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Fruits and vegetables are major sources of nutrients for the majority of the population across the globe. With the rapid increase in population, the objectives of the future agro-industry are to reduce product loss while increasing product quality and productivity considerably. Consequently, farmers need to be assisted with cutting-edge technologies for sustainable, eco-friendly, and efficient farming. Smart farming for early disease recognition and control is the current hot-spot research objective in the fruitage domain. The precision agriculture era has been revolutionized by federating cutting-edge technologies like machine learning, deep learning, and, the Internet-of-Things. However, the existing studies focused on the impact of individual technology on single or multiple cultivars of edible fruits or vegetables. Limited areas of the fruitage disease remain explored, necessitating further investigation into the research gaps and challenges identified for implementing the smart practices in real-field farmlands. In this paper, a comprehensive survey of recent advancements in fruit and vegetable disease identification and classification is presented. The technology-wise state-of-the-art findings, gaps, challenges, and future opportunities for fruitage disease recognition have been presented, covering 99 research articles. Moreover, the corpus of publicly available fruit and vegetable datasets has been investigated, with the existing gaps, improvements, and future requirements. The research paper concludes with challenges and a future outlook that promises to be a very significant and valuable resource for researchers working in the area of agronomic disease monitoring. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is a crucial detection technique extensively employed in various contexts demanding accurate identification of human actions. Currently, mainstream HAR approaches frequently rely on sequential data generated by wearable sensors, with a primary focus on extracting feature representations that align with various human activities. Nevertheless, complex and diverse human activities encompass various kinds of features and substantial variations in the features emphasized across different activities, making it challenging for current artificial intelligence methods to achieve accurate recognition. To address this challenge, this paper introduces a network framework called the Multi-Feature Combining Attention Neural Network (MFCANN). This framework incorporates diverse feature extraction, along with both local and global feature attention mechanisms. It is composed of stacked Multi-Feature Combining Attention Blocks (MFCAB) designed by us. In contrast to traditional convolutional methods, MFCAB parallelly stacks various convolutional components, enabling the extraction of a broader range of features from human activity data. Additionally, we propose the Intra-Module Attention Block (Intra-MAB) and the Inter-Module Attention Block (Inter-MAB), which simultaneously focus on local fine-grained features within module feature maps and global distinguishing features across module feature maps. This aims to achieve more targeted feature learning, reinforcing the network's ability to distinguish between different human activities. Abundant experimental results demonstrate that the proposed MFCANN in this paper outperforms current mainstream deep learning algorithms in HAR tasks, achieving recognition accuracies of 0.9813, 0.9324, and 0.9930 on the UCI-HAR, USC-HAD, and RealWorld datasets respectively. We release our code at https://github.com/RangerHeart/MFCANN. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is a crucial detection technique extensively employed in various contexts demanding accurate identification of human actions. Currently, mainstream HAR approaches frequently rely on sequential data generated by wearable sensors, with a primary focus on extracting feature representations that align with various human activities. Nevertheless, complex and diverse human activities encompass various kinds of features and substantial variations in the features emphasized across different activities, making it challenging for current artificial intelligence methods to achieve accurate recognition. To address this challenge, this paper introduces a network framework called the Multi-Feature Combining Attention Neural Network (MFCANN). This framework incorporates diverse feature extraction, along with both local and global feature attention mechanisms. It is composed of stacked Multi-Feature Combining Attention Blocks (MFCAB) designed by us. In contrast to traditional convolutional methods, MFCAB parallelly stacks various convolutional components, enabling the extraction of a broader range of features from human activity data. Additionally, we propose the Intra-Module Attention Block (Intra-MAB) and the Inter-Module Attention Block (Inter-MAB), which simultaneously focus on local fine-grained features within module feature maps and global distinguishing features across module feature maps. This aims to achieve more targeted feature learning, reinforcing the network's ability to distinguish between different human activities. Abundant experimental results demonstrate that the proposed MFCANN in this paper outperforms current mainstream deep learning algorithms in HAR tasks, achieving recognition accuracies of 0.9813, 0.9324, and 0.9930 on the UCI-HAR, USC-HAD, and RealWorld datasets respectively. We release our code at https://github.com/RangerHeart/MFCANN. [ABSTRACT FROM AUTHOR]

The field of comic recognition (CR) has gained significant attention in recent years due to the growing popularity of digital comics. CR involves the automatic identification and extraction of comic panels, speech bubbles, and text from digital comic images, which has numerous applications in the digital media industry. The objective of this review paper is to conduct a systematic literature analysis of the current state of the art in CR techniques and methods. The authors strive to present all major discoveries related to representation models, techniques, tools, datasets, and comparative evaluations of recognition models. The present study utilizes the conventional Systematic Literature Review (SLR) approach and covers a wide range of 72 articles published in 17 prominent journals and 29 notable conferences and workshops. The available literature on recognition models and techniques is broadly categorized into three groups: computer vision (CV) technique being the most commonly used (75%) in the selected studies, followed by non-CV techniques (13.88%) and hybrid techniques (11.11%). Among the widely used datasets in the selected studies, Manga 109 and Ebdtheque are recognized by 50% of them. Within the field of CR, authors such as Jean-Christophe Burie, Christophe Rigaud, Jean-Marc Ogier, and Zhi Tang have received significant attention. The paper summarizes the findings of the CR research and highlights the need for standardization of accuracy norms and datasets. It emphasizes the importance of exploring and developing hybrid techniques for efficient CR tasks. [ABSTRACT FROM AUTHOR]

Industrial products typically lack defects in smart manufacturing systems, which leads to an extremely imbalanced task of recognizing surface defects. With this imbalanced sample distribution, machine learning and deep learning algorithms preferentially learn features from the majority classes, potentially leading to inaccurate results. Addressing the issue of sample imbalance has thus emerged as a critical area of research within the field of industrial intelligent manufacturing. This paper discusses the imbalanced sample problem of industrial product surface defect detection algorithms, and proposes the existence of "four imbalances and two uncertainties". It also summarizes the industrial product surface dataset and innovatively adds the imbalance rate comparison to the dataset. In this study, data re-sampling, data expansion, feature extraction and identification, and re-weighting of category weights are elaborated at the level of data and algorithm respectively. Additionally, the paper explores prospective directions for future research, including supervised and unsupervised learning, transfer learning, anomaly detection, quality prediction, and future challenges. It is hoped to lay a solid foundation for the more far-reaching development of smart manufacturing and surface defect detection methods. And provide some directions for the research of sample imbalance and long-tail problems. [ABSTRACT FROM AUTHOR]

The fault management process is facilitated by equipping power distribution systems with automated devices, especially remote-controlled switches (RCSs). Although RCS plays a key role in improving the system's reliability, it imposes significant investment, installation, and maintenance costs. Hence, RCSs should be optimally located in distribution feeders for the highest profit. In previous works, reliability-oriented mathematical optimization models have been formulated to reach this goal. However, the number of test solutions exponentially grows with the problem size to find the globally optimal solution. This paper uses machine learning to propose a scalable and easy-to-implement model for optimal switch placement in real power distribution systems. At first, the features of candidate points for installing RCSs are introduced. Then, a learning model is applied to deeply explore the relationship between these features and optimal locations for installing RCSs. After training, the learning-based surrogate model directly determines the optimal RCS placement strategy in real power distribution systems by leveraging knowledge gained from past experiences. Simulation results demonstrate that the proposed surrogate model is approximately 29 times faster than the integer programming-based mathematical model, without a significant loss of accuracy, when implemented on a modified 11 kV network connected to Bus 4 of the Roy Billiton test system. This paper also employs explainable artificial intelligence (XAI) tools to select the most important features, where the Hamming loss is decreased by approximately 5%. [ABSTRACT FROM AUTHOR]

For shafting rotation equipment, in the fault diagnosis based on vibration analysis, the sampling signal is a waveform of a short moment, which is quasi-static information at the corresponding moment. A single quasi-static piece of information does not necessarily contain obvious fault information, however, in a longer period much larger than the sampling period, the time series information composed of multiple quasi-static combinations may well-characterize the fault. Traditional deep learning algorithms often focus on fault features in quasi-static information, ignoring time series features, which results in low diagnostic accuracy. To solve the above problems, this paper firstly presents a fault diagnosis framework of time series and deep fusion network (TDFN). Then, a method to extract time series information and quasi-static information based on expert experience knowledge is proposed, and finally studies the method of using this framework to train the network and fuse the two types of features. Different from existing deep learning methods, TDFN includes time series blocks, depth blocks and fusion blocks. This paper uses laboratory data and industrial rotor data for verification, the results show that the overall accuracy of TDFN reached 93.8% and it does not experience a drop in accuracy after fusion. The diagnosis accuracy is higher than traditional networks, providing guiding suggestions for the management and operation of on-site industrial equipment. [ABSTRACT FROM AUTHOR]

