2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)最新文献_第5页

Demographic and Psychographic Customer Segmentation for Ecommerce Applications 电子商务应用的人口统计和心理客户细分

2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) Pub Date : 2023-05-04 DOI: 10.1109/ICAAIC56838.2023.10140861

Ch. Sai Vamsee, D. Rakesh, I. Prathyusha, B. Dinesh, C. Bharathi

{"title":"Demographic and Psychographic Customer Segmentation for Ecommerce Applications","authors":"Ch. Sai Vamsee, D. Rakesh, I. Prathyusha, B. Dinesh, C. Bharathi","doi":"10.1109/ICAAIC56838.2023.10140861","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140861","url":null,"abstract":"E-commerce transactions are not a new concept anymore. E-commerce is a popular method of shopping, and many businesses utilize it to market and sell their goods. As a result, clients perceive an overabundance of information. Information overload happens when consumers are given too much information about a product and get perplexed. Personalization will help to solve the overloading issue. Personalization techniques may be applied to marketing to draw in new consumers and increase revenue. In e-commerce applications, Customer segmentation is crucial to marketing because it enables managers to identify new clients and steer clear of pursuing the incorrect ones. E-commerce businesses may adjust their offers to better fit the requirements and preferences of their consumers and increase customer satisfaction and loyalty by studying and using demographic and psychographic client segmentation. It enables businesses to comprehend client demands and make efforts to meet them. By coming up with the best marketing plan, it seeks to establish a connection with the most lucrative clients. This research study segments the consumers using K-means clustering and selects the best clustering technique. After clustering, SVR (Support vector Regression) is used to classify the data. The findings of this study can help e-commerce businesses to better target and engage their customers by giving them useful information.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131065241","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

Automated Garbage Classification using Deep Learning 使用深度学习的自动垃圾分类

2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) Pub Date : 2023-05-04 DOI: 10.1109/ICAAIC56838.2023.10141483

Sabitabrata Bhattacharya, Kanumala Bhargav Sai, H. S, Puvirajan, Hussain Peera, G. Jyothi

引用次数: 0

Surface Roughness Prediction in Turning of Monel K 500 using DWT Technique 用DWT技术预测蒙乃尔k500车削过程中的表面粗糙度

2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) Pub Date : 2023-05-04 DOI: 10.1109/ICAAIC56838.2023.10140643

Ganesh V Dilli, R. Bommi

{"title":"Surface Roughness Prediction in Turning of Monel K 500 using DWT Technique","authors":"Ganesh V Dilli, R. Bommi","doi":"10.1109/ICAAIC56838.2023.10140643","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140643","url":null,"abstract":"Research on specimen surface roughness is crucial because of its impact on machined components' functionality. In the meanwhile, a vision system is a cutting-edge method that is becoming more popular for measuring pictures of the specimen to determine the roughness of the machined surface. Scanning electron microscope (SEM) images of the machined surface are captured by a vision system for this study. During the final turning process, two-dimensional pictures of the machined surface of the Monel K 500 alloy are used to estimate the profile of the surface of specimens. Surface roughness of simulated specimens was investigated by image analysis under different simulated machining settings. This study employs a method for identifying surface texture that combines the acquisition of 2D surface pictures with a wavelet transform-based strategy. The Two-Dimensional Wavelet Transform may be utilized in the process of assessing surfaces due to its ability to deconstruct an image of a machined surface into a multi-resolution representation of that surface's multiple attributes. Prediction errors of less than 1.674% were obtained by analysing the histogram frequency difference of a lit area of interest (ROI) in images of rotated surfaces.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123382647","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

Hardware-based Dual Booting Switch with Customizable Setup Script and Cross Platform Support 基于硬件的双启动开关，具有可定制的设置脚本和跨平台支持

2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) Pub Date : 2023-05-04 DOI: 10.1109/ICAAIC56838.2023.10141425

