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Deep Learning Approach for Automatic Cardiovascular Disease Prediction Employing ECG Signals

computer Modeling in engineering & sciences 2023年第11期137卷 1677-1694页

作者： Muhammad Tayyeb Muhammad Umer Khaled Alnowaiser Saima Sadiq Ala’Abdulmajid Eshmawi Rizwan Majeed Abdullah Mohamed Houbing Song Imran Ashraf Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science Khwaja Fareed University of Engineering and Information TechnologyRahim Yar KhanPakistan Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Faculty of Computer Science and Information Technology Universiti Tun Husein OnnMalaysia(UTHM)Bahru80536Malaysia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Information Systems University of MarylandBaltimore CountyBaltimoreMD 21250USA Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Korea

Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed ***,electrocardiogram(ECG)data is analyzed by medical experts to determine the cardiac abnormality,which is *** addition,the diagnosis requires experienced medical experts and is ***,automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning *** study proposes a simple multilayer perceptron(MLP)model for heart disease prediction to reduce computational *** dataset containing averaged signals with window size 10 is used as an *** competing deep learning and machine learning models are used for comparison.K-fold cross-validation is used to validate the *** outcomes reveal that the MLP-based architecture can produce better outcomes than existing approaches with a 94.40%accuracy *** findings of this study show that the proposed system achieves high performance indicating that it has the potential for deployment in a real-world,practical medical environment.

关键词： Cardiovascular disease prediction electrocardiograms deep learning multilayer perceptron

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A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing

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Archives of Computational Methods in engineering 2025年第1期32卷 269-310页

作者： Hosseinzadeh, Mehdi Rahmani, Amir Masoud Husari, Fatimatelbatoul Mahmoud Alsalami, Omar Mutab Marzougui, Mehrez Nguyen, Gia Nhu Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia School of Computer Science Duy Tan University Da Nang Viet Nam Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam13120 Korea Republic of

In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheuristic algorithms that was invented in 2022 based on the foraging and migration behavior of the hummingbird for modeling and solving optimization problems. The algorithm initially starts with an initial random population of solutions. It then uses iterative processes and hummingbird position updates to balance exploration and exploitation toward the most optimal solutions. This paper has a detailed and extensive review of the AHA algorithm considering the aspects of hybrid, improved, binary, multi-objective, and optimization problems. In addition, a wide range of applications of AHA in various fields such as feature selection, image processing, scheduling, Internet of Things, classification, clustering, financial and economic issues, forecasting, wireless sensor networks, and many engineering challenges are explored. The statistical and numerical results showed that the AHA algorithm with deep learning methods, Levy flight, and opposition-based learning had the best performance. Also, the AHA algorithm is most widely used in solving multimodal optimization problems and continuous functions. © The Author(s) under exclusive licence to International Center for Numerical Methods in engineering (CIMNE) 2024.

关键词： Optimization

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Efficient UAV Routing Strategies for Wireless Sensor Network Data Retrieval

Efficient UAV Routing Strategies for Wireless Sensor Network...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Al-Sharhanee, Kareem Ali Malalah Najim, Ali Hamza Alheeti, Khattab M. Ali Satar, Nurhizam Safie Mohd Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad10011 Iraq Department of Computer Technical Al-Diwaniyah Iraq College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

WSNs are applied in many disciplines where certain conditions require the ability to adapt to network sink mobility and changes in the dynamics of the area coverage. To meet these needs, it is necessary to develop integrated systems where the ''terrestrial' systems and the Unmanned Aerial Vehicles (UAVs)' collect data from sensors efficiently. Planning of UAV trajectories is another challenging issue if many WSN nodes are randomly deployed. This challenge has a big impact on the overall effectiveness of the system. This paper presents a novel approach for reducing the traveled distance of unscrewed aerial vehicles (UAVs) to improve the data collection efficiency in WSNs with poor node density. Yet the method that has been developed is aimed at increasing 'the efficiency and efficacy of data collection... with the assistance of Unmanned Aerial Vehicles (UAVs)' which means this work reveals practical application solutions. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Optimal Resource Allocation for NOMA Wireless Networks

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computers, Materials & Continua 2023年第2期74卷 3249-3261页

作者： Fahad R.Albogamy M.A.Aiyashi Fazirul Hisyam Hashim Imran Khan Bong Jun Choi Turabah University College Computer Sciences ProgramTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Jazan UniversityJazanP.O.Box 218Saudi Arabia Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra Malaysia(UPM)43400SerdangMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarP.O.Box 814Pakistan School of Computer Science and Engineering Soongsil UniversitySeoulKorea

The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user ***-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user *** its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power *** contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum ***,it will increase the complexity of its *** power allocation techniques will have a direct impact on the system’s *** a result,in order to boost the system capacity,an efficient power allocation mechanism must be *** research developed an efficient technique based on conjugate gradient to solve the problem of downlink power *** major goal is to maximize the users’maximum weighted sum *** suggested algorithm’s most notable feature is that it converges to the global optimal *** compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.

