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检索条件"任意字段=2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2023"

共 1096 条记录，以下是81-90 订阅

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data and Information Security Issues and Challenges for Banking Sectors in Fiji

Data and Information Security Issues and Challenges for Bank...

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

作者： Lal, Goel Aman Singh, Manpreet Bir, Jashvir School of Information Technology Engineering Mathematics & Physics The University of the South Pacific Suva Fiji Fiji National University National Training & ProductivityCentre Suva Fiji

ISBN: (纸本)9798350341072

The Internet has played a key role in changing how we interact with other people and how we do business today. As a result of the Internet, electronic commerce has emerged, allowing businesses to more effectively interact with their customers and other corporations inside and outside their industries. The internet can be seen as a truly global phenomenon that has made time and distance irrelevant to many transactions. One industry that is using this new communication channel to reach its customers is the banking industry. The transformation from traditional banking to e-banking has been a leap change. The electronic banking system addresses several emerging trends such as 'customers' demand for anytime, anywhere service, product time-to-market imperatives and increasingly complex back-office integration challenges. However, the increase in the use of ICT facilities results in the increase of criminal activities like spamming, credit card frauds, ATM frauds, phishing, identity theft, denial of service and other security bridges have lent credence to the view that ICT is contributing to crime in the banking sector. The challenges that oppose electronic banking are the concerns of security and privacy of information. This study discusses security issues associated with internet connectivity, focusing on three main areas: confidentiality, integrity, and availability of information. Vulnerabilities that companies are exposed to, such as a virus, malware, phishing, Trojan horse, exposes a' company's information assets to attacks. It also discusses preventive measures that organizations should implement in order to protect a company from such threats. Finally, recommendations are made for the decision-makers to consider when making decisions regarding data and information security on financial organizations. © 2023 ieee.

关键词： Decision making

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Nested U-Net-Based Polyp Segmentation for Accurate Diagnosis of Colorectal Cancer

Nested U-Net-Based Polyp Segmentation for Accurate Diagnosis...

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

作者： Bhatt, Bhavini R. Jani, Kuntesh Maltare, Nilesh Vahora, Safvan Gujarat Technological University Department of Computer/ It Engineering Ahmedabad India L. D. College of Engineering Department of Information Technology Ahmedabad India Government Engineering College Department of Information Technology Modasa India

ISBN: (纸本)9798350341072

Colorectal cancer, a prominent global malignancy characterized by abnormal polyp growth in the colon or rectum, presents significant challenges in accurate diagnosis and treatment. Manual polyp segmentation during colonoscopy is hampered by obscured visibility, varying polyp morphology, and potential human error. To address these limitations, automated segmentation methodologies have emerged as crucial tools for improving diagnostic precision and efficiency. While conventional machine learning techniques have been applied to alleviate these complexities, the emergence of deep learning has opened new avenues for intricate pattern recognition and feature extraction. This research proposes an innovative approach to polyp segmentation, harnessing the Nested U-Net architecture complemented by a combination loss function. The proposed methodology strategically integrates augmentation techniques to counteract overfitting, a common concern in deep learning applications. Evaluation metrics such as dice coefficient, Intersection over Union (IoU), precision, and recall are employed to measure the performance of the proposed approach. Compared to U-Net-based polyp segmentation, the proposed approach exhibits a substantial increase of 0.60% in training dice coefficient, 1.15% in training IoU and 0.01% in precision. The results of extensive experimentation demonstrate the superiority of the proposed Nested U-Net-based polyp segmentation over U-Net-based approaches. © 2023 ieee.

关键词： Diseases

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A Novel Smartphone Recommendation System Using Ensemble Machine Learning

A Novel Smartphone Recommendation System Using Ensemble Mach...

