Multi-Layered Non-Local Bayes Model for Lung Cancer Early Diagnosis Prediction with the Internet of Medical Things.

The Internet of Things (IoT) has been influential in predicting major diseases in current practice. The deep learning (DL) technique is vital in monitoring and controlling the functioning of the healthcare system and ensuring an effective decision-making process. In this study, we aimed to develop a framework implementing the IoT and DL to identify lung cancer. The accurate and efficient prediction of disease is a challenging task. The proposed model deploys a DL process with a multi-layered non-local Bayes (NL Bayes) model to manage the process of early diagnosis. The Internet of Medical Things (IoMT) could be useful in determining factors that could enable the effective sorting of quality values through the use of sensors and image processing techniques. We studied the proposed model by analyzing its results with regard to specific attributes such as accuracy, quality, and system process efficiency. In this study, we aimed to overcome problems in the existing process through the practical results of a computational comparison process. The proposed model provided a low error rate (2%, 5%) and an increase in the number of instance values. The experimental results led us to conclude that the proposed model can make predictions based on images with high sensitivity and better precision values compared to other specific results. The proposed model achieved the expected accuracy (81%, 95%), the expected specificity (80%, 98%), and the expected sensitivity (80%, 99%). This model is adequate for real-time health monitoring systems in the prediction of lung cancer and can enable effective decision-making with the use of DL techniques.

Optimization System Based on Convolutional Neural Network and Internet of Medical Things for Early Diagnosis of Lung Cancer.

Recently, deep learning and the Internet of Things (IoT) have been widely used in the healthcare monitoring system for decision making. Disease prediction is one of the emerging applications in current practices. In the method described in this paper, lung cancer prediction is implemented using deep learning and IoT, which is a challenging task in computer-aided diagnosis (CAD). Because lung cancer is a dangerous medical disease that must be identified at a higher detection rate, disease-related information is obtained from IoT medical devices and transmitted to the server. The medical data are then processed and classified into two categories, benign and malignant, using a multi-layer CNN (ML-CNN) model. In addition, a particle swarm optimization method is used to improve the learning ability (loss and accuracy). This step uses medical data (CT scan and sensor information) based on the Internet of Medical Things (IoMT). For this purpose, sensor information and image information from IoMT devices and sensors are gathered, and then classification actions are taken. The performance of the proposed technique is compared with well-known existing methods, such as the Support Vector Machine (SVM), probabilistic neural network (PNN), and conventional CNN, in terms of accuracy, precision, sensitivity, specificity, F-score, and computation time. For this purpose, two lung datasets were tested to evaluate the performance: Lung Image Database Consortium (LIDC) and Linear Imaging and Self-Scanning Sensor (LISS) datasets. Compared to alternative methods, the trial outcomes showed that the suggested technique has the potential to help the radiologist make an accurate and efficient early lung cancer diagnosis. The performance of the proposed ML-CNN was analyzed using Python, where the accuracy (2.5-10.5%) was high when compared to the number of instances, precision (2.3-9.5%) was high when compared to the number of instances, sensitivity (2.4-12.5%) was high when compared to several instances, the F-score (2-30%) was high when compared to the number of cases, the error rate (0.7-11.5%) was low compared to the number of cases, and the computation time (170 ms to 400 ms) was low compared to how many cases were computed for the proposed work, including previous known methods. The proposed ML-CNN architecture shows that this technique outperforms previous works.

Green Technology as a Way of Cleaning the Environment from Petroleum Substances in South-Eastern Poland.

BACKGROUND: In phytoextraction methods, the problem is the obtained contaminated plant biomass, the selection of the appropriate species, resistant to the type and degree of contamination, as well as the long time needed to completely clean the soil. GOAL: when selecting the appropriate method of remediation of soils contaminated with polycyclic aromatic hydrocarbons, not only the effectiveness of the method should be considered, but also the degree of contamination, the location of the site and its current and planned use. METHODS: Descriptive, laboratory and comparative methods were used. RESULTS: Soil contamination with polycyclic aromatic hydrocarbons (PAHs), which can cause mutations and cancer, is of particular concern as it affects not only human health but also vegetation growth and the biological environment. A fast, nature-friendly and cost-effective method is required to remove and minimize the hazardous effects of crude oil. CONCLUSIONS: Green technology is particularly beneficial, especially the phytoextraction technique, in which plants clean the soil of excess petroleum products, prevent its further movement from the site of contamination and prevent erosion of reclaimed soil. Species such as: Trifolium repens, Trifolium pratense, Lotus corniculatus, Agrostis stolonifera, Festuca rubra subsp. trichophylla, Arrhenatherum elatius performed their tasks very well, therefore they can be recommended for use as a factor counteracting environmental degradation.