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
In many real world applications, multimodal biometric systems are popularly used due to its ability to deal with disadvantages of unimodal biometric systems. In this paper, effective and efficient multimodal biometric system are developed using Convolution Neural Network (CNN) which helps to predict identity of a individual based on his/her iris pattern. The proposed iris recognition systems is a combination of CNN with Soft Max classifier. The performance of the proposed iris is evaluated on IITD database obtained in various conditions. The obtained results shows that the proposed system outperforms the previous approaches. And the four layered CNN with Adadelta optimizer has produced the best results of more than 95% accuracy.
Statistical analytical procedures are usually applied for correlation techniques and in estimation of soil spatial trends. Euclidean cluster, scoter plot, correlation matrix, and inverse distance weight are more favorable statistical properties than traditional procedures. The distribution of soil properties experiment are used to understand engineering characteristics and to conclude suitability for engineering construction initial works. The investigational analysis shows the symptomatic properties of statistical analysis estimations. Three Mandals in Medak district, Telangana state, has been selected to locate appropriate places for civil engineering works. The Mandals are Narsingh Mandal, Shankarampet Mandal, Chegunta Mandal. These three mandals have good agriculture fields so it is difficult to recognize good soil source of geo-technical properties. The assessments are preferred to analyze liquid limit, plastic limit, compaction properties, particle size composition, and proctor compaction. The results of soils are plotted on thematic maps by using inverse distance weight (IDW) and reclassify techniques. These maps are a good example to quickly identify source places. Cluster I and II groups have suitable geo-technical properties of the region, and Cluster III gathers low strength soil samples. Pearson correlation reveals matrix theory to understand reasonable and most suitable parameter in the research region. The liquid limit versus plastic limit R-value is 0.5481 and p-value is <0.5, which indicates a positive correlation. The maximum dry density of plastic limit correlation value R is -0.1395, which indicates a negative correlation and inversely proportional to maximum dry density of the plastic limit.
Knowledge about the distribution of herbs (weeds) within a specific area might be a prerequisite for specific treatment. The herbs are taken out from images using image processing described by shape features. A classification based on the features reveals the type and number of weeds for each image. For the classification we use only a maximum of 16 features out of the 81 computed ones. The selection has been done using processing algorithms, which rate the definiteness of the features of prototypes. If no prototypes are available, clustering algorithms are accustomed automatically to generate clusters. In the next step weed classes are assigned to the clusters. Weed maps are generated using the system. Weed maps are compared with the results of a manual weed sampling.
Early image retrieval techniques were based on textual annotation of images. Annotating images manually is a cumbersome and expensive task for large image databases, and is often subjective, context-sensitive and incomplete. Content based image retrieval, uses the visual contents of an image such as color, shape, texture, and spatial layout to represent and index the image. The Region Based Image Retrieval (RBIR) system uses the Discrete Wavelet Transform (DWT) and a Watershed Segmentation algorithm to segment an image into regions. Though k-means clustering algorithm is very fast and simple to implement, it provides only coarse image segmentation. Better retrieval results can be expected by employing a more sophisticated segmentation technique. For this purpose, a novel Texture Gradient based Watershed Segmentation technique is developed. The Watershed Transform is a well established tool for the segmentation of images. However, it is often not effective for textured image regions that are perceptually homogeneous. In order to properly segment such regions the concept of the Texture Gradient is introduced and is implemented using a Non Decimated Wavelet Packet Transform. A marker location algorithm is subsequently used to locate significant homogeneous textured or non textured regions. A marker driven Watershed Transform is then used to properly segment the identified regions. The experimental results demonstrate the superiority of this technique over k-means clustering.
In India, 65% of the people have adopted agriculture for their primary source of income. Several vital factors that affect the farmers are natural calamities and disasters such as unpredicted rain, floods, storm, drought, etc. Added to these one of the major issues faced in agriculture domain is in plant pathology. The identification and verification of plant diseases is a serious concern which needs to be treated well for increasing the yield, plant growth and quality. Many researchers, scientists and scholars have greatly contributed towards plant disease identification. However, still we see many drawbacks such as accuracy in the end results, false acceptance and false rejection problems. The main purpose of this work is to provide a detailed analysis and comparison on existing techniques versus the current trend techniques. This review will help the researchers to choose the adequate technique or method for future use. In this paper, the results of several methods used to identify diseases in leaves have been reviewed strenuously.
