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• Five Machine Learning (ML) models compared for wave height time series prediction. • Complex ML models do not outperform simple AR in wave height time series prediction. • Comment to related papers: signal decomposition in test set series is WRONG. Significant Wave Height (SWH) is crucial in many aspect of ocean engineering. The accurate prediction of SWH has therefore been of immense practical value. Recently, Artificial Intelligence (AI) time series prediction methods have been widely used for single-point short-term SWH time-series forecasting, resulting in many AI-based models claiming to achieve good results. However, the extent to which these complex AI models can outperform traditional methods has largely been overlooked. This study compared five different models - AutoRegressive (AR), eXtreme Gradient Boosting (XGB), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and WaveNet - for their performance on SWH time series prediction at 16 buoy locations. Surprisingly, the results suggest that the differences of performance among different models are negligible, indicating that all these AI models have only "learned" the linear auto-regression from the data. Additionally, we noticed that many recent studies used signal decomposition method for such time series prediction, and most of them decomposed the test sets, which is WRONG. [ABSTRACT FROM AUTHOR]

• AI improves infectious waste management. • Ensemble model exceeds 97.81% accuracy. • Reinforcement learning optimizes classification. • Geometric augmentation boosts model robustness. • Study addresses urgent urban waste challenges. This research paper focuses on effective infectious municipal waste management in urban settings, highlighting a dearth of dedicated research in this domain. Unlike general or specific waste types, infectious waste poses distinct health and environmental risks. Leveraging advanced artificial intelligence techniques, we prioritize infectious waste categorization and optimization, integrating metaheuristics into optimization methods to create a robust dual-ensemble framework. Our model, the "Enhanced Artificial Intelligence for Infectious Municipal Waste Classification System," combines ensemble image segmentation methods and diverse convolutional neural network models. Innovative geometric image augmentation enhances model robustness, diversifies training data, and improves accuracy across waste types. A pivotal aspect is the integration of a reinforcement learning-differential evolution algorithm as a decision fusion strategy, optimizing classification by harmonizing outputs from ensemble methods and convolutional neural network models. Computational results, using a newly collected dataset, demonstrate our model's accuracy exceeding 96.54% while using the existing solid waste dataset we achieve the accuracy of 97.81%, outperforming advanced approaches by margins ranging from 2.02% to 8.80%. This research significantly advances sustainable waste management, showcasing artificial intelligence's transformative potential in addressing intricate waste challenges. It establishes a foundational framework prioritizing efficiency, effectiveness, and sustainability for future waste management solutions. Acknowledging the importance of diverse datasets, customization for urban contexts, and practical integration into existing infrastructures, our work contributes to the broader discourse on the role of artificial intelligence in evolving waste management practices. [ABSTRACT FROM AUTHOR]

Precision Agriculture (PA) has revolutionized crop management by leveraging information technology, satellite positioning data, and remote sensing. One crucial component in PA applications is the Normalized Difference Vegetation Index (NDVI), which offers valuable insights into crop vigor and health. However, discontinuity of optical satellite acquisitions related to cloud cover and the huge load of the required processing time pose challenges to real-time applications. NDVI prediction emerges as an innovative solution to address these limitations. It allows for proactive decision-making by providing accurate estimates, enabling farmers and land managers to plan essential agronomic activities such as irrigation, fertilization, and pest control, based on anticipated future conditions. This study introduces an Artificial Neural Network (ANN) model incorporating NDVI, Normalized Difference Water Index (NDWI), temperatures, and precipitation as predictive variables. The model employs a novel time series slicing algorithm, Boosting Adaptive Time Series Slicer (BATS), to enhance the input training dataset's variability, presenting the model with a broader range of examples. A 2-Bidirectional Long Short-Term Memory (LSTM) forecasting model was developed to predict future NDVI values over short and medium-term horizons. The study area used to train, test and validate the ANN corresponds to a diverse landscape of cultivated corn fields located in Piemonte (NW-Italy). Results showed that NDVI future estimates were accurate; considering three time horizons for predictions (5, 10, and 15 days) RMSE values resulted to be 0.028, 0.038 and 0.050, respectively. Additionally, ablation tests proved that the most important variable for enhancing the model's accuracy is the NDWI, and the most useful timesteps are the four most recent ones. To preliminary investigate the capability of the ANN to operate over a wider and different area it was applied over the entire Europe, using the LUCAS dataset as reference map to locate corn fields. Results show RMSE of 0.062, 0.083 and 0.105 for the 5, 10 and 15 days forecasting horizons, respectively. The methodology proposed in this paper can be a possible alternative to more ordinary approaches for NDVI forecasting that nowadays appears to be a fundamental step for a proactive precision agriculture where crop management can be significantly improved. Future developments should explore the use of sequence-to-sequence ANNs to predict the development of multiple spectral indices over multiple crop types simultaneously. [ABSTRACT FROM AUTHOR]

In recent studies, artificial intelligence models have been developed for the prediction of nitrogen oxide emissions from thermal power plants. This research utilizes an artificial intelligence prediction model with more coal input features than boiler features, including volatility, ash content, sulfur content, fixed carbon, total moisture, calorific value, grinding rate, fuel ratio, coal feeding rate, boiler efficiency, total air volume, and excess air volume. The paper delves into the importance analysis of input features for artificial intelligence models. Moreover, feature importance analysis is not only a prerequisite for predicting nitrogen oxide emissions but also a study providing insights into model performance. An artificial neural networks (ANN) regression model is employed to predict nitrogen oxide emissions, and the results demonstrate that the number of feature importance significantly impacts model performance. The best model performance is achieved with eight specific input features. Finally, after the training and validation processes, the ANN model yields the optimal coefficient of determination (R2) value. • Development of an artificial intelligence prediction model for nitrogen oxide emissions from thermal power plants. • A comprehensive set of input features is utilized for emissions prediction. • Importance analysis of input features significantly impacts model performance. • Optimal model performance was achieved with eight specific input features. • The robust coefficient of determination (R2) value demonstrates the accuracy of the ANN model. [ABSTRACT FROM AUTHOR]

Artificial Intelligence has found wide application, but also poses risks due to unintentional or malicious tampering during deployment. Regular checks are therefore necessary to detect and prevent such risks. Fragile watermarking is a technique used to identify tampering in AI models. However, previous methods have faced challenges including risks of omission, additional information transmission, and inability to locate tampering precisely. In this paper, we propose a method for detecting tampered parameters and bits, which can be used to detect, locate, and restore parameters that have been tampered with. We also propose an adaptive embedding method that maximizes information capacity while maintaining model accuracy. Our approach was tested on multiple neural networks subjected to attacks that modified weight parameters, and our results demonstrate that our method achieved great recovery performance when the modification rate was below 20%. Furthermore, for models where watermarking significantly affected accuracy, we utilized an adaptive bit technique to recover more than 15% of the accuracy loss of the model. • Fragile watermarking for rigorous neural network integrity detection. • Watermarking ensures 100% tampering detection, precise location, and recovery. • Watermarking fragility by combining Mutual- and Self-neural net parameters. • Incorporates watermarking with neural net feature, maintaining watermarked models' performance via gradient descent. • Bit-level training controls step size with carefully set hyperparameters. [ABSTRACT FROM AUTHOR]