The aging of transformer oil–paper insulation (OPI) contributes significantly to transformer failure and reduced service life. Therefore, reliable and precise evaluation of the aging status of transformer insulation condition is crucial in averting transformer failure. This article proposes a novel deep learning-based aging condition assessment technique of transformers by combining FDS (Frequency domain spectroscopy) and squeeze-and-excitation-enabled convolutional neural network (SE-CNN). An intrinsic product-based transformation using a Gramian angular field is employed to dimensionally expand the 1-D FDS signals acquired from OPI samples into 2D images. Each feature extraction channel in the SE-CNN-based aging diagnosis framework is assigned a specific weight, enabling the model to concentrate on the most relevant features while discarding the rest. Furthermore, the effect of moisture variation on the model is also considered under three different conditions to ensure the consequence of compensation for the effect of moisture on FDS. A comparison with traditional machine learning models has also been made to ensure the viability of the proposed methodology. Additionally, the model is interpreted by creating explanation weight plots of the feature coefficients using a local interpretable model-agnostic explanations (LIME) method in Python. The findings demonstrate that the novel aging diagnosis framework visually assesses the transformer's OPI system with the highest accuracy of 99.12% in the test set. Furthermore, it facilitates the automatic extraction of deep aging features, thus providing better generalization and convergence capability. [ABSTRACT FROM AUTHOR]

Data-driven prognostics of systems exploit sensor measurements to predict the degradation evolution and anticipate failures, corresponding to the estimation of the remaining useful life (RUL). This task uses feature engineering to build prognostic indicators (HI) and machine learning (ML) to estimate the RUL. However, high variability in data coming from similar systems operating under different conditions negatively affects the RUL performance. Hence, this paper presents a new methodology that combines feature and ML engineering methods to provide an explainable RUL prediction. The key contributions lie in constructing efficient prognostic indicators that isolate distinct profile trajectories, enabling adaptive RUL extraction for each system. An ensemble of heterogeneous ML predictors is also trained using these indicators and RUL trajectories, effectively addressing variability issues and enhancing RUL performance. The proposed methodology is rigorously validated using NASA-provided turbofan engine data (C-MAPSS), demonstrating its performance, compared to state-of-the-art results, with improved score and accuracy of prediction. • Explainability of RUL calculation considering the variability of degradation trajectories. • Enhancing the adaptability of predictions to different operating conditions. • Handling epistemic uncertainties in RUL estimation. • High accuracy and precision of the estimated remaining useful life. [ABSTRACT FROM AUTHOR]

In the face of uncontrolled offensive content on social media, automated detection emerges as a critical need. This paper tackles this challenge by proposing a novel approach for identifying offensive language in multilingual, code-mixed, and script-mixed settings. The study presents a novel multilingual hybrid dataset constructed by merging diverse monolingual and bilingual resources. Further, we systematically evaluate the impact of input representations (Word2Vec, Global Vectors for Word Representation (or GloVe), Bidirectional Encoder Representations from Transformers (or BERT), and uniform initialization) and deep learning models (Convolutional Neural Network (or CNN), Bidirectional Long Short Term Memory (or Bi-LSTM), Bi-LSTM-Attention, and fine-tuned BERT) on detection accuracy. Our comprehensive experiments on a dataset of 42,560 social media comments from five languages (English, Hindi, German, Tamil, and Malayalam) reveal the superiority of fine-tuned BERT. Notably, it achieves a macro average F 1 -score of 0.79 for monolingual tasks and an impressive 0.86 for code-mixed and script-mixed tasks. These findings significantly advance offensive language detection methodologies and shed light on the complex dynamics of multilingual social media, paving the way for more inclusive and safer online communities. [ABSTRACT FROM AUTHOR]

Efficient forecasting of ship order books holds immense significance in the maritime industry, enabling companies to optimize their operations, allocate resources effectively, and make informed decisions. However, volatile characteristics within historical order books pose challenges in achieving reliable, intelligent, and precise forecasts. This paper presents a novel ensemble deep random vector functional link (edRVFL) algorithm to anticipate future ship order book dynamics. The edRVFL leverages deep feature extraction and ensemble learning to enhance forecasting performance. To further elevate its capabilities, we introduce a discontinuous and parsimonious embedding strategy, which deviates from the conventional dense collection of continuous time steps used in vanilla edRVFL. This parsimonious embedding approach limits the model's complexity and boosts its generalization ability. We extensively evaluate the proposed method using ship order book data, and comparative studies demonstrate its superiority over alternative approaches. Our proposed edRVFL offers a promising solution for accurate and efficient ship order book forecasting, making it a valuable asset in the maritime industry's decision-making processes. The source codes utilized in this research are openly available on GitHub at the following link: https://github.com/crkkkaa/Ship-order-book-forecasting-by-an-ensemble-deep-parsimonious-random-vector-functional-link-network-. • A novel ensemble deep RVFL to anticipate ship order book dynamics is proposed. • We propose a parsimonious embedding algorithm to optimize temporal patterns. • The direct links ablation study is conducted for comparison. [ABSTRACT FROM AUTHOR]

In the face of uncontrolled offensive content on social media, automated detection emerges as a critical need. This paper tackles this challenge by proposing a novel approach for identifying offensive language in multilingual, code-mixed, and script-mixed settings. The study presents a novel multilingual hybrid dataset constructed by merging diverse monolingual and bilingual resources. Further, we systematically evaluate the impact of input representations (Word2Vec, Global Vectors for Word Representation (or GloVe), Bidirectional Encoder Representations from Transformers (or BERT), and uniform initialization) and deep learning models (Convolutional Neural Network (or CNN), Bidirectional Long Short Term Memory (or Bi-LSTM), Bi-LSTM-Attention, and fine-tuned BERT) on detection accuracy. Our comprehensive experiments on a dataset of 42,560 social media comments from five languages (English, Hindi, German, Tamil, and Malayalam) reveal the superiority of fine-tuned BERT. Notably, it achieves a macro average F 1 -score of 0.79 for monolingual tasks and an impressive 0.86 for code-mixed and script-mixed tasks. These findings significantly advance offensive language detection methodologies and shed light on the complex dynamics of multilingual social media, paving the way for more inclusive and safer online communities. [ABSTRACT FROM AUTHOR]

Efficient forecasting of ship order books holds immense significance in the maritime industry, enabling companies to optimize their operations, allocate resources effectively, and make informed decisions. However, volatile characteristics within historical order books pose challenges in achieving reliable, intelligent, and precise forecasts. This paper presents a novel ensemble deep random vector functional link (edRVFL) algorithm to anticipate future ship order book dynamics. The edRVFL leverages deep feature extraction and ensemble learning to enhance forecasting performance. To further elevate its capabilities, we introduce a discontinuous and parsimonious embedding strategy, which deviates from the conventional dense collection of continuous time steps used in vanilla edRVFL. This parsimonious embedding approach limits the model's complexity and boosts its generalization ability. We extensively evaluate the proposed method using ship order book data, and comparative studies demonstrate its superiority over alternative approaches. Our proposed edRVFL offers a promising solution for accurate and efficient ship order book forecasting, making it a valuable asset in the maritime industry's decision-making processes. The source codes utilized in this research are openly available on GitHub at the following link: https://github.com/crkkkaa/Ship-order-book-forecasting-by-an-ensemble-deep-parsimonious-random-vector-functional-link-network-. • A novel ensemble deep RVFL to anticipate ship order book dynamics is proposed. • We propose a parsimonious embedding algorithm to optimize temporal patterns. • The direct links ablation study is conducted for comparison. [ABSTRACT FROM AUTHOR]

