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Go to Editorial ManagerAlzheimer's disease (AD) is a progressive neurodegenerative disorder that severely impacts cognitive functions such as memory, attention, and reasoning, ultimately affecting daily life. Early and accurate detection is crucial for timely intervention and management. Traditional diagnostic methods, including neuroimaging and cognitive assessments, can be expensive and time-consuming, necessitating more accessible and efficient alternatives. This study aims to develop an automated and efficient deep learning-based detection system that uses Electroencephalogram (EEG) signals to accurately classify AD and healthy individuals. A Convolutional Neural Network (CNN) model was designed to extract meaningful features from preprocessed EEG data. The architecture consists of convolutional layers with max pooling, dropout regularization, and fully connected layers to improve classification accuracy. The model was trained and evaluated on a comprehensive EEG dataset, using key performance metrics such as accuracy, recall, precision, and F1-score. The proposed CNN model achieved a high classification accuracy of 94.56%, a low loss of 0.2162, and an AUC value of 0.93828, demonstrating superior classification capability. The results indicate that the model effectively distinguishes between AD and healthy individuals, outperforming several state-of-the-art approaches. The findings highlight the potential of deep learning-based EEG analysis for AD detection, providing an accessible and cost-effective tool for early diagnosis. The high accuracy of the proposed CNN model suggests that it can assist medical professionals in making well-informed decisions, ultimately improving patient outcomes.
Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detection. In this paper, the use of modern learning machine-based approaches was explored. More than 70 state-of-the-art articles (from 2019 to 2024) were extensively explored to highlight the different machine learning and deep learning (DL) techniques of different models used for the detection, classification, and prediction of cancerous lung tumors. The efficient model of Tiny DL must be built to assist physicians who are working in rural medical centers for swift and rapid diagnosis of lung cancer. The combination of lightweight Convolutional Neural Networks and limited resources could produce a portable model with low computational cost that has the ability to substitute the skill and experience of doctors needed in urgent cases.
One of the most common causes of mortality worldwide is Lung cancer, an early diagnosis crucial for a patient’s survival and recovery. Automated segmentation of lung lesions in chest CT has become a pre-eminent focal point for research, particularly with the development of hybrid methods combining traditional image processing with advanced deep learning methods such as CNN. These hybrid approaches aim to minimize individual methods limitations by controlling their merge strengths to enhance segmentation efficiency, precision, and clinical utility. This review comprehensively analyzes different hybrid techniques, such as deep learning improved by rule-based systems, multi-scale feature extraction, and ensemble learning. As well as inspect their clinical effect, particularly in improving diagnostic accuracy and optimizing treatment procedures. Despite their possibility, these approaches still face significant challenges, such as computational complexity, data requirements, and the requirement for explainable AI (XAI). Upcoming advancements in lung lesion segmentation will focus on refining these models to achieve faster processing, improved accuracy, and integration with diagnostic tools to protect transparency and ethical considerations.
The effects of the repeated solution heat treatment on hardness, tensile strength and microstructure of aluminum were investigated. For this purpose, an alloy of AA6061-T6 was undergo to cyclic solution heat treatment process which is composed of repeated period (10 min) held at 520 °C for 1, 4, 8 and 12 cycles. The hardness was tested for five aging times (as quenching, one week, three weeks, one month and five months) to all cycles (1, 4, 8 and 12) firstly and it is found that the hardness of five months as aging time for all cycles has the best results (90Hv) as compared with others (as quenching, one week, three weeks, and one month), so it was adopted for all cycles to implement the tensile test and the microstructure. Hardness results were improved to Vickers hardness of (90Hv) with increasing of cycles up to 8 cycles then decreasing after that to (45Hv). Tensile results were showed an increment (34%) also for the same group of 8 cycles compared with (17%) and (9%) for 4 and 12 cycles, respectively. Microstructure is revealed that whenever cycles are increased, the precipitate phase in alloy is increased also, thus, it is improved the hardness and tensile strength.
This paper proposes robust control for three models of the linear inverted pendulum (one mass linear inverted pendulum model, two masses linear inverted pendulum model and three masses linear inverted pendulum model) which represents the upper, middle and lower body of a bipedal walking robot. The bipedal walking robot is built of light-weight and hard Aluminum sheets with 2 mm thickness. The minimum phase system and non-minimum phase system are studied and investigated for inverted pendulum models. The bipedal walking robot is programmed by Arduino microcontroller UNO. A MATLAB Simulink system is built to embrace the theoretical work. The results showed that one linear inverted pendulum is the worst performance, worst noise rejection and the worst set point tracking to the zero moment point. But two masses linear inverted pendulum models and three masses linear inverted pendulum model have a better performance, a better high-frequency noise rejection characteristic and better set-point tracking to the zero moment point.