Mining Techniques Research Articles

A Review on Rice Crop Disease Classification Using Computational Approach

Rice is a significant cereal crop across the world. In rice cultivation, different types of sowing methods are followed, and thus bring in issues regarding sampling collection. Climate, soil, water level, and a diversified variety of crop seeds (hybrid and traditional varieties) and the period of growth are some of the challenges. This survey mainly focuses on rice crop diseases which affect the parts namely leaves, stems, roots, and spikelet; it mainly focuses on leaf-based diseases. Existing methods for diagnosing leaf disease include statistical approaches, data mining, image processing, machine learning, and deep learning techniques. This review mainly addresses diseases of the rice crop, a framework to diagnose rice crop diseases, and computational approaches in Image Processing, Machine Learning, Deep Learning, and Convolutional Neural Networks. Based on performance indicators, interpretations were made for the following algorithms namely support vector machine (SVM), convolutional neural network (CNN), backpropagational neural network (BPNN), and feedforward neural network (FFNN).

Opinion mining for national security: techniques, domain applications, challenges and research opportunities

BackgroundOpinion mining, or sentiment analysis, is a field in Natural Language Processing (NLP). It extracts people’s thoughts, including assessments, attitudes, and emotions toward individuals, topics, and events. The task is technically challenging but incredibly useful. With the explosive growth of the digital platform in cyberspace, such as blogs and social networks, individuals and organisations are increasingly utilising public opinion for their decision-making. In recent years, significant research concerning mining people’s sentiments based on text in cyberspace using opinion mining has been explored. Researchers have applied numerous opinions mining techniques, including machine learning and lexicon-based approach to analyse and classify people’s sentiments based on a text and discuss the existing gap. Thus, it creates a research opportunity for other researchers to investigate and propose improved methods and new domain applications to fill the gap.MethodsIn this paper, a structured literature review has been done by considering 122 articles to examine all relevant research accomplished in the field of opinion mining application and the suggested Kansei approach to solve the challenges that occur in mining sentiments based on text in cyberspace. Five different platforms database were systematically searched between 2015 and 2021: ACM (Association for Computing Machinery), IEEE (Advancing Technology for Humanity), SCIENCE DIRECT, SpringerLink, and SCOPUS.ResultsThis study analyses various techniques of opinion mining as well as the Kansei approach that will help to enhance techniques in mining people’s sentiment and emotion in cyberspace. Most of the study addressed methods including machine learning, lexicon-based approach, hybrid approach, and Kansei approach in mining the sentiment and emotion based on text. The possible societal impacts of the current opinion mining technique, including machine learning and the Kansei approach, along with major trends and challenges, are highlighted.ConclusionVarious applications of opinion mining techniques in mining people’s sentiment and emotion according to the objective of the research, used method, dataset, summarized in this study. This study serves as a theoretical analysis of the opinion mining method complemented by the Kansei approach in classifying people’s sentiments based on text in cyberspace. Kansei approach can measure people’s impressions using artefacts based on senses including sight, feeling and cognition reported precise results for the assessment of human emotion. Therefore, this research suggests that the Kansei approach should be a complementary factor including in the development of a dictionary focusing on emotion in the national security domain. Also, this theoretical analysis will act as a reference to researchers regarding the Kansei approach as one of the techniques to improve hybrid approaches in opinion mining.

COMMTRUST

Abstract: We propose CommTrust for trust evaluation by mining feedback comments. Our main contributions include: 1) we propose a multidimensional trust model for computing reputation scores from user feedback comments; and 2) we propose an algorithm for mining feedback comments for dimension ratings and weights, combining techniques of natural language processing, opinion mining, and topic modeling. Extensive experiments on eBay and Amazon data demonstrate that CommTrust can effectively address the “all good reputation” issue and rank sellers effectively. To the best of our knowledge, our research is the first piece of work on trust evaluation by mining feedback comments.. An algorithm is proposed to mine feedback comments for dimension weights, ratings, which combine methods of topic modeling, natural language processing and opinion mining. This model has been experimenting with the dataset which includes various user level feedback comments that are obtained on various products. It also finds various multi-dimensional features and their ratings using Gibbs-sampling that generates various categories for feedback and assigns trust score for each dimension under each product level. Keywords: E-Commerce, Feedback mining, Trust score, Topic modeling, Reputation-based trust score

