Data Classification Techniques Research Articles

CONNECTING GEOMETRY AND SEMANTICS VIA ARTIFICIAL INTELLIGENCE: FROM 3D CLASSIFICATION OF HERITAGE DATA TO H-BIM REPRESENTATIONS

Abstract. Cultural heritage information systems, such as H-BIM, are becoming more and more widespread today, thanks to their potential to bring together, around a 3D representation, the wealth of knowledge related to a given object of study. However, the reconstruction of such tools starting from 3D architectural surveying is still largely deemed as a lengthy and time-consuming process, with inherent complexities related to managing and interpreting unstructured and unorganized data derived, e.g., from laser scanning or photogrammetry. Tackling this issue and starting from reality-based surveying, the purpose of this paper is to semi-automatically reconstruct parametric representations for H-BIM-related uses, by means of the most recent 3D data classification techniques that exploit Artificial Intelligence (AI). The presented methodology consists of a first semantic segmentation phase, aiming at the automatic recognition through AI of architectural elements of historic buildings within points clouds; a Random Forest classifier is used for the classification task, evaluating each time the performance of the predictive model. At a second stage, visual programming techniques are applied to the reconstruction of a conceptual mock-up of each detected element and to the subsequent propagation of the 3D information to other objects with similar characteristics. The resulting parametric model can be used for heritage preservation and dissemination purposes, as common practices implemented in modern H-BIM documentation systems. The methodology is tailored to representative case studies related to the typology of the medieval cloister and scattered over the Tuscan territory.

MI-MOTE: Multiple imputation-based minority oversampling technique for imbalanced and incomplete data classification

Class imbalance and data incompleteness problems occur simultaneously in many real-world classification datasets, which negatively affects the training of classifiers. Given an imbalanced and incomplete training dataset, the conventional approach is to address these two problems sequentially by handling data incompleteness first and then focusing on class imbalance. In this study, we propose a multiple imputation-based minority oversampling technique, named MI-MOTE, to address imbalanced and incomplete data classification simultaneously. Majority instances are imputed once and minority instances are oversampled using multiple different imputations without directly manipulating any of their observed values. Accordingly, minority instances are diversified with less data distortion compared to the conventional approach. The proposed method is applied in the data preprocessing phase, meaning it can be used with any type of classifier. Experimental results for benchmark datasets with various missing rates demonstrate the effectiveness of the proposed method.

A hybrid feature selection model based on improved squirrel search algorithm and rank aggregation using fuzzy techniques for biomedical data classification.

Feature selection has gained its importance due to the voluminous nature of the data. Owing to the computational complexity of wrapper approaches, the poor performance of filtering techniques, and the classifier dependency of embedded approaches, hybrid approaches are more commonly used in feature selection. Hybrid approaches use filtering metrics to reduce the computational complexity of wrapper algorithms and are proved to yield better feature subset. Though filtering metrics select the features based on their significance, most of them are unstable and biased towards the metric used. Moreover, the choice of filtering metrics depends largely on the distribution of data and data types. Biomedical datasets contain features with different distribution and types adding to the complexity in the choice of filtering metric. We address this problem by proposing a stable filtering method based on rank aggregation in hybrid feature selection model with Improved Squirrel search algorithm for biomedical datasets. Our proposed model is compared with other well-known and state-of-the-art methods and the results prove that our model exhibited superior performance in terms of classification accuracy and computational time. The robustness of our proposed model is proved by conducting experiments on nine biomedical datasets and with three different classifiers.

A Survey of Data Mining Techniques for Smart Museum Applications

This research aims to find out what data mining techniques are effectively implemented in museums and what application trends are currently being used to improve museum performance towards modern museums based on intelligent system technology. The review was carried out on a number of articles found in journals and proceedings in the 2004-2020 period. It is found that the majority of data mining techniques are implemented in museum virtual guide applications, recommender systems, collection clustering and classification system, and visitor behaviour prediction application. Data classification, clustering, and prediction technique commonly used for museum application. Collections with historical and artistic value contain a lot of knowledge making data mining an important technique to be included in various applications in museums so that they can have an impact on the achievement of museum goals not only in the fields of education and culture but also economics and business.

Regression Tree Based Correlation Technique in Spatial Data Classification

Data mining is the process of discovering useful patterns from large geo-spatial datasets with the help of machine learning methods. . The machine learning methods plays an important role for data analytics modeling and visualization. Geo-spatial data is a significant task in many application domains, such as environmental science, geographic information science, and social networks. However, the existing spatial pattern discovery and prediction techniques failed to predict the event accurately with minimum error and time consumption. . In this paper, a novel Pearson Correlated Regression Tree-based Affine Projective spatial data Classification (PCRT-APSDC) technique is proposed to improve the spatial data classification and minimize error based on the Affine Projective classification technique. The proposed algorithm employs a fuzzy rule procedure that constructs the regression tree. The fuzzy rule is applied for linking the inputs (i.e. spatial data) with the outputs (i.e. classification results). Our goal is to classify the data into two subsets such as fired region and non-fired region. Experimental evaluation is carried out using a forest fire dataset with different factors such as classification accuracy, false-positive rate, and classification time. The results confirm that the proposed technique predicts the fired region with increased spatial data classification accuracy and minimized time as well as false-positive rate than the state-of-the-art methods.

