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14th International Conference on Open Source Systems and Technologies, ICOSST 2020

作者： Rehman, Attique Ur Nawaz, Ali Ali, Mohammad Tahir Abbas, Muhammad College of Electrical and Mechanical Engineering Nust Department of Computer and Software Engineering Rawalpindi Pakistan College of Art and Science Gulf University of Science and Technology Computer Science Department Mubarak Al-Abdullah Kuwait

ISBN: (纸本)9781728190501

Agile development is conventional these days and with the passage of time software developers are rapidly moving from Waterfall to Agile development. Agile methods focus on delivering executable code quickly by increasing the responsiveness of software companies while decreasing development overhead and consider people as the strongest pillar of software development. As agile development overshadows Waterfall methodologies for software development, it comes up with some distinct challenges related to testing of such software. Our study is going to discuss the challenges this approach has stirred up. Some of the challenges are discussed in this paper with possible solutions and approaches used for resolving these challenges. Also, the tools in practice are mentioned to improve the efficiency of the process. (Abstract) © 2020 IEEE.

关键词： software testing

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Evaluating Excellence Webometric’s Criteria Weight using Consistent Fuzzy Preference Relation Method 21

Evaluating Excellence Webometric’s Criteria Weight using Co...

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Proceedings of the 2021 International Conference on computer, Control, Informatics and Its Applications

作者： Tenia Wahyuningrum Condro Kartiko Teotino Gomez Soares Institut Teknologi Telkom Purwokerto Indonesia Department of Software Engineering Institut Teknologi Telkom Purwokerto Indonesia Department of Computer Science Dili Institute of Technology Timor-Leste

ISBN: (纸本)9781450385244

Some methods in the world university ranking institutions still have weaknesses. Among the university rankings, Webometrics focused on quantitative studies related to website and content phenomena and is considered easy to measure because it bases ranking criteria on university activities on the internet. On the webometrics, the percentage of weight on the criteria of excellence which is quite high (40 %), makes the university pay attention to achieving that score. The existing method in weighting the criteria using the analytical hierarchy process (AHP) needs to include =n(n-1)/2 questions for each group of n-criteria paired comparisons. In this study, the Consistent Fuzzy Preference Relation (CFPR) method is used to study the factors/criteria to determine what strategy universities will take to improve the webometrics ranking in terms of excellence. The CFPR techniques are used to reduce expert assessment steps to only as much as n-1 to ensure consistency at the level with n criteria. Based on the calculation results, it can be concluded that the strategy for improving the excellence score is prioritized on three main criteria in sequence, namely improving scholarly rank (A), measuring the number of scientific papers (B), recognizing scholarly ranks (C). The weight of each criterion in the sequence is 0.51, 0.32, and 0.17. Some strategies to increase the excellence score based on the main priority of sub-criteria and sub-sub criteria are explained in this study.

关键词： Webometrics University rank Consistent Fuzzy Preference Relations Excellence

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A New Application of Machine Learning: Detecting Errors in Network Simulations

A New Application of Machine Learning: Detecting Errors in N...

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Simulation Winter Conference

作者： Maciej K. Wozniak Luke Liang Hieu Phan Philippe J. Giabbanelli Department of Intelligent Systems KTH Royal Institute of Technology Stockholm SWEDEN Department of Computer Science & Software Engineering Miami University St. Oxford OH USA

ISBN: (纸本)9781665476621

After designing a simulation and running it locally on a small network instance, the implementation can be scaled-up via parallel and distributed computing (e.g., a cluster) to cope with massive networks. However, implementation changes can create errors (e.g., parallelism errors), which are difficult to identify since the aggregate behavior of an incorrect implementation of a stochastic network simulation can fall within the distributions expected from correct implementations. In this paper, we propose the first approach that applies machine learning to traces of network simulations to detect errors. Our technique transforms simulation traces into images by reordering the network's adjacency matrix, and then training supervised machine learning models. Our evaluation on three simulation models shows that we can easily detect previously encountered types of errors and even confidently detect new errors. This work opens up numerous opportunities by examining other simulation models, representations (i.e., matrix reordering algorithms), or machine learning techniques.

关键词： Training Machine learning algorithms Computational modeling Aggregates Stochastic processes Machine learning Transforms

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Improving VANET's Performance by Incorporated Fog-Cloud Layer (FCL)

arXiv

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arXiv 2022年

作者： Samara, Ghassan Rasmi, Mohammed Sweerky, Nael A. Al daoud, Essam Salem, Amer Abu Computer Science Department Zarqa University Zarqa Jordan Software Engineering Department Zarqa University Zarqa Jordan Departmint of Internet Technology Zarqa University Zarqa Jordan

Because of its usefulness in various fields including as safety applications, traffic control applications, and entertainment applications, VANET is an essential topic that is now being investigated intensively. VANET confronts numerous challenges in terms of reaction time, storage capacity, and reliability, particularly in real-time applications. As a result, merging cloud computing and cloud computing has recently been researched. The goal of this study is to develop a system that merges the fog and cloud layers into a single layer known as the included fog-cloud layer. To lower the time it takes for real-time applications on VANETs to respond while also improving data flow management over the Internet and achieving an efficient perception service while avoiding the high cost of cloud connectivity. © 2022, CC BY.

