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Abstract: This article explores the application of image processing techniques in recyclable waste paper sorting. In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products and save processing chemicals and energy. From 1932 to 2009, different mechanical and optical paper sorting methods have been developed to fill the paper sorting demand. Still, in many countries including Malaysia, waste papers are sorted into different grades using a manual sorting system. Because of inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. Automated paper sorting systems offer significant advantages over human inspection in terms of worker fatigue, throughput, speed, and accuracy. This research attempts to develop a smart vision sensing system that is able to separate the different grades of paper using first-order features. To construct a template database, a statistical approach with intra-class and inter-class variation techniques are applied to the feature selection process. Finally, the K-nearest neighbor (KNN) algorithm is applied for paper object grade identification. The remarkable achievement obtained with the method is the accurate identification and dynamic sorting of all grades of papers using simple image processing techniques. [Copyright &y& Elsevier]

Nowadays, there has been a rapidly increasing number of scientific submissions in multiple research domains. A large number of journals have various acceptance rates, impact factors, and rankings in different publishers. It becomes time-consuming for many researchers to select the most suitable journal to submit their work with the highest acceptance rate. A paper submission recommendation system is more critical for the research community and publishers as it gives scientists another support to complete their submission conveniently. This paper investigates the submission recommendation system for two main research topics: computer science and applied mathematics. Unlike the previous works (Wang et al., 2018; Son et al., 2020) that extract TF–IDF and statistical features as well as utilize numerous machine learning algorithms (logistics regression and multiple perceptrons) for building the recommendation engine, we present an efficient paper submission recommendation algorithm by using different bidirectional transformer encoders and the Mixture of Transformer Encoders technique. We compare the performance between our methodology and other approaches by one dataset from Wang et al. (2018) with 14012 papers in computer science and another dataset collected by us with 223,782 articles in 178 Springer applied mathematics journals in terms of top K accuracy (K = 1 , 3 , 5 , 10). The experimental results show that our proposed method extensively outperforms other state-of-the-art techniques with a significant margin in all top K accuracy for both two datasets. We publish all datasets collected and our implementation codes for further references. 1 1 https://github.com/BinhMisfit/PSRMTE. • Bidirectional transformer encoders can improve the performance of the paper submission recommendation system. • The Mixture of Transformer Encoders framework shows the efficiency in the paper submission recommendation problem. • Proposed techniques can surpass other recent techniques on two datasets related. [ABSTRACT FROM AUTHOR]

• Newbie study using standard ML techniques with HAR Application and Discussions. • Activities found in Literature with the corresponding reference. • Co-occurrences between activities and sensors with the corresponding reference. • Summary and comparison among the different datasets found in Literature. • Summary of the experimentation settings with performance scores found in Literature. Human Activity Recognition (HAR) is an essential area of research related to the ability of smartphones to retrieve information through embedded sensors and recognize the activity that humans are performing. Researchers have recognized people's activities by processing the data received from the sensors with Machine Learning Models. This work is intended to be a hands-on survey with practical's tables capable of guiding the reader through the sensors used in modern smartphones and highly cited developed machine learning models that perform human activity recognition. Several papers in the literature have been studied, paying attention to the preprocessing, feature extraction, feature selection, and classification techniques of the HAR system. In addition, several summary tables illustrating HAR approaches have been provided: most popular human activities in the literature with paper references, the most popular datasets available for download (Analyzing their characteristics, such as the number of subjects involved, the activities recorded, and the sensors with online-availability), co-occurrences between activities and sensors, and a summary table showing the performance obtained by researchers. =The paper's goal is to recommend, through the discussion phase and thanks to the tables, the current state of the art on this topic. [ABSTRACT FROM AUTHOR]

Roundwood sorting is still a manual process in many Swiss sawmills, requiring employees to visually inspect and categorize thousands of logs per day. The heavy workload can be both physically and mentally taxing and can lead to increased rates of human error. State-of-the-art automation systems like X-ray log scanners are expensive and difficult to integrate into existing process lines. This paper proposes a novel recommendation system that leverages recent advances in image classification to automate roundwood classification by quality and species. The system integrates a camera to capture cross-sectional images of logs and record numerical data, such as length, taper, and diameter. The analysis of the resulting dataset highlights the challenges of data imbalance and noise, which makes classification difficult and, in some cases, impossible. However, by using selected datasets with reduced noise, state-of-the-art Convolutional Neural Networks (CNNs) can extract quality and species features. Quality models learn from a manually selected and simplified dataset, featuring samples that experts can clearly classify based on the image's information. Species models are trained on a label-noise-reduced dataset, reflecting real-world complexity. The accuracy on the selected dataset for three quality classes is 80%. The species determination is less challenging and reaches 91% accuracy on a synchronized dataset for the main species spruce and fir. Overall, this paper highlights the potential of Machine Learning in augmenting the roundwood sorting processes and presents a novel system that can improve the efficiency and accuracy of the process. [Display omitted] • Automation of roundwood sorting: Replaces manual sorting with image-based AI. • Integrated camera system in roundwood sorting to collect labeled dataset. • Species prediction: 91% accuracy in spruce–fir distinction. • Quality prediction on complexity reduced dataset: 80% accuracy between three main quality levels. • Efficient, adaptable & scalable system which is easy to integrate into existing process lines. [ABSTRACT FROM AUTHOR]

The emergence of online social networks (OSN) platforms removes communication barriers that are essential to human life, catalyzing social networking growth. However, this emergence has given rise to a negative impact when someone abuses the platform to commit cyberbullying activities. Hence, it is crucial to work on automated cyberbullying-related classification to mitigate the societal phenomena in OSN. The research on the automated classification model for cyberbullying was pioneered over the last decade with growing interest among researchers. It is helpful to track its growth over the decades to elucidate the state-of-arts techniques applied in this field. This paper presents a large amount of literature germane to cyberbullying classification from past to present to provide a comprehensive review. A total of 126 papers were reviewed. This paper emphasizes text-based cyberbullying and multi-modal cyberbullying. The review was presented around the machine learning workflow, encompassing four core sections: dataset analysis, pre-processing analysis, feature analysis, and technique analysis. Based on the critical analysis, limitations are addressed along with the future works that can be conducted to fill the gap in previous research. Furthermore, the review also examined the ethical implications associated with the implementation of these techniques. This review paper is expected to assist readers in fully comprehending the current trend, architecture, and techniques applied to the field. [ABSTRACT FROM AUTHOR]

Truck drivers are essential to modern supply chains. However, the companies that employ them are having trouble keeping them. At many trucking firms, annual turnover rates approach 100 percent or higher. This paper develops a method for predicting individual truck driver turnover events before they happen by applying supervised machine learning classifiers to a new source of operational truck driver data. Our paper uses Electronic Logging Device (ELD) data, which comprise newly federally-mandated, time-stamped work logs collected from approximately 1200 American long-haul truck drivers over 3 years. We train three supervised machine learning classifiers (logistic regression, random forests, and support vector machines) on this data and achieve 60 to 70 percent prediction accuracy and 50 to 60 percent recall across two 5-fold cross-validated experiments. We observe that the quantity and consistency of week-day driving assignments explain most of our models' predictive power. We offer these results as both a new technical tool, as well as novel managerial insights for improving global truck driver retention problems for the benefit of supply chains worldwide. [Display omitted] • Newly available operational data from long haul truck drivers is analyzed. • Three machine learning classifiers are trained on the operational data. • Predictive models achieve 60–70 percent accuracy, 50–60 percent recall. • Models show potential to enable interventions to retain truck drivers. [ABSTRACT FROM AUTHOR]

