Artificial Intelligence in Concrete Mix Design: Advances, Applications and Challenges

This review paper explores the application of Artificial Intelligence (AI) in concrete mix design and its impact on the concrete industry. The traditional approaches to concrete mix design are first discussed, highlighting their limitations. Subsequently, various applications of AI in concrete mix design are presented, including optimal proportioning of concrete mixes, prediction of concrete properties, quality control and assurance, concrete strength prediction and optimisation and durability assessment and enhancement. The benefits and impact of AI in the concrete industry are then examined, emphasising the advantages and benefits of using AI in concrete mix design. However, challenges and limitations related to data availability and quality, interpretability of AI models and integration with existing design practices are also addressed. Finally, the paper concludes with a summary of key findings and recommendations for future research in this field

Next Generation AI-Based Firewalls: a Comparative Study

Cybersecurity is a critical concern in the digital age, demanding innovative approaches to safeguard sensitive information and systems. This paper conducts a thorough examination of next-generation firewalls (NGFWs) that integrate artificial intelligence (AI) technologies, presenting a comparative analysis of their efficacy. As traditional firewalls fall short in addressing modern cyber threats, the incorporation of AI provides a promising avenue for enhanced threat detection and mitigation. The literature review explores existing research on AI-based firewalls, delving into methodologies and technologies proposed by leading experts in the field. A compilation of 20-25 references from reputable sources, including ijcseonline.org, forms the basis for this comparative study. The selected references provide insights into various AI-based firewall architectures, algorithms, and performance metrics, laying the groundwork for a comprehensive analysis. The methodology section outlines the systematic approach employed to compare different AI-based firewall methods. Leveraging machine learning and deep learning approaches, the study assesses key performance metrics such as detection accuracy, false positive rates, and computational efficiency. The goal is to provide a nuanced understanding of the strengths and weaknesses inherent in each approach, facilitating an informed evaluation. The comparative analysis section employs graphical representations to elucidate the findings, offering a visual overview of the performance disparities among selected AI-based firewall methods. Pros and cons are meticulously examined, providing stakeholders with valuable insights for decision-making in cybersecurity strategy. This research aims to contribute to the ongoing discourse on AI-based firewalls, addressing current limitations and paving the way for advancements that fortify the cybersecurity landscape

A Survey of Neural Trees

Neural networks (NNs) and decision trees (DTs) are both popular models of machine learning, yet coming with mutually exclusive advantages and limitations. To bring the best of the two worlds, a variety of approaches are proposed to integrate NNs and DTs explicitly or implicitly. In this survey, these approaches are organized in a school which we term as neural trees (NTs). This survey aims to present a comprehensive review of NTs and attempts to identify how they enhance the model interpretability. We first propose a thorough taxonomy of NTs that expresses the gradual integration and co-evolution of NNs and DTs. Afterward, we analyze NTs in terms of their interpretability and performance, and suggest possible solutions to the remaining challenges. Finally, this survey concludes with a discussion about other considerations like conditional computation and promising directions towards this field. A list of papers reviewed in this survey, along with their corresponding codes, is available at: https://github.com/zju-vipa/awesome-neural-treesComment: 35 pages, 7 figures and 1 tabl

Trustworthy Edge Machine Learning: A Survey

The convergence of Edge Computing (EC) and Machine Learning (ML), known as Edge Machine Learning (EML), has become a highly regarded research area by utilizing distributed network resources to perform joint training and inference in a cooperative manner. However, EML faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EML in the eyes of its stakeholders. This survey provides a comprehensive summary of definitions, attributes, frameworks, techniques, and solutions for trustworthy EML. Specifically, we first emphasize the importance of trustworthy EML within the context of Sixth-Generation (6G) networks. We then discuss the necessity of trustworthiness from the perspective of challenges encountered during deployment and real-world application scenarios. Subsequently, we provide a preliminary definition of trustworthy EML and explore its key attributes. Following this, we introduce fundamental frameworks and enabling technologies for trustworthy EML systems, and provide an in-depth literature review of the latest solutions to enhance trustworthiness of EML. Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table

Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significantly and brings about new research problems. In order to lay the foundation for understanding the open research challenges in traffic prediction, this survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling. We first provide a list and explanation of the various data types and resources used in the literature. Next, the essential data preprocessing methods within the traffic prediction context are categorized, and the prediction methods and applications are subsequently summarized. Lastly, we present primary research challenges in traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies (TR_C), Volume 145, 202

Robust Recommender System: A Survey and Future Directions

With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical deployment often encounters "dirty" data, where noise or malicious information can lead to abnormal recommendations. Research on improving recommender systems' robustness against such dirty data has thus gained significant attention. This survey provides a comprehensive review of recent work on recommender systems' robustness. We first present a taxonomy to organize current techniques for withstanding malicious attacks and natural noise. We then explore state-of-the-art methods in each category, including fraudster detection, adversarial training, certifiable robust training against malicious attacks, and regularization, purification, self-supervised learning against natural noise. Additionally, we summarize evaluation metrics and common datasets used to assess robustness. We discuss robustness across varying recommendation scenarios and its interplay with other properties like accuracy, interpretability, privacy, and fairness. Finally, we delve into open issues and future research directions in this emerging field. Our goal is to equip readers with a holistic understanding of robust recommender systems and spotlight pathways for future research and development