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Software maintenance is an imperative step in software development. Code smells can arise as a result of poor design as well as frequent code changes due to changing needs. Early detection of code smells during software development can help with software maintenance. This work focuses on identifying code smells on Java software using nanopatterns. Nanopatterns are method-level code structures that reflect the presence of code smells. Nanopatterns are extracted using a command-line interface based on the ASM bytecode analysis. Class labels are extracted using three tools, namely inFusion, JDeodorant, and iPlasma. Rules are extracted from nanopatterns using the Apriori algorithm and mapped with the extracted class labels. Best rules are selected using the Border Collie Optimization (BCO) algorithm with the accuracy of the k-NN classifier as the fitness function. The selected rules are stored in the rule base to detect code smells. The objective is to detect a maximum number of code smells with a minimum number of rules. Experiments are carried out on Java software, namely jEdit, Nutch, Lucene, and Rhino. The proposed work detects code smells, namely data class, blob, spaghetti code, functional decomposition, and feature envy, with 98.78% accuracy for jEdit, 97.45% for Nutch, 95.58% for Lucene, and 96.34% for Rhino. The performance of the proposed work is competitive with other well-known methods of detecting code smells.

Due to the COVID-19 pandemic, countries across the globe has enforced lockdown restrictions that influence the people's socio-economic lifecycle. The objective of this paper is to predict the communal emotion of people from different locations during the COVID-19 lockdown. The proposed work aims in developing a deep spatio-temporal analysis framework of geo-tagged tweets to predict the emotions of different topics based on location. An optimized Latent Dirichlet Allocation (LDA) approach is presented for finding the optimal hyper-parameters using grid search. A multi-class emotion classification model is then built via a Recurrent Neural Network (RNN) to predict emotions for each topic based on locations. The proposed work is experimented with the twitter streaming API dataset. The experimental results prove that the presented LDA model-using grid search along with the RNN model for emotion classification outperforms the other state of art methods with an improved accuracy of 94.6%. © 2021 - IOS Press. All rights reserved.

A framework for Computer-Aided Diagnosis (CAD) to diagnose Cavitary TB and Miliary TB from chest Computed Tomography (CT) slices has been designed and implemented. The lung tissues from the CT slic...

A computer-aided diagnosis (CAD) system that employs a super learner to diagnose the presence or absence of a disease has been developed. Each clinical dataset is preprocessed and split into training set (60%) and testing set (40%). A wrapper approach that uses three bioinspired algorithms, namely, cat swarm optimization (CSO), krill herd (KH) ,and bacterial foraging optimization (BFO) with the classification accuracy of support vector machine (SVM) as the fitness function has been used for feature selection. The selected features of each bioinspired algorithm are stored in three separate databases. The features selected by each bioinspired algorithm are used to train three back propagation neural networks (BPNN) independently using the conjugate gradient algorithm (CGA). Classifier testing is performed by using the testing set on each trained classifier, and the diagnostic results obtained are used to evaluate the performance of each classifier. The classification results obtained for each instance of the testing set of the three classifiers and the class label associated with each instance of the testing set will be the candidate instances for training and testing the super learner. The training set comprises of 80% of the instances, and the testing set comprises of 20% of the instances. Experimentation has been carried out using seven clinical datasets from the University of California Irvine (UCI) machine learning repository. The super learner has achieved a classification accuracy of 96.83% for Wisconsin diagnostic breast cancer dataset (WDBC), 86.36% for Statlog heart disease dataset (SHD), 94.74% for hepatocellular carcinoma dataset (HCC), 90.48% for hepatitis dataset (HD), 81.82% for vertebral column dataset (VCD), 84% for Cleveland heart disease dataset (CHD), and 70% for Indian liver patient dataset (ILP).

Clinical time series data contain large set of time-stamped data points that describe the patient’s health. The observations of these data points are done at irregular intervals and hence knowledge...

A novel classification framework for clinical decision making that uses an Extremely Randomized Tree (ERT) based feature selection and a Diverse Intensified Strawberry Optimized Neural network (DISON) is proposed. DISON is a Feed Forward Artificial Neural Network where the optimization of weights and bias is done using a two phase training strategy. Two algorithms namely Strawberry Plant Optimization (SPO) algorithm and Gradient-descent Back-propagation algorithm are used sequentially to identify the optimum weights and bias. The novel two phase training method and the stochastic duplicate-elimination strategy of SPO helps in addressing the issue of local optima associated with conventional neural networks. The relevant attributes are selected based on the feature importance values computed using an ERT classifier. Vertebral Column, Pima Indian diabetes (PID), Cleveland Heart disease (CHD) and Statlog Heart disease (SHD) datasets from the University of California Irvine machine learning repository are used for experimentation. The framework has achieved an accuracy of 87.17% for Vertebral Column, 90.92% for PID, 93.67% for CHD and 94.5% for SHD. The classifier performance has been compared with existing works and is found to be competitive in terms of accuracy, sensitivity and specificity. Wilcoxon test confirms the statistical superiority of the proposed method.