• We analyzed older driver at-fault crashes at unsignalized intersections in Alabama. • We estimated random parameter models. • We introduced a deep learning approach based on Artificial Neural Networks. • We identified variables that increase the likelihood of injuries and fatalities. • We discussed possible countermeasures to address older driver at-fault crashes. Introduction: The research described in this paper explored the factors contributing to the injury severity resulting from the male and female older driver (65 years and older) at-fault crashes at unsignalized intersections in Alabama. Method: Random parameter logit models of injury severity were estimated. The estimated models identified a variety of statistically significant factors influencing the injury severities resulting from older driver at-fault crashes. Results : According to these models, some variables were found to be significant only in one model (male or female) but not in the other one. For example, variables such as driver under the influence of alcohol/drugs, horizontal curve, and stop sign were found significant only in the male model. On the other hand, variables such as intersection approaches on tangents with flat grade, and driver older than 75 years were found significant only in the female model. In addition, variables such as making turning maneuver, freeway-ramp junction, high speed approach, and so forth were found significant in both models. Estimation findings showed that two parameters in the male model and another two parameters in the female model could be modeled as random parameters, indicating their varying influences on the injury severity due to unobserved effects. In addition to the random parameter logit approach, a deep learning approach based on Artificial Neural Networks was introduced to predict the outcome of the crashes based on 164 variables that are listed in the crash database. The artificial intelligence (AI)-based method achieved an accuracy of 76% indicating the role of the variables in deciding the final outcome. Practical Applications: Future plans are set to study the use of AI on large sized datasets to achieve a relatively high-performance, and hence to be able to identify which variables contribute the most to the final outcome. [ABSTRACT FROM AUTHOR]

Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex and opaque machine learning (ML) models. Despite the development of a multitude of methods to explain the decisions of black-box classifiers in recent years, these tools are seldomly used beyond visualization purposes. Only recently, researchers have started to employ explanations in practice to actually improve models. This paper offers a comprehensive overview over techniques that apply XAI practically to obtain better ML models, and systematically categorizes these approaches, comparing their respective strengths and weaknesses. We provide a theoretical perspective on these methods, and show empirically through experiments on toy and realistic settings how explanations can help improve properties such as model generalization ability or reasoning, among others. We further discuss potential caveats and drawbacks of these methods. We conclude that while model improvement based on XAI can have significant beneficial effects even on complex and not easily quantifiable model properties, these methods need to be applied carefully, since their success can vary depending on a number of factors, such as the model and dataset used, or the employed explanation method. [Display omitted] • We show how and why XAI improves several model properties in toy experiments. • We develop a unifying theoretical framework for XAI-based model improvement. • We extensively review and discuss existing approaches that use XAI to improve models. • We provide experiment-supported practical recommendations for XAI-based augmentation. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning has shown useful in the field of robotics but the black-box nature of deep neural networks impedes the applicability of deep reinforcement learning agents for real-world tasks. This is addressed in the field of explainable artificial intelligence, by developing explanation methods that aim to explain such agents to humans. Model trees as surrogate models have proven useful for producing explanations for black-box models used in real-world robotic applications, in particular, due to their capability of providing explanations in real time. In this paper, we provide an overview and analysis of available methods for building model trees for explaining deep reinforcement learning agents solving robotics tasks. We find that multiple outputs are important for the model to be able to grasp the dependencies of coupled output features, i.e. actions. Additionally, our results indicate that introducing domain knowledge via a hierarchy among the input features during the building process results in higher accuracies and a faster building process. [ABSTRACT FROM AUTHOR]

The emergence of artificial intelligence (AI) and its related capabilities has led industries to rethink the existing practices of conventional supply chain management and data analysis. Machine learning (ML), Deep Learning (DL) and their unique ability to predict future data and classify data have led to important research in the supply chain (SC) domain, particularly in identifying and prioritizing supply chain risks. This paper proposes several DL methodologies to exploit the benefit of DL, particularly to identify whether any product will be delivered late due to any unforeseen reason in a complex SC system. Four different DL architectures (Simple-LSTM, Deep-LSTM, 1D-CNN, and TCN-1DSPCNN models) are proposed to extract features, while six variant classifiers: Softmax, random trees (RT), random forest (RF), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM), were used to classify delay or non-delay information. By seamlessly capturing intricate temporal dependencies, these DL models enhance accuracy in robustly identifying supply chain late orders. Leveraging their hierarchical feature learning, these proposed DL models excel in recognizing subtle patterns and correlations, making them ideal for classifying late orders within the supply chain. Their parallel processing prowess facilitates real-time decision support, allowing organizations to address potential delays and allocate resources effectively and proactively. Five-fold cross-validation is presented to avoid over-fitting and to prove the efficiency of the proposed DL models. The total accuracies of the six ML classifiers are 74.03, 75.81, 93.35, 87.72, 93.59, and 95.10, respectively, while the maximum accuracies obtained from four proposed DL methodologies obtained an accuracy of 97.6, 98.63, 100, 100% respectively using the SVM classifier for predicting late orders based on five-fold cross-validation. • This paper investigates a few DL approaches to extract features of SC data. • Both RNN and CNN are applied in the same model. • An improved CNN model has been proposed for feature extraction. • An online dataset is employed to validate the proposed DL architectures. [ABSTRACT FROM AUTHOR]

The growing integration of AI in agriculture necessitates access to diverse data sets for training artificial intelligence (AI) systems. Manufacturers often face challenges due to a perceived shortage of valid data sets, highlighting the need for centralized platforms offering high-quality data. While global AI regulations are yet to be established, existing laws govern data collection, creation, labeling, and processing for AI training in various legal systems. The absence of explicit AI laws prompts a call for platforms supplying data to adhere to relevant regulations, especially in areas where AI controls devices interacting with humans, such as collision warning systems in yard tractors and robots. Compliance is crucial, given the complexity of rules governing data sets featuring people, animals, and buildings. The paper focuses on how to check the compliant data sets in Germany's legal context, focusing on situations in farm yards. People are often visible on such data sets; thus, the data set must preserve the right to informational self-determination and one's image, which is prevalent in many legal systems. Images portraying people typically qualify as personal data, requiring careful anonymization to address privacy concerns. The paper uses artificial neural networks (ANN) to remove images that contradict people's rights on one's image; thus, data sets that comply with data protection laws can be created. It also addresses the lesser-explored issue of the right to one's image in German law. Though AI training may not be impeded, dissemination via digital platforms must adhere to legal boundaries, even if metadata is absent. The paper suggests an AI solution to identify images within legal limits, detect violations, and modify them to create a legally compliant data set respecting the right to one's image. • Discussing legal conform data sets for the agricultural application domain. • Providing a data set of 2694 images covering the categories: – class (I) not legally compliant and – class (II) legally compliant on the right to one's image; cf. Fig. 1. • Identifying the gap in ANN data sets and models for law compliance checks. • Train and benchmark ANN models for checking law compliance – on the right to one's image; – cf. Tab. 1, Tab. 2, and Fig. 3. • Promoting the importance of AI-based law compliance checks for – detecting violations against the law and – developing compliant AI systems in agriculture. [ABSTRACT FROM AUTHOR]

Numerous Artificial Intelligence (AI) solutions are available for achieving thermal comfort. They were either trained with limited datasets or using personalized training with limited field studies. This work assessed the model that used the ASHRAE multiple databases as the shallow supervised learning dataset for an Artificial Neural Network (ANN) based controller suitable for the residential dwellings' node. The learning accuracy can be increased to 96.1%. This paper presented the field studies to show the model performances for the common UK dwellings: the prior 1970s, the new, modular, refurbished, and the use of new materials to improve indoor thermal performance. The result shows that the model was able to perform in different environments and able to acknowledge adaptive human comfort. This was shown by the ability to represent 98.90% of the ASHRAE Standard 55 data, 6.06% improvement from the previous research. As a result, the broader comfort zone acknowledgement can lead to energy saving whilst maintaining comfort by the possibility of lowering the temperature set point. This study also proves that further energy savings can be acquired from the occupants' presence, scheduling, and activities. These factors can increase the comfort probability to more than 10%. [Display omitted] • This paper addresses the gap between the physiology and the psychology thermal comfort approach, dominated by AI solutions. • The work shows a wider comfort zone which has been identified to become progressively narrower over the past several decades. • The field studies represent major UK-dwelling cases that weren't addressed in the previous Artificial Intelligence approach. • The occupant presence and scheduling can contribute to more than a 10% increase in comfort which impacts energy saving. • This work highlights the possibility of achieving indoor thermal comfort with less energy for more sustainable dwellings. [ABSTRACT FROM AUTHOR]

