Robust sliding-Backstepping mode control of a wind system based on the DFIG generator.

This paper presents a new contribution in the field of the optimization of the techniques of control of the wind systems and the improvement of the quality of energy produced in the grid. The Sliding Mode control technique gives quite interesting results, but its major drawback lies in the phenomenon of chattering (oscillations), which reduces the system's precision. We propose in this work a solution to cancel this chattering phenomenon by the implication of the adaptive Backstepping technique to control the powers of the double-fed asynchronous generator (DFIG) connected to the electrical network by two converters (network side and side machine) in the nominal part of the sliding mode model. This hybrid technique will correct errors of precision and stability and the performance of the wind system obtained in terms of efficiency, active and reactive power is significant. First, a review of the wind system was presented. Then, an exhaustive explanation of the Backstepping technique based on the Lyapunov stability and optimization method has been reported. Subsequently, a validation on the Matlab & Simulink environment was carried out to test the performance and robustness of the proposed model. The results obtained from this work, either by follow-up or robustness tests, show a significant performance improvement compared to other control techniques.

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method.

White blood cells (WBCs) are blood cells that fight infections and diseases as a part of the immune system. They are also known as "defender cells." But the imbalance in the number of WBCs in the blood can be hazardous. Leukemia is the most common blood cancer caused by an overabundance of WBCs in the immune system. Acute lymphocytic leukemia (ALL) usually occurs when the bone marrow creates many immature WBCs that destroy healthy cells. People of all ages, including children and adolescents, can be affected by ALL. The rapid proliferation of atypical lymphocyte cells can cause a reduction in new blood cells and increase the chances of death in patients. Therefore, early and precise cancer detection can help with better therapy and a higher survival probability in the case of leukemia. However, diagnosing ALL is time-consuming and complicated, and manual analysis is expensive, with subjective and error-prone outcomes. Thus, detecting normal and malignant cells reliably and accurately is crucial. For this reason, automatic detection using computer-aided diagnostic models can help doctors effectively detect early leukemia. The entire approach may be automated using image processing techniques, reducing physicians' workload and increasing diagnosis accuracy. The impact of deep learning (DL) on medical research has recently proven quite beneficial, offering new avenues and possibilities in the healthcare domain for diagnostic techniques. However, to make that happen soon in DL, the entire community must overcome the explainability limit. Because of the black box operation's shortcomings in artificial intelligence (AI) models' decisions, there is a lack of liability and trust in the outcomes. But explainable artificial intelligence (XAI) can solve this problem by interpreting the predictions of AI systems. This study emphasizes leukemia, specifically ALL. The proposed strategy recognizes acute lymphoblastic leukemia as an automated procedure that applies different transfer learning models to classify ALL. Hence, using local interpretable model-agnostic explanations (LIME) to assure validity and reliability, this method also explains the cause of a specific classification. The proposed method achieved 98.38% accuracy with the InceptionV3 model. Experimental results were found between different transfer learning methods, including ResNet101V2, VGG19, and InceptionResNetV2, later verified with the LIME algorithm for XAI, where the proposed method performed the best. The obtained results and their reliability demonstrate that it can be preferred in identifying ALL, which will assist medical examiners.

Research and Development of an IoT-Based Remote Asthma Patient Monitoring System.

This paper presents the research and development of an Internet of Things- (IoT-) based remote health monitoring system for asthmatic patients. Asthma is an inflammatory disease. Asthma causes the lungs to swell and get narrower, making it difficult to carry air in and out of the lungs. This situation makes breathing very difficult. Remote patient monitoring (RPM) is a method of collecting health-related data from patients who are in a remote location and electronically transmitting it to healthcare providers for evaluation and consultation. The aim of this study is to design a monitoring system that allows doctors to monitor asthmatic patients from a remote area. The proposed system will allow patients to measure oxygen saturation (SpO2), heart rate, body temperature, humidity, volatile gases, room temperature, and electrocardiogram (ECG) using various sensors, which will be displayed in an application. This data is then sent to the doctor to monitor the patient's condition and suggest appropriate actions. Overall, the system consists of an Android application, a website, and various sensors. The Android studio and Java programming language were used to develop the application. For the frontend, the website was built using Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), JavaScript, and jQuery. The system also uses Django, a Python-based open-source web framework, for the backend. The system developed the various sensors using an ESP8266 microcontroller compatible with the Arduino Integrated Development Environment (IDE). The system uses a MAX30100 pulse oximeter and heart rate sensor, a GY-906 MLX90614 noncontact precision thermometer, a DHT11 humidity and temperature sensor, a MQ-135 gas and air quality sensor, and an AD8232 ECG sensor for collecting various parameters that may trigger asthma attacks. Finally, the system developed the Asthma Tracker app and the Asthma Tracker website for remote health monitoring. The system was initially tested on demo patients and later deployed and tested on seven real human test subjects. Overall, the monitoring system produced satisfactory results. The data acquired by the sensors has a high level of accuracy. The system also maintained user-friendliness and low cost.

A Comparative Analysis of Machine Learning Algorithms to Predict Alzheimer's Disease.

Alzheimer's disease has been one of the major concerns recently. Around 45 million people are suffering from this disease. Alzheimer's is a degenerative brain disease with an unspecified cause and pathogenesis which primarily affects older people. The main cause of Alzheimer's disease is Dementia, which progressively damages the brain cells. People lost their thinking ability, reading ability, and many more from this disease. A machine learning system can reduce this problem by predicting the disease. The main aim is to recognize Dementia among various patients. This paper represents the result and analysis regarding detecting Dementia from various machine learning models. The Open Access Series of Imaging Studies (OASIS) dataset has been used for the development of the system. The dataset is small, but it has some significant values. The dataset has been analyzed and applied in several machine learning models. Support vector machine, logistic regression, decision tree, and random forest have been used for prediction. First, the system has been run without fine-tuning and then with fine-tuning. Comparing the results, it is found that the support vector machine provides the best results among the models. It has the best accuracy in detecting Dementia among numerous patients. The system is simple and can easily help people by detecting Dementia among them.

Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods.

One of the most common visual disorders is cataracts, which people suffer from as they get older. The creation of a cloud on the lens of our eyes is known as a cataract. Blurred vision, faded colors, and difficulty seeing in strong light are the main symptoms of this condition. These symptoms frequently result in difficulty doing a variety of tasks. As a result, preliminary cataract detection and prevention may help to minimize the rate of blindness. This paper is aimed at classifying cataract disease using convolutional neural networks based on a publicly available image dataset. In this observation, four different convolutional neural network (CNN) meta-architectures, including InceptionV3, InceptionResnetV2, Xception, and DenseNet121, were applied by using the TensorFlow object detection framework. By using InceptionResnetV2, we were able to attain the avant-garde in cataract disease detection. This model predicted cataract disease with a training loss of 1.09%, a training accuracy of 99.54%, a validation loss of 6.22%, and a validation accuracy of 98.17% on the dataset. This model also has a sensitivity of 96.55% and a specificity of 100%. In addition, the model greatly minimizes training loss while boosting accuracy.