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

作者： Almadhor, Ahmad Abbas, Sidra Sampedro, Gabriel Avelino Abisado, Mideth Gadekallu, Thippa Reddy College of Computer and Information Sciences Jouf University Sakaka72388 Saudi Arabia Comsats University Islamabad Department of Computer Science Islamabad Pakistan University of the Philippines Open University Faculty of Information and Communication Studies Los Baños4031 Philippines De la Salle University Center for Computational Imaging and Visual Innovations 2401 Taft Ave. Manila1004 Philippines College of Computing and Information Technologies National University Manila Philippines Zhongda Group Jiaxing City Zhejiang Province Haiyan County314312 China Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu India College of Information Science and Engineering Jiaxing University Jiaxing314001 China Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798350341072

Due to the proliferation of internet evaluations brought on by the rising demand for smartphones, consumers find it challenging to make accurate selections when purchasing. In this paper, we offer ensemble voting methods based on TF-IDF (Term Frequency-Inverse Document Frequency) features for clas-sifying mobile phone ratings. We use a recently assembled dataset comprising over 13,000 smartphone reviews from the Flipkart website. The suggested approach includes feature extraction using the TF-IDF, data cleaning, balancing, and voting-based model prediction. To identify the recently created Flipkart dataset, the suggested method created an ensemble voting mechanism based on machine learning techniques. According to the experimental findings, the suggested method performs more accurately and efficiently than conventional machine learning techniques. At 98.0 %, the model achieved the greatest accuracy. The suggested method can be expanded to additional e-commerce platforms with sizable datasets of online evaluations and assist customers in making updated purchase preferences. © 2023 ieee.

关键词： Machine learning

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Flower Based Plant Classification System

Flower Based Plant Classification System

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

作者： Muralitharan, Rasendram Inuvaisubramainam, Nilani Jayasinghe, Upul Ragel, Roshan G. Rajeevan, Aarumuham Niladhari, Gramma University of Peradeniya Faculty of Engineering Department of Computer Engineering Sri Lanka Office of the Deputy Chief Secretary Engineering Service Northern Province Sri Lanka Divisional Secretariat Uduvil Sri Lanka

ISBN: (纸本)9798350341072

Plants could be classified using plant flower information. Lot of existing flower-based plant classification systems are available. Some classification system uses DSP techniques to classify the plants. Some classification systems use machine learning techniques to classify the plants. Some classification system uses hybrid techniques to classify the plants. Some classification system uses other methods to classify the plants. Although we have lot of flower-based plant classification systems, the accuracy of the existing flower-based plant classification systems is low. We use machine learning techniques to classify the plants. Our system uses length, width, and mean RGB values of the flowers as the features of the machine learning models. We use K Nearest Neighbour, Support vector machine, Random Forest, and CNN algorithms to classify the plants. We collected the data from five different plants in the Jaffna district, in Sri Lanka. The plants are Nithiyakalyani, Nanthiyavattai, Nochi, Thiruvaththi, and Hibiscus. Each class has 50 samples. The length and width are measured using measuring tape. The RGB mean value is measured using digital camera. The local patterns of the plant species could be found in the first layer of the convolutional neural network models. © 2023 ieee.

关键词： Convolutional neural networks

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Frame Elimination for Recordings of Bat Flight Kinematics from a High-Speed Video Array

Frame Elimination for Recordings of Bat Flight Kinematics fr...

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

作者： Jayathunga, D.P. Ramashini, M. Zaini, Juliana De Silva, Liyanage C. Müller, R. Uva Wellassa University of Sri Lanka Department of Computer Science and Informatics Sri Lanka Universiti Brunei Darussalam Faculty of Integrated Technologies Brunei School of Digital Science Faculty of Integrated Technologies Universiti Brunei Darussalam Brunei Virginia Tech Department of Mechanical Engineering BlacksburgVA24060 United States

ISBN: (纸本)9798350341072

Bat flight is complex because they have the ability to change the shape of their wings while in flight to perform manoeuvres. To accurately capture the subject's complexity, we have set up a synchronized array of 50high-speed cameras. This array is capable of capturing over 50,000 images per second. The video array is designed to cover a large imaging volume that allows bats to perform full-flight manoeuvres. As a result, many video frames do not show the entire bat. Hence, this study uses deep learning techniques to develop an accurate frame elimination process. Until the controlled environment is set up, we used simulated data created from the blender. For the frame elimination purpose, images needed to be classified based on the location of the bat, where it could be fully or partially presented within the frame or maybe not presented. We used different deep learning techniques, including the ordinary CNN, pretrained models, and object detection model - YOLOv5, to achieve this target. Among all the models, we noted that YOLOv5 gave the best accurate unwanted frame elimination results, where the mAP0.5 value is 0.99. © 2023 ieee.

