bluuebunny/crossref_metadata_2025 huggingface dataset:
Kinetics of Iron Bioleaching using Isolated Leptospirillum Ferriphilum: Effect of Temperature
Authors:   Chemical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia., Prabhu. S* Venkatea , G Ramesh. ,   Mechanical Engineering, JCT College of Engineering, Coimbatore, India., Adugna Amare T. ,   Environmental Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia., Beyan Surafel M. ,   Chemical Engineering, Jimma Institute of Technology, Jimma, Ethiopia., Assefa. K Gizachew ,   Chemical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.
Published: October 2019

This study was designed to investigate the effect of temperature on iron bioleaching kinetics using Leptospirillum ferriphilum. The bacteria were isolated and subjected to molecular characterization technique for confirming L. ferriphilum. Using the isolate, bioleaching data were collected in the temperature range of 298–318 K at an initial pH of 1.5 and 5% pulp density with an average particle size being 300 μm. The results of experiments concluded that leaching efficiency increases with temperature and maximum of 93.85% were observed after 20 days at 313 K. The bioleaching kinetics indicated that the maximum rate (rate constant: 0.1452 d1) was found in the experiment conducted at the optimum temperature, and the rate-controlling step was “diffusion through ash layer.” The activation energy was calculated to be 37.59 kJ/mol. From the thermodynamic study of the bioleaching system, ΔH˚ and ΔS˚ were found to be 0.7399 × 10−3 and 28.512 J/mol, respectively.
Redesigning the Academic Role- The Emerging Dimensions of Knowledge Workers
Authors:   ProfessorDepartment of Management Studies SRM Institute of Science and Technology Vadapalani Chennai, Gayathry* Dr.S.
Published: October 2019

The role of teachers is undergoing a paradigm shift in the present education scenario. During earlier days, teachers played the role of tutors and were highly subject oriented. They were not expected to do anything other than mere teaching. They were considered as instructors. With advancement in technology the role of the teachers became as that of a guide and the teachers were leading the students. In the present era of knowledge explosion, the teachers have to be facilitators. They learn along with the students. The role of the teachers has thus undergone a paradigm shift from that of instructors to leaders to learners. This paper makes an earnest attempt to study the role of teachers working for Government and Private Arts & Science Colleges and Engineering Colleges. An attempt has been made to study the association between the different parameters for evaluating the role of teachers working under various streams and different sectors.
Opinion Based Memory Access Algorithms using Collaborative Filtering in Recommender Systems
Authors:   Research Scholar, Dept. of c.sc,Cauvery college for Women,BharathidasanUniversity,Trichy,India., J* Ms.Sangeetha , Janita Dr.Sinthu ,   Professor, Dept. of c.sc,Cauvery college for Women, BharathidasanUniversity,Trichy,India.
Published: October 2019

In recent years, the online shopping and the online advertisement businesses is growing in a vast way. The reason behind this growth is, the peoples are not having sufficient time for go for a shop. Without seeing the quality of the product directly, the people are ready to buy the product by seeing the other user recommendation of the particular product. This leads an interest / the need to develop the researcher an innovative recommendation framework. Based on the opinion prediction rule, the huge size of words and the phrases which are presented in the unstructured data is modified as a numerical values. The sale of the particular product in an online shopping is depends on its description of the quality, the review of the customer. Based on the positive and negative polarity, an Inclusive Similarity-based Clustering (ISC) is proposed to cluster the extracted related keywords from the user reviews. To evaluate the strength, weakness of the product, estimate the respective features, as well as the opinions, the Improved Feature Specific Collaborative Filtering (IFSCF) model for the feature with aspect opinion is proposed. Finally the complete feedback of the product is estimated by propose the Novel Product Feature-based Opinion Score Estimation process. The main challenge in this recommendation system is the fault information estimation of the reviews and the unrelated recommendations of the bestselling or the better quality product. To neglect these issues, an Enhanced Feature Specific Collaborative Filtering Model based on temporal (EFCFM) is proposed in the recommendation system. Hence the developed EFCFM method is investigated by comparing along with the existing methods in terms of subsequent parameters, precision, recall, f-measure, MAE and the RMSE. The outcome shows that the developed EFCFM approach predicts the best product and produce the accurate recommendation to the customers.
Enhancing Packet Delivery Ratio and a lifetime of Wireless Sensor Networks using Energy-Efficient Unequal Clustering Routing Algorithm
Authors:   Electronics and communication engg., CDLSIET, panniwala mota, Sirsa, Haryana, INDIA., handa* Priyanka , singh panag Tripatjot ,   Electronics and communication engg., BBSBEC, Fatehgarh Sahib, Punjab, INDIA.., Sohi Balwinder Singh ,   Electronics and communication engg., CGC,Gharuan, Mohali, Punjab, INDIA.
Published: October 2019

