bluuebunny/crossref_metadata_2025 huggingface dataset:
Open Interest Analysis Based on IPTV Users
Authors:   Departmentof CSE, VRSEC, Vijayawada, India., Balusu Manjusha , Alaparthi Jitendra ,   Department of CSE, VRSEC, Vijayawada, India
Published: August 2019

Current data frameworks make it progressively simple to acquire knowledge into the open intrigue, which is ending up increasingly more significant in differing open and corporate exercises and procedures. They propose and depict a system and a technique for assessing open enthusiasm from the verifiable negative criticism gathered from the IPTV group of spectators.Theexplorationcentersessentiallyaroundthechannel change occasions and their match with the substance data got from shut subtitles. The proposed and depict system and a technique for assessing open enthusiasm from the implicit negative feedback gathered from the IPTV spectators our examination scopes basically on the channel change occasions andtherematchwithsubstancedataacquiredfrom shutcaptions. It can cover an a lot more extensive population, and identify even minor varieties in client conduct. The displayed structuredepends on Concept modeling, viewership profiling and consolidates the understood watcher responses into an interestscore.
Modeling Of Automotive Radiator by Varying Structure of Fin and Coolant
Authors:   Assistant Professor, Department Mechanical& Production Engineering Sathyabama Institute of Science and Technology, Chennai,India., Kavitha Mrs.K.R. , Mercy J.Lilly ,   Associate Professor Department Mechanical& Production Engineering Sathyabama Institute of Science and Technology, Chennai, India., Subramanaiam Prakash ,   Professor Department Mechanical& Production Engineering Sathyabama Institute of Science and Technology, Chennai,India., Krishnamoorthy Dr.Arunagiri ,   Professor of Mechanical Engineering Sathyabama Institute of Science and Technology, Chennai,, Palaniswamy Sivashankari ,   Assistant Professor Institute of Science and Technology of Chennai Tamil nadu India.
Published: August 2019

Radiators are heat exchangers used to transfer thermal energy from one medium to another for the purpose of cooling and heating. The majority of radiators are designed to service in four wheeler and heavy duty vehicles. The radiator is always a origin of heat to its environment, although this may be for either the function for cooling the fluid or of heating this environment, or coolant supplied to it, as for engine cooling. In existing plain fins type radiator are commonly used, which are usually set up in a cross flow arrangement made up of aluminum and copper alloy. Powerful fan and water pump is accompanied in this to greatly improve heat dissipation rate. The addition of fins is one of the way to improve and increase the rate of radiator cooling. This method follow the principle of increasing contact surface. Contact surface can also be increased by varying fin geometrical structure. In this project simple modification has been carried out in the existing fin geometry with a view to improve its heat dissipated rate. Also comparison of conventional coolant with Nanofluids has been carried out by using PTC Creo 3.0 and analysis is carried out by using Ansys v16. The result are compared and optimum fin design is concluded.
Fingerprint Image Segmentation using Deep Features and SVM
Authors:   Computer Science, Karpagam Academy of Higher Education, Coimbatore, India., Joy Reji C , Hemalatha S ,   Computer Science, Karpagam Academy of Higher Education, Coimbatore, India.
Published: August 2019

The task of fingerprint segmentation is the most important step in an automated fingerprint identification system. It is essential to separate the fingerprint foreground with ridge and valley structure from the background, which usually contains unwanted data hindering an accurate feature extraction. In the proposed method, fingerprint segmentation is treated as a classification problem by classifying the given input image into foreground class or background class. Here, we have used an unsupervised learning algorithm by using Stacked Sparse Autoencoder (SSAE) to learn the deep features which can very well distinguish the background region from foreground one. Finally, these deep features are given to the SVM classifier. The experimental results prove that the proposed method meets the state-of-the-art results in a wide range of applications.
Design of Dynamic Digest through Polynomial Product for Improved Avalanche Effect on Keyless Hash Function
Authors:   Research Scholar, Department of Computer Science, School of Engineering and Technology, Pondicherry University, Kalapet, Puducherry, India., Karthik P. , BALA Dr.P.SHANTHI ,   Assistant Professor, Department of Computer Science, School of Engineering and Technology, Pondicherry University, Kalapet, Puducherry, India.
Published: August 2019

The integrity violation of remote data draws more significance after the advent of cloud solutions. The conventional digest functions like MD4, MD5, SHA-160, SHA-2 family, and SHA-3 family provide a more elegant solution for the aforesaid problem. But the breaking of MD4, MD5, and SHA-160 algorithms and the partisal breaking of the families of SHA-2 and SHA-3 raises concerns about their use on cryptographic applications. In addition, they do not provide the facility to decide the hash output length dynamically at runtime. This work attempts to address the aforesaid problems through polynomial products. The proposed design uses a higher-order two-variables polynomial function to establish the hash output. The application of polynomial function at the block-level helps the proposed design to produce variable length hash output. The experimental analysis of avalanche response, the effect of an avalanche on the hash output nibbles, and the analysis of confusion and diffusion prove the proposed design performs exceedingly well than the conventional algorithms. Therefore, the proposed design could be considered as a more suitable alternative for the conventional keyless digest function in the perspective of security.
A Novel CBR-Decision Tree Based Intelligent Car Fault Diagnosis System (CFDS)
Authors:   Department of Computer Science & Engineering, National Institute of Technology, Durgapur, West Bengal, India, De Sumana , Chakraborty Baisakhi ,   Department of Computer Science & Engineering, National Institute of Technology, Durgapur, West Bengal, India
Published: August 2019

