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Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

computers, Materials & Continua 2023年第5期75卷 4485-4499页

作者： Fatma Taher Hamoud Alshammari Lobna Osman Mohamed Elhoseny Abdulaziz Shehab Eman Elayat College of Technological Innovation Zayed UniversityDubaiUAE Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Electronics and Communications Engineering Delta Higher Institute for Engineering&TechnologyMansouraEgypt College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates Department of Information Systems Mansoura UniversityMansoura35516Egypt Department of Teacher Preparation Faculty of Specific EducationMansoura UniversityMansouraEgypt

Cardiac diseases are one of the greatest global health *** to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent *** article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia *** fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection *** ensemble of classifiers is then applied to the fusion’s *** proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of ***,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal ***,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized *** preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection *** short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive *** proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art *** achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.

关键词： Cardiac arrhythmia preprocessing missing values classification model fusion

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Security enhancement of LSB-based audio steganography method

Security enhancement of LSB-based audio steganography method

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2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Jezdimirovic, Jovana Pekez, Nenad Kovacevic, Jelena University of Novi Sad Faculty of Technical Sciences Computing and Control Engineering Department Novi Sad Serbia Systems LLC RT-RK Institute for Computer Based Novi Sad Serbia

ISBN: (纸本)9798350347722

The rapid development of digital technology has brought about the challenge of ensuring information security. Cryptography and steganography are among the various techniques available to address this challenge. These techniques come in different forms and provide reliable means of securing information and communication. Despite the existence of numerous options, new variations are still emerging. This paper concentrates on Least Significant Bit (LSB) coding, which is one of the most widely recognized and frequently utilized steganography techniques. The advantages of LSB-based coding methods are high capacity and low complexity. However, predictability is an issue. Audio files' sizes and data redundancy make audio data perfect for steganography, as it is possible to embed large amounts of secret information and easily transmit the signal via various communication channels. However, the use of LSB coding in audio steganography has a drawback due to the extreme sensitivity of the human auditory system (HAS). This means that any noise added to the audio with data embedded in the LSBs can be detected by the HAS, which is dependent on the number of LSBs used. This work presents an improvement of the LSB-based approach, the XORing of LSBs method, with an emphasis on enhanced security. The method achieves this by randomly picking the pairs of bits to be XOR'd while preserving the characteristics of classic LSB methods. To verify the method, it has been implemented on the Cirrus Logic DSP, proving that it may be efficiently used in a real-Time audio environment without interfering with HAS sensitivity levels. © 2023 IEEE.

关键词： Steganography

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Enhancing Stock Price Prediction: A Hybrid Approach Leveraging Large Language Models and Deep Learning

Enhancing Stock Price Prediction: A Hybrid Approach Leveragi...

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International Conference on computer and Applications (ICCA)

作者： Mohamed Erfan Ahmed Gamal A. Ebrahim Marwa A. Abdelaal Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Computer Science Department Faculty of Computers and Information Sadat Academy for Management Sciences Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Accurate stock price prediction is a challenging yet crucial goal in finance, with significant implications for investment decisions and risk management. This paper presents a comprehensive review of machine learning techniques for stock price prediction, examining traditional methods such as regression and ensemble models, as well as advanced approaches that integrate sentiment analysis and textual data sources. With the emergence of powerful Large Language Models (LLMs) such as ChatGPT, Llama and Gemini, we explore their potential for enhancing predictive accuracy using historical stock data. Key challenges are discussed, including data quality, model interpretability, and adapting to dynamic market conditions. Additionally, this paper proposes a trustworthy stock price prediction model based on LLMs enabling informed investment decision-making. Experimental results demonstrate that ChatGPT-4o model achieved a prediction accuracy of approximately 97%, which can be improved by tuning model parameters. Consequently, the paper highlights the potential of LLMs in improving stock price forecasting.

关键词： Adaptation models Accuracy Biological system modeling Large language models Semantics Predictive models Data models Forecasting Tuning Investment

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Comprehensive Review of Capsule Networks with a Case Study on Potato Leaf Disease Detection Using CapsNet and Attention Mechanism 8th

Comprehensive Review of Capsule Networks with a Case Stud...

