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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Beemkumar, N. Singh, Daljeet Pal Ahmed, Zahid Faculty of Engineering and Technology Department of Mechanical Engineering Bagalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350343274

In this paper, we utilize ANNs to investigate how medicine dosing and management might be improved for the elderly population. The suggested strategy calls for the use of patient-specific data as inputs for the ANNs, such as age, renal and hepatic function, body mass index, and preexisting prescription profiles. The neural network model dynamically changes drug doses using the combination of the Multilayer Perceptron (MLP) algorithm, Backpropagation, and the Stochastic Gradient Descent (SGD) algorithm to maximize therapeutic benefits and reduce the likelihood of adverse drug events. The research highlights the improved accuracy, sensitivity, specificity, precision, and F1 score of the suggested technique compared to numerous conventional methods. As a result, the suggested system has great potential to revolutionize medication management for the elderly population since it is more personalized, flexible, patient-safe, effective, and efficient in its use of resources. © 2023 IEEE.

关键词： Optimization

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Proposed Different Signal Processing Tools for Efficient Optical Wireless Communications

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computers, Materials & Continua 2022年第5期71卷 3293-3318页

作者： Hend Ibrahim Abeer D.Algarni Mahmoud Abdalla Walid El-Shafai Fathi E.Abd El-Samie Naglaa F.Soliman Department of Electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh 11586Saudi Arabia

Optical Wireless Communication(OWC)is a new trend in communication systems to achieve large bandwidth,high bit rate,high security,fast deployment,and low *** basic idea of the OWC is to transmit data on unguided media with *** system requires multi-carrier modulation such as Orthogonal Frequency Division Multiplexing(OFDM).This paper studies optical OFDM performance based on Intensity Modulation with Direct Detection(IM/DD)*** system requires a non-negativity *** paper presents a framework for wireless optical OFDM system that comprises(IM/DD)with different forms,Direct Current biased Optical OFDM(DCO-OFDM),Asymmetrically Clipped Optical OFDM(ACO-OFDM),Asymmetrically DC-biased Optical OFDM(ADO-OFDM),and *** also considers channel coding as a tool for error control,channel equalization for reducing deterioration due to channel effects,and investigation of the turbulence *** evaluation results of the proposed framework reveal enhancement of *** performance of the IM/DD-OFDM system is investigated over different channel models such as AWGN,log-normal turbulence channel model,and ceiling bounce channel *** simulation results show that the BER performance of the IM/DD-OFDM communication system is enhanced while the fading strength is *** results reveal also that Hamming codes,BCH codes,and convolutional codes achieve better BER ***,two algorithms of channel estimation and equalization are considered and *** algorithms include the Least Squares(LS)and the Minimum Mean Square Error(MMSE).The simulation results show that the MMSE algorithm gives better BER performance than the LS algorithm.

关键词： Optical communication systems OWC IM/DD OFDM MMSE LS ADO-OFDM DCO-OFDM ACO-OFDM

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Heart-Net: AMulti-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

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computers, Materials & Continua 2024年第9期80卷 3967-3990页

作者： DeemaMohammed Alsekait Ahmed Younes Shdefat AymanNabil Asif Nawaz Muhammad Rizwan Rashid Rana Zohair Ahmed Hanaa Fathi Diaa Salama Abd Elminaam Department of Computer Science and Information Technology Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia College of Engineering and Technology American University of the Middle EastEgaila15453Kuwait Faculty of Computers Science Misr International UniversityCairo11800Egypt University Institute of Information Technology—PMAS Arid Agriculture University Rawalpjndi46000Pakistan Department of Computer Science International Islamic UniversityIslamabad04436Pakistan Jadara Research Center Jadara UniversityIrbid21110Jordan Information System Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13511Egypt MEU Research Unit Middle East UniversityAmman11831Jordan

Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic *** diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality ***,these methods often require significant time and expertise,making them less accessible in resource-limited *** technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare *** study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant *** is performed using Optimized *** approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health *** proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality *** findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

关键词： Heart diseases magnetic resonance imaging electrocardiogram deep learning classification

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Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model

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computers, Materials & Continua 2023年第7期76卷 975-992页

