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Analytics Predictive for HRM: Using machine learning to Improve Workforce Scheduling and Staff Engagement 2

Analytics Predictive for HRM: Using Machine Learning to Impr...

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2nd IEEE international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Rajyalaxmi, M. Vijai, C. Lourens, Melanie Chatterjee Biswas, Prasanta Jahnavi, K.N. Umesh, U. School of Business Sr University Telangana Warangal India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Commerce and Business Administration Chennai62 India Durban University of Technology Deputy Dean Faculty of Management Sciences South Africa Cdoe Parul University Vadodara India SIndhi Institute of Management Bangalore India Department of Commerce and Management Studies KeralaMyladi Nilambur679329 India

ISBN: (纸本)9798350384277

Human resource management relies heavily on staff engagement in the workplace and workforce scheduling. Increased performance and results are ultimately the result of engaged and enthusiastic employees who feel a feeling of devotion, loyalty, and connection to their company. Workforce scheduling and staff engagement are influenced by various organizational elements, but little is known about the specific elements that influence them. This paper discusses improving workforce scheduling and staff engagement using data-driven methodologies and machine-learning techniques such as random forests, gradient boosting, artificial neural networks (ANN), and additional trees. The study-generated framework depends on historical statistics from the Public Utility Service Billing Organization. The ANN simulated framework, with its feature collection and carefully selected training variables, shows how useful the framework can be as a decision support tool for workforce scheduling in production and service operations. The below variables are employed as organizational elements in the prediction framework and feature significance evaluation, with staff engagement serving as the resulting aspect: equal treatment, diversity, and participation;secure culture;wellness and health;direct managers;quality of evaluations;care quality;harassment and discrimination;assault;and teamwork. In terms of prediction accuracy, all of the techniques yield outcomes that are similar in terms of prediction accuracy and feature significance order. According to the findings, teamwork is the second most important element influencing employee engagement, after secure culture. © 2024 IEEE.

关键词： Personnel training

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Pioneering Minimalist Speech Analysis Through Optimized Spectral Features machine learning Models 6

Pioneering Minimalist Speech Analysis Through Optimized Spec...

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6th IEEE international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Harsha Vardhini, P.A. Shiva Prasad, S. Vishnu Sai, M.H.S. Santoshi, Ch Konduru, Dhanush Vignan Institute of Technology and Science Department of ECE Telangana Deshmukhi India Telangana India Matrusri Engineering College Department of CSE Telangana India Integrated Software Engineering VIT Department of CSE Tamilnadu Vellore India

ISBN: (纸本)9798350306613

In the contemporary landscape, speech processing stands as a cornerstone across diverse applications, with gender identification emerging as a pivotal task in numerous domains, including law enforcement for swift resolution of criminal cases. The discernment of gender from speech recordings, especially in languages such as Telugu, holds paramount importance given its widespread usage and cultural significance in South India. This paper addresses the imperative need for robust gender identification methodologies tailored specifically for Telugu speech. Leveraging the rich heritage of Telugu as one of the most historically significant and widely spoken Dravidian languages, our study delves into the application of sophisticated statistical models, namely Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM), to unravel the gender nuances embedded within Telugu speech patterns. To facilitate this identification task, we meticulously curated a bespoke dataset, drawing from diverse sources to ensure representativeness and reliability. Our methodology encompasses an array of advanced feature extraction techniques, including Mel Frequency Cepstral Coefficient (MFCC), augmented with first and second-order differentials, and Zero Crossing Rate (ZCR). Moreover, to streamline the analysis and optimize accuracy, we employ Principal Component Analysis (PCA) for feature reduction, thus enhancing computational efficiency without compromising on discernment accuracy. The efficacy of our approach is substantiated through rigorous evaluation and comparison analyses, wherein we meticulously assess the performance of both original and reduced feature sets. Our findings underscore the effectiveness of our methodology in achieving notable accuracy levels, thereby underscoring its potential for real-world applications in gender identification from Telugu speech data. © 2024 IEEE.

关键词： machine learning models MFCC PCA speech Analysis ZCR

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Evaluating Lightweight GAN- and Adapted CTGAN-Based data Synthesis for Predictive Maintenance in High-Radiation Environments 11

Evaluating Lightweight GAN- and Adapted CTGAN-Based Data Syn...

