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2024 Asia communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical communications, IPOC 2024

作者： Wang, Xinjun Zhang, Zhenrong Zhu, Kangqi Hua, Nan School of Computer Electronic and Information Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China Department of Electronic Engineering Tsinghua University Beijing China

We evaluate the accuracy and performance of different simulation modes in MegaStarSim, analyze end-to-end routing strategies in large-scale satellite optical networks, and assess link quality across various inter-sate... 详细信息

ISBN: (纸本)9798350379266

关键词： Satellite links

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Redefining Disease Assessment in Raspberries: A Hybrid GAN-SVM Model for Detailed Intensity Measurement 4

Redefining Disease Assessment in Raspberries: A Hybrid GAN-S...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Arora, Dinesh Sharma, Deepika Sharma, Avinash Goyal, Parul Chandigarh Enigineering College Department of Electronics & Communications Engineering Landran Punjab Mohali India Chandigarh College of Technology Depatment of Biotechnolgy Landran Punjab Mohali India Chandigarh Enigineering College Department of Computer Science & Engineering Jhanjeri Punjab Mohali India M. M Engineering College Maharishi Markandeshwar Deemed to be University Computer Science & Engineering Department Mullana Haryana Ambala India

ISBN: (纸本)9798331529635

This study presents a hybrid Generative Adversarial Network (GAN) and Support Vector Machine (SVM) model for accurately categorizing Raspberry Cane Disease (RCD) into four severity levels. Leveraging a dataset of 7,000 high-resolution images, the model achieves 98.8% accuracy. GANs generate synthetic images to address data scarcity, while SVMs ensure precise multi-class classification. The results demonstrate the model's effectiveness in disease severity assessment, highlighting its potential for broader agricultural applications. © 2024 IEEE.

关键词： Generative adversarial networks

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Early Prediction of Chronic Heart Disease Based on Electronic Triage Dataset by using Machine Learning

Early Prediction of Chronic Heart Disease Based on Electroni...

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2023 Al-Sadiq International Conference on Communication and Information Technology, AICCIT 2023

作者： Muhammed, Saleh Mahdi Abdul-Majeed, Ghassan Mahmoud, Mahmoud Shuker Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Department of Computer Science Baghdad Iraq University of Baghdad College of Engineering Department of Computer Science Baghdad Iraq Al/Mansour University College Department of Communications Baghdad Iraq

ISBN: (纸本)9798350341881

Chronic Heart Disease (CHD) is the main reason for mortality around the world, constituting about 15% of all natural deaths, which confirms the importance of early detection to mitigate these rates. CHD can be detected by measuring multiple health parameters such as blood pressure, SpO2, ECG, and other parameters. Therefore, there is a persistent need for effective and precise prediction methods. Traditional techniques for prediction and analysis are limited due to the complexity of the data and correlations. First approach for developing these techniques is by using machine learning (ML) algorithms. ML can examine vast amounts of patient information, including medical records, test outcomes, and imaging scans to identify patterns and predict the possibility of developing heart disease or having a cardiac event. This study aims to improve the accuracy of prediction by utilizing ML techniques to predict CHD using medical data, reduce the incidence of misdiagnosis, and ultimately save lives. Supervised learning techniques, such as Support Vector Machine (SVM), Artificial Neural Networks (ANN), AdaBoost, Logistic Regression (LR), K-Nearest Neighbor (KNN) and others are employed to identify correlations in CHD data and enhance prediction rates. The results indicate that SVM-derived probabilistic models outperform other algorithms in predicting CHD and the accuracy of the model was 96.2%, as revealed through various performance evaluation metrics. © 2023 IEEE.

关键词： Support vector machines

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Maximizing dirty-paper coding rate of RIS-assisted multi-user MIMO broadcast channels

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Intelligent and Converged Networks 2022年第1期3卷 64-73页

作者： Mohamed A.Elmossallamy Radwa Sultan Karim G.Seddik Geoffery Ye Li Zhu Han Qualcomm Inc. BoxboroughMA 01719USA Electrical and Computer Engineering Department and the Computer Science Department University of HoustonHoustonTX 77004USA. the Electrical and Computer Engineering Department Manhattan CollegeRiverdaleNY 10471USA. Electronics and Communications Engineering Department American University in CairoNew Cairo 11835Egypt. Department of Electrical and Electronic Engineering Imperial College LondonLondonSW72AZUK

