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BER Improvement of Cooperative Spectrum Sharing of NOMA in 5G Network

BER Improvement of Cooperative Spectrum Sharing of NOMA in 5...

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Sciences and Techniques of Automatic control and computer Engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Mohamed Hassan Rashid A. Saeed Rania A. Mokhtar Khalid Hamid Elmustafa Sayed Ali Nidhal Odeh Manwinder Singh Othman O. Khalifa Shayla Islam Department of Wireless Communication Lovely Professional University Phagwara Punjab India Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Communication Systems Engineering University of Science & Technology Omdurman Sudan Department of Electronics Engineering Sudan University of Science and Technologu (SUST) Khartoum Sudan School of Electrical Computer and Telecommunications Engineering University of Wollongong Australia Department of Electrical and Computer Engineering International Islamic University Malaysia Gombak Malaysia Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya Department of Computer Science Institute of Computer Science and Digital Innovation UCSI University Malaysia Kuala Lumpur Malaysia

Non-orthogonal multiple access is one of the best methods for addressing the needs of 5G wireless services (NOMA). The proliferation of mobile devices in recent years has increased the importance of cooperative spectrum sharing and utilization in wireless communication due to increases in the bit error rate (BER) brought on by collisions and interference. In this study, MIMO and Massive MIMO (M-MIMO) in the downlink (DL) NOMA power domain (PD) in conjunction with the Cooperative Cognitive Radio Network are proposed as two new ways for improving and evaluating BER in the 5G network (CCRN). Customers of NOMA compete for available channels on the CCRN in this first strategy. The second approach creates a dedicated channel for NOMA users. Three scenarios with varying distances, power location coefficients, and transmission power are used to evaluate the proposed methods in the MATLAB software program. It is assumed that four users will share a 90 MHz BW using QPSK modulation in all three scenarios. When evaluating the performance of the suggested system under the presumption of frequency-selective Rayleigh fading, concurrent interference cancellation (SIC) and unstable channel conditions are also considered.

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Searchable Proxy Re-Encryption Data Sharing Scheme Based on Consortium Chain

Searchable Proxy Re-Encryption Data Sharing Scheme Based on ...

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computer Science and Blockchain (CCSB), international conference on

作者： Chuanping Li Juxiang Tang College of Computer Science and Engineering Chongqing University of Technology Chongqing China

In order to solve the problems of low data retrieval efficiency, lack of flexible authority management, and difficulty in meeting the needs of multi-user massive data sharing in data sharing scenarios, a searchable proxy re-encryption data sharing scheme based on alliance chains is proposed, which realizes the dynamics of cloud data sharing process Fine-grained access control and efficient retrieval of ciphertext keywords. In this scheme: the original data is encrypted using the AES algorithm, the symmetric key and the keyword are respectively encrypted using a non-interactive proxy re-encryption algorithm and a searchable encryption algorithm; the original data ciphertext is stored in the Interplanetary File System (IPFS), and the keyword Metadata such as ciphertext and permission control policies are stored on the alliance chain. Use smart contracts to realize decentralized ciphertext conversion, combined with the advantages of B+ tree index, to achieve efficient search of ciphertext keywords and quick verification of permissions. Designing a non-interactive proxy re-encryption algorithm can generate proxy keys quickly and efficiently, and is suitable for multi-user complex data sharing scenarios. The solution also supports dynamic management of access rights to meet data sharing requirements in different scenarios. The experimental analysis results show that the keyword matching algorithm takes an average of 1lms, which exhibits higher efficiency compared to the global keyword search scheme. In the concurrent access test experiment, the maximum throughput of the system is 24 transactions per second, and the transaction success rate is high. Therefore, the system has a good concurrent access capability to meet the practical use.

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Bio-inspired Heuristic Optimization-based Cascaded Network for Diabetic Retinopathy Screening

Bio-inspired Heuristic Optimization-based Cascaded Network f...

