The analysis of medical images (MI) is an important part of advanced medicine as it helps detect and diagnose various diseases early. Classifying brain tumors through magnetic resonance imaging (MRI) poses a challenge demanding accurate models for effective diagnosis and treatment planning. This paper introduces AG-MSTLN-EL, an attention-aided multi-source transfer learning ensemble learning model leveraging multi-source transfer learning (Visual Geometry Group ResNet and GoogLeNet), attention mechanisms, and ensemble learning to achieve robust and accurate brain tumor classification. Multi-source transfer learning allows knowledge extraction from diverse domains, enhancing generalization. The attention mechanism focuses on specific MRI regions, increasing interpretability and classification performance. Ensemble learning combines k-nearest neighbor, Softmax, and support vector machine classifiers, improving both accuracy and reliability. Evaluating the model\'s performance on a dataset with 3064 brain tumor MRI images, AG-MSTLN-EL outperforms state-of-the-art models in terms of all classification measures. The model\'s innovative combination of transfer learning, attention mechanism, and ensemble learning provides a reliable solution for brain tumor classification. Its superior performance and high interpretability make AG-MSTLN-EL a valuable tool for clinicians and researchers in medical image analysis.

Accurately classifying microalgae species is vital for monitoring marine ecosystems and managing the emergence of marine mucilage, which is crucial for monitoring mucilage phenomena in marine environments. Traditional methods have been inadequate due to time-consuming processes and the need for expert knowledge. The purpose of this article is to employ convolutional neural networks (CNNs) and support vector machines (SVMs) to improve classification accuracy and efficiency. By employing advanced computational techniques, including MobileNet and GoogleNet models, alongside SVM classification, the study demonstrates significant advancements over conventional identification methods. In the classification of a dataset consisting of 7820 images using four different SVM kernel functions, the linear kernel achieved the highest success rate at 98.79 %. It is followed by the RBF kernel at 98.73 %, the polynomial kernel at 97.84 %, and the sigmoid kernel at 97.20 %. This research not only provides a methodological framework for future studies in marine biodiversity monitoring but also highlights the potential for real-time applications in ecological conservation and understanding mucilage dynamics amidst climate change and environmental pollution.

Delamination represents one of the most significant and dangerous damages in composite plates. Recently, many papers have presented the capability of structural health monitoring (SHM) techniques for the investigation of structural delamination with various shapes and thickness depths. However, few studies have been conducted regarding the utilization of convolutional neural network (CNN) methods for automating the non-destructive testing (NDT) techniques database to identify the delamination size and depth. In this paper, an automated system qualified for distinguishing between pristine and damaged structures and classifying three classes of delamination with various depths is presented. This system includes a proposed CNN model and the Lamb wave technique. In this work, a unidirectional composite plate with three samples of delamination inserted at different depths was prepared for numerical and experimental investigations. In the numerical part, the guided wave propagation and interaction with three samples of delamination were studied to observe how the delamination depth can affect the scattered and trapped waves over the delamination region. This numerical study was validated experimentally using an efficient ultrasonic guided waves technique. This technique involved piezoelectric wafer active sensors (PWASs) and a scanning laser Doppler vibrometer (SLDV). Both numerical and experimental studies demonstrate that the delamination depth has a direct effect on the trapped waves\' energy and distribution. Three different datasets were collected from the numerical and experimental studies, involving the numerical wavefield image dataset, experimental wavefield image dataset, and experimental wavenumber spectrum image dataset. These three datasets were used independently with the proposed CNN model to develop a system that can automatically classify four classes (pristine class and three different delamination classes). The results of all three datasets show the capability of the proposed CNN model for predicting the delamination depth with high accuracy. The proposed CNN model results of the three different datasets were validated using the GoogLeNet CNN. The results of both methods show an excellent agreement. The results proved the capability of the wavefield image and wavenumber spectrum datasets to be used as input data to the CNN for the detection of delamination depth.

