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Life Cycle Assessment (LCA) has been used to evaluate the life-cycle Greenhouse Gas (GHG) emissions of pulp and paper production, and most previous studies rely on process-based models for specific product types (e.g., printing paper), industry-average data, or information from a few mills. In this work, a data-mining approach is used to quantify GHG emissions intensities of different paper products manufactured by the U.S. mills. Facility-level emission data collected from publically available governmental databases and mill-level production data collected from the private sector were integrated to track the GHG emissions for different product lines and paper products in mills (in total, 165 mills were matched and analyzed). The results highlight the ranges of GHG emissions intensities by different product groups and categories, and can be used as a transparent data source for LCA practitioners, policymakers, and the pulp and paper industry to perform further analysis on carbon accounting and strategic planning for GHG mitigation. [ABSTRACT FROM AUTHOR]

Abstract: There is a large value in making Pulp and Paper mills more energy efficient. ABB has developed an energy assessment service where opportunities to save energy in the paper machine are identified. The energy assessment is done by quantifying energy flows, benchmarking energy users, data mining and steam sensitivity analysis and by experiments and additional measurements at the paper machine. Energy quantification helped in identifying main energy consumer, benchmarking was useful to assess the gap between operating performance and best performance whereas data mining and steam sensitivity analysis helped in studying the impact of key operating variables on performance of paper machines. After the assessment an action plan was presented to the mill for energy efficiency improvement together with a return on investment. [Copyright &y& Elsevier]

Insight into current scientific applications of Big Data in the precision dairy farming area may help us to understand the inflated expectations around Big Data. The objective of this invited review paper is to give that scientific background and determine whether Big Data has overcome the peak of inflated expectations. A conceptual model was created, and a literature search in Scopus resulted in 1442 scientific peer reviewed papers. After thorough screening on relevance and classification by the authors, 142 papers remained for further analysis. The area of precision dairy farming (with classes in the primary chain (dairy farm, feed, breed, health, food, retail, consumer) and levels for object of interest (animal, farm, network)), the Big Data-V area (with categories on Volume, Velocity, Variety and other V's) and the data analytics area (with categories in analysis methods (supervised learning, unsupervised learning, semi-supervised classification, reinforcement learning) and data characteristics (time-series, streaming, sequence, graph, spatial, multimedia)) were analysed. The animal sublevel, with 83% of the papers, exceeds the farm sublevel and network sublevel. Within the animal sublevel, topics within the dairy farm level prevailed with 58% over the health level (33%). Within the Big Data category, the Volume category was most favoured with 59% of the papers, followed by 37% of papers that included the Variety category. None of the papers included the Velocity category. Supervised learning, representing 87% of the papers, exceeds unsupervised learning (12%). Within supervised learning, 64% of the papers dealt with classification issues and exceeds the regression methods (36%). Time-series were used in 61% of the papers and were mostly dealing with animal-based farm data. Multimedia data appeared in a greater number of recent papers. Based on these results, it can be concluded that Big Data is a relevant topic of research within the precision dairy farming area, but that the full potential of Big Data in this precision dairy farming area is not utilised yet. However, the present authors expect the full potential of Big Data, within the precision dairy farming area, will be reached when multiple Big Data characteristics (Volume, Variety and other V's) and sources (animal, groups, farms and chain parts) are used simultaneously, adding value to operational and strategic decision. [ABSTRACT FROM AUTHOR]

Lower-dimensional representation of scientific text has attracted much attention among researchers due to its impact on many data mining and recommendation tasks. This paper studies two main research streams in scientific literature representation. First, both local and distributed representation viewpoints are reviewed and their advantages and disadvantages in lower dimensional representation are discussed. The paper then proposes a novel hybrid distributed technique for text representation. Using scientific articles as the major source of textual information, both the article's content and citation network are used to build a distributed and universal lower dimensional representation. The superiority of the new technique to the traditional methods is then justified in predicting the existence of links in large citation graphs. [ABSTRACT FROM AUTHOR]

Knowledge discovery for data streaming requires online feature selection to reduce the complexity of real-world datasets and significantly improve the learning process. This is achieved by selecting highly relevant subsets and minimising irrelevant and redundant features. However, researchers have difficulties in addressing various forms of data. The goal of this article is to present a state-of-the-art review of feature subset selection based on the data form for the high-dimensional data used in online streaming. Through a systematic literature review assessing journal and conference papers from the past five years, detailed discussions on traditional feature selection and online feature selection were presented. Subsequently, a taxonomy of the challenges related to OFS provides a comprehensive review of state-of-the-art OFS and the benchmark methods. Several data forms were identified based on the extensive review: group stream, multi-label, capricious, imbalance, and feature drift. Using critical analysis, the evaluation metrics of online feature selection methods were compared from the perspectives of threshold initialisation, accuracy, high dimensionality, running time, relevancy, and redundancy for the optimal feature subset. An online feature selection framework was derived to illustrate the relationship between the application area, data form, online feature selection methods, evaluation metrics, and tools. Finally, the findings and potential directions for future research were thoroughly discussed. It is suggested that future researchers explore the derived framework and aim to advance each method. • Online feature selection methods for high dimensional online streaming data according to data forms and correlation measures. • A taxonomy of OFS challenges and the OFS framework was derived to illustrate the relationship of its ecosystems. • Significant findings in the adjustment of threshold dimensionality of data, relevancy and redundancy state. [ABSTRACT FROM AUTHOR]

Missing data imputation is a critical task in the data pre-processing stage to ensure the quality, stability, and reliability of machine learning models. If missing values are imputed incorrectly, it can result in erroneous predictions and inconsistent model performance. Traditional imputation methods often struggle with complex data patterns having non-linearity and uncertainties. By integrating soft clustering principles, the proposed work provides a flexible framework for imputing missing data by taking into account the underlying inherent data pattern. To address the missing data challenges, this paper presents a novel imputation technique called Radial Basis Kernel Intuitionistic Fuzzy Ⅽ-Means Imputation (KIFCMI), which builds upon the standard Intuitionistic Fuzzy Ⅽ-Means (IFCM) technique. KIFCMI explores the application of centroid-based imputation using IFCM by incorporating the RBF kernel-induced metric in the data space as a replacement for the original Euclidean norm metric. By employing a kernel function, KIFCMI enables the clustering of data that lacks linear separability within the original space, enabling the formation of homogeneous clusters in a space of higher dimensionality. The effectiveness of the proposed imputation technique is validated on ten diverse real-world datasets obtained from the Public Library UCI with 10% and 20% missing data. The comparative analyses of the proposed technique KIFCMI, is carried out with three conventional techniques, namely fuzzy c-means imputation (FCMI), kernelized fuzzy c-means imputation (KFCMI), and intuitionistic fuzzy c-means imputation (IFCMI). The experimental results using two performance measures, namely RMSE and MAE, showcase the robustness and versatility of the proposed technique across other imputation outcomes. This research paper contributes to the evolving landscape of missing data imputation, offering insights into the practical applications of fuzzy clustering techniques. [ABSTRACT FROM AUTHOR]

