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Rao algorithms that include three algorithms are very simple and parameter-less algorithms with effective and desirable performance. This paper modifies these three algorithms, merges them, and establishes a powerful group algorithm. In the first optimization step, the suggested algorithm is tested on 30 standard CEC2014 functions with 50 dimensions to compare it with main algorithms, several well-known algorithms, and modified versions of RAO algorithm. It becomes evident in the first test that the suggested optimizer is effective, and reliable for optimization of real-parameter functions, and it has shown its superiority to original RAO algorithm and several modern and modified versions of RAO algorithms for most of the test functions and achieved more acceptable results than them. Moreover, the suggested algorithm benefits a faster convergence characteristic than original RAO algorithms. The proposed Colonial Competitive RAO (CCRAO) has been applied on five popular engineering problems and its results have been compared with those of recent papers. According to the results, CCRAO is an effective, robust, and reliable optimizer for engineering design problems and can contain all useful features of RAO algorithms altogether. CCRAO has succeeded to converge to the best solution for these engineering problems and surpasses most of the other algorithms. [ABSTRACT FROM AUTHOR]

The development of stereo matching algorithm is still one of the challenging problems, especially in ill-posed regions. Hence, this article presents a survey on the algorithm frameworks related to the stereo matching algorithm. Based on the early survey that had been conducted, two major frameworks available in current stereo matching algorithm development, they are traditional and artificial intelligence (AI) frameworks. Most of the traditional methods are very low accuracy compared to the AI-based approach. This can be observed in the standard benchmarking dataset, such as from the KITTI and the Middlebury, where AI methods rank at the top of the accuracy list. Additionally, the trend for solving computer vision problems uses AI or machine learning tools that become more apparent in recent years. Thus, this paper is focusing on the survey between the deep learning frameworks, which is one of the machine learning tools related to the convolutional neural network (CNN). Several mixed approaches between CNN based method and traditional handcraft method, as well as the end to end CNN method also discussed in this paper. [ABSTRACT FROM AUTHOR]

In the last years, the massive deployment of new photovoltaic (PV) power plants has launched the connection of PV inverters to the electrical network. A single medium-sized ground-mounted PV plant may have thousands of these inverters linked to the grid and even more PV panels on the DC side. Upon reaching such a substantial magnitude of devices involved in grid-connected installations, the effective operation, management, predictive maintenance, and fault detection becomes increasingly challenging without integrating advanced prediction and automated anomaly detection systems. Artificial intelligence algorithms, grounded in data measurements, can be pivotal in addressing this challenge. This paper proposes several regression-based methods to predict PV plants' energy generation, which is useful for detecting transient and long-term anomalies. These models are trained using a Recursive Least Squares (RLS) method and require a minimum number of variables to yield satisfactory outcomes, which is one of the paper's contributions. They mainly rely on energy generation measurements and geolocation. Within the scope of this research, two distinct algorithms have been implemented and validated. The first algorithm, a simplified model, is engineered to analyse the daily efficiency variation, prioritizing the identification of faults and abnormal operational profiles in PV plants. On the other hand, the second algorithm adopts a more intricate model tailored to facilitate long-term diagnosis, enabling the assessment of PV efficiency degradation. In this work, both algorithms are described and their performance is validated using the historical data from more than 20 PV plants placed in different climatic regions. • Photovoltaic systems' modelling using linear regression models. • Photovoltaic fault detection using linear regression coefficients evaluation. • Automatic fault detection for photovoltaic plants with low sensors' availability. • Short-term and long-term efficiency trend evaluation of photovoltaic plants. [ABSTRACT FROM AUTHOR]

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

Intelligent machines are an important carrier of artificial intelligence, which are widely used not only in the industrial field, but also in various fields of daily life and work. Path planning is an important research direction for studying machine intelligence. The main purpose is to search for a feasible path so that the robot can walk along the searched path in a complex environment. This paper aims to study the evolutionary algorithm of intelligent machines based on artificial intelligence. On the basis of introducing the characteristics of artificial potential field algorithm, the concept and characteristics of genetic algorithm, using genetic algorithm as the framework, the artificial potential field algorithm is combined with the crossover of genetic algorithm. In the operator, the optimization performance of the crossover operator is improved. The research results show that the length of the path obtained by the improved algorithm is shorter and better than that of the genetic algorithm of the single-point crossover operator. [ABSTRACT FROM AUTHOR]

This paper presents a Text similarity detection method based on artificial intelligence and natural language processing. The method combines statistical machine learning and deep learning techniques and designs six models from three perspectives: character-level, word-level, and semantic-level. These models include the diff model based on machine learning, cosine similarity model, Jaccard model, TF-IDF model, as well as the SimCSE and SBERT models based on deep learning methods. To fully leverage the characteristics of these models, three scenarios are designed to calculate the similarity scores based on experience and multiple experimental results. The results show that calculating the similarity scores using these three scenarios not only achieves high accuracy but also requires fewer computational resources. As deep learning and natural language processing technologies continue to advance, Text similarity detection methods based on artificial intelligence and natural language processing will continue to be improved and play a more significant role in practice. Future research can explore more models and algorithms to enhance the accuracy and robustness of plagiarism detection. [ABSTRACT FROM AUTHOR]

The peak shear strength of clayey soil-geomembrane interfaces is a vital parameter for the design of relevant engineering infrastructure. However, due to the large number of influence factors and the complex action mechanism, accurate prediction of the peak shear strength for clayey soil-geomembrane interfaces is always a challenge. In this paper, a machine learning model was established by combining Mind Evolutionary Algorithm (MEA) and the ensemble algorithm of Adaptive Boosting Algorithm (ADA)-Back Propagation Artificial Neural Network (BPANN) to predict the peak shear strength of clayey soil-geomembrane interfaces based on the results of 623 laboratory interface direct shear experiments. By comparing with the conventional machine learning algorithms, including Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA) tuned ADA-BPANN, MEA tuned Support Vector Machine (SVM) and Random Forest (RF), the superior performance of MEA tuned ADA-BPANN has been validated, with higher predicting precision, shorter training time, and the avoidance of local optimum and overfitting. By adopting the proposed novel model, sensitivity analysis was carried out, which indicates that normal pressure has the largest influence on the peak shear strength, followed by geomembrane roughness. Furthermore, an analytical equation was proposed to assess the peak shear strength that allows the usage of machine learning skills for the practitioners with limited machine learning knowledge. The present research highlights the potential of the MEA tuned ADA-BPANN model as a useful tool to assist in preciously estimating the peak shear strength of clayey soil-geomembrane interfaces, which can provide benefits for the design of relevant engineering applications. • The ensemble artificial intelligence algorithms in predicting the peak shear strength of interfaces is compared. • An analytical equation for estimating the peak shear strength is proposed. • Physical experiments are conducted to validate the effectiveness of the proposed analytical equation. [ABSTRACT FROM AUTHOR]

