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Analysing Osteoporosis Detection: A Comparative Study of CNN and FNN
Osteoporosis causes progressive loss of bone density and strength, causing a more elevated risk of fracture than in normal healthy bones.
Applications of Knowledge Distillation in Remote Sensing: A Survey
With the ever-growing complexity of models in the field of remote sensing (RS), there is an increasing demand for solutions that balance model accuracy with computational efficiency.
Deep Transfer Learning for Kidney Cancer Diagnosis
Many incurable diseases prevalent across global societies stem from various influences, including lifestyle choices, economic conditions, social factors, and genetics.
Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey
Additionally, DA, which is a subset of DTL, has been modified to enhance the point cloud data's quality by dealing with noise and missing points.
Enhancing Plant Disease Detection: A Novel CNN-Based Approach with Tensor Subspace Learning and HOWSVD-MD
Machine learning has revolutionized the field of agricultural science, particularly in the early detection and management of plant diseases, which are crucial for maintaining crop health and productivity.
Machine Learning and Vision Transformers for Thyroid Carcinoma Diagnosis: A review
The growing interest in developing smart diagnostic systems to help medical experts process extensive data for treating incurable diseases has been notable.
Enhancing Kinship Verification through Multiscale Retinex and Combined Deep-Shallow features
The challenge of kinship verification from facial images represents a cutting-edge and formidable frontier in the realms of pattern recognition and computer vision.
Current Topological and Machine Learning Applications for Bias Detection in Text
Institutional bias can impact patient outcomes, educational attainment, and legal system navigation.
AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions
There has been a growing interest in creating intelligent diagnostic systems to assist medical professionals in analyzing and processing big data for the treatment of incurable diseases.
Federated Learning for Computer Vision
Computer Vision (CV) is playing a significant role in transforming society by utilizing machine learning (ML) tools for a wide range of tasks.
Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions
Chat Generative Pre-trained Transformer (ChatGPT) has gained significant interest and attention since its launch in November 2022.
On the Sensitivity of Deep Load Disaggregation to Adversarial Attacks
Non-intrusive Load Monitoring (NILM) algorithms, commonly referred to as load disaggregation algorithms, are fundamental tools for effective energy management.
Unveiling the Invisible: Enhanced Detection and Analysis of Deteriorated Areas in Solar PV Modules Using Unsupervised Sensing Algorithms and 3D Augmented Reality
However, hot spot and snail trails in PV modules caused mostly by crakes reduce their efficiency and power capacity.
Improving CNN-based Person Re-identification using score Normalization
For example, without normalization, the rank-20 rate accuracies of the GRID, CUHK01, VIPeR and PRID450S datasets were 61. 92%, 83. 90%, 92. 03%, 96. 22%; however, after score normalization, they have increased to 64. 64%, 89. 30%, 92. 78%, and 98. 76%, respectively.
Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives
Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services.
