Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

Coronavirus 2019(COVID-19)is the current global buzzword,putting the world at risk.The pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources,which are already few.Even established nations would not be in a perfect position to manage this epidemic correctly,leaving emerging countries and countries that have not yet begun to grow to address the problem.These problems can be solved by using machine learning models in a realistic way,such as by using computer-aided images during medical examinations.These models help predict the effects of the disease outbreak and help detect the effects in the coming days.In this paper,Multi-Features Decease Analysis(MFDA)is used with different ensemble classifiers to diagnose the disease’s impact with the help of Computed Tomography(CT)scan images.There are various features associated with chest CT images,which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from pneumonia.The current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better results.The model’s performance is assessed using Receiver Operating Characteristic(ROC)curve,the Root Mean Square Error(RMSE),and the Confusion Matrix.It is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient.

Automatic License Plate Recognition System for Vehicles Using a CNN

Automatic License Plate Recognition(ALPR)systems are important in Intelligent Transportation Services(ITS)as they help ensure effective law enforcement and security.These systems play a significant role in border surveillance,ensuring safeguards,and handling vehicle-related crime.The most effective approach for implementing ALPR systems utilizes deep learning via a convolutional neural network(CNN).A CNN works on an input image by assigning significance to various features of the image and differentiating them from each other.CNNs are popular for license plate character recognition.However,little has been reported on the results of these systems with regard to unusual varieties of license plates or their success at night.We present an efficient ALPR system that uses a CNN for character recognition.A combination of pre-processing and morphological operations was applied to enhance input image quality,which aids system efficiency.The system has various features,such as the ability to recognize multi-line,skewed,and multifont license plates.It also works efficiently in night mode and can be used for different vehicle types.An overall accuracy of 98.13%was achieved using the proposed CNN technique.

An AW-HARIS Based Automated Segmentation of Human Liver Using CT Images

In the digestion of amino acids,carbohydrates,and lipids,as well as protein synthesis from the consumed food,the liver has many diverse responsibilities and functions that are to be performed.Liver disease may impact the hormonal and nutritional balance in the human body.The earlier diagnosis of such critical conditions may help to treat the patient effectively.A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human liver.The proposed approach can recognize the abnormalities with better accuracy without training,unlike in supervisory procedures requiring considerable computational efforts for training.In the earlier stages,the CT images are pre-processed through an Adaptive Multiscale Data Condensation Kernel to normalize the underlying noise and enhance the image’s contrast for better segmentation.Then,the preliminary phase’s outcome is being fed as the input for the Anisotropic Weighted—Heuristic Algorithm for Real-time Image Segmentation algorithm that uses texture-related information,which has resulted in precise outcome with acceptable computational latency when compared to that of its counterparts.It is observed that the proposed approach has outperformed in the majority of the cases with an accuracy of 78%.The smart diagnosis approach would help the medical staff accurately predict the abnormality and disease progression in earlier ailment stages.

Explainable Software Fault Localization Model: From Blackbox to Whitebox

The most resource-intensive and laborious part of debugging is finding the exact location of the fault from the more significant number of code snippets.Plenty of machine intelligence models has offered the effective localization of defects.Some models can precisely locate the faulty with more than 95%accuracy,resulting in demand for trustworthy models in fault localization.Confidence and trustworthiness within machine intelligencebased software models can only be achieved via explainable artificial intelligence in Fault Localization(XFL).The current study presents a model for generating counterfactual interpretations for the fault localization model’s decisions.Neural system approximations and disseminated presentation of input information may be achieved by building a nonlinear neural network model.That demonstrates a high level of proficiency in transfer learning,even with minimal training data.The proposed XFL would make the decisionmaking transparent simultaneously without impacting the model’s performance.The proposed XFL ranks the software program statements based on the possible vulnerability score approximated from the training data.The model’s performance is further evaluated using various metrics like the number of assessed statements,confidence level of fault localization,and TopN evaluation strategies.

Unified Computational Modelling for Healthcare Device Security Assessment

This article evaluates the security techniques that are used to maintainthe healthcare devices, and proposes a mathematical model to list these in theorder of priority and preference. To accomplish the stated objective, the articleuses the Fuzzy Analytic Network Process (ANP) integrated with Technical forOrder Preference by Similarities to Ideal Solution (TOPSIS) to find the suitablealternatives of the security techniques for securing the healthcare devices fromtrespassing. The methodology is enlisted to rank the alternatives/ techniquesbased on their weights’ satisfaction degree. Thereafter, the ranks of the alternatives determine the order of priority for the techniques used in healthcare security.The findings of our analysis cite that Machine Learning (ML) based healthcaredevices obtained the highest priority among all the other security techniques.Hence the developers, manufacturers and researchers should focus on the MLtechniques for securing the healthcare devices. The results drawn through theaid of the suggested mathematical model would be a corroborative referencefor the developers and the manufacturers in assessing the security techniques ofthe healthcare devices.