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International Conference on computer, Control, Informatics and Its Applications (IC3INA)

作者： M. Athallah Dzikri Alhady Ira Maryati Roni Salambue Lia Sadita Andre Sihombing Aruni Yasmin Azizah Zul Indra Gita Sastria Department of Computer Science Universitas Riau Pekanbaru Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency (BRIN) Bandung Indonesia Faculty of Computer Science Universitas Indonesia (UI) Depok Indonesia

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

Water segmentation has many benefits, such as for the hydrological cycle research, monitoring flood dynamics, and managing water resources. The U-Net is a famous segmentation architecture for water body segmentation. Water masks, used as labels in the U-Net architecture training process, are generated from water index classification. Since some water indices have been developed that claim beneficial performance, the comparative performance of each method has not yet been discovered. This study aims to compare the performance of several water index methods, including the Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Automated Water Extraction Index (AWEI), and Sentinel-2 Water Index (SWI). The study showed MNDWI obtained the best segmentation results and performance scores in urban areas with an IoU score of 0.97706. Meanwhile, SWI obtained the best segmentation results and performance scores in turbid water bodies with an IoU scores of 0.96075. This indicates that the water index is reliable in building a dataset for training the U-Net model. This study provides recommendations for selecting the appropriate water index that aligns with the characteristics of the segmented water bodies. These recommendations aim to enhance the accuracy and relevance of the water index in different environmental contexts.

关键词： Training Normalized difference water index Urban areas computer architecture Indexes Reliability Floods Informatics Water resources Monitoring

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External Validation of an AI mHealth Tool for Gingivitis Detection among Older Adults at Daycare Centers: A Pilot Study

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International Dental Journal 2025年第3期75卷 1970-1978页

作者： Chau, Reinhard Chun Wang Cheng, Andrew Chi Chung Mao, Kaijing Thu, Khaing Myat Ling, Zhaoting Tew, In Meei Chang, Tien Hsin Tan, Hong Jin McGrath, Colman Lo, Wai-Lun Hsung, Richard Tai-Chiu Lam, Walter Yu Hang Faculty of Dentistry The University of Hong Kong Hong Kong Special Administrative Region China Musketeers Foundation Institute of Data Science The University of Hong Kong Hong Kong Special Administrative Region China Department of Computer Science Hong Kong Chu Hai College Hong Kong Special Administrative Region China Faculty of Dentistry The National University of Malaysia Kuala Lumpur Malaysia School of Dental Medicine University of Pennsylvania Philadelphia PA United States Eastman Dental Institute University College London London United Kingdom

Objectives: Periodontal disease is a significant public health concern among older adults due to its relationship with tooth loss and systemic health disease. However, there are numerous barriers that prevent older adults from receiving routine dental care, highlighting the need for innovative screening tools at the community level. This pilot study aimed first, to evaluate the accuracy of GumAI, a new mHealth tool that uses AI and smartphones to detect gingivitis, and the user acceptance of personalized oral hygiene instructions provided through the new tool, among older adults in day-care community centers. Methods: Participants were invited from 3 day-care community centers. Intraoral photographs were captured and assessed by both GumAI (test) and a panel consisting of 2 calibrated periodontists and a dentist (benchmark). Mean sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and F1 score were calculated to determine GumAI's diagnostic performance in comparison to the benchmark. User acceptance with this tool was assessed using 2 Rasch Theory-based 5-point Likert-type questions. Results: 44 participants were recruited out of 80 invited older adults. GumAI demonstrated a sensitivity of 0.93 and specificity of 0.50 compared to the panel‘s assessments, with a PPV of 0.90 and NPV of 0.56. The accuracy and F1 scores were 0.85 and 0.91, respectively. All participants expressed high acceptance of the process. Conclusion: GumAI demonstrates high sensitivity, PPV, accuracy, and F1 score compared to the panel's assessments but falls relatively short in specificity and NPV. Despite this, the tool was highly accepted by older adults, indicating its potential to enhance gingivitis detection and oral hygiene management in community settings. Further refinements are necessary to improve specificity and validate usability measures. Clinical Relevance: This study may pave the way for broader applications of mHealth systems in co

关键词： Community dentistry Gingivitis Machine learning Mobile health Periodontal diseases Telemedicine

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Malicious Traffic Classification in WSN using Deep Learning Approaches 2

Malicious Traffic Classification in WSN using Deep Learning ...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Gupta, Chhote Lal Prasad Rajassekharan, Dinesh Sharma, Dilip Kumar Elangovan, Mohanraj Myilsamy, Varatharaj Upreti, Kamal Bansal Institute of Engineering and Technology Dept. of Computer Science and Engineering Lucknow India Peninsula College Faculty of Computer Science Peninsula Malaysia Jaypee University of Engineering and Technology Department of Mathematics Madhya Pradesh Guna473226 India K. S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruchengode637215 India V.S.B College of Engineering Technical Campus Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore642109 India Department of Computer Science Delhi-NCR Ghaziabad India

ISBN: (纸本)9798350369960

Classifying malicious traffic in Wireless Sensor Networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data. © 2023 IEEE.

