Image Segmentation Based on Doubly Truncated Generalized Laplace Mixture Model and K Means Clustering

The present paper aims at performance evaluation of Doubly Truncated Generalized Laplace Mixture Model and K-Means clustering (DTGLMM-K) for image analysis concerned to various practical applications like security, surveillance, medical diagnostics and other areas. Among the many algorithms designed and developed for image segmentation the dominance of Gaussian Mixture Model (GMM) has been predominant which has the major drawback of suiting to a particular kind of data. Therefore the present work aims at development of DTGLMM-K algorithm which can be suitable for wide variety of applications and data. Performance evaluation of the developed algorithm has been donethrough various measures like Probabilistic Rand index (PRI), Global Consistency Error (GCE) and Variation of Information (VOI). During the current work case studies forvarious different images having pixel intensities has been carried out and the obtained results indicate the superiority of the developed algorithm for improved image segmentation

Computer Applications in Metallurgical Research

This paper outlines the current efforts in computer applications in metallurgical research at the Defence Metallurgical Research Laboratory, Hyderabad. Work being done on armour penetration studies, optimization of armour profiles for fighting vehicles, computer control of multifunction 2000 tonne forge press, drawing of processing mechanism maps, process modelling of titanium sponge production and methods of curve fitting to experimental data, is described and briefly discussed

Molluscan shell deposits along Pinnakkayal—Valinokkom coast and their exploitation

Marine molluscan shell deposits are distributed at different places between Pinnal(ayal and Valmoltkom on the southwest coast of India and support a good shell lima Indutsry. The different areas where the lime shell deposits occur have been surveyed and the nature and extent of the deposits, the species composition, the methods of exploitation, magnitude of production, utilization and annual turnover are dealt with

PHYTOCHEMICAL AND IN-VITRO ANTIOXIDANT ACTIVITY OF METHANOLIC EXTRACT OF LACTUCA SCARIOLA & CELOSIA ARGENTEA LEAVES.

Antioxidant compounds in food play an important role as a health protecting factor. Primary sources of naturally occurring antioxidants are whole grains, fruits and vegetables. Highly reactive free radicals and oxygen species can initiate degenerative diseases. Antioxidant compounds like phenolic acids, polyphenols and flavonoids are commonly found in plants have been reported to have multiple biological effects, including antioxidant activity. Methanolic leaf extracts of Lactuca scariola Linn and Celosia argentea Linn were assessed for the In vitro Antioxidant activity using  DPPH (1, 1-diphenyl-2-picryl-hydrazyl) radical  , Nitric oxide(NO) and hydrogen peroxide (H2O2)scavenging models using Ascorbic acid as positive control. Analysis of free radical scavenging activities of the extracts revealed a concentration dependent free radical scavenging activity resulting from reduction in DPPH, Nitric oxide and Hydroxyl radical. The methanolic  extract  of Lactuca scariola Linn showed  significant DPPH , Nitric oxide and hydrogen peroxide scavenging activity compared to Celosia argentea Linn extract . IC50  values by  DPPH , Nitric oxide and hydrogen peroxide model for  Lactuca scariola and  Celosia argentea were found to be 192.5±9.014, 394.2±6.009, 434.2±18.78, 233.3±20.73 , 521.4±4.061and 494.2±5.465 respectively

A Comprehensive Analysis on Risk Prediction of Heart Disease using Machine Learning Models

Most of the deaths worldwide are caused by heart disease and the disease has become a major cause of morbidity for many people. In order to prevent such deaths, the mortality rate can be greatly reduced through regular monitoring and early detection of heart disease. Heart disease diagnosis has grown to be a challenging task in the field of clinically provided data analysis. Predicting heart disease is a highly demanding and challenging task with pure accuracy, but it is easy to figure out using advanced Machine Learning (ML) techniques. A Machine Learning approach has been shown to predict heart disease in this approach. By doing this, the disease can be predicted early and the mortality rate and severity can be reduced. The application of machine learning techniques is advancing significantly in the medical field. Interpreting these analyzes in this methodology, which has been shown to specifically aim to discover important features of heart disease by providing ML algorithms for predicting heart disease, has resulted in improved predictive accuracy. The model is trained using classification algorithms such as Decision Tree (DT), K-Nearest Neighbors (K-NN), Random Forest (RF), Support Vector Machine (SVM). The performance of these four algorithms is quantified in different aspects such as accuracy, precision, recall and specificity. SVM has been shown to provide the best performance in this approach for different algorithms although the accuracy varies in different cases

