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5th International Conference on Advancements in Computing, ICAC 2023

作者： Roshani, O.V.D.E. Ayuwardhana, H.M.K.J.J. Rodrigo, P.H.M.S. Hewageegana, R.U. Fernando, Harinda De Silva, Dilshan I. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress. © 2023 IEEE.

关键词： Cost effectiveness

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LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT Image Segmentation

LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, QiSong Wu, JiaRou Wang, YingZhuo Qu, HuiLi Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China The University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350337488

The COVID-19 pandemic has had a profound impact on human society. It has highlighted the need for faster diagnostic methods. Research has shown that combining semantic segmentation with traditional medical approaches can significantly accelerate the process. To address this, leveraging COVID-19 CT images, our team designs a revolutionary semantic segmentation model called Level of Detail Enhancement U-Net (LDE-UNet), which shows the lesion area on CT images. By introducing the LDE block, the model has the unique advantage of overcoming the loss of data details during the downsampling process by emphasizing and transmitting details at the same level. Our SOTA model outperforms the second-best model by at least 0.7% in the most critical indicator precision. Compared with other models, LDE-UNet's strong reliability determines its ability to be used in the medical field to accelerate the localization and division of lesion areas on CT images by professional doctors, thus completing patient diagnosis faster. In addition, we also propose a standardized method for processing medical images. © 2023 IEEE.

关键词： COVID-19 Downsample LDE-UNet Semantic segmentation

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Road Traffic Accident Pattern Analysis with Data Mining 12

Road Traffic Accident Pattern Analysis with Data Mining

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12th International Conference on Awareness Science and Technology, iCAST 2023

作者： Zhe Chai, Albe Bing Lau, Bee Theng Tsun Tee, Mark Kit McCarthy, Chris Faculty of Engineering Computing & Science Swinburne University of Technology Sarawak Campus Kuching Malaysia Department of Computer Science & Software Engineering Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9798350324693

The discovery of Road Traffic Accident (RTA) patterns is vital to formulate mitigation strategies based on the characteristics of RTA. Various studies have applied association rule mining for RTA pattern discovery. However, limited research has conducted a comparative analysis of RTA patterns in different regions. This work is necessary to identify the similarities and differences of RTA characteristics between different regions, especially the developed and developing countries with different RTA trends in fatality rates. This work explored the application of association rule mining (FP-Growth algorithm) to discover and compare the RTA patterns for developed and developing countries. The Orange Data Mining software is used to discover RTA patterns for Ethiopia (12,317 samples) and the UK (United Kingdom) (1,048,575 samples), representing developing and developed countries. Eventually, findings showed similarities and differences in RTA patterns between Ethiopia and the UK. Road characteristics and conditions are the common factors contributing to RTA severity levels, while other crucial factors include the light condition and speed limit. In conclusion, the discovery of RTA patterns in different regions is beneficial to assist in formulating strategies to reduce the risk of RTA. Future work is necessary to discover the RTA patterns from different perspectives, such as seasonal or periodical variations of RTA patterns. © 2023 IEEE.

关键词： Association rules

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Extreme Weather Risk Framework for Power System Location-based Resilience Assessment 13

Extreme Weather Risk Framework for Power System Location-bas...

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2022 IEEE International Conference on Power Systems Technology, POWERCON 2022

作者： Lakshita, Lakshita Nair, Nirmal-Kumar C. The University of Auckland Faculty of Electrical Computer and Software Engineering Auckland New Zealand

ISBN: (数字)9781665417754

ISBN: (纸本)9781665417754

Risk assessment methods for electrical power system network based on the historical extreme weather events, also known as High Impact Low Probability events (HILP) helps support system operators to develop and implement effective resilience strategies. To support the power system's resilience, preventive and corrective technologies have been proposed, assessed, and implemented. Additionally, several risk managements tools have been utilized for the system analysis, one being Failure mode and Effects analysis (FMEA) widely used to improve the reliability of the electricity grids by considering different factors to provide the detailed risk assessment. Although FMEA analysis has been reported to help improve the reliability of system it has not been assessed for power system resilience measures. Therefore, this paper attempts to introduce the FMEA to assess and help improve the power system resilience by proposing location-based risk assessment approach to analyze the risk of outages due to the extreme weather events for different locations in North Island major cities of New Zealand. This FMEA based analysis considers the wind gusts data by estimating the severity of the data set with regards to loss of load due to high wind gust, and the probability of failure detection in the selected region. The weather data analysis has been tested and visualized with the actual transmission network data to identify the risk priority number for different locations/zones based in the Waikato region, New Zealand. © 2022 IEEE.