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8th International Symposium on Intelligent Informatics, ISI 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai603 203 India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819721467

Accurate solutions are needed for a variety of computer vision applications, including medical imaging, object detection, and recognition. Such complicated challenges are beyond the capabilities of artificial intelligence and machine learning, which mainly rely on data and algorithms to learn. The path to Deep Learning (DL) is thus paved. Even though deep learning is incredibly effective at solving complex problems, it is invariant, meaning that it ignores the spatial relationship between the features and instead searches for features only. This can cause the model’s performance to suffer. Additionally, because the model is invariant, gathering a large volume of training data is a difficult undertaking. To address this, the Capsule Network (CapsNet), whose performance outperforms Deep Learning techniques, was introduced. Despite its success, applications of CapsNet and working remain a mystery. Thus, the performance of CapsNet across many applications and obstacles is examined in this study together with the predecessors of CapsNet. Additionally, this paper explores the use of Capsule Networks with an attention mechanism (CapsNet-ATM) for predicting potato leaf diseases. The performance of CapsNet-ATM in this crucial agricultural application is thoroughly assessed and contrasted with the well-known models VGG-16 and VGG-19, providing insight into the system’s efficacy and promise as a reliable tool for the detection and prediction of potato leaf disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Dual-Critic Deep Deterministic Policy Gradient Approach for Task Offloading in Edge-Fog-Cloud Environment

A Dual-Critic Deep Deterministic Policy Gradient Approach fo...

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International Conference on computer and Applications (ICCA)

作者： Moshira A. Ebrahim Gamal A. Ebrahim Hoda K. Mohamed Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt

Computation intensive IoT applications have grown exponentially over the past few years, which has led to the rise in popularity of the edge, fog, and cloud computing paradigms among mobile devices with embedded smart chips. Mobile Edge Computing (MEC) and Fog Computing (FC) usually cooperate with cloud computing to fulfill the high-demand requirements and optimize the network resources of complex computational offloading environments. In this paper, the topic of computational offloading for the hierarchical Edge-Fog-Cloud (EFC) computing is examined where edge, fog, and cloud nodes function as relay nodes that are constructed for latency critical and computations intensive jobs. Hierarchical EFC makes use of both centralized and distributed computing architectures considering diverse channel quality, network selection, and cloud access. This study proposes an improved Deep Reinforcement Learning (DRL) algorithm to find the optimal decision for offloading a computational task in EFC networks. Simulation results validate the proposed DRL-based model by comparing it with other alternative algorithms. Experimental results demonstrate how the proposed approach can efficiently reduce the latency delay by 43% and energy cost by 32% of IoT end devices. Additionally, it can achieve a higher success rate compared with the other related algorithms with more than 16%.

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Predicting Breast Cancer Survival: Comparative Analysis of Machine Learning and Deep Learning Models 5

Predicting Breast Cancer Survival: Comparative Analysis of M...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Bathool, Saliha Alashetty, Ashvini Farquad, M.A.H. School of Engineering And Design Alliance University Bengalore India School of CS &IT. JAIN Deemed-To-Be University Bengalore India Higher Colleges of Technology Faculty of Computer Information Systems Ras Al-Khaimah United Arab Emirates

ISBN: (纸本)9798331530013

The forward-looking study is grounded in the comprehensive chronic cancer death data of Surveillance, Epidemiology, and End Results, one of the most remarkable datasets for breast cancer survival rates ever examined. The approach of this study is to divide patients' data under a particular case feature;in this case, we explore patients' survival rate for breast cancer, which is our main goal. This is done by using the T-test, log-rank test, ANOVA test, and chi-square test, as well as linear regression. In the second part, we include everything from the prediction software to building models. Logistic Regression, Decision Tree and Random Forest Support Vector Machines (S.K.), K-Nearest Neighbors, Naive Bayes, Gradient Boosting, Convolutional Neural Networks, and Long Short-Term Memory Networks Models are heavily evaluated based on various key metrics such as accuracy, precision, and recall. Parameter optimization is performed for each model, and robust validation techniques are implemented to ensure the reliability and generalizability of our findings. The clustered approach is chosen to carry out a comprehensive and systematic explanation of the different factors that may influence the lifespan of women who have survived the cancer and the use of ML and DL models as a predictive tool. Survival anticipation for breast cancer is imperative in the context of personalized therapies. ML and DL models have proven to be powerful instruments in predicting the future states of patients based on their data. © 2024 IEEE.

