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Food ingredient recognition model via image and textual feature extraction and hybrid classification strategy

International Journal of Reasoning-based Intelligent Systems 2024年第1期16卷 74-90页

作者： Madival, Sharanabasappa A. Jawaligi, Shivkumar S. Department of Computer Science and Engineering Faculty of Engineering and Technology Sharnbasva University India Department of Electronic and Communication Engineering Faculty of Engineering and Technology Sharnbasva University Karanataka Kalburgi India

This research work focuses on food recognition, especially, the identification of the ingredients from food images. Here, the developed model includes two stages namely: 1) feature extraction;2) classification. Initially, the image features and textual features will be extracted, where image features like SIFT and improved CNN-based deep features, textural features are extracted. Then, the hybrid classifier is used for the identification of food ingredients that combines the models like neural network (NN) as well as long short-term memory (LSTM). In order to make the accurate results, the weights of NN and LSTM are fine-tuned via the Chebyshev map evaluated teamwork optimisation (CME-TWO) algorithm. At the final stage, the primacy of the offered scheme is proven concerning varied metrics. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Feature extraction

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Dielectric Material Characterization for Reflectarray Antennas Designed at Sub-6GHz and Millimeter Wave Bands of 5G 16

Dielectric Material Characterization for Reflectarray Antenn...

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16th IEEE Malaysia International Conference on Communication, MICC 2023

作者： Khan, Sher Dali Abbasi, Muhammad Inam Ibrahim, Imran Mohd Sohu, Izhar Ahmed Khan, Sanaullah Faculty of Electrical and Electronic Engineering Technology Malaysia Faculty of Electronic and Computer Engineering Malaysia

ISBN: (纸本)9798350304343

The future compliance of reflectarray antenna necessitates a methodical examination of its primary traditional configurations to anticipate improvements. The current design, which utilizes microwave and millimeter wave frequencies, serves as a fundamental foundation for further investigation. This study offers a comparison and characterization of various dielectric substrates employed in Sub-6 GHz and millimeter wave reflectarray antenna, based on unit cells designed at 3.5 GHz and 28 GHz. The analysis utilizes a range of commercially accessible dielectric materials characterized by dielectric permittivity $({{\varepsilon _r}})$ values spanning 2 to 3.6 and loss tangent (tan) values ranging from 0.0009 to 0.001. The performance of different dielectric materials in the design of infinite reflectarray is assessed in terms of bandwidth and reflection loss, providing a comprehensive comparison. It is observed that the bandwidth of patch element unit cells at different levels exhibits significant variations depending on the material properties, with a 10% bandwidth range. Furthermore, it is illustrated that the reflectarray antenna's reflection loss can be deconstructed into dielectric and conductor losses, both contingent upon the material properties utilized in the design. This article presents a comparison of eleven distinct substrate materials that have the potential to result in disruptive designs for both the architecture and components, thereby impacting the performance of the reflectarray antenna. © 2023 IEEE.

关键词： Bandwidth

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Investigation of the Performance Impact of Active Layer Parameter Variations on Inverted Perovskite Solar Cells Using GPVDM 14

Investigation of the Performance Impact of Active Layer Para...

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14th IEEE Regional Symposium on Micro and Nanoelectronics, RSM 2023

作者： Aziz, A.M.A. Muniandy, S. Idris, M.I. Napiah, Z.A.F.M. Zamani, Z.B. Norddin, N.B. Rashid, M. Faculty of Electronic and Computer Engineering Malaysia Faculty of Electronic and Computer Engineering Malaysia Faculty of Electrical and Electronic Engineering Technology Malaysia School of Physics Malaysia

ISBN: (纸本)9798350323689

This research explores the performance of inverted perovskite solar cells (IPSC) using the General-purpose Photovoltaic Device Model (GPVDM) software. Alternatively, inorganic p-Type semiconductors, especially NiOx which is the most widely used HTL, can provide intrinsically higher stability and exhibit lower cost than organic polymer-based HTL. The device structure in the simulation comprises ITO/NiOx/MAPbI3/C60/BCP/Ag. Various factors, including layer thickness, electrical parameters, absorption coefficient and refractive index of each layer, can influence the simulated IPSC's performance. GPVDM provides a comprehensive simulation platform to investigate the impact of these factors on the power conversion efficiency (PCE) of IPSCs. The simulation results from GPVDM exhibit a remarkable match and good agreement with achieving efficiencies of 17.35% and 17.57%. To optimize the results, two cases are analyzed and compared. Notably in Case 2, which employs experimental data for α and n from earlier research, outperforms the preceding journal with an efficiency of 18.23% compared to 17.57%. These simulation findings serve as a valuable guide for the fabrication of IPSCs utilizing NiOx, BCP, and C60 as active layers, offering insights into enhancing their performance. © 2023 IEEE.

