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An Attention Mechanism Approach for Natural Color Retrieval for Plant Monitoring 20

An Attention Mechanism Approach for Natural Color Retrieval ...

20th International Conference on Electrical engineering/electronics, computer, Telecommunications and Information technology, ECTI-CON 2023

作者： Chadavadh, Yanatorn Kasetkasem, Teerasit Patrapornnant, Teera Parittotakapron, Sirichai Isshiki, Tsuyoshi Kasetsart University Faculty of Engineering Department of Electrical Engineering Bangkok Thailand National Electronics and Computer-Technology Center Pathunthani Thailand Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9798350310467

Even though agriculture practices have been continuously developed with the support of modern technologies, many more improvements can be made to enhance agricultural technologies and businesses. One such technology is the use of specific light color combinations to optimize the growth rate of plants. One obvious drawback is that plants' color will change according to the light color combinations. The light color can fool human eyes and may cause errors when monitoring for plant anomalies. Color correction methods should be applied to help restore the natural plant color with the white light source from the unnaturally colored plant images. Our color correction method uses an application of self-dot-product attention, multi-head attention, and channel attention combined with a U-Net-based model. This proposed method performs the color correction with the input image in the RGB color space in two steps. First, a global transformation network provides the global function that maps the input RGB color vectors from every pixel and produces the corrected RGB color vectors. The global mapping function is the same for all pixels in the image. Next, a local transformation network attempts to correct the local color distortions such as light the flickering of LED light due to the AC power supplier. © 2023 IEEE.

关键词： Deep learning

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An empirical intelligent water irrigation system using soft computing and IoT

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International Journal of Information technology (Singapore) 2024年第6期16卷 3983-3989页

作者： Patel, Warish Sangeetha, Y. Gopagoni, Deepa Rani Mallik, D. M. Arvind Agrawal, Rakshal Mishra, Saket Sunil, M.P. Kolandaisamy, Raenu Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat India Department of Information Technology VR Siddhartha Engineering College Andhra Pradesh Vijayawada India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India PES Institute of Technology and Management Karnataka Shivamogga India Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India Centre of Interdisciplinary Research in Business and Technology Chitkara Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka Bengaluru India Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia

Water loss from fruit and vegetable production is mostly caused by leakage in the transportation system and handling of land, as well as by using traditional processing techniques. In order to increase the wellness of plants, save power, and optimize consumption of water, information should be widely applied in designing intelligent irrigation systems, which is possible with the help of the Internet of Things (IoT). In the given paper, an IoT-based soft computing recommendation framework is proposed for performing irrigation in a smarter way. The system works in such a way that IoT devices are positioned in the field to gather information about crops and groundwater levels. The acquired data is then fed to a cloud repository, which makes use of soft computing techniques such as neural networks for analysing and providing effective recommendations to farmers on their smartphones. In this way, the framework is tuned and the farmers are also updated from time to time regarding judicious irrigation processes. Lastly, the performance of the proposed system is validated against traditional techniques by taking crop datasets into consideration. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Efficient water management Intelligent devices IoT Smart irrigation Soft computing

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An Effective Ensemble Learning Method for Fault Diagnosis of Photovoltaic Arrays 3rd

An Effective Ensemble Learning Method for Fault Diagnosis of...

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3rd International Conference on Electronic engineering and Renewable Energy, ICEERE 2022

作者： Mellit, Adel Boubaker, Sahbi Department of Electronics Faculty of Science and Technology University of Jijel Jijel Algeria Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (纸本)9789811962226

In this paper, a novel fault diagnosis method for photovoltaic (PV) arrays is proposed. The method combines three machine learning (ML) algorithms: the first one is an unsupervised ML algorithm (principal component analysis, ‘PCA’) used for features reduction;the second one is a kind of a recurrent neural networks (long short-term memory, ‘LSTM’) employed to predict the PV power;and the third one is a supervised ensemble learning categorical boosting algorithm (CatBoost), used mainly for fault classification improvement. Four types of faults (dust deposit on PV modules surface, partial shading effect, shunted diode and open-circuit diode) are examined in this study. Results showed the effectiveness of the proposed method in terms of detection and classification accuracy, 98.70% and 98.58% respectively, and demonstrate the superiority of this method over other investigated ML algorithms (e.g. decision tree, support vector machine, k-nearest neighbours, and random forest). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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Federated learning-based lightweight selective feature fusion and irregular-aware network for crack detection