关键词： Object detection

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A Modified Feature Selection Algorithm for Intrusion Detection System Based on Student Psychology-Based Optimization with Explainable AI

A Modified Feature Selection Algorithm for Intrusion Detecti...

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

作者： Evan, Md. Raisul Islam Motiur Rahman, Mohammad Tanzir Mehedi, Sk. Nazib, Abdullah Islam, Rafiqul Rahman, Ziaur Mawlana Bhashani Science and Technology University Computer Science and Engineering Tangail1902 Bangladesh Mawlana Bhashani Science and Technology University Information and Communication Technology Tangail1902 Bangladesh School of Electrical Engineering and Robotics Queensland University of Technology BrisbaneQLD Australia School of Computing Mathematics and Engineering Charles Sturt University Bathurst Australia School of Computer Science Queensland University of Technology BrisbaneQLD Australia

ISBN: (纸本)9798350341072

Intrusion Detection Systems (IDS) play a vital role in safeguarding network security by monitoring and identifying both normal and intrusive activities. However, constructing an effective IDS from extensive datasets poses a significant challenge. The process of feature selection from these datasets is crucial, as it not only enhances accuracy but also reduces the time required to build the model. In this paper, we employ a modified version of the Student Psychology-Based Optimization (SPBO) algorithm to identify the most relevant features set for IDS. Following feature selection, various machine learning algorithms are employed for multi-level classification. To evaluate our approach, we utilize the NLS-KDD dataset. Our proposed algorithm surpasses several state-of-the-art feature selection methods in terms of accuracy, precision, recall, Fl-score, and finally faster convergence. Specif-ically, our proposed bio-inspired SPBO algorithm combined with the Random Forest (RF) algorithm achieves a comparable accuracy rate of 99.81 %. To ensure better comprehensibility, we employ two widely used Explainable Artificial Intelligence (XAI) techniques: SHapley Additive exPlanations (SHAP) and Local In-terpretable Model-Agnostic Explanations (LIME). These methods provide insightful explanations for our model's decisions, further enhancing its interpretability. © 2023 ieee.

关键词： Feature Selection

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Predicting Phosphoglycerylation with Transformer Features and Deep Learning

Predicting Phosphoglycerylation with Transformer Features an...

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

作者： Chandra, Abel Sharma, Alok Dehzangi, Iman Tsunoda, Tatsuhiko Sattar, Abdul Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Rutgers University Department of Computer Science CamdenNJ United States Center for Computational and Integrative Biology Rutgers University Camden United States Riken Center for Integrative Medical Sciences Laboratory for Medical Science Mathematics Yokohama Japan School of Science The University of Tokyo Laboratory for Medical Science Mathematics Department of Biological Sciences Tokyo Japan Graduate School of Frontier Sciences The University of Tokyo Laboratory for Medical Science Mathematics Department of Computational Biology and Medical Sciences Tokyo Japan

ISBN: (纸本)9798350341072

Understanding protein sequences can advance treatments for various diseases. However experimentally obtaining this information is laborious, time-consuming, and expensive. Traditional machine learning techniques, like support vector machine, random forest and logistic regression, offer potential to fast-track this process but are sometimes limited by data complexity. Deep learning algorithms, in contrast, tend to yield higher performance. In this study, we employed a convolutional neural network to predict protein phosphoglycerylation. Features were extracted from pre-trained transformer models and compared with conventional features, such as evolutionary information and physicochemical/biochemical properties. Our results indicate significant performance improvements across all feature types, with the combination of transformerbased features and the convolutional neural network being especially effective. This methodology holds potential for other protein property prediction tasks. Our software and datasets used in this study are publicly available at https://***/abelavit/***. © 2023 ieee.

