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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Ayyappan, G. Gopinath, M. Yuktha Varshika, J. Rohan Kumar, S. Sakthi, S. Karthikraja, M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai602105 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Information Technology Chennai602105 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Artificial Intelligence and Machine Learning Chennai602105 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Agriculture Engineering Chennai602105 India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

Lung carcinoma represents a diverse group of malignancies that originate in the respiratory epithelium. The primary histological subtypes include adenocarcinoma, small cell carcinoma, and squamous cell carcinoma, each exhibiting distinct clinical, morphological, and genetic characteristics. This classification is critical for determining appropriate treatment strategies and predicting patient outcomes. This article provides an overview of the histopathological features, molecular profiles, and clinical implications of each subtype, highlighting the importance of accurate classification in the management of lung carcinoma. This study also explores various deep-learning algorithms applied to radiological imaging data for the classification of lung carcinoma. We analyze a dataset comprising Computed Tomography (CT) scans annotated with histopathological findings to distinguish between malignant and benign lung nodules. In deep learning models, Convolutional Neural Networks (CNNs) and their variants, have shown promising results in automated detection and classification tasks. Traditional Data augmentation and Gaussian blur techniques are used to enlarge the dataset and to smoothing the image by reducing noise. This model achieves an accurate classification accuracy of 97.74%, 98.96% precision, and 0.98 AUC-ROC compared to various deep learning algorithms used for classification. © 2024 IEEE.

关键词： Lung cancer

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Adaptive Multi-Link Data Allocation for LEO Satellite Networks

Adaptive Multi-Link Data Allocation for LEO Satellite Networ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zheng, Jinkai Luan, Tom H. Zhao, Jinwei Li, Guanjie Zhang, Yao Pan, Jianping Cheng, Nan Dongguan University of Technology School of Electrical Engineering and Intelligentization Guangdong China Xidian University School of Cyber Engineering Shaanxi China University of Victoria Department of Computer Science VictoriaBC Canada Northwestern Polytechnical University School of Computer Science Shaanxi China Xidian University School of Telecommunications Engineering Shaanxi China

ISBN: (纸本)9798350351255

The rapid development of Low Earth Orbit (LEO) satellite networks has provided ubiquitous Internet access to users around the world, especially in areas where there are no terrestrial networks. However, a dish can only communicate with one of the available satellites when uploading data in the current framework, resulting in low communication efficiency. As the number of satellites continues to increase, the current framework cannot make full use of the user-satellite link resources. In this paper, we first conduct a measurement of Starlink's network performance and report some unique features. Then, we propose an adaptive multi-link data allocation framework for LEO satellite networks where a dish can communicate with multiple satellites at the same time to improve data transmission efficiency. With this framework, data can be split into chunks and uploaded simultaneously over multiple links. Our goal is to determine the data allocation strategies to jointly optimize the transmission latency and data processing costs. To this end, we propose a deep reinforcement learning-based algorithm integrated with the traffic prediction module to determine the optimal data allocation strategies in a dynamic network environment. Through extensive simulations, we demonstrate the effectiveness of our approach compared with baselines. © 2024 IEEE.

关键词： Satellite links

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PageRank-Guided Random Walks for Improved Graph Representations 15

PageRank-Guided Random Walks for Improved Graph Representati...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Ryu, Yejong Choe, Tae Young Park, Taeyoon Kim, Mucheol Chung-Ang University Department of Computer Science and Engineering Yongin City Korea Republic of Chung-Ang University Department of Computer Science and Engineering Seoul Korea Republic of Chung-Ang University Department of Physics Seoul Korea Republic of School of Computer Science & Engineering Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9798350364637

Graph embedding efficiently maps complex graph features within a low-dimensional vector space, facilitating a variety of downstream network prediction tasks. Recent advancements often leverage random walk-based methods due to their proficiency in capturing the structural information of graphs. However, these methods tend to exhibit an inherent bias toward high-centrality nodes, which frequently serve as pivotal bridges in network topology. Such biases disproportionately influence the learning process and can skew embeddings. This leads to inaccurate predictions, particularly for nodes with sparse connections. This paper proposes a novel approach that utilizes the PageRank algorithm to quantify node importance and identify imbalances. By integrating these insights, we aim to mitigate centrality bias in graph embeddings, thus enhancing the representational fairness and predictive accuracy of the model. © 2024 IEEE.

