Intelligent Wearable Devices Enabled Automatic Vehicle Detection and Tracking System with Video-Enabled UAV Networks Using Deep Convolutional Neural Network and IoT Surveillance.

The discipline of computer vision is becoming more popular as a research subject. In a surveillance-based computer vision application, item identification and tracking are the core procedures. They consist of segmenting and tracking an object of interest from a sequence of video frames, and they are both performed using computer vision algorithms. In situations when the camera is fixed and the backdrop remains constant, it is possible to detect items in the background using more straightforward methods. Aerial surveillance, on the other hand, is characterized by the fact that the target, as well as the background and video camera, are all constantly moving. It is feasible to recognize targets in the video data captured by an unmanned aerial vehicle (UAV) using the mean shift tracking technique in combination with a deep convolutional neural network (DCNN). It is critical that the target detection algorithm maintains its accuracy even in the presence of changing lighting conditions, dynamic clutter, and changes in the scene environment. Even though there are several approaches for identifying moving objects in the video, background reduction is the one that is most often used. An adaptive background model is used to create a mean shift tracking technique, which is shown and implemented in this work. In this situation, the background model is provided and updated frame-by-frame, and therefore, the problem of occlusion is fully eliminated from the equation. The target tracking algorithm is fed the same video stream that was used for the target identification algorithm to work with. In MATLAB, the works are simulated, and their performance is evaluated using image-based and video-based metrics to establish how well they operate in the real world.

Glaucoma Detection Using Image Processing and Supervised Learning for Classification.

A difficult challenge in the realm of biomedical engineering is the detection of physiological changes occurring inside the human body, which is a difficult undertaking. At the moment, these irregularities are graded manually, which is very difficult, time-consuming, and tiresome due to the many complexities associated with the methods involved in their identification. In order to identify illnesses at an early stage, the use of computer-assisted diagnostics has acquired increased attention as a result of the requirement of a disease detection system. The major goal of this proposed work is to build a computer-aided design (CAD) system to help in the early identification of glaucoma as well as the screening and treatment of the disease. The fundus camera is the most affordable image analysis modality available, and it meets the financial needs of the general public. The extraction of structural characteristics from the segmented optic disc and the segmented optic cup may be used to characterize glaucoma and determine its severity. For this study, the primary goal is to estimate the potential of the image analysis model for the early identification and diagnosis of glaucoma, as well as for the evaluation of ocular disorders. The suggested CAD system would aid the ophthalmologist in the diagnosis of ocular illnesses by providing a second opinion as a judgment made by human specialists in a controlled environment. An ensemble-based deep learning model for the identification and diagnosis of glaucoma is in its early stages now. This method's initial module is an ensemble-based deep learning model for glaucoma diagnosis, which is the first of its kind ever developed. It was decided to use three pretrained convolutional neural networks for the categorization of glaucoma. These networks included the residual network (ResNet), the visual geometry group network (VGGNet), and the GoogLeNet. It was necessary to use five different data sets in order to determine how well the proposed algorithm performed. These data sets included the DRISHTI-GS, the Optic Nerve Segmentation Database (DRIONS-DB), and the High-Resolution Fundus (HRF). Accuracy of 91.11% for the PSGIMSR data set and the sensitivity of 85.55% and specificity of 95.20% for the suggested ensemble architecture on the PSGIMSR data set were achieved. Similarly, accuracy rates of 95.63%, 98.67%, 95.64%, and 88.96% were achieved using the DRIONS-DB, HRF, DRISHTI-GS, and combined data sets, respectively.

Empirical Analysis of Apnea Syndrome Using an Artificial Intelligence-Based Granger Panel Model Approach.

