2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS)最新文献_第2页

Using Machine Learning Techniques to Predict People At-Risk for Drug Addiction: A Bayesian-Based Model 使用机器学习技术预测有毒瘾风险的人:一个基于贝叶斯的模型

2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS) Pub Date : 2022-10-12 DOI: 10.1109/PAIS56586.2022.9946914

Wafia Abada, Abdelkrim Bouramoul

{"title":"Using Machine Learning Techniques to Predict People At-Risk for Drug Addiction: A Bayesian-Based Model","authors":"Wafia Abada, Abdelkrim Bouramoul","doi":"10.1109/PAIS56586.2022.9946914","DOIUrl":"https://doi.org/10.1109/PAIS56586.2022.9946914","url":null,"abstract":"Drug addiction is the excessive use of substances such as alcohol, cannabis, cocaine or heroin. It can also take the form of physical or psychological dependence on these substances. The diagnosis of drug addiction is based on a set of behaviors or criteria related to the use of the substance in question. This diagnosis is a complex process that requires questioning and analyzing the behavior of the addict. To this end, mental health practitioners and addictologists predict whether a person is addicted to a particular drug based on many factors, such as the person's environment and family relationships. However, this process is not trivial and requires analysis of previous patient behavior while considering the frequency of substance use. This study proposes a machine learning-based model to measure the risk of substance abuse. The dataset used to develop our predictive models is based on many parameters, such as previous instances of significant addiction in confirmed substance abusers and failures in their lives. A Naïve Bayes machine learning algorithm was used, and the performance of this classifier was measured. The different models developed were evaluated using the most commonly used metrics in machine learning: High Detection Rate, False Alarm, Accuracy, Precision, and F-measure. The results show that using machine learning-based models to predict individuals at risk for drug addiction can greatly assist addiction physicians. Bayesian classification yielded an encouraging accuracy score of 91,4%.","PeriodicalId":266229,"journal":{"name":"2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132788845","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 Blockchain-Based Model for Efficient, Privacy-Preserving Online Medical Diagnoses 基于区块链的高效、隐私保护在线医疗诊断模型

2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS) Pub Date : 2022-10-12 DOI: 10.1109/PAIS56586.2022.9946870

K. Rais, M. Derdour, M. Amroune

{"title":"A Blockchain-Based Model for Efficient, Privacy-Preserving Online Medical Diagnoses","authors":"K. Rais, M. Derdour, M. Amroune","doi":"10.1109/PAIS56586.2022.9946870","DOIUrl":"https://doi.org/10.1109/PAIS56586.2022.9946870","url":null,"abstract":"Patient personal data is growing exponentially due to the digitization of medical systems and the increasing number of patients and diseases which can be collected during online diagnostics for commercial use in the manufacture of medicines for example or for scientific research without their authorization as well as to invade the patient's privacy, knowing his medical record, which can affect his psyche. Scaling, maintaining, and managing a centralized security model can be very difficult and expensive. A centralized security infrastructure introduces a single point of failure, making it an easy target for DDoS attacks (Denial-of-service attacks) and Centralized infrastructure is difficult to implement in industrial configurations where peripheral nodes are geographically dis-persed. During this study, we propose a blockchain based medical data retention system, we demonstrate the ability of this technology to maintain the confidentiality and transparency of data stored online, thanks to encrypted information that is difficult to access or use, knowing and verifying all transactions. Our result through this study was largely satisfactory since we demonstrated the power of this technology at two levels of security, namely data security and privacy.","PeriodicalId":266229,"journal":{"name":"2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115138049","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

Secure Mobile Data Offloading in Small Cell Networks 小型蜂窝网络中的安全移动数据卸载

2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS) Pub Date : 2022-10-12 DOI: 10.1109/PAIS56586.2022.9946896

Amira Cheriet, T. Mekhaznia

引用次数: 0

A Deep Learning-based System for Detection At-Risk Students 基于深度学习的风险学生检测系统

2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS) Pub Date : 2022-10-12 DOI: 10.1109/PAIS56586.2022.9946878

Amira Bouamrane, Hafed Zarzour

引用次数: 0

Ensemble learning-based model for fake news detection 基于集成学习的假新闻检测模型

2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS) Pub Date : 2022-10-12 DOI: 10.1109/PAIS56586.2022.9946895

Chahrazad Toumi, Abdelkrim Bouramoul

{"title":"Ensemble learning-based model for fake news detection","authors":"Chahrazad Toumi, Abdelkrim Bouramoul","doi":"10.1109/PAIS56586.2022.9946895","DOIUrl":"https://doi.org/10.1109/PAIS56586.2022.9946895","url":null,"abstract":"Technological advances in the 21st century have improved the life quality of humans worldwide. They have morphed the news-delivering sphere from a monopoly controlled by state-owned media to a free-speech-motivated playfield. News is no longer conveyed solely by journalists' articles and printed newspapers. Social media, and the Internet in general, allow anyone to share or deliver news to a large audience in real-time, very quickly and at a low cost. Unfortunately, fake news has spread widely along with true news online. Fake news can cause significant damage to companies, celebrities, and even ordinary individuals. Therefore, detecting fake news has become an important task. This paper presents an ensemble learning model that uses CNN, LSTM and C-LSTM to recognize fake news. The aim is to create a model that can effectively detect fake news in both short and long news statements. For this purpose, a combination of two datasets, namely the ISOT and LIAR datasets, were used and combined into a single corpus. Evaluation metrics were used to assess the models' performance: accuracy, recall, precision, and F1-score. When compared to the state-of-the-art, the proposed model achieved good results. Compared to the state-of-the-art, the proposed model achieved competitive results. An accuracy of 89.16% and an F1 score of 95.03% were obtained on the combined corpus, a precision of 89.47% on the ISOT dataset, and an accuracy of 53.23% and an F1-score of 71.80% on the LIAR dataset.","PeriodicalId":266229,"journal":{"name":"2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129652292","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 deep-based compound model for lung cancer detection 基于深度的肺癌检测复合模型

2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS) Pub Date : 2022-10-12 DOI: 10.1109/PAIS56586.2022.9946875

Sourour Maalem, Mohammed Mounir Bouhamed, M. Gasmi

引用次数: 1

U Form Microstrip Patch Antenna for Ultra Wideband Communications 用于超宽带通信的U型微带贴片天线

2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS) Pub Date : 2022-10-12 DOI: 10.1109/PAIS56586.2022.9946911

M. H. D. Yaccoub, Hanane Djellab, Aichetou Mohameden, N. Aknin

引用次数: 0

A Formal Integrated Approach for Cyber Physical Systems 网络物理系统的形式化集成方法

2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS) Pub Date : 2022-10-12 DOI: 10.1109/PAIS56586.2022.9946900

Ayoub Bouheroum, Djamel Benmerzoug, S. Hemam, F. Belala, Aya Lehamdi, Radhia Aouissate

引用次数: 0

Analysis and prediction of chaotic time series 混沌时间序列的分析与预测

2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS) Pub Date : 2022-10-12 DOI: 10.1109/PAIS56586.2022.9946871

A. Sahnoune, Elhadj Zeraoulia, D. Berkani

引用次数: 0

Predicted Model based on Boltzmann Restricted Machine for Web Services Recommendation 基于Boltzmann限制机的Web服务推荐预测模型

2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS) Pub Date : 2022-10-12 DOI: 10.1109/PAIS56586.2022.9946882

F. Merabet, Rouabhia Artaa, Zaani Asma, Djamel Benmerzoug

引用次数: 0