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Signature based key exchange for securing data and user from web data stealing attacks
Due to the immense technological growth, web and its related applications are becoming a major part of everyday life. The growth of the internet and technology not only increases the positive benefits but also increases negative activities such as data theft. As web applications are used frequently for many online services, it is the most common and valuable target for the adversary to host any web vulnerabilities. Data theft or data stealing attacks are quite common in the web and the internet with severe consequences. The private data are generally stored on the system which gives an opportunity for the attacker to steal the data from the storage or during transit. However, apart from stealing the critical data from the user, the attacker also steals the sensitive data from the web applications. This type of attack takes several forms for stealing perilous information from the user and web application. Unfortunately, these attacks are easy to execute as the attacker needs only the internet connection, a web server and technical knowledge which are readily available. Several prevention strategies exist to thwart the user and the application from the web attacks, however, they do not provide the complete solution. This paper presents the signature based key exchange to prevent the user as well as the web application from several variations of data stealing attacks through mutual attestation. The experimental results show that the proposed method prevents the user and application from data theft than any other existing methods. BEIESP. -
An efficient scheme for water leakage detection using support vector machines (SVM)-Zig
Water is one of the most essential and valuable resources for all living beings, yet in the present day, there is a scarcity of it. Half of the water loss in large cities and industries is due to leaks and illegal lines. 10%-20% of water loss can be reduced by detecting leaks but without the presence of advanced monitoring systems, this problem is typically worsened. Monitoring the consumption and leak detection for such large areas is a challenging task. To overcome this issue a small prototype is prepared called Zig. Zig is designed for both household and industrial purposes. Its main aim is to monitor the flow and consumption of water at different levels of a building like a first-floor and so on which may represent some industrial and household situation. This work focuses on pressure/flow monitoring method to reduce the operational cost and also to detect leakage. One of the machine learning algorithms, Support Vector Machines (SVM) has been applied to detect the leakage and it is compared with Random Forest algorithm to show that proposed scheme is detecting water leakage better. BEIESP. -
Adaptive uplink scheduling model for WiMAX network using evolutionary computing model
The increased usage of smart phones has led to increase usage an internet based application services. These application requires different quality of service (QoS) and bandwidth requirement. WiMAX is an efficient network to provision high bandwidth connectivity and coverage to end user. To meet QoS requirement the exiting model used adaptive model selection scheme. However, these model induce bandwidth wastage as it does not considers any feedback information for scheduling. This work present an Adaptive Uplink Scheduling (AUS) by optimizing MAC layer using Multi-Objective Genetic Algorithm (MOGA). The MAC scheduler use feedback information from both physical layer and application layer. Further, to meet QoS requirement of application and utilize bandwidth efficiently this paper presented an adaptive modulation selection scheme based on user application requirement using MOGA. Our model provides application level based QoS provisioning for WiMAX network. Experiment are conducted to evaluate performance of AUS over exiting model. The overall result attained shows AUS model attain good performance in term of throughput, successful packet transmission and packet collision. 2019 Institute of Advanced Engineering and Science. All rights reserved. -
On the Mass Accretion Rate and Infrared Excess in Herbig Ae/Be Stars
The present study makes use of the unprecedented capability of the Gaia mission to obtain the stellar parameters such as distance, age, and mass of HAeBe stars. The accuracy of Gaia DR2 astrometry is demonstrated from the comparison of the Gaia DR2 distances of 131 HAeBe stars with the previously estimated values from the literature. This is one of the initial studies to estimate the age and mass of a confirmed sample of HAeBe stars using both the photometry and distance from the Gaia mission. Mass accretion rates are calculated from H? line flux measurements of 106 HAeBe stars. Since we used distances and the stellar masses derived from the Gaia DR2 data in the calculation of the mass accretion rate, our estimates are more accurate than previous studies. The mass accretion rate is found to decay exponentially with age, from which we estimated a disk dissipation timescale of 1.9 0.1 Myr. The mass accretion rate and stellar mass exhibit a power-law relation of the form . From the distinct distribution in the values of the infrared spectral index, n2-4.6, we suggest the possibility of difference in the disk structure between Herbig Be and Herbig Ae stars. 2019. The American Astronomical Society. All rights reserved.. -