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international conference on Advancement in Electrical and Electronic Engineering (ICAEEE)

作者： Eshat Ahmad Shuvo Wahidur Rahman Md Shakhawat Hossain Md. Tarequl Islam Md. Sadiq Iqbal Dept. of Computer Science and Engineering Bangladesh University Adabor Bangladesh Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh Dept. of Computer Science & Engineering & RIoT Center Independent University Bangladesh Dept. of Computer Science & Engineering Khwaja Yunus Ali University Sirajganj Bangladesh

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

Diabetic retinopathy (DR) is a common yet fatal complication of diabetic patients in which high levels of blood sugar damage the blood vessels in the retina, the light-sensitive eye tissue crucial for human vision. Early detection and timely intervention are essential to managing DR and preventing severe vision loss. Traditionally, the examination is performed by ophthalmologists manually examining the retinal fundus images to check for signs of DR. This approach is helpful but subjective, time-consuming and tedious. Artificial intelligence (AI)-guided computer vision has recently become very compelling and practical for image analysis and diagnosis. Existing AI-based methods achieved sufficient accuracy at the cost of high computing resources and large datasets. This paper proposes a cascaded network incorporating deep learning and the traditional machine learning approaches with a bio-inspired heuristic optimization algorithm for DR detection from fundus images. The proposed method achieved sufficient accuracy (97.1%) when trained using limited data and low computing machines. The AI models for the cascaded networks were selected through an exhaustive search in which five popular CNN models were used for extracting features; the Bacterial foraging optimization (BFO) was used to determine optimal features, and seven traditional machine models were used to detect the DR. The ResNet50-BFO-SVC cascaded network was found to be most suitable in this study. The proposed cascaded network brings efficiency, accuracy, scalability, and robustness to DR screening.

关键词： Diabetic retinopathy Accuracy Microorganisms Computational modeling Biological system modeling Scalability Feature extraction

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Gateway malicious command detecting for intelligent fishery sensor networks Malicious instruction detection method based on deep learning 5

Gateway malicious command detecting for intelligent fishery ...

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5th international conference on computer Information Science and Artificial Intelligence, CISAI 2022

作者： Zhang, FengWei Tao, WenXin Sun, YingZe Information Technology Research Center Chinese Academy of Fishery Sciences Beijing China Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9781510662520

Fisheries IoT realizes real-time online detecting and precise modulation of aquaculture process, which greatly enhances the informationization, automation and intelligence level of farms, reduces production costs, promotes the scale of fishery industry, and improves the quality level and market competitiveness of agricultural products. However, the key to large-scale fishery farming lies in accurate water environment modulation, the IoT gateway system is the key to realize the ability of intelligent fishery to achieve accurate water environment modulation. Once the aquatic product networking is attacked by malicious IoT commands, it may cause rapid disintegration of the farming environment ecosystem at any time, resulting in huge economic losses. Due to the size and power consumption of fishery IoT gateways, traditional security protection means are often difficult to be applied in such scenarios. Therefore, this paper proposes an improved malicious command detection method for smart fisheries sensor gateways based on deep learning techniques, aiming to accurately and efficiently identify malicious control commands of smart fisheries sensor networks on IoT gateways. The main contributions of this paper are three: (1) Transforming various types of signals from gateways into feature texts, which are used as inputs for deep learning for command classification. (2) Based on the BiLSTM model, it is improved by adding a multi-headed self-attention mechanism to obtain a smaller size and higher accuracy malicious command detection model applicable to smart fisheries gateways (3) Experimental results show that compared with other models, highly confusing malicious commands elaborated for the command characteristics of short, small size and high change rate of smart fisheries sensor commands The improved BiLSTM model has a higher recognition rate. © 2023 SPIE.

关键词： Gateways (computer networks)

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Estimation of Medical Expenses Using Machine Learning 1

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3rd international conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Suganthi, N. Choudary, K. R. Vamsi Krishna Chanikya, C.H. Kumar, K. Sravan Department of Computer Science and Engineering SRM IST Ramapuram Chennai India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