Optimized Performance and Utilization Analysis of Real-Time Multi Spectral Data/Image Categorization Algorithms for Computer Vision Applications

In the field of computer vision , the process of acquiring, processing, analyzing and understanding multispectral data images is a major requisite. The major tool for digital data analysis and object recognition is data categorization. The basic and main stages involved in data categorization are the identification of an appropriate categorization system, an assortment of training and testing samples and the categorization method. Data categorization (or) classification is to recognize and depict the features of any data that can be later used for knowledge discovery. This work aims to compare supervised data classification techniques. This paper illustrates utilization of various techniques viz., Minimum distance (MD), Maximum likelihood (ML) and Mahalanob is distance (Mad). All the procedures are compared and analyzed for finest results and maximum accuracy.

Design and Implementation of Missing Data Classification Technique for IoT Applications Using Artificial Intelligence

The combination of various sensors with different data methods is a common technique used to increase precision in the classification of IoT health data. However, for even the assessment outcomes, all modalities are barely available and this scarcity of evidence poses significant barriers to multimodal education. Driven by recent developments in deep education, we are providing a cross-neural network for the segmentation of the IoT Health Data Classification, which is trained on data modalities not all available during trials. In IoT Health Data Classification, we train our architecture with a cost function that is especially tailored to unbalanced classes. We are providing the device with a benchmark data set with incomplete data. Assuming that they are not present in the research process, our methodology goes beyond both the CNN training and the collection of two CNNs trained in the missing modality by utilising time data

Improving the Efficiency of Image and Video Forgery Detection Using Hybrid Convolutional Neural Networks

Recently, on the internet, the level of image and video forgery has augmented due to the augmentation in the malware, which has facilitated user (anyone) to upload, download, or share objects online comprising audio, images, or video. Recently, Convolution Neural Network (CNN) has turn into a de-facto technique for classification of multi-dimensional data and it renders standard and also highly effectual network layer arrangements. But these architectures are limited by the speed due to massive number of calculations needed for training in addition to testing the network and also, it might render less accuracy. To trounce these issues, this paper proposed to ameliorate the image and video forgery detection’s efficiency utilizing hybrid CNN. Initially, the intensive along with incremental learning phase is carried out. After that, the hybrid CNN is implemented to detect the image together with video forgery. The developed system was tested on images together with videos for different kinds of forgeries, and it was observed that the proposed work obtains more than 98% accuracy for both testing as well as validation sets.

Deming least square regressed feature selection and Gaussian neuro-fuzzy multi-layered data classifier for early COVID prediction.

Coronavirus disease (COVID‐19) is a harmful disease caused by the new SARS‐CoV‐2 virus. COVID‐19 disease comprises symptoms such as cold, cough, fever, and difficulty in breathing. COVID‐19 has affected many countries and their spread in the world has put humanity at risk. Due to the increasing number of cases and their stress on administration as well as health professionals, different prediction techniques were introduced to predict the coronavirus disease existence in patients. However, the accuracy was not improved, and time consumption was not minimized during the disease prediction. To address these problems, least square regressive Gaussian neuro‐fuzzy multi‐layered data classification (LSRGNFM‐LDC) technique is introduced in this article. LSRGNFM‐LDC technique performs efficient COVID prediction with better accuracy and lesser time consumption through feature selection and classification. The preprocessing is used to eliminate the unwanted data in input features. Preprocessing is applied to reduce the time complexity. Next, Deming Least Square Regressive Feature Selection process is carried out for selecting the most relevant features through identifying the line of best fit. After the feature selection process, Gaussian neuro‐fuzzy classifier in LSRGNFM‐LDC technique performs the data classification process with help of fuzzy if‐then rules for performing prediction process. Finally, the fuzzy if‐then rule classifies the patient data as lower risk level, medium risk level and higher risk level with higher accuracy and lesser time consumption. Experimental evaluation is performed by Novel Corona Virus 2019 Dataset using different metrics like prediction accuracy, prediction time, and error rate. The result shows that LSRGNFM‐LDC technique improves the accuracy and minimizes the time consumption as well as error rate than existing works during COVID prediction.

