Breast cancer detection using Resnet50

 Breast cancer is one of the leading causes of death in women. Early detection through breast ultrasound images is important and can be improved using machine learning models, which are more accurate and faster than manual methods. Previous research has shown that the use of the logistic regression, svm and random forest algorithms in breast can...

Breast cancer Breast cancer detection Resnet50 InceptionresnetV2 ResNet50-Based Effective Model for Breast Cancer

Detection and classification of brain tumor using hybrid deep learning models

 Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely used non-invasive method for obtaining high-contrast grayscale brain images, primarily for tumor diagnosis. The application of Convolutional Neural Networks (CNNs) in deep learning has revolutioniz...

brain tumor brain tumor detection hybrid deep learning models tumor analysis using CNN classification of brain tumor

Machine Learning Based Healthcare System for Investigating the Association Between Depression and Quality of Life

 The present study aims to elucidate the main variables that increase the level of depression at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the tr...

ANN Healthcare System Depression classification accuracy FSVM

Breast cancer detection using Adaboost classifier

 Breast cancer is a deadly disease; an accurate and early diagnosis of breast cancer is the most efficient method to decrease the death rate. But, in the early detection and diagnosis of breast cancer, differentiating abnormal tissues is a challenging task. In this paper, a weight-based AdaBoost algorithm is proposed for an effective detection a...

Regression Neural Network (GRNN) Probabilistic Neural Network (PNN) Ada Booster Breast cancer convolutional neural network (CNN)

Covid-19 outbreak Prediction with the Base of Deep Learning Vgg16

 In this paper, modifications in neoteric architectures such as VGG16, VGG19, ResNet50, and InceptionV3 are proposed for the classification of COVID-19 using chest X-rays. The proposed architectures termed “COV-DLS” consist of two phases: heading model construction and classification. The heading model construction phase utilizes four modified d...

Deep Learning Covid-19 outbreak

LEARNING WITH SELECTED FEATURES

 Feature selection is the task of choosing a small subset of features that is sufficient to predict the target labels well. Here, instead of trying to directly determine which features are better, we attempt to learn the properties of good features. For this purpose we assume that each feature is represented by a set of properties, referred to as me...

MACHINE LEARNING SELECTED FEATURES

TRAFFIC PREDICTION FOR INTELLIGENT TRANSPORTATION SYSTEM USING MACHINE LEARNING

 The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated ...

DEEP LEARNING TRAFFIC PREDICTION

SASDL AND RBATQ: SPARSE AUTOENCODER WITH SWARM BASED DEEP LEARNING AND REINFORCEMENT BASED Q-LEARNING FOR EEG CLASSIFICATION

 The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated ...

DEEP LEARNING SASDL AND RBATQ

SASDL AND RBATQ: SPARSE AUTOENCODER WITH SWARM BASED DEEP LEARNING AND REINFORCEMENT BASED Q-LEARNING FOR EEG CLASSIFICATION

 The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated ...

DEEP LEARNING REINFORCEMENT BASED Q-LEARNING

HUMAN STRESS DETECTION AND PREDICTION USING ANN

 The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training a...

COVID-19 prediction using CNN Algorithm

 Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultra sonography (LU...

Network Traffic Analyzer

 Networking, which is one of the most significant aspects of information technology revolution, is developing increasingly day after day. This is because it offers a huge amount of knowledge, resources and human experiences. On the one hand, it contains a considerable amount of harmful content, because of misusing. On the other hand, sitting for a l...

Automated Identification of Fish Species (AutoFiS)

 There is a need for automatic systems that can reliably detect, track and classify fish and other marine species in underwater videos without human intervention. Conventional computer vision techniques do not perform well in underwater conditions where the background is complex and the shape and textural features of fish are subtle. Data-driven cla...

Automated Animal Identification and Detection of Species (AAIDeS)

 Image sensors are increasingly being used in biodiversity monitoring, with each study generating many thousands or millions of pictures. Efficiently identifying the species captured by each image is a critical challenge for the advancement of this field. Here, we present an automated species identification method for wildlife pictures captured by r...

Seizure Prediction using Hilbert Huang Transform on Field Programmable Gate Array

 The Hilbert Huang Transform (HHT) has been used extensively in the time-frequency analysis of electroencephalography (EEG) signals and Brain-Computer Interfaces. Most studies utilizing the HHT for extracting features in seizure prediction have used intracranial EEG recordings. Invasive implants in the cortex have unknown long term consequences and ...

Low-Power ECG-Based Processor for Predicting Ventricular Arrhythmia

 This paper presents the design of a fully integrated electrocardiogram (ECG) signal processor (ESP) for the prediction of ventricular arrhythmia using a unique set of ECG features and a naive Bayes classifier. Real-time and adaptive techniques for the detection and the delineation of the P-QRS-T waves were investigated to extract the fiducial point...

VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability

 Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (VLSI) design based on the nonlinear support vector machine (SVM). The proposed design mainly consist...

When Does Diversity Help Generalization in Classification Ensembles

 Ensembles, as a widely used and effective technique in the machine learning community, succeed within a key element--``diversity.'' The relationship between diversity and generalization, unfortunately, is not entirely understood and remains an open research issue. To reveal the effect of diversity on the generalization of classification ensembles, ...

Transfer Relation Network for Fault Diagnosis of Rotating Machinery With Small Data

 Many deep-learning methods have been developed for fault diagnosis. However, due to the difficulty of collecting and labeling machine fault data, the datasets in some practical applications are relatively much smaller than the other big data benchmarks. In addition, the fault data come from different machines. Therefore, on some occasions, fault di...

Hyperspectral Image Classification via Discriminant Gabor Ensemble Filter

 For a broad range of applications, hyperspectral image (HSI) classification is a hot topic in remote sensing, and convolutional neural network (CNN)-based methods are drawing increasing attention. However, to train millions of parameters in CNN requires a large number of labeled training samples, which are difficult to collect. A conventional Gabor...

Cross-Lingual Knowledge Transferring by Structural Correspondence and Space Transfer

 The cross-lingual sentiment analysis (CLSA) aims to leverage label-rich resources in the source language to improve the models of a resource-scarce domain in the target language, where monolingual approaches based on machine learning usually suffer from the unavailability of sentiment knowledge. Recently, the transfer learning paradigm that can tra...

Deep-Learning-Based Automatic Selection of Fewest Channels for Brain-Machine Interfaces

 Due to the development of convenient brain-machine interfaces (BMIs), the automatic selection of a minimum channel (electrode) set has attracted increasing interest because the decrease in the number of channels increases the efficiency of BMIs. This study proposes a deep-learning-based technique to automatically search for the minimum number of ch...

Federated Tensor Decomposition-Based Feature Extraction Approach for Industrial IoT

 Data in modern industrial applications and data science presents multidimensional progressively, the dimension and the structural complexity of these data are becoming extremely high, which renders existing data analysis methods and machine learning algorithms inadequate to the extent. In addition, high-dimensional data in actual scenarios often sh...

Classification of Kidney Images Using Cuckoo Search Algorithm and Artificial Neural Network

 Ultrasound (US) imaging is used to provide the structural abnormalities like stones, infections and cysts for kidney diagnosis and also produces information about kidney functions. The goal of this work is to classify the kidney images using US according to relevant features selection. In this work, images of a kidney are classified as abnormal i...

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound

 Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultra sono graphy (L...