bluuebunny/crossref_metadata_2025 huggingface dataset:
IoT Based Automated Sericulture System
Authors:   UG-Final Year B.Tech Students,Department of Electronics and Communication Engineering, School of Engineering and Technology, Jain (Deemed-to-be-University), Bangalore, Karnataka, India, B Srinivas , Kumari Khushi ,   UG-Final Year B.Tech Students,Department of Electronics and Communication Engineering, School of Engineering and Technology, Jain (Deemed-to-be-University), Bangalore, Karnataka, India, Reddy H Goverdhan ,   UG-Final Year B.Tech Students,Department of Electronics and Communication Engineering, School of Engineering and Technology, Jain (Deemed-to-be-University), Bangalore, Karnataka, India, N Niranjan ,   UG-Final Year B.Tech Students,Department of Electronics and Communication Engineering, School of Engineering and Technology, Jain (Deemed-to-be-University), Bangalore, Karnataka, India, S A Hariprasad ,   Director, Department of Electronics and Communication Engineering, School of Engineering and Technology, Jain (Deemed-to-be-University), Bangalore, Karnataka, India, M P Sunil ,   Assistant professor, Department of Electronics and Communication Engineering, School of Engineering and Technology, Jain (Deemed-to-be-University), Bangalore, Karnataka, India
Published: August 2019

Sericulture denotes to the rearing of silkworm to produce silk. Parameters like Temperature, Humidity and Light intensity are the important factors in the progression of silkworms and suitable encouraging must to be done according to the requisites in every stage. Environmental variations assume as the important part in the growth and development of silkworm. Sericulture is the important occupation in India and the techniques used by the agriculturists are yet outdated. Hereafter there is the need of developing modernization in sericulture cultivate. This endeavor gives a thought of providing automation in sericulture cultivate. The model goals at making use of developing technology that is IOT and smart Sericulture using automation. Observing environmental parameters of the silkworm rearing house is the most important aspect to improve vintage of the silk. The specialty of this model comprises enhancement of a system which can observe temperature, humidity, light power through sensors using NodeMCU and in case of any variations in the parameters send a notification on the user mobile application using internet connection. This system permits for data assessment and scheduling to be programmed through the arduino IDE software.
Experimental and FEA evaluation of AA2014/TIC composites
Authors:   Assistant Professor, Department of Mechanical Engineering, Kingston Engineering College, Vellore-632059, Tamilnadu, India, Mohanavel V. , Sundar S.N. ,   Lecturer, Department of Mechanical Engineering, AMK Technolog Polytechnic College, Chennai, Tamilnadu, India., Poongothai R. ,   Lecturer, Department of Mechanical Engineering (Sandwich Course), Central Polytechnic College, Chennai, Tamilnadu, India., Kumar S. Suresh ,   Lecturer, Department of Mechanical Engineering, Panimalar Polytechnic College, Chennai, Tamilnadu, India., Sivaraman V. ,   Lecturer, Department of Mechanical Engineering, Panimalar Polytechnic College, Chennai, Tamilnadu, India.
Published: August 2019

AA2014/titanium carbide composites (Al-MMCs) included with diverse mass fraction of TiC (0, 4 and 8 wt.%) were made through stir casting method. This present work, examines the tensile behavior of the fabricated specimen and finite element modeling based tensile specimen. Experimental tensile specimens are examined through ultimate testing machine (UTM). The modeling of the tensile specimen has been developed in ANSYS 12.0 for finite element analysis, which are the most standard CAE tools. Tensile load values are calculated by experimental method and that load values are given as input to the FEA analysis. The finite element outcomes employing ANSYS APDL software exhibiting stresses and deflections were confirmed with experimental upshots and analytical outcomes.
Diabot: A Predictive Medical Chatbot using Ensemble Learning
Authors:   PG Student, Ramaiah University, Bangalore., Bali Manish , Mohanty Samahit ,   PG Student, Ramaiah University, Bangalore., Chatterjee Dr.Subarna ,   Assistant Professor Ramaiah University, Bangalore, Sarma Manash ,   PG Student, Ramaiah University, Bangalore., Puravankara Rajesh ,   PG Student, Ramaiah University, Bangalore.
Published: August 2019