Numerous Artificial Intelligence (AI) solutions are available for achieving thermal comfort. They were either trained with limited datasets or using personalized training with limited field studies. This work assessed the model that used the ASHRAE multiple databases as the shallow supervised learning dataset for an Artificial Neural Network (ANN) based controller suitable for the residential dwellings' node. The learning accuracy can be increased to 96.1%. This paper presented the field studies to show the model performances for the common UK dwellings: the prior 1970s, the new, modular, refurbished, and the use of new materials to improve indoor thermal performance. The result shows that the model was able to perform in different environments and able to acknowledge adaptive human comfort. This was shown by the ability to represent 98.90% of the ASHRAE Standard 55 data, 6.06% improvement from the previous research. As a result, the broader comfort zone acknowledgement can lead to energy saving whilst maintaining comfort by the possibility of lowering the temperature set point. This study also proves that further energy savings can be acquired from the occupants' presence, scheduling, and activities. These factors can increase the comfort probability to more than 10%. [Display omitted] • This paper addresses the gap between the physiology and the psychology thermal comfort approach, dominated by AI solutions. • The work shows a wider comfort zone which has been identified to become progressively narrower over the past several decades. • The field studies represent major UK-dwelling cases that weren't addressed in the previous Artificial Intelligence approach. • The occupant presence and scheduling can contribute to more than a 10% increase in comfort which impacts energy saving. • This work highlights the possibility of achieving indoor thermal comfort with less energy for more sustainable dwellings. [ABSTRACT FROM AUTHOR]

The motivation of this paper is to investigate the resistance of countries' energy research and development (RD&D) expenditures to random shocks. The analysis includes the five countries (France, Germany, Japan, the United States, and the United Kingdom) that are the biggest investors in RD&D in fossil fuels, renewables, energy efficiency, and nuclear energy. Thus, economies will be able to take precautions against global shocks in producing sustainable energy policies. To achieve this aim, this paper uses the artificial neural network approach, which uses a distributed computing model that can store and generalize data after a learning period. Empirical results widely depend on countries and distinct energy technology policy fields. The key findings provide evidence that RD&D expenditures, except for renewables, do not tend to mean revert. The economies of Japan, Germany, and the United States should make more renewable investments to reduce the resistance of renewable energy sources to shocks and thus produce policies for both environmental sustainability and energy security. The economies of France and the United Kingdom can ensure energy security by continuing their sustainable energy policies. • Applied SDG policies focused on energy technology RD&D are presented. • The stochastic characteristics of energy technology investments are examined. • Carbon-reducing policies are recommended for the top five economies. • Energy technologies are perused through artificial intelligence approach. [ABSTRACT FROM AUTHOR]

The increasing integration of distributed energy resources has led to heightened complexity in distribution network models, posing challenges of uncertainty and volatility to the operation and control of distribution networks. Simultaneously, the widespread deployment of metering and sensing devices has furnished data-driven approaches with copious data sources. As a potent tool for data mining, artificial intelligence (AI) algorithms have the capacity to harness extensive datasets, thus furnishing comprehensive energy services for distribution grids. This paper provides a comprehensive exposition of AI techniques applied in the realm of distribution network voltage control (DNVC). Initially, a brief overview is provided for the four voltage control frameworks and the data calibration of DNVC, serving as the foundation for various voltage control algorithms. Subsequently, in ascending order of algorithmic advancement, the application of improved heuristic algorithms, deep learning (DL) algorithms structured around artificial neural networks (ANNs), and deep reinforcement learning (DRL) algorithms in the DNVC domain are sequentially introduced, and the relationships among the three AI technologies in framework, control principles, and performance are systematically analyzed. Additionally, attention is directed toward the algorithmic progress made in addressing specific challenges within the DNVC process. Lastly, building upon extant research, the paper summarizes four prominent developmental directions for AI in DNVC: safety exploration, information privacy, multi-feeder collaboration, and heightened uncertainty. Corresponding solutions to each of these directions are individually discussed. • The research status and applications of mainly used AI methods in distribution network voltage control is reviewed. • The relationships among the three artificial intelligence methods are systematically analyzed. • The existing algorithms, along with their strengths, limitations, and relationshipsare highlighted. • The research directions of artificial intelligence methods in distribution network voltage control are discussed in detail. [ABSTRACT FROM AUTHOR]

Despite the existence of evidence-based rehabilitation strategies that address biomechanical deficits, the persistence of recurrent ankle problems in 70% of patients with acute ankle sprains highlights the unresolved nature of this issue. Artificial intelligence (AI) emerges as a promising tool to identify definitive predictors for ankle sprains. This paper aims to summarize the use of AI in investigating the ankle biomechanics of healthy and subjects with ankle sprains. Articles published between 2010 and 2023 were searched from five electronic databases. 59 papers were included for analysis with regards to: i). types of motion tested (functional vs. purposeful ankle movement); ii) types of biomechanical parameters measured (kinetic vs kinematic); iii) types of sensor systems used (lab-based vs field-based); and, iv) AI techniques used. Most studies (83.1%) examined biomechanics during functional motion. Single kinematic parameter, specifically ankle range of motion, could obtain accuracy up to 100% in identifying injury status. Wearable sensor exhibited high reliability for use in both laboratory and on-field/clinical settings. AI algorithms primarily utilized electromyography and joint angle information as input data. Support vector machine was the most used supervised learning algorithm (18.64%), while artificial neural network demonstrated the highest accuracy in eight studies. The potential for remote patient monitoring is evident with the adoption of field-based devices. Nevertheless, AI-based sensors are underutilized in detecting ankle motions at risk of sprain. We identify three key challenges: sensor designs, the controllability of AI models, and the integration of AI-sensor models, providing valuable insights for future research. [Display omitted] • Application of sensors and artificial intelligence to analyse ankle biomechanics. • Lab sensors predict parameters, paving the way for post-modern rehabilitation. • Field devices show potential for ankle injury prediction. • Focus on streamlined smart sensors and effective data management in the future. [ABSTRACT FROM AUTHOR]

The advent of reliable and inexpensive sensors and advancements in general computing have made data-heavy algorithms feasible for operational, real-time decision-making applications in the geothermal energy industry. This systematic review aims to provide a starting point for researchers interested in developing data-driven systems, tools, and frameworks to enhance the performance and reliability of above-ground geothermal energy operations. The approach and results of the review are presented to answer the following research questions: how has data analytics been applied in above-ground geothermal operations, what data sets have been used in such studies, which types of machine learning or artificial intelligence algorithms have been used in geothermal studies, and at which stages of geothermal development have studies been applied. Published research articles were retrieved from four literature databases: the International Geothermal Association (IGA) online library, ScienceDirect, SpringerLink, and IEEE Xplore. A total of 830 publications were retrieved using the same search query across the selected databases, from which 63 research papers were selected based on a set of inclusion and exclusion criteria. A full-text evaluation of the selected research papers revealed that machine learning has been used in geothermal for design optimisation, performance monitoring, performance optimisation, fault detection, and other applications. Most of the trained models (95 %) were of the artificial neural network family with other model types generally used as performance benchmarks. The systematic review revealed significant potential for further research and applications in the areas of feature selection, systematic time-series feature engineering, and model evaluation. • A systematic review of AI and data analytics studies in geothermal was conducted. • Most of the reviewed research focused on the development of neural network models. • Design optimisation and condition monitoring are the primary themes in the studies. • Model quality and interpretability can be addressed by applying ML best practices. [ABSTRACT FROM AUTHOR]

