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2nd International Conference on Smart Cities, Automation and Intelligent Computing Systems, ICON-SONICS 2021

作者： Andarini, Rahmi Widjaja, Moeljono Halim, Dareen Kusuma Universitas Multimedia Nusantara Faculty of Engineering and Informatics Deparment of Engineering Physics Tangerang Indonesia Universitas Multimedia Nusantara Faculty of Engineering and Informatics Department of Informatics Tangerang Indonesia Universitas Multimedia Nusantara Faculty of Engineering and Informatics Department of Computer Engineering Tangerang Indonesia

ISBN: (纸本)9781665449922

The Heating, Ventilation and Air Conditioning (HVAC) System is a significant energy user of a building. The main factor that affects the energy consumption of the HVAC system is indoor air temperature, which also will affect the relative humidity. Therefore, monitoring indoor air temperature and relative humidity is not only useful for controlling the energy consumption, but also important to maintain the comfort condition of the occupant. This research aims to model a thermal building condition in classrooms and then validate the model by the data from a monitoring system. Firstly, thermal building simulation was developed in order to find the profile of indoor air temperature and relative humidity of a classroom. Afterward, the results of thermal building modeling were compared to the reading of the monitoring system. It is found that the results of thermal building simulation and the reading of the monitoring system show a similar trend. © 2021 IEEE.

关键词： Energy utilization

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Noise Reduction in Industry Based on Virtual Instrumentation

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computers, Materials & Continua 2021年第10期69卷 1073-1096页

作者： Radek Martinek Rene Jaros Jan Baros Lukas Danys Aleksandra Kawala-Sterniuk Jan Nedoma Zdenek Machacek Jiri Koziorek Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical EngineeringVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia Faculty of Electrical Engineering Automatic Control and InformaticsOpole University of TechnologyOpolePoland Faculty of Electrical Engineering and Computer Science Department of TelecommunicationsVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia

This paper discusses the reduction of background noise in an industrial environment to extend *** the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G *** study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a *** experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from *** tested a hybrid algorithm for noise reduction and its impact on voice command recognition *** virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested *** welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+*** workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

关键词： 5G hybrid algorithms signal processing speech recognition

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Calibrated Mathematical Model of Rotary Vane Pumps Under Healthy and Faulty Conditions in Water Desalination Applications Employing Machine Learning

SSRN

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SSRN 2024年

作者： Abdel Fatah, Amr A. Sorour, Sherif Samy Pester, Andreas Samir, Engy El Seoud, M. Samir Abou The British University in Egypt Faculty of Engineering Egypt The British University in Egypt Faculty of Informatics and Computer Science Egypt The British University in Egypt Faculty of Informatics and Computer Science 11873 Suez-Desert Road P/box 43 Sherouk Egypt

This paper introduces a mathematical model designed to analyze the performance of rotary vane pumps used in brackish water Reverse Osmosis desalination applications, under both healthy and faulty conditions. We employed various machine learning algorithms, especially K nearest neighbor and Large-Short-Term-Memory to calibrate and validate the model against empirical data, enhancing its predictive accuracy and reliability. The model's ability to detect and classify faults is demonstrated through simulated scenarios, reflecting potential real-world operational challenges. This research not only advances understanding of pump mechanics and dynamics but also proposes a methodological framework for integrating machine and deep learning into traditional engineering practices, providing a data-based tool for improving the efficiency and maintenance of hydraulic systems. The developed mathematical model of the vane pump is new and presented for the first time. © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6315-6332页

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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WSports-50: An Image Dataset for Women’s Sport Action Classification 4th

WSports-50: An Image Dataset for Women’s Sport Action Class...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Ray, Palash Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Nasipuri, Mita Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India Department of Computer Science and Information Systems BITS Pilani Rajasthan Pilani India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India

ISBN: (纸本)9789819726134

Sport action recognition is an interesting area in computer vision. Categorization of sport actions, representing difficult and complex body postures, is regarded as a fine-grained visual classification problem. The Convolutional Neural Networks (CNNs) have attained enhanced performance over conventional feature descriptors in recognizing various sport activities. In general, though decent improvement has been gained using deep learning for sport action recognition, however, recognition of women’s sport activities is not widely explored. Even, no benchmark dataset depicting women’s sport action with sufficient variations is available yet for study. Hence, fine-grained image classification of diverse sport categories involving female/women athletics requires immediate research attention. To overcome this limitation, this paper proposes an image dataset comprising worldwide popular 50 sport categories of women players only. A simple deep learning model is proposed that extracts the high-level deep features using a backbone CNN. Then, these features are pooled from a collection of regular regions representing local discriminative information. The spatial pyramid pooling is applied for mining semantic information and enhancing feature aggregation for classification. The proposed method has achieved satisfactory performance on the Women Sports dataset using four standard backbone CNNs. Moreover, our method has achieved better accuracy on the Yoga-82 pose recognition dataset with a significant margin, e.g., 11.6% gain using ResNet-50 base CNN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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Practical big data techniques for end-to-end machine learning deployment: a comprehensive review

