2022 IEEE World Conference on Applied Intelligence and Computing (AIC)最新文献_第6页

Machine Learning Approaches For Disease Prediction:- A Review 疾病预测的机器学习方法:综述

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848838

Sudha, Harkesh Sehrawat, Yudhvir Singh, Vivek Jaglan

{"title":"Machine Learning Approaches For Disease Prediction:- A Review","authors":"Sudha, Harkesh Sehrawat, Yudhvir Singh, Vivek Jaglan","doi":"10.1109/AIC55036.2022.9848838","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848838","url":null,"abstract":"Over recent years, disease prediction catches the attention of researcher’s awareness to cover a large range in medical as well as computer science field. Therefore, several models have been constructed for many different-different diseases diagnose and their forecasting. These models utilise an assortment of patient features to assess the likelihood of results over a definite interval of time and have capability to make better decision making. Patients’ health database contain large amount of information regarding particular disease and several laboratory test results. It has become essential to discover hidden patterns from those longitudinal health-related databases, and machine learning algorithms are playing a vital role to achieve this task. These algorithms assure the superior accuracy of observation and identification of disease. This paper highlighting various diseases, whose diagnose and prediction have been done through machine learning algorithms. It conveys concentration in the direction of machine learning algorithms and attributes that are used for the prediction of diseases and decision-making process accordingly.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"298 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131870311","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

Damage Detection and Smart Warning for Eventual Structure Failures in Mechatronic Systems 机电系统最终结构失效的损伤检测与智能预警

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848855

Ciprian Dragne, V. Chiroiu, M. Iliescu, I. Todirițe

{"title":"Damage Detection and Smart Warning for Eventual Structure Failures in Mechatronic Systems","authors":"Ciprian Dragne, V. Chiroiu, M. Iliescu, I. Todirițe","doi":"10.1109/AIC55036.2022.9848855","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848855","url":null,"abstract":"Structural damages could appear in any mechanical parts. New methods for their structural monitoring would be a good way to estimate structural strength and, consequently, its capacity to efficiently assist people in need, even for health recovery (surgery, severe illness cure), or for improving the lives of the visually impaired. Mechanical damages mean irreversible changes in structural characteristics. Damage when appear, may cause excessive deflection, buckling, fracture, changes in vibration eigenvalues and eigenmodes, etc. Techniques for damage detection give us warnings at the right moment on duty before any dangerous failure of the structure is triggered. Accurate localization of the damaged zone would help to act after warning so that to avoid structural failure. This study presents the concept of a smart warning system based on damage detection and damage localization techniques, as well as an intelligent control system. The studied models are mechatronic systems used in healthcare. A new parameter was developed to explore accurate information related to the parts damaged that have a higher probability of failure, referenced to the system’s users and to the medical procedures.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128085402","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

A Proposed Framework for Stutter Detection: Implementation on Embedded Systems. 一种口吃检测框架:在嵌入式系统上的实现。

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848966

B. Alhalabi, Jonatahn Taylor, Harshal A. Sanghvi, A. Pandya

引用次数: 1

Supervised Learning for Detecting Cognitive Security Anomalies in Real-Time Log Data 基于监督学习的实时日志数据认知安全异常检测

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848922

Md. Al Amin

{"title":"Supervised Learning for Detecting Cognitive Security Anomalies in Real-Time Log Data","authors":"Md. Al Amin","doi":"10.1109/AIC55036.2022.9848922","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848922","url":null,"abstract":"Every system generates a large quantity of logs. Logs are immensely crucial for monitoring a system, inspecting anomalous behaviors, and analyzing errors. By using log data, many recent studies suggest that efficient and accurate machine learning classifiers can use to detect anomalies. The source diversification and the unstructured nature of logs create difficulties in subsequent analysis. Even though the evaluation of automatic log parsing opened up the door to further research. To decrease manual work, many anomaly detection systems based on automated log analysis have been developed. However, due to the lack of a research and comparison of multiple anomaly detection methods, developers may still be unsure about which anomaly detection methods to utilize. Even if developers employ an anomaly detection technique, reimplementation takes time. To address these issues, we present a comprehensive study of detecting security anomalies from real-time log data based on different supervised machine learning algorithms and trained with publicly archived logs data. Two selected production log datasets with 15,923,592 and 365,298 log records were used to evaluate these algorithms. We also proposed an approach that the ability to provide visibility into the system, the proper way to acquire insight using a strong monitoring system that collects metrics, represents data, and automated methods to give notifications to administrators to bring to their awareness a significant change in the system's status. We assessed the model's performance using a variety of well-known assessment metrics, including “precision”, “recall”, “specificity”, “F1 measure”, and “accuracy”.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122237436","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

