Breast Cancer Histopathological Image Classification using Stochastic Dilated Residual Ghost Model

A new deep learning-based classification model called the Stochastic Dilated Residual Ghost (SDRG) was proposed in this work for categorizing histopathology images of breast cancer. The SDRG model used the proposed Multiscale Stochastic Dilated Convolution (MSDC) model, a ghost unit, stochastic upsampling, and downsampling units to categorize breast cancer accurately. This study addresses four primary issues: first, strain normalization was used to manage color divergence, data augmentation with several factors was used to handle the overfitting. The second challenge is extracting and enhancing tiny and low-level information such as edge, contour, and color accuracy; it is done by the proposed multiscale stochastic and dilation unit. The third contribution is to remove redundant or similar information from the convolution neural network using a ghost unit. According to the assessment findings, the SDRG model scored overall 95.65 percent accuracy rates in categorizing images with a precision of 99.17 percent, superior to state-of-the-art approaches.

Performance Evaluation of Pre-processing Techniques on Diabetes Prediction

Medical industry contains a large amount of sensitive data that must be evaluated in order to get insight into records. The nonlinearity, non-normality, correlation structures and complicated diabetic medical records, on the other hand, makes accurate predictions difficult. The Pima Indian Diabetes dataset is one of them, owing to the dataset's imbalance, large number of missing values and difficulty in identifying highly risk factors. Some of these challenges have been solved using computational approaches such as machine learning methods, but they have not performed ideally, with pre-processing techniques being recognized as critical to achieving correct findings. The goal of this work is to apply multiple pre-processing approaches to increase the accuracy of some simple models. These multiple pre-processing techniques are median imputation in which null values are substituted by finding the median of the input variables dependent on whether or not the patient is diabetic and then follow by applying oversampling and under-sampling procedures on both majority and minority votes. These votes are applied in order to address the problem of class imbalance as pointed out from the literature. Finally, the dimension reduction Pearson correlation is used to detect high-risk features since it is effective at quantifying information between attributes and their labels. In this study, these techniques are applied in the same order to Linear Regression, Naive Bayes, Decision Tree, K Nearest Neighbor, Random Forest and Gaussian Boosting classifiers. The utility of the techniques on the mentioned classifiers is validated using performance measures such as Accuracy, Precision and Recall. The Random Forest Classifier is found to be the best-improved model, with 95 percent accuracy, 94.25 percent precision and 95.35 percent recall. Medical practitioners may find the provided strategies beneficial in improving the efficiency of diabetes analysis. Keywords— Classifiers, diabetes, Pima Indian Diabetes dataset, pre-processing techniques

Automatic Approach for Detecting the Seizure Using RCCN Architecture

Seizure is one of the most common neurodegenerative illnesses in humans, and it can result in serious brain damage, strokes, and tumors. Seizures can be detected early, which can assist prevent harm and aid in the treatment of epilepsy sufferers. A seizure prediction system's goal is to correctly detect the pre-ictal brain state, which occurs before a seizure occurs. Patient-independent seizure prediction models have been recognized as a real-world solution to the seizure prediction problem, since they are designed to provide accurate performance across different patients by using the recorded dataset. Furthermore, building such models to adjust to the significant inter-subject variability in EEG data has received little attention. We present a patient-independent deep learning architectures that can train a global function using data from numerous people with its own learning strategy. On the CHB- MIT-EEG dataset, the proposed models reach state-of-the-art accuracy for seizure prediction, with 95.54 percent accuracy. While predicting seizures, the Siamese model trained on the suggested learning technique is able to understand patterns associated to patient differences in data. Our models outperform the competition in terms of patient-independent seizure prediction, and following model adaption, the same architecture may be employed as a patient-specific classifier. We show that the MFCC feature map used by our models contains predictive biomarkers associated to inter-ictal and pre-ictal brain states, and we are the first study to use model interpretation to explain classifier behaviour for the task of seizure prediction.

