Unsupervised Learning Techniques Research Articles

Analyzing Daytime/Nighttime Pedestrian Crash Patterns in Michigan Using Unsupervised Machine Learning Techniques and their Potential as a Decision-Making Tool

Background Data mining applications are becoming increasingly common across various fields. Numerous data mining methodologies have been utilized in pedestrian crash data analysis. However, association and correspondence analyses have yet to be extensively employed in pedestrian safety literature to support decision-making. Material and Methods Road lighting significantly affects pedestrian crashes, highlighting the importance of examining pedestrian crash patterns during daytime and nighttime. This study analyzed pedestrian fatal and injury crashes in Michigan from 2011 to 2021 under varying road lighting conditions. The study identified pedestrian crash patterns using unsupervised machine-learning techniques based on several multidimensional factors. The Association Rules Learning (ARL) technique was used to determine general associations of patterns leading to pedestrian crashes. At the same time, Multiple Correspondence Analysis (MCA) helped understand crash attribute patterns in two notable scenarios: elderly pedestrian crashes during daytime lighting and high-speed midblock crashes during nighttime. These methods revealed key patterns associated with contributing factors to pedestrian crashes in different lighting conditions. Results In the analysis of cloud combinations involving elderly pedestrians during daylight, it was found that: 1) elderly pedestrians were significantly involved in severe injury crashes at two-lane midblock locations with a speed limit between 45 and 65 mph; 2) improper actions by elderly drivers were significantly associated with elderly pedestrians on wide roads (>5 lanes); and 3) the complex interaction between city streets with 3-5 lanes was significantly correlated with drivers 'failed to yield' actions. Additionally, in the analysis of cloud combinations involving nighttime pedestrian crashes at high-speed midblock locations, it was found that 1) a specific age group of alcohol- or drug-involved pedestrians (19-30) was significantly associated with young drivers and improper driver actions; 2) during the winter season, young pedestrians were significantly involved in moderate injury crashes at undivided midblock locations; and 3) elderly drivers and 'failed to yield' actions were highly correlated with 3-5 lane roadways. Discussion The research findings are expected to raise awareness of Michigan pedestrian crash patterns under daytime and nighttime lighting conditions. They will also recommend safety countermeasures for practitioners to enhance pedestrian safety in walkable cities. The proposed methodologies can uncover relationships that need to be well-documented in the existing literature on pedestrian safety. Additionally, these methods can identify valuable relationships without restricting variables to being either dependent or independent, and they can reveal relationships that might be challenging to detect otherwise. The results will also provide decision rules and visualizations that are easy to understand. Conclusion The results showed that the proposed techniques can analyze pedestrian crash data in a way that aligns with understanding pedestrian crash characteristics. Traffic safety administrators could benefit from this methodology as a decision-support tool.

Hydrogeochemical Characteristics and Formation Mechanisms of High-Arsenic Groundwater in the North China Plain: Insights from Hydrogeochemical Analysis and Unsupervised Machine Learning

Hydrochemical data were utilized in this study to elucidate the hydrogeochemical characteristics and genesis of high-arsenic groundwater in the North China Plain, employing both traditional hydrogeochemical analysis and unsupervised machine learning techniques. The findings indicate that the predominant hydrochemical types of groundwater in the study area are HCO3-Ca·Na and SO4·Cl-Na·Ca. The primary mechanism influencing groundwater chemistry has been identified as rock weathering. The unsupervised machine learning framework incorporates various methods, such as principal component analysis (PCA), non-negative matrix factorization (NMF), machine learning models (gradient boosting trees and random forests), and cluster analysis to explore the characteristics and genesis of groundwater hydrochemical types within the study area. This study demonstrated that the formation mechanism of high-arsenic groundwater results from multiple interacting factors.

