COVID-19 Semantic Pneumonia Segmentation and Classification Using Artificial Intelligence.

Coronavirus 2019 (COVID-19) has become a pandemic. The seriousness of COVID-19 can be realized from the number of victims worldwide and large number of deaths. This paper presents an efficient deep semantic segmentation network (DeepLabv3Plus). Initially, the dynamic adaptive histogram equalization is utilized to enhance the images. Data augmentation techniques are then used to augment the enhanced images. The second stage builds a custom convolutional neural network model using several pretrained ImageNet models and compares them to repeatedly trim the best-performing models to reduce complexity and improve memory efficiency. Several experiments were done using different techniques and parameters. Furthermore, the proposed model achieved an average accuracy of 99.6% and an area under the curve of 0.996 in the COVID-19 detection. This paper will discuss how to train a customized smart convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.

Separation of Different Blogs from Skin Disease Data using Artificial Intelligence.

A combination of environmental conditions may cause skin illness everywhere on the earth, and it is one of the most dangerous diseases that can develop as a result. A major goal in the selection of characteristics is to produce predictions about skin disease instances in connection with influencing variables, which is one of the most important tasks. As a consequence of the widespread usage of sensors, the amount of data collected in the health industry is disproportionately large when compared to data collected in other sectors. In the past, researchers have used a variety of machine learning algorithms to determine the relationship between illnesses and other disorders. Forecasting is a procedure that involves many steps, the most important of which are the preprocessing of any scenario and the selection of forecasting features. A major disadvantage of doing business in the health industry is a lack of data availability, which is particularly problematic when data is provided in an unstructured format. Filling in missing numbers and converting between various types of data take somewhat more than 70% of the total time. When dealing with missing data in machine learning applications, the mean, average, and median, as well as the stand mechanism, may all be employed to solve the problem. Previous research has shown that the characteristics chosen for a model's overall performance may have an influence on the overall performance of the model's overall performance. One of the primary goals of this study is to develop an intelligent algorithm for identifying relevant traits in models while simultaneously eliminating nonsignificant attributes that have an impact on model performance. To present a full view of the data, artificial intelligence techniques such as SVM, decision tree, and logistic regression models were used in conjunction with three separate feature combination methodologies, each of which was developed independently. As a consequence of this, their accuracy, F-measure, and precision are all raised by a factor of ten, respectively. We then have a list of the most important features, together with the weights that have been allocated to each of them.

Intelligent Control Techniques for the Detection of Biomedical Ear Infections.

The capacity to carry out one's regular tasks is affected to varying degrees by hearing difficulties. Poorer understanding, slower learning, and an overall reduction in efficiency in academic endeavours are just a few of the negative impacts of hearing impairments on children's performance, which may range from mild to severe. A significant factor in determining whether or not there will be a decrease in performance is the kind and source of impairment. Research has shown that the Artificial Neural Network technique is capable of modelling both linear and nonlinear solution surfaces in a trustworthy way, as demonstrated in previous studies. To improve the precision with which hearing impairment challenges are diagnosed, a neural network backpropagation approach has been developed with the purpose of fine-tuning the diagnostic process. In particular, it highlights the vital role performed by medical informatics in supporting doctors in the identification of diseases as well as the formulation of suitable choices via the use of data management and knowledge discovery. As part of the intelligent control method, it is proposed in this research to construct a Histogram Equalization (HE)-based Adaptive Center-Weighted Median (ACWM) filter, which is then used to segment/detect the OM in tympanic membrane images using different segmentation methods in order to minimise noise and improve the image quality. A tympanic membrane dataset, which is freely accessible, was used in all experiments.

Utilization of solar energy for wastewater treatment: Challenges and progressive research trends.

This article offers a trend of inventions and implementations of photocatalysis process, desalination technologies and solar disinfection techniques adapted particularly for treatment of industrial and domestic wastewater. Photocatalysis treatment of wastewater using solar energy is a promising renewable solution to reduce stresses on global water crisis. Rendering to the United Nation Environment Programme, 1/3 of world population live in water-stressed countries, while by 2025 about 2/3 of world population will face water scarcity. Major pollutants exhibited from numerous sources are critically discussed with focus on potential environmental impacts & hazards. Treatment of wastewater by photocatalysis technique, solar thermal electrochemical process, solar desalination of brackish water and solar advanced oxidation process have been presented and systematically analysed with challenges. Both heterogenous and homogenous photocatalysis techniques employed for wastewater treatment are critically reviewed. For treating domestic wastewater, solar desalination technologies adopted for purifying brackish water into potable water is presented along with key challenges and remedies. Advanced oxidation process using solar energy for degradation of organic pollutant is an important technique to be reviewed due to their effectiveness in wastewater treatment process. Present article focused on three key issues i.e. major pollutants, wastewater treatment techniques and environmental benefits of using solar power for removal of pollutants. The review also provides close ideas on further research needs and major concerns. Drawbacks associated with conventional wastewater treatment options and direct solar energy-based wastewater treatment with energy storage systems to make it convenient during day and night both listed. Although, energy storage systems increase the overall cost of the wastewater treatment plant it also increases the overall efficiency of the system on environmental cost. Cost-efficient wastewater treatment methods using solar power would significantly ensure effective water source utilization, thereby contributing towards sustainable development goals.

Digital printing of a novel electrode for stable flexible organic solar cells with a power conversion efficiency of 8.5.

Poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) mixed with single-wall nanotubes (SWNTs) (10:1) and doped with (0.1 M) perchloric acid (HClO4) in a solution-processed film, working as an excellent thin transparent conducting film (TCF) in organic solar cells, was investigated. This new electrode structure can be an outstanding substitute for conventional indium tin oxide (ITO) for applications in flexible solar cells due to the potential of attaining high transparency with enhanced conductivity, good flexibility, and good durability via a low-cost process over a large area. In addition, solution-processed vanadium oxide (VOx) doped with a small amount of PEDOT-PSS(PH1000) can be applied as a hole transport layer (HTL) for achieving high efficiency and stability. From these viewpoints, we investigate the benefit of using printed SWNTs-PEDOT-PSS doped with HClO4 as a transparent conducting electrode in a flexible organic solar cell. Additionally, we applied a VOx-PEDOT-PSS thin film as a hole transporting layer and a blend of PTB7 (polythieno[3,4-b] thiophene/benzodithiophene): PC71BM (phenyl-C71-butyric acid methyl ester) as an active layer in devices. Zinc oxide (ZnO) nanoparticles were applied as an electron transport layer and Ag was used as the top electrode. The proposed solar cell structure showed an enhancement in short-circuit current, power conversion efficiency, and stability relative to a conventional cell based on ITO. This result suggests a great carrier injection throughout the interfacial layer, high conductivity and transparency, as well as firm adherence for the new electrode.