关键词： data routing deep learning LSTM malicious transmission neural network Traffic data transmission WSN

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A Distributed Framework for Large-scale Protein-protein Interaction data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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An intelligent solar flare prediction model based on X-ray flux curves using Long Short-Term Memory

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Astronomical Techniques and Instruments 2025年第2期2卷 65-72页

作者： Yan Gao Li Zhang Long Xu College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang 550025China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

Solar flares are violent solar outbursts which have a great influence on the space environment surrounding Earth,potentially causing disruption of the ionosphere and interference with the geomagnetic field,thus causing magnetic ***,it is very important to accurately predict the time period of solar *** paper proposes a flare prediction model,based on physical images of active solar *** employ X-ray flux curves recorded directly by the Geostationary Operational Environmental Satellite,used as input data for the model,allowing us to largely avoid the influence of accidental errors,effectively improving the model prediction efficiency.A model based on the X-ray flux curve can predict whether there will be a flare event within 24 *** reverse can also be verified by the peak of the X-ray flux curve to see if a flare has occurred within the past 24 *** True Positive Rate and False Positive Rate of the prediction model,based on physical images of active regions are 0.6070 and 0.2410 respectively,and the accuracy and True Skill Statistics are 0.7590 and *** model can effectively improve prediction efficiency compared with models based on the physical parameters of active regions or magnetic field records,providing a simple method for solar flare prediction.

关键词： Neural Network Long Short-Term Memory Solar flare prediction X-ray flux curve

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Robust Perceptual Hashing for Fingerprint Template Protection 1

Robust Perceptual Hashing for Fingerprint Template Protectio...

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1st International Conference on computer Communications and Intelligent Systems, I3CIS 2022

作者： Birouk, Wafa Lahoulou, Atidel Melit, Ali Bouridane, Ahmed University of Bejaia Faculty of Exact Sciences Computer Science Department Bejaia06000 Algeria University Mohammed Seddik Benyahia Computer Science Department Jijel18000 Algeria LaRIA Laboratory University Mohammed Seddik Benyahia Computer Science Department Jijel18000 Algeria University of Sharjah Cybersecurity and Data Analytics Research Center Sharjah United Arab Emirates

ISBN: (纸本)9781665461887

Perceptual hashing has proven its robustness in securing biometric templates, where several methods have been proposed in order to have a robust and discriminating hash code against acceptable attacks. This paper proposes a robust perceptual hashing scheme for biometric template protection that based on SIFT and Harris functions for the feature ex-traction/selection phase and on DWT and SVD functions for the hash code generation phase. The experimental results in this paper discussed the effect of distance value and image blocks size parameters on hash performances (robustness and discrimination), which are used in the feature selection phase and the hash generation phase of our method respectively. The results show that we have could to choose the optimal parameters values in the generation of our hash code to give a best tradeoff between discrimination and robustness against acceptable image manipulations. © 2022 IEEE.

关键词： Signal reconstruction

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An IoT-based Bearded Dragon Enclosure Integrating YOLO Object Detection for Monitoring Food and Cleanliness

An IoT-based Bearded Dragon Enclosure Integrating YOLO Objec...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Nanmanat Varisthanist Thanapat Tardtong Kittitach Jadee Krittakom Srijiranon Thammasat University Research Unit in Data Innovation and Artificial Intelligence Department of Computer Science Faculty of Science and Technology Thammasat University Pathumthani Thailand

Among exotic reptiles, bearded dragons (Pogona) are popular pets. As ectotherms, bearded dragons require special food and controlled environments, including temperature, humidity, ultraviolet light, and cleanliness. This research applied the Internet of Things (IoT) to control environments within the pet enclosure. In addition, the You Only Look Once (YOLO) deep learning algorithm applied to convolutional neural networks was used to create object detection to count crickets eaten by a dragon and detect feces inside the enclosure. To find the appropriate YOLO structure, three varied experiments were proposed, including different versions YOLO and sizes as well as hyperparameter tuning. Results were that YOLYv5s with 16 batches and 0.01 learning rate was the appropriate structure with an average precision of 0.8246 and 0.6051 for detecting crickets and feces, respectively, at a mean average precision of 0.7149. The proposed enclosure with object detection may improve efficiency and reduce redundancy in breeder tasks.