Condition monitoring and predictive maintenance applications receive ongoing scientific attention in production technology. Larger companies, especially machine and component manufacturers, already offer related products. Small and medium-sized enterprises (SMEs) in particular show interest in developing and offering solutions in this market themselves to gain economic advantages, to improve resource utilization of their machines or to be able to offer these advantages to their own customers. In the development process, however, they often encounter problems already in the digitization of the machines. The first hurdle is to obtain an analysis-capable data set. This is due to the fact that common and established general data mining development process models, such as CRISP-DM, do not focus on production technology, causing difficulties for engineers during deployment. A problem with existing process models is the limited practicality in the engineering domain due to restricted adaptability. In a previous paper, a guideline for engineers for data mining suitable digitization of production machines was developed in order to solve these problems. The related results were provided in the context of a project for condition monitoring of mixing machines. In this paper, the proposed method is applied to components of a 5-axis CNC milling machine in three different monitoring use cases. A complete workflow is presented, including effect analysis, sensor selection, formulation of predictive scenarios, data preparation, training of machine learning algorithms and vizualization. Data and documentation are provided alongside this publication. [ABSTRACT FROM AUTHOR]

Access points to Heliophysics information are often poorly inter-linked to one another, making it challenging for researchers to integrate information, mature their understanding, and incorporate more complex ideas and relationships into their analyses. The authors reason this behavior to be a direct conseaquence of the lack of infrastructure for knowledge and data discovery in Heliophysics. Building an online library resource for Heliophysics addresses this gap in resources, and can also address other troublesome gaps related to community-building, such as access to help from other scientists, example analysis tutorials, and collaboration opportunities. This paper envisions a resource that better connects existing Heliophysics information and knowledge sources, the LIbrary for KnowledgE and Discovery (LIKED), which will be an online resource that interlinks information across databases (e.g., observational and publication), researcher interactions (e.g., conferences and discussion boards), and educational materials. The result is a rich and searchable environment to provide users a more productive experience for research and collaboration. In this paper, we provide the conceptual model for LIKED. We expand to detail how LIKED will serve to connect the various knowledge and research components available internationally together in an uniformly searchable manner. Finally, this work outlines various contributions the community can make towards this goal. [ABSTRACT FROM AUTHOR]

Real-time semantic segmentation, as a key technique for scene understanding, has been an important research topic in the field of computer vision in recent years. However, existing models are unable to achieve good segmentation accuracy on mobile devices due to their huge computational overhead, which makes it difficult to meet actual industrial requirements. To address the problems faced by current semantic segmentation tasks, this paper proposes a lightweight bilateral asymmetric residual network (LBARNet) for real-time semantic segmentation. First, we propose the bilateral asymmetric residual (BAR) module. This module learns multi-scale feature representations with strong semantic information at different stages of the semantic information extraction branch, thus improving pixel classification performance. Secondly, the spatial information extraction (SIE) module is constructed in the spatial detail extraction branch to capture multi-level local features of the shallow network to compensate for the lost geometric information in the downsampling stage. At the same time, we design the attention mechanism perception (AMP) module in the jump connection part to enhance the contextual representation. Finally, we design the dual branch feature fusion (DBF) module to exploit the correspondence between higher-order features and lower-order features to fuse spatial and semantic information appropriately. The experimental results show that LBARNet, without any pre-training and pre-processing and using only 0.6M parameters, achieves 70.8% mloU and 67.2% mloU on the Cityscapes dataset and Camvid dataset, respectively. LBARNet maintain a high segmentation accuracy while using a smaller number of parameters compared to most existing state-of-the-art models. [Display omitted] • This paper proposes a Bilateral Asymmetric Residual (BAR) module, a Spatial Information Extraction (SIE) module, an Attention Mechanism Perception (AMP) module and a Dual Branch Feature Fusion (DBF) module. • A Lightweight Bilateral Asymmetric Residual Network (LBARNet) for real-time image semantic segmentation is proposed in this article. • The experimental results show that LBARNet achieves 70.8% and 67.2% segmentation accuracy on two challenging datasets (Cityscapes and CamVid) using only 0.6M parameters. [ABSTRACT FROM AUTHOR]

Multi-label classification has recently attracted research interest as a data mining task. Many current applications in data mining address problems that have instances belonging to more than one class. This requires the development of new efficient methods. Instance selection has been used in multi-label learning to improve the execution time and classification performance of many learning methods. Following the single-label approach, instance selection has been applied by selecting or unselecting the same instances for all labels. In this paper, we present a different and novel approach. An instance might be useful for some labels and harmful for others; therefore, our algorithm allows each instance to be discarded, selected, or only partially selected for use in the classification of certain labels. An extensive comparison using 45 datasets shows the usefulness of our approach in improving the current instance selection methods for multi-label problems, as well as the ability of our algorithm to compete with other more complex multi-label classification methods. • This paper presents a new framework for instance selection for multi-label datasets. • The method improves several state-of-the-art methods. • The method is scalable to large multi-label datasets. • Interesting new research lines are opened by the framework. [ABSTRACT FROM AUTHOR]

