Detection of Alzheimer’s Using Convolutional Neural Network

Intrusion detection plays a critical role in safeguarding computer networks from malicious activities and unauthorized access. Traditional machine learning (ML) models have been widely employed for intrusion detection, but their effectiveness can be limited due to the complex and evolving nature of network attacks. An efficient Convolutional Neural Network (CNN) that utilizes one-dimensional convolutions, batch normalization, and max pooling layers is designed to capture the attack patterns in network traffic data. By leveraging the inherent feature extraction capabilities of CNNs, the proposed architecture demonstrates improved performance in identifying network intrusions compared to traditional ML models. The effectiveness of the novel CNN architecture is evaluated by conducted comprehensive experiments on two benchmark intrusion detection datasets. The designed CNN performance was compared with four other ML models such as logistic regression, k-nearest neighbors, decision trees, and random forest. The experimental results revealed that the proposed CNN architecture outperformed the traditional ML models with highest accuracy of 97.68% and 97.39% and highest F1-score of 97.68% and 97.40% for. UNSW-NB15 and CICIDS2017 datasets, respectively. The proposed CNN architecture serves as a promising alternative to traditional ML models, offering improved performance in identifying and mitigating network attacks.

The medical diagnosis of diseases involves the detection and classification of patients from medical image data. It has an important Artificial Intelligence (AI) application in Healthcare and Neuroscience fields. In addition, Machine Learning (ML) and Deep Learning (DL) techniques provide important tasks on Neurodegenerative Diseases (ND) diagnosis from Brain MRI (Magnetic Resonance Imaging) data for image processing with good classification results. This chapter aims at comprehensive and comparative study on the performance of medical image classification with those techniques using Brain MRI datasets. We introduce a brief overview of the medical diagnosis of ND with AI applications in the neuroscience domain. In the literature part, we present the understanding of the medical image classification with Machine and Deep Learning techniques. Therefore, we describe the concept of MRI data and the Brain MRI datasets that are used in this work. In the comparison part, we focus on related works of the Artificial Neural Network (ANN) and the Deep Convolutional Neural Network (DCNN) models and we compare these models in the literature. In the methodology part, we combine the ANN and DCNN in order to propose a new Deep model named ANN-DCNN model when it is more accurate and effective to classify from those Brain images. To improve the performance of those Neural Networks in medical diagnosis applications, we discuss the recent insights and future directions.

Recognition of human activity based on convolutional neural network has received the interest of researchers in recent years due to its significant improvement in accuracy. A large number of algorithms based on the deep learning approach have been proposed for activity recognition purpose. However, with the increasing advancements in technologies having limited computational resources, it needs to design an efficient deep learning-based approaches with improved utilization of computational resources. This paper presents a simple and efficient 2-Dimensional convolutional neural network (2-D CNN) architecture with very small size convolutional kernel for human activity recognition. The merit of the proposed CNN architecture over standard deep learning architectures is fewer trainable parameters and lesser memory requirement which enables it to train the proposed CNN architecture on low GPU memory-based devices and also works well with smaller as well as larger size datasets. The proposed approach consists of mainly four stages: namely (1) creation of dataset and data augmentation, (2) designing 2-D convolutional neural network (CNN) architecture, (3) the proposed 2-D CNN architecture trained from scratch up to optimum stage, and (4) evaluation of the trained 2-D CNN architecture. To illustrate the effectiveness of the proposed architecture several extensive experiments are conducted on three publicly available datasets, namely IXMAS, YouTube, and UCF101 dataset. The results of the proposed method and its comparison with other state-of-the-art methods [8-12,14,18-26,29-33] demonstrate the usefulness of the proposed method.

While machine learning approaches are rapidly being applied to hydrologic problems, physics-informed approaches are still relatively rare. Many successful deep-learning applications have focused on point estimates of streamflow trained on stream gauge observations over time. While these approaches show promise for some applications, there is a need for distributed approaches that can produce accurate two-dimensional results of model states, such as ponded water depth. Here, we demonstrate a 2D emulator of the Tilted V catchment benchmark problem with solutions provided by the integrated hydrology model ParFlow. This emulator model can use 2D Convolution Neural Network (CNN), 3D CNN, and U-Net machine learning architectures and produces time-dependent spatial maps of ponded water depth from which hydrographs and other hydrologic quantities of interest may be derived. A comparison of different deep learning architectures and hyperparameters is presented with particular focus on approaches such as 3D CNN (that have a time-dependent learning component) and 2D CNN and U-Net approaches (that use only the current model state to predict the next state in time). In addition to testing model performance, we also use a simplified simulation based inference approach to evaluate the ability to calibrate the emulator to randomly selected simulations and the match between ML calibrated input parameters and underlying physics-based simulation.

