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strategic Human Resource Management Innovations in Finance and Marketing with ML

Strategic Human Resource Management Innovations in Finance a...

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2024 IEEE international conference on intelligent Systems and Advanced Applications, ICISAA 2024

作者： Muzhumathi, R. Vijayakanthan, S. Rajeshkumar, L. Lekshmi, R.S. Chandrasekaran, S. Selvameena, R. Velammal Engineering College Department of Management Studies Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Management Studies Tamil Nadu Chennai India St.Joseph's College of Engineering Department of Management Studies Tamil Nadu Chennai India Saveetha Institute of Medical & Technical Sciences Saveetha School of Management Tamil Nadu Chennai India Dr.M.G.R Educational and Research Institute Department of Computer Science and Engineering Maduravoyal Tamil Nadu Chennai India

ISBN: (纸本)9798331539948

Due to manual procedures, static data analysis, and reactive strategies, traditional strategic Human Resource Management (SHRM) systems in marketing and finance suffer from inefficiencies and delayed decision-making. The proposed system incorporates automation, real-time data processing, and predictive analytics along with machine learning (ML) to overcome these drawbacks. Utilizing Random Forest (RF) for automated talent acquisition, Long Short-Term Memory (LstM) networks for predictive workforce planning, and KNearest Neighbors (KNN) for individualized employee development are some of the key features. The results show notable progress: RF obtains an F1-score of 0.84, an LstM of 0.77, and a KNN of 0.90. Additionally, the proposed system increases training efficacy by improving employee satisfaction from 70% to 85% and skill improvement from 60% to 75%. Employee retention rates increase from 75% in 2020 to 87% in 2023. By transforming HR operations from reactive to proactive, the ML-integrated SHRM system improves corporate performance and decision-making. © 2024 IEEE.

关键词： Marketing

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Towards enhancing the safety of Advanced Air Mobility: Automatic 3D inter-urban modelling for improved weather monitoring 19

Towards enhancing the safety of Advanced Air Mobility: Autom...

引用

19th 3D GeoInfo conference / 31st EG-ICE international Workshop on intelligent Computing in engineering

作者： Aldao Pensado, Enrique Veiga Pineiro, Gonzalo Dominguez Estevez, Pablo Veiga Lopez, Fernando Fontenla Carrera, Gabriel Gonzalez Jorge, Higinio Martin Ortega, Elena Beatriz Univ Vigo Res Inst Phys & Aerosp Sci Campus Lagoas Orense 32004 Spain

Advanced Air Mobility (AAM) is an emerging sector that aims to optimise transportation in urban and interurban environments using Unmanned Aerial Vehicles (UAVs). However, these aircraft are particularly susceptible to adverse weather phenomena, such as turbulence or wind gusts. To ensure the safety of operations, high-resolution real-time atmospheric prediction models, such as those based on Computational Fluid Dynamics (CFD), are required. The development of these systems faces challenges as accurate geometric models of navigation environments are needed to simulate the interaction between the wind and the terrain and infrastructure. These models feature complex geometries, so they cannot be manually crafted, requiring automated processing of geospatial data sources. As the main novelty, this work presents a methodology for the automated modelling of interurban environments for CFD simulations using digital elevation models and georeferenced surface semantic classification data. A practical case study was developed in the outskirts of Vigo (Spain), where we demonstrate the effectiveness of our approach and its integration with the open-source CFD software OpenFOAM.

关键词： Automatic 3D geometry modelling data integration Smart Cities Advanced Air Mobility (AAM) Unmanned Aerial Vehicles (UAVs)

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Machine learning for Indoor Localization using CSI Technique 21

Machine Learning for Indoor Localization using CSI Technique

引用

21st international System-on-Chip Design conference, ISOCC 2024

作者： Lee, Wilaiporn Suwanasri, Cattareeya Pipanmekaporn, Luepol Srisomboon, Kanabadee King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Sciences Bangkok Thailand

ISBN: (纸本)9798350377088

In this research, we propose a low-cost indoor localization technique using the CSI. By using CSI signal as input data, different locations and human activities are classified effectively using machine learning models. According to a low-cost constraint where a single transmitter (Tx) and receiver (Rx) are utilized, we investigate the appropriate transmitter location for achieving accurate position estimation in an indoor environment under two patterns of room environment. Moreover, an appropriate machine learning model for position estimation is examined where decision tree, KNN, Naïve bayes, and ANN are considered. The experimental results demonstrate that the highest accuracy can be achieved using the ANN technique. The shortest distance between a Tx and Rx provides the highest precision for determining the human location in an empty room, whereas placing the Tx and Rx in opposing positions is advantageous for a section with items positioned against the wall. When determining an average accuracy, placing the Tx and Rx in opposing positions shows the highest accuracy among others. © 2024 IEEE.