Detection of hidden drawings using multi-wavelength dynamic speckle, tuneable algorithms, and unsupervised learning

Abstract We implemented an experiment to reveal hidden drawings on papyrus, utilizing an optical technique based on the speckle phenomenon. The goal is to optimize the detection of hidden objects. Our approach proposes using multiple wavelengths for illumination and tuneable algorithms to process the dynamic speckle images. By implementing the suggested method, we generated various results with varying quality, contingent upon the tuneable algorithm parameters. It's feasible to identify the optimal parameter combination to achieve the most effective visualization of the recovered image. To streamline the selection of tuneable algorithms and mitigate reliance on subjective visual judgment, we employed unsupervised machine learning techniques to determine the conditions necessary to achieve optimal results. This approach simplifies the selection procedure and offers an objective and non-invasive method. Furthermore, the proposed procedure holds promise for extending its application to uncover hidden paintings, subsurface archaeological artefacts, and other dynamic speckle experiments.

Identifying Landslide Hotspots Using Unsupervised Clustering: A Case Study

Landslides pose significant threats to life, property, and infrastructure. This study explores applying unsupervised learning techniques to identify and understand landslide-prone areas. We analyzed topographic data by employing K-Means, Hierarchical Clustering, Spectral Clustering, Mean Shift Clustering, and DBSCAN to uncover hidden patterns in landslide occurrence. Evaluation metrics, including the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index, were used to assess the performance of these algorithms. Hierarchical Clustering achieved the highest Silhouette Score of 0.635, indicating excellent cluster separation. However, Mean Shift Clustering outperformed the other methods with a superior Davies-Bouldin Index of 0.603 and the highest Calinski-Harabasz Index of 4121.75, demonstrating the best overall clustering performance. DBSCAN also performed well, with a Silhouette Score of 0.610 and 12 noise points identified. These findings contribute to a deeper understanding of landslide spatial distribution and can inform the development of effective early warning systems and mitigation strategies.

Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions

Background/Objectives: The emergence of antimicrobial resistance (AMR) due to the misuse and overuse of antibiotics has become a critical threat to global public health. There is a dire need to forecast AMR to understand the underlying mechanisms of resistance for the development of effective interventions. This paper explores the capability of machine learning (ML) methods, particularly unsupervised learning methods, to enhance the understanding and prediction of AMR. It aims to determine the patterns from AMR gene data that are clinically relevant and, in public health, capable of informing strategies. Methods: We analyzed AMR gene data in the PanRes dataset by applying unsupervised learning techniques, namely K-means clustering and Principal Component Analysis (PCA). These techniques were applied to identify clusters based on gene length and distribution according to resistance class, offering insights into the resistance genes’ structural and functional properties. Data preprocessing, such as filtering and normalization, was conducted prior to applying machine learning methods to ensure consistency and accuracy. Our methodology included the preprocessing of data and reduction of dimensionality to ensure that our models were both accurate and interpretable. Results: The unsupervised learning models highlighted distinct clusters of AMR genes, with significant patterns in gene length, including their associated resistance classes. Further dimensionality reduction by PCA allows for clearer visualizations of relationships among gene groupings. These patterns provide novel insights into the potential mechanisms of resistance, particularly the role of gene length in different resistance pathways. Conclusions: This study demonstrates the potential of ML, specifically unsupervised approaches, to enhance the understanding of AMR. The identified patterns in resistance genes could support clinical decision-making and inform public health interventions. However, challenges remain, particularly in integrating genomic data and ensuring model interpretability. Further research is needed to advance ML applications in AMR prediction and management.

A fronto-insular network underlies individual variations in anger expression and control

Abstract Anger can be deconstructed into distinct components: a tendency to outwardly express it (anger-out) and the capability to manage it (anger control). These aspects exhibit individual differences that vary across a continuum. Notably, the capacity to express and control anger is of great importance to modulate our reactions in interpersonal situations. The aim of this study was to test the hypothesis that anger expression and control are negatively correlated and that both can be decoded by the same patterns of grey and white matter features of a fronto-temporal brain network. To this aim, a data fusion unsupervised machine learning technique, known as transposed Independent Vector Analysis (tIVA), was used to decompose the brain into covarying GM–WM networks and then backward regression was used to predict both anger expression and control from a sample of 212 healthy subjects. Confirming our hypothesis, results showed that anger control and anger expression are negatively correlated, the more individuals control anger, the less they externalize it. At the neural level, individual differences in anger expression and control can be predicted by the same GM–WM network. As expected, this network included lateral and medial frontal regions, the insula, temporal regions, and the precuneus. The higher the concentration of GM–WM in this brain network, the higher the level of externalization of anger, and the lower the anger control. These results expand previous findings regarding the neural bases of anger by showing that individual differences in anger control and expression can be predicted by morphometric features.

