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Both software debugging and artificial intelligence techniques are hot topics in the current field of software engineering. Debugging techniques, which comprise fault localization and program repair, are an important part of the software development lifecycle for ensuring the quality of software systems. As the scale and complexity of software systems grow, developers intend to improve the effectiveness and efficiency of software debugging via artificial intelligence (artificial intelligence for software debugging, AI4SD). On the other hand, many artificial intelligence models are being integrated into safety-critical areas such as autonomous driving, image recognition, and audio processing, where software debugging is highly necessary and urgent (software debugging for artificial intelligence, SD4AI). An AI-enhanced debugging technique could assist in debugging AI systems more effectively, and a more robust and reliable AI approach could further guarantee and support debugging techniques. Therefore, it is important to take AI4SD and SD4AI into consideration comprehensively. In this paper, we want to show readers the path, the trend, and the potential that these two directions interact with each other. We select and review a total of 165 papers in AI4SD and SD4AI for answering three research questions, and further analyze opportunities and challenges as well as suggest future directions of this cross-cutting area. [ABSTRACT FROM AUTHOR]

It is nowadays widely understood that undeclared work cannot be efficiently combated without a holistic view on the mechanisms underlying its existence. However, the question remains whether we possess all the pieces of the holistic puzzle. To fill the gap, in this paper, we test if the features so far known to affect the behaviour of taxpayers are sufficient to detect noncompliance with outstanding precision. This is done by training seven supervised machine learning models on the compilation of data from the 2019 Special Eurobarometer on undeclared work and relevant figures from other sources. The conducted analysis not only does attest to the completeness of our knowledge concerning the drivers of undeclared work but also paves the way for wide usage of artificial intelligence in monitoring and confronting this detrimental practice. The study, however, exposes the necessity of having at disposal considerably larger datasets compared to those currently available if successful real-world applications of machine learning are to be achieved in this field. Alongside the apparent theoretical contribution, this paper is thus also expected to be of particular importance for policymakers, whose efforts to tackle tax evasion will have to be expedited in the period after the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

In this paper, we continue to consider the problem of recovering the port-Hamiltonian structure for an arbitrary system of differential equations. We complement our previous study on this topic by explaining the choice of machine learning algorithms and discussing some details of their application. We also consider the possibility provided by this approach for a potentially new definition of canonical forms and classification of systems of differential equations. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to survival analysis using topological data analysis (TDA) within Bayesian statistics combined with machine learning algorithms suitable to time-to-event data. The paper brings into the analysis aspects of topological invariance through what is known as persistence homology. TDA demonstrates the existence and statistical significance of a kind of unmeasured heterogeneity originating from the topology of the data as a whole. Combined with machine learning tools persistence homology provides us with new tools to construct a rich set of ways to analyze data and build predictive models that are optimized using inherent topological invariants such as one-dimensional loops as regularization. Specifically, this paper incorporates persistent homology effects in different ways in the analysis of survival data through the technique of functional principal component analysis (FPCA): first, by using topological invariants converted into FPCA factors that shape Bayesian statistical analysis of time-to-event data; second, by using FPCA measures of topological invariants in regularizing the process of optimizing the data and the posterior distributions of the Bayesian estimation; three, by using FPCA factors of measures of topological invariants in machine learning algorithms and deep neural networks suitable for analyzing survival data as a way of going beyond usual parametric and semi-parametric models of survival analysis. The approach is illustrated through a running example of multi-frailty survival analysis of democracies in the period of 1950–2010. [ABSTRACT FROM AUTHOR]

With the development of the era of big data, research data are accumulating, and various research directions emerge endlessly. It is difficult for researchers to grasp the hotspots and development trends of the discipline. Therefore, exploring methods to quickly and accurately identify research fronts is of great significance to scientific and technological innovation. This paper proposes a research front identification method integrating machine learning and main path analysis in conjunction with papers and patents based on the existing research. The innovation of this method is the combination of citation analysis and semantic analysis to identify research front from the perspective of science-technology linkage. This article takes the Internet of Things in supply chain as an example to verify the feasibility and effectiveness of the method. It is of great significance to identify important scientific and technological research fronts in a specific domain by intuitively revealing knowledge diffusion and text mining. The proposed method enriches the application of MPA and helps scholars grasp the latest information, mainstreams and future directions. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) algorithms have been increasingly replacing people in several application domains—in which the majority suffer from data imbalance. In order to solve this problem, published studies implement data preprocessing techniques, cost-sensitive and ensemble learning. These solutions reduce the naturally occurring bias towards the majority sample through ML. This study uses a systematic mapping methodology to assess 9927 papers related to sampling techniques for ML in imbalanced data applications from 7 digital libraries. A filtering process selected 35 representative papers from various domains, such as health, finance, and engineering. As a result of a thorough quantitative analysis of these papers, this study proposes two taxonomies—illustrating sampling techniques and ML models. The results indicate that oversampling and classical ML are the most common preprocessing techniques and models, respectively. However, solutions with neural networks and ensemble ML models have the best performance—with potentially better results through hybrid sampling techniques. Finally, none of the 35 works apply simulation-based synthetic oversampling, indicating a path for future preprocessing solutions. [ABSTRACT FROM AUTHOR]

Decision making is the process of making choices by identifying a decision, gathering information, and assessing alternative resolutions. Therefore, in this paper, they proposed an attention-based hierarchical denoised deep clustering (AHDDC) algorithm to solve the problem, which enables GCN to learn multiple layers of hidden information and uses the attention mechanism to strengthen the information. These accepted articles make contributions and novel aspects of different research in social media data scenarios, traffic data scenarios, data security scenarios, medical data scenarios, knowledge graph learning, and the other graph based deep learning models. [Extracted from the article]

