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46th ICT and Electronics Convention, MIPRO 2023

作者： Lipovac, Josipa Krizanovic, Kresimir University of Zagreb Faculty of Electrical Engineering and Computing Unska 3 Zagreb Croatia

ISBN: (纸本)9789532331042

Metagenomics is a rapidly growing field that allows for studying complex microbial communities. One of the first steps in the metagenomic analysis is the classification of the organisms present in a sample. This is usually done by comparing sequencing reads to a database of known organisms. With the recent development of long-read sequencing technologies, such as PacBio and Oxford Nanopore Technologies (ONT), it is now possible to generate highly accurate assemblies of genomes from metagenomic samples. This is typically done using a combination of reference-based and de novo assembly approaches. Assembling the genomes from the metagenomic sample, prior to classification, could improve classification results and also aid in identifying new, previously unknown species. However, the evaluation of metagenome assemblies is a challenging task and it is important to assess the quality of the assemblies in order to ensure the accuracy of downstream analyses. In this paper, we provide a detailed overview of metagenomic classification, de novo metagenome assembly process, and evaluation of metagenome assembly, highlighting various tools and techniques currently available for each step. We also present initial results showing that metagenomic classification can benefit from a previously assembled metagenome. © 2023 MIPRO Croatian Society.

关键词： Quality control

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Comparative Analysis of Graph Representation Techniques for Code Vulnerability Detection 9

Comparative Analysis of Graph Representation Techniques for ...

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9th International Conference on engineering and Emerging Technology, ICEET 2023

作者： Elahee, Muhammad Irfan Elahee Bin Mohd Illzam Yong, Kelvin Sheng Chek Then, Patrick Hang Hui Yong, Sim Kwan Swinburne University of Technology Sarawak Faculty of Engineering Computing and Science Kuching Malaysia

ISBN: (纸本)9798350316926

The surge in software development and products that use software have intensified the risk of software vulnerabilities. The difficulty of maintaining code security has been exacerbated by the growing software ecosystem and complexity of contemporary software systems. Code vulnerability detection is important in preventing these vulnerabilities, but current methods are time-consuming and may overlook certain vulnerabilities. Deep learning, specifically tree/graph-based neural network, may be a promising solution as it preserves hierarchical and structural information in code, leading to better performance in vulnerability detection. This research engages in a more comprehensive investigation in discussing different methods of code representation, including Abstract Syntax Trees (AST), Control Flow Graphs (CFG) and Program Dependence Graphs (PDG). These graphs undergo thorough analysis and are examined to pinpoint the critical information they extract and how it contributes to vulnerability detection. Notably, it is discovered that distinct graph representations excel at identifying different types of vulnerabilities. The information captured from each graph representations can be used to identify the type of vulnerability and differentiate vulnerable code from non-vulnerable code. Such finding lays the foundation for a more advanced software vulnerability detection tool based on graph representation of codes, offering software vulnerabilities can be efficiently addressed. © 2023 IEEE.

关键词： Flow graphs

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Technology-Enhanced Inclusive Mathematics Learning: Promoting Equity and Lifelong Learning Opportunities for Vision-Impaired Students

Technology-Enhanced Inclusive Mathematics Learning: Promotin...

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2023 IEEE International Humanitarian Technology Conference, IHTC 2023

作者： Al Omoush, Muhammad H. Salih, Sumaia E. Mehigan, Tracey Dublin City University Faculty of Engineering and Computing Dublin Ireland Dublin Ireland

ISBN: (纸本)9798350314311

The paper investigates the dynamic relationship between technology and inclusive mathematics education for students with vision impairments. The paper examines the transformative potential of technology integration in fostering an inclusive educational environment while acknowledging the challenges that need to be addressed. The authors highlight the imperative of teacher training, data privacy, and ethical considerations in effectively integrating technology into educational practices. The evolving role of educators in facilitating meaningful and inclusive learning experiences is emphasized. A collaborative effort involving governments, educational institutions, technology developers, and the community is indispensable in pursuing inclusive education;by collectively addressing challenges, embracing technological advancements, and adhering to ethical principles, a transformative educational landscape can be cultivated. © 2023 IEEE.

关键词： Students

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Angular Position Estimation for Human-Following and Robot Navigation 6th

Angular Position Estimation for Human-Following and Robot Na...

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6th International Conference on Smart Grid and Internet of Things, SGIoT 2022

作者： Asante, Isaac Theng, Lau Bee Tsun, Mark Tee Kit Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Kuching Malaysia

ISBN: (纸本)9783031312748

Mobile robot navigation and human following are two related areas under the field of robotics that have garnered a lot of interest over the years, due to their advantages in the real world, in various settings. Modern techniques depending exclusively on computer vision for environmental data input commonly rely on state-of-the-art object detection methods to obtain data about the location of detected objects in a scene. However, these detection frameworks do not directly provide the angular position of obstacles and human targets in images. In this research project, the Mask R-CNN instance segmentation framework detects static objects and humans in an environment. A chain of algorithms is then used to transform the image’s content and pixel information into a one-dimensional array that can be mapped to the robot’s field of view. The findings show that the result can aid a mobile robot in estimating the angular position of obstacles and a human in a scene, which is necessary for collision-free navigation and robot-human following in an unknown environment. The proposed method also shows adaptivity as it works outdoors and indoors under poor lighting conditions. It can be used as a standalone algorithm in robotics simulations with webcams and static images. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Object detection

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Automated Characterization in Cryocon 22C at Wide Temperature Range using LabVIEW 7

Automated Characterization in Cryocon 22C at Wide Temperatur...

