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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Alsheakh, H. Mahdi, Tareq Nasser Kadhm, Mustafa S. Al Shammari, Abeer Abdulsahib, Fatimah I. Taher, Hazeem B Algriree, Waleed Alobaidi, Marwa Q. Mohammed, Hayder Dhafer College of Science Mustansiriyah University Department of Computer Science Baghdad Iraq College of Pharmacy Mustansiriyah University Clinical Laboratory Science Baghdad Iraq Ministry of Science and Technology Baghdad Iraq Szechenyi Istvan Egyetem Department of Computer Science Gyor9026 Hungary University of Thi-Qar Department of Computer Science Thi-Qar Iraq University Putra Malaysia Dep. of Wireless Communication and Network Engineering Selangor Malaysia National Engineering School of Sfax Sfax University Sfax Tunisia College of Pharmacy Mustansiriyah University Baghdad Iraq

ISBN: (纸本)9798350374131

The Internet of Things (loT) facilitates the interconnection of a vast array of devices, services, and individuals for data exchange in multi-domain loT ecosystems, the social, physical, and cyber domains. That emerges various security, privacy, and trust issues, such as vulnerabilities, threats, and attacks. Addressing these challenges requires innovative solutions. New controls (solutions) are needed. Trust by design can be one of these controls, supporting security and privacy (among other system components). This paper proposes a Trusted loT (TloT), a trust-by-design model, and highlights the author's principal contributions. First, it advocates for implementing Tlo T through trust services. Second, it outlines an architecture for using trust by design in loT, including the design of its top-level structure and activities. Minor contributions include overviews of loT and trust, as well as looking at security and privacy in terms of security services and privacy services, respectively. © 2024 IEEE.

关键词： Internet of things

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A Bayesian take on Gaussian process networks 23

A Bayesian take on Gaussian process networks

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Enrico Giudice Jack Kuipers Giusi Moffa Dep. of Mathematics and Computer Science University of Basel Basel Switzerland Dep. of Biosystems Science and Engineering ETH Zurich Basel Switzerland Dep. of Mathematics and Computer Science University of Basel Basel Switzerland and Division of Psychiatry University College London London UK

Gaussian Process Networks (GPNs) are a class of directed graphical models which employ Gaussian processes as priors for the conditional expectation of each variable given its parents in the network. The model allows the description of continuous joint distributions in a compact but flexible manner with minimal parametric assumptions on the dep.ndencies between variables. Bayesian structure learning of GPNs requires computing the posterior over graphs of the network and is computationally infeasible even in low dimensions. This work implements Monte Carlo and Markov Chain Monte Carlo methods to sample from the posterior distribution of network structures. As such, the approach follows the Bayesian paradigm, comparing models via their marginal likelihood and computing the posterior probability of the GPN features. Simulation studies show that our method outperforms state-of-the-art algorithms in recovering the graphical structure of the network and provides an accurate approximation of its posterior distribution.

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Logistic Regression Classification for Assessing the Risk of Kidney Tumor

Logistic Regression Classification for Assessing the Risk of...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Dalia Alzu'bi Rabia Emhamed Al Mamlook Ahmad Nasayreh Mohammad Aljaidi Rami AlAzab Hasan Gharaibeh Qais Al-Na'amneh Dep. Information and Systems Engineering Concordia University Montreal Canada Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University Zawia Tripoli Libia Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. Surgery and Urology Jordan University of Science and Technology Irbid Jordan Dep. of Cyber Security Applied Science Private University Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

The kidneys have the important function of filtering waste products and toxins from the blood in the human body. Any problems affecting the kidneys can potentially impact their efficiency and overall function. Kidney Tumor (KT) is a disease that specifically affects kidney cells and causes the abnormal growth of tissue in one or both kidneys. Assessing the risk of developing a kidney tumor and identifying the key factors that contribute to this risk are crucial for ensuring patient well-being. Additionally, this information assists doctors and specialists in making faster and more accurate diagnoses, determining appropriate treatment methods, and potentially influencing the course of the disease to minimize its severity and impact. Machine learning algorithms have recently been introduced to evaluate disease risks, and in this study, we specifically focus on examining the risk of kidney tumor development and investigating the influencing factors. We employed a logistic regression model to predict if a patient is at risk of developing a kidney tumor or not, and further categorized the samples into high-risk and low-risk groups. Our model was trained and tested using a unique dataset obtained from the (KAUH) hospital in Jordan, consisting of well-balanced metadata from 120 patients with kidney issues. Our work demonstrated accuracy results ranging from 90% to 98%. Ultimately, specialists can utilize our model as an additional tool to enhance the speed and accuracy of patient diagnoses

关键词： Logistic regression Analytical models Accuracy Predictive models Genetics History Reliability