An in-depth understanding of uncertainty is the first step to making effective decisions under uncertainty. Machine/deep learning (ML/DL) has been hugely leveraged to solve complex problems involved with processing high-dimensional data. However, reasoning and quantifying different uncertainties to achieve effective decision-making have been much less explored in ML/DL than in other Artificial Intelligence (AI) domains. In particular, belief/evidence theories have been studied in Knowledge representation and reasoning (KRR) since the 1960s to reason and measure uncertainties to enhance decision-making effectiveness. Based on our in-depth literature review, only a few studies have leveraged mature uncertainty research in belief/evidence theories in ML/DL to tackle complex problems under different types of uncertainty. Our present survey paper discusses major belief theories and their core ideas dealing with uncertainty causes and types and quantifying them, along with the discussions of their applicability in ML/DL. Particularly, we discuss three main approaches leveraging belief theories in Deep Neural Networks (DNNs), including Evidential DNNs, Fuzzy DNNs, and Rough DNNs, in terms of their uncertainty causes, types, and quantification methods along with their applicability in diverse problem domains. Through an in-depth understanding of the extensive survey on this topic, we discuss insights, lessons learned, limitations of the current state-of-the-art bridging belief theories and ML/DL, and future research directions. This paper conducts an extensive survey by bridging belief theories and deep learning in reasoning and quantifying uncertainty to help researchers initiate uncertainty and decision-making research. • Identifying the causes and types of uncertainty in various belief models. • Uncertainty reasoning and quantification of belief models for decision-making. • Applying uncertainty quantification of belief models into deep learning. • Comparing uncertainty consideration in belief models and deep learning. • Insights, lessons learned, limitations, and future research directions. [ABSTRACT FROM AUTHOR]

A substantial amount of research has been carried out in Explainable Artificial Intelligence (XAI) models, especially in those which explain the deep architectures of neural networks. A number of XAI approaches have been proposed to achieve trust in Artificial Intelligence (AI) models as well as provide explainability of specific decisions made within these models. Among these approaches, global interpretation methods have emerged as the prominent methods of explainability because they have the strength to explain every feature and the structure of the model. This survey attempts to provide a comprehensive review of global interpretation methods that completely explain the behaviour of the AI models. We present a taxonomy of the available global interpretations models and systematically highlight the critical features and algorithms that differentiate them from local as well as hybrid models of explainability. Through examples and case studies from the literature, we evaluate the strengths and weaknesses of the global interpretation models and assess challenges when these methods are put into practice. We conclude the paper by providing the future directions of research in how the existing challenges in global interpretation methods could be addressed and what values and opportunities could be realized by the resolution of these challenges. [ABSTRACT FROM AUTHOR]

• A comprehensive, explainable accident-data-based AI artifact design is presented. • Utilize aggregate SHAP scores to classify crash data into high-level causal factors. • A systems-level taxonomy of driver behaviors linked to injury severity is discussed. • Separating explanation from models yields managerial insights. Automobile traffic accidents represent a significant threat to global public safety, resulting in numerous injuries and fatalities annually. This paper introduces a comprehensive, explainable artificial intelligence (XAI) artifact design, integrating accident data for utilization by diverse stakeholders and decision-makers. It proposes responsible, explanatory, and interpretable models with a systems-level taxonomy categorizing aspects of driver-related behaviors associated with varying injury severity levels, thereby contributing theoretically to explainable analytics. In the initial phase, we employed various advanced techniques such as data missing at random (MAR) with Bayesian dynamic conditional imputation for addressing missing records, synthetic minority oversampling technique for data imbalance issues, and categorical boosting (CatBoost) combined with SHapley Additive exPlanations (SHAP) for determining and analyzing the importance and dependence of risk factors on injury severity. Additionally, exploratory feature analysis was conducted to uncover hidden spatiotemporal elements influencing traffic accidents and injury severity levels. We developed several predictive models in the second phase, including eXtreme Gradient Boosting (XGBoost), random forest (RF), deep neural networks (DNN), and fine-tuned parameters. Using the SHAP approach, we employed model-agnostic interpretation techniques to separate explanations from models. In the final phase, we provided an analysis and summary of the system-level taxonomy across feature categories. This involved classifying crash data into high-level causal factors using aggregate SHAP scores, illustrating how each risk factor contributes to different injury severity levels. [ABSTRACT FROM AUTHOR]

• Low-cost measurement platform for particulate matter monitoring developed. • Artificial intelligence field calibration procedure of low-cost sensor proposed. • Combination of affine scaling and optimized neural network surrogate employed. • Correlation coefficient improved to 0.86 for particulate matter of size 1 μm. • Root-mean-squared error of 3 μm/m3 (1 μm particles) and 4.9 μm/m3 (10 μm particles) Due to detrimental effects of atmospheric particulate matter (PM), its accurate monitoring is of paramount importance, especially in densely populated urban areas. However, precise measurement of PM levels requires expensive and sophisticated equipment. Although low-cost alternatives are gaining popularity, their reliability is questionable, attributed to sensitivity to environmental conditions, inherent instability, and manufacturing imperfections. The objectives of this paper include (i) introduction of an innovative approach to field calibration for low-cost PM sensors using artificial intelligence methods, (ii) implementation of the calibration procedure involving optimized artificial neural network (ANN) and combined multiplicative and additive correction of the low-cost sensor readings, (iii) demonstrating the efficacy of the presented technique using a custom-designed portable PM monitoring platform and reference data acquired from public measurement stations. The results obtained through comprehensive experiments conducted using the aforementioned low-cost sensor and reference data demonstrate remarkable accuracy for the calibrated sensor, with correlation coefficients of 0.86 for PM 1 and PM 2.5 , and 0.76 PM 10 (particles categorized as having diameter equal to or less than 1 μm, 2.5 μm, and 10 μm, respectively), along with low RMSE values of only 3.1, 4.1, and 4.9 µg/m3. [ABSTRACT FROM AUTHOR]

• Presenting a summary of more than ten mostly used Artificial Intelligence (AI) and Machine Learning (ML) methods. • Reviewing the most recent research which has been conducted in the Renewable Energy Systems (RES) field. • Classifying various AI methods and their applications in RES. • Discussing widely used approaches in the different categories of RES. Recent advancements in data science and artificial intelligence, as well as the development of clean and sustainable energy sources, have created numerous opportunities for energy researchers to conduct their studies. Artificial Intelligence (AI) and Machine Learning (ML) techniques have been applied to Renewable Energy Systems (RES) for several years, and their intensity and scope have grown in recent years. Since artificial intelligence and machine learning have a wide range of applications, it may be difficult to select and implement suitable methods for future research. In order to address this issue, this study examines several of the most popular and well-known AI techniques in renewable energy. This paper describes over ten of the most prevalent RES modeling and optimization algorithms, including Artificial Neural Networks (ANN), Long and Short-Term Memory (LSTM), Recurrent and Convolutional Neural Networks (RNNs and CNNs), the Genetic Algorithm (GA), and the Particle Swarm Optimization algorithm (PSO). More than a hundred different studies between 2020 and 2022 have been compiled and organized in the results section according to the method used and the field of application. At the end, the results are discussed, and the limitations of the current study will be mentioned, followed by some suggestions to complete our work and make it more useful. [ABSTRACT FROM AUTHOR]