Community Organizing for Climate Change Adaptation and Mitigation in Kalikatir Village, Gondang Sub District, Mojokerto Regency

This research focused on the following things; a) How are the problems resulting from climate change to the quality of the environment and people in Kalikatir village? b) How are strategies in solving the problem of climate change through adaptation and mitigation efforts? c). What was the meaning of da’wah in this community organizing? Researchers used the Participatory Action Research (PAR) method along with Participatory Rural Appraisal (PRA) techniques for data mining. The dynamics of community organizing included inculturation, knowing, understanding, planning, action, monev, reflection, and report. The results of the research showed that the local climate change presents various problems in the disaster of natural resources, food resistance, economy, and health sectors. Community organizing of people produced a "Farmer Group Learning Center", and also facilitated some access to the information, knowledge, and experience about climate change. Da’wah activities showed in this community organizing on the form of tathwir and tamkin which transformed the teaching of Islam through empowering the community of their human, social, economic, and environmental resources.

Early detection and risk control of safety signals of Chinese medicine

The safety of modern Chinese medicine has attracted increasing attention from society. Experts and scholars have carried out extensive in-depth research on the safety of commonly used Chinese medicines from various aspects such as safety monitoring, clinical research, and experimental analysis. The findings in the safety performance of Chinese medicines can inspire the mining and analysis of relevant signals in the drug safety alerts. A variety of methods are employed for the mining of risk signals or analysis and screening of relevant signals to accurately determine the correlation between medication and adverse reactions. The safety signal acquisition and mining techniques are the technical basis for the safety risk management of medicine products after Chinese medicines are marketed, which is critical in drug safety alerts. To accurately collect the safety signals of Chinese medicines and effectively and rapidly track, determine, and assess the sources of signals are important technical links in drug risk management. The ultimate purpose of safety signal discovery is to achieve normalized risk management through downgrading drugs from a high-risk level to a low-risk level.The five main steps in the standard drug risk management are listed below: to extensively collect predicted risk signals; to accurately identify drug risks by the techniques such as data mining; to evaluate the risks with process-based quality risk control; to employ management measures minimizing the impact of drug risks for risk management; to update and apply risk assessment to clinical evaluation after medication.

Application of Class Based Association Rule Pruning to Generate Optimal Association Rules in Healthcare

The association rule mining approach produces uninteresting association rules. When the set of association rules become large, it becomes less interesting to the user. In order to pick interesting association rules among peak volumes of found association rules, it is critical to aid the decision-maker with an efficient post-processing phase. Theymotivate the need for association analysis performance. Practically it is an overhead to analyze the large set of association rules. In this work, association rule pruning technique called Class Based Association Rule Pruning (CBARP). This pruning techniques is proposed to prune the weak association rules of the healthcare system. The results are compared with Semantic Tree Based Association Rule Mining (STAR) technique and it demonstrate that the CBARP method outperforms other methods for the given support values.

A Novel Approach in Prediction of Crop Production Using Recurrent Cuckoo Search Optimization Neural Networks

Data mining is an information exploration methodology with fascinating and understandable patterns and informative models for vast volumes of data. Agricultural productivity growth is the key to poverty alleviation. However, due to a lack of proper technical guidance in the agriculture field, crop yield differs over different years. Mining techniques were implemented in different applications, such as soil classification, rainfall prediction, and weather forecast, separately. It is proposed that an Artificial Intelligence system can combine the mined extracts of various factors such as soil, rainfall, and crop production to predict the market value to be developed. Smart analysis and a comprehensive prediction model in agriculture helps the farmer to yield the right crops at the right time. The main benefits of the proposed system are as follows: Yielding the right crop at the right time, balancing crop production, economy growth, and planning to reduce crop scarcity. Initially, the database is collected, and the input dataset is preprocessed. Feature selection is carried out followed by feature extraction techniques. The best features were then optimized using the recurrent cuckoo search optimization algorithm, then the optimized output can be given as an input for the process of classification. The classification process is conducted using the Discrete DBN-VGGNet classifier. The performance estimation is made to prove the effectiveness of the proposed scheme.

