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.

Facial Emotion Recognition (FER) is a challenging problem due to various challenges such as variability in expressions and ambiguity in data. Several popular benchmarking datasets, specifically employed for FER tasks exhibit bias towards ethnicity, demography and image capture mechanisms. More specifically, the images in such datasets are captured in a controlled environment and are taken in good light, with straight head orientation, no occlusion or other facial artefacts. When employed for FER, these biases may impair a model's generalizability, rendering it ineffective for FER in novel and unseen datasets. Especially, in applications involving security (access control) and identification of mal-intentions from facial expressions, it may prove inefficient. A criminal may disguise their face with make-up, headgear, and religious facial accessories and can fool the FER models trained on these biased datasets. To that end, this work focuses on understanding these datasets better by identifying such “good-image” bias. Methods to mitigate such bias which allows the FER models to perform better and improve the robustness are also demonstrated. A simple yet effective FER framework for studying bias mitigation is proposed. Using this framework, the performance on popular dataset is analyzed and a significant difference in model performance is observed. Additionally, a knowledge transfer technique and a synthetic image generation technique are proposed to mitigate the identified bias. Finally, using the SFEW dataset, the findings are validated on the FER task, demonstrating the effectiveness of our techniques in mitigating real-world “good-image” bias. The experiments show that the proposed techniques outperform baseline methods by averaged fourfold improvement.

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.

The stemming process is crucial and significant in the pre-processing step of natural language processing. The stemmer oversees the stemming process. It facilitates the extraction of morphological variants of a root or base word from the provided word. Over the period, several stemmers for various vernacular languages have been proposed. However, very few research studies have comprehensively investigated these available stemmers. This article makes multifold contributions. First, we discuss the various stemmers of 15 Indian and 17 non-Indian languages describing their key points, benefits, and drawbacks. All the Indian languages for which stemmers have been built are covered in this study. For the non-Indian languages, stemmers of commonly spoken languages have been covered. Second, we present a language-wise comparative analysis of stemmers based on our identified parameters. Third, we discuss the wordnets and dictionaries available for different languages. Fourth, we provide details of the datasets available for various languages. Fifth, we also provide challenges in existing stemmers and future directions for future researchers. The study presented in this article reveals that significant research has been carried out for the stemmers of influential languages such as English, Arabic, and Urdu. On the other hand, languages with d resources, such as Farsi, Polish, Odia, Amharic, and others, have received the least attention for research. Moreover, rigorous analysis reveals that most of the stemmers suffer from over-stemming errors. With a complete catalogue of available stemmers, this study aims at assisting the researchers and professionals working in the areas such as information retrieval, semantic annotation, word meaning disambiguation, and ontology learning.

Natural Language Processing (NLP) aids the empowerment of intelligent machines by enhancing human language understanding for linguistic-based human-computer communication. Recent developments in processing power, as well as the availability of large volumes of linguistic data, have enhanced the demand for data-driven methods for automatic semantic analysis. This paper proposes multilingual data processing using feature extraction with classification using deep learning architectures. Here, the input text data has been collected based on various languages and processed to remove missing values and null values. The processed data has been extracted using Histogram Equalization based Global Local Entropy (HEGLE) and classified using Kernel-based Radial basis Function (Ker_Rad_BF). These architectures could be utilized to process natural language. We present solutions to the multilingual sentiment analysis issue in this research article by implementing algorithms, and we compare precision factors to discover the optimum option for multilingual sentiment analysis. For the HASOC dataset, the proposed HEGLE_ Ker_Rad_BF achieved an accuracy of 98%, a precision of 97%, a recall of 90.5%, an f-1 score of 85%, RMSE of 55.6%, and a loss curve analysis attained 44%. For the TRAC dataset, the accuracy of 98%, the precision attained is 97%, the Recall is 91%, the F-1 score is 87%, and the RMSE of the proposed neural network is 55%.

Current industry trends demand automation in every aspect, where machines could replace humans. Recent advancements in conversational agents have grabbed a lot of attention from industries, markets, and businesses. Building conversational agents that exhibit human communication characteristics is a need in today's marketplace. Thus, by accumulating emotions, we can build emotionally aware conversational agents. Emotion detection in text-based dialogues has turned into a pivotal component of conversational agents, enhancing their ability to understand and respond to users’ emotional states. This article extensively compares various artificial intelligence techniques adapted to text-based emotion detection for conversational agents. The study covers a wide range of methods, from machine learning models to cutting-edge pre-trained models and deep learning models. We evaluate the performance of these techniques on the benchmark unbalanced Topical-Chat and balanced Empathetic Dialogue datasets. This article offers an overview of the practical implications of emotion detection techniques in conversational systems and their impact on user response. The outcomes of this work contribute to the ongoing development of empathetic conversational agents, emphasizing natural human-machine interactions.