PIN diodes, due to their simple structure and variable resistance characteristics under high-frequency high-power excitation, are often used in radar front-end as limiters to filter high power microwaves (HPM) to prevent its power from entering the internal circuit and causing damage. This paper carries out theoretical derivation and research on the HPM effects of PIN diodes, and then uses an optimized neural network algorithm to replace traditional physical modeling to calculate and predict two types of HPM limiting indicators of PIN diode limiters. We proposes a neural network model for each of the following two prediction scenarios: in the scenario of time-junction temperature curves under different HPM irradiation, the weighted mean squared error (MSE) between the predicted values from the test dataset and the simulated values is below 0.004. While in predicting PIN limiter\'s power limitation threshold, insertion loss, and maximum isolation under different HPM irradiation, the MSE of the test set prediction values and simulation values are all less than 0.03. The method proposed in this research, which applies an optimized neural network algorithm to replace traditional physical modeling algorithms for studying the high-power microwave effects of PIN diode limiters, significantly improves the computational and simulation speed, reduces the calculation cost, and provides a new method for studying the high-power microwave effects of PIN diode limiters.

BACKGROUND: Examining functions and characteristics of DNA sequences is a highly challenging task. When it comes to the human genome, which is made up of exons and introns, this task is more challenging. Human exons and introns contain millions to billions of nucleotides, which contributes to the complexity observed in this sequences. Considering how complicated the subject of genomics is, it is obvious that using signal processing techniques and deep learning tools to build a strong predictive model can be very helpful for the development of the research of the human genome.
RESULTS: After representing human exons and introns with color images using Frequency Chaos Game Representation, two pre-trained convolutional neural network models (Resnet-50 and GoogleNet) and a proposed CNN model having 13 hidden layers were used to classify our obtained images. We have reached a value of 92% for the accuracy rate for Resnet-50 model in about 7 h for the execution time, a value of 91.5% for the accuracy rate for the GoogleNet model in 2 h and a half for the execution time. For our proposed CNN model, we have reached 91.6% for the accuracy rate in 2 h and 37 min.
CONCLUSIONS: Our proposed CNN model is faster than the Resnet-50 model in terms of execution time. It was able to slightly exceed the GoogleNet model for the accuracy rate value.

The field of multimodal robotic musical performing arts has garnered significant interest due to its innovative potential. Conventional robots face limitations in understanding emotions and artistic expression in musical performances. Therefore, this paper explores the application of multimodal robots that integrate visual and auditory perception to enhance the quality and artistic expression in music performance. Our approach involves integrating GRU (Gated Recurrent Unit) and GoogLeNet models for sentiment analysis. The GRU model processes audio data and captures the temporal dynamics of musical elements, including long-term dependencies, to extract emotional information. The GoogLeNet model excels in image processing, extracting complex visual details and aesthetic features. This synergy deepens the understanding of musical and visual elements, aiming to produce more emotionally resonant and interactive robot performances. Experimental results demonstrate the effectiveness of our approach, showing significant improvements in music performance by multimodal robots. These robots, equipped with our method, deliver high-quality, artistic performances that effectively evoke emotional engagement from the audience. Multimodal robots that merge audio-visual perception in music performance enrich the art form and offer diverse human-machine interactions. This research demonstrates the potential of multimodal robots in music performance, promoting the integration of technology and art. It opens new realms in performing arts and human-robot interactions, offering a unique and innovative experience. Our findings provide valuable insights for the development of multimodal robots in the performing arts sector.

