-74813b3e17f460286df-5228 20220409080009243 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 03 30 2020 8 6 department of Electronics, Karnataka State Akkamahadevi Women’s University, Vijayapura, India. Swetha* Mohankumar N M department of Electronic Science, Bangalore University, Bengaluru, India. Mohana H K department of Electronics, Seshadripuram First Grade College, Bengaluru, India. *Devaraju J T department of Electronic Science, Bangalore University, Bengaluru, India. An exponential increase in the number of multimedia users over LTE network necessitates user prioritization and differentiated services support. LTE standard has defined Quality of Service (QoS) class-based user priority rather than priority among users within a QoS class. However, when the cell load exceeds the system capacity, Quality of Experience (QoE) of all the users may deteriorate due to lack of radio resources allocated to them. Under these circumstances, some users of interest may be prioritized over other users in the cell during resource allocation to enhance their QoE. In this paper, Proportional Fair (PF) scheduling algorithm Based User Priority (PFBUP) mechanism is proposed to prioritize organizational users over other users. Performance evaluation of the proposed mechanism is carried out using QualNet 7.1 network simulator by varying priority coefficient for the organizational users. 03 30 2020 5293 5297 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9804.038620 https://www.ijrte.org/portfolio-item/F9804038620/ Department of Computing, Global College of Engineering and Technology, Muscat, Oman. Fatma Asad Al Jarah* Mazhar Hussain Malik Department of Computing, Global College of Engineering and Technology, Muscat, Oman. Since last decade, the exponential growth of the internet users and the size of data over the internet is increasing day by day, which lead to increase the complexity of the systems by implementing policies and security to avoid attacks on systems and networks. It is very important to understand and analyses the real time data traffic of the communication systems. The purpose of this paper to design a customized Java based application which enables analysts to capture the traffic at the bottleneck under the mean field communication environment where a large number of devices are communicating with each other. The sending data for further processing for analysis the trend to overcome vulnerabilities or to manage the effectiveness of the communication systems. The proposed application enables to capture 8 different types of protocol traffic such as HTTP, HTTPS, SMTP, UDP, TCP, ICMP and POP3. The application allows for analysis of the incoming/outgoing traffic in the visual to understand the nature of communication networks which lead to improve the performance of the networks with respect to hardware, software, data storage, security and reliability. 03 30 2020 5643 5646 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9809.038620 https://www.ijrte.org/portfolio-item/F9809038620/ “School of “Computer Science and” Engineering”“Vellore Institute of Technology”“Vellore, India Divya Jain Prof. Swarnalatha P “School of “Computer Science and” Engineering”“Vellore Institute of Technology”“Vellore, India” Monitoring high throughput distributed system by using a statistical analysis of the “historical time series” of an Instrumentation Data”. “The Pipeline has been made to process the information which can be otherwise called data pipeline, is a lot of information handling components associated in arrangement, where yield of one component is the contribution of the next one”. Several codes are giving different visualization for statistical analysis of data. “Network and Cloud Data Centers” generate a lot of data every second; this data can be gathered as period arrangement information. A time-series is a grouping taken at progressive similarly dispersed focuses in time that implies at a particular time interval to a particular time, the estimations of explicit information that was taken is known as information of a time-series. “This time-series information can be gathered utilizing framework measurements like CPU, Memory, and Disk utilization”. The TICK and ELK Stack is abbreviation for a foundation of open source instruments worked “to make collection, storage, graphing, and alerting” on time arrangement data incredibly easy. As an information collector, using Telegraf, “for storing and analyzing” information and the time-series database InfluxDB and Elasticsearch. For plotting and visualizing used Grafana and Kibana. Watchman is utilized for alert refinement and once system metrics usage exceeds the specified threshold, the alert is generated and sends it to the Telegram. 03 30 2020 4590 4596 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9810.038620 https://www.ijrte.org/portfolio-item/F9810038620/ Research Scholar-VTU, Asst. Professor, Information Science & Engg., RNS Institute of Technology, Bengaluru, India. *Mr. R Rajkumar* Dr.M.V. Sudhamani Professor & HoD, Department of Information Science & Engg., RNS Institute of Technology, Bengaluru, India. Development of Content-Based Image Retrieval systems supports retrieval of similar images based on selected features. Selection of appropriate features for this process is a difficult task. In this regard, deep learning concept helps in choosing appropriate features for retrieval. In this work, Content-Based Image Retrieval system is proposed using Convolution Neural Network known as Residual Neural Network model. The dataset used to build retrieval system is collection of web images 50,000 of 250 categories. The model is trained on 40% of image data and tested on 60% of data. When user submits a query image from the client-side, similar features are extracted by the model on server-side. Later, the features of query image are compared with trained images data and similarity is measured using the metric of Euclidean distance. The retrieved resultant images are displayed on Graphical User Interface. The results are comparatively higher with the existing systems. The proposed work is also compared with Google’s Image retrieval system for random query images and our proposed work has shown a better performance by 14.27%. 03 30 2020 4597 4605 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9811.038620 https://www.ijrte.org/portfolio-item/F9811038620/ Civil Engineering Department, National Research Centre, Egypt(NRC) A. M. Abdel-Wahab* Ahmed K. Abdel-Gawad Professor, Department of Civil Engineering, National Research Centre, Egypt. ALAA AL DIN I. AWAD Assistant Professor of Surveying, Public Works Department, Faculty of Engineering, Ain Shams University, Egypt. The Kingdom of Saudi Arabia is heading to catch up with civilization, to be one of the major international country in the first world. One of the important things is to identify the urban changes taking place in the city growth. Also, high resolution satellite images of Jeddah are available from various sources such as GeoEye (2010) and WorldView-3 (2015). Jeddah has different satellite images from different sources with different accuracy and therefore the spectral, spatial resolution and coverage area are different. So, the aim of this research work is to assess, compare, and describe the some ways of dealing with image of the GeoEye (2010) and image of the WorldView-3 (2015). To reach the desired uses of these methodologies and to achieve a degree of accuracy appropriate to the required results, formulating this comparison within the boundaries of the Jeddah Governorate to define the scope of the study and dealing with different geographical factors that characterize the province of Jeddah. In this context, in the current research work, the sample representing the urban area was chosen from 2010 and 2015. The quality control process was done on the product using different methods to detect automatic changes of the Study areas, like (area - geometric shape - number of changes). For Geometric shape of the image in 2015, it is noted that the best result was the ratio ranges between 50-60% in the following methods (IHS to RGB - Image Segmentation - Unsupervised (ISODATA) - Supervised (min)). As for the rest of the results, it ranges between 30% - 40%. As for the number of Vertex of the geometric shape of the image 2015, it is noted that the lowest number of points that make up the buildings is from the Image Segmentation method and the largest number of points that make up the buildings is from the Unsupervised (K-means) method. In the area of the building in 2015, is noted that the best result, where the ratio ranges between 65%-70% in the following methods (IHS to RGB - Image Segmentation - Unsupervised (ISODATA) and Supervised (Maximum)). On the contrary, the lowest result was the ratio reaches 59% in Supervised (Minimum) method. For the number of buildings in 2015, is noted that all the results of the supervised classification and the unsupervised classification are close to what was achieved by digitizing. 