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Color Space Search Algorithm (CSSA): A Novel Optimization Approach

Color Space Search Algorithm (CSSA): A Novel Optimization Ap...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hossam M. J. Mustafa Nedal Nwasra Ahmad Khasawneh Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Computer Science Department Faculty of Information Technology University of Petra Amman Jordan School of Computing Skyline University College Sharjah UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper presents a novel CSSA (Color Space Search Algorithm), introducing a metaheuristic optimization algorithm that addresses critical limitations in existing optimization methods. While current algorithms demonstrate suboptimal performance in multimodal landscapes, revealing premature convergence rates, CSSA introduces a novel approach by leveraging color theory principles in the optimization domain. The proposed algorithm combines complementary color transformations for global exploration with triadic color relationships for local exploitation, which aims to improve the exploration-exploitation balance compared to conventional methods. The algorithm's performance is evaluated across three categories of benchmark functions: unimodal functions, multimodal functions, and fixed-dimensional multimodal functions. Comprehensive experiments compared CSSA against classical (PSO, GA) and modern (MPA, EO, ALO, SSA, DA) optimization algorithms, with performance metrics including best fitness, mean fitness, solution consistency, and convergence rate analysis. Statistical analyses through the Friedman test (ranking 4.22) confirm the algorithm's robust performance, particularly in handling complex optimization landscapes. These results place CSSA as a promising optimization algorithm for complex optimization problems.

关键词： Ranking (statistics) Statistical analysis Metaheuristics Optimization methods Color Evolutionary computation Benchmark testing Optimization Convergence

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Early Detection of Diabetic Foot Ulcers through CoAtnet Model: A Scalable Solution for Healthcare Integration

Early Detection of Diabetic Foot Ulcers through CoAtnet Mode...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Muhammad Abdullah Khan Muhammad Zaman Tanzila Kehkashan Nikola Ivković Vedran Uroš Adnan Akhunzada Faculty of Computer Science The University Of Lahore Sargodha Pakistan Department of Computer Science & IT Superior Univerity Lahore Pakistan Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Malaysia Faculty of Organization and Informatics University of Zagreb Varaždin Croatia Polytechnic Marko Marulić in Knin Knin Croatia Department of Data and Cybersecurity University of Doha for Science and Technology Doha Qatar

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Diabetic foot ulcers are one of the serious complications of diabetes mellitus affecting millions of patients across the globe and could lead to infections and amputations if diagnosed late. The recent trends in machine learning, with high promise, have been embodied in deep learning to solve the detection task for diabetic foot ulcers accurately. However, existing methods, such as the traditional CNN, faced the challenge of focusing on the critical ulcerous regions and thus were not productive in the real-time performances. Thus, this present research focuses on improving a deep learning-based model by employing CoAtNet, which is a customized attention mechanism to develop convolutional mechanisms for enhancing the outcomes related to DFU detection from foot images. The model was trained on the DFUC dataset, along with data augmentation techniques such as rotation, flipping, and contrast adjustment to enhance robustness. Therefore, accuracy was around 98% and F1-score stood at 90%. The suggested solution has outstanding potential for the early detection of DFUs, especially in resource-poor health environments, thus improving the outcome of patients and reducing the occurrence of severe complications like amputation.

关键词： Deep learning Accuracy Attention mechanisms Computational modeling Amputation Wounds data models Robustness Diabetes Medical diagnostic imaging

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A Critical Review of AI-Driven AR Glasses for Realtime Multilingual Communication

A Critical Review of AI-Driven AR Glasses for Realtime Multi...

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Advances in science and Engineering Technology International Conferences (ASET)

作者： Zainab Rasheed Sameh Ghwanmeh Aziz Almahadin Faculty of Mathematics & Data Science Emirates Aviation University Dubai UAE College of Computer Information Technology American University in the Emirates Dubai UAE Faculty of Engineering Technology & Science Higher Colleges of Technology Dubai UAE

ISBN: (数字)9798350344134

ISBN: (纸本)9798350344141

This research paper examines the integration of Artificial Intelligence-driven Augmented Reality glasses within wired and wireless systems to facilitate real-time multilingual communication. The synergy between AI and AR technologies presents a transformative potential in overcoming language barriers, enabling seamless communication among diverse linguistic communities. By harnessing the power of AI, AR glasses offer instant translation and interpretation services, enhancing interconnectivity in multicultural settings. The paper discusses the technical underpinnings of these AR systems, exploring their architecture, functionalities, and the algorithms driving real-time multilingual translation. Furthermore, this study investigates the implications and challenges of embedding AI-powered AR glasses into wired and wireless communication networks. It assesses the system's effectiveness in translating spoken languages and analyzing their potential impact on communication infrastructure. The review of existing literature, coupled with case studies, offers insights into the successful applications of these integrated systems. This comprehensive analysis advocates for the utilization of AI-driven AR glasses as an instrumental tool in bridging language gaps, emphasizing their significance in fostering inclusive and efficient communication ecosystems within wired and wireless networks.

关键词： Accuracy Navigation Wireless networks Instruments Ecosystems Glass Linguistics Real-time systems Iterative algorithms Artificial intelligence

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