Sleep apnea is a serious sleep disorder that occurs when a person's breathing is interrupted during sleep. People with untreated sleep apnea stop breathing repeatedly during their sleep. This study provides an empirical analysis of apnea syndrome using the AI-based Granger panel model approach. Data were collected from the MIT-BIH polysomnographic database (SLPDB). The panel is composed of eighteen patients, while the implementation was done using MATLAB software. The results show that, for the eighteen patients with sleep apnea, there was a significant relationship between ECG-blood pressure (BP), ECG-EEG, and EEG-blood pressure (BP). The study concludes that the long-term interaction between physiological signals can help the physician to understand the risks associated with these interactions. The study would assist physicians to understand the mechanisms underlying obstructive sleep apnea early and also to select the right treatment for the patients by leveraging the potential of artificial intelligence. The researchers were motivated by the need to reduce the morbidity and mortality arising from sleep apnea using AI-enabled technology.

Detection of Breast Cancer Using Histopathological Image Classification Dataset with Deep Learning Techniques.

Cancer is one of the top causes of mortality, and it arises when cells in the body grow abnormally, like in the case of breast cancer. For people all around the world, it has now become a huge issue and a threat to their safety and wellbeing. Breast cancer is one of the major causes of death among females all over the globe, and it is particularly prevalent in the United States. It is possible to diagnose breast cancer using a variety of imaging modalities including mammography, computerized tomography (CT), magnetic resonance imaging (MRI), ultrasound, and biopsies, among others. To analyze the picture, a histopathology study (biopsy) is often performed, which assists in the diagnosis of breast cancer. The goal of this study is to develop improved strategies for various CAD phases that will play a critical role in minimizing the variability gap between and among observers. It created an automatic segmentation approach that is then followed by self-driven post-processing activities to successfully identify the Fourier Transform based Segmentation in the CAD system to improve its performance. When compared to existing techniques, the proposed segmentation technique has several advantages: spatial information is incorporated, there is no need to set any initial parameters beforehand, it is independent of magnification, it automatically determines the inputs for morphological operations to enhance segmented images so that pathologists can analyze the image with greater clarity, and it is fast. Extensive tests were conducted to determine the most effective feature extraction techniques and to investigate how textural, morphological, and graph characteristics impact the accuracy of categorization classification. In addition, a classification strategy for breast cancer detection has been developed that is based on weighted feature selection and uses an upgraded version of the Genetic Algorithm in conjunction with a Convolutional Neural Network Classifier. The practical application of the suggested improved segmentation and classification algorithms for the CAD framework may reduce the number of incorrect diagnoses and increase the accuracy of classification. So, it may serve as a second opinion tool for pathologists and aid in the early detection of diseases.

Biotransformation of organic micropollutants by anaerobic sludge enzymes.

Biotransformation of organic micropollutants (OMPs) in wastewater treatment plants ultimately depends on the enzymatic activities developed in each biological process. However, few research efforts have been made to clarify and identify the role of enzymes on the removal of OMPs, which is an essential knowledge to determine the biotransformation potential of treatment technologies. Therefore, the purpose of the present study was to investigate the enzymatic transformation of 35 OMPs under anaerobic conditions, which have been even less studied than aerobic systems. Initially, 13 OMPs were identified to be significantly biotransformed (>20%) by anaerobic sludge obtained from a full-scale anaerobic digester, predestining them as potential targets of anaerobic enzymes. Native enzymes were extracted from this anaerobic sludge to perform transformation assays with the OMPs. In addition, the effect of detergents to recover membrane enzymes, as well as the effects of cofactors and inhibitors to promote and suppress specific enzymatic activities were evaluated. In total, it was possible to recover enzymatic activities towards 10 out of these 13 target OMPs (acetyl-sulfamethoxazole and its transformation product sulfamethoxazole, acetaminophen, atenolol, clarithromycin, citalopram, climbazole, erythromycin, and terbutryn, venlafaxine) as well as towards 8 non-target OMPs (diclofenac, iopamidol, acyclovir, acesulfame, and 4 different hydroxylated metabolites of carbamazepine). Some enzymatic activities likely involved in the anaerobic biotransformation of these OMPs were identified. Thereby, this study is a starting point to unravel the still enigmatic biotransformation of OMPs in wastewater treatment systems.