Object Tracking Using HSV Values and OpenCV

This research shows how to use colour and movement to automate the process of recognising and tracking things. Video tracking is a technique for detecting a moving object over a long distance using a camera. The main purpose of video tracking is to connect target objects in subsequent video frames. The connection may be particularly troublesome when things move faster than the frame rate. Using HSV colour space values and OpenCV in different video frames, this study proposes a way to track moving objects in real-time. We begin by calculating the HSV value of an item to be monitored, and then we track the object throughout the testing step. The items were shown to be tracked with 90 percent accuracy. Keywords: HSV, OpenCV, Object tracking, Video frames, GUI

COVID-19 Detection and Analysis Using Chest X-Ray and CT- Scan Images

COVID-19 seems to be the most devastating and lethal illness characterized by an unique coronavirus for the human body. Coronavirus, which is considered to have originated in Wuhan, China, and is responsible for a huge number of deaths, spread swiftly around the world in December 2019. Early discovery of COVID-19 by proper diagnosis, especially in situations with no evident symptoms, could reduce the death rate of patients. The primary diagnostic tools for this condition are chest Xrays and CT scans. COVID-19 may be detected using a machine vision technique from chest X-ray pictures and CT scans, according to this study.The model's performance was evaluated using generalised data throughout the testing step. According to recent studies gained using radiological imaging techniques, such images convey crucial data about the COVID-19 virus. This proposed approach, which makes use of modern artificial intelligence (AI) techniques, has shown to be effective in recognising COVID-19, and when combined with radiological imaging, can aid in the correct detection of this disease. The proposed approach was created in order to provide accurate assessments for COVID and non-COVID patients.The results demonstrate that VGG-16 is the best architecture for the reference dataset, with 98.87 percent accuracy in network evaluations and 95.91 percent success in patient status identification. Convolutional layers were developed, with distinct filtering applied to each layer. As a result, the VGG-16 design performed well in the classification of COVID-19 cases. Nevertheless, by modifying it or adding a preprocessing step on top of it, this architecture allows for significant gains. Our methodology can be used to help radiologists validate their first screenings and can also be used to screen patients quickly via the cloud.

Evaluating Annotated Dataset of Customer Reviews for Aspect Based Sentiment Analysis

Sentiment analysis of product reviews on e-commerce platforms aids in determining the preferences of customers. Aspect-based sentiment analysis (ABSA) assists in identifying the contributing aspects and their corresponding polarity, thereby allowing for a more detailed analysis of the customer’s inclination toward product aspects. This analysis helps in the transition from the traditional rating-based recommendation process to an improved aspect-based process. To automate ABSA, a labelled dataset is required to train a supervised machine learning model. As the availability of such dataset is limited due to the involvement of human efforts, an annotated dataset has been provided here for performing ABSA on customer reviews of mobile phones. The dataset comprising of product reviews of Apple-iPhone11 has been manually annotated with predefined aspect categories and aspect sentiments. The dataset’s accuracy has been validated using state-of-the-art machine learning techniques such as Naïve Bayes, Support Vector Machine, Logistic Regression, Random Forest, K-Nearest Neighbor and Multi Layer Perceptron, a sequential model built with Keras API. The MLP model built through Keras Sequential API for classifying review text into aspect categories produced the most accurate result with 67.45 percent accuracy. K- nearest neighbor performed the worst with only 49.92 percent accuracy. The Support Vector Machine had the highest accuracy for classifying review text into aspect sentiments with an accuracy of 79.46 percent. The model built with Keras API had the lowest 76.30 percent accuracy. The contribution is beneficial as a benchmark dataset for ABSA of mobile phone reviews.

Multilayered MapReduce Framework to Link Cybernetic Vulnerabilities and Cybernetic Laws from E-News Articles

The growth of technology evaluation and the influence of smart gazettes, which have a very complex structure, the amount of data in an organization, E-Commerce, and ERP explodes. When data is processed as described, it becomes the engine of every individual. According to projections from 2025, social media, IoT, streaming data, and geodata will generate 80% of unstructured data, and there will be 4.8 billion tech enthusiasts. The most popular social media trend allows users to access publicly available data. Hackers are highly qualified in both the web space and the dark web, and the rise of complexity and digitization of this public access will cause loopholes in legislation. The major goal of this study is to gather information about the cyber vulnerability of electronic news. Data collection, text standardization, and feature extraction were all part of the initial step. In the second step, MapReduce was used to obtain demographic insights using a multi-layered categorization strategy. Cybercrime is classified using a classifier technique, and the model has a 53 percent accuracy rate. Phishing is a result of cyber weaknesses, and it has been discovered in a higher number of metropolitan cities. Men, rather than women, make up the majority of crime victims. Individuals should be made aware of secure access to websites and media, according to the findings of the study. People should be aware of cyber vulnerabilities, as well as cyber laws enacted under the IPC, the IT Act 2000, and CERT-In.

