An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

Breast cancer is one of the leading cancers among women.It has the second-highest mortality rate in women after lung cancer.Timely detection,especially in the early stages,can help increase survival rates.However,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s experience.However,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early stages.The research presented in this paper is focused on the multi-class classification of breast cancer.The deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound dataset.The proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble model.Deep learning models are fine-tuned along with hyperparameter tuning to achieve better results.Subsequently,entropy-based feature selection is used.Breast cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,respectively.The performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

Energy Theft Detection in Smart Grids with Genetic Algorithm-Based Feature Selection

As big data,its technologies,and application continue to advance,the Smart Grid(SG)has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology(ICT)and cloud computing.As a result of the complicated architecture of cloud computing,the distinctive working of advanced metering infrastructures(AMI),and the use of sensitive data,it has become challenging tomake the SG secure.Faults of the SG are categorized into two main categories,Technical Losses(TLs)and Non-Technical Losses(NTLs).Hardware failure,communication issues,ohmic losses,and energy burnout during transmission and propagation of energy are TLs.NTL’s are human-induced errors for malicious purposes such as attacking sensitive data and electricity theft,along with tampering with AMI for bill reduction by fraudulent customers.This research proposes a data-driven methodology based on principles of computational intelligence as well as big data analysis to identify fraudulent customers based on their load profile.In our proposed methodology,a hybrid Genetic Algorithm and Support Vector Machine(GA-SVM)model has been used to extract the relevant subset of feature data from a large and unsupervised public smart grid project dataset in London,UK,for theft detection.A subset of 26 out of 71 features is obtained with a classification accuracy of 96.6%,compared to studies conducted on small and limited datasets.

Augmenting IoT Intrusion Detection System Performance Using Deep Neural Network

Due to their low power consumption and limited computing power,Internet of Things(IoT)devices are difficult to secure.Moreover,the rapid growth of IoT devices in homes increases the risk of cyber-attacks.Intrusion detection systems(IDS)are commonly employed to prevent cyberattacks.These systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate countermeasures.Attempts have been made in the past to detect new attacks using machine learning and deep learning techniques,however,these efforts have been unsuccessful.In this paper,we propose two deep learning models to automatically detect various types of intrusion attacks in IoT networks.Specifically,we experimentally evaluate the use of two Convolutional Neural Networks(CNN)to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 dataset.To accomplish this goal,the network stream data were initially converted to twodimensional images,which were then used to train the neural network models.We also propose two baseline models to demonstrate the performance of the proposed models.Generally,both models achieve high accuracy in detecting the majority of these nine attacks.

A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

Due to the rapid propagation characteristic of the Coronavirus(COVID-19)disease,manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of infection.Despite,new automated diagnostic methods have been brought on board,particularly methods based on artificial intelligence using different medical data such as X-ray imaging.Thoracic imaging,for example,produces several image types that can be processed and analyzed by machine and deep learning methods.X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging machines.Through this paper,we propose a novel Convolutional Neural Network(CNN)model(COV2Net)that can detect COVID-19 virus by analyzing the X-ray images of suspected patients.This model is trained on a dataset containing thousands of X-ray images collected from different sources.The model was tested and evaluated on an independent dataset.In order to approve the performance of the proposed model,three CNN models namely Mobile-Net,Residential Energy Services Network(Res-Net),and Visual Geometry Group 16(VGG-16)have been implemented using transfer learning technique.This experiment consists of a multi-label classification task based on X-ray images for normal patients,patients infected by COVID-19 virus and other patients infected with pneumonia.This proposed model is empowered with Gradient-weighted Class Activation Mapping(Grad-CAM)and Grad-Cam++techniques for a visual explanation and methodology debugging goal.The finding results show that the proposed model COV2Net outperforms the state-of-the-art methods.

关于权的非交换对称空间

本文定义一种关于一个给定权的非交换对称空间,讨论该空间的性质,并且证明在等距同构意义下该空间与权的选择无关.进一步地,本文也引入一种非交换对称Hardy空间,研究此Hardy空间的基本性质,并且给出此Hardy空间也在等距同构意义下与权的选择无关.