Earticle

Malaysia possesses about 1.56 million ha of Peat Swamp Forest (PSF). The PSF safeguard enormous biological diversity, while providing crucial benefits for the sustainable development of human communities. Numbers of threatened plant species are associated with the PSF, including the commercially important Gonystylus bancanus timber. To prevent significant losses of biodiversity, it is important to manage the PSF for both biological conservation and sustainable use. Equally important is to restore all degraded PSF in an attempt to ensure the PSF ecosystem is suitable for the vegetation to grow and rehabilitate back to the normal condition. Prior to plan any forest restoration program, there is a need to properly map the degraded PSF in order to estimate the forest conditions and determine the vegetations status. Most of the time this need to be done at a landscape level and requires a technology that can provide accurate, timely and reliable information for the planner to make decision. This paper describes a study using geospatial technology in combination with ground survey to classify the degraded PSF in South East Pahang Peat Swamp Forest (SEPPSF), Malaysia, into different degree of vegetation classes. With map accuracy of about 83%, the technique proved to be useful in delineating the different degree of PSF degradation from which the information can be used to properly plan forest restoration program in the area. The final output which is in the form of map can be used in developing a Restoration Master Plan for the degraded PSF areas.

Fashion occupies a large part of the industry and has been a part of our lives. One of the ways to analyze trends in fashion is to detect and classify categories in fashion images. In this paper, we present fashion category detection through the utilization of Detectron2's Mask R-CNN, which is easy to learn with custom datasets and has a high model construction and learning speed. Learning is also done based on Fashionpedia, a large-scale fashion segmentation and attribute localization dataset built with fashion ontology. As a result, the average precision (AP) of the bounding box was 52.45 and that of segmentation was 48.77, showing reasonably high performances. We propose a possibility of using fashion category detection and classification work in the field of fashion design.

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

인물소재숙어는 인물을 소재로 구체적이고 독립적인 의의를 가진 언어 구절이 다. 가장 큰 특징은 인물을 중심 의소로 삼으며, 평가를 핵심 내용으로 삼는 것이 다. 인물소재숙어의 의미에 따라 도덕·공로·지혜·우정·용모·부유의 6개로 분류한 다. 중국 전통 가치관의 가장 근원적 특징은 도덕지상으로 현실 생활 속의 사람 과 일의 평가에 대해 모두 도덕에 부합하는지 여부를 기준으로 삼고 있다. 그래 서 인물소재숙어의 6대 요소 중 하나인 도덕이 다른 5개를 이끌고 있다. 영예를 얻어 가문을 빛나게 하는 것은 한족의 독특한 문화심리의 동력이었으며, 인물소재숙어의 탄생을 촉발했고, 인물소재숙어 전파를 촉진하는 내재적 문화 추진력이 되었다.

Character-themed idioms are language units with independent meanings based on characters, whose most prominent feature is that characters are taken as the central sememe and evaluation as the core connotation.According to the connotation of idioms on characters, we can divide them into six categories: moral sentiment, meritorious service, wisdom, friendship and emotion, appearance and appearance, and material wealth.The most fundamental characteristic of Chinese traditional values is the primacy of morality. The evaluation of people and things in real life is based on whether they conform to morality. Therefore, the moral sentiment category of character theme idioms actually dominates the other five categories.It is the unique cultural psychological complex and cultural psychological power of the Han nationality to gain fame and honor the family and progenitors, and it is also the internal cultural driving force that promotes the emergence and dissemination of idioms on character themes.

人物题材熟语就是以人物为题材的具有独立意义的语言单位，其最显著的特点 就是以人物作为中心义素，以评价作为核心内涵。根据人物题材熟语的内涵，我 们将其分为六大类：道德情操类、功勋建树类、智慧才学类、友谊情感类、容貌 仪表类和物质财富类。中国传统价值观最根本的特征是道德至上，对现实生活中 人和事的评价都以是否符合道德为标准。因此，人物题材熟语中的道德情操类实 际上统领了其他五类。获取荣名、光宗耀祖是汉民族独特的文化心理情结和文化 心理动力， 同时也是催生人物题材熟语产生、促进人物题材熟语传播的内在文化 推动力。