关键词： Perovskite solar cells

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A privacy and compliance in regulated anonymous payment system based on blockchain

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3141-3157页

作者： Elfadul, Issameldeen Wu, Lijun Elhabob, Rashad Elkhalil, Ahmed School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer and Information Engineering Qilu Institute of Technology Shandong Jinan250200 China Faculty of Computer Science and Information Technology Karary University Khartoum Sudan

Decentralized Anonymous Payment Systems (DAP), often known as cryptocurrencies, stand out as some of the most innovative and successful applications on the blockchain. These systems have garnered significant attention in the financial industry due to their highly secure and reliable features. Regrettably, the DAP system can be exploited to fund illegal activities such as drug dealing and terrorism. Therefore, governments are increasingly worried about the illicit use of DAP systems, which poses a critical threat to their security. This paper proposes Privacy and Compliance in Regulated Anonymous Payment System Based on Blockchain (PCRAP), which provides government supervision and enforces regulations over transactions without sacrificing the essential idea of the blockchain, that is, without surrendering transaction privacy or anonymity of the participants. The key characteristic of the proposed scheme is using a ring signature and stealth address to ensure the anonymity of both the sender and receiver of the transaction. Moreover, a Merkle Tree is used to guarantee government supervision and enforce regulations. Our proposed scheme satisfies most of the stringent security requirements and complies with the standards of secure payment systems. Additionally, while our work supports government regulations and supervision, it guarantees unconditional anonymity for users. Furthermore, the performance analysis demonstrates that our suggested scheme still remains applicable and effective even when achieving complete anonymity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Blockchain

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Evolutionary recurrent neural network based on equilibrium optimization method for cloud-edge resource management in internet of things

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Neural Computing and Applications 2025年第6期37卷 4957-4969页

作者： Ebrahimi Mood, Sepehr Rouhbakhsh, Adel Souri, Alireza Department of Computer Science Yazd University Yazd Iran Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China

Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most significant being resource management within the cloud environment. Specifically, the allocation of physical machines (PMs) to virtual machines (VMs) in the Infrastructure as a service (IaaS) layer aims to achieve optimal efficiency. To address this challenge, we propose an evolutionary recurrent neural network (RNN) for resource allocation, enhanced by the equilibrium optimization (EO) algorithm. This method improves the generalization performance and reduces the architecture complexity of RNNs, while ensuring better adaptability to various demand patterns in cloud environments. Additionally, the effectiveness of RNNs heavily relies on the quality of training data;hence, we generate appropriate training data to facilitate the development of an efficient resource management model. Through extensive simulations, our method demonstrates superior performance compared to traditional algorithms. However, it is worth noting that this study is primarily based on simulation results, highlighting the need for real-world validation to confirm its practical applicability. The proposed method's applicability to different cloud computing environments and scenarios, including various service models (IaaS, PaaS, and SaaS) and deployment models (public, private, and hybrid), is also briefly discussed. Future studies should explore the computational efficiency and potential optimization techniques of the EO-RNN approach and consider real-world implementations to validate its effectiveness and uncover practical challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Cloud platforms

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Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

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computer Modeling in engineering & Sciences 2024年第3期138卷 2103-2127页

作者： Othman S.Al-Heety Zahriladha Zakaria Ahmed Abu-Khadrah Mahamod Ismail Sarmad Nozad Mahmood Mohammed Mudhafar Shakir Hussein Alsariera Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka(UTeM)Durian TunggalMalaysia College of Computing&Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Electrical Electronic and System EngineeringUniversiti Kebangsaan MalaysiaUKM BangiSelangorMalaysia Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkukIraq Faculty of Engineering and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Batu PahatMalaysia 6Research Center DepartmentThe University of MashreqBaghdadIraq

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the *** this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things *** framework integrates Kalman filtering and *** smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction *** traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction *** evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art ***,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

关键词： Q-learning intelligent transportation system(ITS) traffic control vehicular communication kalman filtering smart city Internet of Things

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Neuroadaptive Containment Control for Nonlinear Multiagent Systems With Input Saturation: An Event-Triggered Communication Approach

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3163-3173页

作者： Wang, Xin Zhang, Sen Li, Huaqing Zhang, Wei Li, Hongyi Huang, Tingwen Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this article, a neuroadaptive event-triggered containment control strategy combined with the dynamic surface control (DSC) approach is proposed for nonlinear multiagent systems (MASs) with input saturation. Based on the event-triggered communication mechanisms, the updates of neural network weight and controllers are implemented solely under triggering conditions of violation, which markedly reduces unnecessary communication resources and minimizes inefficient control costs compared with the traditional control method. Radial basis function neural networks (RBF NNs) are employed to handle the nonlinear uncertainties of MASs. Simultaneously, an adaptive compensatory mechanism is incorporated within the backstepping process to address the nonlinear effect posed by input saturation. Additionally, we demonstrate the system stability by extending the Lyapunov theorem to jump and continuous scenarios while excluding Zeno behavior, realizing that all followers can enter the convex hull constructed by leaders. Finally, the effectiveness of the proposed methodology is verified through application simulations. © 2013 IEEE.

