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3rd International Conference on Image Processing and Robotics, ICIPRoB 2024

作者： Jobaida, Nohori Shidujaman, Mohammad Mahbub, Ahsan Samani, Hooman Premachandra, Chinthaka Uddin, Mohammad Faisal Alam, Saadia Binte Independent University Bangladesh RIoT Center Department of Computer Science and Engineering Dhaka Bangladesh University of the Arts London Creative Computing Institute London United Kingdom Shibaura Institute of Technology Department of Electronic Engineering Tokyo Japan

ISBN: (纸本)9798350374766

Educational robotics is to improve children's and teenagers' skill and competency development via the use of electronic components and robots in an interdisciplinary learning environment. It focuses mostly on the STEAM fields, while it can also touch on subjects like geography, history, and languages. STEAM education relates these 5 subjects those are science, Technology, Engineering, Arts and Mathematics. The capacity to which children could interact with a social robot in a novel learning environment is being studied. A method for measuring how actively children with learning difficulties participate in adequately prepared activities is described, utilizing multimodal machine learning. The goal is to find the child's sustained attention throughout the conversation. To achieve this goal, visual and aural data are recorded between the kid and the robot. Education robots for kids are emerging as tools for learning which can enhance young children's early reading and language skills considering recent technical developments. Social robots are those that can interact with humans on a mental and social level. The most recent studies on social robots and how typically developing kids, aged 0 to 8, pick up early language and literacy abilities have been examined. For the entirety of the study, five main themes were identified. Our designed robot TINY, a theoretical foundation for social robotic education, which stands for Teach, Inspire, Nurture, and Yield. Research has shown that social robots can facilitate early language acquisition, a thorough study has been done on the formation of early reading abilities. Additionally, our robot explores the potential of low-cost support for early language acquisition and literacy development, aiming to provide access to high-quality educational tools for all students. © 2024 IEEE.

关键词： Machine design

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN computer science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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Detecting Users Prone to Spread Fake News on Arabic Twitter 5

Detecting Users Prone to Spread Fake News on Arabic Twitter

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5th Workshop Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection, OSACT 2022

作者： Ali, Zien Sheikh Al-Ali, Abdulaziz Elsayed, Tamer Computer Science and Engineering Department Qatar University Qatar KINDI Center for Computing Research Qatar University Qatar

ISBN: (纸本)9791095546757

The spread of misinformation has become a major concern to our society, and social media is one of its main culprits. Evidently, health misinformation related to vaccinations has slowed down global efforts to fight the COVID-19 pandemic. Studies have shown that fake news spreads substantially faster than real news on social media networks. One way to limit this fast dissemination is by assessing information sources in a semi-automatic way. To this end, we aim to identify users who are prone to spread fake news in Arabic Twitter. Such users play an important role in spreading misinformation and identifying them has the potential to control the spread. We construct an Arabic dataset on Twitter users, which consists of 1,546 users, of which 541 are prone to spread fake news (based on our definition). We use features extracted from users’ recent tweets, e.g., linguistic, statistical, and profile features, to predict whether they are prone to spread fake news or not. To tackle the classification task, multiple learning models are employed and evaluated. Empirical results reveal promising detection performance, where an F1 score of 0.73 was achieved by the logistic regression model. Moreover, when tested on a benchmark English dataset, our approach has outperformed the current state-of-the-art for this task. © European Language Resources Association (ELRA).

关键词： COVID-19

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Evaluate the Performance Parameters for Web browsers Empowered with Fuzzy logic 2

Evaluate the Performance Parameters for Web browsers Empower...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Khaliq, Faiza Afzal, Farheen Rehman, Abd Ur Aziz, Nauman Zafar, Dr Fareeha Riphah International University Riphah School of Computing & Innovation Lahore Pakistan NCBA&E School of Computer Science Lahore Pakistan Riphah Interntional University Riphah School of Computing Lahore Pakistan NCBA &e Lahore Pakistan Applied Science Research Center Applied Science Private University School of Computer Science Amman11937 Jordan GC University Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350335644

In recent years, the Web browser achieved huge popularity in academic as well as industrial circles. A large number of web services and applications are trying to make their performance efficient. Decreasing the waiting of fetch Web browser has become a real challenge. This paper presents a parameter estimation methodology and model that measure the performance of Web browser. A system that displays a wide and natural manner of thinking based on a Multiple Criteria Decision-Making process. In this research paper, the proposed system uses different kinds of parameters to estimate the efficiency of the browser empowered with the Fuzzy logic to elaborate on this problem. © 2023 IEEE.

关键词： Decision making

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MatrikalinDiabetes: User-centered Design of a mHealth App for Gestational Diabetes Mellitus Management and Education Among Bangladeshi Women 26th

MatrikalinDiabetes: User-Centered Design of a mHealth App ...

