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IoT and its Significance on Smart Technologies in the Health Sector - An Extensive Review

IoT and its Significance on Smart Technologies in the Health...

引用

computer science and data engineering (csde), ieee asia-pacific conference on

作者： Deepika Bandhana Neeraj Anand Sharma Department of Computer Science and Mathematics School of Science and Technoloy The University of Fiji Lautoka Fiji

IoT seems to be the trending solution in all sectors notably because of the yield in productivity, efficiency, effective strategies, and results that are associated with adapting to this technology. These positive results are enormously experienced in one of the crucial sectors which determine and ensure the prolonged healthy life expectancy of mankind. It is well noted that a lot of work has been done on this topic in Academia and Corporate field all over the world but this paper will present a selective review that has been done so far by the Academic world as a scholarly article and a resource for the Health Sector in Fiji to earnestly integrate smart technologies in its architecture.

关键词： Productivity Economics computer science Focusing data engineering Internet of Things Intelligent systems

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Classification of Landscape Pictures Based on Residual Network 2

Classification of Landscape Pictures Based on Residual Netwo...

引用

2nd asia-pacific conference on Communications Technology and computer science, ACCTCS 2022

作者： Li, Peiyu Yang, Xinwei Chen, Xunlei Chengdu University of Technology College of Information Science and Technology College of Cyber Securny Oxford Brookes College Chengdu China Chengdu University of Technology School of Mechanical and Electrical Engineering Chengdu China

ISBN: (纸本)9781665400343

The continuous development of artificial intelligence has brought great convenience to our life and work. One of the more mature applications in artificial intelligence is the function of image classification, which provides a new method for us to process massive data. When faced with an explosion of image information on the Internet, we can use deep learning to categorize images and select only the categories we want to stay in. This will save us a lot of time and reduce our workload. This paper is based on deep learning to classify several common types of landscape maps, so that we can easily choose the scenery we want to visit. The residual network structure is adopted in this paper, and the accuracy can reach 87.6% only after low rounds of training, which is better than the previous shallow network structure. © 2022 ieee.

关键词： Image classification

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Neural Network Classification of Resting State fMRI data of ADHD and Healthy Subjects using Dynamic Mode Features

Neural Network Classification of Resting State fMRI Data of ...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： R Harini R Thatchayeni K Lakshmipriya P UdayaPrasanth S Purnima O Uma Maheswari Electronics & Communication Engineering Anna University Guindy Chennai India

Attention deficit hyperactivity disorder (ADHD) is one across the board mental disorder distinguished by inattentiveness, hyperactivity or impulsive behavior. The exact cause is yet unknown but the prevalence of ADHD requires early diagnosis and treatment. This study investigates the decomposed subsystems of resting- state fMRI (rs-fMRI) blood oxygen level dependent (BOLD) signals in order to assess the overall characteristic of the human brain. rs-fMRI BOLD signals were first decomposed into dynamic modes (DMs) which can illuminate the patterns of brain subsystems. Each DM is associated with one Eigenvalue that characterizes functional connectivity dynamics. Thereby the features related to those DM were extracted. Using the features obtained, the classification is performed using neural network-based pattern recognition model and the performance is evaluated. The interpreted results with neural network (NN) classifier obtains an accuracy of 73.7%.

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Explainable Artificial Intelligence for Maize Disease Diagnostics

Explainable Artificial Intelligence for Maize Disease Diagno...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Mubaraka Banadda Ntanda Kimuli Aloysius Solome Nakazzi Othieno Benedict Ernest Mukisa Samuel Owekitiibwa Ggaliwango Marvin Department of Computer Science School of Computing and Informatics Technology (SCIT) Makerere University Kampala Uganda Department of Computer School of Computing and Informatics Technology (SCIT) Makerere University Kampala Uganda

This study focuses on the use of machine learning techniques, specifically Explainable AI and Deep Learning, to diagnose two prevalent diseases in maize crops: maize streak and maize leaf blight. These diseases have been major obstacles for farmers in effectively managing and identifying infections, leading to decreased harvest quantity and quality. By developing and training a customized model using supervised machine learning, combined with Transfer Learning using pretrained models like VGG19, MobileNet, we aim to improve maize disease diagnostics. The accuracy of the generated models was assessed, and the best performing model is selected for further analysis utilizing Explainable AI methods. The final step involves deploying the model on a mobile application, with the web application serving as a platform for visualizing the stored data collected through the mobile app in an external database. The ultimate objective of this research is to equip farmers with the necessary tool to enhance agricultural productivity by enabling precise and efficient diagnosis of maize diseases.

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Performance Comparison of Homomorphic Encrypted Convolutional Neural Network Inference Among HElib, Microsoft SEAL and OpenFHE

Performance Comparison of Homomorphic Encrypted Convolutiona...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Haoyun Zhu Takuya Suzuki Hayato Yamana Department of Computer Science Waseda University Tokyo Japan Department of Computer Science and Communications Engineering Waseda University Tokyo Japan Faculty of Science and Engineering Waseda University Tokyo Japan

Homomorphic encryption (HE) offers a promising solution for maintaining data privacy even during calculation, as it allows for the evaluation of ciphertexts. Several HE libraries have been released, but selecting the best-suited library for HE beginners can be challenging. This paper provides insights into the selection process by comparing three popular HE libraries: HElib, Microsoft SEAL, and OpenFHE. To evaluate the performance of these libraries, we implemented a convolutional neural network inference using each of the three libraries and analyzed its latency. Our experimental results show that Microsoft SEAL achieves the shortest latency, less than 56% of OpenFHE and less than 17% of HElib. In terms of ease of use, OpenFHE surpasses the other libraries because it eliminates the need for programmers to consider rescaling during calculations and has more choices on different key-switching algorithms. In contrast, utilizing automatic rescaling with OpenFHE results in 5x longer latency than manual rescaling.