Accessibility to medical knowledge and healthcare costs are the two major impediments for common man. Conversational agents like Medical chatbots, which are designed keeping in view medical applications can potentially address these issues. Chatbots can either be generic or disease-specific in nature. Diabetes is a non-communicable disease and early detection of the same can let people know about the serious consequences of this disorder and help save human lives. In this paper, we have developed a generic text-to-text ‘Diabot’ – a DIAgnostic chatBOT which engages patients in conversation using advanced Natural Language Understanding (NLU) techniques to provide personalized prediction using the general health dataset and based on the various symptoms sought from the patient. The design is further extended as a DIAbetes chatBOT for specialized Diabetes prediction using the Pima Indian diabetes dataset for suggesting proactive preventive measures to be taken. For prediction, there exists multiple classification algorithms in Machine Learning which can be used based on their accuracy. However, rather than considering only one model and hoping this model is the best or most accurate predictor we can make, the novelty in this paper lies in Ensemble learning, which is a meta-algorithm that combines a myriad of weaker models and averages them to produce one final balanced and accurate model. From literature reviews, it is observed that very little research has happened in ensemble methods to increase prediction accuracy. The paper presents a state-of-the art Diabot design with an undemanding front-end interface for common man using React UI, RASA NLU based text pre-processing, quantitative performance comparison of various machine learning algorithms as standalone classifiers and combining them all in a majority voting ensemble. It is observed that the chatbot is able to interact seamlessly with all patients based on the symptoms sought. The accuracy of Ensemble model is balanced for general health prediction and highest for diabetes prediction among all weak learners considered which provides motivation for further exploring ensemble techniques in this domain.”
EMP-SA: Ensemble Model based Market Prediction using Sentiment Analysis
Authors:   Research Scholar Department of Computer Science, Ramaiah University, Bangalore, (Karnataka), India., Yenkikar Anuradha , Bali Manish ,   Adjunct Professor, Department of Computer Science, IT-ADT University Pune, India., Babu Narendra ,   Associate Professor, Department of Computer Science Engineering, Ramaiah University, Bangalore, (Karnataka), India.
Published: August 2019

Predicting stock market trend is an extremely complicated task and calls for extensive study and insights into the context at hand. Primary requirement for any investor is to assess this trend to help invest for maximizing his returns. The advances in Machine learning and data analytics in particular have changed the way investors can approach this matter. Sentiment analysis or Opinion mining can be carried out by taking into consideration public sentiments regarding the stock market conditions and to understand the ups and down of this most volatile sector. In this paper, public sentiments from Twitter along with news feed related to the stock market conditions for predicting the nature of market is considered to analyse the stock market trend. The data is collected from twitter and various news sites to generate a gross sentiment score regarding the market. The gross sentiment score is used to find a correlation between market price and sentiments to train the proposed models for prediction using Linear and robustness regression techniques such as Ordinary Least squares (OLS), RANSAC, Theil-Sen estimator, Huber Regression and Ridge regression. Ensemble method is used to achieve reliable and better prediction accuracy instead of a single method. Ensemble method combines models and carries out majority voting among them to produce one final model to increase prediction accuracy. The obtained results reveal that public opinion does make a significant impact on market behaviour with the prediction accuracy between 65-91% depending on the dataset.
Geographic Analytics and Visualization for Decision Making: An Application for Online Food Delivery Platform
Authors:   Rajagiri Business School, Kochi, India., George Karan , R Sreejith ,   Asst. Professor, Rajagiri College of Social Sciences, Kochi, India., S Senthil ,   Director, School of CSA, REVA University, Bangalore India.
Published: August 2019

Promoting organizers such as marketing planners frequently utilize geographical information systems to single out appropriate retail stores, setting up advertising campaigns based on regions, and target direct showcasing exercises. Geographical information systems topical maps encourage the visual evaluation of regions. A wide arrangement of elective symbolization, for example, circles, bars, or shading, can be utilized to represent quantitative geospatial data on such maps. In any case, there is little learning on which sort of symbolization is most viable for each case. In a huge scale exploratory study, the creators demonstrate that the symbolization firmly impacts choice execution. GIS-based representations encourage the evaluation of store areas and help organizers to choose the most encouraging options. The choice of the best option requires a visual streamlining which is supported by GIS topical maps. In this study, the manner in which how various GIS-based information portrayals impact advertising analyst’s decision making for online food delivery platforms is explored. The outcomes demonstrate that GIS maps are an important piece of work and that sort of guided representation impacts the initiated decision-making process.
Unified Provisioning Framework for Dynamic Virtual Machine Placement Optimization in Cloud Data Center
Authors:   Computer Science Department, Reva University, Bangalore, India., Shah Darshan , Vinayakmurthi Dr. M. ,   Computer Science Department, Reva University, Bangalore, India., Kumar Dr. Anand ,   M.S. Engineering College, Bangalore, India.
Published: August 2019

Current techniques for Virtual machine placement in cloud data center is avoiding multiple resources due to its complexity and hardness of the problem. Due to this each Virtual Machine Placement increased overall frequency of server consolidation and migration. In this paper, we have overcome these limitations by providing local search-based unified approximation framework which utilized multiple resources of server and reduced the frequency of server consolidation and migration. The framework is evaluated on Azure cloud data center benchmark data sets and it has surpassed existing methods with improvement by 32% in overall virtual machine placement.
Prevalence of Diabetes Mellitus in Tiruchirappalli District using Machine Learning
Authors:   Associate Professor. Department of Computer Science, St. Joseph’s College(Autonomous), Trichy, Arockiam Dr.L. , Sathyapriya S. ,   Ph.D Scholar, Department of Computer Science, St. Joseph’s College(Autonomous), Trichy, Jane V.A. ,   Ph.D Scholar, Department of Computer Science, St. Joseph’s College(Autonomous), Trichy, Kumar A. Dalvin Vinoth ,   Assistant Professor, REVA University, Bangalore.
Published: August 2019

