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Advancement in Data Science, E-learning and information systems (ICADEIS), International Conference on

作者： Alifiah Putri Sinung Suakanto Joko Siswanto Farah Fitriavida Arifin Irfan Darmawan Dina Angela Department of Information System Faculty of Software Engineering Telkom University Bandung Indonesia Industrial Engineering Research Group Bandung of Institute Technology Bandung Indonesia Information Techology Research Group Faculty of Industrial Engineering Institut Teknologi Harapan Bangsa Bandung Indonesia

The recruitment process presents a significant challenge for Human Resource (HR) staff, particularly in their ability to objectively assess candidate competencies and align them with job requirements. While qualitative comparisons between resumes and position requirements may yield candidates who appear suitable, this approach proves ineffective in terms of time and cost. Furthermore, many recruiters lack sufficient knowledge of the positions they are hiring for, making it difficult to justify their selection decisions. This research aims to address this challenge by developing an HR person-job matching application for the recruitment of prospective employees. The study focuses on analyzing and designing applications that can quantitatively measure the competencies of candidates and compare them with job requirements using a person-job matching method. The effectiveness of this approach was evaluated by testing it on a sample of employees from small and medium-sized enterprises (SMEs). The study found that out of the 16 employees across three job positions, the person-job matching application achieved an accuracy rate of 75%. This accuracy percentage was determined by comparing the fit value results with the actual conditions of the employees in their respective workplaces. The findings highlight the potential of the HR person-job matching application to enhance the efficiency and effectiveness of recruitment efforts.

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High Performance Computing Framework for Variable Selection on Genome-wide Association Studies

High Performance Computing Framework for Variable Selection ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Xiang Liu, Xueling Diao, Jing Zheng, Mengyao Li, Jihe Wei, Dehui Xie, Qipeng Li, Xia Jiang, Linshan National University Health System Group Technology Office Singapore National University of Singapore School of Computing Singapore Peking University School Hospital of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases Department of Preventive Dentistry China Harvard University Harvard T.H. Chan School of Public Health United States Peking University School of Software and Microelectronics China Hong Kong University of Science and Technology IoT Thrust Information Hub Guangzhou China Eth Zurich Department of Computer Science Switzerland National University of Singapore Institute of Data Science Singapore

ISBN: (纸本)9798350386226

Variable selection for genome-wide association studies (GWAS) has been a major research focus for decades. With the exponential growth of biological and biomedical data in the era of big data, scientists are confronted with the challenge of extracting meaningful information from vast datasets while managing the inherent heterogeneity in bioinformatics. To date, there are no highly effective tools that support high-dimensional datasets and achieve robust variable selection performance, all while accounting for the non-i.i.d. features and structured relatedness among explanatory and response *** address these challenges, we introduce the first high-performance computing framework for variable selection in GWAS. Our framework integrates various state-of-the-art methods, allowing researchers to easily combine different techniques and fully explore their potential. Additionally, our approach employs novel optimization strategies to solve the problem efficiently, even for high-dimensional data with sparse characteristics. By processing the data holistically, the framework delivers comprehensive analysis and accurate linkage mapping associations. Designed for ease of use, the framework is implemented in Python and offers seamless deployment, making it accessible to a wide range of researchers. © 2024 IEEE.

关键词： Confounding factors genome-wide association study structured relatedness variable selection

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Identification of Molecular Biomarkers and Key Pathways among Idiopathic pulmonary fibrosis (IPF), Chronic Obstructive Pulmonary Disease (COPD) and Lung cancer

Identification of Molecular Biomarkers and Key Pathways amon...

