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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ran Su Jin Xiao Hui Cui Ping Xuan Xikang Feng Leyi Wei Qiangguo Jin School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China Department of Computer Science and Information Technology La Trobe University Melbourne Australia Department of Computer Science School of Engineering Shantou University China School of Software Northwestern Polytechnical University China AIDD Faculty of Applied Science Macao Polytechnic University Macao SAR China School of Information Xiamen University Xiamen China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Gliomas hold a prominent position in neurooncology due to their high malignancy and poor survival rates. Accurately predicting the prognosis and survival risk of glioma patients is crucial for clinical treatment. Recent advances in survival prediction methods emphasize the importance of integrating complementary information from diverse modalities while neglecting the significant modality gap between pathological images and genomic data. To address this issue, we propose a modality-specific knowledge interaction network (MSKI-Net), which integrates whole slide images (WSI), RNA-Seq gene expression data, and copy number variation (CNV) data for glioma survival analysis. The MSKI-Net consists of a modality-specific feature enhancement (MSFE) module, a modality-interactive cross-attention (MICA) module, and a modality-specific knowledge-guided representation learning (MSKR) module. The three modules collaborate by complementing modality-specific features with modality-agnostic knowledge to improve the learning capability of MSKI-Net. Furthermore, we construct a dataset named TCGA mm , which combines WSI, RNA-Seq, and CNV data from The Cancer Genome Atlas (TCGA) to address the issue of data scarcity. Extensive experiments demonstrate that MSKI-Net achieves superior performance in predicting the survival risk of glioma cancer.

关键词： Knowledge engineering Representation learning Pathology Genomics Prediction methods Data models Gene expression Bioinformatics Prognostics and health management Cancer

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Toxic Comment Classification Using S-BERT Vectorization and Random Forest Algorithm

Toxic Comment Classification Using S-BERT Vectorization and ...

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Contemporary computing and Communications (InC4), IEEE International Conference on

作者： Aparna Ashok Kumar Peeta Basa Pati K. Deepa S.Tresa Sangeetha Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Karnataka India Department of Electrical & Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Karnataka India Department of Engineering University of Technology and Applied Sciences AlMusannah Sultanate of Oman

The growing popularity of social media platforms and microblogging websites has led to an increase in the expression of views and opinions. However, conversations and debates on these platforms often lead to the use of toxic comments, which consists of insulting and hateful remarks. To address this issue, it is important for social media systems to be able to recognize harmful comments. With the rising incidence of cyberbullying, it is crucial to study the classification of toxic comments using various algorithms. This study compares the effectiveness of different word and sentence embedding methods, including TF-IDF, InferSent, Bert, and T5 for toxic comments classification. A comparative study is also conducted on the impact of using SMOTE to balance the highly imbalanced dataset. The results of these models are compared and analysed. It is observed that T5 embedding with Random Forest Classifier works best at 0.91 F1-Score.

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

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Procedia computer science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization Classification

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Body Fat Prediction using Various Regression Techniques

Body Fat Prediction using Various Regression Techniques

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Advances in computing, Communication and applied Informatics (ACCAI), International Conference on

作者： Nikhil Mahesh Peeta Basa Pati K. Deepa Suresh Yanan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Department of Engineering University of Technology and Applied Sciences-Al Musannah Sultanate of Oman

Predicting body fat percentage is essential for addressing the obesity problem. This paper compares the performance of several machine learning models based on Regression, to predict the body fat percentage. Using a dataset of 252 participants with information on age, weight, height, and fat percentage, the models were assessed based on multiple performance criteria, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Squared Error(MSE). The results demonstrates that Random Forest Regressor surpass other models with a lower RMSE of 0.276. These findings suggest that machine learning models can be a valuable tool for precise BFP, the use of machine learning provides a faster and more precise method for predicting body fat percentage. Overall, the study’s results suggest that machine learning models can be valuable tool for accurate body fat percentage prediction.

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This is the Table I Want! Interactive Data Transformation on Desktop and in Virtual Reality

arXiv

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arXiv 2023年

作者： In, Sungwon Lin, Tica North, Chris Pfister, Hanspeter Yang, Yalong The Department of Computer Science Virginia Tech BlacksburgVA24060 United States The School of Interactive Computing Georgia Institute of Technology AtlantaGA30309 United States The Department of Computer Science Virginia Tech United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States

Data transformation is an essential step in data science. While experts primarily use programming to transform their data, there is an increasing need to support non-programmers with user interface-based tools. With the rapid development in interaction techniques and computing environments, we report our empirical findings about the effects of interaction techniques and environments on performing data transformation tasks. Specifically, we studied the potential benefits of direct interaction and virtual reality (VR) for data transformation. We compared gesture interaction versus a standard WIMP user interface, each on the desktop and in VR. With the tested data and tasks, we found time performance was similar between desktop and VR. Meanwhile, VR demonstrates preliminary evidence to better support provenance and sense-making throughout the data transformation process. Our exploration of performing data transformation in VR also provides initial affirmation for enabling an iterative and fully immersive data science workflow. © 2023, CC BY.