Venkatesh Chaturvedi, D. Arora

{"title":"Hardware-based Dual Booting Switch with Customizable Setup Script and Cross Platform Support","authors":"Venkatesh Chaturvedi, D. Arora","doi":"10.1109/ICAAIC56838.2023.10141425","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141425","url":null,"abstract":"Many people use numerous operating systems to do a variety of tasks in today's hectic world. While using Linux for programming and coding, someone may utilize Windows for some document editing. There are no major problems faced while dual booting two operating systems aside from the fact that in order to dual boot, one has to open the boot menu every time in order to select which operating system is to be booted. This process of opening the boot menu and selecting the operating system each time is a very redundant task. Hence, the idea of a hardware-based switch that would help in switching the operating system without opening the boot menu every time was born. Initial thoughts for the switch included using a control board such as the Raspberry Pi for programming the switch. Even modification of the bootloader source code in order to create some hardware-based controller was attempted. However, after several days of research and testing, it was concluded that even though previous attempts had been made to create a dual booting switch using control boards, and were successful to an extent, those solutions were too complex for the value they provided. Hence, work was started on creating a dual booting switch without using a control board and with minimum modifications to the bootloader.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126427088","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 Comparative Study of Machine Learning Algorithms for Malware Analysis 恶意软件分析中机器学习算法的比较研究

2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) Pub Date : 2023-05-04 DOI: 10.1109/ICAAIC56838.2023.10141134

Krithik Gopinath, Mayaluri Tejaswi, Hritesh J, Thirumagal E

引用次数: 0

Machine Learning based Automatic Tablet Dispenser 基于机器学习的自动片剂分配器

2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) Pub Date : 2023-05-04 DOI: 10.1109/ICAAIC56838.2023.10141058

Sharmitha D, Keerthana C

引用次数: 0

An Intelligent Framework for Grassy Shoot Disease Severity Detection and Classification in Sugarcane Crop 甘蔗草芽病害严重程度检测与分类的智能框架

2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) Pub Date : 2023-05-04 DOI: 10.1109/ICAAIC56838.2023.10141146

D. Banerjee, V. Kukreja, S. Hariharan, Vishal Jain, S. Dutta

{"title":"An Intelligent Framework for Grassy Shoot Disease Severity Detection and Classification in Sugarcane Crop","authors":"D. Banerjee, V. Kukreja, S. Hariharan, Vishal Jain, S. Dutta","doi":"10.1109/ICAAIC56838.2023.10141146","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141146","url":null,"abstract":"The Grassy Shoot Disease is a severe problem in sugarcane crops, affecting their productivity and causing significant economic losses. The research aims to introduce a model that utilizes both CNN and SVM techniques to make precise predictions about the severity levels of Grassy Shoot Disease in sugarcane cultivation. The methodology involves data preprocessing, CNN-based feature extraction, SVM-based classification, and model evaluation. The data preprocessing phase involved data cleaning, normalization, and augmentation, followed by the extraction of features using a three-layer CNN model. Following feature extraction, the extracted features were fed into an SVM-based classifier with regularisation to avoid overfitting. The classifier's overall accuracy was 81.53%, and its precision, recall, F1-score, and support values ranged from 65.71% to 85.37% depending on the severity level. These results show that the suggested method is a solid method for accurately estimating the degrees of Grassy Shoot Disease severity in sugarcane crops.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123009738","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

Plant Disease Severity Detection and Fertilizer Recommendation using Deep Learning Techniques 基于深度学习技术的植物病害严重程度检测和肥料推荐

2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) Pub Date : 2023-05-04 DOI: 10.1109/ICAAIC56838.2023.10140467

