Rule-based Classification Approach Research Articles

A rule-based protein classification approach using normalized distance-based encoding method

A rule-based protein classification approach using normalized distance-based encoding method

Adaptive fuzzy-evidential classification based on association rule mining

As one of the most promising classification approaches, association classification (AC) integrates data classification and association discovery techniques for generating a compact set of classification association rules. Recently, the fuzzy set and evidence theories are successively applied into AC in order to improve the classification performance in terms of accuracy and interpretability. However, from the perspectives of applicability and universality, there still exists two important issues in the current AC framework. On one hand, several key parameters, such as the number of fused rules in classification and the minimum support threshold in association discovery, are difficult to be accurately predefined in practice. On the other hand, the fixed grid-based fuzzy partition is not benefit to adapt for those datasets with large number of features. In this paper, an association rule-based adaptive fuzzy-evidential classification framework (AR-AFEC) is developed for overcoming the above limitations. To do so, an optimal rule fusion strategy and a dynamic minimum support threshold setting scheme are proposed for adaptively learning the parameters during classification and association mining respectively. In addition, an entropy-based trapezoidal fuzzy partition technique is proposed to adaptively obtain the fuzzy sets defined on each continuous feature domain. Experiments on 26 benchmark datasets and a human activity recognition application demonstrate that the proposal can achieve better accuracy than some state-of-the-art rule-based classification approaches, using less rules with more general structure.

Location-based activity behavior deviation detection for nursing home using IoT devices

With the advancement of the Internet of Things(IoT) and pervasive computing applications, it provides a better opportunity to understand the behavior of the aging population. However, in a nursing home scenario, common sensors and techniques used to track an elderly living alone are not suitable. In this paper, we design a location-based tracking system for a four-story nursing home - The Salvation Army, Peacehaven Nursing Home in Singapore. The main challenge here is to identify the group activity among the nursing home’s residents and to detect if they have any deviated activity behavior. We propose a location-based deviated activity behavior detection system to detect deviated activity behavior by leveraging data fusion technique. In order to compute the features for data fusion, an adaptive method is applied for extracting the group and individual activity time and generate daily hybrid norm for each of the residents. Next, deviated activity behavior detection is executed by considering the difference between daily norm patterns and daily input data for each resident. Lastly, the deviated activity behavior among the residents are classified using a rule-based classification approach. Through the implementation, there are 44.4% of the residents do not have deviated activity behavior, while 37% residents involved in one deviated activity behavior and 18.6% residents have two or more deviated activity behaviors.

Flash-Flood Potential Mapping in Agricultural Land Using Rule-Based Classification Approach on Multi-Temporal Synthetic-Aperture Radar (SAR) Data over Jhajjar and Rohtak Districts of Haryana State

Flash floods are known to be most dangerous flood. In recent decades there is change in the climate and urban development causing increase in the risk of flash flood for human population. Remote sensing and geographical information system playing a very important and feasible role in assessment of the flash flood over a decade. This paper describes the synergetic use of Synthetic-aperture radar (SAR) data to delineate and mapping of the flooded area at its peak. The methodology was tested on the flash flood event that occurs in Jhajjar and Rohtak districts of Haryana in July, 2022. Four Sentinel-1 Synthetic-aperture radar data were acquired and pre-processed, two images are acquired before the occurrence of the event and two SAR images were acquired after the event. For the identification and demarcation of the flooded area, the rule-based classification approach was used. The results of the study suggests the scope of using multi-temporal Sentinel-1 VV polarized C-band SAR data to develop an operational flash-flood area identification and framework.