This paper is about the classification and systematization of Japanese spatial nouns. The three types of frame of reference proposed by Levinson (1996), classifies whole static spatial arrays in the following way: (i) Intrinsic frames of reference (not discussed here), (ii) Absolute frames of reference (not discussed here), and (iii) Relative frames of reference (discussed here). We could classify spatial nouns into two major classes; territory (dimension 2, 3)-oriented and direction (dimension 1)-oriented nouns. The territory-oriented nouns concern a territory of the reference object. The direction-oriented nouns concern the direction of a location with respect to the reference object, such as “X no migi (the right of X)”, “X no temae (in front of X)” and so on. And territoryoriented spatial nouns are further classified into three subclasses: designating internal location of the reference object (internal), designating a location apart from the reference object (external), and designating a location on shape of the reference object (boundary).

This study sought to classify the constructions of pronominal verbs for the purpose of effective education of the pronominal verbs. To this end, the prototype theory was applied to classification of the pronominal verbs. From the perspective of this theory, the pronominal verbs are classified into 7 categories: reflexive, reciprocal, passive, no-agentive, autonomous, parasitic and intrinsic. And the prototype theory was applied to each of theses 7 categories again. The beneficial effects of this categorization are as follows. In the perspective of linguistics, this categorization shows the pronominal verbs very clearly and in detail including exceptional uses which the traditional classification misses. In the perspective of pedagogy, the classification on the basis of the prototype theory enables the learners to grasp the pronominal verbs with ease. Because this classification method is characterized by consistency and classifies pronominal verbs in a very systematic and organized way. This method has great educational effects on the way that these characteristics reduce the learners’ psychological consumption. Cette recherche tente de classer les tours de verbes pronominaux dans le but d’un enseignement efficace de ces derniers aux étudiants. Pour ce faire, nous avons appliqué la théorie du prototype à la classification des tours pronominaux. D’après cette théorie, les tours pronominaux sont classés en 7 catégories: (1) réflexive, (2) réciproque, (3) passive, (4) non-agentive, (5) autonome, (6) parasite et (7) essentielle. La théorie du prototype a ensuite été appliquée à chacune de ces 7 catégories. Les avantages de cette catégorisation sont les suivants: Au point de vue linguistique, cette catégorisation montre les tours pronominaux de manière précise et détaillée englobant des emplois exceptionnels qui échappent à la catégorisation traditionnelle. Au point de vue pédagogique, parce que la classification sur la base de la théorie du prototype est cohérente, systématique et bien organisée, cette méthode permet aux apprenants de saisir plus facilement les verbes pronominaux en reduisant leur consommation morale Cette méthode de catégorisation est donc didactiquement très efficace.

Korea’s “Foreign Trade Act” states that the goods(including technology determined by Presidential Decree) on which restriction, such as export license, is required for maintaining international peace and security as well as national security in accordance with the principles of the International Export Control Regimes, are to be referred to as “Strategic Item” and the list of such goods designated are publicly notified by the “Public Notice on Trade of Strategic Goods(hereinafter, the Public Notice)”. Information security systems, equipment and components above control parameter specified under control number 5A002.a of such list are classified as “Strategic Items”. In fact, this entry covers the majority of the information security equipment such as switch, firewall, server and network equipment. This paper studies negative classification cases of items that meet the control threshold of the list on the Public Notice but nonetheless excluded by the de-control notes available for certain items such as those sold in mass market or those with encryption technology exclusively for authentication. The date of classification results issued through the Information System for Management of Export and Import of Strategic Items(Yestrade) over the past 5 years are used. By Analyzing such data, we try to make recommendations for improvements.