Sensors are regarded as significant components of electronic devices. The sensor nodes deployed with limited resources, such as the power of battery inserted in the sensor nodes. So the lifetime of wireless sensor networks(WSNs) can be increased by using the energy of the sensor nodes in an efficient way. A major part of energy is consumed during the communication of data. Also, the growing demand for usage of wireless sensors applications in different aspects makes the quality-of-service(QoS) to be one of the paramount issues in wireless sensors applications. QoS guarantee in WSNs is difficult and more challenging due to the fact that the sensors have limited resources and the various applications running over these networks have different constraints in their nature and requirements. The packet delivery ratio(PDR) is a major factor of QoS. To achieve high QoS the packet delivery ratio should be maximum. The energy-efficient unequal clustering routing protocol (EEUCR) is evaluated and results show that it enhances the packet delivery ratio(PDR) and a lifetime of WSNs. In this protocol, the area of the network is divided into a number of rings of unequal size and each ring is further divided into a number of clusters. Rings nearer to the base station(BS) have smaller area and area of rings keeps on increasing as the distance from BS increases for balanced energy consumption. The nodes with heterogeneous energy are deployed in the network. Nodes nearer to the base station have higher energy as compared to farther nodes. Static clustering is used but cluster heads(CHs) are not fixed and are elected on the basis of remaining energy. This helps to increase lifetime of EEUCR. PDR of EEUCR is improved because multiple rings help to find better route which further aids to ensure safe reception of packets at the destination. Simulation results are compared with existing protocols and show that this algorithm gives better results.
Asymmetric Sigmoidal Activation Function for Feed-Forward Artificial Neural Networks
Authors:   Research Scholar and Assistant Professor, University School of Information Communication and Technology, Guru Gobind Singh Indrapratha University, N.Delhi, India., Sehrawat* Ruchi , Chandra Pravin ,   University School of Information Communication and Technology, Guru Gobind Singh Indrapratha University, N.Delhi, India., Ghose Udayan ,   University School of Information Communication and Technology, Guru Gobind Singh Indrapratha University, N.Delhi, India.
Published: October 2019

Artificial neural networks of the feed – forward kind, are an established technique under the supervised learning paradigm for the solution of learning tasks. The mathematical result that allows one to assert the usefulness of this technique is that these networks can approximate any continuous function to the desired degree. The requirement imposed on these networks is to have non-linear functions of a specific kind at the hidden nodes of the network. In general, sigmoidal non-linearities, called activation functions, are generally used. In this paper we propose an asymmetric activation function. The networks using the proposed activation function are compared against those using the generally used logistic and the hyperbolic tangent activation function for the solution of 12 function approximation problems. The results obtained allow us to infer that the proposed activation function, in general, reaches deeper minima of the error measures and has better generalization error values.
Conversion of Video Surveillance System into Measurement System Based on Video Camera
Authors:   Saint-Petersburg National Research University of Information Technologies, Mechanics and Optics, St. Petersburg, Russia., Ashirov* Artem Nailevich , Akhmadiev Aidar Ildusovich ,   Kazan National Research Technical University named after A.N. Tupolev-KAI, Kazan, Russia., Ashirova Svetlana Anatolievna ,   Kazan National Research Technical University named after A.N. Tupolev-KAI, Kazan, Russia., Kabitova Evgenia Vladimirovna ,   Kazan National Research Technical University named after A.N. Tupolev-KAI, Kazan, Russia.
Published: October 2019

This work reviews measurement systems applied for static and dynamic detection of object coordinates as well as acquiring information of their position at various distances. The market of measurement systems based on video cameras has been analyzed. Their classification is presented, fields of application are described, advantages and disadvantages are summarized for each type of measurement systems. In addition, it has been revealed that the frequency and extent of measurement errors depend not only on the quality on system components but also on numerous other factors, such as light flare, microvibrations, thermal expansion, etc. The review of measurement systems is necessary for determination of the most efficient method of conversion of video surveillance system into measurement system.
Location-Aware Mobile Cloud using Artificial Intelligence
Authors:   Research scholar at I.K.G. Punjab Technical University, Kapurthala, Punjab, India., Vishal*  , Kaur Prof. Bikrampal ,   Professor in the Department. of Computer Science & Engineering in Chandigarh Engineering College, Landran, Mohali,India., Jangra Dr.Surender ,   Assistant Professor, Department of Computer Science, GTB College, Bhawanigarh, (Sangrur), India.
Published: October 2019

Mobile cloud computing is a rapidly evolving technology these days and it faces major problems of load imbalance due to the high demand for mobile applications. There are many techniques to solve the problem, but the user can improve load by using a more optimized solution. This research deals with the VM allocation and migration by means of location awareness. The research paper also presents a user authentication and server load management system to reduce the overload of the server. A captcha based authentication mechanism is also presented for user verification. The concept of Feedback is also introduced for the mobile servers. This concept makes the selection of mobile server for the job list. ANN (Artificial neural network) is used for location awareness judgment. ANN is a machine learning approach, which is used to minimize the human effort and also minimize the processing time to allocate job to an accurate server with minimum SLA violation. MBFD (Mobile best fit decreasing) algorithm is used for the VM allocation and selection policy. This research has considered SLA (Service level agreement) violation and energy consumption to compute the performance of the work with an aim of reducing energy consumption with maximized resource efficiency. The proposed work model is also compared with the work presented by Xiong in the same area.
Automated Retraining of Machine Learning Models
Authors:   Department of Information and Communication Technology, Manipal Institute of Technology (Manipal Academy of Higher Education), Manipal, India., Kavikondala Akanksha , Muppalla Vivek ,   Department of Information and Communication Technology, Manipal Institute of Technology (Manipal Academy of Higher Education), Manipal, India., Prakasha K* Krishna ,   Department of Information and Communication Technology, Manipal Institute of Technology (Manipal Academy of Higher Education), Manipal, India., Acharya Vasundhara ,   Department of Computer Science & Engineering Manipal Institute of Technology (Manipal Academy of Higher Education), Manipal, India.
Published: October 2019