Co-operative co-evolution approach solves problems by breaking them into subproblems. The proposed framework for Clinical Decision Support System (CDSS) uses a co-operative coevolution approach whi...

Pulmonary emphysema is a condition characterized by the destruction and permanent enlargement of the alveoli of the lungs. The destruction of gas-exchanging alveoli causes shortness of breath followed by a chronic cough and sputum production. A Computer-Aided Diagnosis (CAD) framework for diagnosing pulmonary emphysema from chest Computed Tomography (CT) slices has been designed and implemented in this study. The process of implementing the CAD framework includes segmenting the lung tissues and extracting the regions of interest (ROIs) using the Spatial Intuitionistic Fuzzy C-Means clustering algorithm. The ROIs that were considered in this work were emphysematous lesions - namely, centrilobular, paraseptal, and bullae that were labelled by an expert radiologist. The shape, texture, and run-length features were extracted from each ROI. A wrapper approach that employed four bio-inspired algorithms - namely, Moth-Flame Optimization (MFO), Firefly Optimization (FFO), Artificial Bee Colony Optimization, and Ant Colony Optimization - with the accuracy of the support vector machine classifier as the fitness function was used to select the optimal feature subset. The selected features of each bio-inspired algorithm were trained independently using the Extreme Learning Machine classifier based on the tenfold cross-validation technique. The framework was tested on real-time and public emphysema datasets to perform binary classification of lung CT slices of patients with and without the presence of emphysema. The framework that used MFO and FFO for feature selection produced superior results regarding accuracy, precision, recall, and specificity for the real-time dataset and the public dataset, respectively, when compared to the other bio-inspired algorithms.

In this article, a classification framework that uses quantum-behaved particle swarm optimization neural network (QPSONN) classifiers for diagnosing a disease is discussed. The neural network used for classification is radial basis function neural network (RBFNN). For training the RBFNN K-means clustering algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm has been used. The K-means clustering algorithm is used to find the optimal number of clusters which determines the number of neurons in the hidden layer. The cluster approximation error is used to find the optimal clusters. The weights between the hidden and the output layer is determined using QPSO algorithm based on the mean squared error (MSE). The performance of the developed classifier model has been tested with five clinical datasets, namely Pima Indian Diabetes, Hepatitis, Bupa Liver Disease, Wisconsin Breast Cancer and Cleveland Heart Disease were obtained from the University of California, Irvine (UCI) machine learning repository.

A Computer-Aided Diagnosis (CAD) system to assist a radiologist for diagnosing pulmonary emphysema from chest Computed Tomography (CT) slices has been developed. The lung tissues are segmented from the chest CT slices using Spatial Fuzzy C-Means (SFCM) clustering algorithm and the Regions of Interest (ROIs) are extracted using pixel-based segmentation. The ROIs considered for this work are pulmonary emphysematous lesions namely, centrilobular emphysema, paraseptal emphysema and sub-pleural bullae. The extracted ROIs are then validated and labelled by an expert radiologist. From each ROI, features with respect to shape, texture and run-length are extracted. A competitive coevolution model is proposed for Feature Selection (FS). The model makes use of two bio-inspired algorithms namely, Spider Monkey Optimization (SMO) algorithm and Paddy Field Algorithm (PFA) as its building blocks. FS is performed as a wrapper approach, using the bio-inspired algorithms namely, SMO and PFA with the accuracy of the Support Vector Machine (SVM) classifier as the fitness function. Ten-fold cross validation technique is used in training the SVM classifier using the selected features. The model is tested using two datasets: Real-time emphysema dataset and CT emphysema database (CTED) dataset. The accuracy, precision, recall and specificity obtained for both the datasets are (81.95%, 93.74%), (72.92%, 90.61%), (72.92%, 90.61%), (86.46%, 95.3%), which are better compared to the performance of SMO and PFA algorithms applied individually for FS and the CAD system without performing FS.