Fault diagnostic systems find numerous applications in almost all service domains. Now a days, interest lies on intelligent fault diagnosis. For this, a huge database of cases called Case Base (CB) comprising of fault descriptions and their solutions needs to be maintained. Case Based Reasoning (CBR) is a popular Artificial Intelligence technique that supports huge databases that find popular applications in fault diagnosis systems. CBR is a very useful process for solving problems, detecting and diagnosing faults, learning, reasoning and supporting decisions. As the size of CB increases, the accuracy of the system too increases leading to an increase in computational time complexity. So CBR techniques are coupled with machine learning approaches to reduce the same. This paper proposes a CBR methodology based Intelligent Car Fault Diagnosis System (CFDS) that integrates decision tree as a machine learning technique and jaccard similarity method to diagnose faults of cars accurately in a minimum time. A car fault diagnosis and detection system requires individual expertise gathered from personal experience and technical skills. Many times, not only the fault, but also the car part from where the fault originates or the cause of the fault needs to be known to handle or repair the problem. So, to help car mechanics as an assistant tool, CFDS is proposed; so that they can deal with various types of car faults very easily. Here, the proposed methodology integrates decision trees and jaccard similarity method to diagnose faults where the usage of decision trees is to store cases and jaccard similarity method is used to calculate the similarity percentages between user new query case and stored cases in the CB. User can post a new query about his car problem to the user interface of the CFDS. The CFDS uses the proposed methodology to find the solutions of that problem, and finally then at last these solutions are displayed to the user. To obtain better performance of the CFD system, this paper introduces a novel model of CBR cycle called CR4 model that is slightly modified version of traditional CBR cycle of R4 model, proposed by Aamodt and Plaza in 1994.
K Nearest Neighbor Based Model for Intrusion Detection System
Authors:   Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, India., Nikhitha M. , Jabbar Dr.M.A. ,   Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, India.
Published: August 2019

Network security has become more important in this digital era due to the usage of information and communications technology (ICT). Data security is also one of the major issues in today’s world. Due to the usage of this ICT technologies threat to network is also increasing. So in order to solve these problems the researchers has developed IDS that deals with network traffic to identify the harmful users and hackers in the computer. In this paper, we designed a model for IDS for classification of attacks using K-Nearest Neighbor classifier algorithm. KNN is a supervised and lazy machine learning classifier, it shows its best performance in terms of accuracy and classifications. Experimental analysis was conducted on ISCX dataset to judge the implementation of model. The Experimental outcome shows that our suggested model recorded an improved accuracy of 99.96%.
Topological Properties of Parallel Series Language
Authors:   School of Advanced Sciences, Division of Mathematics, Vellore Institute of Technology, Chennai, India., N Mohana. , Desikan Kalyani ,   School of Advanced Sciences, Division of Mathematics, Vellore Institute of Technology, Chennai, India, Dare V. Rajkumar ,   Department of Mathematics, Madras Christian College, Tambaram, Chennai, India.
Published: August 2019

Parallelism is a process by which a sequential string is broken down into a number of alphabets and used to speed up the acceptance of a string. To identify the parallelisable string, we have used parallel operator || and defined the language as parallel series languages. Algebraic and recognition properties of series parallel posets have been studied by Lodaya in [8]. In this paper, we have introduced finite and infinite parallel series language (parallel strings are connected sequentially). We have considered the set of all parallel series language as topological space and prefix order relation (poset relation) has been used to relate two parallel series strings. Topological concepts like limit, sequence, open set, closed set and basis for parallel series languages and their properties have been derived.
Perception of Investors towards Risk in Various Investment Avenues
Authors:   Research Scholar Department Of Management Studies Jntua- Anatapuramu – A.P, Krishna U M Gopal , Sulatan Dr. Aliya ,   Associate Professor Department Of Management Studies Rgmcet – Kurnool District – A.P, Reddy Dr. T Narayana ,   Associate Professor Department Of Management Studies Jntua – Anatapuramu – A.P
Published: August 2019

