2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)最新文献_第4页

Impact of Hyperparameter Tuning for Identification and Classification of Plant Leaf Diseases: A Deep Learning Approach 超参数调整对植物叶病识别和分类的影响：深度学习方法

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119401

M. V. Shewale, R. Daruwala

引用次数: 0

Progressive Web Application for Plant Disease Detection using CNN 利用CNN进行植物病害检测的渐进式Web应用

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119391

Kristen Pereira, Arjun Pansare, P. Bhavathankar

{"title":"Progressive Web Application for Plant Disease Detection using CNN","authors":"Kristen Pereira, Arjun Pansare, P. Bhavathankar","doi":"10.1109/IATMSI56455.2022.10119391","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119391","url":null,"abstract":"A large portion of India's population relies primarily on agriculture for their livelihood. Farmers suffer a considerable amount of loss due to the innumerable diseases affecting their plants. Detection of such plant diseases with the human eye often yields inaccurate results. Furthermore, to correctly identify the disease, the individual assessing the plant should be an expert in the respective field. The diagnosis of plant illness is a visual task and thus, many computer vision techniques have been used previously for tackling it. Recently, convolutional Neural Networks have shown excellent results in many computer vision tasks. This study develops an application for plant disease classification by comparing the results obtained by training two convolutional neural networks, one from scratch and one by the transfer learning method. Both achieved a validation accuracy of 86 percent and 96 percent, respectively. The system was developed in the form of a web application for both mobile and web devices using the model, which is capable of functioning without any network requirements.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"70 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":"126195396","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

Development of Data Logger for COVID Vaccine Temperature Monitoring System COVID疫苗温度监测系统数据记录仪的研制

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119279

Ketan L. Kasar, V. Rajguru, S. Adhau

{"title":"Development of Data Logger for COVID Vaccine Temperature Monitoring System","authors":"Ketan L. Kasar, V. Rajguru, S. Adhau","doi":"10.1109/IATMSI56455.2022.10119279","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119279","url":null,"abstract":"The Data Logger (DL) is a unique tool created to carry out the typical duty of gathering data in a specific area. This common task can include measuring humidity, temperature, pressure or any other physical quantities. Due to the current pandemic situation, its use in temperature monitoring of Covid vaccine will be crucial. According to World Health Organization (WHO) guidelines, COVID vaccine can be stored and transported at -80 °C, -20°C and +2-8°C and shelf life is reduced as vaccine is transferred from one storage temperature to another. So cost effective, efficient and standalone Data Logger (DL) is the need of the hour. The Data logger is proposed to be developed with the use of ESP8266 Node MCU microcontroller. It takes power from a 5V Battery. DS18B20 sensor will be used for temperature sensing. Here we will use Wi-Fi module of ESP8266 Node MCU to send the temperature data from sensor to the Google Sheet over the internet. This real time data will be stored in the format of time and month/date/year. Data logged in Google Sheet will be displayed to the user with the help of graphical user interface (GUI) which is developed using PYTHON scripting language. GUI will allow user to interact with Data Logger through visual graphs. The Data Logger components are mounted on a double layered PCB.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"1 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":"128721181","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

Stroke Prediction using Optimization and Exploratory Data Analysis 基于优化和探索性数据分析的脑卒中预测

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119295

Ravneet Kaur, Kaustubh Hambarde, Reuben George, Arwa Hussain, Chaitanya Gomkar, S. Sonawani

{"title":"Stroke Prediction using Optimization and Exploratory Data Analysis","authors":"Ravneet Kaur, Kaustubh Hambarde, Reuben George, Arwa Hussain, Chaitanya Gomkar, S. Sonawani","doi":"10.1109/IATMSI56455.2022.10119295","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119295","url":null,"abstract":"A stroke is a medical condition that causes brain damage by rupturing blood vessels. It can also happen if the brain's blood supply and other nutrients are cut off. It is the leading cause of death and disability worldwide., according to the World Health Organization (WHO). It is a potentially fatal illness that primarily affects adults over the age of 65. Doctors devote a significant amount of time and effort to predicting strokes. As a result., the primary goal of the study is to use various Machine Learning approaches to predict the likelihood of stroke occurring using hyper parameter tuning to achieve greater accuracy and optimize the outcomes. After going through the dataset, we discovered that the standard algorithms we used., such as Support Vector Machine (SVC), Decision Tree Classifier, Random Forest Classifier, XGBoost, and KNeighbors, as well as some feature selection methods, could only predict 80 to 85 percent of the time, so we came up with the idea of optimization in machine learning, where we use the technology or concept of hyper parameter tuning, which helped us to gain a prediction of about 95 percent. With this, we also used an Exploratory Data Analysis (EDA) concept for visualization, which helped us to study the attribute. The above-mentioned prognosis was achieved using Hyper Parameter Tuning, which involves checking and analyzing the parameters of each algorithm in such a way that after setting to some predefined parameters, it produces the expected accuracy. To evaluate the data, we employed the EDA approach, in which we compared many associated health behaviors in different combinations with respect to stroke, and each EDA diagram concluded the relationship of these attributes to the cause of stroke. As a result, this study evaluates the performance of various machine learning algorithms that use Hyper parameters tuning with EDA.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"1 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":"122372167","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

iAssist: An online wellness platform to elevate the physical and mental health of the elderly iAssist:一个提升老年人身心健康的在线健康平台

