Information Mining Techniques Research Articles

AI Based Model for Prediction of Heavy/High Impact Rain Events

India is a farming country whose economy is heavily reliant on the development of rainforests. In order to analyse agricultural yields, rainfall estimates are essential and fundamental for all ranchers. The ability to predict the climate with the aid of science and innovation is unsurprising rainfall. To effectively use water resources, horticulture production, and water arranging, it is important to determine the amount of rainfall. Rainfall can be predicted using a variety of information mining techniques. The process of information extraction is used to evaluate rainfall. Probably the most well-known rainfall forecast calculations may be found in this article. Some of the computations compared with this record include Logistic Regression, K-Neighbors Classifier, Random Forest Classifier, and Certificate Tree. It is possible to decompose the process of precisely predicting rainfall from a relative standpoint. To do this, we gathered satellite data. We have taken a number of variables into account in order to predict (temperature, dew point, humidity, wind pressure, wind speed, wind direction, etc.). Key Words: Random Forest Classifier, KNeighbors Classifier, Logistic Regression, Oversampling, Under sampling, and Rainfall Prediction.

Quality Innovation Cultivation Strategy for College Students in the Internet Era Oriented on the Cultivation of Innovative Ability

Abstract Addressing the imperative of cultivating innovation and learning quality in 21st-century college education, this research proposes a strategic CTDE model to enhance college students’ innovative capacities. By implementing a personalized information mining technique, we have established a sophisticated user profile model that strategically distributes learning resources geared towards innovation. The methodology integrates factor analysis with K-means clustering for a customized learning experience. A targeted survey reveals notable achievements: a standout innovation ability score of 3.92 for an individual student and a class-wide enhancement, with improvements ranging from 14% to 21% after detailed work evaluations. These findings affirm the CTDE model’s efficacy in fostering student innovation and quality learning.

Data Mining Techniques and Applications

Abstract: In recent days internet is considered as the main supply for searching the information and collecting data . The extraction of the data from the web offers several query results. Machine-controlled tools are needed through queries from the amount of pages by using the internet to spot the connected info. Data mining method is taken into account an efficient method of extracting the relevant information from databases. This method is employed for the pattern identification. Data mining could be a method that finds helpful patterns from great amount of knowledge. The paper discusses few of the information mining techniques, algorithms and a few of the organizations that have adapted data processing technology to enhance their businesses and located glorious results

Machine learning algorithms for smart and intelligent healthcare system in Society 5.0

The pandemic has shown us that it is quite important to keep track record our health digitally. And at the same time, it also showed us the great potential of Instruments like wearable observing gadgets, video conferences, and even talk bots driven by artificial intelligence (AI) can provide good care from remotely. Real time data collected from different health care devices of cases across globe played an important role in combatting the virus and also help in tracking its progress. The evolution of biomedical imaging techniques, incorporated sensors, and machine learning (ML) in recent years has led in various health benefits. Medical care and biomedical sciences have become information science fields, with a solid requirement for refined information mining techniques to remove the information from the accessible data. Biomedical information contains a few difficulties in information investigation, including high dimensionality, class irregularity, and low quantities of tests. AI is a subfield of AI and computer science which centric the utilization of information and calculations to impersonate the way that people learn, steadily further developing its accuracy. ML is an essential element of the rapidly growing area of information science. Calculations are created using measurable procedures to make characterizations or forecasts, exposing vital experiences inside information mining operations. In this chapter, we explain and compare the different algorithms of ML which could be helpful in detecting different disease at earlier stage. We summarize the algorithms and different steps involved in ML to extract information for betterment of the society which is already exposed to the world of data.

Big Data Analytics in Healthcare: Transforming Information into Actionable Insights

This study investigates how enormous information examination can reform the medical care industry, with a specific accentuation on how it can transform a lot of medical care information into helpful experiences. Using Jupyter Note pad, a popular intelligent processing instrument, the review utilized information mining techniques to gather critical turns of events and examples from enormous scope clinical datasets. Enormous information examination has a ton of potential to further develop direction, distribute assets ideally, and at last work on quiet results in the medical care industry. The primary part of the report portrays the rising sum and intricacy in clinical information, featuring the need for refined scientific methods to remove significant experiences from this enormous measure of information. Jupyter Note pad gives a broad stage that can be utilized to apply various information examination procedures, permitting scientists to actually look at, break down, and decipher clinical information. The review delineates the commitment of huge measures of information in anticipating ailment designs, distinguishing risk factors, and tweaking treatment procedures with the utilization of factual methods and AI Calculations. The concentrate likewise investigates how medical services partners, like executives, policymakers, and doctors, might be affected by these commonsense experiences. The outcomes feature how large information examination may essentially further develop proof based direction, asset productivity, and the improvement of an information driven care biological system. The consolidation of Jupyter Journals into the insightful cycle works on the investigation’s straightforwardness and reproducibility, ensuring the precision of the ends drawn from it. The current review presumes that large information examination, empowered by Jupyter Journal, assumes a basic part in delivering the progressive force of medical services information and sending off another period of very much educated direction and improved patient consideration.