Investment is the process of Allocation of money with the expectation receiving additional returns in the feature. In any investment risk is inherent. An investor will take decisions on financial investment avenue on basis of demographical factors, Socio - Economic factors, Life Style, etc.., and an investor can estimate the risk on particular investment avenue. Risk is an important factor that influences the financial investment decisions. In this study an attempt has been made to find out the impact of knowledge level on risk taking ability of investor and investors perception towards risk in various investment avenues at Kurnool district of Andhrapradesh.
Energy Efficient Methods for Topology Control Problem of Wireless Sensor Network
Authors:   Research scholar, SunRise university, Associate professor, Department of CSE, S R Engineering college, India, Chintham Sandeep , Thirupathi V. ,   Research scholar, Osmania university, Assistant professor, Department of CSE, S R Engineering college, India
Published: August 2019

Energy conservation in sensor networks is depending on duty cycle, data driven and mobility-based methods. Duty cycle methods use synchronous and asynchronous MAC protocols for saving the node energy. Data driven methods employs in-network processing, data aggregation and hierarchical sampling methods for minimizing the energy utilization. This paper delivers energy efficient strategies for information gathering and also a selection in WSN.
A Cognitive Support for Identifying Phishing Websites using Bi-LSTM and RNN
Authors:   Computer science engineering, Hindustan Institute of Technology and Science, Chennai, India., Arivukarasi M. , Antonidoss Dr. A. ,   Computer science engineering, Hindustan Institute of Technology and Science, Chennai, India.
Published: August 2019

Phishing over web is a saturating risk that speaks to fifth of online business sites. Regardless of the broad research in phishing sites location, none adapt with the nonstop improvement in phishing methods. Along these lines, a subjective, dynamic, and self-versatile phishing location framework is expected to consequently distinguish new phishing methodologies. Subjective Computing methods emulate the thinking and learning capacities of human mind. In this paper, we propose a psychological structure for phishing sites recognition. The structure utilizes an intellectual system called a bidirectional long momentary memory (BLSTM) intermittent neural system (RNN). Moreover, we incorporated a Convolutional Neural Network (CNN) for semantically distinguishing items and activities in sites' pictures. Existing phishing site discovery frameworks experience the ill effects of poor picture highlights execution as they utilize just factual and basic highlights of pictures. The system should outflank existing frameworks since it can gain from setting persistently identify new phishing strategies.
An Efficient Watermarking and Key Generation Technique using DWT Algorithm in Three-Dimensional Image
Authors:   Research Scholar Department of Computer ScienceBharathiar University, Coimbatore, Tamilnadu, India., Kumar R. Sai , Napoleon Dr. D. ,   Assistant Professor Department of Computer Science Bharathiar University, Coimbatore, Tamilnadu,
Published: August 2019

Discrete Wavelet Transform is the algorithm which can be used to increase the contrast of an image for better visual quality of an image. The histogram value for original image with highest bins is taken for embedding the data into an image to perform the histogram equalization for repeating the process simultaneously. Information can be embedded into the source image with some bit value, for recovering the original image without any loss of the pixels. DWT is the first algorithm which has achieved the image contrast enhancement accurately. This approach maintained the original visual quality of an image even though themessage bits are embedded into the contrast-enhanced images. The proposed work with an original watermarking scheme based on the least significant bit technique. As a substitute of embedding the data into a simple image as watermarking, least significant bitmethod by utilizing the three wavelets transform is applied in the proposed system in order to enhance the embedding technique using spatial domain. For security, the Huffman coding has used to secure the data embedded into a host image, which can convert the secret message sequence into bit sequence for least significant bit operation. DWT can analyze the signal at multiple resolutions and it can divide the image into two types of quadrants as high and low-frequency quadrants. Here dividing an image into low and high it makes the information to hide.
Data Analytics on Agrometeorological Parameters for Building A Utility System for Farmer Community
Authors:   Assistant Professor, Department of Computer science and Engineering, M S Ramaiah Institute of Technology, Bangalore, (Karnataka), India., B J Sowmya , Mundada Gautam ,   Student at Ramaiah Institute of Technology, Bengaluru, (Karnataka), India., Hegde Pranav ,   B.E student, Ramaiah Institute of Technology, Bengaluru, (Karnataka), India., Dr. Seemas  ,   Professor, Department of Computer science and Engineering, M S Ramaiah Institute of Technology, Bangalore, (Karnataka), India., Srinivasa Dr. K G ,   Professor, Department of National Institute of Technical Teacher Training & Research, Chandigarh, Chandigarh, (Punjab), India.
Published: August 2019

The day-to-day evolution of the world is throwing many challenges, thereby demanding the humans to be on par with the modern tools and technologies that are on offer. These technologies are contributing to the overall growth across all the domains. One such domain that is highly affected by the modern tools and techniques is Agriculture. Gone are the days, when only a handful methodologies and tools were utilized to understand about agriculture. Although the farmers are sceptical to the modernization, the end result has been encouraging at many a times. With the use of analytics, one can get to know more about the type of soil, crops and fertilizers, amount of water to be utilized depending on the climatic conditions and thereby have an effective yield. In this work, the elementary task of portraying the effect of climatic conditions on the production of different varieties of crops is carried out. Also, the application of Multivariate Linear Regression and Artificial Neural Network (ANN) techniques provides a significance outcome in the prediction of yield with the focus on Ragi and Rice. The work provides an accuracy of 65% with the 3-fold cross validation technique and 68% accuracy with ANN model.
PV and Grid interfaced Plug-in EV Battery Charger operating in P-VG, P-V and V-G Modes
Authors:   School of Electrical Engineering, Vellore Institute of Technology (VIT), Chennai, India., Subudhi Partha Sarathi , S. Krithiga ,   School of Electrical Engineering, Vellore Institute of Technology (VIT), Chennai, India.
Published: August 2019