A Weight Based Labeled Classifier Using Machine Learning Technique for Classification of Medical Data

A Weight Based Labeled Classifier Using Machine Learning Technique for Classification of Medical Data

Stochastic Gradient Descent with Logistic Regression Technique for Medical Data Classification

At present times, medical data classification becomes a hot research area and utilizes data mining (DM) techniques for disease diagnosis. The application of automated medical data classification techniques helps to identify the diseases accurately. This paper presents a new stochastic gradient descent with logistic regression based medical data classification (SGDLR-MDC) for disease diagnosis. The presented model executes data pre-processing as the initial stage to perform data conversion and class labeling. The preprocessed data is executed by the classification model to determine the actual class label of the input data. The SGD technique necessitates the utilization of training data with equivalent ground truth labels allocated to the data instances. The outcome from the SGDLR-MDC model signifies the existence or non-existence of the diseases. The experimental results denoted that the SGDLR-MDC model has demonstrated proficient performance with the maximum accuracy of 98.89%, 98.96%, and 99.15% on the applied hepatitis, Indian liver patient (ILP), and thyroid dataset respectively.

Feasibility study on exhaled-breath analysis by untargeted Selected-Ion Flow-Tube Mass Spectrometry in children with cystic fibrosis, asthma, and healthy controls: Comparison of data pretreatment and classification techniques

Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) has been applied in a clinical context as diagnostic tool for breath samples using target biomarkers. Exhaled breath sampling is non-invasive and therefore much more patient friendly compared to bronchoscopy, which is the golden standard for evaluating airway inflammation. In the actual pilot study, 55 exhaled breath samples of children with asthma, cystic-fibrosis and healthy individuals were included. Rather than focusing on the analysis of target biomarkers or on the identification of biomarkers, different data analysis strategies, including a variety of pretreatment, classification and discrimination techniques, are evaluated regarding their capacity to distinguish the three classes based on subtle differences in their full scan SIFT-MS spectra. Proper data-analysis strategies are required because these full scan spectra contain much external, i.e. unwanted, variation. Each SIFT-MS analysis generates three spectra resulting from ion-molecule reactions of analyte molecules with H3O+, NO+ and O2+. Models were built with Linear Discriminant Analysis, Quadratic Discriminant Analysis, Soft Independent Modelling by Class Analogy, Partial Least Squares - Discriminant Analysis, K-Nearest Neighbours, and Classification and Regression Trees. Perfect models, concerning overall sensitivity and specificity (100% for both) were found using Direct Orthogonal Signal Correction (DOSC) pretreatment. Given the uncertainty related to the classification models associated with DOSC pretreatments (i.e. good classification found also for random classes), other models are built applying other preprocessing approaches. A Partial Least Squares - Discriminant Analysis model with a combined pre-processing method considering single value imputation results in 100% sensitivity and specificity for calibration, but was less good predictive. Pareto scaling prior to Quadratic Discriminant Analysis resulted in 41/55 correctly classified samples for calibration and 34/55 for cross-validation. In future, the uncertainty with DOSC and the applicability of the promising preprocessing methods and models must be further studied applying a larger representative data set with a more extensive number of samples for each class. Nevertheless, this pilot study showed already some potential for the untargeted SIFT-MS application as a rapid pattern-recognition technique, useful in the diagnosis of clinical breath samples.

Performance Analysis of Classification Techniques in Data Mining using WEKA

To place similar objects together in to one group and separate the different ones are done with the help of data mining algorithms. In the present days, retrieval of information for decision making is very crucial task. The interdisciplinary field of computer science that deals with this task is known as data mining. Data mining is used to discover the knowledge that is hidden in large volume of data stored in data repositories. So, many researchers attracted towards data mining for decision making. There are number of tools available for data mining also such as ORANGE, WEKA and KNIME etc. But whenever there is discussion of some classification model then WEKA seems as a strong data mining tool. In this paper, various machine learning algorithms of classification are analysed by using the diabetes data set. WEKA tool is used for the same purpose. The purpose of this paper is to present the comparative evaluation of WEKA classifiers in the context of diabetes dataset. These algorithms are compared by using their results calculations received on WEKA.

Study of Feature Extraction Techniques for Sensor Data Classification

Human activity recognition is a rapidly growing area in healthcare systems. The applications include fall detection, ambiguous activity, dangerous behavior, etc. It has become one of the important requirements for the elderly or neurological disorder patients. The devices included are accelerometer and gyroscope, which generate large amounts of data. Accuracy of classification algorithms for this data is highly dependent upon extraction and selection of data features. This research study has extracted time domain features, based on statistical functions as well as rotational features around three axes. Gyroscope data features are also used to enhance accuracy of accelerometer data. Three popular classification techniques are used to classify the accelerometer dataset into activity categories. Binary classification (run -1 / walk-0) is considered. The results have shown SVM and LDA when used with rotation and gyroscope data gives the highest accuracy of 92.0% whereas FDA shows 84% accuracy.