Benefits of cross training: Scale development and validity
Studies related to benefits of cross - training were mainly done either in the context of qualitative research or as comprehension of desk research. The literature scarcely covered the measurement issues, and thus, it became vital to quantify and develop a scale to measure the benefits of cross - training (BCT). Cross -training means training that covers multiple tasks within a department This training technique keeps employees prepared to handle more than a single Job for which they have been Initially hired. This concept Is also called 'worker multlfunctlonallty'. The study aimed to propose and validate an Instrument to measure BCT. The nrst section of the study was exploratory factor analysis (EFA) establishing the benefits of cross training through four dimensions namely Job Stability, Career Advancement, Networking, and Idle lime Management. Confirmatory factor analysis (CFA) was used in the second section to verify the factor structure of the observed variables. The results indicated that cross training the employees in an organization could help practitioners to adopt the same as a strategy in retaining the employees by saving on the costs of recruitment, selection, and staffing. The findings also suggested that cross training helped in securing a job, progressing in one's career, enabling better interaction among the employees, and efficiently managing the idle time in the organization. 2019, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
MHD flow of SWCNT and MWCNT nanoliquids past a rotating stretchable disk with thermal and exponential space dependent heat source
The main purpose of this investigation is to analyze the impacts of a novel exponential space dependent heat source on MHD slip flow of carbon nanoliquids past a stretchable rotating disk. The flow is created due to rotation and stretching of the disk. Aspects of the convective condition and cross-diffusion (Soret and Dufour effects) are also accounted. A comparative study of nanofluids made up of SWCNTs (single-walled carbon nanotube) and MWCNTs (multi-walled carbon nanotube) is presented. The governing partial differential equations system is reduced to nonlinear ordinary boundary value problem. The RungeKuttaFehlberg is utilized for numerical simulations. Embedded dimensionless parameters on the flow fields are examined via graphical illustrations. The rate of heat mass transfer can be controlled by cross-diffusion, exponential space-based heat source and thermal-based heat source effects. It is also proved that q( ) (? ) x q x SWCNT nanoliquid MWCNT nanoliquid -. A novel idea of the exponential space dependent heat source is implemented in the investigation of the slip flow over a rotating deformable disk under the effects of cross-diffusion, temperature based heat source and magnetic field for the first time. A comparison between two different fluids namely SWCNT-H2O nanoliquid and MWCNT-H2O nanoliquid are studied. 2019 IOP Publishing Ltd Printed in the UK. -
Credit card fraud detection using ANN
Fraud on its own was and is devastating a lot of businesses, be them small or large. Particularly in the field of finance where we can see constant attacks on both individuals and enterprises alike. As such, credit cards are the most targeted as they are linked to both personal information and accounts. It is also evident to say that credit card fraud detection research is very much needed to deter and mitigate the impact of fraud on the financial field in general. It is important to identify frauds before it is too late so that the stolen credit card cannot be used for fraudulent transactions. To effectively detect these fraud transactions, we use a data consisting of fraudulent and non-fraudulent transactions to create a model that classifies these transactions with a high accuracy based on a machine learning technique. We used Artificial Neural Network with Logistic Regression to measure and in order to achieve high accuracy, we refined the parameters using the algorithms Back-propagation which has proved to have a high accuracy rate giving the model the ability to distinguish a fraudulent transaction from a normal one. BEIESP. -
Evaluating the performance of machine learning using feature selection methods on dengue dataset
Dengue fever is a mosquito-borne disease transmitted by the bite of an Aedes mosquito infected with a dengue virus. The bites of an infected female Aedes mosquito which gets the virus while feeding on the infected persons blood, transmits the virus to others. Dengue transmission is climate sensitive for several reasons such as temperature, humidity, rainfall, etc. Areas having higher vapor pressure and rainfall rate are most vulnerable to the spreading of the dengue disease. So to find the important features responsible for spreading the dengue we have used the classification algorithms. Machine learning is one of the key methods used in modern day analysis. Many algorithms have been used for medical purposes. Dengue disease is one of the serious contagious diseases. To find the features related to spreading of dengue disease, we have used popular machine learning algorithms. This proposed work focuses on evaluating the performances of the various machine learning techniques like-Random Forest Classifier (RFC), Decision Tree Classifier (DTC) and Linear Support Vector Machine (LSVM). Predictive Mean Matching is applied for preprocessing of the data and percentage split is applied for resampling of the data. Information gain values for each of the attributes are calculated. The attributes are sorted on the basis of information gain values. Feature selection methods (FSMs) such as Forward Selection (FS) and Backward Elimination (BE) are applied to choose the finest subset of the attributes, so that the algorithm runs more efficiently with a lower run time. It also results in the improvement of the accuracy. The attributes selected by the Feature Selection Methods are the main attributes which results in the probable effects of global weather change on human healthiness. BEIESP. -