关键词： Graph embeddings

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Feature Fusion Approach with Inception-RestNet and Canonical Correlation Analysis for Strawberry Leaf Disease Detection 2

Feature Fusion Approach with Inception-RestNet and Canonical...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Gautam, Vinay Maheshwari, Himani Tiwari, Raj Gaurang Agarwal, Ambuj Kumar Trivedi, Naresh Kumar Garg, Naveen Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University School of Computing Uttrakhand Dehradun India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttrakhand Dehradun India

ISBN: (纸本)9798350371314

Strawberries are one of the most demanding horticulture crops due to their flavor and nutritional value. Therefore, the demand for strawberries growing yearly and production of the same is very low. The prime factor of low production is leaf disease because the crop is susceptible to various types of leaf diseases. This causes huge financial losses every year to the farmers. Therefore, adequate action is needed to save strawberry crops from diseases and effectively manage their spread. Farmers were completely dependent on expert skilled personnel to identify crop diseases that may be the reason for the loss of crop yield. Therefore, this research proposed a novel deep-learning method to recognize plant leaf disease to optimize losses. However, with the invention of this method, it is easy to recognize different disease patterns within the plant leaf. The research proposed a feature fusion-based deep learning model named FFIR (Feature Fusion with Inception-RestNet) for recognizing leaf diseases. The features were extracted with a composition of the Inception-RestNet model. Later, the extracted features are fused in a fusion matrix using canonical correlation analysis (CCA). Afterward, the classification was performed with a Convolution Neural network. The proposed model outperformed with an accuracy of 99.34%. © 2024 IEEE.

关键词： Crops

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PBRL-TChain: A performance-enhanced permissioned blockchain for time-critical applications based on reinforcement learning

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Future Generation computer Systems 2024年 154卷 301-313页

作者： Zhang, Yiguang Lin, Junxiong Lu, Zhihui Duan, Qiang Huang, Shih-Chia School of Computer Science Fudan University Shanghai200433 China Information Sciences & Technology Department Pennsylvania State University AbingtonPA United States Shanghai Blockchain Engineering Research Center Shanghai China Department of Electronic Engineering National Taipei University of Technology Taiwan

Permissioned blockchains can provide high security and reliability for various Internet of Things (IoT) systems, such as smart healthcare and vehicular networks. However, the performance issues of permissioned blockchains have been a constraint for fully supporting time-critical tasks with tight requirements of low latency and high throughput. This paper proposes PBRL-TChain, a performance-enhanced permissioned blockchain for time-critical applications based on deep reinforcement learning (DRL). First, we propose a priority ordering mechanism to minimize latency and maximize reliability. Then, we design a fast retransmission mechanism to alleviate the impact of transaction conflicts on the latency performance. Finally, we propose a DRL-based dynamic adjustment method in PBRL-TChain to achieve better performance and reliability. Experiments show that our method outperforms existing methods. Compared with Fabric++ and Athena, it can reduce the latency of time-critical transactions by 10 times, achieving a level of 10 ms, significantly improving the system's performance and reliability. © 2023 Elsevier B.V.

关键词： Blockchain

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Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

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computer Systems Science & engineering 2022年第9期42卷 1273-1287页

作者： S.Prakash P.Vishnu Raja A.Baseera D.Mansoor Hussain V.R.Balaji K.Venkatachalam Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of ECE Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic

Urban living in large modern cities exerts considerable adverse effectson health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanizedcountries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples isbecoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce *** relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, theiterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated usingensemble nonlinear support vector machines and random forests. Thus, instead ofusing the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vectormachines, where the product rule is used to combine probability estimates ofdifferent classifiers. Performance is evaluated in terms of the prediction accuracyand interpretability of the model and the results.

关键词： Chronic disease classification iterative weighted map reduce machine learning methods ensemble nonlinear support vector machines random forests

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VLC positioning in low data rate for V2V communication 2024

VLC positioning in low data rate for V2V communication

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12th Euro-American Conference on Telematics and Information Systems, EATIS 2024

作者： Giraldo, Luis Miguel Soler, Joaquin Perez Botella-Mascarell, Carmen Juan, Vicent Girbes Varea, Sandra Roger Torres, Julio Martos Olcina, Raimundo Garcia Electronical Engineering Department Universitat de València Burjassot Valencia46100 Spain Computer Science Department Informatics Department Universitat de València Burjassot Valencia46100 Spain

ISBN: (纸本)9798400717338

The creation and implementation of a system that bridges visible light communication (VLC) with selected identifiers, envisioned as informational beacons, are outlined. This system is adept at controlling LED panels and orchestrating communication between LED lights and the receiving unit, laying the groundwork for vehicle-to-vehicle (V2V) communication systems tailored for precise positioning. The design and assessment of test-bed requirements cater to the anticipation of future V2V optical wireless communication scenarios. Through analysis, it is demonstrated that commercial outdoor lighting can be effectively utilized for V2V positioning, capable of operating under daylight with a data transmission rate of 100 Kbps. Crucially, the system's ability to accurately detect positioning beacons facilitates the localization of vehicles within designated areas of interest, paving the way for the advancement of V2V communication technologies. This comprehensive approach not only validates the feasibility of using VLC for vehicle positioning but also sets a precedent for future developments in V2V communication systems. © 2024 Owner/Author.