This paper for the first time treats the interpretation of electrochemical noise time-frequency spectra as an image classification problem. It investigates the application of a convolutional neural network (CNN) for deep learning image classification of electrochemical noise time-frequency transient information. Representative slices of these spectra were selected by our transient analysis technique and served as input images for the CNN. Corrosion data from two types of pitting corrosion processes serve as test cases: AISI304 and AA2024-T3 immersed in a 0.01M HCl and 0.1M NaCl solution between 0 and 1ks after immersion, respectively. Continuous wavelet transform (CWT) spectra and modulus maxima (MM) are used to train the CNN, either individually or in a combined form. The classification accuracy of the CNN trained with the combined dataset is 0.97 and with the two individual datasets 0.72 (only CWT spectrum) and 0.84 (only MM). The ability to additionally classify a more progressed form of pitting corrosion of AA2024-T3 between 9 and 10ks after immersion indicates that the proposed method is sufficiently robust using combined datasets with CWT spectra and MM. The pitting processes can effectively be detected and classified by the proposed method. The most important contribution of the present work is to introduce a novel procedure that decreases the classical need for large amounts of raw data for training and validation purposes, while still achieving a satisfactory classification robustness. A relatively small number of individual signals thereby generates a multitude of input images that still contain all relevant kinetic information about the underlying chemo-physical process. [Display omitted] • Electrochemical noise interpretation is treated as an image classification problem. • Continuous wavelet transform spectra are divided into single transient spectra. • A small number of individual signals generates a multitude of input images. • Amount of raw data needed for training the convolutional neural network is reduced. • Modulus maxima increase the classification accuracy of pitting corrosion. [ABSTRACT FROM AUTHOR]

In the aftermath of natural disasters, efficient waste collection becomes a crucial challenge, owing to the dynamic and unpredictable nature of waste generation, coupled with resource constraints. This paper presents an innovative hybrid methodology that synergizes Long Short-Term Memory (LSTM) machine learning with Differential Evolution (DE) optimisation to augment waste collection efforts post-disaster. The approach leverages real-time data to forecast waste generation with high accuracy, facilitating the development of adaptable waste collection strategies. Our approach is designed to dynamically update collection plans in response to evolving scenarios, ensuring timely and effective decision-making. Field tests conducted in an earthquake-prone city have demonstrated the superior performance of this method in managing waste collection under fluctuating conditions. Moreover, an in-depth sensitivity analysis helps in identifying key areas for improvement. Significantly outperforming traditional models, this method offers substantial time savings and equips disaster response teams with a robust tool for addressing the challenges of waste collection. [ABSTRACT FROM AUTHOR]

Implementing key engineering solutions to optimise the operation of energy industries requires daily electricity demand forecasting and including uncertainty, to promote markets insight analysis as part of their strategic planning, regulating and supplying electricity to consumers. This paper proposes hybrid artificial intelligence models combining convolutional neural networks (CNN) as a feature extraction algorithm with extreme learning machines (ELM) as a framework to predict electricity demand with confidence intervals generated by Kernel Density Estimation (KDE) approaches. In order to develop CELM-KDE model, time-lagged series of daily electricity demand with local climate variables based on the air temperature, atmospheric vapour pressure, evaporation, solar radiation, humidity and sea level pressures are used to train the proposed CELM-KDE hybrid model. In order to fully evaluate the newly developed model from a point-based, as well as a probabilistic prediction strategy, the observed and predicted electricity demand as well as the probability distribution of errors are analysed using KDE method that operates without prior data distribution assumptions. Based on observed and predicted electricity demand and the relevant probabilistic confidence intervals generated by the CELM-KDE model, the final results show that the proposed method attains significantly better probability interval predictions than traditionally-used point-based models. The proposed CELM-KDE model is demonstrated to be highly effective in providing a comprehensive coverage of predicted errors, as well as providing greater insights into the average bandwidth and detailed predicted electricity demand in the testing stage. The results also indicate that the proposed hybrid model is a reliable decision support tool to develop engineering solutions in area of energy modelling, monitoring and forecasting, which could potentially be useful to the industry policymakers. We show that the point-and probabilistic-based electricity demand predictive models can be employed as an effective tool to improve accuracy of forecasting and provision of insights for national electricity markets and key energy industry stakeholder application tools. [Display omitted] • A hybrid deep learning model for daily electricity demand prediction is proposed. • A Kernel Density Estimation is applied to derive probabilistic prediction intervals. • The algorithm quantifies uncertainties in point-based electricity demand predictions. • The proposed model outperforms deep learning models using statistical uncertainty indicators. [ABSTRACT FROM AUTHOR]

Conventional malware detection approaches have the overhead of feature extraction, the requirement of domain experts, and are time-consuming and resource-intensive. Learning-based approaches are the mainstay of malware detection as they overcome most of these challenges by significantly improving the detection effectiveness and providing a low false positive rate. The exponential growth of malware variants and first-time-appeared malware, which includes polymorphic and zero-day attacks, are some of the significant challenges to learning-based malware detectors. These challenges have catastrophic impacts on the detection effectiveness of these learning-based malware detectors. This paper proposes a novel deep learning-based framework to detect first-time-appeared malware effectively and efficiently by providing better performance than conventional malware detection approaches. First, it translates and visualises each Windows portable executable (PE) file into a coloured image to eliminate the overhead of feature extraction and the need for domain experts to analyse the features. In the subsequent step, a fine-tuned deep learning model is used to extract the deep features from the last fully connected layer. The step has reduced the cost of training required by the deep learning models if used for end-to-end classification. The third step selects the most important and influential features through a powerful feature selection algorithm. The most important features are then fed to a one-class classifier for final detection. With the one-class classifier, an enclosed boundary around the features of benign data is constructed. Anything outside the boundary is declared as an anomaly/malicious. It has enhanced the framework's ability to detect evolving, unseen, polymorphic, and zero-day attacks, as well as reducing the problem of overfitting. The detection effectiveness of the proposed framework is validated with state-of-the-art deep learning models and conventional approaches. The proposed framework has outperformed with an accuracy of 99.30% on the Malimg dataset. The Wilcoxon signed-rank test is used to validate the statistical significance of the proposed framework. It is evident from the results that the proposed framework is effective and can be used in the defence industry, resulting in more powerful and robust solutions against zero-day and polymorphic attacks. [Display omitted] • A novel approach of combining deep learning and machine learning is proposed. First, deep learning is used to extract deep features. The most influential and meticulous features are selected in the subsequent steps to train the machine learning classifier for final detection. The proposed framework eliminates the need for human efforts for reverse engineering tasks. • The proposed framework consists of four steps. In the first step, all PEs are transformed into coloured images. The second step used a deep learning model to extract the deep features. The subsequent step selects the most important features. Finally, the lightweight and most influential features are sent to the final machine learning classifier for final malware detection. • We demonstrate that the proposed framework is lightweight, resilient, efficient and cost-effective. An in-depth analysis is performed to validate the detection effectiveness and generalisation of the proposed framework on multiple datasets. Our results demonstrate that the proposed framework outperformed conventional and state-of-the-art malware detection approaches. [ABSTRACT FROM AUTHOR]

Conventionally, the extraction of hand-crafted acoustic features has been separated from the task of establishing robust machine-learning models in speech processing. The manual approach of feature engineering is both time-consuming and necessitates specialist knowledge, posing significant hindrances. Moreover, the resulting features may not be ideal for the desired application. The speech community has adopted raw waveform modeling to enhance performance. These techniques learn an optimized representation of the input automatically. With deep learning (DL) advancements, raw waveform modeling has become valuable for tasks like classification and prediction. The primary aim of this survey is to offer valuable insights and fills a gap in the existing literature by providing a comprehensive review of the state-of-the-art in speech and speaker recognition using raw waveform modeling for both adult and children's speech. The article covers papers from 2013–2023 and is the first to review both adult and children's databases. The article focuses on the advantages of raw waveform models. It presents essential concepts and techniques while discussing the challenges and limitations of using raw waveforms for speech and speaker recognition in both adult and children's speech. The article also evaluates recent progress in DL architectures such as SincNet, ResNet, and RawNet, and outlines future research directions in the field. [ABSTRACT FROM AUTHOR]

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance. [ABSTRACT FROM AUTHOR]