03 30 2020 5080 5092 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9813.038620 https://www.ijrte.org/portfolio-item/F9813038620/ Assistant professor of Information technology in SRM IST,Chennai,Tamil Nadu,India. Mr. Kalyanaraman B* M Balasaran department of Information Technology,SRM IST,Chennai,Tamil Nadu,India. Kaushik G Vishwanath department of Information Technology,SRM IST,Chennai,Tamil Nadu,India. Ragul SK department of Information Technology,SRM IST,Chennai,Tamil Nadu,India Bryan John Samuel department of Information Technology,SRM IST,Chennai,Tamil Nadu,India The traditional technique used for image recognition has complexity in the construction of algorithm and the training speed for the system to analyze algorithm is also too high so, the computation of the algorithm becomes very difficult in order to overcome this lack of computation. The proposed system is very efficient in both training as well as the computation speed required for the image recognition. Since, the proposed system uses the traditional LSTM algorithm which is one of the backbone factors of the RNN technique as it predicts the input on the basis of sequential analysis as it uses tanh function in order to remove the negative values of the matrix and it also predicts and removes the error in the input with the use of differential formulas in order to formulate the outcome desired for the image to be recognized. Because of this sequential analysis of the data increases the future scope of image recognition in the field of deep learning, and also because of its efficient use of the algorithm in comparison with the existing algorithm like ANN, CNN. 03 30 2020 5093 5096 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9817.038620 https://www.ijrte.org/portfolio-item/F9817038620/ Assistant Professor, Department of Electrical and Electronics Engineering, NMAMIT Nitte. Girisha Joshi* Pinto Pius A J Professor, Department of Electrical and Electronics Engineering, NMAMIT Nitte Asynchronous motors used in applications like electric vehicles require protection against all possible fault conditions like short circuit, over voltage, over current etc., for their reliable operation. 3-phase inverter is used as power modulator for induction motor. In this paper a novel fault protection scheme is implemented for inverter used in modulating voltage and frequency of power supply driving 3-phase Induction Motor. Function of gate drive circuits used in power electronics converters is to provide isolation for gate signals and shift the voltage level of gate pulses to the required level to enable turn on and turn off the switches as desired. Gate signals are generated using Texas instrument F28069M board. Level shifting is achieved by buffer ICs and fault protection is provided by enabling shut down pin in the DSP on occurrence of fault. Hardware results show that modulation index of gate signals is set to zero under fault conditions and ensure total safety of Inverter driver circuit and power circuit used in the Inverter. 03 30 2020 4606 4610 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9818.038620 https://www.ijrte.org/portfolio-item/F9818038620/ Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Guntur, INDIA. Nitin Ralph Pochont* Vinay Atgur Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Guntur, INDIA. K.M.V. Ravi Teja Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Guntur, INDIA. G Manavendra Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Guntur, INDIA. G.P. Desai Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Guntur, INDIA. P. Vijaya Kumar Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Guntur, INDIA. To protect the environment from the global warming dependency on the fossil fuels have to be reduced. Locally available alternate fuels are greatly prominent for the development of industrialization compared to conventional fuels. This paper mainly deals with the production of Ethanol from a source called “Black Jaggery” and an Optimization of the extracted alcohol to attain the characteristics and properties which would be essential to blend the alcohol with an existing fossil fuel. Black Jaggery being a sugar-based product is fermented in the presence of a yeast enzyme for several days and is distilled to extract the bio-fuel (ethanol) from the source. The extracted oil is characterized for the thermal properties by using thermal constant analyzer TPS-500 which will be helpful for the combustion studies. Obtained results shows that compared to E-5, E-10 and E-20, E-15 blend shows better thermal properties increased thermal conductivity, thermal diffusivity with reduced specific heat. 03 30 2020 5398 5301 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9820.038620 https://www.ijrte.org/portfolio-item/F9820038620/ SRM Institute of Science and Technology, Kattankulathur,Tamil Nadu Sivaprasad Athikkal* Kumaravel Sundaramoorthy National Institute of Technology Calicut, Kerala, India. The idea of DC-DC converter with multi-input is yet to attain a vital role in the field of 'hybrid energy system (HES)' integration and electric vehicle applications. So, the analysis of the dynamic behavior of the multi input converters is crucial in designing a proper controller to achieve a stable performance. This paper reports a 'small signal model (SSM)' and the performance analysis of a 'dual-input DC-DC converter (DIC)'. The parasitic resistances of capacitor and inductor are considered in the modelling. The significant transfer function (TF)s are derived with the help of the SSM, and the Bode plots for the TFs have been obtained. The performance analysis shows that the derived TFs allow better closed loop performance of the system. The simulation of the DIC converter in MATLAB/ Simulink® has been carried out and the simulation waveforms are presented. A hardware setup of the DIC converter is fabricated and experimented in the laboratory. The dynamic performance of the DIC is analyzed under the variations in the source and load conditions. The presented converter with a closed loop controller can be used in the applications to formulate a HES with solar-PV, battery, fuel cell, etc. Also the performance comparison of the DIC converter has been performed with other reported converters which shows that the DIC converter has higher efficiency and several other potential merits. 03 30 2020 5402 5411 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9821.038620 https://www.ijrte.org/portfolio-item/F9821038620/ Research Scholar, Department of Mechanical Engineering, Bangalore University and Assistant Professor, Department of Mechanical Engineering, HKBK College of Engineering, Bengaluru, India. Nadeem Pasha K Dr.S. Ranganatha Department of Mechanical Engineering, University Visveswaraya College of Engineering, Bengaluru, India. Titanium and Titanium alloys are widely used for aircraft as a material having light weight, high strength and corrosion resistance. The titanium and its alloys are compatible with carbon fibre reinforced plastic components with respect to corrosion and thermal behaviour. Response of Titanium grade 2 and grade 12 at different speed during sliding is to be studied. The literature survey shows inadequate studies on wear response of these alloys. Experiments using pin on disc test rigs were conducted. Speed level of 500rpm, 1000rpm, and 1500 rpm were used. The sliding was found to be sensitive to sliding speed. As speed increases from 500 rpm to 1000 rpm the coefficient of friction increased. At speed of 1500 rpm two steady phase of sliding identified. In one of the steady phase the coefficient of friction was found to be more than the coefficient of friction at 1000 rpm. Where in another steady phase of sliding the coefficient of friction was found to be comparable or less then the coefficient of friction at 1000 rpm. 03 30 2020 5810 5814 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9826.038620 https://www.ijrte.org/portfolio-item/F9826038620/ UG student, ECE department, RNS institute of technology, Bangalore, INDIA. Sharath N Payyadi* Varun S D UG student, ECE department, RNS institute of technology, Bangalore, INDIA. Kalluru UG student, ECE department, RNS institute of technology, Bangalore, INDIA. Dr. Archana R Kulkarni Assistant Professor, ECE department, RNS institute of technology, Bangalore, INDIA. In this paper, we propose a technique for early detection of the diseases like blast and blight in paddy crop using one of the deep learning algorithms called