• Artificial Intelligence to predict two-phase frictional pressure gradient. • Collection of a pressure drop database for a wide range of operating conditions. • Effect of the output and input type and numerosity on prediction accuracy. • Good accuracy with non-dimensional output and mixed input values. • Conventional methods always preserve physical trend with respect to neural networks. This paper presents a discussion on several possibilities to predict the frictional pressure gradient during two-phase flow, with both the application of artificial intelligence and the implementation of conventional correlations and predictive methods. To this purpose, a huge database of approximately 8000 data points has been collected from 49 sources available in scientific literature, including 23 working fluids and the following ranges of parameters: mass fluxes from 32.7 to 2000 kg/m2s, saturation temperatures from -190°C to +120°C (reduced pressures from 0.021 to 0.780), tube diameters from 0.5 to 14.0 mm. This consolidated database has been used to train several artificial neural networks (ANNs), by using only two hidden layers (shallow neural networks) and evaluating the effect of: training and testing datasets choice (either test data included or outside the training domain), the number of neurons for each hidden layer (from 1 to 50), the type of output (either dimensional or non-dimensional), the type and number (from 1 to 22) of input parameters. The best results (MAPE of 16.8% and 88% of data within ±30%) have been obtained by using the liquid-only two-phase multiplier Φ L O 2 as non-dimensional output and 12 mixed input parameters. Compared to the statistics of well-established literature correlations for frictional pressure drop (best MAPE of 22% and 73% of data points predicted within a ±30% error range, provided by Mauro et al. mechanistic method), the ANN demonstrates therefore a higher general accuracy. However, the use of Artificial Neural Networks does not guarantee a physical trend, which is instead preserved with conventional prediction methods. [ABSTRACT FROM AUTHOR]

• This paper shows how artificial neural networks can be used to determine overvoltages in power system. • The primary contribution of this paper is a novel method based on artificial neural networks for classification of overvoltages in power systems. • This approach consists of a few steps, as follows: • Modelling of power system components for transient studies in EMTP. This means developing two different models, one for switching overvoltages, and the other one for fast front overvoltages. • Creating and deploying JavaScript to automate the simulation process and parametric analysis. • Overvoltage feature extraction from generated overvoltage waveshape using harmonic analysis in MATLAB. • Overvoltage classification using neural network in MATLAB. Numerous factors, including sudden load reductions, switching transient loads, lightning strikes, and malfunctions of control devices, can result in overvoltage. Overvoltage can harm associated power supply components and result in insulation failure, electronic component damage, flashovers, etc. A machine learning technique called a "neural network" estimates computation results that depend on a lot of inputs. For a variety of reasons, neural networks have recently been used to manage and optimize the power system. This paper presents an artificial neural network (ANN)-based approach to determining overvoltages in power systems. To simulate overvoltages, many simulations were performed in Electromagnetic Transient Program (EMTP). Variations of parameters of interest that have an influence on overvoltages were made using JavaScript that was connected to EMTP models. The extraction of characteristic parameters from overvoltage waveshape is a demanding task, and it was conducted in MATLAB, as was a overvoltage classification methodology based on neural networks. Results were presented and discussed. [ABSTRACT FROM AUTHOR]

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general. [ABSTRACT FROM AUTHOR]

Implementing associative memory experiments with nanoscale memristors is an interesting subject, which can allow robots to mimic human thinking. This paper is concerned with a new emotion model of memristor-based neural network and its circuit implementation. The model has three inputs and two outputs, which can feel happy when it receives good news or feel sad when it receives bad news. Unknown news can be recognized through associative memory, which simulates human emotions. Associative learning and three kinds of forgetting process together make up the full-function emotion model. In addition, the Ag/AgInSbTe/Ta-based model closely related to the actual physical properties of memristors is used to design synaptic structures. The circuits of memristor-based neural networks are also simplified. Furthermore, the new emotion model is able to adjust the changing rate of emotion. The process reflects the fact that humans learn the same thing faster at the second time, compared with the first time. Finally, PSPICE is used to simulate all the circuits of the emotion model. The presented emotion model in this paper offers more possibilities for designing intelligent machines. [ABSTRACT FROM AUTHOR]

Artificial intelligence, and in particular deep learning using convolutional neural networks, has been used extensively for image classification and segmentation, including on medical images for diagnosis and prognosis prediction. Use in radiotherapy prognostic modelling is still limited, however, especially as applied to toxicity and tumour response prediction from radiation dose distributions. We review and summarise studies that applied deep learning to radiotherapy dose data, in particular studies that utilised full three-dimensional dose distributions. Ten papers have reported on deep learning models for outcome prediction utilising spatial dose information, whereas four studies used reduced dimensionality (dose volume histogram) information for prediction. Many of these studies suffer from the same issues that plagued early normal tissue complication probability modelling, including small, single-institutional patient cohorts, lack of external validation, poor data and model reporting, use of late toxicity data without taking time-to-event into account, and nearly exclusive focus on clinician-reported complications. They demonstrate, however, how radiation dose, imaging and clinical data may be technically integrated in convolutional neural networks-based models; and some studies explore how deep learning may help better understand spatial variation in radiosensitivity. In general, there are a number of issues specific to the intersection of radiotherapy outcome modelling and deep learning, for example translation of model developments into treatment plan optimisation, which will require further combined effort from the radiation oncology and artificial intelligence communities. • The use of deep learning for radiotherapy outcome prediction is reviewed. • Ten studies used convolutional neural networks and spatial dose for prediction. • Studies were generally small and single institutional, with lack of external validation. • Deep learning may explore spatial variation in radiosensitivity, but methodology underdeveloped. [ABSTRACT FROM AUTHOR]

Deep neural networks, as applied in the field of nuclear power fault diagnosis, have garnered significant attention alongside advancements in artificial intelligence technology. However, the "black box" nature of deep learning models has raised concerns regarding their deployment in scenarios demanding high safety standards, such as nuclear power plants. In this paper, we innovatively propose the utilization of an explainable artificial intelligence method, grounded in game theory, to conduct a detailed analysis of the diagnostic behavior of neural network models applied to nuclear power plants. By leveraging SHAP, the decision-making processes of these opaque models are demystified, offering insights into how and why they arrive at their predictions. In this study, two of the most widely utilized neural network frameworks are applied across six representative fault diagnosis cases for a comprehensive analysis. Based on its analysis results, a novel SHAP-Enhanced Feature Selection for Efficient Neural Network Fault Diagnosis strategy is proposed, significantly reducing model complexity without sacrificing diagnostic performance. This research breaks through the limitations of data-driven models used in the field of nuclear power fault diagnosis, conducts an interpretable analysis of their behavior, and proposes an improved strategy based on the analysis results, contributing to the practical application of data-driven models in nuclear power. [ABSTRACT FROM AUTHOR]

• The paper explores AI decision-making to enhance transparency of CIN diagnosis using colposcopy images. • The study presents interpretable insights for clinical validation using different XAI methods. • Saliency method showed best result, using gradients to reveal likely malignancy traits. • Expert Clinic Validation of results conducted, along with statistical analyses. Although artificial intelligence techniques have performed well in analysing medical images, it is essential to consider the degree of understanding or interpretation in evaluations. This is especially important when diagnostic support is involved, as the trustworthiness of machine learning predictions needs to be clarified and evaluated. For deep learning algorithms to be translated into clinical practice, they must be made less opaque. To examine the classification of cervical dysplasia-diagnosed patients, we will explore explainable artificial intelligence (XAI) approaches, by implementing seven different interpretation techniques (Saliency, XRAI, Integrated Gradients, Smooth Gradients, Smooth Integrated Gradients, Grad-CAM, Smoothgrad-CAM). These XAI techniques shall be explored in order to assess the performance of four Convolutional Neural Network models (AlexNet, VGG16, MobileNet, and InceptionNet). Our results show that saliency qualities most closely match expert annotations, and there is a moderate to strong association between a model's sensitivity and the agreement between humans and computers. We also discovered a relevant relationship between expert insights and computational learning. This highlights the potential role of human expertise in influencing effective computer learning. [ABSTRACT FROM AUTHOR]