The 46th issue of Financial Innovation (FIN), Volume 10, No.4 (2024) features 30 papers from authors and co-authors representing 25 countries. The papers are categorized into three sub-themes: FinTech, Asset Pricing, and Risk Management and Analysis. The FinTech papers cover topics such as churn prediction in retail banking, text analysis methodologies in blockchain research, user experience in peer-to-peer payment systems, and factors influencing blockchain adoption in Taiwan's banking sector. The Asset Pricing papers examine topics such as financial performance evaluation, prediction of stock outperformance, pricing variance swaps, and modeling OHLC data in candlestick charts. The Risk Management and Analysis papers cover subjects such as the influence of political stability on stock market returns, measuring longevity and mortality risks for life insurance products, predicting defaults in the invoice-trading market, and the impact of hashrate on Bitcoin network security. [Extracted from the article]

An introduction to this special issue of "Machine Learning," focusing on articles based on selected papers presented at the 4th Asian Conference on Machine Learning (ACML) 2012 which was held in Singapore from November 4-6, is presented.

With the rapid advancement of scientific research, collaboration in this area is becoming increasingly important. One of the major challenges is the link prediction problem for research collaboration. Recently, learning-based link prediction methods have received much attention. However, most of these studies have solely concentrated on exploiting a single network and its topology features for prediction, and ignore other factors that may influence link formation. To address this issue, in this paper we propose a link prediction model based on multi-network representation learning. Specifically, we develop new features based on the author's institutions and published papers, and three networks incorporating these features are modeled. Then, the network representation method based on joint training is proposed to embed the networks in a low-dimensional space. Finally, the authors' feature vectors are combined in finer granularity, and collaboration prediction is performed in a supervised manner. The performance of our model is evaluated by comparing it with other link prediction methods on a real-world dataset, and the experimental results show the effectiveness of our model. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is increasingly used to enhance production systems and meet the requirements of a rapidly evolving manufacturing environment. Compared to larger companies, however, small- and medium-sized enterprises (SMEs) lack in terms of resources, available data and skills, which impedes the potential adoption of analytics solutions. This paper proposes a preliminary yet general approach to identify low-cost analytics solutions for manufacturing SMEs, with particular emphasis on ML. The initial studies seem to suggest that, contrarily to what is usually thought at first glance, SMEs seldom need digital solutions that use advanced ML algorithms which require extensive data preparation, laborious parameter tuning and a comprehensive understanding of the underlying problem. If an analytics solution does require learning capabilities, a 'simple solution', which we will characterise in this paper, should be sufficient. [ABSTRACT FROM AUTHOR]

Today, manufacturing enterprises are adopting emerging Industry 4.0 technologies to create industrial intelligence-driven smart factories. This trend, in turn, is stimulating the advent of intelligent supply chains that can sync and support the rapid evolution of advanced industrial practices via supply chain digital transformation. Specifically, Artificial Intelligence (AI) and Machine Learning (ML) are emerging as vital breakthrough technologies that can help firms enhance profit margins, reduce supply chain costs, deliver excellent customer service, and make their supply chains intelligent. This paper identifies and analyzes 338 most influential research papers to scientifically examine the linkages among the AI-ML techniques and their applications in the SCM domain through bibliometric and network analysis, descriptive data analysis, and visual representation, thus furnishing a perspicacious knowledge base. The main contribution of this paper is to identify the unexplored potential and the contexts in which AI and ML can be used in managing and transforming supply chains digitally, including the aspects of intelligent and interpretative evolutions. Additionally, a fundamental contribution of this work is a comprehensive mind map that makes it possible to visualize, understand, and simulate the wide spectrum of findings from the bibliometric analyses. Finally, the study presents research gaps, implications, and future scope as a point of reference for researchers and practitioners. [ABSTRACT FROM AUTHOR]

Technological advancement has led to a dynamic change in how people interact. The interaction can be classified as—social networks (virtual) and real-life (physical) interaction. The relationships among nodes of social networks depend on communication among nodes and have been established by transmitting messages publicly or privately, depending on the trust among nodes. However, it poses three main challenges: (i) nodes can reveal their private message, (ii) private conversation among nodes can be hacked, and (iii) stored messages can be sent to another node, therefore, compromising privacy. The paper presents a privacy-preserving model using two major techniques, Binomial Distribution and Fuzzy Logic. This process has been followed by building predictive models using machine learning to provide potential directions to mitigate security threats posed to social networking platforms. The paper also provides a wholesome discussion of model design for predictive modelling, whereas most state-of-the-art techniques were primarily concerned about delivering potential directions for predictive modelling. The employed statistical and predictive modelling techniques suggest that friendship relationships in real life among people would transcend to ensure a secure and privacy-preserving relationship among them on social networks. Empirical results indicated that XG Boost supersedes the state-of-the-art models by achieving a predictive accuracy of 92.035%. In contrast, introducing new statistical features improves the performance of all our predictive models. [ABSTRACT FROM AUTHOR]

A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users' feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain. [ABSTRACT FROM AUTHOR]

Strong earthquake geological hazards cause significant social and economic losses. The assessment of post-earthquake socioeconomic development levels is one of the important bases from which to measure the recovery capacity of hazard areas. However, the long-term impact of geological hazards is rarely considered in the assessment of the socioeconomic development level of a mountainous earthquake-stricken area. The purpose of this paper is to study the complex relationship between earthquake geological hazard effects and socioeconomic development in the long-term post-earthquake development and reconstruction of mountainous extremely earthquake-stricken areas, to provide a reference for the study area to achieve regional sustainable development goals and high-quality development. On this basis, using the economic, social, ecological environment and other relevant data from 2008 to 2016, and using unsupervised machine learning algorithms, a socioeconomic development level evaluation model based on spectral clustering was established, and the effects of different kernel function scale parameters on the model were analyzed. The optimal parameters were determined, and the spatiotemporal analysis of the socioeconomic development level of the study area was carried out. The research results show that the performance of the evaluation model is optimal when the output category is 4 and the scale parameter is 0.26, and the scale parameter of the kernel function is an important indicator that affects the accuracy of the model. From 2008 to 2016, the socioeconomic development level of Qingchuan has always been at a very low level, Wenchuan and Beichuan have always been at a low level, Shifang and Mianzhu have always been at a high level, and Pengzhou and Dujiangyan have always been at a very high level. The socioeconomic development level of Anxian, Maoxian and Pingwu fluctuates greatly over time. Another interesting finding is that the socioeconomic development level of the mountainous earthquake-stricken area has a strong correlation with its own industrial structure and landslide hazard effects. The work done in this paper is of great significance for understanding the temporal and spatial effects of hazards and understanding the complexities of regional disaster systems. [ABSTRACT FROM AUTHOR]