as CNN algorithm [8]. The entire system works on the Raspberry-pi which acts as processing unit for the proposed system. The well trained keras model to predict the disease in paddy leaf is developed using CNN algorithm [5]. As the symptoms of the blast and blight disease are detected using feature of the paddy leaf, so the model is trained using the diseased image data set. The pi-camera will capture the image of the leaf and the captured image will be processed by the python script running on raspberry pi. The images will be captured at different angle with respect to raspberry-pi. The trained model will predict whether the processed image of the leaf contains the symptoms of the disease. The proposed system also includes the feature which will alert the farmer about spreading of the disease by using the GSM module to send the message to the farmer of the field. Also the pesticides are suggested to farmers to control the further growth of the germs causing the disease. In India paddy is grown as the staple crop. The paddy crop is mostly damaged due to the leaf diseases called as blast and blight disease[4]. About 20%-30% of the yield will be destroyed by the blast and blight diseases respectively [14]. Hence, by implementing the above proposed system the disease can be detected at the early stage and loss can decreased. 03 30 2020 5298 5304 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9835.038620 https://www.ijrte.org/portfolio-item/F9835038620/ Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education , Manipal Chethan Sharma* We take birth, we live and finally we all die. During the short time we live, we all want to be happy. Happiness comes when you are healthy, more the healthier you are- more the happiness. There are various types of robots depending on the area in which they are made use of. Medical robots are used to assist doctors, surgeons and other healthcare workers. The most advanced aspect of medical robotics is surgical robotics, in which a robot actually performs surgery. It might be surprising to know that in few top hospitals the top performing surgeon might not be a human being. Apart from surgeries, robots can be used to perform various other jobs within the healthcare industry. In this paper, we see how robots can reduce the strain of healthcare workers by reducing and automation their job. 03 30 2020 4739 4741 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9837.038620 https://www.ijrte.org/portfolio-item/F9837038620/ School of Software, Hallym University, Chuncheon-si, Gangwon-do, South Korea. Jse Mok Ahn* Jeom Keun Kim School of Software, Hallym University, Chuncheon-si, Gangwon-do, South Korea. The heart rate variability (HRV) is a noninvasive way properly for investigating the activity of the autonomic nervous system (ANS) as well as to predict cardiovascular diseases. To guarantee an accurate HRV analysis, a motion artifact-free HRV recording must be obtained. However, complete removal of a motion artifact is impossible when measuring heartbeats for 5 min, and the motion artifact due to sudden ANS activity must be taken into consideration for the HRV parameters. And, the ANS balance has thus far been evaluated by each individual HRV parameter calculated for a single 5 min HRV segment, leading to the dynamic activity of the ANS within the same period being ignored. Therefore, to resolve this problem, HRV parameters for ultra-short-term segments that are short enough to reflect a sudden motion artifact must be analyzed. The aim of the present study was to evaluate the effects of a motion artifact on the variation in HRV parameters to provide detailed information on ANS activity. The 121 ultra-short-term HRV segments were created by moving a 1-min window forward by a time shift interval of 2 s for the entire 5 min HRV segment. The ratios of Ln LF to Ln HF in these ultra-short-term segments and a single 5 min segment with a motion artifact were 0.89 and 1.06, respectively, while those in a motion artifact-free HRV segment were 0.75 and 0.93, respectively. This variation test for a short-term motion artifact and motion artifact-free HRV dataset was found to affect the SDNN (7.73 and 2.68), SD2 (11.44 and 4.42), TINN (40.33 and 9.92), and Ln HF (0.37 and 0.13) the most in terms of the standard deviation, respectively. Taken together, the mean HRV parameters of many ultra-short-term segments might play an important role in evaluating dynamic ANS activities within a short-term segment, avoiding the false conclusions made by the traditional HRV analysis. 03 30 2020 4611 4616 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9840.038620 https://www.ijrte.org/portfolio-item/F9840038620/ Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, India, S. Gnanavel* M. Sreekrishna Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, India, Nivedha K Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai Preethi S UG Scholar, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai. Pranav V UG Scholar, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai. Neurological disorders of the brain are generally difficult to diagnose at the early stages. Since common symptoms like headaches, fatigue or difficulty in speaking and understanding can be related to any neurological disorder. It can be noted that most of the neurological disorders are curable if detected at an early stage. Thus, the life expectancy of the patient will be increased through an early detection and an early start of the curative procedure. An accurate identification of the disorder can be done by processing the MRI images of the patient. While brain disorders like tumor, stroke can be classified with an abnormal growth of the brain tissue., disorders like Alzheimer’s occur due to degeneration of brain cells. Since all the neurological disorders have common symptoms differentiating them at the beginning stages is considered a challenge. A rule based expert system with a set of rules is used for processing the symptom experienced by the patient. Each symptom is associated with a weighting factor that determines the risk to a particular disorder. Once the risk factor is evaluated the MRI images of the patient is scanned to obtain the severity of the disorder. By utilizing an expert system for analysis of symptom and image processing to detect the region of abnormality we may derive accurate results. Thus an effective prognosis can help patients get into the treatment at the earliest. 03 30 2020 5305 5311 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9841.038620 https://www.ijrte.org/portfolio-item/F9841038620/ M. Tech. degree, Guru Nanak Dev University, Amritsar. Harkiranpreet Kaur* Kiranbir Kaur Assistant Professor, Computer Engineering and Technology department of Guru Nanak Dev University, Amritsar. Edge Computing facilitates low-latency for real- time applications. It can be achieved by installing computing infrastructure at the network edge or bringing cloud benefits closer to the user. In Edge Computing, computing resources are not more than one hop away from the user equipment. The mobility of a user in such a case can cause problems. If a user continues to move, maintaining low-latency becomes challenging. So, service is migrated between edge servers as the user moves. This migration can lead to a time duration for which the service is not available, called Downtime. While migrating, to leverage low- latency, Downtime must be kept to a minimum. In this paper, we introduced an approach to migrate service so that the minimum amount of data is transferred during Downtime. We have also discussed some existing techniques for migrating service and compared them based on how they reduce data to be transferred to lessen the Downtime. Our approach transfers lesser data that causes less Downtime than one of the existing techniques. 