• A feature selection method based on binary dandelion algorithm is proposed. • The algorithm applies a sine-cosine operator and a restart strategy. • Mutual information and quick bit mutation improve the performance of the algorithm. • The algorithm performs well on datasets with larger dimensions. Feature selection (FS) is an important data preprocessing technology for machine learning and data mining. Metaheuristic algorithm (MH) has been widely used in feature selection because of its powerful search function. This paper presents an improved Binary Dandelion Algorithm using Sine Cosine operator and Restart strategy (SCRBDA) for feature selection. First, the sine cosine operator is used in the radius formula of the core dandelions (CD), which significantly enhances the ability of algorithm development and exploration. Secondly, the algorithm uses a restart strategy to increase its ability to get rid of local optimum. Thirdly, mutual information is used to guide the generation of some dandelions, which pays more attention to the correlation between the selected features and categories. Finally, quick bit mutation is used as the mutation strategy to improve the diversity of the population. The SCRBDA proposed in this paper was tested on 18 datasets of different sizes from UCI machine learning database. The SCRBDA was compared with 8 other classical feature selection algorithms, and the performance of the proposed algorithm was evaluated through feature subset size, classification accuracy, fitness value, and F1-score. The experimental results show that SCRBDA achieves the best performance, which has stronger feature reduction ability and achieves better overall performance on most datasets. Especially on large-scale datasets, SCRBDA can obtain extremely smaller feature subsets while maintaining much higher classification accuracy, and satisfactory F1-score. [ABSTRACT FROM AUTHOR]

In this paper the performance of different Machine Learning and Deep Learning approaches is evaluated in problems related to green mobility in big cities. Specifically, the forecasting of bike sharing demand in Madrid and Barcelona (Spain) is approached, for different prediction time-horizons, and also a problem of cable car demand forecasting in Madrid city. An important number of predictive variables are considered, which are grouped into four different sets (categorical/calendrical, persistence-based, meteorological and, as a novelty of the paper, information about analogue past instances), whose relevance is studied for all cases. A feature selection mechanism is also incorporated in order to improve the prediction accuracy of the proposed algorithms. A total of 12 different multivariate regression techniques are implemented, covering from Machine Learning methods to time-series Deep Learning approaches. Excellent results in all the prediction problems approached are reported. Finally, the consequences of obtaining accurate prediction in these three problem of green mobility in big cities are discussed. In addition, it is studied how the results could be exported to other similar cases in more general urban mobility studies. Novelties of the work include: (1) Addressing the forecast problem of passenger flow on a cable car using ML and DL multivariate techniques; (2) using the demand of analogous past instances as an additional feature to solve the demand prediction problems; and (3) the extraction of global conclusions about feature relevance when addressing a demand forecasting problem in green mobility. [ABSTRACT FROM AUTHOR]

The paper presents a machine learning based Smart Intrusion and Fault Identification (SIFI) method to identify the cyber-physical abnormalities in an inverter-based cyber-physical microgrid (CPM). The SIFI method utilizes an ensemble classifier (EC) to assimilate the decisions from three distinct classifiers (C4.5 decision tree, random forest (RF), and forest by penalizing attributes (FPA)). The proposed method employs a voting mechanism to classify and localize abnormal events for improved accuracy. The paper investigates the effects of cyberattacks (Denial-of-Service (DoS) and Malicious Data Injection (MDI) attacks) and physical abnormalities caused by line faults in microgrids. To train the classifiers, SIFI employs dataset with statistical attributes extracted from measurements. The renewable alternative power systems simulation (RAPSim) software tool is utilized to model the proposed system. The effectiveness of the presented model is assessed with respect to mean value error (MVE). The efficiency of the classifier is demonstrated by comparing its performance with other classifiers in terms of the MVE. For physical abnormalities identification and localization, the approach provides enhanced outputs with the MVE of 0.157 % and 0.162 % correspondingly. For identifying MDI anomalies, the model provides better results with a lower error rate. For DoS anomaly detection, the model provides better classification performance with 0.136 % error. The extensive empirical analysis proves that the anticipated ensemble classifier-based SIFI model yields the minimum MVE for identifying physical faults and cyberattacks. Furthermore, the effectiveness of the proposed method is identified by considering a case study in which the SIFI method outperforms well for the MDI and DoS cyber-attacks in the CPM. [ABSTRACT FROM AUTHOR]

In recent years, the application of the internet of things (IoT) in areas such as intelligent transportation, smart cities, and the industrial internet has become increasingly widespread. As a crucial supporting infrastructure, IoT devices are utilized in various fields to construct IoT networks. However, due to the inherent limitations of IoT devices, such as limited computing resources and low memory capacity, security concerns have become increasingly prominent. Among these concerns are Denial-of-Service (DoS) and botnet attacks, which are difficult to prevent due to their large-scale and covert nature. To address these challenges, this paper proposes a Hybrid DoS Attack Intrusion Detection System (HDA-IDS) that combines signature-based detection with anomaly-based detection to effectively identify both known and unknown DoS/botnet attacks. Additionally, this paper introduces a novel anomaly-based detection model called CL-GAN. It integrates CNN-LSTM with GAN to establish a baseline for normal behavior and detect malicious traffic. In contrast to other semi-supervised models, the CL-GAN exhibits superior accuracy, as well as shorter training and testing times, in detecting DoS and botnet attacks. In addition, experimental results demonstrate that the HDA-IDS outperforms other IDSs in detecting DoS and botnet attacks. When tested on datasets such as NSL-KDD, CICIDS2018, and Bot-IoT, the HDA-IDS achieved an average of 5% overall improvement superior performance in terms of accuracy, precision, recall, and F1-Score compared to other works. These results highlight the effectiveness of the proposed system in addressing security issues in IoT networks, and presents a general framework that addresses the challenge of large-scale attacks constructed through the dissemination of false information. [ABSTRACT FROM AUTHOR]

In the era of rapid paper publications in various venues, automatic citation recommendations would be highly useful to researchers when they write papers. Local citation recommendation aims to recommend possible papers to cite given local citation contexts. Previous work mainly computes the similarity score between citation contexts and cited papers on a one-to-one basis, which is quite time-consuming. We train a pair of neural network encoders that map citation contexts and all possible cited papers to the same vector space, respectively. After that, we index the positions of all cited papers in the vector space. This makes our process for searching recommended papers considerably faster. On the other hand, existing methods tend to recommend papers that are highly similar to each other, which makes recommendations lack diversity. Therefore, we extend our algorithm to perform multi-topic recommendations. We generate multi-topic training examples based on the index we mentioned earlier. Furthermore, we specially design a multi-group contrastive learning method to train our model so that it can distinguish different topics. Empirical experiments show that our model outperforms previous methods by a wide margin. Our model is also light weighted and has been deployed online so that researchers can use it to obtain recommended citations for their own paper in real-time. • Proposed FLCR algorithm with sentence-transformer & k-d tree. • Introduced multi-topic citation recommendation for diverse contexts. • Developed large-scale dataset with 1.7 million citation contexts for evaluation. • Demonstrated significant performance improvement over previous methods. • Deployed demo for real-time citation recommendations. [ABSTRACT FROM AUTHOR]

Constructing a deformation prediction model for dams that can accurately capture deformation trends is crucial to ensure their operational safety. The accurate monitoring data are critical to the presentation of the variation patterns of the monitored quantities. However, due to various disturbances, there can be a lot of noise in the raw deformation monitoring data acquired by the sensors. Therefore, the reduction of the noise level in the data sequence is necessary for the improvement of the accuracy and reliability of the prediction model. This paper presents data denoising techniques for the removal of outliers from data sequences, including complete ensemble empirical mode decomposition with adaptive noise, garrote threshold functions, and sample entropy. The ensemble learning algorithm was then employed to complete the deformation prediction of concrete dams. Through example analysis and comparison with a benchmark model, the proposed model's performance was validated, highlighting its effectiveness and superiority. Additionally, this paper enhances the model's interpretability by visualizing the optimization process. The degree of influence of the input factors on the deformation prediction results is also analyzed using the feature importance metric. The proposed model is expected to be applied to deformation monitoring of other civil and hydraulic structures, as it can effectively reduce the impact of noise on prediction. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are currently the major causes of death globally. Among the strategies to prevent cardiovascular issues, the automated classification of heart sound abnormalities is an efficient way to detect early signs of cardiac conditions leading to heart failure or other, even asymptomatic, complications, quite effective for timely interventions. Despite the significant improvements in this field, there are still limitations due to the lack of solutions, available data-sets and poor (mainly binary — normal vs abnormal) classification models and algorithms. This paper presents a Medical Cyber–Physical System (MCPS) for the automatic classification of heart valve diseases onsite, in a timely manner. The proposed MCPS, indeed, can be deployed into personal and mobile devices, addressing the limitations of existing solutions for patients, healthcare practitioners, and researchers, through an efficient and easy accessible tool. It combines different neural network models trained on a new Italian dataset of 132 adult patients covering 9 heart sound categories (1 normal and 8 abnormal), also validated against two main open-access (Physionet/CinC Challenge 2016 and Korean) datasets. The overall MCPS performance (time, processing and energy resource utilization) and the high accuracy of the models (up to 98%) demonstrated the feasibility of the proposed solution, even with few data. The dataset supporting the findings of this paper is available upon request to the authors. • DB of 132 heart sounds of Italian patients in 9 categories, available upon request. • MCPS for automated HVD classification via hierarchical ML model structures. • Compared six ML classifiers (RF, SVM, NB, LR, DNN, LSTM) for HVD and majority voting. • MCPS validated on two open access datasets (Physionet/CinC 2016 and Korean). • Time, processing and energy efficiency of MCPS validated, with usage guidelines. [ABSTRACT FROM AUTHOR]