We briefly review the state-of-the-art machine learning (ML) algorithms for mineral exploration, which mainly include random forest (RF), convolutional neural network (CNN), and graph convolutional network (GCN). In recent years, RF, a representative shallow machine learning algorithm, and CNN, a representative deep learning approach, have been proved to be powerful tools for ML-based mapping for mineral exploration. In the future, GCN deserves more attention for ML-based mapping for mineral exploration because of its ability to capture the spatial anisotropy of mineralization and its applicability within irregular study areas. Finally, we summarize the original contributions of the six papers comprising this special issue. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to systematically sort out and analyze the cutting-edge research on the eligibility criteria of clinical trials. Eligibility criteria are important prerequisites for the success of clinical trials. It directly affects the final results of the clinical trials. Inappropriate eligibility criteria will lead to insufficient recruitment, which is an important reason for the eventual failure of many clinical trials. We have investigated the research status of eligibility criteria for clinical trials on academic platforms such as arXiv and NIH. We have classified and sorted out all the papers we found, so that readers can understand the frontier research in this field. Eligibility criteria are the most important part of a clinical trial study. The ultimate goal of research in this field is to formulate more scientific and reasonable eligibility criteria and speed up the clinical trial process. The global research on the eligibility criteria of clinical trials is mainly divided into four main aspects: natural language processing, patient pre-screening, standard evaluation, and clinical trial query. Compared with the past, people are now using new technologies to study eligibility criteria from a new perspective (big data). In the research process, complex disease concepts, how to choose a suitable dataset, how to prove the validity and scientific of the research results, are challenges faced by researchers (especially for computer-related researchers). Future research will focus on the selection and improvement of artificial intelligence algorithms related to clinical trials and related practical applications such as databases, knowledge graphs, and dictionaries. [ABSTRACT FROM AUTHOR]

In recent years, electromagnetic spectrum has become an indispensable national strategic resource in the information age and an important strategic support for the development of national informatization. With the rapid proliferation of wireless data today, the demand for electromagnetic spectrum is also growing rapidly, and spectrum resources are limited, leading to the increasingly prominent problem of spectrum scarcity. In response to the traditional spectrum prediction methods for spectrum prediction with extended time and low accuracy, this paper analyzes the spectrum correlation of different channels that are in the same service, performs the similarity measure of frequency dimension data, and proves the correlation between the spectrum; and predicts the spectrum data with different time occupancy, and conducts deep mining of the spectrum target behavior from the time–frequency dimension. The experiments mainly use sequence to sequence (Seq2Seq) based Long Short-Term Memory (LSTM) to predict the data of each occupancy degree, and compare with the traditional LSTM network, Autoregressive Integrated Moving Average model (ARIMA), etc. The prediction accuracy of the method in this paper is higher. At the same time, the abnormal spectrum is monitored using the Mahalanobis distance algorithm, the detection accuracy reaches 100%. [ABSTRACT FROM AUTHOR]

Context: Artificial Intelligence (AI) has rapidly developed over the past several decades. Several related AI approaches, such as Machine Learning (ML), have been applied to research on landscape patterns and ecological processes. Objectives: Our goal was to review the methods of AI, particularly ML, used in studies related to landscape ecology and the main topics addressed. We aimed to assess the trend in the number of ML papers and the methods used therein, and provide a synopsis and prospectus of current use and future applications of ML in landscape ecology. Methods: We conducted a systematic literature search and selected 125 papers for review. These were examined and scored according to multiple criteria regarding methods and topic. We applied quantitative statistical methods, including cluster analysis based on titles, abstracts, and keywords and a non-metric multidimensional scaling based on attributes assigned during the review. We used Random Forests machine learning to describe the differences between identified clusters in terms of the topics and methods they included. Results: The most frequent method found was Random Forests, but it is noteworthy to mention the increasing popularity of tools related to Deep Learning. The topics cover both ecologically oriented issues and the landscape-human interface. There has been a rapid increase in ML and AI methods in landscape ecology research, with Deep Learning and complex multi-step pipeline AI methods emerging in the last several years. Conclusions: The rapid increase in the number of ML papers in landscape ecology research, and the range of methods employed in them, suggest explosive growth in application of these methods in landscape ecology. The increase of Deep Learning approaches in the most recent years suggest a major change in analytical paradigms and methodologies that we feel may transform the field and enable analyses of more complex pattern process relationships across vaster data sets than has been possible previously. [ABSTRACT FROM AUTHOR]

A world of AI-assisted writing and reviewing might transform the nature of the scientific paper. [ABSTRACT FROM AUTHOR]

