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24th International Electronics Symposium, IES 2022

作者： Marta, Bayu Sandi Muhammad Amin, A. Sari, Dewi Mutiara Armono, Haryo Dwito Politeknik Elektronika Departement of Informatics and Computer Engineering Negeri Surabaya Indonesia Institut Teknologi Sepuluh Nopember Faculty of Marine Engineering Ocean Engineering Department Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

The implementation of computer vision in the field of underwater image is currently very popular. However, Indonesia's vision system for breakwater work has not yet been implemented. The work of the vision system is still manual using divers [1]. In addition, the application of computer vision in underwater images has a big obstacle because the resulting image in an underwater environment has poor color and contrast. This poor color and contrast are due to a density nearly 1000 times denser than air. This density directly affects the light transmission, which affects the resulting image, which has less color and contrast than the image taken through the air. So it takes image processing such as Image Enhancement and Image Color Restoration [2]. Another issue is building a real-time system. If the system that is built is not real-time, this makes the possibility of data reading errors being significant, and the movement of information is slow [3]. This research presents methods regarding image enhancement and color restoration built in the Graphical User Interface (GUI). The methods applied in this research are Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Multi-Scale Retinex with Color Restoration (MSRCR). Those methods are applied to the images produced by the two cameras and run on the single board processor and laptop for the comparison. The result by using 71 underwater images shows that for Underwater Color Image Quality Evaluation (UCIQE) value of the Histogram Equalization (HE) Method yields 20%, meanwhile for Contrast Limited Adaptive Histogram Equalization (CLAHE) yields 8%, and the Multi-Scale Retinex with Color Restoration (MSRCR) method yields 72%. © 2022 IEEE.

关键词： Image enhancement

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Prediction of COVID-19 Transmission in the United States Using Google Search Trends

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computers, Materials & Continua 2022年第4期71卷 1751-1768页

作者： Meshrif Alruily Mohamed Ezz Ayman Mohamed Mostafa Nacim Yanes Mostafa Abbas Yasser El-Manzalawy College of Computer and Information Sciences Jouf UniversitySakaka72314Saudi Arabia Faculty of Engineering Al-Azhar UniversityCairo11651Egypt Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt RIADI Laboratory La Manouba UniversityManouba2010Tunisia Department of Translational Data Science and Informatics GeisingerDanvillePA17822USA

Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health *** to the exponential spread of the COVID-19 infection worldwide,several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the *** accelerate scientific and public health insights into the spread and impact of COVID-19,Google released the Google COVID-19 search trends symptoms open-access *** objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related ***,we propose a stacked long short-term memory(SLSTM)architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms *** the SLSTM networks trained using historical data only as the base models,our base models for 7 and 14-day-ahead forecasting of COVID cases had the mean absolute percentage error(MAPE)values of 6.6%and 8.8%,*** the other side,our proposed models had improved MAPE values of 3.2%and 5.6%,*** 7 and 14-day-ahead forecasting of COVID-19 deaths,the MAPE values of the base models were 4.8%and 11.4%,while the improved MAPE values of our proposed models were 4.7%and 7.8%,*** found that the Google search trends for“pneumonia,”“shortness of breath,”and“fever”are the most informative search trends for predicting COVID-19 *** also found that the search trends for“hypoxia”and“fever”were the most informative trends for forecasting COVID-19 mortality.

关键词： Forecasting COVID-19 transmission and mortality in the US stacked LSTM SARS-COV-2 and google COVID-19 search trends

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Automated User Experience (UX) Testing for Mobile Application: Strengths and Limitations

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第4期16卷 30-45页

作者： Abuaddous, Hayfa. Y. Saleh, Ashraf Mousa Enaizan, Odai Ghabban, Fahad Al-Badareen, Anas Bassam Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan Department of Management Information System University of Tabuk Tabuk Saudi Arabia College of Computer Science and Engineering Taibah University Medina Saudi Arabia Department of Software Engineering Aqaba University of Technology Aqaba Jordan

