An Efficient Hidden Markov Model for Offline Handwritten Numeral Recognition

Traditionally, the performance of ocr algorithms and systems is based on the recognition of isolated characters. When a system classifies an individual character, its output is typically a character label or a reject marker that corresponds to an unrecognized character. By comparing output labels with the correct labels, the number of correct recognition, substitution errors misrecognized characters, and rejects unrecognized characters are determined. Nowadays, although recognition of printed isolated characters is performed with high accuracy, recognition of handwritten characters still remains an open problem in the research arena. The ability to identify machine printed characters in an automated or a semi automated manner has obvious applications in numerous fields. Since creating an algorithm with a one hundred percent correct recognition rate is quite probably impossible in our world of noise and different font styles, it is important to design character recognition algorithms with these failures in mind so that when mistakes are inevitably made, they will at least be understandable and predictable to the person working with theComment: 6pages, 5 figure

Limb body wall complex or body stalk complex or cyllosomas: a case report

Limb body wall complex (LBWC) is also called Body stalk complex and Cyllosomas. We present this rare congenital malformation complex highlighting the importance of early sonographic imaging findings in LBWC along with differentiation from other anterior abdominal wall defects. Limb body wall complex / Body stalk anomaly refers to a rare complicated polymalformative fetal malformation syndrome of uncertain etiology and results in head, heart, lung, diaphragm, kidney or gonadal abnormalities. LBWC was first described by Van Allen et al; in (1987). The two of the three following anomalies must be present to establish the diagnosis: 1. Exencephaly / Encephalocele with facial clefts, 2. Thoraco-Abdominoschisis / ventral body wall defects and 3. Limb defects. LBWC arises as a result of early amnion disruptions or error in embryonic development. If all components of the syndrome are present, the condition is lethal. LBWC is invariably fatal and incompatible with life. No case of postnatal survival is reported so far. Serum alpha-fetoprotein measurement and ultrasonography examination is the key to the prenatal diagnosis and followed by medical termination of pregnancy. It presents two distinct phenotypes described by Russo et al (1993) and later Cusi et al in (1996), according to the foetoplacental relationships: 1. Placento-cranial and 2.Placento-abdominal types. Among the 168 live births at S.V.S. Medical College & hospital Mahabubnagar (INDIA) during the period of 2010-2011 we came across an aborted female fetus. It was weighing 1800gms, 30 weeks of gestation diagnosed by antenatal ultrasonography as ventral body wall defect. It was associated with ompholocele, severe scoliosis and limb defects. Its confirmation of the diagnosis of Limb body wall complex with Placento-abdominal type was done by postmortem fetography

Wagging the Contact Line: Transverse and Longitudinal Waves

Kinetics of wetting has been explored where the contact line not only sees a steady spreading but also has longitudinal or transverse oscillations imposed on it. The latter case is realized when spreading takes place over a rough surface. The effects of the imposed motion are small, which seem to be due to low spreading rates and small dynamic contact angles used in this study. However, a singularity is seen in viscous dissipation during the movement on the model rough surface, which is interpreted here as an instability that is similar to Haines\u27 jumps and stick-slip phenomena, with possible entrainment of the displaced fluid. This is the first time that all of these have been associated with each other

Wetting Kinetics of a Thin Film Evaporating in Air

The conservation equation and the equations of motion are solved for a case where a thin liquid film moves out of a slot onto a horizontal surface. The liquid is allowed to evaporate into air. The evaporation process is taken to be isothermal. Lubrication theory approximation is used where only the tangential velocity and its dependence only in the normal direction are considered. The dynamics of thin films includes the use of disjoining pressure for a pure liquid and where there is a dissolved polymer. The results show that evaporation is quicker than film thinning such that a spreading regime dominated by the effects of disjoining pressure is never achieved. However, unlike the cases of pinning studied so far, there is no singularity in the evaporative flux near the contact line because of the use of disjoining pressure on evaporation. It is also observed that a balance between the rate of viscous dissipation and surface work is able to quantify the steady state contact angle. Consequently, a more macroscopic (and quantitative) description of contact line can be found that avoids the singularities discussed earlier and also the detailed calculations shown here. However, the detailed calculations are necessary to make the above point

