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Smart Computing and Application (ICSCA), International Conference on

作者： Abdul Mateen Abdul Ahad Salam Zia Ibraheem Shayea Sawsan Ali Knowledge Unit of Systems and Technology (KUST) University of Management and Technology Sialkot Pakistan Istanbul Technical University of Turkey Department of Computer Science University of Sialkot Sialkot Pakistan Department of Electronics and Communication Engineering Istanbul Technical University Department of Computer Science and Engineering University of Ha'il Ha'il Saudi Arabia

Heterogenous 5G wireless Sensor Networks face several constraints throughout the routing process. Reduced costs, minimal energy use, and reliable data transmission between nodes are the primary problems of a heterogenous 5G wireless sensors network. During the transmission process of nodes, vacant holes use the most energy. The two most problematic aspects of heterogenous 5G WSNs are the location mentioned above mistake and the battery drain on individual nodes. However, the network's performance suffers significantly due to data packet loss and excessive energy consumption. Thus, many routing protocols incorporate various energy-saving techniques. In contrast, a routing strategy is suggested in this research to prevent void holes and maximize network stability. In addition, the scalability and efficacy of the proposed protocol are demonstrated by comparing it with state-of-the-art protocols. This study also introduces the idea of location error to increase the network's efficiency. In addition, a scalability study of the suggested systems has been conducted. Extensive simulations were also conducted to evaluate the effectiveness of the proposed technique. The findings confirm that the recommended routing strategy outperforms the competing schemes.

关键词： Wireless sensor networks 5G mobile communication Scalability Packet loss Routing Stability analysis Routing protocols

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Charged quark stars in f(R,T)gravity

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Chinese Physics C 2022年第11期46卷 308-317页

作者： Juan M.Z.Pretel Takol Tangphati Ayan Banerjee Anirudh Pradhan Centro Brasileiro de Pesquisas Físicas Rua Dr.Xavier Sigaud150 URCARio de Janeiro CEP 22290-180RJBrazil Theoretical and Computational Physics Group Theoretical and Computational Science Center(TaCS)Faculty of ScienceKing Mongkut’s University of Technology Thonburi126 Prachauthid Rd.Bangkok 10140Thailand Astrophysics and Cosmology Research Unit School of MathematicsStatistics and Computer ScienceUniversity of KwaZulu-NatalPrivate Bag X54001Durban 4000South Africa Centre for Cosmology Astrophysics and Space ScienceGLA UniversityMathura-281406Uttar PradeshIndia

Recent advances in nuclear theory and new astrophysical observations have led to the need for specific theoretical models applicable to dense-matter physics *** chromodynamics(QCD)predicts the existence of non-nucleonic degrees of freedom at high densities in neutron-star matter,such as quark *** a confining quark matter model,which consists of homogeneous,neutral 3-flavor interacting quark matter with O(m_(s)^(4))corrections,we examine the structure of compact stars composed of a charged perfect fluid in the context of f(R,T)*** system of differential equations describing the structure of charged compact stars has been derived and numerically solved for a gravity model with f(R,T)=R+2β*** simplicity,we assumed that the charge density is proportional to the energy density,namely,ρ_(ch)=αρ.It is demonstrated that the matter-geometry coupling constant β and charge parameter α affect the total gravitational mass and the radius of the star.

关键词： charged stars f(R T)gravity

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A Reliable and Stable V2V Routing Scheme for Warning Message Dissemination in VANETs using Bi-Directional Clustering 22

A Reliable and Stable V2V Routing Scheme for Warning Message...

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Proceedings of the 2022 12th International Conference on Communication and Network Security

作者： Mansoor Khan Shanshan Tu Ghulam Abbas Ziaul Haq Abbas Hisham Alasmary Muhammad Waqas Faculty of Computer Science and Engineering GIK Institute of Engineering and Technology Pakistan Information Technology Beijing University of Technology Beijing 100124 China China Telecommunications and Networking Research Center GIK Institute of Engineering and Technology Pakistan College of Computer Science King Khalid University Kingdom of Saudi Arabia and Information Security and Cybersecurity Unit King Khalid University Kingdom of Saudi Arabia Computer Engineering Department University of Bahrain Computer Engineering 32038 Bahrain. Bahrain and School of Engineering Edith Cowan University Australia

