Accumulation of cells with unimpeded growth is the hallmark for the development of life challenging brain tumor disease. In pre-existing research Machine Learning analytical models are trained on domain specific dataset to achieve goals of an Artificial Intelligence based application in Computer Science for the said disease identification. An ongoing research in the field is presented in this paper where an experimental set of 7038 domain specific images are used to train a model. On experiments conducted on the dataset using six different Machine Learning algorithms the researchers are able to identify Glioma tumor, Meningioma tumors and Pituitary tumor with an accuracy of 96% using RESTNET 5.0 with Transfer Learning Model.
{"title":"Deep Learning-Based Brain Tumor Classification Prototype Using Transfer Learning","authors":"Binju Saju, Laiby Thomas, Fredy Varghese, Arpana Prasad, Neethu Tressa","doi":"10.1109/AICAPS57044.2023.10074201","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074201","url":null,"abstract":"Accumulation of cells with unimpeded growth is the hallmark for the development of life challenging brain tumor disease. In pre-existing research Machine Learning analytical models are trained on domain specific dataset to achieve goals of an Artificial Intelligence based application in Computer Science for the said disease identification. An ongoing research in the field is presented in this paper where an experimental set of 7038 domain specific images are used to train a model. On experiments conducted on the dataset using six different Machine Learning algorithms the researchers are able to identify Glioma tumor, Meningioma tumors and Pituitary tumor with an accuracy of 96% using RESTNET 5.0 with Transfer Learning Model.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125226878","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}
Pub Date : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074104
D. Tayal, Neha Srivastava, Neha
An essential study issue now is the preference of highly discriminative traits from a huge feature collection. By eliminating a significant number of noisy, redundant features, this has the potential to enhance classification performance while lowering the cost of system diagnostics. A feature selection process has been implemented using nature-inspired algorithms. Each of these algorithms needs its starting population to be initialized, and how well that initialization is done has a big impact on the outcome. This paper presents a newly hybrid nature-inspired Algorithm which is comprised by Harris-hawk Algorithm with Visual Geometry Group for selection of traits on High-Dimensional-datasets. Our main idea is to overcome the overfitting issue of feature selection and also overcome convergence problem arise in nature inspired algorithm by introducing visual geometry group Convolution neural network based deep neural network. Then, we compared our upgraded approach to the most significant nature-inspired optimization technique to show that our technique is more accurate and categorized using the Acute lymphoblastic leukemia & Breast cancer High Dimensional datasets.
{"title":"Feature Selection using Enhanced Nature Optimization Technique","authors":"D. Tayal, Neha Srivastava, Neha","doi":"10.1109/AICAPS57044.2023.10074104","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074104","url":null,"abstract":"An essential study issue now is the preference of highly discriminative traits from a huge feature collection. By eliminating a significant number of noisy, redundant features, this has the potential to enhance classification performance while lowering the cost of system diagnostics. A feature selection process has been implemented using nature-inspired algorithms. Each of these algorithms needs its starting population to be initialized, and how well that initialization is done has a big impact on the outcome. This paper presents a newly hybrid nature-inspired Algorithm which is comprised by Harris-hawk Algorithm with Visual Geometry Group for selection of traits on High-Dimensional-datasets. Our main idea is to overcome the overfitting issue of feature selection and also overcome convergence problem arise in nature inspired algorithm by introducing visual geometry group Convolution neural network based deep neural network. Then, we compared our upgraded approach to the most significant nature-inspired optimization technique to show that our technique is more accurate and categorized using the Acute lymphoblastic leukemia & Breast cancer High Dimensional datasets.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115920563","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}
Pub Date : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074523
Mohammed Ashikur Rahman, Adamu Abubakar Ibrahim, A. Tumian
Sepsis is a life-threatening condition of patients in an intensive care unit. Early sepsis detection can reduce the mortality rate and cost of treatment among the patients of the Intensive care unit (ICU). Machine Learning-based model can be used to predict sepsis early using Electronic Health Record (EHR) which consists of big data. Features selection plays a vital role for reducing overfitting and the accuracy of the ML-based prediction model. In this paper, Generalized Linear Model (GLM) was used to select the significant features related to sepsis using MIMIC-III dataset which is a rational database that contains ICU patient’s data at Beth Israel Deaconess Medical center. In addition, developed a sepsis prediction model using Artificial Neural Network (ANN) and Random Forest (RF) and validated those models using confusion matrix. After that, clinical severity scores were also calculated with the same dataset. Finally, compared the Area Under the Receiver Operating Characteristic (AUROC) between ML-based model and clinical severity score. The accuracy of ML-based prediction model with GLM is better than clinical severity scores like SOFA, qSOFA and SIRS.
