2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)最新文献_第6页

Framework for Designing a Multiband Ultra-Wide Band (UWB) Antenna with its Functions 一种多波段超宽带(UWB)天线及其功能设计框架

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010967

Nitin I. Bhopale, S. Pawar

引用次数: 0

Poultry Farm Surveillance System Utilizing IoT and Wireless Sensor Network 利用物联网和无线传感器网络的家禽养殖场监控系统

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010720

A. Shanmugapriya, A. Sangeethadevi, A. Kalaivani

{"title":"Poultry Farm Surveillance System Utilizing IoT and Wireless Sensor Network","authors":"A. Shanmugapriya, A. Sangeethadevi, A. Kalaivani","doi":"10.1109/ICAISS55157.2022.10010720","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10010720","url":null,"abstract":"The majority of poultry farms are manually inspected and handled. The critical variables that must be monitored and managed include heat, moisture, air quality, illumination, drying, and foodstuff feeding. These variables directly affect way chicken is grown. The rate of death for broiler chickens is currently higher than average. By effectively automating the process of keeping track of the heat, moisture, air level of quality, and food feeder, this data analysis aims to manufacture nutritious chickens and decrease the risk of death of chickens to enhance productivity. By linking wireless networks of sensors (WSN) and the Web of Things, this is made possible. Combining IoT and WSN technologies, a prototype was built, and the aforementioned parameters were examined against standard limits. When these variables go above the set thresholds, automatic remedial actions are taken that may help lower the farm's chicken risk of dying. Additionally, the user receives automatic S MS alert notifications from this system. To track and display this information, a web function was developed.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114628002","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

Design of Voice based Virtual Assistant using Internet of Things 基于物联网的语音虚拟助手设计

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010805

V. Sriram, D. Kamalakkannnan, T. Archana, Anandbabu Gopatoti, B. Swapna, Ajay Singh Yadav

{"title":"Design of Voice based Virtual Assistant using Internet of Things","authors":"V. Sriram, D. Kamalakkannnan, T. Archana, Anandbabu Gopatoti, B. Swapna, Ajay Singh Yadav","doi":"10.1109/ICAISS55157.2022.10010805","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10010805","url":null,"abstract":"Virtual assistance is one of the most important features in everyday life. People nowadays rely on the enhanced features of virtual assistance. The proposed system is used for the propulsion of the technology that is been in need. The virtual assistance in the process is costly and needs more manpower to build them. The proposed system embellishes the cost-effective nature and the maintenance of the system will be very efficient the hardware used is made to give a good life balance and they will not get perished as time goes over. The Proposed system recognizes the voice that has been recorded and analyses them for command retrieval. The user can add as many as many voices as they can. The Process of adding the voices are user friendly and the method of making the commands reliable and commendable depend on the performance ratio of the hardware. The most important feature in the proposed system is the hardware construction. In this system Raspberry Pi is used for the construction of the CPU and the voice control unit is designed with voice recognition sensors. The correctness and the effective time being are processed by the microphones connected to the system. The sheerness of the system relies on the microphone only. The microphone plays a major role in the proposed system as such the microphone helps us to recognize the voice and repeat the commands that have been commenced by the user. The Virtual used here is manual assistance through the Linux platform. It is a new venture in the phenomenal wave.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"384 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115911099","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 analysis using neural network-based model for thyroid disease prediction 综合分析基于神经网络的甲状腺疾病预测模型

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10011021

Anu K.P., J. B. Benifa

引用次数: 1

Power Factor Correction: Its Importance and Improvements 功率因数校正:其重要性及改进

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10011116

S. S. Dhruvanth, K. Reddy, P. S. Sai, Ranjith Kumar Gatla, P. Sridhar, T. Babu

引用次数: 0

An Unsupervised Attack Detection Approach for Software Defined Networks 软件定义网络的无监督攻击检测方法

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010577

Ravin Chouhan, M. Atulkar, N. K. Nagwani

{"title":"An Unsupervised Attack Detection Approach for Software Defined Networks","authors":"Ravin Chouhan, M. Atulkar, N. K. Nagwani","doi":"10.1109/ICAISS55157.2022.10010577","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10010577","url":null,"abstract":"Software Defined Networking (SDN) is quickly becoming a vital technology for the future Internet. SDN provides a worldwide network with the capacity to manage network traffic dynamically. One of the main advantages of SDN over traditional networks is that it provides better network security due to centralised control. However, the flexibility offered by SDN architecture raises several additional network security concerns that must be addressed to improve SDN network security. This study proposes an unsupervised learning method to address attacks in the SDN controller. 7 extracted features from southbound traffic have been used to train KMeans, MeanShift, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), AgglomerativeClustering, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), MiniBatchKMeans, Ordering Points To Identify Cluster Structure (OPTICS), and SpectralClustering, which are all well-known unsupervised classifiers. In terms of various well-known performance measuring criteria, such as Silhouette Score (SS), Calinski Harabasz Index (CHI), and Davies Bouldin Index (DBI), BIRCH outperforms all other classifiers.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126453920","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