Data-driven models have been recognized as powerful tools to support Structural Health Monitoring (SHM). This paper contributes to the literature by exploring two data-driven approaches to detect damage through changes in a set of variables that assess the condition of the structure, and accommodates the challenge that may arise due to the influence of environmental and operational variabilities. This influence is reflected in the response of the structure and can reduce the probability of detecting damage in a structure or increase the probability of signaling false positives. This paper conducts a comparative study between a machine learning detection approach (supported by linear regression, random forest, support vector machine, and neural networks) and a cointegration approach, with the aim of detecting damage as early as possible. This study also contributes to the literature by evaluating the merits of the damage detection methods using real data collected from a small-scale structure. The structure is analyzed in a reference state and a perturbed state in which damage is emulated. The results show that both approaches are able to detect damage within the first 24 h, without ever signaling false positives. The cointegration based approach can notably detect damage after 10 h and 15 minutes, while the machine learning approach takes 20 h 30 m to detect damage. • Comparison of Machine Learning and Cointegration in SHM. • Consideration of damages and false alarms. • Usage of a reduced scale structure to reproduce the behavior of a bridge column. • Cointegration is the fastest to detect damage. [ABSTRACT FROM AUTHOR]

Citation counts is a crucial factor in evaluating the quality of research papers. Therefore, it is vital to accurately predict citation counts and explore the mechanisms underlying citations. In this study, we focus on predicting the citation counts in the field of statistics. We collect 55,024 academic papers published in 43 statistics journals between 2001 and 2018. Furthermore, we collect and clean a high-quality dataset and then construct multi-layer networks from different perspectives, including journal networks, author citation networks, co-citation networks, co-authorship networks, and keyword co-occurrence networks. Additionally, we extract 77 factors for citation counts prediction, including 22 traditional and 55 network-related factors. To address the issues of zero-inflated and over-dispersed citation counts, a neural network model is designed to achieve high prediction accuracy. Furthermore, we adopt a leave-one-feature-out approach to investigate the importance of these factors. The proposed neural network model achieves an MAE value of 7.352, which outperforms other machine learning models in the comparison. Thus, this study provides a useful guide for researchers to predict citation counts and can be easily extended to other research fields. • Extracting factors based on multi-layer academic networks. • Extracting research topics using keyword co-occurrence network. • Using the neural network model to predict the citation counts. • Indicating network-related factors possess strong predictive. performance. • Measuring factor importance using the leave-one-feature-out method. [ABSTRACT FROM AUTHOR]

Intelligent Fault-Tolerant Control (IFTC) refers to the applications of machine learning algorithms for fault diagnosis and design of Fault-Tolerant Control (FTC). The overall goal of the FTC is to accommodate defects in the system components while they are in use and maintain stability with little to no performance reduction. These systems are crucial for mission-critical and safety-related applications where the safety of people is at stake and service continuity is crucial. In this review paper, a systematic study has been done for the development of FTC with machine learning, deep learning, and transfer learning algorithms. The challenges and limitations faced with their possible solutions through machine learning theories for the IFTC model are lined up. This paper guides researchers on the different possible types of machine learning algorithms and their advanced forms like deep learning and transfer learning. The differences among these are highlighted by the challenges and limitations of each. The paper is significant such that most of the important literature references from the Scopus database particularly related to important electrical and mechanical industrial problems have been discussed to guide the researchers who want to apply IFTC for specific industrial problems, being the research gap. Finally, future research directions for the development of IFTC are highlighted. [ABSTRACT FROM AUTHOR]

Marine ecosystems and their fish habitats are becoming increasingly important due to their integral role in providing a valuable food source and conservation outcomes. Due to their remote and difficult to access nature, marine environments and fish habitats are often monitored using underwater cameras to record videos and images for understanding fish life and ecology, as well as for preserve the environment. There are currently many permanent underwater camera systems deployed at different places around the globe. In addition, there exists numerous studies that use temporary cameras to survey fish habitats. These cameras generate a massive volume of digital data, which cannot be efficiently analysed by current manual processing methods, which involve a human observer. Deep Learning (DL) is a cutting-edge Artificial Intelligence (AI) technology that has demonstrated unprecedented performance in analysing visual data. Despite its application to a myriad of domains, its use in underwater fish habitat monitoring remains under explored. In this paper, we provide a tutorial that covers the key concepts of DL, which help the reader grasp a high-level understanding of how DL works. The tutorial also explains a step-by-step procedure on how DL algorithms should be developed for challenging applications such as underwater fish monitoring. In addition, we provide a comprehensive survey of key deep learning techniques for fish habitat monitoring including classification, counting, localisation, and segmentation. Furthermore, we survey publicly available underwater fish datasets, and compare various DL techniques in the underwater fish monitoring domains. We also discuss some challenges and opportunities in the emerging field of deep learning for fish habitat processing. This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts. At the same time, it is suitable for computer scientists who would like to survey state-of-the-art DL-based methodologies for fish habitat monitoring. • Marine environments and fish habitats are widely surveyed using underwater cameras. • Deep learning (DL) offers an effective solution for processing underwater videos. • We write a tutorial on the key concepts and steps of DL for underwater monitoring. • We provide a survey of key DL-based visual fish monitoring methods/datasets. • We shed light on challenges/opportunities of DL for underwater fish monitoring. [ABSTRACT FROM AUTHOR]

Flight diversions due to adverse weather have a significant negative impact on airspace users, airports, and the environment. Previous work proposed a tree-based model that learned which flights are more likely to be diverted due to adverse weather conditions at the destination airport from historical traffic and weather data. This model was trained using confident learning to exclude from training unpredictable flight diversions caused by reasons other than weather, such as medical emergencies or technical problems. Aggregated performance metrics computed on the test set demonstrated high precision and moderate recall, and inspection of the Shapley values unravelled the most important features. Initial human observations made during the live trial, however, have raised a potential connection between the quality of predictions and the specific weather conditions that lead to flight diversions. This study aims to provide empirical evidence to support this hypothesis. To accomplish this, a data-driven methodology should be used to characterise the main conditions that cause flight diversions. Concurrently, the effectiveness of the model in each of these conditions should be evaluated. The dataset under consideration, however, presents a challenge because it contains a plethora of high-cardinality categorical features (e.g., destination airport and aircraft operator) as well as skewed numerical features with a wide range of scales (e.g., visibility and wind gust). This scenario significantly increases the difficulty of characterising the main conditions that cause flight diversions and conducting subsequent analyses even with established methods like factor analysis of mixed data. To address this challenge, this paper proposes to utilise supervised clustering, a method that combines state-of-the-art feature attribution, dimensionality reduction, and clustering algorithms to identify the most representative decisions of a model. Following that, the distinctive characteristics of the various decisions (e.g., extreme obscuration at the airport), each associated with a cluster, are determined by examining them. Finally, conventional classification and uncertainty metrics are computed for each cluster to assess the performance of the model. This paper shows that supervised clustering is effective in characterising flight diversions due to weather, and provides valuable insights into the performance of the model in different conditions, highlighting situations where predictions require careful consideration. • Flight diversions due to adverse weather conditions could be predicted with machine learning. • Supervised clustering effectively identifies the common weather conditions causing flight diversions. • Flight diversions in low visibility and windy conditions are highly predictable. [ABSTRACT FROM AUTHOR]

