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2nd International Conference on Information Science and Education, ICISE-IE 2021

作者： Chen, Huan Liu, Ying Shan, Chan Zhang, Hequn School of Journalism and Communication of Minjiang University Fujian Fuzhou350108 China Department of Software Technology Xiamen Institute of Software Technology Fujian Xiamen China Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia College of Information Engineering Jimei University Xiamen361021 China

ISBN: (纸本)9781665438292

In the era of rapid development of intelligent technology, the children's digital publishing industry is facing a disruptive change, and this change may be realized through the reconstruction of the supply and demand chain. The value power of source content creation and the ability to gather users have been deconstructed. The increase in the amount of content requires platform institutions to have a more professional content operation system. The convenience of payment technology has greatly increased the digital content payment rate of users of children's digital products. A development model of the supply and demand chain of children's digital publishing with more innovative flexibility and enabling effects. © 2021 IEEE

关键词： Economics

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Dogodo: IoT Based Mobile Application to Provide Essential Health Services to Dogs 3

Dogodo: IoT Based Mobile Application to Provide Essential He...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Thilakarathne, L.V.I.S. Salay, M.S. Wijethilaka, M. G. R Chethana Fernando, T.S. Sriyaratna, Disni Rupasinghe, Shalini Sri Lanka Institute of Information Technology Faculty of Computing Department of Infotmation Tchnology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Voice of dogs can be heard by people who listen to them. The more you listen, the more you learn about the dogs. This study proposes a platform to identify and observe dogs' behavior and their activities by using the newest technologies. The proposed system will mainly cover the relevant areas that are supposed to be covered to full fill the pet owners' expectations by providing necessary services such as internal health, voice recognition, and emotion translations and external issues such as skin diseases, breeding patterns, and breeding outcomes. Our priority is to emphasize necessary services in the mobile application and provide fluid services with fewer interruptions. Primarily research will offer an IoT device and a mobile application that covers the dog's internal health and external health consecutively. The study revealed current developments and the challenges and possibilities related to the system. © 2021 IEEE.

关键词： Speech recognition

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The Low-Cost IoT Mornitoring the Quality of River: A Case Study of Wang River in Lampang Municipality

The Low-Cost IoT Mornitoring the Quality of River: A Case St...

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International computer Science and engineering Conference (ICSEC)

作者： Narong Krueganta Oran Chieochan Anukit Saokaew Ekkachai Yana Chaowoot Singkaew Weerachai Sawangtook Ekkarat Boonchieng Warakhom Wongchai Sakchai Srimakorn Faculty of Industrial Technology School of Industrial Technology Lampang Rajabhat University Lampang Thailand Faculty of Sciences School of Information Technology Lampang Rajabhat University Lampang Thailand Faculty of Science School of Environmental innovation Lampang Rajabhat University Lampang Thailand Faculty of Industrial Technology School of Computer Engineering Lampang Rajabhat University Lampang Thailand Department of Computer Sciences Faculty of Sciences Chiang Mai University Chiang Mai Thailand Faculty of Industrial Technology School of Industrial Electrical Engineering Lampang Rajabhat University Lampang Thailand Faculty of Industrial Technology School of Software Engineering Lampang Rajabhat University Lampang Thailand

The research project focuses on prototyping an IoT (Internet of Things) system for measuring and monitoring the quality of the Wang River in Lampang Municipality. The system utilizes EC (Electrical Conductivity), pH, and temperature sensors connected to an ESP32 microcontroller with a custom-developed board. Additionally, the IoT system is supported by a solar cell system to provide power. The software programming is implemented using Arduino C++ on the PlatformIO platform, which is integrated with Visual Studio code. The IoT platform chosen for this project is ThingsBoard, specifically the community version. The research findings indicate that the utilization of IoT technology combined with the sensors has improved the prototype of a low-cost IoT monitoring system for assessing the quality of the Wang River in Lampang Municipality.

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A CNN-Based Model for Yield Prediction in MEMS Resonator Manufacturing

A CNN-Based Model for Yield Prediction in MEMS Resonator Man...