We consider a downlink multi-user scenario and investigate the use of reconfigurable intelligent surfaces(RISs)to maximize the dirty-paper-coding(DPC)sum rate of the RIS-assisted broadcast *** from prior works,which maximize the rate achievable by linear precoders,we assume a capacity-achieving DPC scheme is employed at the transmitter and optimize the transmit covariances and RIS reflection coefficients to directly maximize the sum capacity of the broadcast *** propose an optimization algorithm that iteratively alternates between optimizing the transmit covariances using convex optimization and the RIS reflection coefficients using Riemannian manifold *** results show that the proposed technique can be used to effectively improve the sum capacity in a variety of scenarios compared to benchmark schemes.

关键词： broadcast channels dirty-paper coding multiple-input-multiple-output(MIMO) reconfigurable intelligent surfaces

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Smart System for Vechicle Number Plate Recognition Using Convolutional Neural Network(CNN) 1

Smart System for Vechicle Number Plate Recognition Using Con...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Nidhya, R. Kalpana, R. Sudhakar, R. Maranco, M. Smilarubavathy, G. Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Andhra Pradesh India Srm Institute of Science and Technology Department of Networking and Communications Tamilandu Chennai India St. Joseph's Institute of Technology Department of Computer Science & Engineering Tamilandu Chennai India

ISBN: (纸本)9798350328042

In the era of smart cities and advanced transportation systems, the need for efficient and accurate vehicle identification has become paramount. This paper introduces a sophisticated system for vehicle number plate recognition leveraging the capabilities of Convolutional Neural Networks (CNNs). The proposed system aims to enhance security, streamline traffic management, and contribute to the evolution of intelligent transportation infrastructure. Its is also used to check for suspicious vehicles or vehicles that were involved in crime, in that case it is useful for the polices to browse the details of the accused vehicles. Integration of the trained CNN model into a comprehensive system capable of real-time video processing demonstrates the system's applicability in diverse environments. Deployment considerations span various platforms, from centralized servers to edge devices, ensuring adaptability to different deployment scenarios. The existing systems are based on different methodologies but still, it is a challenging task as some of the factors like high speed of the vehicle, non-uniform vehicle number plate, the language of vehicle number and different lighting conditions can affect a lot in the overall recognition rate. Proposed system, driven by CNN technology, stands as a robust solution for accurate and efficient automated identification. © 2023 IEEE.

关键词： Convolutional neural networks

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Weighting Heart Disease Criteria Using Multi-Criteria Decision-Making

Weighting Heart Disease Criteria Using Multi-Criteria Decisi...

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2023 Al-Sadiq International Conference on Communication and Information Technology, AICCIT 2023

作者： Muhammed, Saleh Mahdi Abdul-Majeed, Ghassan Mahmoud, Mahmoud Shuker Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Department of Computer Science Baghdad Iraq University of Baghdad College of Engineering Department of Computer Science Baghdad Iraq Al-Mansour University College Department of Communications Baghdad Iraq

ISBN: (纸本)9798350341881

Heart disease describes a group of conditions that affect the heart and there are many different factors including lifestyle, medical history, family history, and so on that may cause this complex disease. It is therefore important to consider multiple criteria, such as smoking status, blood pressure, and cholesterol levels to make informed decisions about patient priority management and treatment of HD. Multicriteria Decision Making (MCDM) allows us to identify and weigh the importance of each criterion. This approach can enhance the quality of patient care and improve clinical outcomes. Therefore, weighting HD criteria using MCDM is an essential step in achieving better healthcare outcomes in the management of HD. The FWZIC method was used in this study to weigh 11 heart disease parameters with the assistance of four cardiologists. The results of the criterion weights were without any difference in the preferences of the respective expert. Therefore, this method is flexible, robust, and useful in complex real-world problems. © 2023 IEEE.

