Artificial Neural Network based Cancer Cell Classification

This paper addresses the system which achieves auto-segmentation and cell characterization for prediction of percentage of carcinoma (cancerous) cells in the given image with high accuracy. The system has been designed and developed for analysis of medical pathological images based on hybridization of syntactic and statistical approaches, using Artificial Neural Network as a classifier tool (ANN) [2]. This system performs segmentation and classification as is done in human vision system [1] [9] [10] [12], which recognize objects; perceives depth; identifies different textures, curved surfaces, or a surface inclination by texture information and brightness. In this paper, an attempt has been made to present an approach for soft tissue characterization utilizing texture-primitive features and segmentation with Artificial Neural Network (ANN) classifier tool. The present approach directly combines second, third, and fourth steps into one algorithm. This is a semi-supervised approach in which supervision is involved only at the level of defining structure of Artificial Neural Network; afterwards, algorithm itself scans the whole image and performs the segmentation and classification in unsupervised mode. Finally, algorithm was applied to selected pathological images for segmentation and classification. Results were in agreement with those with manual segmentation and were clinically correlated [18] [21]. Keywords: Grey scale images, Histogram equalization, Gausian filtering, Haris corner detector, Threshold, Seed point, Region growing segmentation, Tamura texture feature extraction, Artificial Neural Network(ANN), Artificial Neuron, Synapses, Weights, Activation function, Learning function, Classification matrix

Industrial Fluids Components Health Management Using Deep Learning

The fatigue state of fluid components such as valves, metal surfaces in gas or oil carrying pipelines is important to monitor on regular basis and plan for repair work to avoid risks associated with them, this becomes more crucial when the pipelines are supplying hazard prone fluids. There exist methods for detection of corroded surfaces, scratches and fractures in pipelines, valves, and regulators etcetera. The conventional methods are based on sensors and chemical analysis methods. There are challenges with conventional methods pertaining to the desired metric of scalability and disadvantages of these methods is they are contact based and destructive methods. Therefore, to overcome these limitations of existing methods there is a need for development of non-contact and nondestructive methods. The recent advancements in Artificial Intelligence technology in every domain including health care monitoring, agriculture sector, defense applications and civilian applications etc., have shown that deep learning methods can be explored in industrial applications to develop fault tolerant systems which help fluid components state of health monitoring through computer vision. In this chapter proposes various methods for analysis of health state of fluid components using deep convolutional neural networks and suggest the best models for these applications

Circle fitting of boundaries of microarray spots

A new circle fitting algorithm is presented that generates the best circle to approximate the boundary of a given microarray spot. Here, the best approximation means, the total differential area between the circumference of the circle and the boundary of the spot is kept minimum. The center and the radius of the circle are determined to satisfy this condition. The differential area is used as a quantitative metric to represent the boundary quality of the corresponding microarray spot

An overview on development of policies and barriers for wind energy generation: Indian scenario

In the past wind energy is harnessed for attaining some valuable work like grains grinding, water pumping, and even boat sailing over a long-time. However, times have been changed from conventional utilization of wind energy to electricity generation in modern days. Wind energy is believed to be one of the purest kinds of renewable energy. India's wind energy resource potential is estimated to be 102 GW at 80m and 302 GW at 100m of hub height. The immense potential of wind energy which has been kept uninvestigated can be accomplished through fair framed policies. The present paper focused on a comprehensive analysis of the Indian government planning to expand its wind energy business by offering financial incentives and development policies. In this paper, Indian wind energy policies have been intensely analyzed and various barriers to achieving the success of these schemes and programs have been discussed. The summary of the present paper is to reiterate the work carried on the wind energy sector in terms of enhanced fiscal incentives, minimized energy pricing, offshore wind farm prospects, and market growth stability by the Indian government (both central and state)

Rice leaves disease classification using deep convolutional neural network

The rice disease due to fungus, bacteria, spot and sheath blight, leaf scald effects the crops yield. The farmers have limitation predicting the quality on the crop for large scale evaluation. Therefore, there is a need for an automatic leaves disease prediction tool to assists to apply corrective procedures. Deep learning models have outperformed in several sectors of computer vision. In this paper a deep leaning model based on pre-trained CNN is customized through altering the architecture of the models and apply transfer learning methods and the resulting model named PaddyLeaf15 CNN is evaluated on the benchmark dataset from Kaggle. The results indicate that the proposed model outperforms as compared to VGG-16 and Inception V3 based models with highest model accuracy of 95%

Circle Fitting of Boundaries of Microarray Spots

A new circle fitting algorithm is presented that generates the best circle to approximate the boundary of a given microarray spot. Here, the best approximation means, the total differential area between the circumference of the circle and the boundary of the spot is kept minimum. The center and the radius of the circle are determined to satisfy this condition. The differential area is used as a quantitative metric to represent the boundary quality of the corresponding microarray spot

Rice Leaves Disease Classification Using Deep Convolutional Neural Network

The rice disease due to fungus, bacteria, spot and sheath blight, leaf scald effects the crops yield. The farmers have limitation predicting the quality on the crop for large scale evaluation. Therefore, there is a need for an automatic leaves disease prediction tool to assists to apply corrective procedures. Deep learning models have outperformed in several sectors of computer vision. In this paper a deep leaning model based on pre-trained CNN is customized through altering the architecture of the models and apply transfer learning methods and the resulting model named PaddyLeaf15 CNN is evaluated on the benchmark dataset from Kaggle. The results indicate that the proposed model outperforms as compared to VGG-16 and Inception V3 based models with highest model accuracy of 95%

An Effective Deep Learning Model for Health Monitoring and Detection of COVID-19 Infected Patients: An End-to-End Solution

The COVID-19 infection is the greatest danger to humankind right now because of the devastation it causes to the lives of its victims. It is important that infected people be tested in a timely manner in order to halt the spread of the disease. Physical approaches are time-consuming, expensive, and tedious. As a result, there is a pressing need for a cost-effective and efficient automated tool. A convolutional neural network is presented in this paper for analysing X-ray pictures of patients' chests. For the analysis of COVID-19 infections, this study investigates the most suitable pretrained deep learning models, which can be integrated with mobile or online apps and support the mobility of diagnostic instruments in the form of a portable tool. Patients can use the smartphone app to find the nearest healthcare testing facility, book an appointment, and get instantaneous results, while healthcare professionals can keep track of the details thanks to the web and mobile applications built for this study. Medical practitioners can apply the COVID-19 detection model for chest frontal X-ray pictures with ease. A user-friendly interface is created to make our end-to-end solution paradigm work. Based on the data, it appears that the model could be useful in the real world