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IEEE/SEMI Conference and Workshop on Advanced Semiconductor Manufacturing

作者： Yikang Ding Bo Wen Qi Wang Chong Chen Xiangshu Lei Yufeng Jin Wei Wang School of Electronic and Computer Engineering Peking University Shenzhen China School of Integrated Circuits Peking University Beijing China School of Software & Microelectronics Peking University Beijing China Faculty of Information and Communication Engineering Dalian University of Technology Dalian China School of Electronic and Computer Engineering School of Integrated Circuits Peking University Beijing China School of Integrated Circuits National Key Laboratory of Advanced Micro and Nano Manufacture Technology Peking University Beijing China

ISBN: (数字)9798331531850

ISBN: (纸本)9798331531867

This work presents a novel method for predicting the yield of Micro-electromechanical Systems (MEMS) resonators by using Convolutional Neural Networks (CNNs) to extract morphological features from cross-sections images of MEMS resonators’ beams produced by Deep Reactive-Ion Etching (DRIE). By establishing a model that maps the relationships between the MEMS resonators’ operating frequency and the morphological variations in beams induced by process deviations, this method can predict the operating frequency more than 10 3 times faster than Finite Element Analysis (FEA) simulation, achieving over 96.7% accuracy, advancing the standardization of MEMS resonators manufacturing flow.

关键词： Micromechanical devices Microelectromechanical systems Time-frequency analysis Resonant frequency Standardization Predictive models Feature extraction Manufacturing Convolutional neural networks Resonators

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EEG Feature Extraction with Fast Fourier Transform for Investigating different Brain regions in Cognitive and Reasoning Activity 5

EEG Feature Extraction with Fast Fourier Transform for Inves...

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5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022

作者： Amin, Hafeez Ullah Hafeez, Yasir Reza, Mohammed Faruque Adil, Syed Hasan Hasan, Rumaisa Abu Ali, Syed Saad Azhar University of Nottingham Malaysia School of Computer Science Faculty of Science and Engineering Semenyih43500 Malaysia Universiti Sains Malaysia Dept. of Neurosciences Kota Bharu Kubang Kerian Malaysia Iqra University Software Engineering Department Karachi Pakistan Universiti Teknologi Petronas Centre for Intelligent Signal and Imaging Research Electrical and Electronics Engineering Department Perak Seri Iskandar32610 Malaysia King Fahd University of Petroleum and Minerals Aerospace Engineering Department Dhahran31261 Saudi Arabia

ISBN: (数字)9781665459327

ISBN: (纸本)9781665459327

In this study, cortical brain activity during a pattern matching task (PMT) was measured by employing electroencephalography (EEG). The EEG data were recorded from 128 scalp locations during a pattern-matching task and in rest conditions (eyes open and eyes closed). Spectral Analysis of EEG frequency bands reflected a significant (p © 2022 IEEE.

关键词： Fast Fourier transforms

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A Distributed Feature Selection Approach Over Hadoop for Accurate Lung Cancer Classification Based on Grasshopper Algorithm and Rough Sets

SSRN

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SSRN 2023年

作者： Song, Xu Zhou, Hongyu Teo, Brian Sheng Xian Abo-Alnadr, Ayman Hamed, Ahmed School of Computer and Information Engineering Anyang Normal University Anyang455000 China School of Software Engineering Anyang Normal University Anyang455000 China School of Graduate Studies Shah Alam40100 Malaysia Kafrelsheikh University Egypt Department of Computer Science Faculty of Computers and Information Damanhour University Damanhour22511 Egypt

In the specialized field of lung cancer research, precise feature selection has become paramount, especially given the extensive and intricate datasets available. Many of these datasets house a plethora of features, of which a substantial number may be redundant, leading to potential inaccuracies and increased computational demands. Although the Rough Set (RS) and Multigranular Rough Set (MGRS) models have demonstrated efficacy in feature selection, their computational complexities can be limiting. To address this, we introduce an innovative solution, integrating the MGRS with the Grasshopper Optimization Algorithm (GOA)—a metaheuristic technique derived from grasshopper foraging behaviors. To manage the expansive lung cancer data, we employ the Hadoop framework for streamlined distributed processing. By distributing the enhanced GOA tasks within Hadoop, we aspire to efficiently process large-scale datasets. The proposed algorithm's efficacy is assessed using dedicated lung cancer datasets, benchmarked via classifiers such as Random Forest and K-Nearest Neighbor. Preliminary results highlight the superior performance of our approach compared to prevalent metaheuristic strategies, with the MGRS model enhancing performance notably when employed as an objective function. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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A GSO-based multi-objective technique for performance optimization of blockchain-based industrial Internet of things

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International Journal of Communication Systems 2024年第15期37卷

作者： Zanbouri, Kouros Darbandi, Mehdi Nassr, Mohammad Heidari, Arash Navimipour, Nima Jafari Yalcın, Senay Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Pôle Universitaire Léonard de Vinci Paris France Communication Technology Engineering Department Tartous University Tartus Syria Department of Mathematics and Natural Sciences Gulf University for Science and Technology Mishref Campus Kuwait Department of Computer Engineering Faculty of Engineering and Natural Science İstanbul Atlas University Turkey Department of Software Engineering Haliç University Istanbul Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Research Center of High Technologies and Innovative Engineering Western Caspian University Baku Azerbaijan Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Energy System Engineering School of Engineering and Natural Sciences Bahçeşehir University Istanbul Turkey

The latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain-based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain-based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain-based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision-making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain-based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations. © 2024 John Wiley & Sons Ltd.