Deep neural nets are opaque black-box models with little to no understanding of underlying model dynamics. This issue is more prevalent in the case of multimodal artificial intelligence (AI) systems, where model explainability and interpretability are prime concerns due to data integration from heterogeneous data streams and complex inter and intramodal interactions. However, the traditional explainable models are challenging to apply in the multimodal scenario. We propose a co-learning-based solution for fostering model explainability for the natural language processing (NLP)-based multimodal sentiment analysis application to address this issue. The proposed approach employs explainability by obeying the co-learning principles of dealing with noisy and missing modality either at train or test time to find the modality dominance by extracting the local and global model explanations. The proposed approach is validated with post hoc explainability methods such as local interpretable model-agnostic explanations (LIME) and SHapley Additive exPlanations (SHAP) gradient-based explanations to model the modality contributions and interactions at the fusion level. The co-learning-based system ensures trust and robustness in the model by providing some degree of model explainability along with robustness. The kind of explanations provided is multifaceted and is obtained through a peek inside the black box, hence is specifically helpful for the system designers and model developers to understand the complex model dynamics that are far more challenging in the case of multimodal applications.

The Sign Language Recognition system intends to recognize the Sign language used by the hearing and vocally impaired populace. The interpretation of isolated sign language from static and dynamic gestures is a difficult study field in machine vision. Managing quick hand movement, facial expression, illumination variations, signer variation, and background complexity are amongst the most serious challenges in this arena. While deep learning-based models have been used to accomplish the entirety of the field's state-of-the-art outcomes, the previous issues have not been fully addressed. To overcome these issues, we propose a Hybrid Neural Network Architecture for the recognition of Isolated Indian and Russian Sign Language. In the case of static gesture recognition, the proposed framework deals with the 3D Convolution Net with an atrous convolution mechanism for spatial feature extraction. For dynamic gesture recognition, the proposed framework is an integration of semantic spatial multi-cue feature detection, extraction, and Temporal-Sequential feature extraction. The semantic spatial multi-cue feature detection and extraction module help in the generation of feature maps for Full-frame, pose, face, and hand. For face and hand detection, GradCam and Camshift algorithm have been used. The temporal and sequential module consists of a modified auto-encoder with a GELU activation function for abstract high-level feature extraction and a hybrid attention layer. The hybrid attention layer is an integration of segmentation and spatial attention mechanism. The proposed work also involves creating a novel multi-signer, single, and double-handed Isolated Sign representation dataset for Indian and Russian Sign Language. The experimentation was done on the novel dataset created. The accuracy obtained for Static Isolated Sign Recognition was 99.76%, and the accuracy obtained for Dynamic Isolated Sign Recognition was 99.85%. We have also compared the performance of our proposed work with other baseline models with benchmark datasets, and our proposed work proved to have better performance in terms of Accuracy metrics.

EDF (earliest deadline first) has been proved to be optimal scheduling algorithm for single processor realtime operating systems when the systems are preemptive and underloaded. The limitation of this algorithm is, its performance decreases exponentially when system becomes slightly overloaded. Authors have already proved ability of ACO (Ant Colony Optimization) based scheduling algorithm for real-time operating system which is optimal during underloaded condition and it gives outstanding results in overloaded condition. The limitation of this algorithm is, it takes more time for execution compared to EDF algorithm. In this paper, an adaptive scheduling algorithm is proposed which is combination of both of these algorithms. Basically the new algorithm uses EDF algorithm but when the system becomes overloaded, it will switch to ACO based scheduling algorithm. Again, when the overload disappears, the system will switch to EDF algorithm. Therefore, the proposed algorithm takes the advantages of both algorithms and overcomes the limitations of each other. The proposed algorithm along with EDF algorithm and ACO based scheduling algorithm, is simulated for real-time system and the results are obtained. The performance is measured in terms of Success Ratio and Effective CPU Utilization. Execution Time taken by each scheduling algorithm is also measured. From analysis and experiments it reveals that the proposed algorithm is fast as well as very efficient in both underloaded and overloaded conditions.

Areas in which Genetic Algorithm (GA) excel is their ability to manipulate many parameters simultaneously, their use of parallelism enables them to produce multiple equally good solutions to the same problem. So GAs are most appropriate for multi objective optimization problems, in which there is no single value to be minimized or maximized, but having multiple objectives, usually with tradeoffs involved: one can only be improved at the expense of another. By looking at this strength of GA we have applied MultiObjective GA to support QoS Routing in Mobile Ad-hoc Network (MANET). A MANET is dynamic multi-hop wireless network established by a group of mobile nodes on a shared wireless channel by virtue of their proximity to each other. To support mobility to user generally low configured nodes are in use, so limited resources, dynamic network topology and link variations are the issues with MANET. So, Routing in such a dynamic environment is a challenging issue, lots of work has been done for routing in MANET but QoS (Quality of Service) requirements are not yet supported that way, and in this scenario to find optimal path is a problem of NP class. We have applied MultiObjective Genetic algorithm: to optimize four QoS parameters bandwidth constraints, delay, traffic from adjacent nodes, and number of hops, and to provide adaptive route in MANET. Our experiment is based on Network Simulator NS-2.28 and results show that GA based approach is better than traditional method.