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2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Dudjak, Mario Zoric, Bruno Bajer, Drazen Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350347722

Model selection is an essential step when applying machine learning to classification problems. It is typically carried out by the practitioner who strives to identify the most suitable classifier for a given problem. Given the variety of classifiers available and the difficulty in predicting which one will yield the best performance depending on the characteristics of the problem, this is by no means a simple task. Biomedical problems pose a significant challenge in this regard due to their numerous data intrinsic characteristics that are known to impair classification performance. Given that different classifiers perform well for different biomedical problems, combining them into an ensemble would seem practical. However, the practitioner still needs to determine how to combine them. This paper presents an ensemble-based framework that automates the training and combination of different classifiers in order to relieve practitioners of this burden whilst obtaining highly competitive performance. The effectiveness of the proposed framework was evaluated on several biomedical problems from the literature. © 2023 IEEE.

关键词： Optimization

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Secret Key Optimization for Secure Speech Communications

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computers, Materials & Continua 2022年第8期72卷 3025-3037页

作者： Osama S.Faragallah Mahmoud Farouk Hala S.El-Sayed Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Electrical Engineering Faculty of EngineeringMenoufia UniversityShebin El-Kom 32511Egypt

This paper answers three essential questions for audio speech cryptosystems in time and discrete transform *** first question is,what are the best values of sub-keys that must be used to get the best quality and security for the audio cryptosystem in time and discrete transform *** second question is the relation between the number of sub-keys,the number of secret keys used,and the audio speech signal block’s ***,how many possible secret keys can be used to get the best quality and security results for the audio speech cryptosystem in time and discrete transform *** audio cryptosystem discussed before in recent research is applied to answer the three previous *** simulation results and analysis answer all three questions;first,there is no specific,well-defined format or rule for sub-key values that must be used to get a better *** the second question,yes,there is a direct relationship between the number of applicable keys,number of available sub-keys,and block size of speech audio signal and formulated into a first-order *** the third question,each discrete transform domain has a specific acceptable range of sub-keys that imply a particular number of keys that can be used to get a better cryptosystem.

关键词： Audio cryptosystem chaotic baker map secret key size

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Smart Drive Safe: Harnessing CNN for Enhanced Traffic Sign Recognition and Voice Alerts 5

Smart Drive Safe: Harnessing CNN for Enhanced Traffic Sign R...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Priya, D. Divya Chinnasamy, P. Ayyasamy, Ramesh Kumar Kiran, Ajmeera Jalil, Norazira Binti A. Sangodiah, Anbuselvan Mlr Institue of Technology Department of Computer Science Engineering Hyderabad India School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Srivilliputtur India Faculty of Information and Communication Technology Perak Kampar Malaysia Quest International University Faculty of Computing and Engineering Perak Ipoh Malaysia

ISBN: (纸本)9798350379945

Traffic signs secure their safety on the roads and the protection of others around them;everyone must be aware of traffic signs and other aspects of road safety. Traffic sign detection is a highly relevant computer vision problem that forms the basis of numerous applications in sectors like the automotive industry. Between classes, traffic signs differ widely in terms of colour, shape, and the use of pictograms or text. The project's classification of traffic signs in an image into various categories is based on a deep neural network model. IoT devices that capture traffic signs and notify users of them are used to build a model. © 2024 IEEE.

关键词： Automotive industry

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Intent Classification of Nepali Factoid Questions

Intent Classification of Nepali Factoid Questions

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Timilsina, Sushil Khadka, Ashim Lin, Jianchu Faculty of Science and Technology Gandaki College of Engineering and Science Pokhara Nepal Faculty of Science and Technology Nepal College of Information Technology Lalitpur Nepal Faculty of Computer and Software Engineer Huaiyin Institute of Technology Jiangsu China

ISBN: (纸本)9798350306293

Question Classification plays a vital role in identifying the accurate answer to any question, and is considered as a core component of Question Answering Systems. In Nepali Natural Language Processing, the area of Question Answering has not even been initiated due to a lack of dataset. The dataset of Nepali factual questions has been created by collecting questions from general knowledge books and labelling them with developed taxonomy. The problem of Nepali factoid question classification has been studied with a Support Vector Machine, which works as a baseline classification algorithm and obtained a 0.74 weighted F1-score in the imbalance dataset. The experimental results of the proposed model show the effectiveness of the approach in classifying factoid questions accurately compared with the state-of-the-art approaches. © 2023 IEEE.