K-Nearest Neighbors (KNN) are very popular supervised machine learning algorithm which can be effectively used for both multi-class classification and regression utilizing multiple features of the dataset. It trains extremely fast, that is with time complexity of O(n). However, the time complexity for generating results with KNN is O(n3dk) (where k=number of neighbours, d=dimension of data, n=number of training points) and hence it becomes very difficult to work with larger dataset. Although there are parallelizing and distributed computing techniques available for speeding up KNN, it still have limitations on horizontal scalability, fault tolerance and incremental learning capabilities. To solve these issues, the paper presents a KNN inspired novel Supervised Machine Learning algorithm called Distributed Nearest Hash utilizing unique usage of hashmap and primary key clustering order with help of wide column store databases facilitating easy incremental near-realtime scalability to train unlimited datapoints with O(n) time complexity and also predict them in near realtime with O(1) time complexity considering n >> d. This can be readily integrated in any system that uses KNN as a base classifier or regressor. The experimental results shows that the proposed DNH model is 25% faster than state of the art distributed KNN algorithm. Integrating with fuzzy logic the algorithm can be used for several class classification. Using this could aid in building very powerful smart decision making systems with very little computational power. [ABSTRACT FROM AUTHOR]

The amount of network traffic generated from the Internet of Things (IoT) devices is massive which leads to Big data. In present scenario, IoT devices has taken a leap ahead in terms of growing technology and insecure network traffic flow. The sensitivity of the data in the IoT network is becoming very high, which concern the security in legal and privacy issues. Traditional Intrusion Detection System (IDS) is used as a primary line security to differentiate between the attack and benign network traffic flow. As the size of the network traffic capture increases, it becomes more important to handle such big data. Similarly, when the number of classes and instances increases, the complexity of the data also increases. In this paper, we propose a big data platform based intrusion detection system which can differentiate between the types of network traffic flow generated from the IoT devices. We compared the performance of deep learning algorithms with machine learning algorithms on Apache Spark platform and found that machine learning algorithms outperform deep learning algorithms with greater accuracy and less training time for the model. We evaluated our research based on real world network traffic dataset BoT-IoT [1] for an improved intrusion detection system in IoT network traffic. [ABSTRACT FROM AUTHOR]

In the last decade it became increasingly apparent the inability of technical metrics such as accuracy, sustainability, and non-regressiveness to well characterize the behavior of intelligent systems. In fact, they are nowadays requested to meet also ethical requirements such as explainability, fairness, robustness, and privacy increasing our trust in their use in the wild. Of course often technical and ethical metrics are in tension between each other but the final goal is to be able to develop a new generation of more responsible and trustworthy machine learning. In this paper, we focus our attention on machine learning algorithms and associated predictive models, questioning for the first time, from a theoretical perspective, if it is possible to simultaneously guarantee their performance in terms of both technical and ethical metrics towards machine learning algorithms that we can trust. In particular, we will investigate for the first time both theory and practice of deterministic and randomized algorithms and associated predictive models showing the advantages and disadvantages of the different approaches. For this purpose we will leverage the most recent advances coming from the statistical learning theory: Complexity-Based Methods, Distribution Stability, PAC-Bayes, and Differential Privacy. Results will show that it is possible to develop consistent algorithms which generate predictive models with guarantees on multiple trustworthiness metrics. • AI is nowadays requested to optimize both technical and ethical metrics. • Focus on ML algorithms and associated models (both deterministic and randomized). • We prove that it is possible to develop consistent algorithms and models. • We bound generalization in terms of both technical and ethical metrics. • We leverage the most recent advances coming from the statistical learning theory. [ABSTRACT FROM AUTHOR]

The quest for learning more about the minute intricacies of the universe has fascinated mankind since ages. From stars to planets and asteroids to exoplanets, each research endeavor in astronomy has supplemented our knowledge about the cosmos. NASA's Kepler Mission is one monumental step in this direction wherein, telescopes conduct a survey of the Milky Way galaxy and try to identify thousands of earth-size and other smaller planets in or near the habitable zone, so as to determine the thousands or even millions of stars in our galaxy that might have such orbiting planets. Exoplanet is any new planet outside the solar system which orbits a star. Identifying new exoplanets gives us a chance to precisely understand the planet formation processes. Earlier, it was a laborious endeavour to mine the mission data using traditional algorithms and churn out the possible exoplanets. Ever since the advent of various machine learning algorithms, this process has become quite seamless. However, not all algorithms give equal and promising results when it comes to analysing different types of data. A comparative study of algorithms helps in this regard, thereby identifying the pros and cons of different algorithms for analysing certain forms of data. In this paper, we initially focus on feature set reduction using principal component analysis and thereafter make a detailed comparative analysis of ML algorithms for the identification of exoplanets, in the NASA Kepler mission data. [ABSTRACT FROM AUTHOR]

Grid-interactive building control poses a critical challenge in the context of grid modernization and decarbonization. Recently, various artificial intelligence-based optimal control approaches have been proposed, providing innovative solutions to optimal building control problems. However, researchers and practitioners are now confronted with a dilemma when selecting appropriate control strategies, ranging from model-based (knowledge-incorporating) to learning-based (data-driven) approaches, and hybrid methods combining them. Although each algorithm in existing literature claims superiority over specific baselines, their performance has never been systematically compared and analyzed, owing to the absence of a unified platform for comprehensive evaluation. To fill this knowledge gap, we identify and implement all state-of-the-art approaches within a modular training and evaluation framework, assessing their efficacy in a grid-interactive building control problem. In this paper, we also introduce a streamlined hybrid method that complements existing hybrid approaches. Our comparative study reveals and quantifies the advantages of hybrid methods: on average, they achieve near-optimal control while requiring less than 14% of the online computation of traditional model-based methods. To achieve this performance, they need as few as 2% of the training samples of purely learning-based methods. Finally, we provide insights into the merits, limitations, and implementation of each method to help researchers better understand the state of the art and future directions. The software framework implemented in this study is open-sourced to facilitate future research. [ABSTRACT FROM AUTHOR]