Currently, the fusion of surface Electromyography (EMG) and deep learning is gradually showing immense potential in the research of Lower Limb Motion Intention Recognition (LLMIR). Nevertheless, most deep learning algorithms have poor interpretability without special design or the help of other post-hoc analysis tools, as well as unsatisfactory performance in cross-subject prediction. Hence, this paper presents a novel Interpretable Dual-Branch EMG Network (IDB-EMGNet), in which one branch is dedicated to lower limb motion recognition, and the other is able to predict knee joint angles in advance. The shallow feature extraction module of IDB-EMGNet is constructed using an ante-hoc interpretable SincNet technique, which enables the detection of the spectral range of EMG used for the LLMIR task. An improved bottleneck block with shuffle attention is designed for deep feature extraction, which enhances model performance with only a little increase in complexity. The performance of IDB-EMGNet in both intra-subject and inter-subject scenarios is investigated, where the latter integrates the transfer learning technique. Specifically, by conducting model pre-training on source-domain subjects and transferring the learned knowledge to target-domain subjects, satisfactory performance can be achieved even with less computing resource. Experimental results on two publicly available datasets indicate that the proposed approach exhibits superior applicability to both normal subjects and knee-pathology patients, showing a promising prospect in the controller design of human-robot collaborative exoskeletons. • A novel IDB-EMGNet is proposed for simultaneous discrete and continuous lower limb motion intention recognition. • Interpretability of IDB-EMGNet is enhanced via an ante-hoc SincConv technique. • An IB-Neck block is designed to improve model prediction performance. • Transfer learning technique is utilized to improve model generalization in inter-subject prediction. • The proposed approach performs well for both normal subjects and knee-pathology patients. [ABSTRACT FROM AUTHOR]

Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goal to investigate model generalizability. In the first stage, we developed and compared different Deep Learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models and CNN-LSTM to predict indoor air temperature in a multi-step time horizon. In the second stage, we implemented a Transfer Learning (TL) process, which aims to improve the prediction performance on a new set of buildings (targets), exploiting the knowledge of related or similar buildings (sources). Different TL strategies and source model identification methods were investigated. The study showed that the CNN-LSTM performed the best among the architectures compared, with an average Mean Absolute Error (MAE) of 0.26 °C for one-hour-ahead (twelve 5-min future steps) predictions. Furthermore, the results showed that freezing the LSTM layer and fine-tuning the other layers of the CNN-LSTM achieved the best performance among four TL strategies, which further improved the performance with respect to a machine learning approach by 10%, and proving the effectiveness and generalizability of the proposed approach. A comparison of three different source model identification methods showed that randomly selecting source models constrained by similar building characteristics can provide good TL performance while retaining simplicity comparing with other quantitative source identification methods. • Investigated deep transfer learning techniques for residential thermal dynamics using smart thermostat data. • Compared diverse neural network architectures for indoor temperature prediction. • Explored transfer learning to enhance model accuracy. • Evaluated four transfer learning methods and three source model identification approaches. • Achieved high accuracy and generalizability with the proposed deep transfer learning approach. [ABSTRACT FROM AUTHOR]

Traditional machine learning methods require the assumption that training and test data are drawn from the same distribution, which proves challenging in real-world applications. Moreover, deep learning models require a substantial amount of labeled data for training in classification tasks and limited samples may lead to overfitting. In many real-world scenarios, there is an insufficient supply of labeled samples within the target domain for learning. Transfer learning offers an effective solution, allowing knowledge from a source domain to be transferred to a target domain. Additionally, data augmentation enhances model generalization by increasing data samples, particularly beneficial when dealing with limited target domain data. In this paper, we synergistically enhance the model's performance on classification tasks by integrating transfer learning techniques with a data augmentation strategy. By conducting numerous experiments across various datasets, we verified the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

In recent years, consortium blockchains have attracted considerable interest from the business communities and academia. To satisfy specific application requirements, appropriately configuring or adjusting the parameters is essential but challenging for developers when building the consortium blockchain. In this paper, the parameter configuration and adjustment for the consortium blockchain is transformed as a multi-objective optimization problem, and a novel explainable deep reinforcement learning (DRL) algorithm is proposed to solve the problem. On considering that existing DRL algorithms cannot be directly used in the consortium blockchain as they suffer from lacking of explainability, a causal model for configuring and adjusting the consortium blockchain's parameters is proposed and integrated into the DRL algorithm. The causal model can be used to derive causal explanations of the DRL algorithm to increase its trustworthiness. Furthermore, the causal model-based DRL (C-DRL) algorithm can perform causal inference before taking action to eliminate unreasonable exploration and improve the DRL algorithm's performance. The experimental results demonstrate the proposed algorithm provides the consortium blockchain with adaptive parameter configuration and adjustment to achieve sustainable high performance and security. Furthermore, the proposed algorithm increases the convergence speed of the vanilla algorithm by 49.3% and is more trustworthy than the vanilla DRL algorithm. • To achieve sustainable high performance and security, we transform the parameter configuration and adjustment for the consortium blockchain as a multi-objective optimization problem and provide a formal representation of the problem. • We propose a causal model of consortium blockchain's parameters using the structural causal model (SCM) formalism. • We propose a causal model-based DRL (C-DRL) algorithm to solve the multi-objective optimization problem. The C-DRL algorithm is equipped with the causal model of consortium blockchain's parameters. • The agent can use the causal model to derive the causal explanation for its action to provide trustworthiness. Furthermore, the agent with the causal model can utilize causal inference to eliminate unreasonable exploration and improve the convergence speed of the current DRL algorithm. • Extensive experiments are conducted to validate our proposal. The simulation results show that the agent running the C-DRL algorithm has more reasonable behavior than that running the model-free DRL algorithm. Moreover, the C-DRL algorithm converges faster than the model-free DRL algorithm. [ABSTRACT FROM AUTHOR]

Safety and passengers' comfort affect directly the reliability and availability of trains. Therefore, appropriate monitoring of the variables influencing those two concepts is mandatory for good operation and maintenance costs reduction. Moreover, operators and rail manufacturers are evolving to a condition-based maintenance strategy (CBM). For this purpose, machine learning is being used to predict early main component's failures, avoiding a loss of reliability or availability during commercial operation. From the passengers' point of view, punctuality and comfortability are two of the most relevant factors when evaluating a railway transport service. Comfort is mainly based on the availability of all services in the train, but it is also related to the accelerations received through the car body to the passengers. More precisely, the lateral car body acceleration (Ayc variable in this paper) is a key variable related to comfort perception and it is therefore important to be monitored and controlled. Rolling, suspension, and damping elements must be kept in perfect conditions during operation to achieve a smooth train movement. In this paper, an optimized model based on Long Short Term Memory networks (LSTM) is designed and trained to predict lateral car body accelerations using only the train accelerometers and with no additional sensoring for this specific experiment. This model will be compared with artificial neural networks (ANN) models to evaluate the relevance of time dependency for this purpose. The optimal time frame to group data is a key characteristic of the neural network training process and is also determined in this paper. The model is trained with data corresponding to the correct dynamic behavior of the train. To test the reliability of this method, a real case of excessive train movement is tested. In these situations, an alert could be sent to the operator or the maintainer for a not-programmed maintenance inspection at the depot (predictive maintenance), avoiding future passenger complaints for the loss of comfort or, in extreme cases, speed limitations due to safety reasons for excessive accelerations. The main contributions of this work are the following: - A LSTM-based neural network, predicting lateral car body accelerations achieving better results than a plain ANN for this dataset: 0.0335 m/s2 MAE and a 12% gain on extreme axles. - An optimization of the time frame to group the measurements in this algorithm that is considering as an input the time variable: 5 s. - A real use case of the model in which monitoring the deviation would have helped to take an early maintenance adjustment on the suspension or damping elements to prevent the loss of comfort. - The p-value used to reduce the uncertainty of the results obtained in the real use case is reaching over 0.95 in the coaches with good dynamic behavior and less than 0.15 in the coaches where a dynamic problem related to comfort loss is detected. [ABSTRACT FROM AUTHOR]

