrow domain coverage. The traditional shallow learning mode cannot solve the problem of large vocabulary analysis in English-Chinese transliteration, and the application method is unreasonable. The deep learning algorithm to analyze the translation unit system. First, the web crawler technology is used to evaluate the data of English Chinese bilingual news websites and English Chinese translation websites, according to the translation standards It is required to characterize indicators to reduce interference factors in bilingual translations. Then, the machine translation mode evaluates the translation unit system, forms a unity of principle and flexibility in translation, and corrects Comprehensive analysis of the results of the translation unit system. The analysis results show that the method proposed in this paper can improve the accuracy of analysis and calculation, so as to realize the effective and rational judgment of analysis and better analyze the results. Comprehensive utilization facilitates the effective analysis and realization of analysis. MATLAB simulation shows that under certain evaluation criteria, the deep learning algorithm has a translation unit system for the English Chinese translation theory system The accuracy and timeliness are better than the traditional shallow learning mode.

关键词： Deep learning Analytical models Technological innovation Vocabulary Accuracy Crawlers Interference machine translation MATLAB Standards

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