Context Based Classification of Reviews Using Association Rule Mining, Fuzzy Logics and Ontology

The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules

Context Based Classification of Reviews Using Association Rule Mining, Fuzzy Logics and Ontology

The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules

Forward genetic screen using a gene-breaking trap approach identifies a novel role of grin2bb-associated RNA transcript (grin2bbART) in zebrafish heart function

LncRNA-based control affects cardiac pathophysiologies like myocardial infarction, coronary artery disease, hypertrophy, and myotonic muscular dystrophy. This study used a gene-break transposon (GBT) to screen zebrafish (Danio rerio) for insertional mutagenesis. We identified three insertional mutants where the GBT captured a cardiac gene. One of the adult viable GBT mutants had bradycardia (heart arrhythmia) and enlarged cardiac chambers or hypertrophy; we named it “bigheart.” Bigheart mutant insertion maps to grin2bb or N-methyl D-aspartate receptor (NMDAR2B) gene intron 2 in reverse orientation. Rapid amplification of adjacent cDNA ends analysis suggested a new insertion site transcript in the intron 2 of grin2bb. Analysis of the RNA sequencing of wild-type zebrafish heart chambers revealed a possible new transcript at the insertion site. As this putative lncRNA transcript satisfies the canonical signatures, we called this transcript grin2bb associated RNA transcript (grin2bbART). Using in situ hybridization, we confirmed localized grin2bbART expression in the heart, central nervous system, and muscles in the developing embryos and wild-type adult zebrafish atrium and bulbus arteriosus. The bigheart mutant had reduced Grin2bbART expression. We showed that bigheart gene trap insertion excision reversed cardiac-specific arrhythmia and atrial hypertrophy and restored grin2bbART expression. Morpholino-mediated antisense downregulation of grin2bbART in wild-type zebrafish embryos mimicked bigheart mutants; this suggests grin2bbART is linked to bigheart. Cardiovascular tissues use Grin2bb as a calcium-permeable ion channel. Calcium imaging experiments performed on bigheart mutants indicated calcium mishandling in the heart. The bigheart cardiac transcriptome showed differential expression of calcium homeostasis, cardiac remodeling, and contraction genes. Western blot analysis highlighted Camk2d1 and Hdac1 overexpression. We propose that altered calcium activity due to disruption of grin2bbART, a putative lncRNA in bigheart, altered the Camk2d-Hdac pathway, causing heart arrhythmia and hypertrophy in zebrafish

Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function

Reliability is one of the quantifiable software quality attributes. Software Reliability Growth Models (SRGMs) are used to assess the reliability achieved at different times of testing. Traditional time-based SRGMs may not be accurate enough in all situations where test effort varies with time. To overcome this lacuna, test effort was used instead of time in SRGMs. In the past, finite test effort functions were proposed, which may not be realistic as, at infinite testing time, test effort will be infinite. Hence in this paper, we propose an infinite test effort function in conjunction with a classical Nonhomogeneous Poisson Process (NHPP) model. We use Artificial Neural Network (ANN) for training the proposed model with software failure data. Here it is possible to get a large set of weights for the same model to describe the past failure data equally well. We use machine learning approach to select the appropriate set of weights for the model which will describe both the past and the future data well. We compare the performance of the proposed model with existing model using practical software failure data sets. The proposed log-power TEF based SRGM describes all types of failure data equally well and also improves the accuracy of parameter estimation more than existing TEF and can be used for software release time determination as well

A software reliability growth model addressing learning

Goel proposed generalization of the Goel-Okumoto (G-O) software reliability growth model (SRGM), in order to model the failure intensity function, i.e. the rate of occurrence of failures (ROCOF) that initially increases and then decreases (I/D), which occurs in many projects due to the learning phenomenon of the testing team and a few other causes. The ROCOF of the generalized non-homogenous poisson process (NHPP) model can be expressed in the same mathematical form as that of a two-parameter Weibull function. However, this SRGM is susceptible to wide fluctuations in time between failures and sometimes it seems unable to recognize the I/D pattern of ROCOF present in the datasets and hence does not adequately describe such data. The authors therefore propose a shifted Weibull function ROCOF instead for the generalized NHPP model. This modification to the Goel-generalized NHPP model results in an SRGM that seems to perform better consistently, as confirmed by the goodness of fit statistic and predictive validity metrics, when applied to failure datasets of 11 software projects with widely varying characteristics. A case study on software release time determination using the proposed SRGM is also given.failure intensity function, goodness of fit statistic, mean value function, NHPP model, predictive validity, SRGM,