The proposed system facilitates uninterruptable charging of a photovoltaic (PV) fed plug-in electric vehicle (EV) battery charging system irrespective of solar irradiation conditions by integrating utility grid to the battery charging system. The system employs bidirectional cycloconverter (BDCC) in order to use utility grid as source or sink during different modes of operation which depends on the availability of solar power. During low irradiation condition, the utility grid acts as a backup source in order to facilitate uninterruptable charging of the EV battery. When surplus power is generated from the PV panel, it is fed to the utility grid, which acts as sink in this mode. For uninterruptable EV battery charging, the controller operates the switches and relays in the proposed system corresponding to solar irradiation level. The available literatures define complex control strategies which are solved in this proposed system by adopting a simple dynamic control algorithm. The simulation of the proposed system has been carried out using PSIM simulation software and experimental prototype has been designed, developed and tested for different modes of operations to validate the efficacy of the proposed system.
Diagnosis of Coronary Artery Disease using 1-D Convolutional Neural Network
Authors:   Research Scholar, Department of Computer Engineering, K.I.I.T. University, Bhubaneswar, Odisha India., Mr. Debabrata  , Kumar Dr Santosh ,   Associate Professor Department of Computer Engineering, K.I.I.T. University, Bhubaneswar, Odisha, India., Dr Debabala  ,   Associate Professor Department of Computer Science, Ramadevi Women’s University, Bhubaneswar, Odisha, India.
Published: August 2019

Heart disease is treated as one of the noxious diseases at the present time. Coronary artery disease is a kind of heart syndrome which is statistically growing day by day in the society. It is very tough for medical practitioners to predict Coronary artery disease as it is a complicated task that needs experience and acquaintance. For the detection of the disease doctors normally prescribe various invasive and non-invasive methods like angiography, ECG and echocardiogram. These methods are very expensive and sometimes not able to discover a number of undiagnosed symptoms. Due to these, it is not possible to detect the disease accurately at an early stage. The medical sector today contains a number of useful data that is helpful to detect a disease accurately. Using this data many researchers have proposed a number of intelligent systems for the detection of the disease. In this work, a competent system is implemented using deep learning for the better detection of the disease. The system is constructed using Convolutional Neural Network. The system has three phases. In the first phase data cleaning, data imputation and important feature selection are performed. In the second phase model training and hyperparameter tuning is performed. Finally, in the last phase, the model prediction is performed using the test data. The data set used for experimentation is Cleveland, Hungary, Switzerland and Long beach heart disease data present in the UCI repository. The proposed system gives a classification accuracy of 96.49% during testing, which is highest among all the discussed methods.
Multicast Communication using Different Group Key Managements
Authors:   Department of Computer Science, NMAM Institute of Technology, Karnataka, India., Kumar H S Ranjan , Aithal Dr. Ganesh ,   Dean (Research), Mangalore Institute of Technology and Engineering, Moodbidri, Karnataka, India., Shetty Dr. Surendra ,   Professor and Head, NMAM Institute of Technology, Karnataka, India.
Published: August 2019

The most appropriate communication mechanism for transfer of packets from one source to another is multicast IPv6 communication. Nowadays, multicast communication plays a major role in a large number of communication applications. In this multicast communication, the message privacy attains a highest position. The members of multicast are dynamic so they permit the host members to enter and depart the cluster without the permission of remaining group members and, it can't provide any transfer without the interference of host, this may reduce the performance. For security enhancement in multicast communication Group Key Management (GKM) was introduced. There are three different approaches in GKM and they were mainly involved to overcome the issues of host mobility in multicast communication. The challenges that are encountered by these GKM approaches their requirements and challenges was also discussed in this paper. Finally, some questions and their explanations were provided along with it. These GKM approaches will improve the privacy of multicast communication. So, the packets can deliver to the group members without any interference.
Reliability of Non-Destructive Testing Methods in the Assessment of the Strength of Concrete Columns Reinforced with Two Layers of Transverse Confining Stirrups: Empirical Evidence
Authors:   Department of Civil Engineering, Mangalayatan University, Aligarh, India., Kumar Mahesh , Zaidi S Kaleem Afrough ,   Civil Engineering Section, Aligarh Muslim University, Aligarh, India, Jain SC ,   Department of Mechanical Engineering, IIT, Mandi, India, Krishna KVSM ,   Institute of Business Management, Mangalayatan University, Aligarh, India.
Published: August 2019