Significant research has been carried out in the field of User Behavior Analysis, focused on understanding, modeling and predicting past, present and future behaviors of users. However, the heterogeneity of the approaches makes their comprehension very complicated. Thus, domain and Machine Learning experts have to work together to achieve their objectives. The main motivation for this work is to obtain an understanding of this field by providing a categorization of state-of-the-art works grouping them based on specific features. This paper presents a comprehensive survey of the existing literature in the areas of Cybersecurity, Networks, Safety and Health, and Service Delivery Improvement. The survey is organized based on four different topic-based features which categorize existing works: keywords, application domain, Machine Learning algorithm, and data type. This paper aims to thoroughly analyze the existing references, to promote the dissemination of state-of-the-art approaches discussing their strong and weak points, and to identify open challenges and prospective future research directions. In addition, 127 discussed papers have been scored and ranked according to relevance-based features: paper reputation, maximum author reputation, novelty, innovation and data quality. Both types of features, topic-based and relevance-based have been combined to build a similarity metric enabling a rich visualization of all considered publications. The obtained graphic representation provides a guide of recent advancements in User Behavior Analysis by topic, highlighting the most relevant ones. [ABSTRACT FROM AUTHOR]

In recent years, the global wind power construction is accelerating. Although wind power is a clean energy without pollution, its volatility and irregularity have a great impact on wind power integration. Therefore, scholars pay more and more attention to the ultra-short-term prediction of wind speed. At present, the popular wind speed prediction model usually combines wind speed decomposition algorithm, machine learning algorithm, and intelligent optimization algorithm. The general wind speed decomposition algorithm cannot use the information contained in the factors affecting wind speed. Besides, the current popular optimization algorithms, such as gray wolf optimization algorithm, have strong convergence and better optimization effect, but their structure is complex and their operation complexity is large. And the PSO algorithm has simple structure and fast operation speed. To solve the above problems, a novel combination prediction model is proposed in this paper. This model uses VMD to decompose the wind speed into high-frequency signal and low-frequency signal and then uses principal component analysis and spectral clustering to extract and classify the influencing factors. In addition, aiming at the problem of slow convergence speed in the later stage of PSO iteration, an adaptive improved PSO is proposed. Finally, this paper also designs a rolling train method to adjust the size of training samples. Through four experiments of wind speed series in two periods, it is proved that the combined prediction model proposed in this paper has the following advantages: the model fully extracts the information of wind speed and influencing factors; the improved PSO algorithm has better optimization effect; rolling training method effectively improves the prediction ability of the model; the combined forecasting model has good adaptability and competitiveness. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including the passive-aggressive active (PAA) learning algorithms, the combination of authors, citation network and contents in a single bibliographic model, and the support vector machine (SVM).

Achieving a high level of classification accuracy in medical datasets is a capital need for researchers to provide effective decision systems to assist doctors in work. In many domains of artificial intelligence, ensemble classification methods are able to improve the performance of single classifiers. This paper reports the state of the art of ensemble classification methods in lung cancer detection. We have performed a systematic mapping study to identify the most interesting papers concerning this topic. A total of 65 papers published between 2000 and 2018 were selected after an automatic search in four digital libraries and a careful selection process. As a result, it was observed that diagnosis was the task most commonly studied; homogeneous ensembles and decision trees were the most frequently adopted for constructing ensembles; and the majority voting rule was the predominant combination rule. Few studies considered the parameter tuning of the techniques used. These findings open several perspectives for researchers to enhance lung cancer research by addressing the identified gaps, such as investigating different classification methods, proposing other heterogeneous ensemble methods, and using new combination rules. [ABSTRACT FROM AUTHOR]

External audit is undergoing rapid changes where more and more routine tasks are automated with analytics and artificial intelligence (AI) instruments. The paper addresses a research problem of mapping data analytics to audit tasks and develops a framework aligning audit phases and AI and using data analytics in teaching audit with AI. The paper contributes to the literature on using data analytics with AI in knowledge specific areas and particularly critical for emerging audit analytics, which is data analytics in external financial audit application. The paper employs the process model methodology (Wynn and Clarkson, Research in Engineering Design 29:161–202, 2018) and the hybrid approach of curriculum development (Dzuranin et al., Journal of Accounting Education 43:24–39, 2018). The framework is extended further by inclusion of knowledge areas and skills recommendations for each identified stage. This inclusion is linked to the peak accounting body guidelines to ensure compliance with course certification and future job prospects. The developed framework is implemented using audit management platform MindBridge AI. The developed teaching and learning materials show implementation of the framework on the practical level. The developed framework was evaluated in a focus group with accounting academics and industry professionals. Its implementation was evaluated in a series of workshops and a survey with participants. [ABSTRACT FROM AUTHOR]

People's mental conditions are often reflected in their social media activity due to the internet's anonymity. Psychiatric issues are often detected through such activities and can be addressed in their early stages, potentially preventing the consequences of unattended mental disorders like depression and anxiety. In this paper, the authors have implemented machine learning models and used various embedding techniques to classify posts from the famous social media blog site Reddit as stressful and non-stressful. The dataset used contains user posts that can be analyzed to detect patterns in the social media activity of those diagnosed with mental disorders. This paper uses different NLP (Natural Language Processing) tools such as ELMo (Embeddings from Language Models) word embeddings, BERT (Bidirectional Encoder Representations from Transformers) tokenizers, and BoW (Bag of Words) approach to create word/sentence data that can be fed to machine learning models. The results of each method have been discussed. The results achieved a top F1 score of 0.76, a Precision score of 0.71, and a Recall of 0.74 using only the preprocessed texts and machine learning algorithms to classify the posts. The results achieved by this paper are significant and have the potential to be applied in real-world scenarios to analyze mental stress among social media users. Although this paper focuses on data from Reddit, the techniques used can be transferred to similar social media platforms and could help solve the growing mental health crisis. [ABSTRACT FROM AUTHOR]

