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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Kadry, Seifedine Damasevicius, Robertas Taniar, David Rajinikanth, Venkatesan Lawal, Isah A. Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland Monash University Faculty of Information Technology ClaytonVIC3800 Australia St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India

ISBN: (纸本)9781665441261

Breast Cancer (BC) is one of the harsh conditions, which largely affects the women group. Due to its significance, a range of procedures are available for premature detection and early treatment to save the patient. The clinical level diagnosis of BC will be done using;(i) Image supported detection and (ii) Core-Needle-Biopsy (CNB) assisted confirmation. The proposed work aim to develop a computerized scheme to detect the Breast-Tumor-Section (BTS) from the beast MRI slices. This work implements a joint thresholding and segmentation methodology to enhance and extract the BTS from the 2D MRI slices. A tri-level thresholding based on Slime-Mould-Algorithm and Shannon's-Entropy (SMA+SE) is implemented to enhance the BTS and Watershed-Segmentation (WS) is implemented to mine the BTS. After extracting the BTS, a study between the BTS and Ground-Truth image is performed and the necessary Image-Performance-Values (IPV) are computed. In this work the axial, coronal and sagittal slices of 2D breast MRI are separately examined and the attained results are presented. © 2021 IEEE.

关键词： Tumors

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Automated Detection of Schizophrenia from Brain MRI Slices using Optimized Deep-Features 7

Automated Detection of Schizophrenia from Brain MRI Slices u...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Kadry, Seifedine Taniar, David Damasevicius, Robertas Rajinikanth, Venkatesan Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Monash University Faculty of Information Technology ClaytonVIC3800 Australia Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India

ISBN: (纸本)9781665441261

In humans, the incident rate of mental illness is gradually rising due to various causes. Schizophrenia is one of the chronic mental illness and its happing rate also rising in the current era. The patient with Schizophrenia will experience a confused mental condition and a timely recognition and treatment is essential to reduce the risk. The proposed work aims to implement a methodology to support the automated detection of Schizophrenia from the brain MRI slices of T1 modality (T1W). The assessment of brain MRI is executed using the pre-trained VGG16 system and the deep-features extracted are optimized with the Slime-Mould-Algorithm (SMA) and the reduced features are then considered to train, test and validate the binary classifiers employed in this work. This research is implemented using 500 images of each case (healthy/abnormal) and the attained result with the SVM-Cubic is superior compared to other classifiers considered in the automated disease detection system. © 2021 IEEE.

关键词： Magnetic resonance imaging

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U-Net Supported Segmentation of Ischemic-Stroke-Lesion from Brain MRI Slices 7

U-Net Supported Segmentation of Ischemic-Stroke-Lesion from ...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Kadry, Seifedine Damasevicius, Robertas Taniar, David Rajinikanth, Venkatesan Lawal, Isah A. Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland Monash University Clayton Faculty of Information Technology VIC3800 Australia St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India

ISBN: (纸本)9781665441261

The brain abnormality is one of the major sicknesses in human's health and the untreated brain defect will cause major illness. Ischemic stroke is one of the major medical emergencies and the timely diagnosis and treatment will save the patient from serious sickness. The proposed research employs the U-Net scheme to extort the Ischemic-Stoke-Lesion (ISL) from the brain MRI slices of ISLES2015 database. In this work, a pre-trained U-Net encoder-decoder system is employed to extort the ISL fragment from the chosen test image. After the extraction, a relative assessment is performed with the ground-truth available along with consequent test image. In this work, 20 patients' images (20 patient x 25 slices = 500 images) are adopted for the assessment and the general result achieved with the executed methodology helped to achieve a better value of Jaccard (>90%), Dice (>95%) and Accuracy (>98%) on the considered image dataset. © 2021 IEEE.

关键词： Diagnosis

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SHAPE CONSTRAINTS IN SYMBOLIC REGRESSION USING PENALIZED LEAST SQUARES

arXiv

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arXiv 2024年

作者： Martinek, Viktor Reuter, Julia Frotscher, Ophelia Mostaghim, Sanaz Richter, Markus Herzog, Roland Interdisciplinary Center for Scientific Computing Heidelberg University Heidelberg69120 Germany Otto von Guericke University Faculty of Computer Science Magdeburg39106 Germany Faculty of Mechanical Engineering University of Technology Chemnitz Applied Thermodynamics Chemnitz09107 Germany

We study the addition of shape constraints (SC) and their consideration during the parameter identification step of symbolic regression (SR). SC serve as a means to introduce prior knowledge about the shape of the otherwise unknown model function into SR. Unlike previous works that have explored SC in SR, we propose minimizing SC violations during parameter identification using gradient-based numerical optimization. We test three algorithm variants to evaluate their performance in identifying three symbolic expressions from synthetically generated data sets. This paper examines two benchmark scenarios: one with varying noise levels and another with reduced amounts of training data. The results indicate that incorporating SC into the expression search is particularly beneficial when data is scarce. Compared to using SC only in the selection process, our approach of minimizing violations during parameter identification shows a statistically significant benefit in some of our test cases, without being significantly worse in any instance. © 2024, CC BY-NC-SA.