Data is the most crucial component of a successful ML system. Once a machine learning model is developed, it gets obsolete over time due to presence of new input data being generated every second. In order to keep our predictions accurate we need to find a way to keep our models up to date. Our research work involves finding a mechanism which can retrain the model with new data automatically. This research also involves exploring the possibilities of automating machine learning processes. We started this project by training and testing our model using conventional machine learning methods. The outcome was then compared with the outcome of those experiments conducted using the AutoML methods like TPOT. This helped us in finding an efficient technique to retrain our models. These techniques can be used in areas where people do not deal with the actual working of a ML model but only require the outputs of ML processes.
Do Work Experience, Good Governance, and Independence Influence the Audit Quality?
Authors:   Accounting Department, Faculty of Economics and Communication, Bina Nusantara University, Jakarta, Indonesia, Handoko* Bambang Leo , Widuri Rindang ,   Accounting Department, Binus Graduate Program, Master of Accounting, Bina Nusantara University, Jakarta, Indonesia, Andrian Tommy ,   Accounting Department, Faculty of Economics and Communication, Bina Nusantara University, Jakarta, Indonesia, Darmasaputra Joseph Ivan ,   Accounting Department, Faculty of Economics and Communication, Bina Nusantara University, Jakarta, Indonesia
Published: October 2019

Financial auditors play an important role in the financial reporting of business entities. The auditor must examine the report or bookkeeping company. When conducting its audit work, auditors are required to have competence in accounting or finance and always maintain an independent attitude. Even so, until now there are still many people who doubt the competence and independence of public accountants. This is compounded by the many cases involving the public accounting profession, in many financial statement fraud cases that occurs worldwide such as the Enron, Phar-Mor, Crazy Eddie case. This study aims to analyze in order to obtained statistical result regarding how auditor work experience, understanding of Good Governance (GG), independence of auditor influenced quality of the audit result. The sample in our study was obtained by using a purposive sampling method for certified auditor firm in the Jakarta Special Capital Region. We have 200 auditors as our respondents from various audit firms. Hypothesis testing is done by using ordinary least square. We used determination of coefficient, t test and f test. On the conclusion, we have evidence that good governance variable significantly influences audit quality, while work experience variables and independence variable do not significantly influence audit quality. Our study also stated that all the independent variables give influence magnitude 64.8 percent for explaining quality of audit.
Response of Multi-Storeyed Buildings Having Vertical Irregularities using ETABS
Authors:   Civil department, Pallavi Engineering College, Jawaharlal Nehru Technological University, Hyderabad, India., Kumar* V. Shiva , Kumar M. Manoj ,   Civil department, Pallavi Engineering College, Jawaharlal Nehru Technological University, Hyderabad, India.
Published: October 2019

The objective of this research is to analyze the regular and vertical irregular buildings for understanding the response of vertically irregular buildings by using advanced software like ETABS. The analysis is carried out to understand the behaviour of the buildings by performing Non-Linear Dynamic analysis (Nonlinear Time-History Analysis) in software. The response of buildings is analyzed for three different irregularities, they are i) mass irregularity ii) stiffness irregularity iii) setback irregularity. The response of the vertically irregular buildings with regular building is done by considering the Base shear, Displacement and Story Drift of the buildings. The buildings which are irregular in plan will undergo to torsion effects easily because their centre of mass does not coincide with the centre of gravity, since the torsion will be developed in the building. When comes to the buildings which are irregular in elevation and located in seismic zones, the understanding of behaviour of such vertically irregular buildings will be difficult. It is a challenging task to the structural engineers to study the behaviour of vertically irregular buildings. This paper will provide the response of vertical irregular buildings so the engineers can design the buildings accordingly.
SMT Component Inspection in PCBA’s using Image Processing Techniques
Authors:   ECE Department, VVIET and Research Scholar, MRF, Mysore, India., D B* Anitha , Rao Mahesh ,   ECE Department, MIT, Mysore, India
Published: October 2019

In the present evolving technology, an Automated Optical Inspection is a solution for identifying the various types of defects occurring in assembled PCB with SMT components. As these high-end machines are expensive, moreover the small scale industries can’t afford such a huge investment, in this paper a low cost image processing technique where a good known reference image is compared with the acquired image is being tried. This work provides an automated approach for identifying few of the defects related to the SMT components found in the assembled PCB, using three different techniques namely Contour Analysis, Optical Character Recognition and Pixel Subtraction for identifying shifted components, value of the components and missing components respectively in LabVIEW platform. The time taken for identifying the various defects through different techniques are calculated and tabulated. Using these techniques the number of errors can be decreased in turn the end performance can also be enhanced with the increased production yield.
Potential Use of RTL Co-Simulation
Authors:   Department of ECE, SRMIST, Vadapalani, Chennai India., Karthik* S. , Priyadarsini K. ,   Department of CSE, VISTAS, Pallavaram, Chennai India.
Published: October 2019

