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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111-120 订阅

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Lossless Segmentation of Brain Tumors from MRI Images using 3D U-Net

Lossless Segmentation of Brain Tumors from MRI Images using ...

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

作者： Nuha, Nigar Sultana Farha, Ramisa Rafi, Sowat Hossain Sakib, Syed Nazmus Khan, Md Sabbir Dipto, Shakib Mahmud Reza, Md Tanzim Alam, Md Ashraful Brac University Department of Computer Science and Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9781665453059

2D computer vision and activities related to medical image analysis are being remarkably guided with the help of Convolutional Neural networks (CNNs) in recent years. Since a major portion of the available clinical imaging data is in 3D, we are inspired to further develop 3D CNNs for seeking the advantage of greater spatial context. Despite the fact that many FCNs are previously worked on, current 3D approaches still rely on patch processing due to the utilization of GPU memory, which limits the incorporation of bigger context information for improved performance. Using efficient 3D FCNs in MRI images without any data loss would result in more efficient disease detections. In this paper, we propose an approach to an efficient 3D U net segmentation technique for MRI Images using a lossless preprocessing of an MRI image dataset. Our proposal has the advantage of an impressive reduction of the required GPU memory for 3D Medical Image processing activities and that too, with an enhanced performance which is evaluated by the IoU (Intersection over Union) evaluation metric. Comprehensive experiment results performed with MICCAI BraTS'20 exhibit the viability of the presented strategy, © 2022 ieee.

关键词： Magnetic resonance imaging

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Performance Analysis of Verifiable data Registry Solutions for Decentralized Identifiers

Performance Analysis of Verifiable Data Registry Solutions f...

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

作者： Alizadeh, Morteza Andersson, Karl Schelen, Olov Electrical and Space Engineering Luleå University of Technology Department of Computer Science Skellefteå Sweden

ISBN: (纸本)9781665453059

User identification in decentralized systems is a demanding task. Identification systems should work resiliently and have efficient performance. Moreover, identification systems should protect the data that they must store against hackers and saboteurs. Keeping a system with decentralized identification without any intervention in the middle has attracted attention to improve earlier centralized identification systems. Decentralized Identifiers (DIDs) constitute a solution for identification divided into different modules. The verifiable data registry is one of the main parts of this technology, which is distributed storage of identity properties. We analyze the decentralized identification data registry and compare the performance of verifiable data registry based on blockchain and the Distributed Hash Table (DHT) on different scales of systems. Our evaluation results show that DHT has better performance. Furthermore, a model based on DHT shows that in addition to immutable storage and faster query time, it makes systems handle or search in data storage with lower searching time compared to Ethereum Blockchain as another immutable secure technology. Finally, our results show that DHT is a better solution than other models in different scenarios. Although blockchain has promising results on a small scale, it still has problems with storage and query time in large-scale systems. © 2022 ieee.

关键词： Blockchain

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Security of Internet of Things Devices: Ethical Hacking a Drone and its Mitigation Strategies

Security of Internet of Things Devices: Ethical Hacking a Dr...

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

作者： Karmakar, Gour Petty, Mark Ahmed, Hassan Das, Rajkumar Kamruzzaman, Joarder Institute of Innovation Science and Sustainability Federation University Australia Churchill Campus Australia Federation University Australia Information Technology Service Mount Helen Campus Australia

ISBN: (纸本)9781665453059

Internet of Things (IoT) is enabling us to introduce cost-effective, innovative and intelligent services in business, industrial, and government application domains. Despite these huge potential benefits of IoT applications, since the backbone of IoT is Internet and IoT connects numerous heterogeneous devices, IoT is vulnerable to many different attacks and thus has been a honey pot to the cybercriminals and hackers. For this reason, the attacks against IoT devices are increasing sharply in recent years. To prevent and detect these attacks, ethical hacking of different IoT devices are of paramount importance. This is because the lesson learnt from these ethical hackings can be exploited to develop effective and robust strategies and mitigation approaches to protect IoT devices from these attacks. There exist a few ethical hacking techniques reported in the literature such as hacking Android phones, Windows XP virtual machine and a DNS rebinding attack on IoT devices. In this paper, we implement an approach for the ethical hacking of a Drone and then hijack it. As an outcome of lesson learnt, the mitigation approaches on how to reduce the hacking on a drone is presented in this paper. © 2022 ieee.

关键词： Drones

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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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Explainable AI Based Glaucoma Detection Using Transfer Learning and LIME

Explainable AI Based Glaucoma Detection Using Transfer Learn...

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

作者： Chayan, Touhidul Islam Islam, Anita Rahman, Eftykhar Reza, Md. Tanzim Apon, Tasnim Sakib Alam, Md. Golam Rabiul Brac University Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665453059

Glaucoma is the second driving reason for partial or complete blindness among all the visual deficiencies which mainly occurs because of excessive pressure in the eye due to anxiety or depression which damages the optic nerve and creates complications in vision. Traditional glaucoma screening is a time-consuming process that necessitates the medical professionals' constant attention, and even so time to time due to the time constrains and pressure they fail to classify correctly that leads to wrong treatment. Numerous efforts have been made to automate the entire glaucoma classification procedure however, these existing models in general have a black box characteristics that prevents users from understanding the key reasons behind the prediction and thus medical practitioners generally can not rely on these system. In this article after comparing with various pre-trained models, we propose a transfer learning model that is able to classify Glaucoma with 94.71% accuracy. In addition, we have utilized Local Interpretable Model-Agnostic Explanations(LIME) that introduces explainability in our system. This improvement enables medical professionals obtain important and comprehensive information that aid them in making judgments. It also lessen the opacity and fragility of the traditional deep learning models. © 2022 ieee.