Wide spread literature is available on the approach to assess the strength of Reinforced Concrete structures. However no attention has been made so far to study the similar potential when the RC columns are reinforced with double layer of transverse confining stirrups. An obvious reason could be that studies in double layered stirrups options are quite at their infancy. The authors took the advantage of their involvement in studying the axial compression behaviour in terms of strength and ductility of concrete columns reinforced with double layered transverse confining stirrups, applied the two non-destructive measures, viz., Rebound Hammer Test Method (RHTM) and Ultrasonic Pulse Velocity Test Technique (UPVTT), and destructive testing measure, viz., Compression Testing Method (CTM) on the specimens that they have developed in this study. The results have been subjected to regression techniques to build relevant equations for predictability. The present study attempts to explain the detailed procedures adopted within the frame work of the informed knowledge and had attempted to derive meaningful implications for the use of practitioners and academic fraternity. The findings of this paper drew attention to the superior impact of a combined non-destructive testing approach whereby RHTM and UPVTT have been merged in a fashion to yield better assessment of RC columns with double layers of transverse confining stirrups. The combined influence of RHTM and UPVTT clearly explain, greater characteristic compressive strength as one moves from a specimen with a Normal Strength Concrete (NSC) to a specimen with High Strength Concrete (HSC).
Fuzzy Classification with Comprehensive Learning Gravitational Search Algorithm in Breast Tumor Detection
Authors:   Department of Applied Science, Northcap University, Gurgaon, India., Bala Indu , Malhotra Anshu ,   Department of Applied Science, Northcap University, Gurgaon, India.
Published: August 2019

The research paper herewith presents an effectual diagnosis classification system using fuzzy classifier and a very efficient heuristics algorithm comprehensive learning gravitational search algorithm (CLGSA) which has a good ability to search and finding optimal solutions. The effectiveness of the proposed model is estimating on Wisconsin breast cancer data set available in the UCI Machine learning source in the University of California, Irvine. We testify the data over the parameters of classification of accurateness, sensitivity as well as specificity with a much better and more responsive 10-fold cross validation method; which is considered as a reliable diagnostics model in the medical field. Experiment results have clearly shown that the proposed approach will turn out to be a calculative and decisive medium for cancer detection in the field of medicine.
Numerical Analysis of Protrusion Effect over an Airfoil at Reynolds Number -105
Authors:   Department of Aerospace Engineering, University of Petroleum and Energy Studies, Dehradun, INDIA., Bodavula Aslesha , GUVEN Ugur ,   Department of Aerospace Engineering, University of Petroleum and Energy Studies, Dehradun, INDIA, Yadav Rajesh ,   Department of Aerospace Engineering, University of Petroleum and Energy Studies, Dehradun, INDIA
Published: August 2019

Need of micro aerial vehicles and Unmanned Air Vehicles is increasing due to military, defense and civilian requirements. These vehicles fly at very small Reynolds numbers and have to move in confined spaces with a bare minimum speed, to achieve high lift coefficient is the main concern. The main focus of this research paper is to carry out the computational analysis and study the unsteady flow over NACA0012 airfoil with right angle triangular protrusion at the Reynolds number 105. The location of the protrusion is 0.05c, with three different heights of protrusion 0.005c, 0.01c, and 0.02c, normal to the surface of the airfoil. Geometric modeling and grid generation are created using the ICEM CFD software and numerical analysis carried out using CFD Software at various angle of attacks ranging from 00 to 160 with 20 intervals. Numerical validation has done and compared. The results obtained from the research work is recommend that for smaller protrusions the lift and drag coefficients are unaffected in the low angle of attacks while the lift characteristic is significantly improved at the higher angle of attacks.
Analytical Method for Directional over Current Relays Coordination in Micro Grids
Authors:   Electric Power and Machines Dept. Ain Shams University, Cairo, Egypt., Abul'Wafa Ahmed R. , Mahmoud Bahaa S. ,   Electric Power and Machines Dept. El’Shorouk Academy, Cairo, Egypt., Mohamed Aboul'Fotouh A. ,   Electric Power and Machines Dept. El’Shorouk Academy, Cairo, Egypt.
Published: August 2019

Microgrids are consisting of interconnected loads and distributed generators which can operate with and without utility. The significant implementation of microgrids challenged the protection engineers especially in designing protection microgrid schemes. This challenge comes from the bi-directional fault current variation and the dynamic behavior of microgrids. The implementation of artificial intelligent (AI) techniques for coordinating the directional overcurrent relays (DOCRs) in Microgrids (MGs) is a promising solution. However, all the (AI) techniques estimate the solutions and achieve the objective function after an extensive number of iterations. This paperwork proposes an analytical method for solving the coordination of (DOC) relays in meshed Microgrids which operates in grid connected mode. The proposed analytical method needs a lesser simulation time than the (AI)techniques to find the proper settings for the coordinated directional overcurrent relays (DOCRs). A file code is written using MATLAB software to implement and test this methodology.
An Enhanced Virtual Reality Method for Diagnosing Autistic Children
Authors:   Assistant Professor, SCSE, VIT University, Chennai, India., Vincent Rajiv , Vijayakumar V ,   Professor, SCSE, VIT University, Chennai, India.
Published: August 2019