Mental disorders are neural issues that influence brain cognition and social connectivity. The significant increase in mental disorders needs prompt detection for effective treatment. Psychiatric disorders can be partially diagnosed with constant counseling by using anti-depressants and other inhibitors. However, this is a laborious and also time-consuming process. This leads to investigating the computational techniques that can be applied to detect psychiatric illness in its premature phases. Various works are proposed to detect mental disorders from four data forms: neuro-images, EEG signals, textual data, and gene data. The researchers aim to investigate various models that can detect mental illness using advanced deep learning techniques. Through exploring authoritative databases, the authors gathered papers and studied classic machine learning and primary deep learning techniques for anticipating mental health issues. As the results of various models, the study also presented the difficulties and constraints that machine learning researchers have encountered while studying psychological disorders. The collected articles are categorized into four forms based on the data type used for the analysis. This paper proposes four different hybrid models to detect mental disorders in the early stages from four different data forms. Standardized metrics like area under the receiver operating characteristic curve (AUC–ROC) score and average classification accuracy (ACA) are used to evaluate the models. The proposed models outperformed all the state-of-the-art models in disorder prediction. Furthermore, the study discusses specific suggestions for future studies and development in the use of learning algorithms in the field of mental health. [Display omitted] • We aim to investigate various models which detect mental illness using DL techniques. • The collected articles are grouped into four different forms based on the data type. • We propose four different hybrid models to detect mental disorders at early stages. • To evaluate the models, the metrics accuracy, precision and recall are used. • The proposed models outperformed all the state-of-the-art models in disorder prediction. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has experienced phenomenal expansion over the past few years and has emerged as one of the most dynamic sectors of the international market. Android has become the driving force behind the rapid development of the IoT, and malware is one of the most severe concerns. Android is an open-source platform with a significant number of users. The identification of Android malware has become more critical in cybersecurity due to the meteoric rise of Android malware over the past few years and the severity of the damage it inflicts on smartphone users. Existing traditional Android malware detection methods based on machine learning have achieved encouraging performance. Still, the solution requires a massive workforce of feature engineering to build dynamic or static features and continuous learning through pre-extracted features to maintain high performance in identifying malware. Therefore, technologies for end-to-end malware detection without human interaction are necessary. In response to the rapid evolution of Android malware, a convolutional neural network that can acquire knowledge without requiring feature extraction is ideally suited. This paper offers a unique classification approach for malware based on a convolutional neural network, with feature extraction selected from the current pre-trained models DenseNet169, Xception, InceptionV3, ResNet50, and VGG16. The proposed methods have an advantage over existing detection methods due to their end-to-end learning process. Our suggested approaches convert Android APK files to binary codes and RGB images for usage as inputs to deep learning models. These models are trained and evaluated using the CICInvesAndMal2019 dataset, which consists of 333 benign and 428 malicious applications. Experiments demonstrate that the proposed approaches achieve a classification accuracy of 95.24%, 95.24%, and 95.83% for DenseNet169, InceptionV3, and VGG16, respectively, on a 761-sample dataset of malware, which is superior to other compared algorithms. Compared to previous methods, our suggested methods are more applicable to Android IoT devices since they do not require manual feature engineering and consume fewer resources. [ABSTRACT FROM AUTHOR]

State estimation is a critical process in modern power system control centers that operate under strict real-time requirements. Among its key post-processing components are bad data detection and identification, with the latter being more complex and potentially compromising the system's real-time performance. This paper proposes a novel component in the state estimation process called Anomaly Detection and Identification Module (ADIM), whose goal is to detect anomalies, or erroneous data points, prior to the state estimation process. Our work presents a deep learning-based method that exhibits a commendable level of accuracy in detecting anomalies within a continuous stream of measurements. ADIM's capabilities are demonstrated through comprehensive experiments on a set of test cases that effectively encompass various network configurations, transformer types, and load scenarios. It is shown that ADIM can detect and identify anomalies effectively, significantly reducing the need for the bad data detection component and improving overall state estimation responsiveness. Our work lays the foundation for developing a detection- and identification-based anomaly system for power system measurements. [ABSTRACT FROM AUTHOR]

In the Industry 4.0 era, a myriad of sensors of diverse nature (temperature, pressure, etc.) is spreading throughout the entire value chain of industries, being potentially exploitable for multiple purposes, such as Predictive Maintenance (PdM): the just-in-time maintenance of industrial assets, which results in reduced operating costs, increased operator safety, etc. Nowadays, industrial processes require to be highly configurable, in order to proactively adapt their operation to diverse factors such as user needs, product updates or supply chain uncertainties. This limits current Industry 4.0-PdM solutions, typically consisting of ad-hoc developments intended for specific scenarios, i.e. they are designed to operate under certain conditions (configurations, employed sensors, etc.), being unable to manage changes in their setup. This paper presents a general Deep Learning (DL) architecture, MachNet, which deals with such heterogeneity and is able to address PdM problems of a diverse nature. The modularity of the proposed architecture enables it to deal with an arbitrary number of sensors of different types, also allowing the integration of prior information (age of assets, material type, etc.), which clearly affects performance and is often neglected. In practice, our architecture effortlessly adapts to the assets' specifications and to different PdM problems. That is, MachNet becomes an architectural template that can be instantiated for a given scenario. We tested our proposal in two different PdM-related problems: Health State (HS) and Remaining-useful-Life (RuL) estimation, achieving in both cases comparable or superior performance to other state-of-the-art approaches, with the additional advantage of the generality that MachNet offers. [ABSTRACT FROM AUTHOR]

Wind speed is the main driver of wind power output, but its inherent fluctuations and deviations present significant challenges for power system security and power quality. Accurate short-term wind power forecasting is necessary to ensure the stability and seamless integration of wind energy into the grid, thereby bolstering grid reliability, optimizing power flow management, and minimizing disruptions, ultimately contributing to a more sustainable and efficient energy ecosystem. Non-stationarity is a major challenge in analyzing wind speed data, and change-point detection is essential for optimal resource allocation. This paper addresses the issue of short-term wind power forecasting for stable and effective wind energy system operation. To predict non-stationary data and detect change points, non-stationary data must first be transformed into stationary data. Discrete wavelet transform (DWT) is used to decompose wind speed traces into low- and high-frequency components for more accurate predictions using deep learning algorithms. The proposed approach utilizes a combination of DWT and recurrent neural network (RNN) models, including Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU), complementing each other to efficiently predict short-term and long-term dependencies in wind speed data and achieve the most accurate prediction results. Experimental results substantiate the consistent superiority of the proposed approach over the baseline method, showcasing a significant enhancement in prediction accuracy, with improvements spanning from 11.36% to 16.63% for MAPE, 16.67%–26.47% for RMSE, and 21.74%–30.77% for MAE. [ABSTRACT FROM AUTHOR]

Energy management systems (EMS), as enablers of more efficient energy consumption, monitor and manage appliances to help residents be more energy efficient and thus more frugal. Recent appliance detection and identification techniques for such systems rely on machine learning. However, machine learning solutions for appliance classification on existing low-frequency household metering have not yet been thoroughly investigated. In this paper, we propose CARMEL, a new approach for identifying home appliances from load monitoring in building EMS based on a new data representation technique and a new model that leverages spatio-temporal correlations in the new representation. The proposed data representation technique performs dimensionality expansion of time series that scales linearly, rather than quadratically and, together with the proposed model, outperform the state of the art image transformation models by 5 percentage points. Evaluation on 5 different low-frequency household metering datasets, considering 29 appliances in total, shows that the proposed representation and the corresponding resource-aware deep learning architecture (1) achieve an average weighted F1 score of 0.92 and (2) require only 230 labeled samples and 3x fewer epochs to transfer to new households. • A new, expressive and more efficient transformation of time series signal through sub-window imaging. • A new model that captures spatio-temporal correlations within the appliance traces. • Appliance detection improvement over the state of the art models by 5 percentage points on 5 open-source datasets. • Lower adoption barrier of such data hungry AI technologies by transferring the classification model. [ABSTRACT FROM AUTHOR]

Foreign exchange rate affects many countries' economic status and development. Therefore, it is essential to find the factors affecting the exchange rate price and make reasonable predictions. This paper proposes the novel LASSO-BiLSTM-based ensemble learning method by integrating least absolute shrinkage and selection operator (LASSO) and bidirectional long short-term memory (LSTM) to predict the USD/CNY exchange rate. First, 29 variables are selected to reflect economic activities on market and macroeconomic levels. Then, LASSO-BiLSTM-based ensemble learning approach is adopted with two steps: 1) LASSO is used to select six highly correlated variables with the exchange rate to reduce noises. 2) BiLSTM is employed to forecast the exchange rate with the six chosen variables. Last, to test the effectiveness of BiLSTM, comparisons with four deep learning algorithms, which are extreme learning machine (ELM), kernel extreme learning machine (KELM), long short-term memory (LSTM), and support vector regression (SVR), are conducted. The result shows that LASSO-BiLSTM outperforms the other models in 1-step forecast (MAE: 0.051, RMSE: 0.072, MDA: 0.777). The same conclusion applies to 3-steps and 6-steps forecasts. Overall, the proposed LASSO-BiLSTM-based ensemble learning method demonstrates high potential in time series forecasting. [ABSTRACT FROM AUTHOR]