关键词： Radial basis function networks

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Enhancing Persian Word Sense Disambiguation with Large Language Models Techniques and Applications 14

Enhancing Persian Word Sense Disambiguation with Large Langu...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Arshia, Fatemeh Zahra Sadidpour, Saeedeh Sadat Faculty of Electronic & Computer Engineering Malek Ashtar University of Technology Tehran Iran

ISBN: (纸本)9798331511272

— WSD means the task of word sense disambiguation, which is a very important task in NLP. It assigns not only the meaningful word to the source text but also the proper meaning of the word according to the context. Hence, it is key to the proper accomplishment of NLP in Persian—a language rich in morphology and great polysemy. The recent improvements in LMs have greatly advanced the capabilities of NLP, opening further improvement avenues in WSD performance. This paper presents the integration of LLMs for improving WSD in Persian, considering the linguistic challenges related to this language. In this study, we consider four models of the Persian language: FaBERT, AriaBERT, GPT-2 Persian, and PersianMind-v1.0. We use the supervised fine-tuning method on the SBU-WSD-Corpus. Our methodology will consist of preprocessing the Persian WSD corpus, then fine-tuning the models with the mentioned corpus, and measuring their performance. Results indicate that methods using LLMs significantly improve WSD accuracy against traditional methods, with FaBERT achieving the best accuracy. We have further expounded on their real-life applications, such as sentiment analysis, to show the consequential effect of this advancement on general NLP tasks. The study is concluded with some insights into future research directions, underlining the potential role that LLMs can play in further transforming WSD and related fields. ©2024 IEEE.

关键词： Economic and social effects

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Graphene oxide-coated microbottle resonators for relative humidity sensing

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Optoelectronics Letters 2024年第7期20卷 400-405页

作者： Huda Adnan Zain Malathy Batumalay Md Ashadi Md Johari Hazli Rafis Abdul Rahim Sulaiman Wadi Harun Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur 50603Malaysia Faculty of Data Science and Information Technology INTI International UniversityNilai 71800Malaysia1 Faculty of Electrical and Electronic Engineering Technology Universiti Teknikal Malaysia MelakaMelaka 76100Malaysia Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia MelakaMelaka 76100Malaysia

Graphene oxide(GO)is a 2D coating material used to improve fiber optics sensors’response to relative *** resonators(MBRs)have garnered more attention as sensing media *** MBR with a 190μm diameter was coated with ***,tapered fiber light coupling was used to investigate the relative humidity sensing performance in the range of 35—70%RH at 25℃.The MBR showed a higher Q factor before and after GO *** sensitivity of 0.115 dB/%RH was recorded with the 190μm GO-coated MBR sample compared to a sensitivity of 0.022 dB/%RH for the uncoated MBR *** results show that the MBR can be used in fiber optic sensing applications for environmental sensing.

关键词： humidity oxide bottle

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Efficient Saliency Map Detection for Low-Light Images Based on Image Gradient

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IEEE Transactions on Circuits and Systems for Video technology 2024年第2期34卷 852-865页

作者： Lin, Chun-Yi Haq, Muhamad Amirul Chen, Jiun-Han Ruan, Shanq-Jang Naroska, Edwin National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan Hochschule Niederrhein University of Applied Science Faculty of Electrical Engineering and Computer Science Krefeld47805 Germany

Recently, deep learning has been widely employed across various domains. The Convolution Neural Network (CNN), a popular deep learning algorithm, has been successfully utilized in object recognition tasks, such as face recognition, vehicle recognition, and license plate recognition. However, conventional methods for object recognition may not be appropriate for low-light image recognition due to information loss in the dark regions and unexpected noise that can impair object quality. Therefore, the development of specialized techniques for low-light image enhancement has become a major research focus for object detection. This paper proposed a gradient-based saliency map detection method with an improved ResNet architecture that outperforms previous works in detecting multiple or large objects. Additionally, the proposed method enhances images with the object as the center and emphasizes foreground-background differences. Compared with previous works, this paper achieves1.28× improvements in the parameters and 1.32× faster inference speed than the original ResNet architecture. © 1991-2012 IEEE.

关键词： Neural networks

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