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Bilateral Coupled Epsilon Negative Metamaterial for Dual Band Wireless Communications

computers, Materials & Continua 2022年第4期71卷 1263-1281页

作者： Md Mhedi Hasan Mohammad Tariqul Islam Md Moniruzzaman Mohd Hafiz Uddin Norsuzlin Binti Mohd Sahar Md Samsuzzaman Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaSelangor43600Malaysia Space Science Centre Institute of Climate ChangeUniversiti Kebangsaan MalaysiaSelangor43600Malaysia Department of Computer and Communication Engineering Faculty of Computer Science and EngineeringPatuakhali Science and Technology UniversityPatuakhali8602Bangladesh

This work presents a dual band epsilon negative(ENG)metamaterial with a bilateral coupled split ring resonator(SRR)for use in C and X band wireless communication *** traditional split-ring resonator(SRR)has been amended with this engineered *** proposed metamaterial unit cell is realized on the 1.6 mm thick FR-4 printed media with a dimension of 10×10 *** resonating patch built with a square split outer *** interlinked inner rings are coupled vertically to the outer ring to extend its electrical length as well as to tune the resonance *** simulation is performed using CST studio suite 2019 to design and performance *** transmission coefficient(S21)of the proposed unit cell and array configuration exhibits two resonances at 6.7 and 10.5 GHz with wide bandwidth extended from 4.86 to 8.06 GHz and 10.1 to 11.2 GHz,*** permittivity is noted at frequencies between 6.76–9.5 GHz and 10.5–12 GHz,consecutively,with near-zero refractive index and *** optimal EMR value depicts the compactness of the proposed *** 1×2,2×2 and 4×4 arrays are analyzed that shows similar response compared to the unit *** results of the 2×2 array shows the close similarity of S21 response as compared to *** observed properties of the proposed unit cell ascertain its suitability for wireless communications by enhancing the gain and directivity of the antenna system.

关键词： Metamaterial epsilon negative near-zero index split ring resonator wireless communications

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Detection and Identification of Tomato Disease Based on Deep Learning and Machine Learning algorithms 1

Detection and Identification of Tomato Disease Based on Deep...

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1st IEEE Afro-Mediterranean Conference on Artificial Intelligence, AMCAI 2023

作者： Boukhris, Abdelouafi Antari, Jilali Asri, Hiba Sadiq, Abderrahmane Ibn Zohr University Laboratory of Computer Systems Engineering Mathematics and Applications Polydisciplinary Faculty of Taroudant Taroudant Morocco LISI Laboratory Faculty of Sciences Semlalia Department of Computer Sciences Marrakech Morocco

ISBN: (纸本)9798350394689

Plant diseases negatively affect crop production;the quality and the quantity of crop production are being interrupted. Identifying diseases manually is a hard task and time consuming, also the majority of farmers don't have the necessary knowledge to do this operation. So, we need agriculture engineer which are rare. To address these issues, we need an accurate and automated detection system for crop disease. A new technique based on deep learning to detect crop disease is required. In this paper, we propose a new technique called ALS-NN (Autoencoder Latent Space-Neural network) which combines two models Autoencoder and Neural Network. The Autoencoder compress data into Latent Space representation, and the Neural Network is used for classification based on compressed data. The dataset used in this paper is PlantVillage with 4 types of Tomato diseases. Our system achieved 91% accuracy. © 2023 IEEE.

关键词： Deep learning

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A morphological word embedding for Arabic language 6

A morphological word embedding for Arabic language

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6th International Conference on AI in Computational Linguistics, ACLing 2024

作者： Masbah, Abdelouahab Ababou, Nabil Mazroui, Azzedine Computer Science Research Laboratory Faculty of Sciences Mohammed First University Oujda Morocco Systems Engineering Laboratory Faculty of Science and Technology Sultan Moulay Slimane University Beni Mellal Morocco

Word embedding is a technique for representing words as dense real vectors and is crucial for most natural language processing tasks. Common approaches include non-contextual embeddings such as Word2vec and contextual embeddings like BERT, both of which operate on the principle that words appearing in similar contexts have close vector representations. Despite their effectiveness, these methods often fail to capture some morphological information useful in many natural language processing applications. In this study, we propose an original word embedding of Arabic words based on the morphological characteristics of the words. Tests show that integrating this morphology-based word embedding into a topic detection model outperforms models using Word2vec or AraBERT embeddings. © 2024 Elsevier B.V.. All rights reserved.