One of the most important features of a reliable power system is its capability to supply the demand continuously. This continuous supply has been maintained by the transient stability of the system against large disturbances. The study of this type of stability is examined through different indicators. One of the common indicators to evaluate the transient stability of the power system is the well-known Critical Clearing Time (CCT) index. Conventional methods for calculating CCT have presented good accuracy, however, their computational cost is very high which makes them not suitable for real-time applications and real large-scale networks. Considering their ability to feature extraction of big data, deep neural networks can be utilized as reliable tools to cover these deficiencies. In this regard, to cover the shortcomings of the conventional methods, this paper proposes a method based on deep Convolutional Neural Networks (CNN) to estimate the CCT index in real-time power system applications. Moreover, to analyze a realistic case, nonlinear limiters of the excitation systems which have a considerable effect on transient stability index are considered in this study. Thanks to the using of several deep layers and the comprehensive established database, the accuracy of proposed method is appropriately high. Numerical studies on IEEE standard networks as well as a real case in Iran Power Grid (IPG) represents the advantages of the proposed method. • A deep-learning-based convolutional neural network is designed to estimate CCT index of power system. • Nonlinear limiters of the excitation system are taken into account. • A real case study in Iran grid is considered for simulation. • As a result, the transient stability of system is accurately determined in on-line manner. [ABSTRACT FROM AUTHOR]

Internet of Things is shaping the quality of living standard. With the rapid growth and expansion of adopting IoT-based approaches, their security represents a growing challenge for both manufacturers and consumers. There is a recent rising trend towards employing artificial intelligence approaches to enhance the security of IoT infrastructure. This survey paper focuses on reviewing recent developments in applying artificial intelligence to intrusion detection in the IoT domain. Selected articles are classified according to the applied AI algorithm. This study provides an in-depth survey highlighting the recent advances in artificial intelligence for improving the security of IoT. It summarizes and organizes the recent related research, then presents a comprehensive discussion on research challenges, open issues, and needed future research. [ABSTRACT FROM AUTHOR]

Decoupling the physical world and providing standardized service interfaces is still challenging when developing Location Based Services (LBS). This lack also hinders the possibility of developing Intelligent services on top of LBS architectures. In this paper, we propose a multi-layer Digital Twin-based architecture that aims to enable the development of machine learning-based Intelligent LBS (I-LBS) that are able to adapt, evolve, and perform Continual Learning (CL). The platform uses Digital Twins to ensure physical abstraction and provide cyber–physical knowledge to the I-LBSs, which is defined as an execution graph of operation modules. Finally, we simulated a use-case for this platform in the complex scenario of Healthcare organization and management where the I-LBS classifies allowed/not allowed trajectories of users inside a real-existing hospital scenario depending on their role in the organization. The use case is implemented as a Deep Learning-based reconstruction task of high-resolution trajectories processed by the DT architecture that also deploys the I-LBS. The platform is evaluated in terms of physical complexity and computational time on the DT side and on both a traditional machine learning setting and a replay-based CL one for the intelligence side to demonstrate the flexibility and adaptability features introduced by the components for dynamic or unseen scenarios. [ABSTRACT FROM AUTHOR]

Ship trajectory prediction based on Automatic Identification System (AIS) data has attracted increasing interest as it helps prevent collision accidents and eliminate potential navigational conflicts. Therefore, it is necessary and urgent to conduct a systematic analysis of all the prediction methods to help reveal their advantages to ensure safety at sea in different scenarios. It is particularly important and significant within the context of unmanned ships forming a new hybrid maritime traffic together with manned ships in the future. This paper aims to conduct a comparative analysis of the up-to-date ship trajectory prediction algorithms based on machine learning and deep learning methods. To do so, five classical machine learning methods (i.e., Kalman Filter, Gaussian Process Regression, Support Vector Regression, Random Forest, and Back Propagation Network) and eight deep learning methods (i.e., Recurrent Neural Networks, Long Short-Term Memory, Bi-directional Long Short-Term Memory, Gate Recurrent Unit, Bi-directional Gate Recurrent Unit, Sequence to Sequence, Spatio-Temporal Graph Convolutional Network, and Transformer) are thoroughly analysed and compared from the algorithm essence and applications to excavate their features and adaptability for manned and unmanned ships. The findings reveal the characteristics of various prediction methods and provide valuable implications for different stakeholders to guide the best-fit choice of a particular method as the solution under a specific circumstance. It also makes contributions to the extraction of the research difficulties of ship trajectory prediction and the corresponding solutions that are put forward to guide the development of future research. • Multi-perspective systematic analysis of ship trajectory prediction (STP) research. • Comparative analysis of thirteen methods to reveal their advantages and disadvantages. • Applicability of the thirteen extracted methods under difficult STP circumstances. • New solutions to STP for future hybrid traffic of manned and unmanned ships. [ABSTRACT FROM AUTHOR]

People increasingly use social media platforms to express themselves by posting visuals and texts. As a result, hate content is on the rise, necessitating practical visual caption analysis. Thus, the relationship between image and caption modalities is crucial in visual caption analysis. Contrarily, most methods combine features from the image and caption modalities using deep learning architectures with millions of parameters already trained without integrating a specialized attention module, resulting in less desirable outcomes. This paper suggests a novel multi-modal architecture for identifying hateful memetic information in response to the above observation. The proposed architecture contains a novel "multi-scale kernel attentive visual" (MSKAV) module that uses an efficient multi-branch structure to extract discriminative visual features. Additionally, MSKAV utilizes an adaptive receptive field using multi-scale kernels. MSKAV also incorporates a multi-directional visual attention module to highlight spatial regions of importance. The proposed model also contains a novel "knowledge distillation-based attentional caption" (KDAC) module. It uses a transformer-based self-attentive block to extract discriminative features from meme captions. Thorough experimentation on multi-modal hate speech benchmarks MultiOff, Hateful Memes, and MMHS150K datasets achieved accuracy scores of 0.6250, 0.8750, and 0.8078, respectively. It also reaches impressive AUC scores of 0.6557, 0.8363, and 0.7665 on the three datasets, respectively, beating SOTA multi-modal hate speech identification models. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) in medicine is beginning to alter current procedures in prevention, diagnosis, treatment, amelioration, cure of disease and other physical and mental impairments. In addition to raising concerns about public trust and ethics, advancements in this new emerging technology have also led to a lot of debate around its integration into healthcare. The objective of this work is to introduce researchers to AI and its medical applications, along with their potential pitfalls, in a comprehensive manner. This paper provides a review of current studies that have investigated how to apply AI methodologies to create a smart predictive maintenance model for the industries of the future. We begin with a brief introduction to AI and a decade's worth of its advancements across a variety of industries, including smart grids, train transportation, etc., and most recently, healthcare. In this paper, we explore the various applications of AI across various medical specialties, including radiology, dermatology, haematology, ophthalmology, etc. along with the comparative study by employing several key criteria. Finally, it highlights the challenges for large-scale integration of AI in medical systems along with a summary of the ethical, legal, trust, and future implications of AI in healthcare. [ABSTRACT FROM AUTHOR]

This paper considers the task of rice leaf disease identification using transfer-learned deep learning models. The similarity between various symptoms and the inability to distinguish diseases at glance have become a major challenge in the area. Modern deep CNN-based models have acquired state-of-the-art performance and they frequently incorporate global image as an input to learn the model. The major drawback of such a system for rice leaf disease identification is that the diseases often affect small area; CNN models trained on global images may suffer from irrelevant noisy regions. This paper proposes a Dynamic Mode Decomposition based on attention-driven preprocessing for rice leaf disease identification. The four different categories of rice leaf diseases such as bacterial blight, blast, brown spot and tungro (3416 images in total) are considered for the study. Four types of experiments are conducted in this work, Initially the effectiveness of 10 transfer-learned Deep CNN (DCNN) models for rice leaf disease identification is analyzed with an accuracy of 93.87%, DenseNet121 outperformed other transfer-learned models. Secondly, 3 machine learning models are trained on the deep features extracted from the final fully connected layers of DCNN models. The simulation results indicate that the DenseNet121 deep feature with Random Forest classifier performs better compared to other deep feature and machine learning algorithms. Furthermore, Dynamic mode decomposition (DMD) based attention-driven pre-processing mechanism to localize the infected region is investigated. The sparse component obtained from the DMD algorithm is processed to produce a hard attention map which is further multiplied with the original images to generate hard segmentation map. The influence of hard segmentation maps which localizes the infected regions for rice leaf disease identification is investigated. The performance of 10 DCNN models was evaluated using transfer learning strategy and machine learning models learned on deep features both on original images and DMD preprocessed images. The XceptionNet deep feature with SVM classifier on DMD preprocessed images outperforms other models with a test accuracy of 100%. Finally, the performance of the proposed DMD-based attention-driven pre-processing was investigated on on-field rice leaf images. XceptionNet model achieved a better classification accuracy of 94.33, compared to other transfer-learned models. The Analysis in terms of Accuracy, Precision, Recall and F1-Score revealed that the models trained on DMD pre-processed images exhibit significantly better performance. • Analysis of the transfer learned models on rice leaf disease identification using 10 state-of-the-art DCNN models. • Development of ML-based identification models on deep features extracted from the aforementioned DCNN models. • Attention-driven preprocessing of rice-leaf disease dataset using Dynamic Mode Decomposition based ROI extraction algorithm. • Analysis of the impact of DMD preprocessed images on transfer learned DCNN classifiers and ML models. • The effectiveness of DMD pre-processed images on on-field rice leaf images was evaluated using transfer-learned DCNN models. [ABSTRACT FROM AUTHOR]