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Designing Participatory AI: Creative Professionals' Worries and Expectations about Generative AI

Designing Participatory AI: Creative Professionals' Worries ...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Inie, Nanna Falk, Jeanette Tanimoto, Steve It University of Copenhagen Copenhagen Denmark Dep. of Artificial Intelligence and Human Interfaces University of Salzburg Salzburg Austria Paul G. Allen School of Computer Science & Engineering University of Washington Seattle United States

ISBN: (纸本)9781450394222

Generative AI, i.e., the group of technologies that automatically generate visual or written content based on text prompts, has undergone a leap in complexity and become widely available within just a few years. Such technologies potentially introduce a massive disruption to creative fields. This paper presents the results of a qualitative survey (N = 23) investigating how creative professionals think about generative AI. The results show that the advancement of these AI models prompts important reflections on what defines creativity and how creatives imagine using AI to support their workflows. Based on these reflections, we discuss how we might design participatory AI in the domain of creative expertise with the goal of empowering creative professionals in their present and future coexistence with AI. © 2023 Owner/Author.

关键词： computer programming

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Feature Selection for Robust Spoofing Detection: A Chi-Square-based Machine Learning Approach

Feature Selection for Robust Spoofing Detection: A Chi-Squar...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Qais Al-Na'amneh Mohammad Aljaidi Hasan Gharaibeh Ahmad Nasayreh Rabia Emhamed Al Mamlook Sattam Almatarneh Dalia Alzu'bi Abla Suliman Husien Dep. of Cyber Security Applied Science Private University Amman Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University of Zawia Tripoli Libia Dep. of Data Science and Artificial Intelligence Zarqa University Zarqa Jordan Dep. Information and Systems Engineering Concordia University Montreal Canada

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Nowadays, the Internet of Things (IoT) system is vulnerable to spoofing attacks that can easily where attackers can easily pose as a legal entity of the network. A “spoofing attack” refers to a type of cyber-attack when an attacker purposefully impersonates or masquerades as someone or something else to deceive the target or obtain unauthorized access to systems, information, or resources. In such attacks, the attacker alters their name, IP address, or other attributes to fool the victim into thinking they are engaging with a legitimate entity. Spoofing attacks can take place via a variety of channels, including, ARP and DNS spoofing. Therefore. Spoofing attacks can have serious consequences. We proposed a new approach based on three machine learning models LightGBM, Gradient Boost, and XGBoost to classify attacks on spoofing, we used Chi-square to select the best features to get the highest performance, and we demonstrated that the results using Chi-square achieved higher results than without Chi-square and improved the result with a rate three percent of accuracy. In terms of results, LightGBM outperformed other models by achieving 89%, 91 %, 87 %, and 89 % for accuracy, precision, recall, and f1-score, respectively. This shows the potential and efficiency of Chi-square to achieve the best performance by selecting the best features, thus providing a secure system and identifying cyber-attacks on systems.

关键词： Electric potential Accuracy Law Machine learning Learning (artificial intelligence) Feature extraction Internet of Things

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A Bayesian Take on Gaussian Process Networks

arXiv

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arXiv 2023年

作者： Giudice, Enrico Kuipers, Jack Moffa, Giusi Dep. of Mathematics and Computer Science University of Basel Basel Switzerland Dep. of Biosystems Science and Engineering ETH Zurich Basel Switzerland Dep. of Mathematics and Computer Science University of Basel Basel Switzerland Division of Psychiatry University College London London United Kingdom

关键词： Approximation algorithms

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Deep Feature Extraction Framework Based on DNN for Enhancing Mirai Attachment Classification in Machine Learning

Deep Feature Extraction Framework Based on DNN for Enhancing...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Hasan Gharaibeh Mohammad Aljaidi Ahmad Nasayreh Qais Al-Na'amneh Ameera S. Jaradat Ghassan Samara Rabia Emhamed Al Mamlook Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. of Cyber Security Applied Science Private University Amman Jordan Dep. of Business Administration Trine University Indiana USA Department of Mechanical and Industrial Engineering University of Zawia Tripoli Libia

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Nowadays, the rapid growth of Internet of Things (IoT) devices has changed many parts of our lives by providing seamless connectivity and automation. However, this expansion has created new challenges and vulnerabilities, especially with regard to security. Mirai botnet, a strain of malware that has severely damaged the IoT ecosystem, is one of the prominent risks to IoT security. This research examines the impact of the Mirai botnet on IoT devices. In this study, we present a hybrid model to detect Mirai attacks. Deep neural networks (DNNs) are used to extract significant deep features, which are subsequently passed to the (Light Gradient Boosting Machine) LGBM algorithm for classification. A dataset from the Canadian Institute 2023 that included several kinds of Internet of Things assaults was used to assess the model. The model achieved excellent feature extraction and promising results with accuracy, recall, precision, and F1-score scoring up to 95 % for all. These accuracy results demonstrated the superior performance of the suggested model over competing techniques, which had scores of 71 %, 85%, and 52%, respectively, for Support Vector Machine (SVM), Light Gradient Boosting Machine (LGBM), and Stochastic Gradient Descent (SGD). Furthermore, the model outperforms the DNN model, which received a score of 65%.