Mining weighted sequential patterns in incremental uncertain databases

Due to the rapid development of science and technology, the importance of imprecise, noisy, and uncertain data is increasing at an exponential rate. Thus, mining patterns in uncertain databases have drawn the attention of researchers. Moreover, frequent sequences of items from these databases need to be discovered for meaningful knowledge with great impact. In many real cases, weights of items and patterns are introduced to find interesting sequences as a measure of importance. Hence, a constraint of weight needs to be handled while mining sequential patterns. Besides, due to the dynamic nature of databases, mining important information has become more challenging. Instead of mining patterns from scratch after each increment, incremental mining algorithms utilize previously mined information to update the result immediately. Several algorithms exist to mine frequent patterns and weighted sequences from incremental databases. However, these algorithms are confined to mine the precise ones. Therefore, in this work, we develop an algorithm to mine frequent sequences in an uncertain database in this work. Furthermore, we propose two new techniques for mining when the database is incremental. Extensive experiments have been conducted for performance evaluation. The analysis showed the efficiency of our proposed framework.

Improved minimum-minimum roughness algorithm for clustering categorical data

Clustering is a fundamental technique in data mining and machine learning. Recently, many researchers are interested in the problem of clustering categorical data and several new approaches have been proposed. One of the successful and pioneering clustering algorithms is the Minimum-Minimum Roughness algorithm (MMR) which is a top-down hierarchical clustering algorithm and can handle the uncertainty in clustering categorical data. However, MMR tends to choose the category with less value leaf node with more objects, leading to undesirable clustering results. To overcome such shortcomings, this paper proposes an improved version of the MMR algorithm for clustering categorical data, called IMMR (Improved Minimum-Minimum Roughness). Experimental results on actual data sets taken from UCI show that the IMMR algorithm outperforms MMR in clustering categorical data.

A Mind Genomics-Based Cartography to Assess the Effects of the COVID19 pandemic in the tourism industry

This study aims to understand what Albanians think about the tourism business and the likely impact of Covid-19 on tourism in Albania. A Mind Genomics-based cartography assessed the different aspects of tourism, with a response from 4800 people out of 7000 invited. Mind Genomics is a new science based on regression models, data mining and clustering techniques. Only 38% of Albanians are optimistic about summer vacations. Two mindsets of Albanian respondents emerged from the study. Around 52.5% of participants belong to the pessimistic mindset about having vacations at all. The remainder, 47.5% of participants belong to the “cautious” group; they would wait for a “reasonable” offer to decide

Deep seabed mining and communities: A transdisciplinary approach to ecological risk assessment in the South Pacific.

Deep-sea mineral extraction is a fledgling industry whose guiding principles, legislation, protocols, and regulations are still evolving. Responsible management of the industry is difficult when it is not clearly understood what biological and environmental diversity or ecosystem services may be at risk. But the industry's infancy provides an opportunity to address this challenge by stakeholder-led development and implementation of a multidisciplinary risk assessment framework. This article aims to present the findings of a workshop held in New Zealand that hosted stakeholders from a broad range of interests and regions in the South Pacific associated with the deep-sea mineral activity. The outputs provide stakeholder-informed ecological risk assessment approaches for deep-sea mining activities, identifying tools and techniques to improve the relevance of risk assessment of deep seabed mining projects to communities in the South Pacific. Discussions highlighted the importance of trust or respect among stakeholders, valuing the "life force" of the ocean, the importance of scientific data, and the complications associated with defining acceptable change. This research highlighted the need for a holistic transdisciplinary approach that connects science, management, industry, and community, an approach most likely to provide a "social license" to operate. There is also a need to revise traditional risk assessment methods to make them more relevant to stakeholders. The development of ecotoxicological tools and approaches is an example of how existing practices could be improved to better support deep-sea mineral management. A case study is provided that highlights the current challenges within the legislative framework of New Zealand. Integr Environ Assess Manag 2022;18:664-673. © 2021 SETAC.

Using Machine Learning to Capture Quality Metrics from Natural Language: A Case Study of Diabetic Eye Exams.