关键词： Non-orthogonalmultiple access wireless networks power domain 5G networks

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Improving the review classification of Google apps using combined feature embedding and deep convolutional neural network model

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4257-4272页

作者： Aslam, Naila Alzamzami, Ohoud Xia, Kewen Sadiq, Saima Umer, Muhammad Bisogni, Carmen Ashraf, Imran School of Electronics and Information Engineering Hebei University of Technology Tianjin China Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Computer Science Information Technology The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Salerno Fisciano Italy Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of

Online reviews play an integral part in making mobile applications stand out from the large number of applications available on the Google Play store. Predominantly, users consider posted reviews for appropriate app selection. Manual categorization of such reviews is both inefficient and time-consuming. Therefore, automatic analysis of the sentiments of such reviews provides fast suggestions for new users and facilitates their selection of the appropriate app. However, data imbalance is a major challenge for performing class prediction of such reviews as their distribution is sparse and often leads to low accuracy. This work proposes a framework to overcome this limitation. Extensive experiments are performed using the original and balanced data with the synthetic minority oversampling technique (SMOTE) and adaptive synthetic sampling (ADASYN). Additionally, deep learning and machine learning models are evaluated using FastText, FastText Subword, global vector (GloVe), and their combinations for word representation. Baseline machine learning models, including random forest, extra tree classifier, gradient boosting, Naive Bayes, logistic regression (LR), stochastic gradient descent (SGD), and voting classifier (VC) that combines LR and SGD, are used for comparison. The outcomes show that the convolutional neural network using a combination of word embedding techniques produces the most accurate results. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolutional neural networks

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Development of Authentication Framework Using GAIT for IoT communication Models

Development of Authentication Framework Using GAIT for IoT C...

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2024 International Conference on Optimization Computing and Wireless communication, ICOCWC 2024

作者： Nishat, Humaira Balaji, T.S. Shargunam, S. Sasikala, R. Rani, K. Cvr College of Engineering Department of Electronics and Communication Engineering Telangana Hyderabad India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai600026 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Srivilliputhur India Karpagam College of Engineering Department of Cyber Security Tamilnadu Coimbatore641032 India M. Kumarasamy College of Engineering Department of Information Technology Tamilnadu Karur India

ISBN: (纸本)9798350383348

The data collected by the authorised sensor devices is transferred to the cloud for safe storage. communication occurs via a secure key management system designed for a decentralised network. Subsequently, it is important to guarantee that only authorised individuals are able to get this data from the cloud. Ensuring user authentication is of utmost importance in resource-constrained IoT devices due to the large number of users accessing sensitive data. The current user authentication methods suffer from accuracy, error rate, and implementation time problems. A novel solution is offered to tackle these challenges by introducing a sophisticated stacked ensemble authentication architecture that utilises cellphones for IoT systems. The suggested authentication approach utilises gait analysis to authenticate individuals by recognising their unique patterns of human movement. A smartphone equipped with an accelerometer and gyroscope sensor is used to gather gait characteristics. The proposed system has two components: preprocessors and classifiers. The preprocessor performs noise reduction and feature extraction. Subsequently, stacked ensemble machine learning classification models, including base and meta classifiers, are used to classify the characteristics, such as linear acceleration, rotation rate, and gravity, for the purpose of user authentication. The accuracy and error rate of the outcomes are compared with the schemes that already exist. The observation indicates that the suggested framework attains a 95% accuracy rate, a lower error rate, and optimised execution time. © 2024 IEEE.