ISBN: (纸本)9781450397520

Road traffic injuries cause considerable life and economic losses to individuals, their families, and nations as a whole. To avoid road accidents, Vehicular Ad-hoc Networks (VANETs) play a vital role by enhancing transportation systems to make them intelligent. To enhance road safety, emergency message dissemination schemes are indispensable in VANET-based applications. This paper proposes a routing protocol for emergency message dissemination, which uses stable clustering for the bi-directional highway scenario. To assure the long-term sustainability of a cluster, a median technique for selecting a CH is devised. Further, an extended form of the K-medoids algorithm for a new node joining a cluster is employed, which ensures that both node and cluster have the same direction for bi-directional road traffic. Simulation results reveal that the proposed protocol proffers enhanced throughput and lowers the packet loss rates and end-to-end delay compared with state-of-the-art routing schemes.

关键词： bi-directional traffic warning message dissemination vehicular ad hoc network clustering

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A Machine Learning Algorithm For Developing A Smart Health System

A Machine Learning Algorithm For Developing A Smart Health S...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Shemily P John P Anju S. Barath Kumar P. Vijayaraghavan Nishesh Nigam G. Amirthayogam General Studies Unit University College of Bahrain Kingdom of Bahrain Department of Computer Science and Business System (csbs) Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Department of Biotechnology Karpagam Academy of Higher Education Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science & Engineering (cse) IES Institute of Technology and Management Bhopal M.P India Department of Information Technology Hindustan Institute of Technology and Science (Deemed To Be University) Chennai Tamil Nadu India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The merging of revolutionary technologies like deep learning has greatly affected the healthcare sector, changing diagnosis and decision-making processes. This study tries to explore the role of neural networks in Digital Health research by examining 192 records drawn from the database used by Scopus. Employing a carefully made search term, the study tries to identify nations that excel in book output, primary research subjects, top funding backers, and common research terms in this domain. Notably, the debut paper on AI in smart health appeared in 2011, signalling the origins of a sizeable surge of academic output. India has emerged as the leader in performing study in this area, showing a remarkable growth trend. Furthermore, the research shows that Access from the IEEE is the top magazine, having the largest number of papers within this area. This extensive review serves as an important tool for scholars, policy makers, and healthcare workers, giving deep insights into the development of artificial intelligence in Smart Health study. The results show a positive trend, demonstrating that artificial intelligence in Connected Health is set for substantial increase in the future. The creativity and sincerity of this study highlight its importance in developing knowledge and defining the future environment of healthcare innovation.

关键词： Technological innovation Ethics Machine learning algorithms Databases Reviews Smart healthcare Neural networks

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An electrical model characterization of an electronic nose chemical sensor using a programmable system-on-a-chip based AFE

An electrical model characterization of an electronic nose c...

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2022 International Young Engineers Forum in Electrical and computer Engineering, YEF-ECE 2022

作者： Santos, Joao J.M. Palma, Susana I.C.J. Esteves, Carina Gamboa, Hugo Oliveira, Joao Pedro Roque, Ana C.A. Department of Electrical and Computer Engineering Nova School of Science and Technology Nova University of Lisbon Caparica2829-516 Portugal Associate Laboratory i4HB Institute for Health and Bioeconomy Nova School of Science and Technology Nova University of Lisbon Caparica2829-516 Portugal Ucibio Applied Molecular Biosciences Unit Department of Chemistry Nova School of Science and Technology Nova University of Lisbon Caparica2829-516 Portugal LIBPhys - Laboratory for Instrumentation Biomedical Engineering and Radiation Physics Nova School of Science and Technology Nova University of Lisbon Caparica2829-516 Portugal Uninova Nova School of Science and Technology Nova University of Lisbon Caparica2829-516 Portugal