The prevalence of value-based payment models has led to an increased use of the electronic health record to capture quality measures, necessitating additional documentation requirements for providers. This case study uses text mining and natural language processing techniques to identify the timely completion of diabetic eye exams (DEEs) from 26,203 unique clinician notes for reporting as an electronic clinical quality measure (eCQM). Logistic regression and support vector machine (SVM) using unbalanced and balanced datasets, using the synthetic minority over-sampling technique (SMOTE) algorithm, were evaluated on precision, recall, sensitivity, and f1-score for classifying records positive for DEE. We then integrate a high precision DEE model to evaluate free-text clinical narratives from our clinical EHR system. Logistic regression and SVM models had comparable f1-score and specificity metrics with models trained and validated with no oversampling favoring precision over recall. SVM with and without oversampling resulted in the best precision, 0.96, and recall, 0.85, respectively. These two SVM models were applied to the unannotated 31,585 text segments representing 24,823 unique records and 13,714 unique patients. The number of records classified as positive for DEE using the SVM models ranged from 667 to 8,935 (2.7-36% out of 24,823, respectively). Unique patients classified as positive for DEE ranged from 3.5 to 41.8% highlighting the potential utility of these models. We believe the impact of oversampling on SVM model performance to be caused by the potential of overfitting of the SVM SMOTE model on the synthesized data and the data synthesis process. However, the specificities of SVM with and without SMOTE were comparable, suggesting both models were confident in their negative predictions. By prioritizing to implement the SVM model with higher precision over sensitivity or recall in the categorization of DEEs, we can provide a highly reliable pool of results that can be documented through automation, reducing the burden of secondary review. Although the focus of this work was on completed DEEs, this method could be applied to completing other necessary documentation by extracting information from natural language in clinician notes. By enabling the capture of data for eCQMs from documentation generated by usual clinical practice, this work represents a case study in how such techniques can be leveraged to drive quality without increasing clinician work.

Big Data for Biomedical Education with a Focus on the COVID-19 Era: An Integrative Review of the Literature.

Medical education refers to education and training delivered to medical students in order to become a practitioner. In recent decades, medicine has been radically transformed by scientific and computational/digital advances—including the introduction of new information and communication technologies, the discovery of DNA, and the birth of genomics and post-genomics super-specialties (transcriptomics, proteomics, interactomics, and metabolomics/metabonomics, among others)—which contribute to the generation of an unprecedented amount of data, so-called ‘big data’. While these are well-studied in fields such as medical research and methodology, translational medicine, and clinical practice, they remain overlooked and understudied in the field of medical education. For this purpose, we carried out an integrative review of the literature. Twenty-nine studies were retrieved and synthesized in the present review. Included studies were published between 2012 and 2021. Eleven studies were performed in North America: specifically, nine were conducted in the USA and two studies in Canada. Six studies were carried out in Europe: two in France, two in Germany, one in Italy, and one in several European countries. One additional study was conducted in China. Eight papers were commentaries/theoretical or perspective articles, while five were designed as a case study. Five investigations exploited large databases and datasets, while five additional studies were surveys. Two papers employed visual data analytical/data mining techniques. Finally, other two papers were technical papers, describing the development of software, computational tools and/or learning environments/platforms, while two additional studies were literature reviews (one of which being systematic and bibliometric).The following nine sub-topics could be identified: (I) knowledge and awareness of big data among medical students; (II) difficulties and challenges in integrating and implementing big data teaching into the medical syllabus; (III) exploiting big data to review, improve and enhance medical school curriculum; (IV) exploiting big data to monitor the effectiveness of web-based learning environments among medical students; (V) exploiting big data to capture the determinants and signatures of successful academic performance and counteract/prevent drop-out; (VI) exploiting big data to promote equity, inclusion, and diversity; (VII) exploiting big data to enhance integrity and ethics, avoiding plagiarism and duplication rate; (VIII) empowering medical students, improving and enhancing medical practice; and, (IX) exploiting big data in continuous medical education and learning. These sub-themes were subsequently grouped in the following four major themes/topics: namely, (I) big data and medical curricula; (II) big data and medical academic performance; (III) big data and societal/bioethical issues in biomedical education; and (IV) big data and medical career. Despite the increasing importance of big data in biomedicine, current medical curricula and syllabuses appear inadequate to prepare future medical professionals and practitioners that can leverage on big data in their daily clinical practice. Challenges in integrating, incorporating, and implementing big data teaching into medical school need to be overcome to facilitate the training of the next generation of medical professionals. Finally, in the present integrative review, state-of-art and future potential uses of big data in the field of biomedical discussion are envisaged, with a focus on the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic, which has been acting as a catalyst for innovation and digitalization.