The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects. [ABSTRACT FROM AUTHOR]

Geotechnical reliability analysis provides a novel way to rationally take the underlying geotechnical uncertainties into account and evaluate the stability of geotechnical structures by failure probability (or equivalently, reliability index) from a probabilistic perspective, which has gained great attention in the past few decades. With the rapid development of artificial intelligence techniques, various machine learning (ML) algorithms have been successfully applied in geotechnical reliability analysis and the number of relevant papers has been increasing at an accelerating pace. Although significant advances have been made in the past two decades, a systematic summary of this subject is still lacking. To better conclude current achievements and further shed light on future research, this paper aims to provide a state-of-the-art review of ML in geotechnical reliability analysis applications. Through reviewing the papers published in the period from 2002 to 2022 with the topic of applying ML in the reliability analysis of slopes, tunneling, and excavations, the pros and cons of the developed methods are explicitly tabulated. The great achievements that have been made are systematically summarized from two major aspects. In addition, the four potential challenges and prospective research possibilities underlying geotechnical reliability analysis are also outlined, including multisensor data fusion, time-variant reliability analysis, three-dimensional reliability analysis of practical cases, and ML model selection and optimization. • A state-of-the-art review of ML in geotechnical reliability analysis applications. • Several commonly used ML algorithms and some latest advanced ML methods are summarized. • The potential challenges and prospective research possibilities are outlined. [ABSTRACT FROM AUTHOR]

Biochemical sensors have become indispensable tools for real-time, on-site monitoring and analysis in diverse domains such as healthcare, environmental protection, and food safety. The rapid evolution of artificial intelligence (AI) has opened new frontiers for enhancing the capabilities of these sensors across a spectrum of detection modalities. This paper delves into the recent integration of AI algorithms into biochemical sensors, examining this advancement from a functional standpoint and focusing on the empowerments it brings to electrochemical, electrochemiluminescence, colorimetric, and Raman sensors. AI techniques aim to enhance the capabilities of biochemical sensors beyond traditional techniques and have enabled improved selectivity, drift correction, efficiency, resolution, assisted diagnosis, and biomarker screening from complex multidimensional data. In the end, we provide a personal perspective on future development and address the remaining challenges in the commercialization of AI-based biochemical sensors. • We introduce the common types and functions of AI algorithms used in biochemical sensors. • AI algorithms enhance biochemical sensors' performance, achieving highly sensitive, precise, and high-throughput analysis. • AI-assisted sensors unlock tremendous benefits across healthcare, environmental protection, and food safety. • A personal perspective on the challenges and future opportunities is discussed. [ABSTRACT FROM AUTHOR]

Optimal path planning is a fundamental problem in artificial intelligence (AI) and has wide applications in areas such as robotics, transportation, and logistics. In this paper, we propose a novel approach for ordering algorithms in pathfinding problems using the concept of shell layers. Our approach aims to improve the computational efficiency and distance cost of ordering algorithms. To evaluate the effectiveness of our approach, we compare it with five state-of-the-art techniques commonly used in the field of ordering algorithms on eight different scenarios with varying configurations. Our results show that our proposed approach outperformed the state-of-the-art techniques in terms of time-computational complexity and distance cost in most of the scenarios, demonstrating its potential as a new state-of-the-art technique for ordering algorithms. Specifically, our approach outperformed the nearest neighbor, A*, branch and bound, Christofides, and genetics ordering algorithms. However, we also identified two specific situations where our approach was outperformed by two state-of-the-art algorithms due to the specific distribution of the goal nodes. These findings highlight the importance of evaluating and comparing ordering algorithms in various scenarios. Our approach has significant implications for AI research and development, as it has the potential to improve the performance of pathfinding algorithms in various applications. We also discuss the limitations of our approach and potential areas for further research, such as investigating the effectiveness of our approach in different types of graphs and exploring the potential use of machine learning techniques to optimize the shell layer construction. The code for this study is available at https://github.com/abdullah1aloush1/MuGONA. • Equally-spaced nodes formed over workspace with obstacles, optimizing search process. • Efficient neighboring graph built for equally-spaced nodes, reducing complexity. • Path optimization for navigation by ordering goal nodes, reducing time and distance. • Path smoothing for navigation via interpolation to yield a trajectory on path nodes. [ABSTRACT FROM AUTHOR]

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Background: The application of artificial intelligence to interpret the electrocardiogram (ECG) has predominantly included the use of knowledge engineered rule-based algorithms which have become widely used today in clinical practice. However, over recent decades, there has been a steady increase in the number of research studies that are using machine learning (ML) to read or interrogate ECG data.Objective: The aim of this study is to review the use of ML with ECG data using a time series approach.Methods: Papers that address the subject of ML and the ECG were identified by systematically searching databases that archive papers from January 1995 to October 2019. Time series analysis was used to study the changing popularity of the different types of ML algorithms that have been used with ECG data over the past two decades. Finally, a meta-analysis of how various ML techniques performed for various diagnostic classifications was also undertaken.Results: A total of 757 papers was identified. Based on results, the use of ML with ECG data started to increase sharply (p < 0.001) from 2012. Healthcare applications, especially in heart abnormality classification, were the most common application of ML when using ECG data (p < 0.001). However, many new emerging applications include using ML and the ECG for biometrics and driver drowsiness. The support vector machine was the technique of choice for a decade. However, since 2018, deep learning has been trending upwards and is likely to be the leading technique in the coming few years. Despite the accuracy paradox, accuracy was the most frequently used metric in the studies reviewed, followed by sensitivity, specificity, F1 score and then AUC.Conclusion: Applying ML using ECG data has shown promise. Data scientists and physicians should collaborate to ensure that clinical knowledge is being applied appropriately and is informing the design of ML algorithms. Data scientists also need to consider knowledge guided feature engineering and the explicability of the ML algorithm as well as being transparent in the algorithm's performance to appropriately calibrate human-AI trust. Future work is required to enhance ML performance in ECG classification. [ABSTRACT FROM AUTHOR]

In this paper an Online Monitoring System is presented, which enables a universal condition monitoring of electrical household appliances. The approach involves recording the power consumption of the respective device and evaluating the measured data using machine learning methods and tools. Thereby an artificial intelligence learns the normal condition of the examined device and can subsequently perform a monitoring. A k -Means algorithm is used for clustering and a One-class Support Vector Machine for classification. This makes it possible to detect anomalies in the operating modes and behavior of the device during subsequent operation. In addition to the methodology, the design and real application of a prototype is presented, in which a suitable sensor technology and a software with artificial intelligence are implemented. [ABSTRACT FROM AUTHOR]