This paper mainly updates the image segmentation and multi-target recognition algorithm through the support vector machine algorithm in ML, and verifies the reliability of the new proposed algorithm through simulation experiments, and determines that the ML-based image segmentation and multi-target recognition algorithm can have stronger processing capability in image recognition and processing. Finally, the performance of traditional image segmentation and multi class object recognition algorithms and the image segmentation and multi class object recognition algorithms proposed in this paper were compared through experiments. The results showed that the performance of the ML based image segmentation and multi class object recognition algorithms proposed in this paper has increased by 29.1% on average in all aspects. Article Highlight: This paper studies the different modes of machine learning algorithm models, and combines machine learning algorithm models with image segmentation and multi-target recognition modes, so that image segmentation and multi-target recognition algorithms can be used in image data analysis, data processing, and intelligent processing. Three aspects have better performance. In image data analysis and data processing, the advantages of machine learning models for data processing are mainly used. Through training on large amounts of data, a dedicated machine learning model for image data analysis and processing is established. The intelligent processing is reflected in the continuous improvement of this model and the further improvement of the degree of automation. [ABSTRACT FROM AUTHOR]

An alternative energy source such as solar is one of the most important renewable resources. A reliable solar radiation prediction is essential for various applications in agriculture, industry, transport, and the environment because they reduce greenhouse gases and are environmentally friendly. Solar radiation data series have embedded fluctuations and noise signals due to complexity, stochasticity, non-stationarity, and nonlinearity with uncertain and time-varying nature. Aside from being highly nonlinear, solar radiation is highly influenced by the environment and environmental parameters such as air temperature, cloud cover, surface reflectivity, and aerosols. In addition, the spatial measurements of these variables are not readily available. To tackle these challenges, it is necessary to consider data preprocessing techniques and to develop and test precise solar radiation predicting models at different forecast horizons. There is, however, controversy regarding the performance of such models in various studies. Comparisons are not conducted systematically among the different studies. Using a critical literature review, the authors hope to answer these questions and believe that further investigation of solar radiation can benefit researchers and practitioners alike. This study presents a comprehensive evaluation of solar radiation modeling using artificial intelligence in the last 15 years and provides a novel detailed analysis of the available models. The studies conducted in different climates of the world that were published in distinguished journals were considered (i.e., 90 papers in total) for this purpose. Newly discovered procedures for optimizing forecasts, data cleaning, feature selection, classification methods, and stand-alone or hybrid data-driven models for solar radiation prediction and modeling were evaluated. The results strikingly showed that the most used artificial intelligence methods were artificial neural network, adaptive neuro-fuzzy inference system, and decision tree family of models. In addition, the extreme learning machine, support vector machine, and particle swarm optimization were the most used optimization techniques in solar radiation modeling. In terms of forecast horizons, the most common forecast horizon found in papers was on the daily scale (51% of studies), followed by the hourly scale (26%), and the least common was the monthly scale (18%). Based on the regional studies, the highest number of solar radiation papers originated from Asia, with Europe in second place and African countries in third place. An increasing trend in the number of papers from 2011 to 2015 was noted, and the second peak started from 2018 till the present. Under each section, a summary of findings is provided. The paper concludes with future thoughts and directions on solar radiation modeling. [ABSTRACT FROM AUTHOR]

In most biomedical research paper corpus, document classification is a crucial task. Even due to the global epidemic, it is a crucial task for researchers across a variety of fields to figure out the relevant scientific research papers accurately and quickly from a flood of biomedical research papers. It can also assist learners or researchers in assigning a research paper to an appropriate category and also help to find the relevant research paper within a very short time. A biomedical document classifier needs to be designed differently to go beyond a "general" text classifier because it's not dependent only on the text itself (i.e. on titles and abstracts) but can also utilize other information like entities extracted using some medical taxonomies or bibliometric data. The main objective of this research was to find out the type of information or features and representation method creates influence the biomedical document classification task. For this reason, we run several experiments on conventional text classification methods with different kinds of features extracted from the titles, abstracts, and bibliometric data. These procedures include data cleaning, feature engineering, and multi-class classification. Eleven different variants of input data tables were created and analyzed using ten machine learning algorithms. We also evaluate the data efficiency and interpretability of these models as essential features of any biomedical research paper classification system for handling specifically the COVID-19 related health crisis. Our major findings are that TF-IDF representations outperform the entity extraction methods and the abstract itself provides sufficient information for correct classification. Out of the used machine learning algorithms, the best performance over various forms of document representation was achieved by Random Forest and Neural Network (BERT). Our results lead to a concrete guideline for practitioners on biomedical document classification. [ABSTRACT FROM AUTHOR]

Federated machine learning (Fed ML) is a new distributed machine learning technique using clients' local data applied to collaboratively train a global model without transmitting the datasets. Nodes, the participating devices in the ML training, only send parameter updates (e.g., weight updates in the case of neural networks), which are fused together by the server to build the global model without compromising raw data privacy. Fed ML guarantees its confidentiality by not divulging node data to third party, central servers. Privacy of data is a crucial network security aspect of Fed ML that will enable the technique for use in the context of data-sensitive Internet of Things (IoT) and mobile applications (including smart geo-location and smart grid infrastructure). However, most IoT and mobile devices are particularly energy constrained, which requires optimization of the Fed ML process for efficient training tasks and optimized power consumption. This paper, to the best of our knowledge, is the first Systematic Mapping Study (SMS) on Fed ML for energy constrained devices. First, we selected a total of 67 from 800 papers that satisfy our criteria, then provide a structured overview of the field using a set of carefully chosen research questions. Finally, we attempt to offer an analysis of the state-of-the-art Fed ML techniques and outline potential recommendations for the research community. [ABSTRACT FROM AUTHOR]

A Soft Computing technique is one of the essential problems solving techniques for the researchers present in the world. Compared to other issues solving techniques, the Soft Computing domain gives outstanding performance, so the researchers are concentrating the Soft Computing domain for solving problems. Soft Computing domain categorizes into many sub-domains, like Neural Networks, Machine Learning, and Genetic Algorithm. Through this paper, we considered to study the impact produced by the Institutions as well as authors in the domain of Soft Computing from the year 1999 to 2019 for PubMed database. From the overall study, we found that China has made more number of publications, author productivity, and influential authors. Some countries, like Russia, Saudi Arabia, and Turkey, with minimum author productivity and minimum publications from the Asian continent in the Soft Computing-related domains. From the research, we determined that china dominates in terms of Institutions wise and Author productivity in the field of Soft Computing domain. [ABSTRACT FROM AUTHOR]

