Web Impact Factor and link analysis of Central University Websites of North Eastern States of India

This study was conducted to observe the websites of Ten Central Universities situated in the north-eastern states of India and find out the three different Web Impact Factors viz. Simple, Revived & External Web Impact Factors of the websites under study. This paper shows the status of those websites finding out different number of hyperlinks to and from the websites. The paper also shows how the numbers of webpages in a websites as compared to the number of different links plays huge role in the utility of the website

Rethinking Fine Art Libraries: Issues, Challenges and Status of the Libraries of the Institute of Fine Art in New Delhi, India

Abstract: Purpose and objectives of the study: This purpose of the paper is to explore the collection, services, challenges and prospects of the libraries of the institute of fine arts in New Delhi, India. Fine arts and performing arts library specialize in collecting items relating to any faction of the art including music, poetry, sculpture, painting, theatre, dance, film and recorded sound etc. Scope: This study is confined to the libraries of the institute of the fine arts in Delhi, the capital city of India, which are approved and funded by the concerned government. Methodology: The authors performed a case study to discover the current statues of the libraries of the institute of fine arts in Delhi. Findings: The study finds that the libraries of the institute of fine arts in Delhi are enriched not only by the collection of printed and non-printed materials but also by art objects. The collection developments works of these libraries of the institute of fine arts are done mainly through purchase and receiving donated books. For development of the library collection they have book selection committee and book purchase committee. It is found that less numbers of libraries have wedding out policy. The study reveals that most of the libraries are doing re-binding for preservation. The interest in participating library network and consortium is found to be stronger. Originality: This paper identifies weaknesses in current practices and offers some suggestions. This is about an original initiative undertaken to develop a set of core values

Agent Based Expert System for Online Assessment in Distributed Database Environment with Agent Cloning: A Modification

This paper introduces an alternate architectural framework of an agent based Extended Expert System for Online Assessment (EESOA) in distributed environment for the learners of ODL (Open and Distance Learning) System. The main modification is done in the question collection process by the mobile agent for student assessment form the different interconnected question database servers. Instead of Itinerary Design Pattern (in earlier framework), the Branching Design Pattern is used in this modified framework of EESOA. This necessarily uses the agent cloning process to perform the load balancing while retrieving the question set in the distributed question database environment

Binding Energy calculation of GSK-3 protein of Human against some anti-diabetic compounds of Momordica charantia linn (Bitter melon)

Diabetes is one of the major life threatening diseases worldwide. It creates major health problems in urban India. Glycogen Synthase Kinase-3 (GSK-3) protein of human is known for phosphorylating and inactivating glycogen synthase which also acts as a negative regulator in the hormonal control of glucose homeostasis. In traditional medicine, Momordica charantia is used as antidiabetic plant because of its hypoglycemic effect. Hence to block the active site of the GSK-3 protein three anti-diabetic compounds namely, charantin, momordenol & momordicilin were taken from Momordica charantia for docking study and calculation of binding energy. The aim of present investigation is to find the binding energy of three major insulin-like active compounds against glycogen synthase kinase-3 (GSK-3), one of the key proteins involved in carbohydrate metabolism, with the help of molecular docking using ExomeTM Horizon suite. The study recorded minimum binding energy by momordicilin in comparison to the others

Predication of soil pH using HSI colour image processing and regression over Guwahati, Assam, India

Soil is known to be the most valuable natural source for all agriculture fields. Soil has two properties, namely- physical and chemical. These properties include soil moisture, texture, etc. and the latter include pH value. Soil texture plays an important role in crop cultivation. The physical properties of soil such as texture and granular size determine the water and nutrient holding capacity. Also the chemical property like pH value is very important for plant growth and development. Soil having pH value between 5.5 and 7 is optimal for agricultural purpose. Hence, a detailed study of soil pH property is necessary for cultivation. But laboratory method of soil pH calculation is a very costly and tedious process. Therefore, it is essential to develop an expert-based system that will overcome this issue. However, the system must be able to give correct result and should match with those conducted in laboratory. Farmers analyze pH either in lab or by soil pH card based on soil image color. But this is not an effective method since it relies heavily on human perception. Hence, we have developed an expert based system which can determine the pH of the soil without any human error. For this, we have conducted our experiments with the help of MatLab tool and smart phone as we have concerned about the rural farmers. We have analyzed and compared the proposed system results with the traditional laboratory methods with regression and have found 86 % accuracy in our model