Insights into the variability of microbial community composition and micropollutant degradation in diverse biological wastewater treatment systems.

The biological potential of conventional wastewater treatment plants to remove micropollutants mainly depends on process conditions and the predominant microbial community. To explore this dependence and to connect the occurrence of genera with operating conditions, five pilot-scale reactors with different process conditions were combined into two reactor cascades and fed with the effluent of the primary clarifier of a municipal WWTP. All reactors and the WWTP were analyzed for the removal of 33 micropollutants by LC-MS/MS and the presence of the microbial community using 16S rRNA gene sequencing. The overall removal of the micropollutants was slightly improved (ca. 20%) by the reactor cascades in comparison to the WWTP while certain compounds such as diatrizoate, venlafaxine or diclofenac showed an enhanced removal (ca. 70% in one or both cascades). To explore the diverse bacteria in more detail, the general community was divided into a core and a specialized community. Despite their profoundly different operating parameters (especially redox conditions), the different treatments share a core community consisted of 143 genera (9% of the overall community). Furthermore, the alpha- and beta-biodiversity as well as the occurrence of several genera belonging to the specialized microbial community could be linked to the prevalent process conditions of the individual treatments. Members of the specialized community also correlated with the removal of certain groups of micropollutants. Hence, the comparison of the specialized community with micropollutant removal and operating conditions via correlation analysis is a valuable tool for an extended evaluation of prevalent process conditions. Based on an extended data set this approach could also be used to identify organisms as indicators for operating conditions which are beneficial for an improved removal of specific micropollutants.

Micropollutant degradation via extracted native enzymes from activated sludge.

A procedure was developed to assess the biodegradation of micropollutants in cell-free lysates produced from activated sludge of a municipal wastewater treatment plant (WWTP). This proof-of-principle provides the basis for further investigations of micropollutant biodegradation via native enzymes in a solution of reduced complexity, facilitating downstream protein analysis. Differently produced lysates, containing a variety of native enzymes, showed significant enzymatic activities of acid phosphatase, ß-galactosidase and ß-glucuronidase in conventional colorimetric enzyme assays, whereas heat-deactivated controls did not. To determine the enzymatic activity towards micropollutants, 20 compounds were spiked to the cell-free lysates under aerobic conditions and were monitored via LC-ESI-MS/MS. The micropollutants were selected to span a wide range of different biodegradabilities in conventional activated sludge treatment via distinct primary degradation reactions. Of the 20 spiked micropollutants, 18 could be degraded by intact sludge under assay conditions, while six showed reproducible degradation in the lysates compared to the heat-deactivated negative controls: acetaminophen, N-acetyl-sulfamethoxazole (acetyl-SMX), atenolol, bezafibrate, erythromycin and 10,11-dihydro-10-hydroxycarbamazepine (10-OH-CBZ). The primary biotransformation of the first four compounds can be attributed to amide hydrolysis. However, the observed biotransformations in the lysates were differently influenced by experimental parameters such as sludge pre-treatment and the addition of ammonium sulfate or peptidase inhibitors, suggesting that different hydrolase enzymes were involved in the primary degradation, among them possibly peptidases. Furthermore, the transformation of 10-OH-CBZ to 9-CA-ADIN was caused by a biologically-mediated oxidation, which indicates that in addition to hydrolases further enzyme classes (probably oxidoreductases) are present in the native lysates. Although the full variety of indigenous enzymatic activity of the activated sludge source material could not be restored, experimental modifications, e.g. different lysate filtration, significantly enhanced specific enzyme activities (e.g. >96% removal of the antibiotic erythromycin). Therefore, the approach presented in this study provides the experimental basis for a further elucidation of the enzymatic processes underlying wastewater treatment on the level of native proteins.