UNASSIGNED: Developing efficient methods to infer relations among different faces consisting of numerous expressions or on the same face at different times (e.g., disease progression) is an open issue in imaging related research. In this study, we present a novel method for facial feature extraction, characterization, and identification based on classical computer vision coupled with deep learning and, more specifically, convolutional neural networks.
UNASSIGNED: We describe the hybrid face characterization system named FRetrAIval (FRAI), which is a hybrid of the GoogleNet and the AlexNet Neural Network (NN) models. Images analyzed by the FRAI network are preprocessed by computer vision techniques such as the oriented gradient-based algorithm that can extract only the face region from any kind of picture. The Aligned Face dataset (AFD) was used to train and test the FRAI solution for extracting image features. The Labeled Faces in the Wild (LFW) holdout dataset has been used for external validation.
UNASSIGNED: Overall, in comparison to previous techniques, our methodology has shown much better results on k-Nearest Neighbors (KNN) by yielding the maximum precision, recall, F1, and F2 score values (92.00, 92.66, 92.33, and 92.52%, respectively) for AFD and (95.00% for each variable) for LFW dataset, which were used as training and testing datasets. The FRAI model may be potentially used in healthcare and criminology as well as many other applications where it is important to quickly identify face features such as fingerprint for a specific identification target.

UNASSIGNED: Lung cancer is the most common type of cancer, accounting for 12.8% of cancer cases worldwide. As initially non-specific symptoms occur, it is difficult to diagnose in the early stages.
UNASSIGNED: Image processing techniques developed using machine learning methods have played a crucial role in the development of decision support systems. This study aimed to classify benign and malignant lung lesions with a deep learning approach and convolutional neural networks (CNNs).
UNASSIGNED: The image dataset includes 4459 Computed tomography (CT) scans (benign, 2242; malignant, 2217). The research type was retrospective; the case-control analysis. A method based on GoogLeNet architecture, which is one of the deep learning approaches, was used to make maximum inference on images and minimize manual control.
UNASSIGNED: The dataset used to develop the CNNs model is included in the training (3567) and testing (892) datasets. The model\'s highest accuracy rate in the training phase was estimated as 0.98. According to accuracy, sensitivity, specificity, positive predictive value, and negative predictive values of testing data, the highest classification performance ratio was positive predictive value with 0.984.
UNASSIGNED: The deep learning methods are beneficial in the diagnosis and classification of lung cancer through computed tomography images.

Deep learning is playing a major role in identifying complicated structure, and it outperforms in term of training and classification tasks in comparison to traditional algorithms. In this work, a local cloud-based solution is developed for classification of Alzheimer\'s disease (AD) as MRI scans as input modality. The multi-classification is used for AD variety and is classified into four stages. In order to leverage the capabilities of the pre-trained GoogLeNet model, transfer learning is employed. The GoogLeNet model, which is pre-trained for image classification tasks, is fine-tuned for the specific purpose of multi-class AD classification. Through this process, a better accuracy of 98% is achieved. As a result, a local cloud web application for Alzheimer\'s prediction is developed using the proposed architectures of GoogLeNet. This application enables doctors to remotely check for the presence of AD in patients.

Pepper leaf disease identification based on convolutional neural networks (CNNs) is one of the interesting research areas. However, most existing CNN-based pepper leaf disease detection models are suboptimal in terms of accuracy and computing performance. In particular, it is challenging to apply CNNs on embedded portable devices due to a large amount of computation and memory consumption for leaf disease recognition in large fields. Therefore, this paper introduces an enhanced lightweight model based on GoogLeNet architecture. The initial step involves compressing the Inception structure to reduce model parameters, leading to a remarkable enhancement in recognition speed. Furthermore, the network incorporates the spatial pyramid pooling structure to seamlessly integrate local and global features. Subsequently, the proposed improved model has been trained on the real dataset of 9183 images, containing 6 types of pepper diseases. The cross-validation results show that the model accuracy is 97.87%, which is 6% higher than that of GoogLeNet based on Inception-V1 and Inception-V3. The memory requirement of the model is only 10.3 MB, which is reduced by 52.31%-86.69%, comparing to GoogLeNet. We have also compared the model with the existing CNN-based models including AlexNet, ResNet-50 and MobileNet-V2. The result shows that the average inference time of the proposed model decreases by 61.49%, 41.78% and 23.81%, respectively. The results show that the proposed enhanced model can significantly improve performance in terms of accuracy and computing efficiency, which has potential to improve productivity in the pepper farming industry.