Proceedings of the 4th International Conference on Information Management & Machine Intelligence最新文献

Skin Disease Diagnostic techniques using deep learning 使用深度学习的皮肤病诊断技术

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590917

Babli Kumari, A. Jatain, Yojna Arora

{"title":"Skin Disease Diagnostic techniques using deep learning","authors":"Babli Kumari, A. Jatain, Yojna Arora","doi":"10.1145/3590837.3590917","DOIUrl":"https://doi.org/10.1145/3590837.3590917","url":null,"abstract":"On our planet, skin cancer is among the most dangerous diseases. It is, however, difficult to diagnose skin cancer correctly. A variety of tasks have recently been shown to be excelled by machine learning and deep learning algorithms. In the case of skin diseases, these algorithms are very useful. In this article, we examine various machine learning and deep learning techniques and their use in diagnosing skin diseases. In this paper, we discuss common skin diseases and the method of acquiring images from dermatology, and we present several freely available datasets. Our focus shifts to exploring popular machine learning and deep learning architectures and popular frameworks for implementing machine and deep learning algorithms once we have introduced machine learning and deep learning concepts. Following that, performance evaluation metrics are presented. Here we are going to review the literature on machine and deep learning and how these technologies can be used to detect skin diseases. Furthermore, we discuss potential research directions and the challenges in the area. In this paper, the principal goal is to describe contemporary machine learning and deep learning methods for skin disease diagnosis","PeriodicalId":112926,"journal":{"name":"Proceedings of the 4th International Conference on Information Management & Machine Intelligence","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117171597","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

Benchmark for Investigating the Security in Software Development Phases 研究软件开发阶段安全性的基准

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590860

J. Qurashi, S. Sandhu, P. Bhari

引用次数: 0

RDF-ML: A Proposed SPARQL Tool for Machine Learning on Semantic Web Data RDF-ML:一个用于语义Web数据机器学习的SPARQL工具

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590944

Rupal Gupta, S. K. Malik

引用次数: 0

A Review on Software Defect Prediction Using Machine Learning 基于机器学习的软件缺陷预测研究综述

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590918

Shikha Gautam, A. Khunteta, Debolina Ghosh

{"title":"A Review on Software Defect Prediction Using Machine Learning","authors":"Shikha Gautam, A. Khunteta, Debolina Ghosh","doi":"10.1145/3590837.3590918","DOIUrl":"https://doi.org/10.1145/3590837.3590918","url":null,"abstract":"Software plays an important role in many of the systems and devices that make up our modern societies. In order to provide their customers with software of a higher quality in a shorter amount of time, numerous software companies are developing software systems of varying sizes for various purposes. It is too challenging to produce high-quality software in a shorter amount of time due to the constraints of software development and the growing size of software data. Therefore, prior to delivering the software product, defect prediction can significantly contribute to a project's success in terms of; cost and quality to evaluate the quality of their software. The goal of the literature review is to investigate about the current trends of software defect prediction approaches. Conclusion of the literature review introduce that many machine learning algorithms are implemented named with Random forest, Logistic regression, Naïve Bayes and Artificial neutral Network etc. with different software metrics like CK metrics, Source code metric etc. The performance measurement of the model done by various methods like accuracy, precision etc.","PeriodicalId":112926,"journal":{"name":"Proceedings of the 4th International Conference on Information Management & Machine Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122490081","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

Effective and light weight security system for highly confidential cloud data such as PHR 针对PHR等高度机密云数据的高效轻量级安全系统

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590904

Sandeepkumar E V, Kayalvizhi Jayavel

{"title":"Effective and light weight security system for highly confidential cloud data such as PHR","authors":"Sandeepkumar E V, Kayalvizhi Jayavel","doi":"10.1145/3590837.3590904","DOIUrl":"https://doi.org/10.1145/3590837.3590904","url":null,"abstract":"The server in a cloud storage system can hold a very large amount of Personal health records (PHR) data or information. The cloud platform's storage servers provide archival services for a lengthy time frame. The third party basically functions as an administrator for the efficiency of cloud storage. This is why we're starting up our cloud storage service. One of the biggest difficulties with the cloud is that it is vulnerable to hacking. Ordinary methods of encryption are used to safeguard the information from prying eyes. All of the secret messages' code words are kept in a system of varying symbols. Deletion coding is carried out in a manner analogous to that which is used to calculate the unequal code word cyphers required for a communication in a distributed setting. When the message symbols are stored in different servers in a dispersed environment, the cryptographic term signs are also calculated independently and stored. For this reason, we introduce and include a threshold proxy re-encryption scheme. Fully Homomorphic Encryption is a promising approach to securing sensitive PHR data by limiting who can view it. When sending encrypted PHR data, the proxy re-encryption mechanism re-encrypts the PHR data again before sending it on to the recipient or storage server. Allocation is completed when secure access control has maximised performance. In light of this, we expect to see the Schmidt-Samoa Public Key Encryption (SSPKE) method developed on the Enhanced v Boosting Algorithm (EBA) by PHR data Hiding Architecture. Additionally, in this initiative, we employ a procedure of multi-party protocol admission control to operate and access the user's PHR data without jeopardising the sensitive cloud PHR data privacy. The results of the experiments show the beneficial effect when various metrics, such as total processing time, server response time, and PHR data decomposition rate, are taken into account for the application of PHR.","PeriodicalId":112926,"journal":{"name":"Proceedings of the 4th International Conference on Information Management & Machine Intelligence","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117328479","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 Review on Gsm Based LPG Leakage Detection and Controller 基于Gsm的LPG泄漏检测与控制器研究进展

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590861

Md Tabish Zahir, Mudit Sagar, Yasir Imam, Anshu Yadav, P. Yadav

引用次数: 0

Online Grading of Fruits using Deep Learning Models and Computer Vision 使用深度学习模型和计算机视觉的水果在线分级

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590842

Shathanaa Rajmohan, Mani Tej Mendem, Shankar Sreenu Vanam, Pavan Kumar Thalapally

引用次数: 0

Sales Prediction Scheme Using RFM based Clustering and Regressor Model for Ecommerce Company 基于RFM聚类回归模型的电子商务公司销售预测方案

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590937

Nithya Chalapathy, Helen Josephine V.L.