2020 5207 5211 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E9850.038620 https://www.ijrte.org/portfolio-item/F9850038620/ Pursuing Computer Science Engineering, VR Siddhartha Engineering College, Vijayawada, Andhra Pradesh. N. Monish Sai* B. Vijay Prakash Pursuing Computer Science Engineering, VR Siddhartha Engineering College, Vijayawada, Andhra Pradesh. N. Renuka Pursuing Computer Science Engineering, VR Siddhartha Engineering College, Vijayawada, Andhra Pradesh. K. Deepu Pursuing Computer Science Engineering, VR Siddhartha Engineering College, Vijayawada, Andhra Pradesh. B. Jayanag Assistant professor in VR Siddhartha Engineering College In the fastmoving world, it is nearly impossible to check and verify the details of each and every vehicle manually. So, in order to untangle this problem, we came up with vehicle monitoring system. The prime idea of our project focusses on drawing out the vehicle registration number from the number plates captured using the camera and process the captured number plate image using various algorithms and eventually store the processed data. This data can be used for monitoring the vehicles in parking lots. Management of cars in parking lots requires a lot of human intervention. Parking lots have been installed with cameras, but none of them serve the purpose. They are just installed for video surveillance and none of them solve the problem. Previously, i.e. in the mini project part, we proposed an Automatic Vehicle Plate Recognition system, which captures the vehicle image, processes the number plate and gives out the details of the vehicle. With the help of this module, we propose a system which can be used in parking lots to monitor the vehicles entering and exiting the lot. We can even run a background check on vehicles using the number plate details and alert the police department in case if the vehicle is suspicious. 03 30 2020 5212 5215 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9854.038620 https://www.ijrte.org/portfolio-item/F9854038620/ Management Department, STIEM Bongaya, Makassar, Indonesia. Suseno Hadi Purnomo* Heslina Management department, STIEM Bongaya, Makassar, Indonesia. Muhammad Tafsir Management department, STIEM Bongaya, Makassar, Indonesia. This study aims to determine the effect of intellectual capital and literacy sharia banking to banking shariah service usage . This research uses quantitative methods and descriptive analysis of primary and secondary data using questionnaires, documentation, library studies and direct observation, the number of samples is 98 people through incidental sampling techniques using multiple linear regression as an analytical tool. Finding this research are intellectual capital has no influence on the use of sharia banking services while sharia banking literacy has an influence on the use of sharia banking services. The study involved 98 respondents banking shariah customer were selected at random. Instruments used for capital intellectual and shariah banking literacy developed by researchers from some of the instruments that have been used in some study previous. Instruments validated by a post hoc analysis of factors involving 98 respondents. 03 30 2020 5647 5651 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9855.038620 https://www.ijrte.org/portfolio-item/F9855038620/ Professor, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Hyderabad, India. Dr M Rama Bai Mrs. J. Sreedevi Assistant Professor, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Hyderabad, India Ms. B. Pragna Student, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Hyderabad, India. Generative Adversarial Networks (GANs) use deep learning methods like neural nets for generative modeling. Neural style transferring of images and facial character generation of anime images are previously implemented by applying GAN methods but were not successful in giving a promising output. In this work, Image Processing is applied on the datasets in the mode along with the training of GAN system. The problem of applying GAN to generate specific images is addressed by using a clean and problem specific dataset for anime facial character generation. Modeling is done by applying Convolutional Neural Nets, GANs empirically. Neural style transfer, Automatic Anime characters are generated with high-resolution, and this model tackles the limitations by progressively increasing the resolution of both generated images and structural conditions during training. This model can be used to develop unique anime characters or the image generated can be used as inspiration by artists and graphic designers, can be used as filters in famous apps such as snapchat for style transferring. With different evaluations and result analysis, it is observed that this model is a stable and high-quality model. 03 30 2020 5312 5316 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9856.038620 https://www.ijrte.org/portfolio-item/F9856038620/ Assistant Professor, Faculty of Computing and Software Engineering, AMIT, Arbaminch University Dr. Anusuya Ramasamy* Mr. Joseph Wondwosen Research Scholar, Faculty of Computing and Software Engineering, AMIT, Arbaminch University, Ethiopia. Digital Image Processing is application of computer algorithms to process, manipulate and interpret images. As a field it is playing an increasingly important role in many aspects of people’s daily life. Even though Image Processing has accomplished a great deal on its own, nowadays researches are being conducted in using it with Deep Learning (which is part of a broader family, Machine Learning) to achieve better performance in detecting and classifying objects in an image. Car’s License Plate Recognition is one of the hottest research topics in the domain of Image Processing (Computer Vision). It is having wide range of applications since license number is the primary and mandatory identifier of motor vehicles. When it comes to license plates in Ethiopia, they have unique features like Amharic characters, differing dimensions and plate formats. Although there is a research conducted on ELPR, it was attempted using the conventional image processing techniques but never with deep learning. In this proposed research an attempt is going to be made in tackling the problem of ELPR with deep learning and image processing. Tensorflow is going to be used in building the deep learning model and all the image processing is going to be done with OpenCV-Python. So, at the end of this research a deep learning model that recognizes Ethiopian license plates with better accuracy is going to be built. 03 30 2020 5730 5737 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9857.038620 https://www.ijrte.org/portfolio-item/F9857038620/ Electrical Engineering Department,BIT Sindri, Dhanbad, Jharkhand, India. Rajendra Murmu* Arvind Kumar Singh Electrical Engineering Department, NERIST, Nirjuli, Arunachal Pradesh, India. Present research demonstrates an experimental work and simulation of FPGA based PMSM drives consists of PI and Fuzzy logic controller, for speed control under load, zero load and random change in load conditions. It also delineates the overall performance of a closed loop vector Permanent Magnet Synchronous Motor (PMSM) drive consisting of two loops, current for inner and speed for outer loops for better speed tracking systems. The resistive load which is connected across the armature of dc shunt motor and coupled with PMSM is varied. The resultant speed and torque are studied in details. Result showed that in case of fuzzy logic controller, the peak overshoot and settling time can be minimized. This FPGA based PMSM drives can be used for different paramount application under constant speed. 03 30 2020 5317 5321 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9860.038620 https://www.ijrte.org/portfolio-item/F9860038620/ M.Tech Environmental Engineering, Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu District Augustine Crispin C* Sivakumar R Professor, Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu Industrial pollution and urbanization is a major threat to the water environment.The advent of urbanization and industrialization for economic growth has adversely affected the biological diversity. Lake water quality deterioration has been evident in the lakes surrounding the city of Coimbatore. The growth of industries in the city has led to the increase of population day by day in the city The present study is mainly aimed at studying the nature and impact of water pollution in the sub basins of noyyal river basin in coimbatore-sulur subwatershed which has a major impact on the Environment, Health and Socio-Economic status. To understand the magnitude of the impact, water samples were collected in and around the Coimbatore city namely Sulur lake, Singanallur lake, Valankulam, Ukkadam lake and Noyyal river stream which falls in Coimbatore-Sulur subwatershed and analyzed for physical, chemical and bacterial characteristics. The study showed that the chemical characteristics were relatively higher (TDS-957mg/l), (Cl-439.58mg/l), (NO3-56.28mg/l) than the Bureau of Indian Standard acceptable limits and the presence of Escherichia Coli(60cfu/100ml) and Total Coliform(400cfu/100ml) are menacing in all the water samples leading to major health impact in human beings and also the quality of water is deteriorated. 