Autism Spectrum Disorder (ASD) is a neurodevelopment condition that influences substantial children and adults. Currently the identification of this disorder is carried out by specialists who follow regular questionnaires and lookout for definite behavioral indications over physical monitoring. These approaches for the identification are not only particular, but hard to reiteration, expensive and also tremendously time taking. This paper talks about a procedure to support the likelihoods of the existence of ASD in children by automated optical and pulse analysis. This paper focuses on the differentiating abilities of the autistic kids, especially with colours. We investigate the colour perception and cognition of kids with ASD who have preference to certain colours. In comparison with the former approaches, our methodology is ideal to predict ASD in children by using the infrared and pulse rate sensor which helps to analyze the colours that they are not comfortable with. The design we have modelled is envisioned for investigation and treatment of autism and tests included contributors with this condition.
Unconstrained Ear Recognition through Domain Adaptive Deep Learning Models of Convolutional Neural Network
Authors:   Department of Graduate Studies, Technological Institute of the Philippines, Quezon City, Philippines., Alejo Marwin B. , Hate Cris Paulo G. ,   Department of Graduate Studies, Technological Institute of the Philippines, Quezon City, Philippines.
Published: August 2019

Limited ear dataset yields to the adaption of domain adaptive deep learning or transfer learning in the development of ear biometric recognition. Ear recognition is a variation of biometrics that is becoming popular in various areas of research due to the advantages of ears towards human identity recognition. In this paper, handpicked CNN architectures: AlexNet, GoogLeNet, Inception-v3, Inception-ResNet-v2, ResNet-18, ResNet-50, SqueezeNet, ShuffleNet, and MobileNet-v2 are explored and compared for use in an unconstrained ear biometric recognition. 250 unconstrained ear images are collected and acquired from the web through web crawlers and are preprocessed with basic image processing methods including the use of contrast limited adaptive histogram equalization for ear image quality improvement. Each CNN architecture is analyzed structurally and are fine-tuned to satisfy the requirements of ear recognition. Earlier layers of CNN architectures are used as feature extractors. Last 2-3 layers of each CNN architectures are fine-tuned thus, are replaced with layers of the same kind for ear recognition models to classify 10 classes of ears instead of 1000. 80 percent of acquired unconstrained ear images is used for training and the remaining 20 percent is reserved for testing and validation. Results of each architectures are compared in terms of their training time, training and validation outputs as such learned features and losses, and test results in terms of above-95% accuracy confidence. Above all the used architectures, ResNet, AlexNet, and GoogleNet achieved an accuracy confidence of 97-100% and is best for use in unconstrained ear biometric recognition while ShuffleNet, despite of achieving approximately 90%, shows promising result for use in mobile version of unconstrained ear biometric recognition.
Dynamics Modeling and Optimization of a Wrapper Flow Pack Mechanism
Authors:   Department of Engineering and Architecture, University of Parma, Parma, Italy and Department of Innovative Technologies, SUPSI, Manno, Switzerland., Silvestri Marco
Published: August 2019

This paper is about a mechanical solution to vibration problem of a wrapper flow pack mechanism, which is used in packaging industry. The target mechanism is the welding head of the machine where the vibration is generated, due to eccentric masses rotating with a cut-on-the-fly motion law. Consequently, production rate is limited by the vibration at 200 pieces per minute. To reduce the vibration so that the production rate can exceed over 200 ppm, two ways of improvements are considered. The first one is optimizing the motion law. The other one consists in mechanical modifications which eliminates or balance out the inertia forces derived from the eccentric rotating mass. This research implements the second way of adding balance weights and discusses the properties of it. During analysis, the software ADAMS which provides kinematic simulation is used as the analytical tool to test and examine the solutions proposed.
Resizable Drone Designs: for Maneuvering Through Paths of Varying Dimensions
Authors:   Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India., Prakash. R Varun , G Kirubakarn. ,   Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India., Kumar. D Dinesh ,   Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India, Nainar. A Vignesh ,   Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India.
Published: August 2019

