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Human Resource Demand Prediction and Configuration Model Based on Grey Wolf Optimization and Recurrent Neural Network

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Computational Intelligence and Neuroscience 2022年第0期2022卷 5613407页

作者： Rajagopal, Navaneetha Krishnan Saini, Mankeshva Huerta-Soto, Rosario Vílchez-Vásquez, Rosa Kumar, J.N.V.R. Swarup Gupta, Shashi Kant Perumal, Sasikumar Business Studies University of Technology and Applied Sciences Salalah Oman Department of Management Studies Government Engineering College Jhalawar Rajasthan Jhalrapatan India Graduate School Universidad Cesar Vallejo Lima Peru Faculty of Science Universidad Nacional Santiago Antunez de Mayolo Huaraz Peru MIEEE Department of Computer Science and Engineering SR Gudlavalleru Engineering College Gudlavalleru India Computer Science Engineering Integral University UP Lucknow India Department of Computer Science Wollo University Kombolcha Institute of Technology Post Box No. 208 Kombolcha Ethiopia

Business development is dependent on a well-structured human resources (HR) system that maximizes the efficiency of an organization's human resources input and output. It is tough to provide adequate instructions for HR's unique task. In a time when the domestic labor market is still maturing, it is difficult for companies to make successful adjustments in HR structures to meet fluctuations in demand for human resources caused by shifting corporate strategies, operations, and size. Data on corporate human resources are often insufficient or inaccurate, which creates substantial nonlinearity and uncertainty when attempting to predict staffing needs, since human resource demand is influenced by numerous variables. The aim of this research is to predict the human resource demand using novel methods. Recurrent neural networks (RNNs) and grey wolf optimization (GWO) are used in this study to develop a new quantitative forecasting method for HR demand prediction. Initially, we collect the dataset and preprocess using normalization. The features are extracted using principal component analysis (PCA) and the proposed RNN with GWO effectively predicts the needs of HR. Moreover, organizations may be able to estimate personnel demand based on current circumstances, making forecasting more relevant and adaptive and enabling enterprises to accomplish their objectives via efficient human resource planning. © 2022 Navaneetha Krishnan Rajagopal et al.

关键词： Personnel

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Conventional/Phase Shift Dual Drive Mach-Zehnder Modulation Measured Type Based Radio over Fiber Systems

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Journal of Optical Communications 2024年第s1期45卷 s123-s131页

作者： Amiri, I.S. Rashed, Ahmed Nabih Zaki Rahman, Ziaur Paul, Bikash Kumar Ahmed, Kawsar Computational Optics Research Group Advanced Institute of Materials Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Applied Sciences Ton Duc Thang University Ho Chi Minh City Viet Nam Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt School of Computer Science and Software Engineering RMIT University 414-418 Swanston Street MelbourneVIC3000 Australia Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Department of Software Engineering Daffodil International University Sukrabad Dhanmondi Dhaka1207 Bangladesh

This work clarified the conventional/phase shift dual Mach-Zehnder modulation measured technique for upgrading radio over fiber (ROF) systems. Four levels of pulse amplitude modulation and non-return to zero code scheme are employed in different previous studies for upgrading ROF systems. The possible transmission distance is extended to 150 km and a bit rate of 40 Gb/s for achieved acceptable max. Q factor of 13.7498 and minimum bit error rates of 1.82×10-43. The total optical received power is measured at fiber cable length for maximum distance. Also, in the same way, the signal power levels are measured after fiber optic cable and avalanche photo detector optical receiver. The proposed model has presented the enhancement percentage ratio of 13.654% over the previous models in signal quality enhancement. © 2020 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Q factor measurement

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Adaptive computer Vision Solutions for Individuals with Special Needs