03 30 2020 5097 5101 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9861.038620 https://www.ijrte.org/portfolio-item/F9861038620/ Assistant Professor, Dayananda Sagar College of Engineering, Bangalore, India. Dharmendra P* Abhinav Verma Dept. of Aeronautical Engineering, Dayananda Sagar College of Engineering, Bangalore, India. Chandana JP Reddy Dept. of Aeronautical Engineering, Dayananda Sagar College of Engineering, Bangalore, India. Jitvan Suri S Dept. of Aeronautical Engineering, Dayananda Sagar College of Engineering, Bangalore, India. Vishal M Dept. of Aeronautical Engineering, Dayananda Sagar College of Engineering, Bangalore, India. The requirement for improving the aerodynamic efficiency and delaying the formation of stall over the wing has been of prime importance within the field of aviation. The main objective of the project is to further improve upon these two parameters. The configuration used for analysis consists of a NACA 2412 airfoil of chord length 0.982m with a 64mm cylinder at the leading edge. Analysis is completed using ANSYS Fluent, with a freestream velocity of 10m/s. The aerodynamic characteristics of three configuration bare airfoil, Airfoil with static cylinder and Airfoil with rotating cylinder are tabulated and plotted. The comparison is then followed by pressure and velocity contours to visualize the flow over each configuration. The rotating cylinder configuration shows a improvement in the aerodynamics characteristics. The rotating cylinder configuration gives the most favourable result. This study has a potential application in high lift devices and can be used as stall delaying device. 03 30 2020 4742 4750 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E9862.038620 https://www.ijrte.org/portfolio-item/F9862038620/ Assistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India N. Pooranam* S.K.Sai Sabareshwar Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India K.R.Shyam Sundar Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India. P.B.Rahav Krithik Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India. In the modern era disposing wastes in a safe way is essential now a day; the main issues are garbage overflow. This creates unhygienic and unclean in the environment, it produces bad smell. This may leads in spreading some deadly diseases and human illness; environment should be kept clean and hygienic in society every place should be monitored in a high expense, to reduce this process the proposed approach helps in reducing entire process. The model is fully automated, where the smart dustbin automatically disposes the waste in a fixed location. Therefore this IOT connected through wired and wireless without user intervention. In the design system PIC controller will help in providing connectively in an efficient way. It reduces man work and improves maintenance of the dustbin. This process is controlled by a smart app which includes the performance measure is high which can be monitored through smart device. The design is equipped with high sensor devices which gives alert when it leads to any danger for the environment. 03 30 2020 5106 5110 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9868.038620 https://www.ijrte.org/portfolio-item/F9868038620/ Department of Electronics and Telecommunication, Pimpri Chinchwad College of Engineering, Nigdi, Pimpri-Chinchwad, Maharashtra, India. Prathamesh N. Narkhede* Narendra S. Pandit Department of Electronics and Telecommunication, Pimpri Chinchwad College of Engineering, Nigdi, Pimpri-Chinchwad, Maharashtra, India. Sahil A. Palaskar Department of Electronics and Telecommunication, Pimpri Chinchwad College of Engineering, Nigdi, Pimpri-Chinchwad, Maharashtra, India. Dr. Varsha Harpale Assistant Professor, Department of Electronics and Telecommunication, Pimpri Chinchwad College of Engineering, Nigdi, Pimpri-Chinchwad, Maharashtra, India. Dr. Deepti Khurge Assistant Professor, Department of Electronics and Telecommunication, Pimpri Chinchwad College of Engineering, Nigdi, Pimpri-Chinchwad, Maharashtra, India. Noise cancellation has slowly become a necessity instead of luxury. This is due to the increasing levels of noise pollution in today’s society. Exposure to prolonged and excessive noise has resulted in serious health problems ranging from stress, poor concentration and fatigue from lack of sleep, to more serious issues such as cardiovascular disease, cognitive impairment, tinnitus and hearing loss. Such noise pollution levels are also harmful to fetuses as low frequencies are not attenuated in the same way as high frequencies are attenuated by the mother’s womb. Recent techniques such as noise cancelling headphones, earbuds are being implemented that provide noise cancellation limited to a single person and not to a small room as a whole. Thus our paper proposes a duct system, which will be using the concept of active noise cancellation with the application of digital adoptive feedforward control, where an anti-noise signal is generated, having same amplitude as that of the noise signal but of reverse phase which when added together will cancel each other out. The adaptive filter is controlled by a microphone located in the duct to sense the noise reduction and adjust the entire system for optimum operation. The noise cancelling headphones that are being in use nowadays also operate on the same principle that is being implemented by our proposed system. But due to complex designs in the circuits such devices usually end up being too costly. So our system aims at providing a low cost solution. The system is mainly aimed at lower range of frequencies up to 1 KHz. Active noise control is being preferred as passive silencers are bulky and the attenuations achieved for low frequencies are relatively small. 03 30 2020 5575 5579 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9870.038620 https://www.ijrte.org/portfolio-item/F9870038620/ Professor, department of Computer Science and Engineering, Sree vidyanikethan Engineering College, Tirupati, India. Dr. A. V. Sriharsha* Ms. K. Yochana PG Scholar, department of Computer Science and Engineering, Sree vidyanikethan Engineering College, Tirupati, India, In recent years, the Convolutional neural networks (CNN) has been active in various Artificial intelligence applications as well as computer vision tasks. We suggested an effective technique in this study to decrease the number of duplicates in feature maps of CNN. Proposed a novel convolution scheme Octave convolution (Octconv) to minimize the duplicates in the feature maps and boost the CNNs performance. The principle concept of this method is to separate the Convolutional filters into a higher frequency and lower frequency sections. In this report, we made an attempt for minimizing the spatial redundancy directly from output feature maps of CNN using the following 3 steps: First, divide the channels into higher and lower frequency parts depending on the information of the image using Multi-scale representation. Second, reduce the number of FLOPs from the low frequencies. Third, before sending the output to combine both the higher frequency and lower frequency information of the image. The key purpose of this abstract is to improve CNNs efficiency by reducing spatial redundancy in the feature maps of the convolution layer. 03 30 2020 5412 5418 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9871.038620 https://www.ijrte.org/portfolio-item/F9871038620/ Research Scholar, Department of Mathematics, Birla Institute of Technology and Science-Pilani, Hyderabad, Telangana, India. V Selva Kumar* Dipak Kumar Satpathi Associate Professor, Department of Mathematics, Birla Institute of Technology and Science-Pilani, Hyderabad, Telangana, India. P. T. V. Praveen Kumar Assistant Professor, Department of Mathematics, Birla Institute of Technology and Science-Pilani, Hyderabad, Telangana, India. V.V. Haragopal Visiting Professor, Department of Mathematics, Birla Institute of Technology and Science-Pilani, Hyderabad, Telangana, India In India the insurance industry is in its growth stage. It consists of 58 insurance companies of which 24 in life and 34 are non-life insurance. The Non-life Insurance companies which cater to motor insurance business presently utilize different trend models to forecast paid claim amount. Motor Insurance Claim amount prediction is one of the most difficult tasks to accomplish in financial forecasting due to the complex nature of data points. The main objective of this study is to determine a reliable time series forecasting model to predict own damage (OD) claim amount of motor insurance data in India from 1981 to 2016. In this context, the annual time series claim data was collected and modeled by using the Generalized linear model (GLM), Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) method. The validation of the model has been done by comparison of predicted and actual values for the period of 36 years. Also, different types of possible models were evaluated using Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) for accuracy. The results showed that ANN outperformed other traditional time series models (GLM & ARIMA) for predicting the future own damage claim amount with a lesser residual error. Further, the outcome of these data analytics studies would help Insurance companies to have an idea about the expected future claim amounts with more accuracy. Thus, predicting the Motor insurance's own damage claim will help insurance companies to budget their future revenue. 