User Experience (UX) evaluation has a significant importance for any interactive application. Mobile device applications have additional limitations to convey good user experiences (UX) due to the usage and features of mobile devices in ubiquitous computing contexts. There are a lot of automated tools available for evaluating, such applications particularly developed for Android and iOS. Automated UX evaluation provides a number of strengths which are able to overcome the limitations of non-automated (moderated) counterparts. Automated UX evaluation is regarded as unexplored field, especially when it is adopted for the UX evaluation of complex mobile application. Despite these, automated UX evaluation tools are unexplored and should only be used in coupling with traditional UX evaluation techniques. Hence, this study aims to discuss the literature in automated UX evaluation and to highlight the UX measurements that merit further investigation. This paper presents the most fre-quently used automated UX evaluation tools for mobile applications. Therefore, the study analyzes existing automated UX tools and frameworks and identifies which UX attributes and measurements of automated UX evaluation are likely to be of use in future research. Moreover, we discuss the strengths and limitations of automated UX evaluation methods for mobile applications. Finally, the paper provides an overview of automated UX evaluation techniques and tools. It also presents the strengths and limitations for comparing various evaluation methods. © 2022,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Automation

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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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The Improvement of Personal Service in the MSMEs Marketplace Using a Rank-Based Collaborative Filtering Approach

The Improvement of Personal Service in the MSMEs Marketplace...

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2022 International Conference on Electrical and Information Technology, IEIT 2022

作者： Lestari, Sri Yulmaini Aswin Sulyono Nul Fikri, Ruki Rizal Pratama, Yan Aditiya Informatics Engineering Study Program Faculty of Computer Science Lampung Bandar Indonesia Management Study Program Faculty of Business Economics Lampung Bandar Indonesia

ISBN: (数字)9781665453035

ISBN: (纸本)9781665453035

Indonesian MSMEs in 2022 will begin to enter the transformational recovery phase. Various preparations must be made in order to adapt. One that has been done is to increase the number of MSMEs entering the digital ecosystem, such as the MSME marketplace. The MSME Marketplace is used for marketing various MSME products online. The MSME Marketplace must be supported by methods that are able to provide good and fast personal services, considering the types and variety of products offered are increasing in number and varying from time to time. Therefore, this study proposes the Porat_Rank method to produce product recommendations that match user interests quickly so that users are served well. The experimental results show that the Porat_Rank method is superior to the Copelan Score method both in producing recommendations and in processing speed. © 2022 IEEE.

关键词： Collaborative filtering

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Procedural Modeling Method for Automatic Generation of Multi-Layout Virtual Content with Existing Elements

Procedural Modeling Method for Automatic Generation of Multi...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Emir Cogo Ehlimana Cogo Damir Pozderac Selma Rizvić Department of Computer Science and Informatics Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina

Procedural modeling is used to generate virtual content in organized layouts of exterior and interior elements. There is a large number of existing layout generation methods, and newer approaches propose the generation of multiple layout types within the same generation session. This introduces additional constraints when manually created layout elements need to be combined with the automatically generated content. Existing approaches are either designed to work with existing elements for a single layout type, or require a high amount of manual work for adding existing elements within multiple layouts. This paper presents a method that enables the application of existing subdivision methods on multiple layout types by inserting existing content into the generation result. This method can generate test cases by creating variations of partially generated layouts for procedural modeling methods that can work with existing content.

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Twister2 Cross-platform resource scheduler for big data

Twister2 Cross-platform resource scheduler for big data

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作者： Uyar, Ahmet Gunduz, Gurhan Kamburugamuve, Supun Wickramasinghe, Pulasthi Widanage, Chathura Govindarajan, Kannan Perera, Niranda Abeykoon, Vibhatha Akkas, Selahattin Fox, Geoffrey School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Engineering Faculty of Engineering Mugla Sitki Kocman University Mugla Turkey

Twister2 is an open-source big data hosting environment designed to process both batch and streaming data at scale. Twister2 runs jobs in both high-performance computing (HPC) and big data clusters. It provides a cross-platform resource scheduler to run jobs in diverse environments. Twister2 is designed with a layered architecture to support various clusters and big data problems. In this paper, we present the cross-platform resource scheduler of Twister2. We identify required services and explain implementation details. We present job startup delays for single jobs and multiple concurrent jobs in Kubernetes and OpenMPI clusters. We compare job startup delays for Twister2 and Spark at a Kubernetes cluster. In addition, we compare the performance of terasort algorithm on Kubernetes and bare metal clusters at AWS cloud. © 2021 John Wiley & Sons, Ltd.