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Discover Data 2025年第1期3卷 1-18页

作者： El-Sayed, Ahmed Abougabal, Mohamed Lazem, Shaimaa Computer and Systems Engineering Department Faculty of Engineering Alexandria University Alexandria Egypt Informatics Research Institute City of Scientific Research and Technological Applications New Borg El-Arab Egypt

In recent years, data generated from diverse sources has grown exponentially, giving rise to new challenges for processing and analysis under the umbrella of “big data". Although various methods and platforms have been proposed to tackle these issues, there is still a need for a conceptual guide that synthesizes state-of-the-art tools and techniques across the full machine learning (ML) workflow. In this survey, we consolidate research on big data infrastructures, distributed processing frameworks, and ML methods, mapping out an end-to-end conceptual framework that can serve as a reference architecture for end-to-end deployment. Specifically, we discuss key aspects of big data analytics (storage solutions, platforms like Hadoop and Spark, NoSQL databases), preprocessing approaches (dimensionality reduction, instance selection, noise handling), ML algorithms (supervised, unsupervised, and emerging deep learning paradigms), and deployment considerations (monitoring, continuous integration, and versioning). By gathering and integrating these elements, our survey provides a comprehensive overview of existing solutions, clarifies the design choices developers must consider, and identifies research gaps that remain to be addressed.

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An Enhancement of DES, AES Based on Imperceptibility Along with LSB

An Enhancement of DES, AES Based on Imperceptibility Along w...

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2022 International Seminar on Application for Technology of Information and Communication, iSemantic 2022

作者： Sari, Christy Atika Wahyu Utomo, Danang Sari, Wellia Shinta Sinaga, Daurat Doheir, Mohamed Dian Nuswantoro University Faculty of Computer Science Department of Informatics Engineering Semarang Indonesia University Teknikal Malaysia Melaka Melaka Malaysia

ISBN: (数字)9781665488396

ISBN: (纸本)9781665488396

A data or information becomes important if only certain people can receive the information. For example, keeping a confidential documentation file. Files stored do not guarantee to be free from access and abuse by crackers such as wanting to modify or distribute the contents of the file on the basis of providing benefits for them. The security of information can be increased by using cryptography to encrypt messages into a secret cipher. The steganographic method is a method used to protect messages by inserting them into other media. In this study, a combination of cryptographic algorithms, namely Data Encryption Standard and Advanced Encryption Standard, was used for the Least Significant Bit steganography method. DES has the advantage of using S-Box. DES is not immune to brute force attacks and has a key length of only 56 bits, thus it is necessary to optimize the DES algorithm. AES has a more complicated key randomization process using a certain loop where AES is an extension of DES. On the other hand, to obtain a high imperceptibility value, LSB is used. The results of the combination of 2 cryptographic algorithms with steganography were tested with parameters such as PSNR and MSE which got values of 73.66 dB and 0.016, which means they are ideal values for stego images that have a high level of similarity with the original image. Encryption and decryption processing, the best time is 0.438 seconds with various message file sizes used. Meanwhile, in the embedding results, the image file size is slightly larger. © 2022 IEEE.

关键词： Cryptography

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Comparison of Uzbek-Turkish Inflection Suffixes 7

Comparison of Uzbek-Turkish Inflection Suffixes

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7th International Conference on computer Science and engineering, UBMK 2022

作者： Adali, Ecref Rajabovich, Mengliyev Bakhtiyor Mirdjonovna, Khamroyeva Shahlo Istanbul Technical University Computer Engineering and Informatics Faculty Istanbul Turkey Tashkent State University Uzbek Language and Literature Tashkent Uzbekistan

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

Creating a parallel corpus is one of the necessary resources for machine translation and can he considered as the first translation dictionary. However, it is clear that a successful translation cannot he made with a translation dictionary alone;The morphological features of the languages should also he added to the compilation. Therefore, in this paper, we present comparatively the morphology of the Uzbek and Turkish languages, which we have prepared especially in order to create a parallel corpus of Uzhek-Turkish languages and make machine translations, in terms of inflectional suffixes. © 2022 IEEE.

关键词： Computational linguistics

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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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