• Pinch force strength is accurately predicted using sEMG. • Different pinch movements are accurately classified using sEMG. • Similarity of EMG features is evaluated. • An EMG feature-to-image method is proposed. The movement of intelligent EMG-driven prosthesis mainly relies on the synergy of different fingers to achieve function of grasping objects. The paper proposes a novel scheme for force prediction and movement classification about pinch between different fingers based on surface electromyography (sEMG) using machine learning. The pinch force and sEMG signals are recorded synchronously by a data acquisition device. Eight features are extracted, which are proven to have better performance in the estimation of sEMG-to-force. We present a novel feature selection method that uses the one-dimensional time series similarity assessment based on Manhattan distance to eliminate the repetitive information between features. Three optimal features carrying less repetitive information are retained. Seven machine learning algorithms are used to predict force strength. The results show that the Long Short Term Memory (LSTM) has the best performance of force prediction, achieving a R 2 of 0.9517 and RMSE of 3.2723. The paper proposes a novel method of converting EMG feature sequences to the normalized gray image in order to classify the finger movement. Five classifiers based on image feature extraction and the Convolutional Neural Network (CNN) are developed respectively. The experimental results indicate that the CNN performs best, achieving an accuracy of 97.66%. In this way, it not only realizes the accurate force prediction, but also realizes the movement classification between different fingers. The proposed methodology has the potential to realize simultaneous force and movement control of prosthetic hand. [ABSTRACT FROM AUTHOR]

In this paper we present SurvLIMEpy, an open-source Python package that implements the SurvLIME algorithm. This method allows to compute local feature importance for machine learning algorithms designed for modelling Survival Analysis data. The presented implementation uses a matrix-wise formulation, which allows to speed up the execution time. Additionally, SurvLIMEpy assists the user with visualisation tools to better understand the result of the algorithm. The package supports a wide variety of survival models, from the Cox Proportional Hazards Model to deep learning models such as DeepHit or DeepSurv. Two types of experiments are presented in this paper. First, by means of simulated data, we study the ability of the algorithm to capture the importance of the features. Second, we use three open source survival datasets together with a set of survival algorithms in order to demonstrate how SurvLIMEpy behaves when applied to different models. • Python package implementing SurvLIME algorithm for computing feature importance. • It supports a wide variety of survival models, including deep learning models. • Fast and efficient implementation due to matrix-wise optimisation problems. • An open-source implementation available on GitHub. • Stable release provided to PyPI. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a distributed machine learning method to protect users' privacy and security. It currently faces the following two problems (McMahan et al., 2017): (1) The performance of the global model degrades when dealing with Non-Independent Identically Distributed (Non-IID) data. Other existing classical methods do not have a rigorous theory based on error processing ; (2) In the process of global model aggregation, the classical FL algorithm either directly averages local models or solely considers the proportion of local data to assign weights to the models, without accounting for the discrepancies between the local models. In this paper, a federated aggregation algorithm Federated Maximum Mean Discrepancy (FedMMD) is proposed to address the deviation between the local models and Non-IID. First of all, this paper utilizes the Dilated Convolution Meet Transformer (DCMT) model for local model feature extraction. This approach aims to capture more feature information and minimize the impact of Non-IID scenarios. Secondly, the learning stability of the local model data participating in the aggregation is compared with Maximum Mean Discrepancy (MMD). The weights of the local models involved in the aggregation are determined using the SKNQ (Student–Keuls–Newman-Q) method and the entropy weight method. The SKNQ method calculates and compares the MMD across multiple clients, while the entropy weight method assigns different weights to clients based on their deviations. On the standard dataset, the FedAvg algorithm (McMahan et al., 2017), the FedProx algorithm (Li et al., 2020) and the FedMMD algorithm are compared. The experimental results demonstrate that the FedMMD algorithm, used in training the global model, not only enhances the accuracy of learning IID and Non-IID data but also improves the generalization capability of the global model. • An DCMT model is proposed to alleviate the model fluctuations caused by NonIID data. • An MMD method is proposed to compared horizontally before the model is aggregated. • Entropy weight method and the SKNQ method are combined to allocate local models' weight when they are fused into global model. [ABSTRACT FROM AUTHOR]

With the wide application of the shield tunneling method in tunnel engineering, the untimely and incorrect attitude control of shield systems has become an essential factor affecting the quality of shield tunneling. The use of multi-agent deep reinforcement learning (MADRL) algorithm to learn correct and timely shield attitude correction policies is beneficial for handling shield attitude control tasks which are complex and separable. However, the sensing data of the shield systems are complicated and widely distributed, which affects the learning efficiency of the agents. To resolve this problem, this paper proposes a MADRL framework based on state classification and assignment (SCA) for adaptive shield attitude control. SCA-MADRL uses a clustering algorithm to classify states and uses a planning model to make the agent's learning space a consistent state space so that each agent can learn efficiently in its own learning space. The state classification algorithm in the framework can improve the learning efficiency of the agent, and the proposed assignment algorithm can further enhance the learning effect when reusing the agent. This paper verifies the necessity and effectiveness of using task decomposition to implement MADRL for equipment control and provides a practical framework for automated and intelligent shield systems. [ABSTRACT FROM AUTHOR]

Federated learning (FL) has emerged as a promising privacy-preserving solution, which facilitates collaborative learning. However, FL is also vulnerable to poisoning attacks, as it has no control over the participant's behavior. Machine learning (ML) models are heavily trained for low generalization errors. Generative models learn the patterns in the input data to discover out-of-distribution samples, which can be used to poison the model, thereby degrading its performance. This paper proposes a novel approach to generate poisoned (adversarial) samples using hyperdimensional computing (HDC), projecting an input sample to a large HD space and perturbing it in the vicinity of the target class HDC model. This perturbation preserves the semantics of the original samples and adds hidden backdoor/noise into it. It generates a large set of adversarial samples equal to the HD space. It is observed that a trained ML model successfully misclassifies 60%–70% of the generated samples. The adversary uses these samples to frame data poisoning attacks, called hyperdimensional data poisoning attacks (HDPA). HDPA increases the attack impact by 5–10 × than existing poisoning attacks against the byzantine-robust defenses. Furthermore, we propose a hyperdimension-based confidence metric (HDBC) to check the model's conformance, which requires neither access to an ML model nor any additional calibration. • We frame a data poisoning attack in FL, called HDPA. • HDPA achieves 5–10× higher attack impact than existing attacks. • This paper presents an adversarial sample generation approach using the HDC model. • It does not even require the black-box access of the trained ML model. • A hyperdimension-based confidence metric is proposed to evaluate a model confidence. [ABSTRACT FROM AUTHOR]

Android has been a significant target of malware applications due to the exponential growth of mobile devices. This may result in severe threats to Android users such as financial loss, information leakage etc. The security of smartphones has encouraged the researchers in applying machine learning techniques to enhance the Android malware detection rate as the traditional approaches are not effective in distinguishing unknown malware. In recent years, many malicious apps have been detected on Android devices using machine learning based approaches. The problem with these detection systems is that they give a high false positive and false negative rate. Therefore, it is imperative to develop methods for better identifying and classifying malware. The main contribution of this paper is to improve the classification accuracy by fusing the base classifiers. This paper proposes a fusion approach named as MalDetect that enables an effective combination of machine learning techniques to improve the identification rate of malware. The proposed approach is based on the base classifiers and a set of ranking algorithms defined on their predictive error rate. The purpose of this method is to improve the detection rate of Android malware. Two datasets i.e. Drebin and AndroMD are used to illustrate the effectiveness of the proposed approach. Experimental results show that the proposed approach performs better than the individual base classifiers and traditional ensemble learning techniques for identifying Android malware. Further, the proposed approach is compared with the renowned Stacking ensemble technique that uses a meta -classifier. From the experimental outcomes, it is found that the weighted F-measure obtained from the Stacking for Drebin and AndroMD datasets is 0.9811 and 0.9775 respectively whereas the weighted F-measure acquired from the proposed approach is 0.9857 and 0.9790 respectively. [ABSTRACT FROM AUTHOR]

