Nutrition Deficiency Prediction using Machine Learning Techniques

Despite the fact that many developing nations have experienced economic progress, Nutrition- deficiency remains a pervasive problem in the society, with millions of impoverished people's diets lacking in essential macro and micronutrients essential for optimal human health. Lack of awareness of food consumed daily causes Nutrition deficiency among general population, data from multiple health records are used for research and prediction. It investigates the importance of a well-balanced diet for our daily life. The Healthy Food Diversity Index (HFDI) is a supplement to the popular Household Dietary Diversity Score (HDDS). It's a tool for determining the diversity of household food. The HDDS has been established as a reliable source of information, but it has several limitations as a measure of dietary diversity that is linked to nutritional quality.  In this paper, various machine learning techniques such as Random Forest classifier (RF), Support-Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Logistic Regression (LR) are used  to predict Nutrition-Deficiency using house hold risk factors and they compared their Accuracy, Sensitivity and Specificity. The predictions were also compared to the anthropometric classifications used by the National school feeding program to prove the efficiency of the proposed approach

Wave attenuation by coastal heterospecific vegetation - modeling of synthetic plant meadows by Response Surface Methodology (RSM)

193-202Knowing the interactions between wave and aquatic vegetation is becoming increasingly important because of the phenomenon of plant-induced wave attenuation for the development of sustainable coastal management systems. Many of the wave-vegetation interaction studies focus on monotypic coastal plant meadows, while coastal plant meadows are typically heterospecific in nature, and the work on heterospecific plant meadows is still very limited. This research aims therefore to explain the heterospecific vegetation-wave interactions using a three-level four-factor surface response methodology (RSM) using controlled laboratory wave flume conditions. Heterospecific seagrass species, Cymodocea serrulata is physically simulated using synthetic plant mimics to establish a relationship between wave attenuation (E%) and four direct control factors, i.e. wave period (T), water depth (h), bed roughness factor (f) and plant density (N), using an empirical model. The model developed was evaluated using the methodology of variance analysis (ANOVA) and analyzed for the key and interaction effects of the parameters studied. The findings showed that both individually and in combination, all the parameters considered are significantly successful on E%. All model-based findings were compared with a new collection of experimental data, and validation tests were carried out

Wave attenuation by coastal vegetation – An Empirical study on synthetic models

1143-1152The interaction between wave and submerged vegetation is the primary cause of wave-induced attenuation. Studies on wave vegetation interactions mostly considered aquatic monotypic plant meadows and limited work have been reported on heterospecific plant meadows, common in tropical shores of southern India. An attempt was made to understand the heterospecific vegetation meadow-wave interactions through a two-level, four factors, full factorial experimental design-based laboratory flume study under controlled conditions. Simulated vegetation mimics were used as a simplified representation of heterospecific seagrass species, Halophila spinulosa, Halophila ovalis to develop a linear empirical model. Four input variables viz., water depth (h), wave period (T), plant density (N) and bed roughness factor (f), were considered for the study with the wave attenuation (E %) as a response. The developed model was tested for adequacy by using the analysis of variance (ANOVA) technique. Main and interaction effects of the input variables were analyzed, compared, and validated. The results show that all the four input variables are statistically significant with the T has the highest significant individual effect on wave attenuation followed by f, h, and N. Evidential two-way interaction effects, mainly between h with the rest of the parameters were also observed

A Prediction Of Welding Process Control Variables By Prediction Of Weld Bead Geometry Using Factorial Design Approach

Plasma Enhanced Shielded Metal Arc Welding (PESMAW) is a modified version of the age old manual metal are welding (MMA) where the cellulose based flux coated solid wires are replaced by tubular low hydrogen flux coated electrodes. PESMAW process is aimed to eliminate the usage of cellulose in the electrode coating so as to save some trees and hence make the welding process partially green. The high heat content of the cellulose supported arc is achieved by controlled supply of auxiliary plasma gas through the tubular wire directed into the arc. This paper discusses the influence of the welding process parameters to the weld bead characteristics of weldments made by PESMAW process using mild steel as base metal. Two level fractional factorial design was adopted to investigate and quantify the direct and interactive effects of four major control parameters. “Bend on plate” technique was used to lay weldments and bead geometry was measured using standard metallurgical procedures. Statistical models were made from the obtained results and were analyzed and tested by using analysis of variance technique and students’t’ test. The main and interactive effects of control parameters were studied and presented in graphical form