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Optoelectronics Letters 2025年

作者： LIU Hui HE Tian CHENG Xu SHI Fan NIKOLI? Sa?a V. ZHANG Jianhua School of Computer Science and Engineering， Tianjin University of Technology Department of Electronics， Faculty of Electronic Engineering， University of Nis

Employing deep learning for pavement crack detection can significantly enhance accuracy， and federated learning can help to overcome the challenges of data silos and data security. This paper proposes a lightweight method to detect cracks. We use lightweight encoder modules to extract multi-scale features， and further feature fusion and modelling by Selective Fusion Blocks and Irregular-aware Blocks. Moreover， this method is the first to combine federated learning with crack detection， effectively resolving the tension between data privacy and data sharing in decentralised devices. Experiments were conducted to compare our proposed method with other crack detection methods on two publicly available datasets， including the original method without federated learning and five state-of-the-art methods combined with the same federated learning framework. The experiment proves that our model obtains comparable F1 score with minimal parameters and computational effort. For example， compared to the original method， the number of parameters and computation are reduced by 67.1% and 73.6%， respectively.

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Gauss Gradient and SURF Features for Landmine Detection from GPR Images

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computers, Materials & Continua 2022年第6期71卷 4457-4486页

作者： Fatma M.El-Ghamry Walid El-Shafai Mahmouad I.Abdalla Ghada M.El-Banby Abeer D.Algarni Moawad I.Dessouky Adel S.Elfishawy Fathi E.Abd El-Samie Naglaa F.Soliman Department of Electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazigSharqia44519Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Recently,ground-penetrating radar(GPR)has been extended as a well-known area to investigate the subsurface ***,its output has a low resolution,and it needs more processing for more *** paper presents two algorithms for landmine detection from GPR *** first algorithm depends on a multi-scale technique.A Gaussian kernel with a particular scale is convolved with the image,and after that,two gradients are estimated;horizontal and vertical ***,histogram and cumulative histogram are estimated for the overall gradient *** bin values on the cumulative histogram are used for discrimination between images with and without ***,a neural classifier is used to classify images with cumulative histograms as feature *** second algorithm is based on scale-space analysis with the number of speeded-up robust feature(SURF)points as the key parameter for *** addition,this paper presents a framework for size reduction of GPR images based on decimation for efficient *** further classification steps can be performed on images after *** sensitivity of classification accuracy to the interpolation process is studied in detail.

关键词： GPR images cumulative histogram gradient image neural classifier SURF

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Investigating the Ethical Implications of Artificial Intelligence and Establishing Guidelines for Responsible AI Development

Investigating the Ethical Implications of Artificial Intelli...

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2024 International Conference on Advances in Computing Research on Science engineering and technology, ACROSET 2024

作者： Sukumar, R. Gambhir, Vinima Seth, Jyoti Faculty of Engineering and Technology Department of Electronics and Communication Engineering Bangalore India Atlas SkillTech University Department of Isme Mumbai India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350388800

