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12th International Conference on Human-Computer Interaction, INTERACCION 2022

作者： Córcoles Molina, Pablo Penichet, Victor M.R. Lozano, Maria Dolores Garrido, Juan Enrique Garcia-Garcia, Jose Maria Albertos-Marco, Félix Capgemini Software Valencia Spain Department of Computing Systems Universidad de Castilla-La Mancha Albacete Spain Albacete Research Institute of Informatics Universidad de Castilla-La Mancha Albacete Spain Dept. Informática e Ingeniería Industrial Universidad de Lleida Lleida Spain Department of Technologies and Information Systems Universidad de Castilla-La Mancha Talavera de la Reina Spain

ISBN: (纸本)9781450397025

In the past years, rehabilitation systems have been proposed to allow patients to perform physical therapy from their own homes. To assist them in this task, 3D avatars can be used to guide them through the different exercises. In this paper, we have transformed and improved a movement-based physical rehabilitation system, called Rehabilita, to enhance the user experience of the patients. By adding a 3D avatar to guide users through physical therapy, and updating the UI elements of the system, we have improved the user experience of the patients. These modifications have followed the proposals of a heuristic evaluation proposed in a previous work. An evaluation with real users was later carried out to measure the effectiveness and efficiency of the modifications performed over the original system. The assessment results show that users can follow the steps of an exercise more easily, although some limitations must be discussed. © 2022 ACM.

关键词： User interfaces

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Heat and drought impact on carbon exchange in an age-sequence of temperate pine forests

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Ecological Processes 2022年第1期11卷 108-125页

作者： M.Altaf Arain Bing Xu Jason J.Brodeur Myroslava Khomik Matthias Peichl Eric Beamesderfer Natalia Restrepo‑Couple Robin Thorne School of Earth Environment and Society and McMaster Centre for Climate ChangeMcMaster University1280 Main Street WestHamiltonON L8S 4K1Canada Department of Biological Sciences University of CalgaryAlbertaCanada Department of Geography and Environmental Management University of WaterlooWaterlooONCanada Department of Forest Ecology and Management Swedish University of Agricultural SciencesUmeåSweden School of Informatics Computingand Cyber SystemsNorthern Arizona UniversityFlagstaffAZUSA Department of Ecology and Evolutionary Biology University of ArizonaTucsonAZUSA Department of Geography Wilfrid Laurier UniversityWaterlooONCanada

Background:Most North American temperate forests are plantation or regrowth forests,which are actively *** forests are in different stages of their growth cycles and their ability to sequester atmospheric carbon is affected by extreme weather *** this study,the impact of heat and drought events on carbon sequestration in an age‑sequence(80,45,and 17 years as of 2019)of eastern white pine(Pinus strobus L.)forests in southern Ontario,Canada was examined using eddy covariance flux measurements from 2003 to ***:Over the 17‑year study period,the mean annual values of net ecosystem productivity(NEP)were 180±96,538±177 and 64±165 g C m^(–2)yr^(–1)in the 80‑,45‑and 17‑year‑old stands,respectively,with the highest annual carbon sequestration rate observed in the 45‑year‑old *** found that air temperature(Ta)was the dominant control on NEP in all three different‑aged stands and drought,which was a limiting factor for both gross ecosystem productivity(GEP)and ecosystems respiration(RE),had a smaller impact on ***,the simultaneous occurrence of heat and drought events during the early growing seasons or over the consecutive years had a significant negative impact on annual NEP in all three *** observed a similar trend of NEP decline in all three stands over three consecutive years that experienced extreme weather events,with 2016 being a hot and dry,2017 being a dry,and 2018 being a hot *** youngest stand became a net source of carbon for all three of these years and the oldest stand became a small source of carbon for the first time in 2018 since observations started in ***,in 2019,all three stands reverted to annual net carbon ***:Our study results indicate that the timing,frequency and concurrent or consecutive occurrence of extreme weather events may have significant implications for carbon sequestration in temperate conifer forests in Eastern North *** study is one of few globally available t

关键词： Carbon fluxes Net ecosystem productivity Ecosystem respiration Extreme weather events Drought Temperate forest White pine Eddy covariance

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Dataset transformation using hybrid method of polar-based cartesian and image filtering technique for annual rainfall clustering

Dataset transformation using hybrid method of polar-based ca...