Assessing the detection of lateral movement through unsupervised learning techniques

Lateral movement (LM) is an umbrella term for techniques through which attackers spread from an entry point to the rest of the network. Typically, LM involves both pivoting through multiple systems and privilege escalation. As LM techniques proliferate and evolve, there is a need for advanced security controls able to detect and possibly nip such attacks in the bud. Based on the published literature, we argue that although LM-focused intrusion detection systems have received considerable attention, a prominent issue remains largely unaddressed. This concerns the detection of LM through unsupervised machine learning (ML) techniques. This work contributes to this field by capitalizing on the LMD-2023 dataset containing traces of 15 diverse LM attack techniques as they were logged by the system monitor (Sysmon) service of the MS Windows platform. We provide a panorama of this sub-field and associated methodologies, exploring the potential of standard ML-based detection. In further detail, in addition to analyzing feature selection and preprocessing, we detail and evaluate a plethora of unsupervised ML techniques, both shallow and deep. The derived scores for the best performer in terms of the AUC and F1 metrics are quite promising, around 94.7%/93% and 95.2%/93.8%, for the best shallow and deep neural network model, respectively. On top of that, in an effort to further improve on those metrics, we devise and evaluate a two-stage ML model, surpassing the previous best score by approximately 3.5%. Overall, to our knowledge, this work provides the first full-blown study on LM detection via unsupervised learning techniques, therefore it is anticipated to serve as a groundwork for anyone working in this timely field.

Seal and reservoir risk evaluation using hierarchical clustering analysis with seismic attributes in Northwestern Australia

Assessing the presence and quality of reservoir rocks and their sealing capacity is crucial for various applications, including hydrocarbon, geothermal, and CO2 sequestration projects. Typically, exploration geoscientists rely on seismic attributes and borehole logs into interpretation to integrate diverse data for estimating reservoirs and seals. However, for all seismic interpreters, the process is time-consuming.In this study, we explore the application of Hierarchical Clustering Analysis (HCA), an unsupervised machine learning technique, to streamline the integration of multidisciplinary information. While HCA and similar techniques may occasionally misclassify critical data, we demonstrate how to enhance their accuracy by carefully selecting the number of clusters and their calibration with borehole data.The novelty of our work is the innovative transformation of HCA clusters into a 3D lithology model, which can significantly facilitate the estimation of reservoir rock and seal-rock juxtaposition risk. Using the HCA clustering hierarchy, five clusters effectively discern the presence and quality of seal and reservoir rock in two different datasets. The classification, in combination with the fault probability, addresses the seal risk offshore the Northern Carnarvon Basin.

Advanced classification of hot subdwarf binaries using artificial intelligence techniques and Gaia DR3 data

Context. Hot subdwarf stars are compact blue evolved objects, burning helium in their cores surrounded by a tiny hydrogen envelope. In the Hertzsprung-Russell Diagram they are located by the blue end of the Horizontal Branch. Most models agree on a quite probable common envelope binary evolution scenario in the Red Giant phase. However, the current binarity rate for these objects is yet unsolved, but key, question in this field. Aims. This study aims to develop a novel classification method for identifying hot subdwarf binaries within large datasets using Artificial Intelligence techniques and data from the third Gaia data release (GDR3). The results will be compared with those obtained previously using Virtual Observatory techniques on coincident samples. Methods. The methods used for hot subdwarf binary classification include supervised and unsupervised machine learning techniques. Specifically, we have used Support Vector Machines (SVM) to classify 3084 hot subdwarf stars based on their colour-magnitude properties. Among these, 2815 objects have Gaia DR3 BP/RP spectra, which were classified using Self-Organizing Maps (SOM) and Convolutional Neural Networks (CNN). In order to ensure spectral quality, previously to SOM and CNN classification, our 2815 BP/RP set were pre-analysed with two different approaches: the cosine similarity technique and the Uniform Manifold Approximation and Projection (UMAP) technique. Additional analysis onto a golden sample of 88 well-defined objects, is also presented. Results. The findings demonstrate a high agreement level (∼70–90%) with the classifications from the Virtual Observatory Sed Analyzer (VOSA) tool. This shows that the SVM, SOM, and CNN methods effectively classify sources with an accuracy comparable to human inspection or non-AI techniques. Notably, SVM in a radial basis function achieves 70.97% reproducibility for binary targets using photometry, and CNN reaches 84.94% for binary detection using spectroscopy. We also found that the single–binary differences are especially observable on the infrared flux in our Gaia DR3 BP/BR spectra, at wavelengths larger than ∼700 nm. Conclusions. We find that all the methods used are in fairly good agreement and are particularly effective to discern between single and binary systems. The agreement is also consistent with the results previously obtained with VOSA. In global terms, considering all quality metrics, CNN is the method that provides the best accuracy. The methods also appear effective for detecting peculiarities in the spectra. While promising, challenges in dealing with uncertain compositions highlight the need for caution, suggesting further research is needed to refine techniques and enhance automated classification reliability, particularly for large-scale surveys.

