Search

Skin cancer especially melanoma is one of the most common cancers around the world and needs early and accurate detection to improve the results for patients. Traditional methods for diagnosis have problems like being subjective and inconsistent which shows the need for better computer-based solutions. While deep learning techniques show promise in automating detection through dermoscopic image analysis existing models struggle with limited generalizability high computational demands and class imbalance in datasets. To address these limitations, this work proposes the Multi-Center Validation of Ladybug Beetle Optimized Convolutional Capsule Neural Networks with Explainable AI (LOCapsNet-XAI) for skin cancer classification using dermography images. The proposed workflow for skin cancer classification using LOCapsNet-XAI begins with the acquisition of multi-center clinical data contains diverse dermoscopic images from various healthcare institutions to create a representative training dataset. Next, image preprocessing techniques such as Anisotropic Diffusion and Kuwahara Filtering are employed to enhance image clarity by reducing noise while preserving important features like lesion boundaries. Following preprocessing, feature extraction is performed using the innovative Convolutional Capsule Neural Network (CapsNet) architecture, which effectively captures complex patterns and spatial relationships within the images. The model's parameters are then optimized using the Ladybug Beetle Optimization Algorithm (LBOA), which enhances the exploration and exploitation capabilities to improve classification performance. To foster trust in AI-assisted diagnoses, Explainable AI methodologies specifically Grad-CAM++ are integrated into the framework, that provides the clinicians with visual insights into the model's decision-making processes. Finally, the workflow culminates in the classification of skin lesions by accurately identifying them as benign or malignant and facilitating informed clinical decision making. The proposed LOCapsNet-XAI method achieves exceptional performance metrics including 99.992% accuracy; 99.99% precision; 99.988% specificity; 99.99% recall; 99.98% F1-score; 91ms computation time and an AUC value of 0.99. These findings underscore the capability of the proposed model to enhance early skin cancer detection and improve clinical outcomes. In conclusion, LOCapsNet-XAI represents a significant advancement in the automated detection of skin cancer facilitating reliable and interpretable diagnostics in diverse clinical environments. [ABSTRACT FROM AUTHOR]

The rise of digital libraries has transformed the way academic writing in the English language is conducted. This article explores the multifaceted impacts of digital libraries on English language academic writing, focusing on accessibility, the breadth of resources, citation practices, and the enhancement of writing quality. By providing scholars with an extensive and diverse collection of resources, digital libraries have made academic research more inclusive and efficient. This paper evaluates both the positive and negative effects of digital libraries, analyzing how these platforms have contributed to the evolution of writing practices, research methodologies, and knowledge dissemination in academic settings. The findings suggest that digital libraries play a pivotal role in enhancing the quality of academic writing but also raise concerns about plagiarism and information overload. [ABSTRACT FROM AUTHOR]

BACKGROUND: Connectome is understanding the complex organization of the human brain's structural and functional connectivity is essential for gaining insights into cognitive processes and disorders. OBJECTIVE: To improve the prediction accuracy of brain disorder issues, the current study investigates dysconnected subnetworks and graph structures associated with schizophrenia. METHOD: By using the proposed structural connectivity-deep graph neural network (sc-DGNN) model and compared with machine learning (ML) and deep learning (DL) models.This work attempts to focus on eighty-eight subjects of diffusion magnetic resonance imaging (dMRI), three classical ML, and five DL models. RESULT: The structural connectivity-deep graph neural network (sc-DGNN) model is proposed to effectively predict dysconnectedness associated with schizophrenia and exhibits superior performance compared to traditional ML and DL (GNNs) methods in terms of accuracy, sensitivity, specificity, precision, F1-score, and Area under receiver operating characteristic (AUC). CONCLUSION: The classification task on schizophrenia using structural connectivity matrices and experimental results showed that linear discriminant analysis (LDA) performed 72% accuracy rate in ML models and sc-DGNN performed at a 93% accuracy rate in DL models to distinguish between schizophrenia and healthy patients. [ABSTRACT FROM AUTHOR]

