Transfer Learning is an efficient approach of solving classification problem with little amount of data. In this paper, we applied Transfer Learning to the well-known AlexNet Convolution Neural Network (AlexNet CNN) for human recognition based on ear images. We adopted and fine-tuned AlexNet CNN to suit our problem domain. The last fully connected layer is replaced with another fully connected layer to recognize 10 classes instead of 1000 classes. Another Rectified Linear Unit (ReLU) layer is also added to improve the non-linear problem-solving ability of the network. To train the fine-tuned network, we allocate 250 ear images taken from 10 subjects for training, and 50 ear images are used for validation and testing. The proposed fine-tuned network works well in our application as we get 100% validation accuracy.

Identifying people based on their footprint has not yet gained enough attention from the researchers. Therefore, in this paper, an investigation of human identification conducted based on the footprint. Transfer Learning used as the main concept of this investigation. The aim of using Transfer Learning is to overcome the need for a large-scale dataset and achieve high accuracy with a low-scale dataset. Five well-known models used, namely, Alexnet, Vgg16, Vgg19, Googlenet, and Inception v3. Each of these models fine-tuned to fit-in the paper’s topic. A dataset of 30 individuals constructed in order to train the models. The right and left footprint of each individual captured with iPhone camera. The models trained and evaluated based on the same settings. The evaluation shows that Inception v3 model achieved the highest accuracy compared to all other four models.

Ear biometric research can be traced back as early as 1949. Off late, more work of ear biometrics has been done mainly focusing within constrained environment. Illumination invariant problem in unconstrained environment remains less explored for ear biometric. In this paper, we propose an illumination-invariant ear authentication approach using biometric authentication methods. Ear regions are firstly segmented from side face images using biased normalized cuts and morphological operations. Shape features are then extracted using Log-Gabor filters where the Hamming distance is finally applied for authentication. A authentication rate of 92.66% is reported for intra-class matching on 50 subjects

This paper investigates the application of convolutional neural networks (CNNs) for the early detection of brain tumors to enhance diagnostic accuracy. Brain tumors present a significant global health challenge, and early detection is vital for successful treatments and patient outcomes. The study includes a comprehensive literature review of recent advancements in brain tumor detection techniques. The main focus is on the development and evaluation of CNN models, including EfficientNetB3, residual networks-50 (ResNet50) and visual geometry group-19 (VGG-19), for binary image classification using magnetic resonance imaging (MRI) scans. These models demonstrate promising results in terms of accuracy, precision, and recall metrics. However, challenges related to overfitting and limited dataset size are acknowledged. The study highlights the potential of artificial intelligence (AI) in improving brain tumor detection and emphasizes the need for further research and validation in real-world clinical settings. EfficientNetB3 reached 99.44% training accuracy but showed potential overfitting with validation accuracy dropping to 89.47%. ResNet50 steadily improved to 83.62% training accuracy and 89.47% validation accuracy. VGG19 struggled, with only 62% accuracy.

Can Convolution Neural Network (CNN) Triumph in Ear Recognition of Uniform Illumination Invariant?

The integration of Artificial Intelligence (AI) with Wireless Sensor Networks (WSNs) has substantially advanced the capabilities of these systems to collect, process, and respond to large volumes of sensor data. Machine learning (ML) algorithms, a key component of AI, enable WSNs to detect patterns, trends, and anomalies, thereby enhancing their adaptability and performance in dynamic environments. One particularly impactful application is in aging-in-place systems, where elderly individuals can be continuously monitored for vital signs and daily activities, maintaining independence while benefiting from timely health and safety interventions. As the global elderly population grows, there is a pressing need for intelligent monitoring solutions to support independent living. This review aims to consolidate and critically evaluate existing ML algorithms and sensor technologies used in WSNs for aging-in-place applications. It introduces a structured taxonomy of ML approaches—including supervised, unsupervised, hybrid, and reinforcement learning—and presents a comparative analysis of wearable and environmental sensors. Unlike prior reviews, this work integrates technical performance evaluation with application-specific relevance, offering a unified view of algorithmic capabilities, sensor trade-offs, and practical deployment challenges. Based on a systematic selection process, 124 peer-reviewed articles were analyzed. The review also identifies open research challenges, highlights limitations of current solutions, and proposes targeted future directions to guide the development of adaptive, reliable, and user-centered aging-in-place systems.