어떤 학생이 학생설계융합전공을 이수하는가? - 군집분석을 활용한 기이수 집단 분석

The purpose of this study is to propose solutions aimed at activating student-designed convergence majors and improving completion rates at A University. For this, cluster analysis, an unsupervised machine learning technique, is used and determine whether the groups derived through clustering analysis effectively distinguish between students who applied to complete the student-designed convergence major and those who did not. The findings are summarized as follows: The analysis of group differences identified two central factors that distinguished the groups: 'When did you start thinking about your career?' and 'Are you confident about your major?' From these findings, the characteristics of students suited for completing the student-designed convergence major can be summarized as follows: First, the student-designed convergence major should be expected to synergize with the primary major and encourage completion among students who have established a clear career direction. Second, even if students are not confident about their major, those with a high degree of decision-making regarding their career path and a clear direction can be encouraged to complete a student-designed convergence major.

Analysing Virtual Labs Through Integrated Multi-Channel Eye-Tracking Technology: A Proposal for an Explanatory Fit Model

This study deals with an analysis of the cognitive load indicators produced in virtual simulation tasks through supervised and unsupervised machine learning techniques. The objectives were (1) to identify the most important cognitive load indicators through the use of supervised and unsupervised machine learning techniques; (2) to study which type of task presentation was most effective at reducing the task’s intrinsic load and increasing its germane load; and (3) to propose an explanatory model and find its fit indicators. We worked with a sample of 48 health sciences and biomedical engineering students from the University of Burgos (Spain). The results indicate that being able to see the task before performing it increases the germane load and decreases the intrinsic load. Similarly, allowing students a choice of presentation channel for the task respects how they process information. In addition, indicators of cognitive load were found to be grouped into components of position, speed, psychogalvanic response, and skin conductance. An explanatory model was proposed and obtained acceptable fit indicators.

A robust deep learning approach for identification of RNA 5-methyluridine sites

RNA 5-methyluridine (m5U) sites play a significant role in understanding RNA modifications, which influence numerous biological processes such as gene expression and cellular functioning. Consequently, the identification of m5U sites can play a vital role in the integrity, structure, and function of RNA molecules. Therefore, this study introduces GRUpred-m5U, a novel deep learning-based framework based on a gated recurrent unit in mature RNA and full transcript RNA datasets. We used three descriptor groups: nucleic acid composition, pseudo nucleic acid composition, and physicochemical properties, which include five feature extraction methods ENAC, Kmer, DPCP, DPCP type 2, and PseDNC. Initially, we aggregated all the feature extraction methods and created a new merged set. Three hybrid models were developed employing deep-learning methods and evaluated through 10-fold cross-validation with seven evaluation metrics. After a comprehensive evaluation, the GRUpred-m5U model outperformed the other applied models, obtaining 98.41% and 96.70% accuracy on the two datasets, respectively. To our knowledge, the proposed model outperformed all the existing state-of-the-art technology. The proposed supervised machine learning model was evaluated using unsupervised machine learning techniques such as principal component analysis (PCA), and it was observed that the proposed method provided a valid performance for identifying m5U. Considering its multi-layered construction, the GRUpred-m5U model has tremendous potential for future applications in the biological industry. The model, which consisted of neurons processing complicated input, excelled at pattern recognition and produced reliable results. Despite its greater size, the model obtained accurate results, essential in detecting m5U.