전 세계적으로 한류 콘텐츠가 유행하며 국내뿐만 아니라 해외에서도 국내 저작물의 저작권 침해 사례가 지속 적으로 증가하고 있다. 이에 대응하기 위한 국내 불법 저작물 유통 사이트를 탐지하는 기술에 대한 연구가 활발 하게 진행되고 있으며 최근 연구는 국내 불법 저작물 유통 사이트들이 광고 배너를 상당수 포함하고 있는 특징을 활용한 연구가 존재한다. 그러나 해외 불법 저작물 유통 사이트의 경우 광고 배너를 포함하지 않거나 국내 사이 트에 비해 적은 양의 광고를 포함하고 있어 국내와 같은 탐지 기술의 적용이 제한적이다. 본 연구에서는 저작물 불법 공유 사이트가 게시물과 저작물의 이미지를 유사한 계층 구조로 포함하는 특징을 이용하여 링크 트리 및 텍 스트 유사도 비교 기반의 탐지 기술을 제안한다. 대량의 링크로 작성된 대규모 트리의 정확한 유사도 비교를 위 해 GNN(Graph Neural Network)를 활용한다. 본 연구의 실험에서 일반 사이트와 저작물 불법 유통 사이트를 분 류에 95% 이상의 높은 정확도를 보였고 해당 알고리즘을 적용하여 불법 유통 사이트의 자동화된 탐지를 통해 신 속한 저작권 침해 대응이 가능할 것으로 기대된다.

The global popularity of K-content(Korean Wave) has led to a continuous increase in copyright infringement cases involving domestic works, not only within the country but also overseas. In response to this trend, there is active research on technologies for detecting illegal distribution sites of domestic copyrighted materials, with recent studies utilizing the characteristics of domestic illegal distribution sites that often include a significant number of advertising banners. However, the application of detection techniques similar to those used domestically is limited for overseas illegal distribution sites. These sites may not include advertising banners or may have significantly fewer ads compared to domestic sites, making the application of detection technologies used domestically challenging. In this study, we propose a detection technique based on the similarity comparison of links and text trees, leveraging the characteristic of including illegal sharing posts and images of copyrighted materials in a similar hierarchical structure. Additionally, to accurately compare the similarity of large-scale trees composed of a massive number of links, we utilize Graph Neural Network (GNN). The experiments conducted in this study demonstrated a high accuracy rate of over 95% in classifying regular sites and sites involved in the illegal distribution of copyrighted materials. Applying this algorithm to automate the detection of illegal distribution sites is expected to enable swift responses to copyright infringements.

참여정부에 균형발전의 대표적인 정책으로 추진되었던 혁신도시는 국가의 균형발전과 혁신역량을 지방으로 이전하기 위해 전국적으로 10개의 혁신도시가 건설되었고, 처음 기본구상이 발표된지 15년의 시간이 흐른 지금은 혁신도시의 건설과 분양이 대부분 완료되었고, 공공기관들의 이전도 대부분 이루어져 있다. 그러나 그동안은 혁신도시의 건설과 공공기관의 이전에 초점이 맞추어져 혁신도시의 기능을 발휘하게 하는 부분이나 이전한 공공기관이나 종사자 가족들의 주거생활에 대한 만족도 등은 아직까지는 부족한 실정이고, 이전기관 종사자나 종사자 가족들 그리고 혁신도시의 이전을 담당하는 공무원들로부터 다양한 불만과 요구들이 계속되고 있다. 2017ㄴ녀 새정부가 출범하면서 혁신도시 시즌 2가 발표되었고 혁신도시의 완성과 기능제고를 위한 다양한 정책들이 다시 제안되고 있다. 본 연구는 혁신도시의 건설과 공공기관의 이전이 혁신도시 자체의 발전을 이끌어 가는 것에서 멈추지 않고 주변지역들의 산업과 경제발전을 함께 유도해내기 위한 중심적인 역할이 필요하다는 전제에서 시작한다. 이를 위해 혁신도시의 건설과정과 이전 대상도시의 기능과 요구를 살표보고, 혁신도시와 모도시와 연계된 주변의 지역들과 혁신도시가 함께 발전할 수 있는 방안을 찾아보고자한다. 이를 통해 혁신도시의 건설효과가 주변지역으로 확산되고 국가의 균형발전에 기여할 수 있는 방안을 제시해보고자 한다.