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26th International Conference on Human-computer Interaction, HCII 2024

作者： Ratul, Mohammad Arshad Hossain Yanoor Bristy, Tunisha Sayeed, Noorjahan Islam, Ashraful Chaudhry, Beenish Moalla Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Louisiana United States

ISBN: (纸本)9783031768088

This study employs the User-centered Design (UCD) methodology to develop a mobile health (mHealth) application (app) specifically tailored for Bangladeshi women with Gestational Diabetes Mellitus (GDM). GDM affects approximately 10% of pregnant women globally and around 35% within Bangladesh, as reported by the World Health Organization (WHO). Despite its high prevalence, there is a significant lack of awareness and education about GDM in Bangladesh, compounded by language barriers that make existing digital solutions less accessible. The MatrikalinDiabetes mHealth app aims to overcome these barriers by providing comprehensive management and educational resources for GDM in the Bangla language, addressing both pre-pregnancy and post-pregnancy needs. To ensure the app meets the actual needs of potential users, personas were created based on a literature review, and a survey was conducted. This process informed the development of both a low-fidelity, paper-based prototype and a high-fidelity digital prototype. Key features of the MatrikalinDiabetes app include tracking of food and water intake, physical activity monitoring, reminders, GDM education, chat forums for community support, blood glucose level (BGL) monitoring, an SOS button for emergency contacts, and quizzes for user engagement. Preliminary feedback indicated that 53.8% of participants favored the integrated features and expressed willingness to use and recommend the app. The survey of the high-fidelity prototype yielded positive responses, with 57.9% of participants strongly agreeing and 42.1% agreeing that the features were beneficial. The user-friendly design of the high-fidelity prototype ensures intuitive interaction, aiding in health management, enhancing GDM understanding, and promoting a healthier lifestyle among pregnant women in Bangladesh. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Obstetrics

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Mechanism design optimization through CAD-based Bayesian optimization and quantified constraints

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Discover Mechanical Engineering 2024年第1期3卷 1-18页

作者： Ben Yahya, Abdelmajid Ramos Garces, Santiago Van Oosterwyck, Nick De Boi, Ivan Cuyt, Annie Derammelaere, Stijn Department of Electromechanics CoSysLab University of Antwerp Antwerp Belgium AnSyMo/CoSys Flanders Make The Strategic Research Center For The Manufacturing Industry Antwerp Belgium Department of Electromechanics InViLab University of Antwerp Antwerp Belgium Department of Computer Science University of Antwerp Antwerp Belgium Division of Computing Science and Mathematics University of Stirling Stirling United Kingdom

This study addresses the challenge of mechanism design optimization, particularly focusing on the energy efficiency and design space of reciprocating mechanisms. The research question centers on how to effectively utilize computer-Aided Design (CAD) simulations alongside Bayesian optimization (BO) and a constrained design space to streamline the design optimization process, overcoming the limitations of traditional kinematic and dynamic analysis methods. The objective is to investigate and develop a novel optimization framework that integrates CAD-based simulations with a BO approach. To achieve this, the study employs a methodological approach. At first, the feasibility of a chosen mechanism design is evaluated through a sequence of CAD-motion simulations to quantify the (in)feasibility of this design. When this design appears to be feasible, a CAD-based design evaluation method is started, in which the objective value is extracted by a sequence of CAD-motion simulations. In this paper, we advocate the use of non-parametric Gaussian processes to build a surrogate model of the objective function and the feasible design space constrained by static and dynamic constraints. The main research results demonstrate that the proposed CAD-based Bayesian optimization framework can effectively identify optimal design parameters that minimize the root mean square (RMS) torque while adhering to specified static and dynamic constraints. This optimization approach significantly reduces the complexity associated with analytic methods, making it scalable to more complex mechanisms and implementable by machine builders. In conclusion, the study successfully develops a novel optimization framework that leverages CAD-based simulations and Bayesian optimization to streamline the design process of mechanisms. The results of an emergency ventilator case study with three design parameters show a reduction of the RMS torque with 71% after 255 CAD-based design evaluations. Moreover, the resu

关键词： Bayesian optimization Dimensional synthesis Mechanical systems Motion control

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Enhancing Cyclone center Identification in Radar Images through Deep Learning and Match Recognition 15

Enhancing Cyclone Center Identification in Radar Images thro...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Shanmugam, Thirumurugan Chandrashekar, Chirag Subburaj, Maheswari Sivaraman, Arun Kumar Sultana, Ajmery Thangavel, Senthil Kumar University of Technology and Applied Sciences College of Computing and Information Sciences Suhar Oman Vellore Institute of Technology School of Computer Science and Engineering Chennai India Photon Interactive Inc Digital Engineering Solution Center ON Canada Algoma University Brampton School of Computer Science and Technology ON Canada Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

