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IoT-based Blood Glucose Detection System 6

IoT-Based Blood Glucose Detection System

引用

6th IEEE international conference on Electrical, Electronics and System Engineering, ICEESE 2023

作者： Lim, Shau Ibrahim, Siti Zuraidah Karim, Mohd Nazri A Boonsong, Wasana Masrakin, Khuzairi Mohd Noh, Faridah Hanim Universiti Malaysia Faculty of Electronic Engineering & Technology Perlis Arau Malaysia Faculty of Electronic Engineering & Technology University Malaysia Perlis Arau Malaysia Rajamangala University of Technology Srivijaya Faculty of Industrial Education and Technology Department of Electrical Education Songkla Thailand University Tun Hussein Onn Malaysia Faculty of Engineering Technology Johor Malaysia

ISBN: (纸本)9781665453714

This study presents a non-invasive blood glucose concentration (BGC) system that is based on the internet of Things (IoT) and employs near-infrared sensors (NIR) to detect blood glucose levels through the scanning of fingers. The purpose is to develop a BGC system and assess the accuracy of the proposed system. Constant monitoring and data-driven insights for optimum treatment and enhanced quality of life are made possible by IoT-based systems, and non-invasive glucose monitoring is a critical component of diabetes care. The system that was built comprises of a Light Emitting Diode (LED) that emits signals that are sent via the fingertip, and a phototransistor that is positioned next to the LED to detect any reflected signals. The results of the scanning are then stored in ThingSpeak. The technique used to determine the blood glucose level involves analyzing the variation in received signal intensity that results from reflection. Using regression analysis, a mathematical relationship between glucose concentration and voltage was established and installed in the Arduino. Two sorts of testing were carried out to evaluate the system: in vitro tests on glucose solution and in vivo experiments on the human body. By comparing 10 readings acquired from both types of experiments, the results of the studies indicate that the device's glucose detection accuracy ranges from 1.13 to 16.41%. IoT approach is feasible due to its continuous monitoring capabilities, data analysis, and remote access features, making it a promising tool for effective diabetes control. © 2023 IEEE.

关键词： internet of things

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Driving Training-based Optimization-Enabled Key Generation Approach for Secure and Efficient Authentication with Data Sharing Approach in IoT Healthcare 1

引用

international conference on Advances in signal Processing and Communication Engineering, ICASPACE 2023

作者： Waykar, Sanjay B. Kadu, Rajesh D. Karlekar, Nandkishore P. Shahare, Yogesh Mahatma Gandhi Mission’s College of Engineering and Technology Maharashtra Kamothe Navi Mumbai India School of Computing MIT-ADT University Pune India Department of Business Analytics ITM Skill University Kharghar Navi Mumbai India

ISBN: (数字)9789819705627

ISBN: (纸本)9789819705610

Blockchain technology (BT) offers a solution to the interoperability issues that currently plague health information systems. Additionally, BT provides the cutting-edge standard that ensures the safe exchange of computerized health data among healthcare providers, institutions, people, and medical specialists. Recently, both business and academics recognized the internet of Things (IoT) as one of the top study fields. Nevertheless, a significant issue in IoT healthcare across a number of product domains is the secrecy of patient health data transmission. For secure and effective authentication with data sharing strategy in IoT healthcare, a method called Driving Training-based Optimization (DTBO) Key generation is presented to address these issues. The entities involved in the proposed DTBO_Key generation technique are the user, owner, authenticating minor, blockchain, and IoT server. The suggested technique consists of seven stages: initialization, registration phase, key creation, login phase, data protection, authentication process, and verification phase. A minimum memory consumption of 2.983 MB, a computation cost of 28.008%, and an accuracy of 0.9 were attained using the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： internet of things

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Autoencoder-based OFDM for Agricultural image Transmission 10

Autoencoder-based OFDM for Agricultural Image Transmission

引用

10th international conference on Advanced Cloud and Big Data (CBD)

作者： Li, Dongbo Liu, Xiangyu Shao, Yuxuan Sun, Yuchen Cheng, Siyao Liu, Jie Harbin Inst Technol Fac Comp IoT & Ambient Intelligence Res Ctr Harbin Peoples R China Harbin Inst Technol Fac Comp Harbin Peoples R China Harbin Inst Technol Shenzhen Int Res Inst AI Shenzhen Peoples R China

ISBN: (纸本)9798350309713

With the rapid development of the internet of Things (IoT), smart agricultural puts forward higher demands on the transmission of agricultural big data. This paper proposes an end-to-end learning communication with autoencoderbased orthogonal frequency division multiplexing (OFDM-AE) for agricultural image transmission, which solves the problems of delay, congestion and high complexity caused by the processing method to information of independent modularization for the conventional OFDM. It is proposed to construct AE based on convolutional neural network (CNN) to realize global joint optimization of end-to-end communication system. In this paper, the network architecture of OFDM-AE is designed and trained on massive agricultural image data. We analyze the performance of the proposed OFDM-AE in different signal-to-noise ratio (SNR) cases. The experimental results show that the OFDM-AE can retain the image feature information and has a very advantageous complexity performance compared to the conventional OFDM with various modulation methods.

