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2nd EAI International Conference on Computational Intelligence and Communications, CICom 2021

作者： Nyangaresi, Vincent Omollo Al Sibahee, Mustafa A. Abduljabbar, Zaid Ameen Alhassani, Abdulhadi Abduljaleel, Iman Qays Abood, Enas Wahab Faculty of Biological & Physical Sciences Tom Mboya University College Homabay Kenya College of Big Data and Internet Shenzhen Technology University Shenzhen China Computer Technology Engineering Department Iraq University College Basrah Iraq Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen China Communications Engineering Department Iraq University College Basrah Iraq Department of Computer Science College of Computer Science and Information Technology University of Basrah Basrah Iraq Department of Mathematics College of Science University of Basrah Basrah Iraq

ISBN: (纸本)9783031195228

Security and efficiency during the handover process are critical challenges in 5G heterogeneous networks owing to its strict low latency and security requirements. The 5G networks support many services and communication paradigms. Examples of these applications include as Internet of Things (IoT), vehicular ad hoc networks (VANETs), device-to-device communication (D2D), among others. As such, security lapses in the core network can easily escalate to the supported communication devices and networks and vice versa. Unfortunately, legacy cellular networks deploy the received signal strength indicator (RSSI) as the only parameter during the handover process. This results in increased handover latencies, packet losses, and high handover failure probability. Consequently, other protocols have been introduced that deploy additional handover parameters such as data rates, service costs, battery power, bandwidth, and energy consumption. However, these schemes only deal with efficiency of the handoff process but rarely consider security and privacy issues. Conversely, security and privacy protocols rarely address efficiency aspects of the handover process. In this paper, an algorithm that leverages on artificial neural networks coupled with fuzzy logic for target cell selection is presented. Based on the obtained results, there is a 56.1% reduction in handover latency and a 38.8% reduction in packet losses when the proposed scheme is deployed. In terms of security, it upholds both backward and forward key secrecy. In addition, desynchronization and replay and attacks are effectively thwarted in the proposed algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： 5G mobile communication systems

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Generating All Invertible Matrices by Row Operations 35

Generating All Invertible Matrices by Row Operations

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35th International Symposium on Algorithms and Computation, ISAAC 2024

作者： Gregor, Petr Hoang, Hung P. Merino, Arturo Mička, Ondřej Department of Theoretical Computer Science and Mathematical Logic Charles University Prague Czech Republic Algorithm and Complexity Group Faculty of Informatics TU Wien Austria Institute of Engineering Sciences Universidad de O’Higgins Rancagua Chile

ISBN: (纸本)9783959773546

We show that all invertible n × n matrices over any finite field Fq can be generated in a Gray code fashion. More specifically, there exists a listing such that (1) each matrix appears exactly once, and (2) two consecutive matrices differ by adding or subtracting one row from a previous or subsequent row, or by multiplying or dividing a row by the generator of the multiplicative group of Fq. This even holds in the more general setting where the pairs of rows that can be added or subtracted are specified by an arbitrary transition tree that has to satisfy some mild constraints. Moreover, we can prescribe the first and the last matrix if n ≥ 3, or n = 2 and q > 2. In other words, the corresponding flip graph on all invertible n × n matrices over Fq is Hamilton connected if it is not a cycle. This solves yet another special case of Lovász conjecture on Hamiltonicity of vertex-transitive graphs. © Petr Gregor, Hung P. Hoang, Arturo Merino, and Ondřej Mička.

关键词： Matrix algebra

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Video integrity verification based on blockchain 2

Video integrity verification based on blockchain

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： Kamal, Randa Hemdan, Ezz El-Din El-Fishway, Nawal Menoufia University Department of Computer Science and Engineering Faculty of Electronic Engineering Menouf Egypt

ISBN: (纸本)9781665418423

with the existence of the new revolution of software techniques, tampering with the videos became easier than ever. For retaining the integrity of the captured scenes, novel methods are required. Blockchain is now one of the most interesting integrity verification techniques. Blockchain provides trust among the participant nodes;"users"without the need of a trusted third party. In this paper, we propose a new blockchain-based framework for integrity verification of videos. In the proposed framework, the scene is captured and hashed in its original state and the captured frame;"video frame"is stored as a reference frame. The hash value, frame name, and timestamp of the capturing time are stored in a CSV file. The genesis block of a permissioned blockchain is created with this data. Other frames are captured for the same scene every predefined period and hashed. If the new hash value is identical to the reference hash, then the new frame is deleted to preserve storage space, and the metadata of the frame is stored in the same CSV file. If the hash values are not identical, then the name of the frame is changed to the timestamp of the capturing time, the frame then is propagated to all participant nodes, an email is sent to a predefined mail to take the appropriate action, and finally, a new block will be created with the new metadata. The proposed system ensures the integrity of the captured videos, ease to use and robustness. © 2021 IEEE.

