2022 16th International Conference on Open Source Systems and Technologies (ICOSST)最新文献_第2页

The Effect of Big Data Analytics in Enhancing Agility in Cybersecurity Incident Response 大数据分析在提高网络安全事件响应敏捷性中的作用

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016853

Ayesha Naseer, A. M. Siddiqui

{"title":"The Effect of Big Data Analytics in Enhancing Agility in Cybersecurity Incident Response","authors":"Ayesha Naseer, A. M. Siddiqui","doi":"10.1109/ICOSST57195.2022.10016853","DOIUrl":"https://doi.org/10.1109/ICOSST57195.2022.10016853","url":null,"abstract":"The ongoing automation of business operations is putting enterprises at risk of cyber attacks more than ever before. Incident response teams are employed by the enterprises for the identification, management, and elimination of cybersecurity attacks along with for the recovery of business operations timely and effectively. In this paper, we argue that to effectively react to the cybersecurity attacks enterprises should build agility in their incident response method and big data analytics performs an effective role in developing agility in incident response. Grounded on twenty-one in depth expert interviews, we develop a framework that explains the salient features and effect of big data analytics in the incident response method at three stages, i.e., manual analysis, basic analysis, and advanced analysis. The agile properties of flexibility, innovation and swiftness are instilled in the incident response method by practicing big data analytics at higher stages of analysis. The results informed that the key features of big data analytics can be firstly utilize to estimate the existing analytical capability and secondly as an assisting tool to enhance incident response method capability.","PeriodicalId":238082,"journal":{"name":"2022 16th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127985026","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 Effective Early Stage Detection of Lung Cancer Using Fuzzy Local Information cMean and GoogLeNet 基于模糊局部信息均值和GoogLeNet的肺癌早期有效检测

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016866

Sobia Shafiq, Muhammad Adeel Asghar, Muhammad Emad Amjad, Jawwad Ibrahim

{"title":"An Effective Early Stage Detection of Lung Cancer Using Fuzzy Local Information cMean and GoogLeNet","authors":"Sobia Shafiq, Muhammad Adeel Asghar, Muhammad Emad Amjad, Jawwad Ibrahim","doi":"10.1109/ICOSST57195.2022.10016866","DOIUrl":"https://doi.org/10.1109/ICOSST57195.2022.10016866","url":null,"abstract":"Cancer is one of the main causes of death worldwide, accounting for an incredible 5 million fatalities per year. In this article, innovative machine learning algorithms are used to detect lung cancer at an early stage. To extract features, computed tomographic scan images were used. In the initial stage of lung nodule, preprocessing is accomplished for data cleaning and resizing of dataset. In the second stage, a set of features was recovered from the preprocessed images using Fuzzy Local Information cMean (FLIcM). Aside from this, deep features were retrieved and merged together for improved performance using GoogLeNet. To detect small cell lung cancer (SCLC), scans with no tumours after categorization using Sup-port Vector Machine (SVM) were enhanced using Contrasted Limited Adaptive Histogram Equalization (CLAHE) to recognise small cell lung cancers. Other than simple nodules, which are noncancerous cells, the suggested model has shown to be the most effective at detecting SCLC; as a result, we were able to reach a classification performance of 91.5 %. The suggested model improves classification performance by 3 % when employing a diffused feature set for early stage detection of SCLC, compared without using CLAHE.","PeriodicalId":238082,"journal":{"name":"2022 16th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124025337","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