The Innovative city was promoted as one of the policies for the balanced development of the country in order to develop suitable for its respective characteristics in the national land, to enhance regional competitiveness through regional cooperation and cooperation, to improve the quality of life for the people, and to achieve balanced development of the land. During the last decade after the decision to move of Innovative city, since the construction of Innovative cities and the relocation of public institutions are focused, satisfaction with the residential life of the public institutions or the family members of the employees who have transferred the function of the Innovative city is insufficient. The various complaints and demands continue to be made by the moving workers, their families, and the officials responsible for the movement of Innovative cities. It is necessary to evaluate whether the Innovative city is achieving the goal of constructing an Innovative city and to judge the completion of the policy, rather than evaluating the urban construction index such as the city construction or the moving of public institutions. This research was started from the premise of need to play a central role in guiding together the industries and economic development in the surrounding area, not only from the point of the construction of Innovative cities and relocation of public institutions leading to the development of the innovative cities themselves. Therefore, I will divide the Innovative city into new classification based on the relationship with the surrounding area, and research the ways to develop with the Innovative cities, the mother cities, and the surrounding regions. Through this process, I would like to suggest the ways to contribute to the balanced development of the land, which was one of the main purposes of the innovative cities’ construction. The main contents of this research are as follows: First, the classification of 10 Innovative cities nationwide based on the conventional location conditions is excluded, and attempt new classification from the viewpoint of development of cooperation with the surrounding regions, Second, the various indicators of Innovative cities and surrounding areas by classified type, and third, I propose the policies for the mutual development of innovative cities and surrounding regions.

게임 산업의 급격한 양적인 팽창으로 인해 국내외 게임 시장은 포화상태에 이르기 시작하였다. 이러한 현상은 기업이 지속 성장하는데 있어 반드시 극복해야할 과제라 판단된다. 게임 기업은 이러한 제한적인 성장에서 벗어나 지속적인 성장 전략이 필요한 것이다. 대표적인 예가 최근 게임 기업들은 게임을 통한 긍정적인 효과를 바탕으로 타 산업과의 융합으로 인한 시장 확대로 고려하고 있다. 이러한 게임 기능성에 대한 성장은 기능성 게임이 갖고 있는 ‘효과성’ 때문이다. 오락적 기능뿐만 아니라 교육, 국방, 의료 등 특정한 목적을 결합하여 더 큰 시너지 효과를 낼 것이라는 기대감 때문인 것이다. 그러나 효과성에 대한 연구 범위가 기능성게임으로 한정되어 있으며, 산업 전반에 파급하는데 제한적인 한계를 나타내고 있다. 따라서 초기 기능성 게임에만 국한되어 있는 연구범위를 확대하여 게임 콘텐츠 기능성 효과에 대해 연구를 수행함으로서 게임 산업 육성을 위한 방안을 모색할 수 있을 것이라 판단된다. 이에 본 연구에서는 게임 산업을 대표하는 대표적인 게임 콘텐츠의 기능성 효과를 측정할 수 있는 항목을 도출함으로서 타산업과 융합 또는 활용을 통해 얻어질 수 있는 게임 기능성 효과에 대한 토대를 마련하고자 한다.

Due to the rapid quantitative expansion of the game industry for domestic and international gaming market is saturated. This phenomenon continues to grow, there must be a corporate challenge is to overcome. Game companies away from these restrictive growth requires sustained growth strategy. Thus, a positive effect through the game on convergence with other industries, the market appears to expand. Because the game has serious games market is growing effectiveness. Entertainment features as well as education, defense, health care by combining a specific purpose, such as greater expectations that synergy is due. However, effectiveness studies are limited in scope serious game, spread across industries is limited to showing the limitations. Therefore limited to serious game to expand the scope of the research about the effects of game content by exploring ways for the development of the game industry would be able to be determined. In this study, the functionality of the game content to measure the effectiveness of the items were extracted. Thereby be achieved through convergence with other industries, the functional effect of the game is to lay the foundation for.

