2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)最新文献

Frequency Analysis Attack on Ceaser Cipher using Quantum Support Vector Machine 基于量子支持向量机的频率分析攻击

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119286

Vishnu Ajith, Mahima Mary Mathews, P. V.

引用次数: 0

Natural Language Query in Bengali to SQL Generation Using Named Entity Recognition 使用命名实体识别的孟加拉语自然语言查询到SQL生成

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119243

K. Mandal, Prasenjit Mukheriee, Baisakhi Chakraborty

{"title":"Natural Language Query in Bengali to SQL Generation Using Named Entity Recognition","authors":"K. Mandal, Prasenjit Mukheriee, Baisakhi Chakraborty","doi":"10.1109/IATMSI56455.2022.10119243","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119243","url":null,"abstract":"Various search strategies are used to search the data from the database. Adapting the searching language and grasping its numerous syntaxes are the key hurdles that a user encounters when accessing these data. Thus, we propose a system that translates natural language queries into Structured Query Language (SQL) queries and retrieves the relevant data from a database. This proposed system allows inexperienced users to access a database without prior knowledge of query languages. The current approach applies machine learning and rule-based approaches because the machine learning approach gives better results for large-size data, whereas the rule-based approach performs well in small-size datasets. This system receives health queries in Bengali. Tokenization is applied to the user's query. The Bengali Natural Language Processing (BNLP) toolkit removes punctuation marks from the token list. After removing punctuation marks, the proposed system uses a predefined Bengali stop words list to provide a score for each token. The score facilitates the finding of nominal words. The stemming method is performed to obtain the nominal root word. The pattern is created to generate all possible nominal compounds in Bengali. A new set of proposed rules and named entity recognition module of the BNLP toolkit is utilized to predict entities and attributes using the pattern. The proposed system maintains a healthcare database. Finally, the SQL is formed using entities, and attributes and the relevant result is obtained from the database.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127489974","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 and Implementation of LQR PI Controller for Second-order Time Delay Process 二阶时滞过程LQR PI控制器的设计与实现

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119366

Harshavardhana Reddy K, Sachin Sharma, Avagaddi Prasad, Shivarama Krishna K

引用次数: 0

Deploying and Analyzing Classification Algorithms for Intrusion Detection 入侵检测分类算法的部署与分析

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119264

Himanshu Pandey, Saumya Bhadauria

{"title":"Deploying and Analyzing Classification Algorithms for Intrusion Detection","authors":"Himanshu Pandey, Saumya Bhadauria","doi":"10.1109/IATMSI56455.2022.10119264","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119264","url":null,"abstract":"Intrusion Detection Systems that use anomaly de-tection can detect unknown assaults, but they are less accurate, resulting in many false alarms. In this paper, machine learning techniques are examined in order to create IDSs that may be used in existing computer networks. In order to improve detection quality, a three-step optimization technique is first provided: 1) rebalancing the dataset with augmented data, 2) optimizing model performance, and 3) integrating the results of the best models through ensemble learning. This method has problems because the models are trained on previously known assaults and so do not do anomaly detection. To solve the existing issues, we studied the accuracy, sensitivity, roc curve, false positive rate of various binary and multi-class classifiers like KNN, Linear SVM, Quadratic SVM, multi-layer perceptron(MLP), and some other general classification algorithms, which inferred to us that some advancements could be made to the existing models. We developed a new and better LSTM (Long Short Term Memory) technique, a deep learning technique for recognizing attacks and storing them in long-term memory in order to counter future attacks.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126176769","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

Automation in Agriculture and Smart Farming Techniques using Deep Learning 农业自动化和使用深度学习的智能农业技术

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119251

Nitin Panuganti, Pinku Ranjan, Kawaljeet Singh Batra, Jayant Kumar Rai

{"title":"Automation in Agriculture and Smart Farming Techniques using Deep Learning","authors":"Nitin Panuganti, Pinku Ranjan, Kawaljeet Singh Batra, Jayant Kumar Rai","doi":"10.1109/IATMSI56455.2022.10119251","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119251","url":null,"abstract":"Agriculture is considered to be a field of great importance and with a serious economic impact in all successful countries. Due to the substantial increase in world population, it has become a relevant concern to be able to meet people's daily dietary needs. Henceforth, it has become inevitable to make a transition to smart agricultural techniques to achieve the set food security goals. In recent times, several deep learning methods, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been vigorously studied, applied, and researched in different fields, including farming and agriculture. In this project, we aim at analyzing existing research on deep learning techniques in smart farming and agriculture and propose solutions for different aspects of farming using various deep learning architectures. Furthermore, we studied the farming parameters such as weather reports, plant irrigation information, pests that affect common crops, germination periods of the flowers/seeds, disease/anomaly detection in their leaves, etc., and proposed modular solutions for each of the respective areas of smart farming. Additionally, we also compared relevant studies regarding farming and focused agricultural methods, problems being faced, the method for collecting data being used, and the deep learning model suggested.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116171424","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