关键词： Virtual reality

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Combining Dense and Sparse Rewards to Improve Deep Reinforcement Learning Policies in Reach-Avoid Games with Faster Evaders in Two vs. One Scenarios

Combining Dense and Sparse Rewards to Improve Deep Reinforce...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Jefferson Silveira Kalena McCloskey Camille-Alain Rabbath Craig Williams Sidney Givigi Department of Electrical & Computer Engineering The Ingenuity Labs Research Institute Queen’s University Kingston Canada Department of Applied Mathematics and Engineering Queen’s University Kingston Canada School of Computing Queen’s University Kingston Canada Department of Physics and Space Science Royal Military College of Canada Kingston Canada School of Computing and the Ingenuity Labs Research Institute Queen’s University Kingston Canada

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper investigates a variation of the reach-avoid game, a multi-agent pursuit and evasion scenario applicable to aerial defense, with faster evaders. Using Deep Reinforcement Learning techniques, the study proposes a different reward function that combines dense (distance-based) and sparse (outcome-based) rewards. Focused on the defender’s perspective in aerial defense, this new reward function resulted in effective learned policies against faster evaders, outperforming traditional differential game and DRL strategies with dense-only rewards. Moreover, the learned policy demonstrated versatility across different instances of the problem, including changes in pursuer speeds and winning radii, illustrating its versatility in unseen situations during training.

关键词： Training Games Differential games Deep reinforcement learning Information technology

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The Summer Heat of Cryptojacking Season: Detecting Cryptojacking using Heatmap and Fuzzy

The Summer Heat of Cryptojacking Season: Detecting Cryptojac...

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Cyber Resilience (ICCR), International Conference on

作者： Ahmad Firdaus Ghassan Saleh AlDharhani Zahian Ismail Mohd Faizal Ab Razak Faculty of Computing College of Computing & Applied Sciences Universiti Malaysia Pahang Pekan Pahang Head of Department Digital Innovation Institute of Computer Science & Digital Innovation (ICSDI) UCSI University Kuala Lumpur Malaysia

ISBN: (数字)9781665461221

ISBN: (纸本)9781665461238

Cryptojacking is a subset of cybercrime in which hackers use unauthorised devices (computers, smartphones, tablets, and even servers) to mine cryptocurrencies. Similar to many other forms of cybercrime, the objective of cryptojacking is achieve profit illegally. It is also designed to remain entirely concealed from the victim's view. However, its attacks continue to evolve and spread, and their number continues to rise. Therefore, it is essential to detect cryptojacking malware, as it poses a significant risk to users. However, in machine learning intelligence detection, an excessive number of insignificant features will diminish the detection's accuracy. For machine learning-based detection, it's important to find important features in a minimal amount of data. This study therefore proposes the Pearson correlation coefficient (PMCC), a measure of the linear relationship between all features. After that, this study employs the heatmap method to visualise the PMCC value as a colour version of heat. We utilised The Fuzzy Lattice Reasoning (FLR) classifier for classification algorithms in machine learning. This experiment utilised actual cryptojacking samples and achieved a 100 percent detection accuracy rate in simulation.

关键词： Heating systems Correlation coefficient Lattices Machine learning Static analysis Feature extraction Cognition

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Impact on mental health due to COVID-19 pandemic: A cross-sectional study in Bangladesh

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Clinical eHealth 2023年第1期6卷 42-52页

作者： Pathan, Refat Khan Biswas, Munmun Yasmin, Suraiya Uddin, Mohammad Amaz Das, Anindya Khandaker, Mayeen Uddin Faruque, M.R.I. Sarker, Moniruzzaman Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Dept. of Computer Science and Engineering BGC Trust University Bangladesh Chittagong 4381 Bangladesh Department of Computer and Information Science Tokyo University of Agriculture and Technology Koganei - 184-0012 Tokyo Japan Dept. of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Center for Applied Physics and Radiation Technologies School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Space Science Centre Universiti Kebangsaan Malaysia 43600 UKM Selangor Bangi Malaysia Department of Marketing Sunway University Business School Sunway University Selangor Bandar Sunway 47500 Malaysia Faculty of Graduate Studies Daffodil International University Daffodil Smart City Birulia Savar Dhaka – 1216 Bangladesh