Machine learning is a part of AI which develops algorithms to learn patterns and make decision form the massive data. Recently, Machine learning has been used to resolving various critical medical problems. Diabetes is one of the dangerous disease, which can lead to more complicated, including deaths if not timely treated. The study is designed for providing the prevalence of Diabetes Mellitus in Tiruchirappalli district using machine learning algorithms and it was detected that the polluted air causes diabetes disease and also increases the risk of that disease. This proposed work helps the people in preventing diabetes disease using various diabetic attributes with an aim to enhance the quality of healthcare and lessen the diagnoses cost of the disease. In future, the work done may be extended by considering many other attributes and by implementing it through various algorithms to improve the prediction accuracy of diabetes mellitus.
Credit Card Fraud Analysis using Robust Space Invariant Artificial Neural Networks (RSIANN)
Authors:   Research scholar (Rg. No: R16PCS09), REVA University, Asst. Professor, Anurag group of Institutions (Autonomous), Hyderabad., Deepika S. , Senthil S. ,   Professor & Director- School of Computer Science and Applications, Reva University, Bangalure.
Published: August 2019

One of the impact factor for any organizations or banks revenue and service quality is credit card fraud activities. Hence, need of efficient approach for detect early potential fraud and/or prevent them. In this paper, we considered pre-processing and used deep convolution neural network called as Space Invariant Artificial Neural Networks for classifying fraudsters. Available Credit card fraud dataset may not have sufficient information hence need pre-processing. The proposed approach has pre-processing phrase to make as robust. This approach used leverage layers and suitable tuning parameters for getting good classification accuracy. In neural network applications, choosing of tuning parameters and model selection has great role in solving the problems. We have done careful analysis and selected leverage layers and corresponding parameter values. The proposed architecture tested with all possible tuning parameters to evaluate the performance on pre-processed credit card fraud records. We found the proposed robust SIANN (RSIANN) is outperformed other state-of-art machine learning (ML) algorithms (Support vector machine (SVM), random forest (RF), Navie bayes and deep convolution neural network (DCNN) in terms of accuracy (85%). Thus, this model analyses the transaction and decide it fraud or not.
Automatic Railway Level Crossing System with Embedded Precision Solar Panel using IoT
Authors:   School of Computer Science and Applications, REVA University, Bangalore, India, Densil Raj V. Francis , Kumar Dr. V. Arul ,   School of Computer Science and Applications, REVA University, Bangalore, India, Vinoth Kumar Dr. A. Dalvin ,   School of Computer Science and Applications, REVA University, Bangalore, India
Published: August 2019

Indian Railways is one of the public transportation modes in the country. It is the largest interconnected rail routes which carry many numbers of people from one end to another end. In this, various level crossings are operated on the rail routes and some of the level crossing gates are operated manually. This process is carried out by getting the information from the previous station once the train left the station. In this process, lots of mishaps are occurring due to human interventions and also there is no safety for road users. This issue can be overcome by making the same process in an automated manner. Hence, in this research work, a new automated level crossing model is proposed using the technology “Internet of Things” (IoT). This model will provide
Influence of Axle Speed and Transverse Speed in Alloys Distribution of Friction Stir Welded Aluminum Alloy 6082
Authors:   Department of Mechanical Engineering, PSNA College of Engineering and Technology, Dindigul, India., Yasin J. , Saravanakumar M. ,   Department of Mechanical Engineering, PSNA College of Engineering and Technology, Dindigul, India., nivas C. Madasamy @ Prabu ,   Department of Mechanical Engineering, PSNA College of Engineering and Technology, Dindigul, India.
Published: August 2019

In numerous auxiliary applications, steels are predominantly supplanted by aluminium alloys on account of its great quality weight proportion. One noteworthy issue emerges in this material is a reduction of mechanical properties during the joining process. To wipe out shortages of mechanical properties, it is essential to discover a superior method of metal exchange. In this analysis, a comparative joint of grating friction stir welds 6082-T6 was created by changing the axle speeds between 800 rpm to 1800 rpm and the welding speeds between 30 mm/min to 120mm/min. An imperfection free welding was acquired at 1400 rpm axle speed and 65 mm/min welding speed with the most extreme tensile strength of 158.61 MPa. Examining electron microscopy (SEM) and Electron dispersive spectroscopy (EDS) test were directed to explore the molecule dissemination of the weld tests for optimum weld. From the results, presents of porosity are higher in optimum weld. In order to reduce the porosities, major elements were added and repeated the tests again.
Classification of crops using ANN
Authors:   CSE Depatment, Sharnbasva University, Kalaburgi, India,, Anandkmar  , Y S Dr. Lalitha ,   ECE Department, Don Bosco Institute of Technology, Bengaluru, India,
Published: August 2019

