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An Efficient Deep Learning Approach to Detect Neurodegenerative Diseases Using Retinal Images

An Efficient Deep Learning Approach to Detect Neurodegenerat...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Chowdhury Mohammad Irfanuddin Wasique Islam Shafin Koushik Ahmed Md. Hasib Khan Md. Ashraful Alam Rafeed Rahman Shakib Mahmud Dipto Department of Computer Science and Engineering Brac University Dhaka Bangladesh Dept. of CSE Computer Vision and Intelligent Systems (CVIS) Research Lab Brac University Dhaka Bangladesh Department of Computer Science and Engineering University of Liberal Arts Bangladesh (ULAB) Dhaka Bangladesh

Neurodegenerative disorders like Multiple Sclerosis, Parkinson's disease, and Alzheimer's disease typically get diagnosed through brain MRI, CT scans, genetic testing, and various laboratory screening tests that are often tedious, time-consuming, and beyond the reach of the financial capability of many people especially, those living in the developing world. Moreover, they often pose potential hazards for patients with certain pre-existing conditions. To remedy this, we propose an efficient deep-learning approach that enables the detection of such neurodegenerative diseases through retinal images rather than brain images. Our proposed system can proactively detect such disorders simply through retinal scans, which are fast and cost-effective compared to traditional diagnostic approaches, which require expensive and sophisticated equipment. We used a dataset containing retinal cross-sectional images of 21 Multiple Sclerosis patients and 14 healthy individuals for our research, and our model achieved 98.85% accuracy while classifying healthy and diseased individuals from retinal scans.

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Providing Automated Feedback on the Gestalt Principle of Symmetry by Means of Machine Learning

Providing Automated Feedback on the Gestalt Principle of Sym...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Bertram Haskins Marna Haskins School of IT Nelson Mandela University Port Elizabeth South Africa Noroff Fagskole (Online) Noroff School of Technology and Digital Media Kristiansand Norway

The Gestalt Principles are sometimes used within the field of design to help students understand the underlying structure of images. Assessment activities focused on determining the Gestalt Principles contained in an image is a very subjective exercise, with students and teachers not necessarily agreeing on the content of an image. This subjectivity becomes more problematic when dealing with large class groups or when multiple markers are employed. This study proposes a multi-layer perceptron-based model for classifying images as exhibiting the principle of symmetry. The model exhibits a high level of accuracy when classifying images as containing aspects of symmetry, whether across the entire image or only partially, or not. The model forms part of a larger process which generates images, based on original input images, and highlights areas of symmetry. These annotated images may be used to help provide feedback to students.

关键词： computer science Machine learning data engineering

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Graph Convolutional Networks for Lithium-Ion Battery Health Estimation 10

Graph Convolutional Networks for Lithium-Ion Battery Health ...

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10th ieee International Power Electronics and Motion Control conference, IPEMC 2024 ECCE asia

作者： He, Yuxin Deng, Zhongwei Nie, Wenhao Jiang, Chunlin University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu611731 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China

ISBN: (纸本)9798350351330

Assessing the state of health (SOH) is essential for guaranteeing the safety and maintenance of lithium-ion batteries (LIBs). Nevertheless, due to the complex operating conditions, accurately estimating the capacity of LIBs is challenging. This study proposes a data-driven approach leveraging graph convolutional networks (GCN) to estimate the SOH of LIBs by utilizing multiple voltage segments. Then, the extracted voltage sequence is input into a graph generation layer(GGL), which transforms traditional sequential data into graph structured data. Finally, aggregating features from multiple nodes, and battery SOH estimation is achieved through a multi-layer perceptron. The efficacy of this approach in estimating battery SOH is validated using the MIT-Stanford dataset. Based on the results, the GCN method is able to achieve a high accuracy of 0.41% MAE and 0.63% RMSE, representing a substantial improvement over alternative methods. © 2024 ieee.

关键词： Lithium-ion batteries

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Predicting Habitual Activities for Reducing Perceived Latency

Predicting Habitual Activities for Reducing Perceived Latenc...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： YuFeng Deng Sathiamoorthy Manoharan Xinfeng Ye School of Computer Science University of Auckland Auckland New Zealand

Human activities are usually habitual, following some patterns. The patterns make these activities predictable. A number of our activities these days are online, and these activities require us to access resources from online sources, incurring data transfer latencies. Being able to predict the patterns in online activities allows automatic prefetching of these resources so that the perceived latency of accesses can be vastly reduced. While there is a lot of prior research to predict short-term activities, research relating to predicting long-term habitual activities is sparse. This paper attempts to address the general area of how long-term habitual activities can be predicted. It proposes a model using long short-term memory (LSTM) capable of learning long-term habitual activities and predicting future activities based on this model. Our experimental evaluation shows that the prediction accuracy is promising.