Design of metaheuristic rough set-based feature selection and rule-based medical data classification model on MapReduce framework

Abstract Recently, big data analytics have gained significant attention in healthcare industry due to generation of massive quantities of data in various forms such as electronic health records, sensors, medical imaging, and pharmaceutical details. However, the data gathered from various sources are intrinsically uncertain owing to noise, incompleteness, and inconsistency. The analysis of such huge data necessitates advanced analytical techniques using machine learning and computational intelligence for effective decision making. To handle data uncertainty in healthcare sector, this article presents a novel metaheuristic rough set-based feature selection with rule-based medical data classification (MRSFS-RMDC) technique on MapReduce framework. The proposed MRSFS-RMDC technique designs a butterfly optimization algorithm for minimal rough set selection. In addition, Hadoop MapReduce is applied to process massive quantity of data. Moreover, a rule-based classification approach named Repeated Incremental Pruning for Error Reduction (RIPPER) is used with the inclusion of a set of conditional rules. The RIPPER will scale in a linear way with the number of training records utilized and is suitable to build models with data uncertainty. The proposed MRSFS-RMDC technique is validated using benchmark dataset and the results are inspected under varying aspects. The experimental results highlighted the supremacy of the MRSFS-RMDC technique over the recent state of art methods in terms of different performance measures. The proposed methodology has achieved a higher F-score of 96.49%.

The Importance of Agronomic Knowledge for Crop Detection by Sentinel-2 in the CAP Controls Framework: A Possible Rule-Based Classification Approach

Farmers are supported by European Union (EU) through contributions related to the common agricultural policy (CAP). To obtain grants, farmers have to apply every year according to the national/regional procedure that, presently, relies on the Geo-Spatial Aid Application (GSAA). To ensure the properness of applications, national/regional payment agencies (PA) operate random controls through in-field surveys. EU regulation n. 809/2014 has introduced a new approach to CAP controls based on Copernicus Sentinel-2 (S2) data. These are expected to better address PA checks on the field, suggesting eventual inconsistencies between satellite-based deductions and farmers’ declarations. Within this framework, this work proposed a hierarchical (HI) approach to the classification of crops (soya, corn, wheat, rice, and meadow) explicitly aimed at supporting CAP controls in agriculture, with special concerns about the Piemonte Region (NW Italy) agricultural situation. To demonstrate the effectiveness of the proposed approach, a comparison is made between HI and other, more ordinary approaches. In particular, two algorithms were considered as references: the minimum distance (MD) and the random forest (RF). Tests were operated in a study area located in the southern part of the Vercelli province (Piemonte), which is mainly devoted to agriculture. Training and validation steps were performed for all the classification approaches (HI, MD, RF) using the same ground data. MD and RF were based on S2-derived NDVI image time series (TS) for the 2020 year. Differently, HI was built according to a rule-based approach developing according to the following steps: (a) TS standard deviation analysis in the time domain for meadows mapping; (b) MD classification of winter part of TS in the time domain for wheat detection; (c) MD classification of summer part of TS in the time domain for corn classification; (d) selection of a proper summer multi-spectral image (SMSI) useful for separating rice from soya with MD operated in the spectral domain. To separate crops of interest from other classes, MD-based classifications belonging to HI were thresholded by Otsu’s method. Overall accuracy for MD, RF, and HI were found to be 63%, 80%, and 89%, respectively. It is worth remarking that thanks to the SMSI-based approach of HI, a significant improvement was obtained in soya and rice classification.

Evaluation of fuzzy membership functions for linguistic rule-based classifier focused on explainability, interpretability and reliability

Objective:Decision support systems focus on their interpretability with a different caution. The majority of approaches utilize reliable optimization techniques to achieve high recognition accuracy. However, accuracy improvement is done at the cost of the readability of the decision support system. This paper proposes a novel technique of linguistic rule extraction focused on: explainability, interpretability, and reliability, called EasIeR. Methods:Proposed solution performs a direct analysis of the learning set without omitting real data imperfections. The proposed algorithm processes the imprecision of data with linguistic values described by fuzzy membership functions and the uncertainty of knowledge employing a fuzzy belief function. It describes the learning set with the most appropriate fuzzy if-then rules. For this purpose, a new method of evaluation of membership functions in a set of rules and their iterative refinement is proposed. Results:EasIeR achieves the best results of trade-off criteria between the generalization quality and the interpretability of the knowledge base when compared with state-of-art fuzzy rule-based classification approaches. Achieving the simplest rule base and competitive generalization quality is verified by cross-validation experiments with benchmark data sets. Significance:The new method of rule evaluation allows for better determination of their quality in decision support, and the selection algorithm quickly provides a clear and easy to interpret knowledge base. This rule evaluation technique will be proficient for all fuzzy rule-based classification systems.

