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Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects

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IEEE Transactions on Evolutionary Computation 2025年

作者： Molina, Daniel Poyatos, Javier Ser, Javier Del Garcia, Salvador Ishibuchi, Hisao Triguero, Isaac Xue, Bing Yao, Xin Herrera, Francisco TECNALIA Derio48160 Spain Bizkaia Leioa48940 Spain Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China University of Granada Department of Computer Science and Artificial Intelligence Granada18071 Spain School of Engineering and Computer Science Victoria University of Wellington Wellington6012 New Zealand University of Birmingham CERCIA School of Computer Science BirminghamB15 2SQ United Kingdom Lingnan University School of Data Science Hong Kong Hong Kong

In Artificial Intelligence, there is an increasing demand for adaptive models capable of dealing with a diverse spectrum of learning tasks, surpassing the limitations of systems devised to cope with a single task. The recent emergence of General-Purpose Artificial Intelligence Systems (GPAIS) poses model configuration and adaptability challenges at far greater complexity scales than the optimal design of traditional Machine Learning models. Evolutionary Computation (EC) has been a useful tool for both the design and optimization of Machine Learning models, endowing them with the capability to configure and/or adapt themselves to the task under consideration. Therefore, their application to GPAIS is a natural choice. This paper aims to analyze the role of EC in the field of GPAIS, exploring the use of EC for their design or enrichment. We also match GPAIS properties to Machine Learning areas in which EC has had a notable contribution, highlighting recent milestones of EC for GPAIS. Furthermore, we discuss the challenges of harnessing the benefits of EC for GPAIS, presenting different strategies to both design and improve GPAIS with EC, covering tangential areas, identifying research niches, and outlining potential research directions for EC and GPAIS. © 1997-2012 IEEE.

关键词： Adversarial machine learning

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Interdependency patterns of potential seaport risk factors in relation to supply chain disruption in Indonesia

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Journal of Shipping and Trade 2023年第1期8卷 6页

作者： Do. Bagus, Muhammad Reza Hanaoka, Shinya Department of Transdisciplinary Science and Engineering Tokyo Institute of Technology 2-12-1 O-okayama Meguro-ku Tokyo 152-8550 Japan Department of Information System College of Informatics and Computer Management Tidore Mandiri (STMIK Tidore Mandiri) Tidore Indonesia

As an integral part of the global supply chain network, Indonesian supply chain entities should understand conditional seaport risk factors that could lead to seaport threats that affect supply chain continuity. This study aims to provide a procedure for evaluating the interdependencies, implications, and correlations among various seaport risk factors for supply chain threats, specifically by investigating current practices in the developed economic region of Indonesian seaport operations. The study uses a rough set method to solve feature selection problems and multivariate analysis of variance to assess the correlation between dependent and independent variables. We find 39 conditional seaport risk factors that are potentially influenced by about 21 dependent factors related to seaport-fulcrum supply chain entities. Furthermore, threats from the planning process, infrastructure, seaport service process, distribution process, financial costs of nuclear enterprises and security existed and affluent highest potential risk in Indonesia. © 2023, The Author(s).

关键词： Association rule mining Genetic algorithm Interdependency risk analysis Rule rough set theory Seaport risk factors

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Design and Implementation of a QoL Improvement System for Visually Impaired Patients 5

Design and Implementation of a QoL Improvement System for Vi...

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5th International Conference on Advances in Computational Tools for engineering Applications, ACTEA 2023

作者： Abi Zeid Daou, Roy Beaino, Jad Hawat, Johnny Ibrahim, Ramy Hayek, Ali Boercsoek, Josef Olmedo, Jose Javier Serrano Université la Sagesse Faculty of Engineering Mechatronics Department Beirut Lebanon MART Learning Education and Research Center Chananir Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Hamm59063 Germany University of Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Germany Centro de Tecnología Biomédica Universidad Politécnica de Madrid Lab of Bioinstrumentation and Nanomedicine Spain

ISBN: (纸本)9798350309959

Visually impaired people encounter several challenges in their mobility and navigation. Their daily activities are obstructed due to their inability to adapt or recognize accurately their surroundings, especially outside their house which they are more familiar with. Thus, it becomes the main reason of accidents, falling off, getting lost in unknown areas, etc. Furthermore, one of the sensory systems that helps the body to process data about the external environment is the visual system. Blind people also lose touch with the outside world, develops poor motor habits, which results in postural problems as a result. This project will assist visually impaired people in their daily life and simplify normal tasks through a system combining two previously designed projects, 'Smart Shoes for Blind and Visually Impaired People', and 'Human posture monitoring device'. The multifunctional system is developed with the goal of securing safe movements for visually impaired people as well as maintaining a good back posture by detecting leaning postures (LP). The purpose of the smart shoe is to identify obstacles and protect the user from unintended accidents. A compatible Android application has been developed to alert the user when there is an obstruction or when he is walking on a wet surface. Voice alarms will be used to acoustically alert the user. If the user collapses, a message with their position will be sent right away to a relative. On the other hand, the smart vest will identify the position of the user's back and alert him to maintain a straight posture through the same application as well. As the system is dealing with human health, some safety measurements would be taken into consideration to implement a safe electrical system in order to reduce error and to increase accuracy. © 2023 IEEE.