LEVERAGING DATA MINING TECHNIQUES IN ENHANCING THE EFFICACY OF HEALTHCARE IN THIRD WORLD COUNTRIES: SPOTLIGHT ON TURKISH CASE STUDY

This paper presents a utilization of the information mining technique to decide the financial profiles of the public clinics in Turkey. The review depends on the information accumulated in 2004, covering 645 public clinics run by the Ministry of Health (MoH) as the fundamental supplier of essential and optional wellbeing administrations in Turkey. The public medical clinics, as of now financed by a combination of assets allotted from the overall spending plan and separately worked rotating reserves, need critical answers for their financial issues as a piece of a continuous public change exertion. The examination takes on the Chi-Square Automatic Interaction Detector (CHAID) choice tree calculation, as one of the most efficient and cutting-edge information digging technique utilized for division. The investigation has discovered that the public clinics could be sorted by the CHAID into 12 unique profiles as far as their financial execution. These profiles have directed us in deciding the key financial markers to be engaged upon in the public emergency clinics and present accepted procedures to work on their individual financial exhibitions. The findings have likewise permitted strategy ideas regarding the financial techniques that might be considered in working on the financial execution of the public medical clinics toward an effective wellbeing area change in Turkey.

Churn Prediction in Telecom sector using Deep Neural Network with Flask Application

Agitate Analysis is one of the overall utilized examination on Subscription Oriented Industries to break down client practices to anticipate the clients which are going to leave the help understanding from an organization. It depends on Deep Learning techniques and calculations and become so significant for organizations in the present business conditions as acquiring another client’s expense is more than holding the current ones. The paper audits the significant examinations on Customer Churn Analysis on Telecommunication Industry in writing to introduce general data to perusers about the regularly utilized information mining techniques utilized, results and execution of the strategies and revealing an insight to additional studies. To stay up with the latest, contemplates distributed in most recent five years and basically most recent two years have been incorporated.

Prediction of Employee Turn Over Using Random Forest Classifier with Intensive Optimized Pca Algorithm

Employee turnover is the important issue in the recent day organizations. In this paper, a data mining based employee turnover predictor is developed in which ORACLE ERP dataset was used for sample training to predict the employee turnover with much higher accuracy. This paper deploys impactful algorithms and methodologies for the accurate prediction employee turnover taking place in any organization. First of all preprocessing is done as a precautionary step as always before proceeding with the core part of the proposed work. New Intensive Optimized PCA-Principal Component Analysis is used for feature selection and RFC-Random Forest Classifier is used for the classification purposes to classify accordingly to make the prediction more feasible. For classifying and predicting accurately, a methodology called Random Forest Classifier (RFC) classifier is deployed. The main objective of this work is to utilize Random Forest Classification methodology to break down fundamental purposes lying behind the worker turnover by making use of the information mining technique refer as Intensive Optimized PCA for feature selection. Comparative study taking the proposed novel work with the existing is made for showing the efficiency of this work. The performance of this proposed method was found to perform better with improved yields of ROC, accuracy, precision, recall, and F1 score when compared to other existing methodologies.

A Semi-Supervised Deep Rule-Based Approach for Complex Satellite Sensor Image Analysis.

Large-scale (large-area), fine spatial resolution satellite sensor images are valuable data sources for Earth observation while not yet fully exploited by research communities for practical applications. Often, such images exhibit highly complex geometrical structures and spatial patterns, and distinctive characteristics of multiple land-use categories may appear at the same region. Autonomous information extraction from these images is essential in the field of pattern recognition within remote sensing, but this task is extremely challenging due to the spectral and spatial complexity captured in satellite sensor imagery. In this research, a semi-supervised deep rule-based approach for satellite sensor image analysis (SeRBIA) is proposed, where large-scale satellite sensor images are analysed autonomously and classified into detailed land-use categories. Using an ensemble feature descriptor derived from pre-trained AlexNet and VGG-VD-16 models, SeRBIA is capable of learning continuously from both labelled and unlabelled images through self-adaptation without human involvement or intervention. Extensive numerical experiments were conducted on both benchmark datasets and real-world satellite sensor images to comprehensively test the validity and effectiveness of the proposed method. The novel information mining technique developed here can be applied to analyse large-scale satellite sensor images with high accuracy and interpretability, across a wide range of real-world applications.