关键词： Adversarial machine learning

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The Pros and Cons of Using ChatGPT in Medical Education: A Scoping Review

The Pros and Cons of Using ChatGPT in Medical Education: A S...

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21st international conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2023

作者： Mohammad, Bushra Supti, Turjana Alzubaidi, Mahmood Shah, Hurmat Alam, Tanvir Shah, Zubair Househ, Mowafa College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9781643684000

This scoping review explores the advantages and disadvantages of using ChatGPT in medical education. We searched PubMed, Google Scholar, Medline, Scopus, and Science Direct to identify relevant studies. Two reviewers independently conducted study selection and data extraction, followed by a narrative synthesis. Out of 197 references, 25 studies met the eligibility criteria. The primary applications of ChatGPT in medical education include automated scoring, teaching assistance, personalized learning, research assistance, quick access to information, generating case scenarios and exam questions, content creation for learning facilitation, and language translation. We also discuss the challenges and limitations of using ChatGPT in medical education, such as its inability to reason beyond existing knowledge, generation of incorrect information, bias, potential undermining of students' critical thinking skills, and ethical concerns. These concerns include using ChatGPT for exam and assignment cheating by students and researchers, as well as issues related to patients' privacy. © 2023 The authors and IOS Press.

关键词： Medical education

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Coronary and Heart Disease Risk Estimation Based on Predictive data Analysis 21

Coronary and Heart Disease Risk Estimation Based on Predicti...

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21st IEEE international Symposium on intelligent Systems and Informatics, SISY 2023

作者： Martinovic, Goran Drenjancevic, Ines Zagar, Drago Bacun, Tatjana J.J. Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia J.J. Strossmayer University of Osijek Faculty of Medicine Osijek Osijek Croatia J.J. Strossmayer University of Osijek Faculty of Medicine Osijek University Hospital Osijek Osijek Croatia

ISBN: (纸本)9798350343366

Coronary and heart diseases are a very important medical problem and a global challenge. The problem might be solved by both finding an efficient medicine or treatment for the suffering patients and taking care of prevention. This paper deals with a cloud-supported web system for coronary disease risk estimation based on data analysis. Risk estimation is based on logistic regression, which is selected according to evaluation and comparison with other machine learning and statistical approaches. This method estimates the influence of every single factor as well as a general risk level for coronary and heart diseases. As a result, the system provides coronary disease risk estimation for the given risk factor values, as well as advice generation for factors whose values exceed the range of normal values. The system enables input of personal, statistical and measured data, risk estimation and generation of advice, warnings and alarms. The results obtained show application credibility for characteristic use cases and a satisfactory solution in a cloud environment. © 2023 IEEE.

关键词： Risk assessment

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Unsupervised Anomaly Detection and Root Cause Analysis for an Industrial Press Machine based on Skip-Connected Autoencoder 21

Unsupervised Anomaly Detection and Root Cause Analysis for a...

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21st IEEE international conference on Machine learning and Applications (IEEE ICMLA)

作者： Sun, Chenwei Trat, Martin Bender, Janek Ovtcharova, Jivka Jeppesen, George Baer, Jan FZI Res Ctr Informat Technol Intelligent Syst & Prod Engn Karlsruhe Germany Dieffenbacher GmbH Maschinen & Anlagenbau Eppingen Germany

ISBN: (纸本)9781665462839

We propose an unsupervised-learning-based method for anomaly detection and root cause analysis for an industrial press machine. A skip-connected autoencoder with 55% performance improvement measured by reconstruction root mean square error to vanilla variant in average is used to train the collected multivariant time series data in different schemes. We then conduct a stacked evaluation method for both machinelevel anomalies with the root cause localization and anomaly on specific cylinder tracks. Both real-world and synthetic anomalies embedded in real data are used for evaluation. The result shows that the multi-models training scheme and the relatively short window length can gain better performance, i.e., fewer anomaly false alarms and misses.

关键词： autoencoder skip connection multivariant time series anomaly detection root cause analysis

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Research on bridge detection algorithm based on remote sensing image 7

Research on bridge detection algorithm based on remote sensi...

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7th international conference on Mechatronics and intelligent Robotics, ICMIR 2023

作者： Zhang, Zanpeng Luo, Lingyu China Merchants Chongqing Communications Technology Research and Design Institute Co Ltd Chongqing400067 China China Merchants Chongqing Highway Engineering Testing Center Co Ltd Chongqing400067 China Chongqing Jiangbei District Heji Primary School Chongqing400000 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

In the 21st century, with the rapid development of artificial intelligence (AI) technology, rapid and automatic recognition of ground objects in remote sensing images has gradually become the current research focus. This technology solves the problem that a single remote sensing image can not cover the target area and can not meet the actual operation requirements. Because of the advantages of high efficiency, low cost and reliable mosaic image quality, it is widely used in engineering surveying and mapping, traffic monitoring, military investigation, disaster investigation and other fields. With the rapid development of aerial remote sensing technology, the number of optical remote sensing images has increased explosively, which provides necessary data support for using deep learning method to detect targets in remote sensing images. Based on the research background of automatic detection and recognition of bridge targets in visible light remote sensing images, this paper studies the problems of feature extraction, river detection, river region correction and bridge target recognition, and constructs a complete automatic detection and location system of bridge targets. After that, the bridge is accurately detected using binary morphology combined with the feature of bridge crossing both banks. © 2023 SPIE. All rights reserved.