According to the demand of sports athletes' scientific training, combined with data mining technology, a set of sports training model which is suitable for China's national conditions and has a higher level of scientific research is established. Starting from the data mining technology, this paper expounds its connotation and several common methods in detail. Based on the demand of sports training, this paper studies the quality control of sports training and the statistical system of tactical combat. On this basis, an improved output method of Aprilio algorithm is introduced to enhance the correctness of systematic evaluation. On this basis, three algorithms, classic, DC-Apriori and modified Apriori, are simulated and compared. The results show that the improved Apriori algorithm studied in this project can provide better decision support for the training model. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, knowledge management has become an important means for enterprises, organizations, and individuals to gain advantages in competition. The design and implementation of Knowledge management is one of the key links of knowledge management. This paper discussed the design of Knowledge management based on Support Vector Machine (SVM), in order to improve the efficiency and quality of knowledge management. This paper introduced the application of SVM in Knowledge management, and discussed how to design an excellent Knowledge management. The research results showed that the response time of the Knowledge management designed in this paper was 87 seconds when the number of users was 300. When the number of users exceeded 300, although the response time was longer, its growth rate slowed down. SVM can be used in knowledge classification, entity recognition and relationship extraction, and can greatly improve the efficiency of Information extraction and knowledge management. [ABSTRACT FROM AUTHOR]

This paper explores the utilization of randomized SVD (rSVD) in the context of kernel matrices arising from radial basis functions (RBFs) for the purpose of solving interpolation and Poisson problems. We propose a truncated version of rSVD, called trSVD, which yields a stable solution with a reduced condition number in comparison to the non-truncated variant, particularly when manipulating the scale or shape parameter of RBFs. Notably, trSVD exhibits exceptional proficiency in capturing the most significant singular values, enabling the extraction of critical information from the data. When compared to the conventional truncated SVD (tSVD), trSVD achieves comparable accuracy while demonstrating improved efficiency. Furthermore, we explore the potential of trSVD by employing scale parameter strategies, such as leave-one-out cross-validation and effective condition number. Then, we apply trSVD to solve a 2D Poisson equation, thereby showcasing its efficacy in handling partial differential equations. In summary, this study offers an efficient and accurate solver for RBF problems, demonstrating its practical applicability. The code implementation is provided to the scientific community for their access and reference. [ABSTRACT FROM AUTHOR]

The domain of Social Collaboration Analytics (SCA) is gathering momentum due to the increasing importance of Enterprise Collaboration Systems (ECS) that are used to build performant digital workplaces. ECS collect and store large amounts of digital trace data, which reflect the collaborative work of employees. Collaborative work activity is usually supported by multiple systems, which complicates the analysis of the digital traces. The cross-system analysis of collaborative work is challenging because of the nature of the log files that are stored in heterogeneous formats and at different levels of log granularity. These challenges cannot easily be overcome with the help of existing approaches for data preprocessing. In this paper, approaches and frameworks for data mining, data preprocessing and SCA are adapted to design a special method for Social Process Mining (SPM). The DaProXSA approach was developed to obtain interpretable, high-quality event logs of multiple ECS and builds on existing literature as well as on findings from an analysis of leading ECS. DaProXSA contributes to the aim of discovering and interpreting patterns and processes that occur in computer-mediated collaborative work. [ABSTRACT FROM AUTHOR]

Today, higher education institutions are focused on understanding which factors are associated with the failure or success of students to, early on, be able to implement measures that can reduce the low performance of students and even dropout. The retention rate is positively and negatively influenced by factors belonging to several dimensions (personal, environmental, and institutional). We aim to use information from those dimensions to identify students enrolled in a Computer Networks course at risk of failing the subject. Besides, this needs to happen as early as possible, to be able to provide the students, for example, with extra support or resources to try to prevent that negative outcome. For predicting the grade level on the first test, the best accuracy obtained was 55%. However, most C-level grades were correctly classified, with 63% accuracy in predicting the students that are most at risk of failing, which is one of our main objectives. As for the prediction of the second test's grade level, the best accuracy obtained was 89% and concerned data regarding the students' interaction with the LMS together with students' grades history. All the C-level grades were correctly classified (100% accuracy) and so we were able to correctly predict every student at a high risk of failing. Using the procedure described in this paper, we are able to anticipate the students needing extra support, and provide them with different resources, to try to prevent their negative outcome. [ABSTRACT FROM AUTHOR]

Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life sciences today. Topics include a wide range from machine learning and Big Data analytics, knowledge discovery and data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support and reasoning under uncertainty, temporal and spatial representation and inference, and methodological aspects of explainable AI (XAI) with applications of biotechnology. In this pre-Editorial paper, we provide an overview of open research issues and challenges for each of the topics addressed in this special issue. Potential authors can directly use this as a guideline for developing their paper. • This pre-Editorial provides an overview of some hot topics in AI for life. • The benefit for the reader is to get a short, crisp, concise intro into AI. • Authors interested in this Special Issue will get a scope of topics. • The take away message is that in the future no biotechnology can do without AI. [ABSTRACT FROM AUTHOR]

In this paper, we examine the possibility of employing the idea of progressive-inductive (π) aggregation in the k-means algorithm. We base our work on the interactive visualization framework called Skydive which is a tightly coupled system that combines data aggregation and visual aggregation for smooth user interaction. It is based on an aggregate pyramid data structure (π-cube), allowing a visualization client to quickly filter the data. Skydive was designed for interactive data visualization in terms of expressiveness and efficiency. Therefore, the underlying data structure enabled access to data on desired levels of granularity. In this paper, we utilize this concept to speed up the process of clustering proposing π-means - a modified version of the k-means algorithm. This will allow us to explore further possible applications of π-cube towards interactive data exploration. [ABSTRACT FROM AUTHOR]

Social media platforms generate vast amounts of data that can be mined to gain insights into user behavior, preferences, and opinions. With the widespread adoption of mobile devices and the Android platform, social media data mining on Android has become an important area of research. In this paper, we present a systematic review of existing research related to social media data mining on Android. We conducted a comprehensive literature search and identified relevant research papers published between 2015 and 2022. We analyzed the papers to identify key themes and trends, and we discuss the strengths and weaknesses of current approaches. Our review revealed that most of the research in this area focuses on text data mining, and that machine learning techniques are commonly used for data analysis. We also found that there is a lack of research on image and video data mining, and that privacy and ethical issues are important considerations in this field. Based on our review, we propose future research directions for social media data mining on Android, including the development of more efficient and accurate machine learning algorithms, and the integration of multimedia data analysis. Our paper provides a valuable overview of existing research in this field and offers insights for researchers and practitioners interested in social media data mining on Android. [ABSTRACT FROM AUTHOR]