In this paper, a novel Fault Detection and Isolation (FDI) method for Proton Exchange Membrane (PEM) electrolyzers is presented. The challenge of limited availability of labeled fault data is addressed through the utilization of Bond Graphs (BG) and Self-Supervised Learning (SSL). The Linear Fractional Transformation-Bond Graph (LFT-BG) model is employed to generate uncertain residuals and pseudo labels, enabling the training of a deep neural network through self-supervised learning. Additionally, the BG-XAI method is introduced, leveraging eXplainable AI (XAI) and structural equations from Bond Graphs to provide explanations for the decisions made by the deep learning model. The superior performance of the proposed approach, particularly in dealing with limited labeled data, is demonstrated through comparative assessments against various state-of-the-art SSL methods. The demo code of the proposed method is available in this repository: https://github.com/mohan696matlab/SSL_based_Hybrid_FDI. • Uses LFT-bond graph model to generate residuals and pseudo labels for pre-training. • Introduces BG-XAI, a novel explainable AI technique using occlusion. • The proposed hybrid FDI is applied to a PEM electrolyzer stack, showing superior performance. • It deals with limited labeled fault data in renewable energy, offering a reliable solution for diagnosis. [ABSTRACT FROM AUTHOR]

Appropriate weight initialization settings, along with the ReLU activation function, have become cornerstones of modern deep learning, enabling the training and deployment of highly effective and efficient neural network models across diverse areas of artificial intelligence. The problem of "dying ReLU," where ReLU neurons become inactive and yield zero output, presents a significant challenge in the training of deep neural networks with ReLU activation function. Theoretical research and various methods have been introduced to address the problem. However, even with these methods and research, training remains challenging for extremely deep and narrow feedforward networks with ReLU activation function. In this paper, we propose a novel weight initialization method to address this issue. We establish several properties of our initial weight matrix and demonstrate how these properties enable the effective propagation of signal vectors. Through a series of experiments and comparisons with existing methods, we demonstrate the effectiveness of the novel initialization method. [ABSTRACT FROM AUTHOR]

High occupational injury and fatality rate in the construction industry is a serious global concern. Recognizing AI as a solution to enhance safety performance, this study reviews 153 papers to assess and categorize current AI applications in construction, focusing on text, visual, and audio data, while also identifying challenges and future research opportunities. Real-time monitoring, hazard detection, and information extraction are identified as key areas where AI is applied, with a notable reliance on deep neural networks, object recognition, and Natural Language Processing. The review highlights major challenges, including the need for high-quality data management, semantic feature representation, and occluded object detection. Additionally, it underscores the untapped potential of audio-based AI and the advancements possible with Large Language Models for text interpretation. The findings emphasize the need for integrated, multi-faceted AI systems and advocate for responsible AI deployment to mitigate safety risks on construction sites. [Display omitted] • Poor Safety remains a global challenge for construction. • AI emerges as an effective approach for enhancing safety. • Study reviews and categorizes AI applications in construction safety. • The key insights, opportunities, and challenges for AI integration are identified. [ABSTRACT FROM AUTHOR]

Due to its insensitivity to light intensity and the capability to capture multidimensional information, polarimetric imaging technology has been proven to have advantages over traditional intensity-based imaging techniques for object detection tasks in adverse environmental conditions, particularly in road traffic scenarios. Recently, with the rapid development of artificial intelligence technology, deep learning (DL)-powered object detection techniques can further enhance recognition accuracy and algorithm robustness. This improvement is made possible by the ability of DL technology to leverage large datasets and extract deeper levels of target-specific features. However, constructing large-scale polarimetric datasets poses challenges as obtaining polarimetric information requires multiple captures of intensity images. In other words, the workload is several times higher compared to traditional imaging techniques. To address the current scarcity of polarimetric datasets and evaluate the practical performance of various algorithms on polarimetric datasets, this paper proposes a Polarimetric Object Detection Benchmark (PODB) dataset. The PODB provides an integrated quality evaluation framework for DL-based object detection algorithms in complex road scenes by incorporating polarimetric imaging. Besides, we conducted extensive object detection experiments using the PODB, which allowed for a comprehensive validation and performance evaluation of effective benchmark algorithms. Furthermore, a physics-based multi-scale image fusion cascaded object detection neural network model is proposed. By combining the multidimensional information provided by polarized images with an adaptive learning multi-decision object detection neural network model, the recognition accuracy of complex road scenes in adverse weather conditions has been improved by approximately 10%. Additionally, we anticipate that PODB will serve as an effective platform for evaluating and comparing the performance of object detection algorithms, as well as providing researchers with a baseline for future studies in developing new DL-based methods. • Polarimetric imaging excels in challenging conditions, like road traffic. • Scarce polarimetric road data due to acquisition difficulties. • Proposed Polarimetric Object Detection Benchmark dataset for deep learning-based algorithms. • Introduction of a novel multi-scale image fusion cascaded neural network model. • Achieving a 10% accuracy boost for complex, adverse weather road scenes. [ABSTRACT FROM AUTHOR]

The conventional standardized theoretical models (such as Webster, Alcelik, Indo-HCM) used for the delay estimation revolve around the mathematical hypothesis and assumptions, making them static with limitations in accommodating the dynamic traffic behaviors. Recent advances in artificial intelligence make machine learning techniques suitable for estimating vehicle control delay compared to conventional methods. This paper demonstrates the application of several machine learning models developed by focusing on the fluctuations of traffic observed at an intersection having heterogeneous traffic conditions in Ahmedabad city for delay estimation. Several parameters are extracted from on-field and video surveys. However, not all parameters are relevant for accurately estimating vehicle control delay. Hence, a feature selection process consisting of several feature-scoring techniques from filter, wrapper, and embedded methods is applied to all the parameters. This process gave insights into the most statistically relevant independent parameters, and out of all the parameters, cycle time was found to be insignificant, with a feature score of 0 from almost all techniques. Hence, it was removed, and then the prominent parameters were then used to build a vehicle control delay model using support vector regression (SVR), K-nearest neighbor (KNN), artificial neural network (ANN), random forest regression (RF), and decision tree regression (DT). Error distribution, standard deviation of errors, coefficient of Determination (R2), and Root Mean Squared Error (RMSE) are the parameters used for evaluating the performance of the machine learning models. RF outperformed them all with a standard deviation of errors, R2, and RMSE of 11.065, 0.926, and 11.081 on testing data. But ANN, KNN, and DT also performed satisfactorily. Compared with conventional standardized theoretical models, all the machine learning models except SVR performed better. [ABSTRACT FROM AUTHOR]