In financial engineering, portfolio optimization has been of consistent interest. Portfolio optimization is a process of modulating asset distributions to maximize expected returns and minimize risks. Despite numerous studies on shallow learning models, they have shown limited success in analyzing the complex nature of massive stock data, a task where recent deep learning models excel. However, the deterministic nature of conventional deep learning models impedes their consideration of portfolio risk due to an inherent lack of uncertainty quantification in their predictions. This paper proposes a novel portfolio weighting strategy, incorporating both risk and return considerations within a deep learning framework. We propose the Predictive Auxiliary Classifier Generative Adversarial Networks (PredACGAN), a probabilistic deep learning model, to measure prediction uncertainty. The PredACGAN generator leverages latent vectors and historical stock prices to predict future returns. The model synthesizes predictive distributions from various latent vectors and past prices. The associated risk is produced via the entropy of these distributions, facilitating portfolio optimization through both return and risk considerations. The proposed algorithm removes high-risk assets from the investment universe at rebalancing moments, enabling PredACGAN to optimize portfolios considering both return and risk. We evaluated PredACGAN and the accompanying algorithm with S&P 500 stocks from 1990 to 2020, with portfolios rebalanced monthly based on PredACGAN predictions and risk measures. The PredACGAN portfolios yielded 9.123% annual returns and a 1.054 Sharpe ratio, outperforming a risk-agnostic portfolio yielding 1.024% annual returns and a 0.236 Sharpe ratio. The PredACGAN portfolio also exhibited lower maximum drawdowns, highlighting its effectiveness. • Proposed a novel portfolio optimization algorithm and a deep learning model. • Demonstrated the validity of the proposed algorithm in the stock market context. • Showcased the superior performance of algorithm with S&P 500 and NASDAQ 100 stocks. [ABSTRACT FROM AUTHOR]

To solve the difficulties in extracting general features of few-shot high-dimensional structural health monitoring data and making accurate repair decision, a civil aircraft structural repair decision method based on deep meta-learning (DML) is proposed in this paper. Firstly, multi-source structural health monitoring data such as component name, cumulative flight hours, inspection level and state parameters are collected as model input. In addition, the damage condition, repair level and repair definition are used as data labels. Secondly, DML model is used for few-shot learning of repair cases. Monitoring data and corresponding labels make up different target datasets, which can be used for training of base-learner and meta-learner. Thirdly, the historical cases in manual are used for in-sample prediction by meta-learning model, while the new cases are utilized for out-of-sample prediction by fine-tuning the initialized deep meta-learning model. Finally, the aircraft aluminum lap joint damage dataset is used for case study. The results show that the proposed deep meta-learning model can achieve higher accuracy when dealing with few-shot high-dimensional input. Compared with other models, the task oriented DML model has more strong generalization ability and higher classification accuracy in analyzing multi-task cases. • Few repair cases with multi-source structural health monitoring data and various repair information are collected to make up few-shot high-dimensional tasks. • Deep meta-learning model based on CNN and MAML is proposed for multitask structural repair decision. • Continuous classification decisions can be achieved by real-time applying and updating of deep meta-learning. [ABSTRACT FROM AUTHOR]

Process mining focuses on the analysis of event log data to build various process analytical capabilities. Predictive process analytics has emerged as one of such key capabilities and it uses machine learning techniques to construct process prediction models. In recent years, deep neural networks have gained increasing interest in process prediction since they can handle multi-dimensional sequential inputs with minimal information loss. However, they are considered black-box models and existing studies in explaining deep neural network-based process predictions rely on only event-level features for explanation. In this paper, we propose a new approach for generating explanations for process outcome predictions at multiple levels. The approach is underpinned by three different prediction models: a transparent model for generating global explanations based on case-level features, an attention-based deep neural network for generating local explanations based on event-level features, and a novel eXplainable Dual-learning Deep network (XD 2 -net) for generating local explanations based on case-level features. Using three publicly available datasets, we have tested the applicability of the approach and further examined the multi-level explanations generated by the approach through an elaborate case study. Unlike others, the design of our approach promotes the idea of leveraging the complementary capabilities of different models and utilizing their strengths, rather than focusing on model performance competition. This will contribute towards generating more comprehensive explanations that meet the needs of different end users and purposes in the future. [ABSTRACT FROM AUTHOR]

In the context of mechanical engineering design, the field of machine learning accelerated topology optimization is dominated by image-based models trained in a supervised manner on datasets with a limited diversity of boundary conditions. State-of-the-art methods show poor generalization capabilities and are strongly coupled to finite element mesh resolution, hindering scalability. In this paper, we leverage the explicit topology parameterization of the moving morphable components (MMC) framework to train a deep learning model that directly generates geometric design variables using a model architecture that is independent of the finite element mesh used for structural analysis. The developed model is trained on a large dataset of boundary conditions. Despite achieving state-of-the-art regression loss, evaluations reveal that direct-design approaches generate topologies with poor mechanical performance. Specifically, the model-generated topologies have, on average, a stiffness 11.48% lower than conventional MMC designs, as evidenced by in-distribution and out-of-distribution test samples. We demonstrate that this is due to the incompatibility between the regression loss function typically used in literature and the topology optimization objective of compliance minimization. To address this issue, we propose a novel acceleration approach that leverages the trained model to generate improved initial designs for conventional optimization. Specifically, the deep learning model is used to generate an initial design, which is then refined by conventional optimization to arrive at a final, optimal design. This approach shows a computation time-saving of 36.84% without sacrificing the final mechanical performance of the optimal topology compared to conventional optimization starting from a uniform initial layout. [ABSTRACT FROM AUTHOR]

Level Crossings are among the most critical railway assets, concerning both the risk of accidents and their maintainability, due to intersections with promiscuous traffic and difficulties in remotely monitoring their health status. Failures can be originated from several factors, including malfunctions in the bar mechanisms and warning devices, such as light signals and bells. This paper focuses on the intelligent detection of anomalies in warning bells through non-intrusive acoustic monitoring by: (1) introducing a new concept for autonomous monitoring of level crossings; (2) generating and sharing a specific dataset collecting relevant audio signals from publicly available audio recordings; (3) implementing and evaluating a solution combining deep learning and transfer learning for warning bell detection. The results show a high accuracy in detecting anomalies and suggest viability of the approach in real-world applications, especially where network cameras with on-board microphones are installed for multi-purpose level crossing surveillance. • New concept for autonomous monitoring of level crossings through audio–video analytics. • Novel approach for level crossing bell detection using deep learning and transfer learning. • Generation of a specific dataset collecting relevant audio signals. • Results show high accuracy and suggest feasibility within smart railway maintenance. [ABSTRACT FROM AUTHOR]

Home automation is seen as a potential pillar of the smart city revolution that combines smart mobility, lifestyle and ecosystem governed by intelligent sensors connected to the internet. Households can save money and be more comfortable with automated appliances. The cost of electricity and user comfort are fundamentally contradictory, so they can be presented as a dynamic multi-objective optimization problem with fluctuating priorities for the customer to use various devices at different times. For this reason, this paper proposes an advanced Intelligent Home Energy Management (IHEM) approach based on reinforcement learning to achieve home demand response (DR) efficiency. The optimal formulation of the one-hour-ahead energy consumption scheduling problem is considered a Markov Decision Process (MDP) with discrete time steps. An efficient Neural Network (NN)-based approach with a Q-learning algorithm is developed to address this problem, enabling the IHEM system to achieve better cost-effective scheduling performance. The accurate data of electricity price and energy supplied by the Photovoltaic (PV) system are analyzed in sliding periods by machine learning for uncertainty prediction. Using the newly developed approach, which has the dual objective of minimizing the electricity bill, it is possible to obtain scheduling decisions for appliances and energy storage. The results show that the proposed optimization method reduces the monthly electricity costs by 20% compared to the Integer Linear Programming (ILP)-based HEMS method. • Artificial intelligence (AI) approach to monitor household energy consumption. • An efficient deep reinforcement learning algorithm to control activity recognition in smart homes. • Design and implementation of Intelligent home energy management system. • Converge promptly and considerably reduce operating expenses adopting AI-based approach. [ABSTRACT FROM AUTHOR]

