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applied Artificial Intelligence: Risk Mitigation Matters 18th

Applied Artificial Intelligence: Risk Mitigation Matters

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18th IFIP WG 5.1 international conference on Product Lifecycle Management (PLM)

作者： Jastroch, Norbert MET Commun D-61352 Bad Homburg Germany

ISBN: (纸本)9783030943356;9783030943349

Digital technology is the main driver of the transformation process that is already on its way and expected to take up speed. Science and engineering are challenged to realize the significant innovation potential while keeping an eye on economic and societal sustainability. Research methodology in science as well as development practice in engineering provide well-established approaches to risk management and mitigation relating to this technological transformation. Artificial intelligence, though, brings in new features to address which this chapter shall help to deal with. As such we take into view machine learning, automated decision making and autonomous systems, and data utilization. We look upon characteristic risks within the application lifecycle, and on functional, societal, and cybersecurity risks. We derive suggestions for an approach to proactive risk management addressing the lifecycle of Artificial Intelligence applications. Along with a preparatory section on terminological clarification regarding artificial intelligence, data, and risk this paper is intended to build awareness of risk mitigation matters and set the scene for the development of accountable risk management approaches.

关键词： Artificial intelligence Risk management Risk mitigation data analytics machine learning Automated decision making Autonomous systems

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On Phishing: URL Lexical and Network Traffic Features Analysis and Knowledge Extraction using machine learning Algorithms (A Comparison Study) 5

On Phishing: URL Lexical and Network Traffic Features Analys...

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5th international conference on data Science and Information Technology, DSIT 2022

作者： Fadheel, Wesam Al-Mawee, Wassnaa Carr, Steve Western Michigan University Department of Computer Science KalamazooMI49008 United States

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

Phishing is a criminal act in which a Phisher creates almost identical website connections exploiting URL Lexical characteristics to dupe unsuspecting users into exposing sensitive information such as financial data, address, and other personal information. Phishers recently sought to trick security experts by masking malicious URLs with obfuscation techniques to make them appear legitimate. this action leads us to conclude that URL lexical features analysis approaches are absolute procedures, and extract analysis techniques are required. this research is a first step toward designing and developing a decision-making system that uses a combination of URL Lexical, and Network Traffic features to detect and classify malicious URLs rather than relying solely on Lexical or URL Network Traffic features. To achieve our goal, we examined and assessed the usage of URL Lexical and Network Traffic features to detect malicious URLs. In the study, three methodologies are used: Complete Features, KMO test as a features selection method, and PCA as a dimensionality method, which are tested by LR, SVM, and KNN classification algorithms and evaluated by the Confusion Matrix Accuracy measure. Using Network Traffics features (ISCXURL dataset), the W/O approach: LR, SVM, and KNN has 92%, 94%, and 93% accuracy. the KMO approach: SVM has 91% accuracy. the PCA approach: LR and SVM have 92% and 94% accuracy, surpassing the use of Lexical features (UCI dataset). In contrast, using Lexical features (UCI dataset), the KMO approach: LR and KNN has 90% and 94% accuracy. the PCA approach: KNN has 95% accuracy, surpassing the use of Network Traffic features (ISCXURL dataset). As a result, we are confident in proceeding with the next step of designing and developing a decision-making application that detects and classifies malicious URLs utilizing URL Lexical and Network Traffic features. © 2022 IEEE.

关键词： learning algorithms

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A Survey on the Impact of data analytics and machine learning Techniques in E-commerce

A Survey on the Impact of Data Analytics and Machine Learnin...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： S Dheva Rajan Sivajee Vavilapalli Shahriar Hasan Ritesh Kumar Nafisa Rafa Iskandar Muda Information Technology University of Technology and Applied Sciences Almusannah Sultanate of Oman Department of Management Studies Vignan’s Institute of information technology (A) Duvvada Visakhapatnam Department of Finance University of Dhaka Bangladesh Maharishi School of Engineering and Technology Lucknow Uttar Pradesh India BBA in Marketing Institute of Business Administration University of Dhaka Dhaka Bangladesh Department of Doctoral Program Faculty Economic and Business Universitas Sumatera Utara Medan Indonesia

Current article has been developed for understanding the impact of data analytics and machine learning technique in e-commerce. If the observation has been developed for the current market of this technology it can be detected that real-life industry increases their investment which showed the interest in getting advantages. Identification of the problem of costing and different algorithm suitability objectives has been set for understanding the real impact of e-commerce. Secondary data collection has been used for understanding the different usability of machine learning and data analytics for e-commerce. It has been detected that using machine learning brings the benefits of automation and improves continuous growth. data analytics provide the scope of accuracy build-up. Apart from this different area of application and the different techniques has been analyzed. Understanding become easier and the scope of future benefits can be identified.