This paper focuses on home smart voice devices to address the problems of traditional mental health diagnosis and evaluation (e.g., the inability to monitor, analyze and assess mental health in real-time and the heavy reliance on psychologists and counselors for judgment). It constructs a model for monitoring, examining, and evaluating children's mental health using interactive communication and voice recordings between children and intelligent voice devices. The model uses techniques such as natural language processing and data mining based on a random forest algorithm. Meanwhile, this paper explores and investigates the feasibility of implementing the model on the ground in terms of technology development and technology preference. [ABSTRACT FROM AUTHOR]

Recent developments in the field of artificial intelligence has led to renewed interest in natural language processing. Named entity recognition (NER) is a classic problem in natural language processing. Investigating named entities is a continuing concern within information extraction. Names of people, organizations, locations, events, etc. are named entities (NEs). Although extensive research has been carried out on NER, limited study exists that explores NER in the Marathi language. The paper aims to provide a conceptual theoretical framework based on the gazetteer matching technique for named entity recognition system development for the Marathi language. A combination of rules and regular expressions is considered for temporal and numerical pattern recognition. The techniques presented in this paper are systematic, clear, and effective for morphologically rich language processing tool development. The system described in the paper reports a satisfactory performance with 62.64% NE identification and 72.27% NE classification accuracy. [ABSTRACT FROM AUTHOR]

COVID-19 is a pandemic that has resulted in numerous fatalities and infections in recent years, with a rising tendency in both the number of infections and deaths and the pace of recovery. Accurate forecasting models are important for making accurate forecasts and taking relevant actions. As a result, accurate short-term forecasting of the number of new cases that are contaminated and recovered is essential for making the best use of the resources at hand and stopping or delaying the spread of such illnesses. This paper shows the various techniques for forecasting the covid-19 cases. This paper classifies the various models according to their category and shows the merits and demerits of various fore-casting techniques. The research provides insight into potential issues that may arise during the forecasting of covid-19 instances for predicting the positive, negative, and death cases in this pandemic. In this paper, numerous forecasting techniques and their categories have been studied. The goal of this work is to aggregate the findings of several forecasting techniques to aid in the fight against the pandemic. [ABSTRACT FROM AUTHOR]

In the urban rail transportation industry, collecting various data, including the number of trips, the number of fleet breakdowns, the number of breakdowns of railway lines, etc., contributes significantly to future planning and optimization of resources and costs. We are faced with big data analysis due to the daily production of large volumes of data in the urban rail transport industry. This study aims to find a trip prediction model for Tehran Metro to do trip decision-making. This paper provides a brief introduction to knowledge discovery from failure databases and presents the data mining methodology. The relevancy of data mining for a trip prediction model will be depicted. In this sense, considering the relevance of data collected on the metro control center and intelligent trip prediction, this paper presents a functional architecture of a predictive trip model using data mining techniques. Data Mining will identify behavior patterns, allowing a more accurate early detection of faults in the daily operation. This research has investigated three situations, including delayed trips, incomplete trips, and canceled trips, using actual data recorded over the past four years, including non-programmed failures and their impact on trips. A model with more than 90% (90.48%) accuracy suggests a trip status forecast on this line. Also, the research has used Rapid Miner software and the Decision tree technique in data mining and its rules. Also, the kappa index of 0.792 is calculated for the validation model. Therefore, the proposed model enables the prediction of three types of incomplete traffic, delayed trips, and cancellation of trips due to emergency failure at the time of the operation of the Tehran Metro. [ABSTRACT FROM AUTHOR]

Numerous research on stunting supplementation interventions in Indonesia have been published. The information can be extracted through data mining, especially from academic research databases. In this paper, we presented a text mining-based literature review strategy to create a pipeline that researchers can use to accelerate the development of stunting supplementation intervention research in Indonesia. Utilizing various NLP (Natural Language Processing) techniques, data were crawled, processed, and visualized using Python. The crawling dataset used a module from the Pubmed API (Application Programming Interface) to collect literature papers. The NLTK (Natural Language Toolkit) module and itertools were used to process text data. The n-grams model was applied to process tokens into bigrams and trigrams. Text information was visualized using Matplotlib and Word cloud packages. There is an increasing number of publication in stunting supplementation intervention according to our result, which was observed from 2015 to 2021. West Java was the province where most of the stunting research has been conducted, as determined by research abstracts. Top occurrences obtained from the bigram and trigrams models calculation produced different terms. The word pairings that occurred the most frequently in the bigram and trigram model analyses were "child-aged" and "iron-folic-acid," respectively. The findings of this study are expected to help researchers to obtain the latest research topics related to stunting supplementation interventions in Indonesia. [ABSTRACT FROM AUTHOR]

Big data and its analysis are crucial for other applications like the Internet of Things (IoT), data mining, cloud computing, etc. The volume of data is expanding along with the internet and other digital technologies. Traditional systems, however, cannot process this volume of data. Analyzing this much data will take a lot of work. However, there are several difficulties in assuring data security and privacy, such as safeguarding against data leakage and security breaches, flaws in open databases, and third-party data sharing, among others. This paper's goal is to analyze the benefits of blockchain in big data as well as its drawbacks and identify potential future approaches. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive exploration of the development of an AI-driven Gen-Alpha education guidance indicator for Oman, employing cutting-edge machine learning techniques. The primary objective is to deliver a highly personalized support system and guidance mechanism that accompanies students throughout their academic journey. The research places a spotlight on the pivotal role of Artificial Intelligence (AI) within the realm of education. It skillfully demonstrates AI's robust capabilities in accurately forecasting student performance through the effective use of data mining and advanced machine learning techniques. An integral aspect of this research is the application of AI in aiding students to navigate the involved process of subject selection for their higher education pursuits. This multidimensional process encompasses exhaustive data collection, the detailed creation of a sophisticated machine learning model, and the application of a diverse array of state-of-the-art algorithms. Among these algorithms, K-Nearest Neighbors (KNN), Decision Trees, Random Forest, and Support Vector Machines (SVM) stand out prominently. Notably, the SVM algorithm emerges as the outright winner, delivering an extraordinary accuracy rate of 77%. This remarkable achievement underscores the model's unwavering robustness and its potential to redefine the educational landscape in Oman. In essence, this paper transcends the boundaries of conventional research and offers conclusive validation of AI's revolutionary potential in reshaping educational paradigms. By facilitating data-driven decision-making, the AI-driven Gen-Alpha education guidance indicator empowers students to embark on their educational journeys well-informed and confident, thereby playing a pivotal role in advancing Oman's education system. [ABSTRACT FROM AUTHOR]