关键词： Natural language processing systems

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Leveraging AI to Unlock the Potential of Data Science 15

Leveraging AI to Unlock the Potential of Data Science

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

作者： Yadav, Rakesh Kumar Sudhamsu, Gadug Goyal, Manish Kumar Yuvaraj, S. Deshpande, Leena A. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

The application of synthetic Intelligence (AI) to records science has spread out a massive array of new possibilities. Via the combination of advanced evaluation strategies, effective understanding-pushed system gaining knowledge of algorithms, and modern computing architectures, AI is allowing researchers to quick and as it should be discover insights from huge and complicated datasets. Furthermore, AI tools are offering corporations with the ability to make higher and more knowledgeable choices from the evaluation of hundreds of thousands of statistics points. The skills of information technology powered by means of AI are revolutionizing industries which includes healthcare, finance, and manufacturing-producing savings, uncovering efficiencies, and identifying new opportunities. As AI-driven records technology maintains to strengthen, organizations that capitalize on its ability stand to gain precious insights and unlock new degrees of overall performance. © 2024 IEEE.

关键词： Smart manufacturing

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IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with Deep Learning

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computers, Materials & Continua 2023年第2期74卷 3085-3100页

作者： Saeed Masoud Alshahrani Fatma S.Alrayes Hamed Alqahtani Jaber S.Alzahrani Mohammed Maray Sana Alazwari Mohamed A.Shamseldin Mesfer Al Duhayyim Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Nowadays,Internet of Things(IoT)has penetrated all facets of human life while on the other hand,IoT devices are heavily prone to *** has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security *** is one of the dreadfulmalicious entities that has affected many users for the past few *** is challenging to recognize Botnet since it has excellent carrying and hidden *** approaches have been employed to identify the source of Botnet at earlier *** Learning(ML)and Deep Learning(DL)techniques are developed based on heavy influence from Botnet detection *** spite of this,it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet *** current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizingRat SwarmOptimizer with Deep Learning(BDC-RSODL)*** presented BDC-RSODL model includes a series of processes like pre-processing,feature subset selection,classification,and parameter ***,the network data is pre-processed to make it compatible for further ***,RSO algorithm is exploited for effective selection of subset of ***,Long Short TermMemory(LSTM)algorithm is utilized for both identification and classification of ***,Sine Cosine Algorithm(SCA)is executed for fine-tuning the hyperparameters related to LSTM *** order to validate the promising 3086 CMC,2023,vol.74,no.2 performance of BDC-RSODL system,a comprehensive comparison analysis was *** obtained results confirmed the supremacy of BDCRSODL model over recent approaches.

关键词： Internet of things cloud computing long short termmemory deep learning sine cosine algorithm feature selection

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Reconfigurable Intelligent Surface Direct Data Modulation With Adaptive Beam Steering Backscatter Communication

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IEEE Internet of Things Journal 2025年第11期12卷 15600-15619页

作者： Amri, Muhammad Miftahul Aziz, Arif Abdul Tran, Nguyen Minh Park, Je Hyeon Kim, Dong In Choi, Kae Won Sungkyunkwan University College of Information and Communication Engineering Department of Electrical and Computer Engineering Suwon16419 Korea Republic of University Center of Excellence for Intelligent Sensing-IoT Telkom University Bandung40257 Indonesia VNU University of Engineering and Technology Faculty of Electronics and Telecommunications Department of Wireless Communications Hanoi100000 Viet Nam

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for next-generation wireless communications due to its ability to manipulate electromagnetic (EM) waves. This work introduces a novel RIS direct modulation scheme based on the backscatter communication concept. In this work, the RIS acts as a sole information modulator. Unlike conventional RIS systems that relay incoming datacarrying waves, this work enables RIS to encode its own information into unmodulated waves without requiring any hardware modifications. We propose an adaptive algorithm that maximizes modulation order while meeting the quality of service (QoS) requirements by selecting appropriate constellation points to minimize error probability. The proposed scheme was validated through simulations and experiments using a passive 5.8 GHz RIS prototype. The validations demonstrate consistent performance in both distributed and co-located transmitter- receiver scenarios. These results highlight its feasibility as an alternative approach for RIS-assisted communications and contribute to further exploration of RIS-enabled symbiotic radio (SR) and over-the-air (OTA) modulation systems. © 2014 IEEE.