This paper’s objective is to highlight the potential use of C/RTL Co-Simulation and application of it in satisfying the time constraints involved in verification of industrial, complex, monolithic. Co-Simulation performed in Xilinx software has provided a platform for further analysis of designs. Steps before performing co-simulation has provided statistical data about how much resources the system design requires which can be further analyzed, part-by-part, using profiling method. The profiling allows demarcation of a distinctive line between resource-intensive processes and time-intensive processes. A further study into this matter would purge the need of resource-intensive and time-consuming devices. This could be a small step towards attaining a level of production where the outcome is a better device and error-less production of these devices.
Exploration of Neighbor Kernels and Feature Estimators for Heart Disease Prediction using Machine Learning
Authors:   Lecturer, Department of Computer Science, College of Science and Arts, Khamis Mushayt, King Khalid university, Abha, Asir, Saudi Arabia., Mathew* Rincy Merlin , Devi M. Shyamala ,   Associate Professor, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, TamilNadu, India., Basheer Shakila ,   Assistant Professor, Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdul Rahman university, Riyadh-11671, Saudi Arabia
Published: October 2019

In the growing era of technological world, the people are suffered with various diseases. The common disease faced by the population irrespective of the age is the heart disease. Though the world is blooming in technological aspects, the prediction and the identification of the heart disease still remains a challenging issue. Due to the deficiency of the availability of patient symptoms, the prediction of heart disease is a disputed charge. With this overview, we have used Heart Disease Prediction dataset extorted from UCI Machine Learning Repository for the analysis and comparison of various parameters in the classification algorithms. The parameter analysis of various classification algorithms of heart disease classes are done in five ways. Firstly, the analysis of dataset is done by exploiting the correlation matrix, feature importance analysis, Target distribution of the dataset and Disease probability based on the density distribution of age and sex. Secondly, the dataset is fitted to K-Nearest Neighbor classifier to analyze the performance for the various combinations of neighbors with and without PCA. Thirdly, the dataset is fitted to Support Vector classifier to analyze the performance for the various combinations of kernels with and without PCA. Fourth, the dataset is fitted to Decision Tree classifier to analyze the performance for the various combinations of features with and without PCA. Fifth, the dataset is fitted to Random Forest classifier to analyze the performance for the various levels of estimators with and without PCA. The implementation is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that for KNN classifier, the performance for 12 neighbours is found to be effective with 0.52 before applying PCA and 0.53 after applying PCA. For Support Vector classifier, the rbf kernel is found to be effective with the score of 0.519 with and without PCA. For Decision Tree classifier, before applying PCA, the score is 0.47 for 7 features and after applying PCA, the score is 0.49 for 4 features. For, Random Forest Classifier, before applying PCA, the score is 0.53 for 500 estimators and after applying PCA, the score is 0.52 for 500 estimators.
Exploration of Multiple Linear Regression with Ensembling Schemes for Roof Fall Assessment using Machine Learning
Authors:   Associate Professor, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, TamilNadu, India., Devi* M. Shyamala , Basheer Shakila ,   Assistant Professor, Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdul Rahman university, Riyadh-11671, Saudi Arabia, Mathew Rincy Merlin ,   Lecturer, Department of Computer Science, College of Science and Arts, Khamis Mushayt, King Khalid university, Abha, Asir, Saudi Arabia.
Published: October 2019

Roof fall of the building is the major threat to the society as it results in severe damages to the life of the people. Recently, engineers are focusing on the prediction of roof fall of the building in order to avoid the damage to the environment and people. Early prediction of Roof fall is the social responsibility of the engineers towards existence of health and wealth of the nation. This paper attempts to identify the essential attributes of the Roof fall dataset that are taken from the UCI Machine learning repository for predicting the existence of roof fall. In this paper, the important features are extorted from the various ensembling methods like Gradient Boosting Regressor, Random Forest Regressor, AdaBoost Regressor and Extra Trees Regressor. The extracted feature importance of each of the ensembling methods is then fitted with multiple linear regression to analyze the performance. The same extracted feature importance of each of the ensembling methods are subjected to feature scaling and then fitted with multiple linear regression to analyze the performance. The Performance analysis is done with the performance parameters such as Mean Squared Log Error (MSLE), Mean Absolute error (MAE), R2 Score, Mean Squared error (MSE) and Explained Variance Score (EVS). The execution is carried out using python code in Spyder Anaconda Navigator IP Console. Experimental results shows that before feature scaling, Extra Tree Regressor is found to be effective with the MSE of 0.06, MAE of 0.07, R2 Score of 87%, EVS of 0.89 and MSLE of 0.02 as compared to other ensembling methods. In the same way, after applying feature scaling, the feature importance extracted from the Extra Tree Regressor is found to be effective with the MSE of 0.04, MAE of 0.03, R2 Score of 96%, EVS of 0.9 and MSLE of 0.01 as compared to other ensembling methods.
Outlier Detection in High Dimensional Data Based on Elastic Net Regression
Authors:   Dept. of CSE, ANU, Guntur, Andhra Pradesh, India, Anuradha* Ch. , Ramesh Dr. M. ,   Dept. of CSE, RVR & JC College of Engineering, Guntur, Andhra Pradesh, India, Murty Dr. Patnala S.R. Chandra ,   Dept. of CSE, ANU, Guntur, Andhra Pradesh, India
Published: October 2019