关键词： Blockchain

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Advances in Medical Image Processing for Liver Tumour Recognition: A Comprehensive Survey

Advances in Medical Image Processing for Liver Tumour Recogn...

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computer, Communication and Control (IC4), International Conference on

作者： B. Deepa C. N. Marimuthu S. Geetha S. Maheswari Faculty of Information and Communication Engineering Anna University Chennai India Department of Electronics and Communication Engineering Nandha Engineering College Erode India Department of Computer Science and Engineering Nandha Engineering College Erode India

The last several decades have seen a lot of activity in the medical image processing domain especially when it comes to the segmentation of liver and liver tumours. The survival rate of liver cancer is rather low when compared to some other types of cancer. Nevertheless, early detection greatly improves the prognosis of liver cancer patients. As a consequence, multiple researchers have developed automated deep-learning (DL) systems for forecasting the development of cancer cells using various medical imaging modalities. A summary of some previously published studies on liver cancer diagnosis is currently lacking in review publications. But new mechanisms and architecture in the prognosis of liver cancer were beyond the possibility of these investigations. This review emphases on the topologies of DL for liver cancer diagnosis. The survey that follows examines the datasets used, describes existing DL-based designs, assesses the benefits and drawbacks of earlier studies, and discusses image processing techniques. Furthermore, a thorough examination of several imaging modalities, performance metrics and results, challenges and future research goals are provided. The comparison study of different methods reveals that HFCNN has the highest accuracy, at 97.22%.

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Mathematical Modelling of Quantum Kernel Method for Biomedical Data Analysis

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computers, Materials & Continua 2022年第6期71卷 5441-5457页

作者： Mahmoud Ragab Ehab Bahauden Ashary Maha Farouk S.Sabir Adel A.Bahaddad Romany F.Mansour Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

This study presents a novelmethod to detect themedical application based on Quantum Computing(QC)and a few Machine Learning(ML)*** has a primary advantage i.e.,it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of ***,this model is suggested for medical application only by recent researchers.A novel strategy i.e.,Quantum KernelMethod(QKM)is proposed in this paper for data *** this QKM process,Linear Tunicate Swarm Algorithm(LTSA),the optimization technique is used to calculate the loss function initially and is aimed at medical *** output of optimization is either 0 or 1 i.e.,odd or even in *** this output value,the data is identified according to the ***,the method also reduces time,saves cost and improves the efficiency by feature selection process i.e.,Filter *** the features are extracted,QKM is deployed as a classification model,while the loss function is minimized by *** motivation of the minimal objective is to remain ***,some computations can be performed more efficiently by the proposed *** testing,the test data was evaluated by minimal loss *** outcomes were assessed in terms of accuracy,computational time,and so *** this,databases like Lymphography,Dermatology,and Arrhythmia were used.

关键词： Medical data classification feature selection qkm classifier ltsa optimization

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A Convolutional Neural Network for Ultra-Wideband Radar-Based Hand Gesture Recognition

A Convolutional Neural Network for Ultra-Wideband Radar-Base...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350326413

New developments in sensing technology have enabled the creation of improved assistive devices that enhance daily eldercare routines and offer personalized care to users. Wearable or ambient sensors can now detect a person's behavior, but the need for constant recharging due to their high energy consumption makes wearing such devices around the clock difficult. In this paper, a new sensing technique based on deep learning (DL) approaches for hand gesture recognition (HGR) using an ultra-wideband (UWB) radar sensor is introduced. The study examines the recognition performance of a convolutional neural network (CNN) for HGR and utilizes the UWB-gestures dataset to train and test the DL networks in various experiments with different scenarios. The experimental results demonstrate that the CNN achieved the highest F1-score of 97.19%. © 2023 IEEE.

关键词： Convolutional neural networks

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Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN

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computers, Materials & Continua 2023年第2期74卷 2641-2659页

作者： Muhammad Nadeem Ali Arshad Saman Riaz SyedaWajiha Zahra Muhammad Rashid Shahab S.Band Amir Mosavi Department of Computer Science Abasyn UniversityIslamabad44000Pakistan Department of Computer Science National University of TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliuYunlin64002Taiwan Institute of Information Society University of Public ServiceBudapest1083Hungary John von Neumann Faculty of Informatics Obuda UniversityBudapestHungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaSlovakia

Cloud computing is one of the most attractive and cost-saving models,which provides online services to *** computing allows the user to access data directly from any *** nowadays,cloud security is one of the biggest issues that *** types of malware are wreaking havoc on the *** on the cloud server are happening from both internal and external *** paper has developed a tool to prevent the cloud server from spamming *** an attacker attempts to use different spamming techniques on a cloud server,the attacker will be intercepted through two effective techniques:Cloudflare and K-nearest neighbors(KNN)*** will block those IP addresses that the attacker will use and prevent spamming ***,the KNN classifiers will determine which area the spammer belongs *** the end of the article,various prevention techniques for securing cloud servers will be discussed,a comparison will be made with different papers,a conclusion will be drawn based on different results.