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In this paper, the Convolutional Neural Network (CNN) architecture, which is one of the deep learning architectures, is used to classify the basic circuit components drawn by hand. During the training and testing stages of the model, a new dataset containing images of 863 circuit components manually drawn by different people is created. The data set contains images of four different classes of circuit components such as resistor, inductor, capacitor and voltage source. All images have been fixed to the same size and converted to grayscale to increase recognition performance and reduce process complexity. In the study, training for four classes is performed with CNN architecture. Based on the CNN architecture, four new CNN models are employed with different the number of layers. The training and validation results of these models are compared separately, the model with the highest training and validation performance is observed with four layer CNN model (CNN-4). This model obtained 84.41% accuracy rate at classification task.

Brain tumors pose a significant risk to human life, making accurate grading essential for effective treatment planning and improved survival rates. Magnetic Resonance Imaging (MRI) plays a crucial role in this process. The objective of this study was to develop an automated brain tumor grading system utilizing deep learning techniques. A dataset comprising 293 MRI scans from patients was obtained from the Department of Radiology at Bahawal Victoria Hospital in Bahawalpur, Pakistan. The proposed approach integrates a specialized Convolutional Neural Network (CNN) with pre-trained models to classify brain tumors into low-grade (LGT) and high-grade (HGT) categories with high accuracy. To assess the model's robustness, experiments were conducted using various methods: (1) raw MRI slices, (2) MRI segments containing only the tumor area, (3) feature-extracted slices derived from the original images through the proposed CNN architecture, and (4) feature-extracted slices from tumor area-only segmented images using the proposed CNN. The MRI slices and the features extracted from them were labeled using machine learning models, including Support Vector Machine (SVM) and CNN architectures based on transfer learning, such as MobileNet, Inception V3, and ResNet-50. Additionally, a custom model was specifically developed for this research. The proposed model achieved an impressive peak accuracy of 99.45 %, with classification accuracies of 99.56 % for low-grade tumors and 99.49 % for high-grade tumors, surpassing traditional methods. These results not only enhance the accuracy of brain tumor grading but also improve computational efficiency by reducing processing time and the number of iterations required.

Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review

This study addresses the task of detecting offensive language in Urdu text on social media, where harmful and offensive comments can lead to emotional distress. Using a dataset of Urdu comments collected from YouTube news channels, both deep learning (DL) and machine learning (ML) models were employed for offensive language detection. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF) and the Count Vectorizer approach to capture unigrams, bigrams, and trigrams. Four ML models—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Gaussian Naive Bayes (NB)—were evaluated, with SVM achieving comparatively stronger performance among the ML approaches. For the deep learning framework, Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) architectures were implemented using custom Word2Vec and FastText embeddings. The experimental results indicated that the CNN model outperformed the other evaluated approaches, suggesting its effectiveness for detecting offensive language in Urdu text. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1240 Dimensions.Open Alex.

In this paper, we study the performance of variants of well-known Convolutional Neural Network (CNN) architectures on different audio tasks. We show that tuning the Receptive Field (RF) of CNNs is crucial to their generalization. An insufficient RF limits the CNN's ability to fit the training data. In contrast, CNNs with an excessive RF tend to over-fit the training data and fail to generalize to unseen testing data. As state-of-the-art CNN architectures - in computer vision and other domains - tend to go deeper in terms of number of layers, their RF size increases and therefore they degrade in performance in several audio classification and tagging tasks. We study well-known CNN architectures and how their building blocks affect their receptive field. We propose several systematic approaches to control the RF of CNNs and systematically test the resulting architectures on different audio classification and tagging tasks and datasets. The experiments show that regularizing the RF of CNNs using our proposed approaches can drastically improve the generalization of models, out-performing complex architectures and pre-trained models on larger datasets. The proposed CNNs achieve state-of-the-art results in multiple tasks, from acoustic scene classification to emotion and theme detection in music to instrument recognition, as demonstrated by top ranks in several pertinent challenges (DCASE, MediaEval).