Background: Computer-aided diagnosis (CAD) systems for the detection of lung disorders play an important role in clinical decision making. CAD systems provide a second opinion to the physician in interpreting computed tomography (CT) images. In this work, a CAD system to diagnose pulmonary hamartoma nodules from chest CT images is proposed. Methods: Segmentation of lung parenchyma from CT images is carried out using Otsu’s thresholding method. Nodules are considered to be the region of interests (ROIs) in this work. Texture, shape and run length based features are extracted from the ROIs. Cosine similarity measure (CSM) and rough dependency measure (RDM) are used independently as filter evaluation functions with ant colony optimization (ACO) to select two subsets of features. The selected subsets are used to train two classifiers namely support vector machine (SVM) and Naive Bayes (NB) classifiers using 10-fold cross validation. All the four trained classifiers are tested and the performance measures are estimated. Results: CT slices of patients affected with pulmonary cancer and hamartoma are used for experimentation. From the lung parenchymal tissues of 300 CT slices, 390 nodules are extracted. The feature selection algorithms, ACO-CSM and ACO-RDM are run for different feature subset sizes. The selected features are used to train SVM and NB classifiers. From the results obtained, it is inferred that SVM classifier with the feature subsets chosen by ACO-RDM feature selection approach yielded a maximum classification accuracy of 94.36% with 38 features. Conclusion: From the results, it can be clearly inferred that selecting relevant features to train the classifier has a definite impact on the performance of the classifier. Keywords: Computer aided diagnosis, Pulmonary hamartoma, Ant colony optimization, Cosine similarity, Rough dependency, Support vector machine

Software maintenance is an essential part of any software that finds its use in the day-to-day activities of any organization. During the maintenance phase bugs detected must be corrected and the software must evolve with respect to changing requirements without ripple effects. Software maintenance is a cumbersome process if code smells exist in the software. The impact of poor design is code smells. In code smells detection, majority of the existing approaches are rule based, where a rule represents the combination of metrics and threshold. In rule based approach, defining the rules that detect the code smells are time consuming because identifying the correct threshold value is a tedious task, which can be fixed only through trial and error method. To address this issue, in this work Euclidean distance based Genetic Algorithm and Particle Swarm Optimization (EGAPSO) is used. Instead of relying on threshold value, this approach detects all code smells based on similarity between the system under study and the set of defect examples, where the former is the initial model and the latter is the base example. The approach is tested on the open source projects, namely Gantt Project and Log4j for identifying the five code smells namely Blob, Functional Decomposition, Spaghetti Code, Data Class and Feature Envy. Finally, the approach is compared with code smell detection using Genetic Algorithm (GA), DEtection and CORrection (DECOR), Parallel Evolutionary Algorithm (PEA) and Multi-Objective Genetic Programming (MOGP). The result of EGAPSO proves to be effective when compared to other code smell detection approaches. Keywords: Software maintenance, Code smell, Software metrics, Search based software engineering, Euclidean distance, Open source software

This article explains the selection of important parameters from an execution pattern which brings out the details of the application of test cases. Hence, execution profiles are captured and a new execution profile-based clustering approach is chosen for test case selection, which uses three new features. These are Function frequency, Branches taken and Block percentage. The test cases are clustered using the extracted features. The experiments show that the proposed FBB selects smaller size of more relevant test cases which are more fault revealing compared to the existing Function Call Profile approach.

In this article, a classification framework that uses quantum-behaved particle swarm optimization neural network (QPSONN) classifiers for diagnosing a disease is discussed. The neural network used for classification is radial basis function neural network (RBFNN). For training the RBFNN K-means clustering algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm has been used. The K-means clustering algorithm is used to find the optimal number of clusters which determines the number of neurons in the hidden layer. The cluster approximation error is used to find the optimal clusters. The weights between the hidden and the output layer is determined using QPSO algorithm based on the mean squared error (MSE). The performance of the developed classifier model has been tested with five clinical datasets, namely Pima Indian Diabetes, Hepatitis, Bupa Liver Disease, Wisconsin Breast Cancer and Cleveland Heart Disease were obtained from the University of California, Irvine (UCI) machine learning repository.

In wireless sensor networks (WSN), the nodes are used to collect and gather the data from different environments. Hence, the network consumes more energy which is the main and challenging issue in WSNs. Since the sensor is operating under battery, recharging is impossible and hence the lifetime of each sensor is an important issue. Therefore, it is necessary to introduce new and efficient techniques to extend the network lifetime. In this paper, a new delay constrained energy efficient routing technique is proposed for performing effective routing in WSNs. This approach introduces a delay constraint based reliable routing approach which reduces the energy consumption by constructing efficient clusters without increasing the end-to-end delay. Moreover, the proposed technique called the rule based clustering for routing model provides better performance in terms of network lifetime than the other existing techniques since they consume more energy during the formation of clusters and finding the shortest path. Moreover, additional overhead on the cluster head selection is tackled also using rules in this proposed model in an efficient manner by building balanced clusters. The main advantage of the proposed approach is that it extends the lifetime of the network and increases the throughput, energy efficiency, link quality and scalability. The experimental verification of this technique has been carried out using MATLAB simulations and proved that this model increases the packet delivery rate, network performance and reduces the delay and energy consumption.