A Systematic Overview on Some of the Traditionally Used Plants of Assam

Assam is a state of the North Eastern part of India. There are different types of plants well distributed in various parts of the state. These plants have various medicinal properties and are used traditionally to cure diseases because of its fewer side effects. A large number of surveys have been documented about different kinds of traditionally used plants of Assam. The purpose of this Study is to do a Systematic overview on various aspects like its taxonomical characters, geographical habitat, Cultivation along with its physicochemical constituents, Pharmacological and medicinal properties so that it can be useful to treat the various diseases efficiently. Various traditional plants such as Carica papaya, Cocos nucifera, Murraya koenigii, Ocimum sanctum, Musa paradisiaca, Averrhoa carambola, Aegle marmelos, Azadirachta indica, Citrus limonum, Psidium guajav, Hibiscus rosa sinensis, Catharanthus roseus, Trichosanthes dioica, Cynodon dactylon, Brassica rapa were reviewd. Keywords: Assam, plants, Physicochemical constituents, Pharmacological and Medicinal uses

Smartphone assist deep neural network model to recognize the high-quality tea using leaf maturity and its effect on leaf chlorophyll

Immature and tender tea leaves always produce high-quality tea than mature tea leaves. Depending on the maturity and age of the leaf, the colour and texture of the tea leaf are different. The photosynthesis capacity of the tea leaf also changes with the change of leaf maturity. Though the tea farmer plucks, classifies, and recognizes the best tea leaves (immature and tender) by viewing the visual symptoms and position of the leaves, the method is not authentic all time and leads to the overall degradation of the tea quality. The present study presents a smartphone assist tea leaf recognition system by analyzing the colour and texture properties of the tea leaf. The six different colour features and 4 Haralick texture features were extracted in the colour and grey domain of the leaf images. Three types of tea leaves, i.e., mature, immature, and tender, were classified using Deep Neural Network (DNN) with ADAM (Adaptive Moment Estimation) optimizer. With an accuracy of 97%, the DNN outperformed the Support Vector Machine (SVM) and K Nearest Neighbor (KNN). The SVM and KNN reported a total of 94.42% and 95.53% accuracy, respectively. The investigated system using DNN with an average precision and recall value of 98.67 and 98.34, respectively, may detect and classify the tea leaf maturity status. The system also can be used in AI-based tea plucking robotic systems or machines

Performance analysis of support vector machine for early identification of citrus diseases

Early citrus disease detection is necessary for optimum citrus productivity. But detecting a citrus disease at an early stage requires expert views or laboratory tests. But getting an expert view of all time is impossible for rural farmers. The present study aimed to create a low-cost, intelligent, affordable citrus disease classification system. This study offered a Support Vector Machine (SVM) based smart classification method for categorizing various citrus diseases. Citrus photos were subjected to a variety of image processing techniques to categorize the diseases using SVM and the kernel. Prior to classification, the images were segmented and the hue channel threshold value was used to differentiate the diseased area from the remaining portion of the image. The segmented image’s color and grey domains were used to extract 13 different texture and color features. This study outlined three different SVM kernel types- Linear, Gaussian, and Polynomial, and evaluated their accuracy and confusion matrix performances. The Radial Based Function with a polynomial kernel derived from the SVM outperformed the SVM's linear and Gaussian kernel

Smartphone assisting convolutional neural networks for soil texture classification in dry and wet humid conditions in West Guwahati, Assam

Soil texture using a hydrometer or pipette method requires expertise, although these are accurate. A soil expert may help the farmer to detect the soil texture by analyzing the visual texture of the soil, which is not always accurate. This paper presents the smartphone image-based sand and clay soil classification in wet and dry humid conditions using Self Convolution Neural Network (SCNN) and finetuned MobileNet.A soil dataset of 576 soil images was prepared using a low-cost smartphone under natural light conditions. Different augmentation techniques such as shift, range, rotation, and zoom were applied to the soil dataset to increase the number of images in the soil dataset. The best performance of the MobileNet was reported at epoch 15 with a testing and training loss of 0.0091 and 0.0194, respectively. Though the SCNN model performed best at epoch 10 with a testing accuracy of 99.85%, the MobileNet reported less computation time (167.8s) than the SCNN (273.2s). The precision and recall of the models were 99.62 (MobileNet) and 99.84 (SCNN). The accuracy of the SCNN reported itself as the best model, whereas the computing time of the MobileNet reported itself as the best model in different humid conditions. The model can be used to replicate the traditional soil texture analysis method and the farmers can use it for better productivity