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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Aghalya, S. Sarasu, R. Mishra, Nilamadhab Arunachalam, G. Gobinath, D. Naganathan, Suresh Babu Thandullu St. Joseph's College of Engineering Omr Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Spatial Informatics Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The study presents an advanced neural network model for real-time remote evaluation and treatment of rheumatic diseases using sensor fusion. The model was developed for use in this investigation. The suggested system uses a wide variety of sensing modalities, such as image, motion, and biometric sensors. This is accomplished via artificial intelligence (AI) and the data gleaned from sensors. The neural network, which was trained on a dataset that was both varied and labeled, performs autonomous analyses of input data in order to accurately predict and categorize rheumatic diseases. The integration of the model into remote medical treatment is made easier by the fact that it is run on a Raspberry Pi inside a tele-rheumatology framework. This unique strategy not only improves diagnostic accuracy but also expands rheumatology services to geographically inaccessible or disadvantaged groups. As a result, the landscape of rheumatic healthcare delivery is completely transformed as a result of this new approach. For the purpose of ensuring the ethical use of this revolutionary technology, the procedures for data security and ethical concerns have been given priority. © 2024 IEEE.

关键词： Neural network models

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Optimal Vaccine Distribution using Cloud-based Reinforcement Learning for Logistics Management 8

Optimal Vaccine Distribution using Cloud-based Reinforcement...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Ranganathan, Chitra Sabapathy Riazulhameed, Arshadh Ariff Mohamed Abuthahir Menaka, R. Mohankumar, N. Raj, T. Saju Sujatha, S. Mphasis Corporation Tamil Nadu Chennai India Blackstraw Technologies Pvt Ltd Tamil Nadu Chennai India Velalar College of Engineering and Technology Department of Information Technology Tamil Nadu Erode India Symbiosis Institute of Technology Nagpur Campus Maharashtra Pune India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

Vaccination systems require efficient vaccine delivery, which is particularly important during worldwide epidemics such as pandemics. This paper proposes a new method to improve vaccine distribution by using cloud reinforcement learning (RL) for logistics management. Suboptimal resource allocation and vaccine delivery delays are common results of traditional techniques' inability to adapt to unpredictable and changing contexts. The proposed system allows for real-time adaptive decision-making using cloud computing resources and RL approaches. It takes geographical dispersion, population density, vaccination demand, and transportation restrictions. Integrating with preexisting logistical infrastructure is a breeze due to the cloud-based architecture's scalability and flexibility. Simulations and case studies show that the technique is successful, outperforming standard approaches in distribution efficiency and resource use. It offers a method to improve vaccination delivery networks, which will help in the face of future public health crises. © 2024 IEEE.

关键词： Electronic health record

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Detection of Malicious Node in VANETs Using Digital Twin

Detection of Malicious Node in VANETs Using Digital Twin

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International Conference on Big Data Intelligence and Computing, DataCom 2022

作者： Arya, Varsha Gaurav, Akshat Gupta, Brij B. Hsu, Ching-Hsien Baghban, Hojjat Insights2Techinfo Jaipur India Ronin Institute MontclairNJ07043 United States International Center for AI and Cyber Security Research and Innovation and Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Lebanese American University Beirut1102 Lebanon Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan

ISBN: (纸本)9789819922321

Vehicular ad-hoc network (VANET) plays an essential role in helping the development of smart cars and intelligent transportation systems. By easing congestion and speeding up data transfer, VANET makes it possible for connected cars to function more smoothly. However, VANETs are affected by different types of cyber attacks, such as distributed denial of services (DDoS) attacks. A digital twin (DT) is a replica of a physical system that operates in tandem with the actual thing, allowing for continuous monitoring and management. The DT prepares the way for the monitoring of a physical entity on a regular basis and for its automated management. The improved efficiency in keeping tabs on the physical world is largely attributable to DT. For this reason, academics are advocating for its use in a variety of settings. In this research, we use DT to solve the problem of identifying and stopping malignant nodes on a VANET infrastructure. In this paper, we proposed a framework that uses the concepts of DT for the identification of malignant nodes. Our suggested approach employs machine learning to distinguish between regular traffic and attack traffic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vehicular ad hoc networks

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Artificial Intelligence based Performance Models to Support Hydrologic Rainfall Conditions using Ensembling Approach 2

Artificial Intelligence based Performance Models to Support ...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： William, P. Yadav, M. Geeta Lakhanpal, Sorabh Shrivastava, Anurag Rawat, Kishan Singh Chaudhary, Swati Savitribai Phule Pune University Sanjivani College of Engineering Department of Information Technology Pune India Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad500043 India Lovely Professional University Phagwara India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Civil Engineering Uttarakhand Dehradun248002 India Lloyd Institute of Engineering and Technology Uttar Pradesh Greater Noida201308 India