An unmanned aerial vehicle, commonly known as a drone, is an aircraft without a human pilot aboard. Essentially, a drone is a flying robot that can be remotely controlled or fly autonomously through software-controlled flight plans in their embedded systems, Flying robots are increasingly adopted in search and rescue missions because of their capability to quickly collect and stream information from remote and dangerous areas. Their maneuverability and hovering capabilities allow them to navigate through complex structures, inspect damaged buildings, and even explore underground tunnels and caves. Since their size is fixed, maneuvering over the compact areas and tunnels of variable size becomes an issue. To overcome this issue, we propose a model of quadrotor design which has the capability to change its size. The arm length of the quadrotor is changed dynamically so that it can fly in areas of variable sizes that would be hard to reach with the quadrotor of fixed arm length. On the other hand, our model is cost-effective, since the arm of the drone is designed with PVC (Polyvinyl Chloride). Using this model, drones will be able to move over compact areas and passages of variable sizes, thus aiding in better exploration during search and rescue operations.
Predictive Data Analysis to Identify Heart Anomalies
Authors:   Department of Computer Science and engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India., samreen Sabha , Cherukuri Dr.Kiran Mai ,   Department of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India., Goud Dommati Venkatsai ,   Department of Computer Science and Engineering, , VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India
Published: August 2019

Heart disease is a usually used word to describe diseases related to heart, when heart is not efficiently performing at is best, most of this disease is acquired because of unhealthy lifestyle and unhealthy food. Heart diseases need regular care to improve the patient’s quality of life. We can analyze cardiac disabilities of a individual by factors like historical health data and risk factors .The fusion of algorithms with clinical data can forecast the results of any disease so, incorporating these two things for Predicting and diagnosis of the heart functionality using the computational algorithms where the user interface in developed in R studio. Foremost objective of the system is for majorly predicting the heart anomalies collected using the real time clinical data .The proposed method uses the performance comparison of the algorithms and as well as the datasets like random forest and logistic regression to calculate which gives highest accuracy rate performance and this study also involves use of two different datasets, one which is available in the existing dataset for heart disease and another which was collected from the hospital in real time, so this can help in making an efficient system that can be utilized to predict the probability of heart diseases of any individual. Thus this can form a foundation for any therapy or treatment to be given this would increase the efficiency as well as help the medical staff and doctors to predict heart disease and more accurately. Computer diagnosis and prediction of a disease can solve many medical problems by predicting it beforehand.
Cipher Text Policy Attribute Based Encryption (Cp-Abe) using Concealed Policy in Public Cloud
Authors:   Research Scholar, Department of Computer Science, Sri Ramakrishna College of Arts & Science for Women, Coimbatore, Tamilnadu, India, Ashmita A.C. , Yamini Dr.C. ,   Associate Professor, Department of Computer Science, Sri Ramakrishna College of Arts & Science for Women, Coimbatore, Tamilnadu, India
Published: August 2019

Cloud computing focus on the data storage and the management. Since the cloud is open source for the user there have to protect the privacy and the security of the data which are less maintenances. There are several approaches designed to establish a secure data. The Signature work of this scheme is to allow the user to verify the shared in the cloud using the secret key. The anonymzed access policy can be viewed by the Cloud Service Provider (CSP) and the user; hence the recipient information will not be leaked or disclosed. The novel idea of this method is to conserve the privacy of the access policy using Cipher text Policy Access Based Encryption (CP- ABE) scheme. We have proposed a novel system, “Concealed Policy using mandatory and Role based”, using time elapsed of 10 secs. Within the required time the user, should response to the cloud using their Secret key(SK) to decrypt the file, and the owners with the Public key (PK), must encrypt the file using the mandatory access and upload it to the Third Party Auditor (TPA).This will improves the security to the user in the cloud environment. The concealed policy relies on two stages, there is Mandatory policy and Role based policy using the signature verification. Which are more efficient to the user to encrypt and decrypt the file. Without verification of the signature, the legitimate user cannot access the particulars in the cloud environment. If the signature of the user is valid, there can access (decrypt) the file using the SK. whereas, invalid signature occurs the TPA (Third Party Auditor) traces the reasonable user responsible for the signature and check with the mandatory access to encrypt the file, Nether less the access den ailed.
The Impact Behaviour of Carbon Fiber-Epoxy Composite Leading Edge using Finite Element Method
Authors:   MJIIT UTM, Kuala Lumpur, Malaysia., Nurihan O. , Aminanda Y. ,   Faculty of Engineering, UTB Brunei Darussalam., Ali J.S.M ,   Kuliyyah of Engineering, IIUM, Gombak, Kuala Lumpur, Rasid Z.A. ,   MJIIT UTM, Kuala Lumpur, Malaysia., Yahaya H. ,   MJIIT UTM, Kuala Lumpur, Malaysia.
Published: August 2019