关键词： Convolutional neural networks

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A QUBO Formulation for the K-densest Common Subgraph Isomorphism Problem Via Quantum Annealing

A QUBO Formulation for the K-densest Common Subgraph Isomorp...

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2020 ieee asia-pacific conference on computer science and data engineering, csde 2020

作者： Huang, Nan The University of Auckland School of Computer Science New Zealand

ISBN: (纸本)9781665419741

In this paper, we extend the algorithm of [6] to solve the k-densest maximum common subgraph problem. By solving the k-densest maximum common subgraph isomorphism problem, we find high complexity common substructures between input graphs. We prove that our proposed objective function always gives the right answer to the corresponding QUBO problem. © 2020 ieee.

关键词： Set theory

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Performance Analysis of Machine Learning Algorithms for Classification of Infection Severity Levels on Rubber Leaves

Performance Analysis of Machine Learning Algorithms for Clas...

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

作者： Lazim, Siti Saripa Rabiah Mat Sulaiman, Zulkefly Nawi, Nazmi Mat Mustafah, Anas Mohd Institute of Plantation Studies Universiti Putra Malaysia Malaysia Unversiti Putra Malaysia Faculty of Agriculture Department of Plant Science Serdang43400 Malaysia Universiti Putra Malaysia Department of Biological and Agricultural Engineering Selangor Malaysia

ISBN: (纸本)9798350341072

White root disease (WRD) infection in rubber plantations which is caused by Rigidiporus micropores can lead to a significant yield loss. At the early infection stage, it is very difficult to diagnose the disease because infected trees do not exhibit any symptoms. Thus, this study was carried out to investigate the potential application of spectroscopic technology and machine learning algorithms to classify severity level of infected trees at early stage based on spectral data. A total of 50 leaf samples were used in this work, representing five severity levels;healthy, light, moderate, severe and very severe infection. A visible shortwave near-infrared (VSNIR) spectrometer was used to record the spectral data of the leaf samples. The chlorophyll content of each leaf was measured using SPAD meter. Four classification algorithms investigated in this study were artificial neural network (ANN), support vector machine (SVM), knearest neighbour (kNN) and random forest (RF). The result of the study demonstrates good classification accuracy of 90, 82, 78, and 72% for ANN, SVM, kNN and RF, respectively. This work shows that the spectroscopic measurement combined with classification techniques are promising strategy to classify severity level of WRD based on the spectral data of the rubber leaves. © 2023 ieee.

关键词： Rubber

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Using Novel Hybrid Convolutional Neural Network for Dysarthria Diagnosis

Using Novel Hybrid Convolutional Neural Network for Dysarthr...

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

作者： Thilakarathne, Navod Neranjan Galajit, Kasorn Karnjana, Jessada Pa, Win Pa Yassin, Hayati Universiti Brunei Darussalam Faculty of Integrated Technologies Brunei Nectec National Science and Technology Development Agency Pathum Thani Thailand University of Computer Studies Yangon Myanmar

ISBN: (纸本)9798350341072

Dysarthria is a motor speech disorder characterized by articulation and phonation difficulties resulting from speech muscle weakness, paralysis, or incoordination. A precise and timely diagnosis of dysarthria is essential for effective treatment and management of the condition, as it may deteriorate over time or be a precursor to a much more serious disease. On the other hand, this is becoming a severe problem in recent times owing to the rising ageing population and the prevalence of neurological disorders among such people. Hence early detection of dysarthria is deemed essential for the timely management of the disease. In recent years, Artificial Intelligence (AI) applications have shown promising results in various audio processing tasks and incorporated into pathological voice analysis for disease diagnosis. The majority of previous studies on dysarthria detection employed Machine Learning (ML) and Deep Learning (DL) models as the disease classification models. In light of this, this study presents a novel hybrid approach for classifying dysarthria based on audio data using Convolutional Neural Network (CNN) and Support Vector Machine (SVM). According to the experimental results, the proposed classification schema achieves an accuracy of 98.25 % compared to previous research work. Overall, our proposed method aims to automate the classification process, enabling faster and more reliable diagnoses. © 2023 ieee.

关键词： Support vector machines

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