关键词： GAIT Authentication Internet of Things (IoT) KNN

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Packet Drop Reduction by Volunteer nodes in Mobile Wireless Sensor Networks 2

Packet Drop Reduction by Volunteer nodes in Mobile Wireless ...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Mishra, Nilamadhab Latha, P. Rekha, K. Sashi MuthuKumaran, B. School of Computer Science and Engineering Vit Bhopal University Madhya Pradesh Bhopal India St.Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350347579

In Mobile Wireless Sensor Networks (MWSN), minimizing packet drop is vital. Many strategies are proposed to manage the packet drops. Though, these strategies need to be more capable of handling the packet drops proficiently. This approach proposes a Packet Drop Reduction using Volunteer nodes (PDRV) in MWSN. Volunteer nodes aim to reduce packet drops and delays in the network. In this approach, the volunteer node listens to the discharge data packet and then allocates the transmitter node. These nodes are selected based on node linkage, energy, communication processing time, and bandwidth. In addition, PDRV verifies the hash signature with One Time Key (OTK) for transmitting secure data. Hence, it reduces both the packet drops and the delay. Here, the volunteer nodes are selected by the node ability. The node linkage. energy, communication processing time, and bandwidth evaluate node ability. Experimental outcomes illustrate that the PDRV mechanism has reduced the network delay and packet losses. © 2023 IEEE.

关键词： Bandwidth

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection systems

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computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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Super Six - Axis Cord Influenced Linear Laser for Personal Flower Management in Cultivation with Live Control systems is Connected to The Robot Manipulator 11

Super Six - Axis Cord Influenced Linear Laser for Personal F...

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11th International Conference on system Modeling and Advancement in Research Trends, SMART 2022

作者： Narasimhayya, B.E. Vinay Kumar, S.B. Department of Computer Science and Engineering Bangalore India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Bangalore India

ISBN: (纸本)9781665487320

In today's agribusiness, using robotics, machine learning, and also the web of things has become standard practise. The agricultural business is being forced to embrace such new methods in order to address issues like massive unemployment, rising food consumption with declining quality farmland, efficiency per square land, and deteriorating food quality due to global warming. As a result of the aforementioned difficulties and the world's increasing population, research and innovation in agricultural technology have become more crucial nowadays. In this document, we suggest an AI-powered wire automaton with six levels of autonomy and an omega automaton with degrees of opportunity that can help with agricultural tasks like sorting, pest management, picking strawberries and flowers, fertiliser application, and tracking of plant development, NPK tiers, and soil moisture. The robotic is called Farmbot. The Based criteria may communicate crop information to distant servers where it can be processed and retrieved to get further insight into soil quality. It can also be managed and watched remotely by a home computer or via a mobile internet software. The Farmbot may be programmed to do routine tasks including continuous monitoring, collecting fruit, cleaning, applying fertiliser, and gathering information about each plant. The Farmbot has direct exposure to just about any area of the field thanks to its placement over a moveable four wheels bot. This Farmbot's goal is mainly on providing individualised plant maintenance and aiding producers in raising output. As a design illustration of a functioning version for the imagined Farmbot, a 3D depiction is shown. The simulation's outcomes are shown. © 2022 IEEE.

关键词： Remote control

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Deep Reinforcement Learning for Autonomous Drone Navigation in Cluttered Environments 5

Deep Reinforcement Learning for Autonomous Drone Navigation ...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Chandrashekhar, A. Rawate, Amit Dhanamathi, A. Dasari, Rakeshnag Shinkar, Rajeshri Pravin Pavithra, G. Icfai Foundation for Higher Education Faculty of Science & Technology Department of Mechanical Engineering Telangana Hyderabad India Maharashtra Institute of Technology Department of Electronics and Computer Engineering Maharashtra Aurangabad India Roever Engineering College Department Of Computer Science and Engineering Tamilnadu Perambalur India Acharya Nagarjuna University Department of CSE Nagarjuna Nagar Guntur India SIES College of Arts Science and Commerce Department of Computer Science Sector 5 Nerul Navi Mumbai India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350351378

The application of Deep Reinforcement Learning (DRL) to enhance the autonomous navigation capabilities of drones in environments that are complex and congested. The complications involved with guiding drones across restricted places, overcoming obstructions, and navigating among clutter are addressed in this article. Through the utilization of DRL techniques, the framework that has been proposed gives drones the ability to learn and alter their navigation strategies on their own through the process of trial and error, hence optimization of real-time decision-making. The incorporation of deep neural networks for the processing of sensory data makes it possible for drones to comprehend their environment, which in turn makes it easier for them to make educated judgments with regard to safe navigation. In order to demonstrate the potential of DRL in boosting the autonomy and robustness of drone navigation systems in demanding environments, the effectiveness of the technique is evaluated using simulations and experiments conducted in the real world. The findings make a contribution to the development of autonomous drone technology, which has implications for a variety of applications including environmental monitoring, search and rescue operations, and surveillance. © 2024 IEEE.

关键词： Drones

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