关键词： Accidents

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Authentication using Biometric Data from Mobile Cloud Computing in Smart Cities 3

Authentication using Biometric Data from Mobile Cloud Comput...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Prakash, R.Om Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Near East University Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near Artificial Intelligence Engineering Dept Near East University Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Noida Instue Engineering and Technology U.P Greator Noida201306 India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

Smart cities are cities that are designed to be more efficient, sustainable, and connected. As our cities grow and become more complex, it's important to find new ways to address the challenges that arise, such as security, privacy, and mobility. This is where mobile cloud computing and biometric authentication come in. Mobile cloud computing is a way to provide cloud computing services to mobile devices, which means people can access services and data from anywhere and at any time. Biometric authentication is a method of identifying people based on their unique physical or behavioral characteristics, like fingerprints, facial recognition, or voice recognition. These two technologies can be combined to create a secure and personalized environment for residents and visitors in smart cities. By using mobile devices, IoT devices, wireless sensor networks, and edge computing, we can collect real-time data that can be used for big data analytics and machine learning. This means we can optimize city services, like traffic management, waste management, or energy consumption, and enhance the quality of life for people living in smart cities. However, the use of these technologies also poses significant security and privacy risks, so it's important to design systems that are secure and privacy-preserving. This requires interdisciplinary research and collaboration between experts in different fields. In summary, mobile cloud computing and biometric authentication have the potential to transform smart cities by creating a more secure and personalized environment for residents and visitors. But we must also be mindful of the potential risks and work together to address them. © 2023 IEEE.

关键词： Mobile cloud computing

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Pipette: Efficient Fine-Grained Reads for SSDs

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2023年第12期42卷 4721-4734页

作者： Bai, Shuhan Wan, Hu Huang, Yun Sun, Xuan Wu, Fei Xie, Changsheng Hsieh, Hung-Chih Kuo, Tei-Wei Xue, Chun Jason Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan430074 China City University of Hong Kong Department of Computer Science Pokfulam Hong Kong Imperial College London Department of Computing LondonSW7 2BX United Kingdom Yeestor Microelectronics Company Ltd. Department of Research and Development Shenzhen518057 China National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Big data applications, such as recommendation system and social network, often generate a huge number of fine-grained reads to the storage. Block-oriented storage devices upon the traditional storage system rely on the paging mechanism to migrate pages to the host DRAM, tending to suffer from these fine-grained read operations in terms of I/O traffic as well as performance. Motivated by this challenge, an efficient fine-grained read framework, Pipette, is proposed in this article as an extension to the traditional I/O framework. With adaptive design for caching, merging, and scheduling, Pipette explores locality and acceleration for fine-grained read requests to establish an efficient byte-granular read path upon the dedicated byte-addressable interface. When the Pipette prototype on an SSD runs popular workloads, we measured throughput gains by up to 50% and 54% with traffic reduction in the range of 41.3× and 56.5× . © 1982-2012 IEEE.

关键词： Merging

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Data analysis and interpretable machine learning for HVAC predictive control: A case-study based implementation

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science and Technology for the Built Environment 2023年第7期29卷 698-718页

作者： Mao, Jianqiao Grammenos, Ryan Karagiannis, Konstantinos Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom General Technology Ltd Athens15233 Greece

Energy efficiency and thermal comfort levels are key attributes to be considered in the design and implementation of a Heating, Ventilation and Air Conditioning (HVAC) system. With the increased availability of Internet of Things (IoT) devices, it is now possible to continuously monitor multiple variables that influence a user’s thermal comfort and the system’s energy efficiency, thus acting preemptively to optimize these factors. To this end, this paper reports on a case study with a two-fold aim;first, to analyze the performance of a conventional HVAC system through data analytics;secondly, to explore the use of interpretable machine learning techniques for HVAC predictive control. A new Interpretable Machine Learning (IML) algorithm called Permutation Feature-based Frequency Response Analysis (PF-FRA) is also proposed. Results demonstrate that the proposed model can generate accurate forecasts of Room Temperature (RT) levels by taking into account historical RT information, as well as additional environmental and time-series features. Our proposed model achieves 0.4017 °C and 0.9417 °C of Mean Absolute Error (MAE) for 1-h and 8-h ahead RT prediction, respectively. Tools such as surrogate models and Shapley graphs are employed to interpret the model’s global and local behaviors with the aim of increasing trust in the model. © Copyright © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： Energy efficiency

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Security and Privacy Concerns in 'Communication Systems' Blockchain Technology and the Internet of Things 3

Security and Privacy Concerns in 'Communication Systems' Blo...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Prakash, R.Om Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Noida Instue Engineering and Technology U.P Greator Noida201306 India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