Learning and semiautomatic intention labeling for classification models: a COVID-19 dialog attendance study for chatbots

Abstract It is increasingly common to use chatbots as an interface to services. One of the main components of a chatbot is the Natural Language Understanding (NLU) model, which is responsible for interpreting the text and extracting the intent and entities present in that text. It’s possible to focus only on one of these tasks of NLU, such as intent classification. To train an NLU intent classification model, it’s generally necessary to use a considerable amount of annotated data, where each sentence of the dataset receives a label indicating an intent. Performing manually labeling data is arduous and impracticable, depending on the data volume. Thus, an unsupervised machine learning technique, such as data clustering, could be applied to find and label patterns in the data. For this task, it is essential to have an effective vector embedding representation of texts that depicts the semantic information and helps the machine understand the context, intent, and other nuances of the entire text. This paper extensively evaluates different text embedding models for clustering and labeling. We also apply some operations to improve the dataset’s quality, such as removing sentences and establishing various strategies for distance thresholds (cosine similarity) for the clusters’ centroids. Then, we trained some intent classification Models with two different architectures, one built with the Rasa framework and the other with a neural network (NN) using the attendance text from the Coronavirus Platform Service of Ceará, Brazil. We also manually annotated a dataset to be used as validation data. We conducted a study on semiautomatic labeling, implemented through clustering and visual inspection, which introduced some labeling errors to the intent classification models. However, it would be unfeasible to annotate the entire dataset manually. Nevertheless, results of competitive accuracy were still achieved with the trained models.

Historical insights at scale: A corpus-wide machine learning analysis of early modern astronomic tables.

Understanding the evolution and dissemination of human knowledge over time faces challenges due to the abundance of historical materials and limited specialist resources. However, the digitization of historical archives presents an opportunity for AI-supported analysis. This study advances historical analysis by using an atomization-recomposition method that relies on unsupervised machine learning and explainable AI techniques. Focusing on the "Sacrobosco Collection," consisting of 359 early modern printed editions of astronomy textbooks from European universities (1472-1650), totaling 76,000 pages, our analysis uncovers temporal and geographic patterns in knowledge transformation. We highlight the relevant role of astronomy textbooks in shaping a unified mathematical culture, driven by competition among educational institutions and market dynamics. This approach deepens our understanding by grounding insights in historical context, integrating with traditional methodologies. Case studies illustrate how communities embraced scientific advancements, reshaping astronomic and geographical views and exploring scientific roots amidst a changing world.

Enhanced Collaborative Filtering: Combining Autoencoder and Opposite User Inference to Solve Sparsity and Gray Sheep Issues

In recent years, the study of recommendation systems has become crucial, capturing the interest of scientists and academics worldwide. Music, books, movies, news, conferences, courses, and learning materials are some examples of using the recommender system. Among the various strategies employed, collaborative filtering stands out as one of the most common and effective approaches. This method identifies similar active users to make item recommendations. However, collaborative filtering has two major challenges: sparsity and gray sheep. Inspired by the remarkable success of deep learning across a multitude of application areas, we have integrated deep learning techniques into our proposed method to effectively address the aforementioned challenges. In this paper, we present a new method called Enriched_AE, focused on autoencoder, a well-regarded unsupervised deep learning technique renowned for its superior ability in data dimensionality reduction, feature extraction, and data reconstruction, with an augmented rating matrix. This matrix not only includes real users but also incorporates virtual users inferred from opposing ratings given by real users. By doing so, we aim to enhance the accuracy of predictions, thus enabling more effective recommendation generation. Through experimental analysis of the MovieLens 100K dataset, we observe that our method achieves notable reductions in both RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error), underscoring its superiority over the state-of-the-art collaborative filtering models.