作者： Hadeel Alsolai Mohammed Rizwanullah Mashael Maashi Mahmoud Othman Amani A.Alneil Amgad Atta Abdelmageed Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityP.O.box 103786Riyadh11543Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Soil classification is one of the emanating topics and major concerns in many *** the population has been increasing at a rapid pace,the demand for food also increases *** approaches used by agriculturalists are inadequate to satisfy the rising demand,and thus they have hindered soil *** comes a demand for computer-related soil classification methods to support *** study introduces a Gradient-Based Optimizer and Deep Learning(DL)for Automated Soil Clas-sification(GBODL-ASC)*** presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision *** the presented GBODL-ASC technique,three major processes are *** the initial stage,the presented GBODL-ASC technique applies the GBO algorithm with the EfficientNet prototype to generate feature *** soil categorization,the GBODL-ASC procedure uses an arithmetic optimization algorithm(AOA)with a Back Propagation Neural Network(BPNN)*** design of GBO and AOA algorithms assist in the proper selection of parameter values for the EfficientNet and BPNN models,*** demonstrate the significant soil classification outcomes of the GBODL-ASC methodology,a wide-ranging simulation analysis is performed on a soil dataset comprising 156 images and five *** simulation values show the betterment of the GBODL-ASC model through other models with maximum precision of 95.64%.

关键词： Soil classification earth sciences machine learning parameter optimization metaheuristics

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Soliton Solutions for Coupled Nonlinear Generalized Zakharov Equations with Anti-cubic Nonlinearity Using Various Techniques

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International Journal of Applied and Computational Mathematics 2024年第1期10卷 1-30页

作者： AlQahtani, Salman A. Alngar, Mohamed E. M. New Emerging Technologies and 5G Network and Beyond Research Chair Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Basic Science Department Faculty of Computers and Artificial Intelligence Modern University for Technology and Information Cairo 11585 Egypt

This study introduces novel exact solutions for the coupled nonlinear generalized Zakharov equations with anti-cubic nonlinearity. Utilizing a variety of mathematical approaches, including the extended trial function method, the rational exp (-ψ(ξ))-expansion method, and three well-known auxiliary equation methods, a spectrum of solutions is derived. These solutions encompass dark solitons, bright solitons, singular solitons, Jacobi elliptic functions, periodic waves, and rational solutions. The unique contribution lies in the use of a phase variable θ(ξ) instead of a phase constant, providing fresh insights into wave behavior. A comparative analysis underscores the originality of the derived solutions, setting this work apart from prior studies. These findings advance our understanding of nonlinear dynamics and mathematical modeling, with potential applications in various domains. © 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Coupled nonlinear generalized Zakharov equations Extended trial function method Rational exp(- ψ(ξ))-expansion method Soliton solutions Three famous auxiliary equation methods

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Moving Towards Nano-Devices in the Context of Smart Bio-Tech Wearables

Moving Towards Nano-Devices in the Context of Smart Bio-Tech...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Ranganathaswamy, M.K. Srivastav, Vaibhav Choudhary, Deeksha Department of Mechanical Engineering Faculty of Engineering and Technology Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineeing & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

Because of the development of wireless capabilities, the network and, in specific, data centers have become a significant problem in our everyday lives. In order to maximize miniaturization and lower the opportunity burden among this equipment, the Network of Things (IoT) attempts to incorporate sensor and actuators into commonplace things. The idea is to use wireless and satellite networks to link these parts to the Internet, producing hard numbers that can then be saved and processed later. The Internet and pcs all provide innovative elements, but Interface TV (iTV) mixes conventional TV with a comparable level of involvement. With the advancement of Smart tvs, computational capability has been added to the device, enabling it to provide services beyond the norm, execute programmes, and realise its networking potential. This study proposes a methodology that comprises and unifies an IoT platform, a genuine tv Streaming show, and a network of wireless sensors. It includes everything from wireless sensor network implementation and transmission to web content accessibility guidelines and finished products through a genuine interactive television application. Hard data provision has now been trialled in a housing area with the goal of enhancing residents' life experiences. Additionally, it combines the potential for using this data to build procedures aimed at lowering energy consumption, enhancing longevity, and promoting the effective use of already available resources. For any implementation with comparable features, the conceptual approach serves as the foundation. © 2023 IEEE.

关键词： cloud computing data centers IoT IoT platform microdevices network of things Next-gen biotechnology Sensors and ThinkSpeak smart TV UN Wearable Devices wearables wireless sensors

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The rise of mobile development: a comparison between Ionic and Flutter 14

The rise of mobile development: a comparison between Ionic a...