Fuzzy system for classification of microarray data using a hybrid ant stem optimisation algorithm

Microarray data analysis provides an effective methodology for the diagnosis of diseases and cancers. Although much research has been performed on applying several techniques for microarray data classification during the past years, it has been shown that conventional machine learning and statistical techniques have intrinsic drawbacks in achieving accurate and robust classifications. This paper presents fuzzy based classification system to analyse microarray data. The mutual information approach is used in this approach to extract the most informative genes from the microarray dataset. In the design of fuzzy expert systems, a novel hybrid ant stem (HAS) algorithm used to extract the if-then rules using the membership functions from the given diabetes microarray data is presented. The performance of the proposed technique evaluated using the two diabetes microarray dataset are simulated. These results prove that the proposed HAS algorithm produces a highly accurate fuzzy expert system.

A review on the use of data mining techniques for medical data classification

A review on the use of data mining techniques for medical data classification

Fuzzy system for classification of microarray data using a hybrid ant stem optimisation algorithm

Microarray data analysis provides an effective methodology for the diagnosis of diseases and cancers. Although much research has been performed on applying several techniques for microarray data classification during the past years, it has been shown that conventional machine learning and statistical techniques have intrinsic drawbacks in achieving accurate and robust classifications. This paper presents fuzzy based classification system to analyse microarray data. The mutual information approach is used in this approach to extract the most informative genes from the microarray dataset. In the design of fuzzy expert systems, a novel hybrid ant stem (HAS) algorithm used to extract the if-then rules using the membership functions from the given diabetes microarray data is presented. The performance of the proposed technique evaluated using the two diabetes microarray dataset are simulated. These results prove that the proposed HAS algorithm produces a highly accurate fuzzy expert system.

Deepint.net: A Rapid Deployment Platform for Smart Territories.

This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris—Vélib’ Métropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques.

Predicting Influential Blogger’s by a Novel, Hybrid and Optimized Case Based Reasoning Approach With Balanced Random Forest Using Imbalanced Data

Bloggers possess the capability of understanding and influencing mass psychology to a wide community of fans and followers by posting their online valuable content. Their dominance over audience can be used as a helping hand in the corporate world which desires to disseminate their product or services among diversified people belonging to varying localities, and is always on the lookout for suitable and quick ways to grasp public access. Due to this reason, influential bloggers are preferred in the online market to initiate marketing campaigns which is a thought-provoking task due to loads of blogger communities. The novelty of this paper lies in the proposed Framework for Influential Blogger Prediction based on Blogger and Blog Features (IBP-BBF) using Case-Based Reasoning (CBR) which is not only capable of handling labeled data but also unstructured data (blogs) and imbalanced data in an optimized way. Detailed labelled and unstructured data are collected by online survey of 129 bloggers and text mining of their 32,200 blogs respectively. The classification results are compared and validated with state-of-the-art machine learning techniques by using standard evaluation measures respectively in the context of imbalanced data. The results show that the proposed IBP-BBF framework through CBR modeling outperforms existing techniques in classifying and adapting the influential blogger prediction. The IBP-BBF framework performed better as compared to baseline imbalanced data classification techniques. It is found that the Balanced Random Forest contributes towards the performance of CBR approach than Balanced Bagging Classifier and RUSBoost classifier. By using the CBR approach, baseline techniques can be optimized for influential blogger identification in a better way.

Comprehensive Performance Assessment of Deep Learning Models in Early Prediction and Risk Identification of Chronic Kidney Disease

The incidence of chronic kidney disease (CKD) is rising rapidly around the globe. Asymptomatic CKD is common and guideline-directed monitoring to predict CKD by various factors is underutilized. Computer-aided automated diagnostic (CAD) can play a major role to predict CKD. CAD systems such as deep learning algorithms are pivotal in disease diagnosis due to their high classification accuracy. In this paper, various clinical features of CKD were utilized and seven state-of-the-art deep learning algorithms (ANN, LSTM, GRU, Bidirectional LSTM, Bidirectional GRU, MLP, and Simple RNN) were implemented for the prediction and classification of CKD. The proposed algorithms were applied based on artificial intelligence by extracting and evaluating features using five different approaches from pre-processed and fitted CKD datasets. In this study, we have measured accuracy, precision, recall, and calculated the loss and validation loss in prediction. Further, the study analyzed computation time and prediction ratio, and AUC to evaluate the model performance along with statistical significance to compare their performances. While classifying CKD, algorithms such as ANN, Simple RNN, and MLP provided high accuracy of 99%, 96%, 97% respectively, and a good prediction ratio along with reduced time. The model outperforms traditional data classification techniques by providing superior predictive ability. Subsequently, the study proposed the integration of best performing DL models in the IoMT. This proposal will assist predictive analytics to advance CKD prediction by using deep learning more efficiently and effectively. The study is the first fundamental step toward a comprehensive performance assessment to classify and predict CKD using deep learning models and its associated risk factors.