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11th IEEE/ACM international conference on Big data computing, Applications and Technologies, BDCAT 2024

作者： Al-Hasan, Tamim M. Zhai, Xiaojun Mcdonald-Maier, Klaus D. Bensaali, Faycal Cryer, Alice University of Essex Colchester School of Computer Science and Electrical Engineering EssexCO4 3SQ United Kingdom Qatar University Al Tarfa College of Engineering Department of Electrical Engineering Doha Qatar United Kingdom Atomic Energy Authority Culham Science Centre Abingdon Remote Applications in Challenging Environments OxfordshireOX14 3DB United Kingdom

ISBN: (纸本)9798350367300

This paper presents a comparative analysis of two developed Generative Adversarial Network (GAN) architectures for synthesizing sensor data in predictive maintenance (PdM) applications within high-radiation environments. The study ad-dresses the challenge of data scarcity in such settings, where experimental runs are constrained by the risk of device failure and economic considerations. The two GAN models: GAN-1 uses the Conditional Tabular GAN (CTGAN) architecture, and GAN-2 employs a custom network. These models generated synthetic datasets that were used to train and evaluate three machine learning algorithms: Random Forest, k-Nearest Neighbours, and eXtreme Gradient Boosting. The performance of these PdM models trained on synthetic data was compared against models trained on the original limited dataset. Results demonstrate that GAN-1 produced synthetic data closely mirroring the characteristics of the original dataset, enabling PdM models to achieve comparable performance levels. This study highlights the potential of GAN-based data synthesis in enhancing PdM model development for high-radiation environments, offering a viable solution to the challenges of limited data availability in such harsh settings. The findings have significant implications for improving operational reliability and safety in nuclear and other extreme environments where electronic systems are deployed. © 2024 IEEE.

关键词： Gamma rays

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13th international conference on Telematics and computing, WITCOM 2024

13th International Conference on Telematics and Computing, W...

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13th international conference on Telematics and computing, WITCOM 2024

ISBN: (纸本)9783031772894

The proceedings contain 41 papers. The special focus in this conference is on Telematics and computing. The topics include: Real–Time Monitoring of a Photobioreactor for Microalgae Growth Based on Microcontrollers and IoT Technology;color Phase Shifting Profilometry Using Multi-frequency Temporal Phase Unwrapping;mapping Gender-Based Violence: Integrating machine learning and Geospatial Analysis in Mexico City;Neural Architecture Search Using DyNNO for Air Pollution Forecasting;efficient Computation Techniques for a Standard Topology Optimisation Routine;machine Translation Evolution from Natural Translation to GPT Technology;disruptions to V2V and V2I LiFi Communication in Traffic Lights for Smart City Applications;application of Convolutional Neural Networks for the Classification and Evaluation of Fruit Ripeness;simulation and Performance of Delta Modulator for CubeSat-Based Communications;integrating Satellite data for Seabird Species Distribution Modeling: Acquisition and Pre-processing of Predictor Variables;generative Artificial Intelligence in the Context of Urban Spaces;voice-Based User Interface Development for Android Mobile Applications;A Topic Modeling Approach Using Transformers for Open-Ended Questions at Instituto Profesional DUOC UC;microstrip Patch Antenna Implementation Based on Computational Simulation to Be Applied to Wi-Fi 6 Internet Access;development of a Convolutional Neural Networks Model as a Sign Language Interpreter;Energy Consumption Analysis of a WSN for Terrestrial Mammal Species Monitoring;Fine-Tuning the TOPSIS Technique and Transferring Knowledge of Different Driving Styles;artificial Immune Network Algorithm for the Multiple Sequence Alignment Problem of Alzheimer’s Disease Amyloid-Secretase-Pathway.

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Navigating learning Pathways: A Multifaceted Approach to Understanding Student Engagement and Progress in Online Programming Courses 12

Navigating Learning Pathways: A Multifaceted Approach to Und...