Now more than ever, artificial intelligence (AI) presents both unprecedented potential and difficulties. This article delves deeply into the ethical concerns raised by AI and lays forth principles that should be followed in the creation of ethical AI systems. In the opening, we recognize the revolutionary potential of AI as well as the ethical quandaries it poses. In order to guarantee that AI technologies are created and implemented in a responsible and fair way, a deep ethical examination is required in light of the proliferation of autonomous systems. To address these ethical difficulties, we suggest the adoption of an Ethical Analysis Framework (EAF). The Ethical Assessment Framework (EAF) provides a framework for systematically assessing the fairness, transparency, privacy, accountability, and bias of AI systems. It's a cornerstone for the community of AI ethics researchers, developers, and policymakers working together to keep AI honest. Our approach places special importance on the gathering and preparation of data. We emphasize how important ethically sound data is in defining the moral implications of AI. We found that when compared to other approaches to assessing the morality of AI, our suggested technique consistently outperformed the others. The suggested technique establishes itself as an innovative and effective way of navigating the growing ethical environment by providing a systematic approach to addressing AI's ethical implications and supporting responsible AI research. For instance, as compared to more conventional methods, our technique increased fairness evaluations by an average of 15%. In conclusion, there is a wide range of concerns related to the morality of AI that must be constantly monitored and addressed via joint effort. Our method places an emphasis on making sure AI is progressing in a way that is responsible, ethical, and fair to all members of society. © 2024 IEEE.

关键词： Ethical technology

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Blockchain-based color medical image cryptosystem for industrial Internet of Healthcare Things (IoHT)

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Multimedia Tools and Applications 2024年 1-55页

作者： Khallaf, Fatma El-Shafai, Walid El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Ahram Canadian University 6th October City Giza Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

In recent years, the proliferation of smart devices and associated technologies, such as the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Internet of Medical Things (IoMT), has witnessed a substantial growth. However, the limited processing power and storage capacity of smart devices make them vulnerable to cyberattacks, rendering traditional security and cryptography techniques inadequate. To address these challenges, blockchain (BC) technology has emerged as a promising solution. This study introduces an efficient framework for the Internet of Healthcare Things (IoHT), presenting a novel cryptosystem for color medical images using BC technology in conjunction with the IoT, Secure Hash Algorithm 256-bit (SHA256), shuffling, and bitwise XOR operations. The encryption scheme is specifically designed for an IIoT grid network computing system, relying on diffusion and confusion principles. In this paper, the proposed cryptosystem strength is evaluated against differential attacks with several comprehensive metrics. Simulation results and theoretical analysis demonstrate the cryptosystem effectiveness, showcasing its ability to provide high levels of security and immunity to data leakage. The proposed cryptosystem offers a versatile range of technical solutions and strategies that are adaptable to various scenarios. The evaluation metrics, with approximate values of 99.61% for Number of Pixels Change Rate (NPCR), 33.46% for Unified Average Changed Intensity (UACI), and 8 for information entropy, closely align with the desired ideal outcomes. Consequently, this paper contributes to the advancement of secure and private systems for medical image encryption based on BC technology, potentially mitigating the risks associated with cyberattacks on smart medical devices. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Improving the review classification of Google apps using combined feature embedding and deep convolutional neural network model

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4257-4272页

作者： Aslam, Naila Alzamzami, Ohoud Xia, Kewen Sadiq, Saima Umer, Muhammad Bisogni, Carmen Ashraf, Imran School of Electronics and Information Engineering Hebei University of Technology Tianjin China Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Computer Science Information Technology The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Salerno Fisciano Italy Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of

Online reviews play an integral part in making mobile applications stand out from the large number of applications available on the Google Play store. Predominantly, users consider posted reviews for appropriate app selection. Manual categorization of such reviews is both inefficient and time-consuming. Therefore, automatic analysis of the sentiments of such reviews provides fast suggestions for new users and facilitates their selection of the appropriate app. However, data imbalance is a major challenge for performing class prediction of such reviews as their distribution is sparse and often leads to low accuracy. This work proposes a framework to overcome this limitation. Extensive experiments are performed using the original and balanced data with the synthetic minority oversampling technique (SMOTE) and adaptive synthetic sampling (ADASYN). Additionally, deep learning and machine learning models are evaluated using FastText, FastText Subword, global vector (GloVe), and their combinations for word representation. Baseline machine learning models, including random forest, extra tree classifier, gradient boosting, Naive Bayes, logistic regression (LR), stochastic gradient descent (SGD), and voting classifier (VC) that combines LR and SGD, are used for comparison. The outcomes show that the convolutional neural network using a combination of word embedding techniques produces the most accurate results. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolutional neural networks