Outlier detection in large datasets is the dynamic research area in computer science such as data mining, database systems, and distributed systems. Outlier detection faces many challenges due to the absence of data samples from the outlier class. Massive algorithms have been projected to conquer the challenges in this field to improve the efficiency of regression approach for large datasets. Currently, no particular efficient regression technique is designed for outlier detection. In this research, we proposed an ElasticNet regression model for detecting the outliers in high dimensional data. To validate the efficiency and competence of our projected algorithm, it is implemented in the open source software called Weka Explorer. The parameters such as Mean absolute error 0.0022, RMSE 0.0387, Relative absolute error (RAE) 0.4562 and Root relative squared error (RSE) 7.8722 are calculated using annthyroid dataset. ElasticNet model consumes less computational time, generates fast convergence results, provides high accuracy and correctly classified accuracy is 98.25%.
Development of Multilevel Cascaded H-bridge Inverter Based Dynamic Voltage Restorer (DVR)
Authors:   M-Tech in Power Systems EEE department, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India., Bai* Bondili Saibindu , Awaar Vinay Kumar ,   Electrical and Electronics Engineering department, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India., Jugge Dr. Praveen ,   Electrical and Electronics Engineering department, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India.
Published: October 2019

The objective of this study is to present the development of a dynamic voltage restorer (DVR) which employs a cascaded H-bridge multilevel inverter. DVR is a power electronic converter based on custom power device used to compensate for voltage variations. DVR does not need active energy storage systems like batteries as the zero energy compensation method is used. The control system may lead to challenges as compared with other DVR systems. To overcome this challenge a control method is used. In addition to minimize the delay of DVR and to mitigate the voltage sags, a three phase estimation method is used. The estimation method and control scheme is performed by using MATLAB/SIMULINK.
Physical and Biochemical Assessment of Sugarcane Bagasse (Saccharum Officinarum)
Authors:   Scholar (Ph D) at Department of Biotechnology, School Of Applied Sciences REVA University, Rukmini Knowledge Park, Kattigenahalli, Yelahanka, Bangalore-560064, Karnataka. Assistant Professor at Sir M Visvesvaraya Institute of Technology, Hunasamaranahalli Bangalore, Chandra* Ishwar , Ramesh N ,   School of Applied Sciences, REVA University, Rukmini Knowledge Park, Kattigenahalli, Yelahanka, Bangalore-560 064 Karnataka., Upadhyay Anima ,   Department of Chemistry, Sir M Visvesvaraya Institute of Technology, Krishnadeveraya Nagar, Hunasamaranahalli Bangalore-562157, Affiliated Visvesvaraya Technological University, Belgaum
Published: October 2019

Bagasse was milled into small size of 0.200152 mm volume surface mean diameter. The powder was found to be a rich source of many macro and micro nutrients, namely Nitrogen, Phosphorus, Potassium, Calcium, Magnesium, Sulphur, Iron, Manganese, Zinc, and Copper. FTIR spectrum indicates the possible presence of polysaccharides like cellulose, lignin and hemicelluloses, and polyphenols in the bagasse sample. Micro-images of the bagasse obtained through SEM shows the features of fiber structures. The fiber cells are cross-linked and surface seems to be rough and thick-walled. The fiber pith was found to have pits at some places. Proximate analysis through EDX shows the high proportions of Carbon (44.51%) and Oxygen (55.49%).
An Integrated Evaluation of Manufactured Sand and Metakaolin Impacts on Concrete Properties
Authors:   department of Civil Engineering, Vignan’s Foundation for science, technology&Research.Guntur,India., Pratap Chowdary* Muppalla Venkata Sai Surya , Poluraju P. ,   department of Civil Engineering, V.R.Siddartha Engineering College, , Vijayawada India., babu T.Suresh ,   department of Civil Engineering, Visvodaya Engineering College, Kavali, India., Aadi SS. ,   Profesor,department of civil Engineering, Vignan’s Foundation for science, technology& Research
Published: October 2019

This paper deals with M25 Concrete mix in which replacing Natural Sand by the Manufacturing Sand of 35% and 65% at Cement by Metakaolin of 0, 5, 10, 15 and 20 percentages is compared with concrete had cement with Metakaolin at different percentages without replacement of natural sand .Workability is determined for Concrete and Cylindrical specimens of 150mm*300mm of size are casted to test Concrete properties such as Split Tensile strength(STS) and Compressive Strength(CS) of Concrete. These specimens are placed under curing of 7days, 28days and 60days; after that time placed under testing and compared the results with Normal Concrete.
Gsm Based Face Recognition using Pir Sensor on Raspberry Pi 3
Authors:   M Tech Scholar, Department of ECE, GRIET, Hyderabad, India., Rakesh* G. , Kora Padmavathi ,   Professor, Department of ECE, GRIET, Hyderabad, India., Swaraja K. ,   Department of ECE, GRIET, Hyderabad, India., Meenakshi K. ,   Professor, Department of ECE, GRIET, Hyderabad, India, Karuna G. ,   Professor, Department of CSE, GRIET, Hyderabad, India.
Published: October 2019