Improving out-of-sample forecasts of stock price indexes with forecast reconciliation and clustering

In this paper, we propose a novel approach to improving forecasts of stock market indexes by considering common stock prices as hierarchical time series, combining clustering with forecast reconciliation. We propose grouping the individual stock price series in various ways including via metadata and using unsupervised learning techniques. The proposed approach is applied to the Dow Jones Industrial Average Index and the Standard & Poor 500 Index and their component stocks, and the results obtained with different grouping approaches are compared. The results empirically demonstrate that the combined use of clustering and reconciliation improves the forecast accuracy of the stock market indexes and their constituents.

Exploring spatial and seasonal water quality variations in Kelani River, Sri Lanka: a latent variable approach.

Water quality degradation poses a significant challenge globally, especially in developing nations like Sri Lanka. Extensive monitoring programs designed to address escalating river pollution collect multiple water quality parameters over extended periods and varied locations. However, the sheer volume of data can be overwhelming, making it difficult to process effectively and interpret accurately using conventional methods. In this study, latent variable (LV) and unsupervised machine learning techniques were used to investigate spatial and seasonal variations of surface water quality for 17 parameters across 17 locations along the Kelani River, Sri Lanka, using monthly water quality parameters from 2016 to 2020. Pearson's correlation matrix identified 10 parameters significantly affecting water quality variations and factor analysis (FA) generated five LVs, accounting for 77% of the total variance in the dataset. The identified LVs showed multiple methods of river pollution.Hierarchical clustering analysis and self-organizing mapping methods clustered stations in a closely analogous manner. Stations near industrial zones and the river mouth showed higher water quality variance, often exceeding national guidelines. Correlation testing revealed strong relationships between water quality and catchment hydrometeorological variations during monsoonal seasons. Spatial analyses showed increased LV variance in the Lower Kelani River Basin, indicating higher pollutant levels in different seasons. Industrial effluents (LV-2 and LV-4) and domestic and municipal sewage (LV-3 and LV-5) exhibit greater seasonal fluctuations. The results showed that the proposed LV approach has the potential to assist authorities in addressing water pollution amidst the complexity of multiple water quality parameters.

Towards the construction of regional marine radiocarbon calibration curves: an unsupervised machine learning approach

Abstract. Radiocarbon may serve as a powerful dating tool in palaeoceanography, but its accuracy is limited by the need to calibrate radiocarbon dates to calendar ages. A key problem is that marine radiocarbon dates must be corrected for past offsets from either the contemporary atmosphere (i.e. “reservoir age” offsets) or a modelled estimate of the global average surface ocean (i.e. delta-R offsets). This presents a challenge because the spatial distribution of reservoir ages and delta-R offsets can vary significantly, particularly over periods of major marine hydrographic and/or carbon cycle change such as the last deglaciation. Modern reservoir age and delta-R estimates therefore have limited applicability. While forward modelling of past R-age variability has been proposed as a means of resolving this problem, this requires accurate a priori knowledge of past global radiocarbon budget closure (i.e. production, and cycling), which we currently lack. In this context, the construction of empirical regional marine calibration curves could provide a way forward. However, the spatial reach of such calibrations and their robustness subject to (uncertain) temporal changes in climate and ocean circulation would need to be tested. Here, we use unsupervised machine learning techniques to define distinct regions of the surface ocean that exhibit coherent behaviour in terms of their radiocarbon age offsets from the contemporary atmosphere (R ages), regardless of the causes of R-age variability. We apply multiple algorithms (k-means, k-medoids, and hierarchical clustering) to outputs from two different numerical models spanning a range of climate states, forcings, and timescales of adjustment. Comparisons between the cluster assignments across model runs confirm some robust regional patterns that likely stem from constraints imposed by large-scale ocean and atmospheric physics. At the coarsest scale, regions of coherent R-age variability correspond to the major ocean basins. By further dividing basin-scale shape-based clusters into amplitude-based subclusters, we recover regional associations, such as increased high-latitude R ages, or the propagation of R-age anomalies from regions of deep mixing in the Southern Ocean to upwelling sites in the eastern equatorial Pacific, which cohere with known modern oceanographic processes. We show that the medoids (i.e. the most representative locations) for these regional sub-clusters provide significantly better approximations of simulated local R-age variability than constant offsets from the global surface average. This remains true when cluster assignments obtained from one model simulation are applied to simulated R-age time series from another. Further, model-based clusters are found to be broadly consistent with existing reservoir age reconstructions that span the last ∼30 kyr. We therefore propose that machine learning provides a promising approach to the problem of defining regions for which empirical marine radiocarbon calibration curves may eventually be generated.

