Although the conventional image processing methods can detect fabric defects, defect detection is an open research problem due to diversity of types. In this paper, feasibility VGG16 deep learning architecture for has been demonstrated. A new database used. The pre-trained model on built. Thus, training time reduced. experimental results show that outperforms traditional Shearlet transform and ...

This study investigated the combined prognostic value of pretreatment anemia and cervical node necrosis (CNN) in patients with nasopharyngeal carcinoma (NPC). Retrospective review of 1302 patients with newly diagnosed nonmetastatic NPC treated with intensity-modulated radiotherapy (IMRT) ± chemotherapy. Patients were classified into four groups according to anemia and CNN status. Survival was c...

Today, many eye diseases jeopardize our everyday lives, such as Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), and Glaucoma. Glaucoma is an incurable unavoidable disease that damages the vision of optic nerves quality life. Classification has been active field research for past ten years. Several approaches classification are established, beginning with conventional segmenta...

Background: Feature extraction process is noteworthy in order to categorize brain tumors. Handcrafted feature consists of profound limitations. Similarly, without appropriate classifier, the promising improved results can’t be obtained. Objective: This paper proposes a hybrid model for classifying tumors more accurately and rapidly preferable choice aggravating tasks. The main objective this re...

In Bangladesh, most people depend on agriculture for their livelihood. The country's economy and agricultural production are hampered if plants affected by diseases. Crop pests also disrupt production. So, this paper proposes leaf disease, disease severity stage, pest detection strategies with suggestions prevention using five notable Convolutional Neural Network models (CNN) such as VGG16, Res...

Background: Deep learning has revolutionized artificial intelligence and has transformed many fields. It allows processing high-dimensional data (such as signals or images) without the need for feature engineering. The aim of this research is to develop a deep learning-based system to decode motor intent from electromyogram (EMG) signals. Methods: A myoelectric system based on convolutional ne...

This paper presents an efficient and robust approach for reducing the size of deep neural networks by pruning entire neurons. It exploits maxout units for combining neurons into more complex convex functions and it makes use of a local relevance measurement that ranks neurons according to their activation on the training set for pruning them. Additionally, a parameter reduction comparison betwe...

Otitis Media (OM) is a type of infectious disease caused by viruses and/or bacteria in the middle ear cavity. In current study, it aimed to detect eardrum region images for diagnosing OM using artificial intelligence methods. The Convolution Neural Networks (CNN) model and deep features this obtained with otoscope device were used. order separate these as Normal Abnormal, end-to-end VGG16 was d...

Fruit classification is a computer vision task that aims to classify fruit classes correctly, given an image. Nearly all studies have used RGB color images as inputs, few costly hyperspectral images, and classical ML-based colorized depth images. Depth apparent benefits such invariance lighting, less storage requirement, better foreground-background separation, more pronounced curvature details...

This presentation investigates the dynamics of discrete-time cellular neural networks (DT-CNN). In contrast to classical neural networks that are mostly gradient-like systems, DT-CNN possesses both complete stability and chaotic behaviors as different parameters are considered. An energylike function which decreases along orbits of DT-CNN as well as the existence of a globally attracting set ar...