Epilepsy is one of the most paramount neurological diseases, affecting about 1% of the world's population. Seizure detection and classification are difficult tasks and are ongoing challenges in biomedical signal processing to enhance medical diagnosis. This paper presents and highlights the unique frequency and amplitude information found within multiple seizure types, including their morphologies, to aid the development of future seizure classification algorithms. Whilst many published works in the literature have reported on seizure detection using electroencephalogram (EEG), there has yet to be an exhaustive review detailing multi-seizure type classification using EEG. Therefore, this paper also includes a detailed review of multi-seizure type classification performance based on the Temple University Hospital Seizure Corpus (TUSZ) dataset for focal and generalised classification, and multi-seizure type classification. Deep learning techniques have a higher overall average performance for focal and generalised classification compared to machine learning techniques, whereas hybrid deep learning approaches have the highest overall average performance for multi-seizure type classification. Finally, this paper also highlights the limitations of the TUSZ dataset and suggests some future work, including the curation of a standardised training and testing dataset from the TUSZ that would allow a proper comparison of classification methods and spur advancement in the field. [ABSTRACT FROM AUTHOR]

Every day, humans make hundreds of decisions, most of which are based on information we gather from our surroundings. The majority of this perception when it comes to driving is visual. Autonomous vehicles that are capable of recognizing items around them and making quick decisions in response to these impulses are self-driving cars. Self-driving cars are the main concern and an emerging subject for every automobile giant in the world. Death due to human failure in a road accident increases with every passing month and with technology assisting humans in every possible field, it is essential to prioritize road safety with it. In a thorough investigation of traffic safety, it was discovered that human error was the sole cause of 57% of accidents and a contributory factor in more than 90% of them. In comparison, only 2.4% of crashes were the result of a mechanical issue, and 4.7% were the result of environmental variables alone. Autonomous cars not only help reduce human errors while driving but also help in reducing driver's fatigue to a greater extent. This study examines the work of a number of scholars in order to provide a quick summary of how computer vision is being used in autonomous cars today. Previous research has shown the use of deep learning algorithms with the help of LiDAR sensors but they come with the problem of being relied on costly hardware. This paper focuses on the vertical and lateral movement of the car while using a more budget friendly yet effective option using just simple computer vision algorithms like Color space transformation, Canny edge detection and Hough line transformation to make the car detect lane lines and steering the car so that it remains bound to them. Simple computer vision requires less operational hardware which indicates that using cost effective boards like Raspberry Pi and Nvidia Jetson Nano can yield much more powerful results. The paper also highlights the reconstruction of a ready-made remote-controlled car that helps understanding autonomous cars even better. [ABSTRACT FROM AUTHOR]

In the 5G smart grid, battery liquid leakage negatively affects the safe storage and transportation of electricity. Image edge detection help locate the liquid leakage phenomenon. This paper proposes a digitalization-based algorithm that converts the image to the codeword and judges the edge by the code weight. Its main steps are image sampling, quantization, coding and judging. Compared with traditional Sobel and Canny operators, the algorithm dramatically enhances detection ability with machine learning. Moreover, this paper applies the algorithm to detect the leakage of infrared battery images in the 5G smart grid; The results show that it can accurately extract the liquid leakage location, improving fault diagnosis and early risk warning. [ABSTRACT FROM AUTHOR]

Financial time series prediction has many applications in economics, but producing profitable strategies certainly has a special place among them, a daunting challenge. Statistical and machine learning techniques are intensively researched in the search for a holy grail of stock markets forecasting. However, it is not clear to prospecting researchers how good those popular models are regarding useful predictions on a real scenario. This paper contributes to that discussion, providing decisive evidences contrary to the use of basic out-of-the-box models, specifically Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF) and Naive-Bayes (NB). Results consider optimistic and unreal variables often found in literature, as well as a more close-to-real simulation of the models usage. Specifically, current day closing prices direction forecasting results are contrasted with those on next day forecasts. As expected, when forecasting the current day, accuracy is almost perfect. However, when used to forecast next day closing direction, with a strict data separation policy and without direction or snooping bias, ANN, SVM, RF and NB produce results essentially equal to random guessing. The main achieved result is the demonstration of how a machine learning approach would fare in a support decision system for forecasting short-term future market direction, regardless of the level of market development, considering more than 100 securities in a 10 years period. Consequences for algorithmic trading relate to discouraging usage of the considered models as implemented here. On a more abstract sense, this paper presents more evidence to the Efficient Market Hypothesis (EMH). [ABSTRACT FROM AUTHOR]

Automatic fault diagnosis in many systems is performed via the analysis of vibration data in the time or frequency domain. In the literature, many approaches for extracting featured from signals for fault diagnosis have been proposed. In this paper, we apply a variety of transform functions including Fourier, Wavelet, etc. to extract features from the vibration data, which includes the statistical features and some features automatically extracted via Convolutional Neural Networks (CNNs). For each of these feature extraction approaches, a learning algorithm is trained to diagnose the faults and their results are aggregated in an ensemble machine learning algorithm. The weights of the base learner algorithms are optimized via an evolutionary algorithm to achieve the best-weighted voting scheme. The architecture of CNNs has a significant effect on the performance of the algorithm, thus, in this paper, an evolutionary algorithm is proposed to find the best architecture for CNNs in fault diagnosis. The CNNs are trained via gradient descent algorithms which suffer from getting stuck in local optima. To manage this, we propose an evolutionary algorithm that benefits from the speed of gradient descent and the global search of evolutionary algorithms. The proposed algorithm is tested on a number of benchmark problems and the experimental results are presented. • An ensemble algorithm is proposed for fault diagnosis. • Different feature extractors are exploited. • An evolutionary training algorithm is proposed. [ABSTRACT FROM AUTHOR]

In this paper we investigate the role of self-learning agents in multi-agent models of financial markets. We develop an agent-based simulation model of a financial market and, in addition to the agents with fixed strategies used in previous research, we introduce an agent with a self-learning strategy. To model the behavior of such an agent, we use deep reinforcement learning algorithms, namely deep deterministic policy gradient (DDPG). Next, we conduct a comparative analysis of the results of the constructed model with outcomes of previously proposed models, as well as with the characteristics of real market. To conduct comparative analysis, we use stylized facts of asset returns that allow us to evaluate and compare the characteristics of the markets. Our results show that a model with a self-learning agent gives a better approximation of the real market than a model with classic agents. In particular, unlike the model with classical agents, the model with a self-learning agent turns out to be not so heavy-tailed. Thus, we demonstrate that for a complete understanding of market processes simulation models should take into account self-learning agents that have a significant presence at modern financial markets. Finally, we present the python package 1 1 https://github.com/Kiri28/Agent-Based%5fMarket%5fsimulator. , which was developed by us as part of the research implementation. This package allows to simulate the financial market, as well as create your own agents and evaluate their impact on the market. • The paper explores the role of self-learning agents in modern financial market models. • We use stylized facts based analysis to compare simulated markets with each other. • Using a comparative analysis, we show that our model better describes real markets. • The paper provides our Python package for simulating financial markets. [ABSTRACT FROM AUTHOR]

Applying machine learning (ML) methods to multi-factor authentication is becoming increasingly popular. However, there is no comprehensive methodology to evaluate biometric systems based on machine learning in the literature. This paper proposes a general methodology for evaluation the ML-based systems for impostor recognition/detection using biometric traits. This includes creation of learning and testing sets with appropriate size balance (proportion) between these sets, selecting the number of instances coming from different users, evaluation of the influence of the impostors number on their detection rate, and the impact of the number of records representing user's behavior. In addition, we propose how the real data (possibly affected by account takeover attempts) could be used to extend the enrollment data to support the impostor detection. The proposed approach was used for a systematic comparison of an extensive set of ML and statistical methods. For some of them, the false acceptance rate (FAR) close to zero and false rejection rate (FRR) smaller than 0.05 in a supervised experiment were accomplished, proving the merit of certain ML-based approaches. Moreover, using the method proposed in the paper, a classifier trained on experimental data achieved FAR below 0.05 on the real-world data collected at an actual financial web page. • General methodology for evaluating ML impostor recognition using biometric traits. • Evaluation of the influence of the impostors number on their detection rate. • Evaluation of the impact of the number of records representing user's behavior. • The proposed approach tested on ML methods reaching FAR and FRR smaller than 0.05. • Test-bed trained classifier achieved FAR < 0.05 on data from financial service. [ABSTRACT FROM AUTHOR]

