2023 3rd International Conference on Smart Data Intelligence (ICSMDI)最新文献_第9页

Comparative Analysis of Classification algorithms for Classifying Psychotypes 心理类型分类算法的比较分析

2023 3rd International Conference on Smart Data Intelligence (ICSMDI) Pub Date : 2023-03-01 DOI: 10.1109/ICSMDI57622.2023.00091

Kalyani Adawadkar, V. Gandhi

{"title":"Comparative Analysis of Classification algorithms for Classifying Psychotypes","authors":"Kalyani Adawadkar, V. Gandhi","doi":"10.1109/ICSMDI57622.2023.00091","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00091","url":null,"abstract":"Machine learning is a subdomain of Artificial Intelligence that makes a machine learn with the help of data. Classification algorithms follow a supervised learning methodology which allows labels to be assigned to the observations so that unobserved data can be labelled based on the training data. This paper intends to study different classification (Supervised learning) algorithms with the help of the MBTI dataset. MBTI Test is a Meyers Briggs Type Indicator test which helps us to identify an individual based on one of the 16 personality types. 4 classification algorithms namely, k-nearest neighbours. Decision Tree, Support Vector Machine and Random Forest algorithm are implemented on the KPMI Dataset. The evaluation metries (accuracy, precision, recall and f1-score) related to each of the classification algorithms are measured. A comparison of the metrics is tabulated to throw light on the best algorithm for the given dataset. As per the MBTI test, an individual belongs to 1 of 16 personality types based on whether an individual is an extrovert(E)-introvert(I), sensing(S)-intuitive(N), thinking(T)-feeling(F) or judging(J)-perceiving(P). The MBTI dataset is visualized to know the psycho-type of the employees. The visualization helps to identify the highly and rarely found psycho type. It is visualized that in employees of the MBTI dataset, most of the psycho-types are satisfied with their jobs. In the future, this algorithm will help in identifying student personality type, related student behaviour analysis, and its predictions related to their career choice, and remedial measures for improvement in personality.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131434137","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

Extracting Information and Size Prediction of Objects in Underwater Images using Image Processing Technique 基于图像处理技术的水下图像中目标信息提取与大小预测

2023 3rd International Conference on Smart Data Intelligence (ICSMDI) Pub Date : 2023-03-01 DOI: 10.1109/ICSMDI57622.2023.00051

G. Lakshmi, E. Salomon, Samruddhi Tendulkar

引用次数: 0

Text Representation for Sentiment Analysis: From Static to Dynamic 情感分析的文本表示:从静态到动态

2023 3rd International Conference on Smart Data Intelligence (ICSMDI) Pub Date : 2023-03-01 DOI: 10.1109/ICSMDI57622.2023.00025

P. M. Gavali, Suresh K. Shiragave

引用次数: 0

Deep Learning with Histogram of Oriented Gradients- based Computer-Aided Diagnosis for Breast Cancer Detection and Classification 基于面向梯度直方图的深度学习计算机辅助诊断乳腺癌检测与分类

2023 3rd International Conference on Smart Data Intelligence (ICSMDI) Pub Date : 2023-03-01 DOI: 10.1109/ICSMDI57622.2023.00099

A. Ponraj, R. Canessane

{"title":"Deep Learning with Histogram of Oriented Gradients- based Computer-Aided Diagnosis for Breast Cancer Detection and Classification","authors":"A. Ponraj, R. Canessane","doi":"10.1109/ICSMDI57622.2023.00099","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00099","url":null,"abstract":"In the modern era, cancer is a major public health concern. Breast cancer is one of the leading causes of death among women. Breast cancer is becoming the top cause of death in women worldwide. Early identification of breast cancer allows patients to receive proper treatment, improving their chances of survival. The proposed Generative Adversarial Networks (GAN) approach is designed to aid in the detection and diagnosis of breast cancer. GANs are deep learning algorithms that generate new data instances that mimic the training data. GAN is made up of two parts: a generator that learns to generate false data and a discriminator that learns from this false data. Furthermore, the histogram of oriented gradients (HOG) is utilized as a feature descriptor in image processing and other computer vision techniques. Gradient orientation in the detection window and region of interest is determined by the histogram of oriented gradients descriptor approach. Using an image dataset and deep learning techniques, the proposed research (GAN-HOG) aims to improve the efficiency and performance of breast cancer diagnosis. The deep learning method is used here to analyze image data by segmenting and classifying the input photographs from the dataset. Unlike many existing nonlinear classification models, the proposed method employs a conditional distribution for the outputs. The proposed model GAN-HOG had an accuracy of 98.435%, a ResNet50 accuracy of 87.826%, a DCNN accuracy of 92.547%, a VGG16 accuracy of 89.453%, and an SVM accuracy of 95.546%.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123870408","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: 2

Prediction of Road Traffic using an ELM-based Neural Network 基于elm的神经网络道路交通预测

2023 3rd International Conference on Smart Data Intelligence (ICSMDI) Pub Date : 2023-03-01 DOI: 10.1109/ICSMDI57622.2023.00097

R. S. Ali Fathima, R. Sumathi

引用次数: 0

Integration of Android Application and Smart Conventional Chess Board Android应用与智能传统棋盘的集成

2023 3rd International Conference on Smart Data Intelligence (ICSMDI) Pub Date : 2023-03-01 DOI: 10.1109/ICSMDI57622.2023.00106

