2023 4th International Conference for Emerging Technology (INCET)最新文献_第9页

Artificial Intelligence Energy Efficiency in Low Power Applications 低功耗应用中的人工智能能效

2023 4th International Conference for Emerging Technology (INCET) Pub Date : 2023-05-26 DOI: 10.1109/INCET57972.2023.10170102

V. Sudha, R. P. Devi, K. Kavitha, A. Prakash, G. Ramachandran

{"title":"Artificial Intelligence Energy Efficiency in Low Power Applications","authors":"V. Sudha, R. P. Devi, K. Kavitha, A. Prakash, G. Ramachandran","doi":"10.1109/INCET57972.2023.10170102","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170102","url":null,"abstract":"In the direction of independent on-device AI .By deploying AI to edge devices, on-device AI may power a variety of functions in our daily lives, such as search and rescue with unmanned aerial vehicles, health care in robots, and augmented reality (AR)/mixed reality (XR) glasses (UAVs).However, it can be difficult to implement DL on edge devices and use it in practical applications. Real applications of on-device AI are not possible because the computational and energy costs of model inference are excessively high for edge devices with constrained computing power and battery capacity. Additionally, pre-trained models may not be accurate for new input instances because they cannot dynamically adapt to the real world after being deployed to edge devices. Two projects are carried out in order to achieve effective and adaptive on-device AI. A machine-learning-based analogue circuit regression model offers an alternate propose methodology for dealing with swiftly increasing invent complexity. The more modern technology structures are proposed, such as SOI or FinFET, the more robust calculation engine is needed to meet various design specifications while assuring operative resilience.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117116307","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

ServiceArc: A Systematic Approach towards Daily Wage Labour Management through Automation System ServiceArc:通过自动化系统实现日薪劳动管理的系统化方法

2023 4th International Conference for Emerging Technology (INCET) Pub Date : 2023-05-26 DOI: 10.1109/INCET57972.2023.10170058

Apurva Jadhav, Piyush Atre, Akshay Andhare, Ujjwal Chaturvedi, Priyanka P. Boraste, D. Medhane

{"title":"ServiceArc: A Systematic Approach towards Daily Wage Labour Management through Automation System","authors":"Apurva Jadhav, Piyush Atre, Akshay Andhare, Ujjwal Chaturvedi, Priyanka P. Boraste, D. Medhane","doi":"10.1109/INCET57972.2023.10170058","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170058","url":null,"abstract":"Daily wage labourers and workers are an integral part of the economy of any country. Especially in a country like India where the estimated population of daily wage workers is nearly around 187 million, they are the backbone of our economy. The problem here is that a lot of workers are financially exploited by contractors who are able to take advantage of the helplessness of the labour who need to find work every single day. In order to tackle this issue, the proposed system aims to establish a meeting point between the labour, contractors, and customers. Therefore, we are designing and implementing an Android application for the purpose of enabling daily wage labour and workers to connect with their customers and contractors and facilitate a fully-fledged platform for end users to obtain their services. Upon registration on the application, the labour can directly come in contact with customers by making themselves available and thus provide their services. The users can also obtain services through the contractor in case multiple labours are required by them. Multi-lingual support, GPS, standard prices, a payment interface, and a rating system are some of the key features of the application.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123065251","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

Comparative Analysis of Machine learning techniques for Forecasting Ionospheric Total Electron Content Data 预测电离层总电子含量数据的机器学习技术比较分析

2023 4th International Conference for Emerging Technology (INCET) Pub Date : 2023-05-26 DOI: 10.1109/INCET57972.2023.10169972

