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Breaking News Recognition Using OCR
Identifying and recognition of breaking news in most of the TV channels in different backgrounds with varying positions from a static image plays a significant role in journalism and multimedia image processing. Now a days its very challenging to isolate only breaking news from headlines due to overlapping of many categories of news, keeping all this in mind, a novel methodology is proposed in this paper for detecting specific text as a breaking news from a given multimedia image. Basic digital image processing techniques are used to detect text from the images. The methods like MSER (Maximally Stable Extremal Regions) and SWT (Stroke Width Transform) are used for text detection. The proposed work focuses on extraction of text in breaking news images also discusses the different methods to overcome existing challenges in text detection along with different types of breaking news datasets collected from various news channels are used to identify text from images and comparative study of different text detection methods. The comparative study proves that MSER and SWT is a better technique to detect text in images. Finally using OCR (Optical Character Recognition) technique to extract the breaking news text from the detected regions will help in easy indexing and analysis for journalism and common people. Extensive experiments are carried out to demonstrate the effectiveness of the proposed approach. 2019, Springer Nature Singapore Pte Ltd. -
Secured Electronic Transactions Using Visual Encryption: An E-Commerce Instance
The developments in the information technology has led the modern world to rely on the internet for digital information. People around the world depend on the internet for every information from general information to sharing confidential data over the network. Technology has grown so wide that even all the transactions are happening through online platforms. Along with this, there has been a rise in the security threats of the user's confidential data which are shared by the users while buying goods online. To maintain the security there are many authentication techniques available today like OTP, CAPTCHA etc. These security measures taken till now have been adequate till the present day but over time the hackers would find out ways to attack. Data breaches and hacking of user data would increase day by day if preventive measures are not taken time to time. This project focuses on providing security to the user's confidential data with a two way authentication technique. The aim of the project is to include one more security level to the existing online transaction system. The visual cryptographic method is used along with the OTP generating system. 2018 IEEE. -
Gravity modulation effect on ferromagnetic convection in a Darcy-Brinkman layer of porous medium
The influence of a timedependent body force on the threshold of convective instability in a magnetic fluid filled horizontal porous layer is investigated. The gravity modulation effect is treated by employing a perturbation method. The correction Rayleigh number is computedas a function of the modulation frequency, porous and magnetic parameters. It is expounded that, for small and reasonable values of the modulation frequency, gravity modulation and magnetic mechanism have opposing influence on the stability. The study further explicates that, when the gravity modulation frequency increases beyond all bounds, manifestation of the disappearance of the magnetic and porous medium effects on the stability is highly likely. Published under licence by IOP Publishing Ltd. -
Early detection of breast cancer using ER specific novel NIR fluorescent dye conjugate: A phantom study using FD-f-DOT system
Fluorescence diffuse optical tomography (f-DOT) is an imaging technique that can quantify the spatial distribution of fluorescent tracers in small animals and human soft tissues. Efficacy of f-DOT imaging can be improved by tagging a functional group to the dye. A novel estrogen receptor (ER) specific near-infrared (NIR) fluorescent dye conjugate was synthesized which can be effectively used for detecting breast cancer tissues at an early stage. Our novel dye, Near Infrared Dye Conjugate-2 (NIRDC-2), is a conjugate of 17?-estradiol with an analogue of Indocyanine Green dye, bis1,1-(4-sulfobutyl) indotricarbocyanine-5-carboxylic acid, sodium salt. Our present study focuses on imaging cylindrical silicone phantoms using Frequency Domain f-DOT system. Background absorption and scattering coefficients were 0.01mm-1 and 1mm-1 respectively. 10?M concentration of NIRDC-2 and Indocyanine Green (ICG) were administered separately into a cylindrical hole (target) of size 8mm diameter in the phantom. In-silico studies were performed to analyze the properties of dyes using experimental data. Absorption coefficient of 0.0002 mm-1 was recovered for the background. Fluorophore absorption coefficient at the target recovered were 0.000173 mm-1 and 0.000408 mm-1 for ICG and NIRDC-2 respectively. In comparison with ICG, our novel dye had a two fold higher target to background contrast. Recovered target position was accurate but size altered. In concurrence with the recovered fluorescent property and the cell lines studies carried out earlier, binding properties of NIRDC-2 makes it a potential probe for the early tumor detection using f-DOT system. COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. -
Ambient monitoring in smart home for independent living
Ambient monitoring is a much discussed area in the domain of smart home research. Ambient monitoring system supports and encourages the elders to live independently. In this paper, we deliberate upon the framework of an ambient monitoring system for elders. The necessity of the smart home system for elders, the role of activity recognition in a smart home system and influence of the segmentation method in activity recognition are discussed. In this work, a new segmentation method called area-based segmentation using optimal change point detection is proposed. This segmentation method is implemented and results are analysed by using real sensor data which is collected from smart home test bed. Set of features are extracted from the segmented data, and the activities are classified using Naive Bayes, kNN and SVM classifiers. This research work gives an insight to the researchers into the application of activity recognition in smart homes. Springer Nature Singapore Pte Ltd. 2019. -