关键词： Adaptive algorithms

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Propagation of EM Waves Through Biomaterial Based on Harvest Waste - Influence of Biomaterial Density, Moisture and Temperature 33rd

Propagation of EM Waves Through Biomaterial Based on Harvest...

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33rd International Conference on Organization and technology of Maintenance, OTO 2024

作者： Mandrić, Vanja Rupčić, Slavko Pavković, Bogdan Bogdanović, Nataša Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia Department of Information and Communication Technologies Academy of Applied Technical and Preschool Studies Niš Serbia

ISBN: (纸本)9783031805967

This paper deals with the simulation calculation of the transmission (S21) parameter in a biomaterial calculated parametrically on the basis harvest waste (residues) as a function of the density, moisture and temperature of the material. Since this material is essentially wood, empirical and semi-empirical models are used to determine the electromagnetic parameters of the wood material as a function of the density, moisture and temperature of the material according to the available literature. The structural - geometric and environmental parameters of the material that are the subject of the research are: density, moisture and temperature of the material, and the electromagnetic parameters that "describe" them are: relative permittivity Εr, loss tangent tan δ and electrical conductivity σ. The simulation model is based on a system for measuring coupling parameters in free space, which consists of two antennas, between which a 600 × 600 mm sample of the biomaterial is placed, and a network analyzer. With increasing density and moisture significantly increases the relative dielectric constant, the loss tangent and the electrical conductivity of the measured biosamples. The temperature change in a wide temperature range (20 ℃–100 ℃) has no significant influence on the electromagnetic parameters. The measured results were used to verify the simulation model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electric conductivity

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A Coarse to Fine Detection Method for Prohibited Object in X-ray Images Based on Progressive Transformer Decoder 24

A Coarse to Fine Detection Method for Prohibited Object in X...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Ma, Chunjie Du, Lina Gao, Zan Zhuo, Li Wang, Meng Shandong Jinan China School of Computer Science and Technology Shandong Jianzhu University Shandong Jinan China Faculty of Information Technology Beijing University Of Technology Beijing China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9798400706868

Currently, Transformer-based prohibited object detection methods in X-ray images appear constantly, but there are still some shortcomings such as poor performance and high computational complexity for prohibited object detection with heavily occlusion. Therefore, a coarse to fine detection method for prohibited object in X-ray images based on progressive Transformer decoder is proposed in this paper. Firstly, a coarse to fine framework is proposed, which includes two stages: coarse detection and fine detection. Through adaptive inference in stages, the computational efficiency of the model is effectively improved. Then, a position and class object queries method is proposed, which improves the convergence speed and detection accuracy of the model by fusing the position and class information of prohibited object with object queries. Finally, a progressive Transformer decoder is proposed, which distinguishes high and low score queries by decreasing confidence thresholds, so that high-score queries are not affected by low-score queries in the decoding stage, and the model can focus more on decoding low-score queries, which usually correspond to prohibited object with severe occlusion. The experimental results on three public benchmark datasets (SIXray, OPIXray, HiXray) demonstrate that compared with the baseline DETR, the proposed method achieves the state-of-the-art detection accuracy with a 21.6% reduction in model computational complexity. Especially for prohibited objects with heavily occlusion, accurate detection can be carried out. © 2024 ACM.

关键词： Decoding

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TinyML-Based Gait Recognition for User Detection on an STM32L4 IoT Node 9

TinyML-Based Gait Recognition for User Detection on an STM32...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Hassan, Hateem Abdullah, M. Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Hussain, Usman Khan, Zohaib Ahmed Javaid, Arslan University Lahore Department of Computer Engineering Information Technology Punjab Pakistan Bahria University Lahore Campus Dept. of Computer Science Pakistan University of Central Punjab Lahore Pakistan Faculty of Information Technology Department of Computer Science Pakistan National College of Business Administration And Economics Department of Computer Science Lahore Pakistan Information Technology University Lahore Pakistan University of Home Economics Pakistan

ISBN: (纸本)9798350394474

Gait recognition stands out as a robust biometric tool for discerning users. Yet, adapting it to devices with limited resources, such as the STM32L4 microcontroller, demands cutting-edge strategies. This research unveils an innovative TinyML methodology, leveraging a specially-designed Convolutional Neural Network (CNN). Notably, our bespoke CNN model showcased a remarkable 97.3% accuracy in pinpointing users on the STM32L4 system. © 2024 IEEE.

关键词： Convolutional neural networks

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