Search

Abstract A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition.

Iodine complexes have known antimicrobial properties along with reported in-vitro antiviral activity for several viruses. Renessans is one such product with iodine complexes and ascorbic acid. The present study was designed to determine its efficacy for SARS-CoV-2 in Rhesus macaque. Rhesus macaque were assigned to: A) prophylactic group (n = 3), (B) treatment group (n = 3), (C) infection control group (n = 4), and (D) negative control group (n = 4). Groups A, B, and C were challenged with 2 × 106 TCID of SARS-CoV-2. The prophylactic group (A) was administered Renessans from 5 days before infection till 8 days postinfection (DPI). The treatment group (B) was administered Renessans from 3 till 8 DPI. Group C was administered water-insoluble fractions only. Nasal swabs from all monkeys of groups A, B, and C remained positive for SARS-CoV-2 till 2 and 7 DPI, while the swabs became negative for groups A and B at 14 DPI. Likewise, fecal matter of monkeys in group A returned negative results during the experiment, while that of group B had significantly decreased viral load (101.5 genome copies/mL) compared to group C (103 genome copies/mL). Hence, it is concluded that Renessans has in-vivo SARS-CoV-2 activity and may result in early clearance of SARS-CoV-2.

Bilharzia or schistosomiasis is one of the most fatal and factitious disease happens through pollute which become a significant reason of deaths in the world. Prediction and factors identification that become causes of disease in early stage, may escort to treatment before it becomes critical. Data mining techniques are used to assist medical professionals effectively in diseases' classification. This research investigates the recovery and death factors which contributes to schistosomiasis disease preprocessed dataset, collected from Hubei, China. A computerized learning method, association rule mining (Apriori) is used to spot factors. Different tools were used for analysis and model evaluation with minimum support and minimum confidence indicated higher than 90% to generate rules. In addition, attributes indicating recovery and death of individuals were identified. Strong associations of disease factors; BMI, viability, nourishment, extent to ascites etc. determined and classified through Apriori algorithm. Further, results generated by association rule mining method may useful for professionals in treatment decision with better precision.

Diabetes is a common, chronic disease. Prediction of diabetes at an early stage can lead to improved treatment. Data mining techniques are widely used for prediction of disease at an early stage. In this research paper, diabetes is predicted using significant attributes, and the relationship of the differing attributes is also characterized. Various tools are used to determine significant attribute selection, and for clustering, prediction, and association rule mining for diabetes. Significant attributes selection was done via the principal component analysis method. Our findings indicate a strong association of diabetes with body mass index (BMI) and with glucose level, which was extracted via the Apriori method. Artificial neural network (ANN), random forest (RF) and K-means clustering techniques were implemented for the prediction of diabetes. The ANN technique provided a best accuracy of 75.7%, and may be useful to assist medical professionals with treatment decisions. Keywords: Association rule mining, Artificial neural network (ANN), Data mining, Diabetes, K-means clustering, Random forest

A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy (VTDR), which is a leading cause of visual impairment and blindness worldwide. However, previous automated VTDR detection methods have mainly relied on manual feature extraction and classification, leading to errors. This paper proposes a novel VTDR detection and classification model that combines different models through majority voting. Our proposed methodology involves preprocessing, data augmentation, feature extraction, and classification stages. We use a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model for feature extraction and selection and an improved SVM-RBF with a Decision Tree (DT) and K-Nearest Neighbor (KNN) for classification. We tested ourmodel on the IDRiD dataset and achieved an accuracy of 98.06%, a sensitivity of 83.67%, and a specificity of 100% for DR detection and evaluation tests, respectively. Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) in diagnosing diabetic retinopathy, a major contributor to global vision impairment, is becoming increasingly pronounced. Notably, the detection of vision-threatening diabetic retinopathy (VTDR) has been significantly fortified through automated techniques. Traditionally, the reliance on manual analysis of retinal images, albeit slow and error-prone, constituted the conventional approach. Addressing this, our study introduces a novel methodology that amplifies the robustness and precision of the detection process. This is complemented by the groundbreaking Hierarchical Block Attention (HBA) and HBA-U-Net architecture, which notably propel attention mechanisms in image segmentation. This innovative model refines image processing without imposing excessive computational demands by honing in on individual pixel intricacies, spatial relationships, and channel-specific attention. Building upon this innovation, our proposed method employs a multi-stage strategy encompassing data pre-processing, feature extraction via a hybrid CNN-SVD model, and classification employing an amalgamation of Improved Support Vector Machine-Radial Basis Function (ISVM-RBF), DT, and KNN techniques. Rigorously tested on the IDRiD dataset classified into five severity tiers, the hybrid model yields remarkable performance, achieving a 99.18% accuracy, 98.15% sensitivity, and 100% specificity in VTDR detection, thus surpassing existing methods. These results underscore a more potent avenue for diagnosing and addressing this crucial ocular condition while underscoring AI's transformative potential in medical care, particularly in ophthalmology. [ABSTRACT FROM AUTHOR]

The rise of vision-threatening diabetic retinopathy (VTDR) underscores the imperative for advanced and efficient early detection mechanisms. With the integration of the Internet of Things (IoT) and 5G technologies, there is transformative potential for VTDR diagnosis, facilitating real-time processing of the burgeoning volume of fundus images (FIs). Combined with artificial intelligence (AI), this offers a robust platform for managing vast healthcare datasets and achieving unparalleled disease detection precision. Our study introduces a novel AI-driven VTDR detection framework that integrates multiple models through majority voting. This comprehensive approach encompasses pre-processing, data augmentation, feature extraction using a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model, and classification through an enhanced SVM-RBF combined with a decision tree (DT) and K-nearest neighbor (KNN). Validated on the IDRiD dataset, our model boasts an accuracy of 99.89%, a sensitivity of 84.40%, and a specificity of 100%, marking a significant improvement over the traditional method. The convergence of the IoT, 5G, and AI technologies herald a transformative era in healthcare, ensuring timely and accurate VTDR diagnoses, especially in geographically underserved regions. [ABSTRACT FROM AUTHOR]

Lysine Lipoylation is a protective and conserved Post Translational Modification (PTM) in proteomics research like prokaryotes and eukaryotes. It is connected with many biological processes and closely linked with many metabolic diseases. To develop a perfect and accurate classification model for identifying lipoylation sites at the protein level, the computational methods and several other factors play a key role in this purpose. Usually, most of the techniques and different traditional experimental models have a very high cost. They are time-consuming; so, it is required to construct a predictor model to extract lysine lipoylation sites. This study proposes a model that could predict lysine lipoylation sites with the help of a classification method known as Artificial Neural Network (ANN). The ANN algorithm deals with the noise problem and imbalance classification in lipoylation sites dataset samples. As the result shows in ten-fold cross-validation, a brilliant performance is achieved through the predictor model with an accuracy of 99.88%, and also achieved 0.9976 as the highest value of MCC. So, the predictor model is a very useful and helpful tool for lipoylation sites prediction. Some of the residues around lysine lipoylation sites play a vital part in prediction, as demonstrated during feature analysis. The wonderful results reported through the evaluation and prediction of this model can provide an informative and relative explanation for lipoylation and its molecular mechanisms. [ABSTRACT FROM AUTHOR]