이 연구는 예비교사들이 삼엽충을 분류하면서 보이 는 탐구 특성을 분석하는 것이다. 연구를 위해 교사 양 성 대학에 다니는 예비교사 70명이 참여하였다. 연구 에 이용된 분류 과제는 삼엽충 화석 사진 9장이었다. 예비교사들의 탐구활동은 그룹별로 삼엽충 화석을 관 찰한 후 삼엽충의 진화 과정을 분류한 후 계통도를 구 성하는 것이었다. 구체적인 연구결과는 다음과 같다. 첫째, 예비교사들은 삼엽충의 외형적인 특징을 잘 관 찰해내고 그 내용을 바탕으로 체계적인 분류 결과를 도출하였다. 둘째, 예비교사들은 다양한 분류 기준을 사용하여 삼엽충을 분류하였다. 셋째, 예비교사들의 계통도와 과학자들의 계통도는 매우 유사하였다. 예비 교사들은 과학자들이 주장하는 삼엽충 화석의 일반적 인 진화 과정인 단순한 형태에서 복잡한 형태로의 변화를 관찰과 추리를 통해 분석해낸 후 이를 근거로 계통도를 구성하였다. 이러한 결과는 충분한 시간이 제공되는 그룹별 탐구활동이 매우 효과적이며 예비교사 들에게 탐구 활동에 대한 경험이 매우 중요함을 다시 한번 시사해주었다.

This study was to analyze the inquiry characteristics of preservice teachers as they classify trilobites. For the study, 70 preservice teachers attending teacher training university participated. The classification tasks used in the study were 9 photos of trilobite fossils. The preservice teachers' inquiry activity was to classify the evolutionary processes of trilobites after observing trilobite fossils by group and then to construct a phylogenetic tree. The results of the study are as follows. First, preservice teachers observed the external features of the trilobites and constructed systematic classification results based on their observed contents. Second, preservice teachers classified trilobites using various classification criteria. Third, the phylogenetic tree of preservice teachers and the phylogenetic tree of scientists were very similar. The preservice teachers constructed a sphylogenetic tree based on the observation and inference of the change from a simple form to a complex form, which is a general evolution process of the trilobite fossil claimed by scientists. These results suggest that group-based inquiry activities with sufficient time are very effective and that the experience of inquiry activities is very important for preservice teachers.

One of the areas most at risk of forest fires in Turkey is the Mediterranean region. Although the reasons for fire outbreaks change over the years, they result in the loss of forest assets in the area due to fire. Fires also cause major ecological damage. Forest fires reduce or eliminate many forms of vegetation from the land surface. The Kavaklıdere-Muğla-Yatağan-Yılanlı fire, which occurred between 2 and 8 August 2021, damaged a large area. Therefore, the study aims to investigate the land use and land cover (LULC) changes in 2020, 2021 and 2024 in Kavaklıdere district, Muğla. In this study, six LULC classes, agriculture (A), bare + other (BO), forest (F), urban (U), water (W) and burned areas (BA), were obtained using Sentinel-2 satellite imagery with the random forest classification technique on the Google Earth Engine (GEE) platform. In addition, land surface temperature (LST) data were obtained using the splitwindow algorithm on Landsat-8 data. The results show that LULC changes are clearly in fireaffected areas and that LST values are particularly high in burned areas compared to other classes. The results demonstrate the effectiveness of remote sensing techniques in monitoring changes in land cover and surface temperature following fire.

Vision Transformer (ViT) 기반 모델들은 이미지 분류 등 다양한 시각적 인식 작업에서 강력한 성능 을 보이지만, 모든 토큰을 동일하게 활용하는 방식은 불필요한 연산 증가와 중요한 정보 희석 문제를 초래할 수 있다. 특히, Global Average Pooling (GAP)은 모든 토큰을 평균화하여 특징을 요약하는데, 이 과정에서 핵심 정보 손실 위험이 존재한다. 본 논문에서는 GAP 과정에서 중요한 토큰만을 선별적으로 샘플링하여 활용하는 새로운 방법을 제안한다. 이 방법은 추가적인 학습 가능한 파라미터 없이 동작하 며, 연산량을 줄이면서도 효과적인 특징 요약이 가능함을 실험을 통해 확인하였다.