关键词： Big data

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Latent Communication in Artificial Neural Networks

arXiv

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arXiv 2024年

作者： Moschella, Luca GLADIA research lab Department of Computer Science Faculty of Information Engineering Informatics and Statistics Italy

As NNs (Neural Networks) permeate various scientific and industrial domains, understanding the universality and reusability of their representations becomes crucial. At their core, these networks create intermediate neural representations, indicated as latent spaces, of the input data and subsequently leverage them to perform specific downstream tasks. This dissertation focuses on the universality and reusability of neural representations. Do the latent representations crafted by a NN remain exclusive to a particular trained instance, or can they generalize across models, adapting to factors such as randomness during training, model architecture, or even data domain? This adaptive quality introduces the notion of Latent Communication – a phenomenon that describes when representations can be unified or reused across neural spaces. A salient observation from our research is the emergence of similarities in latent representations, even when these originate from distinct or seemingly unrelated NNs. By exploiting a partial correspondence between the two data distributions that establishes a semantic link, we found that these representations can either be projected into a universal representation (Moschella*, Maiorca*, et al., 2023), coined as Relative Representation, or be directly translated from one space to another (Maiorca* et al., 2023). Intriguingly, this holds even when the transformation relating the spaces is unknown (Cannistraci, Moschella, Fumero, et al., 2024) and when the semantic bridge between them is minimal (Cannistraci, Moschella, Maiorca, et al., 2023). Latent Communication allows for a bridge between independently trained NN, irrespective of their training regimen, architecture, or the data modality they were trained on – as long as the data semantic content stays the same (e.g., images and their captions). This holds true for both generation, classification and retrieval downstream tasks;in supervised, weakly supervised, and unsupervised settings;and

关键词： Neural networks

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Semantic Segmentation of Brain Tumors: A Performance Evaluation Using DeepLabV3+, UNet, and Intel’s OpenVINO Toolkit

Semantic Segmentation of Brain Tumors: A Performance Evaluat...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Medina Kapo Amila Akagic Emir Buza Department of Computer Science and Informatics Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Artificial intelligence, Machine Learning, and Deep Learning are increasingly making significant contributions to the field of medicine. Individual patient conditions, disease localization, and various influencing factors underscore the complexity of disease diagnosis and treatment planning. Introducing new technologies can revolutionize medical diagnostics, facilitating swift and accurate assessments. Among the noninvasive diagnostic methods, Magnetic Resonance Imaging (MRI) stands out, particularly in tumor diagnosis. UNet, renowned for its effectiveness in medical image analysis, serves as a robust model for semantic segmentation, as does DeepLabV3+. However, these models are inherently complex, and their inference process can be time-consuming. By leveraging the OpenVINO toolkit, the inference process is significantly reduced. In this study, nearly a 2-fold acceleration is achieved in inference time with the DeepLabV3+ model and a roughly 1.2-fold improvement with the UNet model on CPU. Moreover, when employing GPU with FP16 precision, the acceleration reached almost 2.5fold for UNet and nearly 3-fold for DeepLabV3+, showcasing the substantial performance enhancements attainable through optimized hardware utilization.

关键词： Deep learning Adaptation models Semantic segmentation Magnetic resonance imaging Graphics processing units Brain modeling Hardware Medical diagnosis Medical diagnostic imaging Tumors

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Testing DOM-utilizing functionalities through mutation testing

Testing DOM-utilizing functionalities through mutation testi...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Damir Pozderac Mujo Hadžić Irfan Prazina Vensada Okanovic Department of Computer Science and Informatics Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina

Software development is implemented in several key phases, one of which is software testing. Software testing consists of selecting techniques for the purpose of finding software defects and bugs in the process of writing code. There are several ways and approaches that lead us to that purpose, with the goal of selecting the most adequate method in terms of cost, complexity, and efficiency. In this paper, we will take a deeper dive into mutation testing techniques. Mutation testing techniques are fault-based and focus more on test structures than the input data, which is considered the testing start point. The basic concept of mutation testing consists of a few steps, which will be covered in this paper, and metrics that measure how effective the tests really are. With a few code examples, we will show why code coverage, which is mostly taken as a measure while testing, is sometimes not the most reliable source and does not give a full picture when talking about the quality of written tests.

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