关键词： Intrusion prevention system spamming KNN classification spam cyber security botnet

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ScCADE: A Superior Tool for Predicting Perturbation Responses in Single-Cell Gene Expression Using Contrastive Learning and Attention Mechanisms

ScCADE: A Superior Tool for Predicting Perturbation Response...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ou, Jingfeng Li, Jiawei Xia, Zhiliang Dai, Shurui Ding, Yulian Guo, Yan Jiang, Limin Tang, Jijun Southern University of Science and Technology College of Engineering Shenzhen518055 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518107 China Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Miami Department of Public Health Sciences MiamiFL33136 United States

ISBN: (纸本)9798350386226

The advent of single-cell transcriptomics has revolutionized our ability to analyze cellular heterogeneity and dynamics at a fine resolution, yet covering the vast array of potential perturbations remains challenging due to biological variability. To address this, we propose scCADE, a novel computational approach utilizing contrastive learning and an attention mechanism to decouple gene expression signatures and predict cellular responses to perturbations. scCADE excels in predicting responses in cells to perturbations observed in other cells but not yet seen in the target cells. Through rigorous ablation studies and validation across three datasets involving drug and gene editing perturbations, scCADE consistently outperformed existing methods, underscoring its efficacy and potential to advance genomics and personalized medicine by accurately forecasting responses to novel perturbations. © 2024 IEEE.

关键词： Contrastive Learning

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A Deep Learning Approach for Detecting Diabetic Retinopathy

A Deep Learning Approach for Detecting Diabetic Retinopathy

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International Conference on computer and Applications (ICCA)

作者： Mohamed Hossam Hassan Nabil Laila Hammam Hany Ayad Bastawrous Gamal A. Ebrahim Electrical Engineering Department The British University in Egypt Cairo Egypt Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlans Computer and Systems Engineerig Department Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The World Health Organization (WHO) reports that diabetic retinopathy affects one-third of diabetics, regardless of their stage of the disease. Several research efforts are focused on its automated detection and diagnosis. Identifying diabetic retinopathy is crucial due to the damage that occurs to the blood vessels of the eye retina, leading to vision blur or even complete blindness. Thus, an annual checkup is needed for people with diabetes. Moreover, uncontrolled sugar levels for diabetes patients could worsen the current stage of diabetic retinopathy. Consequently, automated detection can greatly contribute to the treatment of disease. This can be carried out through several algorithms, including deep learning models and support vector machines, in addition to transfer learning. This contribution proposes a new approach for diabetic retinopathy automated detection based on convolutional neural network (CNN) models. The proposed model provides both binary and multi-class detection. Both scenarios have shown promising results, where the training accuracies of both the binary classification and the multi-class are 92% and 94%, respectively.

关键词： Deep learning Training Support vector machines Diabetic retinopathy Computational modeling Transfer learning Retina Convolutional neural networks Sugar Diseases

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Segmentation of butterflies from complex agro-ecological images using quantum mechanics and spatial refinement

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Digital Signal Processing: A Review Journal 2023年 135卷

作者： Filali, Idir Achour, Brahim Ramdani, Mohamed LARI laboratory Department of Computer Science Faculty of Electronic Engineering and Computer Science Mouloud Mammeri University Tizi-Ouzou15000 Algeria

The presence of butterflies is an important indicator of environmental health and stability. Its pollination ability increases agricultural yields. However, some butterflies may also cause crop degradation. Butterfly segmentation in complex agro-ecological environments is therefore a preliminary step whose accuracy affects species recognition. We propose in this paper a segmentation method composed of a sequence of three steps. First, we combine background connectivity with local distinctiveness measures to locate roughly butterfly areas. Second, we deploy an adaptation of the Schrödinger equation on a graph propagation process that checks regional similarities to those areas. Finally, we perform a spatial refinement that optimizes the butterfly detection. Both objective and subjective comparative experiments suggest that our method performs well against image artifacts. Obtained results outperform those of some state-of-the-art methods that reach high performance. © 2023 Elsevier Inc.

关键词： Quantum theory

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