The paper presents advancements in healthcare data capture through the application of image-based extraction techniques, which include sophisticated image processing techniques such as resizing and adaptive thresholding, for prescription information. With the increasing digitization of medical records, automating the extraction of relevant data from prescription documents has become crucial. This research explores the utilization of image processing and optical character recognition (OCR) methodologies to extract prescription information accurately. By converting prescription documents into image format and employing OCR algorithms, the text content is extracted and parsed for critical details such as medication names, dosages, and patient instructions. Notably, our methodology excels in overcoming limitations associated with handwritten documents, achieving an impressive accuracy rate of 98%. This image-based approach offers a streamlined and efficient method for capturing prescription data, reducing manual data entry efforts, and minimizing potential errors. Experimental evaluations demonstrate the effectiveness and accuracy of the proposed approach, highlighting its potential to enhance healthcare data capture and improve patient care. [ABSTRACT FROM AUTHOR]

Financial anomalies must be detected in order for financial institutions and regulatory bodies to manage risks and avoid fraudulent behavior. Financial anomaly detection is the practice of identifying unexpected or irregular financial transactions or patterns that may indicate fraudulent behavior or errors. It is crucial in today's digital era to prevent fraud, limit financial losses, and maintain secure financial systems. Various types of financial anomalies, such as credit card fraud, money laundering, financial statement fraud, and cryptocurrency fraud, pose significant risks to individuals and organizations. This review critically evaluates a selected research article on the use of blockchain technology in conjunction with data mining techniques to detect financial anomalies. The paper employs the case study method to demonstrate how well the suggested integrated system works in spotting financial anomalies. This review evaluates the article's methodology and conclusions and discusses its implications for practice. The report claims that merging data mining methods with blockchain technology can increase the precision and effectiveness of financial anomaly identification. This research advances knowledge about how block-chain technology and data mining techniques can be used to find financial abnormalities while also offering suggestions for further study and use. [ABSTRACT FROM AUTHOR]

Techniques for classifying data using data mining are now a day prevalent in agriculture. The method of classifying seeds involves grouping various seed varieties according to their morphological characteristics. To accomplish categorization of the typical Charotar region (generally comprising Anand and Kheda districts of the Gujarat State of India) Gujarat Wheat (GW) varieties (TRITICUM – AESTIVUM) viz. GW 273, GW 496, GW 322, LOK-1, and GDW 1255 (TRITICUM – DURUM), Weka Explorer was used. The features used are area, perimeter, solidity, aspect ratio, major and minor axis of seed kernel, Hue, Saturation, Value, and SF1 (empirical). Features reduction was done using Information Gain (IG) and its modified version Gain Ratio (GR). This paper compares performance of Tree based data mining algorithms in classifying wheat varieties. For classification we used purely tree-based machine learning algorithms viz. J48, Random Forest, Hoeffding Tree, Logistic Model Tree (LMT), and REPTree. LMT- logistics regression method gives higher accuracy 96.4% compared to other classifiers. Hoeffding Tree classifiers stood second with 96% accuracy. For validation 10-fold cross validation was used. By reducing the number of folds in cross validation performance of most algorithms decreased except J48. The percentage of correctly classified instance increased for all algorithms when features were selected by GR except for J48. [ABSTRACT FROM AUTHOR]

This paper demonstrates the application of a fully automated machine learning (ML) pipeline on time series data from the domain of production engineering. The workflow aims to streamline the machine learning (ML) process and reduce manual effort by incorporating automated machine learning (AutoML) and automated featurization techniques. A comparative study is created to evaluate the proposed workflow against existing state-of-the-art methods using six datasets from production engineering, five widely used ones and a newly presented one. The study compares the performance of the automated workflow with the manual data mining process described in the literature. The results lead to two main conclusions: first, an automated featurization process cannot match the performance of manual feature generation, but still delivers good results without requiring manual work. Second, if the manually generated features are used, AutoML can outperform the manual process in solving the combined problem of algorithm selection and hyperparameter optimization, leading to better prediction results. The presented approach serves as a useful reference for other methods, showing the practicality of baseline workflow without human bias. Overall, the proposed data processing pipeline demonstrates the potential of AutoML in production engineering and provides valuable insights for practitioners in this domain. [ABSTRACT FROM AUTHOR]

The metaheuristics approach is important for solving many engineering applications, including healthcare. In the healthcare sector, many problems related to patients' treatments are not covered, and it is one of the issues for healthcare services across the globe. Healthcare support functions like clinical decision support, patient profiling, customized care, and health tracking could be the area where data analysis effectively provides end-user support. This paper focused on the metaheuristics algorithm concept with data mining technologies to provide a healthcare sector solution mechanism. This paper identifies the data analysis problem of the healthcare sector. It reviews various recent technologies used in healthcare and related sectors. It focused on the importance of metaheuristics algorithms, artificial intelligence, and implementation methodologies. [ABSTRACT FROM AUTHOR]

The concept of data lake was proposed more than a decade ago. Although progress has been made in data lake research and applications, there are also numerous issues and challenges need to be addressed. In this paper, we survey some recent developments, provide our observations, as well as our expectations on future research and practice in this area. We start with a discussion on general terminology in data lakes, then review various aspects of data lakes, including an examination on metadata and related issues, information granularities involved in data lakes, unique features such as mixed lazy and eager approach in data lake lifecycle, relationship between data lakes and data mining, interplay between data lakes and data warehousing techniques, as well as some notable developments related to applications of data lakes. We present our observations while we examine these aspects, and wrap up this paper by expressing our hope of developing a unified framework for data lake lifecycle evaluation. [ABSTRACT FROM AUTHOR]

