Artificial Intelligence and Machine Learning最新文献_第2页

Performance and Efficiency Assessment of Drone in Search and Rescue Operation 无人机在搜救行动中的性能与效率评估

Artificial Intelligence and Machine Learning Pub Date : 2022-07-23 DOI: 10.5121/csit.2022.121201

Tauheed Khan Mohd, Vuong Nguyen, T. Hoang, P. M. Zeyede, Beamlak Abdisa

{"title":"Performance and Efficiency Assessment of Drone in Search and Rescue Operation","authors":"Tauheed Khan Mohd, Vuong Nguyen, T. Hoang, P. M. Zeyede, Beamlak Abdisa","doi":"10.5121/csit.2022.121201","DOIUrl":"https://doi.org/10.5121/csit.2022.121201","url":null,"abstract":"With the development of technology, human beings have successfully predicted and prevented the damage caused by natural disasters. However, due to climate change, society has witnessed the rising actions of forest fire, earthquake, tsunami, etc., and there are many which people cannot prevent, and the level of dangerous situations are increasing rapidly for the Search and Rescue (SAR) operation. Not to mention, more and more people are turning their attention and hobbies to exploring wilderness where they might get lost or, worse, get injured. For that reason, to raise the chance of survival for the victims and reduce the risk for the search team, the use of Unmanned aerial vehicles (UAV) has been proposed. The plan is the headquarters will deploy a fleet of drones to get into the areas where human cannot enter easily and then report the situations as well as the condition of the victims with images and videos. In most research papers, it seems very promising; however, there is still much work that needs to be done. In this paper, some of the features which included for future researches are which algorithm is the most optimal, what standard structure should be used for the drones so it can complete the missions under any kind of circumstances, and how to set up a communication line that guaranty the effective to reduce the level of miscommunication.","PeriodicalId":174755,"journal":{"name":"Artificial Intelligence and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130554970","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

An Intelligent Video Editing Automate Framework using AI and Computer Vision 一个使用人工智能和计算机视觉的智能视频编辑自动化框架

Artificial Intelligence and Machine Learning Pub Date : 2022-07-23 DOI: 10.5121/csit.2022.121216

Haolin Xie, Yu Sun

引用次数: 0

A Novel Intelligent Image-Processing Parking Systems 一种新型智能图像处理停车系统

Artificial Intelligence and Machine Learning Pub Date : 2022-07-23 DOI: 10.5121/csit.2022.121212

Sree Veera Venkata Sai Saran Naraharisetti, Benjamin Greenfield, Benjamin Placzek, Steven Atilho, Mohamad Nassar, M. Mekni

引用次数: 0

An Approach CPS for the Smart Monitoring of Industrial Systems 一种用于工业系统智能监控的CPS方法

Artificial Intelligence and Machine Learning Pub Date : 2022-07-23 DOI: 10.5121/csit.2022.121205

Nesrine Jlassi, Cédrick Béler, O. Khlaief, K. Medjaher

引用次数: 0

EmailTracker: An Intelligent Analytical System to Assist Email Event Tracking using Artificial Intelligence and Big Data EmailTracker:利用人工智能和大数据协助电子邮件事件跟踪的智能分析系统

Artificial Intelligence and Machine Learning Pub Date : 2022-07-23 DOI: 10.5121/csit.2022.121222

Joyce Zheng, Yu Sun

引用次数: 0

Implementing Risk Score to Protect from Android Pattern Lock Attacks 实现风险评分，以防止Android模式锁定攻击

Artificial Intelligence and Machine Learning Pub Date : 2022-07-23 DOI: 10.5121/csit.2022.121208

