Face Detection using Principal Component Analysis in Real Time Database

Face Recognition is the process of identification of a person by their facial image. In this system, a holistic Principal Component Analysis (PCA) based method, namely Eigenface method is studied and implemented on the Faces 94 database. This approach treats face recognition as a two-dimensional recognition problem. Face images are projected onto a face space that encodes best variation among known face images. The face space is defined by eigenface which are eigenvectors of the set of faces, which may not correspond to general facial features such as eyes, nose, and lips. Face will be categorized as known or unknown face after matching with the present database. Experimental results in this thesis showed that an accuracy of 98.8158% was achieved. The variable reducing theory of PCA accounts for the smaller face space than the training set of face

Customer Segmentation and Business Sales Forecasting using Machine Learning for Business Development

This study explores the application of machine learning techniques for business development, focusing on sales prediction and customer segmentation, using a Walmart dataset. Performance metrics include Mean Absolute Error (MAE) and R2 scores. Our hybrid approach combines the BIRCH algorithm with time-lagged machine learning (TL-ML). The results reveal that customer segmentation significantly improves model performance across all metrics. Among the techniques tested, models incorporating customer segmentation (CS-RFR and CS-TL-ML) outperform standard Random Forest Regressor models. Specifically, CS-TL-ML shows a slight advantage in terms of both lower MAE and higher R2 scores, confirming its efficacy for sales prediction and customer segmentation tasks

Set Down Study of Projectile in Flight Through Imaging

Deformation study of projectile immediately after firing is essential for its successful impact. A projectile that undergoes more than the tolerated amount of deformation in the barrel may not produce the requisite results. The study of projectile deformation before its impact requires it to be imaged in flight and perform some computation on the acquired image. Often the deformation tolerance is of the order of tens of micrometer and the acquired image cannot produce image with such accuracy because of photographic limitations. Therefore, it demands sub-pixel manipulation of the captured projectile image. In this work the diameter of a projectile is estimated from its image which became blur because of slow shutter speed. First the blurred image is restored and then various interpolation methods are used for sub-pixel measurement. Two adaptive geometrical texture based interpolation schemes are also proposed in this research. The proposed methods produce very good results as compared to the existing methods.Science Journal, Vol. 64, No. 6, November 2014, pp.530-535, DOI:http://dx.doi.org/10.14429/dsj.64.811

Physicochemical and biological factors controlling water column metabolism in Sundarbans estuary, India

BACKGROUND: Sundarbans is the single largest deltaic mangrove forest in the world, formed at estuarine phase of the Ganges - Brahmaputra river system. Primary productivity of marine and coastal phytoplankton contributes to 15% of global oceanic production. But unfortunately estuarine dynamics of tropical and subtropical estuaries have not yet received proper attention in spite of the fact that they experience considerable anthropogenic interventions and a baseline data is required for any future comparison. This study is an endeavor to this end to estimate the primary productivity (gross and net), community respiration and nitrification rates in different rivers and tidal creeks around Jharkhali island, a part of Sundarbans estuary surrounded by the mangrove forest during a period of three years starting from November’08 to October’11. RESULTS: Various physical and chemical parameters of water column like pH, temperature, conductivity, dissolved oxygen, turbidity, suspended particulate matter, secchi disc index, tidal fluctuation and tidal current velocity, standing crop and nutrients were measured along with water column productivity. Relationship of net water column productivity with algal biomass (standing crop), nutrient loading and turbidity were determined experimentally. Correlations of bacterial abundance with community respiration and nitrification rates were also explored. Annual integrated phytoplankton production rate of this tidal estuary was estimated to be 151.07 gC m(-2) y(-1). Gross primary productivity showed marked inter annual variation being lowest in monsoon and highest in postmonsoon period. CONCLUSION: Average primary production was a function of nutrient loading and light penetration in the water column. High aquatic turbidity, conductivity and suspended particulate matter were the limiting factors to attenuate light penetration with negative influence on primary production. Community respiration and nitrification rates of the estuary were influenced by the bacterial abundance. The estuary was phosphorus limited in postmonsoon whereas nitrogen-limited in premonsoon and monsoon period. High algal biomass and primary productivity indicated the estuary to be in eutrophic state in most of the time throughout the year. Our study also indicated a seasonal shifting between autotrophic and heterotrophic conditions in Sundarban estuarine ecosystem and it is a tropical, well mixed (high tidal influx) and marine dominated (no fresh water connection) system

Forecasting United States Presidential election 2016 using multiple regression models

The paper analyses economic and non-economic factors in order to develop a forecasting model for 2016 US Presidential election and predict it. The discussions on forthcoming US Presidential election mention that campaign fund amount and unemployment will be a deciding factor in the election, but our research indicates that campaign fund amount and unemployment are not significant factors for predicting the vote share of the incumbent party. But in case of non–incumbent major opposition party (challenger party) campaign fund amount does play a role. Apart from unemployment other economic factors such as inflation, exchange rate, interest rate, deficit/surplus, gold prices are also found to be insignificant. Growth of economy is found to be significant factor for non-incumbent major opposition party and not for incumbent party. The study also finds that non-economic factors such as June Gallup rating, Gallup index, average Gallup, power of period factor, military intervention, president running, percentage of white voters and youth voters voting for the party are significant factors for forecasting the vote share of either incumbent party or non-incumbent major opposition party/challenger party. The proposed models forecasts with 95% confidence interval that Democratic party is likely to get vote share of 48.11% with a standard error of ±2.18% and the non-incumbent Republican party is likely to get vote share of 40.26% with a standard error ±2.35%

Forecasting United States Presidential election 2016 using multiple regression models