Hand gesture recognition using EMD and VMD techniques 手势识别使用EMD和VMD技术

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119304

Bhavana Sharma, J. Panda

引用次数: 0

Successful Delivery Probability and Performance Analysis of Network Coding with Multi-generation Mixing in Wired Network 有线网络中多代混合网络编码的成功交付概率及性能分析

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119444

Zhou Ting, Amit Yadav, Asif Khan, D. Yadav

引用次数: 0

An Exploratory Analysis of Delhi Air Quality Using Statistics and Machine Learning Models 使用统计和机器学习模型对德里空气质量进行探索性分析

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119423

Anwesha Chakravarty, S. S, S. S

{"title":"An Exploratory Analysis of Delhi Air Quality Using Statistics and Machine Learning Models","authors":"Anwesha Chakravarty, S. S, S. S","doi":"10.1109/IATMSI56455.2022.10119423","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119423","url":null,"abstract":"Air pollution is one of the most significant concerns of the present era, which has severe and alarming effects on human health and the environment, thereby escalating the climate change issue. Hence, in-depth analysis of air pollution data and accurate air quality forecasting is crucial in controlling the growing pollution levels. It also aids in designing appropriate policies to prevent exposure to toxic pollutants and taking necessary precautionary measures. Air quality in Delhi, the capital of India, is inferior compared to other major cities in the world. In this study, daily and hourly concentrations of air pollutants in the Delhi region were collected and analyzed using various methods. A comparative analysis is performed based on months, seasons, and the topography of different stations. The effect of the Covid-19 lockdown on the reduction of pollutant levels is also studied. A correlation analysis is performed on the available data to show the relationships and dependencies among different pollutants, their relationship with weather parameters, and the correlations between the stations. Various machine learning models were used for air quality forecasting, like Linear Regression, Vector Auto Regression, Gradient Boosting Machine, Random Forest, and Decision Tree Regression. The performance of these models was compared using RMSE, MAE, and MAPE metrics. This study is focused on the dire state of air pollution in Delhi, the primary reasons behind it, and the efficacy of calculated lockdowns in bringing down pollution levels. It also highlights the potential of Linear Regression and Decision Tree Regression models in predicting the air quality for different time intervals.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128789274","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

Medical Waste Classification using Deep Learning and Convolutional Neural Networks 使用深度学习和卷积神经网络的医疗废物分类

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119431

Mark Verma, Arun Kumar, Somesh Kumar

{"title":"Medical Waste Classification using Deep Learning and Convolutional Neural Networks","authors":"Mark Verma, Arun Kumar, Somesh Kumar","doi":"10.1109/IATMSI56455.2022.10119431","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119431","url":null,"abstract":"With the rise of attention to healthcare since the start of the century, which the recent pandemic has emphasized, the number of hospitals and clinics has increased exponentially. The growth in hospitals and patients has also resulted in increased medical waste. The different kinds of medical waste must be segregated and disposed of properly to prevent the spread of bacteria and viruses and cross-contamination. However, it is not economically feasible to hire a workforce that can segregate said waste. With the perceived popularity of deep learning and image classification systems, creating a Deep Learning model to categorize the different kinds of medical waste is possible. Hence using a deep learning-based classification method in which an appropriate pre-trained model is selected for practical implementation, followed by transfer learning methods to improve classification results, is appropriate. Different types of medical waste are grouped into umbrella categories(general, hazardous, infectious). An ideal situation would be where images are uploaded, and the machine can classify the presented waste appropriately with little to no waiting times. Three out of four of the modified pre-trained models with different architectures were able to achieve an accuracy above 95 percent.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128361591","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 and Analysis of Frequency Reconfigurable Cylindrical Dielectric Resonator Antenna for Cognitive Radio 认知无线电中频率可重构圆柱介质谐振器天线的设计与分析

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119371

Jayant Kumar Rai, Pinku Ranjan

引用次数: 1