This paper presents study on image processing techniques used classify the crop images. Advanced computing technology helps to improve yield of agriculture products with increasing population of the world and less resources of food. Identification of automatic crop classification based on types is the most important problem. Automatic identification of crop type could help farmers for application of fertilization, pesticides and harvesting of different crop species on-time for the improvement of the production processes of food industries. In this work, Artificial Neural network was used to classification of Guava, Papaya, Banana and Pomegranate crop images. The result shows 90% accuracy in the classification.
Predicting Academic Performance of Tertiary Students using Classification Algorithm
Authors:   Research Scholar, Department of PG & Research Dr. N.G.P Arts & Science College, Coimbatore, India, Jayaprakash Sujith , V. Dr. Jaiganesh ,   Assistant Professor, Department of PG & Research Dr. N.G.P Arts & Science College, Coimbatore, India
Published: August 2019

Classification algorithms have paved the way for several recommender systems in the field of Medicine, Entertainment, Politics, Education, etc. Recently there is a growing interest among researchers to analyze or predict the academic progression of students from High Schools to Tertiary Education. Better performance of students will directly reciprocate in the growth of an institution. Hence, setting up a supervised learning system will act as a gauge to provide a benchmark education. This paper aims to recommend a system based on a predictive model which will aid the institution to measure the performance of students based on various parameters.
A Novel Zero-Sum Polymatrix Game Theory Bidding Strategy for Power Supply Market
Authors:   Department of Electrical Engineering, Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India., Kumar Saurabh , Dwivedi Bharti ,   Department of Electrical Engineering, Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India.
Published: August 2019

The competitive power system market involves very high financial risk due to the essential requirements of real-time bidding decision making. Decisions once taken cannot be altered easily because multiple generators participate in bidding process while simultaneously dispatching to meet the load demand most economically. In order to avoid such risks it becomes pertinent to re-structure the bidding strategies from time to time to meet upcoming techno-economical challenges. In this paper, three generating units are studied using Matrix Laboratory software with a novel approach for deciding the best strategy from the most economical strategy viewpoint. A scenario of different formulations is created for muti-player game, which then is solved with the help of zero-sum polymatrix game theory. A systematic tabular layout of revenues pertaining to each formulation in terms of mixed strategies is developed. The minimax and maximin revenues, identified using Game theoretic approach, gave the most economical strategy. Thus exact and self-enforcing generalized method for best bidding strategies of all three generators are logically derived for the most optimal solution.
Sorptivity and Durability Assessment of Dolomite Impregnated Ternary Concrete
Authors:   Civil Engineering Department, S R Engineering College, Warangal, India., Khan MD Ikramullah , Challa Bhavani ,   Civil Engineering Department, S R Engineering College, Warangal, India., Varma S. Haripriya ,   Civil Engineering Department, S R Engineering College, Warangal, India., Abdy Sayyed Mohd Abbas ,   Civil Engineering Department, S R Engineering College, Warangal, India.
Published: August 2019

Traditional testing methods such as absorption test and permeability test are normally not providing accurate results of nature of concrete and there is a need for another type of test to predict the durability of concrete. In this work, industrial by-product like dolomite, marble dust and fly ash are utilized as fraction of cement replacing with 2%, 4%, 6%, 8%, and 10% dolomite, 10% fly ash and 10% marble dust by the weight of cement. The study is conducted on mix designed concrete of M30 grade and compared with conventional concrete. The specimens are casted and tested to examine various properties of concrete like compressive strength, split tensile strength, durability and sorptivity. Durability test is done by hydrochloric acid (HCl) and sulphuric acid (H2SO4) on dolomite powder, promising results were obtained in the sorptivity test which shows the dense nature of concrete by the usage of dolomite powder.
Optimization of Machining Parameters using ANOVA and Grey Relational Analysis while Turning Aluminium 7075
Authors:   Department of Mechanical Engineering, GITAM (Deemed to be University), Hyderabad, India., Siva Surya Mulugundam , Vepa K. S ,   Department of Mechanical Engineering, GITAM (Deemed to be University), Hyderabad, India., Karanam Malleswari ,   Department of Mechanical Engineering, GITAM (Deemed to be University), Hyderabad, India.
Published: August 2019

In this research a detailed study is carried out on machining parameters for turning operation on aluminium 7075 with high speed steel. This grade of aluminium is known for its applications in aerospace industry and research about its machining parameters will lead to more developments in the field of production. Aim of this work is to optimize turning operation. Machining parameters viz. speed, feed and depth of cut are taken as input parameters. Material removal rate (MRR), tool wear (TWR), surface roughness (SR) are taken as output parameters and the set of optimized parameters means reduction in total production cost. The experiments are planned using Taguchi’s L9 orthogonal array. Grey relational analysis (GRA) is used for multi objective optimization using grey relational grades. Application of analysis of variance(ANOVA) helps in the identification of most prominent parameters among speed, feed and depth of cut.
Design and Analysis of the Scroll Expander with Next Generation refrigerant in an ORC
Authors:   Mechanical Engineering, Pillai College of Engineering, Mumbai, India., Babar Prashant S. , Shaligram Prof A. G. ,   Mechanical Engineering, Pillai College of Engineering, Mumbai, India.
Published: August 2019