Discretization is one of the commonly used data preprocessing technique to improve the efficiency of the knowledge extraction process on clinical data. Generally, clinical data contains numeric attributes with continuous values. Data discretization simplifies the original data by transforming continuous data attribute values into a finite set of intervals. Although discretization is capable of handling continuous attributes on clinical data, there are cases where discretization is not an appropriate technique for handling continuous attributes. There are instances where attribute values are vague, imprecise and have multiple distributions with different classes, which challenges the process of mining in clinical data. Hence, there is a need for fuzzy discretization to pre-process the clinical data before mining. The aim of this study is to derive fuzzy discretization from crisp-interval discretization using geometric approach for constructing fuzzy sets, where overlapping region between the fuzzy sets is represented as geometric area. This study comprises of three steps: First, non-overlapping fuzzy sets are constructed using intervals generated from crisp-interval discretization. Second, area of overlapping between the fuzzy sets is computed based on the geometric approach and an average area of overlapping is estimated. Third, fuzzy sets are redesigned based on the estimated average area of overlapping. Fuzzy discretizations for three, five and seven intervals have been examined using Pima Indian Diabetes dataset (PID) and Bupa Liver Disorder dataset (BLD) taken from the University of California Irvine machine learning repository. The variation in performance of crisp and fuzzy discretization methods is measured using six classification approaches namely, tree based approach, probabilistic induction based approach, rule-based approach, network learning approach, kernel-based approach and distance-based approach and a rule-based fuzzy inference system. The results show that the classification accuracy remains stable with less deviation across different classifiers with varying intervals.

A framework for clinical diagnosis which uses bioinspired algorithms for feature selection and gradient descendant backpropagation neural network for classification has been designed and implemented. The clinical data are subjected to data preprocessing, feature selection, and classification. Hot deck imputation has been used for handling missing values and min-max normalization is used for data transformation. Wrapper approach that employs bioinspired algorithms, namely, Differential Evolution, Lion Optimization, and Glowworm Swarm Optimization with accuracy of AdaBoostSVM classifier as fitness function has been used for feature selection. Each bioinspired algorithm selects a subset of features yielding three feature subsets. Correlation-based ensemble feature selection is performed to select the optimal features from the three feature subsets. The optimal features selected through correlation-based ensemble feature selection are used to train a gradient descendant backpropagation neural network. Ten-fold cross-validation technique has been used to train and test the performance of the classifier. Hepatitis dataset and Wisconsin Diagnostic Breast Cancer (WDBC) dataset from University of California Irvine (UCI) Machine Learning repository have been used to evaluate the classification accuracy. An accuracy of 98.47% is obtained for Wisconsin Diagnostic Breast Cancer dataset, and 95.51% is obtained for Hepatitis dataset. The proposed framework can be tailored to develop clinical decision-making systems for any health disorders to assist physicians in clinical diagnosis.

Display Omitted A CAD system for disease diagnosis using Differential Evolution with Global Information (DEGI) based Back Propagation (BP) is proposed.The CAD system is trained and tested using six benchmark dataset.DEGI overcomes the premature convergence due to stagnation.The CAD system can be used by junior clinicians for medical decision support. A Computer-Aided Diagnostic (CAD) system that uses Artificial Neural Network (ANN) trained by drawing in the relative advantages of Differential Evolution (DE), Particle Swarm Optimization (PSO) and gradient descent based backpropagation (BP) for classifying clinical datasets is proposed. The DE algorithm with a modified best mutation operation is used to enhance the search exploration of PSO. The ANN is trained using PSO and the global best value obtained is used as a seed by the BP. Local search is performed using BP, in which the weights of the Neural Network (NN) are adjusted to obtain an optimal set of NN weights. Three benchmark clinical datasets namely, Pima Indian Diabetes, Wisconsin Breast Cancer and Cleveland Heart Disease, obtained from the University of California Irvine (UCI) machine learning repository have been used. The performance of the trained neural network classifier proposed in this work is compared with the existing gradient descent backpropagation, differential evolution with backpropagation and particle swarm optimization with gradient descent backpropagation algorithms. The experimental results show that DEGI-BP provides 85.71% accuracy for diabetes, 98.52% for breast cancer and 86.66% for heart disease datasets. This CAD system can be used by junior clinicians as an aid for medical decision support.