This research focuses on improving the identification of cyclone centers using deep learning and match recognition applied to radar images. Accurately pinpointing the cyclone's center is vital for predicting its intensity and trajectory. However, challenges persist in automatically locating the center due to the diverse nature of cyclone morphology and structure. To address this, the deep convolutional network's capability is leveraged to capture various structural features in images by proposing two-step approach for cyclone center localization. Initially, a pre-trained EfficientDet model is employed using transfer learning to obtain weights. Subsequently, these weights, along with the data, are utilized in a deep learning model to provide precise coordinates of the cyclone center in the respective image. The effectiveness of existing deep learning and machine learning models show that the cyclone prediction systems have an accuracy ranging from 86% to 92% or better, and cyclone eye detection accuracy surpassing 87%. Experiment outcomes indicate that the proposed methodology outperforms conventional methods and existing works, showcasing its potential for enhancing cyclone monitoring and forecasting. © 2024 IEEE.

关键词： Cyclone detection deep learning efficient det and YOLO model transfer learning

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Time-aware Medication Recommendation via Intervention of Dynamic Treatment Regimes 25

Time-aware Medication Recommendation via Intervention of Dyn...

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34th ACM Web Conference, WWW 2025

作者： Li, Yishuo Zhang, Qi Lu, Wenpeng Peng, Xueping Zhang, Weiyu Si, Jiasheng Gong, Yongshun Hu, Liang Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Department of Computer Science and Technology Tongji University Shanghai China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia School of Software Shandong University Jinan China

ISBN: (纸本)9798400712746

Medication recommendation aims to suggest personalized drug combinations to patients based on their longitudinal medical histories stored in electronic health record (EHR) datasets. Patients’ Dynamic Treatment Regimes (DTRs) determine how patients’ drug combinations change along with the evolution of disease treatment. DTRs are effective for comprehending disease-treatment dynamics and for recommending a timely and personalized combination of medications for patients. However, existing medication recommender systems (MRSs) overlook the multiple treatment pathways generated by the intervention of DTRs and can only recommend a single treatment paradigm, ignoring the fact that patients may be at different treatment stages and thus require different treatment regime. Such disregard leads to a significant limitation in recommending personalized medication combinations tailored to different treatment stages, yielding greatly compromised accuracy and applicability of MRSs. Moreover, existing methods often overlook the time interval information over patients’ successive visits, which is critical to indicate patients’ treatment evolution. To address these significant gaps, we propose a Time-aware Medication Recommendation Framework via Intervention of Dynamic Treatment Regimes, called MR-DTR. To explicitly illustrate the intervention processes of DTRs on similar patients, we employ a co-guided graph to connect various patient sequences. In addition, to fully utilize the time interval information, we design a time-aware guidance mechanism dedicated to the co-guided graph to efficiently learn medication representation using the patient’s guidance information. We also introduce relative time intervals in the encoder to act as positional information. Extensive experiments on two real-world datasets demonstrate that MR-DTR surpasses state-of-the-art models in terms of recommendation performance. © 2025 Copyright held by the owner/author(s).

关键词： Electronic health record

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Secure Image Communication Using Galois Field,Hyper 3D Logistic Map,and B92 Quantum Protocol

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computers, Materials & Continua 2024年第12期81卷 4435-4463页

作者： De Rosal Ignatius Moses Setiadi Nova Rijati Ahmad Rofiqul Muslikh Bonifacius Vicky Indriyono Aceng Sambas Faculty of Computer Science Universitas Dian NuswantoroSemarang50131Central JavaIndonesia Research Center for Quantum Computing and Materials Informatics Faculty of Computer ScienceUniversitas Dian NuswantoroSemarang50131Central JavaIndonesia Faculty of Information Technology Universitas MerdekaMalang65146East JavaIndonesia Faculty of Economics and Business Universitas STRADA IndonesiaKediri64133East JavaIndonesia Faculty of Informatics and Computing Universiti Sultan Zainal AbidinCampus BesutBesut22200TerengganuMalaysia Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaTamansari GobrasTasikmalaya46196Indonesia

In this paper,we propose a novel secure image communication system that integrates quantum key distribution and hyperchaotic encryption techniques to ensure enhanced security for both key distribution and plaintext ***,we leverage the B92 Quantum Key Distribution(QKD)protocol to secure the distribution of encryption keys,which are further processed through Galois Field(GF(28))operations for increased *** encrypted plaintext is secured using a newly developed Hyper 3D Logistic Map(H3LM),a chaotic system that generates complex and unpredictable sequences,thereby ensuring strong confusion and diffusion in the encryption *** hybrid approach offers a robust defense against quantum and classical cryptographic attacks,combining the advantages of quantum-level key distribution with the unpredictability of hyperchaos-based *** proposed method demonstrates high sensitivity to key changes and resilience to noise,compression,and cropping attacks,ensuring both secure key transmission and robust image encryption.

关键词： Galois field hyper 3D logistic map hyperchaotic map image security improved logistic map secure communication quantum security

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