2021 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS)最新文献_第4页

iHAS: An Intelligent Home Automation Based System for Smart City iHAS:面向智慧城市的智能家庭自动化系统

2021 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS) Pub Date : 2021-12-01 DOI: 10.1109/iSES52644.2021.00023

Shaik Mulla Shabber, Mohan Bansal, P. M. Devi, Prateek Jain

引用次数: 7

Intelligent Approaches for Natural Language Processing for Indic Languages 印度语自然语言处理的智能方法

2021 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS) Pub Date : 2021-12-01 DOI: 10.1109/iSES52644.2021.00084

Rashi Kumar, V. Sahula

引用次数: 1

Averting and Mitigating the Effects of Uncertainties with Optimal Control in Industrial Networked Control System 用最优控制避免和减轻工业网络控制系统中的不确定性影响

2021 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS) Pub Date : 2021-12-01 DOI: 10.1109/iSES52644.2021.00080

Brijraj Singh Solanki, R. Kumawat, S. Srinivasan

引用次数: 0

Direction of Arrival Estimation in Automotive Radar with Sailfish Optimization Algorithm 基于旗鱼优化算法的汽车雷达到达方向估计

2021 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS) Pub Date : 2021-12-01 DOI: 10.1109/iSES52644.2021.00049

P. Geetha, S. Nanda, Rajendra Prasad Yadav

引用次数: 1

Global Level Smart Vaccination Tracking System using Blockchain and IoT 使用区块链和物联网的全球级智能疫苗接种跟踪系统

2021 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS) Pub Date : 2021-12-01 DOI: 10.1109/iSES52644.2021.00106

G. Nithin, B. S. Egala, A. K. Pradhan

{"title":"Global Level Smart Vaccination Tracking System using Blockchain and IoT","authors":"G. Nithin, B. S. Egala, A. K. Pradhan","doi":"10.1109/iSES52644.2021.00106","DOIUrl":"https://doi.org/10.1109/iSES52644.2021.00106","url":null,"abstract":"The COVID-19 outbreak highlighted the smart healthcare infrastructure requirement to speed up vaccination and treatment. Present vaccination supply chain models are fragmented in nature, and they are suitable for a pandemic like COVID-19. Most of these vaccination supply chain models are cloud-centric and depend on humans. Due to this, the transparency in the supply chain and vaccination process is questionable. Moreover, we con’t trace where the vaccination programs are facing issues in real-time. Furthermore, traditional supply chain models are vulnerable to a single point of failure and lack people-centric service capabilities. This paper has proposed a novel supply chain model for COVID-19 using robust technologies such as Blockchain and the Internet of Things. Besides, it automates the entire vaccination supplication chain, and it records management without compromising data integrity. We have evaluated our proposed model using Ethereum based decentralized application (DApp) to showcase its real-time capabilities. The DApp contains two divisions to deal with internal (intra) and worldwide (inter) use cases. From the system analysis, it is clear that it provides digital records integrity, availability, and system scalability by eliminating a single point of failure. Finally, the proposed system eliminates human interference in digital record management, which is prone to errors and alternation.","PeriodicalId":293167,"journal":{"name":"2021 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116741309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

VLSI Architecture of Sigmoid Activation Function for Rapid Prototyping of Machine Learning Applications. 用于机器学习应用快速成型的Sigmoid激活函数VLSI架构。

2021 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS) Pub Date : 2021-12-01 DOI: 10.1109/iSES52644.2021.00036

Binit Kumar Pandit, A. Banerjee

{"title":"VLSI Architecture of Sigmoid Activation Function for Rapid Prototyping of Machine Learning Applications.","authors":"Binit Kumar Pandit, A. Banerjee","doi":"10.1109/iSES52644.2021.00036","DOIUrl":"https://doi.org/10.1109/iSES52644.2021.00036","url":null,"abstract":"This paper presents a novel VLSI architecture design of the Sigmoid activation function using Chebyshev’s polynomial approximation for efficient hardware realization. The Sigmoid activation function is one of the key components for completing the classification task and provides generality to the deep networks. The complexity of the sigmoid function leads to low accuracy and longer latency in dedicated hardware design. Therefore, an accurate and fast hardware architecture of the sigmoid function is explored. Chebyshev’s polynomial approximation method is capable of reducing the sum of products (SOP) terms leading to optimum utilization of available hardware resources in FPGAs. The availability of a large number of embedded array multipliers in new FPGA families like Zynq, Kintex7, Virtex7, etc., makes hardware realization of non-linear functions like sigmoid easier and robust. The proposed VLSI architecture has been implemented and tested for its correctness on Xilinx’s Zynq UltraScale+ MPSoC ZCU104 Evaluation Kit using Xilinx Vivado 2018.3. software platform. It can be further used for any end-to-end prototyping using FPGAs and deployed for high-performance real-time applications.","PeriodicalId":293167,"journal":{"name":"2021 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114987991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Data Aggregation in Internet of Things aiming at Precision Agriculture 面向精准农业的物联网数据聚合