关键词： Training computer science Computational modeling Prefetching Neural networks Predictive models data transfer

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Deep Reinforcement Learning Aided Device Profiling for Anomaly Detection in IIoT Over Zero Trust Security Network

Deep Reinforcement Learning Aided Device Profiling for Anoma...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Anamika Singh Rajesh Kumar Dhanaraj Ching-Hsien Hsu Anupam Kumar Sharma Pardeep Kumar School of Computing Science and Engineering Galgotias University Greater Noida U.P India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) Pune India Dept. Computer Science and Information Engineering Asia University Taiwan Manav Rachna International Institute of Research and Studies India

The application of internet of things in industries is the fourth revolution of industries and is referred to as industry 4.0 or industrial internet of things. The shifting of manual and conventional approach to a digital platform is witnessed as a key consideration for the emergence of industrial internet of things. Industrial internet of things works around thousands of devices connected remotely and the overall monitoring, functioning and storage is performed virtually by and on devices, hence more prone to cyber threads. With the amplification in the growth and application of more complicated networks, the requirements for the security are also increased significantly. Zero trust security stands as an efficient solution for all the security questions. Zero trust security involves continuous monitoring of devices for verifying the authenticity before permitting the access to network. Continuous monitoring for detecting the honest and malicious device adversely effects the productivity of the IIoT environment. The proposed paper presents a novel approach to generate a unique profile of devices via deep reinforcement learning to avoid irrelevant authentication cycles and produce more optimized results. The proposed work surpasses the existing approaches (CRH-SRP) [1] and Cyber Security via Determinism [2] with enhanced data confidentiality rate by 3.25% and reduced false positive rate by 30.5% respectively.

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Statistical Methods for data mining Mathematics students' online presence

Statistical Methods for Data mining Mathematics students' on...

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8th ieee asia-pacific conference on computer science and data engineering (ieee csde)

作者： Naseem, Mohammed Reddy, Emmenual Nand, Ravneil Univ South Pacific Sch Informat Technol Engn Math & Phys Suva Fiji

ISBN: (纸本)9781665495523

COVID-19 has caused major changes in every aspect of human endeavor, including efforts in the higher education sector. A sudden shift from face-to-face and blended settings to a completely online delivery mode has introduced changes to conventional teaching methods, and made learning rely heavily on technology and the Internet. Hence, students' online engagement with these tools has become even more important for their academic success. Therefore, there is a need to investigate the effects of various indicators of students' online presence on their academic performance. This paper explores the effectiveness of online presence in Higher Education Institutes, where COVID-19 has shifted the deliveries to online mode. The chosen indicator is frequency that will be adequately used to quantify the effectiveness of online presence on student performance in online mathematics courses. Statistical methods are used to measure the correlation and association between students' online presence indicators and their performance. As such, it would allow to build models to predict future outcomes or occurrences and student performances, with a major focus on mathematics and statistics courses. The results show that there is an increase in student online interaction in courses during COVID-19 era;however, it is consistent with the Online Measureable Presence Model (OMPM) model where frequency was the dominant indicator of student performance.

关键词： online presence mathematics data mining virtual learning environments higher education

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Gender Classification in Bangla Language Using Deep Learning-Based Voice Analysis

Gender Classification in Bangla Language Using Deep Learning...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Md. Zarif Labib Sayema Binte Monsur Abtahi Maskawath Shuvo Tasmia Azrine Talukder Juhaer Hakim Syed Muaz Ali Md. Ashraful Alam Department of Computer Science & Engineering BRAC University Dhaka Bangladesh

Gender classification based on voice analysis is one of the essential tasks in speech and audio processing, with various applications such as speech recognition systems, voice assistants, call center analytics, Etc. For speech synthesis, human-computer interaction, and speaker identification - gender classification plays a vital role. Although extensive research on this topic has been done in various languages, studies can hardly be found regarding gender classification in the Bangla language. Our research aims to recognize gender in the Bangla language using deep learning approaches and voice analysis. The proposed strategy in this study consists of three stages: i) Pre-processing of the data; ii) Feature extraction utilizing the Short-Time Fourier Transforms (STFT) and Mel-Frequency Cepstral Coefficients (MFCC); iii) Classification using Convolutional Neural Network (CNN) models such as ResNet50, EfficientNetB0, InceptionV3, and DenseNet-121. Notably, 12 distinct feature combinations are used for model training and testing, using both the MFCC and STFT features singly or in combination. After thorough training and testing, InceptionV3 and EfficientNetB0 CNN models with MFCC features as input resulted in the highest accuracy of 92%, which demonstrates the system's excellent accuracy rate and its potential for use in practical settings.