Healthcare providers, patients, and policymakers are all faced with a serious dilemma due to the rising prices of healthcare services. A precise forecast of medical costs can help in resource allocation, insurance pricing, and financial planning for patients and healthcare systems. The implementation of machine learning algorithms to forecast medical costs based on specific patient data is thoroughly examined in this research article. The patient demographics included in the dataset for this study include age, gender, BMI, smoking habits, region, and the number of dependents, among other characteristics. These characteristics were gathered from a variety of healthcare insurance claims. Predictive models are constructed using a variety of regression and ensemble machine learning techniques, including Decision Tree, Random Forest, and Linear Regression. To improve model performance, feature engineering and data pretreatment methods are used. The models are assessed for accuracy and generalizability using measures like mean absolute error (MAE), mean squared error (MSE), and R-squared (R2). Age, BMI, and smoking habits are among the things that are known to have a major impact on predicting medical expenses. The study also examines how geographic region affects cost estimation, highlighting regional variances in healthcare prices. In summary, this study shows how machine learning can accurately forecast medical costs based on patient characteristics. The created models provide insightful cost estimation and can be used to enhance budgeting, resource allocation for healthcare, and insurance pricing. This study offers useful applications with important economic and societal ramifications, contributing to the increasing body of research at the confluence of healthcare and machine learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Budget control

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Democratizing access to collaborative music making over the network using air instruments 22

Democratizing access to collaborative music making over the ...

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17th international Audio Mostly conference (AM)

作者： Cocchiara, Davide Turchet, Luca Univ Trento Dept Informat Engn & Comp Sci Trento Italy

ISBN: (纸本)9781450397018

To date, scarce research has been conducted on the development of tools capable of fostering the democratization of the access to collaborative music making over the network. This paper describes a system based on interconnected air instruments conceived for introducing musically untrained people to collaborative music playing. The system consists of an application controlling synthesizers via real-time finger tracking on input from a consumer-grade camera, which is used in conjunction with a basic networking music performance system communicating control messages. Moving fingers in the air is one of the simplest movements that everybody can afford, thus it was selected as an optimal candidate to build a musical instrument accessible to everybody. A user study was conducted to assess the experience in interacting with the system, involving ten pairs of participants with no musical expertise. Overall, results showed that participants deemed that the system was effective in providing a high user experience, adequate to enable non-musicians to play together at a distance. Moreover, the system was judged as capable of promoting music playing for non-musicians thus fostering easiness of access to music making in a collaborative fashion. A critical reflection on the results is provided, along with a discussion of the study limitations and possible future works.

关键词： Networked music performance systems digital musical instruments computer vision Internet of Musical Things

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Identification of Ransomware families by Analyzing Network Traffic Using Machine Learning Techniques 3

Identification of Ransomware families by Analyzing Network T...

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3rd IEEE international conference on Transdisciplinary AI, TransAI 2021

作者： Almousa, May Osawere, Janet Anwar, Mohd Information Technology Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Computer Science Department College of Engineering North Carolina A&T State University NC United States

ISBN: (纸本)9781665434126

The number of prominent ransomware attacks has increased recently. In this research, we detect ransomware by analyzing network traffic by using machine learning algorithms and comparing their detection performances. We have developed multi-class classification models to detect families of ransomware by using the selected network traffic features, which focus on the Transmission control Protocol (TCP). Our experiment showed that decision trees performed best for classifying ransomware families with 99.83% accuracy, which is slightly better than the random forest algorithm with 99.61% accuracy. The experimental result without feature selection classified six ransomware families with high accuracy. On the other hand, classifiers with feature selection gave nearly the same result as those without feature selection. However, using feature selection gives the advantage of lower memory usage and reduced processing time, thereby increasing speed. We discovered the following ten important features for detecting ransomware: time delta, frame length, IP length, IP destination, IP source, TCP length, TCP sequence, TCP next sequence, TCP header length, and TCP initial round trip. © 2021 IEEE.

关键词： Transmission control protocol

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High-quality 3D Object Detection Based on Instance-aware Sampling 3

High-quality 3D Object Detection Based on Instance-aware Sam...