Fake news refuting is an essential part of online social networks (OSNs) management. Identifying the potential voluntary fake news refuters who can spontaneously repost the fake news refutation messages on the OSNs, and intentionally exposing such messages to them can contribute a lot to fake news regulation. Therefore, to fully utilize available user features and enhance fake news refuter identification, the paper proposes a novel paradigm for refuter classification problem, which leverages the pre-trained techniques and mixture of expert system to unify the structured and unstructured data. Specifically, the unstructured text records are first understood by the pre-trained ERNIE2 model, and then feed into Chi-Merge grounded Convolutional Neural Networks, which are expert classifiers in our framework, along with the structured profile data for further transformation. Besides, features extracted from text data are used for determining weights for multiple expert classifiers. Experiments on near 60 k real-world Weibo user data prove that our framework outperforms the large-scale pre-trained deep learning methods and traditional machine learning classification models. This paper provides a novel perspective of fake news refutation from the refuter's side and sheds light on artificial intelligence-driven OSNs management. [ABSTRACT FROM AUTHOR]

• Machine learning approaches to resolve identity issues. • Implementing TensorFlow to investigate policing dataset. • Predictive models using ML techniques to identify fraudulent or genuine IDs. This paper focuses on applications of various machine learning techniques on an anonymized policing dataset used in EU SPIRIT Horizon 2020 project to identify fraudulent identities and help Law Enforcement Agencies (LEAs) in their investigation in finding potential criminals and identity resolution. Lack of qualitative data and appropriate methodology to carry out research on criminal fraudulent identities is a common reason for fewer research in this area. Additionally, it is a very sensitive data to work with and minor inaccuracy in prediction of result causes massive impact in the society as genuine people could be questioned whereas criminals could be sent free. Both of these issues are addressed in this paper by application of 39 million records from policing dataset and working towards higher accuracy while building the model. Various machine learning approaches are applied to train the dataset to make predictions and the research focus on being able to predict the 5 suspected fraudulent identities out of 39 million records in the policing dataset. One of the applied machine learning techniques include TensorFlow along with Keras model which has seldomly been applied by researchers in detection of criminal data. To compare the results and test accuracy of TensorFlow model, other machine learning techniques such as Support Vector Machine, Naïve Bayes and K-nearest Neighbours are also applied to have a comparative study on the obtained outcomes from each model. The goal of this research is to find fraudulent IDs amongst all the anonymized IDs in the criminal dataset using TensorFlow and three other machine learning models and select the most optimal model out of them. Since the model is comparing two names so string-matching techniques such as Levenshtein edit distance, Hamming Distance, Jaro-Winkler and Soundex were applied to select an effective approach first before building the model and analysing the results. TensorFlow model demonstrated highest accuracy with relatively least execution time and the only model to successfully predict all the 5 suspects from the policing dataset. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurodegenerative disorder that produces both motor and non-motor complications, degrading the quality of life of PD patients. Over the past two decades, the use of wearable devices in combination with machine learning algorithms has provided promising methods for more objective and continuous monitoring of PD. Recent advances in artificial intelligence have provided new methods and algorithms for data analysis, such as deep learning (DL). The aim of this article is to provide a comprehensive review of current applications where DL algorithms are employed for the assessment of motor and non-motor manifestations (NMM) using data collected via wearable sensors. This paper provides the reader with a summary of the current applications of DL and wearable devices for the diagnosis, prognosis, and monitoring of PD, in the hope of improving the adoption, applicability, and impact of both technologies as support tools. Following PRISMA (Systematic Reviews and Meta-Analyses) guidelines, sixty-nine studies were selected and analyzed. For each study, information on sample size, sensor configuration, DL approaches, validation methods and results according to the specific symptom under study were extracted and summarized. Furthermore, quality assessment was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) method. The majority of studies (74%) were published within the last three years, demonstrating the increasing focus on wearable technology and DL approaches for PD assessment. However, most papers focused on monitoring (59%) and computer-assisted diagnosis (37%), while few papers attempted to predict treatment response. Motor symptoms (86%) were treated much more frequently than NMM (14%). Inertial sensors were the most commonly used technology, followed by force sensors and microphones. Finally, convolutional neural networks (52%) were preferred to other DL approaches, while extracted features (38%) and raw data (37%) were similarly used as input for DL models. The results of this review highlight several challenges related to the use of wearable technology and DL methods in the assessment of PD, despite the advantages this technology could bring in the development and implementation of automated systems for PD assessment. • Deep learning and wearable technology are used in diagnosis and monitoring. • Growing interest in the use of deep learning and wearables in Parkinson's disease. • The adoption of deep learning in Parkinson's disease is currently under development. • Technical and methodological challenges difficult the technology adoption. [ABSTRACT FROM AUTHOR]

• We have developed a clustering algorithm for an unknown number of clusters by MOGA. • It works with continuous and categorical featured data sets. • It can work with data sets having missing values. • The final solution is selected by majority vote by all non-dominated solutions. • Context-sensitive and cluster-orient genetic operators are designed. Many clustering algorithms categorized as K -clustering algorithm require the user to predict the number of clusters (K) to do clustering. Due to lack of domain knowledge an accurate value of K is difficult to predict. The problem becomes critical when the dimensionality of data points is large; clusters differ widely in shape, size, and density; and when clusters are overlapping in nature. Determining the suitable K is an optimization problem. Automatic clustering algorithms can discover the optimal K. This paper presents an automatic clustering algorithm which is superior to K -clustering algorithm as it can discover an optimal value of K. Iterative hill-climbing algorithms like K -Means work on a single solution and converge to a local optimum solution. Here, Genetic Algorithms (GA s) find out near global optimum solutions, i.e. optimal K as well as the optimal cluster centroids. Single-objective clustering algorithms are adequate for efficiently grouping linearly separable clusters. For non-linearly separable clusters they are not so good. So for grouping non-linearly separable clusters, we apply Multi-Objective Genetic Algorithm (MOGA) by minimizing the intra-cluster distance and maximizing inter-cluster distance. Many existing MOGA based clustering algorithms are suitable for either numeric or categorical features. This paper pioneered employing MOGA for automatic clustering with mixed types of features. Statistical testing on experimental results on real-life benchmark data sets from the University of California at Irvine (UCI) machine learning repository proves the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• The paper focuses on a pick-up routing problem with a three-dimensional (3D-PRP) loading constraint. • The paper combines genetic algorithm for pick-up routing with machine learning for three-dimensional loading problem. • K-means algorithm is developed for clustering phase. • The paper includes four expert systems. • Results of a real dataset are highly promising. This paper presents a combinatorial problem called a pick-up routing problem with a three-dimensional (3D-PRP) loading constraint, clustered backhauls at the operational level, and train loading at the tactical level for an intermodal transportation network. A two-phase approach, called clustering first, packing-routing second, is proposed for use during the first stage. The clustering of backhauls is carried out using the k-means algorithm. A hybrid approach is provided, which combines the packing of orders by first solving a 3D loading problem for each cluster using machine learning with a best-fit-first strategy, with routing using a genetic algorithm. During the second stage, the train-loading problem is solved using a mixed integer programming approach to minimise the total costs by incorporating various cost types, in which detention and demurrage costs are taken into account. All solution approaches are computationally evaluated on real-world data provided by an international logistics firm and new randomly generated instances. Comparisons are carried out using both exact solution methods and heuristic approaches, and the proposed approach was shown to be more effective for real-world problems. [ABSTRACT FROM AUTHOR]

