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IEEE 20th international conference on Automation science and Engineering (CASE)

作者： Velasquez, Ibis Pencole, Yannick Le Corronc, Euriell Univ Toulouse INSA CNRS LAAS CNRSUT3 Toulouse France

ISBN: (纸本)9798350358513;9798350358520

In this paper, we introduce the problem of learning max-plus linear models from event data available through unlabeled logs. We present a method for obtaining these models when the logs contain input and output event dates generated by a system in both normal conditions and abnormal conditions caused by failures. The properties of the method are presented, as well as results from a simulated example.

关键词： Adversarial machine learning

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Terrorism Attack Classification Using machine learning: The Effectiveness of Using Textual Features Extracted from GTD dataset

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Computer Modeling in Engineering & sciences 2024年第2期138卷 1427-1467页

作者： Mohammed Abdalsalam Chunlin Li Abdelghani Dahou Natalia Kryvinska School of Computer Science and Technology Wuhan University of TechnologyWuhan430070China LDDI Laboratory Faculty of Science and TechnologyUniversity of Ahmed DRAIAAdrar01000Algeria Information Systems Department Faculty of ManagementComenius UniversityBratislava82005Slovakia

One of the biggest dangers to society today is terrorism, where attacks have become one of the most significantrisks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) havebecome the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management,medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related,initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terroristattacks. The proposed framework posits that neglected text attributes included in the Global Terrorism database(GTD) can influence the accuracy of the model’s classification of terrorist attacks, where each part of the datacan provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomyhas one or more tags attached to it, referred as “related tags.” We applied machine learning classifiers to classifyterrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts andlearns contextual features from text attributes to acquiremore information from text data. The extracted contextualfeatures are combined with the “key features” of the dataset and used to perform the final classification. Thestudy explored different experimental setups with various classifiers to evaluate the model’s performance. Theexperimental results show that the proposed framework outperforms the latest techniques for classifying terroristattacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.

关键词： Artificial intelligence machine learning natural language processing data analytic DistilBERT feature extraction terrorism classification GTD dataset

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Comparison of Oil Field Production Prediction Methods Based on machine learning 13th

Comparison of Oil Field Production Prediction Methods Based ...

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13th international conference on Frontier Computing, FC 2023

作者： Zhu, Xiaoyu No. 5 Oil Production Plant of Daqing Oilfield Co. Ltd Heilongjiang163513 China

ISBN: (纸本)9789819994151

machine learning is a branch of computer science and has been applied to the field of artificial intelligence. It is a form of data analysis in which computers are used for learning without explicit programming. machine learning technology is increasingly used in oil and gas exploration. The method based on machine learning shows promising results in predicting oil production of different oil wells. In this case, the main goal of applying machine learning technology is to effectively mine large data sets and extract information from them, even if they are not predefined by humans or engineers. The main feature used to predict oilfield production is the number of wells, which can be found in the well report. In order to predict future oilfield production from these data, there are many technologies. machine learning technology has been applied to this problem. It helps them identify patterns in different types of data sets and helps them develop better predictions. It helps improve accuracy while reducing costs by using less time and resources. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Behavioral Analytics for Predictive Modeling of Mental Health Disorders: A Review of machine learning Techniques and Challenges

Behavioral Analytics for Predictive Modeling of Mental Healt...

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2025 international conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Vats, Prashant Kshirsagar, Pravin R. Upreti, Kamal Lalit, Keshav Tak, Tan Kuan Mahajan, Shubham Manipal University Jaipur Dept. of Computer Science and Engg Jaipur India J D College of Engineering & Management Department of Electronics & Telecommunication Engineering Nagpur India Dept. of Computer Science Delhi NCR India Manipal Institute of Technology Dept. of Computer Science and Engg Karnataka Manipal India Singapore Institute of Technology Singapore Amity University Gurugram India

ISBN: (纸本)9798331527495

Mental health issues, including anxiety, stress, and depression, may remain untreated until they escalate to a severe level. The issues significantly impact an individual's overall well-being and productivity. Timely identification is crucial for the effectiveness of both intervention and therapy. The application of machine learning techniques makes behavioral analytics a powerful tool for mental health disease prediction modeling. By analyzing behavioral data, this technology facilitates the early detection of various illnesses. This work aims to provide a thorough overview of the use of machine learning techniques, including models that employ Deep structured learning as well as both unsupervised as well as supervised learning, to behavioral data, including activity levels, speech patterns, and facial movements, in order to identify signs of mental health. The benefits and drawbacks of a broad range of machine learning algorithms are examined, with a focus on how these computer algorithms may be applied to identify patterns linked to illnesses like stress, anxiety, and emotional depression. This study looks into the problems that this business encounters as well. These difficulties include combining behavioral data with extra environmental issues and physiological features from the immediate surroundings, the necessity for large and diverse datasets, the need for security of information, and the capacity to understand models. © 2025 IEEE.