We Developed An Associate Approach To The Detection And Identification Of Human Faces And Describe A Operating, Near-Real-Time Face Recognition System That Tracks A Subject’s Face And So Acknowledges The Person By Comparison Characteristics Of The Face To Database. Our Approach Treats Face Recognition As A Two-Dimensional Recognition Downside, Taking Advantage Of The Very Fact That Faces Area Unit Area Unit Normally Upright And Therefore Is Also Delineate By A Small Set Of 2-D Characteristic Views. Face Pictures Are Projected Onto A Feature Area (“Face Space”) That Best Encodes The Variation Among Database Images. The Face Area Is Outlined By The “Eigenfaces”, That Area Unit The Eigenvectors Of The Set Of Faces; They Do Not Essentially Correspond To Isolated Options Like Eyes, Ears, And Noses. The Framework Provides The Flexibility To Be Told To Acknowledge New Faces
Valorization of Specially Designed Concrete by Using Sugarcane Bagasse Ash and Inducing the Special Benefits of Waste Tin Fiber Reinforced Concrete
Authors:   Professor, Department of Civil Engineering, CMR Technical Campus, Kandlakoya, Medchal Road, Hyderabad – 501401, Telangana, India., SOUNTHARARAJAN* Dr VM , Sivasankar Dr S. ,   Associate Professor, Department of Civil Engineering, CMR Technical Campus, Kandlakoya, Medchal Road, Hyderabad – 501401, Telangana, India., Kumar Mr. R Vinodh ,   Assistant Professor, Department of Civil Engineering,, Meenakshi College of Engineering, Chennai, Tamil Nadu., Modak Mr. Nabajyoti ,   Assistant Professor, Department of Civil Engineering, Anand Institute of Higher Technology, Chennai, Tamil Nadu, India., Bai K. Dilli ,   Pursuing M.Tech – Structural Engineering at CMR Institute of Technology, Kandlakoya, Medchal Road, Hyderabad – 501401, Telangana, India.
Published: October 2019

This research work has been investigated the agriculture solid waste of sugarcane bagasse ash (SCBA) materials replacing Portland cement and produces the assured quality of concrete. The current research work for various mixes of experimental test results shows the higher compressive strength was 37.51MPa at 28-days, 38.10 MPa at 56-days, the best mix consisting of SCBA (wet sieving method) content up to 15% (by weight of binding materials) along with 1.5% of waste tin fibers and also an excellent improvement trend was noted in flexural rigidity of concrete to addition of tin fibers shows the higher bending stress for all mixes except reference as well as more than 15% of SCBA concrete at different curing days. However, this study focused on the indirect measurement of tensile strength in SCBA concrete obtained the higher split tensile strength was 3.75MPa at 28-days, 3.95MPa at 56-days. It is concluded based on the various test results for different curing days the optimum replacement level of SCBA up to 15% of Portland cement was fixed and achieve the target strength of M25 grade of Portland cement concrete at 28 days.
Improved Diabetes Prediction Model for Predicting Type-Ii Diabetes
Authors:   Post-graduate student, Department of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India., Nimmagadda* Sai Poojitha , Yeruva Dr. Sagar ,   Associate Professor, Department of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India., Siempu Dr. Rakesh ,   Assistant Professor, Department of Civil Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India.
Published: October 2019

The state or disorder where the body cannot effectively use the insulin is called Diabetes. If the insulin levels are not maintained properly, the diabetes is one such disorder where it damages all other body parts. It is estimated that the diabetes is the 7th leading cause of deaths as per World Health Organisation report. Early recognition of diabetes, decreases the risk of serious ailments, which includes, heart diseases, brain stroke, eye related diseases, kidney diseases, nerve related diseases etc. In the present work, pima indians diabetes data set is considered as the best dataset and different models viz., hierarchical clustering with decision tree, hierarchical clustering with support vector machines, hierarchical clustering with logistic regression and k means with logistic regression are developed and implemented for identifying and predicting the diabetes. The accuracies of these prediction models range between 0.90 and 0.946. An Improved Diabetes Prediction Algorithm (IDPA) combining the hierarchical clustering algorithm and Naïve Bayes classification algorithm is developed to identify and predict the Type-II diabetes and has shown an accuracy of 0.96. In this IDPA, firstly, the grouping of data into two groups i.e. diabetes and non-diabetes is done by applying the hierarchical clustering algorithm. Then, the filtering is done by comparing the group value to the class value followed by applying Naïve Bayes classification algorithm for predicting diabetes. The results show that the proposed novel method i.e. IDPA can predict the diabetes with higher accuracy levels (0.96) than the traditional/existing methods and other methods which were implemented. This model can be used to predict diabetes early, thereby reducing the serious complications of diabetes.
Anfis Controller Based Reconfigurable Solar Converter for Single Stage Power Conversion-PV Battery System
Authors:   Electrical and Electronics Engineering, Anurag Group of Institutions, Hyderabad, India., Thaduri* Kavya , S Mamatha. ,   Electrical and Electronics Engineering, Anurag Group of Institutions, Hyderabad, India., Kumar.T Anil ,   Electrical and Electronics Engineering, Anurag Group of Institutions, Hyderabad, India.
Published: October 2019

