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

Brain Tumor Detection and Classification Using Artificial Intelligence

 Health is very important for human life. In particular, the health of the brain, which is the executive of the vital resource, is very important. Diagnosis for human health is provided by magnetic resonance imaging (MRI) devices, which help health decision makers in critical organs such as brain health. Images from these devices are a source of...

Brain Tumor Detection Classification Using Artificial Intelligence Tumor Detection Using CNN Brain Disease Analysis Disease Detection

Enhancing Digital Image Forgery Detection Using Transfer Learning

 Nowadays, digital images are a main source of shared information in social media. Meanwhile, malicious software can forge such images for fake information. So, it’s crucial to identify these forgeries. This problem was tackled in the literature by various digital image forgery detection techniques. But most of these techniques are tied to detec...

Digital Image Forgery Transfer Learning MobileNetV2 Convolutional neural network (CNN) ResNet VGG

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

ENRICHING THE TRANSFER LEARNING WITH PRE-TRAINED LEXICON EMBEDDINGFOR LOW-RESOURCE NEURAL MACHINE TRANSLATION

 Most State-Of-The-Art (SOTA) Neural Machine Translation (NMT) systems today achieve outstanding results based only on large parallel corpora. The large-scale parallel corpora for high-resource languages is easily obtainable. However, the translation quality of NMT for morphologically rich languages is still unsatisfactory, mainly because of the dat...

MACHINE LEARNING ENRICHING THE TRANSFER LEARNING

AN ONLINE TRANSFER LEARNING FRAMEWORK WITH EXTREME LEARNING MACHINFOR AUTOMATED CREDIT SCORINGE

 Automated Credit Scoring (ACS) is the process of predicting user credit based on historical data. It involves analyzing and predicting the association between the data and particular credit values based on similar data. Recently, ACS has been handled as a machine learning problem, and numerous models were developed to address it. In this paper, we ...

MACHINE LEARNING AN ONLINE TRANSFER LEARNING

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

  In recent months, coronavirus disease 2019 (COVID-19) has infected millions of people worldwide. In addition to the clinical tests like reverse transcription- polymerase chain reaction (RT-PCR), medical imaging techniques such as computed tomography (CT) can be used as a rapid technique to detect and evaluate patients infected by COVID...

MACHINE LEARNING Deep Learning

META-TRANSFER LEARNING THROUGH HARD TASKS

 Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples o...

MACHINE LEARNING META-TRANSFER

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

MODULARIZING DEEP LEARNING VIA PAIRWISE LEARNING WITH KERNELS

 By redefining the conventional notions of layers, we present an alternative view on finitely wide, fully trainable deep neural networks as stacked linear models in feature spaces, leading to a kernel machine interpretation. Based on this construction, we then propose a provably optimal modular learning framework for classification that does not req...

DEEP LEARNING MODULARIZING DEEP LEARNING

FEDERATED DEEP LEARNING FOR THE DIAGNOSIS OF CEREBELLAR ATAXIA: PRIVACY PRESERVATION AND AUTO-CRAFTED FEATURE EXTRACTOR

 Cerebellar ataxia (CA) is concerned with the incoordination of movement caused by cerebellar dysfunction. Movements of the eyes, speech, trunk, and limbs are affected. Conventional machine learning approaches utilizing centralised databases have been used to objectively diagnose and quantify the severity of CA . Although these approaches achieved ...

DEEP LEARNING AUTO-CRAFTED FEATURE EXTRACTOR

A DEEP LEARNING APPROACH FOR TASK OFFLOADING IN MULTI-UAV AIDED MOBILE EDGE COMPUTING

 A number of electric devices in buildings can be considered as important demand response (DR) resources, for instance, the battery energy storage system (BESS) and the heat, ventilation, and air conditioning (HVAC) systems. The conventional model-based DR methods rely on efficient ondemand computing resources. However, the current buildings suffer ...

DEEP LEARNING AIDED MOBILE EDGE COMPUTING

A DEEP LEARNING APPROACH FOR TASK OFFLOADING IN MULTI-UAV AIDED MOBILE EDGE COMPUTING

 A number of electric devices in buildings can be considered as important demand response (DR) resources, for instance, the battery energy storage system (BESS) and the heat, ventilation, and air conditioning (HVAC) systems. The conventional model-based DR methods rely on efficient ondemand computing resources. However, the current buildings suffer ...

DEEP LEARNING AIDED MOBILE EDGE COMPUTING

A HYBRID CLOUD AND EDGE CONTROL STRATEGY FOR DEMAND RESPONSES USING DEEP REINFORCEMENT LEARNING AND TRANSFER LEARNING

 A HYBRID CLOUD AND EDGE CONTROL STRATEGY FOR DEMAND RESPONSES USING DEEP REINFORCEMENT LEARNING AND TRANSFER LEARNING...