To optimize the operation of his wind farm, the farm manager needs to make precise diagnostic decisions for scheduling efficiently the maintenance actions. To assist him in this task, this paper proposes an approach aimed at designing a hybrid diagnoser model for two main goals. The first one is to detect anomalies at an early stage, and the second one is to provide specific deductions or explanations about the potential cause of the problem. In this way, the developed system can help the maintenance manager understand why a particular decision needs to be made. To do this, an original approach coupling the artificial intelligence technique with a discrete event system is proposed to diagnose, identify, and explain a probable source of a problem. Thus, the proposed method uses a hybrid approach consisting of two model blocks. The first block consists of autoencoder models to extract feature representation to diagnose the health state of the system. The second one is a discrete event-based model to create and visualize rule-based anomaly alerts and triggers to provide plausible explanations to the operator to improve his task. Then, this work introduces a methodology to jointly train these two models and learn all parameters of the hybrid diagnoser. It is shown how the information extracted from the neural network model is used to automatically construct the event-based model to explain the occurrence of an anomaly. The proposed system achieved significant results in explaining and detecting early five types of anomalies in wind turbine systems with accuracy up to 80%. The results demonstrate that the approach can deliver performance gains of up to 20% compared to standard techniques such as Long short-term memory and Random cut forest. • Anomalies detection and explanation in wind turbine systems. • Hybrid learning of an autoencoder and event-based model. • Computation of a set of rules for identifying the root causes of anomalies. • Explainable predictive maintenance for improving decision-making processes. [ABSTRACT FROM AUTHOR]

• A new AI-based model learning method for HCF of the joints in large-span steel structures is proposed. • The PSO-ANN-ABC AI algorithm is applied to firstly predict fatigue resistance for welded spherical joints. • A subroutine for HCF damages is developed to solve the problem non-stationary amplitudes and frequencies. Welded spherical joints are critical components in large-span bridges and spatial steel structures prone to high cycle fatigue (HCF) loadings. Challenges exist for assessing fatigue life of this joints in small data from physical experiments and no detailed fatigue classification in design codes. This paper investigates the fatigue resistance of welded spherical joints and the real-time pre-damage evolution under non-stationary HCF loadings by proposing AI-based macro model learning from fine FE models and fatigue tests. Firstly, the structural features of welded spherical joints about stress concentration and weld defects were analyzed and quantified, so modified S-N curve was built. Then, based on artificial intelligence (AI-based) learning, stress concentration factors (SCF) were obtained by an artificial neural network with particle swarm optimization and the modified fatigue resistance of welded spherical joints was derived by artificial bee colony algorithms. Finally, the macro linkage element was proposed as a user element subroutine (UEL), by which non-stationary fatigue pre-damage evolution based on the time-domain incremental analytical method was realized. The results not only indicated the damage value close to the ones derived by commonly used rain flow count methods, but also provided the pre-damage evolution curve for real-time monitoring. [ABSTRACT FROM AUTHOR]

To mitigate the high cost of integrated ship design, in this paper, we propose an innovative integrated hull design methodology utilizing artificial intelligence-aided design technology. The AIAD methodology leverages a deep neural network model constructed from multiple fidelities datasets to curtail the exponential growth in data requirements resulting from the proliferation of design variables caused by integration. Combining the proposed bump-free deformation method and optimization algorithms with the multi-fidelity deep network, the AIAD-based design approach facilitates the achievement of optimal hull design solutions with remarkable fidelity while minimizing design workload. Utilizing the developed design method, we performed a numerical test on integrated-hull design for container ships. Through a comparative analysis and sensitive analysis of design results and workload, the optimized results obtained demonstrated the effectiveness of the proposed AIAA integrated-hull design methodology in achieving optimal designs while preserving high fidelity and low workload. The proposed new approach has shown promises to expedite ship integrated-hull design, addressing the longstanding issue of its high cost. • An artificial intelligence-based design method is proposed, reducing the workload brought by the integrated-hull design. • Balanced the conflicting relationship between fidelity and cost of the deep learning model by using multi-fidelity datasets. • A bump-free deformation method is provided, which solves the local concave-convex after deformation. • Carried out the integrated-hull design of a container ship hull, achieving a 3% reduction in resistance. [ABSTRACT FROM AUTHOR]

Date fruit (Phoenix dactylifera L.) is known for its diversity in terms of genotypes that differ in shape, color, texture, taste, and other physical and sensory attributes, in addition to biochemical compounds. This richness generates complex, long, and tedious work at the scale of farm as well as in handling, processing, and storage facilities to prepare standardized, and homogenous final products that deal with consumer preferences and needs. To alleviate this, an increased importance and particular trend were observed recently for the use of artificial intelligence (AI) techniques and computer vision technology since they are considered as accurate, objective, non-destructive, fast, and efficient emerging technologies. In this context, this manuscript provides comprehensive insights into key applications of AI techniques and computer vision technology, using different classification models and algorithms developed on the basis (for example) of neural network classifiers, such as convolutional and artificial neural network, on harvest and postharvest assessment of date fruit. Thus, the importance of date fruit dataset used in the AI domain was discussed with link to all accessible datasets in the field of date palm. In addition, available and relevant AI tools and approaches applied to the postharvest of date fruit from maturity distinguishing until storage through other key handling operations, sorting, grading, etc., were detailed in the five sections of this paper. In terms of conclusion, this review showed that these smart technologies can play a substantial role and offer new opportunities to improve the whole value chain of date palm sector. • Harvesting and post-harvest handling impact quality and storability of dates. • These operations are tedious and subjective leading to quantitative/qualitative losses. • Artificial intelligence (AI) techniques are emergent technologies that can reduce these losses. • AI techniques are more accurate, fast, non-destructive and objective. [ABSTRACT FROM AUTHOR]

Extensive research has been conducted on analyzing human movements, driven by its diverse practical applications such as human–robot interaction, human learning, and clinical diagnosis. However, the current state-of-the-art still encounters scientific challenges when it comes to modeling human movements. There are two key aspects that need to be addressed. Firstly, new models should consider the stochastic nature of human movement and the physical structure of the human body to accurately predict the patterns in full-body motion descriptors over time. Secondly, while deep learning algorithms have been utilized, they lack explainability in terms of predicting body posture sequences, making it essential to improve their comprehensible representation of human movement. This paper aims to tackle these challenges by presenting three innovative methods for creating explainable representations of human movement. The study formulates human body movement as a state-space model based on the Gesture Operational Model (GOM). Model parameters are estimated through either one-shot training employing Kalman Filters or data-intensive training utilizing artificial neural networks. The trained models are utilized for analyzing the dexterity of expert professionals in full-body movements, enabling the identification of dynamic associations between body joints and gesture recognition. Additionally, these models are employed to generate artificial professional movements. • Three innovative methods for explainable human movement models. • Body dexterity analysis: Models reveal how artisans and operators perform tasks. • Minimal motion descriptors to enhance task recognition. • Artificial generation of full-body professional movements. • Extensive experimentation with real-world datasets. [ABSTRACT FROM AUTHOR]

• Proposing and validating a novel CNN-DDPG based AI-trader application. • Can be generalized for other business operations such as demand forecasting in logistics and risk hedging. • This paper lays the foundation for further studies with AI applications. Artificial Intelligence (AI) is well-developed as a part of human life. In both financial markets and business operations, AI is getting more and more important. In this paper, we build a novel "Reinforcement Learning" (RL) framework based AI-trader. We adopt an actor-critic RL algorithm called "Deep Deterministic Policy Gradient" (DDPG) to find the optimal policy. Our proposed DDPG has two different convolutional neutral networks (CNNs) based function approximators. The proposed AI-trader's performance is shown to outperform other methods with the use of real stock-index future data. We further discuss the generalization and implications of the proposed method for business operations. [ABSTRACT FROM AUTHOR]