Nayana Shenvi, Hassanali Virani

{"title":"Comparative Analysis of Machine learning techniques for Forecasting Ionospheric Total Electron Content Data","authors":"Nayana Shenvi, Hassanali Virani","doi":"10.1109/INCET57972.2023.10169972","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10169972","url":null,"abstract":"The ionosphere is a highly dynamic region of the Earth's atmosphere that plays a crucial role in global navigation and communication systems. Accurate forecasting of ionospheric activity is essential for mitigating its impact on these systems. In recent years, machine learning techniques have shown promise in predicting ionospheric activity, but there is limited research on their comparative performance. This paper presents a comparative analysis of various machine learning techniques for forecasting ionospheric total electron content (TEC) data. Specifically, we compare the performance of five popular machine learning techniques- linear regression, multi-layer perceptron neural networks, K-nearest neighbors, support vector regression and random forest regressor. We use TEC data along with exogenous parameters namely By, Bz, Vp, Np, F10.7, Kp, Dst and Ap. We evaluate the performance of the models at different latitudes and during solar quiet and active years. Our results show that the Random Forest Regressor (RFR) outperformed the other techniques with the lowest root mean square error (RMSE) and mean absolute error (MAE). The R2 value suggests that the RFR model provides the best fit to the TEC data compared to other models evaluated and can be used for ionospheric TEC forecasting.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124805545","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

A Comprehensive Data Driven Approach on Crop Yield and Fertilizer Efficiency 作物产量和肥效的综合数据驱动方法

2023 4th International Conference for Emerging Technology (INCET) Pub Date : 2023-05-26 DOI: 10.1109/INCET57972.2023.10170643

Kiran Kesarapu, Nelluru Sai Kiran, Erothi Manju Dhara, R. Rupa, Gurpreet Singh Chhabra

引用次数: 0

Prediction of Diseases in Potato Plant using Pre-trained and Traditional Machine Learning Models 基于预训练和传统机器学习模型的马铃薯病害预测

2023 4th International Conference for Emerging Technology (INCET) Pub Date : 2023-05-26 DOI: 10.1109/INCET57972.2023.10170149

Swati Laxmi Sahu, Renta Chintala Bhargavi

{"title":"Prediction of Diseases in Potato Plant using Pre-trained and Traditional Machine Learning Models","authors":"Swati Laxmi Sahu, Renta Chintala Bhargavi","doi":"10.1109/INCET57972.2023.10170149","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170149","url":null,"abstract":"Potato, among the most vegetables is commercially significant and well-known vegetable which is known for its high nutritional content and delicious flavor. India is one of the world’s leading producers of potato. Unfortunately, plant diseases in potato have been one of the causes of decreased production. So, it is necessary to detect them. Collecting images of plants diseases is a big challenge as it is a very time-consuming process. Often, we do not have sufficient data to train our deep learning models, so data augmentation techniques are used for increasing the dataset which lead to poor generalization. This study focuses on detecting whether the plant is healthy or diseased. In this proposed method, limited dataset is used for potato plant disease classification without using any data augmentation techniques. Popular pre-trained models — VGG16, InceptionResNetV2, ResNet50V2 are used for feature extraction and traditional machine learning algorithms — XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest are used as classifiers. From the study, it is observed that the combination of VGG16 model as a feature extractor and SVM as a classifier achieved the highest accuracy of 93% compared to rest of the combination of models and algorithms. The method proposed in this study can be used for potato plant disease detection with limited dataset.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123757433","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

Heart Disease Prediction Using a Soft Voting Ensemble of Gradient Boosting Models, RandomForest, and Gaussian Naive Bayes 使用梯度增强模型、随机森林和高斯朴素贝叶斯的软投票集成进行心脏病预测

2023 4th International Conference for Emerging Technology (INCET) Pub Date : 2023-05-26 DOI: 10.1109/INCET57972.2023.10170399

Kaustav Sen, Bindu Verma

引用次数: 0

Human Activity Recognition for Office Surveillance 办公室监控的人体活动识别

2023 4th International Conference for Emerging Technology (INCET) Pub Date : 2023-05-26 DOI: 10.1109/INCET57972.2023.10170132

