The fight against lung cancer knows no boundaries of age, gender, or ethnicity. The key to conquering this global challenge lies in timely detection, which dramatically enhances survival rates and quality of life post-diagnosis. This survey aims to address the lack of comprehensive reviews in the domain of automated lung cancer diagnosis dedicated to image processing through the lens of artificial intelligence and computer-aided diagnosis (CAD) systems. Although there is growing interest in this field, there is a dearth of literature offering a detailed examination of the framework architecture of these systems. To fill this gap, this study adopted a focused approach, analyzing 131 original articles from 2019 to 2024, sourced from Scopus and Web of Science indexed repositories. In this paper, a structured framework was introduced to enable a thorough analysis, evaluation and validation of existing CAD techniques. The review investigated raw imaging data and framework components, identified optimization opportunities, such as refining pre-processing techniques and improving feature extraction methods. Additionally, the study conducted a comparative analysis among various CAD systems, aiding researchers in selecting optimal methods for lung cancer diagnosis. Moreover, the study established detailed guidelines for documenting model specifications in CAD systems, enhancing reproducibility. Ultimately, this framework provides a roadmap for future research in the field, addressing the limitations of current CAD systems, and contributing to improved accuracy and efficiency in lung cancer detection.

Abstract Advances in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are enabling machines to emulate human-like behaviors. In the context of social computing, lifelike characters are crucial as they facilitate natural and intuitive interactions between humans and computers. Chatbots, a key application of such technologies, are computer programs that use Natural Language Processing (NLP) to engage in text-based conversations. They are widely used in customer service and other domains, but the challenge lies in designing chatbots that feel more human to enhance user engagement. Research has shown that incorporating emotions into chatbots is critical for achieving this goal. Effective emotion recognition systems must be able to process real-time text interactions, understand users’ sentiments on various topics, address their concerns, and respond appropriately based on the detected emotions. This paper proposes a meta-learning ensemble approach for text-based emotion detection in conversational data. The proposed method combines the outputs of multiple well-established machine learning algorithms to improve accuracy in recognizing emotions in text. A comparative analysis was conducted on two conversational datasets, demonstrating that the meta-learning ensemble method outperforms individual machine learning algorithms on both datasets. The proposed approach achieved 73% classification accuracy on the Empathetic Dialogues dataset, while on the EmoContext dataset, it achieved 95.1% classification accuracy, significantly outperforming results over individual machine learning algorithms. The conclusions demonstrate that utilizing a meta-learner for model fusion successfully leverages the advantages of separate algorithms while alleviating their intrinsic shortcomings, resulting in enhanced overall performance.

Abstract Reinforcement learning, characterized by trial-and-error learning and delayed rewards, is central to decision-making processes. Its core component, the reward function, is traditionally handcrafted, but designing these functions is often challenging or impossible in real-world scenarios. Inverse reinforcement learning (IRL) addresses this issue by extracting reward functions from expert demonstrations, facilitating optimal policy derivation and offering a deeper understanding of expert behavior. This comprehensive review focuses on three key aspects: the diverse methodologies employed in IRL, its wide-ranging applications across fields such as robotics, autonomous vehicles, and human intent analysis, and the importance of curated datasets in advancing IRL research. A structured analysis of IRL techniques is provided, applications are categorized by domain, and the role of benchmark datasets in evaluating performance and guiding future developments is emphasized. The unique value of IRL in bridging the gap between human and artificial learning is highlighted, demonstrating its potential to unlock advancements in machine learning, decision making, and explainable AI. By summarizing the current state of IRL research and advocating for future directions, this review serves as a valuable resource for researchers and practitioners seeking to explore and advance the field.

Abstract Icing accumulation on wind turbine blades significantly diminishes power output and revenue generation. Traditional icing detection methods, including sensor-based and model-based approaches, heavily rely on domain knowledge, contrasting with data-centric methods. However, a balanced distribution of normal and abnormal instances in wind turbine data is imperative. In this research, we propose a framework for blade icing detection utilizing a multi-head attention mechanism-based transformer. Supervisory control and data acquisition (SCADA) data is collected from wind turbines on Hitra Island, Norway, with a 10-min average interval over 12 months. To address dimensionality challenges, an autoencoder-based data compression technique is employed, followed by the application of a multi-head attention transformer for icing detection. We investigate and compare the performance of two baseline deep learning methods: convolutional neural network (CNN) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), against our proposed transformer framework. The results demonstrate superior accuracy and F1-score by the proposed model compared to CNN and CNN-LSTM. Additionally, we delve into a recommendation engine grounded in Bayesian inference. This engine assesses the risk associated with specific control actions, estimating conditional risk for icing and non-icing events on wind turbine blades. This Bayesian recommendation engine holds promise for real-time deployment scenarios.