03 30 2020 4751 4757 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9873.038620 https://www.ijrte.org/portfolio-item/F9873038620/ CSE Department, Thapar Institute of Engineering and Technology, Patiala (INDIA). Vinod Kumar Bhalla Ravinanda Kumar CSE Department, Thapar Institute of Engineering and Technology, Patiala (INDIA). Manish Kumar Singla EIE Department, Thapar Institute of Engineering and Technology, Patiala (INDIA). Parag Nijhawan EIE Department, Thapar Institute of Engineering and Technology, Patiala (INDIA) Target recognition from the data obtained from radars poses great challenge to manual analysis of the target with high speed and accuracy. So to overcome this challenge automatic target recognition system is developed using soft computing machine learning tool. The problem becomes more complex when the images are clicked from various angles. An automated classification scheme is proposed in this paper. Principal Component Analysis is used for feature extraction and to reduce the high dominions in the images data. It is known that principal component analysis is widely used from in various fields like space science. Support vector machine is used as a tool. All major kernel functions are applied to gain the maximum accuracy. This framework is evaluated and found effective as compared to results than other methods. 03 30 2020 5598 5603 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9874.038620 https://www.ijrte.org/portfolio-item/F9874038620/ Computer science and engineering, SRM institute of science and technology, Kattankulathur, India. Pooja Nair* G. Abirami Computer science and engineering, SRM institute of science and technology, Kattankulathur, India. The project proposes an application that reviews and analysis tweets on twitter application by doing software repository mining on the information gathered. The main purpose of this project is to investigate a few computational strategies to estimate the effect of web based life. Propelled by the techniques recently created to break down software systems and other unique frameworks, these strategies measure different static and dynamic parts of interpersonal organizations, the possibility and advantages of these estimation techniques with regards to Twitter is shown. By investigating the tweets the connection between the imperativeness of the news and the volume of the related tweets can be seen, which gets refreshed after every constant period of time. Using this strategy the tweets are ranked according to the highest priority. 03 30 2020 5419 5422 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9876.038620 https://www.ijrte.org/portfolio-item/F9876038620/ Asst. Prof, SCOPE, VIT University, Chennai. Pradeep K V* Anusha K. Assoc. Prof. SCOPE, VIT University, Chennai. Nachiyappan S. Asst. Prof.(Sr), SCOPE, VIT University, Chennai. To develop an effective intrusion detection system we definitely need a standardize dataset with a huge number of correct instances without missing values. This would significantly help the system to train and test for real-time use. Previously for research purpose, KDD-CUP’99 dataset has been used, but later on, it has been seen that it is not so useful for training the model as it consists a lot of missing and abundant values. All this issue have been tackled in NSL dataset. To analyze the capabilities of the dataset for intrusion detection system we have analyzed various machine learning classification algorithm to classify the attack over any network. This paper has explored many facts about the dataset and the computation time. 03 30 2020 4726 4730 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9877.038620 https://www.ijrte.org/portfolio-item/F9877038620/ Associate Professor, Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, India Parag Nijhawan* Vinod Kumar Bhalla Assistant Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, India Jyoti Gupta PhD Scholar, Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, India. Manish Kumar Singla PhD Scholar, Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, India. Scientists, researchers and business analyst need to process large volume of data to convert the data to meaningful information. Historically all the professionals are doing this manually and later with the invention of computers by developing algorithms and coding programs. Eventually size of data is growing with the advancement of social economic developments and advancement of technological sectors. This posed greater challenge to the technocrats. So there was demand to automatically process and analyze the data. Scientists started working towards the direction of artificial intelligence (AI). They developed the branch of AI as machine learning (ML). Large number of areas are using these techniques and getting benefit of these models. Financial data analyst, business analyst, medical researchers, software professionals are applying machine learning to increase the speed of large amount of data for decision making by drawing patterns with minimal and no human intervention. The purpose of this paper is to discuss a few widely publicized ML algorithms and their advantages and benefits 03 30 2020 5604 5611 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9881.038620 https://www.ijrte.org/portfolio-item/F9881038620/ Assistant Professor, School of Computing,Department of Computer Science and Engineering, Vel tech university, Avadi Dr.P. Durgadevi* Ms.A. Akila Assistant Professor, School of Computing,Department of Computer Science and Engineering, Vel tech university, Avadi.. Dr.Veeramakali Assistant Professor, School of Computing,Department of Computer Science and Engineering, Vel tech university, Avadi Theoretical energy recognition in remote sensor systems has received intense research interest in the late years. Radio variation, channel distortion, and blockage bring great strength and responsiveness to packets broadcast over a remote channel. A twin innovation is effective communication that can drastically increase the channel range and reduce transmission vigor consumption in disrupting channel. Growth in the direct range brings with it a reduced fault rate. In this paper, an acceptable correspondence method is proposed for each tab with active sending and receiving clusters. It consists of two stages, the precise routing phase, the selective and transmitting stage. In the routing phase, the basic route between the source and the sink hub is started. In the second stage, centers of fundamental development toward flattering team leaders select additional touch centers with minimal biomass costs from their surroundings, and then spread from bundle to cluster to the recently established endurance cluster. Reductions in error rate and regeneration are proven by the fact that malpractice funds become long-term obligation systems. 03 30 2020 5652 5656 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9884.038620 https://www.ijrte.org/portfolio-item/F9884038620/ Assoc. professor, Department of ECE, Lakireddy Bali Reddy College of Engineering, Mylavaram, Mr. B. V. N. R. Siva Kumar* M.David Vinod Project Students, Department of ECE, Lakireddy Bali Reddy College of Engineering, Mylavaram,. V. Venkateswarlu Project Students, Department of ECE, Lakireddy Bali Reddy College of Engineering, Mylavaram,. K. Anusha Project Students, Department of ECE, Lakireddy Bali Reddy College of Engineering, Mylavaram Ch. Thrinath Project Students, Department of ECE, Lakireddy Bali Reddy College of Engineering, Mylavaram In this paper, we are designing a new enhanced and practical brilliant automated medical caretaker is intended to decrease the physical weight of attendants in emergency clinics. This robot is executed utilizing LABVIEW as creating stage, which makes the equipment and programming all the more simple and solid. 