An Exploration of e-Cigarette-Related Search Items on YouTube: Network Analysis.

Backgrounde-Cigarette use among youth is high, which may be due in part to pro–e-cigarette content on social media such as YouTube. YouTube is also a valuable resource for learning about e-cigarette use, trends, marketing, and e-cigarette user perceptions. However, there is a lack of understanding on how similar e-cigarette–related search items result in similar or relatively mutually exclusive search results. This study uses novel methods to evaluate the relationship between e-cigarette–related search items and results.ObjectiveThe aim of this study is to apply network modeling and rule-based classification to characterize the relationships between e-cigarette–related search items on YouTube and gauge the level of importance of each search item as part of an e-cigarette information network on YouTube.MethodsWe used 16 fictitious YouTube profiles to retrieve 4201 distinct videos from 18 keywords related to e-cigarettes. We used network modeling to represent the relationships between the search items. Moreover, we developed a rule-based classification approach to classify videos. We used betweenness centrality (BC) and correlations between nodes (ie, search items) to help us gain knowledge of the underlying structure of the information network.ResultsBy modeling search items and videos as a network, we observed that broad search items such as e-cig had the most connections to other search items, and specific search items such as cigalike had the least connections. Search items with similar words (eg, vape and vaping) and search items with similar meaning (eg, e-liquid and e-juice) yielded a high degree of connectedness. We also found that each node had 18 (SD 34.8) connections (common videos) on average. BC indicated that general search items such as electronic cigarette and vaping had high importance in the network (BC=0.00836). Our rule-based classification sorted videos into four categories: e-cigarette devices (34%-57%), cannabis vaping (16%-28%), e-liquid (14%-37%), and other (8%-22%).ConclusionsOur findings indicate that search items on YouTube have unique relationships that vary in strength and importance. Our methods can not only be used to successfully identify the important, overlapping, and unique e-cigarette–related search items but also help determine which search items are more likely to act as a gateway to e-cigarette–related content.

Incorporating Rule-based Pattern Recognition Approach for Document Structure Classification on Cloud-based Document Management System

Numerous documents in the form of emails, business letters, reports and transactions among others are created and received by many organizations. Thus, organizing them is a challenge. There are various ways to organize documents such as according to the header, sender, or content. However, following the rules in organizing documents might not be consistent with organizations like schools as this may vary from one person to another and is prone to human errors. The manual organization also requires a lot of time and may lead to difficulty in finding the documents’ location. Thus, this study aimed to develop an intelligent document organizing system named Docudile. It is a self-organizing system that classifies each document and seamlessly places them in the computer directory using the rule-based pattern recognition for quick and accurate locating of documents. A cloud-based document management system with storage that syncs documents from local storage to cloud server was also developed to mitigate the inaccessibility of the documents when they are accessed from a remote area. Term Frequency-Inverse Document Frequency (TF-IDF) was used to retrieve the documents. Results showed that the system yielded 98 and 89% accuracy in classifying and retrieving the documents, respectively, based on the rule-based pattern recognition. Compared with Naïve Bayes Classifier and support vector machine accuracy results, it was found that using cosine feature similarity of the rule-based pattern recognition obtained a better accuracy in classifying school-related documents. Furthermore, this study recommends that supporting documents should go with the main document during the classification.