Article presents the relevance of creating Smart Grid for public power networks and industrial enterprises. The article describes an experiment in which data were collected from current and voltage sensors from several different consumers of electric energy, using the created Smart Socket - Smart Energy device. The methods and algorithms of intellectual and spectral analysis were used to analyze and process experimental data. In this paper, we developed an algorithm for identifying the type of consumer of electric energy in the network of general power supply using the machine learning model XGBoost - extreme gradient boosted decision trees. [ABSTRACT FROM AUTHOR]

Abstract The aim of this paper is to investigate the power quality analysis by using 2D discrete orthonormal S-transform, machine learning and multi-objective evolutionary algorithms. The fact that PQ signals are one-dimensional (1D) signals due to their nature leads to the search for feature extraction approaches based on 1D signal processing methods. Due to the electric network is getting more and more complicated day by day, it is necessary to determine effectively the disturbances events. In the proposed method, extraction of a new feature based on two-dimensional (2D) signal processing by 2D Fast Discrete Orthonormal Stockwell Transform (2D-FDOST) method and determination of the most suitable feature group by Non-dominated Sorting Genetic Algorithm II (NSGA-II) method are performed. Eleven different PQ events are synthetically produced based on mathematical modelling. 1D signals are transformed into 2D signals with equal row and column numbers. Statistical and image-based features are created on the amplitude and phase matrices obtained by 2D-FDOST method from 2D signals. The NSGA-II method, which is one of the multi-objective evolutionary optimization methods, is used to convert a large number of feature vectors into a small number of useful feature groups. NSGA-II produces the optimal solution for two different fitness functions that calculate the number of features and classifier performance. By using different machine learning classifiers for selected features, a model classifying PQ disturbances with high performance and robust structure against noisy situations is created. Highlights • The power quality (PQ) disturbances data is transformed into two dimensional image matrices. • Features are generated for the PQ disturbances data by using the 2D-FDOST method. • The NSGA-II method selects the feature group with the least number of features and the highest KNN classifier performance. • The different classifier models created using the selected feature group are compared with one another. • PQ disturbances classifier is presented for the best performance. [ABSTRACT FROM AUTHOR]

Abstract Multi-view representation learning for social images has recently made remarkable achievements in many tasks, such as cross-view classification and cross-modal retrieval. Since social images usually contain link information besides the multi-modal contents (e.g., text description, and visual content), simply employing the data content may result in sub-optimal multi-view representation of the social images. In this paper, we propose a Deep Multi-View Embedding Model (DMVEM) to learn joint embeddings for the three views including the visual content, the associated text descriptions, and their relations. To effectively encode the link information, a weighted relation network is built based on the linkages between social images, which is then embedded into a low dimensional vector space using the Skip-Gram model. The learned vector is regarded as the third view besides the visual content and text description. To learn a joint representation from the three views, a deep learning model with three-branch nonlinear neural network is proposed. A three-view bi-directional loss function is used to capture the correlation between the three views. The stacked autoencoder is adopted to preserve the self-structure and reconstructability of the learned representation for each view. Comprehensive experiments are conducted in the tasks of image-to-text, text-to-image, and image-to-image searches. Compared to the state-of-the-art multi-view embedding methods, our approach achieves significant improvement of performance. Highlights • We investigate the problem of multi-view learning for the linkage-embedded social images using deep model. • The learned representation can effectively capture both the content and the network structure. • We propose a deep multi-view model DMVEM to effectively learn a joint representation from the three views of social image. • The experimental results demonstrate the superiority of the proposed model against the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

This study is informed by two research gaps. One, Artificial Intelligence's (AI's) Machine Learning (ML) techniques have the potential to help separate information and misinformation, but this capability has yet to be empirically verified in the context of COVID-19. Two, while older adults can be particularly susceptible to the virus as well as its online infodemic, their information processing behaviour amid the pandemic has not been understood. Therefore, this study explores and understands how ML techniques (Study 1), and humans, particularly older adults (Study 2), process the online infodemic regarding COVID-19 prevention and cure. Study 1 employed ML techniques to classify information and misinformation. They achieved a classification accuracy of 86.7% with the Decision Tree classifier, and 86.67% with the Convolutional Neural Network model. Study 2 then investigated older adults' information processing behaviour during the COVID-19 infodemic period using some of the posts from Study 1. Twenty older adults were interviewed. They were found to be more willing to trust traditional media rather than new media. They were often left confused about the veracity of online content related to COVID-19 prevention and cure. Overall, the paper breaks new ground by highlighting how humans' information processing differs from how algorithms operate. It offers fresh insights into how during a pandemic, older adults—a vulnerable demographic segment—interact with online information and misinformation. On the methodological front, the paper represents an intersection of two very disparate paradigms—ML techniques and interview data analyzed using thematic analysis and concepts drawn from grounded theory to enrich the scholarly understanding of human interaction with cutting-edge technologies. • Machine learning techniques classified COVID-19 information and misinformation. • Older adults were interviewed to understand their information processing behaviour. • Older adults were more inclined to trust traditional media rather than new media. • Humans failed to classify COVID-19 information and misinformation algorithmically. • Corrective messages with attractive presentation are not always seen as convincing. [ABSTRACT FROM AUTHOR]

Abstract The world is moving exponential towards the technical growth in every field. The most obliging and gifted is the field of Healthcare and medical diagnosis with the advanced techniques of Artificial Intelligence and Machine Learning; it's a boon to the patients affected by cancer. Brain Tumor is one of the severe syndrome causes for the death of Human beings. Magnetic Resonance Imaging is vital medical diagnosis tool for revealing of tumors. Precise segmentation and classification of the tumors yet relics a puzzling task because of its large structural and spatial erraticism among tumors. In this paper an effort is made to discuss and implement the different Machine Learning classification algorithms on Brain Tumor MR Images for 20 Patients i.e 150*20 Slices of MR Sequences (MR Images) for thirteen attributes (statistical features), using the Dataset of BRATS 2015. Contribution to the heart of technology that can be used for tumor classification exhausting the machine learning algorithms namely the Naive Bayes, Logistic Regression, Multi-layer Perceptron, Support Vector Machine and Decision tree using the Waikato Environment for Knowledge Analysis tool and MATLAB. The experimental outcomes of proposed technique have been assessed for performance and quality examination on magnetic resonance brain images. [ABSTRACT FROM AUTHOR]

