Conservation of mangrove forest covers in Kochi coast

Mangroves are salt-tolerant plants of tropical and subtropical intertidal regions of the world. The specific regions where these plants occur are called mangrove ecosystems. They are breeding, feeding and nursery grounds for many estuarine and marine organisms, including finfish and shell fish. India has only 2.66% of the world’s mangroves, covering an estimated area of 4,827 sq. k

Puducherry mangroves under sewage pollution threat need conservation

Indian mangroves have a rich diversity of soil-dwelling organisms which include micro, meio and macro forms. Mangrove ecosystem provides an ideal nursery and breeding ground for most of the marine and brackish water fish and shellfish. India has only 2.66% of the world’s mangroves1, covering an estimated area of 4827 sq. km. The present study area lies within the margins of lat. 11°90′107″– 11°90′703″N and long. 79°80′547″– 79°81′851″E. Mangrove exists as fringing vegetation over 168 ha distributed along the sides of Ariankuppam estuary, which empties into the Bay of Bengal (Coromandal coast) at Veerampatinam on the southeastern coast of Indi

Checklist and Spatial Distribution of Molluscan Fauna in Minicoy Island, Lakshadweep, India

Among the various animal groups represented in the macrobenthic fauna of Minicoy lagoon studied, mollusks were the dominant group. Molluscan fauna were investigated from six selected stations in the sea grass beds and mangroves of the Minicoy lagoon, Lakshadweep during 1999-2001. A total of 70 species of mollusk (52 gastropods, 12 bivalves) and an additionally 7 soft mollusks are reported in the present study. The total density of molluscan fauna varied from 137-604 (no. 0.25m2), while the highest biomass was obtained during postmonsoon season at southern seagrass bed and the least was observed during premonsoon season at northern seagrass bed. Among these Gafrarium divarticatum, Terebralia palustris are found the most dominant species of Minicoy Island, India

New Polychaete Records from Seagrass Beds at Minicoy Island, Lakshadweep, India

Species composition, distribution and taxonomic description of polychaete fauna in the seagrass beds of the Minicoy lagoon, Lakshadweep, India were studied during 1999 - 2001. In 4 stations, 27 species of polychaetes belonging to 14 genera were identified. Of these 27 species, 10 species of polychaetes, belonging to 8 genera under 6 families, comprise new distributional records from Minicoy Island, and the descriptions of these species are provided. Among these, Glycera lancadivae, G. tesselata, and Eurythoe matthaii are found to be the most dominant species

Advanced Machine Learning Techniques for Alzheimer’s Disease Detection: A PCA and Improved XGBoost Approach

Alzheimer’s Disease (AD) is one of the main fields in clinical medicine that contributes to the existing difficulties in research. The presented work is dedicated to the Machine Learning approach to the identification and detection of the Alzheimer’s Disease, including the Image Enhancement techniques. The work of they also use Principal Component Analysis (PCA) together with the contemporary approach in improving the image quality of the brain images obtained from public databases. The focus of this research is the enhanced XGBoost classification model as applied with the help of two other classification methods to assure its effectiveness. A lot of tests were performed on the Alzheimer’s Disease dataset with an analytical feature extraction procedure to enhance the model results. These proposed methodologies are tested against conventional algorithms with an emphasis on accuracy, precision, recall and F1-score. The first estimates suggest an increase in the level of AD detection accuracy and its superiority over conventional approaches. Apart from showing a correlation between PCA and new pre-processing methodologies, this study also underscores the enhanced diagnostic aptitude of the improved XGBoost classifier

Knowledge management in technical education using lean concept

Innovation, Flexibility and Rapid change-are the keywords for 21st century business environment.Industries that have traditionally delivered manufactured goods must streamline their processes and focus on the rapidly changing needs of their customers and the capabilities of their suppliers.Lean is one of the promising alternative strategy for achieving continuous improvement in business performance through identifying a company’s value stream and then systematically removing all waste.Educational Institutions are now focusing on knowledge management, and knowledge is a new paradigm for the way of work.The key issue in knowledge management in educational institution is faculty-subject allocation problem which can be solved by using the lean concept.This paper mainly concentrates on minimizing the knowledge wastage in technical institution by properly allocating the faculty to subjects. The faculty-subject allocation problem is solved using a meta-heuristic approach and a decision support system can be developed

Soil temperature prediction based on ensemble tree bagger machine learning algorithm for agricultural decision making

This study focuses on predicting surface soil temperature (ST) at a 5 cm depth, which significantly influences agricultural decisions such as sowing time, irrigation management and soil-plant-atmosphere dynamics. Machine learning (ML) algorithms were used to predict ST using above-ground weather variables viz., air temperature (T), relative humidity (RH), wind velocity (WV) and sunshine duration (SS) measured at 15-min intervals. Six regression-based ML models (Ensemble, Gaussian Process Regression, Support Vector Machine, Tree, Neural Network and Kernel) were trained and tested for predictive accuracy. The Ensemble Bagging Tree model showed the highest precision, with RMSE values of 2.04 and 1.9 for validation and testing, respectively. Various combinations of the weather variables were tested and the model performed best when using above mentioned variables. Among the predictors, T had the greatest impact on ST prediction, as indicated by mean absolute Shapley values. The Shapley values of the variables revealed that T had a critical role in the model output, with time, SS, RH and WV following in importance. Additionally, as a model explainable artificial intelligence (xAI) metrics, SHapley Additive exPlanations (SHAP) were analysed and found that SHAP dependency had a defined relationship between the predictors and ST at a 5 cm depth. This study highlights the effectiveness of machine learning in predicting soil temperature and emphasizes the role of weather variables in agricultural decision-making. decision-making

Conservation and Management of Tuna Fisheries in the Indian Ocean and Indian EEZ

The focus of the study is to explore the recent trend and stock status of Indian Ocean and Indian EEZ, and its conservation and sustainable management of tuna fishereis. In the Indian Ocean, tuna catches increased rapidly from about 179,959 t in 1980 to about 832,246 t in 1995. They have continued to increase up to 2005 where the catch reached 1,318,648 t, forming about 26% of the world catch. However, since 2006 onwards there was a decline in tuna catch and in 2010 the catch was only 1,257,908 t. Tuna production in India continued to increase with fluctuations from 63,633 t during 2001-2005 to an of average 78,400 t during 2006-2010, and in 2010 the catch declined again to only 65,863 t. Tuna is an important but not a well managed fishery in the Indian Ocean and Indian EEZ. The Indian Ocean stock is currently overfished and has no proper management regulations aimed at with sustaining the stock. In the present study, sustainable management system is evaluated with information on tuna landings, stock status and major issues on tuna fishery. To address these major issues, appropriate tuna fishing policies are proposed to help sustainable development and management of tuna fishery resource in the Indian Ocean