Buddepu Sudhir, Devalaraju Charan Teja, Kurra Sai, Peddinti Sridhar, T. Daniya

{"title":"Plant Disease Severity Detection and Fertilizer Recommendation using Deep Learning Techniques","authors":"Buddepu Sudhir, Devalaraju Charan Teja, Kurra Sai, Peddinti Sridhar, T. Daniya","doi":"10.1109/ICAAIC56838.2023.10140467","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140467","url":null,"abstract":"In India, the agriculture industry plays a significant role in the economy and employs a sizable section of the workforce. The demand for food is increasing and analysis of agriculture data can help improve practices and increase productivity by providing insights into crop diseases and weather conditions. Plant diseases can greatly impact agricultural productivity, and early detection is crucial to avoiding losses. The proposed project makes use of different ML techniques such as KNN, SVM, and DL techniques such as CNN and ANN to detect plant diseases in an efficient and effective manner. These techniques can be trained on large datasets to learn patterns and make predictions, making them well suited for this task. The Deep Learning system includes a system that automatically scans leaf images and detects disease based on visual symptoms. This system also calculates severity level of disease and suggests suitable amount of fertilizer for disease to soak in their crop according to severity level. A user interface was created to help farmers and agriculture workers for easy usage by simple capturing leaf image and get suggestions, this helps farmers to increase their crop production and to maintain quality of crop.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122394503","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

Pre- Stroke Detection using K- Nearest Neighbour and Random Forest Algorithm 基于K近邻和随机森林算法的预中风检测

2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) Pub Date : 2023-05-04 DOI: 10.1109/ICAAIC56838.2023.10140476

K. B. Rakshna, P. Tamil Selvan, S. Varshini, J. Chitra

{"title":"Pre- Stroke Detection using K- Nearest Neighbour and Random Forest Algorithm","authors":"K. B. Rakshna, P. Tamil Selvan, S. Varshini, J. Chitra","doi":"10.1109/ICAAIC56838.2023.10140476","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140476","url":null,"abstract":"Stroke is one of the deadliest diseases in the world because it causes the brain's blood vessels to burst, injuring the brain. Symptoms may appear when the brain's blood and other nutrient flow is interrupted. There are various imaging techniques to detect stroke like CT, MRI etc., but these techniques are expensive, time consuming and in these techniques, people need to depend on radiologists for disease diagnosis. The existing model incorporates only software prediction so real time prediction is not possible and also early detection of stroke cannot be predicted so that the treatment given for stroke gets delayed and the severity of the disease is increased To overcome this the proposed system uses a microcontroller and various types of sensors to detect the vital parameters like heart rate, SpO2, temperature, lump, and it also uses machine learning algorithm to detect the stroke in advance. For the accurate detection of the stroke, an efficient Machine Learning technique should be used, and it was created through a unique examination of many ML algorithms. KNN and the random forest algorithm were two machine learning algorithms employed in this case to recognize strokes. The accuracy level of KNN is less than random forest algorithm that is 52% and 94% respectively.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131261145","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

Framework of Air Pollution Assessment in Smart Cities using IoT with Machine Learning Approach 使用物联网和机器学习方法的智慧城市空气污染评估框架

2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) Pub Date : 2023-05-04 DOI: 10.1109/ICAAIC56838.2023.10140834

H. Varade, Sonal C. Bhangale, Sandip R. Thorat, Pravin B. Khatkale, S. Sharma, P. William

{"title":"Framework of Air Pollution Assessment in Smart Cities using IoT with Machine Learning Approach","authors":"H. Varade, Sonal C. Bhangale, Sandip R. Thorat, Pravin B. Khatkale, S. Sharma, P. William","doi":"10.1109/ICAAIC56838.2023.10140834","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140834","url":null,"abstract":"Exhale and inhale filthy air has major health consequences. Air pollution's influence may be mitigated by conducting regular monitoring and keeping a record of it. Government organizations may also take proactive measures to protect the environment by accurately anticipating pollution levels in real time. In future smart cities, we propose using the Internet of Things and machine learning to track pollution levels in the air we breathe. The Pearson correlation test is performed to see whether pollutants and meteorological indicators have a high link. A cloud-centric IoT middleware architecture is used in this research instead of a standard sensor network to gather data from both air pollution and current weather sensors. This means that both reliability and cost have been greatly improved. Sulphur Dioxide (SO2) and Particulate Matter levels were predicted using an Artificial Neural Network (ANN) (PM2.5). The positive results show that ANNs may be used to monitor and forecast air pollution. RMSE values of 0.0128 and 0.0001 for SO2 and PM2.5 were found using our models.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131502156","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: 10