2021 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS) Pub Date : 2021-12-01 DOI: 10.1109/iSES52644.2021.00068

H. Sarma

引用次数: 2

Alternate Crop Prediction using Artificial Intelligence: A Case Study in Assam 利用人工智能进行作物交替预测:以阿萨姆邦为例

2021 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS) Pub Date : 2021-12-01 DOI: 10.1109/iSES52644.2021.00067

Bhabesh Mali, Santanu Saha, Daimalu Brahma, P. Singh, Sukumar Nandi

{"title":"Alternate Crop Prediction using Artificial Intelligence: A Case Study in Assam","authors":"Bhabesh Mali, Santanu Saha, Daimalu Brahma, P. Singh, Sukumar Nandi","doi":"10.1109/iSES52644.2021.00067","DOIUrl":"https://doi.org/10.1109/iSES52644.2021.00067","url":null,"abstract":"In recent years, there has been a lot of utilization of Artificial Intelligence and Machine Learning in the field of agriculture to address various types of challenges faced by this sector. In an agro-based country, the focus of the agricultural sector is to achieve the maximum yield of the crops grown and make profits out of it. There has been a severe loss of crops due to the various climatic variations, pest infestation, improper soil treatment, inadequate rainfall, inadequate nutrients etc. In various research studies, the use of machine learning has been found very helpful in addressing various crop-related problems including crop prediction based on various factors. Motivated from this, we, in this paper conducted a case study in Assam for the prediction of alternate crops using artificial intelligence and with an objective to help out the farmers. With our proposed solution, the farmers will be able to predict a particular crop that will be most suitable to grow according to the season, pH of the soil, temperature, rainfall and type of the soil, keeping an eye to get the maximum yield followed by maximum profit. We have used Artificial Neural Networks (ANN) to predict the right crop to be grown. The proposed model efficiently predicts the alternate crop by preserving the original data distribution with an accuracy of about 90.89% for the test data and by using the k-fold Cross-Validation, the accuracy is about 91.57%.","PeriodicalId":293167,"journal":{"name":"2021 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133666864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AccGuard: Secure and Trusted Computation on Remote FPGA Accelerators AccGuard:远程FPGA加速器上的安全可信计算

2021 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS) Pub Date : 2021-12-01 DOI: 10.1109/iSES52644.2021.00093

Wei Ren, Junhao Pan, Deming Chen

引用次数: 3

An Automated MDD Detection System based on Machine Learning Methods in Smart Connected Healthcare 智能互联医疗中基于机器学习方法的MDD自动检测系统

2021 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS) Pub Date : 2021-12-01 DOI: 10.1109/iSES52644.2021.00019

G. Sharma, A. Joshi, E. Pilli

{"title":"An Automated MDD Detection System based on Machine Learning Methods in Smart Connected Healthcare","authors":"G. Sharma, A. Joshi, E. Pilli","doi":"10.1109/iSES52644.2021.00019","DOIUrl":"https://doi.org/10.1109/iSES52644.2021.00019","url":null,"abstract":"Electroencephalography (EEG)-based depression detection in the early stage is a very challenging and important research area in artificial intelligence as it can save the lives of several people. This paper presents EEG-based machine learning models involving 30 healthy subjects and 33 major depressive disorder (MDD) subjects to diagnose MDD. The model with the best performance has been evaluated on the Internet of Medical Things (IoMT) framework for smart healthcare. The main idea behind this study is to recognize features and classifiers which can best discriminate the healthy and depressive subjects. This study has three main steps of analysis: 1) Linear, non-linear, fractal dimension, statistical, time, coherence features have been extracted from EEG signals. Their effects are investigated, and quality features are identified. 2) Three feature selection methods, Principle component analysis (PCA), Neighbourhood component analysis (NBA), and Relief-based algorithm (RBA), are utilized for the selection of most relevant features, and their performance is compared. 3) For discriminating normal and depressed subjects, radial-basis function (RBF) based support vector machine (SVM), K- nearest neighbor (KNN), logistic regression (LR), decision tree (DT), naïve Bayes classification (NBC), bagged tree (BT) and linear discriminant analysis (LDA) classifier are used. This paper concludes that non-linear features with an RBF-SVM classifier achieve the best classification accuracy of 98.90%. The findings in this study are utilized to develop a model to detect depression in remote applications and smart healthcare.","PeriodicalId":293167,"journal":{"name":"2021 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124970930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5