Introduction: Palatable foods have potent reinforcing effects that are mediated, partly, by an abrupt augmentation of dopaminergic neurotransmitters in the brain’s reward pathways. Food addiction to highly palatable foods contributes to the increasing prevalence of obesity. Aim: To evaluate the effect of food addiction on neurobehavioural parameters and the Behavioral Satiety Sequence (BSS) during restriction to a highly palatable diet in a food addiction rat model. Materials and Methods: The experimental study was conducted in the Pharmacology Department at PSG Institute of Medical Sciences and Research, Coimbatore, Tamil Nadu, India from April 2018 to January 2019. The study included six animals out of 28 female Sprague Dawley rats aged approximately 10 weeks and weighing 180-220 g were allocated to the control group and were fed a Standard Chow diet (SC) throughout the study. Twenty-two animals (induction group) were exposed to free choice between SC and a highly palatable Cafeteria Diet (CD), a mix of equal amounts of Bounty®, Snickers®, Mars®, and Milka® chocolate made into homogenous food pellets. After six weeks, 12 animals were randomly selected to have restricted access to the CD diet with free access to SC, while the rest continued to receive a free choice between the two diets for two weeks. Central Nervous System (CNS) parameters, BSS, 24-hour food intake, and body weight were recorded. Results: A significant decrease of 25.4 seconds (18.27%) in the mean time the CD restricted group stayed on the rotarod (113.6±8.31) seconds. and a reduced locomotor activity score of 46.92 (20.94%) in the CD access group in the actophotometer (177.08±5.29) compared to SC was noted. In the open field test, the CD restricted group demonstrated a 47.33% increase in grooming (12.0±1.11) and a 30% lower number of central squares crossed (2.80±0.57). In the Elevated Plus Maze (EPM), the time spent in the open arm (15.00±8.10) was statistically lower (p=0.045). No statistically significant difference between groups was recorded in the forced swim or tail suspension tests. Disruption of the BSS in varying dimensions was apparent in both CD groups. Conclusion: The study has shown that anxiety and altered feeding behaviour were evident in the CD restricted rats in the food addiction model. [ABSTRACT FROM AUTHOR]

Aims: The aim of the study was to compare the activity of sodium hypochlorite (NaOCl) against Enterococcus faecalis when used with four different irrigation protocols. Methodology: Sixty-five single-rooted mandibular premolars with closed apex were prepared till size 35/0.04. The specimens were sterilized and infected with E. faecalis colonies that were cultured separately. The canals were randomly divided into four experimental groups based on irrigation activation protocol, with each group having 15 specimens each - Group 1: control, Group 2: manual dynamic agitation (MDA), Group 3: passive ultrasonic irrigation (PUI), Group 4: intracanal heating (ICH), and Group 5: passive ultrasonic irrigation followed by ICH (PUI ICH). The dentinal shavings were collected and sampled before (S1) and after (S2) the different irrigation techniques were performed. The colony-forming units were counted, and the bacterial reduction was calculated for each group. Results: A significant reduction in the number of E. faecalis colonies was observed for all the experimental groups (P < 0.001). The groups with ICH of NaOCl showed a considerable reduction in bacterial colonies than other groups (P < 0.001), with Group 5 that combined ultrasonics with ICH showed the highest reduction. Conclusion: ICH of NaOCl may be used as an adjunct to root canal irrigation to reduce the bacterial concentration from root canal spaces. [ABSTRACT FROM AUTHOR]