• This paper proposes the Recursive General Regression Neural Network Oracle. • The algorithm was compared to several other classifiers. • It had the highest performance metrics such as accuracy and area under the curve. • The algorithm had also bested the literature in the performance metrics. This research introduces the Recursive General Regression Neural Network Oracle (R-GRNN Oracle) and is demonstrated on several binary classification datasets. The traditional GRNN Oracle classifier (Masters et al., 1998) combines the predictive powers of several machine learning classifiers by weighing the amount of error each classifier has on the final predictions. Each classifier is assigned a weight based on the percentage of errors it contributes to the final predictions as the classifiers evaluate the dataset. The proposed R-GRNN Oracle is an enhancement to the GRNN Oracle in which the proposed algorithm consists of an oracle within an oracle – where the inner oracle acts as a classifier with its own predictions and error contribution. By combining the inner oracle with other classifiers, the R-GRNN Oracle produces superior results. The classifiers considered in this study are: Support Vector Machine (SVM), Multilayer Perceptron (MLP), Probabilistic Neural Network (PNN), Gaussian Naïve Bayes (GNB), K-Nearest Neighbor (KNN), and Random Forest (RF). To demonstrate the effectiveness of the proposed approach, several datasets were used, with the primary one being the publicly available Spambase dataset. The predictions of SVM, MLP, KNN, and RF were used to create the first GRNN Oracle, which was then enhanced with the high performances of SVM and RF to create the second oracle, the R-GRNN Oracle. The combined recursive model was 93.24% accurate using 10-fold cross validation, higher than the 91.94% of the inner GRNN Oracle and the 91.29% achieved by RF, the highest performance by a stand-alone classifier. The R-GRNN Oracle was not only the most accurate, but it also had the highest AUC, sensitivity, specificity, precision, and F1-score (97.99%, 91.86%, 94.40%, 93.28%, and 92.57%, respectively). The research contribution of this paper is introducing the concept of recursion (a concept not fully explored in machine learning models and applications) and testing this structure's ability on further enhancing the performance of the traditional oracle. The recursive model has also been applied to several other datasets: The Human Resources, Bank Marketing, and Monoclonal Gammopathy of Undetermined Significance (MGUS) datasets. The results of these implementations are summarized in this paper. [ABSTRACT FROM AUTHOR]

The stock market is a dynamic, complex, and chaotic environment, which makes predictions for the stock market difficult. Many prediction methods are applied to the stock market, but most are supervised learning and cannot effectively parse the trading information present in the stock market. This paper proposes a prediction model that combines unsupervised learning with reinforcement learning to address this problem. Firstly, we capture the stock trend from historical stock data and construct the trading environment state of the market by the growing neural gas (GNG) algorithm in unsupervised learning. Secondly, the reward function is restructured to provide timely feedback on the trading information present in the stock trading market. Finally, a novel trading agent algorithm, Triple Q-learning, is designed to execute the corresponding trading behavior and make comprehensive predictions of the stock market based on the environment state constructed by GNG. Experimental results on several stock datasets demonstrate that the proposed model outperforms other comparative models in this paper. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technologies are increasingly applied to traffic flow prediction (TFP) to enhance prediction accuracy. This study utilizes bibliometric methods and network analysis measures to gain insights into the research status, development process, opportunities, and challenges of AI-based TFP research based on the literature data retrieved from the Web of Science core collection. The study first conducts basic statistical analysis of all papers. Subsequently, cooperation network analysis is conducted to identify the most productive countries/territories, institutions, and authors, the cooperative relationships, and the formed research communities. Co-citation network analysis is then employed to identify publications that have made outstanding contributions to the AI-based TFP field. Finally, the main path analysis of the paper citation network is used to analyze the knowledge diffusion process, while the keyword co-occurrence analysis is conducted to reveal the evolution characteristics of the research topics. Based on the bibliometric analysis results, we gain insights into the opportunities and challenges in this field from the perspectives of data, models, and applications, and provide pertinent suggestions for future research. Overall, this study can assist researchers in capturing the state-of-the-art and research directions in AI-based TFP. [ABSTRACT FROM AUTHOR]

• An adaptive threshold-based noise determination method is developed. • A noise score is designed to evaluate the noise level of each instance. • The iterative filtering process combined with a bagging filter is applied. • Thorough analysis of the experimental results is provided. The quality of training data plays a decisive role in the establishment of intelligent models. Since raw data obtained from the real world are usually entwined with noise due to variety of causes, noise filtering has become an important aspect of machine learning techniques. In contrast with the extensive research conducted on noise elimination for classification purposes, papers addressing this problem for regression tasks are rather scarce. In this paper, we propose a novel noise filter to clean noisy instances with real-valued label noise. Aiming at the deficiency of the existing noise determination criterion, a new adaptive threshold-based method is first proposed. It allows a noisy instance to be adaptively defined according to the fitting difficulty levels of different datasets, and areas with different densities. Embedded with this criterion, an effective noise filtering procedure is also designed. An ensemble filtering scheme and an iterative filtering process are combined to detect as many potential noisy samples as possible from the original training set. According to the acquire noise detection information, a noise score for evaluating the noise level is specifically developed. The potential noisy samples whose scores exceed a reasonable threshold are further filtered, which can compensate for the possible errors incurred during the previous procedure, and contribute to more reliable filtering results. The validity of the proposed method is studied in exhaustive experiments. We discuss reasonable hyperparameters, and compare the developed method with several state-of-the-art noise filters. The outcomes show that the prediction accuracy of the utilized regressor can greatly benefit from preprocessing the given raw dataset by using our method. Simultaneously, the method is able to acquire a good balance between the elimination of noisy samples and the retention of clean samples, and consistently achieves a better noise filtering performance. [ABSTRACT FROM AUTHOR]

This paper proposes a robust algorithm using two convolutional neural networks (CNN) to find a lost gamma source in a room despite various unpredictable obstacles. This algorithm is an improved and complementary method to our previous research, which does not limit the use of the algorithm to a specific geometry, and the presence of obstacles does not reduce the performance and inability of the method. In our previous research, an algorithm including a CNN was used to estimate the location of the lost gamma source in known geometry. In this paper, by adding another CNN to the previous algorithm, the algorithm can be used in environments with unknown geometry. The environmental simulations and data generation are performed using the Geant4 simulation program. The method has been tested in a designed scenario to check its effectiveness. The results show that by using these two CNNs, an agent can intelligently find the gamma source place in a room with multiple obstacles without human intervention with 95 percent accuracy. [ABSTRACT FROM AUTHOR]

Background —Nowadays, with the rise of Internet access and mobile devices around the globe, more people are using social networks for collaboration and receiving real-time information. Twitter, the microblogging site that is becoming a critical source of communication, has also grabbed the attention of spammers to distract users. So far, researchers have introduced various defense techniques to detect spams and combat spammers' activities. To overcome this problem, many novel techniques have been offered by researchers, which have greatly enhanced spam detection performance. Objective —The purpose of this paper is to identify, taxonomically classify, and compare current Twitter spam detection approaches in a systematic way. Method —This study presents a comprehensive Systematic Literature Review (SLR) method for spam detection on Twitter regarding 70 most relevant papers published between 2010 and October 2022. Literature review analysis reveals that most of the existing Twitter spam detection techniques are based on textual content and messages (tweets) that rely on Machine Learning (ML)-based algorithms. The major differences in these ML algorithms which use various classification and clustering algorithms are related to various feature selection methods. Hence, we propose a classification based on different feature selection analyses, namely content analysis, user analysis, tweet analysis, network analysis, and hybrid analysis. Results —Various parameters are identified to investigate the Twitter spam detection approaches, and each of the papers was examined to find the research methodology and present comparative studies on current approaches. Conclusion —This paper demonstrates that the existing Twitter spam detection approaches have encountered several open issues, including scalability, streaming data analysis, and processing. The most obvious unresolved issues are spam drift and non-English tweets. [ABSTRACT FROM AUTHOR]

• The feature representation of the network was extracted from a randomly searched sequence of vertices. • In networks of scientific documents, representation learning needs to consider similarity and citation relationships. • This paper proposed a method to search and learn multiple paths connecting similar documents in a scientific document network. • From scientific papers and patent datasets, we found empirical evidence that our algorithm is suitable for representation learning. • This paper can contribute to preparing the basis for inducing innovation in science and technology from scientific documents. A network is a structure that can represent an organic relationship of observations. Network representation learning has the advantage of extracting latent features in a network. In recent years, various algorithms have been developed to embed network representation in a low-dimensional feature space. Conventional algorithms learn a sequence of vertices that are randomly searched from a network and convert a vertex into a vector. This study focused on the representation learning of document networks. Thus, this study proposes a scalable network representation learning algorithm that preserves the structure and content of the document network and reduces data sparsity. To do this, we used the shortest walks to extract a sequence of vertices that reflected semantic similarity from a network. The shortest walk is a method for finding several shortest paths that connect similar vertices. After this, the sequence of vertices was inputted into the language model, assuming that the sequence was a sentence. Scientific papers and patent datasets provided empirical evidence for the validity of our approach. The former, the bibliography network of scientific paper, consists of 17,716 nodes and 105,734 edges. The latter is a patent describing a technology for energy, representing a network of 24,894 nodes and 28,314 edges. The results showed that the proposed algorithm exhibited the lowest error of the tested methods in multi-class classification and link prediction tasks. The results of these tasks provided evidence that our algorithm is suitable for extracting feature representations of document networks. [ABSTRACT FROM AUTHOR]