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End-to-end Image Matting Based on Salient Portrait 2

End-to-end Image Matting Based on Salient Portrait

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2nd asia-pacific conference on Communications Technology and computer science, ACCTCS 2022

作者： Fei, Che Wei, Hu Colloge of Information Science and Technology Beijing University of Chemical Technology Beijing China

ISBN: (纸本)9781665400343

The aim of portrait fine segmentation is to accurately estimate the opacity of each pixel in the foreground of the portrait. At present, known methods often require auxiliary inputs that are difficult to obtain or multi-stage models with high computational cost, which cannot meet the application requirements of real-time. In order to have the advantages of both speed and precision, this paper designs a two-sided branch network structure (BIMNET) for single-stage real-time portrait segmentation. One of the two branches is used for detail prediction, and the edge detail feature map is generated by three nested U structures. The other is used for semantic prediction, which obtains high-level semantic features through a fast subsampling semantic path. Finally, the feature fusion module fuses the features of the two at different levels, and finishes the fine segmentation task with the guidance of three output levels. In order to reduce the extra inference cost and improve the robustness of the model, the multi-prediction output heads were designed only in the model training stage. In this paper, for the first time, a strategy combining bilateral network and fine segmentation is adopted to verify the effectiveness of single-stage real-time portrait segmentation, and the balance between precision and speed is achieved. In addition, we collect and annotate the portrait data as an extension of the training data, and all the data sets will be released. © 2022 ieee.

关键词： Semantics

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Blockchain-based Reviewer Selection

Blockchain-based Reviewer Selection

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Jianmin Wen Sathiamoorthy Manoharan Xinfeng Ye School of Computer Science The University of Auckland Auckland New Zealand

Universities require their students to undertake projects as partial fulfilment of the students' degree. These projects typically go through an independent external review process so that academic standards are upheld. Finding appropriate external reviewers with the domain knowledge is a time-consuming process. In this paper, we propose a system that helps advisors find examiners for their students' projects. The system uses machine learning techniques to identify the subject areas of the students' projects from the text of students' dissertations. It then matches scholars who have research interests in the identified subject areas. The system is implemented using a blockchain and aims to provide a transparent environment that allows scholars to freely participate in the system. In this project, we collected a set of papers from several top journals and conferences. Using this dataset, we have evaluated the performance of several classification algorithms, e.g., multinomial Naive Bayes, K-Means Clustering with multinomial logistic regression and Labeled-LDA. The experimental study shows that the weighted multinomial Naive Bayes model performs best among the three models in terms of accuracy and computational time.

关键词： computer science Computational modeling Machine learning data engineering Classification algorithms Blockchains Standards

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A Deep Reinforcement Learning Method for Charging Station Management and Load Balancing

A Deep Reinforcement Learning Method for Charging Station Ma...

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2023 ieee Transportation Electrification conference and Expo, asia-pacific, ITEC asia-pacific 2023

作者： Liu, Jie Liu, Zifan Tang, Xiaoying School of Science and Engineering Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Guangdong Provincial Key Laboratory of Future Networks of Intelligence China

ISBN: (纸本)9798350314274

Electric vehicles (EVs) and the corresponding renewable energy-based charging stations (CSs) are rapidly gaining popularity as an effective solution for environmental protection. However, the increasing adoption of EVs and CSs can lead to load fluctuations and pose safety risks to the grid. To address this issue and develop a practical management strategy for CSs, we propose a load-constraint hierarchical deep reinforcement learning approach. Our method aims to achieve two main objectives: 1) maximize the total profit and balance the load for CSs, taking into account the unpredictable behavior of EV users;and 2) separate pricing and charging scheduling actions to enhance training efficiency. We conducted experiments using real data, and the results validate the effectiveness of our algorithm in achieving load balancing and maximizing profits for CSs. © 2023 ieee.

关键词： Renewable energy

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