A blockchain-based evaluation approach to analyse customer satisfaction using AI techniques.

Due to technological advancements and consumer demands, online shopping creates new features and adapts to new standards. A robust customer satisfaction prediction model concerning trust and privacy platforms can encourage an organization to make better decisions about its service and quality. This study presented an approach to predict consumer satisfaction using the blockchain-based framework combining the Multi-Dimensional Naive Bayes-K Nearest Neighbor (MDNB-KNN) and the Multi-Objective Logistic Particle Swarm Optimization Algorithm (MOL-PSOA).

LSTM-DGWO-Based Sentiment Analysis Framework for Analyzing Online Customer Reviews.

Sentiment analysis furnishes consumer concerns regarding products, enabling product enhancement development. Existing sentiment analysis using machine learning techniques is computationally intensive and less reliable. Deep learning in sentiment analysis approaches such as long short term memory has adequately evolved, and the selection of optimal hyperparameters is a significant issue.

Identification of α-enolase as a prognostic and diagnostic precancer biomarker in oral submucous fibrosis.

Aims: Diagnostic ambiguities regarding the malignant potentiality of oral submucous fibrosis (OSF), an oral precancerous condition having dysplastic and non-dysplastic isoforms are the major failure for early intervention of oral squamous cell carcinoma (OSCC) patients. Our goal is to identify proteomic signatures from biopsies that can be used as precancer diagnostic marker for patient suffering from OSF.

Methods: The high throughput techniques adopting de novo peptide sequencing (1D SDS-PAGE coupled nanoLC MALDI tandem mass spectrometry (MS/MS)-based peptide mass fingerprint), immunohistochemistry (IHC), Western blot (WB) and real-time PCR (RT-PCR) analysis are considered for such biomarker identification and multilevel validations.

Computer-aided molecular pathology interpretation in exploring prospective markers for oral submucous fibrosis progression.

Background: Evaluation of molecular pathology markers using a computer-aided quantitative assessment framework would help to assess the altered states of cellular proliferation, hypoxia, and neoangiogenesis in oral submucous fibrosis and could improve diagnostic interpretation in gauging its malignant potentiality.

Methods: Immunohistochemical (IHC) expression of c-Myc, hypoxia-inducible factor-1-alpha (HIF-1α), vascular endothelial growth factor (VEGF), VEGFRII, and CD105 were evaluated in 58 biopsies of oral submucous fibrosis using computer-aided quantification. After digital stain separation of original chromogenic IHC images, quantification of the diaminobenzidine (DAB) reaction pattern was performed based on intensity and extent of cytoplasmic, nuclear, and stromal expression.

Correlated analysis of semi-quantitative immunohistochemical features of E-cadherin, VEGF and CD105 in assessing malignant potentiality of oral submucous fibrosis.

Oral submucous fibrosis, a potentially premalignant condition for oral squamous cell carcinoma, manifests both non-dysplastic and dysplastic grades. Early and specific identification of its malignant potentiality suffers from diagnostic limitations that may be addressed by correlated molecular pathology attributes having histopathological backdrop. Present study correlates expressional alteration in prime epithelial marker E-cadherin, with neo-angiogenic molecules viz.

Computational analysis of p63(+) nuclei distribution pattern by graph theoretic approach in an oral pre-cancer (sub-mucous fibrosis).

Background: Oral submucous fibrosis (OSF) is a pre-cancerous condition with features of chronic, inflammatory and progressive sub-epithelial fibrotic disorder of the buccal mucosa. In this study, malignant potentiality of OSF has been assessed by quantification of immunohistochemical expression of epithelial prime regulator-p63 molecule in correlation to its malignant (oral squamous cell carcinoma [OSCC] and normal counterpart [normal oral mucosa [NOM]). Attributes of spatial extent and distribution of p63(+) expression in the epithelium have been investigated.

Development of hedge operator based fuzzy divergence measure and its application in segmentation of chronic myelogenous leukocytes from microscopic image of peripheral blood smear.

This paper introduces a hedge operator based fuzzy divergence measure and its application in segmentation of leukocytes in case of chronic myelogenous leukemia using light microscopic images of peripheral blood smears. The concept of modified discrimination measure is applied to develop the measure of divergence based on Shannon exponential entropy and Yager's measure of entropy. These two measures of divergence are compared with the existing literatures and validated by ground truth images.

Joint learning of ultrasonic backscattering statistical physics and signal confidence primal for characterizing atherosclerotic plaques using intravascular ultrasound.

