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8th International Conference on Smart City Applications, SCA 2023

作者： Wasim Raed, Mhd. Huseyinov, Ilham Ozdemir, Ghina Kotenko, Igor Fedorchenko, Elena Department of Computer Engineering Istanbul Aydin University Istanbul Turkey Software Engineering Department Istanbul Aydin University Istanbul Turkey St. Petersburg Federal Research Center of the Russian Academy of Sciences St. Petersburg Russia

ISBN: (纸本)9783031538230

There exists a global challenge related to the boosting number of elderly suffering from chronic diseases like Dementia (EWD). Hence, there is a drastic need for cost-effective disruptive technologies that enable and guarantee the quality of life for EWD via implementing telehealth. The purpose of this paper is to propose a telehealth system based on the Internet of Things for the well-being of the elderly suffering from Dementia in a smart home setting. We propose a smart home solution as a whole and a solution based on an NFC-based working prototype of a pill intake in a box for monitoring and recording the proper intake of prescribed medication for the elderly suffering from chronic diseases. A smart home solution was effectively implemented using the Cisco Packet Tracer simulator 7.3. The proposed IoT-based solution ensures the safe wandering of the elderly in a smart home setting using a Cisco packet tracer simulation tool. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cost effectiveness

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Optimizing GPS and Gyroscope Data for Military Missions

Optimizing GPS and Gyroscope Data for Military Missions

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2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Maidanov, Adil K. Canbolat, Huseyin Atanov, Sabyrzhan K. L.N. Gumilyov Eurasian National University Department of Computer and Software Engineering Astana Kazakhstan Ankara Yildirim Beyazit University Department of Electrical and Electronics Engineering Ankara Turkey

ISBN: (纸本)9798350335040

The use of unmanned aerial vehicles (UAVs) in military missions has greatly increased in recent years. To ensure precise and efficient movements, it is essential to estimate the UAV's orientation accurately. Two commonly used methods for this estimation are the Kalman filter and the Madgwick filter. In this article, we present a method to combine Kalman and Madgwick filters to optimize the orientation estimation of a UAV in military missions. The Kalman filter estimates the orientation with low noise and high accuracy. In contrast, the Madgwick filter is used to correct the orientation quickly in the presence of rapid changes. Our results show that combining these two filters leads to better orientation estimation than using either filter separately. This improved orientation estimation leads to more precise and efficient UAV movements, making it an essential tool for military missions. The proposed method is verified through simulations and experiments, including an Arduino model with a gyroscope and GPS tracker. This method can be easily integrated into the existing control systems of UAVs. It can be adapted to different missions, providing valuable insights for researchers and engineers in the UAV control field. The combination of the Kalman and Madgwick filters offers a robust solution to the challenge of orientation estimation, providing a reliable and efficient tool for a specific purpose © 2023 IEEE.

关键词： Kalman filters

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K-Banhatti Sombor Invariants of Certain computer Networks

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computers, Materials & Continua 2022年第10期73卷 15-31页

作者： Khalid Hamid Muhammad Waseem Iqbal Abaid Ur Rehman Virk Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Mathematics University of SialkotSialkot51013Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Any number that can be uniquely determined by a graph is called a graph *** the last twenty years’countless mathematical graph invariants have been characterized and utilized for correlation ***,no reliable examination has been embraced to decide,how much these invariants are related with a network graph or molecular *** this paper,it will discuss three different variants of bridge networks with good potential of prediction in the field of computer science,mathematics,chemistry,pharmacy,informatics and biology in context with physical and chemical structures and networks,because k-banhatti sombor invariants are freshly presented and have numerous prediction qualities for different variants of bridge graphs or *** study solved the topology of a bridge graph/networks of three different types with two invariants KBanhatti Sombor Indices and its reduced *** deduced results can be used for the modeling of computer networks like Local area network(LAN),Metropolitan area network(MAN),and Wide area network(WAN),backbone of internet and other networks/structures of computers,power generation,bio-informatics and chemical compounds synthesis.

关键词： Bridge networks invariants k-banhatti sombor indices maple network graph molecular graph

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Research and development of an information system for speech recognition based on neural network algorithms metrics 4

Research and development of an information system for speech...