The government of Bangladesh has implemented the “Stay Home” policy following the WHO recommendation to resist the community transmission of Covid-19. As a result, the routine activities of all government, semi-government establishments, including educational institutions, are severely affected, and the country's economic growth becomes slowed down. To overcome such a situation, the relevant authorities have introduced the “Work from Home” policy for the employees and “Remote Education” for students. However, due to the persistence of multi-dimensional socio-economic problems, many employees and students face big challenges in performing their regular jobs while adopting such a policy. Consequently, enormous psychological anxiety has been developed for all people, including students, parents, employees, etc., and concurrently created severe changes in their behavior. This study aims to analyze the reasons for the behavioral changes of the employees, students, academic staff, and family members of different ages due to psychological anxiety, stress, or physical issues. A comprehensive online-based survey has been carried out on people working in various sectors in Bangladesh. A modified Apriori Algorithm has been used to sort out the associations between the causes and types of behavioral changes. Analyzed data revealed a massive human behavioral change in most participants. This finding indicates that the negligence of those significant human behavioral changes may cause a higher risk of creating psychological imbalance. Therefore, there is a need to have a solid understanding of the reasons for the behavioral changes and set up standard guidelines to maintain “Work from Home” in this Covid-19 situation to avoid psychological imbalance. Based on this study, some suggestions have been given for implementation by the government on an urgent basis. © 2023

关键词： Apriori algorithm Behavioral changes Covid-19 outbreak Family crisis Work from home

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Users Acceptance Online Attendance Application Using Barcode Scanner: The Case Study in al Buraimi University College (BUC) Students

Users Acceptance Online Attendance Application Using Barcode...

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2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2022

作者： Alsideiri, Abir Alsharida, Rawan A. Hammood, Maytham Shakir, Mohanaad Binti Cob, Zaihisma Rashid Al Shamsi, Ibrahim Muttar, Ahmed Kh. Abood, Rehab Sheker, Mustefa Buraimi Information Technology Department Oman Tikrit University Computer Science Department Tikrit Iraq Cob University of Buraimi College of Business Buraimi Oman Universiti Tenaga National College of Computing and Informatics Malaysia Applied Science University College of Administrative Sciences Manama Bahrain Al-Fallujah Secondary School for Girls General Directorate of Anbar Education Fallujah Iraq Department of the Sewage of Fallujah Ministry of Municipalities and Public Works Fallujah Iraq

ISBN: (数字)9781665469197

ISBN: (纸本)9781665469197

Marking students' attendance within a class session is the prime task of the subject requirements since marking the attendance will regulate the students to attend the session. Moreover, it verifies how many students are attending the class. Previously, attendance was recorded manually by pen and paper. This method takes a long time, especially if the number of students is significant and adds workload to the teacher, and sometimes the data may be prone to error. This paper presents a mobile application for student attendance to avoid these issues. This application will ease the process of attending to the students in the registration instead of the traditional ones where the teacher spends time calling students to know who is present or absent. The students will use their mobile to log in to the application and select the current class to open the camera to scan the code provided by the teacher, and automatically their attendance will be registered. This application is mainly designed for Buraimi University College lecturers and students. © 2022 IEEE.

关键词： E-learning

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UNet with Two-Fold Training for Effective Segmentation of Lung Section in Chest X-Ray 3

UNet with Two-Fold Training for Effective Segmentation of Lu...

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3rd International Conference on Intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Gnanasoundharam, J. Abed Mohammed, Mazin Glan Devadhas, G. Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq Vimal Jyothi Engineering College Department of Electronics and Instrumentation Engineering Chemperi India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Segmentation and evaluation of the Region of Interest (ROI) in medical imaging is a prime task for disease screening and decision-making. Due to accuracy, Convolutional-Neural-Network (CNN) based ROI segmentation has been widely employed in recent years to evaluate a class of medical images recorded using chosen modality. The proposed work aims to demonstrate the segmentation performance of the UNet scheme with a one-fold and two-fold training process. To experimentally verify the merit of the proposed scheme, segmentation of the lung section from the chest X-ray is studied. This research includes the following parts;(i) Resizing the test image and image mask to pixels, (ii) Training the UNet with one-fold and two-fold approaches, (iii) Extracting the ROI, (iv) Comparing the ROI with the mask to compute the image metrics and (v) Validating and confirming the segmentation performance of UNet. The performance of UNet is then verified with UNet+ and UNet++. The investigational ending substantiates that the proposed approach helps to get better Jaccard (>95%), Dice ((>97%), and Accuracy (>98%) in two-fold training compared to other methods considered in this study. © 2022 IEEE.

关键词： Decision making

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