关键词： Industries data analysis machine learning algorithms Decision making machine learning Electronic commerce Object recognition

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Feature selection methods for high-dimensional biomedical time-to-event data: a review 22

Feature selection methods for high-dimensional biomedical ti...

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5th international conference on Big data Technologies, ICBDT 2022

作者： Tan, Huaning Deng, Chutong Chen, Shaobo Luo, Qianlin Hu, Guoqiang Quan, Yujuan International School Jinan University China College of Information Science and Technology Jinan University China

ISBN: (纸本)9781450396875

In digital era, time-to-event data collected from biomedical studies and healthcare are often of high dimensionality, presenting computational challenges for traditional survival models. To make full use of these data, feature selection (FS), a data processing technique for dimensionality reduction, shows great significance. this work introduces statistical, machine learning, and deep learning FS methods for time-to-event data, mainly focusing on lasso, elastic net, adaptive lasso, adaptive elastic net, random survival forest, and XGBoost. We also describe three state-of-art FS methods - BASIL, FilterDeepHit+, and SparseDeepHit+. then, we compare C-Index of 4 basic FS methods in experiment. Finally, we discuss future challenges and draw a conclusion. © 2022 ACM.

关键词： Feature Selection

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Detection of Android Malwares on IOT Platform Using PCA and machine learning 5th

Detection of Android Malwares on IOT Platform Using PCA and ...

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5th international conference on Recent Innovations in Computing, ICRIC 2022

作者： Mantoo, Bilal Ahmad Malhotra, Sumit Gupta, Kiran Department of Computer Science and Engineering Presidency University Itgalpur Yelahanka Bangaluru560064 India Department of Computer Science and Engineering Chandigarh University Gharaun Punjab Mohali193101 India

ISBN: (纸本)9789811998751

the Android operating system is considered to be the most advanced and popular smart phone operating system and has a dramatic increase in the market with approximately 1.5 billion Android-based devices shipped by 2021. this spike increased in the market leads to high number of malwares in the Android platform. the reason is simple, because the mutating nature of Android malwares reduces the efficiency of malware detection tools in that are already in the market. Furthermore, the vast number of features in the Android platform provides a big challenge to the current tools to detect the Android malware on IOT-based platform. In this paper, we used a real physical device, i.e., Android smart phone, smart tablets, Android watches instead of protected environment like genymotion for analysis and extract the feature from 10,650 applications of malware and benign. Large spaces of feature set are reduced with the use of principal component analysis approach which extracts the best features from the dataset. the data is then fed to the deep learning model with different hidden layers;the algorithms are applied for dynamic features as well as the combination with permissions. the results reveals that the deep learning model with three hidden layers gives overall best accuracy as compared to the previous works done on the same field using protected environment. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Android malware

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Web URLs Phishing Detection Model with Random Forest Algorithm

Web URLs Phishing Detection Model with Random Forest Algorit...

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Big data analytics and Practices (IBDAP), international conference on

作者： Aulia Kharisma Putri Jansen Wiratama Samuel Ady Sanjaya Santo Fernandi Wijaya Monika Evelin Johan Ahmad Faza Information System Universitas Multimedia Nusantara Tangerang Indonesia

ISBN: (数字)9798350391749

ISBN: (纸本)9798350391756

As internet users grow and technology evolves, so do the security risks, one example being phishing. Phishing is an attempt to obtain important information from someone, such as username, password, and other sensitive data, by providing a fake website that resembles the original. this research focuses on the problem of phishing website URLs that are increasing in number. Creating a model using an Algorithm that can detect phishing website URLs. the classification algorithm model that will be used in this research is Random Forest, which will be evaluated based on the confusion matrix value. the accuracy result is 99%. Second, the f1 score test result is 99.1%. the third result of recall testing is 99.3%. the last test result is a precision of 98.9%. With high accuracy, f1 score, recall, and precision values, the model created using the Random Forest algorithm can be applied well to applications in web URLs, phishing detecting, analyzing fake URL patterns, and identifying suspected links as fake web URLs.