Cancer-related deadly diseases affect both developed and underdeveloped nations worldwide. Effective network learning is crucial to more reliably identify and categorize breast carcinoma in vast and unbalanced image datasets. The absence of early cancer symptoms makes the early identification process challenging. Therefore, from the perspectives of diagnosis, prevention, and therapy, cancer continues to be among the healthcare concerns that numerous researchers work to advance. It is highly essential to design an innovative breast cancer detection model by considering the complications presented in the classical techniques. Initially, breast cancer images are gathered from online sources and it is further subjected to the segmentation region. Here, it is segmented using Adaptive Trans-Dense-Unet (A-TDUNet), and their parameters are tuned using the developed Modified Sheep Flock Optimization Algorithm (MSFOA). The segmented images are further subjected to the breast cancer detection stage and effective breast cancer detection is performed by Multiscale Dilated Densenet with Attention Mechanism (MDD-AM). Throughout the result validation, the Net Present Value (NPV) and accuracy rate of the designed approach are 96.719% and 93.494%. Hence, the implemented breast cancer detection model secured a better efficacy rate than the baseline detection methods in diverse experimental conditions. [ABSTRACT FROM AUTHOR]

Information Retrieval is the most predominant topic in the field of Information Systems as the generation of data over various systems and channels is growing every day. The proposed system which works on the offline basis is designed for the purpose of organizing data in a defined manner and for the purpose of increasing relevancy in the retrieval of the required information from this largely generated data. In the proposed system two novel algorithms, Dynamic Path Selection Clustering (DPSC) algorithm for clustering and the Rearward Binary Window Match (RBWM) algorithm for search process are introduced to overcome the difficulty in data organization and search. The evaluation of the entire system is done and the results are compared along with the results of the existing techniques. [ABSTRACT FROM AUTHOR]

Heart disease is a major cause of death for many people in the world. Each year the death rate of people affected with heart disease increased a lot. Machine learning models have been widely used for the prediction of heart disease fromthe different University of California Irvine (UCI)Machine Learning Repositories. But, due to certain data, it predicts less accurately, whereas, for large data, its sub-model deep learning is used. Our literature work has identified that only traditional methods are used for the prediction of heart disease. It will produce less accuracy. To produce more efficacy, Euclidean Distance was used in this work for data pre-processing that will clean the unwanted data andmetaheuristics bio-inspired algorithmsuch as elephant herding optimization (EHO) is utilized for feature selection. Then, this article proposes deep learning models such as convolutional neural network (CNN) and Inception-ResNet-v2 model for the prediction of heart disease fromthe benchmark dataset such as the UCI Cleveland heart dataset. Finally, the proposed hybrid model utilizes a convolutional neural network with an Inception-ResNet-v2 in the third layer of the architecture that classifies heart disease with the promising result of 98.77%, accuracy for the Cleveland dataset which outperforms all the other state-of-the-art methods. In future work, this model can be used to predict other diseases such as cancer, brain tumor and COVID-19 in available datasets for the betterment of human lives. [ABSTRACT FROM AUTHOR]

Behavioral monitoring can be used to monitor aquatic ecosystems and water quality over time. Using precise and rapid fish performance detection, fishermen may make educated management decisions on recirculating aquaculture systems while decreasing labor. Sensors and procedures for recognizing fish behavior are often developed and prepared by researchers in big numbers. Deep learning (DL) techniques have revolutionized the capability to automatically analyze videos, which were utilized for behavior analysis, live fish detection, biomass estimation, water quality monitoring, and species classification. The benefit of DL is that it could automatically study the extraction of image features and reveals brilliant performance in identifying sequential actions. This paper focuses on the design of Dwarf Mongoose Optimization with Transfer Learning-based fish behavior classification (DMOTLB-FBC) model. The presented DMOTLB-FBC technique intends to effectively monitor and classify fish behaviors. Initially, the DMOTLB-FBC technique follows Gaussian filtering (GFI) technique for noise removal process. Besides, a transfer learning (TL)-based neural architectural search network (NASNet) model is used to produce a collection of feature vectors. For fish behavior classification, graph convolution network (GCN) model is employed in this work. To improve the fish behavior classification results of the DMOTLB-FBC technique, the DWO algorithm is applied as a hyperparameter optimizer of the GCN model. The experimentation analysis of the DMOTLB-FBC technique is tested on fish video dataset and the widespread comparison study reported the enhancements of the DMOTLB-FBC technique over other recent approaches. [ABSTRACT FROM AUTHOR]