Even though accurate detection of dangerous malignancies from mammogram images is mostly dependent on radiologists' experience, specialists occasionally differ in their assessments. Computer-aided diagnosis provides a better solution for image diagnosis that can help experts make more reliable decisions. In medical applications for diagnosing cancerous growths from mammogram images, computerized and accurate classification of breast cancer mammogram images is critical. The deep learning approach has been widely applied in medical image processing and has had considerable success in biological image classification. The Convolutional Neural Network (CNN), Inception, and EfficientNet are proposed in this paper. The proposed models attain better performance compared to the conventional CNN. The models are used to automatically classify breast cancer mammogram images from Kaggle into benign and malignant. Simulation results demonstrated that EfficientNet, with an accuracy between 97.13 and 99.27%, and overall accuracy of 98.29%, perform better than the other models in this paper. [ABSTRACT FROM AUTHOR]

This study investigates the impact of air pollution on health outcomes in Middle Eastern countries, a region facing severe environmental challenges. As such, these are important in an effort to add up to policy-level as well as interventional changes that can be put in practice in the area of public health. Numeration analysis and association with health parameters was carried out by using Analytical tools such as, AIR Data, ARIMA,ANN, SVM and Exponential smoothing. Amongst the models, Support Vector Machine came again on top, with high accuracy yielding Mean Absolute Percentage Error of approximately 1%. Mortality of Air pollution in Qat from the case of Mortality of Air Pollution in Qatar is 959 while Auto regressive Integrated Moving average is 11.096, Exponential Smoothing 9.892 and Artificial Neural Networks are the source of inspiration for the development of this paper 4.61. The above perceptions indicate that there is need to adapt modeling strategies depending on the context and establish that it is possible to implement ML models in public health planning basket. This paper publishes the methodological frameworks for the purpose of modeling and analysis of the EHDs and serves as policy prescription for the policy makers to intending to reduce the effects of air borne pollution on health. [ABSTRACT FROM AUTHOR]

Grit, a non-cognitive skill that indicates perseverance and passion for long-term goals, has been shown to predict academic achievement. This paper provides evidence that grit also predicts student outcomes during the challenging period of the Covid-19 pandemic. We use a unique dataset from a digital learning platform in the United Arab Emirates to construct a behavioral measure of grit. We find that controlling for baseline achievement, students who were grittier according to this measure before the pandemic, register lower declines in math and science scores during the coronavirus period. Using machine learning, behavioral data obtained from the platform prior to the pandemic can explain 77% of the variance in academic resilience. A survey measure of grit coming from the same students, on the other hand, does not have significant predictive power over performance changes. Our findings have implications for interventions on non-cognitive skills, as well as how data from digital learning platforms can be used to predict student behavior and outcomes, which we expect will be increasingly relevant as AI-based learning technologies become more common. [ABSTRACT FROM AUTHOR]

The image object recognition and detection technology are widely used in many scenarios. In recent years, big data has become increasingly abundant, and big data-driven artificial intelligence models have attracted more and more attention. Evolutionary computation has also provided a powerful driving force for the optimization and improvement of deep learning models. In this paper, we propose an image object detection method based on self-supervised and data-driven learning. Differ from other methods, our approach stands out due to its innovative use of multispectral data fusion and evolutionary computation for model optimization. Specifically, our method uniquely combines visible light images and infrared images to detect and identify image targets. Firstly, we utilize a self-supervised learning method and the AutoEncoder model to perform high-dimensional feature extraction on the two types of images. Secondly, we fuse the extracted features from the visible light and infrared images to detect and identify objects. Thirdly, we introduce a model parameter optimization method using evolutionary learning algorithms to enhance model performance. Validation on public datasets shows that our method achieves comparable or superior performance to existing methods. [ABSTRACT FROM AUTHOR]

To address the problems of low accuracy in fault diagnosis of oil-immersed transformers, poor state perception ability and real-time collaboration during diagnosis feedback, a fault diagnosis method for transformers based on the integration of digital twins is proposed. Firstly, fault sample balance is achieved through Iterative Nearest Neighbor Oversampling (INNOS), Secondly, nine-dimensional ratio features are extracted, and the correlation between dissolved gases in oil and fault types is established. Then, sparse principal component analysis (SPCA) is used for feature fusion and dimensionality reduction. Finally, the Aquila Optimizer (AO) is introduced to optimize the parameters of the Kernel Extreme Learning Machine (KELM), establishing the optimal AO-KELM diagnosis model. The final fault diagnosis accuracy reaches 98.1013%. Combining transformer digital twin models, real-time interaction mapping between physical entities and virtual space is achieved, enabling online diagnosis of transformer faults. Experimental results show that the method proposed in this paper has high diagnostic accuracy and strong stability, providing reference for the intelligent operation and maintenance of transformers. [ABSTRACT FROM AUTHOR]

The field of cognitive computing, conversational AI has witnessed remarkable progress, largely driven by the development of the Generative Pre-trained Transformer (GPT) series, notably ChatGPT. These transformer-based models have revolutionized natural language understanding by effectively capturing context and long-range dependencies. In light of this, this paper conducts a comprehensive exploration of ChatGPT, encompassing its architectural design, training methodology, real-world applications, and future potential within the conversational AI landscape. The paper studies the ChatGPT ability for advanced control and responsiveness, exhibiting a superior capacity for comprehending language and generating precise, informative responses. The comprehensive survey depicts ChatGPT excels in sustaining context and engaging in multi-turn dialogues, thereby fostering more interactive and meaningful conversations. Furthermore, its adaptability for integration into various systems and scalability has broadened its applicability across diverse domains, including customer service, education, content generation, healthcare, gaming, research, and exploration. Additionally, the paper presents alternative conversational AI models, such as Amazon Codewhisperer, Google Bard (LaMDA), Microsoft Bing AI, DeepMind Sparrow, and Character AI, providing a comparative analysis that underscores ChatGPT's advantages in terms of inference capabilities and future promise. Recognizing the evolution and profound impact of ChatGPT holds paramount significance for researchers and developers at the forefront of AI innovation. In a rapidly evolving conversational AI landscape, ChatGPT emerges as a pivotal player, capable of reshaping the way we interact with AI systems across a wide array of applications. [ABSTRACT FROM AUTHOR]

