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3rd International Workshop and Challenge on computer Vision in Endoscopy, EndoCV 2021

作者： Haithami, Mahmood Ahmed, Amr Liao, Iman Yi Jalab, Hamid Computer Science Department University of Nottingham Malaysia Campus Computer System and Technology Department University of Malaya

Detecting cancers at their early stage would decrease mortality rate. For instance, detecting all polyps during colonoscopy would increase the chances of a better prognoses. However, endoscopists are facing difficulties due to the heavy workload of analyzing endoscopic images. Hence, assisting endoscopist while screening would decrease polyp miss rate. In this study, we propose a new deep learning segmentation model to segment polyps found in endoscopic images extracted during Colonoscopy screening. The propose model modifies SegNet architecture to embed Gated recurrent units (GRU) units within the convolution layers to collect contextual information. Therefore, both global and local information are extracted and propagated through the entire layers. This has led to better segmentation performance compared to that of using state of the art SegNet. Four experiments were conducted and the proposed model achieved a better intersection over union "IoU" by 1.36%, 1.71%, and 1.47% on validation sets and 0.24% on a test set, compared to the state of the art SegNet. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Semantic Segmentation

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Robust Spectral Efficiency Maximization in 5G Networks Using Non-Linear Stochastic Precoding 2

Robust Spectral Efficiency Maximization in 5G Networks Using...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Ponnaian, Geetha Jayanthi, N. Sarasu, R. Sajitha, L.P. Ramalakshmi, R. Manimegalai, L. Department of Computer Science and Engineering Thandalam Chennai India Pallavaram Tamil Nadu Chennai600117 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences India R. M. K Engineering College Department of Computer Science and Business System Tamilnadu Kavaraipettai India Ramco Institute of Technology Department of Electronics and Communication Engineering Rajapalayam India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350371055

This research focuses on enhancing the spectral efficiency of 5G networks through the utilization of nonlinear stochastic precoding techniques. The proposed approach aims to mitigate the detrimental effects of channel impairments and noise by employing advanced precoding schemes that exploit the statistical properties of the wireless channel. By incorporating nonlinear precoding algorithms and stochastic optimization methods, the system can dynamically adapt to varying channel conditions, resulting in improved data transmission rates and overall spectral efficiency. Through extensive simulations and analysis, the effectiveness of the proposed method is demonstrated, showcasing its potential to significantly enhance the performance of 5G networks in real-world scenarios. © 2024 IEEE.

关键词： 5G networks channel impairments nonlinear stochastic precoding spectral efficiency stochastic optimization

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Analysis of cloud services using OWASP security design

Analysis of cloud services using OWASP security design

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Poongavanam, Elumalaivasan Munirathinam, T. Murugesan, Sankar Kayalvizhi, V. Department of Data Science and Business System SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350305708

Cloud computing is extremely popular model among enterprises. It provides more availability of cloud resources when needed with high scalability and extremely cost-effective model. Security is one of the least understood concepts to provide and access for OWASP applications in cloud computing. Many enterprises especially banking clients are risk-averse and have less cloud adoption rate. In this study, security research in the area of cloud security is reviewed and AWS (Amazon Web Service) is the top provider of cloud computing. Since, we have also demonstrated how it works in the security exploration. This paper's major goal is to make cloud computing security is a key activity and prevent unauthorized access to the apps used in the real world. © 2023 IEEE.

关键词： cloud computing AWS On demand services security

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D2-MLP: Dynamic Decomposed MLP Mixer for Medical Image Segmentation

D2-MLP: Dynamic Decomposed MLP Mixer for Medical Image Segme...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yang, Jin Yang, Jing Yu, Xiaobing Qiu, Peijie Prajapat, Sunil Mallinckrodt Institute of Radiology Washington University School of Medicine St. Louis United States Department of Computer System and Technology University of Malaya Kuala Lumpur Malaysia Central University of Himachal Pradesh Dharamshala India

ISBN: (纸本)9798350368741

Convolutional neural networks are widely used in various segmentation tasks in medical images. However, they are challenged to learn global features adaptively due to the inherent locality of convolutional operations. In contrast, MLP Mixers are proposed as a backbone to learn global information across channels with low complexity. However, they cannot capture spatial features efficiently. Additionally, they lack effective mechanisms to fuse and mix features adaptively. To tackle these limitations, we propose a novel Dynamic Decomposed Mixer module. It is designed to employ novel Mixers to extract features and aggregate information across different spatial locations and channels. Additionally, it uses novel dynamic mixing mechanisms to model inter-dependencies between channel and spatial feature representations and to fuse them adaptively. Subsequently, we incorporate it into a U-shaped Transformer-based architecture to generate a novel network, termed the Dynamic Decomposed MLP Mixer. We evaluated it for medical image segmentation on two datasets, and it achieved superior segmentation performance than other state-of-the-art methods. © 2025 IEEE.