• HeuMetaDEP integrates DEP, ML paradigm, and HOAs to solve TSP problems. • DEP using ML paradigm improves the predictive performance of the ensemble. • Three HOAs select meta-characteristics and optimize hyper-parameters. • Seven sets of meta-characteristics are constructed for addressing the TSP problems. • A closeness degree measurement function is designed for meta-characteristics extraction. The ensemble learning paradigm demonstrates powerful advantages in solving Time Series Prediction (TSP) problems. But static ensemble methods do not always play a positive role in predictive performance. Dynamic Ensemble Pruning (DEP) methods aim to select an optimal subensemble for each test sample. By fusing the Meta-learning paradigm with the Heuristic optimization algorithm, this paper develops a hybrid Dynamic Ensemble Pruning (HeuMetaDEP) framework for TSP. The MetaDEP subalgorithm designed based on Meta-Learning (ML) trains a meta-predictor, which can judge whether each base leaner in the ensemble has enough ability to predict the query sample, to dynamically acquire the proper subensemble for each sample. Based on different aspects of the generalization ability of the base learner, seven sets of meta-characteristics are constructed for the training of the meta-predictor. And the Heuristic Optimization Algorithm (HOA) is used to filter the meta-characteristics and select the optimal parameters for a specific TSP problem. In this paper, we apply three HOAs, including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, and Artificial Fish Swarm Algorithm (AFSA) to form a set of three specific algorithms, collectively abbreviated as the HeuMetaDEP algorithms. An experimental evaluation conducted on eight benchmark time series datasets shows that the proposed HeuMetaDEP algorithms are significantly superior on more than half of the datasets, and achieve an average RMSE drop of 12.49% and an average MAE drop of 12.73% on the eight datasets, compared with the five naive algorithms. In addition, the three groups of extended experiments demonstrate that the proposed algorithms have better generalization performance compared with other advanced DEP algorithms, except under the small-sample condition. [ABSTRACT FROM AUTHOR]

In real-world scenarios, new audio classes with insufficient samples usually emerge continually, which motivates the study of few-shot class-incremental audio classification (FCAC) in this paper. FCAC aims to enable the model to recognize new audio classes while remembering the base ones continually. To solve the FCAC problem, the discriminability of the prototypes is vital to the model's classification performance. Thus, we proposed a method to learn the discriminative prototypes from two aspects. First, since the generalization ability of the embedding module (EM) significantly affects the discriminability of the prototypes, the proposed method employs a scheme of pseudo-episodic incremental training to train the EM by simulating the test scenario. Second, to enable the model to achieve a balanced classification performance on both base and new audio classes, the proposed method employs a selective-attention module to adjust different prototypes to enhance their discriminability. Extensive experimental results demonstrate that the proposed method achieves state-of-the-art performance in solving the FCAC problem. Notably, the proposed method achieves a comprehensive performance score (CPS) of 87.82% and 59.25% on the Neural Synthesis musical notes of 100 classes (NSynth-100) and Free sound clips of 89 classes (FSC-89) datasets, respectively, which outperforms the comparison methods. Our code is available at https://github.com/chester-w-xie/DPL_FCAC. • The few-shot class-incremental audio classification problem is studied in this paper. • The proposed method expands the model with discriminative prototypes. • A scheme is proposed to enhance the generalization ability of the embedding module. • A selective-attention-based module is proposed for prototype adjustment. • The proposed method achieves state-of-the-art performance in solving the problem. [ABSTRACT FROM AUTHOR]

The increase in the demand for aircraft operations has caused the ATM system to become overloaded as it no longer has sufficient capacity to respond to this increase in demand. For this reason, many projects have emerged with the aim of increasing the capacity of the ATM system through the development of new technologies. This paper proposes a solution that would allow predicting and evaluating the traffic density in a three-dimensional basis in one or several ATC sectors. The final goal of this methodology is to analyse the complexity of these ATC sectors. This paper proposes, first, the two-dimensional structuring of traffic density in a set of air traffic flows identified from historical operational data in the sector of analysis. In the vertical dimension, the traffic will be still structured in flight levels. As a subsequent step, a prediction of this structured traffic density is attempted by means of machine learning models. This proposed methodology will try to facilitate the work of the ATC service by allowing them to have a picture of the air traffic organisation of the ATC sector before the real operation occurs. The application of this methodology will allow the adjustment of the ATC service resources. In addition, it will allow the complexity of the sectors to be assessed, as this complexity will strongly depend on how the traffic is structured. [ABSTRACT FROM AUTHOR]

In literature, many studies try to analyze healthcare usage and generated decision support systems. In this paper, the aim is to generate a decision support system for insurance companies in the United States according to the federal dataset. The data set contains variables from the United States Healthcare Administration formal data group and they are; group of age, group of healthcare insurance, group of nationality, Corporate Social Responsibility (CSR) usage, and Advance Premium Tax Credit (APTC) usage. The best model for each state in the United States is determined with the dataset from 2014 November to 2015 February. In this study, various statistical models are attempted to generate independent and optimal models for each state. In the model selection process, three different metrics are used. Accuracy rate, f1score and precision score are used in the model selection process and models are compared to each other according to these three metrics for each state uniquely. Consequently, the proposed model is used with a decision support scheme to support customer service workers in healthcare insurance companies. The decision support system under consideration can facilitate a substantial decrease in the duration of interactions between customer service representatives and clients. Additionally, this system can generate more logically sound and efficient customer offers. [ABSTRACT FROM AUTHOR]

The progression of the fuzzy domain is propelled synergistically by the evolution of science via academic papers and the advancement of technology through technical patents. However, the intrinsic interrelationships within the science and technology (S&T) interactive landscape of this domain remain largely unknown, as few studies attempt to explore these close interrelationships. To bridge this research gap, based on 161,060 academic publications and 36,693 granted patents, we measured their mutual attribution between S&T from the perspective of textual semantics and structure information on top of a quantitative network monitoring process. The results showed that the fuzzy domain lacked technology-oriented topics in terms of semantic relations and presented an interactive pattern where science was ahead of technology; that is, science worked as an engineer to expand the knowledge boundaries of the research field, and technology existed more as a beneficiary. This article presents cutting-edge insights into the intrinsic S&T interactions of the fuzzy domain in a visual and quantitative approach for the first time and aims to aid researchers in theoretically grasping the S&T innovation laws. [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]

The past work on Information Retrieval (IR) targeting web document collections shows that incorporating a measure that measures the quality of web documents, or rather the document prior (e.g., PageRank), into an IR system improves the retrieval effectiveness. In this study, we introduce new document priors and empirically investigate their effect by employing them as features in a learning to rank (LTR) deployment. The experiments are performed on the two standard Web IR test collections: the ClueWeb09 and the ClueWeb12 datasets, which include 500 and 733 million web documents, respectively, and the associated TREC & NTCIR query sets with a total number of 1,204 queries. A strong baseline is formed by using standard features introduced in the previous works, with respect to which the effect of newly introduced features in this paper is empirically compared. We test our features by LambdaMART, which is state-of-the-art LTR technique. The results reveal that the features introduced in this work led improvement in retrieval performance on the test collections in use. The introduced features are classified into 5 groups with respect to functional properties and each group is also analyzed in detail. • Measuring the quality of a web document is a challenging task. • A web page contains various elements that indicate quality. • Document priors can be used as query-independent features in LTR deployments. • Introduced query-independent features can measure the quality of web documents. • Contribution of the new feature groups to the retrieval performance is examined. [ABSTRACT FROM AUTHOR]