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检索条件"任意字段=2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022"

共 960 条记录，以下是61-70 订阅

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Harnessing computer Vision to Identify Pests and Predators: A Comparative Model Analysis

Harnessing Computer Vision to Identify Pests and Predators: ...

引用

2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Mohaghegh, Mahsa Jaber, Lara Stratford, James Maxwell, William Tamatea, Connor Crawford, Lachlan School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9798350341072

This study focuses on developing an intelligent system using machine learning algorithms and motion-sensor camera traps to achieve real-time classification of pest predators in the setting of the bushlands of New Zealand. The primary goal is to provide an optimized tool for ecological monitoring, aiding in the preservation of native Kiwi bird habitats and aligning with the eco-city initiatives. By training and comparing various deep learning models, including Convolutional Neural Networks (CNNs), Multi-Layer Perceptron (MLP), and Vision Transformers (ViT), the system aims to assist in accurately identifying and managing pest populations. We found that our best-performing model on our data was ResNet-50 with an overall accuracy of 98.15% and an average f1-score of 0.982 across the five classes. This was closely followed by DenseNet121 with an overall accuracy of 97.95% and an average F1 score of 0.978 and our CNN with a Vision Transformer model with an overall accuracy of 97.58% and an average F1 score of 0.976. © 2023 ieee.

关键词： Convolutional neural networks

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Detecting data Exfiltration Using Seeds Based Graph Clustering

Detecting Data Exfiltration Using Seeds Based Graph Clusteri...

引用

2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Cheng, Adriel Millar, Kyle Defence Science and Technology Group Information Sciences Division Department of Defence Australia

ISBN: (纸本)9781665453059

Identifying devices of interest within Internet Protocol (IP) networks is essential for Cyber security and network management. However, the large variety and number of devices within networks, and the ubiquitous encryption of network traffic poses significant barriers. We present a scalable and encryption resilient technique to identify devices of interest based solely on their affiliation to public internet services. In this work, devices of interest are those that are suspected of engaging with suspicious or unexpected servers;e.g., facilitating the malicious exfiltration of sensitive intellectual property data. Graph-based clustering was used to reveal devices of interest based on the similarity of their network behaviour to a set of pre-known malicious devices acting as seeds. The motivation for our technique was driven by a case study for which a subset of malicious devices were known;however, the vast majority remained undiscovered. We conducted experiments to demonstrate viability of our technique as applied to this use-case. Detection accuracies of 94% were achieved, and malicious devices from the case study were successfully identified to aid data driven decisions by Cyber analysts. © 2022 ieee.

关键词： Network security

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Can LLMs Determine Political Leaning of Polish News Articles?

Can LLMs Determine Political Leaning of Polish News Articles...

引用

2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Szwoch, Joanna Staszkow, Mateusz Rzepka, Rafal Araki, Kenji Graduate School of Information Science and Technology Hokkaido University Sapporo Japan Mateusz Staszków Software Development Warsaw Poland Hokkaido University Faculty of Information Science and Technology Sapporo Japan

ISBN: (纸本)9798350341072

This study explores how Large Language Models (LLMs) can be used to analyze the political and establishment leanings of opening paragraphs in Polish online news articles that discuss controversial topics. This task, often time-consuming and costly when performed by humans, holds promise for enhancing media fairness and balance cost-effectively. It is one of the first attempts to analyze this problem in an under-resourced language like Polish. Findings reveal the challenge of task difficulty for LLMs and human annotators. Accuracy of LLMs varies, with better performance on questions related to political leaning than establishment stance. Notably, the text-davinci-002 model achieves the highest scores in wing orientation detection, whereas gpt-3.5-turbo excels in recognizing pro-/anti-government bias. This study highlights LLMs' limitations for this demanding task in an under-resourced language like Polish, emphasizing the need for alternative detection approaches. © 2023 ieee.

关键词： Computational linguistics

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A Novel Knowledge Distillation Framework with Intermediate Loss for Android Malware Detection

A Novel Knowledge Distillation Framework with Intermediate L...

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Xia, Mengzhen Xu, Zhicheng Zhu, Huijuan Jiangsu University Computer Science and Communication Engineering ZhenJiang China Jiangsu University Mathematical Sciences ZhenJiang China

ISBN: (纸本)9781665453059

With the popularity and self-contained functions of Android operating system, malicious attackers have targeted it primarily. Along with attackers are gradually skilled in avoid deep learning detectors, many in-depth researches on Android malware detection have been done. However, these works have complex models and enormous parameters. To settle this dilemma, we proposed a knowledge distillation architecture with intermediate loss to narrow the capability gap by promoting the student network emulate valuable hint knowledge from the intermediate layers of teacher network, based on Multi-Layer Perceptron (MLP). Besides, our framework utilizes static based features, namely permissions and vulnerabilities to effectively characterize applications and construct dataset. We evaluated our framework from various performance metrics and compared with other state-of-the-art deep neural networks. The experimen-tal results indicates that our framework own better performance and is perspective. © 2022 ieee.

关键词： Distillation

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The Longest Almost Increasing Subsequence Problem with Sliding Windows

The Longest Almost Increasing Subsequence Problem with Slidi...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Ho, Cheng-Han Yang, Chang-Biau National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350341072

In this paper, we first define the longest almost increasing subsequence with sliding windows (LaISW), which is a generalized combination of the longest increasing subsequence with sliding windows (LISW) problem and the longest almost increasing subsequence (LaIS) problem. Given a numeric sequence A, along with a tolerance constant c and a window size w, the LaISW problem aims to find the longest almost increasing subsequence (LaIS) within all windows of size w, where an almost increasing subsequence allows for slight decreases less than c. Then, we propose an efficient algorithm to solve the LaISW problem. In our algorithm, we calculate the change of drop out (occurrence) of each element in the row tower, rather than building the entire row tower. The time and space complexities of our algorithm are O(nL) and O(L), respectively, where n denotes the length of the input sequence, and L denotes the length of the LaISW answer. © 2023 ieee.