A k-NN method for lung cancer prognosis with the use of a genetic algorithm for feature selection

Lung cancer is one of the most common diseases for human beings everywhere throughout the world. Early identification of this disease is the main conceivable approach to enhance the possibility of patients’ survival. In this paper, a k-Nearest-Neighbors technique, for which a genetic algorithm is applied for the efficient feature selection to reduce the dataset dimensions and enhance the classifier pace, is employed for diagnosing the stage of patients’ disease. To improve the accuracy of the proposed algorithm, the best value for k is determined using an experimental procedure. The implementation of the proposed approach on a lung cancer database reveals 100% accuracy. This implies that one could use the algorithm to find a correlation between the clinical information and data mining techniques to support lung cancer staging diagnosis efficiently.

Novel Methods for Crop Prediction based on Influencing Parameters in Indian Regions using Data Mining

Agriculture plays a crucial role for the production of food in Indian regions. Indian regions mainly produces crops like rice, wheat, maize and many other types of crop. There are several factors required for the productivity of any harvest, but we know that soil, climate, pesticides, Fertilizers and ground water is most influencing essential factor for the productivity of any harvest. Let us consider soil which is the key element as it provides nutrients for proper development and growth of crops. Secondly, climate is also having major role in agriculture as crop growth depends on rainfall, humidity, temperature etc. Thirdly, Pesticides is widely used to control pest and prevents the damage of crops. Fourthly, Fertilizers can improve the quality of crops. Finally, ground water which will enrich nutrients in soil. The current preparation centers around different information mining procedures utilized in various regions of India and anticipate future harvest along with reasonable information mining procedure saw during the period(1920-2019).The parameters considered for the examination were soil, atmosphere, water thickness, pesticides and composts and Crop informational collection. The Classification calculations utilized in preparation were Adaptive boosting classification, Excess tree classification, neural based classification, Multiple Process classification, Decision making classification, K-closest neighbors, Bayesian theory classification, decision Forest classification, support group machine, and Randomized Gradient Classification. By using the techniques mentioned above we can improve the harvest prediction using information mining techniques which in turn help the farmers to take better decisions in future and it can be used in other technologies like image analyzing etc. The Experimental results show predicted crop, suitable algorithm and algorithm accuracy in that particular state of India respectively.

An Advanced Education Prognostic Miniature Exploitation Random Forest Technique

The precept goal of the higher instructive affiliation is to offer excessive notable and important education to its understudies. the 2 desires of statistics mining in Indian guidance framework is to have a look at and enhance the account technique facts mining propels development;, type out & speak approximately very last outcomes this is created via the usage of the usage of an information mining technique. This paper centers at some stage in the model portrayal for reading the numerous records mining techniques in Indian guidance framework. moreover the paper surveys a similar research of ID3, k-manner, Naïve Bayes, Random wooded vicinity calculation. in this paper, we've got have been given proposed the approach of Random wooded location to foresee the profession choice for the 12th going out understudies. The usage of Random wooded location has helped the understudies to truely receive a proper desire consistent with their advantage and aptitudes and acts a lifelong advertising representative tool package deal.

Advanced Scholastic Analytics by Implementing Data Mining Techniques

Over the past few years, the adaption of Edification management System in the sector of education has increased. Mining, clustering of essential data and finding out unique patterns from the field of education to research student’s behaviors and performance is widely recognized as Educational Data Mining (EDM) and it is a progressing profession involved with the production of new techniques for discovering distinctive and progressively large-scale information from educational environments and employing those techniques for deeper comprehend learning. It also provides an inherent understanding of teaching and learning processes for the efficient scheduling of education. This paper recommends the use of two information mining techniques in educational data. First, in admittance information, the association rule was implemented to discover some knowledge to support admission schedules. Second, a decision tree was implemented in grades and graduate student job information to estimate work type after graduation. The findings of this research provide an excellent understanding of admittance scheduling and work prediction.

