Gill Nets of Kerala

The fishing industry the world over is passing through a critical situation.The landings of marine fishes seemed to have reached saturation in major fishing areas of the world.As a general rule fisheries employing fishing gear and techniques used by small scale and artisanal fishermen either from shore or from onboard small fishing craft come under small-scale fisheries.This study on gill nets of Kerala, the fishing method depended upon by maximum fishermen of the state focuses on the importance of this selective and low energy fishing method in the marine fishing sector of the state.The study opens with the conceptual framework by briefly reviewing the crisis in the marine fisheries sector. Maximum fishermen depend upon gill net, which is, an important selective and low energy fishing gear. A review of relevant literature on aspects such as material, selectivity and techno-economic efficiency together with scope and main objectives of the study form the major part of the compass of the introductory chapter.This survey provided the inputs for selection of centres. The chapter presents the basis for selection of sample centres, sample units and methodology for field and experimental study.The subject matter of the fourth chapter is a basic study on gear aterials. The weathering resistance, which is an important criterion to assess the material performance, was studied for polyamide monofilament in comparison to polyamide multifilament and polyethylene twisted monofilament.The study provides supporting evidence of oxidation and characteristic C-O stretching in polyethylene and cyclic lactam .formation and presence of OH in polyamide.The study indicates that small mesh gill netting can be encouraged as a selective fishing method in the inshore waters with restrained use of 30 and 32 mm mesh sizes. The economic efficiency was assessed using standard indices such as rate of return, internal rate of return, pay back period, fishery income, energy efficiency and factor productivity. The effect of size and cost of capital and cost of production on the economics of operation is also discussed in this chapter. It was observed that level of technology did not have direct effect on economic performance.

Studies on some fish larvae of the arabian sea and bay of bengal

Though several scientific expeditions surveyed tha Indian ocean. their findings on larval fishes were fragmentory and incomplete. During the International Indian ocean expedition (1960-1965) zooplankton samples were collected from vast area at the lndian ocean adopting standard procedures by participating vessels. These planktons provided an opportunity for a general study of the zooplanltton as well as their anotomy. abundance and distribution.. Realising the imortance of the analysis and classification of fish larvae at the family level being one of the most important information required then for the Indian Ocean region the fish larval portion sorted out from the above zooplankton samples was allocated to the author by the UNESCO consultative committee for the Indian Ocean Biological centre. In spite of the emhasis laid on The study of the family scombridae. owing to the paucity of the scombridae material in tne collection. much could not be done about it.However the record and description of the very early stages of morakel larvae made in 1967 as part of this study was the first of its kind. The present study on the distribution aspects o fish larva of such large area in the Indian ocean is also unique and first of its kind

Classification of Handwritten Characters: Definition, Identification and Utilisation of Regional Characteristics for Lower Case English Characters

A new procedure for the classification of lower case English language characters is presented in this work . The character image is binarised and the binary image is further grouped into sixteen smaller areas ,called Cells . Each cell is assigned a name depending upon the contour present in the cell and occupancy of the image contour in the cell. A data reduction procedure called Filtering is adopted to eliminate undesirable redundant information for reducing complexity during further processing steps . The filtered data is fed into a primitive extractor where extraction of primitives is done . Syntactic methods are employed for the classification of the character . A decision tree is used for the interaction of the various components in the scheme . 1ike the primitive extraction and character recognition. A character is recognized by the primitive by primitive construction of its description . Openended inventories are used for including variants of the characters and also adding new members to the general class . Computer implementation of the proposal is discussed at the end using handwritten character samples . Results are analyzed and suggestions for future studies are made. The advantages of the proposal are discussed in detail .

