2022 5th International Conference on Computational Intelligence and Networks (CINE)最新文献_第3页

Automatic Crop Securing System Using IoT 使用物联网的自动作物保护系统

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037267

Adepu Sai Aashrith, Chakka Manaswini, G. Preetham, B. Panigrahi, P. Sarangi

{"title":"Automatic Crop Securing System Using IoT","authors":"Adepu Sai Aashrith, Chakka Manaswini, G. Preetham, B. Panigrahi, P. Sarangi","doi":"10.1109/CINE56307.2022.10037267","DOIUrl":"https://doi.org/10.1109/CINE56307.2022.10037267","url":null,"abstract":"In terms of the development of a country's economy, agriculture is crucial as it is the main source for the survival of human life. But due to unexpected or unseasonal rains, there are lots of issues where farmers suffer a lot as the crops are destroyed and washed away due to heavy rains. By considering this issue we build our project which helps the farmers to be free from worries when there are heavy unexpected rains. The model which we built helps the farmers from unseasonal rains and saves water. This saved water can be used for various purposes and the main use among it is can reuse the saved water for farming, which decreases the regular water usage for the farmers. In this model, an automated sheet is inculcated which works by taking the inputs from the rain sensor and moisture sensor and protecting the whole field from unexpected or unseasonal rains. When it rains, the rain sensor turns on, and the soil sensor embedded in the ground begins responding to how much water is in the soil. If there is more water in the soil than is necessary, the controller receives the inputs which indicate the DC motor to run which opens the sheet automatically to close the crops using a polystyrene sheet. If there is any issue opening the sheet, information is passed to the farmers and then the operation is performed manually.","PeriodicalId":336238,"journal":{"name":"2022 5th International Conference on Computational Intelligence and Networks (CINE)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132588176","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

An IoT Assisted Fog Enabled Framework for Smart Green House 智能温室的物联网辅助雾支持框架

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037339

Niva Tripathy, S. Tripathy, Mamata Rath, Jhum Swain

{"title":"An IoT Assisted Fog Enabled Framework for Smart Green House","authors":"Niva Tripathy, S. Tripathy, Mamata Rath, Jhum Swain","doi":"10.1109/CINE56307.2022.10037339","DOIUrl":"https://doi.org/10.1109/CINE56307.2022.10037339","url":null,"abstract":"The Internet of Things (IoT) has transformed smart agriculture by increasing efficiency and lowering production costs in addition to boosting productivity and optimizing resource use. This article outlines the future of automation and emphasizes the possibilities of sensors and IoT in the field of greenhouse farming. Through a variety of sensors, the various parameters like humidity, pH and EC value, temperature, UV light intensity, and CO2 level are tracked in order to provide valuable insight into early fault detection and diagnosis. A growing computing technique to enhance and support cloud computing is called fog computing. Fog computing platforms include a number of features that enable delivery services to users more quickly and improve the Quality of Service (QoS) of IoT devices, such as being close to edge users, being an open platform, and supporting mobility. As a result, it is turning into a crucial strategy for user-centered IoT-based applications. The core operating system that directs and oversees all of the operations is a Decision Support System (DSS) in the Fog layer described in this article. The paper also discusses the various difficulties associated with greenhouse farming and spotlights a novel, smart, and sustainable IoT-Fog solution. This article's model is highly suited to the changing environment, redefining sustainability in the process.","PeriodicalId":336238,"journal":{"name":"2022 5th International Conference on Computational Intelligence and Networks (CINE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130677787","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

Vision-Based System to Detect Antistatic Gloves and Antistatic ESD Protection in Laboratory 基于视觉的实验室防静电手套及防静电检测系统

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037543

Jyoti Madake, Varunavi Shettigar, Shruti A. Vedpathak, S. Bhatlawande, S. Shilaskar

引用次数: 0

EEG-Based Brain Computer Interface for Emotion Recognition 基于脑电图的情绪识别脑机接口

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037255

KM Shahin Bano, Prachet Bhuyan, Abhishek Ray

{"title":"EEG-Based Brain Computer Interface for Emotion Recognition","authors":"KM Shahin Bano, Prachet Bhuyan, Abhishek Ray","doi":"10.1109/CINE56307.2022.10037255","DOIUrl":"https://doi.org/10.1109/CINE56307.2022.10037255","url":null,"abstract":"Emotion recognition using electroencephalography (EEG) signal could be a current focus in brain-computer interface research, that is convenient and a reliable technique. EEG-based emotion detection studies are employed in a very spread of fields, including defence, aerospace, and medicine, among others. The purpose of this study is to discover the relationship between EEG signals and human emotions. EEG signals are commonly used to categorise emotions into three groups: positive, negative, and neutral. We first extracted features from the EEG signals in order to classify emotions and used a deep learning classifier: recurrent neural network (RNN) and gated recurrent unit (GRU). Second, a Muse EEG headband with four electrodes (TP9, AF7, AF8, TP10) is used to record brain activity. Positive and negative emotional states are elicited with lucid valence film clips, and neutral resting data with no stimuli is also recorded for one minute per session. EEG data was collected for 3 minutes per state from two people (one male and one female) (positive, neutral, and negative) [5]. This study helps to spot human emotions supported by EEG signals within the brain-computer interface and helps to know the emotion of the mind.","PeriodicalId":336238,"journal":{"name":"2022 5th International Conference on Computational Intelligence and Networks (CINE)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114339331","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