YOLOv5 Crop Detection Deep Learning Model using Artificial Intelligence (AI) and Edge Computing 基于人工智能和边缘计算的YOLOv5作物检测深度学习模型

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010894

S. Bhavan, Mohana

引用次数: 0

Integrating Data-Oriented Intelligent Evaluation Framework Based Image Detection System 集成面向数据的基于智能评估框架的图像检测系统

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010726

Rui Wang

引用次数: 0

A Study and Research Direction towards Healthcare Data Management System in FoG and IoT Networks 基于FoG和IoT网络的医疗数据管理系统研究与研究方向

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10011044

Tina Victoria A, M. Kowsigan

{"title":"A Study and Research Direction towards Healthcare Data Management System in FoG and IoT Networks","authors":"Tina Victoria A, M. Kowsigan","doi":"10.1109/ICAISS55157.2022.10011044","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10011044","url":null,"abstract":"Due to the rise in the count of sensing devices for healthcare, the researchers are showing high interest due to the new complicated and broader range of application scenarios. Several numbers of connected devices are presented in the “Internet of Things (IoT)-enabled Wireless Sensor Network (WSN),” which has several ranges of sensing support systems correlated with clinical health constraints through new research limitations and innovative solutions. WSN consists of sensor nodes, which are coordinated with healthcare to monitor aging family members or patients through utilizing sensing devices to measure walk step counting, fall detection, temperature, blood pressure, heartbeat, and glucose level. Data aggregation towards FoG-assisted technologies uses “peer-to-peer communication” among sensing devices and wearables to assist health. In these cases, secure data gathering and transmission for centralized servers are more complicated for protecting against various attacks towards illicit accessing of data. Conventional solutions are suffered from energy, communication, and storage overheads. The main intention of this paper is to categorize different algorithms suited for “FoG-assisted healthcare data” management systems in IoT-enabled WSNs to assist beginners in this research area. Moreover, this review focuses on diverse tools used for implementation and performance metrics analyzed for validation. Finally, the research gaps and challenges to be focused on in the future are also given, thus giving ideas for future researchers to get into the data management model.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129353275","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

Performance Enhancement of Machine Learning Algorithms on Heart Stroke Prediction Application using Sampling and Feature Selection Techniques 利用采样和特征选择技术提高机器学习算法在心脏中风预测应用中的性能

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10011040

Naga Sreeharsha Reddy Ambati, Sree Harrsha Singara, Syam Sukesh Konjeti, Selvi C

{"title":"Performance Enhancement of Machine Learning Algorithms on Heart Stroke Prediction Application using Sampling and Feature Selection Techniques","authors":"Naga Sreeharsha Reddy Ambati, Sree Harrsha Singara, Syam Sukesh Konjeti, Selvi C","doi":"10.1109/ICAISS55157.2022.10011040","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10011040","url":null,"abstract":"A heart stroke occurs when the flow of blood to a certain area of the heart is restricted, most often by a blood clot. Strokes are a significant contributor to serious impairment in the adult population and a leading cause of fatalities. As a result, many individuals die, and some become permanently disabled. Therefore, the stroke must be precisely predicted to begin treatment as soon as possible. This project uses Kaggle's Stroke Prediction dataset to predict heart stroke where the classes are not balanced. The accuracy of the existing stroke predictions, which used a downsampling technique to balance the data, was 75%. However, the existing models did not employ any Resampling and Feature Selection (FS) techniques to improve their accuracy. In order to achieve the highest level of accuracy for stroke prediction, the stroke dataset has undergone a comparative analysis of several resampling approaches and FS methods across various Machine Learning (ML) algorithms. To obtain better accuracy, classifiers are trained with the K-Fold cross-validation mechanism. Appropriate pre-processing techniques are applied to fill in the missing values and convert the existing categorical data into numerical data. Re-sampling strategies are used to balance the dataset so that the trained model will produce accurate results for all the target variable's classes. Similarly to that, methods for FS are used to extract the best features from the dataset that will aid to improve accuracy. From the experimental results, it has been observed that the Instance Hardness Threshold re-sampling technique along with the Exhaustive feature selection method across the Random Forest classifier yields a better accuracy of 97.9%.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129510051","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