Distribution and exploitation of oyster resources along the southeast and southwest coasts of India

The oysters are sedentary bivalve molluscs which are gregarious and found in aggregates attached by their right shell valve to hard substrata, forming oyster beds or oyster banks in varied environments, intertidal and subtidal zones in shallow coastal waters, bays, creeks, lagoons, backwaters and estuarine environment. In India, natural stocks of oysters are exploited on a small scale at a number of places as a subsistence fishery and oysters are not cultured commercially

Blockchain-Enabled On-Path Caching for Efficient and Reliable Content Delivery in Information-Centric Networks

As the demand for online content continues to grow, traditional Content Distribution Networks (CDNs) are facing significant challenges in terms of scalability and performance. Information-Centric Networking (ICN) is a promising new approach to content delivery that aims to address these issues by placing content at the center of the network architecture. One of the key features of ICNs is on-path caching, which allows content to be cached at intermediate routers along the path from the source to the destination. On-path caching in ICNs still faces some challenges, such as the scalability of the cache and the management of cache consistency. To address these challenges, this paper proposes several alternative caching schemes that can be integrated into ICNs using blockchain technology. These schemes include Bloom filters, content-based routing, and hybrid caching, which combine the advantages of off-path and on-path cachings. The proposed blockchain-enabled on-path caching mechanism ensures the integrity and authenticity of cached content, and smart contracts automate the caching process and incentivize caching nodes. To evaluate the performance of these caching alternatives, the authors conduct experiments using real-world datasets. The results show that on-path caching can significantly reduce network congestion and improve content delivery efficiency. The Bloom filter caching scheme achieved a cache hit rate of over 90% while reducing the cache size by up to 80% compared to traditional caching. The content-based routing scheme also achieved high cache hit rates while maintaining low latency

The timing of death in patients with tuberculosis who die during anti-tuberculosis treatment in Andhra Pradesh, South India

Background: India has 2.0 million estimated tuberculosis (TB) cases per annum with an estimated 280,000 TBrelated deaths per year. Understanding when in the course of TB treatment patients die is important for determining the type of intervention to be offered and crucially when this intervention should be given. The objectives of the current study were to determine in a large cohort of TB patients in India:- i) treatment outcomes including the number who died while on treatment, ii) the month of death and iii) characteristics associated with “early” death, occurring in the initial 8 weeks of treatment. Methods: This was a retrospective study in 16 selected Designated Microscopy Centres (DMCs) in Hyderabad, Krishna and Adilabad districts of Andhra Pradesh, South India. A review was performed of treatment cards and medical records of all TB patients (adults and children) registered and placed on standardized anti-tuberculosis treatment from January 2005 to September 2009. Results: There were 8,240 TB patients (5183 males) of whom 492 (6%) were known to have died during treatment. Case-fatality was higher in those previously treated (12%) and lower in those with extra-pulmonary TB (2%). There was an even distribution of deaths during anti-tuberculosis treatment, with 28% of all patients dying in the first 8 weeks of treatment. Increasing age and new as compared to recurrent TB disease were significantly associated with “early death”. Conclusion: In this large cohort of TB patients, deaths occurred with an even frequency throughout anti-TB treatment. Reasons may relate to i) the treatment of the disease itself, raising concerns about drug adherence, quality of anti-tuberculosis drugs or the presence of undetected drug resistance and ii) co-morbidities, such as HIV/ AIDS and diabetes mellitus, which are known to influence mortality. More research in this area from prospective and retrospective studies is needed