Background/Objectives: This paper presents a hybrid framework for classification of leukemia gene expression data. The framework used in this work consists of three subsystems, namely, class based dimension reduction subsystem, feature selection subsystem and classification subsystem. Methods/Statistical Analysis: This work uses class based dimension reduction techniques by employing PCA and Canonical Correlation Analysis (CCA) to the leukemia gene expression dataset. Acute Lymphoblastic Leukemia (ALL) class is subjected to Principal Component Analysis (PCA) and Acute Myeloid Leukemia (AML) class to CCA thus obtaining dimension reduced data. The feature selection subsystem uses Genetic Algorithm (GA) to select an optimal subset of informative genes. The classification subsystem utilizes these informative genes to train the NN and the classifier is obtained. Findings: The performance of the hybrid framework, GA-PCA and CCA, is analyzed and compared with that of single dimension reduction techniques, namely, GA-PCA and GA-CCA. The experimental results show that the proposed framework achieved accuracy of 88.23%. The sensitivity of the system is 85% and specificity of the system is 92.85%. This aids in determining the informative genes that are relevant to leukemia gene expression data. Applications/Improvements: The classification accuracy of GA-PCA and CCA has shown improvement when compared to that of single dimension reduction technique. Hence, combining more than one method yields higher classification accuracy and aids in identification of new classes.

Background and objectives Rule-based classification is a typical data mining task that is being used in several medical diagnosis and decision support systems. The rules stored in the rule base have an impact on classification efficiency. Rule sets that are extracted with data mining tools and techniques are optimized using heuristic or meta-heuristic approaches in order to improve the quality of the rule base. In this work, a meta-heuristic approach called Wind-driven Swarm Optimization (WSO) is used. The uniqueness of this work lies in the biological inspiration that underlies the algorithm. Methods WSO uses Jval, a new metric, to evaluate the efficiency of a rule-based classifier. Rules are extracted from decision trees. WSO is used to obtain different permutations and combinations of rules whereby the optimal ruleset that satisfies the requirement of the developer is used for predicting the test data. The performance of various extensions of decision trees, namely, RIPPER, PART, FURIA and Decision Tables are analyzed. The efficiency of WSO is also compared with the traditional Particle Swarm Optimization. Results Experiments were carried out with six benchmark medical datasets. The traditional C4.5 algorithm yields 62.89% accuracy with 43 rules for liver disorders dataset where as WSO yields 64.60% with 19 rules. For Heart disease dataset, C4.5 is 68.64% accurate with 98 rules where as WSO is 77.8% accurate with 34 rules. The normalized standard deviation for accuracy of PSO and WSO are 0.5921 and 0.5846 respectively. Conclusion WSO provides accurate and concise rulesets. PSO yields results similar to that of WSO but the novelty of WSO lies in its biological motivation and it is customization for rule base optimization. The trade-off between the prediction accuracy and the size of the rule base is optimized during the design and development of rule-based clinical decision support system. The efficiency of a decision support system relies on the content of the rule base and classification accuracy.

Cu–(5–20%) W composite preforms, with a density of 94% were prepared through mechanical milling, mixing, compaction, sintering and hot extrusion. The X-ray Diffraction analysis, Particle Size analysis, Transmission Electron Microscope, Scanning Electron Microscope and Energy Dispersive Spectrum were used for the characterization studies. The pore size during different sintering atmospheres and the pore size reduction during extrusion, were studied through Auto CAD 2010 software. The wear experiments were conducted using the pin-on-disc wear tester. The various regions in the wear mechanisms were identified through the wear distribution map. The Radial Basis Functional Neural Network has been used in an attempt to predict the mechanical and tribological behavior of composites, and useful conclusions have been made.

Wireless sensor network is a collection of distributed tiny sensor nodes, which sense the natural phenomenon and transmits the sensed information to the base station using multi-hop communication. Most of the existing systems use the routing techniques which consume lot of energy during data communication. Hence, the energy of sensor nodes depletes and results in decrease its lifetime. To overcome the above limitation in this paper we propose a new routing technique called agent based fuzzy swarm optimization (FSO) routing technique which provides dynamic clustering and effective optimized routing. Because of this technique the energy of sensor nodes has been conserved and used effectively which ultimately increases the lifetime of the network. The proposed system has been implemented by using MATLAB and for correctness purpose the results are taken after repeated measurement.