关键词： Optical communication

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Evaluation and Integration of Advanced AI Chatbots for Biological Pathway Curation

Evaluation and Integration of Advanced AI Chatbots for Biolo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Azam, Muhammad He, Fei Santosh, Krpns Xu, Dong Popescu, Mihail University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Department of Health Management and Informatics ColumbiaMO United States

ISBN: (纸本)9798350386226

The rapid expansion of biological literature presents significant challenges in manually curating pathway knowledge from images for biological and medical research. Recent advancements in AI, particularly multimodal AI chatbots like GPT-4V, Claude, and Gemini, offer promising capabilities for image understanding and extraction of biological interactions. This study evaluates the effectiveness of these AI chatbots in curating gene interactions from 50 curated tumor signaling pathway images. Our results highlight the superior performance of GPT-4V, achieving a precision of 0.778, a recall of 0.664, an F1-score of 0.716 in non-directional analysis, and a precision of 0.566, a recall of 0.471, an F1-score of 0.512 in directional analysis. Claude followed with a precision of 0.735, recall of 0.594, and F1-score of 0.6571 in non-directional analysis, and a precision of 0.500, recall of 0.400, and F1-score of 0.445 in directional analysis. Gemini recorded the lowest performance metrics across both analyses. We further developed a model fusion integrating Claude-3 chatbots and our pathway curation pipeline, achieving notable improvements. © 2024 IEEE.

关键词： AI Chatbots Bio-Curation Gene Interactions Precision Oncology Signaling Pathways

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Compact Physical and Electrical Patch Antenna Engineered for 5G Mobile Devices and Multiband Systems

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Progress in Electromagnetics Research B 2025年 111卷 71-81页

作者： Jemaludin, Nazrin Haziq Al-Gburi, Ahmed Jamal Abdullah Abdulhameed, Muhannad K. Hashim, Sarah R. Mohammed, Dunya Zeki Saeidi, Tale Gupta, Anupma Zakaria, Zahriladha Nurhayati, Nurhayati Elabd, Rania Hamdy Jalan Hang Tuah Jaya Durian Tunggal Melaka76100 Malaysia Computer Scince Department University of Kerbala Karbala Iraq Air Conditioning Engineering Department Faculty of Engineering Warith Al-Anbiyaa University Iraq Department of Cyber Security Al-Zahraa University for Women Karbala Iraq Department of Electronic and Communications Engineering Gilgamesh University Baghdad Iraq Co. Donegal LetterkennyF92 FC93 Ireland Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Tamilnadu Chennai602105 India Department of Electrical Engineering Universitas Negeri Surabaya Surabaya60231 Indonesia Department Electronic and Communication Higher Institute of Engineering and Technology in New Damietta Damietta Egypt

This article presents a compact multi-band microstrip patch antenna designed for 5G, Ku, and K-band applications. The antenna operates at 3.5 GHz and 15.6 GHz, supporting 5G communications (3.3–3.6 GHz) and satellite applications (15.6–20 GHz). Fabricated on an FR4 substrate (Εr = 4.3, tan δ = 0.025) with dimensions of 13 × 10 × 1.6 mm3 (0.15λo × 0.12λo × 0.02λo), where λo represents the wavelength at the lower frequency of 3.5 GHz, the antenna achieves return losses of −19 dB and −39 dB at the respective frequencies, with peak gains of −2.8 dBi and 3.7 dBi. The design’s consistency is validated through a comparative analysis with recent studies. The antenna was placed near the ear and mouth area of a human head phantom model to perform a comprehensive SAR analysis. SAR analysis confirms compliance with safety standards, maintaining SAR levels below 2 W/kg. The proposed design demonstrates promising performance for modern communication systems. © (2024), (Electromagnetics Academy). All Rights Reserved.

关键词： Slot antennas

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An Effective Heart Disease Prediction Method using Extreme Gradient Boosting Algorithm Compared with Convolutional Neural Networks 9

An Effective Heart Disease Prediction Method using Extreme G...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Anitha, Cuddapah Rajkumar, S. Dhanalakshmi, R. Mohan Babu University Computer Science and Systems Engineering Sree Vidyaniketan Engineering College Andhra Pradesh Tirupati India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Saveetha School of Engineering SIMATS Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350397376

Heart disease is one of the most serious and common problems to human beings around the face;% to 90% of people are affected by heart problems in which, and Several methods exist to identify heart disease in an earlier stage, but few methods fail to manage the accuracy due to the large volume of data. In addition, the methods require more time to process those features. To overcome these research issues, effective intelligent techniques are proposed to predict heart disease in an earlier stage. Additionally, our model's balanced accuracy (89.5%) outperforms the XGBoost classifiers' accuracy rates. Excellent recall values and area under the curve, together with high specificity and test accuracy, are obtained using the CNN classifier. Designers contrast the XGboost, KNN, SVM, and Naive Bayes Classifiers' classification precision on various healthcare datasets. Finally, this leads to a reduction in the overall error rate and also improves the recognition accuracy by more than 90%. © 2023 IEEE.

关键词： Convolutional neural networks

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