关键词： Embeddings

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Evaluating Pretrained Deep Learning Models for COVID-19 Detection in Medical Imaging 1

Evaluating Pretrained Deep Learning Models for COVID-19 Dete...

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1st Renewable Energy Technologies and Modern Communications systems: Future and Challenges Conference, RETMCS 2024

作者： AL-Shehri, Bushra A. Sabbeh, Sahar F. Dessouky, Mohamed M. Department of Data Science College of Computer Science and Engineering University of Jeddah Jeddah23218 Saudi Arabia Faculty of Computers and Artificial Intelligence Information Systems Department Benha University Benha13518 Egypt Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah23218 Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

ISBN: (纸本)9798331519445

COVID-19 is a respiratory disease for which reverse transcription-polymerase chain reaction (RT-PCR) is the standard detection method. This study introduces a hybrid deep learning approach to support the diagnosis of respiratory abnormalities, using COVID-19 as a case study. Multiple datasets were utilized, including the COVID-19 Radiography Dataset (Kaggle), the SARS-CoV-2 CT-Scan Dataset, and proprietary data from the King Faisal Medical Complex. Seven pre-trained deep learning models—VGG16, VGG19, MobileNet, DenseNet201, ResNet50, InceptionResNetV2, and InceptionV3—were evaluated for their performance. InceptionResNetV2 and InceptionV3 achieved perfect accuracy (100%) on the COVID-19 Radiography Dataset and 99% accuracy on the SARS-CoV-2 CT-Scan Dataset. Additionally, augmentation techniques were explored to improve model robustness. This research contributes significantly to medical imaging, offering practical insights for advancing diagnostic and monitoring practices for COVID-19 and other respiratory conditions. ©2024 IEEE.

关键词： COVID-19

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A Run-Length and Discrete Cosine Transform Based Technique for Image Splicing Detection 3

A Run-Length and Discrete Cosine Transform Based Technique f...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Arafa, Tamer K. Elgendi, Basem I. Shaheen, Samir I. Itcs School Nile University Cairo Egypt Computer and Systems Department Electronics Research Institute Cairo Egypt Cairo University Faculty of Engineering Computer Engineering Dept Cairo Egypt

ISBN: (纸本)9798350306231

Digital images have emerged as the most popular means for sharing information in articles, newspapers, and even courtrooms. However, the widespread use of advanced digital imaging tools has made it easier to forge images. One such technique is image splicing, where multiple source images are merged into a single destination image to conceal or alter its content. Image splicing is an effective forgery technique, as it is difficult to detect by the naked eye. Detection of image splicing is a pattern recognition problem, based on finding image features that are sensitive to splicing. In this paper, we present a new image splicing detection technique based on Run-length features and discrete cosine transform (DCT). Our proposed technique uses support vector machine (SVM) to classify authentic and spliced images. We used SVM instead of generative models to reduce energy consumption and carbon footprint in machine learning applications. Experimental results show that our technique achieves a detection accuracy of 85%, outperforming other steganalysis-based techniques. © 2023 IEEE.

关键词： Discrete cosine transforms

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2269-2291页

作者： Zefeng Zheng Luyao Teng Wei Zhang Naiqi Wu Shaohua Teng the School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China the School of Information Engineering Guangzhou Panyu PolytechicGuangzhou 511483China the Faculty of Information Technology Monash University20 Exhibition Walk ClaytonVIC 3800Australia IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macao University of Science and TechnologyMacao 999078China

Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by ***,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of *** address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target *** analyses show that DDS avoids repeated sampling during the *** the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly *** addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA *** experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.

关键词： Cross-domain risk dual density sampling intra-domain risk maximum mean discrepancy knowledge transfer learning resource-limited domain adaptation

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Uncertainty propagation in the internet of things

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Discover Internet of Things 2024年第1期4卷 1-20页

作者： Pal, Shantanu Khalifa, Sara Miller, Dimity Dedeoglu, Volkan Dorri, Ali Ramachandran, Gowri Moghadam, Peyman Kusy, Brano Jurdak, Raja The School of Information Technology Faculty of Science Engineering and Built Environment Deakin University MelbourneVIC 3125 Australia The School of Information Systems Faculty of Science Queensland University of Technology BrisbaneQLD4000 Australia The School of Electrical Engineering and Robotics Faculty of Engineering Queensland University of Technology BrisbaneQLD4000 Australia The Distributed Sensing Systems Group Data61 CSIRO PullenvaleQLD 4069 Australia The School of Computer Science Faculty of Science Queensland University of Technology BrisbaneQLD 4000 Australia The CSIRO Robotics Data61 CSIRO PullenvaleQLD 4069 Australia