关键词： internet of Things big data autoencoder OFDM agricultural image transmission

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The Droop Control Stabilization Method of MMC-MTDC System based on DC Voltage Differential Regulation 7

The Droop Control Stabilization Method of MMC-MTDC System Ba...

引用

7th IEEE international conference on Energy internet, ICEI 2023

作者： Sun, Siluo Wang, Jun Wang, Chenyu Wang, Weisheng Zhang, Tianfang Ren, Shuai Electric Power Research Institute State Grid Liaoning Electric Power Company Shenyang China College of Information Science & Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350386684

The stability of modern power systems is often influenced by power electronic devices. The stability of the direct current (DC) voltage plays a crucial role in ensuring the overall stability of the power system. In this case, the multi-terminal DC transmission system technology based on modular multilevel converter can effectively improve the stability of DC voltage, and then enhance the stability of the whole new power system. A modular multilevel converter (MMC) is a power electronic device for high voltage direct current transmission that is primarily composed of multiple identical modules, each containing multiple switching devices and capacitors. De voltage stability is one of the important factors in multi-terminal Direct current transmission system (MTDC). Because the multi-terminal HVDC system needs to transport a large amount of direct current energy over long distances, the maintenance of DC voltage stability in the transmission process has an important impact on the reliability and economy of the system. In this paper, a MMC-MTDC control method based on DC voltage differential regulation is proposed. By establishing the topology model of MMC-MTDC and taking the compensation amount of small signal into account in the sag control method based on the small-signal modular modeling, the DC voltage differential regulation of multi-terminal DC transmission system can be realized under various working conditions. It provides theoretical basis and technical support for MTDC voltage stabilization. © 2023 IEEE.

关键词： HVDC power transmission

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Triplets-based Steganographic Scheme to Improve the image Quality in Spatial Domain 3

Triplets-Based Steganographic Scheme to Improve the Image Qu...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Meftah Mafazy, Muhammad Terri Agustinus, Jeems Ikhwanul Haq, Thoha Jean De La Croix, Ntivuguruzwa Ahmad, Tohari Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia African Centre of Excellence in Internet of Things University of Rwanda Kigali Rwanda

ISBN: (纸本)9798350317022

Advances in information and communications technology have changed the way various sectors, from finance to the military, operate. However, this evolution has also increased cybersecurity threats, posing potential risks to user privacy and security. In response to these challenges, data masking methods have emerged as noteworthy solutions. These methods involve hiding information within other data, such as audio and video files, to add an extra layer of security. Retaining the quality of the stego image proves to be a primary challenge in steganography. In addressing the limitations of previous digital image steganography techniques, this research introduces a new algorithm centered on triplets. Our experiments using images from the SIPI dataset, our results showcase a notable improvement in performance, with signal-to-noise ratio (PSNR) results that vary from 43.080 dB to 65.564 dB. These experimental outcomes underscore a substantial enhancement compared to existing methods, affirming the efficacy of the proposed algorithm in bolstering cybersecurity defenses. © 2023 IEEE.

关键词： data hiding National Security network infrastructure spatial domain steganography

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Machine Learning Algorithm to Detect EEG based Emotion states using Virtual-Video stimuli 1

Machine Learning Algorithm to Detect EEG based Emotion state...

引用

1st IEEE international conference on Advances in Electronics, Communication, Computing and Intelligent Information systems, ICAECIS 2023

作者： Thejaswini, S. Ramesh Babu, N. Mamatha, K.R. B M S Institute of Technology and Management Dept. of Ete Karnataka Bengaluru India K S School of Engineering and Management Dept. of Cse Karnataka Bengaluru India

ISBN: (纸本)9798350348057

The interpretation and detection of human emotions are crucial functions of the center-nervous system. Physiological signals are used widely to develop emotion recognition systems in recent years. Detection of emotions using electroencephalogram is a challenging task since they exhibit nonlinear nature. In this paper, three popular machine-learning models are implemented on an acquired database using a Novel approach. The EEG signals are acquired by showing Virtual Reality Video clips for eight discrete emotions. Thirty-four features are extracted in time and frequency domains. The frequency bands decomposition is carried out using 4-level Discrete-Wavelet-Transforms. The generated feature vectors are used on KNN, SVM, and ANN algorithm-based classifiers to classify the data into four, three, and two emotion states. The overall accuracy using ANN, SVM, and KNN classifiers is 85.50%, 73.50%, and 66.75% respectively for 4 classes. The performance of ANN classifiers gave a better accuracy compared to other models. © 2023 IEEE.

关键词： Electroencephalography

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RSSI and Machine Learning-based Indoor Localization systems for Smart Cities 7

RSSI and Machine Learning-Based Indoor Localization Systems ...