关键词： Convolutional neural networks

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Responsible Artificial Intelligence for Preterm Birth Prediction in Vulnerable Populations

Responsible Artificial Intelligence for Preterm Birth Predic...

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

作者： Marvin, Ggaliwango Nakatumba-Nabende, Joyce Hellen, Nakayiza Alam, Md. Golam Rabiul Makerere University Department of Computer Science Kampala Uganda Brac University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665453059

Newborn and child mortality prevention is one of the prioritized Sustainable Development Goals (SDG) targets of the World Health Organization (WHO) expected by 2030. This SDG Target 3.2 has prompted for a lot of solutions for the most affected regions like Sub-Saharan Africa, Central and Southern asia in order to stop preventable deaths of children under the age of 5 years and newborns although available solutions are not yet sufficient to achieve the Goal. The 4th Industrial revolution has further advanced the need for a reliable interdisciplinary approach that leverages technological advancements in achieving the SDG Target 3.2. In this work, we present a trustworthy Artificial Intelligence (AI) solution that blends with the data driven emerging technologies to reduce this global burden by transparently and interpretably predicting preterm births for patients and physicians for predictive and preventive action towards lowering neonatal deaths and increasing child survival. This AI solution can globally improve maternal and child healthcare among nations the run curative healthcare systems. We used Random Forest and KNeighbors and obtained an accuracy of 100% and 78% with respectively with Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) class balancing techniques. With interpretability of the random forest algorithm, we can responsibly improve AI technology adoption for maternal and child health and provide useful automated data driven insights to maternal healthcare management stakeholders and policy makers for a sustainable healthcare system in developing countries. © 2022 ieee.

关键词： Developing countries

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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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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 Decentralized Employee Performance Appraisal Framework for Recruitment, Performance Prediction and Ranking using Permissioned Blockchain and Machine Learning

A Decentralized Employee Performance Appraisal Framework for...

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

作者： Anjum, Afra Antara Majumder, Shaikat Islam, Sadaath Rabiul Alam, Md. Golam Brac University Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9781665453059

Recruitment is a crucial task for Human Resource Management (HRM) and therefore determines the selection of skilled employees-who play key roles in a company's success. Employees perform well only when their skill set matches their job requirements. However, the current recruitment system fails to provide a single solution that verifies employee records and predicts employee-job compatibility. This paper proposes a recruitment system using blockchain, a machine learning model, and a multi-criteria decision analysis method. Here, blockchain technology is used to hold employee records in an encrypted manner, including their performance appraisals, in a decentralized system that allows data verification by supported organisations. QR code is used to verify encrypted employee records which are then decrypted within the system and used to predict their performance for the hiring company, using a classification model. Here, after comparing the performance of different classification models, the best-performing model, SVM Classification, has been used. Finally, the system ranks eligible candidates, based on employee records including their predicted performance appraisal rating, using a TOPSIS algorithm. The SVM classification model and TOPSIS algorithm are deployed in a Streamlit application, which predicts employee performance appraisal ratings and ranks candidates by generating scores using TOPSIS. The scores are then sorted from highest to lowest, therefore ranking candidates from most suitable to least suitable for a job role. © 2022 ieee.

关键词： Smart contract

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A Secure data Processing System in Edge Computing-Powered AIoT

A Secure Data Processing System in Edge Computing-Powered AI...

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

作者： Oh, Jihyeon Lee, JoonYoung Park, YoHan Park, Youngho School of Electronic and Electrical Engineering Kyungpook National University Daegu Korea Republic of School of Computer Engineering Keimyung University Daegu Korea Republic of

ISBN: (纸本)9781665453059

Artificial Intelligent of Things (AIoT) has been utilized a lot of field since AIoT improves the overall performance of the system for the realization of smart life. As the attention about AIoT grows, the amount of data generated by IoT devices increases. Since IoT devices have low capability, it is difficult for IoT devices to store and analyze a huge amount of data. Cloud server has been introduced to handle the overgrowing data. However, cloud server centrally processes the enormous volume of data, and it leads to the problems of response delay and security. By adopting the edge server, the delay problem can be solved due to the closer communication distance. Many research also has been presented to solve the security problem in the AIoT environment. Unfortunately, it is still an ongoing issue. Although many studies have suggested countermeasures, they suffer from various physical and information security-related attacks. It causes the serious consequences such as data leakage and misuse. Thus, it is necessary to study on secure data processing and analysis in the AIoT environment. In this paper, we propose the system model for secure data processing framework by applying mutual authentication between IoT devices, edge server, and cloud server. © 2022 ieee.

关键词： Edge computing

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