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14th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2022

作者： Novac, Ovidiu Constantin Novac, Cornelia Mihaela Ciora, Bogdan Gordan, Cornelia Emilia Gordan, Mircea Ioan Bujdoso, Gyongyi University of Oradea Electrical Engineering and Information Technology Faculty Department of Computers and Information Technology Oradea Romania University of Oradea Electrical Electrical Engineering and Information Technology Faculty Department of Electrical Engineering Oradea Romania University of Oradea Management in Information Technology Engineering and Information Technology Faculty Oradea Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of of Electronics and Telecommunications Oradea Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of Electrical Engineering Oradea Romania University of Debrecen Faculty of Informatics Department of Computer Science and Library and Information Science Debrecen Hungary

ISBN: (数字)9781665495356

ISBN: (纸本)9781665495356

A dive into the world of mobile development frameworks. An overview regarding the mobile technologies, in general but also a close look at two major mobile development frameworks, which are Ionic and Flutter. They are being analyzed at an individual level and furthermore, a comparison between these two technologies is taking place. © 2022 IEEE.

关键词： computers Games Software Marine vehicles Artificial intelligence

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Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

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SN computer Science 2024年第5期5卷 549页

作者： Hamza, Manar Ahmed Hashim, Aisha Hassan Abdalla Motwakel, Abdelwahed Elhameed, Elmouez Samir Abd Osman, Mohammed Kumar, Arun Singla, Chinu Munjal, Muskaan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur 53100 Malaysia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Management Information Systems College of Business Administration in Hawtat bani Tamim Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science College of Post-Graduated Studies University of Science and Technology Omdurman Sudan Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Natural Language Processing (NLP) with Deep Learning (DL) for Tweets Classification includes use of advanced neural network designs to analyse and classify Twitter messages. DL techniques like recurrent neural network (RNN) or transformer-based frameworks like BERT are used to mechanically learn difficult linguistic patterns and contextual info from tweet data. These techniques able to capture subtleties of language with sarcasm, sentiment, and context-specific meanings and making them suitable for tasks like sentiment analysis or topic classification in realm of social media. Leveraging deep symbols learned from great amounts of textual data, these NLP techniques permit precise and nuanced classification of tweets, donating to enhanced information retrieval, sentiment tracking, and trend analysis in dynamic and fast-paced world of social media communication. In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. The goal of the AOADL-TC technique is to identify and discriminate different kinds of sentiments that exist in the tweet data. At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. At last, tuning of parameters related to parallel BiGRU model performed by AOA. An wide set of tests carried out to illustrate better performance of AOADL-TC model. The experimental outcomes portrayed that AOADL-TC technique demonstrates the supremacy of the AOADL-TC technique in terms of different evaluation metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Machine learning Natural language processing Sentiment analysis Social media

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Global video object segmentation with spatial constraint module

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Computational Visual Media 2023年第2期9卷 385-400页

作者： Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory *** some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past *** algorithm uses a global context(GC)module to achieve highperformance,real-time *** GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real ***,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current *** SCM effectively alleviates mismatching of similar targets yet consumes few additional *** added a refinement module to the decoder to improve boundary *** model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

关键词： video object segmentation semantic segmentation global context(GC)module spatial constraint

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Unsupervised Anomaly Detection for IoT Data based on Robust Adversarial Learning 24

Unsupervised Anomaly Detection for IoT Data based on Robust ...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Qiao, Yan Zhang, Benchu Zhang, Zeyu School of Computer Science and Information Engineering Hefei University of Technology Hefei China Macau University of Science and Technology Faculty of Information Technology China

ISBN: (纸本)9798350319934

Anomaly detection based on Generative Adversarial Networks (GANs) has been demonstrated its superiority on in imaging area. However, most existing GAN-based methods require noise-free datasets to accurately train the network, whereas neither collecting an absolute clean dataset or labeling the anomalies in the dataset are quite difficult for Internet of Things (IoT) applications due to the harsh environment and the high dimensionality of the data. In this paper, we propose a robust GAN-based model for anomaly detection in IoT, which can accurately learn the data pattern unsupervisedly even with polluted training data. The new model adopts the framework of Bidirectional GAN (BiGAN) to enable an efficient representation mapping, and integrates the mechanism of Robust Principal Component Analysis (RPCA) to rule out the noises of data from the training set. To improve its performance, a new objective function and scoring function are elaborately designed, while a proximal method and Alternating Direction Method of Multiplier (ADMM) are incorporated into the training process. Comparing our method with four state-of-the-art anomaly detection methods, the experimental results strongly confirm the superiority of our method with polluted IoT datasets. © 2022 IEEE.

关键词： Anomaly detection

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