关键词： Support vector machines Accuracy Machine learning algorithms Botnet Artificial neural networks Feature extraction Boosting

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Thermal performance of the alkali activated binder developed from rice husk ash and bottom ash 3rd

Thermal performance of the alkali activated binder developed...

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3rd International Conference on Material engineering Research, ICMER 2020

作者： Khan, Khan Asudullah Ashwin, Raut Rama Chandrudu, C. Sashidhara, C. Dep. of Civil Engineering Jawaharlal Nehru Technological University Anantapur India Dep. of Civil Engineering SreeDattha Institute of Engineering and Science Hyderabad India Department of Civil Engineering Chiranjeevi Reddy Institute of Engineering and Technology Anantapur India

ISBN: (纸本)9783035737127

Currently, construction industry is unsustainable in nature due to the over dep.ndency on the energy consumptive materials. Thus, to transform the industry from unsustainable to sustainable focus is on innovative materials developed from locally available waste. In this research, focus is on development of alkali activated binder using combination of fly ash (FA), rice husk ash (RHA) and bottom ash (BA) as an alumina silicate source material. Further, thermal performance of the developed material has been studied to understand its impact on the heat transfer and insulation characteristic s of the material. The alkali activated binder developed from bottom ash shows better performance than rice husk ash (RHA) based binder. However, the thermal performance of the rice husk ash binder is more suited for the insulation due to its low thermal characteristics. The lowest thermal conductivity for the developed binder was having value of 0.31 W/m2K for rice husk ash (RHA) based binder. © 2021 Trans Tech Publications Ltd, Switzerland.

关键词： Thermal insulation

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Bayesian Network Structure Discovery Using Antlion Optimization Algorithm.

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2022年第1期7卷 46-65页

作者： Awla, Hoshang Qasim Kareem, Shahab Wahhab Mohammed, Amin Salih Computer science department Faculty of Science Soran University Erbil Iraq Dep. of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Poly-technic University Erbil Iraq Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Erbil Iraq

Bayesian networks have recently been used for discovering an optimal learning structure in machine learning. Bayes networks can describe possible dep.ndencies of explanatory variables. As a novel approach to studying the structure of a Bayesian network, the authors present the Antlion Optimization Algorithm (ALO). In the algorithm;deletion, rewind, insertion, and change are utilized to produce ALO to reach the best hull solution. Essentially, the technique used in the ALO algorithm imitates the antlions’ behaviors while hunting. The suggested approach is contrasted with simulated Annealing, Simulated Annealing Hybrid Bee, Greedy Search Hybrid Bee, optimization inspired by Pigeon, and greedy search using the BDe Score function. The researchers also studied the representation of the confusion matrix of these techniques using different reference data sets. The findings of the assessments reveal that the proposed algorithm works better than the other algorithms and has better consistency and score values. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values). © 2022. All Rights Reserved.

关键词： Antlion Optimization Bayesian Network Hunting Search Local & Global Search Metaheuristics Search and Score Structure Learning

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Machine Learning-Enhanced Standard Deviation to Detect and Handle of Outlier in the 3D Point-Cloud Data

Machine Learning-Enhanced Standard Deviation to Detect and H...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Tanhatalab, Mohammad Rasoul Forrer, Manuel Nelz, Michael Trento University Dep. of Information Engineering and Computer Science Italy Ferrisol Co Graz Austria Nelo Intelligence Eichenau Germany

ISBN: (纸本)9798331507022

Outlier removal is an important step in 3D point cloud data processing. Various methodologies for outlier removal will be analyzed shortly in this paper by describing a range of statistical and machine-learning techniques. Thus, the intent is to analyze different techniques and evaluate their performance in order to justify unique advantages and limitations of each technique. These limitations and advantages advance the discussion by introducing a new approach combining statistical and machine learning techniques on a real-world dataset. It not only identifies the spatial position of outliers but also enhances the measurement precision at those specific points. This work has a lot of significance from many aspects for researchers, engineers, and practitioners. Especially in robotics, industrial processes, augmented reality, and autonomous driving, the nature of 3D point cloud data requires sophisticated outlier handling. We believe our work will serve as a reference to optimize data quality in these dynamic applications under development. © 2025 IEEE.

关键词： Population statistics

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