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119367

Vaishnavi S Desai, Anika Tibrewala, Krithika Saravanan, Preethika K, Tanvi Mantri, Isha Ghiria

引用次数: 0

NGO Portal - A Platform to connect NGOs with prospective members 非政府组织门户-一个连接非政府组织与潜在会员的平台

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/iatmsi56455.2022.10119384

Jay Kumar Vagairya, Minakshi Poonia, Pinku Ranjan, Somesh Kumar

引用次数: 0

Using a Two-Stage HOG-SVM / CNN Model to Identify and Classify Forms of Brown Planthoppers 利用两阶段HOG-SVM / CNN模型对褐飞虱进行形态识别和分类

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119374

Christopher G. Harris, I. Andika, Y. Trisyono

引用次数: 0

Design High Frequency Phase Locked Loop Using Single Ended VCO for High Speed Applications 采用单端压控振荡器设计高速应用的高频锁相环

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119339

Rachana Ahirwar, Hemant Kumar Shankhwar, Gaurav Kaushal, M. Pattanaik, P. Srivastava

{"title":"Design High Frequency Phase Locked Loop Using Single Ended VCO for High Speed Applications","authors":"Rachana Ahirwar, Hemant Kumar Shankhwar, Gaurav Kaushal, M. Pattanaik, P. Srivastava","doi":"10.1109/IATMSI56455.2022.10119339","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119339","url":null,"abstract":"The requirement for rapid, reliable computing has grown as the semiconductor industry has progressed and the process technology has scaled. The demand for high-processing, low power integrated circuits (ICs) are growing all the time as a result, the need of wireless and wire line communication systems for large data rates have grown to the multi-gigabit per second level. The current study focuses on the design of the PLL system in the Cadence Virtuoso analog design environment tool utilizing the SCL 180nm manufacturing technology (scl pdk 180 nm). A single-ended voltage control oscillator is selected for its superior performance like small chip size, low power consumption, and wide frequency range. In the Cadence Virtuoso tool, the Spectre simulator is used to verify the result of the simulations. The proposed PLL has achieved an output frequency of 7.2 GHz, and power consumption of 3.09 mW. Further jitters, phase noise, and spur are reduced and then compared to the recently reported paper.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"216 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":"121628783","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

Hybrid Algorithm based Optimal Routing Protocol for Wireless Sensor Networks 基于混合算法的无线传感器网络最优路由协议

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119277

Ayush Singh, A. Ojha, P. Chanak

{"title":"Hybrid Algorithm based Optimal Routing Protocol for Wireless Sensor Networks","authors":"Ayush Singh, A. Ojha, P. Chanak","doi":"10.1109/IATMSI56455.2022.10119277","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119277","url":null,"abstract":"Wireless Sensor Networks (WSNs) are widely applied in various applications such as environment monitoring, precision agriculture, healthcare, and surveillance. Sensor nodes accumulate data and transmit it to a central unit or Base Station (BS). The traditional data routing mechanisms cause high energy depletion at sensor nodes. It causes the premature death of sensor nodes. The lifetimes of sensor nodes have a direct impact on the lifetime of WSNs. To get more prolonged operation of WSNs, the lifetime of sensor nodes should be increased. This paper applies the Fuzzy C-means algorithm to create different clusters of sensor nodes. Furthermore, this paper proposes a hybrid data routing algorithm that designs an optimal route among cluster heads for data transmission. A hybrid of Ant Colony Optimization (ACO) and Bacterial Foraging Optimization (BFO) algorithm is used to identify the optimal route with in the network. The optimal data collection path minimizes the transmission distance between nodes, minimizes data collection delay and saves energy at sensor nodes. This approach has been compared with other state of the art approaches in terms of residual energy, number of alive nodes and average transmission delay. The results show that the proposed hybrid approach outperforms existing methods.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"69 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":"121656980","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

Analysis of Speech Emotion Recognition and Detection using Deep Learning 基于深度学习的语音情感识别与检测分析

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119297

Rajeev Ranjan

{"title":"Analysis of Speech Emotion Recognition and Detection using Deep Learning","authors":"Rajeev Ranjan","doi":"10.1109/IATMSI56455.2022.10119297","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119297","url":null,"abstract":"Speech emotion recognition (SER) is a mechanism to identify emotions from speech or voice. With the help of SER, robots can understand human feelings. In this challenging world, SER is one of the latest inventions that help to know about human emotion when saying words. Before the widespread use of deep learning, SER relied on various approaches, including support vector machines (SVM) and hidden Markov models (HMM) with several distinct and preprocessing technical features. SER is a challenging task of computational human interaction. This topic has gotten so much attention in the past couple of years. Numerous techniques have been used in speech emotion recognition to extract emotions from voice signals, including several well-developed speech examinations and classification methods. In the traditional way of speech emotion, recognition features are extracted from the speech signals. Then the features are selected, which is collectively known as the selection module, and then the emotions are recognized. This is a very lengthy and time taking process. This paper design an algorithm based on feature extraction and model creation that recognizes the emotion based on the deep learning technique.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"1 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":"122685700","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