Organic Rankine Cycle (ORC) system is used to extract the waste heat from low to medium temperature heat source. The ORC system works as a reverse process of refrigeration. It has the four basic components of Condenser, Refrigerant pump, Evaporator and the Expander. The waste heat is absorbed in the Evaporator and the energy gained is used to rotate the expander which generate the power. The Expander used in the ORC are of different types like the scroll, Screw, Rotary, Turbo. Various refrigerants are used as the working fluid in the ORC system depending upon its physical and thermodynamic properties like R134a, R245fa etc. R245fa is currently used refrigerant in the ORC system. In recent research and development of the refrigerants, R1233zd(E) is also having suitable properties to be used in the ORC system. This refrigerant has zero ODP and very low GWP. This research work includes the analytical and the CFD analysis over the working fluid in the Scroll Expander of the ORC system. The working fluids considered for the analysis is R245fa which currently has a wide usage, and another is R1233zd(E) which has the suitable properties for ORC and is a green refrigerant. A specification for ORC system is considered and the p-h chart is plotted on the refrigerant charts for R245fa & R1233zd(E). The Scroll Fluid Geometry is drafted using ANSYS Design Modeller & Mesh. The Analytical & CFD analysis method is used to find the Force, Torque & Power output parameters for both the refrigerants under consideration at different operating angular positions. The results projected using the Analytical & the CFD method is almost similar with slight variation. The efficiency and the power output using the refrigerant R1233zd(E) is slightly less in comparison with the refrigerant R245fa. Considering the power output results from the scroll expander with both the working fluid under analysis, the refrigerant R1233zd(E) is a good alternate working fluid over the refrigerant R245fa in the ORC system with its Zero ODP and low GWP with a slight compromise over the power output from the ORC system.
Robustious Feature Selection Based Genetic Algorithm (RFS-GA) For Cross Domain Opinion Mining
Authors:   Associate professor, Department of Computer Applications, Nehru College of Management, Coimbatore,(Tamil Nadu), India., blessie Dr. E. Chandra , Gnanapriya S. ,   Ph. D Research Scholar, Department of Computer Applications, Nehru College of Management, Coimbatore,(Tamil Nadu), India.
Published: August 2019

Day by day the requirement of information for processing the sentiment analysis is getting increased multiple times. For these kind of reasons, feature selection is utilized to detect the opinion among different reviews and comments. Sentiment analysis is becoming like phenomenon due to increase of social media’s popularity. Currently, significant advancements are shown in this research domain, but still multiple challenges are to be solved – i.e., sentiment analysis in cross domains. In this paper rumbustious feature selection based genetic algorithm is proposed to address the problem of analyzing the sentiments in cross domain. It performs classification based optimistic-class and pessimistic-class. The dataset used to this research work includes books, DVDs, gadgets and kitchen appliances. Initially the features selection is performed and opinion mining is performed by Genetic Algorithm. Benchmark performance metrics are selected for measuring the performance of proposed work against existing method. Results depict that the proposed work has better performance than that of the existing work as far as chosen performance metrics.
Colon Cancer Biopsy Image Analysis using Deep Learning
Authors:   Department of Computer Science and Engineering Government College of Engineering, Aurangabad, Maharashtra, India., Ubale Anuja , Shikalpure Assistant Prof. S. G. ,   Department of Computer Science and Engineering ,Government College of Engineering, Aurangabad, Maharashtra, India.
Published: August 2019

According to WHO (world health organization) colon cancer is third most commonly identified cancer in male as well as female. It is cancer of colon or rectum, located in inner side of colon wall or rectum wall in large intestine. There are different method used to diagnoses and staging of colon cancer like, colonoscopy, blood testing, biopsy method .Clear analysis of these method used for diagnose the disease. In these method some methods are time consuming if approach is manual analysis and perform on medical images. That’s the reason it required some automated, efficient and accrete machine or technique. To design such machine biopsy image analysis by deep learning play an important role to design and creation of machine that used for biopsy method diagnoses. We work on three model, CNN, VGG16 and InceptionV3 .it provide different accuracy. CNN gives 72%, VGG16 gives 71% and InceptionV3 gives 89% accuracy .Hence proposed method used to significant accuracy improvement automated diagnoses system.
Developing Mathematical Models & Methodologies to Overcome Lean Strategy Selection & Leanness Assessment Problems
Authors:   Research Scholar, Department of Mechanical Engineering, KoneruLakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522502, India., Deshpande Rakhi Ashok , RAO DR. P. V. RAMA ,   Professor, Department of Mechanical Engineering, KoneruLakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522502, India.
Published: August 2019

This investigation inspects the impact of lean strategies among chose manufacturing organizations. In order to remain focused; creating cheaper products at a quicker rate Lean Manufacturing would encourage the business. In this paper we discuss about adopting lean strategies by manufacturing industries with the intention of eliminating wastes. Here we create mathematical models and a systematic methodology for choosing fitting lean strategies. The main objectives of this paper are to assess the leanness levels in manufacturing organizations. In the work we will evaluate both quantitative and subjective info factors. Different tools like MATLAB, Microsoft Access and Excel, Visual Basic, DST tool will be used for leanness assessment problems, methodologies to overcome lean strategy selection. and developing mathematical models. As a final product we endeavor to limitation the sensible disorder of executing unique lean techniques which might be used by any maker for studying leanness and separating their leanness benchmark Accordingly, we propose that assembling partnerships close by various sorts of associations (gain enterprises complete) must obtain lean thinking, acquaintance or culture with forgo inefficient practices along these lines clearing a course for incredible practices that may draw in most significant worry for the associations.
Performance of ZF and CB Precoding Techniques for Large-Scale MU-MIMO System
Authors:   Research Scholar, Department of Electronics and Communication Engineering, Guru Jambheshwar University of Science & Technology, Hisar, Haryana, Singh Jagtar , Kedia Dr. Deepak ,   Professor, Department of Electronics and Communication Engineering, Guru Jambheshwar University of Science & Technology, Hisar, Haryana,
Published: August 2019