ISBN: (纸本)9781665467315

The diagnosis of many diseases involves invasive detection methods, which are both painful and stressing for patients. In the last decades, the ever-growing development in electronic nose (E-Nose) technology made them great candidates for non-invasive disease detection methods. Such devices mimic the human olfactory system through a set of sensors which produce signals that can be associated with diseases. Recently, a class of low-cost and innovative ionogel sensors, developed by our group demonstrated their full applicability in E-Nose systems, opening a new and promising approach to the field. However, the operation of such sensor needs a background calibration phase which relies on the correct characterization and parameterization of the corresponding electrical sensor *** paper proposes a model characterization methodology based on a set of frequency responses acquisitions of the sensor, under several humidity conditions. To obtain a flexible acquisition tool capable of acquiring accurate results, an analog front-end (AFE) circuit to interface with the interdigitated electrode (IDE) sensors is presented. Such AFE circuit is fully implemented using a programmable system-on-a-chip (PSoC), helping to reduce system size and cost. Lastly, a comparison between the electrical model and data acquired with the proposed system is presented. © 2022 IEEE.

关键词： System-on-chip

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Synthesizing realistic images for improved early detection of novel pandemics: A GAN enabled approach

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Heliyon 2025年第6期11卷

作者： Rounaq, Sumera Shah, Shahid Munir Aljawarneh, Mahmoud Mohammad Khan, Sarah Muhammad, Ghulam Department of Computer Science DHA Suffa University Karachi Pakistan Faculty of Engineering Sciences and Technology Hamdard University Karachi Pakistan Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman Jordan System Architect Low Code Solutions London United Kingdom Department of Computer Science Bahria University Karachi Pakistan

Infectious diseases like the novel Coronavirus (COVID-19) affect millions of individuals if not managed well in time. Thus, to reduce the transmission rate, effective diagnostic techniques must be identified. Early detection and diagnosis may be aided by Artificial Intelligence (AI) based automated diagnostics;however, these algorithms need a lot of pertinent data to be trained. In the early stages of a pandemic, gathering a lot of data is never an easy task. This presents challenges for accurate automatic diagnosis of such infectious diseases. To address this issue, a customized Generative Adversarial Networks (GAN) based architecture i. e., CoroWGAN-GP is proposed to synthesize images and to expand datasets. A dataset of 588 images with COVID-19, Normal, and Viral Pneumonia categories (taken from Cohen's repository [1]) was expanded using the proposed CoroWGAN-GP approach and used to train a proposed customized deep CNN architecture i. e., DeepCNN-CovNet19. Experimental results demonstrate that the proposed model achieved a considerable detection accuracy. i. e., 98.0 % in detecting the disease. The effectiveness of the proposed method was further evaluated by comparing its results with the state-of-the-art methods in the literature. Comparative analyses indicate that the proposed approach offers a promising solution for the accurate diagnosis of COVID-19-like pandemics. © 2025 The Authors

关键词： Chest X-ray Convolutional neural networks COVID-19 CXR GAN Pandemic Wasserstein GAN

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ISOMETRIC PAIRS WITH COMPACT + NORMAL CROSS-COMMUTATOR

arXiv

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arXiv 2024年

作者： De, Sandipan Sarkar, Jaydeb Shankar, P. Sankar, T.R. School of Mathematics and Computer Science Indian Institute of Technology Goa Ponda Goa Farmagudi403401 India Indian Statistical Institute Statistics and Mathematics Unit 8th Mile Mysore Road Bangalore560 059 India Department of Mathematics Cochin University of Science and Technology Kerala Kochi682022 India

We represent and classify pairs of commuting isometries (V1, V2) acting on Hilbert spaces that satisfy the condition [V1∗, V2] = compact + normal, where [V1∗, V2]:= V1∗V2 − V2V1∗ is the cross-commutator of (V1, V2). The precise description of such pairs also gives a complete and concrete set of unitary invariants. The basic building blocks of representations of such pairs consist of four distinguished pairs of commuting isometries. One of them relies on some peculiar examples of invariant subspaces tracing back to Rudin’s intricate constructions of analytic functions on the bidisc. Along the way, we present a rank formula for a general pair of commuting isometries that looks to be the first of its *** Codes 47A15, 32A10, 42B30, 47A13, 30H10, 47A65, 46E30, 47B47, 15A15 Copyright © 2024, The Authors. All rights reserved.