DEEP LEARNING TRANSFER LEARNING

A SYSTEMATIC REVIEW ON RECENT ADVANCEMENTS IN DEEP AND MACHINE LEARNING BASED DETECTION AND CLASSIFICATION OF ACUTE LYMPHOBLASTIC LEUKEMIA

 Automatic Leukemia or blood cancer detection is a challenging job and is very much required in healthcare centers. It has a significant role in early diagnosis and treatment planning. Leukemia is a hematological disorder that starts from the bone marrow and affects white blood cells (WBCs). Microscopic analysis of WBCs is a preferred approach for a...

DEEP AND MACHINE LEARNING ACUTE LYMPHOBLASTIC LEUKEMIA

A SYSTEMATIC REVIEW ON RECENT ADVANCEMENTS IN DEEP AND MACHINE LEARNING BASED DETECTION AND CLASSIFICATION OF ACUTE LYMPHOBLASTIC LEUKEMIA

 Automatic Leukemia or blood cancer detection is a challenging job and is very much required in healthcare centers. It has a significant role in early diagnosis and treatment planning. Leukemia is a hematological disorder that starts from the bone marrow and affects white blood cells (WBCs). Microscopic analysis of WBCs is a preferred approach for a...

DEEP AND MACHINE LEARNING ACUTE LYMPHOBLASTIC LEUKEMIA

MODULARIZING DEEP LEARNING VIA PAIRWISE LEARNING WITH KERNELS

 By redefining the conventional notions of layers, we present an alternative view on finitely wide, fully trainable deep neural networks as stacked linear models in feature spaces, leading to a kernel machine interpretation. Based on this construction, we then propose a provably optimal modular learning framework for classification that does not req...

DEEP LEARNING VIA PAIRWISE LEARNING

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

EAD-NET A NOVEL LESION SEGMENTATION METHOD IN DIABETIC RETINOPATHY USING NEURAL NETWORKS

 Diabetic retinopathy (DR) is a common chronic fundus disease, which has four different kinds of microvessel structure and microvascular lesions: microaneurysms (MAs), hemorrhages (HEs), hard exudates, and soft exudates. Accurate detection and counting of them are a basic but important work. The manual annotation of these lesions is a labor-intensiv...

EARLY DETECTION OF MALPOSITIONED CATHETERS AND LINES ON CHEST X-RAYS USING DEEP LEARNING

 Hospital patients can have catheters and lines inserted during the course of their admission and serious complications can arise if they are positioned incorrectly. Early recognition of malpositioned tubes is the key to preventing risky complications (even death), even more so now that millions of COVID-19 patients are in the need of these tubes an...

A Transfer Learning Approach for Securing Resource-Constrained IoT Devices

 In recent years, Internet of Things (IoT) security has attracted significant interest by researchers due to new characteristics of IoT such as heterogeneity of devices, resource constraints, and new types of attacks targeting IoT. Intrusion detection, which is an indispensable part of a security system, is also included in these studies. In order t...

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...

Research Review for Broad Learning System Algorithms, Theory, and Applications

 In recent years, the appearance of the broad learning system (BLS) is poised to revolutionize conventional artificial intelligence methods. It represents a step toward building more efficient and effective machine-learning methods that can be extended to a broader range of necessary research fields. In this survey, we provide a comprehensive overvi...

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...

Composite Kernel of Mutual Learning on Mid-Level Features for Hyperspectral Image Classification

 By training different models and averaging their predictions, the performance of the machine-learning algorithm can be improved. The performance optimization of multiple models is supposed to generalize further data well. This requires the knowledge transfer of generalization information between models. In this article, a multiple kernel mutual lea...

Adaptive Knowledge Transfer by Continual Weighted Updating of Filter Kernels for Few-shot Fault Diagnosis of Machines

 Deep learning (DL) based diagnosis models have to be trained by large quantities of monitoring data of machines. However, in real-case scenarios, machines operate under the normal condition in most of their life time while faults seldom happen. Therefore, though massive data are accessible, most are data of the normal condition while fault data are...

Transfer Learning Promotes 6G Wireless Communications Recent Advances and Future Challenges

 In the coming 6G communications, network densification, high throughput, positioning accuracy, energy efficiency, and many other key performance indicator requirements are becoming increasingly strict. In the future, how to improve work efficiency while saving costs is one of the foremost research directions in wireless communications. Being able t...

A Survey on Curriculum Learning

 ? Curriculum learning (CL) is a training strategy that trains a machine learning model from easier data to harder data, which imitates the meaningful learning order in human curricula. ? As an easy-to-use plug-in, the CL strategy has demonstrated its power in improving the generalization capacity and convergence rate of various models in a wide r...

Representation Learning from Limited Educational Data with Crowd sourced Labels

 Representation learning has been proven to play an important role in the unprecedented success of machine learning models in numerous tasks, such as machine translation, face recognition and recommendation.The majority of existing representation learning approaches often require large amounts of consistent and noise-free labels. However, due to var...

Automatic detection of tuberculosis related abnormalities in Chest X-ray Images using hierarchical feature extraction scheme

 Machine learning techniques have been widely used for abnormality detection in medical images. Chest X-ray images (CXR) are among the non-invasive diagnostic tools used to detect various disease pathologies. The ambiguous anatomical structure of soft tissues is one of the major challenges for segregating normal and abnormal images. The main objecti...