The Internet of Things (IoT) detects context through sensors capturing data from dynamic physical environments, in order to inform automation decisions within cyber physical systems (CPS). Diverse types of uncertainty in the IoT pipeline can propagate within and across nodes, involving complex interactions with security, privacy, and trust that remain largely unexplored. This paper conducts an in-depth analysis of the types of uncertainties in IoT and how they propagate within IoT nodes and networks. We consider adversarial uncertainty in the context of distributed IoT networks to capture perturbations due to malicious actors in the network that can influence IoT security, privacy and trust. We examine the propagation of adversarial uncertainty and well-known uncertainty types, namely aleatoric and epistemic uncertainty, within the five distinct stages of sensing, communication, storage, processing and decision-making in an IoT pipeline, across network layer boundaries, and across nodes within an IoT network. Using this mapping, we analyse the interactions between the uncertainty types and their propagation, and security, privacy, and trust in IoT. Based on this analysis, we discuss guidelines and considerations for mitigating uncertainty in IoT through a smart grid use case study. © The Author(s) 2024.

关键词： Differential privacy

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Adaptive Graph Embedding With Consistency and Specificity for Domain Adaptation

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2094-2107页

作者： Shaohua Teng Zefeng Zheng Naiqi Wu Luyao Teng Wei Zhang School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macao University of Science and TechnologyMacao 999078China School of Information Engineering Guangzhou Panyu PolytechicGuangzhou 511483China Faculty of Information Technology Monash University20 Exhibition Walk ClaytonVIC 3800Australia IEEE

Domain adaptation(DA) aims to find a subspace,where the discrepancies between the source and target domains are reduced. Based on this subspace, the classifier trained by the labeled source samples can classify unlabeled target samples *** approaches leverage Graph Embedding Learning to explore such a subspace. Unfortunately, due to 1) the interaction of the consistency and specificity between samples, and 2) the joint impact of the degenerated features and incorrect labels in the samples, the existing approaches might assign unsuitable similarity, which restricts their performance. In this paper, we propose an approach called adaptive graph embedding with consistency and specificity(AGE-CS) to cope with these issues. AGE-CS consists of two methods, i.e., graph embedding with consistency and specificity(GECS), and adaptive graph embedding(AGE).GECS jointly learns the similarity of samples under the geometric distance and semantic similarity metrics, while AGE adaptively adjusts the relative importance between the geometric distance and semantic similarity during the iterations. By AGE-CS,the neighborhood samples with the same label are rewarded,while the neighborhood samples with different labels are punished. As a result, compact structures are preserved, and advanced performance is achieved. Extensive experiments on five benchmark datasets demonstrate that the proposed method performs better than other Graph Embedding methods.

关键词： Adaptive adjustment consistency and specificity domain adaptation graph embedding geometrical and semantic metrics

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Improved Ingredients-based Recipe Recommendation Software using Machine Learning 11

Improved Ingredients-based Recipe Recommendation Software us...

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11th IEEE International Conference on Intelligent Computing and Information systems, ICICIS 2023

作者： Ashraf, Mariam Tarek, Khadija Fathy, Naglaa Mahmoud, Mariam Emad, Rana Khaled, Khadija Amr, Noran Ain Shams University Faculty of Computer and Information Science Software Engineering Department Cairo Egypt Ain Shams University Faculty of Computer and Information Science Information Systems Department Cairo Egypt

ISBN: (纸本)9798350322101

In this paper we propose an improved recipe recommendation system that employs image recognition of food ingredients. The system is currently a mobile application that performs image recognition on uploaded or camera-captured images and recommends recipes containing the recognized ingredients. We used the ResNet-V2 architecture to build a convolutional neural network model for image recognition, which was able to identify 33 different food ingredients with an accuracy rate of 89%. The recommendation system uses the identified ingredient labels, as well as user preferences and restrictions, to display a list of recipes containing the identified ingredients. This feature allows users to discover new and exciting recipes based on the ingredients they currently have at home, without having to worry about dietary restrictions or other preferences. Overall, our system provides a convenient and personalized way for users to discover and prepare delicious meals based on their unique needs and preferences. © 2023 IEEE.

关键词： Image recognition

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