Development of E-Tourism to Achieve Excellence and Sustainable Development in Tourism: Ha’il Region Case Study

E-tourism is one of the most important levers for social and economic development. The World Tourism Organization (UNWTO) has declared 2017 the year of sustainable tourism for development. When relying on sophisticated communication technologies. Certainly, these innovations in the field of this analysis aim to determine the main factors of e-tourism achieving excellence in tourism in the Ha’il region. Tourism has disrupted this sector with new and different dimensions of tourism development. This has led various regions and countries to accelerate the search for the tools necessary to achieve excellence and sustainable development in tourism. This research aims to study the development factors of e-tourism to achieve excellence and sustainable development in the tourism of the Ha’il region. Therefore, the gap in this research is represented by the lack of a comprehensive analysis of the interaction between e-tourism development factors and factors that promote tourism excellence in the Ha’il region to achieve sustainable development. For this reason, we applied two analyses: in the first one, we used a statistical analysis based on the survey sent to 500 employees from tourism companies. This analysis aimed to determine the main factors that influence e-tourism to achieve excellence in tourism in the Ha’il region. The results of the statistical analysis gave us the following elements: There are only four main factors that allow authorities to achieve excellence in tourism in the Ha’il region, namely Supporting Technology (ST), Competition Pressure (CP), Human Resource Skills (HRS), and Socio-Cultural (SC) factors. Then, we proceed with a second analysis to determine the main factors of excellence in tourism that drive the achievement of sustainable development in tourism in the Ha’il region. For this reason, we applied an artificial intelligence method based on the unsupervised machine learning technique. The results obtained from the machine learning technique show that five factors determine the excellence in tourism and reveal the sustainable development of Ha’il’s tourism. These factors are ST, CP, Financial Performance (FP), HRS, and Government Commitment (GC). Consequently, we consider that all the factors found in both analyses are important to realise excellence in tourism and sustainable development in tourism. However, to achieve sustainable development in tourism in Ha’il, Saudi authorities must develop the three common factors found in the two analyses, namely ST, CP, and HRS. Therefore, this study is expected to help introduce ways of excellence in e-tourism by marketing it using artificial intelligence, enabling the development of the tourism sector in an enormous area. It will also contribute significantly to the formulation of plans, policies, and strategies that will largely ensure the development of Ha’il’s tourism sector.

A concise review towards a novel target specific multi-source unsupervised transfer learning technique for GDP estimation using CO2 emission data

Though economic growths of most of the nations have seen exponential rise due to industrialization, it has also caused proportional increase in their carbon emissions. This paper exploits this proportionate relationship of carbon emission with GDP to predict the per-capita GDP of those nations whose GDP values are missing in the world bank database. The reason behind the same was, those countries were either war-torn or politically isolated/unstable. To achieve the objective of predicting the missing GDP values of those countries from their carbon emissions, this paper exploits the non-linear relationship among the carbon emissions from solid fuels, liquid fuels, and gaseous fuels. It is so because even the differential utilization of these fuels impact economy differently. Use of traditional solid fuel for cooking points toward energy poverty, and access to clean cooking gas indicates higher living standard. However, the available data from the war-torn or isolated countries are very little, and hence insufficient for building a robust predictive machine learning model. So, this paper employs multi-source unsupervised transfer learning to precisely estimate the missing per-capita GDP of those nations. It suitably enlarges the training domains for the prediction models to be more robust. We empirically evaluate the proposed methodology for different regression techniques to estimate the missing GDP values of eleven different nations belonging to diverse strata of economies viz. developed economies, developing, and/or least developing economies. Proposed methodology profoundly improves the prediction preciseness of these regression techniques in estimating the missing per-capita GDP of the considered nations.