Tour Spot Recommendation System via Content-Based Filtering 基于内容过滤的旅游景点推荐系统

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016820

Mishal Muneer, Uzair Rasheed, Sadia Khalid, M. Ahmad

{"title":"Tour Spot Recommendation System via Content-Based Filtering","authors":"Mishal Muneer, Uzair Rasheed, Sadia Khalid, M. Ahmad","doi":"10.1109/ICOSST57195.2022.10016820","DOIUrl":"https://doi.org/10.1109/ICOSST57195.2022.10016820","url":null,"abstract":"Online recommendation systems have gained prominence. The online recommendation systems provide a better and faster way to choose Tour Spots for traveling and transactions. Recommender systems are powerful emerging technologies that help consumers to find the best places for touring according to their interests. The recommendation system recommends the most appropriate destinations for the end users. Internet travel portals also compete with each other regularly by taking into consideration several characteristics. Recommendation systems are one of the best methods to raise income and attract consumers. Established systems retrieved irrelevant information that resulted in low customer satisfaction and disappointment of customers. In this research, the Tour Spot recommendation system through content-based filtering is proposed and designed. It recommends the best picnic spot according to the budget and interest of the user. The proposed system improves the tourist experience and recommends the best place according to the things they would like; most significantly it is budget friendly as it suggests different places according to the interests and needs of the user. Data collection, including user information, consolidated user engagement reports, and tour spot data, is the primary challenge of implementing a travel recommendation system. User information originates mainly from information entered by the user during the registration process. When the system has the data of user likes and dislikes, then the system compares the user preferences to the dataset that the author makes for the tour recommendation systems in the dataset first identifies all the features that must be considered at the time of selection of travel destination. This Article also considers user likes and dislikes and the previous history of users to recommend a similar tour spot.","PeriodicalId":238082,"journal":{"name":"2022 16th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114753397","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

Intent Detection in Urdu Queries using Fine-tuned BERT models 乌尔都语查询中使用微调BERT模型的意图检测

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016834

S. Shams, Bareera Sadia, Muhammad Aslam

引用次数: 0

Development of Localization-Based Waiter Robot Using RP-LIDAR 基于RP-LIDAR的定位服务员机器人开发

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016804

Mudasser Waqas, Zeeshan Ali, Muzzamil Waqas, Awais Zulafqar

引用次数: 0

Owl Ontology for Liver Diseases Using Protégé & WEB-V-OWL 肝病Owl本体，使用proproteus & WEB-V-OWL

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016808

Zaeem Anwaar, F. Khan, Mehwish Naz

引用次数: 0

Enforcing Safety in Cooperative Perception of Autonomous Driving Systems through Logistic Chaos Map-based End-to-End Encryption 基于Logistic混沌映射的端到端加密增强自动驾驶系统协同感知中的安全性

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016879

Manzoor Hussain, Jang-Eui Hong

{"title":"Enforcing Safety in Cooperative Perception of Autonomous Driving Systems through Logistic Chaos Map-based End-to-End Encryption","authors":"Manzoor Hussain, Jang-Eui Hong","doi":"10.1109/ICOSST57195.2022.10016879","DOIUrl":"https://doi.org/10.1109/ICOSST57195.2022.10016879","url":null,"abstract":"Collaboration among multiple cyber-physical systems (CPSs) requires improved safety, reliability, and performance. Collaborative CPSs share common goals and collaborate to achieve them. Connected and autonomous vehicles (CAVs) are typical examples of collaborative CPSs. The Cooperative perception in CAVs is an emerging technology that enables the CAVs to share their local perception with others, thereby improving efficiency and road safety. However, in cooperative perception, malicious vehicles may send phantom vehicle information, and additionally, vehicles may unintentionally be malicious due to faulty sensors. These issues pose serious driving hazards as they can incur traffic accidents. Therefore, this article uses logistic chaos map-based end-to-end encryption techniques to avoid malicious vehicle information in cooperative perception. The cooperative perception is achieved via sharing the camera sensor image frames between two vehicles. Using the CARLA simulator, we demonstrated the real-time logistic chaos map-based encryption in the cooperative perception of CAVs. Unlike existing baseline approaches such as Cooper and F -Cooper, in our cooperative perception, we first encrypt the image frames before sharing and then decrypt the image frame at receiving end to avoid malicious information. The experimental results, such as the histogram, adjacent pixel correlation, and key sensitivity analysis, demonstrated that cooperative perception using logistic map-based encryption is safer and more secure than existing methods. In addition, our cooperative perception system increased the detection rate up to two times than the individual perception system of CAVs.","PeriodicalId":238082,"journal":{"name":"2022 16th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114400423","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

Relationship between Organization's Physical and Psychological Environment and Employees' Mental Satisfaction: An Empirical Analysis of Employee Turnover in Software Industry 组织身心环境与员工心理满意度的关系——基于软件行业员工离职的实证分析

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016849

Sadia Khalid, Usman Qamar, Uzair Rahseed, Wasi Haider Butt

引用次数: 0