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2021 International Seminar on Intelligent Technology and Its Application, ISITIA 2021

作者： Suprapty, Bedi Wiguna, Anggri Sartika Wajiansyah, Agusma Malani, Rheo Politeknik Negeri Samarinda The Applied Modern Computing Robotic Systems Unit East Kalimantan Indonesia Universitas Pgri Kanjuruhan Malang Faculty of Science Technology Informatics Department East Java Indonesia

ISBN: (纸本)9781665428477

Clustering is categorized as unsupervised learning because there is no class label information available in grouping a dataset. For this reason, an assessment of the quality of the clustering results is critical. In general, two essential clustering parameters are the similarities between cluster members in a cluster and the cluster centers' separation. Various approaches can be used to improve a clustering algorithm's performance: raw data pre-processing, cluster center initialization techniques, objective function assignment, modification of specific steps, and others. The subject of this study is the pattern of rainfall every month during the year of the observation period obtained from the clustering process. This study aims to improve the performance of K-Mean Clustering through manipulation of raw data pre-processing into certain datasets. Image filtering technique is used to generate a dataset based on the relationship between neighboring rainfall values. Polar-based Cartesian data space transformation is used to generate a dataset based on a range of rainfall values for each month during the year of the observation period. Four scenarios have been used to test the performance of the proposed method. The study results show that the proposed method produces the highest performance ratio (54.79%) of all scenarios' total average GOS (Global Optimum Solution). Meanwhile, increasing GOS to the original method also resulted in the highest increase in GOS ratio (63.68%) compared to other methods. Further studies will focus on the application of the proposed methods for improving the performance of SOM and Fuzzy C-Mean Clustering. © 2021 IEEE.

关键词： K-means clustering

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Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

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Computers, Materials & Continua 2022年第9期72卷 5117-5134页

作者： Hiam Alquran Wan Azani Mustafa Isam Abu Qasmieh Yasmeen Mohd Yacob Mohammed Alsalatie Yazan Al-Issa Ali Mohammad Alqudah Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Faculty of Electrical Engineering Technology Campus Pauh PutraUniversiti Malaysia Perlis02000ArauPerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia Perlis(UniMAP)02000ArauPerlisMalaysia The Institute of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan

Cervical cancer is screened by pap smear methodology for detection and classification *** smear images of the cervical region are employed to detect and classify the abnormality of cervical *** this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes *** smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images *** features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)*** success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%*** top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high *** the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%*** proposed system is a built-in cascading manner with five models of polynomial(SVM)*** overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

关键词： Classification deep learning machine learning pap smear images resnet101 support vector machines

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PPO-Based Energy Efficiency Maximization For RIS-Assisted Multi-User Miso systems 100

PPO-Based Energy Efficiency Maximization For RIS-Assisted Mu...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Iqbal, Amjad Al-Habashna, Ala'a Wainer, Gabriel Boudreau, Gary Bouali, Faouzi Carleton University Department of Systems and Computer Engineering 1125 Colonel By Dr. OttawaONK1S 5B6 Canada Al Hussein Technical University School of Computing and Informatics Amman Jordan Ericsson Canada 349 Terry Fox Dr. KanataONK2K 2V6 Canada Coventry University CoventryCV6 5NU United Kingdom

ISBN: (纸本)9798331517786

In this paper, we explore the integration of a reconfigurable intelligent surface (RIS) with a multi-antenna base station (BS) for downlink multi-user multiple-input-single-output (MU-MISO) systems. We aim to enhance energy efficiency (EE) by jointly optimizing beamforming and phase shifts at the BS and RIS, respectively, while ensuring each mobile user meets their link budget requirements. The resulting optimization problem is inherently non-convex. To address this challenge, we employ proximal policy optimization (PPO), known for efficiently managing non-convex problems and reducing training overhead in continuous action spaces through a clip factor. Furthermore, by leveraging deep neural networks (DNN), the proposed PPO-based solution provides the optimum values for the beamforming at the BS and the phase shift at the RIS, respectively. Finally, we demonstrate the effectiveness and accuracy of the proposed PPO-based algorithm through an extensive simulation campaign, comparing its performance against baseline methods (i.e., fractional programming (FP) and deep deterministic policy gradient (DDPG)). The results show that our proposed PPO-based algorithm outperforms the considered baseline approaches (i.e., FP and DDPG) in terms of EE by 34.2% and 15.8%, respectively. © 2024 IEEE.

