Automated UI & UX Framework
A consistent UI leaves an overall impression on user’s psychology, aesthetics and taste. Human–computer interaction (HCI) is the study of how humans interact with computer systems. Many disciplines contribute to HCI, including computer science, psychology, ergonomics, engineering, and graphic design. HCI is a broad term that covers all aspects of the way in which people interact with computers. In their daily lives, people are coming into contact with an increasing number of computer-based technologies. Some of these computer systems, such as personal computers, we use directly. We come into contact with other systems less directly — for example, we have all seen cashiers use laser scanners and digital cash registers when we shop. We have taken the same but inextensible line and made more solid justified by linking with other scientific pillars and concluded some of the best holistic base work for future innovations. It is done by inspecting various theories of Colour, Shape, Wave, Fonts, Design language and other miscellaneous theories in detail.
Published by: Karamvir Singh Rajpal, Maninder Kaur
Author: Karamvir Singh Rajpal
Paper ID: V3I1-1224
Paper Status: published
Published: January 18, 2017
Analyse the Mechanical Properties of Metakaolin Using As a Partial Replacement of Cement in Concrete
Cement concrete is the most extensively used construction material. Maintenance and repair of concrete structures is a growing problem involving significant expenditure. As a result carried out worldwide, it has been made possible to process the material to satisfy more stringent performance requirements, especially long–term durability. HPC is the latest development in concrete. It has become very popular and is being used in many prestigious projects such as Nuclear power projects, flyovers multi-storeyed buildings. When using HPC, the addition of supplementary materials in cement has dramatically increased along with the development of concrete industry, due to the consideration of cost saving, energy saving, environmental concerns both in terms of damage caused by the extraction of raw materials and carbon dioxide emission during cement manufacture have brought pressures to reduce cement consumption. Metakaolin looks to be a promising supplementary cementitious material for high-performance concrete. Properties of concrete with metakaolin are mostly preferred additives in high-performance concrete. A possible lower cost, due to large availability in our country itself may be advantages to metakaolin usage in HPC. The substitution proportion of metakaolin is to be used was 5%, 10%, 15%, and 20% by the weight of cement. To make this cubes and cylinders to determine the strength and durability of concrete of it. The results indicate that the replacing mix up to till the last percent has to note and effect on strength in comparing with mixer without metakaolin.
Published by: M. Narmatha, Dr. T Felixkala
Author: M. Narmatha
Paper ID: V3I1-1223
Paper Status: published
Published: January 18, 2017
Anatomical Variations of Nose and Para-Nasal Sinuses: CT Scan review in South Gujarat
To identify frequency and characters of anatomic variations in paranasal sinuses in computed tomography scan of paranasal sinuses. Methods: The retrospective study was conducted at the SSG Hospital, Baroda, and comprised computed tomography scans of 75 patients who had presented between December 2016 and January 2017. The scans were reviewed for the presence of deviated nasal septum, paradoxical middle turbinate, Haller cell, Onodi cell, and pneumatization of the middle turbinate and uncinate process. Results: The mean age of the patients was 32±13.15 years. The most frequent variant being the deviated nasal septum 32 (63%) and the middle nasal Concha 16 (22%). Conclusion: Computed tomography is excellent means of providing anatomical information of paranasal sinuses considering the wide range of variations in the anatomy, each and every para-nasal sinus case should be planned individually and carefully to avoid dreadful complications and maximize patients’ benefit. Keywords: Anatomic variations, Para-nasal sinuses, deviated nasal septum, Concha bullosa.
Published by: Dr. Abhishek S, Dr. Bhavtik Kapadia, Dr. Nandakishore G. Patil, Dr. Girbide Shubhangi
Author: Dr. Abhishek S
Paper ID: V3I1-1219
Paper Status: published
Published: January 18, 2017
Human Face Detection using Fusion Technique
Nowadays face detection and recognition has become an important tool for identification in industry, Educational institutes, verifying websites, hosting images and social networking site. Face Recognition is nothing but Features such as eyes, nose, lips etc. are extracted from a face, these features are processed and compared with similarly processed faces present in the database. If a face is recognized it is known or the system may show a similar face existing in a database else it is an unknown face. In proposed system, an input image can be taken as a static image or by capturing an image. The system is trying to improve efficiency. The system is using ANN (Artificial Neural Network) and Euclidean Distance Measure is working collaboratively for detection of the face. Over here, features are been marked using ELBP (Elliptical local binary pattern) using specific values. Facial features such as forehead, eyes, nose, lips and cheeks. The system basically converts RGB values of features to HSV (Hue saturation value) and stores this HSV values. These HSV values are compared with the feature values of HSV which are stored in databases and if these values are matched with the database face image values then the face is detected otherwise it is not detected. These features distances are calculated using Euclidean distance algorithm. For improving the efficiency OCA (Optimized comparison algorithm) plays an important role as in OCA two features are taken for comparison with the database image. Two features lips and cheeks are taken into consideration and it is compared with the all the database image. Whatever images have got is further compared with the optimized database and finally, face is recognized otherwise user not found message will be printed. Also for real time application live streaming is facilitated in the system for recognition and continuous processing is done. This way system facilitates to efficiently recognize the faces and also helps to improve the accuracy of the system.