脓毒症是重症监护病房患者的一种危及生命的疾病。早期脓毒症检测可以降低重症监护病房(ICU)患者的死亡率和治疗费用。基于机器学习的模型可以利用由大数据组成的电子病历(Electronic Health Record, EHR)对败血症进行早期预测。特征选择对于减少过拟合和基于ml的预测模型的准确性起着至关重要的作用。本文采用基于Beth Israel Deaconess Medical center ICU患者数据的理性数据库MIMIC-III数据集，采用广义线性模型(Generalized Linear Model, GLM)选择脓毒症相关的显著特征。此外，利用人工神经网络(ANN)和随机森林(RF)建立了脓毒症预测模型，并利用混淆矩阵对模型进行了验证。之后，使用相同的数据集计算临床严重程度评分。最后，比较基于ml模型的受试者工作特征面积(Area Under Receiver Operating Characteristic, AUROC)与临床严重程度评分。基于ml的GLM预测模型的准确性优于SOFA、qSOFA、SIRS等临床严重程度评分。
{"title":"Feature Selection using Generalized Linear Model for Machine Learning-based Sepsis Prediction","authors":"Mohammed Ashikur Rahman, Adamu Abubakar Ibrahim, A. Tumian","doi":"10.1109/AICAPS57044.2023.10074523","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074523","url":null,"abstract":"Sepsis is a life-threatening condition of patients in an intensive care unit. Early sepsis detection can reduce the mortality rate and cost of treatment among the patients of the Intensive care unit (ICU). Machine Learning-based model can be used to predict sepsis early using Electronic Health Record (EHR) which consists of big data. Features selection plays a vital role for reducing overfitting and the accuracy of the ML-based prediction model. In this paper, Generalized Linear Model (GLM) was used to select the significant features related to sepsis using MIMIC-III dataset which is a rational database that contains ICU patient’s data at Beth Israel Deaconess Medical center. In addition, developed a sepsis prediction model using Artificial Neural Network (ANN) and Random Forest (RF) and validated those models using confusion matrix. After that, clinical severity scores were also calculated with the same dataset. Finally, compared the Area Under the Receiver Operating Characteristic (AUROC) between ML-based model and clinical severity score. The accuracy of ML-based prediction model with GLM is better than clinical severity scores like SOFA, qSOFA and SIRS.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130959458","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}
Pub Date : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074003
Mehanas Shahul, P. P
This work contains the classification of patients in an Emergency Department in a hospital according to their critical conditions. Machine learning can be applied based on the patient’s condition to quickly determine if the patient requires urgent medical intervention from the clinicians or not. Basic vital signs like Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Respiratory Rate (RR), Oxygen saturation (SPO2), Random Blood Sugar (RBS), Temperature, Pulse Rate (PR) are used as the input for the patients’ risk level identification. High-risk or non-risk categories are considered as the output for patient classification. Basic machine learning techniques such as LR, Gaussian NB, SVM, KNN and DT are used for the classification. Precision, recall, and F1-score are considered for the evaluation. The decision tree gives best F1-score of 77.67 for the risk level classification of the imbalanced dataset.
{"title":"Machine Learning Based Patient Classification In Emergency Department","authors":"Mehanas Shahul, P. P","doi":"10.1109/AICAPS57044.2023.10074003","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074003","url":null,"abstract":"This work contains the classification of patients in an Emergency Department in a hospital according to their critical conditions. Machine learning can be applied based on the patient’s condition to quickly determine if the patient requires urgent medical intervention from the clinicians or not. Basic vital signs like Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Respiratory Rate (RR), Oxygen saturation (SPO2), Random Blood Sugar (RBS), Temperature, Pulse Rate (PR) are used as the input for the patients’ risk level identification. High-risk or non-risk categories are considered as the output for patient classification. Basic machine learning techniques such as LR, Gaussian NB, SVM, KNN and DT are used for the classification. Precision, recall, and F1-score are considered for the evaluation. The decision tree gives best F1-score of 77.67 for the risk level classification of the imbalanced dataset.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"87 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128850682","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}
Pub Date : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074171
D. Sarath Chandra, Gagandeep Kaur, Mahua Bhattacharya
The Internet of Things (IoT) has revolutionized every aspect of the everyday lives of the average person by making everything smart and intelligent. The Internet of Things (IoT) is a collection of interconnected devices that can self-configure. The purpose of this paper is to recommend an IoT-based smart farming system that will help farmers acquire real-time data for effective environmental monitoring that improves overall production and product quality. The major goal of this research is to propose an Internet of Things (IoT) based smart farming system to help farmers obtain live data of temperature, and soil moisture for efficient environment monitoring that enhances crop productivity.