The usage of Building Automation Systems (BAS) and Energy Management Systems (EMS) is indeed becoming ever more common and sophisticated, and seeking to promote energy savings by integrating new sources of data, such as user preferences, in real-time. This paper reviews the existing systems and proposes an innovation in HVAC systems management: a system that tracks the occupants’ preferences, learns from them, and manages HVAC automatically. Our hypothesis was that by developing a learning system based on feedback acquired through the mobile devices of room occupants to optimize the control of a HVAC system, in order to minimize energy consumption while maximizing average user comfort. A prototype solution is described and evaluated by simulation. We show that ambient intelligent systems can be used to control a building’s EMS, effectively reducing energy consumption while maintaining acceptable comfort levels. Our results indicate that employing a k-means machine learning technique enables the automatic configuration of an HVAC system to reduce energy consumption while keeping the majority of occupants within acceptable comfort levels. The developed prototype provides occupants with feedback on ambient variables on a mobile user interface. © 2017 Elsevier Science. All rights reserved. [ABSTRACT FROM AUTHOR]

Buying and selling stocks are the main activities of investment in the financial markets. During several years, financial experts have designed various indices and indicators to develop intelligent methods to decide whether buy or sell or no trade a specific stock. In this paper, the league championship algorithm (LCA) is adapted and equipped with a network structure (which provides the implicit memory function, compact structure and gives the ability of reusing nodes) for stock trading rule extraction process. The proposed algorithm is able to extract and save various stock trading rules for various kinds of stock market conditions. Each individual in LCA makes role as a sport team which could learn from weaknesses and strengths of others to enhance its performance. The network structure used for each individual is equipped with reinforcement learning to strength the searching power and extracting better rules. The backward Q-learning (BQL), which is a combination of Sarsa learning and Q-learning algorithms, is also hybridized with LCA to increase the learning speed to extract better rules. A trend clustering technique is also developed to determine uptrends and downtrends and to remedy delay in the trend direction reflection along with moving average of stock prices. Finally, to use the trading rules extracted and saved in the rule pools, a new approach through average matching degree is introduced which could create trinary signals of buying, selling, and no trade. The effectiveness of the proposed approach is tested on a set of 20 companies, which have high liquidity and large market capitalization, chosen from various fields of Tehran Stock Exchange. The used historical price data was adjusted through factors of dividends and capital increase. Computational tests and simulations indicate that our hybrid method clearly offers higher profits and lower losses than those of genetic network programming method with rule accumulation (GNP-RA) on all stocks and those of the Buy&Hold strategy on 17 out of 20 instances (85%), especially when the price uptrends are sharp. [ABSTRACT FROM AUTHOR]

Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using machine learning algorithms often faces problems and raises questions that must be resolved. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities. The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems; (ii) identify open questions in the use or research of machine learning algorithms; and (iii) assist new researchers to position new research activity in this domain appropriately. The results of this study identify existing classes of recommender systems, characterize adopted machine learning approaches, discuss the use of big data technologies, identify types of machine learning algorithms and their application domains, and analyzes both main and alternative performance metrics. [ABSTRACT FROM AUTHOR]

Adaptive graph learning methods for clustering, which adjust a data similarity matrix while taking into account its clustering capability, have drawn increasing attention in recent years due to their promising clustering performance. Existing adaptive graph learning methods are based on either original data or linearly projected data and thus rely on the assumption that either representation is a good indicator of the underlying data structure. However, this assumption is sometimes not met in high dimensional data. Studies have shown that high-dimensional data in many problems tend to lie on an embedded nonlinear manifold structure. Motivated by this observation, in this paper, we develop dual data representations, i.e., original data and a nonlinear embedding of the data obtained via an Extreme Learning Machine (ELM)-based neural network, and propose to use them as the more reliable basis for graph learning. The resulting algorithm based on ELM and Constrained Laplacian Rank (ELM-CLR) further improves the clustering capability and robustness, while retaining the advantages of adaptive graph learning, such as not requiring any post-processing to extract cluster indicators. The empirical study shows that the proposed algorithm outperforms the state-of-the-art graph-based clustering methods on a broad range of benchmark datasets. [ABSTRACT FROM AUTHOR]

Advancement of Artificial Intelligence (AI) capabilities in medicine can help address many pressing problems in healthcare. However, AI research endeavors in healthcare may not be clinically relevant, may have unrealistic expectations, or may not be explicit enough about their limitations. A diverse and well-functioning multidisciplinary team (MDT) can help identify appropriate and achievable AI research agendas in healthcare, and advance medical AI technologies by developing AI algorithms as well as addressing the shortage of appropriately labeled datasets for machine learning. In this paper, our team of engineers, clinicians and machine learning experts share their experience and lessons learned from their two-year-long collaboration on a natural language processing (NLP) research project. We highlight specific challenges encountered in cross-disciplinary teamwork, dataset creation for NLP research, and expectation setting for current medical AI technologies. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates. Over the past decade, several machine-learning techniques have been used for cardiovascular disease diagnosis and prediction. Each problem requires some degree of understanding of the problem, in terms of cardiovascular medicine and statistics, to apply the optimal machine-learning algorithm. In the near future, AI will result in a paradigm shift toward precision cardiovascular medicine. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI's application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine. [ABSTRACT FROM AUTHOR]

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time decisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and preferences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area. • Review of Artificial Intelligence/Machine Learning for energy demand-side response. • Sub-areas of energy demand response for which AI/ML techniques have been used. • Discussion of pros and cons of using specific AI/ML techniques in each sub-area. • Insights into commercial initiatives/industrial R&D projects using AI techniques. • Discussion of the field's evolution and potential future research paths. [ABSTRACT FROM AUTHOR]