关键词： Image segmentation

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iNUMAlloc: Towards intelligent Memory Allocation for AI Accelerators with NUMA 21

iNUMAlloc: Towards Intelligent Memory Allocation for AI Acce...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xu, Yuanchao Qian, Ruyi Wang, Yida Huo, Qirun Capital Normal University College of Information Engineering Beijing China Skl of Computer Architecture Institute of Computing Technology Cas Beijing China

ISBN: (纸本)9798350329223

The amazing success of deep neural network benefits from the rise of big data. As deep learning models are becoming more scale than ever before, their requirements for memory bandwidth are growing at a tremendous pace. Some AI accelerators adopt non-uniform memory access (NUMA) architecture to mitigate this issue and hence complicate device memory allocation. Although extensive studies have been conducted on how to mitigate resource contention and reduce latency, almost all of them target on CPU-oriented NUMA systems but not on AI accelerators where memory allocation precedes task scheduling. The current memory allocator generally adopts an interleaved memory allocation strategy, which is very easy to implement but far from *** tackle this issue, this paper proposes iNUMAlloc, an intelligent memory allocator specialized for AI accelerators with NUMA architecture by combining program behavior and predictable hardware resources altogether. Preliminary evaluation shows that it can help to improve the accuracy and efficiency of memory allocation, thereby achieving stable execution time. © 2023 IEEE.

关键词： Deep neural networks

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A Comparative study on Multi-modal Fusion for automated Lung Disease Diagnostics 4

A Comparative Study on Multi-modal Fusion for Automated Lung...

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4th international conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Kumar, Sachin Mallick, Pradeep Kumar Vorfolomeyeva, Olga South Ural State University Big Data and Machine Learning Lab Chelyabinsk Russia Kiit Deemed to Be University School of Computer Engineering Odisha Bhubaneswar India West Kazakhstan State University Faculty of Arts and Culture Uralsk Kazakhstan

ISBN: (纸本)9798350349856

High-quality X-rays are now available to diagnose lung diseases with the help of radiologists. However, the diagnostic process is time consuming and depends on specialist availability in medical institutions. Patient information may include chest X-rays of varying quality, medical test results, doctors' notes and prescriptions, and medication details, among others. In this study, we present a model for classifying pulmonary diseases using multimodal data from patient clinical studies and radiographic images. Various methods were used to generate artificial samples for both images and tabular data on the laboratory study results during data preparation. We also proposed a method for establishing a correspondence between the generated modals. The late fusion architecture of the proposed multimodal model was implemented. We conducted experiments on pulmonary data-set with two modalities. Results shows that an increase in accuracy and other parameters were observed for multimodal data fusion using our model in comparison with image only modality and clinical data only modality. It strengthen the fact that multimodality provides more insight for the fusion model to learn and provide a more precise diagnosis than single modality. © 2024 IEEE.

关键词： Biological organs

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Dynamic Graph-Enhanced Sequential LstM for Context-Aware Point-of-Interest Recommendation 21

Dynamic Graph-Enhanced Sequential LSTM for Context-Aware Poi...

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21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Yuwen, Zhang Shaobo, Ren School of Computer Science and Engineering School of Cyber Security University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331519254

Next Point-of-Interest (POI) recommendation is a critical task in location-based services, aiming to predict the user's next POI based on historical check-in data. Existing approaches often rely on static graphs or sequential models to capture user preferences, which are limited by the sparsity of check-in data and the inability to dynamically model spatio-temporal transitions. To address these challenges, we propose the Dynamic Graph-Enhanced LstM (DGEL) model for next POI recommendation. DGEL constructs dynamic POI transition graphs by combining Transformer-based graph learning with embedding techniques, capturing implicit relationships between POIs. Additionally, a Customized Sequential LstM (CS-LstM) is introduced to model long-term and short-term dependencies, incorporating spatial, temporal, and relational signals. Experimental results on Gowalla and Foursquare datasets demonstrate that DGEL achieves significant performance improvements over state-of-the-art baselines, highlighting its effectiveness in leveraging dynamic graphs and structured sequential learning for POI recommendation. © 2024 IEEE.

关键词： Knowledge graph

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