关键词： Blood pressure

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Genetic Neural Network for Diabetes Likelihood Prediction Using Risk Factors 15

Genetic Neural Network for Diabetes Likelihood Prediction Us...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Gamara, Rex Paolo C. Teologo, Antipas T. Recto, King Harold A. Neyra, Romano Q. Bandala, Argel A. Feu Institute of Technology Department of Electronics Engineering Manila Philippines Ateneo de Manila University Computer and Communications Engineering Department of Electronics Quezon City Philippines Feu Institute of Technology Office of the Senior Director for Engineering Manila Philippines De la Salle University Department of Electronics and Computer Engineering Manila Philippines

ISBN: (纸本)9798350381177

Diabetes mellitus is a disease incorporated with carbohydrate metabolism whereas the body becomes unable to generate or react with insulin which leads to abnormal levels of blood sugar (glucose). In a worldwide perspective, Diabetes mellitus is ranked as the 9th leading cause of death based on the records of the World Health Organization and according to the International Diabetes Federation, there are about 463 million diabetic people worldwide in 2019 which is projected to increase to 700 million diabetic people by year 2045. In a regional perspective, about 251 million (45%) diabetic people resides on the Western Pacific and Southeast Asian region, whereas about 140 million people are undiagnosed of the disease. In this study, a genetic algorithm-optimized neural network using MATLAB was developed based on the risk factors. The experimental results show that the best validation performance has a value of 0.014129 and with a regression model coefficient R2 value of 0.95864. © 2023 IEEE.

关键词： Genetic algorithms

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Towards a Comprehensive Model for the Study of computer Networks Performance using Information Theory 10

Towards a Comprehensive Model for the Study of Computer Netw...

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10th International Japan-Africa Conference on Electronics, communications, and Computations, JAC-ECC 2022

作者： Nomeir, Mohamed W. Mokhtar, Bassem Alexandria University Faculty of Engineering Department of Electrical Engineering Alexandria Egypt Egypt-Japan University of Science and Technology School of Electronics Communications and Computer Engineering Computer Science and Engineering Department Alexandria Egypt

ISBN: (纸本)9781665464635

Improving computer networks performance is a tremendous need in our life for meeting ever-changing quality of service (QoS) requirements of interesting applications. We are searching continuously on how to increase the performance of computer networks. One of the most important factors that aid in determining network performance is the available and accessible network information. In this paper, we deal with this problem in an information theoretic framework. Our approach is different than most recent papers in this field, where we use predefined measures that normally work on any computer network and we apply it to a general network model. We examine these measures on a specific model that is more comprehensive and more applicable than previous models discussed in other papers. Also, one of the important reasons in our extended study is to investigate the shortcomings in adopting these measures and how they appear in the results. We discuss briefly how to enhance these measures to be compatible with real networks and to give more precise and efficient results. Our analytical study of the developed model shows that obtained results can be used to guide future network designs since our model can be easily manipulated to get many real network models. © 2022 IEEE.

关键词： Information theory

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Monitoring and accelerating plant growth using IoT and Hydroponics

Monitoring and accelerating plant growth using IoT and Hydro...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Kumar, Abhishek Savaridassan, P. SRM Institute of Science and Technology Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350348217

Growing plants without soil is known as hydroponics, and it is particularly helpful in places with little space and bad weather. A novel method of growing plants without soil in an inert medium is called hydroponics. By avoiding pesticides and other substances that degrade soil and land quality, hydroponics enables farmers to conserve water while enhancing crop quality. Due to its high efficiency and role as an environmentally benign substitute for soil culture, it has the ability to close the gap left by India's low agricultural productivity. This farming process has leveraged several new technologies like Internet of Things (IoT), making integration of intelligent management and real-time data monitoring and collection systems possible. This farming process also allows for a greater degree of automation and refinement in the control process of the hydroponic systems. Via this paper, we have discussed the development of a hydroponic subsystem that leverages agents for cloud-based monitoring, data collection, and storage for future analysis and heuristics. This paper presents an intuitive and low-cost approach to monitoring and accelerating plant growth based on Hydroponics and IoT with tools such as automated environment parameter monitoring and error rectification, sub-second streaming of telemetry from ground units to cloud servers and allowing users to access the status of the plant or system of plants being monitored as a plant health index from anywhere in the world, via a mobile application. The end goal of this paper is to understand the benefits one obtains by adopting a Hydroponics based cultivation system, which would show up as lower water consumption, minimal space usage, increased control over nutrients being introduced, and much more effective protection against pests and diseases when coupled with the feature of emphasizing each plant genus being cultivated via plant profile templates. This approach will not only revolutionize the millennial approach

关键词： Hydroponics

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