A Weighted Frequent Item-Set Mining using WD-FIM Algorithm

Smart systems are the one of the most significant inventions of our times. These systems rely on powerful information mining techniques to achieve intelligence in decision making. Frequent item set mining (FIM), has become one of the most significant research area of data mining. The information present in databases is in-general ambiguous and uncertain. In such databases, one should think of weighted FIM to discover item sets which are significant from end user’s perspective. Be that as it may, with introduction of weight-factor for FIM makes the weighted continuous item sets may not fulfil the descending conclusion property anymore. Subsequently, the pursuit space of successive item set can't be limited by descending conclusion property which prompts a poor time effectiveness. In this paper, we introduce two properties for FIM, first one is, weight judgment downward closure property (WD-FIM), it is for weighted FIM and the second one is existence property for its subsets. In view of above two properties, the WD-FIM calculation is proposed to limit the looking through space of the weighted regular item sets and improve the time effectiveness. In addition, the culmination and time productivity of WD-FIM calculation are examined hypothetically. At last, the exhibition of the proposed WD-FIM calculation is confirmed on both engineered and genuine data sets.

Sentiment Analysis for Product Recommendation System Using Enhanced Stochastic Learning Algorithm

E-Commerce has been known as a rapidly growing commercial enterprise, and even though on line purchasing has no longer accompanied those identical boom patterns within the beyond, it's miles now being diagnosed for its capability. Sentiment evaluation is one of the current research subjects in the subject of textual content mining. Opinions and sentiments mining from natural language are very difficult task. Sentiment analysis is the best solution. This gives important information for decision making in various domains. Various sentiment detection methods are available which affect the quality of result. In this project we are finding the sentiments of people related to the services of E-shopping websites. The sentiments include reviews, ratings and emoticons. The main goal is to recommend the products to users which are posted in E-shopping website and analyzing which one is the best. For this we use hybrid learning algorithm which analyze various feedbacks related to the services. Text mining algorithm is used to find scores of each word. Then sentiments are classified as negative, positive and neutral. It has been observed that the pre-processing of the data is greatly affecting the quality of detected sentiments. Finally analysis takes place based on classification. To find out fake review in the website can be analyzed. This device will discover fake critiques made via posting fake remarks about a product via figuring out the MAC deal with in conjunction with assessment posting styles. User will login to the device using his consumer identification and password and could view various merchandise and will give assessment approximately the product. To discover the evaluation is fake or authentic, system will find out the MAC address of the consumer if the machine observes fake assessment send by way of the identical MAC Address many a times it'll inform the admin to do away with that overview from the device. This gadget uses information mining technique. This machine allows the user to find out accurate overview of the product.

Sentiment Analysis for Product Recommendation System Using Hybrid Algorithm

E-Commerce has been known as a rapidly growing commercial enterprise, and even though on line purchasing has no longer accompanied those identical boom patterns within the beyond, it's miles now being diagnosed for its capability. Sentiment evaluation is one of the current research subjects in the subject of textual content mining. Opinions and sentiments mining from natural language are very difficult task. Sentiment analysis is the best solution. This gives important information for decision making in various domains. Various sentiment detection methods are available which affect the quality of result. In this paper, finding the sentiments of people related to the services of E-shopping websites. The sentiments include reviews, ratings and emoticons. The main goal is to recommend the products to users which are posted in E-shopping website and analyzing which one is the best and use hybrid learning algorithm which analyze various feedbacks related to the services. Text mining algorithm is used to find scores of each word. Then sentiments are classified as negative, positive and neutral. It has been observed that the pre-processing of the data is greatly affecting the quality of detected sentiments. Finally analysis takes place based on classification. To find out fake review in the website can be analyzed. This device will discover fake critiques made via posting fake remarks about a product via figuring out the MAC deal with in conjunction with assessment posting styles. User will login to the device using his consumer identification and password and could view various merchandise and will give assessment approximately the product. To discover the evaluation is fake or authentic, system will find out the MAC address of the consumer if the machine observes fake assessment send by way of the identical MAC Address many a times it'll inform the admin to do away with that overview from the device. This gadget uses information mining technique. This machine allows the user to find out accurate overview of the product.