Assessing Human Stress Through Smartphone Usage
Stress occurs in a human being when they are faced with exigent situations in life. Assessing stress has been always challenging. Smartphones have become a part of everyones day-to-day activity in the present time. Considering humansmartphone interaction, sensing of stress in an individual can be assessed as todays youth spends most of their time with smartphones. Taking this into consideration, a study is carried out in this paper on assessing stress of an individual based on their interaction with the smartphone. In this work, humansmartphone interaction features, like swipe, scroll, and text input, are examined. Text input is incorporated by disabling the autocorrection and spelling checker features of the keyboard. Moreover, sensor data is used by Google activity recognition API to analyze the physical activity of the individual to assess the stress level. 2019, Springer Nature Singapore Pte Ltd. -
Reliable monitoring security system to prevent MAC spoofing in ubiquitous wireless network
Ubiquitous computing is a new paradigm in the world of information technology. Security plays a vital role in such networking environments. However, there are various methods available to generate different Media Access Control (MAC) addresses for the same system, which enables an attacker to spoof into the network. MAC spoofing is one of the major concerns in such an environment where MAC address can be spoofed using a wide range of tools and methods. Different methods can be prioritized to get cache table and attributes of ARP spoofing while targeting the identification of the attack. The routing trace-based technique is the predominant method to analyse MAC spoofing. In this paper, a detailed survey has been done on different methods to detect and prevent such risks. Based on the survey, a new proposal of security architecture has been proposed. This architecture makes use of Monitoring System (MS) that generates frequent network traces into MS table, server data and MS cache which ensures that the MAC spoofing is identified and blocked from the same environment. 2019, Springer Nature Singapore Pte Ltd. -
A Particle Swarm Optimization-Backpropagation (PSO-BP) Model for the Prediction of Earthquake in Japan
Japan is a country that suffers a lot of earthquakes and disasters because it lies across four major tectonic plates. Subduction zones at the Japanese island curves are geologically complex and create various earthquakes from various sources. Earthquake prediction helps in evacuating areas, which are suspected and could save the lives of people. Artificial neural network is a computing model inspired by biological neurons, which learn from examples and can be able to do predictions. In this paper, we present an artificial neural network with PSO-BP model for the prediction of an earthquake in Japan. In PSO-BP model, particle swarm optimization method is used to optimize the input parameters of backpropagation neural network. Information regarding all major, minor and aftershock earthquake is taken into account for the input of backpropagation neural network. These parameters are taken from Japan seismic catalogue provided by USGS (United States Geological Survey) such as latitude, longitude, magnitude, depth, etc., of earthquake. 2019, Springer Nature Singapore Pte Ltd. -
The Quantification of Human Facial Expression Using Trapezoidal Fuzzy Membership Function
Fuzzy Inference System is an interesting approach. Major benefit of the FIS is, it permits the natural narration in linguistic terms of tribulations that can be resolved rather than in requisites of associations between accurate arithmetical points. This helps, handling with the complicated systems in easy way, is the major motive why fuzzy system is broadly incorporated in practice. In the present research paper, an effective approach is proposed that quantifies the human facial expression using Mamdani implication based fuzzy logic system. The recent principle engages in retrieving arithmetical values from persons face and feed them to a fuzzy classifier. Fuzzification and Defuzzification process issues trapezoidal fuzzy membership function for input as well as output. The diverse characteristic of this method is its effortlessness and maximum correctness. Experimental outcome on Image dataset depicts excellent accomplishment of the proposed methodology. In this paper, a legitimate procedure proposed for quantification of human facial expression from the features of the face by means of Mamdani type fuzzy inference system, which is proficient to set up a convenient membership association involving the various dimensions of the happy expression. Values representing features of the face are fed to a Mamdani-type fuzzy classifier. This system recognizes three levels of same happy expression namely Normal, Bit Smiley and Loud Laugh. The total output expressions for this proposed scheme is three. Another discrete element of the proposed methodology is the membership method model of expression outcome which stands on various surveys and readings of psychology. Springer Nature Singapore Pte Ltd. 2019. -
Segmentation and Recognition of E. coli Bacteria Cell in Digital Microscopic Images Based on Enhanced Particle Filtering Framework