An Efficient CNN-LSTM Approach for Smartphone Sensor-Based Human Activity Recognition System 基于智能手机传感器的人体活动识别系统中一种高效CNN-LSTM方法

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037495

Nurul Amin Choudhury, B. Soni

{"title":"An Efficient CNN-LSTM Approach for Smartphone Sensor-Based Human Activity Recognition System","authors":"Nurul Amin Choudhury, B. Soni","doi":"10.1109/CINE56307.2022.10037495","DOIUrl":"https://doi.org/10.1109/CINE56307.2022.10037495","url":null,"abstract":"Human Activity Recognition - HAR is one of the most popular area in the filed of sensor technology and smart learning algorithms. Deep learning algorithms are immensely exploited in HAR systems as it eliminates the need of manual feature engineering. Researchers use normal and hybrid deep learning schemes for training and comparing the models. This paper proposes an efficient CNN-LSTM model for recognising daily human activities using smartphone sensor data. A contemporary CNN-LSTM model is created using time distributed feature extraction layers as it can efficiently handle hierarchical features and can selects the relevant features easily using LSTM memorization scheme. The proposed CNN-LSTM model is compared with two other models - DNN and LSTM in terms of accuracy, precision, recall, F1- score, training loss and computational times. The proposed model managed to outperform other models optimally in all the evaluation metrics. Using holdout training and testing split, the model managed to achieve an average accuracy of 97.609% and 98.69% with relu activation function and 100 training iteration. On validating the different models, the hybrid models takes less computational time and managed to achieve an computational efficiency of (76.23 ± 140.76)% from other models.","PeriodicalId":336238,"journal":{"name":"2022 5th International Conference on Computational Intelligence and Networks (CINE)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133723624","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

Breast Cancer Prediction Using Long Short-Term Memory Algorithm 利用长短期记忆算法预测乳腺癌

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037258

M. Behera, Archana Sarangi, Debahuti Mishra, Shubhendu Kumar Sarangi

{"title":"Breast Cancer Prediction Using Long Short-Term Memory Algorithm","authors":"M. Behera, Archana Sarangi, Debahuti Mishra, Shubhendu Kumar Sarangi","doi":"10.1109/CINE56307.2022.10037258","DOIUrl":"https://doi.org/10.1109/CINE56307.2022.10037258","url":null,"abstract":"Breast cancer (BC) isconsidered the second leading cause of death in both developed and developing countries, with 8% of women being diagnosed with the disease at some time in their life. So, it's more crucial to identify BC and the damaged breast region. In today's world, Machine Learning (ML) algorithms are frequently employed in the classification of breast cancer datasets. These algorithms have quite a significant level of classification accuracy and diagnostic capability. Because a specific classifier may or may not perform well enough for such datasets, a comparison examination of classifiers is necessary in order to get maximum performance in such significant breast cancer predictions. Deep learning is the branch of machine learning with architecture and functions inspired by the human brain. It's especially effective for classifying enormous data sets because the findings are fast and accurate. In this paper, we have used five different machine learning algorithms: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and long short-term memory (LSTM) on BC dataset. The outcomes produced by KNN, SVM, RF, and DT classifier will all be compared to the LSTM classifier on the basis of confusion matrix, precision, F1 score, Recall, and accuracy. This study's main aim is to diagnose the best machine-learning algorithm for breast cancer prediction. It is observed that the LSTM algorithmoutperforms all other discussed algorithms with 96% accuracy.","PeriodicalId":336238,"journal":{"name":"2022 5th International Conference on Computational Intelligence and Networks (CINE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115264666","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

Smart Prediction of Severity in Vehicular Crashes: A Machine Learning Approach 车辆碰撞严重程度的智能预测:机器学习方法

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037474

Subha Koley, Sourav Mondal, P. Ghosal

{"title":"Smart Prediction of Severity in Vehicular Crashes: A Machine Learning Approach","authors":"Subha Koley, Sourav Mondal, P. Ghosal","doi":"10.1109/CINE56307.2022.10037474","DOIUrl":"https://doi.org/10.1109/CINE56307.2022.10037474","url":null,"abstract":"The number of vehicles on the road is increasing steadily every day, and even with substantial technological breakthroughs in vehicle safety systems, the number of injuries and fatalities related to traffic accidents is still very high. It has been noted that victims have frequently passed away as a result of wasting valuable time looking for appropriate hospitals. It goes without saying that locating a suitable hospital for collision victims will be quicker and easier if the severity of their injuries can be assessed right away. In this article, a machine learning-based model has been proposed that can measure the severity of a vehicular crash immediately after an accident. Real road accident data has been used in the proposed model to train the system using various machine learning models and other relevant information related to a crash have been collected from the OBD system of vehicles. The proposed system provides a crash severity report according to three predefined categories i.e. Slight, Serious and Fatal which may be very much useful for choosing appropriate hospitals without wasting precious time.","PeriodicalId":336238,"journal":{"name":"2022 5th International Conference on Computational Intelligence and Networks (CINE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121323075","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

Hindi Document Extractive Summarization: Neural Method on A New Data Set 印地语文档抽取摘要:基于新数据集的神经方法

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037327

Kunal Tawatia, Nishant Jain, Suman Kundu

引用次数: 1

Smart Attendance Tracking and Performance Monitoring System Based on CCTV Camera Input 基于闭路电视摄像机输入的智能考勤与绩效监控系统

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037279

Rupam Sau, Anwesha Bandyopadhyay, Sourav Sarkar, Rohit Kumar Guha, Maitreyee Ganguly

引用次数: 0