Named Entity Recognition (NER) plays a pivotal role in knowledge extraction and improving the intelligence of edge computing. The effectiveness of span-based NER models predominantly depends on the representation of spans. Existing methods primarily utilize semantic features to represent spans, often neglecting other vital information. This paper proposes a method incorporating Part of Speech (POS) information into span representations to overcome this limitation. Central to this methodology is a span POS encoder designed to extract the POS feature of spans. For migrating the method to edge devices, this paper introduces a fast span POS encoder, which significantly reduces the time complexity of POS feature extraction. Building upon this innovation, a span-based NER model named IPSI (I ncorporating P art of S peech I nformation in span representation) is developed, exhibiting outstanding performance on nested and flat datasets. Comparison the original and fast span POS encoders reveals that while the fast encoder slightly compromises performance, it markedly accelerates the training and inference processes. Finally, through a series of experiments and sample analyses, this article explores the intrinsic mechanism through which the span POS feature influences entity recognition and further illustrates the importance of the POS feature. • A span POS encoder is proposed to extract the POS feature of a span. • An efficient variation of the span POS encoder is developed to cater to the limited computational capacity of edge devices. • A NER model is developed, which incorporates the POS feature to enhance span representation, outperforming competitor models. • The significance of the POS feature for NER is substantiated through a series of experiments. [ABSTRACT FROM AUTHOR]

Concept-cognitive learning (CCL), an emerging intelligence learning paradigm, has recently become a popular research subject in artificial intelligence and cognitive computing. A central notion of CCL is cognitive and learning things via concepts. In this process, concepts play a fundamental role when mining and fusing knowledge from data to wisdom. With the in-depth research and expansion of CCL in scopes, goals, and methodologies, some difficulties have gradually emerged, including some vague terminology, ambiguous views, and scattered research. Hence, a systematic and comprehensive review of the development process and advanced research about CCL is particularly necessary at the moment. This paper summarizes the theoretical significance, application value, and future development potential of CCL. More importantly, by synthesizing the reviewed related research, we can acquire some interesting results and answer three essential questions: (1) why examine a cognitive and learning framework based on concept? (2) what is the concept-cognitive learning? (3) how to make concept-cognitive learning? The findings of this work could act as a valuable guide for related studies in quest of a clear understanding of the closely related research issues around concept-cognitive learning. • It is the first paper that attempts to provide an in-depth analysis of the advancement of CCL. • It is a multi-view categorization of CCL in research scopes, goals, and methodologies. • It is an elucidation of the main research gaps and suggestions for the related study of CCL. • It acquires some interesting results by synthesizing the reviewed related research of CCL. [ABSTRACT FROM AUTHOR]

This paper reviews the recent progress of small angle scattering (SAS) techniques, mainly including X-ray small angle scattering technique (SAXS) and neutron small angle scattering (SANS) technique, in the study of metal-organic framework (MOF) colloidal materials (CMOFs). First, we introduce the application research of SAXS technique in pristine MOFs materials, and review the studies on synthesis mechanism of MOF materials, the pore structures and fractal characteristics, as well as the spatial distribution and morphological evolution of foreign molecules in MOF composites and MOF-derived materials. Then, the applications of SANS technique in MOFs are summarized, with emphasis on SANS data processing method, structure modeling and quantitative structural information extraction. Finally, the characteristics and developments of SAS techniques are commented and prospected. It can be found that most studies on MOF materials with SAS techniques focus mainly on nanoporous structure characterization and the evolution of pore structures, or the spatial distribution of other foreign molecules loaded in MOFs. Indeed, SAS techniques take an irreplaceable role in revealing the structure and evolution of nanopores in CMOFs. We expect that this paper will help to understand the research status of SAS techniques on MOF materials and better to apply SAS techniques to conduct further research on MOF and related materials. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper aims to support medical decision making on predicting the diagnosis of COVID-19. Thus, a set of Data Mining (DM) models was developed using prediction techniques and classification models. These models try to understand whether the vital signs of patients have a correlation with a diagnosis. To achieve the objective of the paper, initially, the data was acquired and collected from several data sources such as bedside monitors and electronic nursing records from the Intensive Care Unit of the Santo António Hospital. Secondly, the data was transformed so that it could be used in DM models. The models were induced using the following algorithms: Decision Trees, Random Forest, Naive Bayes, and Support Vector Machine. The analysis of the sensitivity, specificity, and accuracy were the metrics used to identify the most relevant measures to predict COVID-19 diagnosis. This work demonstrates that the models created had promising results. [ABSTRACT FROM AUTHOR]

Clustering is one of the most common technologies in data mining. The size, dimension and sparsity of data are all different aspects that restrict clustering analysis. Clustering machine learning is called unsupervised learning, which is different from classification in that the data objects used in clustering analysis have no class marks, so they need to be calculated by the clustering learning algorithm, while the classified data objects need class marks. At present, most of the sparse data algorithms with high attribute dimensions are oriented to binary data, and there is no evaluation method of clustering results, which brings great limitations to their application. The BP neural network, RBF neural network and Elman neural network model with feedback function, which are mature and widely used in pest prediction models, are studied. The purpose of this paper is to study diseases and pests based on sparse clustering algorithm. In this paper, algorithm formulas, models and data graphs are established to study. From the research, it can be seen that the damage index of pests and diseases is very high, reaching about 50. 54%. Through the research in this paper, we can know that the future will lay a certain foundation for the research of diseases and pests. [ABSTRACT FROM AUTHOR]

Accurate and effective access to agricultural and pastoral information is of great practical importance for scientific planning in agriculture and pastoral husbandry and food security reserves. This paper utilised intra-annual monthly synthetic data from Sentinel-1 and Sentinel-2, combined with band information and index information to simultaneously obtain intra-annual phenological change information and extracted different types of information for random forest classification, thus obtaining the fine-grained agricultural and pastoral map of Jingyang District. On this basis, the effects of different data sources and the importance of different features on the classification results were further analysed. The results show that in the classification results of the Sentinel-1 data method, March feature is the most important and May feature has the least influence on the classification results. In the classification results of the Sentinel-2 data method and the combined Sentinel-1 data and Sentinel-2 data, the December feature has the greatest importance and the February feature has the least impact on the classification results. The NDVI index has the highest importance and the EVI index has the lowest importance score. The importance of band features varies in the classification results of different data sources. The accuracy of the agricultural and pastoral information in Jingyang District is 83.31% and the kappa is 0.76. Rice is the largest crop with an area of 239,830,800 m2, mainly located in the northwest and mountainous areas of Jingyang District. It is followed by green manure with an area of 60,274,000 m2, mainly in the mountainous part. This study demonstrates the feasibility of extracting agricultural and pastoral information from remote sensing images, and also provides basic data for future agricultural and pastoral planning in Jingyang District. [ABSTRACT FROM AUTHOR]