Machine learning and artificial intelligence have created a myriad of opportunities for power systems. For example, learning techniques have been widely used for forecasting, data-driven frequency and voltage control, clustering of demand profiles, state estimation, theft detection, etc. Despite their satisfactory performances, these methods can face challenges associated with adversarial attacks that may result in system failure. In this paper, we systematically compare the effects of a set of attacks using different learning algorithms for a set of power systems problems (classification, forecasting, and control). We first show that most of the machine learning models proposed for power system applications are vulnerable to adversarial attacks. Then we adopt a series of common defense algorithms to improve performance under the different attacks. We identify GAN as the most effective defense algorithm across all power system applications tested, among all attacks and attack levels. It is of interest to notice that for the three power system problems different application datasets can be much more vulnerable. As the first systematic study of attack and defense mechanisms for a variety of power systems applications we hope to motivate the power systems and machine learning communities to continue studying how to increase the robustness of learning models against attacks. • We study the effects of attacks using different learning algorithms for power systems. • We test which defense algorithms are effective based on the attack and case study. • GAN is the most effective defense algorithm for all power system applications tested. • The power systems applications we study are classification, forecasting, and control. • Attack vulnerability varies widely based on the dataset studied in the control case. [ABSTRACT FROM AUTHOR]

The recent development in the theory of Hierarchical Temporal Memory (HTM) - Cortical Learning Algorithms (CLA) which models the structural and algorithmic properties of neocortex has brought in a new paradigm in the field of machine intelligence. As the theory of HTM-CLA continues to evolve, the ways of inferring the patterns and structures recognized by the HTM-CLA algorithm are still a big challenge. Moreover, the existing methods used to infer the classification output from HTM-CLA are far from satisfactory. In this paper, we propose two new classifiers using similarity evaluation methods based on dot similarity (H-DS) and mean-shift clustering (H-MSC) to obtain classification from the sparse distributed representation (SDR) output of HTM-CLA. We validate and benchmark the performance of our proposed classifiers using three datasets from the UCI machine learning repository. The results show that the proposed classifiers enhance the classification performance of HTM-CLA and their performance is also comparable to other traditional machine learning technique such as decision tree. [ABSTRACT FROM AUTHOR]

• A dataset is created using velocities of wheels and distance of obstacle from AMR. • The left and right turn of AMR is framed as a two class problem. • The parameters of the proposed algorithm are updated by SGD optimization technique. • The proposed algorithm provides better results with other existing algorithms. In this paper, a novel approach based on Machine Learning (ML) concept, i.e., the Adaptive Stochastic Gradient Descent Linear Regression (ASGDLR) algorithm, is developed to segregate an AMR's directional movement as right and left turn. Moreover, the developed algorithm is employed for path planning and navigational purposes. Here, real-time velocities of the right and left wheel and distance from obstacle data have been acquired by two Infrared (IR) sensors and one Ultrasonic (US) sensor positioned on the AMR. The weights of the proposed ASGDLR model are iteratively updated by applying Stochastic Gradient Descent (SGD) optimization technique by considering the difference between the actual velocity and model output velocity as an error signal. For the performance analysis of the proposed algorithm, three different performance indices, such as Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE), have been evaluated. The efficacy of the proposed algorithm is compared with six different AI-based regression algorithms for validation purposes. Moreover, the performance of the proposed algorithm is investigated for both obstacle avoidance and path planning in dense and cluttered environments. The simulation findings indicate that our proposed algorithm can accomplish tasks more efficiently than others while eliminating their shortcomings. [Display omitted] [ABSTRACT FROM AUTHOR]

SANS has warned about the new variants of SSH dictionary attacks that are very stealthy in comparison with a simple attack. In this paper, we propose a new method to detect simple and stealthy attacks by combining two key innovations. First, on the basis of our assumptions, we employ two criteria: "the existence of a connection protocol" and "the inter-arrival time of an auth-packet and the next". These criteria are not available, though, owing to the confidentiality and flexibility of the SSH protocol. Second, we resolve this problem by identifying "the transition point of each sub-protocol" through flow features and machine learning algorithms. We evaluate the effectiveness through experiments on real network traffic at the edges in campus networks. The experimental results show that our method provides high accuracy with acceptable computational complexity. [ABSTRACT FROM AUTHOR]

Classically, unsupervised machine learning techniques are applied on data sets with fixed number of attributes (variables). However, many problems encountered in the field of informetrics face us with the need to extend these kinds of methods in a way such that they may be computed over a set of nonincreasingly ordered vectors of unequal lengths. Thus, in this paper, some new dissimilarity measures (metrics) are introduced and studied. Owing to that we may use, e.g. hierarchical clustering algorithms in order to determine an input data set's partition consisting of sets of producers that are homogeneous not only with respect to the quality of information resources, but also their quantity. [ABSTRACT FROM AUTHOR]

In this paper we describe a method for registration of 3D point clouds that represent objects of interest. A stereovision system is used to capture point clouds of a static environment, robot arm and an unknown object. By moving the robot arm in the environment the proposed system defines known occupied zones and is able to identify the robot arm. In order to identify a complete point cloud presentation of the robot gripper it is rotated in front of a stereovision camera and its geometry is captured from different angles. Iterative closest point algorithm is used to determine a rigid transformation between every new robot pose so the original point cloud can be appended with the transformed one. When the robot is holding a new object the registration procedure is repeated and known elements (environment, robot arm and gripper) are removed so that the object can be identified. [ABSTRACT FROM AUTHOR]

Multilayer perceptron (MLP) and support vector machine (SVM), two popular learning machines, are increasingly being used as alternatives to classical statistical models for ground-level ozone prediction. However, employing learning machines without sufficient awareness about their limitations can lead to unsatisfactory results in modeling the ozone evolving mechanism, especially during ozone formation episodes. With the spirit of literature review and justification, this paper discusses, with respect to the concerning of ozone prediction, the recently developed algorithms/technologies for treating the most prominent model-performance-degradation limitations. MLP has the “black-box” property, i.e., it hardly provides physical explanation for the trained model, overfitting and local minima problems, and SVM has parameters identification and class imbalance problems. This commentary article aims to stress that the underlying philosophy of using learning machines is by no means as trivial as simply fitting models to the data because it causes difficulties, controversies or unresolved problems. This article also aims to serve as a reference point for further technical readings for experts in relevant fields. [ABSTRACT FROM AUTHOR]

Autonomous vehicles are the latest engineering research sensations. The development of these needs the collaboration of studies in the automotive and artificial intelligence domains. Self-driving cars face a slew of security issues when left on their own without human supervision. However, the massive volumes of data required for vehicle operation can't be processed rationally by humans. This is where modern intelligence systems can help. They can facilitate clear decision-making regarding the state of the systems. A unique technique based on Machine Learning (ML) procedures is proposed in this paper. Two cluster models have been constructed and coupled with three Convolutional Neural Networks (CNN) models based on Demand-Side Management (DSM). These models are integrated to determine the optimal operating time, stability, and management model. Data from the previous three years have been used in this study. The results show that the proposed CNN and cluster 2 models are the best for energy optimization. The best thing is that this real-time model can meet the demands of large traffic. [Display omitted] [ABSTRACT FROM AUTHOR]