MAPPING COASTAL AND WETLAND VEGETATION COMMUNITIES USING MULTI-TEMPORAL SENTINEL-2 DATA

Abstract. Operational monitoring of complex vegetation communities, such as the ones growing in coastal and wetland areas, can be effectively supported by satellite remote sensing, providing quantitative spatialized information on vegetation parameters, as well as on their temporal evolution. With this work, we explored and evaluated the potential of Sentinel-2 data for assessing the status and evolution of coastal vegetation as the primary indicator of ecosystem conditions, by mapping the different plant communities of Venice lagoon (Northeast Italy) via a rule-based classification approach exploiting synoptic seasonal features of spectral indices and multispectral reflectance. The results demonstrated that coastal and wetland vegetation community type maps derived for two different years scored a good overall accuracy around 80%, with some misclassification in the coastal areas and overestimation of salt marsh communities coverage, and that virtual collaborative environments can facilitate the use of Sentinel-2 data and products to multidisciplinary users.

Inundation Mapping of Kerala Flood Event in 2018 using ALOS-2 and Temporal Sentinel-1 SAR Images

In August 2018, the southern Indian state of Kerala received unusually heavy rainfall leading to largescale flooding and destruction. Reliable flood inundation maps derived from remote sensing techniques help in flood disaster management activities. The freely available Sentinel-1A/B SAR data have the potential for flood inundation mapping due to its all-weather imaging capability. In this study, temporal dual-pol Sentinel-1 SAR data have been utilized. Single-date ALOS-2/PALSAR-2 commercial SAR data were also used to fill the gap between Sentinel-1 acquisitions during the peak flood-period. Two flood-mapping approaches, viz. rule-based classification in case of temporal SAR data and histogram-based thresholding approach in case of single-date imagery, were utilized in the study. Also, flood inundation mapping with different data constraints, i.e. availability of single-date and multi-date imagery has been analysed and discussed. The obtained results were validated with multiple data sources like survey data and secondary data from government agencies. An overall accuracy of 90.6% and a critical success index of 81.6% were achieved with the proposed rule-based classification approach. This study highlights the potential of the combination of Sentinel-1 and ALOS-2/PALSAR-2 data for flood inundation mapping.

Risk Assessment Model for Pluvial Flood Prediction Using Fuzzy-Based Classification Technique

Both developed and developing countries are promoting risk management and refining the ability to alleviate the effects of disaster both man-made and natural, which have become a threat to human life and the world’s economy. The variability in climate change, rapid urbanization and fast-growing socio-economic development has naturally increased the risk associated with flooding. A recent report showed that flood have affected more individuals than any other category of disaster in the 21st century with the highest percentage of 43% of all disaster events in 2019 and Africa been the second vulnerable continent after Asia. So, it is highly important to devise a scientific method for flood risk reduction since it cannot be eradicated. Machine learning can improve the risk management. The paper proposes a pluvial flood detection and prediction system based on machine learning techniques. The proposed model will employ a fuzzy rule-based classification approach for pluvial flood risk assessment. Keywords : Machine Learning, Pluvial Flood, Risk, Fuzzy Rule-Based, Prediction DOI: 10.7176/CEIS/12-1-07 Publication date: January 31 st 2021

Design and implementation of a Luganda text normalization module for a speech synthesis software program

This paper describes a Luganda text normalization module, a crucial component needed for a Luganda Text to Speech system. We describe the use of a rule-based approach for detection, classification and verbalization of Luganda text. At the core of this module are the Luganda grammar rules that were hand-built to normalize Non-Standard Words (NSWs) from different semiotic and noun classes. Input text is first analyzed, matched against handcrafted patterns developed using regular expressions to detect any NSWs. Upon detection, NSWs are tokenized and classified into one of the semiotic classes and then if necessary, into one of the Luganda noun classes. These are subsequently verbalized, each according to its semiotic as well as noun class, and a new text file is produced. We tested the module with 7 datasets and achieved average detection and normalization rates of 82% and 77.7% respectively.