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7th International Conference on Computer Applications in Electrical engineering-Recent Advances, CERA 2023

作者： Bharadwaj, Jyotiska Bogdanović, Lip Marković, Lovro Jorhat India University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9798350305005

This paper presents the development of an algorithm using LabVIEW, a B2912B SMU, and a Model 22C Cryogenic temperature controller for automating the characterization of various semiconductor devices. The algorithm is carried out using LabVIEW. By removing human error and enhancing precision through temperature control, the research aims to streamline the measurement procedure. Users enter the desired temperature values into the LabVIEW program. The Cryocon 22C is then automatically turned on by the algorithm to use a PID control method to stabilize the temperature near the sample. Once the temperature has stabilized, the software performs automatic characterization of the sample, including Gummel plot and Gummel plot negative sweep. To accurately capture the temperature-dependent features of the sample, measurements are performed at various temperatures. The method has the potential to increase productivity in a variety of semiconductor characterization and research applications due to its success in achieving temperature stability and precise measurements. © 2023 IEEE.

关键词： Automation

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The Bigger Picture: Scene Understanding for Recognizing Accidents within Mixed-Traffic Scenario using Deep Learning 13

The Bigger Picture: Scene Understanding for Recognizing Acci...

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13th IEEE International Conference on Control System, computing and engineering, ICCSCE 2023

作者： Tee, Fu Swee Theng, Lau Bee Shiong, Brian Loh Chung Tsun, Mark Tee Kit Computing and Science Swinburne University of Technology Sarawak Faculty of Engineering Kuching Malaysia

ISBN: (纸本)9798350323184

Detecting road traffic accidents in a timely manner is crucial for reducing traffic fatalities. Computer vision techniques are increasingly being used for road traffic accident recognition because of their ability to automatically analyze traffic scenes without needing manual human interpretation. To date, Deep learning has made significant paces in addressing time-series problems and is increasingly being utilized for road traffic accident detection. However, detecting accidents in a mixed traffic flow environment is challenging due to difficulties in detecting smaller-sized road users and annotating unsafe trajectories, particularly in scenarios where larger-sized road users overshadow smaller-sized road users. Therefore, it may be more effective to consider the entire traffic scene as a whole, instead of focusing on individual objects. This paper proposes a Deep Learning model that analyzes the entire traffic scene using a recurrent neural network, ConvLSTM2D, for road traffic accident detection in mixed traffic flow environments. The model is evaluated on 139 videos of a testing dataset, achieving an accuracy of 84%. Future work will focus on further analyzing potential accident features across video frames to enhance the model's performance. © 2023 IEEE.

关键词： Accidents

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Innovative fusion of VGG16, MobileNet, EfficientNet, AlexNet, and ResNet50 for MRI-based brain tumor identification

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Iran Journal of Computer Science 2024年第1期8卷 185-215页

作者： Kia, Marjan Sadeghi, Soroush Safarpour, Homayoun Kamsari, Mohammadreza Jafarzadeh Ghoushchi, Saeid Ranjbarzadeh, Ramin Department of Business Information System Business Analysis (ISBA) Park University Parkville United States School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Software Engineering University of Szeged Szeged Hungary Faculty of Electrical Engineering Malek-Ashtar University of Technology(MUT) Esfahan Iran Faculty of Industrial Engineering Urmia University of Technology Urmia Iran School of Computing Faculty of Engineering and Computing Dublin City University Dublin Ireland

This study presents a novel approach for brain MRI classification by integrating multiple state-of-the-art deep learning (DL) architectures, including VGG16, EfficientNet, MobileNet, AlexNet, and ResNet50, with an attention mechanism. The primary objective was to accurately detect the presence or absence of brain tumors—a critical challenge in medical diagnosis. The methodology was validated on a comprehensive dataset comprising 3264 brain MRI scans, systematically divided into training, validation, and testing subsets. Preprocessing techniques tailored for MRI data were employed to enhance input quality. Each model was initially developed and evaluated individually to establish a performance baseline before their integration. The proposed ensemble technique capitalized on the complementary strengths of these models to enhance classification accuracy and robustness. The inclusion of an attention mechanism further refined the models by emphasizing critical regions in the MRI scans, thereby boosting predictive performance. In addition, data augmentation techniques were applied to mitigate overfitting and increase dataset resilience. Performance metrics, including precision, accuracy, and recall, demonstrated significant improvements across all evaluations, particularly for the models incorporating the attention mechanism. These findings highlight the potential of combining advanced DL architectures with attention mechanisms as a promising strategy for precise and reliable brain tumor classification in MRI scans. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： AlexNet Attention mechanism Brain tumor classification MobileNet ResNet50