关键词： Budget control

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A Bibliometric Analysis and Research Landscape of Artificial Intelligence in Education 14

A Bibliometric Analysis and Research Landscape of Artificial...

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14th IEEE International Conference on Control System, computing and Engineering, ICCSCE 2024

作者： Ajibade, Samuel-Soma M. Issa, Bayan Jasser, Muhammed Basheer Hassan, Farrukh Aldharhani, Ghassan Saleh Al-Hadi, Ismail Ahmed Al-Qasem Akintoye, Kayode Akinlekan Tabuena, Almighty Cortezo School of Engineering and Technology Sunway University Department of Computing and Information Systems No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey University of Aleppo Faculty of Informatics Engineering Syria Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia UKM University Faculty of Information Science and Technology Selangor Bangi Malaysia The Federal Polytechnic Department of Computer Science Ado Ekiti Ekiti State Nigeria University of the City of Valenzuela College of Arts and Sciences Philippines

ISBN: (纸本)9798350364507

This study examines the academic field of Artificial Intelligence in Education (AI-ED) by examining the publications and indexed documents in Scopus from 2010 to 2023. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI-ED research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AIED research. The Scopus search resulted in the discovery of 10,474 published documents on AI-ED. Additionally, trends analysis indicated a significant increase of over 3,000% in publications between 2010 and 2023. Conference papers are the most often utilized document type, including 6757 publications or 64.5% of the overall amount. The ACM International Conference Proceeding Series is the primary source for published materials, accounting for 675 publications or 6.44% of the total publications on the topic. The most prolific researchers in the field of AI-ED are Breazeal, C and Chai C.S, each having published 18 documents. Additionally, the two institutions that have produced the highest number of published documents are Carnegie Mellon University (CMU) and the University of NC State University (NCSU). The output of the affiliations active in the topic is primarily attributed to the funders, specifically the National Science Foundation, which is the leading funding institution based in the United States and has contributed to 354 publications. An analysis of keyword co-occurrence revealed 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED). Future research in the field of artificial intelligence in education will focus on employing sophisticated deep learning, machine learning, and neural network algorithms to precisely predict students' learning patterns. © 2024 IEEE.

关键词： Adversarial machine learning

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Texture and Orientation-based Feature Extraction for Robust Facial Expression Recognition

Texture and Orientation-based Feature Extraction for Robust ...

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International Conference on Software Engineering Research, Management and Applications (SERA)

作者： Sajid Ali Khan Mohammed Bin Abdulrahman Alawairdhi Mousa AL-Akhras Department of Software Engineering Foundation University Islamabad Islamabad Pakistan College of Computing and Informatics Saudi Electronic University Saudi Arabia Computer Information Systems Department King Abdullah II School for Information Technology The University of Jordan Amman

Facial expressions are the most effective way to characterize people’s motives, emotions, and feelings. Several new methods are proposed each year; however, the accuracy of facial expression recognition still needs to be improved especially in uncontrolled conditions. In this paper, we propose a hybrid facial expression model that considers both texture and orientation features to classify expressions. Two types of descriptors namely Local binary pattern and Weber local descriptor are used to preserve the local intensity information and orientation of edges. In the next step, computing the Histograms of oriented gradients (HOG) features from the Local binary pattern and Weber local descriptor images to capture micro-expressions. Then, the AdaBoost feature selection algorithm is utilized to choose the best features from the combined HOG features. The results of the experiments demonstrate that the method proposed in this study performs better than existing methods.

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Quantifying Object-Oriented System Complexity: Introducing a Powerful Measurement Tool

Quantifying Object-Oriented System Complexity: Introducing a...