Foreign Body Shape Classification Using Intuitionistic Fuzzy Rule Based Approach on Pediatric Radiography Images

Segmentation is the significant key stage in image analysis towards partitioning an image into different regions which have homogeneous features such as color, shape, and texture which is very important in classifying different region shapes in an image. In general, images are considered fuzzy due to the uncertainty present in terms of vagueness. The regions contain imprecise gray levels and uncertain data values which makes the task of defining the membership function difficult due to lack of precise knowledge. The intuitionistic fuzzy rule-based shape classification approach is used to classify the different shapes, such as circular, polygon, sharp, and irregular of the aspired foreign body on pediatric radiography images. Experimental results show the effectiveness of the proposed method in contrast to conventional fuzzy rule base algorithm.

Enhancing timeliness of drug overdose mortality surveillance: A machine learning approach.

BackgroundTimely data is key to effective public health responses to epidemics. Drug overdose deaths are identified in surveillance systems through ICD-10 codes present on death certificates. ICD-10 coding takes time, but free-text information is available on death certificates prior to ICD-10 coding. The objective of this study was to develop a machine learning method to classify free-text death certificates as drug overdoses to provide faster drug overdose mortality surveillance.MethodsUsing 2017–2018 Kentucky death certificate data, free-text fields were tokenized and features were created from these tokens using natural language processing (NLP). Word, bigram, and trigram features were created as well as features indicating the part-of-speech of each word. These features were then used to train machine learning classifiers on 2017 data. The resulting models were tested on 2018 Kentucky data and compared to a simple rule-based classification approach. Documented code for this method is available for reuse and extensions: https://github.com/pjward5656/dcnlp.ResultsThe top scoring machine learning model achieved 0.96 positive predictive value (PPV) and 0.98 sensitivity for an F-score of 0.97 in identification of fatal drug overdoses on test data. This machine learning model achieved significantly higher performance for sensitivity (p<0.001) than the rule-based approach. Additional feature engineering may improve the model’s prediction. This model can be deployed on death certificates as soon as the free-text is available, eliminating the time needed to code the death certificates.ConclusionMachine learning using natural language processing is a relatively new approach in the context of surveillance of health conditions. This method presents an accessible application of machine learning that improves the timeliness of drug overdose mortality surveillance. As such, it can be employed to inform public health responses to the drug overdose epidemic in near-real time as opposed to several weeks following events.

Sentiment Analysis and Opinion Summarization of Product Feedback

With the exponential growth of online shopping platforms, user interaction is made direct through their reviews and ratings. User’s opinions and experiences are a significant source of valuable information in decision making process. In recent days, almost every website encourages users to express and exchange their views, suggestions and opinions related to product, services, policies, etc. publicly. Opinion mining is an extensive branch of Artificial Intelligence and a form of Natural Language Processing which illustrates the attitude of the customers, in specific services or products. Also known as Sentiment Analysis, it aims at determining the response and mood or attitude of the speaker or the overall contextual and emotional polarity or reaction. Existing algorithms determine sentiment by training on datasets, lexicon-based approach by calculating polarity and rule-based approach for classification. Opinion Summarization is the process of consolidating a large amount of sentiments and opinions into a clear and brief statement for an easier grasp on the underlying context. Major summarization methods include, Extractive method, Sentence Ranking, Abstractive method and Clustering of Textual Segments. Hence it is important to judge and classify these reviews and present a laconic opinion so it would be easier for users to obtain a gist and overall polarity on the various reviews instead of going through all of them