Clustering is an essential tool in data mining research and applications. It is the subject of active research in many fields of study, such as computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning. Several clustering techniques have been proposed and implemented, and most of them successfully find excellent quality or optimal clustering results in the domains mentioned earlier. However, there has been a gradual shift in the choice of clustering methods among domain experts and practitioners alike, which is precipitated by the fact that most traditional clustering algorithms still depend on the number of clusters provided a priori. These conventional clustering algorithms cannot effectively handle real-world data clustering analysis problems where the number of clusters in data objects cannot be easily identified. Also, they cannot effectively manage problems where the optimal number of clusters for a high-dimensional dataset cannot be easily determined. Therefore, there is a need for improved, flexible, and efficient clustering techniques. Recently, a variety of efficient clustering algorithms have been proposed in the literature, and these algorithms produced good results when evaluated on real-world clustering problems. This study presents an up-to-date systematic and comprehensive review of traditional and state-of-the-art clustering techniques for different domains. This survey considers clustering from a more practical perspective. It shows the outstanding role of clustering in various disciplines, such as education, marketing, medicine, biology, and bioinformatics. It also discusses the application of clustering to different fields attracting intensive efforts among the scientific community, such as big data, artificial intelligence, and robotics. This survey paper will be beneficial for both practitioners and researchers. It will serve as a good reference point for researchers and practitioners to design improved and efficient state-of-the-art clustering algorithms. • Provide an up-to-date comprehensive review of the different clustering techniques. • Highlight novel and most recent practical applications areas of clustering. • Provide a convenient research path for new researchers. • Help experts develop new algorithms for emerging challenges in the research area. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel density-based radar scanning clustering algorithm. Its main objective is to quickly discover and accurately extract individual clusters by employing the radar scanning strategy. By using this algorithm, the number of clusters does not need to be specified beforehand. Two techniques are utilized in our proposed method. First, we use a fast mean-shift algorithm with adaptive radius and active subsets to effectively locate the centers, reducing the computational time significantly. Second, we employ the shape of the probability density function of the distribution of distances between a selected point and the other points in the data set. This is performed to determine the critical parameters of the radiuses of the fast mean-shift algorithm and radiuses of clusters. The new algorithm has four merits. It reduces the computational complexity, overcomes problems caused by high dimensionality, is capable of dealing with heterogeneous spherical data sets, and lastly, is robust to noise and outliers. After applying our proposed method to several kinds of synthetic and real-world data sets, the results indicate that the density-based radar scanning algorithm is efficient and accurate. • The proposed algorithm discovers and extracts clusters by a radar scanning strategy. • The number of clusters does not need to be given beforehand. • This algorithm reduces time complexity and solves high dimensionality. • It is capable of dealing with heterogeneous data. • It is robust and invulnerable to noise and outliers. [ABSTRACT FROM AUTHOR]

[Display omitted] • Tracked motion of 30 human subjects including healthy subjects and abnormal subjectswith help of smartphone embedded sensors. • Used Dynamic Time Warping algorithm to map similarites in pattern of different category of subjects based on locomotor disorders. • Used different machine learning algorithms and tested the performance boost for this approach. The study of walking pattern also known as gait is affected by several underlying musculoskeletal and neurological factors. All humans have contrasting gait pattern however the pattern lies within a predictable range for all human with no underlying health disorders affecting gait. However, deviation from a regular distribution range can indicate underlying health conditions. The gait could be regularly and inexpensively monitored in real-time with help of smartphones embedded sensors such as tri-axial accelerometers and tri-axial gyroscopes as smartphones are widely available. The advancements in computational power and artificial intelligence led to the development of several supervised machine learning algorithms which could be used to detect certain patterns associated with different gait conditions. However, the machine learning has widely been used to study different gait diseases, with this paper we have proposed a more efficient motion tracking approach and a new algorithm utilizing Dynamic Time Warping for an optimized locomotor disorder detection with affordable, practically applicable and globally deployable setup to monitor gait. In this experiment, we have tracked motion of 30 healthy and gait deformed human subjects with the proposed motion tracking approach and utilizing the proposed algorithm, we have achieved 35.95 times faster classification with up to 95.20% detection accuracy. [ABSTRACT FROM AUTHOR]

• We study fairness of AI algorithms in Privileged Group Selection Bias data settings. • A typical domain which often presents such selection bias is AI-based hiring. • We demonstrate that such selection bias can indeed lead to a high algorithmic bias. • We propose three in-process and pre-process fairness mechanisms. • Our methods improve fairness considerably with a minimal compromise in accuracy. An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms. Since they now touch on many aspects of our lives, it is crucial to develop AI algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision-making may be inherently prone to unfairness, even when there is no intention for it. In this paper, we study the fairness of AI algorithms in data settings for which unprivileged groups are extremely under-represented compared to privileged groups. A typical domain which often presents such Privileged Group Selection Bias (PGSB) is AI-based hiring, which stems from an inherent lack of labeled information for rejected applicants. We first demonstrate that such a selection bias can lead to a high algorithmic bias, even if privileged and unprivileged groups are treated exactly the same. We then propose several methods to overcome this type of bias. In particular, we suggest three in-process and pre-process fairness mechanisms, combined with both supervised and semi-supervised learning algorithms. An extensive evaluation that was conducted using two real-world datasets, reveals that the proposed methods are able to improve fairness considerably, with only a minimal compromise in accuracy. This is despite the limited information available for unprivileged groups and the inherent trade-off between fairness and accuracy. [ABSTRACT FROM AUTHOR]

The new form of artificial intelligence training, i.e. federated learning, is becoming more popular in the last few years. It is an optimization problem that includes additional mechanisms such as aggregation and data transmission. In this paper, we propose a hybridization of this type of training with a meta-heuristic. The meta-heuristic algorithm is adapted to manage the entire process as well as to analyze the best models to minimize attacks on this type of collaboration. The proposed solution is based on minimizing the general model error, with additional control mechanisms for incoming models, or adapting the aggregation method depending on the quality of the model. The innovative solution has been analyzed in terms of its application to the problem of image classification using classical and convolutional neural networks, and the most popular meta-heuristic algorithms. The proposal was analyzed in terms of the accuracy of the general model as well as for security against poisoning attacks. We reached 91% of accuracy using the proposed method with the Red Fox Optimization Algorithm and 95% in terms of detection of poisoned samples in the database. • A hybridization of meta-heuristic methods with federated learning. • A meta-heuristic for managing training process of neural networks. • A new technique for analyzing models and choosing the best ones for aggregation. • Additional verification of models to reduce the likelihood of an attack by poisoning. • A mechanism for cleaning the data set in case of its poisoning. [ABSTRACT FROM AUTHOR]