In this paper, analysis of Reconfigurable Solar Converter (RSC) based on Adaptive Neural Fuzzy Interface System (ANFIS) controller is presented for PV battery application. The major concept of RSC is to use single stage three phase grid tie solar PV converter system to perform both dc/ac and dc/dc operations. The conversion stages are decreased and cost, weight reduces. ANFIS controller improves the performance of the system. An effective Mat lab / Simulation result presents the attractive performance of proposed system.
An Adaptive Whale Optimization Algorithm Guided Smart City Big Data Feature Identification for Fair Resource Utilization
Authors:   Faculty of Engineering and Technology, M. D. University, Rohtak (Haryana), India., Sharma* Kapil , Tayal Sandeep ,   Assistant Professor, Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India.
Published: October 2019

World improvement is the development of every single province of the world. Smart city implies changed hardware to adjusted individuals. Smart cities have the most indispensable part in altering distinctive regions of human life, touching segments like transportation, wellbeing, vitality, and instruction. Productively to make measurements to improve distinctive smart city benefits huge information frameworks are put away, prepared, and mined in smart cities. For the change and course of action of huge information applications for smart cities, different difficulties are faces. In this paper, we propose a wrapper display based ideal element recognizable proof calculation for ideal use of assets given highlight subset age. Nine component determination techniques used for compelling element extraction. At last, which includes best add to the ideal usage of assets got by means of a novel element recognizable proof calculation made by the application out of a Whale Optimization Algorithm with Adaptive Multi-Population (WOA-AMP) system as inquiry process in a wrapper display driven by the notable relapse demonstrate regression model Random Forest with Support Vector Machine (RF-SVM). Our proposed calculation gives the exact method to choose the most agreeable feature blend, which prompts ideal asset usage.
Use of ERP System to Manage the Economy of Agricultural Complex
Authors:   Saint Petersburg State University, St. Petersburg, 198504, Russia., Bondarenko* Lidiya Alekseevna , Zubov Afanasy Vladimirovich ,   Saint Petersburg State University, St. Petersburg, 198504, Russia., Orlov Vyacheslav Borisovich ,   Saint Petersburg State University, St. Petersburg, 198504, Russia., Ugegov Nikita Sergeevich ,   Saint Petersburg State University, St. Petersburg, 198504, Russia., Kolyada Leonid Gerasimovich ,   Saint Petersburg State University, St. Petersburg, 198504, Russia.
Published: October 2019

The article deals with the issues of the practical application of industrial information system, namely, Enterprise Resource Planning (ERP) in the management of the economy of a diversified agro-industrial complex in the risk farming area. Features of ERP system application are considered taking into account both multidirectional activity of the agricultural enterprise (crop production, animal husbandry, and industrial production), and the complex system of taxation: the presence of a common taxation system and taxation of various activities. Particular attention is paid to the mechanism of closing accounts, and the formation of the actual cost in terms of agricultural production, which is not so much complicated as a laborious and routine task. A huge number of transactions, multiple calculations of the costs distribution bases, the complex structure of the divisions of the very enterprise make the task of automating of process closing period extremely important. The article presents the experience of both the use of ERP system and the training method of specialists in the use of such industrial information systems for automated accounting and management accounting, payroll and personnel accounting, tested at St. Petersburg State University. In consequence of the implementation of the algorithms described in the article, one managed to achieve an increase in both quantitative economic indicators of the agricultural complex, and qualitative indicators, related to the training of specialists in the field of application of ERP systems in agro-industrial complexes.
Digital Technologies of the European Union in Personal Data Protection
Authors:   Russian State Social University (RSSU), Moscow, Russian Federation, Tchinaryan* Elena Olegovna , Lavrentieva Maria Sergeyevna ,   Russian State Social University (RSSU), Moscow, Russian Federation, Kuchenin Evgeny Sergeevich ,   Russian State Social University (RSSU), Moscow, Russian Federation, Neznamova Alla Andreevna ,   Russian State Social University (RSSU), Moscow, Russian Federation
Published: October 2019