In the present era of sustainable smart manufacturing within the industry 4.0 framework, industries thrive to achieve sustainable development. Machining processes play a substantial role in smart manufacturing. At the same time, the cutting tool is the most significant element of any machining process. The excessive tool wear or sudden failure of the cutting tool causes unplanned downtime, and it also affects the quality of finished products, economics, and effectiveness of the process. Among all the aspects, the vibrations that occur during machining and cutting forces are the most critical parameters, which accelerates the rate of tool wear. Active Vibration Control (AVC) techniques have emerged as promising approaches for mitigating the detrimental effects of vibration on tool performance. To realise the maximum potential of AVC, however, requires a methodical and exhaustive understanding of the existing literature. This study demonstrates the significance of conducting a systematic literature review on improving the Useful Life of cutting tools employing AVC and estimating through data-driven methods. A systematic literature review on AVC and remaining useful life (RUL) estimation of a cutting tool is performed using the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" (PRISMA) methodology. However, the study primarily highlights the active vibration control through MR fluid and its characteristics, modelling, and control techniques. Moreover, the data-driven approach for the RUL prediction is discussed briefly through data acquisition, data processing, feature extraction and ranking techniques together with decision-making algorithms. This review presents a structured method for identifying, evaluating, and synthesising relevant studies, thus providing a comprehensive overview of the current state of research in the field. This review seeks to identify gaps, trends, and research directions in the application of AVC for tool longevity by analysing a wide variety of literature, including peer-reviewed journal articles, conference proceedings, and technical reports. Researchers, engineers, and practitioners engaged in tool design, maintenance, and optimization will benefit from the findings of this systematic literature review. The findings will provide a consolidated knowledge base for informed decision-making, allowing for the identification of knowledge deficits, research opportunities, and avenues for further study. The ultimate objective of this review is to contribute to the advancement of AVC techniques for extending the RUL of tools, nurturing innovation, and promoting sustainable and efficient practises across a variety of industrial sectors.

This paper proposes a rectified complex spherical fuzzy set (rCTSFS) model that enables the phase term of a complex number to function truthfully to its inherent meaning of representing directions, phases, or color hues. In addition, this paper proposes the score and accuracy functions for rectified complex spherical fuzzy numbers (rCTSFn), which allows the three types of membership degrees of an rCTSFn to fulfill human judgment/intuition. The proposed rCTSFS model proves to be a productive extension of the complex spherical fuzzy set (CSFS), complex fuzzy set (CFS), and spherical fuzzy set (SFS) models. On the other hand, Dombi t-norms prove more flexible and comprehensive than some of the other families of triangular norms, such as the algebraic t-norms and the Einstein t-norms, due to the presence of a parameter γ. The parameter γ determines the amount of aggressiveness at estimating the maximum and the minimum of a population based on a sample obtained. Therefore, this paper proposes two Dombi-Choquet integral operators, namely, the rectified complex t-spherical fuzzy arithmetic Dombi-Choquet integral (rCTSFAγ,wλ) operator and the rectified complex t-spherical fuzzy geometric Dombi-Choquet integral (rCTSFGγ,wλ) operator. A multi-criteria decision making (abbr. MCDM) algorithm utilizing the two Dombi-Choquet integral operators is innovated. The proposed Dombi-Choquet MCDM algorithm for the rCTSFS model is applied to an MCDM problem related to diabetic retinopathy detection on five real-life fundus images of different severity levels taken from the Messidor2 dataset. Our newly proposed algorithm proves to be the only algorithm that yields the correct diagnostic results that match the hard truth. On the other hand, none of the 50 algorithms observed among recent works in literature can produce the correct diagnostic results, even after lending the fuzzification procedure innovated in this work to them.

Exploring the finest shortest-path traveling salesman optimization application is a typical NP-hard problem. Similarly the solution of the large-scale optimization applications is also a big challenging issue in front of scientists. First, African Vultures Optimization Algorithm (AVOA) was developed to resolve continuous applications where it performed fine. In the last few months, many enhanced strategies of AVOA have been offered in recent literature works and it has been extensively utilized to resolve large-scale engineering optimization applications. This study offers a newly modified dimension learning hunting (DLH)-based AVOA called DLHAV algorithm to resolve highly complex continuous and discrete applications. It helps improve the imbalance amid the hunting (or exploitation) and search (or exploration), the lack of crowd diversity, slow convergence speed, trapping in local optima, and early convergence of the AVOA variant. The proposed strategy benefits from a newly driven approach called the DLH search approach congenital from the separate exploitation behavior of vultures in the search domain. DLH exploration strategy utilizes a distinct method to make the best neighborhood for all vultures in which the nearest member information can be supplied amid vultures. DLH helps in improving the balance amid global and local and sustains diversity. To scrutinize the performance of DLHAV, the solutions of the DLHAV method are verified on 29-CEC'17 and 10-CEC'20 with familiar comparative methods and some other classical optimization approaches over many familiar traveling salesman problem/large-scale instances. With the intention of attaining unbiased and rigorous comparison, descriptive statistics such as standard deviation and mean have been applied, and the statistical Friedman test is also conducted. The experimental solution carried out in this study has revealed that the proposed algorithm outperforms significantly over the other alternative optimizers.

This article proposes the Mamdani complex fuzzy inference system (Mamdani CFIS) to improve performance of the classical FIS and complex FIS. The applicability of the proposed CFIS is demonstrated by applying it to six commonly available datasets from UCI Machine Learning under the comparison with Mamdani FIS and the Adaptive Neuro Complex Fuzzy Inference System (ANCFIS). It is successfully proven that the proposed Mamdani CFIS is computationally less expensive and presents a more efficient method to handle time-series data and time-periodic phenomena, among all the fuzzy IS found thus far in the literature. Furthermore, the novelty of CFIS mainly lies in its implementation of the complex number throughout the entire procedures of computation. This gives much greater flexibility of implementing unexpected, nonlinear fluctuations.