본 연구는 KTAS 강사 매뉴얼의 추천답안과 ChatGPT가 분류한 KTAS 결과 일치도를 확인하는 것을 목표로 하였다. KTAS 강사 매뉴얼에 제공된 성인 9사례, 소아 5사례, 특수상황 10사례, 총 24사례를 대상으로 ChatGPT가 분류한 결과와 KTAS 강사매뉴얼의 추천답안 간의 일치도를 평가하였다. 분석 결과, KTAS 강사매뉴얼과 ChatGPT 간의 일치에 대한 Cohen’s kappa .518로 중증도의 일치도를 확인하였 다. 성인 Cohen’s kappa .429, 소아 Cohen’s kappa .286, 특수 상황 Cohen’s kappa .625이었다. 또한, 질병 Cohen’s kappa는 .511, 질병 외 Cohen’s kappa는 .474이었다. KTAS 강사매뉴얼과 ChatGPT 간의 일치에 대한 ICC는 .798로 전반적인 일치도가 높음을 보여주었다. 본 연구는 ChatGPT가 응급의료 분야에서 KTAS 분류 도구로서 일정 수준의 신뢰성을 보일수 있음을 확인하였지만, 특히 소아나 질병 외의 사례에 대한 추가적인 자료가 필요함을 알 수 있었다.

The goal of this study was to confirm the consistency between the answers recommended in the KTAS instructor manual and the KTAS results classified by ChatGPT. The consistency between ChatGPT's classification results and the instructor manual's recommended results was evaluated for nine adult cases, five pediatric cases, and ten special cases provided in the KTAS instructor manual. As a result of the analysis, the agreement between the KTAS instructor manual and ChatGPT was Cohen’s kappa of .518, Cohen’s kappa of .429 for adults, Cohen’s kappa of .286 for pediatric, and Cohen’s kappa of .625 for special cases. In addition, the kappa of the disease was .511 and the kappa of the exclusion disease was .474. The ICC for agreement between the KTAS instructor‘s manual and ChatGPT was .798, confirming a high overall agreement. This study confirmed that ChatGPT has a certain level of reliability as a KTAS classification tool in the field of emergency medicine, but it was found that additional data is needed, especially for cases pediatric or excluding diseases.

본 연구에서 탄도 미사일(로켓)과 드론을 분류하는 문제에 대해서 다루었다. 효율적인 방공작전을 위하여 미사일과 드론에 따라서 조치해야하는 상황이 다르기 때문에 미사일과 드론을 분류하는 문제는 중요하다. 우리는 머신러닝 모델 중에 서포트 벡터 모델을 이용하여 각 비행 특성을 고려하여 미사일 또는 드론의 분류 방법을 제안하였다. 미사일과 드론 데이터를 수집하 기 위해서 오픈 미사일 시뮬레이터실험과 실제 드론 비행을 실시하여 직접 데이터를 구축하였다. 실험결과는 서포트벡터 머신 을 통해서 미사일과 드론에 대하여 100% 정확도로 분류 가능한 것을 볼 수가 있었다.

This study addresses the problem of classifying ballistic missiles (rockets) and drones. The classification of missiles an d drones is important because different actions need to be taken for effective air defense operations depending on whether the object is a missile or a drone. We propose a method for classifying missiles and drones by considering their flight cha racteristics using a support vector machine (SVM) model. To collect missile and drone data, we constructed a dataset thro ugh open missile simulator experiments and actual drone flights. The experimental results show that the support vector m achine can classify missiles and drones with 100% accuracy.

Due to the simplicity of the k-nearest neighbor classification algorithm, it has been widely used in many fields. Until now, when the sample size is enormous and the feature attributes are outsized, the productivity of the k-nearest neighbor algorithm classification has also significantly increased. This work demonstrates that a k-nearest neighbor-based data mining technique has been utilized for data index to gather data and analyze an outpatient facility's clinical data set. Therefore, the investigational results show that the suggested algorithm can effectively improve the classification effectiveness of the KNN algorithm in processing a large set of data. Data extraction and fetching techniques can classify possible user/customer behavior using the k-nearest neighbor algorithm based on the user or consumer's impression, entailing prospects, responders, active entities, and different entities. Data mining methods have been utilized to uncover undisclosed patterns and relations. Undoubtedly, the information in a novel manner is reasonable to the healthcare stakeholders and to anticipate future patterns and practices in health-related practices. Many examinations and work have focused on various data mining strategies and approaches. The advanced growth of data science, information, and communication technology has directed the progress of medical-based details toward new artificial intelligence-based processes and data sciences.