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International Conference on Computer and information technology (ICCIT)

作者： Mst. Farjana Yasmin Md. Faruk Hosen Md. Abul Basar Khairul Alam Shadhin Arifa Ferdoushi Trisha Muhammad Shahin Uddin Department of Computing and Information System Daffodil International University (DIU) Ashulia Dhaka Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail Bangladesh Department of Computer Science and Engineering Green University of Bangladesh (GUB) Purbachal American City Kanchon Bangladesh Department of Software Engineering Daffodil International University (DIU) Ashulia Dhaka Bangladesh

Lung cancer (LC), idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the most fatal disorders in the globe, generating frequent human issues. Having IPF and COPD are the risk factors of the LC, but the molecular mechanisms that underlie among IPF, COPD, and LC are not yet elucidated. In this study, we looked for shared molecular indicators and pathways that might explain how individuals with IPF, COPD, and LC are related to one another. GSE24206, GSE76925 and GSE18842 microarray datasets are utilized for IPF, COPD and LC samples. The preprocessing of datasets has done using R language, and then concordant differentially expressed genes (DEGs) are discovered. After that the protein-protein interactions (PPIs) are built using the similar DEGs, and the hub genes are determined using topological analysis. ETS1, MSH2, SORD, RORA and NEDD9 are the PPI network’s top 5 hub genes. The pathways of KEGG demonstrated that the concordant DEGs are related to the colorectal cancer and pathways in cancer. Future work for this project will focus on miRNA, TF, and gene ontology (GO) analyses, as well as module analysis networks. Finally, based on the concordant DEGs, a number of potential medications have been suggested.

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Identification of Candidate Drugs and Molecular Pathways for Pulmonary Arterial Hypertension and Chronic Kidney Disease

Identification of Candidate Drugs and Molecular Pathways for...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md. Faruk Hosen Mst. Farjana Yasmin Md. Abul Basar Arifa Ferdoushi Trisha Khairul Alam Shadhin Md. Sarwar Mollah Muhammad Shahin Uddin Department of Computing and Information System Daffodil International University (DIU) Ashulia Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh (GUB) Purbachal American City Kanchon Bangladesh Department of Software Engineering Daffodil International University (DIU) Ashulia Dhaka Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail Bangladesh

Pulmonary arterial hypertension (PAH) is biggest preventable cause of early deaths, and hence one of the global preferences of World Health Organization for determent. PAH also induces the vast majority of patients with chronic kidney disease (CKD). Both PAH and CKD are inherently connected, but the common molecular pathways and genes of these two disorders have not yet been demonstrated. In this study, we aimed to determine the similar molecular pathways and therapeutic hub proteins in PAH and CKD that may be used to anticipate the progression of the disease. We used Limma package to conduct differential analysis of gene transcripts of PAH and CKD obtained from the Gene Expression repository. The functional annotations of the genes were determined with the use of pathway analysis. Then proteinâprotein interaction (PPI) networks were constructed to determine the hub proteins, and a clustering technique was applied to determine the most significant PPI elements. We separately evaluated the PAH and CKD gene expression-based datasets and discovered 30 differentially expressed genes (DEGs) shared by both PAH and CKD. The pathways of KEGG revealed that the most prevalent DEGs are associated with the Malaria and African trypanosomiasis. Gene ontology (GO), TF and miRNA analysis and module investigation is treated the forthcoming work of this study. Finally, various drug compounds have been suggested based on the concordant DEGs.

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software Development in Small software Companies: Exploring the Usage of Procedures, Techniques, Methods and Models in Practice 21

Software Development in Small Software Companies: Exploring ...

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Proceedings of the 2021 European Symposium on software Engineering

作者： Micheal Tuape Victoria Hasheela-Mufeti Anna Kayanda Jussi Kasurinen Department of Software Engineering Lappeenranta-Lahti University of Technology Finland Department of Computing mathematical and Statistical Sciences University of Namibia Namibia Department of Information System College of Business Education Tanzania

ISBN: (纸本)9781450385060

Small software companies have a challenge with utilizing process tools, which affects practice with significant quality-related challenges. This affects the software industry significantly because SSCs dominate the industry, and most of all, over 80 percent of software products are produced by SSCs. This cross-sectional survey was conducted in 3 countries attracting 115 respondents with the primary objective of investigating the software practice concerning the utilization of process tools in SSCs. The study focused on the tools used in requirements engineering and software testing as critical process areas for quality software products. Our findings indicate that the number of personnel intertwines with the complexities arising from lengthy procedures of the tools and processes, aggregating into difficulty in tool usage. Due to the constant evolution of practices, the volatility in processes also causes slow adoption of other tools, for instance, testing that must accompany the main engineering tools during a project. These findings are significant in informing theory and communicating to the practitioners what they should do regarding process tools.