Adaptive Computer Vision Solutions for Individuals with Spec...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Issam Jebreen Shadi Nashwan Ahmad Al-Qerem Ali Mohd Ali Mohammad A Al Khaldy Mohammed Rajab Faculty of Information Technology Middle East University Jordan Computer Science Department Zarka University Jordan College of Engineering Technology Al-Balqa Applied University Jordan Business Intelligence Data Analytics University of Petra Amman Jordan University Headquarter University Of Anbar Iraq

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

With the rapid evolution of artificial intelligence (AI) and computer vision, assistive technologies have become increasingly adept at supporting individuals with special needs. This paper provides a comprehensive examination of two critical areas: AI-powered visual assistance systems for the visually impaired and sign language recognition and generation frameworks for the Deaf and hard-of-hearing communities. We review the latest research trends, algorithms, and tools, highlighting emerging deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformers in both domains. We then propose a unified framework that leverages edge computing, multimodal learning, and advanced human-computer interaction (HCI) techniques to address challenges such as real-time object recognition, complex gesture capture, data scarcity, and cultural variations in sign languages. We further detail an experimental design and set of results that demonstrate the feasibility and benefits of these approaches, followed by a discussion of ethical considerations, limitations, and future directions. Our goal is to illustrate a path toward more inclusive AI systems that offer robust solutions for accessibility, ultimately bridging communication gaps and fostering independence for individuals with special needs [1]–[11].

关键词： Sign language computer vision Visualization Ethics Visual impairment Assistive technologies Transformers Real-time systems Object recognition Artificial intelligence

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Neuro-Tech Revolution: AI-EEG Integration for Efficient Depression Diagnosis

TechRxiv

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TechRxiv 2023年

作者： Gabdrakhimov, Bekarys Detlefsen, Nicki Skafte Uyanik, Cihan Ejaz, Osama Khan, Muhammed Ahmed Hasan, Muhammad Abul Qazi, Saad Ahmed Puthusserypady, Sadasivan The Department of Health Technology Technical University of Denmark Kgs. Lyngby2800 Denmark The Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby2800 Denmark The Department of Electrical Engineering Stanford University Palo Alto94304 United States The Neurocomputation Lab NED University of Engineering and Technology Karachi75600 Pakistan

Major depressive disorder (MDD) is a common mental disorder affecting the lives of about 280 million people and increasing rates of suicidal mortality. The current methods of diagnosis of depression are subjective, time-consuming, expensive, and inaccurate because of its heterogeneous symptoms that overlap with other disorders. In this paper, we exploit the potential of the fusion of artificial intelligence (AI) and electroencephalogram (EEG) to revolutionize the automatic diagnosis of depression and compare the classification performance of machine learning (ML) and deep learning (DL) based techniques. Results from the analysis of data recorded from 46 subjects (23 MDD and 23 Control) show that the ML methods, particularly the ensemble model with the Dempster-Shafer combination rule outperforms other models, achieving an accuracy of 99.62% and showing robustness to the variations in the data. Our work also includes a study on the effect of various hyper-parameters, in particular the number of EEG channels, feature selection methods, number of selected features, and segmentation length on the model performance. The AI-EEG integration can enhance the accuracy of diagnosis, enable personalized treatment plans, and improve patient outcomes. Continued research, development, and validation of AI algorithms, in conjunction with ethical considerations, will be crucial to harness the full potential of this technology in mental healthcare. © 2023, CC BY.

关键词： Electroencephalography

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What Students Want to Have in the Post-Pandemic?: A Study about Computing science Study Preferences and Mode of Study in Southeast Asia 22

What Students Want to Have in the Post-Pandemic?: A Study ab...