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12th international conference on Information and Education Technology, ICIET 2024

作者： Wang, Xiaonan Su, Yancong Sun, Yi Shigyo, Kento Ohtsuki, Kazuhiro Kiyomitsu, Hidenari Graduate School Of Intercultural Studies Kobe University Kobe Japan School Of Design And Art Xiamen University Of Technology Xiamen China Graduate School Of Information Technology Kobe Institute Of Computing Kobe Japan Hong Kong University Of Science And Technology Academy Of Interdisciplinary Studies Hong Kong Hong Kong

ISBN: (纸本)9798350371772

In an era when online education is becoming increasingly prevalent, and many institutions are transitioning onto online platforms, it has become crucial to understand online learning behaviors. The user data kept by online learning platforms has supported relevant research that has made great strides in analyzing both engagement and dropout rates in online courses. In online programming courses, learners struggle to understand the code and even code by themselves, while instructors barely have access to check everyone's code and give advice in real-time. It becomes critical for instructors to quickly grasp how learners are progressing and encountering difficulties in their online programming learning. This study combines learners' programming activity data, click stream data from video learning, and textual data such as accompanying notes and questions. A visual representation that combines multiple learning data is proposed, and learners' learning Trajectories Map in an online programming course is created from experimentally collected data. The results show a strong correlation between learners' programming activities and their engagement with the learning material. In addition, by comparing the learners' learning trajectories, it was possible to observe their different learning strategies, which provided more detailed clues for studying behavioral patterns and learner characteristics in online learning. This study introduces a new tool for educational evaluation, provides a more comprehensive perspective, and addresses the limitations of relying only on a single indicator for evaluation. It is of great significance to educational technology and computer-assisted education. © 2024 IEEE.

关键词： data visualization

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Portable Cross-Facility Workflows for X-ray Ptychography

Portable Cross-Facility Workflows for X-ray Ptychography

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2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Vong, Albert Henke, Steven F. Hoidn, Oliver Parraga, Hannah Zhou, Tao Mehta, Apurva Schwarz, Nicholas Argonne National Laboratory LemontIL United States SLAC National Accelerator Laboratory Menlo ParkCA United States

ISBN: (纸本)9798350355543

In this paper, we describe portable software work-flows for processing ptychography data from large-scale experimental facilities. The Advanced Photon Source (APS) and Linac Coherent Light Source (LCLS) are examples of such facilities, which generate large amounts of high-energy x-rays used for scientific research. Both facilities have recently undergone upgrades that increase data output by orders-of-magnitude. The increased data rates necessitate a robust processing pipelines that provides timely feedback to beamline scientists and scientific users. X-ray ptychography, a data-intensive computational imaging technique, stands to benefit from robust software workflows that facilitate the use of large computing facilities. Here we describe a file-based workflow that can be deployed at any ptychography beamline. We detail its use during two experiments: the first reconstructed 98 experimental scans sent from APS to the Argonne Leadership computing Facility (ALCF). The second experiment demonstrated cross-facility capabilities by reconstructing 15 experimental scans sent from users at the Linac Coherent Light Source (LCLS) to ALCF. © 2024 IEEE.

关键词： Cross-facility workflows data management Ptychography Software workflows

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Innovative Approaches to Teaching Quantum Computer Programming and Quantum Software Engineering 5

Innovative Approaches to Teaching Quantum Computer Programmi...

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2024 international conference on Quantum computing and Engineering

作者： Haghparast, Majid Moguel, Enrique Garcia-Alonso, Jose Mikkonen, Tommi Manuel Murillo, Juan Univ Jyvaskyla Jyvaskyla Finland Univ Extremadura Caceres Spain

ISBN: (纸本)9798331541378

Quantum computing is an emerging field that promises to revolutionize various domains, such as simulation optimization, data processing, and more, by leveraging the principles of quantum mechanics. This paper outlines innovative pedagogical strategies developed by university lecturers in Finland and Spain for teaching quantum computer programming and quantum software engineering. Our curriculum integrates essential tools and methodologies such as containerization with Docker, Qiskit, PennyLane, and Ocean SDK to provide a comprehensive learning experience. The approach consists of several steps, from introducing the fundamentals of quantum mechanics to hands-on labs focusing on practical use cases. We believe quantum computer programming is an important topic and one that is hard to teach, so having a teaching agenda and guidelines for teaching can be of great help.

关键词： Quantum computer programming quantum software engineering quantum computing quantum information quantum programming education Qiskit Pennylane Ocean SDK Containerization Docker Quantum Serverless

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AI-Enhanced Quality Control Systems for Intelligent Industrial Applications

AI-Enhanced Quality Control Systems for Intelligent Industri...