关键词： Convolutional neural networks

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Experimental evaluation of MU-MIMO in TDD environment for 5G NR using exploiting channel reciprocity

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4651-4666页

作者： Kumar, Rajesh Sinwar, Deepak Singh, Vijander Department of Computer and Communication Engineering Manipal University Jaipur Rajasthan Jaipur 303007 India Department of IoT and Intelligent Systems Manipal University Jaipur Rajasthan Jaipur 303007 India Department of ICT and Natural Sciences Faculty of Information Technology and Electrical Engineering NTNU - Norwegian University of Science and Technology Ålesund Norway

5G New Radio (NR) operates in two frequency ranges viz. Frequency Range 1 (sub-6 GHz band) and Frequency Range 2 (millimeter wave communication). It utilizes Time Division Duplex (TDD) communication, allowing the same frequency band for uplink and downlink transmission using sounding reference signals. These signals help estimate channel conditions for scheduling downlink traffic. Multi-User Multiple Input Multiple Output (MU-MIMO) technology enables simultaneous data transmission to multiple users using multiple antennas. This study examines MU-MIMO performance in TDD by analyzing variables like radio conditions, modulation schemes, code rates, precoding algorithms, and sub-carrier spacing. Simulation results show that Zero Forcing (ZF) precoding outperforms Block Diagonalization (BD) and Regularized Zero Forcing (RZF) in good radio conditions. However, in bad radio conditions, RZF performs better than ZF. Choosing higher code rates in 16 and 64-QAM and lower code rates in 256-QAM from the MCS table improves throughput. This indicates that MCS and user selection are crucial for higher throughput in TDD-based MU-MIMO for 5G NR. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： 5G new radio Beamforming MU-MIMO Precoding Sounding reference signal Time division duplex

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When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios engineering

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 809-815页

作者： Fei-Yue Wang Qinghai Miao Lingxi Li Qinghua Ni Xuan Li Juanjuan Li Lili Fan Yonglin Tian Qing-Long Han School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIN 46202USA Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

关键词： something Intelligence replace

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Mitigating Backscattered Light of Stimulated Raman Scattering in OTDR Active Monitoring 9

Mitigating Backscattered Light of Stimulated Raman Scatterin...

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9th IEEE International Conference on Photonics, ICP 2022

作者： Amran, R. Hussein, A.H. Anas, S.B.A. Ghazali, M.S. Yaakob, S. Abu Bakar, M.H. Khairi, K. Department of Computer and Communication Systems Engineering Faculty of Engineering Upm Serdang Selangor43400 Malaysia Tm Innovation Centre Cyberjaya Selangor63000 Malaysia

ISBN: (数字)9781665427661

ISBN: (纸本)9781665427661

The mitigation of backscattered light of stimulated Raman scattering (SRS) is proposed and simulated in this paper using long-period fiber Bragg grating (LPFBG). The developed setup allows only the 1650 nm monitoring signal to be received by the optical time-domain reflectometer (OTDR) while leaving the unwanted signal or noise behind. The LPFBG filter was able to reduce other backscattered signals by around 17 dB, which improves the performance of OTDR active fiber monitoring. © 2022 IEEE.

关键词： Stimulated Raman scattering

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Cluster Availability Model with Information Update Following Node Recovery

Cluster Availability Model with Information Update Following...

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2023 Wave Electronics and its Application in Information and Telecommunication systems, WECONF 2023

作者： Bogatyrev, V.A. Bogatyrev, S.V. Sivov, V.V. Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Security Saint Petersburg Russia ITMO University Faculty of Software Engineering and Computer Technology Saint-Petersburg Russia

ISBN: (纸本)9798350348293

A model for evaluating the availability of a fault-tolerant cluster with a constraint on the maximum query service time is proposed. Node recovery in the cluster involves replacing the entire computational node with the latest backup results before the failure. Information update after the backup is achieved through the replication of this information from the working nodes of the cluster. The purpose of the article is to assess the reliability of the cluster and its readiness to timely service requests critical to the waiting time. Restoring up-to-date data in a cluster node after previously entering the data saved during backup into it requires the use of resources of the node in which the data is being restored and the node containing up-to-date data. The specified pair of nodes is excluded from the request servicing process, which reduces the likelihood of timely maintenance. Information recovery in case of failures of all nodes containing replicas of up-to-date data requires more time compared to replication from the memory of a functioning node. © 2023 IEEE.

关键词： Fault tolerance

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