Non-Enzymatic Glucose Sensors Composed of Polyaniline Nanofibers with High Electrochemical Performance.

The measurement of glucose concentration is a fundamental daily care for diabetes patients, and therefore, its detection with accuracy is of prime importance in the field of health care. In this study, the fabrication of an electrochemical sensor for glucose sensing was successfully designed. The electrode material was fabricated using polyaniline and systematically characterized using scanning electron microscopy, high-resolution transmission electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, and UV-visible spectroscopy. The polyaniline nanofiber-modified electrode showed excellent detection ability for glucose with a linear range of 10 µM to 1 mM and a detection limit of 10.6 µM. The stability of the same electrode was tested for 7 days. The electrode shows high sensitivity for glucose detection in the presence of interferences. The polyaniline-modified electrode does not affect the presence of interferences and has a low detection limit. It is also cost-effective and does not require complex sample preparation steps. This makes it a potential tool for glucose detection in pharmacy and medical diagnostics.

Electrochemical Sensing of H2O2 by Employing a Flexible Fe3O4/Graphene/Carbon Cloth as Working Electrode.

We report the synthesis of Fe3O4/graphene (Fe3O4/Gr) nanocomposite for highly selective and highly sensitive peroxide sensor application. The nanocomposites were produced by a modified co-precipitation method. Further, structural, chemical, and morphological characterization of the Fe3O4/Gr was investigated by standard characterization techniques, such as X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscope (TEM) and high-resolution TEM (HRTEM), Fourier transform infrared (FTIR), and X-ray photoelectron spectroscopy (XPS). The average crystal size of Fe3O4 nanoparticles was calculated as 14.5 nm. Moreover, nanocomposite (Fe3O4/Gr) was employed to fabricate the flexible electrode using polymeric carbon fiber cloth or carbon cloth (pCFC or CC) as support. The electrochemical performance of as-fabricated Fe3O4/Gr/CC was evaluated toward H2O2 with excellent electrocatalytic activity. It was found that Fe3O4/Gr/CC-based electrodes show a good linear range, high sensitivity, and a low detection limit for H2O2 detection. The linear range for the optimized sensor was found to be in the range of 10-110 µM and limit of detection was calculated as 4.79 µM with a sensitivity of 0.037 µA µM-1 cm-2. The cost-effective materials used in this work as compared to noble metals provide satisfactory results. As well as showing high stability, the proposed biosensor is also highly reproducible.

Facile Fabrication of CuO Nanoparticles Embedded in N-Doped Carbon Nanostructure for Electrochemical Sensing of Dopamine.

In the present study, a highly selective and sensitive electrochemical sensing platform for the detection of dopamine was developed with CuO nanoparticles embedded in N-doped carbon nanostructure (CuO@NDC). The successfully fabricated nanostructures were characterized by standard instrumentation techniques. The fabricated CuO@NDC nanostructures were used for the development of dopamine electrochemical sensor. The reaction mechanism of a dopamine on the electrode surface is a three-electron three-proton process. The proposed sensor's performance was shown to be superior to several recently reported investigations. Under optimized conditions, the linear equation for detecting dopamine by differential pulse voltammetry is I pa (µA) = 0.07701 c (µM) - 0.1232 (R 2 = 0.996), and the linear range is 5-75 µM. The limit of detection (LOD) and sensitivity were calculated as 0.868 µM and 421.1 µA/µM, respectively. The sensor has simple preparation, low cost, high sensitivity, good stability, and good reproducibility.

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.