DIAGNOSTYKA PĘCHERZYCY Z WYKORZYSTANIEM SZTUCZNEJ INTELIGENCJI: PODEJŚCIE OPARTE NA UCZENIU MASZYNOWYM DO AUTOMATYCZNEGO WYKRYWANIA ZMIAN SKÓRNYCH

Pemphigus is a skin disease that can cause a serious damage to human skin. Pemphigus can result in other issues including painful patches and infected blisters, which can result in sepsis, weight loss, and starvation, all of which can be life-threatening, tooth decay and gum disease. Early prediction of Pemphigus may save us from fatal disease. Machine learning has the potential to offer a highly efficient approach for decision-making and precise forecasting. The healthcare sector is experiencing remarkable advancements through the utilization of machine learning techniques. Therefore, to identify Pemphigus using images, we suggested machine learning-based techniques. This proposed system uses a large dataset collected from various web sources to detect Pemphigus. Augmentation has been applied on our dataset using techniques such as zoom, flip, brightness, distortion, magnitude, height, width to enhance the breadth and variety of the dataset and improve model’s performance. Five popular machine learning algorithms has been employed to train and evaluate model, these are K-Nearest Neighbor (referred to as KNN), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and Convolutional Neural Network (CNN). Our outcome indicate that the CNN based model outperformed the other algorithms by achieving accuracy of 93% whereas LR, KNN, RF and DT achieved accuracies of 78%, 70%, 85% and 75% respectively.Pęcherzyca to choroba skóry, która może powodować poważne uszkodzenia ludzkiej skóry. Pęcherzyca może powodować inne problemy,  w tym bolesne plamy i zakażone pęcherze, które mogą skutkować sepsą, utratą masy ciała i łaknienia, co może zagrażać życiu, próchnicą zębów i chorób dziąseł. Wczesne wykrycie pęcherzycy może uchronić przed śmiertelną chorobą. Uczenie maszynowe może zaoferować wysoce efektywne podejście do podejmowania decyzji i precyzyjnego prognozowania. Sektor opieki zdrowotnej doświadcza niezwykłych postępów dzięki wykorzystaniu technik uczenia maszynowego. Dlatego do identyfikacji pęcherzycy za pomocą obrazów zaproponowano techniki oparte na uczeniu maszynowym. Proponowany system wykorzystuje duży zbiór danych zebranych z różnych źródeł internetowych do wykrywania pęcherzycy. W zbiorze danych zastosowano augmentację przy użyciu technik takich jak powiększanie, odwracanie, zmiana jasności, zniekształcenie, zmiana wielkości, wysokość i szerokości, aby zwiększyć zakres i różnorodność zbioru danych oraz poprawić wydajność modelu. Do uczenia i oceny modelu wykorzystano pięć popularnych algorytmów uczenia maszynowego, są to: K-Nearest Neighbor (określany jako KNN), drzewo decyzyjne (DT), regresja logistyczna (LR), las losowy (RF) i konwolucyjną sieć neuronowa (CNN). Uzyskane wyniki wskazują, że model oparty na CNN był lepszy od innych algorytmów, osiągając dokładność na poziomie 93%, podczas gdy LR, KNN, RF i DT osiągnęły dokładność odpowiednio 78%, 70%, 85% i 75%.

An overview of the experimental research use of lysimeters

The lysimeter is most often defined as a box filled with soil with an intact structure for measuring the amount of infiltration and evapotranspiration in natural conditions. At the bottom of the device there is an outflow for atmospheric precipitation water infiltrating to a measuring container. Lysimeter studies are included in the group of dynamic leaching tests in which the leaching solution is added in a specified volume over a specific period of time. Lysimeter studies find applications in, amongst others, agrotechnics, hydrogeology and geochemistry. Lysimeter tests may vary in terms of the type of soil used (anthropogenic soil, natural soil), sample size, leaching solution, duration of the research and the purpose for conducting it. Lysimeter experiments provide more accurate results for leaching tests compared with static leaching tests. Unlike several-day tests, they should last for at least a year. There are about 2,500 lysimeters installed in nearly 200 stations around Europe. The vast majority of these (84%) are non-weighing lysimeters. There are a few challenges for lysimeter research mostly connected with the construction of the lysimeter, estimating leaching results and calibrating numerical transport models with data obtained from lysimeters. This review is devoted to the analysis of the principal types of lysimeters described in the literature within the context of their application. The aim of this study is to highlight the role of lysimeters in leaching studies