Image processing and pattern recognitions play an important role in biomedical image analysis. Using these techniques, one can aid biomedical experts to identify the microbial particles in electron microscopy images. So far, many algorithms and methods are proposed in the state-of-the-art literature. But still, the exact identification of region of interest in biomedical image is a research topic. In this paper, E. coli bacteria particle segmentation and classification is proposed. For the current research work, the hybrid algorithm is developed based on sequential importance sampling (SIS) framework, particle filtering, and Chan–Vese level set method. The proposed research work produces 95.50% of average classification accuracy. 2019, Springer Nature Singapore Pte Ltd. -
Discriminative Gait Features Based on Signal Properties of Silhouette Centroids
Among the biometric recognition systems, gait recognition plays an important role due to its attractive advantages over other biometric systems. One of the crucial tasks in gait recognition research is the extraction of discriminative features. In this paper, a novel and efficient discriminative feature vector using the signal characteristics of motion of centroids across video frames is proposed. These centroid based features are obtained from the upper and lower regions of the gait silhouette frames in a gait cycle. Since gait cycle contains the sequence of motion pattern and this pattern possesses uniqueness over individuals, extracting the centroid features can better represent the dynamic variations. These variations can be viewed as a signal and therefore the signal properties obtained from the centroid features contains more discriminant information of an individual. Experiments are carried out with CASIA gait dataset B and the proposed feature achieves 97.3% of accuracy using SVM classifier. 2019, Springer Nature Singapore Pte Ltd. -
Automatic Classification of Normal and Affected Vegetables Based on Back Propagation Neural Network and Machine Vision
This article presents a neural network and machine vision-based approach to classify the vegetables as normal or affected. The farmers will have great difficulty if there is a change from one disease control to another. The examination through an open eye to classify the diseases by name is more expensive. The texture and color features are used to identify and classify different vegetables into normal or affected using a neural network and machine vision. The mixture of both the features is proved to be more effective. The results of experiments show that the proposed methodology extensively supports the accuracy in automatic detection of affected and normal vegetables. The applications in packing and grading of vegetables are the outcome of this research article. 2019, Springer Nature Singapore Pte Ltd. -
Extraction of features from video files using different image algebraic point operations
In the human-computer interaction (HCI) field, facial feature analysis and extraction are the most decisive stages which can lead to a robust and efficient classification system like facial expression recognition, emotion classification. In this paper, an approach to the problem of automatic facial feature extraction from different videos are presented using several image algebraic operations. These operations deal with pixel intensity values individually through some mathematical theory involved in image analysis and transformations. In this paper, 11 operations (point subtraction, point addition, point multiplication, point division, edge detecting, average neighborhood filtering, image stretching, log operation, exponential operation, inverse filtering, and image thresholding) are implemented and tested on the images (video frames) extracted from three different self-recorded videos named as video1, video2, video3. The videos are in .avi, .mp4 and .wmv format respectively. The work is tested on two types of data: grayscale and RGB (Red, Green, Blue). To assess the efficiency of each operation, three factors are considered: processing time, frames per second (FPS) and sharpness of edges of feature points based on image gradients. The implementation has been done in MATLAB R2017a. 2019 Association for Computing Machinery. -
Thermal Studies of Multiwalled Carbon Nanotube Reinforced with Silicone Elastomer Nanocomposites
This article studies the enhancement in the properties of silicon elastomer (SiR) reinforced by multiwalled carbon nanotube (MWCNT). Multiwalled carbon nanotube filled silicone rubber composites were prepared. The effects of loading levels of MWCNT on the thermal properties of silicone elastomer were investigated. SEM studies reveal the smooth distribution of MWCNT in silicon matrix. At higher concentration nanoparticles collapse together to form agglomerates. The high resolution transmission electron microscopy (HR-TEM) photographs shows excellent/homogeneous distribution of MWCNT in silicon matrix and agglomeration occurs at higher concentrations. Thermal properties of nanocomposites have been characterized using differential scanning calorimetry (DSC) and thermo-gravimetric analysis (TGA). The transition temperature appears at below -25C for MWCNT reinforced SiR nanocomposites. TGA thermogram, shows that temperature at 10%, 20%, 30%, and 50% weight loss for SiR nanocomposites is higher than as compared to unfilled SiR. The results indicate that the addition of MWCNT significantly enhanced the thermal stability of silicon elastomer. 2018 Elsevier Ltd. -
Effective Emoticon Based Framework for Sentimental Analysis of Web Data
The Explosive development in the social media domain has created a platform for mass generation of textual and emoticon based web data from micro blogging sites. Sentimental Analysis refers to analysis of sentiments or emotions from such heterogeneous reviews are the present urge of the market. Thus, an effective emoticon based framework is proposed which generates scores of both textual and emoticons into seven layered categories using SentiWordNet and weighs performance of various machine learning techniques like SVM/SMO, K-Nearest Neighbor (IBK), Multilayer Perception (MLP) and Naive Bayes (NB). Using Jsoup crawler input reviews are obtained and processed with initial pre-processing model for emoticons and text data followed by stemming and POS tagger. Projected framework is investigated on college and hospital dataset obtaining upper attainment level by Kappa statistic metrics having 98.4% correctness and lesses bug value. Proposed Framework showcases greater competence score with lesser FP Rate based on weighted average of correctness measures. The investigational outcomes are tested on training data with Ten-Fold cross validation. The outcome reveals that suggested emoticon based framework for the task of Sentimental analysis can be efficaciously applied in online decision job. 2019, Springer Nature Singapore Pte Ltd. -