{"title":"Smart Irrigation Management System for Precision Agriculture","authors":"D. Sarath Chandra, Gagandeep Kaur, Mahua Bhattacharya","doi":"10.1109/AICAPS57044.2023.10074171","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074171","url":null,"abstract":"The Internet of Things (IoT) has revolutionized every aspect of the everyday lives of the average person by making everything smart and intelligent. The Internet of Things (IoT) is a collection of interconnected devices that can self-configure. The purpose of this paper is to recommend an IoT-based smart farming system that will help farmers acquire real-time data for effective environmental monitoring that improves overall production and product quality. The major goal of this research is to propose an Internet of Things (IoT) based smart farming system to help farmers obtain live data of temperature, and soil moisture for efficient environment monitoring that enhances crop productivity.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114433383","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}
Pub Date : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074322
S. Poonkodi, M. Kanchana
The lung segmentation process plays a vital role in diagnosing lung carcinoma. Segmentation techniques segment the lung region and remove the borders, blood vessels, and void spaces in the CT images. For segmentation, segmenting the highlighted vital features and suppressing the unwanted features is important. In the paper, we proposed the new segmentation techniques combined with an attention mechanism to achieve accurate segmentation. In this model, we introduced the multiscale spatial and channel attention mechanism with the 3D-UNet model named MSCAUNet-3D. this model performs two stages: pre-processing and segmentation. In pre-processing, adaptive Histogram Equalization (AHE) and Gaussian Adaptive Bilateral Filter (GABF) are utilized for removing noise and enhancing the image. In segmentation, we introduce the MSCAUNet-3D for accurate segmentation. To evaluate this model, Dice Coefficient (DC), Jaccard Similarity Coefficient or Index (JI), and Relative Absolute Volume Difference (RAVD) performance measures are utilized. The proposed model yields 91.4, 90.4, and 89.4 in DC, JI, and RAVD, respectively, which shows that the proposed model outperforms the other models.
{"title":"MSCAUNet-3D: Multiscale Spatial Channel Attention 3D-UNet for Lung Carcinoma Segmentation on CT Image","authors":"S. Poonkodi, M. Kanchana","doi":"10.1109/AICAPS57044.2023.10074322","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074322","url":null,"abstract":"The lung segmentation process plays a vital role in diagnosing lung carcinoma. Segmentation techniques segment the lung region and remove the borders, blood vessels, and void spaces in the CT images. For segmentation, segmenting the highlighted vital features and suppressing the unwanted features is important. In the paper, we proposed the new segmentation techniques combined with an attention mechanism to achieve accurate segmentation. In this model, we introduced the multiscale spatial and channel attention mechanism with the 3D-UNet model named MSCAUNet-3D. this model performs two stages: pre-processing and segmentation. In pre-processing, adaptive Histogram Equalization (AHE) and Gaussian Adaptive Bilateral Filter (GABF) are utilized for removing noise and enhancing the image. In segmentation, we introduce the MSCAUNet-3D for accurate segmentation. To evaluate this model, Dice Coefficient (DC), Jaccard Similarity Coefficient or Index (JI), and Relative Absolute Volume Difference (RAVD) performance measures are utilized. The proposed model yields 91.4, 90.4, and 89.4 in DC, JI, and RAVD, respectively, which shows that the proposed model outperforms the other models.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124116809","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}
Pub Date : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074445
Nirupama Parida, Bunil Kumar Balabantaray, R. Nayak, Jitendra Kumar Rout
Prediction of stock market data is difficult because of its complex and highly volatile nature. In this work the historical data as well as the technical indicators are implemented for the purpose of prediction. Different features are extracted using the CNN technique and further the prediction is performed using the dropout based LSTM technique. The basic aim of this study is optimization of the prediction accuracy of the stock price. Different technical indicators and historical data are taken as input data. The sub max layer is substituted with KELM (Kernel Based Extreme Learning Machine). This paper shows a CNN based hybrid system applied on a variety of sources comprising of different stock market. Various matrices are used for observing the accurateness of the proposed model. Two different stock market data are considered for this purpose. The extracted features shows more accurate result. Further it is observed that the proposed model outrun different other methods discussed in this paper
由于股票市场数据的复杂性和高度波动性，对其进行预测是困难的。在这项工作中，采用历史数据和技术指标进行预测。使用CNN技术提取不同的特征，并进一步使用基于dropout的LSTM技术进行预测。本研究的基本目的是优化股票价格的预测精度。不同的技术指标和历史数据作为输入数据。submax层用KELM (Kernel Based Extreme Learning Machine)代替。本文介绍了一种基于CNN的混合系统，应用于由不同股票市场组成的多种来源。使用各种矩阵来观察所提出模型的准确性。为此考虑了两种不同的股票市场数据。特征提取结果更加准确。进一步观察到，所提出的模型优于本文讨论的其他方法