ISBN: (纸本)9798350325799

The architectural design of the BC and the scale of its installation have a major impact on the effectiveness of the BC to lower the peak flow load of stormwater that enters an urban drainage system. In order to get the most of BC's benefits, designers must constantly make sure they are using the appropriate settings. In all, there are 18 design parameters, each of which may be customized in a variety of ways. Therefore, it could be difficult to find the characteristics that would provide the stormwater management model (SWMM) the most accurate results for the India models. The purpose of this study was to investigate the effects that BC design factors that are essential to hydrologic dynamics, such as depth, inflow and overflow, length, and time-to-peak, have on the rates of surface infiltration and outflow as well as storage as a function of a broad spectrum of a wide variety of different types and amounts of precipitation. In the first part of the methodology, the one-factor-at-a-time (OAT) method was used to choose the seven BC design criteria that were deemed to be the most important. This sampling was done in a completely arbitrary manner. The simulations were run by making use of the SWMM Python Wrapper, also known as Py SWMM. This wrapper ensured that the values of the other parameters stayed unchanged while randomly picking samples for one of the parameters. It is essential to examine these three aspects side by side. © 2023 IEEE.

关键词： Python

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On a Piecewise Linear Function Approximation for Quantum Computation

On a Piecewise Linear Function Approximation for Quantum Com...

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IEEE Asia-Pacific Conference on Circuits and Systems

作者： Hideaki Okazaki Dept. of Informatics Faculty of Informatics Computer Science Course (or Field) Graduate school of Electrical and Information Engineering Shonan Institute of Technology Fujisawa Kanagawa Japan

In quantum computing, although correspondences between quantum circuits and polynomials have been explored, a correspondence between a quantum circuit and a low-degree polynomial, even a linear function has not fully clarified. In this paper, a quantum circuit approximating a linear function for building piecewise linear functions, is proposed. First, for qubit operations, a dimensionless form in $[0,1]$ of a linear function is provided. Secondly, a theorem of a parameterized quantum circuit approximating linear functions are provided. Thirdly, by using Yao: an open source Julia package, the quantum circuit simulation results to approximate the linear functions are presented. Finally, the principal results are summarized.

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Multi-party secure communication using blockchain over 5G

Multi-party secure communication using blockchain over 5G

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Archana, K. Kareem, Z.H. Al-Farhani, Liwa H. Bagyalakshmi, K. Jenvi, Ignatia K. Majella Kumar, Ashok Department of Computer Engineering Pimpri Chinchwad College of Engineering and Research Maharashtra Pune India Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq System Analysis Control and Information Processing dep Academy of Engineering RUDN University Moscow Russia Sri Ranganathar Institute of Engineering and Technology Coimbatore India Mathematics Department Saveetha School of Engineering SIMATS Saveetha Nagar Chennai India Department of Computer Science Banasthali Vidyapith Rajasthan Banasthali India

ISBN: (纸本)9781032669663

One of the most critical problems facing the blockchain technology industry right now is how to protect the privacy of users’ data on the blockchain in a way that is both effective and cheap. Based on the Pedersen commitment and the Schnorr protocol, this study comes up with a secure multi-party computing protocol (BPLSM). By making the structure of the protocol and doing formal proof calculations, it has been shown that the protocol can be used in the blockchain network to combine private messages for efficient signing while keeping people’s identities secret. Furthermore, by looking at the nature and security of the protocol, it is also possible to find that the BPLSM protocol on the blockchain has a low cost of computing and a high level of information secrecy. Furthermore, it was found that the BPLSM protocol takes less time to check than the current mainstream BLS signature in a simple multi-party transaction with a fixed number of participants. © 2024 The Author(s).

关键词： Blockchain

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Fuzzy-Based Multiobjective Multifactor Dimensionality Reduction for Epistasis Analysis

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023年第1期20卷 378-387页

作者： Yang, Cheng-Hong Huang, Hsiu-Chen Hou, Ming-Feng Chuang, Li-Yeh Lin, Yu-Da National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung80708 Taiwan Kaohsiung Medical University Biomedical Engineering and School of Dentistry Kaohsiung80708 Taiwan Chia-Yi Christian Hospital Department of Community Health Chia-Yi60002 Taiwan Kaohsiung Medical University Hospital Division of Breast Surgery Department of Surgery Kaohsiung80708 Taiwan Kaohsiung Medical University Biomedical Engineering Kaohsiung80708 Taiwan I-Shou University Department of Chemical Engineering Institute of Biotechnology and Chemical Engineering Kaohsiung84001 Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu 880011 Taiwan