Video genre identification using clustering-based shot detection algorithm

Rapid growth in storage technology and data acquisition has significantly increased the volume of multimedia data online. A challenging problem is to analyze that multimedia data which are in massive quantity. In recent years, indexing of video files based on contents has gained increased popularity in research community. There are also attempts at identifying if a video clip is containing a specific genre of video, e.g., an sports video, movie, drama, animation or talk show. These proposed techniques, however, use a long list of audio visual features in achieving this classification task, which obviously decreases processing efficiency. Based on certain patterns in audio visual features and basic grammar of talk show, this research differentiates multimedia contents of talk shows from rest of the video genres. Our multimodal rule-based classification approach exploits shots and scenes in a video as classification features. The contents from popular multimedia databases like DailyMotion, YouTube and movies from Hollywood and Bollywood are used as dataset to test overall system of genre identification. The system achieves precision and recall of 98% and 100%, respectively, on 600 selected videos of more than 600 h of duration to classify multimedia content as either ‘TalkShow’ or ‘OtherVideo’ category.

Detecting Travel Modes Using Rule-Based Classification System and Gaussian Process Classifier

Travel modes are generally derived from Global Positioning System (GPS) data on the basis of either a rule-based or machine learning classification method. The rule-based classification approach is generally easy to understand, whereas the machine learning classification method has better generalization. However, studies that jointly explore both methods are limited. The present research proposes a two-stage method that aims to impute travel modes from GPS trajectory data. In the first stage, rules are employed to detect subway modes. In the second stage, a Gaussian process classifier based on sequential forward selection methods is utilized to derive the remaining travel modes. On the basis of the four selected features constituting the feature set (i.e., average speed, average acceleration, heading change, and low-speed point rate), over 97% of the samples with subway modes are correctly identified and 93.04% of segments in the walk-based balanced test subset are accurately detected. Over 92% of the car and bus samples are correctly detected for the training and test datasets. Results provide a new perspective in selecting classification methods for the detection of travel modes and other travel characteristics from GPS trajectory data. Furthermore, high differentiation is achieved between the bus and car modes without the bus transit geographic information system sources of bus networks. Therefore, reasonable extracted features contribute to the detection of travel modes, particularly between bus and car modes.

Edu-ACoCM

Online learning repositories are the heart of learning content management systems. This article proposes a model that utilizes the educational content of learning repositories, to create and display multiple learning paths to the students. When a student specifies a topic to study, the model creates the learning paths in form of a tree, with student specified learning concepts as the root node and its co-existing concepts as the child nodes. The model also proposes to automatically extract three co-existing concepts: prerequisites, subsequent topics and features. Use of pattern-based mining and a rule-based classification approach is proposed for the extraction of co-existing concepts. Automatically extracted results are checked for meaningfulness and usefulness against expert generated results. Evaluation of the authors' model on various learning materials shows the appropriate generation of learning paths depicting the co-exiting concepts. The average F1 score obtained for automatic prerequisite extraction is 78%, automatic subsequent topic extraction is 83% and automatic feature extraction is 88%.

New Associative Classification Method Based on Rule Pruning for Classification of Datasets

In data mining, a rule-based classification approach called Associative Classification (AC) normally builds accurate classifiers from supervised learning data sets. It extracts “If-Then” rules and associates each of the generated rules with two computed parameters; support and confidence. These two parameters are utilized to differentiate the rules’ superiority during the building of a classifier’s step. In current AC algorithms, whenever a rule is inserted into a classifier, all of its corresponding training data is discarded. However, the discarded data actually are used to compute support and confidence of other rules and will affect other lower ranked rules since rules normally have common training data examples. The use of static support and confidence may result in very large less-accurate classifiers.Thus, a procedure that amends other rules’ support and confidence is important. This paper proposes a new procedure named Active Pruning Rules (APR) to overcome the above problem so then the classifiers’ performance - especially predictive accuracy and reducing rule redundancy - will be further improved. The experimental results obtained from a number of University of California Irvine (UCI) data sets and real adult autism classification data set showed that APR is highly competitive to other AC and rule-based classifiers and often produces smaller yet more predictive classifiers.