关键词： Channel and Spatial Features Dynamic Networks Medical Image Segmentation MLP Mixer

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ConTNet: cross attention convolution and transformer for aneurysm image segmentation

ConTNet: cross attention convolution and transformer for ane...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhao, Yawu Wang, Shudong Zhang, Yulin Ren, Yande Zhai, Xue Wu, Wenhao Pang, Shanchen China University of Petroleum School of Computer Science and Technology Qingdao China Shandong University of Science and Technology College of Mathematics and System Science Qingdao China Affiliated Hospital of Qingdao University Department of Medical Imaging Center Qingdao China

ISBN: (纸本)9798350337488

In recent years, Convolutional Neural Neural Networks (CNNs) and Transformer architectures have significantly advanced the field of medical image segmentation. Since CNNs can only obtain effective local feature representations, there is difficulty in establishing long-range dependencies. However, Transformer has gained extensive attention from researchers due to its powerful global context modeling capability. Therefore, to integrate the advantages of the two architectures, we propose a network of ConTNet that can combine local and global information, consisting of two parallel encoders, namely, the Transformer and the CNN encoder. The CNN encoder is a stack of deep convolution and Criss-cross attention module (CCAM), which aims to acquire local features while strengthening the connection with the surrounding pixel points. In addition, two different forms of features are fused and fed into the encoder to ensure semantic consistency. Extensive experiments on the aneurysm and polyp segmentation datasets demonstrate that ConTNet performs better due to other state-of-the-art methods. © 2023 IEEE.

关键词： CNN Criss-cross attention module Medical image segmentation Transformer

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A Comparative Analysis on Three Duplication Elements in Copy-Move Forgery using PatchMatch-Based Detection Method 8

A Comparative Analysis on Three Duplication Elements in Copy...

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8th IEEE International Conference on Software Engineering and computer systems, ICSECS 2023

作者： Nor Azaimi, Nur Izzati Abd Warif, Nor Bakiah Ismail, Nor-Syahidatul N Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Centre for Information Security Research Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Department of Computer System and Networking Pekan Pahang Malaysia

ISBN: (纸本)9798350310931

Image forgery is the alteration of a digital image to hide some of the important and useful information. Copy-move forgery (CMF) is one of the most difficult to detect because the copied part of the image has the same characteristics as the original image. Most of the existing datasets only highlight additional attacks in the copied part. Since there are no categories of duplication elements in the datasets, this research analyzed three categories of duplication elements in CMF which are animals, food and non-living things using DEFACTO and CoMo3Dataset. The analysis is performed on PatchMatch-based detection method and the results show that the method able to maintain at least 83% for all duplication elements in both DEFACTO and CoMo3Dataset. Furthermore, the method is able to detect a minimum 92% score for the food category in both datasets. © 2023 IEEE.

关键词： Copy-Move forgery DEFACTO duplication regions PatchMatch

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Exams Item Analysis, Online Examination, and Education Evaluation system Via Internet 20

Exams Item Analysis, Online Examination, and Education Evalu...

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20th International Conference on Electrical Engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON 2023

作者： Chaidee, Kamonlak Boontasri, Kanchana Chaidee, Ekkachai Punthura, Kittichai Kumtip, Narong Pankham, Yotsanan Rajamangala University of Technology Lanna Chiangrai Faculty of Business Administration and Liberal Arts Department of Computer Information System Chiang Rai Thailand Rajamangala University of Technology Lanna Chiangrai Faculty of Science and Agricultural Technology Department of Information Technology Chiang Rai Thailand Rajamangala University of Technology Lanna Chiangrai Faculty of Electrical Engineering Department of Electrical Engineering Chiang Rai Thailand

ISBN: (纸本)9798350310467

The objective of this study is to develop an exams item bank management system on the internet. The exam item analysis system is used to determine the reliability of the exams using K-R 20 and K-R 21 formulas. The difficulty and power of discrimination values are determined using a choice analysis and percentages of high and lower groups technique. The create exams set system can be performed randomly or identified through the difficulty level. The examination can be performed online and the exam results can be immediately announced. The grading system is used to support the decision-making to evaluate learning achievement. The system's accuracy was tested and found to be accurate compared with the hand calculation results. The system was tested with a sample group and satisfaction was assessed through questionnaires. The satisfaction of teachers and students was highest, while that of administrators was high. The developed exams item bank management system operates as a web application with a security system for supporting in learning activity. © 2023 IEEE.

