Artificial neural network and its applications in quality process control, document recognition and biomedical imaging

In computer-vision based system a digital image obtained by a digital camera would usually have 24-bit color image. The analysis of an image with that many levels might require complicated image processing techniques and higher computational costs. But in real-time application, where a part has to be inspected within a few milliseconds, either we have to reduce the image to a more manageable number of gray levels, usually two levels (binary image), and at the same time retain all necessary features of the original image or develop a complicated technique. A binary image can be obtained by thresholding the original image into two levels. Therefore, thresholding of a given image into binary image is a necessary step for most image analysis and recognition techniques. In this thesis, we have studied the effectiveness of using artificial neural network (ANN) in pharmaceutical, document recognition and biomedical imaging applications for image thresholding and classification purposes. Finally, we have developed edge-based, ANN-based and region-growing based image thresholding techniques to extract low contrast objects of interest and classify them into respective classes in those applications. Real-time quality inspection of gelatin capsules in pharmaceutical applications is an important issue from the point of view of industry\u27s productivity and competitiveness. Computer vision-based automatic quality inspection and controller system is one of the solutions to this problem. Machine vision systems provide quality control and real-time feedback for industrial processes, overcoming physical limitations and subjective judgment of humans. In this thesis, we have developed an image processing system using edge-based image thresholding techniques for quality inspection that satisfy the industrial requirements in pharmaceutical applications to pass the accepted and rejected capsules. In document recognition application, success of OCR mostly depends on the quality of the thresholded image. Non-uniform illumination, low contrast and complex background make it challenging in this application. In this thesis, optimal parameters for ANN-based local thresholding approach for gray scale composite document image with non-uniform background is proposed. An exhaustive search was conducted to select the optimal features and found that pixel value, mean and entropy are the most significant features at window size 3x3 in this application. For other applications, it might be different, but the procedure to find the optimal parameters is same. The average recognition rate 99.25% shows that the proposed 3 features at window size 3x3 are optimal in terms of recognition rate and PSNR compare to the ANN-based thresholding technique with different parameters presented in the literature. In biomedical imaging application, breast cancer continues to be a public health problem. In this thesis we presented a computer aided diagnosis (CAD) system for mass detection and classification in digitized mammograms, which performs mass detection on regions of interest (ROI) followed by the benign-malignant classification on detected masses. Three layers ANN with seven features is proposed for classifying the marked regions into benign and malignant and 90.91% sensitivity and 83.87% specificity is achieved that is very much promising compare to the radiologist\u27s sensitivity 75%

Jizz and the joy of pattern recognition:virtuosity, discipline and the agency of insight in UK naturalists’ arts of seeing

Approaches to visual skilling from anthropology and STS have tended to highlight the forces of discipline and control in understanding how shared visual accounts of the world are created in the face of potential differences brought about by multi-sensorial perception. Drawing upon a range of observational and interview material from an immersion in naturalist training and biological recording activities between 2003 and 2009, I focus upon jizz, a distinct form of gestalt perception much coveted by naturalist communities in the UK. Jizz is described as a tacit and embodied way of seeing that instantaneously reveals the identity of a species, relying upon but simultaneously suspending the arduous and meticulous study of an organism’s diagnostic characteristics. I explore the potential and limitations of jizz to allow for both visual precision and an enchanted and varied form of encounter with nature. In so doing, I explore how the specific characteristics of wild, intangible and irreverent virtuoso performance work closely together with disciplining taxonomic standards. As such, discipline and irreverence work together, are mutually enabling, and allow for an accommodation rather than a segregation of potential difference brought about by perceptual variety

The effect of insect herbivory on the growth and fitness of introduced Verbascum thapsus L.

A majority of the plant species that are introduced into new ranges either do not become established, or become naturalized yet do not attain high densities and are thus considered ecologically and economically unproblematic. The factors that limit these relatively “benign” species are not well studied. The biotic resistance hypothesis predicts that herbivores, pathogens and competition reduce growth and reproduction of individual plants and so suppress population growth of non-native species. We explored the effect of insect herbivory and surrounding vegetation on growth and fitness of the non-native biennial plant Verbascum thapsus (common mullein) in Colorado, USA. Mullein is widespread in its introduced North American range, yet is infrequently considered a management concern because populations are often ephemeral and restricted to disturbed sites. To evaluate the impact of insect herbivores on mullein performance, we reduced herbivory using an insecticide treatment and compared sprayed plants to those exposed to ambient levels of herbivory. Reducing herbivory increased survival from rosette to reproduction by 7%, from 70–77%. Of plants that survived, reducing herbivory increased plant area in the first year and plant height, the length of the reproductive spike, and seed set during the second year. Reducing herbivory also had a marked effect on plant fitness, increasing seed set by 50%, from about 48,000 seeds per plant under ambient herbivory to about 98,000 per plant under reduced herbivory. Our findings also highlight that the relationship between herbivory and performance is complex. Among plants exposed to ambient herbivory, we observed a positive relationship between damage and performance, suggesting that, as predicted by the plant vigor hypothesis, insect herbivores choose the largest plants for feeding when their choice is not restricted by insecticide treatment. In contrast to the strong effects of experimentally reduced herbivory, we found that cover of other plants surrounding our focal plants explained relatively little variation in performance outcomes. Overall, we found that herbivore-induced impacts on individual plant performance and seed set are substantial, and thus may help prevent this naturalized species from becoming dominant in undisturbed recipient communities