Epistasis detection is vital for understanding disease susceptibility in genetics. Multiobjective multifactor dimensionality reduction (MOMDR) was previously proposed to detect epistasis. MOMDR was performed using binary classification to distinguish the high-risk (H) and low-risk (L) groups to reduce multifactor dimensionality. However, the binary classification does not reflect the uncertainty of the H and L classification. In this study, we proposed an empirical fuzzy MOMDR (EFMOMDR) to address the limitations of binary classification using the degree of membership through an empirical fuzzy approach. The EFMOMDR can simultaneously consider two incorporated fuzzy-based measures, including correct classification rate and likelihood rate, and does not require parameter tuning. Simulation studies revealed that EFMOMDR has higher 7.14% detection success rates than MOMDR, indicating that the limitations of binary classification of MOMDR have been successfully improved by empirical fuzzy. Moreover, EFMOMDR was used to analyze coronary artery disease in the Wellcome Trust Case Control Consortium dataset. © 2004-2012 IEEE.

关键词： Maximum likelihood estimation

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Using Machine Learning Techniques for Cancer Classification

Using Machine Learning Techniques for Cancer Classification

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Reddy, Ch. Srinivasa Singh, Rajesh Bhavani, R. Dasgupta, Sanhita Singh, Yashpal Pratan Singh, Suryabhan Vigan's Institute of Information Technology Department of IT Duvvada India Uttaranchal University Uttaranchal Institute of Technology India Saveetha Institute of Medical and Technical Sciences Institute of Computer Science and Engineering Saveetha School of Engineering Chennai600124 India Bir Bikram Department of Computer Memorial College Tripura Agartala India Jain Deemed to Be University Department of Computer Science & Engineering Karnataka Bengaluru India Deen Dayal Upadhyaya Gorakhpur University Institute of Engineering and Technology Department of Infomation Technology Gorakhpur India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Given the extremely high death and suffering rates caused by breast cancer, women are particularly at risk. Because of this, it is imperative that algorithms be developed to find breast disease's early warning signs. Decision trees, random forests, regression analysis, and Mri are four categorised algorithms that detect cancer, and the end results were contrasted. Machine learning is essential for predicting breast cancer's early signs. Three data augmentation different classifiers will be used in this research. when categorising algorithms Data with unbalanced classifications are a serious issue that calls for careful treatment and preprocessing. We'll train the various M.L. models using a large dataset of breast cancer patients. The optimal method for this issue is ultimately determined by comparing all the strategies in terms of efficiency and precision. Our research will demonstrate the efficacy of several methods for this category of breast cancer so that we can use the proper strategy. © 2022 IEEE.

关键词： Convolutional neural networks

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Intra-Datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinola, Damola Gideon Adetiba, Emmanuel Abayomi, Abdultaofeek Nnaji, Uche Thakur, Surendra Moyo, Sibusiso Covenant University Covenant Applied Informatics and Communications African Center of Excellence Department of Electrical and Information Engineering Ota Nigeria Durban University of Technology HRA Institute for Systems Science Durban South Africa Mangosuthu University of Technology Department of Information and Communication Technology Durban South Africa Covenant University Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Ota Nigeria Durban University of Technology KZN E-Skills CoLab Durban South Africa Stellenbosch University School for Data Science and Computational Thinking Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358155

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users' requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named;the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment. © 2024 IEEE.

关键词： Cloud platforms

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Invited: Algorithms and Architectures for Accelerating Long Read Sequence Analysis 23

Invited: Algorithms and Architectures for Accelerating Long ...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Hasindu Gamaarachchi Kisaru Liyanage Sri Parameswaran School of Computer Science & Engineering UNSW Sydney and Kinghorn Centre for Clinical Genomics Garvan Institute of Medical Research School of Electrical and Information Engineering University of Sydney.

ISBN: (纸本)9798350323481

Genome sequencing is continuing to revolutionize the medical, forensics, agricultural, and biosecurity fields. The enormous amounts of data from modern sequencing technology, require significant time and cost to obtain accurate results using general-purpose computing resources. The emergence of third-generation sequencers called nanopore sequencers offers small, cheap, and portable genome sequencing capabilities. However, the bottleneck during computational analysis limits scalability and portability. This paper introduces the necessity for one, optimization of existing algorithms; two, efficient utilization of existing heterogeneous systems; and three, novel algorithms and domain-specific architectures for rapid in situ analysis of third-generation sequencing data. State-of-the-art examples are described and future challenges are outlined.

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