{"title":"A Deep Learning based Approach to Stock Market Price Prediction using Technical indicators","authors":"Nirupama Parida, Bunil Kumar Balabantaray, R. Nayak, Jitendra Kumar Rout","doi":"10.1109/AICAPS57044.2023.10074445","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074445","url":null,"abstract":"Prediction of stock market data is difficult because of its complex and highly volatile nature. In this work the historical data as well as the technical indicators are implemented for the purpose of prediction. Different features are extracted using the CNN technique and further the prediction is performed using the dropout based LSTM technique. The basic aim of this study is optimization of the prediction accuracy of the stock price. Different technical indicators and historical data are taken as input data. The sub max layer is substituted with KELM (Kernel Based Extreme Learning Machine). This paper shows a CNN based hybrid system applied on a variety of sources comprising of different stock market. Various matrices are used for observing the accurateness of the proposed model. Two different stock market data are considered for this purpose. The extracted features shows more accurate result. Further it is observed that the proposed model outrun different other methods discussed in this paper","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128657010","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}
Pub Date : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074221
Nitin Lodha, Harshvardhan Singh Gahlaut
In this paper, we aim to help in identifying the people that are violating social distancing norms set by the government (necessary during the COVID-19 pandemic in public places), by providing an efficient real-time deep learning-based framework to automate the process of monitoring the social distancing via object detection and tracking approaches. Our system is divided into two subsystems: one that deals with crowd detection and control, and the other that sends information to the police authorities. Our system technologies, including as IoT, image processing, web cams, BLE, OpenCV, and Cloud, are being considered for inclusion in the proposed framework. The image processing is divided into two sections, the first of which is the extraction of frames from real-time movies, and the second of which is the processing of the frame to determine the number of individuals in the crowd. Even in a crowd, dissemination may be restricted if people adhere to social distancing standards. As a result, the image processing model primarily targets the number of people who do not adhere to social distancing norms and stand too close together.
{"title":"Covid-19 crowd detection and alert system using image processing","authors":"Nitin Lodha, Harshvardhan Singh Gahlaut","doi":"10.1109/AICAPS57044.2023.10074221","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074221","url":null,"abstract":"In this paper, we aim to help in identifying the people that are violating social distancing norms set by the government (necessary during the COVID-19 pandemic in public places), by providing an efficient real-time deep learning-based framework to automate the process of monitoring the social distancing via object detection and tracking approaches. Our system is divided into two subsystems: one that deals with crowd detection and control, and the other that sends information to the police authorities. Our system technologies, including as IoT, image processing, web cams, BLE, OpenCV, and Cloud, are being considered for inclusion in the proposed framework. The image processing is divided into two sections, the first of which is the extraction of frames from real-time movies, and the second of which is the processing of the frame to determine the number of individuals in the crowd. Even in a crowd, dissemination may be restricted if people adhere to social distancing standards. As a result, the image processing model primarily targets the number of people who do not adhere to social distancing norms and stand too close together.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123350693","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}
Pub Date : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074143
S. Shilaskar, S. Bhatlawande, Jayesh B. Deshmukh, Shreya A. Dehankar
All over the world, agriculture is crucial to the growth of the food industry. Agriculture in our nation is dependent on the monsoons, which are insufficient water sources. So, we have to use fully mechanized irrigation in agriculture. For giving crops enough water without squandering it. And all we know Animals, both domestic and wild, frequently destroy the crops on farms, and one reason for this is the crops’ low production. In this generation where all peoples do multiple works where, it is not possible for people to follow the traditional method of protecting the crops and giving sufficient water to the crop, and staying awake at all times on the farm for protecting crops from animals. so, by using some sensors like Soil moisture sensor, PIR sensor, etc to collect all real-time data from the field, collected by Esp8266 (NodeMcu) for further procedure. This system ensures the supply of sufficient water to plants and protects the field from animals. and the whole system will get the electricity supply from the solar panels so there is almost negligible chance of system failure due to electricity.