Intravascular Ultrasound (IVUS) is a predominant imaging modality in interventional cardiology. It provides real-time cross-sectional images of arteries and assists clinicians to infer about atherosclerotic plaques composition. These plaques are heterogeneous in nature and constitute fibrous tissue, lipid deposits and calcifications.

Hunting for necrosis in the shadows of intravascular ultrasound.

Coronary artery disease leads to failure of coronary circulation secondary to accumulation of atherosclerotic plaques. In adjunction to primary imaging of such vascular plaques using coronary angiography or alternatively magnetic resonance imaging, intravascular ultrasound (IVUS) is used predominantly for diagnosis and reporting of their vulnerability. In addition to plaque burden estimation, necrosis detection is an important aspect in reporting of IVUS.

Epithelio-mesenchymal transitional attributes in oral sub-mucous fibrosis.

Evaluating molecular attributes in association with its epithelial and sub-epithelial changes of oral sub-mucous fibrosis is meaningful in exploring the plausibility of an epithelio-mesenchymal transition (EMT) and malignant potentiality of this pathosis. In this study histopathological and histochemical attributes for basement membrane and connective tissue in biopsies of oral sub-mucous fibrosis (n = 55) and normal oral mucosa (n = 16) were assessed and expressions of p63, E-cadherin, β-catenin, N-cadherin and TWIST were analyzed immunohistochemically. The p63 and its isoforms (TA and ∆N), PARD3, E-cadherin and β-catenin were also assessed transcriptomically by q-PCR and EMT players like TWIST1, ZEB1, MMP9 and micro-RNA 205 were searched in gene expression microarrays.

Assessment of molecular events during in vitro re-epithelialization under honey-alginate matrix ambience.

Re-epithelialization is one of the most important stages of cutaneous regeneration and its success requires supportive micro-ambience which may be provided with suitable bio-matrix. Biocompatibility and efficacy of such bio-matrix in re-epithelialization could be explored by multimodal analysis of structural and functional attributes of in vitro wound healing model including evaluation of prime molecular expressions of the epithelial cells during repair. Present study examines the influence of honey-alginate and alginate matrices on re-epithelialization in keratinocyte (HaCaT) population in a 2-D wound model.

Finding an optimum immuno-histochemical feature set to distinguish benign phyllodes from fibroadenoma.

Purpose: Benign phyllodes and fibroadenoma are two well-known breast tumors with remarkable diagnostic ambiguity. The present study is aimed at determining an optimum set of immuno-histochemical features to distinguish them by analyzing important observations on expressions of important genes in fibro-glandular tissue.

Methods: Immuno-histochemically, the expressions of p63 and α-SMA in myoepithelial cells and collagen I, III and CD105 in stroma of tumors and their normal counterpart were studied.

Breast fine needle aspiration cytology practices and commonly perceived diagnostic significance of cytological features: A pan- India survey.

Background: Methodical and meticulous understanding of clinico-pathological procedures and decision making process of cancer diagnosis and identification of aspects that are well-suited for computer-aided analysis are first steps toward development of assistive computational tool for analysis of breast fine needle aspiration cytology (FNAC) slides.

Aims: To identify variables in practice of FNAC as used for diagnosis of breast lesions and commonly perceived diagnostic significance of cytological features for diagnosis of benign or malignant condition of breast lesions.

Materials And Methods: An India-wide questionnaire-based survey of cytopathologists/pathologists' breast FNAC reporting practices and their opinion on diagnostic significance of cytological features in diagnosis of benign or malignant nature of breast lesion were conducted.

Application of higher order cumulants to ECG signals for the cardiac health diagnosis.

Electrocardiogram (ECG) is the P-QRS-T wave which indicates the electrical activity of the heart. The subtle changes in the amplitude and duration of the ECG signal depict the cardiac abnormality. It is very difficult to decipher these minute changes by the naked eye.

Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm.

Oral cancer (OC) is the sixth most common cancer in the world. In India it is the most common malignant neoplasm. Histopathological images have widely been used in the differential diagnosis of normal, oral precancerous (oral sub-mucous fibrosis (OSF)) and cancer lesions.

Brownian motion curve-based textural classification and its application in cancer diagnosis.

Objective: To develop an automated diagnostic methodology based on textural features of the oral mucosal epithelium to discriminate normal and oral submucous fibrosis (OSF).

Study Design: A total of 83 normal and 29 OSF images from histopathologic sections of the oral mucosa are considered. The proposed diagnostic mechanism consists of two parts: feature extraction using Brownian motion curve (BMC) and design ofa suitable classifier.

ApoTome to visualize E-cadherin and p63 expression in oral pre-cancer.