关键词： Supervised learning

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Parametric Cyber Defense: A Sophisticated machine learning Architecture for Advanced Intrusion Detection and Threat Classification 5

Parametric Cyber Defense: A Sophisticated Machine Learning A...

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5th international conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Lakshmi, S. Maalan, M.R. Kishore Kumar, R. St. Joseph's College of Engineering Dept. of Computer Science Engineering Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

In contemporary network environments, effective countermeasures against cybersecurity threats require highly effective Intrusion Detection Systems (IDS). This paper proposes an advanced methodology to enhance IDS by employing parametric analysis on network traffic characteristics. Through careful data selection and preprocessing from a comprehensive dataset, data integrity is maintained, with features such as packet size and transfer duration being leveraged. machine learning algorithms, including Decision Tree, Extra Tree, and xgBoost, are applied to detect and classify various cyberattack categories. In addition, advanced feature selection and dimensionality reduction techniques are utilized to improve model efficiency. Extensive experimentation demonstrates superior accuracy in identifying and classifying cyber threats while reducing the occurrence of false positives. The proposed framework strengthens cybersecurity infrastructures through the integration of advanced machine learning techniques to address evolving threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Trusted Off-chain machine learning Scheme Based on ZK-SNARK and Oracle 48

Trusted Off-chain Machine Learning Scheme Based on ZK-SNARK ...

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48th Annual IEEE international Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Yuan, Zehui Xue, Zeng Feng, Libo Xian, Deng Zhang, Zhixing Wu, Peng Fang, Fake Xie, Jiale Yunnan Univ Sch Software Kunming Yunnan Peoples R China Kunming Shipbldg Equipment Grp Co Ltd Kunming Yunnan Peoples R China

ISBN: (纸本)9798350376975;9798350376968

Decentralized applications (Dapps), based on smart contract technology, have been increasingly applied in various fields such as healthcare, industrial IoT, agriculture, financial services, supply chain management, and insurance. In certain complex business scenarios, blockchain may require machine learning models to assist contract business. However, on-chain computations are often costly and slow, and there are limitations on contract size. Due to the transparency of on-chain data, there are also privacy concerns regarding user data during model training and inference. To address these challenges, we propose a trusted off-chain machine learning solution that integrates ZK-SNARK and Oracle technologies. Following the principle of "off-chain computation, on-chain verification",our approach leverages ZK-SNARK to delegate the computation tasks of machine learning models to a trusted environment under the Oracle off-chain server. This solution significantly reduces the computational costs of the blockchain. User data and models are executed off-chain, effectively safeguarding user privacy. The execution results generate zero-knowledge proofs returned for on-chain verification. We have implemented TOMLS-ZKSO and conducted relevant experiments on insurance contract business on the Chainmaker. Experimental results demonstrate the effectiveness of our approach, with model proof generation taking approximately 0.4 seconds and Dapp response time around 0.52 seconds.

关键词： Blockchain Zero Knowledge machine learning Smart contract Privacy,Oracle Zero Knowledge Proof

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Unraveling the Single Tangent Space Fallacy: An Analysis and Clarification for Applying Riemannian Geometry in Robot learning

Unraveling the Single Tangent Space Fallacy: An Analysis and...

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IEEE international conference on Robotics and Automation (ICRA)

作者： Jaquier, Noemie Rozo, Leonel Asfour, Tamim Karlsruhe Inst Technol Inst Anthropomat & Robot Karlsruhe Germany Bosch Ctr Artificial Intelligence Renningen Germany

ISBN: (纸本)9798350384581;9798350384574

In the realm of robotics, numerous downstream robotics tasks leverage machine learning methods for processing, modeling, or synthesizing data. Often, this data comprises variables that inherently carry geometric constraints, such as the unit-norm condition of quaternions representing rigid-body orientations or the positive definiteness of stiffness and manipulability ellipsoids. Handling such geometric constraints effectively requires the incorporation of tools from differential geometry into the formulation of machine learning methods. In this context, Riemannian manifolds emerge as a powerful mathematical framework to handle such geometric constraints. Nevertheless, their recent adoption in robot learning has been largely characterized by a mathematically-flawed simplification, hereinafter referred to as the "single tangent space fallacy". This approach involves merely projecting the data of interest onto a single tangent (Euclidean) space, over which an off-the-shelf learning algorithm is applied. This paper provides a theoretical elucidation of various misconceptions surrounding this approach and offers experimental evidence of its shortcomings. Finally, it presents valuable insights to promote best practices when employing Riemannian geometry within robot learning applications.