关键词： Small software companies Utilization of process tools software development practice Survey methodology

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Development of a neural network model for forecasting energy consumption in energy management systems

Development of a neural network model for forecasting energy...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Valeriya Tynchenko Ilia Panfilov Ksenia Degtyareva Natalia Kandaurova Tatyana Dolgova Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow Russia Software Research and Training Laboratory Siberaim Federal Iniversity Krasnoyarsk Russia Information-Control Systems Department Reshetnev Siberian State University of Science and Technology Department of Systems Analysis and Operations Research Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Center for Continuing Education Bauman Moscow State Technical University Moscow Russia Department of Information Economic System Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This paper explores the use of neural networks to predict energy consumption based on data on the production and use of various fuels, including oil, gas and coal. The aim of the study is to develop a model that can take into account the complex relationships between key parameters such as production volumes, production costs, CO₂ emissions, energy efficiency, inventory levels and transportation costs. The relevance of the study is due to the need to improve the accuracy of forecasting for effective planning and management of resources in the energy industry, especially in conditions of unstable market and environmental factors. In the course of the work, a multi-layered fully connected neural network model was developed, which was trained and tested on the prepared data. The evaluation of the model using the coefficient of determination and the average absolute error demonstrated its high accuracy and suitability for practical application. Additionally, an analysis of the importance of the signs was carried out, which showed that the most significant factors are the import/export of energy and the share of alternative sources. Visualization of the results confirmed that the model is able to make accurate predictions, minimizing errors. The results obtained confirm that the use of neural network models to predict energy consumption is a promising area that can significantly improve decision-making processes in the energy sector and increase resource efficiency.

关键词： Industries Energy consumption Analytical models Accuracy Costs Neural networks Production Predictive models Data models Forecasting

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Regularized Hypothesis-Induced Wasserstein Divergence for Unsupervised Domain Adaptation

SSRN

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SSRN 2023年

作者： Si, Lingyu Dong, Hongwei Qiang, Wenwen Zheng, Changwen Yu, Junzhi Sun, Fuchun Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China College of Engineering Peking University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

Unsupervised domain adaptation (UDA) aims to align the data distributions of source and target domains to learn domain-invariant feature representations. The performance of such representations heavily relies on the measurement of the discrepancy between the distributions. Recent UDA methods utilize the hypothesis-based measurement, also known as classifier-induced divergence, to quantify the difference between the two domain distributions, for instance, H∆H-divergence. Nevertheless, the classifier-induced divergences face several challenges, including the fake covariate alignment problem and the inconsistency problem, rendering them inadequate for discerning the dissimilarity between different categories of output by the classifier. This paper proposes a novel method called Regularized Hypothesis-Induced Wasserstein Divergence-Based Representation Learning for Unsupervised Domain Adaptation (RHWD) to address these issues. Specifically, RHWD introduces a new hypothesis-induced Wasserstein divergence (HWD) to effectively quantify the difference between the two distributions, which aims to overcome the fake covariate alignment problem. Furthermore, RHWD includes a consistency regularization term to mitigate the inconsistency problem. We train RHWD adversarially in three iterative steps. Additionally, we provide empirical evaluations on standard benchmarks, which demonstrate the effectiveness of our proposed © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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The Impact of 5G technology on Agile Project Management: A Cross-Industry Analysis

The Impact of 5G Technology on Agile Project Management: A C...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Mahesh Kumar Mishra Krishnamoorthy Selvaraj Kathari Santosh Mohd Aarif S.Suma Christal Mary B Kiran Bala Delta System and Software Inc McKinney Texas USA Dept of ERP Encloud service LLC Keller Texas US Department of MBA CMR Institute of Technology Bengaluru Bengaluru India IOSRP Noida UP India Department of Information Technology Panimalar Engineering College Poonamalle Chennai Department of Artificial Intelligence and Data Science K.Ramakrishnan college of Engineering Trichy