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Enhancing Martian Mineral Identification Using an Artificial Neural Network With Extracted Spectral Features In CRISM MTRDR Data

Enhancing Martian Mineral Identification Using an Artificial...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Priyanka Kumari Sampriti Soor Amba Shetty B. S. Daya Sagar Department of Water Resources and Ocean Engineering NIT Karnataka Surathkal India School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Systems Science and Informatics Unit Indian Statistical Institute Bangalore India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Creating a supervised learning model for mineral identification is challenging due to the lack of ground-truth data. This study utilizes a method from existing literature that generates a training dataset by augmenting available spectra in the MICA spectral library. However, rather than using entire spectra for identification, this study extracts spectral features from each spectrum for model training. It employs the apparent continuum removal method, Segmented Curve Fitting, to identify the most informative or distinguishable parts in the spectral domain. Spectral features are then extracted based on band-centers and band-areas for each selected part. The model is evaluated against a Targeted Reduced Data Record (TRDR) dataset obtained using a hierarchical Bayesian model, demonstrating improved identification performance than the existing supervised models. Finally, using this model, dominant minerals are identified in MTRDR data from the Nilli Fossae region of Mars, and a corresponding mineral map is presented.

关键词： Training Absorption Supervised learning Artificial neural networks Feature extraction Data models Libraries

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Wearable Analytics and Early Diagnostic of COVID-19 Based on Two Cohorts 14

Wearable Analytics and Early Diagnostic of COVID-19 Based on...

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14th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2022

作者： Skibinska, Justyna Hosek, Jiri Channa, Asma Brno University of Technology Unit of Electrical Engineering Tampere University Faculty of Electrical Engineering and Communication Brno Czech Republic Brno University of Technology Faculty of Electrical Engineering and Communication Brno Czech Republic University Politehnica of Bucharest University Mediterranea of Reggio Calabria Computer Science Department Diies Department Bucharest Romania

ISBN: (数字)9798350398663

ISBN: (纸本)9798350398663

The outbreak of the COVID-19 pandemic forced a need to create screening tests to diagnose the disease. To answer this challenge, this paper introduces the support methodology for COVID-19 early detection based on wearable and machine learning likewise on two various cohorts. We compare the level of detection of the COVID-19 disease, Influenza, and Healthy Control (HC) thanks to the usage of machine learning classifiers likewise changes in heart rate and daily activity. The features obtained as the parameters of the ratio of heart rate to the variable of the number of steps proved to have the highest statistical importance. The COVID-19 cases versus HC were possible to be distinguished with 0.73 accuracy by the XGBoost algorithm, whereas COVID-19 cases, Influenza vs. HC were able to be differentiated on similar level of accuracy: in 0.72 by Support Vector Machine. The multiclass classification between the cases achieved a 0.57 F1-score for three classes by XGBoost. For early diagnosis, this solution could serve as an extra test for clinicians during the pandemic, and the result shows which metric could be useful for creating the machine learning model. © 2022 IEEE.

关键词： COVID-19

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Designing Cellular Manufacturing System in Presence of Alternative Process Plans

arXiv

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arXiv 2024年

作者： Uddin, Md. Kutub Islam, Md. Saiful Jahin, Md. Abrar Irfan, Md. Tanjid Hossen Seam, Md. Saiful Islam Mridha, M.F. Department of Mechanical Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Industrial Engineering and Management Khulna University of Engineering & Technology Khulna9203 Bangladesh Physics and Biology Unit Okinawa Institute of Science and Technology Graduate University Okinawa904-0412 Japan Department of Computer Science American International University-Bangladesh Dhaka1229 Bangladesh

In the design of cellular manufacturing systems (CMS), numerous technological and managerial decisions must be made at both the design and operational stages. The first step in designing a CMS involves grouping parts and machines. In this paper, four integer programming formulations are presented for grouping parts and machines in a CMS at both the design and operational levels for a generalized grouping problem, where each part has more than one process plan, and each operation of a process plan can be performed on more than one machine. The minimization of inter-cell and intra-cell movements is achieved by assigning the maximum possible number of consecutive operations of a part type to the same cell and to the same machine, respectively. The suitability of minimizing inter-cell and intra-cell movements as an objective, compared to other objectives such as minimizing investment costs on machines, operating costs, etc., is discussed. Numerical examples are included to illustrate the workings of the formulations. © 2024, CC BY.

关键词： Cellular manufacturing

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