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Proceedings of the 2022 5th International Conference on Education Technology Management

作者： Peter ChunYu Yau Yongmin Kim Dennis Wong Patrick Pang Stanley Kan Qi Cao School of Computing Science University of Glasgow United Kingdom James Watt School of Engineering University of Glasow United Kingdom Department of Computer Science State University of New York Korea Korea Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9781450398015

After two years of online learning, minor changes happened in some tiny places which we may not always notice. These changes may impact the school management, education arrangement, and curriculum design: what do students expect from their teachers, and the school? Are they expecting forever online learning, and getting their degree completely online? In this study, we examined 93 final-year students from the school of computing science which mainly used work-based learning applied to learn pedagogy. We analyze their viewpoint on how COVID-19 changed their understanding of teaching and learning: what is the role of teachers and students? And how do they see the arrangement for the internship program? We found that over 92% of students agree that a creditable degree can better secure a job; over 68% of the students believed that face-to-face teaching is still a better choice, but at the same time, over 73% wish to have the materials recorded so that they can access them at their convenience time. We also examined some of the course design elements, especially those related to the internship program, to achieve a possible curriculum improvement for the students in the Asia Pacific region.

关键词： post-covid applied learning work-study program remote learning internship mode of study home-based learning vocational education

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Evaluating the Effect of Volatile Federated Timeseries on Modern DNNs: Attention over Long/Short Memory

Evaluating the Effect of Volatile Federated Timeseries on Mo...

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International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Ilias Siniosoglou Konstantinos Xouveroudis Vasileios Argyriou Thomas Lagkas Sotirios K. Goudos Konstantinos E. Psannis Panagiotis Sarigiannidis Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece R&D Department MetaMind Innovations P.C. Kozani Greece Department of Networks and Digital Media Kingston University Kingston Upon Thames United Kingdom Department of Computer Science International Hellenic University Greece Physics Department Aritostle University of Thessaloniki Thessaloniki Greece Department of Applied Informatics School of Information Sciences University of Macedonia Thessaloniki Greece

In regards to the field of trend forecasting in time series, many popular Deep Learning (DL) methods such as Long Short Term Memory (LSTM) models have been the gold standard for a long time. However, depending on the domain and application, it has been shown that a new approach can be implemented and possibly be more beneficial, the Transformer deep neural networks. Moreover, one can incorporate Federated Learning (FL) in order to further enhance the prospective utility of the models, enabling multiple data providers to jointly train on a common model, while maintaining the privacy of their data. In this paper, we use an experimental Federated Learning System that employs both Transformer and LSTM models on a variety of datasets. The sytem receives data from multiple clients and uses federation to create an optimized global model. The potential of Federated Learning in real-time forecasting is explored by comparing the federated approach with conventional local training. Furthermore, a comparison is made between the performance of the Transformer and its equivalent LSTM in order to determine which one is more effective in each given domain, which shows that the Transformer model can produce better results, especially when optimised by the FL process.

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A Fully Programmable On-Chip Planar Waveguide for Machine Learning

A Fully Programmable On-Chip Planar Waveguide for Machine Le...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Martin M. Stein Tatsuhiro Onodera Benjamin A. Ash Mandar M. Sohoni Melissa Bosch Ryotatsu Yanagimoto Marc Jankowski Timothy P. McKenna Tianyu Wang Gennady Shvets Maxim R. Shcherbakov Logan G. Wright Peter L. McMahon School of Applied and Engineering Physics Cornell University Ithaca NY USA NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale CA USA E. L. Ginzton Laboratory Stanford University Stanford CA USA Department of Electrical Engineering and Computer Science UC Irvine Irvine CA USA Department of Applied Physics Yale University New Haven CT USA Kavli Institute at Cornell for Nanoscale Science Cornell University Ithaca NY USA

We introduce a device containing a planar waveguide whose spatial refractive index profile $n(x, z)$ can be programmed in real time. We demonstrate use this device as an optical neural network.

ISBN: (纸本)9798350369311

We introduce a device containing a planar waveguide whose spatial refractive index profile $n(x, z)$ can be programmed in real time. We demonstrate use this device as an optical neural network.