Every year, new Convolutional Neural Network (CNN) architectures appear to deal with different problems in the activity of image and video recognition. These architectures usually work along the ImageNet dataset for looking for the best performance of the CNNs without taking into account the video task where they are used. This can represent a problem if the task is Human Action Recognition (HAR) in video, since the CNN architectures are pre-trained with an image dataset that can practically contain any object, while HAR problem requires consecutive frames of people doing actions. To prove the idea that using CNNs pre-trained on an image dataset does not always achieve the best performance on a video dataset and that, therefore, it is worth comparing the performance of different CNNs under similar circumstances for the HAR problem, this work proposes an analysis between eight different CNN architectures. Each one of the CNN was exclusively trained with RGB images, which were extracted from the frames of the different classes of videos of HMDB51 dataset. To make the classification of an activity in video, we average the predictions taking into account the successes. We also made some ensembles with the best performance CNNs to measure the improvement in accuracy. Our results suggest that Xception is a strong baseline model that could be used by the community to make their comparisons of their proposals more robust.

Machine learning (ML) within the edge internet of things (IoT) is instrumental in making significant shifts in various industrial domains, including smart farming. To increase the efficiency of farming operations and ensure ML accessibility for both small and large-scale farming, the need for a low-cost ML-enabled framework is more pressing. In this paper, we present an end-to-end solution that utilizes tiny ML (TinyML) for the low-cost adoption of ML in classification tasks with a focus on the post-harvest process of olive fruits. We performed dataset collection to build a dataset that consists of several varieties of olive fruits, with the aim of automating the classification and sorting of these fruits. We employed simple image segmentation techniques by means of morphological segmentation to create a dataset that consists of more than 16,500 individually labeled fruits. Then, a convolutional neural network (CNN) was trained on this dataset to classify the quality and category of the fruits, thereby enhancing the efficiency of the olive post-harvesting process. The goal of this study is to show the feasibility of compressing ML models into low-cost edge devices with computationally constrained settings for tasks like olive fruit classification. The trained CNN was efficiently compressed to fit into a low-cost edge controller, maintaining a small model size suitable for edge computing. The performance of this CNN model on the edge device, focusing on metrics like inference time and memory requirements, demonstrated its feasibility with an accuracy of classification of more than 97.0% and minimal edge inference delays ranging from 6 to 55 inferences per second. In summary, the results of this study present a framework that is feasible and efficient for compressing CNN models on edge devices, which can be utilized and expanded in many agricultural applications and also show the practical insights for implementing the used CNN architectures into edge IoT devices and show the trade-offs for employing them using TinyML.

This work presents the development and evaluation of Artificial Intelligence (AI) models for the automatic classification of brain tumors in Magnetic Resonance Imaging (MRI) scans. Several deep learning architectures were implemented and compared, including VGG-19, ResNet50, EfficientNetB3, Xception, MobileNetV2, DenseNet201, InceptionV3, Vision Transformer (ViT), and an Ensemble model. The models were developed in Python (version 3.12.4) using the Keras and TensorFlow frameworks and trained on a public Brain Tumor MRI dataset containing 7023 images. Data augmentation and hyperparameter optimization techniques were applied to improve model generalization. The results showed high classification performance, with accuracies ranging from 89.47% to 98.17%. The Vision Transformer achieved the best performance, reaching 98.17% accuracy, outperforming traditional Convolutional Neural Network (CNN) architectures. Explainable AI (XAI) methods Grad-CAM, LIME, and Occlusion Sensitivity were employed to assess model interpretability, showing that the models predominantly focused on tumor regions. The proposed approach demonstrated the effectiveness of AI-based systems in supporting early diagnosis of brain tumors, reducing analysis time and assisting healthcare professionals.

COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.

Alzheimer's disease is a progressive neurodegenerative fatal disease characterized by a decrease in mental functions. Although there is no definitive treatment for the disease, there are some treatment methods that delay the course of the disease in case of early diagnosis. Therefore, early diagnosis and classification of the disease are important to determine the most appropriate treatment. The most commonly used method for imaging the brain with a high soft‐tissue resolution is magnetic resonance imaging (MRI). Brain MRI help in the diagnosis of Alzheimer's disease with some specific imaging findings. In this study, we aimed to classify Alzheimer's disease in brain MRI using machine learning architectures. An mRMR‐based hybrid CNN was proposed in the study. First, features of MRI in Darknet53, InceptionV3, and Resnet101 models were extracted. These extracted features were concatenated. Then the obtained features were optimized using the mRMR method. SVM and KNN classifiers were used to classify the optimized features. The accuracy value obtained in the proposed model was 99.1%.