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Flying colors is a Hotel Booking Application; this application provides different staying solutions to customers, it is to book customers a hotel room online based on date and location along with the type of hotel. This architecture is based on agile development which is specially designed to work on multiple regions, channels, and quick future enhancements. PEGA7 Business Process Management (BPM) Tool which enables significant enhancement in building the application and data management experiences mobile functionality, user experience, and analytics. The goal of developing this application using PEGA is to improve the efficiency and performance, the liveliness of usual processes in the business. An automated system is developed to deliver high security and end to end delivery of works and speeds up the process drastically.

There are vast Image Fusion (IF) procedures are being projected in many ways by different analysts. Each coordinate point, it means pixel-level spatial space IF strategies, for the most part, lead to differentiate decrease. Pyramidal IF plans with the end goal like Laplacian, inclination, discriminate, quantity of-low-pass, and the morphological pyramids, and so forth abandonment to existing any spatial direction selectivity in the disintegration technique, also later regularly source obstructive impacts. The generally applied Discrete Wavelet Transform (DWT) can keep ghostly statistics efficiently, however, it will not be able to communicate spatial attributes adequately. Thus, DWT based grouping tactics will not be able to save the striking highlights of input images effectively and present antiques and irregularities in the intertwined outcomes. As of late, a limited Multiscale Geometric Analysis (MGA) instruments were grown, for illustration, Curvelet, Contourlet, NSCT, and so on which don’t experience the ill effects of the issues of wavelet. There are many IF and MIF techniques that work dependent on MGA devices were likewise projected. It is hard for the doctor to analyze the malady from the MRI and CT image independently for the patient. So in our proposed work, the MRI and CT clinical image are converted into a 3-D image for a better finding of the illness influenced part progressively precise and clear.

The information gathered from singular hubs is collected at a base station or host PC. To lessen vitality utilization, numerous frameworks additionally act in-organize the conglomeration of sensor information in the middle of the road hubs on the way to the base station. Most existing total calculations and frameworks do exclude any security arrangements, and subsequently, these frameworks are defenseless against a wide assortment of assaults. To address the security issue, propose one improved new technique. Secure data aggregation provides security to network from an attacker. Secure information accumulation procedure can likewise improve the power and exactness of data. Iterative separating calculation is one of the calculations to perform secure information accumulation and gives security to the whole system. Iterative sifting calculation all the while total information from numerous sources and give trust appraisal of these sources, through relating weight factor appointed to the information given by each source. Iterative sifting calculation with vitality model is utilized for giving more exactness to the Wireless sensor arrange condition.

In the research, the VLSI architecture design for Wallace tree encoder with modified full adder is proposed. In analog to digital conversion process, Wallace tree encoder is utilized in the process of converting the thermometer code to binary. This can be termed to be a high speed application and a flash type of flash ADC, which is a resistor ladder, encoder and comparator circuit. A suitable encoder is required for getting binary code from comparator output. Reducing energy of the encoder is a vital concern whereas designing the minimal power flash form ADC. Wallace tree encoders diminishes the mistake due to the availability of zeroes in the sequence of the once presence to the series of zeroes in a comparator output, but it consumes more power. Hence in the proposed work, a low power Wallace tree encoder is designed using pass transistor logic (PTL) full adder. The proposed design dissipates only 74.15nW power and delay also reduced to 0.0495ns. The circuit is designed using CADENCE 5.1.0 EDA equipped and simulated with the application of spectre virtuoso.

In the Mobile Ad hoc Network, the entire nodes taken as routers and contribute transmission when the nodes are not in the range of transmission for the senders. Directing conventions for the ad hoc systems are intended for the indisposed system setting, on the supposition that all the hubs in the system are reliable. Dependability of the directing convention is endangered in the genuine setting as systems are assaulted by pernicious hubs which regularly will in general upset the correspondence. Right now, it is proposed to contemplate the exhibition of the DSR convention under deceitful conditions. Another strategy is proposed to recognize untrue nodes dependent on the RREQ control parcel arrangement.