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A Model for the Use of Machine Learning in the Detection of Cancer by Means of Thermographic Images in a Mobile Environment

A Model for the Use of Machine Learning in the Detection of ...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Nicholas Lennox Bertram Haskins Noroff Accelerate Noroff School of Technology and Digital Media Kristiansand Norway School of IT Nelson Mandela University Port Elizabeth South Africa

Mobile technology and thermographic imaging have evolved to a point where their confluence point yields a promise greater than their individual parts. One such application may be the detection of breast cancer; yielding a non-invasive, portable solution. This study presents a model for the use of machine learning in the detection of cancer by means of thermographic images in a mobile environment. Aspects of the model, such as the training of a machine learning model for the classification of thermographic images have been implemented and a high-level system design is suggested for the further implementation of the model.

关键词： Training computer science Computational modeling Imaging Machine learning data engineering data models

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An Efficient Use of Toffoli Quantum Gate for Boosting the Embedding Capacity in DNA Sequence

An Efficient Use of Toffoli Quantum Gate for Boosting the Em...

引用

computer science and data engineering (csde), ieee asia-pacific conference on

作者： Mahbubun Nahar AHM Kamal Dept. of Computer Science and Engineering Jatiya Kabi Kazi Nazrul Islam University Trishal Bangladesh

While hiding data in DNA sequence, the steganograpy schemes try to increase the embedding payload, enhance the security of implanted data, protect the stego expansion and preserve the biological characteristics of DNA sequence. In this research, we have proposed a new data hiding method where the scheme implants secrets in DNA sequence using a classical Toffoli quantum gate mechanism. The proposed scheme implants 4-bits of information per nucleotide. Experimental results demonstrate that the proposed scheme achieves massive improvement over the others which is 4 to 12-times higher than the competing schemes.

关键词： computer science Quantum computing DNA Implants Logic gates Electrocardiography data engineering

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Leveraging a Randomized Key Matrix to Enhance the Security of Symmetric Substitution Ciphers

Leveraging a Randomized Key Matrix to Enhance the Security o...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Shubham Gandhi Om Khare Mihika Dravid Mihika Sanghvi Sunil Mane Aadesh Gajaralwar Saloni Gandhi Department of Computer Science and Engineering COEP Technological University Department of Computer Engineering BRACT's Vishwakarma Institute of Information Technology Pune Maharashtra India Independent Researcher

An innovative strategy to enhance the security of symmetric substitution ciphers is presented, through the implementation of a randomized key matrix suitable for various file formats, including but not limited to binary and text files. Despite their historical relevance, symmetric substitution ciphers have been limited by vulnerabilities to cryptanalytic methods like frequency analysis and known plaintext attacks. The aim of our research is to mitigate these vulnerabilities by employing a polyalphabetic substitution strategy that incorporates a distinct randomized key matrix. This matrix plays a pivotal role in generating a unique random key, comprising characters, encompassing both uppercase and lowercase letters, numeric, and special characters, to derive the corresponding ciphertext. The effectiveness of the proposed methodology in enhancing the security of conventional substitution methods for file encryption and decryption is supported by comprehensive testing and analysis, which encompass computational speed, frequency analysis, keyspace examination, Kasiski test, entropy analysis, and the utilization of a large language model.

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Research and Application of Air Quality Prediction Model Based on Urban Big data 2

Research and Application of Air Quality Prediction Model Bas...

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2nd asia-pacific conference on Communications Technology and computer science, ACCTCS 2022

作者： Liu, Bofan Reading Academy Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China

ISBN: (纸本)9781665400343

The urban scale gradually expands with the development of the economy, and many problems arise in urbanization. Specifically, the urban traffic environment is gradually worsening. The traffic pressure is increasing with the urban population growth, the tail gas emissions from private cars and the factory exhaust gas produced with the industrial development around the city have caused terrible environmental influence. Therefore, with the rapid economic growth, urban air quality and residents' health have gradually become a hot research topic. In the previous research on air quality, many studies were conducted from a single time dimension, ignoring the continuation of air pollutants on the time scale and the diffusivity on the spatial scale. Therefore, on this basis, this paper proposes a prediction air quality model based on temporal and spatial distribution. By analyzing historical data, we can predict the air quality value from the time and space scales, and then the required results are obtained according to the meteorological conditions to improve forecast accuracy. © 2022 ieee.

关键词： Air quality

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Analysis of Optimization Method of Laboratory Management System Based on data Processing and Intelligent Sensor Technology of the Internet of Things 2

Analysis of Optimization Method of Laboratory Management Sys...

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2nd asia-pacific conference on Communications Technology and computer science, ACCTCS 2022

作者： Duan, Ruobing Zeng, Hang Cheng, Jinjie Zheng, Zhuoling School of Information Science Guangzhou Xinhua University Guangdong Dongguan523133 China

ISBN: (纸本)9781665400343

At present, colleges and universities have done much research on the management of laboratories and equipment on campus. The focus of the work is on the optimization of manual laboratory management. The work content management of the laboratory is still concentrated in the manual management stage. The workload is very large, and the content is very complicated. Therefore, it is necessary to optimize the above problems. In the process of optimization, artificial intelligence and the Internet of Things, as new information age technologies, have received extensive attention. This paper firstly introduces the data processing and intelligent sensor technology of the Internet of Things from the perspective of the traditional laboratory management system. On this basis, it gradually combines artificial intelligence and cloud computing to improve laboratory management's core content. Based on the above technical core, this paper designs and optimizes the laboratory management system of artificial intelligence algorithms. © 2022 ieee.

关键词： Internet of things

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