A User-based Collaborative Filtering Method to deal with Sparsity in Recommendation Systems by an unsupervised learning of Users’ Hidden Preferences 一种基于用户的协同过滤方法，通过对用户隐藏偏好的无监督学习来处理推荐系统中的稀疏性

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848963

M. M. Reddy, Prabu Mohandas

{"title":"A User-based Collaborative Filtering Method to deal with Sparsity in Recommendation Systems by an unsupervised learning of Users’ Hidden Preferences","authors":"M. M. Reddy, Prabu Mohandas","doi":"10.1109/AIC55036.2022.9848963","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848963","url":null,"abstract":"This paper focusses on sparsity in User-based Collaborative Filtering (UCF) type of Recommendation Systems. UCF mainly depends on Users’ Similarity Calculation (USC). The idea of the proposed method to overcome the sparsity problem of UCF is, overcoming the sparsity effect on USC through imputation of missing ratings. In the proposed method imputation is carried out by finding the hid-den preferences of users through an unsupervised learning using K-Means, basing on given ratings and item features. The proposed method aims to provide adequate information to the similarity metric used for USC, even with sparse rating data. The proposed method is compared with some other approaches in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Squared Error (MSE) values with varying levels of Sparsity and the RMSE, MAE and MSE are found to be the least for the proposed method. Also the RMSE of the proposed method for all the levels is close to 1 with the rating range of the dataset used being [1], [5] and the fact that the error-rate being almost constant across the sparsity levels shows that the proposed method is not greatly affected by sparsity.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125892484","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

Identification of Image Forgery based on various Corner Detection methods 基于各种角点检测方法的图像伪造识别

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848905

Anupama Debnath, Smita Das

引用次数: 1

Using Genetic Algorithm to Optimize Information Dissemination 利用遗传算法优化信息传播

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848845

Kundan Kandhway

引用次数: 0

A Formally Verified Message Validation Protocol for Intelligent IoT E-Health Systems 面向智能物联网电子健康系统的正式验证消息验证协议

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848874

V. O. Nyangaresi, Junchao Ma

{"title":"A Formally Verified Message Validation Protocol for Intelligent IoT E-Health Systems","authors":"V. O. Nyangaresi, Junchao Ma","doi":"10.1109/AIC55036.2022.9848874","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848874","url":null,"abstract":"High volumes of private and sensitive data are transmitted in intelligent Internet of Things (IoT) e-health systems environment. As such, proper protection should be accorded to these networks owing to the devastating effects of any successful compromise. Consequently, numerous security protocols have been presented over the recent past. Unfortunately, most of these schemes deploy cryptographic primitives that always result in extensive communication, storage and computation overheads. In addition, majority of these protocols ignore attack models such as forgery, side-channeling and physical attacks. To this effect, a protocol that leverages on lightweight collision-resistant one-way hashing functions and pseudonyms is presented. The security analysis using both formal and informal techniques show its resilience against attacks as well as the existence of authentication among the communicating entities. In addition, it is shown that the secrecy of the derived session is preserved. In terms of efficiency, it is demonstrated that the proposed protocol incurs the least storage and communication overheads.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129915058","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: 8

Automated Vehicle speed Estimation and License Plate Detection for Smart Cities Development 面向智慧城市发展的自动车速估算与车牌检测

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848890

Divya Sharma, S. Sharma, Vaibhav Bhatnagar

引用次数: 1

Secure Content-Based Image Retrieval Scheme for Multi-User Scenario Over Cloud 云上多用户场景下基于内容的安全图像检索方案

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848928

Anubhav Sharma, S. Shrivastava, Anshul Sarawagi

{"title":"Secure Content-Based Image Retrieval Scheme for Multi-User Scenario Over Cloud","authors":"Anubhav Sharma, S. Shrivastava, Anshul Sarawagi","doi":"10.1109/AIC55036.2022.9848928","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848928","url":null,"abstract":"Since the public cloud is not a credible entity when providing image retrieval services through the public cloud, it may steal the sensitive information of the image data. In recent years, cipher text image retrieval methods have been proposed to protect image privacy. However, the traditional privacy protection image retrieval scheme has low search efficiency and cannot support multi-user scenarios. Therefore, a safe and efficient multi-user outsourcing image retrieval scheme based on access control is proposed. This scheme adopts one-time encryption and matrix transformation methods to realize cipher text image retrieval based on the similarity of Euclidean distance (referred to as Euclidean distance). It uses matrix decomposition and proxy re-encryption to learn multi-user outsourced image retrieval. In addition, the local sensitive hash algorithm is used to construct the index to improve the efficiency of cipher text image retrieval. In particular, a lightweight access control strategy based on role polynomial functions is proposed, which can flexibly set image access permissions and prevent malicious users from stealing private information. Security analysis demonstrates that the proposed scheme can protect the confidentiality of images and query requests; the experimental results show that the proposed method can achieve efficient image retrieval.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129779387","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