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4th IEEE International Conference on Smart Information Systems and Technologies, SIST 2024

作者： Kanashev, Maksat Mukasheva, Assel Yedilkhan, Didar Rakhmetulaeva, Sabina Gnatyuk, Sergiy Nazym, Mirambayeva Almaty University of Power Engineering and Telecommunications Almaty Kazakhstan The School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan The Department of Computer Engineering Astana IT University Astana Kazakhstan The Department of Information Systems International University of Information Technologies Almaty Kazakhstan The Faculty of Cybersecurity Computer and Software Engineering National Aviation University National Aviation University Kyiv Ukraine

ISBN: (纸本)9798350374865

This article examines the parameters of speech recognition in audio recordings containing a variety of phrases uttered by different people with different accents and intonations. The aim of the study is to evaluate the recognition speed, the accuracy of audio-to-text conversion and resource consumption when using SpeechRecognition and Levenshtein libraries. During the analysis, the above libraries were used to record and recognize audio files, as well as to calculate the distance between two lines and determine the recognition accuracy. The findings of this study can be useful for improving speech recognition systems and creating more adaptive and efficient solutions for audio data processing. The results of comparing the accuracy of speech recognition models are recorded in JSON format, after which, using the matplotlib library, they are visualized as graphs, comparing the speed and accuracy of recognition. Visualization of data in this way contributes to their improvement and more effective integration into educational systems. In conclusion, the authors emphasize the prospects for further development of speech recognition technologies in different fields. © 2024 IEEE.

关键词： Audio recordings

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Foreign Object Recognition Model on Coal Mine Belt Conveyors Based on Improved MobileNetV3 7

Foreign Object Recognition Model on Coal Mine Belt Conveyors...

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7th International Conference on Communication and Information Systems, ICCIS 2023

作者： Hu, Yujia Zhang, Zhiwei College of Computer Science Sichuan University Department of Artificial Intelligence Chengdu China College of Computer Science Xi'an Aeronautical Institute Department of Software Engineering Xi'an China

ISBN: (纸本)9798350330540

Coal mine belt conveyors are commonly used equipment in coal mining enterprises which transport coal from the coal faces using belts. Currently, video image monitoring serves as the predominant approach to identify foreign objects within the transported coal, such as large gangue and rock bolts. In recent years, the utilization of Convolutional Neural Networks has become prevalent in automatically recognizing foreign objects within images. Nevertheless, existing models suffer from issues including low recognition accuracy and slow speed. In response to the aforementioned problems, this paper proposes an improved coal foreign object recognition model based on MobileNetV3. Firstly, the Coordinate Attention mechanism is introduced into MobileNetV3 to fully consider both channel and spatial features. Secondly, the ReLU6 activation function in MobileNetV3 is replaced with the ELU activation function to augment the model's capacity to express information within the negative value range. Finally, superfluous network layers are removed to reduce computational complexity and improve recognition speed. Experimental results demonstrate that the proposed improved MobileNetV3 model achieves an accuracy of 90.8% in recognizing foreign object images, which is 2.5 to 4.0 percentage points higher compared to commonly used neural networks including AlexNet and EfficientNet. Moreover, compared with MobileNetV3, the proposed model reduces the number of parameters and computational complexity by 40.3% and 12.0% respectively. These results indicate that the proposed model reaches higher recognition accuracy while reducing network size and computational load. As a result, it provides a feasible approach for enhancing safety in coal mining enterprises. © 2023 IEEE.

关键词： Attention mechanism Image foreign object recognition MobileNetV3

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Metaverse virtual social center for elderly communication in time of social distancing

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Virtual Reality & Intelligent Hardware 2023年第1期5卷 68-80页

作者： Hui LIANG Jiupeng LI Yi WANG Junjun PAN Yazhou ZHANG Xiaohang DONG Department of Software Engineering Zhengzhou University of Light IndustryZhengzhou 450002China General Hospital of Pingmei Shenma Group Pingdingshan 467000China Department of Computer Science and Technology Beihang UniversityBeijing 100191China

Background The lack of social activities among the elderly due to physical limitations can result in loneliness and depression. The spread of COVID-19 has made it difficult for the elderly to conduct stable social activities, increasing feelings of loneliness. The metaverse is a virtual world that mirrors reality. This allows the elderly to overcome the constraints of reality and perform social activities stably and continuously, providing new ideas for alleviating loneliness. Methods By analyzing their needs, a virtual social center framework for the elderly was proposed in this study. In addition, a prototype system was designed according to this framework. The elderly can socialize in virtual reality with metaverse-related technologies and human–computer interaction tools. Additionally, a test was conducted jointly with the chief physician of the geriatric rehabilitation department of a tertiary hospital. Results The results demonstrated that the mental state of the elderly who had used the virtual social center was significantly better than that of those who had not used it. Conclusions Thus, virtual social centers can help relieve loneliness and depression among the elderly with increasing global epidemics and an aging society. Hence,they have promotional value.

关键词： Elderly Loneliness and depression Metaverse Virtual social center HCI

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Artificial Intelligence-based Speech Signal for COVID-19 Diagnostics 6

Artificial Intelligence-based Speech Signal for COVID-19 Dia...