{"title":"Sales Prediction Scheme Using RFM based Clustering and Regressor Model for Ecommerce Company","authors":"Nithya Chalapathy, Helen Josephine V.L.","doi":"10.1145/3590837.3590937","DOIUrl":"https://doi.org/10.1145/3590837.3590937","url":null,"abstract":"Machine learning models are being used for better insights and decision making across many industries today. It shows to be quite useful for businesses in the ecommerce industry as well due to the vast amount of data generated and its potential. This research aimed to find insights on future sales of an ecommerce company [1]. The vast number of variables including both categorical and continuous variables under product data, customer information, transaction information, led us to implement a prediction model using regressors rather than just time series forecasting techniques. First an RFM (Recency, Frequency and Monetary) based clustering algorithm was used to get customer related information and then integrate those results into a regressor to achieve the desired goal of prediction of sales. Two schemes were tested one being predictions on individual clusters and the other where the clusters were one hot encoded back into the main data. Results show quite high accuracy of prediction. The high R-squared also indicated that our hypothesis of including the variables contributed significantly to the predicted sales values was correct in this case. This research fulfills an identified need to understand how machine learning algorithms can be implemented by multiple algorithms being integrated in sequential and logical orders thus helping derive business specific strategies rather than making it a mere technical process by providing empirical results about how the predicted sales values along with given inputs can contribute in business decision making relating to marketing, inventory management, dynamic pricing or many more such strategies.","PeriodicalId":112926,"journal":{"name":"Proceedings of the 4th International Conference on Information Management & Machine Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124348309","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 Practical Approach to detect Indoor and Outdoor Scene Recognition 一种实用的室内外场景识别检测方法

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590923

Vaishali Sharma, Nitesh Nagpal, Ankit Shandilya, Aman Dureja, Ajay Dureja

{"title":"A Practical Approach to detect Indoor and Outdoor Scene Recognition","authors":"Vaishali Sharma, Nitesh Nagpal, Ankit Shandilya, Aman Dureja, Ajay Dureja","doi":"10.1145/3590837.3590923","DOIUrl":"https://doi.org/10.1145/3590837.3590923","url":null,"abstract":"In computer vision recognition of scenes is a long-time research problem. Scene can be defined as the real-time environment view which consists of a lot of views (like road, tree, building, parks, etc.) in a meaningful manner. The problem of scene recognition can be explained as assessment of labels such as “road”, “building”, “hall”, “bedroom” or in an extra simplified way “Indoor scene” and “Outdoor scenes is classified on an input image based on the object or environment of the image. A huge amount of data is created and available every second in this growing era of digital data. Scene recognition is still a rising area that did not attain much success as compared to image recognition due to the vast variability of features in the scenic environment. Because of this reason, there is not a lot of work being completed these days in this area. This project focuses on the evaluation of problem statements using all the literature surveys completed lately and offering solutions for that problem statement. For resolving the proposed problem declaration ResNet and VGG variants are used ResNet18, 50 and 152 & VGG16, 19, and VGG19 with batch normalization are implemented. The entire scene recognition procedure is discussed in this report and the main motive is to form a foundation that can be further continued in proposing a new algorithm. Before deep learning the design and implementation of the scene recognition model depended on the low dimensional portrayal of the scene. The utilization of deep learning especially CNN for scene recognition has gotten extraordinary attention from the computer vision community.","PeriodicalId":112926,"journal":{"name":"Proceedings of the 4th International Conference on Information Management & Machine Intelligence","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127963394","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

Missing Person Detection Using AI 使用AI进行失踪人员检测

Proceedings of the 4th International Conference on Information Management & Machine Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3590837.3590931

Dhanush M S, T. M

{"title":"Missing Person Detection Using AI","authors":"Dhanush M S, T. M","doi":"10.1145/3590837.3590931","DOIUrl":"https://doi.org/10.1145/3590837.3590931","url":null,"abstract":"The focus of the planned study is on finding lost people in crowded environments including public events, festivals, temples, and meetings. In today's busy environments, single-person identification is a challenging endeavor. This problem is addressed by applying a deep learning idea to arrive at a workable solution. Individuals are recognized through the use of a Convolutional Neural Network (CNN). Several face characteristics are used to positively identify the missing person. The use of Face Detection is crucial to the success of this endeavor. The ImageNet Large-Scale Visual Recognition Challenge participant AlexNet is used. The main takeaway is that the model's depth is crucial to its excellent performance, which is computationally expensive but is made possible by the use of graphics processing units (GPUs) during training. With sufficient training using a wide variety of images, it is possible to locate the sought-after object in the allotted space. The project's momentum is based on watching the live feed. Faces are extracted from the video and saved to a database. A collection of photos is available for use in making identifications. We use our own dataset to train the AlexNet's many layers. The images in the database are labelled according to whether or not they contain a person by utilizing the pretrained network. Additionally, the KLT method should be used to determine the person's location and enable real-time tracking.","PeriodicalId":112926,"journal":{"name":"Proceedings of the 4th International Conference on Information Management & Machine Intelligence","volume":"508 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115886664","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