关键词： Multiobjective optimization

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Membership-Mappings for Practical Secure Distributed Deep Learning

arXiv

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arXiv 2022年

作者： Kumar, Mohit Zhang, Weiping Fischer, Lukas Freudenthaler, Bernhard The Software Competence Center Hagenberg Austria The Faculty of Computer Science and Electrical Engineering University of Rostock Germany The Northwestern Polytechnical University Suzhouzhong Road Suzhou China

This study leverages the data representation capability of fuzzy based membership-mappings for practical secure distributed deep learning using fully homomorphic encryption. The impracticality issue of secure machine (deep) learning with fully homomorphic encrypted data, arising from large computational overhead, is addressed via applying fuzzy attributes. Fuzzy attributes are induced by globally convergent and robust variational membership-mappings based local deep models. Fuzzy attributes combine the local deep models in a robust and flexible manner such that the global model can be evaluated homomorphically in an efficient manner using a boolean circuit composed of bootstrapped binary gates. The proposed method, while preserving privacy in a distributed learning scenario, remains accurate, practical, and scalable. The method is evaluated through numerous experiments including demonstrations through MNIST dataset and Freiburg Groceries Dataset. Further, a biomedical application related to mental stress detection on individuals is considered. © 2022, CC BY.

关键词： Mapping

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A Hybrid Method for Analyzing the Situation Based on Cumulative Fully Vaccinated and Confirmed Cases of Covid-19 in Malaysia

A Hybrid Method for Analyzing the Situation Based on Cumulat...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Marzia Ahmed Mohd Herwan Sulaiman Ahmad Johari Mohamad Mostafijur Rahman Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang (UMP) Pahang Malaysia Department of Software Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh

SARS-CoV-2 is an infection that affects several organs and has a wide range of symptoms in addition to producing severe acute respiratory syndrome. Millions of individuals were infected when it first started because of how quickly it travelled from its starting location to nearby countries. Anticipating positive Covid-19 incidences is required in order to better understand future risk and take the proper preventative and precautionary measures. As a result, it is critical to create mathematical models that are durable and have as few prediction errors as possible. This study suggests a unique hybrid strategy for examining the status of Covid-19 confirmed patients in conjunction with complete vaccination. First, the selective opposition technique is initially included into the Grey Wolf Optimizer (GWO) in this study to improve the exploration and exploitation capacity for the given challenge. Second, to execute the prediction task with the optimized hyper-parameter values, the Least Squares Support Vector Machines (LSSVM) method is integrated with Selective Opposition based GWO as an objective function. The data source includes daily occurrences of confirmed cases in Malaysia from February 24, 2021 to July 27, 2022. Based on the experimental results, this paper shows that SOGWO-LSSVM outperforms a few other hybrid techniques with ideally adjusted parameters.

关键词： COVID-19 Support vector machines Pandemics Soft sensors Time series analysis Predictive models Mathematical models

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A High-Resolution Approach for Dynamic Traffic Police Gestures Recognition Based on Spatial Context and Temporal Features Fusion

A High-Resolution Approach for Dynamic Traffic Police Gestur...

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International Conference on Image, Vision and Computing (ICIVC)

作者： Jian He Shengsheng Jiang Xin Wei Cheng Zhang Ruihai Dong Beijing Engineering Research Center for IOT Software and Systems Beijing University of Technology Beijing China Faculty of Information Technology Beijing University of Technology Beijing China School of Computer Science University College Dublin Dublin Ireland

According to the requirement of recognizing traffic police gestures for driver assistance systems and intelligent vehicles, a universal model for dynamic traffic police gesture recognition is firstly introduced, of which can accurately present the spatial context (such as the relative lengths of skeletons, the angles between each skeleton w. r. t. gravity, and part features) of the traffic police gestures. Secondly, an architecture which can respectively extract spatial context and temporal features of dynamic traffic police gesture is proposed. Meanwhile, deep neural network and LSTM are introduced to build a high-resolution traffic police gestures recognizer (namely HRTPGR). At last, the open Police Gesture Dataset is used to train and test TPGR, and the experimental results show that the TPGR achieves a state-of-the-art accuracy with 98.7% for dynamic traffic police gestures recognition, and has strong anti-interference ability to light, background and gesture shape changes.

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