关键词： Equations of state

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Next Generation Image Watermarking via Combined DWT-SVD Technique 2

Next Generation Image Watermarking via Combined DWT-SVD Tech...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Na'amneh, Qais Almomani, Ammar Nasayreh, Ahmad Nahar, Khalid M.O. Gharaibeh, Hasan Al Mamlook, Rabia Emhamed Alauthman, Mohammad Applied Science Private University Department Of Cyber Security Amman Jordan School Of Computing Skyline University College P.O. Box 1797 Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I. T. Department Irbid Jordan Yarmouk University Department Of Computer Sciences Irbid211633 Jordan Yarmouk University Faculty Of Information Technology Computer Sciences Irbid211633 Jordan Trine University Dep. Of Business Administration IN United States University Of Zawia Dep.of Mechanical And Industrial Engineering Tripoli Libya University Of Petra Faculty Of Information Technology Department Of Information Security Amman Jordan

ISBN: (纸本)9798350394962

Personal computers and the Internet are used in different areas and are easier to use. Most data is easy to transmit and duplicate in digital format, and being tampered with and stolen easily leads to issues for content owners and service providers. The Paper describes a secure (tampering-resistant) method for watermarking images for protection and authentication and digital media like audio, video, and multimedia data, and a combination of singular value decomposition (SVD) and Discrete wavelet transform (DWT) for insertion watermark by MATLAB to increase accuracy, simplicity, and flexibility Readability, secure robustness, and imperceptibility the visibility of the image. The proposed algorithm consists of three main steps: Firstly, we decomposed the original image into four sub-bands and the original watermark. Secondly, embed the watermark in the LL sub-band by replacing the singular value for the watermark. Finally, embed a watermark in the other three sub-bands by adding LL values for a watermark in each sub-band in the original image. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Developing an Instrument for Cloud-Based E-Learning Adoption: Higher Education Institutions Perspective

Developing an Instrument for Cloud-Based E-Learning Adoption...

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International Conference on Computer, Communication and Computational Sciences, IC4S 2019

作者： AlAjmi, Qasim Arshah, Ruzaini Abdullah Kamaludin, Adzhar Al-Sharafi, Mohammed A. College of Arts & Humanities A’ Sharqiyah University Ibra Oman Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak GambangPahang26300 Malaysia Faculty of Educational Technology A‘Sharqia University Ibra Oman

ISBN: (纸本)9789811544088

Cloud-Based E-learning is indeed the new paradigm shift in the information and communication technology. As much as the field is still in its infancy stage, various educational institutions have adopted the cloud-based E-learning around the world at all the levels of education. Cloud-based E-learning has shown significant progress and success among all the fields and subjects of education including language teaching, mathematics, science, engineering, and technology. Concisely, this paper is to show the instrument development that can evaluate the adoption of the cloud-based E-learning based on two vital learning aspects: the approach of systematic review and the interaction among the concerned and experts’ point of views. The practitioners in the field of education may find this instrument appealing because of its effectiveness of use. The purpose of this article is to develop multi-dimensional instrument that can be applied to measure the adoption factors for cloud-based E-learning perceived by HEIs, and to come up with a set of validated items. A systematic research design was implemented, multiple stage followed, six experts evaluated this instrument, piloting on 40 out of 100 respondents. Data collection triangulated by in-person interview, telephone interview, and questionnaire interview. Finally, empirically validation was done along with response bias. Finally, valid instrument is developed. © 2021, Springer Nature Singapore Pte Ltd.

关键词： E-learning

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Bootstrapping Simulation in Determining the Prognostic Factors of Lung Cancer Disease by Parametric Survival Analysis

Bootstrapping Simulation in Determining the Prognostic Facto...

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2023 IEEE International Conference on computing, ICOCO 2023

作者： Muhamad Jamil, Siti Afiqah Affendi Abdullah, Mohd Asrul Ibrahim, Nurain Mansor, Mahayaudin M. Md Ghani, Nor Azura Universiti Tun Hussein Onn Malaysia Faculty of Applied Sciences and Technology Pagoh Campus Johor84600 Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi Mara Selangor Shah Alam40450 Malaysia Kompleks Al-Khawarizmi Universiti Teknologi Mara Selangor Shah Alam40450 Malaysia

ISBN: (纸本)9798350302684

Big data analytics focuses on getting useful insights, trends and pattern out of complex and large data. Increasing the sample by resampling the data, in biostatistics expertise, can be employed using the bootstrapping techniques. The world of bootstrapping is very large and expanding where it does not only compute the confidence interval but also perform a standard resampling method. Nevertheless, survival analysis study mostly allows the data to be not normally distributed because of the censored observations. Small number of samples also one of the reasons why this study has to perform bootstrapping to overcome the issues of biasness. Bootstrapping method is said to be one of the best methods in handling skewed data. Thus, by considering bootstrapping method, this study aims to find the most significant prognostic factors of lung cancer disease that affect the survival times with the presence of censored observations by using the parametric survival analysis. Therefore, based on 100, 150, 250 and 600 number of sampling sizes, exponential distribution appeared to fit all the assigned sample sizes. Weibull and log-logistic distribution seems to fit the data only for 100 number of samples. Races and two of the interaction terms in the model appeared to be the most significant prognostic factors affecting the survival time of lung cancer. © 2023 IEEE.