Published by: Rupali Balasaheb Pawar, Deepak Dharrao, Priya Pise
Author: Rupali Balasaheb Pawar
Paper ID: V3I1-1217
Paper Status: published
Published: January 18, 2017
An Application Of Multi Objective Programming Techniques: A Case Study of Central India (Uttar Pradesh, Madhya Pradesh, Rajasthan)
In India and abroad, the commonly used decision modeling in real life rests on the assumption that the decision maker seeks to optimize a well-defined single objective using traditional mathematics programming approach. A farmer may be interested in maximizing his cash income, with certain emphasis on risk minimization. On the other at county level especially in a developing country a planner may aspire for a plan while maximizes food grains production and also to some extent considers employment maximization etc as the goals. Keeping in view the objectives of the study, state-wise secondary data on different variables for the period 1980-81 to 2014-15 were collected from Statistical Abstracts of Punjab, Fertilizer Statistics, Agricultural Statistics at a glance and the reports of the Commission for Agricultural Costs and Prices, published by Ministry of Agriculture By taking its deviations of observed Yt from its estimated value we got the error or the risk coefficients for each year for each crop. These risk coefficients were taken in the matrix formulation in the MOTAD format suggested by Hazell (1971 a and b). To give a meaningful explanation to the level of risk, total mean absolute deviations in gross returns were derived as under: Min A = 1/S Σ│ (chj-gj) xj│ Where A is the minimum average absolute deviation defined as the mean over (h=1………s) years, of the sum of the deviations of gross returns (chj) from the trend in gross returns (gj) multiplied by activity levels x j (j = 1………n). Where A is an unbiased estimator of the population mean absolute income deviation Where A = estimated mean absolute deviation S = no. of years chj = gross returns of the jth activity in hth year gj = sample mean of gross returns of jth activity x j = activity level This was minimized subject to the following constraints: Σaij xj ≤ bi (for all i = 1………….m, j =1……..n) Total activity requirements for the i th constraint, the sum of the unit activity requirements aij for the constraint i times the activity levels ‘xj‘do not exceed the level of the i th constraint bi for all ‘i’ and x j 0 all activity levels are non negative. Where a ij = per unit technical requirement for the jth activity of the ith resource. bi = the ith resource constraint level m = no. of constraints n = no. of activities.
Published by: Prince Singh, Dr. Seema Manchanda
Author: Prince Singh
Paper ID: V3I1-1213
Paper Status: published
Published: January 17, 2017
An Application of Multi Objective Programming Techniques: A Case Study Of South India (Andhra Pradesh, Karnataka)
In India and abroad, the commonly used decision modeling in real life rests on the assumption that the decision maker seeks to optimize a well-defined single objective using traditional mathematics programming approach. A farmer may be interested in maximizing his cash income, with a certain emphasis on risk minimization. On the other, at county level especially in a developing country, a planner may aspire for a plan while maximizes food grains production and also to some extent considers employment maximization etc as the goals. Keeping in view the objectives of the study, state-wise secondary data on different variables for the period 1980-81 to 2014-15 were collected from Statistical Abstracts of Punjab, Fertilizer Statistics, Agricultural Statistics at a glance and the reports of the Commission for Agricultural Costs and Prices, published by Ministry of Agriculture By taking its deviations of observed Yt from its estimated value we got the error or the risk coefficients for each year for each crop. These risk coefficients were taken in the matrix formulation in the MOTAD format suggested by Hazell (1971 a and b). To give a meaningful explanation to the level of risk, total mean absolute deviations in gross returns were derived as under: Min A = 1/S Σ│ (chj-gj) xj│ Where A is the minimum average absolute deviation defined as the mean over (h=1………s) years, of the sum of the deviations of gross returns (chj) from the trend in gross returns (gj) multiplied by activity levels x j (j = 1………n). Where A is an unbiased estimator of the population mean absolute income deviation Where A = estimated mean absolute deviation S = no. of years chj = gross returns of the jth activity in hth year gj = sample mean of gross returns of jth activity x j = activity level This was minimized subject to the following constraints: Σaij xj ≤ bi (for all i = 1………….m, j =1……..n) Total activity requirements for the i th constraint, the sum of the unit activity requirements aij for the constraint i times the activity levels ‘xj‘do not exceed the level of the i th constraint bi for all ‘i’ and x j 0 all activity levels are non-negative. Where a ij = per unit technical requirement for the jth activity of the ith resource = the ith resource constraint level m = no. of constraints n = no. of activities
Published by: Prince Singh, Seema Manchanda
Author: Prince Singh
Paper ID: V3I1-1212
Paper Status: published
Published: January 17, 2017