关键词： Decision making

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Design and Implementation of an On-Board system for a Spider Robot 1st

Design and Implementation of an On-Board System for a Spider...

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Nauman, Nasir Mustafa, Hirra Ashraf, Abrar Khalil, Hisham Department of Computer Science Superior University Lahore Pakistan Department of Computer Science Sharif College of Engineering and Technology Lahore Lahore Pakistan Embedded System and Robotics Lab Department of Technology The University of Lahore Lahore Pakistan

ISBN: (纸本)9783031776199

This paper presents the design of a system onboard spider robot for rescue services. The spider has the six-leg walking pattern that contains the total number of 18 servo motors. In order to reduce the payload, the abdomen of the spider is made with RF4 printed circuit board (PCB). Whereas, the abdomen is also used as the main structure of the robot to attach the rest of the system. Wherein, two pulse-width modulations (PWM) module are controlled by Arduino Nano, the Bluetooth module is used for communication link and bulk converter for the power supply. The spider robot is operated with an in-house developed Android application. It has been observed that the spider robot has a stable walking response along with the multiple excise performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Machine design

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Predicting environment effects on breast cancer by implementing machine learning

arXiv

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arXiv 2023年

作者： Farooq, Muhammad Shoaib Ilyas, Mehreen Department of Computer Science School of System and Technology University of Management and Technology Lahore54000 Pakistan

The biggest Breast cancer is increasingly a major factor in female fatalities, overtaking heart disease. While genetic factors are important in the growth of breast cancer, new research indicates that environmental factors also play a substantial role in its occurrence and progression. The literature on the various environmental factors that may affect breast cancer risk, incidence, and outcomes is thoroughly reviewed in this study report. The study starts by looking at how lifestyle decisions, such as eating habits, exercise routines, and alcohol consumption, may affect hormonal imbalances and inflammation, two important factors driving the development of breast cancer. Additionally, it explores the part played by environmental contaminants such pesticides, endocrine-disrupting chemicals (EDCs), and industrial emissions, all of which have been linked to a higher risk of developing breast cancer due to their interference with hormone signaling and DNA damage. Algorithms for machine learning are used to express predictions. Logistic Regression, Random Forest, KNN Algorithm, SVC and extra tree classifier. Metrics including the confusion matrix correlation coefficient, F1-score, Precision, Recall, and ROC curve were used to evaluate the models. The best accuracy among all the classifiers is Random Forest with 0.91% accuracy and ROC curve 0.901% of Logistic Regression. The accuracy of the multiple algorithms for machine learning utilized in this research was good, which is important and indicates that these techniques could serve as replacement forecasting techniques in breast cancer survival analysis, notably in the Asia region. © 2023, CC BY.

关键词： Forecasting

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Studying of Machine Learning Models for Forecasting Macroeconomic Indicators 27

Studying of Machine Learning Models for Forecasting Macroeco...

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27th International Conference on Advanced Communications technology, ICACT 2025

作者： Ngoc, Lan Dong Thi Ngo-Thi-Thu-Trang Kim, Kyoung-Mok Pham, Vung Nguyen, Ha-Nam Department of Economic Information System Academy of Finance Hanoi Viet Nam Faculty of Information Technology University of Engineering and Technology Vietnam National University Hanoi Viet Nam Posts and Telecommunications Institute of Technology Hanoi122300 Viet Nam Computer Science Department Sam Houston State University HuntsvilleTX77340 United States Department of Medical Information Sahmyook Health University Seoul Korea Republic of Faculty of Information Technology Electric Power University Hanoi Viet Nam

ISBN: (纸本)9791188428137

This study explores the application of machine learning models in forecasting macro-economic indicators, including GDP, inflation rate, unemployment rate, and exchange rate across 11 Southeast Asian countries. The models used include Linear Regression, ARIMA, Random Forest, XGBoost, LSTM, and SVM. We conducted a performance comparison of each model based on MAE, RMSE, and R2 metrics to evaluate the accuracy of the forecasts. The experimental results indicate that Random Forest and XGBoost models excel in predicting nonlinear and complex indicators such as GDP and unemployment rate, while ARIMA and Linear Regression models perform better in time series with clear regular patterns, like inflation rate. The LSTM model shows inconsistent effective-ness, requiring large data volumes and complex optimization processes. SVM demonstrates potential in handling nonlinear data but requires careful tuning. This study concludes that using machine learning models presents significant potential for improving the accuracy of macroeconomic forecasting. However, model tuning and optimization are essential to match the characteristics of each type of economic indicator. Future research directions include developing hybrid models and integrating additional factors such as market sentiment, social and environmental indicators (ESG) to enhance forecasting outcomes. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Inflation

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