关键词： Risk assessment

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Project Assessment in Offshore software Maintenance Outsourcing Using Deep Extreme Learning Machines

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computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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Image Reconstruction Improvement of Variable Coded Aperture using Deep Learning Method for Gamma and Lensless Imaging Applications

Image Reconstruction Improvement of Variable Coded Aperture ...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Schwarz, Ariel Shemer, Amir Danan, Eliezer Cohen, Noa E. Danan, Yossef Department of Electrical and Electronics Engineering Azrieli College of Engineering Jerusalem9103501 Israel Faculty of Engineering Bar-Ilan University Ramat-Gan5290002 Israel School of Software Engineering and Computer Science Azrieli College of Engineering Jerusalem9103501 Israel

ISBN: (纸本)9798350345995

In gamma ray imaging for nuclear medicine, coded aperture is used to improve sensitivity. one of the main reconstructing methods is inverse filtering (deconvolution), where the recorded image is cross-correlated with periodic inverse filter of coded array. The reconstruction is free of coding noise for arbitrary array. However, amplification of quantum noise affect the reconstructed image. Although it is improved by Wiener filtering, the major problem is small terms in the spectral distribution of coded masks. Statistically and experimentally pinhole arrays have at least one term which is zero, resulting unacceptably noisy reconstruction. In our previous research we presented new approach of variable coded aperture (VCA) design for far and near field imaging applications [1-4]. The imaging system is based on time multiplexing method using variable multi pinhole array. The unique variable design enables to overcome the spatial frequencies cutoff and small terms in the Fourier transform exists in static multi pinhole array. The overall pinholes positions are designed to avoid spatial frequencies loss. However, traces of duplications can still be detected in reconstruction using Wiener filtering. Furthermore, since coded aperture blocks most of the photons that enter the detector, the dynamic range of the image is limited, thus leading to low contrast image and inadequate colors gamut. © 2023 IEEE.

关键词： Quantum noise

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Occluded Person Re-Identification using Region-based Convolutional Neural Networks 23

Occluded Person Re-Identification using Region-based Convolu...

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3rd International Conference on Human Machine Interaction, ICHMI 2023

作者： Islam, Atiqul Tsun, Mark Tee Kit Bee Theng, Lau Chua, Caslon Faculty of Engineering Computing & Science Swinburne University of Technology Sarawak Campus Kuching Malaysia Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9798400700088

The ability to continuously follow a target person in a dynamically changing environment remains a major challenge that indoor companion robots confront. Ongoing human following is complicated by close similarity matching and target occlusions. Person Re-Identification (re-ID) is a computer vision task that can help with the problem of intentional or unintentional occlusion of the subject or person of interest. Numerous research has been conducted on re-ID where the basic assumption is that the gallery set will only have non-occluded person images. This paper examines the problem from a human detection perspective and surveys the state-of-the-art of person re-ID, their achievement and shortcomings. A region-based convolutional neural network (R-CNN) Re-ID is proposed in this research. The findings from the prototyping and evaluation demonstrate that the proposed person Re-ID model is reliable for indoor re-ID situations. Thus, future works in this direction include investigating other factors, such as the number of persons in a scene and varying viewing angles of the probed target person, which can impact the performance of the proposed re-ID model. © 2023 ACM.

关键词： Convolutional neural networks

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Intelligent Model for Optimizing Gantt Chart in the Planning Stage 11

Intelligent Model for Optimizing Gantt Chart in the Planning...

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11th International Conference on Information Technology, ICIT 2023

作者： Bdair, Noor Alzyadat, Wael Alzubi, Shadi Alzaytoonh University of Jordan Faculty of Science and IT Dept of Software Engineering Jordan Alzaytoonh University of Jordan Faculty of Science and IT Dept. of of Computer Science Jordan

ISBN: (纸本)9798350320060

Scheduling tasks under resource constraints is essential for project success, and this research focuses on scheduling tasks efficiently to minimize completion time. To tackle this problem, two optimization techniques are employed: Linear programming (LP) and particle swarm optimization (PSO). PSO significantly reduces the duration, leading to a reduction in project duration. But the LP algorithm does not improve the project duration. Using suitable algorithms to maximize project efficiency and success is essential for successful project management. This research provides valuable information for project managers and decision makers, highlighting the importance of utilizing suitable algorithms to maximize project efficiency and success. © 2023 IEEE.