Swift and precise classification of rock masses significantly enhances the prudent and efficient advancement of rock mass engineering. To expeditiously and accurately determine the engineering classification of slope rock masses, this paper introduces a hybrid Data Comprehensive Weight Analysis (DCWA)-Equilibrium Optimizer (EO)-AdaBoost ensemble prediction model. This model is formulated to prognosticate revised Basic Quality value ([BQ]) and the engineering classification of slope rock masses, utilizing the Basic Quality (BQ) classification method. Six elements have been chosen to formulate the prognostic index, and a dataset comprising information on the slope rock mass of 266 groups has been assembled for the purpose of training and evaluating the predictive accuracy of the established model. Two slope sections in DEZIWA open pit mine is chosen to validate the correctness of established DCWA-EO-AdaBoost model in the field through numerical simulation. In comparison to AdaBoost, DCWA-AdaBoost, DCWA-EO-AdaBoost, K-Nearest Neighbor (KNN), Back Propagation (BP), Decision Tree (DT), and Random Forest (RF) models, it is discerned that the DCWA-EO-AdaBoost hybrid model exhibits elevated coefficient of determination (R2, 0.986), variance accounted for (VA, 98.64%), prediction accuracy (A, 92.31%), and kappa coefficient (KC, 88.87%). Conversely, the mean absolute error (E, 5.97%) and root mean square error (ER, 38.37) are diminished, affirming its reliability and superiority. Field validation reveals that, combine the DCWA-EO-AdaBoost model and BQ classification method, mechanical parameters used for slope stability analysis can be obtained accurately, and can obtains slope stability analysis results basically consistent with the engineering practice through numerical simulation, which can prove the correctness of the established prediction model and clarify the use of the established model in the process of slope stability analysis. This attests that the established predictive model holds substantial merit, offering a noteworthy reference for the expeditious and precise acquisition of [BQ] and the classification of slope rock mass based on the BQ method, and can be adopted in the process of slope stability analysis. [ABSTRACT FROM AUTHOR]

Time series data are sequences of values that are obtained by sampling a signal at a fixed frequency, and time series classification algorithms distinguish time series into different categories. Among many time series classification algorithms, subseries-based algorithms have received widespread attention because of their high accuracy and low computational complexity. However, subseries-based algorithms consider the similarity of subseries only by shape and ignore semantic similarity. Therefore, the purpose of this paper is to determine how to solve the problem that subseries-based time series classification algorithms ignore the semantic similarity between subseries. To address this issue, we introduce the deep graph kernel technique to capture the semantic similarity between subseries. To verify the performance of the method, we test the proposed algorithm on publicly available datasets from the UCR repository and the experimental results prove that the deep graph kernel has an important role in enhancing the accuracy of the algorithm and that the proposed algorithm performs quite well in terms of accuracy and has a considerable advantage over other representative algorithms. [ABSTRACT FROM AUTHOR]

Quarry dust, conventionally considered waste, has emerged as a potential solution for sustainable construction materials. This paper comprehensively review the mechanical properties of blocks manufactured from quarry dust, with a particular focus on the transformative role of machine learning (ML) in predicting and optimizing these properties. By systematically reviewing existing literature and case studies, this paper evaluates the efficacy of ML methodologies, addressing challenges related to data quality, feature selection, and model optimization. It underscores how ML can enhance accuracy in predicting mechanical properties, providing a valuable tool for engineers and researchers to optimize the design and composition of blocks made from quarry dust. This synthesis of mechanical properties and ML applications contributes to advancing sustainable construction practices, offering insights into the future integration of technology for predictive modeling in material science. [ABSTRACT FROM AUTHOR]

The classification of network traffic has become increasingly crucial due to the rapid growth in the number of internet users. Conventional approaches, such as identifying traffic based on port numbers and payload inspection are becoming ineffective due to the dynamic and encrypted nature of modern network traffic. A number of researchers have implemented Software Defined Networking (SDN) based traffic classification using Machine Learning (ML) and Deep Learning (DL) models. However, the studies had various limitations such as encrypted traffic detection, payload inspection, poor detection accuracy, and challenges with testing models both in offline and real-time traffic modes. ML models together with SDN are adopted nowadays to enhance classification performance. In this paper, both supervised (Logistic Regression, Decision Tree, Random Forest, AdaBoost, and Support Vector Machine) and unsupervised (K-means clustering) ML models were used to classify Domain Name System (DNS), Telnet, Ping, and Voice traffic flows simulated using the Distributed Internet Traffic Generator (D-ITG) tool. The use of this tool effectively manages and classifies traffic types based on their application. The study discussed the dataset used, model selection, implementation of the model, and implementation techniques (such as pre-processing, feature extraction, ML algorithm, and model evaluation metrics). The proposed model in SDN was implemented in Mininet for designing the network architecture and generating network traffic. Anaconda Python environment was utilized for traffic classification using various ML techniques. Among the models tested, the Decision Tree supervised learning achieved the highest accuracy of 99.81%, outperforming other supervised and unsupervised learning algorithms. These results indicate that the integration of ML with SDN provides an efficient classification method for identifying and accurately classifying both offline and real-time network traffic, enhanced quality of service (QoS), detection of encrypted packets, deep packet inspection and management. [ABSTRACT FROM AUTHOR]