R. N., Raajkumar Sendilkumar, Kowshika Elangovan, Gautham Palaniyappan Arumugam

{"title":"Integration of Android Application and Smart Conventional Chess Board","authors":"R. N., Raajkumar Sendilkumar, Kowshika Elangovan, Gautham Palaniyappan Arumugam","doi":"10.1109/ICSMDI57622.2023.00106","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00106","url":null,"abstract":"This paper describes the design and integration of android application and smart conventional chess board using Arduino. This board was designed for the chess players to provide an actual physical experience of the game. Even though, there are plenty of online and offline chess applications available in mobile phones and desktops, the actual experience cannot be gained. Digital displays release blue light, which can damage light-sensitive cells in the retina of the eye and may also lead to several other health issues. This board provides a solution for the players from different locations, to be played in the physical board instead of looking into displays. The primary functions of the chess board are automatic detection of position and movement of each coin. A framework is built to determine the position of each coin in the two-dimensional structure using reed switches. Arduino is used as the microcontroller in this board. An electro magnet is used for the movement of chess coin from one position to another position within the chess board. With the use of rack and pinion gears, stepper motors are used to move the electro magnet to the desired location on the board. The changes in the position of the coins are frequently monitored and sent to cloud over the internet. An android application is developed using a programming language Java for monitoring and controlling the movement of chess coins over the internet.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117071599","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

Classification of Maturity Stages of Coconuts using Deep Learning on Embedded Platforms 在嵌入式平台上使用深度学习的椰子成熟阶段分类

2023 3rd International Conference on Smart Data Intelligence (ICSMDI) Pub Date : 2023-03-01 DOI: 10.1109/ICSMDI57622.2023.00067

Sneha Varur, Sangamesh Mainale, Sushmita Korishetty, A. Shanbhag, Uday Kulkarni, M. M

{"title":"Classification of Maturity Stages of Coconuts using Deep Learning on Embedded Platforms","authors":"Sneha Varur, Sangamesh Mainale, Sushmita Korishetty, A. Shanbhag, Uday Kulkarni, M. M","doi":"10.1109/ICSMDI57622.2023.00067","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00067","url":null,"abstract":"India stands 3rd in producing coconuts in the world, with respect to area and yield collectively contributing to sustain millions of families. These coconuts are typically harvested by climbing the trees with the use of ropes, which is a challenging task. The need to find the right coconut maturity stage is essential since different coconut stages have various benefits. Maturity detection takes the front seat in deciding the value of the coconut and is directly linked to the quality of the product. This study has observed the maturity stages of coconuts and segregated them into five classes. Further, different state of the art architectures such as Xception, ResNet50V2, ResNet152V2 and MobileNetV2 are compared to address the task of detecting the maturity stages of coconuts. Among these architectures, MobileNetV2 architecture gave the best results. MobileNetV2 was trained on the proposed dataset. It is observed that the model gives 99 % accuracy on test data. Further, the model was deployed on an Android device, making it easier for farmers to recognize different stages of coconut maturity for harvesting and other applications.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126995357","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

Deep Learning Based Lumpy Skin Disease (LSD) Detection 基于深度学习的肿块性皮肤病(LSD)检测

2023 3rd International Conference on Smart Data Intelligence (ICSMDI) Pub Date : 2023-03-01 DOI: 10.1109/ICSMDI57622.2023.00087

Dhiren Dommeti, Siva Ramakrishna Nallapati, Chalamalasetti Lokesh, Singasani P Bhuvanesh, Venkata Vara Prasad Padyala, P. V V S Srinivas

{"title":"Deep Learning Based Lumpy Skin Disease (LSD) Detection","authors":"Dhiren Dommeti, Siva Ramakrishna Nallapati, Chalamalasetti Lokesh, Singasani P Bhuvanesh, Venkata Vara Prasad Padyala, P. V V S Srinivas","doi":"10.1109/ICSMDI57622.2023.00087","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00087","url":null,"abstract":"The emergence of the lumpy skin disease has become a major threat to the livestock industry in recent years, causing high economic losses and health risks to both animals and humans. This virus is difficult to detect due to its complexity, making the early detection and accurate diagnosis of this virus essential. This study will explore the utilization of convolutional neural networks (CNNs) to efficiently and accurately detect and identify the LSDV than traditional methods. Further, the advantages of using CNNs for this purpose has been discussed and some of the applications of this new technology has also been explored. Additionally, the future potential of using CNNs to perform virus detection is also discussed. However, Lumpy disease is classified differently based on its severity. To determine the extent to which the animal is impacted by lumpy skin disease, it is necessary to recognize various stages of the disease. This research study referred to the use of several CNN architectures and Regression algorithms to detect the Lumpy skin disease virus as early as possible. The architectures explored are and the EfficientNet-EfficientNetB7 architecture, MobileNetV2, EfficientNet-EfficientNetB3 architecture, VGG16, InceptionV3, ResNet50, VGG19, Xception and DenseNet201. The paper thoroughly describes all of the steps required to carry out the disease detection model, from data collection to process and outcome.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123082774","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

Intelligent Analysis Framework of Power Marketing Big Data based on Multi-Dimensional KNN Algorithm 基于多维KNN算法的电力营销大数据智能分析框架

2023 3rd International Conference on Smart Data Intelligence (ICSMDI) Pub Date : 2023-03-01 DOI: 10.1109/ICSMDI57622.2023.00082

Yaoyu Wang, Chen Tan, Chengfei Qi, Hongzhang Xiong

引用次数: 0

Artificial Intelligence Powered Early Detection of Heart Disease 人工智能助力心脏病早期检测

2023 3rd International Conference on Smart Data Intelligence (ICSMDI) Pub Date : 2023-03-01 DOI: 10.1109/ICSMDI57622.2023.00095

T. A. Mohanaprakash, A. P, Navaneethakrİshan. M, Savija J, Ramya M, Anbarasa Pandian A

引用次数: 0