03 30 2020 5657 5660 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9886.038620 https://www.ijrte.org/portfolio-item/F9886038620/ Computer Science Department, College of Arts and Science, Prince Sattam Bin Abdul-Aziz University,Wadi Al Dawassir, Riyadh, K.S.A. Mr.Mujthaba Gulam Muqeeth* Dr.Manjur Kolhar Computer Science Department, College of Arts and Science, Prince Sattam Bin Abdul-Aziz University, Wadi Al Dawassir, Riyadh, K.S.A. Dr.Aballa AlAmeen Computer Science Department, College of Arts and Science, Prince Sattam Bin Abdul-Aziz University, Wadi Al Dawassir, Riyadh, K.S.A. Dr.Mohammed Rahmath Computer Science Department, College of Arts and Science, Prince Sattam Bin Abdul-Aziz University, Wadi Al Dawassir, Riyadh, K.S.A. Almighty created human being with numerous wants and needs which makes them associated with their own data, choices and preferences. To grow and develop any business or organizations it is very obligatory to know their clients requests or customer needs based on their data. The evolving role of data makes it very vital element in any organization and carried with convinced operations. In this paper we are going to present a study of Data Science and its relevance with Artificial Intelligence, machine learning and deep learning. The incorporation of these intellectual sciences in data science is useful for perming numerous operations in our research we tried to demonstrate the data science operations like data cleaning, data processing, data modeling, data visualization and data presentations techniques. To grow any business it is mandatory to know their customer needs and satisfy their future expectations by smart decision makings. The intellectual algorithms or data operations in the data science make the data to be more effective in decision making and decision polices. We also focus on how data science incorporates mathematical & statistical methods, logical reasoning with applications of Artificial Intelligence techniques. We also focus on various data operations tools which exists in the market like python, SAS, R and many others. At last we focusses on how data science field going to meet the future expectations of many businesses. This research paper may become as successful reference for the people to carry out their research and meet the expectations of data science field with business growing decisions. 03 30 2020 5661 5668 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9887.038620 https://www.ijrte.org/portfolio-item/F9887038620/ Department of Management, Amrita Vishwa Vidhyapeetham, Kochi, India. Reuben Thomas Mathew* Krishnapriya S Department of Management, Amrita Vishwa Vidhyapeetham, Kochi, India. Athira K Department of Management, Amrita Vishwa Vidhyapeetham, Kochi, India. Adwaith KT Department of Management, Amrita Vishwa Vidhyapeetham, Kochi, India. The Indian railways, a historical inheritance, the 4th largest railway network in the world by size, is an important force in our economy. The digitalization has enabled the customer to get the railway services on their finger tip. IRCTC, the subsidiary of the Indian railway offers variety of service like catering, tourism and the online ticket operation. As technology advancement is taking place, but the IRCTC has been widely criticized for many lacunas in meeting the customer needs and preference. So the study focuses on how Indian Railway needs to revamp the website to make it more contributing to customer expectations. An analysis of customer reviews, revealed that the customers experience many problems while using the IRCTC website to make their choice about the type of travel, coach preference, seat preferences, age group, payment gateways, time and date of travel, etc. The study attempts to find out the user-friendliness of IRCTC website from the point of view of the customers using four identified dimensions or variables. The analysis was done using various web metrics and a text-mining based on the customer reviews. It helped to know about the clicks rates pattern, visit rate, the various activities performed by the customer, time spent, type of device used, keywords used etc. The result shows that majority users have stated negatively towards the features and usability of the website. Based on the analysis of the study a brief summary of findings have been made and a meaningful conclusion have been obtained. 03 30 2020 4805 4810 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9897.038620 https://www.ijrte.org/portfolio-item/F9897038620/ Candidate of Economic Science, Docent of Cathedra of Government and Municipal Governance, South-Russia Institute – branch of Russian Academy of National Economy and Public Administration under President of RF. Rostov-na-Donu, Russia. Dudukalov Egor Martynenko Tatiana Doctor of Economic Science, Зrofessor of Cathedra of Economic Theory and Entrepreneurship, South-Russia Institute – branch of Russian Academy of National Economy and Public Administration under President of RF. Rostov-na-Donu, Russia. Ignatova Tatiana* Doctor of Economic Science, Professor, Head of Cathedra of Economic Theory and Entrepreneurship, South-Russia Institute – branch of Russian Academy of National Economy and Public Administration under President of RF. Rostov-na-Donu, Russia. Ivanova Daria Evgenievna Senior Lecturer of Cathedra of Economic Theory and Entrepreneurship, South-Russia Institute – branch of Russian Academy of National Economy and Public Administration under President of RF. Rostov-na-Donu, Russia. Mnatsakanova Emma Aspirant of Cathedra of Economic Theory and Entrepreneurship, South-Russia Institute – branch of Russian Academy of National Economy and Public Administration under President of RF. Rostov-na-Donu, Russia. The authors argue that outsourcing technology should be implemented into governance systems with consideration of their public or private nature. Functional review as a method of the administrative reform study showed lack of market-based governance methods, which proved to be effective in the private sector. Authors consider how market-oriented were introduced into the national security system of Russian Federation. The authors’ aim is to find out the reasons for current state of affairs. Outsourcing was considered as such an innovative method of public administration, which was supposed to help to save financial resources and to leave governance structures from their usual functions. An analysis of outsourcing practices in the Russian army shows that instead of saving money, the cost of services received on the market increased by many times, and its use in the Armed Forces increased threats to national security instead of getting preferences. 03 30 2020 4758 4761 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9899.038620 https://www.ijrte.org/portfolio-item/F9899038620/ Computer Science Department, College of Arts and Science, Applied Science University, Kingdome of Bahrain. Yazeed Ahmad Al-Sbou* Due to the advances in computer networks, Internet and multimedia communications, Quality of Service (QoS) monitoring and assessment become an increasingly important. The importance of assessing QoS stems from the fact it may reflect the resources availability of a network that may provide solutions for QoS provision, routing, monitoring, management … etc. In the literature, several monitoring and measurement approached and methods have been developed to quantify and predict the QoS of multimedia applications transmitted over such networks. In this research, a new QoS prediction system will be designed. The proposed system is based on using the Nonlinear Autoregressive with eXogenous input model (NARX) using recurrent neural network. This prediction system uses in addition to the QoS parameters; previous measured QoS values will used as inputs to this model. The expected output of this new model is the forecasted QoS. The proposed model will be trained, tested, validated and then optimized to provide a good estimate of the QoS provided by the given network. Simulation results are expected to show that the proposed model will have high accurate QoS prediction capabilities compared to other QoS assessment systems adopted in the literature. 03 30 2020 4762 4770 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9901.038620 https://www.ijrte.org/portfolio-item/F9901038620/ M.E, IT-Department, UIET-PU, Chandigarh Vagisha Sharma* Dr. Amandeep Verma Assistant Professor in IT-Department at UIET-PU, Chandigarh. Dr. Neelam Goel Assistant Professor in IT-Department at UIET-PU, Chandigarh. Production of crops with better quality is the necessary attribute for the economic growth of any country. The agricultural sector provides employment to many people and accounts for major portion of gross domestic product in many countries around the world. Therefore, for enhanced agricultural productivity the detection of diseases in plants at an early stage is quite significant. The traditional approaches for disease detection in plants required considerable amount of time, intense research, and constant monitoring of the farm. However, optimized solutions have been obtained over the past few years due to technological advances that have resulted in better yields for the farmers. Machine learning and image processing are used to detect the disease on the agricultural harvest. The image processing steps for plant disease identification include acquiring of images, pre-processing, segmentation and feature extraction. In this review paper, we focused mainly on the most utilized classification mechanisms in disease detection of plants such as Convolutional Neural Network, Support Vector Machine, K-Nearest Neighbor, and Artificial Neural Network. It has been observed from the analysis that Convolutional Neural Network approach provides better accuracy compared to the traditional approaches. 