Autoencoders are dimension reduction models in the field of machine learning which can be thought of as a neural network counterpart of principal components analysis (PCA). Due to their flexibility and good performance, autoencoders have been recently used for estimating nonlinear factor models in finance. The main weakness of autoencoders is that the results are less explainable than those obtained with the PCA. In this paper, we propose the adoption of the Shapley value to improve the explainability of autoencoders in the context of nonlinear factor models. In particular, we measure the relevance of nonlinear latent factors using a forecast-based Shapley value approach that measures each latent factor's contributions in determining the out-of-sample accuracy in factor-augmented models. Considering the interesting empirical instance of the commodity market, we identify the most relevant latent factors for each commodity based on their out-of-sample forecasting ability. [ABSTRACT FROM AUTHOR]

Federated Learning is an effective solution to address the issues of data isolation and privacy leakage in machine learning. However, ensuring the security of network devices and architectures deploying federated learning remains a challenge due to network attacks. This paper proposes an attention-based Graph Neural Network for detecting cross-level and cross-department network attacks. This method enables collaborative model training while protecting data privacy on distributed devices. By organizing network traffic information in chronological order and constructing a graph structure based on log density, enhances the accuracy of network attack detection. The introduction of an attention mechanism and the construction of a Federated Graph Attention Network (FedGAT) model are used to evaluate the interactivity between nodes in the graph, thereby improving the precision of internal network interactions. Experimental results demonstrate that our method achieves comparable accuracy and robustness to traditional detection methods while prioritizing privacy protection and data security. [ABSTRACT FROM AUTHOR]

Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making. [ABSTRACT FROM AUTHOR]

The accurate classification of human activities in crime scenes during forensics (criminalistics) is of utmost importance in classifying suspicious and unlawful activities, easing their acceptability and interpretability by judges during legal procedures in courts or by other non-experts in the field of forensics. This paper implements machine learning (ML) algorithms: support vector machine (SVM) and decision tree (DT), to demonstrate with a high accuracy, how data emanating from smartphones' sensors reveal and isolate relevant information about static and dynamic human activities in criminalistics. Smartphones' data from five different sensors (accelerometer, gravity, orientation, Gyroscope and light), related to ten recurrent crime scenes activities, grouped into three classes of events (normal, felony and none-felony events) are classified by the proposed algorithms, with novelty being the classification decisions based on the entire period of the events and not instantaneous decision makings. Three independent data-subsets were made, with permutations done between them and at each time, two sets used for training and the third set used for testing. Time- and frequency-domain features were initially used separately and then combined for the model training and testing. The best average training accuracies of 100% and 97.8% were obtained for the DT and SVM, respectively, and the testing accuracies of 89.1% were obtained for both algorithms. We therefore believe that these results will serve as a solid persuasive and convincing argument to judges and non-experts of the field of forensics to accept and easily interpret computer-aided classification of suspicious activities emanating from criminalistic studies. [ABSTRACT FROM AUTHOR]

Financial news is one of the most influential sources for making long-term investment decisions. The goal of this paper is to learn whether it is possible, based on texts from news headlines, to select stocks with low forecasted volatility which outrun the market represented by the Standard and Poor's 500 Index. We solve several binary classification problems using a range of machine learning and deep learning principles. The best classifiers are interpreted with a model-agnostic technique, LIME, to extract key words having the greatest impact on the probabilities of market outrun and low volatility. [ABSTRACT FROM AUTHOR]

The use of AI algorithms in criminal trials has been the subject of very lively ethical and legal debates recently. While there are concerns over the lack of accuracy and the harmful biases that certain algorithms display, new algorithms seem more promising and might lead to more accurate legal decisions. Algorithms seem especially relevant for bail decisions, because such decisions involve statistical data to which human reasoners struggle to give adequate weight. While getting the right legal outcome is a strong desideratum of criminal trials, advocates of the relational theory of procedural justice give us good reason to think that fairness and perceived fairness of legal procedures have a value that is independent from the outcome. According to this literature, one key aspect of fairness is trustworthiness. In this paper, I argue that using certain algorithms to assist bail decisions could increase three different aspects of judges' trustworthiness: (1) actual trustworthiness, (2) rich trustworthiness, and (3) perceived trustworthiness. [ABSTRACT FROM AUTHOR]

The widespread usage of machine learning systems and econometric methods in the credit domain has transformed the decision-making process for evaluating loan applications. Automated analysis of credit applications diminishes the subjectivity of the decision-making process. On the other hand, since machine learning is based on past decisions recorded in the financial institutions' datasets, the process very often consolidates existing bias and prejudice against groups defined by race, sex, sexual orientation, and other attributes. Therefore, the interest in identifying, preventing, and mitigating algorithmic discrimination has grown exponentially in many areas, such as Computer Science, Economics, Law, and Social Science. We conducted a comprehensive systematic literature review to understand (1) the research settings, including the discrimination theory foundation, the legal framework, and the applicable fairness metric; (2) the addressed issues and solutions; and (3) the open challenges for potential future research. We explored five sources: ACM Digital Library, Google Scholar, IEEE Digital Library, Springer Link, and Scopus. Following inclusion and exclusion criteria, we selected 78 papers written in English and published between 2017 and 2022. According to the meta-analysis of this literature survey, algorithmic discrimination has been addressed mainly by looking at the CS, Law, and Economics perspectives. There has been great interest in this topic in the financial area, especially the discrimination in providing access to the mortgage market and differential treatment (different fees, number of parcels, and interest rates). Most attention has been devoted to the potential discrimination due to bias in the dataset. Researchers are still only dealing with direct discrimination, addressed by algorithmic fairness, while indirect discrimination (structural discrimination) has not received the same attention. [ABSTRACT FROM AUTHOR]