Community based open source geographical classical data analysis
The traditional Geographical Information Systems (GIS) have to be migrated to the internet eventually much like every other software today. The article has explored ways of utilizing the Open Geospatial Consortium (OGC) standards to come up with ways of achieving a workflow for the development of a service-based implementation of a customized Web Processing Service (WPS). The proposed concept has explored multiple workflows using various combinations of the publishing and development options and the simplest and the least resource intensive one has been identified as the outcome of this project. The workflow identified was then split into two section to make it even more simplify and adaptable, aiding development from the WPS that has to publish. The development process used for the final workflow is done without the use of a resource intensive IDE keeping in mind the major aim of the proposed model is to reduce the dependency on resource intensive software and services. The proposed model is built solely on open source platforms which are in tandem with second stipulation of proposed model is promoting community-based development. The proposed system provides the better execution time and retrieval time. The execution time is compared with similar system, open source Geographical system provide less execution time. The retrieval time is also reduced this indicated Quality of Service is increased. BEIESP. -
A method to secure FIR system using blockchain
In India, we can see that technology has touched in every aspect of our life. There exist technology in all the fields e.g. education, agricultural, business, government etc. and we can also understand how beneficial it is, as it saves the time, money and human power. In spite of being technologically advanced, the system lacks in security perspective. When we talk about today, India has moved to the era of digitalization after the launch of the campaign Digital India, the Indian Police Department has replaced the manual system with the centralized online process to register the complaint. The main objective of this paper is to provide a method to secure the FIR system using blockchain technology. This introduces to the essential principal of blockchain technology and its future in the police department of India. Blockchain technology will also explain to protect the FIR from the malfeasance. BEIESP. -
Implementing artificial intelligence agent within connect 4 using unity3d and machine learning concepts
Nowadays, we come across games that have unbelievably realistic graphics that it usually becomes hard to distinguish between reality and the virtual world when we are exposed to a virtual reality gaming console. Implementing the concepts of Artificial Intelligence (AI) and Machine-Learning (ML) makes the game self-sustainable and way too intelligent on its own, by making use of self-learning methodologies which can give the user a better gaming experience. The use of AI and ML in games can give a better dimension to the gaming experience in general as the virtual world can behave unpredictably, thus improving the overall stigma of the game. In this paper, we have implemented Connect-4, a multiplayer game, using ML concepts in Unity3D. The machine learning toolkit ML-Agents, which depends on Reinforcement Learning (RL) technique, is provided using Unity3D. This toolkit is used for training the game agent which can distinguish its good moves and mistakes while training, so that the agent will not go for same mistakes over and over during actual game with human player. With this paper, authors have increased intelligence of game agent of Connect 4 using Reinforcement Learning, Unity3D and ML-Agents toolkit. BEIESP. -
Inhibiting extracellular cathepsin d reduces hepatic steatosis in spraguedawley rats y
Dietary and lifestyle changes are leading to an increased occurrence of non-alcoholic fatty liver disease (NAFLD). Using a hyperlipidemic murine model for non-alcoholic steatohepatitis (NASH), we have previously demonstrated that the lysosomal protease cathepsin D (CTSD) is involved with lipid dysregulation and inflammation. However, despite identifying CTSD as a major player in NAFLD pathogenesis, the specific role of extracellular CTSD in NAFLD has not yet been investigated. Given that inhibition of intracellular CTSD is highly unfavorable due to its fundamental physiological function, we here investigated the impact of a highly specific and potent small-molecule inhibitor of extracellular CTSD (CTD-002) in the context of NAFLD. Treatment of bone marrow-derived macrophages with CTD-002, and incubation of hepatic HepG2 cells with a conditioned medium derived from CTD-002-treated macrophages, resulted in reduced levels of inflammation and improved cholesterol metabolism. Treatment with CTD-002 improved hepatic steatosis in high fat diet-fed rats. Additionally, plasma levels of insulin and hepatic transaminases were significantly reduced upon CTD-002 administration. Collectively, our findings demonstrate for the first time that modulation of extracellular CTSD can serve as a novel therapeutic modality for NAFLD. 2019 by the authors. -