• The paper presents the validation of a generalised ANN model for a broad of component conditions and material properties. • Validation of theoretical ANN models with experimental data is rare in the literature. • The results of the validation show ANN to be a better approach for fatigue analysis than existing frequency domain methods. • The results of the study also show that rainflow time domain fatigue analysis is not universally agreeable with experimental results. The use of artificial intelligence especially based on artificial neural networks (ANN) is now prevalent in many fields of data analysis and interpretation. There have been a number of papers published in the literature on the use of ANN for fatigue characterisation. Most of these have however been developed for rather focussed application with limited capability for fatigue life prediction for a broad scope of material and loading conditions. The authors recently presented a uniquely generalised ANN model that is capable of making fatigue life prediction for a broad range of material fatigue properties and loading spectral forms. The model was developed using simulated data albeit subject to conceivable constraints between possible materials properties and load forms. This paper presents a validation of the ANN model using a Society of Automotive Engineers (SAE) random fatigue loading experimental test data. The capabilities and potentials of the model are demonstrated by comparison with the SAE random load fatigue test results and with results obtained from other predictive methods. The performance of the ANN is highly encouraging as a general tool for random loading fatigue analysis. [ABSTRACT FROM AUTHOR]

The rising overheads of data-movement and limitations of general-purpose processing architectures have led to a huge surge in the interest in "processing-in-memory" (PIM) approach and "neural networks" (NN) architectures. Spintronic memories facilitate efficient implementation of PIM approach and NN accelerators, and offer several advantages over conventional memories. In this paper, we present a survey of spintronic-architectures for PIM and NNs. We organize the works based on main attributes to underscore their similarities and differences. This paper will be useful for researchers in the area of artificial intelligence, hardware architecture, chip design and memory system. [ABSTRACT FROM AUTHOR]

• This paper presents the Survey of AI Methods for Renewable Energy Forecasting. • Taxonomy of AI for solar and wind renewable energy is presented. • Combinations of ML and metaheuristic methods for renewable energy are discussed. • Prospects of improved metaheuristic algorithms for renewable energy forecasting are presented. The efforts to revolutionize electric power generation and produce clean and sustainable electricity have led to the exploration of renewable energy systems (RES). This form of energy is replenished and cost-effective in terms of production and maintenance. However, RES, such as solar and wind energies, is intermittent; this is one of the drawbacks of its usage. In order to overcome this limitation, studies have been undertaken to forecast its availability and power output. The current trending method of forecasting availability and the power generated by the RES is the artificial intelligence (AI) method. However, with all its potential, traditional AI, such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and many more, does not have it all. Because of this, metaheuristic algorithms are being explored as optimization techniques to increase the performance accuracy of these AI forecasting methods and overcome some challenges of traditional AI models. This study presents an insightful survey of AI (traditional AI and metaheuristic) methods explored to forecast the availability of solar and wind renewable energy systems. A survey of the existing surveyed literature was presented. The taxonomy of the explored AI in RES was formulated, and the theoretical backgrounds of some of the traditional AI algorithms were presented. Also, the various forms of metaheuristic algorithms and the improved versions applied to optimize classical AI methods to forecast RES systems' availability and power output were surveyed. A conceptual framework of the hybrid AI application in RES was formulated. Finally, the survey discussion, insight, challenges of the existing models and future directions were presented. [ABSTRACT FROM AUTHOR]

Online social networks can be used for mental healthcare monitoring using Artificial Intelligence and Machine Learning techniques for detecting various mental health disorders and corresponding risk assessment. Recent research in this domain has primarily been focused on leveraging deep neural networks and various Transformer based Large Language Models, which have now become state-of-the-art for most natural language processing and computational linguistic tasks due to their unmatched prediction accuracy. Unlike conventional machine learning algorithms, these deep neural networks are black box architectures, where it is difficult to interpret and explain their predicted outcome. However, a black box classification outcome is insufficient for healthcare applications. Such systems will not be widely adopted and trusted by healthcare practitioners if they are not able to understand and explain the reasoning behind the predicted decisions made by an AI and ML based healthcare diagnostic system. The key objective of our research is to demonstrate the applications of model agnostic, post hoc surrogate XAI techniques for providing explainability to classification decisions of pretrained LLMs (Transformers) based mental healthcare diagnostic systems fine-tuned (or trained) to detect depressive and suicidal behavior using UGC from online social networks. For this, we have used the two most recent and popular techniques, SHAP and LIME. We have conducted extensive and in-depth experiments with four datasets and six pretrained LLMs, three of which have already been domain-adapted using mental health related datasets. We have also performed Few Shot Learning experiments with these three pretrained mental health domain-adapted LLMs. The results of qualitative and descriptive data analysis in this paper demonstrate that in order to build a comprehensive understanding of a person's psychological state, emotion, and behavior and to discover the causes, symptoms, and triggers of mental health issues, it is essential to utilize e X pl AI nable (XAI) techniques with Transformer based LLMs (supervised). Alternatively, Transformer based unsupervised topic modeling technique BERTopic may be used for mental health risk monitoring and cause or symptom extraction when supervised training of LLMs is not feasible due to dataset annotation or availability challenges. [ABSTRACT FROM AUTHOR]

Robustness has become an important consideration in deep learning. With the help of explainable AI, mismatches between an explained model's decision strategy and the user's domain knowledge (e.g. Clever Hans effects) have been identified as a starting point for improving faulty models. However, it is less clear what to do when the user and the explanation agree. In this paper, we demonstrate that acceptance of explanations by the user is not a guarantee for a machine learning model to be robust against Clever Hans effects, which may remain undetected. Such hidden flaws of the model can nevertheless be mitigated, and we demonstrate this by contributing a new method, Explanation-Guided Exposure Minimization (EGEM), that preemptively prunes variations in the ML model that have not been the subject of positive explanation feedback. Experiments demonstrate that our approach leads to models that strongly reduce their reliance on hidden Clever Hans strategies, and consequently achieve higher accuracy on new data. • Explainable AI may not detect all Clever Hans effects in practical ML scenarios. • Hidden Clever Hans effects can be mitigated using Exposure Minimization. • We propose a solution in the form of a soft-pruning scheme on network activations. • We demonstrate the practical benefits of our approach on image and text data. [ABSTRACT FROM AUTHOR]

As an emerging digital product, artificial intelligence models face the risk of being modified. Malicious tampering will severely damage model functions, which is different from normal modifications. In addition, tampering localization for targeted repair can effectively reduce the cost. Therefore, it is crucial to achieve model content authentication and locate the tampering location. We proposed a novel semi-fragile neural network watermarking method in this paper to address these issues. Specifically, with the precondition of maintaining model performance, we proposed a method that generates a set of semi-fragile samples for a model to achieve the content authentication and tampering localization. The experiment results show that the content authentication of the model can be achieved by analyzing the output results of the model for the semi-fragile samples. When the model is processed normally, the output results are consistent with the expected label, while when the model is maliciously tampered with, the model produces unstable output. Furthermore, the tamper localization of the model can be further achieved through the information hidden in the semi-fragile samples, resulting in an average accuracy of more than 99.42%. In addition, our method is also effective for other deep neural networks. [ABSTRACT FROM AUTHOR]

As the main research content of artificial intelligence, the artificial neural network has been widely concerned because of its excellent performance in the fields such as computer vision and natural language processing since it was proposed in the 1940s. The neuron model of the traditional neural network was proposed by McCulloch and Pitts in 1943 (MP neurons), But MP neurons is too simple to representing biological neurons. Based on this, this paper studies the attention mechanism and proposes vectorized neuron and its activation function. Firstly, we propose vectorized neurons, then use the attention mechanism to dynamically generate connection weights between vectorized neurons. Nextly, we construct a new type of neural network with vectorized neurons, which we called neural functional group (NFG). Finally, we tested the proposed neural functional group model on two tasks: image classifcation and few-shot learning. The vectorized neuron can be conditionally activated through its activation function. Besides, the vectorized neuron has the potential of representing complex biological neurons, which is difficult for MP neuron. The experimental results show that it can achieve higher accuracy with fewer parameters than convolutional neural networks (CNN) and capsule networks in image classication task; it also competitive to CNN based feature extractor in few-shot learning task. [ABSTRACT FROM AUTHOR]