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Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

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Computer Systems Science & engineering 2023年第8期46卷 2141-2157页

作者： J.Prabakaran P.Selvaraj Department of Networking communications Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia Department of Computing Technologies Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia

In healthcare systems,the Internet of Things(IoT)innovation and development approached new ways to evaluate patient data.A cloud-based platform tends to process data generated by IoT medical devices instead of high storage,and computational *** this paper,an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography(CT)images of patients with pneumonia,Covid-19,tuberculosis(TB),and ***,the CT images are captured and transmitted to the fog node through IoT *** the fog node,the image gets modified into a convenient and efficient format for further *** encryption Standard(AES)algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating ***,the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning *** different pre-trained deep learning architectures(Inception-ResNet-v2,VGG-19,ResNet-50)used transfer learning is adopted for feature *** softmax of heterogeneous base classifiers assists to make individual *** a meta-classifier,the ensemble approach is employed to obtain final optimal *** predicted image is consigned to the recurrent neural network with long short-term memory(RNN-LSTM)for severity analysis,and the patient is directed to seek therapy based on the *** proposed method achieved 98.6%accuracy,0.978 precision,0.982 recalls,and 0.974 F1-score on five class *** experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective.

关键词： Intelligent health care cloud computing fog computing ensemble learning RNN-LSTM

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UAV-Based Lane-Changing Reduction Using Traffic Video Analyser

UAV-Based Lane-Changing Reduction Using Traffic Video Analys...

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ASEAN Australian engineering Congress, AAEC 2022

作者： Bong, Jit Boon Chong, Elizabeth Eu Mee Choo, Chung Siung Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Kuching Malaysia

ISBN: (纸本)9789819955466

Multi-lane roundabouts allow drivers to change lanes by merging and diverging among circulating lanes, yielding weaving in the roundabout. Weaving yields traffic conflicts as crossing traffic slows down the circulating flow. Speed reduction creates significant gaps that reduce capacity. Thus, weaving is crucial due to its significant effect on roundabout capacity. However, little information is available on weaving in multi-lane roundabouts with inscribed circle diameters (ICD) of more than 100 m. A possible reason for the lack of weaving data is the difficulty of collecting lane change data due to the traffic flow complexity, which involves several lane changes and the required resources. Typical data collection methods include using ground-level recorders and surveyors. Ground-level recorders could only provide limited data points due to view coverage constraints. Increasing the workforce and equipment can overcome this limitation, but it may prolong the post-recording analysis due to more views for analysis. Conversely, Unmanned Aerial Vehicles (UAVs) can capture complex traffic interaction data in a single session. Extraction of UAV-based recording by traffic video analyser could provide a wide range of outputs in a shorter time than manual analysis. This paper proposes a novel methodology to extract UAV-based lane change data using a traffic video analyser. The fieldwork involves flying a UAV at a multi-lane roundabout with an ICD of 151 m in Kuching, Malaysia. Turning movement and lane change were obtained using a traffic video analyser. Virtual gates are specified at 10 m intervals to get lane change location. A lane change can be detected when a vehicle crosses two consecutive virtual gates over two adjacent circulating lanes. Weaving occurs when two lane change swap paths along circulating lanes. The findings show that 86% of lane change happens in the weaving section. This paper demonstrated that using UAV and traffic video analyser is feasible to investig

关键词： Antennas

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Few-shot learning and modeling of 3D reservoir properties for predicting oil reservoir production

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Neural computing and Applications 2024年第23期36卷 14527-14541页

作者： Cirac, Gabriel Avansi, Guilherme Daniel Farfan, Jeanfranco Schiozer, Denis José Rocha, Anderson Institute of Computing Universidade de Campinas SP Campinas Brazil Department of Energy Faculty of Mechanical Engineering Center for Petroleum Studies Universidade de Campinas SP Campinas Brazil

The oil and gas industry employs numerical simulation tools extensively in reservoir analysis and strategic planning. This study presents a machine-learning proxy model, employing a Few-shot Learning approach with a Deep Convolutional Generative Adversarial Network (DC-GAN) to reduce computational costs using fewer training samples. The DC-GAN generates new training samples by synthesizing spatial attributes, reservoir parameters, and time series data, thus enhancing the sample variability and diversity needed for accurate production prediction. Also, the study proposes a straightforward and efficient method for data augmentation that primarily involves replicating the initial training dataset. The accumulated production forecast generated from geostatistical realizations enables intelligent reservoir management through risk analysis. The technique can reduce the processing footprint by 70%. Unlike most reservoir studies that employ synthetic datasets, this investigation adopts a real, high-dimensional, and complex reservoir model. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Risk analysis

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