Texture Description of low grade and high grade Glioma using Statistical features in Brain MRIs

Low grade and High grade Gliomas are tumors that originate in the glial cells. The main challenge in brain tumor diagnosis is whether a tumor is benign or malignant, primary or metastatic and low or high grade. Based on the patient's MRI, a radiologist could not differentiate whether it is a low grade Glioma or a high grade Glioma. Because both of these are almost visually similar, autopsy confirms the diagnosis of low grade with high-grade and infiltrative features. In this paper, textural description of Grade I and grade III Glioma are extracted using First order statistics and Gray Level Co-occurance Matrix Method (GLCM). Textural features are extracted from 16X16 sub image of the segmented Region of Interest(ROI) .In the proposed method, first order statistical features such as contrast, Intensity , Entropy, Kurtosis and spectral energy and GLCM features extracted were showed promising results. The ranges of these first order statistics and GLCM based features extracted are highly discriminant between grade I and Grade III. In this study which gives statistical textural information of grade I and grade III Glioma which is very useful for further classification and analysis and thus assisting Radiologist in greater extent.

Fractal based techniques for classification of mammograms and identification of microcalcifications

After skin cancer, breast cancer accounts for the second greatest number of cancer diagnoses in women. Currently the etiologies of breast cancer are unknown, and there is no generally accepted therapy for preventing it. Therefore, the best way to improve the prognosis for breast cancer is early detection and treatment. Computer aided detection systems (CAD) for detecting masses or micro-calcifications in mammograms have already been used and proven to be a potentially powerful tool , so the radiologists are attracted by the effectiveness of clinical application of CAD systems. Fractal geometry is well suited for describing the complex physiological structures that defy the traditional Euclidean geometry, which is based on smooth shapes. The major contribution of this research include the development of • A new fractal feature to accurately classify mammograms into normal and normal (i)With masses (benign or malignant) (ii) with microcalcifications (benign or malignant) • A novel fast fractal modeling method to identify the presence of microcalcifications by fractal modeling of mammograms and then subtracting the modeled image from the original mammogram. The performances of these methods were evaluated using different standard statistical analysis methods. The results obtained indicate that the developed methods are highly beneficial for assisting radiologists in making diagnostic decisions. The mammograms for the study were obtained from the two online databases namely, MIAS (Mammographic Image Analysis Society) and DDSM (Digital Database for Screening Mammography.

classification and content based retrieval of digital mammograms and placental sonograms

Image processing has been a challenging and multidisciplinary research area since decades with continuing improvements in its various branches especially Medical Imaging. The healthcare industry was very much benefited with the advances in Image Processing techniques for the efficient management of large volumes of clinical data. The popularity and growth of Image Processing field attracts researchers from many disciplines including Computer Science and Medical Science due to its applicability to the real world. In the meantime, Computer Science is becoming an important driving force for the further development of Medical Sciences. The objective of this study is to make use of the basic concepts in Medical Image Processing and develop methods and tools for clinicians’ assistance. This work is motivated from clinical applications of digital mammograms and placental sonograms, and uses real medical images for proposing a method intended to assist radiologists in the diagnostic process. The study consists of two domains of Pattern recognition, Classification and Content Based Retrieval. Mammogram images of breast cancer patients and placental images are used for this study. Cancer is a disaster to human race. The accuracy in characterizing images using simplified user friendly Computer Aided Diagnosis techniques helps radiologists in detecting cancers at an early stage. Breast cancer which accounts for the major cause of cancer death in women can be fully cured if detected at an early stage. Studies relating to placental characteristics and abnormalities are important in foetal monitoring. The diagnostic variability in sonographic examination of placenta can be overlooked by detailed placental texture analysis by focusing on placental grading. The work aims on early breast cancer detection and placental maturity analysis. This dissertation is a stepping stone in combing various application domains of healthcare and technology.

FEATURE SELECTION AND COMPARISON OF TWO NAÏVE BAYES CLASSIFICATION METHODS IN THE CONTEXT OF SPAM FILTERING

Treating e-mail filtering as a binary text classification problem, researchers have applied several statistical learning algorithms to email corpora with promising results. This paper examines the performance of a Naive Bayes classifier using different approaches to feature selection and tokenization on different email corpora