Prediction and measurement of weld dilution in robotic CO2 arc welding

Weld dilution is an important feature of weld bead geometry that determines the mechanical and chemical properties of a welded joint. For robotic CO2 arc welding, several welding process parameters are reported to be controlling the dilution. This paper investigates the relationship between four of these process parameters and dilution by depositing ‘bead on plate’ robotic CO2 arc welds over mild steel plates. Two level four factor full factorial design method was used for conducting the experimental runs and linear regression models were developed accordingly. The adequacy of the models were tested by applying students ‘t’ test and the predicted values from the models were plotted against the observed values through scatter diagram. Results showed that the proposed two level full factorial empirical models could predict the weld dilution with reasonable accuracy and ensure uniform weld quality. It can be concluded that robotic CO2 arc welding is a very simple and effective tool for quantifying the main and interactive effects of welding parameters on dilution. Future works should focus in analyzing the influence of variable pure gasses as well as the gas mixture on dilution percentage in robotic arc welding

An Investigation on Relationship between Process Control Para meters and Weld Penetration for Robotic CO2 Arc Welding using Fa ctorial Design Approach

Weld penetration is an important physical characteristic of a weldment that affects the stress carrying capacity of the weld joint. Several welding parameters seem to influence weld penetration. This paper presents the relationship between weld penetration and four direct welding process parameters of robotic CO2 arc welding process on structural carbon steel. Two level, full factorial design was applied to investigate and quantify the direct and interactive effects of four process parameters on weld penetration. The upper and lower limits of the process control variables were identified through trial and error methodology, and the experiments were conducted using ‘bead on plate’ mode. The performance of the model was then tested by using analysis of variance technique and the significance of the coefficients was tested by applying student’s‘t’ test. Commercial computer programs were used for statistical analysis. The main and interactive effects of different welding parameters are studied by presenting it in graphical form

Millets - Neglected Cereal with High Potential in Health Benefits in Malnutrition

In a world grappling with malnutrition, millets emerge as unsung heroes, offering a beacon of hope for improved global health. This chapter delves into the treasure trove of millets, revealing their often-overlooked potential as a nutritional powerhouse. Millets, a diverse group of cereal grains, hold the promise of mitigating malnutrition on a global scale. Firstly, we explore the exceptional nutritional value of millets, demonstrating how they pack a punch with essential vitamins, minerals, and dietary fiber. A comparative analysis with other grains underscores their superiority in providing a balanced diet. We then uncover the diverse varieties of millets and their suitability for various regions and climates, making them an adaptable and sustainable choice for farmers worldwide. Millet farming techniques, including their resilience to adverse conditions, are discussed, shedding light on their role in food security. The health implications of millet consumption are another focus, revealing their potential in preventing chronic diseases and improving overall well-being. Case studies underscore the tangible impact of millet-based interventions on malnutrition reduction. However, challenges persist, such as limited awareness and policy support. Nonetheless, millets hold immense promise for enhancing global health and nutrition. This chapter advocates for the integration of millets into our diets, promoting sustainable agriculture, and addressing malnutrition's root causes. As we delve into the world of millets, we find not only a neglected cereal but a beacon of hope for a healthier, more sustainable future

Role of Herbal Medicine in Cardiovascular Activities

Herbal medicine has gained substantial attention for its potential role in supporting cardiovascular health. This chapter explores the intricate interplay between herbal compounds and cardiovascular activities, shedding light on their mechanisms of action and therapeutic applications. With a historical backdrop of traditional herbal medicine, the prevalence of cardiovascular diseases serves as a compelling backdrop for the investigation. The chapter delves into the multifaceted mechanisms by which herbal compounds influence the cardiovascular system. Notably, herbs exhibit vasodilatory effects, contributing to blood pressure regulation, and harbor potent antioxidant and anti-inflammatory properties that collectively mitigate oxidative stress and inflammation within the cardiovascular milieu. Furthermore, certain herbs intricately modulate lipid metabolism, holding promise in the management of dyslipidemia. A thorough analysis of well-known herbal treatments clarifies each one's unique contributions to cardiovascular health. Hawthorn is revealed to be the champion of heart health, and garlic demonstrates its mastery of cholesterol reduction. Ginkgo Biloba is notable for its capacity to improve circulation, and turmeric demonstrates powerful anti-inflammatory properties. The chapter also looks at herbal medicine's potential as an intervention for regulating fluid balance, arrhythmias, and hypertension. Examined is the potential for resveratrol-rich plants and green tea to protect against heart disease. Along with considerations for safety, interactions, and future study, the symbiotic relationship between stress reduction, adaptogenic herbs, and heart health is also discussed in this article. This chapter concludes with a thorough examination of the crucial part herbal medicine plays in cardiovascular health. The complex interaction between herbal substances and circulatory functions, from mechanisms of action to clinical applications, shows promise for a more integrative and holistic approach to cardiovascular car