关键词： Adversarial machine learning

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A simple yet efficient data-driven model for the prediction of aircraft noise impact

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AEROSPACE science AND TECHNOLOGY 2025年 163卷

作者： Wu, Chunhui Redonnet, Stephane Hong Kong Univ Sci & Technol Kowloon Clear Water Bay Hong Kong Peoples R China

As part of a more global research effort towards greener aviation, the present study focuses on the prediction of noise impacts by the air traffic around major airports. To this end, a simple yet efficient data-driven model is developed which, relying on machine learning (multivariable linear regression), allows assessing the noise levels and annoyance incurred by air traffic upon the sole knowledge of aircraft characteristics and operational features. This model is then applied to the air traffic occurring in Hong Kong city, which presents some unique specificities, e.g., complex airspace, non-standard aircraft types and/or operations, significant weather variations;Upon a prior training using a pre-existing experimental database of actual aircraft flying in and out of the Hong Kong international Airport (HKIA), the model proves efficient in predicting most flights' noise impacts, which are here quantified through three specific sound metrics. In a second stage, this model is further simplified via a systematic exploration and subsequent reduction of its constitutive variables, thereby leading to a reduced order model of similar accuracy and lower complexity. As a byproduct, this model reduction allows discriminating the main drivers underlying the noise impacts by aircraft. Overall, this study demonstrates how a datadriven model based on a relatively simple machine learning approach can effectively predict the noise impacts of air traffic upon the sole knowledge of aircraft characteristics and operational features, even when they are non-standard as in Hong Kong.

关键词： Green aviation Aircraft noise impact Field-test measurements Psychoacoustic annoyance machine learning data-driven model

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Adopting machine learning for Better Leadership and Organizational Outcomes 3

Adopting Machine Learning for Better Leadership and Organiza...

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international conference on Advances in Computing, Communication and applied Informatics (ACCAI)

作者： Subrahmanyam, Satya Khalife, Danielle Farah, Assaad Holy Spirit Univ Kaslik Sch Business Jounieh Lebanon Amer Univ Dubai Sch Business Dubai U Arab Emirates

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Utilizing machine learning (ML), a revolutionary trend in the fields of modern business and leadership, opens up new ways to improve leadership styles and propel company success. This research looks at how machine learning techniques can be used to improve leadership strategies and the general efficiency of an organization. In the area of leadership development, this research looks at how machine learning might be able to look at a lot of data and find trends, patterns, and insights that might not be seen with more traditional methods. In addition, it shows how important machine learning algorithms are in predictive analytics, which helps managers see problems coming, lower risks, and take advantage of new opportunities. This research looks at how systems that use machine learning might improve the openness, fairness, and responsibility of decision-making processes in order to build trust and teamwork. It also looks at the problems and moral issues that might come up when ML is used in leadership, emphasizing how important it is to have responsible ML governance and constant oversight to get rid of biases and make sure fair results. This research shows how machine learning may have a big effect on leadership and the success of organizations. When leaders want to use AI-powered technology to stay ahead of the competition, boost innovation, and help their businesses grow in the digital age, our services can help.

关键词： machine learning Leadership Organizational outcomes Predictive analytics data-driven decision-making Performance optimization Talent management Employee engagement

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Application of machine learning Algorithms for Predicting Employee Attrition

Application of Machine Learning Algorithms for Predicting Em...

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2024 IEEE international conference on Future machine learning and data science, FMLDS 2024

作者： Dabbagh, Mohammad Saleem, Kashif Al-Jumaily, Adel Tahir, Mohammad Amphawan, Angela School of It & Engineering Melbourne Institute of Technology Sydney Australia University of Turku Department of Computing Turku Finland School of Engineering and Technology Sunway University Selangor Malaysia

ISBN: (纸本)9798350391213

Employers today are becoming more concerned about keeping their workforces, yet they mostly find it difficult to discover the actual causes of employee loss. Employee attrition can be caused by a variety of factors, including cultural and financial ones, but satisfaction levels are frequently under-looked. The goal of this research is to find patterns and trends by analysing the various factors that influence employee attrition, such as total working years, job satisfaction, age group and distance from home. The significant rise in the application of machine learning algorithms in recent years may provide new opportunities to improve the accuracy of employee attrition prediction. In this paper, we have applied two machine learning algorithms, Random Forest and Decision Tree Classifiers on a real dataset of an Australian company to predict employee attrition. Our findings highlight the superior performance of Random Forest over Decision Tree algorithm by achieving an accuracy value of 89.46%. © 2024 IEEE.

关键词： Adversarial machine learning

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