IEEE

A Hybrid Machine Learning Approach Utilizing CNN Feature Extraction with Traditional Classifier to Identify Strawberry Leaf Diseases

Abstract: Strawberry production is globally significant because it contains high nutrients. Strawberry leaf disease shapes a significant barrier to strawberry cultivation worldwide. Numerous ...
IEEE

A Novel Deep Learning Model for Classifying Power Quality Problems in PV-integrated Microgrids using CNN-LSTM

Abstract: Distributed generation based on photo-voltaic (PV) modules play a vital role to provide continuous power supply for consumers. Power quality (PQ) events caused by integration of distributed ...
IEEE

Faster R-CNN vs SSD: A Deep Learning Approach for Nodule Detection in Lung Cancer Diagnosis

Abstract: Deep learning-based object identification technology has numerous uses, including facial recognition, commercial analytics, and medical imaging analysis. An object detector has a backbone ...
IEEE

Comparative Analysis of CNN Architectures With Transfer Learning for Brain Tumor Detection and Classification From MRI Scans

Abstract: In the realm of neuroscience, one of the most profound topics of investigation is brain tumors, as they greatly influence brain operations and overall health. These brain tumors can be ...
IEEE

A Hybrid Design Method of Lamb Wave Mode Filter Based on Machine Learning and COM Model

Abstract: This paper reports a acoustic filter design process using a hybrid approach based on machine learning and COM model. Machine learning is deployed to accurately predict the filter structure ...
IEEE

PCB Defect Detection Using Machine Learning: YOLOv5 and Inception v3 CNN-Based Approach

Abstract: Identification of PCB board defects early in the manufacturing process is crucial, as PCB quality control plays an important role in the electronics manufacturing industry. Defective PCBs ...
IEEE

Digital In-Memory Compute for Machine Learning Applications With Input and Model Security

Abstract: Digital in-memory compute (IMC) architectures allow for a balance of the high accuracy and precision necessary for many machine learning applications, with high data reuse and parallelism to ...
IEEE

A Novel CNN Architecture for Alzheimer's Disease Classification Using MRI Images: Comparison with Traditional Machine Learning Models

Abstract: Automated medical image processing has significantly improved with recent advances in deep learning and imaging technologies, particularly in the area of neuroimaging-based Alzheimer's ...
IEEE

Deep Learning Models for Precision Agriculture: Evaluating CNN Architectures for Accurate Plant Disease Detection

Abstract: Early and precise detection of plant diseases is crucial for enhancing crop yield and minimizing agricultural losses. This paper evaluates the performance of deep learning-based ...
IEEE

Comparison of CNN Model with Different Machine Learning Models for Fake News Detection

Abstract: Fake news continues to be a critical issue in today's era for any citizen concerned about political integrity and the state of governance. The use of internet has experienced exponential ...
IEEE

Improved Deep Learning-Based Microwave Inversion With Experimental Training Data

Abstract: Deep learning-based inversion methods show great promise. The most common way to develop deep learning inversion techniques is to use synthetic (i.e., computationally-generated) data for ...