引用

7th SLAAI - international conference on Artificial Intelligence, SLAAI-ICAI 2023

作者： Rathnayake, R.M.M.R. Maduranga, M.W.P. Dissanayake, Maheshi B. General Sir John Kotelawala Defence University Department of Computer Engineering Ratmalana Sri Lanka University of Peradeniya Department of Electrical and Electronics Engineering Kandy Sri Lanka

ISBN: (纸本)9798350319262

Widespread use of the internet of Things (IoT) is being made in a number of application areas to save costs and improve performance. These types of applications also include services that are dependent on localization. When a sensor node is localized, an IoT network may use that information to identify it. A Received signal Strength Indicator (RSSI), which gauges the intensity of the signal received from the sensor nodes, is typically used in conjunction with supervised Machine Learning (ML) algorithms in localization methods. In this study, a unique indoor and outdoor localization and navigation technique is presented that makes use of LoRA nodes and a highly accurate machine learning model. The suggested method has cutting-edge features and shows considerable gains in accuracy and dependability. These discoveries offer potential answers for practical applications and boost localization technology. © 2023 IEEE.

关键词： Sensor nodes

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A Low-Complexity MMSE-HIC Scheme for NOMA-based LEO Satellite IoT systems 22

A Low-Complexity MMSE-HIC Scheme for NOMA-Based LEO Satellit...

引用

22nd IEEE international conference on Communication technology, ICCT 2022

作者： Sun, Weizong Yang, Dewei Kuang, Jingming Chen, Dong Long, Zheren School of Information and Electronics Beijing Institute of Technology Beijing China Institute of Telecommunication and Navigation Satellites China Academy of Space Technology Beijing China

ISBN: (纸本)9781665470674

A low-complexity minimum mean square error (MMSE) equalizer with hybrid interference cancellation (HIC) for non-orthogonal multiple access (NOMA) based Low Earth Orbit (LEO) satellite internet of Things (IoT) systems is presented in this paper. First, a HIC grouping strategy is proposed with the signal-to-interference-plus-noise ratio (SINR) threshold, which can effectively reduce the number of iteration and decoding times. Besides, a reduced-complexity MMSE equalizer based on the matrix expansion and Greville criterion is adopted to avoid the direct computation of matrix inversion. The results show that our proposed scheme can significantly reduce the overhead and processing delay while preserving a good performance. © 2022 IEEE.

关键词： internet of things

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Android malware detection method based on improved genetic algorithm 3

Android malware detection method based on improved genetic a...

引用

3rd international conference on Advanced Algorithms and signal image Processing, AASIP 2023

作者： Zhu, Mingguang Wang, Xingzhen Beijing University of Technology Beijing100124 China

ISBN: (纸本)9781510668522

Android is currently one of the most popular mobile operating systems in the world. The popularity and flexibility of the Android platform makes it a prime target for malicious attackers. Android malware with malicious charges and privacy theft poses a serious threat to users' security. With the rapid update of Android apps and the growing number of malware types, existing Android app stores all lack comprehensive malware detection methods and prevention mechanisms. Previous studies have shown that the detection methods based on feature codes are easily bypassed by deformed malicious codes and rely on the feature code database. However, the detection method based on machine learning can deal with different varieties of malicious code to some extent, but this method needs to transform the application program into feature vector through feature engineering, and then build a classifier to detect and classify the malware quickly. In the field of machine learning, the feature information selected by traditional feature selection methods suffers from low contribution value and high redundancy, which leads to the low accuracy of detection models. The classical genetic algorithm also has disadvantages such as slow convergence speed, poor local search ability, and easily influenced by parameters. Therefore, this paper proposes an improved hybrid adaptive genetic algorithm (HAGA) as feature selection and combines with a machine learning classifier, and then uses the accuracy of the classifier as an evaluation criterion to provide an effective method for Android malware detection. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Android malware

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Spark Collaborative Filtering based Food Recommendation System 2

Spark Collaborative Filtering Based Food Recommendation Syst...

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2nd IEEE international conference on image Processing and Computer Applications, ICIPCA 2024

作者： Zhang, Lian Lin, Rushao Chen, Gongxing Ran, Yan Guangdong Polytechnic of Science and Trade Guangzhou China Guangzhou College of Technology and Business Guangzhou China Guangdong Polytechnic Normal University Guangzhou China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350360240

With the advent of the era of big data and artificial intelligence, the catering industry has experienced great changes, and a wealth of food information has appeared on the market, but the ability of users to collect, filter, organise and summarise information is limited, especially when dealing with a large amount of data, and it becomes extremely difficult for users to obtain usable information from it. The emergence of food recommendation systems not only solves this problem, but also improves the coverage of the information retrieved by the user and formulates the content of food recommendations. In this paper, the least squares method (ALS) is used as a collaborative filtering algorithm to train the hidden semantic model, combined with the matrix decomposition recommendation algorithm, in order to solve the data sparsity problem existing in the recommendation system, and to find the best model through parameter optimisation. In real-time recommendation, this paper introduces reward factor and penalty factor as weights to optimise the recommendation of recently rated dishes for users, which effectively improves the personalisation effect. Combined with Redis, Flume, Kafka and other big data technologies, this paper builds a powerful system that can handle large-scale data, and realises data visualisation through Echarts, providing a fully functional, efficient and stable food recommendation service. © 2024 IEEE.

关键词： Collaborative filtering

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