The paper analyses economic and non-economic factors in order to develop a forecasting model for 2016 US Presidential election and predict it. The discussions on forthcoming US Presidential election mention that campaign fund amount and unemployment will be a deciding factor in the election, but our research indicates that campaign fund amount and unemployment are not significant factors for predicting the vote share of the incumbent party. But in case of non–incumbent major opposition party (challenger party) campaign fund amount does play a role. Apart from unemployment other economic factors such as inflation, exchange rate, interest rate, deficit/surplus, gold prices are also found to be insignificant. Growth of economy is found to be significant factor for non-incumbent major opposition party and not for incumbent party. The study also finds that non-economic factors such as June Gallup rating, Gallup index, average Gallup, power of period factor, military intervention, president running, percentage of white voters and youth voters voting for the party are significant factors for forecasting the vote share of either incumbent party or non-incumbent major opposition party/challenger party. The proposed models forecasts with 95% confidence interval that Democratic party is likely to get vote share of 48.11% with a standard error of ±2.18% and the non-incumbent Republican party is likely to get vote share of 40.26% with a standard error ±2.35%

Effect of a novel succinamic acid derivative as potential anti-diabetic agent in experimental diabetic rats

4-((benzyloxy) amino)-2-hydroxy-4-oxobutanoic acid which is a succinamic acid derivative has been synthesized in 3 step reaction with malic acid. Its structure confirmation was done by various techniques like 1H NMR, 13C NMR, & HRMS and is recently proposed as an insulinotropic agent for the treatment of non-insulin dependent diabetes mellitus. In the present study, the effect of 4-((benzyloxy) amino)-2-hydroxy-4-oxobutanoic acid on plasma glucose, serum insulin, serum lipid profile and lipid peroxidation in streptozotocin–nicotinamide induced type 2 diabetic model was investigated.  4-((benzyloxy) amino)-2-hydroxy-4-oxobutanoic acid was administered orally (20 mg/kg b.w.) to streptozotocin + nicotinamide (STZ + NAD) induced diabetic rats for 28 days. A significant increase in fasting blood glucose levels, HbA1c levels, Serum lipid profile (TG & TC) and in  the levels of Malonaldialdehyde (MDA, end product of lipid peroxidation) was observed in STZ +NAD diabetic rats whereas the levels of high density lipoprotein-cholesterol (HDL-C) and serum insulin levels were significantly decreased  in STZ + NAD induced diabetic rats The effect of 4-((benzyloxy)amino)-2-hydroxy-4-oxobutanoic acid was compared with glibenclamide, a reference drug. Treatment with 4-((benzyloxy) amino)-2-hydroxy-4-oxobutanoic acid and glibenclamide resulted in a significant reduction of fasting blood glucose levels with increase in plasma insulin levels in diabetic treated rats. 4-((benzyloxy) amino)-2-hydroxy-4-oxobutanoic acid also resulted in a significant improvement in serum lipids and lipid peroxidation products. Our results suggest the potential role of 4-((benzyloxy) amino)-2-hydroxy-4-oxobutanoic acid in the management of type-2 diabetes mellitus experimental rats. Keywords: 4-((benzyloxy) amino)-2-hydroxy-4-oxobutanoic acid, dyslipidemia, streptozotocin induced diabetes, lipid peroxidatio

Psychiatric comorbidities among people with epilepsy: A population-based assessment in disadvantaged communities

Psychiatric disorders are frequent among people with epilepsy but often under-recognized. The diagnosis and treatment of these disorders in low- and low-middle-income countries (LMICs) are challenging. METHODS: This cross-sectional survey included people recruited during a community epilepsy screening program involving 59,509 individuals from poor communities in Ludhiana in Northwest India. Adults (age ≥18 years) with confirmed epilepsy on antiseizure medications were screened for depression and anxiety using the Neurological Disorders Depression Inventory for Epilepsy (NDDI-E) and Generalized Anxiety Disorder-7 (GAD-7) twice over two years of follow-up. They were later interviewed for symptoms using the Brief Psychiatric Rating Scale, which was then confirmed by assessments by an experienced psychiatrist. RESULTS: Of the 240 people with confirmed epilepsy, 167 (70%) were adults, of whom, 116 (70%) eventually participated in the study. The NDDI-E with a cut-off of 15 identified depression in 14 (12%) of 116 people after one year of follow-up and 17 (15%) at two years. The GAD-7 using a cut-off of 6 identified 22 (19%) at one year and 32 (28%) with anxiety at two years. The area under the curves for NDDI-E was estimated as 0.62 (95%CI, 0.51-0.73; SE: 0.06; p = 0.04) and for GAD-7 as 0.62 (95%CI, 0.46-0.78; SE: 0.08; p = 0.12). Brief Psychiatric Rating Scale identified 63 (54%) people with psychiatric symptoms, for whom, a psychiatric diagnosis was confirmed in 60 (52%). A psychiatric diagnosis was associated with education below high school [Odds Ratio (OR): 2.59, 95%CI, 1.12-5.1; p = 0.03], later age of seizure onset (OR, 1.05, 95%CI: 1.0-1.10; p = 0.04), seizure frequency of at least one/year at enrolment (OR, 2.36, 95%CI: 1.0-5.58; p = 0.05) and the use of clobazam (OR, 5.09, 95%CI, 1.40-18.42; p = 0.01). CONCLUSION: Depression and anxiety are common in people with epilepsy. Our findings underscore the low yields of screening instruments, NDDI-E and GAD-7, and comparatively better professionally-administered diagnostic assessments in resource-limited settings in LMICs. Moreover, previously established cut-offs do not apply to the community studied