关键词： Fog

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Utility-Aware Data Anonymization Model for Healthcare Information

Utility-Aware Data Anonymization Model for Healthcare Inform...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Fadi Alhaddadin Jairo Gutierrez Department of Research and Innovation Center (CRIC) CUC-Ulster University Doha Qatar Department Computer Science and Software Engineering Auckland Univesity of Technology Auckland New Zealand

The use of collected data is a valuable source for analysis that benefits both medical research and practice. Information privacy is considered a significant challenge that hinders using such information for research purposes. In terms of research, releasing patients’ information for research purposes may lead to privacy breaches for patients in various cases. Individual patients may not wish to be identifiable when using information about their health for research. This work proposes a utility-aware data anonymization model for sharing patients’ health information for research purposes in a privacy-preserving manner. The proposed model is interactive and involves a number of operations that are performed on patients’ information before releasing it for research purposes according to certain requirements specified by the data user (researcher).

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Enhanced Prediction of Fuel Consumption and Carbon Emissions in Light-Duty Vehicles using Convolutional Recurrent Imputation Network

Enhanced Prediction of Fuel Consumption and Carbon Emissions...

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International Conference on computer, Electrical & Communication engineering (ICCECE)

作者： T. Lalitha Jerzy Ryszard Szymański Marta Żurek-Mortka Mithileysh Sathiyanarayanan K. R. Srilekha Computer Science School of Information Science Presidency University Bengaluru Karnataka India Department of Electrical Drives and Industrial Electronics Faculty of Transport Electrical Engineering and Computer Sciences Casimir Pulaski University of Radom Poland Europe Lukasiewicz Research Network Institute for Sustainable Technologies Kazimierza Pułaskiego Radom Poland Europe MIT Square London I M.Tech Integrated(Software Engineering) VIT University Vellore Tamilnadu India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

Fuel utilization and emission estimation are essential for assessing the consequence of materials and strict emission control measures due to the startling rate of weather changes. The world's socioeconomic issues are significantly impacted by the growing worldwide average temperature and its effects on climate change. The melting of the polar ice caps, that is a direct result of this rise, leads to a number of other problems, such as the polar animal’s death, coastal flooding, and exposure to bacteria and earliest microorganisms that are frozen in the snow and could cause many more global pandemics and invisible illnesses. In this paper, Enhanced Prediction of Fuel Consumption and Carbon Emissions in Light-Duty Vehicles using Dynamic Graph Convolutional Recurrent Imputation Network (EP-FCCE-DGCRIN) are proposed.. The effectiveness of the EP-FCCE-DGCRIN technique is compared to other existing methods. This research offers evidence-based suggestions to lessen the environmental effects of vehicles for both manufacturers and consumers depending upon the outcomes of the Descriptive Statistics analysis.

关键词： Temperature measurement Pandemics Snow Sea measurements Carbon dioxide Imputation Fuels Vehicle dynamics Socioeconomics Meteorology

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A 3d convolutional neural network for bacterial image classification 3rd

A 3d convolutional neural network for bacterial image classi...

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3rd International Conference on Big-Data and Cloud Computing, ICBDCC 2019

作者： Mhathesh, T.S.R. Andrew, J. Martin Sagayam, K. Henesey, Lawrence Department of Bioinformatics Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Systems and Software Engineering School of Engineering Blekinge Institute of Technology Karlskrona Sweden

ISBN: (纸本)9789811552847

Identification and analysis of biological microscopy images need high focus and years of experience to master the art. The rise of deep neural networks enables analyst to achieve the desired results with reduced time and cost. Light sheet fluorescence microscopies are one of the types of 3D microcopy images. Processing microscopy images is tedious process as it consists of low-level features. It is necessary to use proper image processing techniques to extract the low-level features of the biological microscopy images. Deep neural networks (DNN) are efficient in extracting the features of images and able to classify with high accuracy. Convolutional neural networks (CNN) are one of the types of neural networks that can provide promising results with less error rates. The ability of CNN to extract the low-level features of images makes it popular for image classification. In this paper, a CNN-based 3D bacterial image classification is proposed. 3D images contain more in-depth features than 2D images. The proposed CNN model is trained on 3D light sheet fluorescence microscopy images of larval zebrafish. The proposed CNN model classifies the bacterial and non-bacterial images effectively. Intense experimental analyses are carried out to find the optimal complexity and to get better classification accuracy. The proposed model provides better results than human comprehension and other traditional machine learning approaches like random forest, support vector classifier, etc. The details of network architecture, regularization, and hyperparameter optimization techniques are also presented. © Springer Nature Singapore Pte Ltd 2021.

关键词： Image classification

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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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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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A Neural Collaborative Filtering Model for Adverse Drug Reaction Prediction

A Neural Collaborative Filtering Model for Adverse Drug Reac...

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Data Analytics, Computing and Artificial Intelligence (ICDACAI), International Conference on

作者： Zetong Xiong Zihao Du Xi Rong Yuting Yang Xiaoyi Zhang School of Information and Safety Engineering Zhongnan University of Economic and Law Wuhan China Department of Computing and Software McMaster University Hamilton Canada School of Engineering Tufts University Medford MA United States Department of Computer Science and Engineering The Ohio State University Columbus USA Department of Mathematics University of California San Diego USA

As adverse drug reactions (ADRs) can cause serious consequences to medication treatment, identifying and predicting adverse drug reactions (ADRs) play a crucial role in drug effects and drug use safety. In this paper, we reviewed existing machine learning methods in ADR prediction and proposed a neural collaborative filtering model (NCF) for the prediction of monopharmacy ADRs. NCF is based on dimension reduction by matrix factorization (MF) and combined with an MLP model to enhance prediction performance by handling complex non-linear relationships using MLN. Our NCF model optimizes the MF model by replacing dot product in the MF model with MLP, integrating drug and ADR embeddings from monopharmacy ADR benchmark data and drug-related chemical, physical and biological descriptors in a two-layer deep neural network. The model was tested by using 10-fold cross-validation. On 10-fold cross-validation, the resulting AUC, AUPR, and running time indicated a better performance and higher efficiency on drug-ADR prediction achieved by applying our NCF model.

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