Image-based Skin Disease Detection and Classification through Bioinspired Machine Learning Approaches

A self-learning disease detection model will be useful for identifying skin infections in suspected individuals using skin images of infected patients. To detect skin diseases, some AI-based bioinspired models employ skin images. Skin infection is a common problem that is currently faced due to various reasons, such as food, water, environmental factors, and many others. Skin infections such as psoriasis, skin cancer, monkeypox, and tomato flu, among others, have a lower death rate but a significant impact on quality of life. Neural Networks (NNs) and Swarm intelligence (SI) based approaches are employed for skin disease diagnosis and classification through image processing. In this paper, the convolutional neural networks-based Cuckoo search algorithm (CNN-CS) is trained using the well-known multi-objective optimization technique cuckoo search. The performance of the suggested CNN-CS model is evaluated by comparing it with three commonly used metaheuristic-based classifiers: CNN-GA, CNN-BAT, and CNN-PSO. This comparison was based on various measures, including accuracy, precision, recall, and F1-score. These measures are calculated using the confusion matrices from the testing phase. The results of the experiments revealed that the proposed model has outperformed the others, achieving an accuracy of 97.72%

IoT Health Devices: Exploring Security Risks in the Connected Landscape

The concept of the Internet of Things (IoT) spans decades, and the same can be said for its inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable potential in expanding care. However, the application of the IoT in healthcare is fraught with an array of challenges, and also, through it, numerous vulnerabilities that translate to wider attack surfaces and deeper degrees of damage possible to both consumers and their confidence within health systems, as a result of patient-specific data being available to access. Further, when IoT health devices (IoTHDs) are developed, a diverse range of attacks are possible. To understand the risks in this new landscape, it is important to understand the architecture of IoTHDs, operations, and the social dynamics that may govern their interactions. This paper aims to document and create a map regarding IoTHDs, lay the groundwork for better understanding security risks in emerging IoTHD modalities through a multi-layer approach, and suggest means for improved governance and interaction. We also discuss technological innovations expected to set the stage for novel exploits leading into the middle and latter parts of the 21st century

A review of lysimeter experiments carried out on municipal landfill waste

The groundwater risk assessment in the vicinity of landfill sites requires, among others, representative monitoring and testing for pollutants leaching from the waste. Lysimeter studies can serve as an example of dynamic leaching tests. However, due to the bacteriological composition of the municipal waste, they are rarely carried out. These tests allow for the proper design of the landfill protection system against migration of pollutants into the ground, assessment of bacteriological, biochemical and chemical risk for the groundwater, determination of the water balance of leachate as well as examination of the course of processes taking place in the waste landfill with a diversified access to oxygen. This paper addresses the issue of performing lysimeter studies on a sample of municipal waste in various scientific centers. It analyzes the size of lysimeters, their construction, the method of water supply, the duration of the experiment, the scope of research, and the purpose of lysimeter studies

An Intelligent System for Monitoring Skin Diseases

The practical increase of interest in intelligent technologies has caused a rapid development of all activities in terms of sensors and automatic mechanisms for smart operations. The implementations concentrate on technologies which avoid unnecessary actions on user side while examining health conditions. One of important aspects is the constant inspection of the skin health due to possible diseases such as melanomas that can develop under excessive influence of the sunlight. Smart homes can be equipped with a variety of motion sensors and cameras which can be used to detect and identify possible disease development. In this work, we present a smart home system which is using in-built sensors and proposed artificial intelligence methods to diagnose the skin health condition of the residents of the house. The proposed solution has been tested and discussed due to potential use in practice