关键词： Linear programming

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Deployment of Polar Codes for Mission-Critical Machine-Type Communication Over Wireless Networks

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computers, Materials & Continua 2022年第4期71卷 573-592页

作者： Najib Ahmed Mohammed Ali Mohammed Mansoor Rodina Binti Ahmad Saaidal Razalli Bin Azzuhri Department of Software Engineering Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala Lumpur Malaysia 2Department of Computer System&TechnologyFaculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia

Mission critical Machine-type Communication(mcMTC),also referred to as Ultra-reliable Low Latency Communication(URLLC),has become a research *** is primarily characterized by communication that provides ultra-high reliability and very low latency to concurrently transmit short commands to a massive number of connected *** the reduction in physical(PHY)layer overhead and improvement in channel coding techniques are pivotal in reducing latency and improving reliability,the current wireless standards dedicated to support mcMTC rely heavily on adopting the bottom layers of general-purpose wireless standards and customizing only the upper *** mcMTC has a significant technical impact on the design of all layers of the communication protocol *** this paper,an innovative bottom-up approach has been proposed for mcMTC applications through PHY layer targeted at improving the transmission reliability by implementing ultra-reliable channel coding scheme in the PHY layer of IEEE 802.11a standard bearing in mind short packet transmission *** achieve this aim,we analyzed and compared the channel coding performance of convolutional codes(CCs),low-density parity-check(LDPC)codes,and polar codes in wireless network on the condition of short data packet *** Viterbi decoding algorithm(VA),logarithmic belief propagation(Log-BP)algorithm,and cyclic redundancy check(CRC)successive cancellation list(SCL)(CRC-SCL)decoding algorithm were adopted to CC,LDPC codes,and polar codes,***,a new PHY layer for mcMTC has been *** reliability of the proposed approach has been validated by simulation in terms of Bit error rate(BER)and packet error rate(PER)***-to-noise ratio(SNR).The simulation results demonstrate that the reliability of IEEE 802.11a standard has been significantly improved to be at PER=10−5 or even better with the implementation of polar *** results also show that the general-purpose wireless net

关键词： Machine-type communication mission-critical applications MTC URLLC M2M wireless networks polar codes

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Hybrid Deep Learning Model of LSTM and BiLSTM for Transjakarta Passenger Prediction 3

Hybrid Deep Learning Model of LSTM and BiLSTM for Transjakar...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Siswanto, Joko Hendry Rahardja, Untung Sembiring, Irwan Lisangan, Erick Alfons Hakim, Muhammad Iman Nur Wibowo, Feri Polytechnic of Road Transportation Safety Road Transportation System Engineering Tegal Indonesia Satya Wacana Christian University Faculty of Information Technology Indonesia University of Raharja Faculty of Science and Technology Tangerang Indonesia Satya Wacana Christian University Faculty of Computer Science Indonesia The University of Western Australia Computer Science and Software Engineering Perth Australia Polytechnic of Road Transportation Safety Automotive Engineering Technology Tegal Indonesia Muhammadiyah University Purwokerto Faculty of Engineering and Science Banyumas Indonesia

ISBN: (纸本)9798350367492

A crucial role in the BRT transportation system's planning, development, and operation is the prediction of passenger numbers. Using time-series data, it is necessary to develop careful prediction models, appropriate techniques, and an indepth understanding of the number of BRT Transjakarta passengers. A prediction model is sought based on comparing combined LSTM and BiLSTM experiments using three evaluation matrices and time. Historical data used from daily passenger data for 13 BRT Transjakarta corridors (1/01/2021-31/12/2023). The best prediction model was obtained from the BiLSTM-CNN combination with the lowest MSLE (0.0425), MAPE (0.1598), and SMAPE (15.9387) evaluation matrix values. However, it required a longer time (00:02:14). Predictions of passenger numbers on the 13 Transjakarta BRT corridors for the next 12 months can be made simultaneously by predicting fluctuations occurring simultaneously. The strongest positive correlation is in corridor 9-6, while the strongest negative correlation is in corridor 12-5. The prediction results must be understood by stakeholders, managers, and technopreneurs to develop and support appropriate strategies to increase the number of BRT passengers. © 2024 IEEE.

关键词： Prediction models

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