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IEEE International Conference on System Engineering and Technology

作者： Muhammed Basheer Jasser Lee Ming Zhen Bayan Issa Ling Mee Hong Ismail Ahmed Al-Qasem Al-Hadi Department of Computing and Information Systems Sunway University Sunway City Selangor Malaysia Faculty of Informatics Engineering University of Aleppo Syria Institute of Computer Science & Digital Innovation UCSI University Kuala Lumpur Malaysia

A lot of metrics and tools have been devised to measure the complexity of software systems. Through measurement, software professionals can have a better understanding and control of a software’s complexity. In this paper, we reviewed complexity metrics and tools for object-oriented systems. The existing tools measure either implemented systems which may be source code or compile binaries, or class diagrams. The tools that measure implemented systems already offer a wide variety of features to satisfy different needs. However, the tools that measure class diagrams are not only poor in amount, but all take in class diagram XMI files, with none of them taking in PlantUML class diagram source code. PlantUML is a popular text-based UML diagramming tool that enables users to define UML diagrams with a domain specific language then renders the diagram for them. Hence, we developed a web application that measures class diagram complexity via PlantUML class diagram source code. It measures five class metrics and seven system metrics visualized as tables and bar charts, with the results exportable to PDF and CSV. Our tool’s measurements were also evaluated to be correct when it is within our scope of supported syntax.

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Lexicon-enhanced machine learning techniques for predicting risks in cryptocurrencies

Lexicon-enhanced machine learning techniques for predicting ...

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4th International Conference on Distributed Sensing and Intelligent systems (ICDSIS 2023)

作者： A. Darwiesh M. Elhoseny A. H. El-Baz M. A. Nour Mathematics Department Faculty of Science Damietta University New Damietta Egypt Information Systems Department College of Computing and Informatics University of Sharjah Sharjah UAE Computer Science Department Faculty of Computers and Artificial Intelligence Damietta University New Damietta Egypt

ISBN: (数字)9781837240258

This study presents a new approach to predict cryptocurrencies risks based on social media platform indicators. This method can help cryptocurrency investors in their future decision-making. It utilizes advanced techniques such machine learning and natural language processing. Furthermore, a case study on American cryptocurrency investors is provided to help them identify and assess the risks associated with their purchases. Moreover, performance metrics are computed to demonstrate the capabilities of the new approach.

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2D Basement Relief Inversion using Sparse Regularization.

arXiv

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arXiv 2024年

作者： Barboza, Francisco Márcio da Cunha Romão Silva, E. Arthur Anthony de Carvalho, Bruno Motta Department of Computing and Technology Federal University of Rio Grande do Norte Rio Grande do Norte Caicó Brazil Graduate Program on Systems and Computing Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil

Basement relief gravimetry is a key application in geophysics, particularly important for oil exploration and mineral prospecting. It involves solving an inverse geophysical problem, where the parameters of a geological model are inferred from observed data. In this context, the geological model consists of the depths of constant-density prisms representing the basement relief, and the data correspond to the gravitational anomalies caused by these prisms. Inverse geophysical problems are typically ill-posed, as defined by Hadamard, meaning that small perturbations in the data can result in large variations in the solutions. To address this instability, regularization techniques, such as those proposed by Tikhonov, are employed to stabilize the solutions. This study presents a comparative analysis of various regularization techniques applied to the gravimetric inversion problem, including Smoothness Constraints, Total Variation, the Discrete Cosine Transform (DCT), and the Discrete Wavelet Transform (DWT) using Daubechies D4 wavelets. Optimization methods are commonly used in inverse geophysical problems because of their ability to find optimal parameters that minimize the objective function—in this case, the depths of the prisms that best explain the observed gravitational anomalies. The Genetic Algorithm (GA) was selected as the optimization technique. GA is based on Darwinian evolutionary theory, specifically the principle of natural selection, where the fittest individuals in a population are selected to pass on their traits. In optimization, this translates to selecting solutions that most effectively minimize the objective function. The results, evaluated using fit metrics and cumulative error analysis, demonstrate the effectiveness of all the regularization techniques and the Genetic Algorithm. Among the methods tested, the Smoothness constraint was briefly the most effective for the first and second synthetic models. For the third model, which was based on re

关键词： Discrete wavelet transforms

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