P. J. Subrahmanya Hande, Rakeshgowda D S, Naveen Kumar, Nandana K A, P. Kanwal

引用次数: 0

Fuzzy Logic Based PV-Battery system for a Standalone Microgrid 基于模糊逻辑的独立微电网光伏电池系统

2023 4th International Conference for Emerging Technology (INCET) Pub Date : 2023-05-26 DOI: 10.1109/INCET57972.2023.10170223

Narendra Kumar Mourya, Bharti Koul

引用次数: 0

Securing Data storage in Cloud after Migration using Immutable Data Dispersion 使用不可变数据分散保护迁移后的云数据存储

2023 4th International Conference for Emerging Technology (INCET) Pub Date : 2023-05-26 DOI: 10.1109/INCET57972.2023.10170274

Rajesh Kumar C, Aroul Canessane R

{"title":"Securing Data storage in Cloud after Migration using Immutable Data Dispersion","authors":"Rajesh Kumar C, Aroul Canessane R","doi":"10.1109/INCET57972.2023.10170274","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170274","url":null,"abstract":"Cloud computing has emerged as a technology behemoth with applications in a wide range of fields. When data is being migrated from offline data centres and stored in multiple cloud environments part of the control is always with the Cloud Service Providers(CSPs) leads to security concerns. The data stored in the cloud may sometimes be compromised even though the CSPs may take precautions to avoid such situations. In this paper, we discuss securely storing the data using the data dispersion technique by breaking the data into multiple segments and combining it with encryption along with replication. The division of data and storing it in the cloud helps in protecting the complete data even if an attacker tries to access the data it will not be easy for him to make sense of the retrieved data because the data is already being encrypted and combined with dispersion and replication adds to the complexity of retrieval. Security is achieved as the dispersed data is spread across multiple locations which makes it difficult for an attacker to get all the segments. In most scenarios be able it depends on traditional encryption techniques alone to protect the data. Here, We propose focusing more on how data is stored in the cloud to relieve the system of costly computational methodologies. In this strategy, the trade-off between security and the data retrieval time must also be considered.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129262617","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

A Solar PV Array Fed High-Gain Reboost Luo Converter by Grey Wolf Optimizer Algorithm based MPPT in BLDC Motor Drive for Electric Vehicles 基于灰狼优化算法的无刷直流电机驱动太阳能光伏阵列高增益再升压变换器

2023 4th International Conference for Emerging Technology (INCET) Pub Date : 2023-05-26 DOI: 10.1109/INCET57972.2023.10170173

T. Muthamizhan, A. Sivakumar

{"title":"A Solar PV Array Fed High-Gain Reboost Luo Converter by Grey Wolf Optimizer Algorithm based MPPT in BLDC Motor Drive for Electric Vehicles","authors":"T. Muthamizhan, A. Sivakumar","doi":"10.1109/INCET57972.2023.10170173","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170173","url":null,"abstract":"Permanent magnet brushless dc (PMBLDC) machines can indeed be powered by photovoltaic systems (PV) and seem to be cheap, compact, and reliable for use in electric vehicle applications. The PMBLDC motors have increasingly gained a significant amount of attention to the incredibly quiet operation and low maintenance cost. Additionally, these motors are the most recent alternative option for researchers and industrial needs. Furthermore, the control of the PMBLDC motor is by electronic commutation, requires rotor-position sensing by regulating the current to the excitation of phase windings. Using reboost Luo dc-dc converters to limit current to the inverter is the most desirable method for regulating a PMBLDC motor. The maximum power point tracking (MPPT) of photovoltaic system employing a reboost Luo converter is handled by proportional Integral (PI) controller-based MPPT, and the controller’s parameters are optimised with the help of a grey-wolf algorithm. The reboost Luo converter increases the voltage from a photovoltaic (PV) source by boosting the PV source’s voltage by a factor of eight. MATLAB/SIMULINK is used to carry out the simulation, which is subsequently cross-checked against the experimental data by means of an FPGA controller.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129487383","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