Developing Service Marketing Through Successful Involvement of Customer

Customer Relationship Management has its roots in service marketing which is based in turn on the formative work of Berry and the IMP. Its purpose is to integrate marketing, sales, and service functions through business process automation, technological solutions and information resources to maximize each customer contact. In this way, service marketing systems facilitate relationships among enterprises, their customers, suppliers, and employees and so provide the technological means to put relationship marketing philosophy into practice. Organizations that fail to keep up with competitor's service marketing capabilities risk being seriously disadvantaged. However, the use of technology on its own is not sufficient and firms must combine developments in IT with a philosophy that calls for the re-organization of the entire firm around its customers. This shift will not be easily achieved. Our purpose, based on collaborative Canfield/CSC Computer Sciences Corporation studies, is to identify the pitfalls, and offer advice on the successful implementation of service marketing systems in support of relationship marketing strategies, including an audit of the organization's readiness to proceed

Technical Efficiency of Maize Production in Fluoride Affected Locales, Tamil Nadu: A Stochastic Frontier Approach

To estimate the technical efficiency of maize production among fluoride affected and non affected locales of Tamil Nadu. A multi-stage sampling method involving a combination of purposive and random sampling procedures was employed in drawing up the samples for collecting primary data. The sample size is about 120. Stochastic frontier production function is used to estimate technical efficiency of maize. The result of stochastic frontier production function indicated that FYM, Potassium, machine power, irrigation and management index have significant influence on yield of maize in less fluoride affected locale, while, seed rate, nitrogen, phosphorous, machine power and irrigation are significantly influence the yield of maize in moderately fluoride affected locale, in case of highly fluoride affected locale, seed rate, nitrogen, phosphorous, potassium and irrigation are significantly influencing the yield of maize, while, nitrogen, potassium, irrigation and management index are significantly influences the yield of maize in non affected locale. The study suggests that awareness of fluoride contamination and averting measures must be disseminated to the farmers

An Impressive Method to Get Better Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE) Values Using Stationary Wavelet Transform (SWT)

Impulse noise in images is present because of bit errors in transmission or introduced during the signal acquisition stage. There are two types of impulse noise, they are salt and pepper noise and random valued noise. In our proposed method, first we apply the Stationary wavelet transform for noise added image. It will separate into four bands like LL, LH, HL and HH. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 02019;s and 2552019;s are present in the selected window and when all the pixel values are 02019;s and 2552019;s then the noise pixel is replaced by mean value of all the elements present in the selected window. This proposed algorithm shows better results than the Standard median filter (MF), decision based algorithm (DBA). The proposed method performs well in removing low to medium density impulse noise with detail preservation up to a noise density of 70% and it gives better Peak signal-to-noise ratio (PSNR) and mean square error (MSE) values

Early Identification of Alzheimer’s Disease Using Medical Imaging: A Review From a Machine Learning Approach Perspective

Alzheimer’s disease (AD) is the leading cause of dementia in aged adults, affecting up to 70% of the dementia patients, and posing a serious public health hazard in the twenty-first century. AD is a progressive, irreversible and neuro-degenerative disease with a long pre-clinical period, affecting brain cells leading to memory loss, misperception, learning problems, and improper decisions. Given its significance, presently no treatment options are available, although disease advancement can be retarded through medication. Unfortunately, AD is diagnosed at a very later stage, after irreversible damages to the brain cells have occurred, when there is no scope to prevent further cognitive decline. The use of non-invasive neuroimaging procedures capable of detecting AD at preliminary stages is crucial for providing treatment retarding disease progression, and has stood as a promising area of research. We conducted a comprehensive assessment of papers employing machine learning to predict AD using neuroimaging data. Most of the studies employed brain images from Alzheimer’s disease neuroimaging initiative (ADNI) dataset, consisting of magnetic resonance image (MRI) and positron emission tomography (PET) images. The most widely used method, the support vector machine (SVM), has a mean accuracy of 75.4 percent, whereas convolutional neural networks(CNN) have a mean accuracy of 78.5 percent. Better classification accuracy has been achieved by combining MRI and PET, rather using single neuroimaging technique. Overall, more complicated models, like deep learning, paired with multimodal and multidimensional data (neuroimaging, cognitive, clinical, behavioral and genetic) produced superlative results. However, promising results have been achieved, still there is a room for performance improvement of the proposed methods, providing assistance to healthcare professionals and clinician

Developing New Drug Antagonist for Alpha Adrenergic Receptors for Hypertension Diseases

Alpha-adrenergic receptors play a vital role in the regulation of blood pressure(BP). Hypertension referred as blood pressure above the normal range. hyper tension can cause severe complications such as stroke, coronary heart disease and kidney failure. Phentolamine is a potential antagonist for a. Alpha-adrenergic receptor. New phentolamine derivatives will be developed through FEP (Free Energy perturbation) methods. AMBER Force fields will apply for energy Calculation. Molecular modeling includes energy minimization, molecular dynamics, mounte-carlo simulations and QSAR properties will be performed for all phentolamine analogs. The interactions studies between alpha - adrenergic Receptor and Phentolamine analogs in solvent mode and protein complex mode will be performed. The best antagonist for alpha-Adrenergic Receptor will be identified