关键词： Optical planar waveguides Refractive index Waveguide lasers Optical fiber networks Optical variables control Real-time systems Planar waveguides System-on-chip Optical refraction Optical devices

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Muscle Deformation Using Position Based Dynamics 13th

Muscle Deformation Using Position Based Dynamics

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13th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2020

作者： Kohout, Josef Červenka, Martin NTIS - New Technologies for the Information Society Faculty of Applied Sciences University of West Bohemia Univerzitní 8 Plzeň Czech Republic Department of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Univerzitní 8 Plzeň Czech Republic

ISBN: (纸本)9783030723781

This paper describes an approach to personalized musculoskeletal modelling, in which the muscle represented by its triangular mesh is subject to deformation, based on a modified position-based dynamic (PBD) method, followed by decomposition of its volume into a set of muscle fibres. The PBD was enhanced by respecting some muscle-specific features, mainly its anisotropy. The proposed method builds no internal structures and works only with the muscle surface model. It runs in real-time on commodity hardware while maintaining visual plausibility of the resulting deformation. For decomposition, the state-of-the-art Kukačka method is used. Experiments with the gluteus maximus, gluteus medius, iliacus and adductor brevis deforming during the simulation of the hip flexion and decomposed into 100 fibres of 15 line segments show that the approach is capable of achieving promising results comparable with those in the literature, at least in the term of muscle fibre lengths. © 2021, Springer Nature Switzerland AG.

关键词： Deformation

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Predictive Modeling of Dental Health Outcomes Based on Fluoride Concentrations using AI

Predictive Modeling of Dental Health Outcomes Based on Fluor...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Swathi Pai M Annepu Arudra Dankan Gowda V Parashuram Shankar Vadar Mahadeo Ramchandra Jadhav Mandeep Kaur Department of Artificial intelligence and machine learning NITTE (Deemed to be University) NMAM Institute of Technology Nitte Karnataka India Department of Computer Science & Engineering Rajiv Gandhi Institute of Technology Bangalore Karnataka India Department of Electronics & Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India Yashwantrao Chavan School of Rural Development Shivaji University Kolhapur Maharashtra India Department of Applied Science Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Electronics and Communication Engineering Punjabi University Patiala Punjab India

Oral health is a crucial aspect of general health, and the presence of fluoride in drinking water has been consistently linked to its improvement. This work utilizes deep learning and machine learning approaches to develop prediction models that can estimate oral health consequences based on fluoride concentrations. Our analysis is based on a comprehensive dataset that includes fluoride levels and oral health indicators from several geographic locations. The dataset covers a broad range of demographic and environmental factors. The study involves a thorough data pretreatment procedure, which includes activities such as data cleaning, standardization, and feature engineering. All of these processes contribute to improving and making the input variables more relevant. The approach used encompasses a range of machine learning algorithms, including as neural networks, decision trees, and ensemble approaches, which are used to create prediction models. Thoroughly adjusting hyperparameters and using cross-validation methods are used to maximize the effectiveness of the model.

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Good Quantum LDPC Codes with Linear Time Decoders

arXiv

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arXiv 2022年

作者： Dinur, Irit Hsieh, Min-Hsiu Lin, Ting-Chun Vidick, Thomas Department of Applied Math and Computer Science The Weizmann Institute of Science Hong Hai Research Institute Taipei Taiwan Department of Physics University of California San DiegoCA United States Department of Computing and Mathematical Sciences California Institute of Technology CA United States

We construct a new explicit family of good quantum low-density parity-check codes which additionally have linear time decoders. Our codes are based on a three-term chain pFm2*mqV ÝÑ δ0 pFm2 qE ÝÑ δ1 FF2 where V (X-checks) are the vertices, E (qubits) are the edges, and F (Z-checks) are the squares of a left-right Cayley complex, and where the maps are defined based on a pair of constant-size random codes CA, CB: Fm2 Ñ F∆2 where ∆ is the regularity of the underlying Cayley graphs. One of the main ingredients in the analysis is a proof of an essentially-optimal robustness property for the tensor product of two random codes. © 2022, CC BY.

关键词： Decoding

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