Blur Image Detection and Classification using Edge Detection Techniques
Breast Cancer Detection using Machine Learning and Image Processing
Sensor Based Sign Language Recognition System
Contour Based X-Ray Image Classification System for Detection of Covid-19
A Survey on Deep Learning Techniques in Real-Time Applications
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 expectation of sun oriented radiation is essential for a few applications in renewable vitality research. There are various land variables, which influence sun powered radiation forecast, the recognizable proof of these variables for precise sun powered radiation expectation is vital. This paper explores a mixture strategy for the pressure of sun powered radiation utilizing prescient investigation. The forecast of moment insightful sun oriented radiation is performed by utilizing diverse models of Artificial Neural Networks (ANN), to be specific Multi-Layer Perceptron Neural System (MLPNN), Levenberg-Marquardt, Scaled Conjugate Gradient. Root Mean Square Error (RMSE) is utilized to assess the forecast precision of the three ANN models utilized. The data and information picked up from the present study could enhance the precision of examination concerning atmosphere studies and help in blockage control.
Motion estimation is an early procedure for video compression and is related to the compression efficiency by reducing temporal redundancies. Motion estimation is the most important part of a video encoder and half of coding complexity or computational time depends on it. There were various Maximization-Expectation (ME) algorithms proposed and implemented to minimize the computational time. High compression gain is achieved using different coding techniques in H.264/AVC codec with respect to other standards. Computational complexity of block based motion estimation has been increased using these techniques, which results in encoder's computation to increase by 80%. In this paper, Star Diamond-Diamond Search Algorithm has been proposed for Block Matching Motion Estimation technique. This proposed technique provides reduction in computational complexity and encoding time without compromising the quality of the video sequence.
The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. Epilepsy is one of the most common neurological diseases and the most common neurological chronic disease in childhood. Electroencephalography (EEG) still remains one in all the foremost helpful and effective tools in understanding and treatment of brain disorder. EEG signal once broken down into the frequency subbands, offers many applied mathematics features in every band. A number of these features, that will be used for detection of brain disorder are explored in this paper. The objective of this study is the analysis of epileptic seizure by using these features more suitable in real time, and for a reliable automatic epileptic seizure detection system to enhance the patient's care and the quality of life.
Music is considered as an all inclusive dialect and has impacted the human presence at different levels. As of late music treatment has developed as a test of research with a clinical approach, including science and craftsmanship. Music Therapy is one of the clinical and experience-based employments of mediations to finish individualized objectives inside a therapeudic approach by a certified/proficient Singer, who tends to the physical, enthusiastic, intellectual, and social needs of people. There is no eliteness about helpful music as a wide range of music have demonstrated this capacity, be it present day or conventional. Indeed, even the general population who are in the last leg of their excursion are understood by music. The primitive social orders have utilized music to the greatest degree. All human and social exercises utilized rhythms and music, as a component of living.
In this venture, the authors have focused on executing an electronic contraption on music treatment. This incorporates the database of various ragas for various clutters, where the individual with confusion can tune in to music and cure his issue viably.
There has been a huge development in the field of weather forecasting in the recent years. In observation method manual extractions of the weather parameters and climate knowledge analysis area unit are time overwhelming and labor demanding. Weather parameters ought to be determined with terribly high level of accuracy, timely and controlled setting to extend accuracy of the forecasts and limited watching is also a current topic of discussion due to its application in various fields. In order to improve the efficiency in predicting various schemes algorithms are being discussed to weather forecasting.
With the emergence and huge transformational potential capability of Big Data Storage (like store, manage and analyse huge amounts of heterogeneous data), finally derives the benefit of data-driven society and economic impacts. Since, the new wave of heterogeneous data rises from different sources, such as the Internet of Things (IoT), Sensor Networks, Open Data on the Web, data from mobile applications, and social networking, comprising that the natural growth of datasets available within the organisations, that certainly creates a demand for new data management storage strategies provide a new scales of data environment. Health sector is a first-rate scenario in this regard, to provide better health services to the society through the way of best integration and analysis of health related data by using current state-of-the-art in Big Data Storage (BDS) technologies, which identifies data-store related trends and capable of handling massive data. This survey paper discusses about the various emerging paradigms connected with BDS technologies, which gives options to both Hadoop and Spark, a fast and newly impacted computing avatar [i.e. In- Memory cluster (Multiple computers linked together, through a fast LAN, that effectively function as a single coputer) Computing] by replacing capacity of MapReduce through Resilient Distributed Datasets (RDD). It forecasts the entire features and availability options behind BDS, to deliver better data model in any Big Data (BD) dependent applications.