关键词： Data Analytics

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Sequence-to-Sequence Knowledge Graph Completion Based on Gated Attention Unit 29

Sequence-to-Sequence Knowledge Graph Completion Based on Gat...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yi, Fengge Wei, Xiumei Liu, Xiaojing Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

ISBN: (纸本)9798350330717

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5's proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment. © 2023 IEEE.

关键词： gated attention unit knowledge graph completion sequence-to-sequence model

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Code Refactoring for Software Reusability: An Experimental Study

Code Refactoring for Software Reusability: An Experimental S...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdullah Almogahed Hairulnizam Mahdin Mazidah Mat Rejab Abdulwadood Alawadhi Samera Obaid Barraood Manal Othman Omar Al-Jamili Abdulwahab Ali Almazroi Shazlyn Milleana Shaharudin Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Johor Malaysia InterNetWorks Research Laboratory School of Computing Universiti Utara Malaysia Sintok Malaysia College of Computers and Information Technology Hadhramout University Hadhramout Yemen Computer Science Department College of Applied Science Taiz University Taiz Yemen Faculty of Engineering and Computing University of Science and Technology Yemen Department of Information Technology College of Computing and Information Technology at Khulais University of Jeddah Jeddah Saudi Arabia Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Malaysia

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Refactoring plays a critical role in enhancing the quality of software systems, enabling them to accommodate growing user bases, feature sets, and performance demands. Software reusability is an essential aspect of software quality. It is crucial in modern software development as it offers several benefits, including increased productivity, improved code quality, and cost-effectiveness. However, there is a lack of studies investigating how specific refactoring techniques affect reusability. Therefore, this study aims to explore the effects of code refactoring on software reusability. This study was conducted in four stages: selecting refactoring techniques, selecting a large dataset, selecting software metrics to measure the reusability, and applying the refactoring techniques. The five most common refactoring techniques (Extract Interface, Encapsulate Field, Extract Class, Inline Class, and Inline Method) used in practices were selected and applied in the jEdit dataset. The refactoring techniques were carried out 338 times through five experiments in the jEdit dataset. The finding reveals that Extract Interface, Encapsulate Field, and Extract Class significantly improve the code reusability while Inline Class and Inline Method impair it. Developers and software architects can use this knowledge to make well-informed judgments about refactoring techniques and develop more robust and reusable software systems.

关键词： Productivity Industries Codes Software quality Software systems Maintenance Software measurement

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Support Vector Machine for Multiclass Classification of Redundant Instances

Support Vector Machine for Multiclass Classification of Redu...

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Intelligent computing and Networking - Proceedings of IC-ICN 2023

作者： Surbakti, Herison Kumar, Athmakuri Naveen Tara, Saikumar Keshta, Ismail khare, Amit Prasad, G.N.R. Faculty of Computing and Digital Technology HELP University Kuala Lumpur Malaysia Senior Software Engineer Glosoft Technologies Pvt Ltd Chennai India Department of ECE BVRIT Hyderabad College of Engineering for Women Hyderabad India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Technology JIET Institute of Design and Technology Rajasthan Jodhpur India Telangana Hyderabad500091 India

ISBN: (纸本)9789819931767

In recent years, support vector machine has become one of the most important classification techniques in pattern recognition, machine learning, and data mining due to its superior classification effect and solid theoretical base. However, its training time will increase dramatically as the number of samples increases, and training will become more sophisticated when dealing with problems involving multiple classifications. A quick training data reduction approach MOIS appropriate for multi-classification tasks is presented as a solution for the aforementioned issues. While eliminating redundant training samples, the boundary samples that play a vital role are chosen in order to considerably reduce training data and the problem of unequal distribution between categories. The experimental results demonstrate that MOIS may maintain or even improve the classification performance of support vector machines while substantially enhancing training efficiency. On the Opt digit dataset, the suggested method improves classification accuracy from 98.94% to 99.05%, while training time is reduced to 15% of the original;in HCL2000, the proposed method improves classification accuracy from 98.94% to 99.05%. When the accuracy rate is marginally increased (from 99.29% to 99.30%) on the first 100 categories dataset, the training time is dramatically reduced to less than 6% of the original. Additionally, MOIS has a high operational efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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