Composite material has been widely used in aircrafts due to its high strength to weight ratio that leads to weight saving of the aircrafts. Equally important, aircraft material should be tough i.e. it should have the ability to absorb high energy and thus resist fracture. The aircraft’s wing design requires the material to have high toughness as parts of the wing especially its leading edge is subjected to impact loadings. Using finite element software of LS-DYNA, this research focuses on studying the impact behaviour of composite panels that represent the leading edges of wings when the panels are subjected to rigid sphere projectile. Three shapes of panels are used: flat, semi-circular and semi ellipse while panels can be of 2, 4 and 8 layers to vary its thickness. The panels are made of laminated composites with woven carbon fibres and the angle of orientations are [0/90] n, [0/45]n and [45/-45]n where n will give the number of layer for the composite. The Mat-58 material type suitable for woven type fibre is used where failure criteria of Hashin is applied. It was found that the simulation results are in a very close agreement with the finding from experiments conducted earlier. Furthermore, the optimum stacking sequence was found to be the [0/45]2 stacking sequences
A New Algorithm Based Charge Controller for Global MPPT of Si Solar Cell
Authors:   Department of Electrical Engineering, Tripura Institute of Technology, Narsingarh, Agartala-799009, West Tripura, India., Debbarma Sajal , Debbarma Shivaji ,   Department of Electrical Engineering, Tripura Institute of Technology, Narsingarh, Agartala-799009, West Tripura, India., Saha Saptadip ,   Department of Electrical Engineering, National Institute of Technology Agartala, Jirania, Agartala-799046, West Tripura, India.
Published: August 2019

This work illustrates the development of a new hybrid MPPT algorithm and the construction of an efficient charge controller based on this algorithm. The existing perturb & observe (P&O) methodology and incremental conductance (IC) method are unable to trace the global maximum power point (GMPP) once there's a frequent modification in solar irradiance. These algorithms stick to local maxima in such scenario so the controller efficiency drops. Here a new hybrid MPPT algorithm was designed to track the GMPP efficiently, which involves the concept of Savitzky Golay digital filter technique. This filter is employed to swish a collection of digital information points that additional will increase the S/N of the signal while not greatly distorting it. The substitute sub-sets of niggardly observations fact are passable about a low-degree polynomial by the come nigh of neat least squares by means of convolution. This algorithm first smoothens the output of photovoltaic using the described technique and removes the local maxima and then further applies the P&O method to find out the GMPP. The circuit was simulated using Proteus and later on, an experimental setup was built using Arduino UNO (ATMEGA328P) to run the algorithm in real-time environment. The controller successfully tracked the GMPP voltages for variable irradiance both in simulation and real-time environment.
An Efficient R-Apriori Algorithm for Frequent Item set Mining in Python
Authors:   Department of Computer Science, VISTAS, Chennai, India., Uthra S. , Rohini K. ,   Department of Information Technology,School of Computing Sciences, VISTAS, Chennai, India.
Published: August 2019

The mining of affiliation rules remains a well-enjoyed and successful procedure for getting critical data from monstrous data sets. It attempts to look out feasible connections between things in monstrous data sets upheld exchanges. Visit examples ought to be created to frame these affiliations. The "R-APRIORI" standard and its arrangement of improved variations, that were one in all the soonest visit design age calculations arranged, remain a most well known option because of their easy to execute and parallel to the common inclination. despite the fact that there are a few conservative single-machine methodologies for Apriori, the huge amount of data by and by open so much surpasses the capacity of 1 machine. In this way, it's important to scale over numerous machines to satisfy the regularly developing requests of this data. Guide cut back could be a well-loved distributable adaptation to non-critical failure structure. Be that as it may, genuine circle I/O in each Map cut back activity obstructs the efficient usage unvaried Map cut back information handling calculations like Apriori Platforms. An as of late arranged distributable data stream stage Sparkle beats the Map cut back I/O circle bottlenecks. Shimmer so gives an ideal stage to circulation Apriori. In any case, the principal computationally costly errand inside the execution of Apriori is to thought of applicant sets with everysingle possible go after singleton visit things and to check each match with each managing record. Here we tend to propose a spic and span approach that drastically decreases this methodology multifaceted nature by dispensing with the progression of creating applicants and maintaining a strategic distance from costly examinations. We stock out in– profundity trials to discover the power and quantifiability of our methodology. Our investigations demonstrate that our methodology commonly beats Sparkle'sexemplary Apriori and dynamic for different data sets.
Authorship Trends in Telemedicine Research
Authors:   Dr. C.L. Metha Central Library, Misrimal Navajee Munoth Jain Engineering College, Chennai, India, Viswanathan V. , Elango B. ,   Library, IFET Engineering College, Villupuram, India, Yugapriya S. ,   Former Assistant Librarian, Apollo Hospitals, Chennai, India
Published: August 2019

Telemedicine is the remote delivery of healthcare services, such as health assessments or consultations, over the telecommunications infrastructure. The aim of the study is to analyze the research output on telemedicine. The publications indexed in Scopus database have been used for this study for a period of twenty five years. Retrieved data has been analyzed with various bibliometric tools such as Co-Authorship Index, Multi Authorship Index, Citations per Paper, Relative Citation Index and Relative Uncitedness Index. Highly cited publications and frequently used words in titles are discussed. Analysis reveals that 36949 publications were published in the area of telemedicine with maximum number of publications in the year 2015. Nearly 80% of the total research output has been published with co-authors. Nearly one third of the publications (32.28%) have yet to receive citations.