03 30 2020 5423 5430 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9902.038620 https://www.ijrte.org/portfolio-item/F9902038620/ Assistant Professor in the Department of Computer Science & Engineering. Anusha Anchan* Manasa G.R. Assistant Professor in the Department of Computer Science & Engineering. Present era of Urbanization, mechanization, and globalization has attracted more and more Air pollution problems. However, PM 2.5 (Particulate Matter) majorly present at air, having diameter below 2.5 μm. With its high concentration leading to health issues such as lung cancer, cardiovascular disease, respiratory disease etc. With respect to this, presented work approach is building of supervised learning models, XGBoost(Extreme Gradient Boosting) along with MLR(Multiple Linear Regression),RF(Random Forest) and MLP (Multilayer Perceptron) to estimate PM2.5 congregation. The air quality data of city Changping in Beijing is taken into consideration for Analaysis. The accuracy of prediction of the four approaches is measured by using contrasting discovered value verses predicted value of PM2.5 with the help of three measuring matrices. The consequences reveals that the Random Forest algorithm outperforms other data mining strategies for the considered data. Prediction of PM2.5 concentrations will assist governing bodies in warning people who are at peak risk, and taking right measures to reduce its quantity in air also to reduce its impact on human life. 03 30 2020 4771 4776 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9912.038620 https://www.ijrte.org/portfolio-item/F9912038620/ Department of Electronics and Communication Engineering, Nirma University, Ahmedabad, Gujarat, India Piyush Bhatasana* Metal interconnects are used to make the interconnections between different part of the circuitry to realize any System on Chip (SoC) design. For the advanced process technologies, the metal interconnects affects the performance of the design. For nanometer process technologies, the coupling effect in the interconnect causes crosstalk and noise. These noise and crosstalk must be affect the operating speed of the design. This is most responsible candidate for the timing aspect of the design. Thus, the physical design and verification of the advanced process technologies should be include the effects of noise and crosstalk. If the timing of a design is not verified, then the design may not perform at the desired operating speed. The power and area are the other factors, that also to be consider with timing for a faster design. There will always be a trade-off between these three factors. Static Timing Analysis (STA) is one of the many techniques used by the designers to verify the timing of the design and also for closing the design with respect to timing, which is called as timing closure. 03 30 2020 5322 5325 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9913.038620 https://www.ijrte.org/portfolio-item/F9913038620/ Asst. Prof.(Sr), SCOPE, VIT University, Chennai. Nachiyappan S.* Pradeep K V Asst. Prof, SCOPE, VIT University, Chennai. Anusha K. Assoc. Prof. SCOPE, VIT University, Chennai. The main goal of blur detection and classification of images using DNN with tensorflow and Keras network. It is to detect and classify an image with natural blur, artificial blur and distorted. As this paper has been a survey and an algorithm has been proposed and implemented, so has to detect and classify accordingly. The proposed algorithm has been implemented and its accuracy has been increased as compared to the existing model of classifying images. 03 30 2020 4777 4780 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9920.038620 https://www.ijrte.org/portfolio-item/F9920038620/ Ph.D. Research Scholar, Amity University, Jaipur, Rajasthan Ajay P. Tushir* The aim of the study is to analyze the effectiveness of the protection provided for privacy and the related interests of women in cyberspace. This research provides a review and analysis of state changes, guidelines, etc. regulatory tools to protect women's privacy and related interests in cyberspace. These instruments constitute an area of law and politics that has reached considerable maturity, diffusion and normative importance over the decades. Awareness of the law and policies in this area is the main objective and will reflect the state of India. All about regulation is a large body of academic commentary that analyzes privacy issues in cyberspace from different angles. This research will present a predictive analysis of sexual and cybercrime crimes against women in India and the laws that prevent cyber victimization in general and women in particular 03 30 2020 5338 5346 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9927.038620 https://www.ijrte.org/portfolio-item/F9927038620/ Ph.D Scholar, Faculty of Management studies, Manav Rachna international Institute of Research and studies. Khaled Sediqian* Dr. Nandini Srivastava Professor in Charge-CDP Manav Rachna international Institute of Research and studies A career search is a long process which requires effort. It can take months before you can find a job that suits your unique needs almost everyone is well aware of the fact that finding a job in Afghanistan is difficult and requires an iron shoe. For recruitment any candidate is required to go for interviews with different companies, it doesn't matter if you've just graduated from a reputable university or have decided to enter the job market after finishing high school, finding a job with the right income and conditions is a challenge. Finding a job is difficult for many job seekers because they choose the toughest way to find a job. There are generally different ways to look for a job that anyone can choose from depending on their abilities. Traditionally, job-search approaches have been broken down into' formal' and' informal' approaches. Formal approaches such as jobs agency services or reacting to advertising published in magazines, journals, newspaper and, more recently, the Internet. The use of personal contacts is one of the informal job-search techniques most studied.in this survey In order to meet the objective; both quantitative as well as qualitative research technique has been used. On the other hand both secondary as well as primary data used for this research paper. For primary data collection, a semi- structured questionnaire designed to collect the information. Books, articles, journals and database are used as secondary sources. Therefor the outcome of this research will help to understand more about employee job search opinion of on various job search methods in Afghanistan. 03 30 2020 4731 4735 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9929.038620 https://www.ijrte.org/portfolio-item/F9929038620/ Computer Science Department, B.V. Raju institute of technology, Narsapur, Telangana, India, Nishath Ansari* Suresh Dara Computer Sscience Department, B.V. Raju institute of technology, Narsapur, Telangana, India. Jaala Shruthi Computer Science Department, B.V. Raju institute of technology, Narsapur, Telangana, India. In the paper, we have used a deep learning technique to identify dual faces i.e. nothing but detecting dual shot faces. As the data is emerging day by day with high dimensionality, recognizing dual faces is a major problem. So wasting time on identifying images is like fiddling around. In order to save time and get absolute accuracy we have implemented a fast preprocessing technique named as Convolutional Neural Network (CNN) along with feature extraction technique which is used to knob the relevant features to detect and identify images/faces. By performing this robust method, our intention is to detect dual images in an efficient way. This technique results in decreased feature cardinality and preserves unique efficiency of the data. The experiment is performed on extensive well liked face detecting benchmark datasets, Wider Face and FDDB. CNN with FE demonstrates the results with superiority and the accuracy was in-depth analyzed by CNN classifier. 03 30 2020 5669 5672 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F9930.038620 https://www.ijrte.org/portfolio-item/F9930038620/