Project management and organisation are completely transformed by the introduction of 5G technology, which has brought in a new era of connection. This research investigates how 5G networks will affect Enterprise Resource Planning (ERP), Agile Project Management (APM) and the larger context of the technology-Organization-Environment (TOE) paradigm through a cross-industry examination. Agile approaches may benefit from improved adaptive design, cooperation and connectivity as a result of 5G technology being included into project management procedures. A team of developers can work together more easily and make decisions more quickly with the real-time data transfer made possible by 5G networks' higher capacity and speed. This study looks at how Agile methods for managing projects are more effective and flexible when using 5G's low latency and high data throughput. This research explores how Enterprise Resource Planning (ERP) systems may be affected by 5G. With 5G networks' enhanced capabilities, current information synchronisation is made possible in new ways, enabling ERP systems to function with increased precision and speed. This study investigates how supply chain administration, resource allocation and general organisational agility are improved by 5G-enabled ERP. This study evaluates how various sectors' organisational structures, procedures, and objectives are affected by the deployment of 5G infrastructure. This research outlines critical elements that influence the successful implementation of 5G into project management and ERP systems, including technology preparedness, organisational culture, and external factors. This research provides important insights into the potential for transformation of 5G technology through a thorough cross-industry assessment. It also gives organisations an established basis on which to adapt their Agile Project Management and Enterprise Resource Planning practises in the dynamic context of today's digital world.

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Extracting Relations between Organizational Patterns Using Association Mining 20

Extracting Relations between Organizational Patterns Using A...

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2020 European Conference on Pattern Languages of Programs, EuroPLoP 2020

作者： Waseeb, Shakirullah Khail, Waheedullah Sulaiman Wahaj, Haji Gul Vranić, Valentino Software Engineering Department Nangarhar University Afghanistan Institute of Informatics Information Systems and Software Engineering Slovak University of Technology in Bratislava Slovakia Database and Information System Department Nangarhar University Afghanistan

ISBN: (纸本)9781450377690

Patterns are powerful when used in combinations. Identifying relationships between patterns is challenging. The existing approaches and pattern formats reflect the relationships with other patterns in a very informal and traditional way. We are proposing an automatic approach which discover such relationships from the patterns descriptive text using text mining and natural language processing techniques. In this work, we demonstrate how it contributes in inference of relationships and its strength among patterns. © 2020 ACM.

关键词： Natural language processing systems

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Applications of Blockchain technology for Secure Transaction of Electronic Health Records

Applications of Blockchain Technology for Secure Transaction...

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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： Arjun Reddy Kunduru Ramya Thatikonda Srinivas A Vaddadi B. Lakshmi Yabesh Abraham Durairaj Isravel Vivek Khirasaria Software Developer Orlando FL USA Information Technology University of the Cumberlands USA Principal System Reliability Architect Department - Risk & Reliability University of the Cumberlands MBA Department Panimalar Engineering College India School of Management SA College of Arts and Science Faculty of Engineering and Technology Sharda University Uzbekistan

The healthcare industry is not immune to the trend of adopting new IT systems, and this includes hospitals and doctors’ offices. To store a patient’s medical history (including diagnoses and treatments), medical history (including diagnostic pictures), and financial data (including demographics and insurance details), a computerised database known as an “Electronic Health Record” (EHR) is used. Because medical records are typically private and must be shielded from prying eyes, ensuring their secure transfer is a top priority for healthcare systems. Electronic health records (EHRs) are used in the proposed study to ensure the security of all sent medical data, including the Chest X-ray MedPix images. This article describes a blockchain-based solution that patients can use to keep all their data in one secure location. This system was built with the Ganache tool on the Ethereum network, making use of Solidity and *** as well as other languages, tools, and approaches. In this study, a systematic method is described wherein blockchain smart contracts are used to record patient information and execute tasks in a distributed environment. After a smart contract has been deployed, all transactional communication occurs within its encrypted confines, protecting both the parties and their personal information. As an added bonus, the validated and broadcasted changes intended by the transaction can be checked. To ensure that government agencies have quick access to protected records, there is a cryptocurrency wallet (MetaMask) that stores the information in an encrypted, decentralised manner. Both patients and physicians can use the wallet to log in. In addition, the system will manage and protect all of the doctor and patient’s information. Things like the following are in scope for the proposed system: Users may now access the same information at the same time because to blockchain’s distributed ledger capabilities, which boosts productivity, establishes trust, and lowe

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