Large Scale Multi User-MIMO (MU-MIMO) is a key technology with reference to 5G to achieve higher spectrum as well as energy efficiency. The new technology refers to the use of a large number of antennas at the base station serving many user terminals in the same time and frequency resource allowing the channel vectors nearly orthogonal as a result, there is a reduction in inter-user interference and users may be served with the significant data rate. The linear precoding techniques play a vital role in the reduction of interference among users and cells. In this paper, we have derived, analyzed and compared two important precoding techniques i.e. Zero-forcing (ZF) and Conjugate beamforming (CB) for large-scale multiuser-MIMO. We analyze these precoding techniques with respect to spectral efficiency and downlink power with imperfect channel state information (CSI) as well as with perfect CSI. It is shown that ZF performs better as compared to CB precoding for achieving higher spectral efficiency and requires lower downlink power. CB outperforms the ZF in terms of downlink transmit power when there is a requirement to achieve low spectral efficiency and also for cell- edge users, hence energy efficient in these cases. It is shown from simulation results that ZF precoding is the better choice for attaining higher spectral and energy efficiency for a large scale multiuser-MIMO communication system.
Experimental Optimization of a Solar Distillation Unit
Authors:   Mechanical Engineering Department, Nirma University, Ahmedabad, India., Singhal Ath S , Raval Tejas ,   Mechanical Engineering Department, Nirma University, Ahmedabad, India., Bhatt Anand ,   Mechanical Engineering Department, Nirma University, Ahmedabad, India.
Published: August 2019

Today global warming is a big issue. Ambient temperature have drastically increased, leading to severe droughts especially in those areas which receive less than average rainfall. Inadequate supply of quality drinking water especially in the rural areas have led to develop a distillation system with affordable cost and less maintenance in rural areas. Countries like India and South Africa are getting ample amount of solar energy almost throughout the year. Solar based water distillation unit- Solar still can be great solution to those kind of areas. Efficiency of the solar distillation can be improved by increasing evaporation rate which is a function of incident radiation. Further by increasing concentration of total dissolved salts in the water, base plate absorptivity and efficient energy absorbing material, by providing additional heat with solar water pre-heaters efficiency of solar still can be improved. Here experiments were carried out in order to find out optimum water bath depth and salt concentration in water in solar still for maximizing the yield. Experiments are also carried out by adding heat storage material and effect of salt concentration on yield.
Microgrid Stability Enhancement by using Coordination of SFCL, SMES and Distributed Generation Units with Fuzzy Logic Controller
Authors:   PG Research Scholar, E.E.E Dept., JNTUA, Anantapuram, AP, India., Ayesha Shaik , Gowd K. Jithendra ,   Assistant Professor, E.E.E Dept., JNTUA, Anantapuram, AP, India., Lakshmi D. Dharani ,   Ph.D. Research Scholar, E.E.E Dept., JNTUA, Anantapuram, AP, India.
Published: August 2019

By the coordination of the superconducting fault current limiter (SFCL), superconducting magnetic energy storage (SMES) and distributed generation (DG) units, the stability of the microgrid is increased under short circuit fault conditions. And by this coordination control, the microgrid is smoothly separated from the main network under severe fault and attains a fault ride through (FRT) operation under minor fault. In this paper, to overcome the drawbacks of the PI controller a fuzzy logic controller (FLC) is used in the controller of the SFCL. This proposed method is carried out in a MATLAB/Simulink. The results show the achievement of a better control strategy.
Heavy Metal Degradable Halophilic Bacteria Isolation from Soil in Samut Sakhon, Thailand
Authors:   Industrail Microbiology, Suan Sunandha Rajabhat University, Bangkok, Thailand, Chutrtong Jaruwan
Published: August 2019