Innovative Techniques in Underground Mining for the Prevention of Gas Dynamic Phenomena

In the last decades, rigorous research has been carried out with the end of understanding the gas dynamic phenomenon and although different preventive techniques have been employed, even today there are numerous accidents even with the loss of life. This work analyses an alternative and innovative method of fracturing and degassing coal, by generating CO2 with a pyrotechnic device called PYROC (Pyrotechnic Break Cartridges). Medium-scale tests of generation of CO2 into coal samples are carried out and their effect is analysed comparing the initial and final permeabilities of the coal samples once the generation of CO2 has finished. These permeabilities are calculated by injecting methane. Besides, the influence of different parameters as the length of the boreholes, the pressure of the gas or the initial permeability of the coal have been analysed with a numerical simulation of one face of one of the sublevels of a mine. The results show that the method increases the safety in mining operations because it fractures and degasses the coal, increases the permeability of the coal in the borehole of injection from 9.5 mD to 31 mD, decreases the methane gas pressure below pre-detonation levels for 1 min, achieves decompressed lengths between 8 and 10 m ahead of the face with pressures of injection of 50 MPa, relaxes the total length of the borehole for initial coal permeability values equal to or greater than 0.002 mD, and allows to work with low permeable coals with high induced stresses and high methane concentrations.

Candidate wells selection and ranking based on data mining and multi-criteria decision analysis techniques

Candidate wells selection and ranking based on data mining and multi-criteria decision analysis techniques

Penerapan Metode K-Means Untuk Menganalisis Minat Nasabah

Insurance is a mechanism of protection or protection from the risk of loss by transferring the risk to another party. Sometimes a product that has just emerged becomes a product that is superior in terms of sales, so that interest in a product is not absolutely measured from the year the product was released. The constraint factors include the marketing of the product when it was launched. Offering products with low premiums along with the benefits that customers want. However, insurance companies still have difficulty in classifying superior products that are in great demand by prospective customers. For this reason, a technique for grouping insurance products is needed to make it easier for companies to see superior products and choose products that suit the needs of their customers. Analyzing and processing data using the K-Means method in the clustering of insurance products is the aim of this study. The application of the K-Means algorithm is to help calculate the purity value from the results of the clustering carried out so that the clustering of insurance products is in accordance with the needs of its customers. The application of the K-Means method with clustering techniques for data mining produces information on insurance products that are more attractive to potential customers. This is very appropriate in grouping data types because it is easier to implement and its application can filter quickly and precisely. Calculations using the K-Means method with a data sample of 55 customers obtained 3 clusters, namely cluster 1 for fire insurance which has 30 customers, cluster 2 for accident insurance 24 people and cluster 3 for health insurance 1 person.

The concepts of method and technique in mining

The mining science and mining industry often use the terms of technique, technology, method and methodology. However, the notions of these terms are yet uncertain. These terms are often messed, and the meanings of the results and predictions are distorted. The present research allows a deeper insight into the concepts of method, technique, methodology and technology. We improve definitions of these notions. Their structures are exhibited, and the difference between a method and a technique is exposed. Methodology and technology are similar on a system basis as the relevant instruments of mental and physical work with various challenges and unclear results. The essences of some concepts critical in mining are disclosed. Some definitions are proposed: method is a finite sequence of different intellectual operations which alter perceptions till advent of a new reasoning; technique is a finite sequence of different physical operations which alter substance up to a new physical state (form); methodology is a scientifically and methodically founded system of elaboration of inferences on a complex problem, which stage-by-stage and multiply clarifies consciousness until vision of a remote desired result appears; technology is a technically and technologically sound system of physical operations included in stage-by-stage and multiple transformation of a substance to its remote desired state (form). The hierarchy chart is developed, and the scopes of the discussed notions are shown. The studies provide more intimate knowledge on the subject to mining engineers and scientists, and improve interpretation of the discussed notions and their scopes.The authors highly appreciate the participation of Professor, Doctor of Engineering Sciences H. U. Li, VostNII Science Center, in this paper preparation.