{"title":"IoT Based Smart Irrigation and Farm Protection System","authors":"S. Shilaskar, S. Bhatlawande, Jayesh B. Deshmukh, Shreya A. Dehankar","doi":"10.1109/AICAPS57044.2023.10074143","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074143","url":null,"abstract":"All over the world, agriculture is crucial to the growth of the food industry. Agriculture in our nation is dependent on the monsoons, which are insufficient water sources. So, we have to use fully mechanized irrigation in agriculture. For giving crops enough water without squandering it. And all we know Animals, both domestic and wild, frequently destroy the crops on farms, and one reason for this is the crops’ low production. In this generation where all peoples do multiple works where, it is not possible for people to follow the traditional method of protecting the crops and giving sufficient water to the crop, and staying awake at all times on the farm for protecting crops from animals. so, by using some sensors like Soil moisture sensor, PIR sensor, etc to collect all real-time data from the field, collected by Esp8266 (NodeMcu) for further procedure. This system ensures the supply of sufficient water to plants and protects the field from animals. and the whole system will get the electricity supply from the solar panels so there is almost negligible chance of system failure due to electricity.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121446633","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}
Pub Date : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074294
J. C I, M. Vivekanandan, Praveen Kumar Premkamal, R. R
Many real world activities in computer science scenarios are linked with concurrency and security related issues and have to handle large number of processes to be executed in parallel with false safe security solutions. There are many traditional methods in programming languages to handle concurrency. Concurrency is one of the major issues that need to be addressed by most of the servers when dealing with the group communication operations. Security of the data as well as the credibility of the users are the other aspects when a group of users involve in real-time communication. Many light-weighted servers are designed to carryout elementary operations of request handling, file sharing etc. In design of such servers having large number of clients, the request service handling will be based on the individual server programs. Keeping track of individual credibility and establishing concurrency solutions in server design is challenging. The whole work describes the significance and implementation of an Erlang based XMPP server in comparison with a Python based XMPP server with a view to service the client request handling operations for sending messages, group chatting, buddy-list creation, presence identification integrated with XML messaging pattern as per the XMPP protocol. We also accomplish the security and credibility of the users using a blockchain based interface that keep track of user activities during group communication. The security analysis is also performed for blockchain based interface.
{"title":"Blockchain based Secure Erlang Server for Request based Group Communication over XMPP","authors":"J. C I, M. Vivekanandan, Praveen Kumar Premkamal, R. R","doi":"10.1109/AICAPS57044.2023.10074294","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074294","url":null,"abstract":"Many real world activities in computer science scenarios are linked with concurrency and security related issues and have to handle large number of processes to be executed in parallel with false safe security solutions. There are many traditional methods in programming languages to handle concurrency. Concurrency is one of the major issues that need to be addressed by most of the servers when dealing with the group communication operations. Security of the data as well as the credibility of the users are the other aspects when a group of users involve in real-time communication. Many light-weighted servers are designed to carryout elementary operations of request handling, file sharing etc. In design of such servers having large number of clients, the request service handling will be based on the individual server programs. Keeping track of individual credibility and establishing concurrency solutions in server design is challenging. The whole work describes the significance and implementation of an Erlang based XMPP server in comparison with a Python based XMPP server with a view to service the client request handling operations for sending messages, group chatting, buddy-list creation, presence identification integrated with XML messaging pattern as per the XMPP protocol. We also accomplish the security and credibility of the users using a blockchain based interface that keep track of user activities during group communication. The security analysis is also performed for blockchain based interface.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"515 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125009883","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}
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