Skin cancer classification using machine learning for dermoscopy image
Skin cancer is highly ambiguous and difficult to identify and cure in the last stage. To increase the survival rate, it is important to recognize the stages of skin cancer for effective treatment. The main aim of the paper is to classify the various stages of skin cancer using dermoscopy images from the data repository of ISIC and PH2. The data is pre -processed with the help of median filter and wiener filter for removing the noise. Segmentation is processed using Watershed and Morphological. After the segmentation, features were extracted using Grey Level Co-occurrence Matrix (GLCM), Color, Geometrical shapes in order to improve the accuracy of dermoscopy image. Finally, the dataset is classified with some popular methods like KNN with 89%, Ensemble with 84% and SVM works better than the other two methods by giving the highest accuracy of 92%. BEIESP. -
Opinion mining on newspaper headlines using SVM and NLP
Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Development of perceived prenatal maternal stress scale
Background: Pregnancy is a state, which is often associated with extreme joy and happiness. Women undergo a number of physiological and psychological changes during pregnancy, which are often stressful if aligned with other adverse life events, compromising their health and well-being. However, there exists no comprehensive psychological instruments for measuring this stress. Objectives: The study was conducted to develop a multidimensional scale to assess prenatal maternal stress (PNMS) comprehensively. Methods: The initial phase of the study focuses on developing items and assessing the content validity of these items. The second phase focuses on pilot-testing and field-testing the newly developed perceived PNMS scale (PPNMSS) among 356 pregnant women belonging to different parity and trimester from November 2015 to October 2016. Results: The underlying factor structure of the 28-item PPNMSS had explored using exploratory factor analysis. The final scale is retained with 15 items having considerable item loading under four major factors as follows: perceived social support, pregnancy-specific concerns, intimate partner relations, and financial concerns. Reliability of each of these dimensions was assessed using Cronbach's alpha. Convergent and divergent validity of the scale was assessed by correlating the scores with perceived stress scale and the World Health Organization (five) well-being index (1998 version). Conclusions: As a comprehensive scale, PPNMSS is efficient to measure PNMS, which facilitates an early detection of stress and depression among pregnant women and timely intervention by health care professionals. -
Enhanced encryption technique for secure iot data transmission
Internet of things is the latest booming innovation in the current period, which lets the physical entity to process and intervene with the virtual entities. As all the entities relate to each other, it generates load of data, which lacks proper security and privacy standards. Cryptography is one of the domains of Network Security, which is one such mechanism that helps the data transmission process to be secure enough over the wireless or wired channel and along with that, it provides authenticity, confidentiality, integrity of data and prevents repudiation. In this paper, we have proposed an alternate enhanced cryptographic solution combing the characteristic of symmetric, asymmetric encryption algorithms and Public Key Server. Here, the key pairs of end points (Users Device and IoT device) are generated using Elliptic Curve Cryptography and the respective public keys are registered in Public Key Server along with their unique MAC address. Thereafter, both the ends will agree on one common private secret key, which will be the base for further cryptographic process using AES algorithm. This model can be called as multi-phase protection mechanism. It will make the process of data transmission secure enough that no intermediate can tamper the data. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Implementation of FOAF, AIISO and DOAP ontologies for creating an academic community network using semantic frameworks
Web 2.0 delivers the information which is then displayed in human readable content, omitting the crucial information which can be drawn from the data by the applications. Web 3.0 or semantic web is an extension to the current web, with an ambition to determine the drawbacks of the current web. The semantic web has already proven its influence in several communities around the globe, such as social media, music industry, healthcare domain, online blogs or articles, etc.; Among the several tools and technologies, ontologies or vocabularies are the foundation pillar for the semantic web. In this paper, the developed system aims at improving the collaboration and academic relations among staff which is directly related to our education community by providing a better networking platform which lets the agents discuss their achievements, titles, domain interests, and various other activities. Results have been analyzed to show how new facts, information can be implied from the presented knowledge of several agents and help generate a relationship graph by utilizing various semantic tools. The system discussed in this paper processes all the information in a format which can be understood by both humans and the machines, to interpret the underlying meaning about it and provide effective results. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Priority based prediction mechanism for ranking providers in federated cloud architecture