This research was conducted to select halophilic microorganisms that can reduce heavy metals in water to be used in wastewater treatment. Halophilic bacteria from salt field soil were isolated in nutrient broth containing 5 heavy metals compound (Zn, Co, Cr, Hg, and Cu). 20 isolates which growth in heavy metal containing media were screened. After that, experiment was conducted to determine the maximum concentration of heavy metals that the isolated could growth. The concentration of heavy metals was obtained from 50-500 mg/L. In media which add Zn, Co, Cr and Cu compound, the isolated were found to be able to grow at concentrations of 450-500 mg/L but they did not grow in media which add Hg (50-500 mg/L). To check the heavy metal degrading ability, the isolates were cultured in nutrient broth which add the most concentrated of heavy metal that they could grow and incubated at 37 ° C for 15 days. Measured the remaining heavy metals and counted the number of microorganisms on days 3, 5,7,10 and 15 were done. It was found that microorganisms were capable to reduce two heavy metals, Cr and Co. Cr reduced by the isolated HL7 and 10-20. Co reduced by the isolated HL1, 3, 4, 5, 7, 8, 10, 11 and 15. Zn and Cu decreased at the decimal level, which were not significant. The results of this research showed that the isolated of halophilic bacteria which isolated from soil in Samut Sakhon had the ability to reduce some heavy metals, which can be applied in wastewater treatment.
Fabrication and Characterization of Plant Mediated Green Zinc Nanoparticles for Antileishmanial Properties
Authors:   Department of Plant Sciences, SBK Women’s Univevrsity, Quetta, Pakistan., Gul Safia , Hashim Yumi Zuhanis Has-Yun ,   International Institute for Halal Research and Training, Iinternational Islamic University Malaysia, Selangor, Malaysia., Puad Noor Illi Mohamad ,   Biotechnology Engineering Department, Iinternational Islamic University Malaysia, Selangor, Malaysia.
Published: August 2019

Infectious diseases outbreak caused by different pathogenic bacteria and the emergence of antibiotic resistance microbial raise new concerns. This situation triggered pharmaceutical companies and researchers to seek for alternatives antimicrobial agents. The discovery of green nanoparticle in pharmaceutical area not only proven to have a strong efficacy as antimicrobial but also promote an eco-friendly compound where the production does not produce hazardous chemicals and waste. In the present study, metallic nanoparticles (Zn) were synthesized using plants extract from Viola yedoensis and Curcuma longa species. The green nanoparticles were successfully synthesized by incorporating plant extracts with the zinc salt with different molar concentrations. The mixture were prepared at different pH, zinc salt (ZnSO4) concentrations and interval times of incubation. The result showed 15 mM concentration of ZnSO4 at pH 7 and incubation at the room temperature for 24 hours were the best conditions for green nanoparticles production. The color intensity changes during the reduction reaction of ZnSO4 by plant extract to form zinc nanoparticles (ZnNPs) was measure using UV spectrophotometer. ZnNPs with the highest color intensity (viola-358nm; turmeric-375nm) were further characterized to determine the elements and morphology of the synthesized ZnNPs using FTIR, XRD and SEM. FTIR analysis demonstrated the presence of aromatic rings and strong bond of O-H and C-H indicating the presence of alcohol and alkene in turmeric ZnNPs. While for viola ZnNPs, the presence of aromatic ring and strong bond of amine showed the existence of alcohol and alkene. The average size of turmeric and viola ZnNPs measured by XRD and SEM were 24-28 nm with spherical shape and 42–60 nm with hexagonal shape, respectively. The biological activity of both ZnNPs were validated by conducting antimicrobial tests towards Leishmanial tropical in which ZnNPs have strong antileishmanial activities.
Factors and Constraints for implementing a Unified Data Platform for Operational Intelligence and Analysis of the Power Sector
Authors:   PhD Scholar University of Petroleum and Energy Studies, Dehradun, Plexus Infratech Private Limited, New Delhi, India., Ghosh Pratik , Banerjee Dr. Ratna ,   Assistant Professor (Senior Scale), University of Petroleum and Energy Studies, Dehradun, India., Kandpal Dr. Vinay ,   Assistant Professor, University of Petroleum & Energy Studies, Dehradun, India., Verghese Dr. Jacob T. ,   Chairman, Steag Energy Services India Private Limited, New Delhi, India.
Published: August 2019

The research tries to identify the various factors that need to be integrated to implement a Unified Data Platform (UDP) for the power sector and its interoperations.Data and tools from generation, transmission, distribution and trading were readily available but in silos and we added to it the complexity of interoperability between stakeholders affecting the organizations, applications, information systems, social, economic, regulatory and technical infrastructure. The different data silos were studied to identify the factors which need to be analysed and integrated for these silos to interoperate with each other for attaining operational excellence in a competitive environment.The research was important as the last few decades have seen an organic growth of the power sector in India. This growth inherently bought with it the requirement for humongous enhancement of existing systems, restructuring of the sector, modification of the policies and develop frameworks to handle the scale of future capacities. The integration and interconnection of different stakeholders in the sector has increased the quantum of software, hardware, tools and data by manifold. The unification of data at a higher level will improve the operational efficiencies and reduce the losses at silos level due to lack of data unification.
Amplify-And-Forward Based Cascaded RF-UWOC System
Authors:   Department of Electronics and Communication Engineering, Indian Institute of Information Technology Guwahati, Assam, India., Anees Sanya
Published: August 2019