Precise histopathological localization of E-cadherin and p63 is of immense importance in understanding the integrity of oral mucosal stratified epithelium in normal and diseased conditions. Necessarily immunohistochemical imaging should have minimum bleaching impact on the dyes and ability to produce clear and crisp images. Here ApoTome provides an alternative with metal halide light source and structured illumination under the assistance of grids, along with integrated image processing modality to generate crisp images with digital interface.

Textural characterization of histopathological images for oral sub-mucous fibrosis detection.

In the field of quantitative microscopy, textural information plays a significant role very often in tissue characterization and diagnosis, in addition to morphology and intensity. The aim of this work is to improve the classification accuracy based on textural features for the development of a computer assisted screening of oral sub-mucous fibrosis (OSF). In fact, a systematic approach is introduced in order to grade the histopathological tissue sections into normal, OSF without dysplasia and OSF with dysplasia, which would help the oral onco-pathologists to screen the subjects rapidly.

Texture based segmentation of epithelial layer from oral histological images.

The objective of this paper is to provide a texture based segmentation algorithm for better delineation of the epithelial layer from histological images in discriminating normal and oral sub-mucous fibrosis (OSF). As per literature and oral clinicians, it is established that the OSF initially originates and propagates in the epithelial layer. So, more accurate segmentation of this layer is extremely important for a clinician to make a diagnostic decision.

Development of cardiac prescreening device for rural population using ultralow-power embedded system.

The invention is inspired by the desire to understand the opportunities and expectations of developing economies in terms of healthcare. The designed system is a point-of-care (POC) device that can deliver heart-care services to the rural population and bridge the rural-urban divide in healthcare delivery. The product design incorporates several innovations including the effective use of adaptive and multiresolution signal-processing techniques for acquisition, denoising, segmentation, and characterization of the heart sounds (HS) and murmurs using an ultralow-power embedded Mixed Signal Processor.

Computer vision approach to morphometric feature analysis of basal cell nuclei for evaluating malignant potentiality of oral submucous fibrosis.

This research work presents a quantitative approach for analysis of histomorphometric features of the basal cell nuclei in respect to their size, shape and intensity of staining, from surface epithelium of Oral Submucous Fibrosis showing dysplasia (OSFD) to that of the Normal Oral Mucosa (NOM). For all biological activity, the basal cells of the surface epithelium form the proliferative compartment and therefore their morphometric changes will spell the intricate biological behavior pertaining to normal cellular functions as well as in premalignant and malignant status. In view of this, the changes in shape, size and intensity of staining of the nuclei in the basal cell layer of the NOM and OSFD have been studied.

Assessment of malignant potential of oral submucous fibrosis through evaluation of p63, E-cadherin and CD105 expression.

Background: The assessment of malignant potential of oral submucous fibrosis grades vis-à-vis their progression towards malignancy is associated with expression of possible multiple molecular markers.

Aims: To analyse p63, E-cadherin and CD105 expression in this premalignant pathosis with a view to unravel and understand the expression of these molecules as markers.

Methods: The oral mucosal biopsies (normal, oral submucous fibrosis with and without dysplasia) were studied with routine H&E, and by immunohistochemistry for p63, E-cadherin and CD105 expression.

Automated screening of arrhythmia using wavelet based machine learning techniques.

Arrhythmia is one of the preventive cardiac problems frequently occurs all over the globe. In order to screen such disease at early stage, this work attempts to develop a system approach based on registration, feature extraction using discrete wavelet transform (DWT), feature validation and classification of electrocardiogram (ECG). This diagnostic issue is set as a two-class pattern classification problem (normal sinus rhythm versus arrhythmia) where MIT-BIH database is considered for training, testing and clinical validation.

Statistical analysis of textural features for improved classification of oral histopathological images.

The objective of this paper is to provide an improved technique, which can assist oncopathologists in correct screening of oral precancerous conditions specially oral submucous fibrosis (OSF) with significant accuracy on the basis of collagen fibres in the sub-epithelial connective tissue. The proposed scheme is composed of collagen fibres segmentation, its textural feature extraction and selection, screening perfomance enhancement under Gaussian transformation and finally classification. In this study, collagen fibres are segmented on R,G,B color channels using back-probagation neural network from 60 normal and 59 OSF histological images followed by histogram specification for reducing the stain intensity variation.

Automated leukocyte recognition using fuzzy divergence.

This paper aims at introducing an automated approach to leukocyte recognition using fuzzy divergence and modified thresholding techniques. The recognition is done through the segmentation of nuclei where Gamma, Gaussian and Cauchy type of fuzzy membership functions are studied for the image pixels. It is in fact found that Cauchy leads better segmentation as compared to others.