Enhancements to randomized web proxy caching algorithms using data mining classifier model
Web proxy caching system is an intermediary between the users and servers that tries to alleviate the loads on the servers by caching selective web pages, behaves as the proxy for the server, and services the requests that are made to the servers by the users. In this paper, the performance of a proxy system is measured by the number of hits at the proxy. The higher number of hits at the proxy server reflects the effectiveness of the proxy system. The number of hits is determined by the replacement policies chosen by the proxy systems. Traditional replacement policies that are based on time and size are reactive and do not consider the events that will possibly happen in the future. The outcomes of the paper are proactive strategies that augment the traditional replacement policies with data mining techniques. In this work, the performance of the randomized replacement policies such as LRU-C, LRU-S, HARM, and RRGVF are adapted by the data mining classifier based on the weight assignment policy. Experiments were conducted on various data sets. Hit ratio and byte hit ratio were chosen as parameters for performance. Springer Nature Singapore Pte Ltd. 2019. -
A review on ensembles-based approach to overcome class imbalance problem
Predictive analytics incorporate various statistical techniques from predictive modelling, machine learning and data mining to analyse large database for future prediction. Data mining is a powerful technology to help organization to concentrate on most important data by extracting useful information from large database. With the improvement in technology day by day large amount of data are collected in raw form and as a result necessity of using data mining techniques in various domains are increasing. Class imbalance is an open challenge problem in data mining and machine learning. It occurs due to imbalanced data set. A data set is considered as imbalanced when a data set contains number of instance in one class vastly outnumber the number of instances in other class. When traditional data mining algorithms trained with imbalanced data sets, it gives suboptimal classification model. Recently class imbalance problem have gain significance attention from data mining and machine learning researcher community due to its presence in many real world problem such as remote-sensing, pollution detection, risk management, fraud detection and medical diagnosis. Several methods have been proposed to overcome the problem of class imbalance problem. In this paper, our goal is to review various methods which are proposed to overcome the effect of imbalance data on classification learning algorithms. Springer Nature Singapore Pte Ltd 2019. -
Mechanical and abrasive wear behaviour of waste silk fiber reinforced epoxy biocomposites using taguchi method
The aim of this research article is to study the static mechanical properties and abrasive wear behavior of epoxy biocomposites reinforced with different weight percentage of waste silk fibers. The effect of parameters such as velocity (A), load (B), fiber loading (C) and abrading distance (D) on abrasive wear has been considered using Taguchi's L25 orthogonal array. The objective is to examine parameters which significantly affect the abrasive wear of biocomposites. The addition of silk fiber has resulted in improved flexural properties of the epoxy matrix. The results of ANOVA indicated that the parameter which played a significant role was abrading distance followed by fiber loading, load and sliding velocity. 2019 Trans Tech Publications Ltd, Switzerland. -
Intelligence-Software Cost Estimation Model for Optimizing Project Management
With the evolution of pervasive and ubiquitous application, the rise of web-based application as well as its components is quite rising as such applications are used both for development and analysis of the web component by developers. The estimation of software cost is controlled by multiple factors right from human-driven to process driven. Most importantly, some of the factors are never even can be guessed. At present, there are no records of literature to offer a robust cost estimation model to address this problem. Therefore, the proposed system introduces an intellectual model of software cost model that is mainly targets to perform optimization of entire cost estimation modeling by incorporating predictive approach. Powered by deep learning approach, the outcome of the proposed model is found to be cost effective in comparison to existing cost estimation modeling. 2019, Springer Nature Switzerland AG. -
A review on feature selection algorithms
A large number of data are increasing in multiple fields such as social media, bioinformatics and health care. These data contain redundant, irrelevant or noisy data which causes high dimensionality. Feature selection is generally used in data mining to define the tools and techniques available for reducing inputs to a controllable size for processing and analysis. Feature selection is also used for dimension reduction, machine learning and other data mining applications. A survey of different feature selection methods are presented in this paper for obtaining relevant features. It also introduces feature selection algorithm called genetic algorithm for detection and diagnosis of biological problems. Genetic algorithm is mainly focused in the field of medicines which can be beneficial for physicians to solve complex problems. Finally, this paper concludes with various challenges and applications in feature selection. Springer Nature Singapore Pte Ltd 2019.