A cascaded radio frequency-underwater wireless optical communication (RF-UWOC) system using an amplify-and-forward relay is considered. UWOC supports high speed communication for underwater surveillance, monitoring of climate change, and different deep-sea mining activities making it a s suitable substitute to underwater acoustic communication. The proposed fixed-gain AF based cascaded radio-optical system can connect underwater with land. The system comprises of Nakagami-m faded radio link and mixture Exponential-Generalized Gamma (EGG) faded UWOC link. Salinity, air bubbles, and thermal fluctuations in water causes underwater optical turbulence (UOT). Optical signals are modulated by subcarrier intensity modulation technique at the relay and heterodyne or direct detection is performed at destination. For this proposed communication system, outage and error analyses are carried out. It is observed that the performance of the considered system deteriorates more because of the underwater optical turbulence (UOT) due to thermal fluctuations and air bubbles as compared to UOT introduced by salinity.
Dynamic Power Optimization of 32 Bit MIPS Processor using Clock Gating for Low Power Applications
Authors:   Ph.D Scholar, Jawaharlal Nehru technological University, Kakinada, Andhra Pradesh, India., Prasanth V. , Babulu K. ,   Professor of ECE, Jawaharlal Nehru technological University/ Kakinada, Andhra Pradesh, India, Kamaraju M. ,   Professor, Electronics and communication Engineering Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh, India.
Published: August 2019

The demand for low power processor is increasing day by day in mobile application for video, audio, mixed signal processing, gaming console and battery-operated electronic devices. Power consumption is the main issue in batter operated devices which constantly reduces battery life. Compared to static power Dynamic power yields more power consumption in digital design. Clock power is one of the major factors in total power consumption which results in high dynamic power consumption. In this paper, a 32-bit MIPS processor is designed to maximize the performance while considering the battery life of the device. Clock gating and data gating method is adopted in this paper and to reduce dynamic power. This design is implemented on 28nm kintex-7 FPGA Board and power is analyzed
Implementation of Fuzzy Possibilistic Product Partition C-Means and Modified Fuzzy Possibilistic C-Means Clustering to Pick the Low Performers using R-Tool
Authors:   Department of Computer Applications, Kongu Arts and Science College, Erode -638107, Tamil Nadu, India., Thilagaraj T. , Sengottaiyan Dr.N. ,   Director, Sri Shanmugha College of Engineering and Technology, Sankari - 637304 , Tamil Nadu, India,
Published: August 2019

The different techniques like clustering, classification, association rule and regression are available in data mining to deal with a huge number of datasets that are available in the education field. The main purpose of educational data mining is to extract useful information that will create a good impact on educational institutions. The identification of risk students, improving the graduation rates and placement opportunities will assess the institutional performance. The clustering is one of the famous techniques to deal with noisy and disjoint groups. The clustering technique is used to measure the distance between data objects of a similar group and also it finds the different cluster center in each iteration. The placement creates the opportunity to learn specific skills on their subject or industry and improves their knowledge in various sectors. In this paper, we are going to discuss Fuzzy Possibilistic Product Partition C-Means (FPPPCM) and Modified Fuzzy Possibilistic C-Means Clustering (MFPCM) performance while dealing with the student placement performance details. The improvement of the educational system will depend on reducing the low performing students rate. The main aim of this paper to pick the low performers by using FPPPCM and MFPCM algorithms. This will helps academia to identify the low performers and provide proper training to them in an early stage. And also the efficiency of FPPPCM and MFPCM is going to analyze with different parameters.
Securing Authentication Database of Online Voting System using PIN Based Lossless Image Secret Sharing Scheme
Authors:   CSE Department, GITAM Institute of Technology, GITAM University, Visakhapatnam.Computer Department, Pimpri Chinchwad College of Engineering, Pune, India., Kharat Reena , Naidu P.Sanyasi ,   CSE Department, GITAM Institute Of Technology, GITAM University, Visakhapatnam
Published: August 2019

Voting is vital part of democratic country as it allows citizen to choose their own government. Government plays important role in progress of each country. Result of election is good if most of the people votes. In today's digital world, it is necessary to have secure online voting system at place which will save voter's time and motivate citizen to vote. Secure authentication is the main issue in online voting system. The authentication module should allow authentic voter to cast their vote. Even though secure authentication is provided, someone having access to authentication database may use it to get authentic entry into the system. Research in this paper shows new and secure authentication module which provides confidentiality to authentication database so that no one can use the authentication database to pass through the system. We have achieved this using PIN based image secret sharing.
Effect of Boron Carbide on Tribological properties of Zinc Aluminium Metal matrix composites
Authors:   Assistant Professor, Department of Mechanical Engineering, Nitte Meenakshi Institute of Technology, Bangalore, India., Kumar MV Puneeth
Published: August 2019

Metal-Matrix Composites (MMCs) are novel composite materials in which nano/micro-phase such as particles or rods like structured materials are reinforced with metals or alloys to improve the physical, mechanical, wear and other properties. The results of previous works have shown that the addition of B4C reinforcement to aluminium alloys has improved the hardness, tensile strength and Young’s modulus. Hence, the present work is focused on development and characterization of B4C reinforced MMCs. evaluating the outcome of the reinforcement on the physical properties such as wear property studies were carried out. The wear results exhibited reduction in wear volume compared to the unreinforced alloy samples and the coefficient of friction is beneath at lofty loads.