关键词： Uniform resource locators Adaptation models Analytical models machine learning algorithms Accuracy Phishing Forestry

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machine learning Recognition Mechanism Based on WI-FI Signal Optimization in the Detection of Driver’s Emotional Fluctuations 5th

Machine Learning Recognition Mechanism Based on WI-FI Signal...

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5th international conference on Recent Innovations in Computing, ICRIC 2022

作者： Jinnuo, Zhu Goyal, S.B. Nanchang Institute of Science & Technology Nanchang China City University Petaling Jaya Malaysia

ISBN: (纸本)9789811998751

With the development of the automobile industry, the use of vehicles has become the most basic means of transportation in people’s daily life. However, due to the rapid increase in the number of vehicles, more and more drivers are causing traffic accidents due to emotional fluctuations during driving. In order to reduce the occurrence of this problem, the artificial intelligence machine learning mechanism under Industry 4.0 technology can currently capture and detect the instantaneous facial emotions of drivers during driving. through the literature survey, it is found that in the machine learning mode, the existing mechanism mainly relies on human vision and biological signals sensors to identify the driver’s emotional fluctuations during driving. However, due to the frequent distortion of visual detection methods and the problems of invasiveness and privacy invasion of biological signals, the investment cost is relatively high. In order to solve the problems under the existing algorithm, this paper firstly formulates the corresponding emotional fluctuation recognition model according to the existing WI-FI signal detection principle and designs the antenna position according to the Fresnel zone to achieve the best signal acquisition effect. In addition, the driver’s action status while using the brake and accelerator is collected. the emotion recognition coefficient is calculated by the collected data, and the fluctuation recognition is performed by using the recognition wash and LSTM emotion discriminator. Finally, according to the evaluation data, the recognition rate of about 85% in the real scene is achieved. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Signal detection

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To evaluate the learning attention and effectiveness in three remote learning approaches using EEG, eyetracking and traditional exam 5

To evaluate the learning attention and effectiveness in thre...

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5th IEEE international conference on Artificial Intelligence and Virtual Reality (IEEE AIVR)

作者： Wang, Yu-Pei Chao, Yi-Ping Chang Gung Univ Dept Comp Sci & Engn Taoyuan Taiwan

ISBN: (纸本)9781665457255

In past three years, many schools have switched to remote learning in response to the new pneumonia epidemic, but remote learning may be less effective due to unsupervised learning or the surrounding environment. this study investigated the impact of three remote learning methods, including (1) 2D video without face (slide + teachers lecture);(2) 2D video with face (slide + teachers lecture + showing face);and (3) immersive virtual reality (VR) using 360-degree video. Total 17 students were recruited in our experiment. Based on the grade point average (GPA) in last semester, all participants were divided into two groups: higher GPA and lower GPA first, and then subdivided into three groups with different remote learning method. In order to realize the degree of attention for each participant during the experiment, electroencephalography (EEG) and eye tracking data were collected simultaneously. Moreover, the exam after experiment was also applied to each participant for realizing the learning effectiveness. the results showed that no significant difference exists in the the exam score after watching video between three remote learning methods. the reason might be that the discrimination index of exam were less to distinguish the learning effectiveness. From the EEG scores, the VR group shows higher attention level. However, the VR group shows the worst eye tracking attention score. It might be caused by that the VR content is relatively rich than 2D video. Moreover, for the EEG analysis, we used two different machine learning models for the attention classification, and the final results showed that logistic regression had better classification results. In the future, we would like to use EEG combined with VR for real-time attention alerts to give immediate alerts when students are not concentrating in order to obtain better learning results.

关键词： VR EEG Eye Tracking logistic regression

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LtrGCN: Large-Scale Graph Convolutional Networks-Based learning to Rank for Web Search

LtrGCN: Large-Scale Graph Convolutional Networks-Based Learn...