Yasir Al-Qaraghuli, Caroline Hillier

{"title":"Implementing Risk Score to Protect from Android Pattern Lock Attacks","authors":"Yasir Al-Qaraghuli, Caroline Hillier","doi":"10.5121/csit.2022.121208","DOIUrl":"https://doi.org/10.5121/csit.2022.121208","url":null,"abstract":"Cyberattacks on Android devices have increased in frequency and commonly occur in physical settings with shoulder surfing and brute-force attacks. These attacks are most common with devices secured by the pattern lock mechanism. This work aims to investigate the various methods that increase the security of Android lock patterns. Research showed that these pattern lock screens are especially vulnerable due to users employing a set of common lock patterns. We propose a pattern-matching algorithm that recognizes these common lock patterns and increases the Risk Score if these passcodes are attempted. The blocking of common passcodes, and identification during the unlocking, reduces the risk of the aforementioned threats to device security. The algorithm we implemented succeeds in deterring users from configuring their devices with commonly used patterns. Overall, our algorithm achieves advanced security compared to current systems by detecting unusual inputs and locking the device when suspicious activity is detected. Our test results show 80% satisfaction from human test subjects when settings the passcode. The algorithm eliminates the use of commonly used patterns and 79% acceptance using our proposed algorithm and blocks access to the device depending on the accuracy score. The proposed algorithm shows remarkable success with limiting brute-force attacks as it proves effective in denying common passcodes.","PeriodicalId":174755,"journal":{"name":"Artificial Intelligence and Machine Learning","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131733883","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 Pedestrian Counting Scheme for Video Images 一种视频图像行人计数方案

Artificial Intelligence and Machine Learning Pub Date : 2022-07-23 DOI: 10.5121/csit.2022.121211

C. Cheng, Yi-Fan Wu

{"title":"A Pedestrian Counting Scheme for Video Images","authors":"C. Cheng, Yi-Fan Wu","doi":"10.5121/csit.2022.121211","DOIUrl":"https://doi.org/10.5121/csit.2022.121211","url":null,"abstract":"Pedestrian counting aims to compute the numbers of pedestrians entering and leaving an area of interest based on object detection and tracking techniques. This paper proposes a simple and effective approach of pedestrian counting that can effectively solve the problem of pedestrian occlusion.Firstly, the moving objects are detected by the median filtering and foreground extraction with the improved mixed Gaussian model. And then the HOG (Histogram of oriented gradient) features detection and the SVM (Support vector machine) classification are applied to identify the pedestrians. A pedestrian dataset containing 1500 positive samples, 12000 negative samples, and 420 hard examples, which gave the false discriminant results with the initial classifier, also considered as negative samples to enhance classification capability is employed. In addition, the Kalman filtering with BLOB analysis for dynamic target tracking is chosen to predict pedestrian trajectory.This method greatly reduces the target misjudgment caused by overlapping and completes the two-way counting. Experiments on pedestrian tracking and counting in video images demonstrate promising performance with satisfactory recognition rate and processing time.","PeriodicalId":174755,"journal":{"name":"Artificial Intelligence and Machine Learning","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123623974","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

Cryptographic Algorithms Identification based on Deep Learning 基于深度学习的密码算法识别

Artificial Intelligence and Machine Learning Pub Date : 2022-07-23 DOI: 10.5121/csit.2022.121217

Ruiqi Xia, Manman Li, Shaozhen Chen

引用次数: 0

Evaluation of Machines Learning Algorithms in Detection of Malware-based Phishing Attacks for Securing E-Mail Communication 基于恶意软件的网络钓鱼攻击检测中的机器学习算法评估

Artificial Intelligence and Machine Learning Pub Date : 2022-07-23 DOI: 10.5121/csit.2022.121202

Kambey L. Kisambu, Mohamedi M. Mjahidi

引用次数: 0

Artificial Intelligence and Machine Learning: 31st Benelux AI Conference, BNAIC 2019, and 28th Belgian-Dutch Machine Learning Conference, BENELEARN 2019, Brussels, Belgium, November 6-8, 2019, Revised Selected Papers 人工智能和机器学习:第31届比荷卢人工智能会议(BNAIC 2019)和第28届比荷机器学习会议(BENELEARN 2019)，比利时布鲁塞尔，2019年11月6日至8日，修订论文选集

Artificial Intelligence and Machine Learning Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-65154-1

T. Saloky, J. Šeminský

引用次数: 6