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2024 international conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Sreeja, S.P. Sreenivasulu, D. Jain, Alok Mittal, Mudit Subbiah, Ram Ziara, Sajjad New Horizon College of Engineering Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Mechanical GRIET Bachupally Telangana Hyderabad India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (纸本)9798350355338

Since AI has been used in industrial settings;significant changes have been made to quality control and maintenance. Adaptive Defect Net (ADN), We believe Reinforce Optimize (RO), Predictive AI-Maintain (PAM), and Predictive AI-Adaptive (PAM) can tackle smart industrial challenges. We tested and compared these tactics to famous ones. ADN's machine vision and deep learning make it quick at issue detection. We proved it was the most accurate technique to detect even the slightest issues via testing. This ensures our products fulfill our requirements. The RO offers a unique perspective on quality control improvement. Since it can mend itself and learn from its errors, it reduces check-taking staff and expenses. We observed that it may boost flexibility and ability. PAM predicts repair characteristics will occur much faster than in scheduled maintenance. Advance machine learning and sensor data analysis detect issues before they occur in predictive asset maintenance (PAM). This reduces expensive unexpected downtime and extends equipment life. The strategies exhibited greatly effect smart workplace adoption in many contexts. ADN, RO, and PAM combined provide incredible precision, speed, and flexibility in maintenance and quality control. These innovative concepts have the potential to significantly reduce costs, improve goods, and fundamentally alter the manufacturing process. The way industry does maintenance and quality control is going to undergo a radical transformation thanks to these AI-powered techniques. In the long run, this will make things more economical, dependable, and efficient. © 2024 IEEE.

关键词： Predictive maintenance

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Beyond Aligned Target Face: StyleGAN-based Face-Swapping via Inverted Identity learning

Beyond Aligned Target Face: StyleGAN-based Face-Swapping via...

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IEEE international conference on Multimedia and Expo (ICME)

作者： Li, Yuanhang Mao, Qi Jin, Libiao Commun Univ China Sch Informat & Commun Engn Beijing Peoples R China Commun Univ China State Key Lab Media Convergence & Commun Beijing Peoples R China

ISBN: (纸本)9798350379822;9798350379815

Recent face-swapping methods based on StyleGAN encounter limitations in transferring identity exclusively to aligned and fixed-size target images, constraining their applicability in real-world scenarios. In this work, we introduce a pioneering Non-Aligned Target Face Swapping method that enables the transfer of identity from an aligned source image to any non-aligned target image. The direct reconstruction of identity representation from an aligned source image and other attribute representations from non-aligned target images often leads to generated images with aligned identity but unaligned attributes. To overcome this challenge, we propose an inverted identity learning strategy that calculates identity loss after transforming face-swapping results back into a cropped alignment space. Comprehensive qualitative and quantitative experiments showcase the effectiveness of our method compared to current state-of-the-art StyleGAN-based techniques on both aligned and simulated nonaligned data. Furthermore, we validate the generalizability of our method using non-aligned face inputs from diverse out-of-domain datasets, enhancing its potential for real-world applications.

关键词： Face-swapping StyleGAN non-aligned

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Edge Caching with Federated Unlearning in Cluster-Centric Small Cell Networks 6

Edge Caching with Federated Unlearning in Cluster-Centric Sm...

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6th international conference on Next Generation data-Driven Networks, NGDN 2024

作者： Cui, Lingrui Wang, Pengfei Han, Yuqi Dalian University of Technology School of Computer Science and Technology Dalian116024 China

ISBN: (纸本)9798350388374

Edge content caching based on federated learning has emerged as a promising technology to address the increasing personalized demands of compute-intensive and latency-sensitive applications within small cell network scenarios. Existing content caching schemes often struggle to accommodate personalized caching requirements while processing invalid data such as poisoned and stale data. To address these challenges, this paper proposes a federated unlearning-enabled small cell network caching paradigm (FedSCC). Firstly, we introduce a personalized federated learning caching scheme based on user classification. This scheme categorizes users according to their preferences and caches content tailored to the preferences of corresponding user classes in small base stations, enhancing cache hit rates and reducing service latency. Secondly, we present a content caching algorithm based on federated unlearning to further improve the caching paradigm's performance. By mitigating the adverse effects of invalid data on the global model and notably reducing service latency, this algorithm significantly enhances cache performance. Experimental results demonstrate that in small-cell network scenarios characterized by personalized caching demands, FedSCC can elevate cache hit rates by at least 21.73% compared to baselines. Additionally, cache hit rates exhibit a significant improvement of 27.05% in experiments involving invalid data. © 2024 IEEE.

关键词： Federated learning

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