关键词： Decoding

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BiProSimGCN: Prediction of Drugs-Proteins Interactions Using Graph Convolutional Neural Network and Proteins Similarity

BiProSimGCN: Prediction of Drugs-Proteins Interactions Using...

引用

2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Ghahramani, S. AmirAli Gh. Hosseinioun, Morteza Nosrati, Amirhossein Kavousi, Kaveh Sharif University of Technology Department of Computer Engineering Kish Island Iran Institute of Biochemistry and Biophysics University of Tehran Department of Bioinformatics Tehran Iran

ISBN: (纸本)9798350341072

The drug discovery process faces challenges due to its length, cost, and experimental failures. Researchers are addressing these issues through 'drug repurposing,' seeking new applications for existing pharmaceuticals. To predict drug-target interactions, computational methods, like drug-target interaction prediction, utilize bipartite graphs and link prediction. In this paper, we propose BiProSimGCN, a framework based on convolutional graph neural networks, to predict drug-protein interactions. BiProSimGCN integrates data from the bipartite drug-protein network and the protein similarity network, aiming to reveal latent factors of drugs and proteins. The performance of BiProSimGCN is evaluated using a dataset with information about drugs, target proteins, and their interactions, as well as benchmark datasets with interactions involving enzymes, ion channels, GPCRs, and nuclear receptors. The results indicate that BiProSimGCN outperforms several models from the literature and achieves comparable performance with others. © 2023 ieee.

关键词： Proteins

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Neural Network Classification of Resting State fMRI data of ADHD and Healthy Subjects using Dynamic Mode Features

Neural Network Classification of Resting State fMRI Data of ...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Harini, R. Thatchayeni, R. Lakshmipriya, K. Udayaprasanth, P. Purnima, S. Maheswari, O. Uma Anna University Electronics & Communication Engineering Chennai Guindy India

ISBN: (纸本)9798350341072

Attention deficit hyperactivity disorder (ADHD) is one across the board mental disorder distinguished by inattentiveness, hyperactivity or impulsive behavior. The exact cause is yet unknown but the prevalence of ADHD requires early diagnosis and treatment. This study investigates the decomposed subsystems of resting- state fMRI (rs-fMRI) blood oxygen level dependent (BOLD) signals in order to assess the overall characteristic of the human brain. rs-fMRI BOLD signals were first decomposed into dynamic modes (DMs) which can illuminate the patterns of brain subsystems. Each DM is associated with one Eigenvalue that characterizes functional connectivity dynamics. Thereby the features related to those DM were extracted. Using the features obtained, the classification is performed using neural network-based pattern recognition model and the performance is evaluated. The interpreted results with neural network (NN) classifier obtains an accuracy of 73.7%. © 2023 ieee.

关键词： Pattern recognition

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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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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

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

ISBN: (纸本)9798350341072

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. © 2023 ieee.

关键词： Anomaly detection

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Leveraging Web Application to Enhance Transport Mobility: Optimize Bus Schedules and Ride-Share in Fiji's Central Division

Leveraging Web Application to Enhance Transport Mobility: Op...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Kumar, Raymond Krishnil Sharma, Neeraj Anand The University of Fiji Department of Computer Science and Mathematics Saweni Campus Fiji

ISBN: (纸本)9798350341072

Transportation is a way to travel from point A to B however the increasing demand for public and private transportation has caused issues such as traffic congestion amongst others. The research discusses the development of a web application to enhance transport mobility in Fiji's Central Division. It highlights the challenges faced by the public transportation system in Fiji, including inadequate bus schedules and ride-sharing options, which lead to traffic congestion and increased greenhouse emissions. The research emphasizes the importance of having a sustainable and efficient transport system to address traffic congestion and environmental impact. The web application will allow users to access real-time bus schedules and book ride-sharing services providing them with cost-effective and convenient travel choices. The research uses a qualitative approach to understand customers' experience. The research proposes a web application that will use the latest development methodologies to show real-time schedules of buses and ridesharing to benefit the users and the environment. © 2023 ieee.

关键词： Buses

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Lung Cancer Detection Using Machine Learning Methods

Lung Cancer Detection Using Machine Learning Methods

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Arka, Dipak Debnath Tafhim, Sad Md. Anan, Rawnak Muntaha Rahat, Nusaibah Ishan, Samiu Mostafa Tanvir, Sifat Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

Cancer-related death is now more common than ever with cancer of the lungs being the leading cause of it. In such regards, the survival percentage for individuals with lung cancer must be increased through early identification. In this paper, we propose a machine learning-based approach for lung cancer detection using data from patients. The proposed method has three stages: preprocessing, feature extraction, and classification. In preprocessing, we extracted the necessary data from the dataset we collected. In the feature extraction stage, we processed the raw data while keeping the original information unchanged, in order for the classifier to work on. Finally, in the classification stage, we use 6 different classifier models to classify the extracted features. We evaluated the proposed method on a publicly available dataset from Kaggle of lung cancer causes and age. The results of the trial confirm the feasibility of the suggested strategy to achieve high accuracy, sensitivity, specificity, and AUC score. In conclusion, using machine learning, The suggested approach offers a powerful and efficient way to detect lung cancer. The proposed method can potentially assist in the detection of lung cancer, thereby improving the survival rate of lung cancer patients. © 2023 ieee.

关键词： Feature extraction

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