Feature Selection (FS) is critically important for optimising the performance of Intrusion Detection Systems (IDSs) used in Wireless Sensor Networks (WSNs). However, selecting an optimal number of relevant features from massive IDS data sets has been an ongoing FS research problem. Many approaches use Machine Learning (ML), such as nature-inspired population-based and Differential Evolution (DE)-based algorithms for FS. However, the main drawback of DE is that it has a premature convergence issue, which leads to false convergence. In this paper, a novel mutation strategy is proposed to alleviate premature convergence and to develop a modified DE-based FS algorithm called DE with maturity extension (DE-ME). The novel mutation is achieved by changing the mutation factor in the DE\rnd\1 based on K-Nearest Neighbour (KNN) as the most efficient implementation without adding further complexity to the design process. The proposed DE-ME algorithm improves the FS operation with significant performance improvements in terms of overcoming premature convergence and increasing accuracy for selection. The DE-ME algorithm was used to select the most effective features from 41 features in the Network Security Laboratory Knowledge Discovery in Databases (NSL-KDD) dataset. The accuracy of DE-ME is high at 99.66 %, its False Positive Rate (FPR) is low at 0.464 %, and the true positive rate (TPR) achieved is 99.80 %. In addition, the number of features selected by DE-ME is 6 out of 41 features in the dataset. [ABSTRACT FROM AUTHOR]

Pattern mining is a fundamental technique of data mining to discover interesting correlations in the data set. There are several variations of pattern mining, such as frequent itemset mining, sequence mining, and high utility itemset mining. High utility itemset mining is an emerging data science task, aims to extract knowledge based on a domain objective. The utility of a pattern shows its effectiveness or benefit that can be calculated based on user priority and domain-specific understanding. The sequential pattern mining (SPM) issue is much examined and expanded in various directions. Sequential pattern mining enumerates sequential patterns in a sequence data collection. Researchers have paid more attention in recent years to frequent pattern mining over uncertain transaction dataset. In recent years, mining itemsets in big data have received extensive attention based on the Apache Hadoop and Spark framework. This paper seeks to give a broad overview of the distinct approaches to pattern mining in the Big Data domain. Initially, we investigate the problem involved with pattern mining approaches and associated techniques such as Apache Hadoop, Apache Spark, parallel and distributed processing. Then we examine major developments in parallel, distributed, and scalable pattern mining, analyze them in the big data perspective and identify difficulties in designing the algorithms. In particular, we study four varieties of itemsets mining, i.e., parallel frequent itemsets mining, high utility itemset mining, sequential patterns mining and frequent itemset mining in uncertain data. This paper concludes with a discussion of open issues and opportunity. It also provides direction for further enhancement of existing approaches. [ABSTRACT FROM AUTHOR]

Condition monitoring plays an important role in production systems, since a failure of a single device could block the whole production line. State-of-the-art technologies such as vibration monitoring, acoustic emission technique, shock pulse method or similar are often applied through a variety of sensors in order to monitor a big amount of different devices. However, such multi-sensor deployment requires not only high investment and high effort, but qualified personnel resources for the analysis of the various data, especially when it comes to a real-time condition monitoring. Additionally, retrofitting of plants and peripheral devices has its own obstacles, due to either limited resources, high complexity, or other diverse restrictions. To foster condition monitoring in industrial applications hardware-software combinations are needed which excel in low applications costs and reduced installation efforts. In this paper, an economic solution for condition monitoring is presented based on a central power measurement device, which can be applied with a 100 Hz resolution on a device group such as pumps or the exhaust system. Mainly, this paper deals with the detection of active individual devices in a group by their power signature. A spectral analysis is carried out to obtain the power signature of every device. Finally, the device detection is realised through supervised data mining models, which are trained with the data from a manufacturing execution system and the high-resolution power signal. This approach allows the detection of a specific device between similar ones, as for every device of interest an individual model is developed. [ABSTRACT FROM AUTHOR]

Manufacturing processes have undergone tremendous technological progress in recent decades. To meet the agile philosophy in industry, data-driven algorithms need to handle growing complexity, particularly in Computer Numerical Control machining. To enhance the scalability of machine learning in real-world applications, this paper presents a benchmark dataset for process monitoring of brownfield milling machines based on acceleration data. The data is collected from a real-world production plant using a smart data collection system over a two-years period. In this work, the edge-to-cloud setup is presented followed by an extensive description of the different normal and abnormal processes. An analysis of the dataset highlights the challenges of machine learning in industry caused by the environmental and industrial factors. The new dataset is published with this paper and available at: https://github.com/boschresearch/CNC_Machining. [ABSTRACT FROM AUTHOR]

• Gas turbine life management monitors and assesses turbine status and conditions. • Inlet and exit air passages impact gas turbine performance, not physical attributes. • Study gap: An incomplete gas-turbine model limits big data's predictive analysis. • Big data analytics drive optimized informed integrated health monitoring decisions for gas turbines. • Data mining extends gas turbine life by forecasting faults for the future. In addition to power generation, gas turbines (GT) are crucial components in many engineering applications, including those for aircraft, ships, and other machinery that needs a prime mover. One of the main concerns was predicting a comprehensive GT model, including intake, exit, and power-generator characteristics. Hence, this study provides a comprehensive review of GT's main components, parameters, faults, and predicting models using machine learning and data mining applications. Forty-four publications between 2014 and 2023 were chosen for this review study based on the primary keywords associated with the paper title. This study focuses solely on the GT model prediction aspects, considering the significance of field distribution, applications of data mining, research goals, benefits, constraints, and contributions to aviation, the oil and gas industry, and marine propulsion fields. Based on the examined studies, ANN, ELM, and KF applications show promise for potential GT life management. The typical limitations of GT life prediction, as observed in 93.2% of the review papers, involved the absence of a comprehensive GT model that incorporates all vibration factors and the use of substantial amounts of real data for predictive modelling. Another notable finding that underscores the knowledge gap in this area is the utilization of complete GT units to monitor unit life and facilitate life management. Apparently, the development of GT life management is still riddled with several limitations, which call for more improvement. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization, increasingly more data are being acquired by intelligent transportation systems (ITSs), which is of great significance for traffic flow forecasting. Efficient intelligent traffic management systems (TMSs) depend on accurately forecasting traffic flows as well as reasonably researching and judging traffic states. However, the current prediction models used in transportation forecasting tasks are traditional temporal- and spatial-dimensional prediction approaches. Especially when faced with road channelization, the numbers of lanes and lane types increase significantly, causing significant increases in the dimensionality and complexity of traffic data, and research models utilizing the cross-interaction relationships among multiple dimensions have not been considered. This has led to unsatisfactory prediction results in complex traffic environments. Therefore, it is very important to explore the exchange–correlation of complex dimensional data and the mining of hidden attributes. This paper presents a method for performing multidimensional cross-attention and spatiotemporal graph convolution. This method fully considers cross-information and constructs an attention cross-view between every pair of dimensions among the channel, time and space domains to model the cross-dimensional dependencies of traffic data. We innovatively propose a triple cross-attention and graph convolutional network (TC-GCN), which can achieve further improved traffic forecasting performance. The TC-GCN is verified on two real-world traffic datasets, namely, METR-LA and PEMS-BAY, and the experimental results are compared with those of multiple advanced baselines, showing that the proposed approach outperforms most of these baselines, which proves the effectiveness of the method proposed in this paper. • TC-GCN: A framework capturing time/space/feature dimension interrelations. • It addresses spatiotemporal information misalignment in traffic analysis. • Features tailored readout for spatiotemporal and time/feature perspectives. • We introduce time block mechanism for dynamic temporal data preservation. [ABSTRACT FROM AUTHOR]