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6th International Conference on Future Networks and Distributed Systems, ICFNDS 2022

作者： Alfaidi, Aseel Alshahrani, Abdullah Aljohani, Maha Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Saudi Arabia Department of Software Engineering College of Computer Science and Engineering University of Jeddah Saudi Arabia

ISBN: (纸本)9781450399050

The speech signal has numerous features that represent the characteristics of a specific language and recognize emotions. It also contains information that can be used to identify the mental, psychological, and physical states of the speaker. Recently, the acoustic analysis of speech signals offers a practical, automated, and scalable method for medical diagnosis and monitoring symptoms of many diseases. In this paper, we explore the deep acoustic features from confirmed positive and negative cases of COVID-19 and compare the performance of the acoustic features and COVID-19 symptoms in terms of their ability to diagnose COVID-19. The proposed methodology consists of the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images to extract deep audio features. In addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 from acoustic features compared to COVID-19 symptoms, achieving an accuracy of 97%. The experimental results show that the proposed method remarkably improves the accuracy of COVID-19 detection over the handcrafted features used in previous studies. © 2022 ACM.

关键词： COVID-19

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FPGA-ENABLED HAND GESTURE RECOGNITION FOR SMART HOME APPLICATIONS

FPGA-ENABLED HAND GESTURE RECOGNITION FOR SMART HOME APPLICA...

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2025 International Conference on Energy, Power, Environment, Control and Computing, ICEPECC 2025

作者： Batool, Misbah Saleem, Mohsin Khan, Maryam N. Siddiqui, Faisal Salim, Ahmed Department of Electrical Engineering Namal University Mianwali Pakistan Software Development Cell Namal University Mianwali Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Pakistan

ISBN: (纸本)9781837243235

Machine learning (ML) algorithms trained with hand gesture data have achieved accuracy rates close to 95%. However, despite this high accuracy, a trained ML algorithm requires hardware implementation to be effectively utilized in practical applications. In the proposed approach, we select one of the three investigated ML algorithms (CNN, ResNet50, and MobileNetV2) that demonstrates the best accuracy and make it compatible with loading onto a Field Programmable Gate Array (FPGA). This allows the algorithm to be effectively utilized in real-world applications. The optimized algorithm was implemented on a Zynq 7000 board to enhance performance, achieving greater efficiency, accuracy, and lower power consumption compared to traditional CPU-based or other architectures. The implementation was tested using a custom dataset containing six distinct hand gestures for detection and computation. Resource-efficient utilization led to a 2% increase in accuracy and a 5% reduction in processing time for the CNN-trained model on the FPGA compared to the CPU. Similarly, the ResNet50-trained model on the FPGA achieved 7% higher accuracy and 9% faster processing time compared to its execution on a CPU. Hand gesture recognition integrated with FPGAs can achieve real-time responsiveness, energy efficiency, and reliability, making them ideal for resource-constrained environments that demand high performance. This demonstrates the FPGA's advantage in smart home solutions, which combine accuracy, speed, and low power consumption. © The Institution of engineering & Technology 2025.

关键词： Convolutional neural networks

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Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

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Operations Research Forum 2024年第3期5卷 58页

作者： Wei, Li Solihin, Mahmud Iwan Saruchi, Sarah ‘Atifah Astuti, Winda Hong, Lim Wei Kit, Ang Chun School of Computer Science and Technology (School of Software) Guangxi University of Science and Technology Guangxi 545006 China Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur 56000 Malaysia Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan 26600 Malaysia Computer Engineering Department Automotive and Robotics Engineering Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Anomaly detection computer vision Deep learning Defect detection High-precision cylindrical parts Image processing Optical illumination

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Online Sexism Detection and Classification by Injecting User Gender Information

Online Sexism Detection and Classification by Injecting User...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Das, Amit Rahgouy, Mostafa Zhang, Zheng Bhattacharya, Tathagata Dozier, Gerry Seals, Cheryl D. Auburn University Department of Computer Science and Software Engineering AuburnAL United States Auburn University at Montgomery Department of Computer Science MontgomeryAL United States

ISBN: (纸本)9798350322347

This paper investigates the potential effects that user gender information has on online sexism detection, in terms of both binary and multi class detection. Social media has recently developed into a center for sexist posts that target especially women. Since most sexist comments are made especially against people of a particular gender, whether the gender information of the users could be effective or not for sexism detection, is still an important question. Here we try to address this issue using Natural Language Processing (NLP) and machine learning models. Experiments showed that combining user gender information with textual features improved classification performance both in terms of binary classification and multi class classification. The effectiveness of the proposed strategy is demonstrated by the experimental results reported in this research. © 2023 IEEE.

关键词： Classification (of information)

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