Detecting objects in hospital indoor environments is critical for scene understanding and can have various applications in healthcare. Deep learning algorithms have proven to be effective in object recognition from images or videos, but the availability of annotated datasets plays a crucial role in their successful application. However, there is a shortage of datasets for object detection in hospital settings, hindering the advancement of hospital indoor object detection algorithms. In this paper, we present the Hospital Indoor Object Detection (HIOD) dataset, consisting of 4,417 images covering 56 object categories. The HIOD dataset represents the frequently encountered objects in hospitals and comprises 51,869 annotated objects. The dataset is characterized by dense annotation, with an average of 11.7 objects and 6.8 object categories per image. An object detection benchmark was established using the HIOD dataset and eight state-of-the-art object detectors. The benchmark provides a comprehensive evaluation of the performance of the selected object detectors on a large and diverse set of images of objects commonly seen in hospital environments. The results of the benchmark can be used to compare and analyze the performance of different object detectors and identify their strengths and weaknesses for use in hospital environments. In the benchmark, one-stage detectors have shown superior performance compared to two-stage detectors of similar parameter sizes. In particular, YOLOv6-L was able to attain a mean Average Precision (mAP) of 51.7% while operating at a detection speed of 255 FPS. The benchmark and dataset can serve as a valuable resource for researchers and practitioners in the field of computer vision and robotics, helping to advance the development of more effective and efficient object detection algorithms for developing automated operations in hospitals such as robotic disinfection and patient assistance. • A new object detection dataset for artificial intelligence applications in hospitals. • A benchmark to compare and analyze the performance of different object detectors. • Improve object detection via hyperparameter optimization and transfer learning. • Evaluate the effects of image size and backbone on object detection performance. [ABSTRACT FROM AUTHOR]

Cancer is considered to be a key cause of substantial fatality and morbidity in the world. A report from the International Agency for Research on Cancer (IARC) states that 27 million new cases of cancer are expected before 2030. 1 in 18 men and 1 in 46 women are estimated to develop lung cancer over a lifetime. This paper discusses an overview of lung cancer, along with publicly available benchmark data sets for research purposes. Recent research performed in medical image analysis of lung cancer using deep learning algorithms is compared using various technical aspects such as efficiency, advantages, and limitations. These discussed approaches provide insight into techniques that can be used to perform the detection and classification of lung cancer. Numerous techniques adapted in the acquisition of the images, extraction of relevant features, segmentation of region affected, selection of optimal features, and classification are also discussed. The paper is concluded by stating the clinical, technical challenges and prominent future directions. [ABSTRACT FROM AUTHOR]

Driver distraction is one of the main causes of fatal traffic accidents. Therefore, the ability to detect driver inattention is essential in building a safe yet intelligent transportation system. Currently, the available driver distraction detection systems are not widely available or limited to specific class actions. Various research efforts have approached the problem through different techniques, including the usage of intrusive sensors, which are not feasible for mass production. Most of the work in early 2010s used traditional machine learning approaches to perform the detection task. With the emergence of deep learning algorithms, many research has been conducted to perform distraction detection using neural networks. Furthermore, most of the work in the field is conducted under simulation or lab environment, and did not validate the proposed system under naturalistic scenario. Most importantly, the research efforts in the field could be further subdivided into many subtasks. Thus, this paper aims to provide a comprehensive review of approaches used to detect driving distractions through various methods. We review all recent papers from 2014–2021 and categorized them according to the sensors used. Based on the reviewed articles, a simplified framework to visualize the detection flow, starting from the used sensors, collected data, measured data, computed events, inferred behaviour, and finally its inferred distraction type is proposed. Besides providing an in-depth review and concise summary of various published works, the practicality and relevancy of driver distraction detection towards increasing vehicle automation are discussed. Further, several open research challenges and provide suggestions for future research directions are provided. We believe that this review will remain helpful despite the development towards a higher level of vehicle automation. • An overview of usage of machine learning in driver distraction detection is provided. • Deep learning outperforms traditional machine learning in driver distraction detection. • A framework for driver distraction detection is introduced. • Research gap and future research for driver distraction detection is discussed. [ABSTRACT FROM AUTHOR]

Individuals in any country are badly impacted both economically and physically whenever an epidemic of infectious illnesses breaks out. A novel coronavirus strain was responsible for the outbreak of the coronavirus sickness in 2019. Corona Virus Disease 2019 (COVID-19) is the name that the World Health Organization (WHO) officially gave to the pneumonia that was caused by the novel coronavirus on February 11, 2020. The use of models that are informed by machine learning is currently a major focus of study in the field of improved forecasting. By displaying annual trends, forecasting models can be of use in performing impact assessments of potential outcomes. In this paper, proposed forecast models consisting of time series models such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), generalized regression unit (GRU), and dense-LSTM have been evaluated for time series prediction of confirmed cases, deaths, and recoveries in 12 major countries that have been affected by COVID-19. Tensorflow1.0 was used for programming. Indices known as mean absolute error (MAE), root means square error (RMSE), Median Absolute Error (MEDAE) and r2 score are utilized in the process of evaluating the performance of models. We presented various ways to time-series forecasting by making use of LSTM models (LSTM, BiLSTM), and we compared these proposed methods to other machine learning models to evaluate the performance of the models. Our study suggests that LSTM based models are among the most advanced models to forecast time series data. [ABSTRACT FROM AUTHOR]

When addressing product quality standards in manufacturing lines, a critical issue is the identification of the parameters that define the quality of the final product and their tracking. The problem of process control under inconsistent working condition of an automatic machinery, i.e. when some parameters are highly variable, is still quite unexplored in literature. This objective becomes even more challenging when the most important process variables are not directly measurable. This paper demonstrates that it is possible to achieve quality control by coupling a soft sensor, whose predictive model is a neural network, with an anomaly detector. The methodology has been applied to automatic machinery placed in a manufacturing line, where high variability in production rate has an important effect on the measured physical variables. This makes automated and accurate quality control difficult, due to the fact that in this test case the data collected are accelerometers signals, extremely sensible to variation in machine productivity by definition. It is shown that this approach outperforms many other classification methods (Support Vector Machines, Ensemble Bagged Tree, Discriminant Analysis, K-nearest neighbours and the direct application of a Neural Network) proposed in the past, achieving satisfactory results evaluated on the basis of four metrics (Accuracy, precision, recall and F 1 -score), even if anomalous data have been collected in a limited number of machine's working points. In particular, an accuracy over 92% has been reached also for production rates where only nominal conditions are collected. This procedure exceeds the direct training of a neural network (accuracy of 57.6% at new production rates), as well as the application of shallow methods based on the extraction of dimensionless features (around 35% in accuracy at new production rates). • Quality control is arduous when it is not possible to measure process variables. • Hard to build robust soft sensor with system working under inconsistent conditions. • A neural network can be trained at specific machine working points. • Anomaly detection is achieved by deep learning and a probabilistic model. • Accuracy higher than 92% for each production rate tested. [ABSTRACT FROM AUTHOR]

Automatic seismic phase detection and phase arrival time picking are always fundamental tasks in seismology. With the emergence of machine learning, and deep learning in particular, a number of neural network architectures are being used to explore the correlations between seismic waveforms and hidden information of interest. In this paper, we present a sequential attention (SEA) module that can learn both local and long-distance corrections to effectively extract sequential features. This module is lightweight and plug-in-play. Based on the SEA module, a fully convolutional neural network with sequential attention mechanism was constructed to detect seismic events and determine the arrival time of phases. Further experiments on STEAD dataset, in comparison with existing methods, show that our proposed model outperforms other algorithms with a higher F1-score (100% for seismic detection, 98.8% for P arrival picking, and 89.0% for S arrival picking) and fewer residual errors. Comparatively, our model has a quarter of the model parameters and computational complexity as well as a 36.7% reduction in processing time compared to the current state-of-the-art method. This demonstrates that the proposed architecture is effective for seismic sequential learning tasks and, as a result, can serve as an improved alternative backbone. [ABSTRACT FROM AUTHOR]