A persistent challenge in the cognitive development of autonomous robotics is the unsupervised and unstructured nature of skill transfer learning where the Self Organizing Map (SOM) has been used as the enabling technology. The Growing Self-Organizing Map (GSOM) algorithm is an unsupervised, structure-adapting machine learning algorithm conventionally used for data exploration, clustering, visualization, outlier detection and dimensionality reduction. In this paper, we present the design and development of a new distributed algorithm based on the GSOM for unsupervised skill transfer learning in autonomous robotics settings which overcomes the key limitations of the SOM in real-life scenarios. We posit this new algorithm will be directly applicable to skill transfer learning scenarios that require unsupervised, incremental and on-going self-learning of multi-tasks and knowledge transfer. The distributed and scalable properties of the proposed algorithm handle large volumes of data required for unsupervised skill transfer learning, based on data parallelization. It generates multiple maps representing diverse skill knowledge, which are then projected together to a single embedding. The new algorithm is positioned within an autonomous developmental robotics framework for knowledge acquisition and skill transfer learning. This framework was further adapted to three contemporary distributed computing platforms, Hadoop, Spark and Hama. Empirical evaluation of these three adaptations using several benchmark and real-life datasets demonstrates its practical value and computational efficiency for unsupervised skill transfer learning in autonomous robots. • Propose a novel Distributed GSOM algorithm enabling transfer learning in robotics environments at scale. • Distributed GSOM algorithm is adapted for MapReduce, BSP and RDD distributed computing paradigms. • Proposed adaptations are implemented on Apache Hadoop, Hama and Spark respectively. • Performance demonstrating super-linear speedups compared to the serial implementation. • Empirically demonstrate better performance of Distributed GSOM over distributed SOM. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) technologies are widely used to study the computational drug repositioning to find potential new uses for marketed drugs, which have a huge impact on drug development. However, due to the sparsity of datasets and the linear limitations of traditional AI or machine learning models lead to the inability of most methods to effectively obtain hidden feature of drugs and diseases To end this, Predictive Intelligence (PI) has been proposed in recent years. Hence, in this work, we propose a PI powered Attentional Stacking Matrix Factorization (PI-ASMF). Firstly, in order to extract the effective latent factor of the drugs or diseases the ASMF model superimposes the drug–disease similarity information and the drug–disease association information into the PI model, which ensures that it can learn the effective latent factor and alleviates the cold start problem to a certain extent. Subsequently, the latent factors of drugs and diseases are element-wise multiplied with their respective attentional vectors to learn the adaptive weights of each feature. Finally, the latent factors of drugs and diseases are fed into the prediction module to generate predictive values of potential drug–disease associations. The AUC and AUPR values of PI-ASMF model on two real datasets are 0.892 and 0.168, 0.913 and 0.25, respectively, which verified its superiority and validity. • The novel PI-ASMF model is proposed in this paper. • The PI-ASMF model can learn the effective drug or disease latent factor. • The PI-ASMF model can capture more complex drug–disease associations. • The results prove the effectiveness and superiority of the PI-ASMF model. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) algorithms have shown their capability to complement human analysis in the understanding of complex phenomena. Their advantages essentially rely on the flexibility of their construction that allows capturing complex relationships between inputs and outputs of a problem and also on their ability to adapt themselves via a learning phase to the available information. As a result, AI is widely used in many scientific fields and especially those related to the nuclear industry. This work deals an application of AI based on several classical machine learning approaches for the study of Reactivity Initiated Accidents (RIA) in the CABRI experimental pulse reactor located at the Cadarache research center, southern France. We focus on the interpretation of the fuel rod behaviour during the power pulse using the online fuel motion monitoring system called the hodoscope. The objective of this paper is to investigate how AI algorithms can be used for the automatic detection of a generic fuel delocalization from the signals recorded by the hodoscope. [ABSTRACT FROM AUTHOR]

Recently, the trending 5G technology encourages extensive applications of on-device machine learning, which collects user data for model training. This requires cost-effective techniques to preserve the privacy and the security of model training within the resource-constrained environment. Traditional learning methods rely on the trust among the system for privacy and security. However, with the increase of the learning scale, maintaining every edge device's trustworthiness could be expensive. To cost-effectively establish trust in a trustless environment, this paper proposes democratic learning (DemL), which makes the first step to explore hardware/software co-design for blockchain-secured decentralized on-device learning. By utilizing blockchain's decentralization and tamper-proofing, our design secures AI learning in a trustless environment. To tackle the extra overhead introduced by blockchain, we propose PoMC (an algorithm and architecture co-design) as a novel blockchain consensus mechanism, which first exploits cross-domain reuse (AI learning and blockchain consensus) in AI learning architecture. Evaluation results show our DemL can protect AI learning from privacy leakage and model pollution, and demonstrated that privacy and security come with trivial hardware overhead and power consumption (2%). We believe that our work will open the door of synergizing blockchain and on-device learning for security and privacy. [ABSTRACT FROM AUTHOR]

Industry 4.0 is the new industrial revolution. By connecting every machine and activity through network sensors to the Internet, a huge amount of data is generated. Machine Learning (ML) and Deep Learning (DL) are two subsets of Artificial Intelligence (AI), which are used to evaluate the generated data and produce valuable information about the manufacturing enterprise, while introducing in parallel the Industrial AI (IAI). In this paper, the principles of the Industry 4.0 are highlighted, by giving emphasis to the features, requirements, and challenges behind Industry 4.0. In addition, a new architecture for AIA is presented. Furthermore, the most important ML and DL algorithms used in Industry 4.0 are presented and compiled in detail. Each algorithm is discussed and evaluated in terms of its features, its applications, and its efficiency. Then, we focus on one of the most important Industry 4.0 fields, namely the smart grid, where ML and DL models are presented and analyzed in terms of efficiency and effectiveness in smart grid applications. Lastly, trends and challenges in the field of data analysis in the context of the new Industrial era are highlighted and discussed such as scalability, cybersecurity, and big data. [ABSTRACT FROM AUTHOR]