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Design and Performance Analysis of a 38 GHz Microstrip Patch Antenna with Slits Loading for 5G Millimeter-Wave Communications 3

Design and Performance Analysis of a 38 GHz Microstrip Patch...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Ali, Mohammed A. M. Gaid, AbdulGuddoos S. A. Saeed, Mamoon M. Saeed, Rashid A. Taiz University Faculty of Engineering & Information Technology Dept. of Communications & Computer Taiz Yemen Faculty of Engineering Dept. of Communications & Electronics Engineering Sana'a Yemen College of Computers and Information Technology Taif University Dept. of Computer Engineering Taif21944 Saudi Arabia

ISBN: (纸本)9798350305333

In this paper, we present a compact, rectangular microstrip patch antenna with small rectangular slits designed for 5G wireless applications in the 38GHz band. The antenna is built on a Roger RT duroid 5880 substrate with a relative permittivity of 2.2, a height of 0.381 mm, and a loss tangent of 0.0009. The microstrip line technique is utilized for power feeding to the radiating patch. To enhance the antenna's performance, three slits are incorporated into the front side of the radiating patch. The design, simulations, and optimizations are conducted using the High-Frequency Structure Simulator (HFSS), while computer Simulation technology (CST) is used for validating the HFSS results. The antenna has a compact overall size of 6.12 × 6 × 0.381 mm3. Moreover, the proposed antenna exhibits impressive characteristics, including a resonance frequency of 38.67 GHz, a reflection coefficient (S11) of - 47.82 dB, and an impedance bandwidth of 2.35 GHz (37.45 GHz - 39.8 GHz). In addition, the antenna's gain varies from 7.84 dBi at the lower frequency of the operational band to 7.9 dBi at the higher frequency, with a maximum gain of 8.037 dBi at the resonance frequency. Likewise, the radiation efficiency is above 99% across the operational band. The results obtained from both simulators are in a good agreement and surpass those of many previously published antennas, indicating that the proposed antenna is a promising solution for 5G wireless communication applications. © 2023 IEEE.

关键词： Millimeter waves

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Robust and High Accuracy Algorithm for Detection of Pupil Images

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computers, Materials & Continua 2022年第10期73卷 33-50页

作者： Waleed El Nahal Hatim G.Zaini Raghad H.Zaini Sherif S.M.Ghoneim Ashraf Mohamed Ali Hassan Electronics and Communications Engineering Department Faculty of EngineeringMSA UniversityCO12585Egypt Computer Engineering Department College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Faculty of Health and Rehabilitation Sciences Princess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electrical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Electronics and Communications Engineering Department Faculty of EngineeringSinai UniversityArishCO45511Egypt

Recently,many researchers have tried to develop a robust,fast,and accurate *** algorithm is for eye-tracking and detecting pupil position in many applications such as head-mounted eye tracking,gaze-based human-computer interaction,medical applications(such as deaf and diabetes patients),and attention *** real-world conditions challenge the eye appearance,such as illumination,reflections,and *** the other hand,individual differences in eye physiology and other sources of noise,such as contact lenses or *** present work introduces a robust pupil detection algorithm with and higher accuracy than the previous attempts for real-time analytics *** proposed circular hough transform with morphing canny edge detection for Pupillometery(CHMCEP)algorithm can detect even the blurred or noisy images by using different filtering methods in the pre-processing or start phase to remove the blur and noise and finally the second filtering process before the circular Hough transform for the center fitting to make sure better *** performance of the proposed CHMCEP algorithm was tested against recent pupil detection *** and results show that the proposed CHMCEP algorithm achieved detection rates of 87.11,78.54,58,and 78 according to´Swirski,ExCuSe,Else,and labeled pupils in the wild(LPW)data sets,*** results show that the proposed approach performs better than the other pupil detection methods by a large margin by providing exact and robust pupil positions on challenging ordinary eye pictures.

关键词： Pupil detection eye tracking pupil edge morphing techniques eye images dataset

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