The purpose of the article is to consider issues related to the legal protection of personal data in the European Union (EU). Based on a systematic approach and the method of comparative law, it is determined that the legal mechanisms of the EU most extensively regulate their scope, create a rigid framework for European and foreign companies and world corporations, and introduce independent regulatory authorities. This system of personal data protection is the most progressive at the moment. It is revealed that in the 20th-century mankind has experienced a rapid breakthrough of its development when the vector of technology progress was a reoriented towards information infrastructure, huge in its scale and universal coverage. Digital technologies led to the third industrial revolution, and they have entered into everyday life, both professional and domestic. Finally, the authors came to the conclusion that personal data protection rules are increasingly expanding. The world community has already realized the need to protect personal information, prevent its uncontrolled use, and the need to take sufficient measures to ensure the protection of information about the private life of everyone. Issues of cross-border transfer of personal data have become particularly important, and the trend towards the implementation of regulations on the personal data protection of an extraterritorial nature can be clearly seen.
Parameters Influencing Size of Micro Nano Bubble
Authors:   Research Scientist, University of Petroleum and Energy Studies., Sairam* Pinisetti Swami , Gunupuru Ravi ,   Assistant Professor, Dept. of Chemistry, University of Petroleum and Energy Studies, Dehradun, UttraKhand, Mehrotra Amritansh ,   Pursuing Bachelor of Tchnology, Electrical Engineering, University of Petroleum and Energy Studies, Dehradun, UttraKhand., Singh Samarth Pratap ,   Pursuing Bachelor of Science, Chemistry, University of Petroleum and Energy Studies. Dehradun, UttraKhand., Pandey Jitendra K ,   Associate Professors, University of Petroleum and Energy Studies. Dehradun, Uttrakhand.
Published: October 2019

Micro Nano Bubble (MNB) have been researched widely for their potental use in different applications such as disinfection, removal of toxic material from water, separation etc. Various parameters such as water temperature, system operating pressure, gas types and gas flow rates contribute in the size of the MNB generated. This research illustrates the influence of various parameters and their significance in size of MNBs generated. Experiments conducted showed that feed gas as a vital parameter for generating bubbles in nano size.
Monitoring Land Cover Changes in the Tropics using Satellite Remote Sensing Data
Authors:   Associate Professor , Fakulti Teknologi Maklumat Dan Komunikasi (FTMK), Universiti Teknikal Malaysia Melaka (UTeM) located in Melaka, Malaysia., Ahmad* Asmala , Hashim Ummi Kalsom Mohamad ,   Associate Professor , Fakulti Teknologi Maklumat Dan Komunikasi (FTMK), Universiti Teknikal Malaysia Melaka (UTeM) located in Melaka, Malaysia., Sakidin Hamzah ,   senior lecturer , Department of Fundamental and Applied Sciences, Universiti Teknologi Petronas (UTP) located in Perak, Malaysia., Sufahani Suliadi Firdaus ,   senior lecturer , Faculty of Applied Science and Technology, Universiti Tun Hussein Onn (UTHM) located in Johor, Malaysia, Mat Amin Abd Rahman ,   senior lecturer, Faculty of Applied Science, University Technology Mara, Terengganu, Malaysia.
Published: October 2019

Changes in land cover are inevitable phenomena that occur in all parts of the world. Land cover changes can occur due to natural phenomena that include runoff, soil erosion and sedimentation besides man-made phenomena that include deforestation, urbanization and conversion of land covers to suit human needs. Several works on change detection have been carried out elsewhere, however there were lack of effort in analyzing the issues that affect the performance of existing change detection techniques. The study presented in this paper aims to detect changes of land covers by using remote sensing satellite data. The study involves detection of land cover changes using remote sensing techniques. This makes use satellite data taken at different times over a particular area of interest. The data has resolution of 30 m and records surface reflectance at approximately 0.4 to 0.7 micrometers wavelengths. The study area is located in Selangor, Malaysia and occupied with tropical land covers including coastal swamp water, sediment plumes, urban, industry, water, bare land, cleared land, oil palm, rubber and coconut. Initially, region of interests (ROI) were drawn on each of the land covers in order to extract the training pixels. Landsat satellite bands 1, 2, 3, 4, 5 and 7 were then used as the input for the three supervised classification methods namely Support Vector Machine (SVM), Maximum Likelihood (ML) and Neural Network (NN). Different sizes of training pixels were used as the input for the classification methods so that the performance can be better understood. The accuracy of the classifications was then assessed by analyzing the classifications with a set of reference pixels using a confusion matrix. The classification methods were then used to identify the conversion of land cover from year 2000 to 2005 within the study area. The outcomes of the land cover change detection were reported in terms quantitative and qualitative analyses. The study shows that SVM gives a more accurate and realistic land cover change detection compared to ML and NN mainly due to not being much influenced by the size of the training pixels. The findings of the study serve as important input for decision makers in managing natural resources and environment in the tropics systematically and efficiently.
Implementation of Low Cost Antenna Rotator Based on Microcontroller
Authors:   School of Applied Science, Telkom University, Bandung, Indonesia., Nugraha* Aditama , Nurmantris Dwi Andi ,   School of Applied Science, Telkom University, Bandung, Indonesia., Anwar Radial ,   School of Applied Science, Telkom University, Bandung, Indonesia.
Published: October 2019

An antenna rotator is a device to buffer and rotate an under test antenna. In this paper, an automatic antenna rotator using microcontroller can be made at a minimal cost. The designed microcontroller, used to set servo motor to rotate the antenna using remote control. The rotator antenna has three movement features to help the antenna measurement. The measurement results are represented in polarization and radiation pattern graphs.