One of the most significant sources of information from customers is customer complaints. Successful and effective complaint management can end complaint crises and ensure client loyalty, which is a sign of great service performance. In this paper, we proposed a novel customer temperament-centered and e-CCH system-based data collection and data mining method titled "3D" model for customer complaint data analysis. Three phases are (1) Development and launch of e-Customer Complaint Handling system, (2) Data collection and transfer of learning by e-Customer Complaint Handling system, and (3) Data mining by e-Customer Complaint Handling system. An advanced e lectronic C ustomer C omplaint H andling System called the e-CCH system was then developed and launched. This system adapts the seasonal associations model based on Hippocrates's customer temperament theory to the whole stages of customer complaint reporting and handling. With this system, we conducted a dataset collection work from restaurant chains of two brands over four years. As a result, we collect thousands of real-world temperament-centred customer complaint cases by four years to form the one-of-a-kind CCH dataset. This one-of-a-kind CCH dataset was open-sourced with detailed customer complaint attributes and heuristic decision-making for valuable industrial handling manner. After further analysis of this dataset, we found that customers with different temperament types tend to have different types of complaints. In addition, adapting the temperament theory to the e-CCH system can classify customer types better and provide personalized solutions. To our best knowledge, this rich and the one-of-a-kind CCH dataset reported in this paper is the first comprehensive study of customer complaint handling in an industrial service management context. Meanwhile, data mining with cross analysis and correspondence analysis and an ChatGPT experiment for transfer of learning based on this yearly and one-of-a-kind industrial customer complaint dataset was analyzed and discussed. In addition, how this dataset may contribute to more realistic complaint-handling theoretic studies for better service failure recovery and interactive marketing is discussed in-depth. [ABSTRACT FROM AUTHOR]

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

Driver fatigue detection based on EEG has received a lot of attention. In this paper, from the initial brain functional networks of EEG, we first identify multilayer differential core networks based on node and edge discrepancy between different states to capture more discriminant information for the task. Then, the discriminant features are constructed from the resulting multilayer core networks by tensor decomposition and feature selection. The resulting discriminant features contain not only structural information about the network of individual frequency bands, but also additional relational information between the networks of different frequency bands. Our method has the strong ability of information extraction and anti-interference, which can be directly applied to the initial network to identify and utilize weak links with discriminative power. The classification results on the publicly available dataset show that the average accuracy of our method is 9.5% higher than that of the baseline method. When applied to the initial functional network without any pre-processing, the proposed method of this paper can achieve a classification accuracy of 91.14%, which is higher than the results of state-of-the-art related studies on the same dataset. These results indicate that the proposed method is effective and reliable for detecting driver fatigue from EEG. [ABSTRACT FROM AUTHOR]

In this paper, we present a survey of modeling and simulation approaches to describe information retrieval basics. We investigate its methods, its challenges, its models, its components and its applications. Our contribution is twofold: on the one hand, reviewing the literature on discovery some search techniques that help to get pertinent results and reach an effective search, and on the other hand, discussing the different research perspectives for study and compare more techniques used in information retrieval. This paper will also shedding the light on some of the famous AI applications in the legal field. [ABSTRACT FROM AUTHOR]

In data mining, outlier detection is applied in different domains. It has very large applications such as energy consumption analysis, forecasting hurricanes in meteorological data, fraud and intrusion detection, event detection and system monitoring in sensor networks, etc. Most of existing outlier detection techniques depend on the properties of a particular type of data and can not deal with a large volume of data well, which mean that there is a necessity for improved methodologies and techniques to be applied to a large amount of data with different types in other application areas. In this paper, a parallel outlier detection technique is developed to detect the outliers in the sequential data. Although there are many types of outliers, this paper concentrates on the contextual anomalies. The proposed technique uses a graph approach to detect the outliers. It is very flexible, fast, and no labeled data is needed comparing to many previous approaches. The experimental results show the detected contextual outliers in the sequential data, as well as the efficient scaling up to handle the massive data by increasing the number of processors. The results prove that the parallelism of the proposed technique is very valuable. [ABSTRACT FROM AUTHOR]

Co-location patterns in spatial dataset are the interesting collection of dissimilar objects which are located in proximity. We keep similar objects in an entity set and maintain that no two objects in a co-location pattern belong to an entity set. Location proximity is based on Euclidean distance measure. However, algorithms for mining patterns in transactional datasets are not directly applicable to spatial datasets for mining co-location patterns. Conventional methods are not applicable to distributed temporal data and many applications generating spatial dataset are inherently distributive in nature. In this paper, a Map-Reduce based approach is proposed to find all co-location patterns from a spatial dataset distributed over nodes. This approach is modularized one and consists of four algorithms. With the first three algorithms in the first approach and by proposing an algorithm for dynamic datasets, this paper contains another approach for the co-location patterns set, that also updates in an incremental manner (not from scratch) whenever certain changes occur in the dataset. Experimental results on larger datasets are also presented. [ABSTRACT FROM AUTHOR]