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2023 3rd international conference on Mechatronics, Automation and Intelligent control, MAIC 2023

作者： Zhang, Xiao Xu, Bingrong Lu, Liping School of Computer Science and Artificial Intelligence Wuhan University of Technology Hubei Wuhan430070 China School of Automation Wuhan University of Technology Hubei Wuhan430070 China

Most existing 3D object detection that is researched on point clouds uses random down-sampling algorithms, which preserve the primary shape and feature information of the point clouds and extract robust features of sampling point clouds to decrease the consumption of computing resources and improve processing inference efficiency. On the other hand, this method of sampling may disregard significant areas within the point clouds, thereby diminishing the precision of detecting comparatively minute entities such as pedestrians and cyclists. In this research, an instance perception-based object detection method is presented that adaptively chooses additional foreground points in response to the properties of smaller objects in the initial point clouds. In addition, it offers end-to-end training and addresses the issue that the typical hand-designed multi-scale grouping receptive fields do not accurately reflect the size of the item. To effectively enhance the recall rate of foreground points, we present an instance-aware algorithm, which relies on farthest point sampling. Specifically, we employ dynamic gating networks to attain instance perception. This approach enables sample candidates to encompass a larger number of foreground objects. Then we use an aggregation layer to efficiently capture robust contextual features with rich spatial information. Through experiments performed on the KITTI dataset, we substantiate the effectiveness of our IAS when compared with current advanced 3D object detection methods. We have achieved a notable enhancement, especially in identifying diminutive objects. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Analytical Study Based Modeling for Covid-19 Using Advance Technology 3

Analytical Study Based Modeling for Covid-19 Using Advance T...

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3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Patel, Meghna Patel, Hiral Parikh, Satyen Ganpat University A. M. Patel Institute of Computer Studies Mehsana India Ganpat University Department of Computer Science Mehsana India Ganpat University Faculty of Computer Applications Mehsana India

ISBN: (纸本)9781665438117

computer based intelligence innovations and apparatuses assume a key job in each part of the COVID-19 emergency reaction. Understanding the infection and quickening clinical exploration on medications and medicines is one of the factor. Recognizing and diagnosing the infection, and anticipating its advancement helping with forestalling or easing back the infection's spread through reconnaissance and contact following reacting to the wellbeing emergency through customized data and learning checking the recuperation and improving early admonition instruments. To help encourage the utilization of AI all through the emergency, strategy creators ought to energize the sharing of clinical, sub-atomic, and logical datasets and models on synergistic stages to help AI specialists assemble successful instruments for the clinical network, and ought to guarantee that scientists approach the essential processing limit. To understand the full guarantee of AI to battle COVID-19, arrangement producers must guarantee that AI frameworks are reliable and lined up with the OECD AI Principles: they should regard human rights and protection;be straightforward, logical, vigorous, secure and safe;and on-screen characters engaged with their turn of events and utilize ought to stay responsible. Before the world was even mindful of the danger presented by the coronavirus (COVID-19), manmade reasoning (AI) frameworks had distinguished the episode of an obscure kind of pneumonia in the People's Republic of China (in the future "China"). By analyzing different aspects, the intelligent system plays the vital role to generate COVID-19 awareness and which ultimately reduce the infection to others. Proposed paper focus on awareness. © 2021 IEEE.

关键词： Viruses

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Object Localization Assistive System Based on CV and Vibrotactile Encoding 44

Object Localization Assistive System Based on CV and Vibrota...

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44th Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Wei, Zhikai Song, Aiguo Hu, Xuhui Southeast Univ Sch Instrument Sci & Engn State Key Lab Bioelect Nanjing 210096 Jiangsu Peoples R China Southeast Univ Sch Instrument Sci & Engn Jiangsu Key Lab Remote Measurement & Control Nanjing 210096 Jiangsu Peoples R China

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Intelligent assistive systems can navigate blind people, but most of them could only give non-intuitive cues or inefficient guidance. Based on computer vision and vibrotactile encoding, this paper presents an interactive system that provides blind people with intuitive spatial cognition. Different from the traditional auditory feedback strategy based on speech cues, this paper firstly introduces a vibration-encoded feedback method that leverages the haptic neural pathway and enables the users to interact with objects other than manipulating an assistance device. Based on this strategy, a wearable visual module based on an RGB-D camera is adopted for 3D spatial object localization, which contributes to accurate perception and quick object localization in the real environment. The experimental results on target blind individuals indicate that vibrotactile feedback reduces the task completion time by over 25% compared with the mainstream voice prompt feedback scheme. The proposed object localization system provides a more intuitive spatial navigation and comfortable wearability for blindness assistance.

关键词： Location awareness Visualization Three-dimensional displays Navigation Interactive systems Blindness Neural pathways

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