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5th international Workshop on learning with Imbalanced Domains - theory and Applications / European conference on machine learning and Principles and Practice of Knowledge Discovery in databases (ECML PKDD)

作者： Li, Yuchen Xiong, Haoyi Kong, Linghe Wang, Shuaiqiang Sun, Zeyi Chen, Hongyang Chen, Guihai Yin, Dawei Shanghai Jiao Tong Univ Shanghai Peoples R China Baidu Inc Beijing Peoples R China Zhejiang Lab Hangzhou Peoples R China

ISBN: (纸本)9783031434266;9783031434273

While traditional learning to Rank (LTR) models use query-webpage pairs to perform regression tasks to predict the ranking scores, they usually fail to capture the structure of interactions between queries and webpages over an extremely large bipartite graph. In recent years, Graph Convolutional Neural Networks (GCNs) have demonstrated their unique advantages in link prediction over bipartite graphs and have been successfully used for user-item recommendations. However, it is still difficult to scale-up GCNs for web search, due to the (1) extreme sparsity of links in query-webpage bipartite graphs caused by the expense of ranking scores annotation and (2) imbalance between queries (billions) and web-pages (trillions) for web-scale search as well as the imbalance in annotations. In this work, we introduce the Q-subgraph and W-subgraph to represent every query and webpage with the structure of interaction preserved, and then propose LtrGCN-an LTR pipeline that samples Q-subgraphs and W-subgraphs from all query-webpage pairs, learns to extract features from Q-subgraphs and W-subgraphs, and predict ranking scores in an end-to-end manner. We carried out extensive experiments to evaluate LtrGCN using two real-world datasets and online experiments based on the A/B test at a large-scale search engine. the offline results show that LtrGCN could achieve Delta NDCG(5) = 2.89%-3.97% compared to baselines. We deploy LtrGCN with realistic traffic at a large-scale search engine, where we can still observe significant improvement. LtrGCN performs consistently in both offline and online experiments.

关键词： learning to Rank Graph Convolutional Networks Web Search

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From Clicks to Class Participation: Demystifying Student Engagement Factors with Attribute Ranking and Predictive analytics

From Clicks to Class Participation: Demystifying Student Eng...

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international conference on Artificial Intelligence and data Sciences (AiDAS)

作者： Rozita Hanapi Mohd Norhisham Razali Syazleena Yahya Sharidatul Akma Abu Seman Suhaida Halamy Emelia Abdul Rahim Faculty of Business and Management Samarahan Campus Universiti Teknologi MARA Malaysia Kementerian Sumber Asli dan Kelestarian Alam Institut Tanah dan Ukur Negara (INSTUN) Perak Malaysia Faculty of Business and Management Puncak Alam Campus Universiti Teknologi MARA Malaysia School of Information Science College of Computing Informatics and Mathematics Samarahan Campus Universiti Teknologi MARA Malaysia

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

Student engagement is important in virtual and physical learning environments to ensure students are connected with the learning materials and the learning environment. However, understanding student engagement is difficult due to limited observation and uncertain participation metrics. this paper explores the application of machine learning techniques to analyze student engagement levels in online and face-to-face learning environments to uncover actionable insights. the data about student engagement was collected comprising diverse student attributes such as class interest, attendance, effort, and technology proficiency. Firstly, the dataset underwent preprocessing to handle missing values and normalize attributes. Attributes selection using Information Gain and Correlation are employed to identify key predictors influencing student engagement. Subsequently, classification algorithms were applied to build predictive models. Model performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. Afterwards, trend analysis was carried out using a data visualization approach. the insights obtained from these analyses guide to the identification of critical factors such as class interest, attendance, and technology proficiency, which significantly impact engagement levels. the findings highlight the effectiveness of machine learning in discerning patterns and predicting student behaviors in virtual learning environments with Logistic Regression yielded 93.30% classification accuracy. Practical implications such as targeted interventions and curriculum adaptations can be designed to enhance engagement based on the identified predictors.

关键词： Support vector machines Measurement Logistic regression Accuracy data visualization Predictive models Market research Classification algorithms Predictive analytics Random forests

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