Machine Learning Based Parametric Estimation Approach for Poll Prediction

Background: The process of election prediction started long back when common practice for election predictions were traditional methods like pundits, hereditary factor etc. However, in recent times new methods and techniques are being used for election forecasting like Data mining, Data Science, Big data, and numerous machine learning techniques. By using such computational techniques the whole process of political forecasting is changed and poll predictions are carried out through them. Objective: The main objective of this research work is to propose an election prediction model for developing areas especially for the state of Jammu and Kashmir (India). Methods: The election prediction model is developed in Jupyter notebook web application using different supervised machine learning techniques. To obtain the optimal results, we perform the hyperparameter tuning of all the proposed classifiers. For measuring the performance of poll prediction system we used confusion matrix along with AUROC curve which depicts that this methods can be well suited for political forecasting. An important contribution of this article is to design a Prediction system which can be used for making prediction in other fields like cardiovascular disease predictions, weather forecasting etc. Results: This model is tested and trained with real-time dataset of the state Jammu and Kashmir (India). We applied features selection techniques like Random Forest, Decision Tree Classifier, Gradient boosting Classifier and Extra Gradient Boosting and obtained eight most important parameters like (Central Influence, Religion Followers, Party Wave, Party Abbreviations, Sensitive Areas, Vote Bank, Incumbent Party, and Caste Factor) for poll predictions with their mean weightages. By applying different classifier to get mean weightage of different parameters for this election prediction models, it has been observed that Party wave got maximum mean weightage of 0.82% as compared to others parameters. After obtaining the vital parameters for political forecasting, we applied various machine learning algorithms like Decision tree, Random forest, K-nearest neighbor and support vector machine for the early prediction of elections. Experimental results show that Support Vector Machine outperformed with a higher accuracy of 0.84% in contrast to others classifiers. Conclusion: In this paper, a clear overview of election prediction models, their potentials, techniques, parameters as well as limitations are outlined. We conclude this work by stating that election predictions can indeed be forecasted with significant parameters however, with caution due to the limitations which were outlined in developing nations like sensitive areas, social unrest, religion etc. This research work may be considered as the first attempt to use multiple classifier for forecasting the Assembly election results of the state Jammu and Kashmir (India).

An analysis of form and function of a research article between and within publishers and journals

Abstract The identification and subsequent analysis of research articles for machine learning and natural language processing is a complicated task given the lack of consistent article organization principles and heading naming conventions across publishers and journals. Given this, an understanding of how research articles organizationally follow a common function and their use of various heading terms, or forms, is a critical step in applying machine learning techniques for data and information mining across a corpus of articles. To address this need, the authors developed and implemented an article heading form and function analysis across 12 publishers including both research articles and nonresearch articles. Our aim was to (a) identify each of the labeled sections used by research articles, define these sections based on their rhetorical function, and determine frequency of use; (b) within the given data set, determine all of the alternative labels used to identify these sections; and (c) determine whether these sections can be used to consistently determine (1) whether an article is a true research article, or (2) whether an article is not a research article. The results indicated wide variability in the organization of research articles with 24 common sections, known by 186 different names both within and across publishing houses.

Social Media Content Analysis and Classification Using Data Mining and ML

Students' natural conversations on social media such as Twitter and Whatsapp are useful to understand their learning experiences feelings. Collecting and analyzing data from such media can be a difficult task. However, the large scale of data is required for automatic data analysis techniques to classify Twitter data. The proposed new system is a combination of qualitative analysis and large-scale data mining and ML techniques. This system focuses on engineering students' Twitter posts, which are collected from engineering colleges, to understand issues and problems in their learning. The authors first conduct a qualitative analysis using ML studio on tweets collected from engineering colleges using term #DStudentsproblems, engineeringProblem, Aluminisuggestions, and ladyEngineer. Collected tweets are related to engineering students' college lives. In the proposed system, a multi-label classification algorithm to classify tweets reflecting students' problems such as soft skill issues, heavy study load, lack of social engagement, and sleep problems is used.