Cloud computing is a growing and excellent technology, as exponentially increasing the interest among users to utilize cloud applications; they need to depend on any one of the particular service provider. Now a days number of service providers also rapidly increasing in wide range, this leads ambiguity and distrust among the users. In this paper, enhanced broker based federated cloud architecture is proposed to resolve the selection of service provider issue using grading techniques and results proved that better performance improvement than single service provider selection. This broker architecture also addresses to selects the appropriate service provider automatically in the federated cloud architecture for the users submitted requests by previous experience with help of Bayesian network model. The former one implemented through concept of grade system. It is constructed for categorizing the providers based on the level of available resources. Grade and grade values distributed by applying the grade distribution algorithm for distinguishes the components. Total grade values computed for every service provider and sorted using quick sort algorithm to grade the cloud service providers. Priority based feedback decision tree technique added with this for separates similar grade cloud service provider in the selected list. Second Bayesian network model also used to rank the cloud service providers according to the previous performance of the providers with customers. Probability of satisfied customers feedback calculated for individual Service Measurements Index of Cloud Service Providers. 2018, Springer Science+Business Media, LLC, part of Springer Nature. -
3D face reconstruction techniques: Passive methods
In the recent literature, 3D face reconstruction received wide interest and has become one of the significant areas of research. 3D face reconstruction provides in depth details on geometrics, texture and color of the face, which are utilized in different applications. It supports a multitude of applications, ranging from face recognition and surveillance to medical imaging, gaming, animation, and virtual reality. This paper attempts to consolidate the research works that have happened in the history of 3D face reconstruction. Also, we try to classify the existing methods based on the input for the process. The databases used in the recent works are discussed and the performance evaluation of methods on different databases is analyzed. The challenges addressed in recent studies are mainly focused on the faster reconstruction of 3D Images, improved accuracy of reconstructed images, human pose identification, image reproduction with higher resolution. Researchers have also tried to address occlusion related problems. Passive methods, used by different researchers are analyzed and their effects on different parameters are discussed in this work. Finally, possible future areas for improvement in terms of reconstructions are presented for the benefit of researchers. BEIESP. -
Grandparenting in Urban Bangalore, India: Support and Involvement From the Standpoint of Young Adult University Students
A variety of caregivers, including grandparents, help raise children. Among grandparents, most Western samples evidence a matrilateral (i.e., mothers kin) bias in caregiving, and many studies show more positive impacts and stronger relationships with grandmothers than grandfathers. The aim of the present study is to test competing hypotheses about a potential laterality bias and explore contrasts between grandmothers and grandfathers in a sample of urban young adult university students in Bangalore, India. A sample of 377 (252 women) relatively mobile and high socioeconomic status individuals 17 to 25 years of age completed a survey consisting of sociodemographic and grandparenting questions. Results reveal generally little evidence of either a patrilateral or matrilateral bias, though findings varied for some outcomes. As illustrations, there were no differences in residential proximity or the most recent time when a participant saw matrilateral or patrilateral grandparents, whereas maternal grandmothers were more approving of ones choice of a life partner than were paternal grandmothers. In inductively coded responses to an open-ended item about the roles of grandparents, maternal grandmothers were more often identified as guides and less often deemed non-significant than paternal grandmothers, while paternal grandfathers were less often viewed as guardians and more often noted for their influence compared with maternal grandparents. Findings also revealed differences between grandmothers and grandfathers, such as grandmothers playing more prominent roles in community and religious festivals. Findings are interpreted within changing residential, work, education, and family dynamics in urban India as well as a primary importance on parents relative to grandparents. The Author(s) 2019.