Performance analysis of breast cancer histopathology image classification using transfer learning models

Convolutional neural networks (CNN) which are deep learning-based methods are being currently successfully deployed and have gained much popularity in medical image analysis. CNN can handle enormous amounts of medical data which makes it possible for accurate detection and classification of breast cancer from histopathological images. In the proposed method, we have implemented transfer learning-based classification of breast cancer histopathological images using DenseNet121, DenseNet201, VGG16, VGG19, InceptionV3, and MobileNetV2 and made a performance analysis of the different models on the publicly available dataset of BreakHis. These networks were pre-trained on the ImageNet database and initialized with weights which are fine-tuned by training with input histopathological images. These models are trained with images of the BreakHis dataset with multiple image magnifications. From the comparative study of these pre-trained models on histopathology images, it is inferred that DenseNet121 achieves the highest breast cancer classification accuracy of 0.965 compared to other models and contemporary methods

Silver effect of Co–Ni composite material on energy storage and structural behavior for Li-ion batteries

Ag powder has been comparatively applied to the Co–Ni materials preparing by mixing method and the prepared electrodes were used as negative electrodes for Li-ion batteries applications. The prepared Co–Ni and Ag–Co–Ni with 10 wt.% of Ag composite electrodes are characterized by XRD, FE-SEM with EDX, impedance and electrochemical charge-discharge studies. These electrochemical studies are demonstrated at current rates of 0.1 C and 0.5 C between 0.01 and 2.0 V vs. Li/Li+. The porous Co–Ni and Ag–Co–Ni composite materials are electrochemically tested in lithium half cells. The porous Ag–Co–Ni composite material demonstrates that the initial and end of discharge capacity up to 20th cycles is, respectively, 860 and 715 mAh g−1 at 0.1 C rate maintaining at approximately 83%. The porous Ag–Co–Ni composite electrode may be a good candidate for high power lithium-ion batteries

Forward genetic screen using a gene-breaking trap approach identifies a novel role of grin2bb-associated RNA transcript (grin2bbART) in zebrafish heart function.

LncRNA-based control affects cardiac pathophysiologies like myocardial infarction, coronary artery disease, hypertrophy, and myotonic muscular dystrophy. This study used a gene-break transposon (GBT) to screen zebrafish (Danio rerio) for insertional mutagenesis. We identified three insertional mutants where the GBT captured a cardiac gene. One of the adult viable GBT mutants had bradycardia (heart arrhythmia) and enlarged cardiac chambers or hypertrophy; we named it bigheart. Bigheart mutant insertion maps to grin2bb or N-methyl D-aspartate receptor (NMDAR2B) gene intron 2 in reverse orientation. Rapid amplification of adjacent cDNA ends analysis suggested a new insertion site transcript in the intron 2 of grin2bb. Analysis of the RNA sequencing of wild-type zebrafish heart chambers revealed a possible new transcript at the insertion site. As this putative lncRNA transcript satisfies the canonical signatures, we called this transcript grin2bb associated RNA transcript (grin2bbART). Using in situ hybridization, we confirmed localized grin2bbART expression in the heart, central nervous system, and muscles in the developing embryos and wild-type adult zebrafish atrium and bulbus arteriosus. The bigheart mutant had reduced Grin2bbART expression. We showed that bigheart gene trap insertion excision reversed cardiac-specific arrhythmia and atrial hypertrophy and restored grin2bbART expression. Morpholino-mediated antisense downregulation of grin2bbART in wild-type zebrafish embryos mimicked bigheart mutants; this suggests grin2bbART is linked to bigheart. Cardiovascular tissues use Grin2bb as a calcium-permeable ion channel. Calcium imaging experiments performed on bigheart mutants indicated calcium mishandling in the heart. The bigheart cardiac transcriptome showed differential expression of calcium homeostasis, cardiac remodeling, and contraction genes. Western blot analysis highlighted Camk2d1 and Hdac1 overexpression. We propose that altered calcium activity due to disruption of grin2bbART, a putative lncRNA in bigheart, altered the Camk2d-Hdac pathway, causing heart arrhythmia and hypertrophy in zebrafish