This framework identifies objects utilizing You Only Look Once (YOLO) approach and to distinguish the missing article continuously and alert the client. Since the items are been investigated in each frame, this framework has immaterial dormancy. YOLO calculation has a few favorable circumstances when contrasted with other item identification calculations. In a single assessment, bounding boxes and class probabilities can be predicted through a Solitary neural system directly from the full picture. The framework can be improved truly by distinguishing proof execution, because the full framework is a unique pipeline. In various estimations like Deformable parts, the model uses a sliding window approach where the classifier runs at similarly partitioned territories over the image. Strategies like R-CNN, first creates bounding boxes and afterward run the classifier on the crates.

Autism Spectrum Disorder (ASD) is a neurodevelopmental syndrome that starts at a very early age of 2–3 years. It is a fast growing developmental disorder that shows prevalent cognitive and behavioral abnormalities. Studies shows that the non-linear complexity in EEG signals can provide information regarding the neuronal activity of the brain. The recent trend in research is towards understanding the neuronal behavior in the nervous system, to visualize and understand the brain activities in children with neurodevelopment disorder. Biological neuron models communicate through sequences of spikes. Inspired by this, is the development of a new class of neural models, called spiking neural networks (SNNs). These networks aim towards the accurate modelling of synaptic interactions between neurons, taking into account the time of spike firing. So, this work focuses on the connectivity of the EEG signals using a stochastic learning algorithm and thereby analyze the channels that actively contributes for an Audio and Visual stimuli.

Stress diagnosis is a common factor used for monitoring patient's health. In the current generation, stress diagnosis is an important factor to identify certain behavioral pattern of a person. Physical stress is easily identified but then mental stress is not predictable at ease. On a specific base, mental stress for patients with cerebral palsy is difficult to diagnose. For this condition, the EMG signals are used. The patients are monitored with certain criteria level for which neural networks are implemented with its designing algorithm for identifying the diagnostic work by the equipment itself instead of doctor monitoring.

Depression is a common phenomenon in the present scenario. Due to the fast pace at which our lives move and immense pressure that we face adolescents, office goers and even the elders face depression. Diagnosing depression in the early curable stages is very important and may even save the life of a patient. EEG signal analysis has been used for medical research like epilepsy, sleep disorder, insomnia etc. Similarly, video signal analysis has been used for facial features detection, eye movement, emotion recognition etc. Collaborating both the methods accuracy of depression detection can be improved upon. This paper describes a novel method for combining both EEG signal analysis and facial emotion recognition through video analysis to successfully categorize depression into various levels. For this aim, power spectrum of three frequency bands (alpha, beta, and theta) and the whole bands of EEG are used as features along with standard deviation, mean and entropy.

Ischemic stroke is a condition in which brain stops working due to lack of blood supply resulting in death of brain cells. Magnetic Resonance Imaging is widely used to detect Ischemic Strokes in brain. This paper gives an automated algorithm to detect the stroke using Image processing techniques. The algorithm consists of six phases. Data in the form of MRI images is collected in first phase. The preprocessing is performed including filtering on the raw data collected. Midline is traced in third phase for acquiring a symmetrical image. It is followed by bifurcation of image in fourth phase. Finally the image quality matrix is formed for texture analysis in fifth phase and neural network is applied in sixth phase for classification of normal and infected brain. The advantage is that the strokes can be detected in its early stage. The algorithm proposed is simple, efficient and less time consuming. The efficiency is found to be 98%.

In this work, a method called Discrete Sine Transform is used in order to classify EEG signals of the brain and are used for the purpose of early autism detection. Autism is a complex behavioral disorder. The EEG signals are obtained by using electrodes that are attached to the brain. Each electrode measures the signals in different regions of the brain as the subject responds to different stimuli. The signals are processed by applying Discrete Sine Transform (DST) and then it is passed as an input to an artificial neural network. DST is used as it greatly simplifies the process and reduces the complexity. The computing tool of a neural network is used in order to objectively estimate whether a subject is suffering from autism. The network is trained with a particular dataset and upon applying a testing input the output was achieved. This study looked at EEG signals, an indirect measure of brain connectivity, and identified patterns that distinguished subjects at an increased risk for autism.

A novel approach for 3D anatomical facial reconstruction from 2D CT images using Delaunay Triangulation is proposed in this paper. The method involves individual slice thresholding, contour finding, point cloud generation, Delaunay triangulation and redundant tetrahedron removal. The results are compared with standard marching cube algorithm and with standard commercial 3D software. The performance is studied based on time complexity and improvement in resolution.