Visualization of Hidden Structure and Shape in Ct Image via Non-Linear Perspective Foreground and Back Ground Projection

The neurologist analyse the brain images to diagnose the disease via structure and shape of the part in the scanned Medical images such as CT, MRI, and PET.The Medical image segmentation perform less in the regions where no or little contrast,artefacts over the different boundary regions. The manual process of segmentation show poor boundary differentiation dueto discernibility in shape and location, intra and inter observer reliability. In this paper, we propose a dyadic Cat optimization (DCO) algorithm to segment the regions in the brain from CT and MRI image via Non- linear perspective Foreground and Back Ground projection. The DCO algorithm remove the artefacts in the boundary regions and provide the exact structure and shape of the brain regions. The DCO algorithm show the region boundary such as plerygomaxillary fissure, occipital lobe, vaginal process zygomatic arch, maxilla and piriform aperture with high visibility in the regions of inadequately visible boundary and distinguish the deformable shape. The DCO algorithm show the increased SSIM and 90 percent accuracy.

A more accurate Parameterization based on cosmic Age (MAPAge)

Recently, several statistically significant tensions between different cosmological datasets have raised doubts about the standard Lambda cold dark matter (ΛCDM) model. A recent letter (Huang 2020) suggests to use “Parameterization based on cosmic Age” (PAge) to approximate a broad class of beyond-ΛCDM models, with a typical accuracy ∼1% in angular diameter distances at z ≲ 10. In this work, we extend PAge to a More Accurate Parameterization based on cosmic Age (MAPAge) by adding a new degree of freedom η 2. The parameter η 2 describes the difference between physically motivated models and their phenomenological PAge approximations. The accuracy of MAPAge, typically of order 10−3 in angular diameter distances at z ≲ 10, is significantly better than PAge. We compare PAge and MAPAge with current observational data and forecast data. The conjecture in Huang (2020), that PAge approximation is sufficiently good for current observations, is quantitatively confirmed in this work. We also show that the extension from PAge to MAPAge is important for future observations, which typically require sub-percent accuracy in theoretical predictions.

Diagnosis of Brain Tumor Using Nano Segmentation and Advanced-Convolutional Neural Networks Classification

The field of nanotechnology has lately acquired prominence according to the raised level of correct identification and performance in the patients using Computer-Aided Diagnosis (CAD). Nano-scale imaging model enables for a high level of precision and accuracy in determining if a brain tumour is malignant or benign. This contributes to people with brain tumours having a better standard of living. In this study, We present a revolutionary Semantic nano-segmentation methodology for the nanoscale classification of brain tumours. The suggested Advanced-Convolutional Neural Networks-based Semantic Nano-segmentation will aid radiologists in detecting brain tumours even when lesions are minor. ResNet-50 was employed in the suggested Advanced-Convolutional Neural Networks (A-CNN) approach. The tumour image is partitioned using Semantic Nano-segmentation, that has averaged dice and SSIM values of 0.9704 and 0.2133, correspondingly. The input is a nano-image, and the tumour image is segmented using Semantic Nano-segmentation, which has averaged dice and SSIM values of 0.9704 and 0.2133, respectively. The suggested Semantic nano segments achieves 93.2 percent and 92.7 percent accuracy for benign and malignant tumour pictures, correspondingly. For malignant or benign pictures, The accuracy of the A-CNN methodology of correct segmentation is 99.57 percent and 95.7 percent, respectively. This unique nano-method is designed to detect tumour areas in nanometers (nm) and hence accurately assess the illness. The suggested technique’s closeness to with regard to True Positive values, the ROC curve implies that it outperforms earlier approaches. A comparison analysis is conducted on ResNet-50 using testing and training data at rates of 90%–10%, 80%–20%, and 70%–30%, corresponding, indicating the utility of the suggested work.