This article examines the security and privacy concerns associated with the integration of blockchain technology and the Internet of Things (IoT) in communication systems. As the IoT continues to grow and evolve, there is an increasing need to ensure the security and privacy of the vast amounts of data generated and exchanged between connected devices. Blockchain technology offers promising solutions for enhancing security and privacy in communication systems;however, it also presents its own set of challenges and vulnerabilities. This article explores the existing literature on the topic and analyzes the materials and methods used in previous studies. The results and discussions delve into the key security and privacy concerns in communication systems with blockchain and IoT, including data integrity, authentication, and confidentiality, scalability, and consensus mechanisms. Finally, the article concludes with recommendations and future directions for addressing these concerns and advancing the security and privacy of communication systems in the context of blockchain technology and the IoT. © 2023 IEEE.

关键词： Internet of things

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Investigating How Data Poising Attacks Can Impact An EEG-Based Federated Learning Model

Investigating How Data Poising Attacks Can Impact An EEG-Bas...

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Cyber Resilience (ICCR), International Conference on

作者： Shamma Alshebli Muna Alshehhi Chan Yeob Yeun Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Computer Science C2PS Khalifa University Abu Dhabi United Arab Emirates

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Detecting potential security threats from individuals within an organization can be achieved using an Electroencephalogram (EEG), which captures the brain's electrical activity. The concept is based on the premise that certain brainwave patterns might be associated with malicious intentions or deceptive behaviors. Recent research on insider threat detection has utilized traditional machine learning classifiers to recognize patterns in brainwave data that correlate with malicious intent. However, these methods pose privacy and data security concerns because they require access to all user data. A recently introduced framework, Federated Learning (FL), offers a solution to this problem. FL aims to develop a global model classifier without the need to access users' local data, thus safeguarding their privacy and sensitive information. Thus, we developed an FL-based insider threat detection model trained on a dataset that contains the EEG signals of 17 participants captured from five electrodes across five power bands using the Emotiv Insight. The model's accuracy within our framework attained a rate of (94.71%) for MLP. However, this method faces potential security threats and attacks, as clients could act maliciously, or external malicious actors might disrupt the network. Therefore, we additionally explore the data poisoning attacks, emphasizing label-flipping scenarios within our federated learning system for EEG-based insider threat detection and illustrating how factors such as the number of poisoned clients and the percentage of poisoning affect an FL-based system. Based on our findings, a higher number of poisoned clients is much more damaging to FL-based systems and should thus be a focal point of consideration in the security design process of these systems.

关键词： Adaptation models Analytical models Power measurement Federated learning Brain modeling Data models Threat assessment

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The Main Threat to computer Network Security in Smart Cities 3

The Main Threat to Computer Network Security in Smart Cities

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Vikash, Om Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Friedrich- Alexander University Clean Energy Processing Department Chemical and Biological Engineerin Erlangen Germany Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Dehradun Department of Computer Science and Engineering India

ISBN: (纸本)9798350330724

As the concept of smart cities continues to gain momentum, the integration of various technologies has become essential for creating sustainable and efficient urban environments. However, with the proliferation of interconnected devices and systems, the risk of network security threats has increased significantly. This research article aims to identify and analyze the main threats to network security in smart cities. By exploring the potential vulnerabilities and challenges posed by the interconnected infrastructure, this study provides insights into the key areas that require attention to ensure the resilience and protection of smart cities. The findings of this research emphasize the need for robust security measures and proactive strategies to mitigate the identified threats. © 2023 IEEE.

关键词： Smart city

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Machine learning based fraudulent detection system for financial transactions

Machine learning based fraudulent detection system for finan...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Alam, Wahaj Ali, Raja Hashim Ali, Nisar Imad, Muhammad ul Abideen, Zain Shah, Muhammad Huzaifa Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14476 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain

ISBN: (纸本)9798331504465

Maintaining the integrity of financial systems and preventing people and organizations from suffering financial losses depend heavily on the ability to spot fraudulent financial transactions. Traditional rule-based fraud detection methods have trouble identifying intricate patterns and developing fraud tactics. Machine learning approaches have become powerful fraud detection tools in recent years, utilizing the strength of data-driven models to spot fraudulent actions. The Existing rule-based fraud detection has difficulty in identifying complex patterns and evolving fraud tactics. This study thoroughly investigates the use of machine learning techniques, including decision trees and random forests, for financial transaction fraud detection, while also exploring feature engineering approaches to extract essential data from transaction records such as temporal, spatial, and relational features. In this study, real-world financial transaction datasets are used to conduct experimental evaluations, comparing the performance of various machine learning models based on accuracy, precision, recall, and F1-score. The results indicate that certain algorithms outperform others, demonstrating promising and favorable outcomes for CatBoost algorithm. This significance of our study lies in showcasing the effectiveness of machine learning techniques, compared to traditional rule-based methods, for detecting fraud in financial transactions, leading to more promising outcomes and contributing to the integrity and security of financial systems. © 2024 IEEE.

关键词： Adversarial machine learning

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