Quantifying Seagrass Distribution in Coastal Water With Deep Learning Models

Coastal ecosystems are critically affected by seagrass, both economically and ecologically. However, reliable seagrass distribution information is lacking in nearly all parts of the world because of the excessive costs associated with its assessment. In this paper, we develop two deep learning models for automatic seagrass distribution quantification based on 8-band satellite imagery. Specifically, we implemented a deep capsule network (DCN) and a deep convolutional neural network (CNN) to assess seagrass distribution through regression. The DCN model first determines whether seagrass is presented in the image through classification. Second, if seagrass is presented in the image, it quantifies the seagrass through regression. During training, the regression and classification modules are jointly optimized to achieve end-to-end learning. The CNN model is strictly trained for regression in seagrass and non-seagrass patches. In addition, we propose a transfer learning approach to transfer knowledge in the trained deep models at one location to perform seagrass quantification at a different location. We evaluate the proposed methods in three WorldView-2 satellite images taken from the coastal area in Florida. Experimental results show that the proposed deep DCN and CNN models performed similarly and achieved much better results than a linear regression model and a support vector machine. We also demonstrate that using transfer learning techniques for the quantification of seagrass significantly improved the results as compared to directly applying the deep models to new locations

FLY-CAPS- A Hybrid Firefly Feature Optimized Capsule Networks for Plant Disease Classification in Resource Constriant Internet of Things (IoT)

Recent advancements in artificial intelligence, automation, and the Internet of Things (IoT) enable farmers to better monitor and diagnose all agricultural procedures with super-intellectual accuracy. These technologies also contribute to boosting the productivity of agriculture, which increases the country’s economy. Though these technologies help farmers increase productivity, the detection of plant diseases still needs heightened scrutiny for prevention and cultivation. Plant disease categorization has expanded with the introduction of deep learning algorithms, but it still needs more innovation in terms of accuracy and computing burden. Thus, a novel deep learning model based on capsule networks with firefly optimization and potent multi-layered feedforward prediction networks is proposed in this research. The handcrafted features in this proposed system are optimized before being extracted using a capsule network, which reduces the complexity overhead and is suitable for IoT devices with limited resources. Finally fed to the feed forward layers for better classification. The extensive experimentation has been tested with the Plant Village databases, which contain more than 50,000 images of healthy and infected plants. Performance criteria including recall, specificity, recall, accuracy, and f1-score are used to assess the proposed algorithm's performance. Additionally, its efficiency and computational cost are contrasted with those of other recent models. The suggested model has greater performance (95%) with reduced computing overhead, according to experimental data, which is advantageous for the new prediction approach and the welfare of the farmer

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

In Agriculture, orchards are the deciding factor in the country’s economy. There are many orchards, and citrus and sugarcane will cover 60 percent of them. These citrus orchards satisfy the necessity of citrus fruits and citrus products, and these citrus fruits contain more vitamin C. The citrus orchards have had some problems generating good yields and quality products. Pathogenic diseases, pests, and water shortages are the three main problems that plants face. Farmers can find these problems early on with the support of machine learning and deep learning, which may also change how they feel about technology.  By doing this in agriculture, the farmers can cut off the major issues of yield and quality losses. This review gives enormous methods for identifying and classifying plant pathogens, pests, and water stresses using image-based work. In this review, the researchers present detailed information about citrus pathogens, pests, and water deficits. Methods and techniques that are currently available will be used to validate the problem. These will include pre-processing for intensification, segmentation, feature extraction, and selection processes, machine learning-based classifiers, and deep learning models. In this work, researchers thoroughly examine and outline the various research opportunities in the field. This review provides a comprehensive analysis of citrus plants and orchards; Researchers used a systematic review to ensure comprehensive coverage of this topic

Improved Sketch-to-Photo Generation Using Filter Aided Generative Adversarial Network

Generating a photographic face image from given input sketch is most challenging task in computer vision. Mainly the sketches drawn by sketch artist used in human identification. Sketch to photo synthesis is very important applications in law enforcement as well as character design, educational training. In recent years Generative Adversarial Network (GAN) shows excellent performance on sketch to photo synthesis problem.  Quality of hand drawn sketches affects the quality generated photo. It might be possible that while handling the hand drawn sketches, accidently by touching the user hand on pencil sketch or similar activities causes noise in given sketch. Likewise different styles like shading, darkness of pencil used by sketch artist may cause unnecessary noise in sketches. In recent year many sketches to photo synthesis methods are proposed, but they are mainly focused on network architecture to get better performance. In this paper we proposed Filter-aided GAN framework to remove such noise while synthesizing photo images from hand drawn sketches. Here we implement and compare different filtering methods with GAN.  Quantitative and qualitative result shows that proposed Filter-aided GAN generate the photo images which are visually pleasant and closer to ground truth image

A spatially-organized multicellular innate immune response in lymph nodes limits systemic pathogen spread

The lymphatic network that transports interstitial fluid and antigens to lymph nodes constitutes a conduit system that can be hijacked by invading pathogens to achieve systemic spread unless dissemination is blocked in the lymph node itself. Here, we show that a network of diverse lymphoid cells (natural killer cells, γδ T cells, natural killer T cells, and innate-like CD8+ T cells) are spatially prepositioned close to lymphatic sinus-lining sentinel macrophages where they can rapidly and efficiently receive inflammasome-generated IL-18 and additional cytokine signals from the pathogen-sensing phagocytes. This leads to rapid IFNγ secretion by the strategically positioned innate lymphocytes, fostering antimicrobial resistance in the macrophage population. Interference with this innate immune response loop allows systemic spread of lymph-borne bacteria. These findings extend our understanding of the functional significance of cellular positioning and local intercellular communication within lymph nodes while emphasizing the role of these organs as highly active locations of innate host defense