Human Body Odour as a Biometric Indicator for Person Identification
Integrated Edge Thresholded Deep Network for Image Denoising
A Pattern-Based Approach to Detect Irony in Twitter Sentiment Analysis
Multimodal Biometrics System with Face, Ear, and Foot Fusion Techniques
IoT-Based Ultrasonic Sensors and Buzzer Systems in Home Security
An Efficient Foot Ulcer Determination System for Diabetic Patients
Landslide Susceptibility Mapping through Weightages Derived from Statistical Information Value Model
Statistical Wavelet based Adaptive Noise Filtering Technique for MRI Modality
Real Time Sign Language: A Review
Remote Sensing Schemes Mingled with Information and Communication Technologies (ICTS) for Flood Disaster Management
FPGA Implementation of Shearlet Transform Based Invisible Image Watermarking Algorithm
A Comprehensive Study on Different Pattern Recognition Techniques
User Authentication and Identification Using NeuralNetwork
Flexible Generalized Mixture Model Cluster Analysis withElliptically-Contoured Distributions
Efficient Detection of Suspected areas in Mammographic Breast Cancer Images
The Internet of Things (IOT) is a prominent research area that provides many interesting solutions to various problems experience by various departments. Smart homes applications is one such branch that evolve from IOT with the huge challenges of data storage and handling. Activity recognition is the major challenge in smart homes application that consolidates multiclass learning. The effectiveness of ensemble learner in handling multiclass problem and collective dissicion delivered prompt its uses in the smart homes application. In this paper we deal with activities recognition problems on various ensemble learners including bagging, boosting and random forest. The standard van Kasteren dataset contains three housedata with eight activities of different days. We perform our experiment on the preprocessed collected data and applied six learners i.e. three from individual learner and three from ensemble learners. On performing the extensive experiment it was found that the group of ensemble learner outcast the simple learners.
In today’s world, it is known that, it is a big problem to search for a person who is lost, who has stolen any items from us, who is accused of any crime or who performs any unauthorized work in an organization. Sometimes, we also know about this person, but we are unable to find the current or past working details, acting, or moving status of this accused person; how will we find that person, secure society and aware a specific area of people from human crimes is a big challenge of the security system. For this, an effort has been made to design an online trapping system based on web enable application to solve the above problems. Data were gathered from people who wants to complain online, control all services, and trap the criminal. This system provides the facility to trap a person who is accused in any crime and moving anywhere.
Signature is being widely used as a personal identification or a verification system, which also comes with wide variety of problems which is getting exposed to forgery. Human errors could add more complexity into the process, hence there is always a need for automated system. Verification can be either online or offline-based. Verification can be performed or accomplished in either ways i.e. online-based or offline-based. The online-based works on image which is digitally acquired as signature uses dynamic information of the signature, when the signature is signed. This paper proposes an offline-system which integrates Android, Matlab, Java where the whole algorithm or the heart of the process takes place in Matlab, Java provides the server and android acts as UI interface. For verification, techniques which are based on geometric features and corner features combined with the training of neural network have been used. Several geometric features have been combined which includes Occupancy region, region where Centroid exist, deviation, even pixels Harris and Scale Invariant Feature Transform (SIFT) features and also Kurtosis, Skewness. The proposed methodology technique includes pre-processing of a scanned signature image at the beginning. Neural network is used as a decision maker for real or forged, while the efficiency of correct recognition is around 90.24% with a threshold of genuine at 60%. The simulation shows that the proposed method has a clear discriminative nature between real and forged signatures.
The power distribution system is considered as the important component of a power system because the consistent delivery of power to the consumers depends on it. Due to massive growth in the consumption of power, the damaged insulators on the electric poles prompt the breakage of the power supply which leads to considerable loss occurring for the power industry and hence to the national economy. As the insulators protect the power distribution system from heavy transients, there must be a monitoring system to regularly check the condition of the insulators. Regular monitoring of the overhead power line insulators requires taking pictures of the poles, sending them to the processing unit and applying image processing techniques to classify the insulator health condition into either healthy or risky and subsequent necessary replacement of the damaged insulator can be done by the maintenance personnel. Using the above procedure, the breakage condition of the insulators can be determined. The insulator images are extracted from the acquired pole image input and then individual insulator's statistical features are obtained based on curvelet transform and contourlet transform coefficients. The obtained features of insulator images are given to SVM (Support Vector Machines) classifier in determining the health condition of an insulator and the experiment results are validated. The health condition monitoring of power system insulators can be done reliably and hence this method of automatic classification would reduce the human efforts to a greater extent.
Counterfeit currency has always been a threat to the economy of the country. Despite the addition of several security features to prevent this, people have always found ways to duplicate currency. It is also difficult in most cases for a common man to identify a fake note and thus, one falls prey to such tricks of counterfeit currency. Hence, it becomes important to adopt newer and better methods to counter the same for all the denominations of currency in circulation, the most vulnerable being the ones with higher denomination. While there exist a number of ways to check for the correctness of the older variants of Indian currency, the newer lot that has been released however has incorporated in themselves, a number of changes and newer features. This paper aims to bring out some of the vital parameters to be extracted to distinguish between real and fake notes in the new set of Indian currency of denominations 2000, 500, 200, 50 and 10 brought out by the Reserve Bank of India (RBI), using various image processing techniques.