Proposed Balanced Constraint Measure-Based Algorithm for Privacy Preserved Information Distributing

Security insurance against mining calculations, an advancing exploration territory, finds the pessimistic symptoms of information mining techniques that start from the protection dissemination of people and associations. Late research in security insurance information mining has focused on finding a legitimate soundness between data protection and learning revelation. In this paper, we present a calculation that creates a disinfected database which keeps up the best possible harmony between data security and information revelation. We initially create the significant sicknesses from the first database, at that point convert the first database into a cleaned (sanitized ) database through our proposed arbitrary cleaning(sanitization) process. Next, we assess the produced cleaned database based on Knowledge Discovery (KD) and Privacy Factor (PF). This assessment procedure computes the KD and PF by looking at the noteworthy illnesses created from the first database versus the sterilized database. On the off chance that the estimations of KD and PF are fulfilled by the client determined limit esteem, the sterilized database is prepared for distributing, else our proposed calculation rehashes the sterilization procedure until the KD and PF qualities are fulfilled. At long last, our proposed calculation is assessed by contrasting it and comparative well known calculations on the criteria of KD and PF. The trial results demonstrate that our proposed calculation performed well at 77% running time contrasted with comparative prevalent calculations. The memory prerequisite for our proposed calculation is 25%, while past calculations need 31% memory. Our proposed calculation additionally performed 53% superior to past calculations regarding number of critical sicknesses influenced by the proposed cleansing procedure.

Towards a visual guide for communicating uncertainty in Visual Analytics

This article presents a first step towards the definition of a visual guide for communicating uncertainty which is to fit into existing visualisation frameworks and toolkits. The first entry in our guide is made by a set of visual variables appropriate for representing areal uncertainty in algorithm mechanics. Such visualisations show users how data points are distributed in the classification space and allow them to understand the “goodness-of-fit” of their data to the algorithm. This is important for Visual Analytics applications, which combine Information Visualisation with information mining techniques in an interactive decision-making process. Model uncertainties stemming from widely spread data points need to be visualised so that the user can make adjustments and improve the analysis.To capitalise on established knowledge and meaning, we explore whether popular visual variables for representing areal uncertainty in the domain of geospatial visualisation may also be effective for representing uncertainty in the visualisation of the mechanics of K-means clustering and Linear Regression algorithms, as both use a spatial distribution of data points. In a study with 500 participants we find that overall the visual means opacity performs best, followed by texture, but that grid and blur may be unsuitable for quantifying uncertainty. The performance of contour lines appears to depend on the algorithm visualisation. Using this study, we extend the validity of a set of domain-specific findings from geospatial visualisation to the visualisation of algorithm mechanics and use these to form the first building blocks of a cross-disciplinary visual guide for representing uncertainty, laying promising foundations for future work.

Evaluation of Concrete Plants Readiness to Produce High Quality Concrete for Municipal Constructions Using Past Information

The only way to test the ability of concrete plants to produce high quality concrete is to test their final products. Also, the process of testing and controlling concrete quality is time consuming and expensive. In this regard, having a quick, cheap and efficient way to predict the readiness of concrete plants to produce high quality concrete is very valuable. In this paper, a probabilistic multi-attribute algorithm has been developed to address this problem. In this algorithm, the goal is to evaluate readiness of concrete plants to produce high quality concrete based on the error rate of concrete compressive strength. Using past information and data mining techniques, this algorithm predicts the readiness level of concrete plants by similarity of their production factors to past information. Readiness alternatives for plants are ranked using data mining techniques for order preference based on their production factors (PF) and by evaluating the similarity/difference of each PF to past information. A case study of 20 concrete plants is used to illustrate the capability of the new algorithm; with results showing that the algorithm generated nondominated solutions can assist plant managers to set efficient production plan, a task both difficult, cost and time-consuming using current methods. In the case study, lab test totally confirm the algorithm outcomes thus it has been successfully verified.

Cyber Attack Classification in Microsoft Azure Using Deep Learning Algorithm

The security of necessary data is consistently an exceptionally fundamental issue for the current computerized world. Intrusion Detection System (IDS) and numerous security procedures are broadly utilized against digital assaults. Information mining and Machine Learning techniques have likewise been utilized by scientists to acquire high discovery rate and low bogus caution rate. Proposed work expects to plan and advance a methodology for improving the digital assault recognition framework utilizing the cloud. Employments of distributed computing are increments logically. They are utilizing customary Machine Learning. Techniques do not uphold well preparation of massive datasets, so new methodologies and stages are required. This paper recommends that cloud-based AI procedure can be utilized to characterize assault into a cloud-based AI stage. The work proposes an assault arrangement system utilizing UNSW-NB15 dataset. The classifier is a manufacture which depends on the 'Multiclass Decision Forest' Machine Learning Algorithm and is sent on Microsoft's Azure Machine Learning (Azure MACHINE LEARNING) stage. Purplish blue machine learning is a public cloud platform. The results acquired by the proposed model are assessed regarding precision, and the examination is finished with benchmarks gave by rivalry overseers.