A Hierarchical Framework for the Classification of Multispectral Imagery

AbstractOut of the abundant digital image data available, multispectral imagery is one which gives us information about the earth we live in. To gain knowledge from multispectral imagery, it is essential to classify the data present in the image based on spectral information. Classification plays a significant role in understanding the remotely sensed data obtained from the satellites. This paper brings out a new classification scheme based on a hierarchical framework. The hierarchical model proposed in this paper helps to understand the imagery at different levels of abstractness and concreteness to serve different applications like town planning, facility management and so on. The model depicts classification of the multispectral imagery on three abstract levels. The algorithm proposed outputs classification at different levels with an average accuracy of 72.6% in level 1 and 78.3% in level 2. The time sensitivity analysis of the algorithm shows that it outperforms the traditional SVM classifier. A detailed analysis of the algorithm proposed is detailed in this paper with respect to the parameters influencing the classification accuracy

FACE RECOGNITION IN EIGEN DOMAIN WITH NEURO-FUZZY CLASSIFIER AND EVOLUTIONARY OPTIMIZATION

Face Recognition is a nascent field of research with many challenges. The proposed system focuses on recognizing faces in a faster and more accurate way using eigenface approach and genetic algorithm by considering the entire problem as an optimization problem. It consists of two stages: Eigenface approach is used for feature extraction and genetic algorithm based feed forward Neuro-Fuzzy System is used for face recognition. Classification of face images to a particular class is done using an artificial neural network. The training of neural network is done using genetic algorithm, a machine learning approach which optimizes the weights used in the neural network. This is an efficient optimization technique and an evolutionary classification method. The algorithm has been tested on 200 images (20 classes). A recognition score for test lot is calculated by considering almost all the variants of feature extraction. Test results gave a recognition rate of 97.01%

Survival and Comparative study on Different Artificial Intelligence Techniques for Crop Yield Prediction

Agriculture is an essential, important sector in the wide-reaching context. Farming helps to satisfy the basic need of food for every living being. Agriculture is considered the broadest economic sector. The crop yield is a significant part of food security and improves the drastic manner by human population. The quality and quantity of the yield touch the high rate of production. Farmers require timely advice to predict crop productivity. The strategic analysis also helps to increase crop production to meet the growing food demand. The forecasting of crop yield is a process of forecasting crop yield by using historical data. Machine learning provides a revolution in the agricultural field by changing the income scenario and growing an optimum crop. Many researchers carried out their research to deal with forecasting crop yield. In this way, accurate prediction of crop yield was improved. But, failed to reduce the crop yield prediction time and the accuracy level was not enhanced by existing methods

An Overview of Carbon Footprint Mitigation Strategies. Machine Learning for Societal Improvement, Modernization, and Progress

Among the most pressing issues in the world today is the impact of globalization and energy consumption on the environment. Despite the growing regulatory framework to prevent ecological degradation, sustainability continues to be a problem. Machine learning can help with the transition toward a net-zero carbon society. Substantial work has been done in this direction. Changing electrical systems, transportation, buildings, industry, and land use are all necessary to reduce greenhouse gas emissions. Considering the carbon footprint aspect of sustainability, this chapter provides a detailed overview of how machine learning can be applied to forge a path to ecological sustainability in each of these areas. The chapter highlights how various machine learning algorithms are used to increase the use of renewable energy, efficient transportation, and waste management systems to reduce the carbon footprint. The authors summarize the findings from the current research literature and conclude by providing a few future directions

BATCH MODE BIOREMEDIATION STUDY ON REACTIVE BLUE-HER BY Cladosporium oxysporum

The fungus, Cladosporium oxysporum Berk. and Curt. was screened for their ability to decolourize Reactive Blue HER in aqueous solutions and observed that had higher dye decolourization (99.81 %) achieved by live biomass, dead biomass. In batch mode (Agitated mode), the lower concentrations (10 mg/L) of Reactive Blue showed complete removal (100%) at an equilibrium time of 60 min for both live and autoclaved biomass. Dye adsorption isotherm, Langmuir model fitted well with 2 R    values more than 0.9. RL (equilibrium parameter) values (indicating type of isotherm) of live and autoclaved mycelia at different adsorbate concentrations were always less than one and more than zero thereby indicating favourable adsorption of dyes onto the adsorbent. With the increase of dosage, no re-stabilization phenomenon or removal reduction was observed. Highest adsorption was observed in autoclaved biomass, 48.31 mg/g. Agitated mode was found to be more efficient and easy to operate for the removal of dyes