Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Generation of Standard Deviation Attribute Pro ﬁle s for Spectral–Spatial Classi ﬁcation of Remote Sensing Data

: In recent years, the development of high-resolution remote sensing image extends the visual field of the terrain features. Back Propagation neural network is widely used in remote sensing image classification in recent years, it is a self-adaptive dynamical system which is widely connected by large amount of neural units, and it bases on distributing store and parallel processing. It study by exercise and had the capacity of integrating the information, synthesis reasoning, and rapid overall processing capacity. It can solve the regular problem arise from remote sensing image processing; therefore, it is widely used in the application of remote sensing. Utilizing data mining tasks such as classification on spatial data is more complex than those on non-spatial data. It is because spatial data mining algorithms have to consider not only objects of interest itself but also neighbors of the objects in order to extract useful and interesting patterns. One of classification algorithms namely the ID3 algorithm which originally designed for a non-spatial dataset has been improved by other researchers in the previous work to construct a spatial decision tree from a spatial dataset containing polygon features only. The objective of this paper is to propose a new spatial decision tree algorithm based on the ID3 algorithm for discrete features represented in points, lines and polygons. As in the Improved ID3 algorithm that use information gain in the attribute selection, the proposed algorithm uses the spatial information gain to choose the best splitting layer from a set of explanatory layers. The new formula for spatial information gain is proposed using spatial measures for point, line and polygon features. In this proposed work, the use of Back Propagation neural network classifier and decision tree approach will be implemented on the high resolution remote sensing images of their pattern recognition Proposed system will shows better performance compare to existing RF and SVM based classification techniques.
ESTIMATION OF PARAMETERS OF THE HIV DISEASE DYNAMICAL SYSTEM USING ROUTINE OBSERVATIONAL DATA

Biological models have considerably assisted in the study of disease dynamics and subsequent issues related to antiviral policy and treatment. The study reported in this paper has been motivated by the HIV dynamical system of nonlinear ordinary differential equations which models the pathogenesis of the disease in order to assess the effectiveness of antiviral therapies among other parameters. we have considered nonlinear models for multivariate longitudinal data as applied to estimation of parameters in a disease dynamic model for HIV. We have studied the efficacy of treatment and other dynamic parameters in the presence of a multiplicity of covariates. In particular, we have sh own that factors like age of a patient at initiation of treatment, compliance to treatment and supplementary treatment have a significant bearing on an individual's response to treatment. We are mindful of the fact that inclusion of many covariates in a complicated nonlinear model for the dynamic system could make computation of parameters more demanding, but inclusion of those significant variables, as demonstrated in this discussio n, will make estimation and inference to be more meaningful and practical.
Optical Character Recognition for Cursive Handwriting

- Recognition of Handwritten text has been one of the active and challenging areas of research in the field of image processing and pattern recognition. It has numerous applications which include, reading aid for blind, historical document recognition, bank cheques. In this paper we focus on recognition of cursive English alphabet in a given scanned text document with the help of SVM. The first step is image acquisition which acquires the scanned image followed by normalization of scanned image followed by feature extraction, then making image suitable for segmentation where image is decomposed into sub images and finally each character classification using SVM classifier. Feature Extraction improves recognition rate by making all the characters into a common height. A new combination methods have been developed and has been used for this recognition, which is implemented in MATLAB 2013 a.
Comparative Analysis of Original AODV and AODV-DOR Routing Protocols in Mobile Ad-hoc Networks

An ad hoc network is a group of movable wireless nodes that wants to share or exchange the information without any fixed infrastructure or centralized control. Ad hoc network is a selfconfiguring network of movable devices which may be connected by a wireless links. The union of these mobile devices forms an arbitrary topology. Due to very high dynamic environment nature of MANET, routing is a very crucial task for measuring the performance. A number of protocols have been familiarized to be rectified the routing mechanism which can find the path between any source host to destination host within the network. This paper focuses on the most popular and widely used Ad hoc On-demand Distance Vector (AODV) reactive MANETs routing protocol with two different flag settings. This paper provides the performance (Average End to End Delay and Packet Delivery Ratio) comparisons of original AODV and AODV-DOR (AODV with destination only reply) in different network conditions. For our simulation we used discrete event simulator known as Network Simulator version 2.35
c-MET and KRAS: Signalling and Clinical Implications in Colorectal Cancer

Colorectal cancer (CRC) is the third leading cause of death from cancer in North America. The KRAS gene is mutated in approximately 40-50% of all CRC, and this mutation precludes treatment with promising targeted therapeutics. c-MET is a receptor tyrosine kinase that is overexpressed in ~70% of CRCs, and expression is correlated with disease progression. We hypothesized that high c-MET plus mutant KRAS would result poor survival of CRC patients, by activating unique signalling pathways that may be targeted for therapeutic purposes. To this end, we used phosphoproteomics in a KRAS mutant cell line, and identified proteins phosphorylated on tyrosine in response to HGF stimulation, including a subset of those that contain SRC family kinase consensus motifs. Small molecule inhibitors of either SRC or c-MET reduced tyrosine phosphorylation of both proteins, indicating reciprocal signalling. We chose the c-MET target p190RhoGAP for future study, as it is often ubiquitously bound to p120RasGAP via phosphorylated tyrosine. We found that RasGAP expression is mediated in part by KRAS signalling, and that expression of RasGAP could partly rescue tumourigenicity of a CRC cell line where the mutant KRAS allele has been inactivated, indicating the requirement of both mutant KRAS and RasGAP expression in this model. We then conclude by looking at CRC patient
A Review on Techniques to Sense the Spectrum for Cognitive Radio Networks

- Radio spectrum is the most valuable resource in wireless communication. In recent past the growth of wireless devices and multimedia applications has led to increase in spectral usage causing spectral congestion problem. Cognitive radio provides tempting solution to this problem by making best use of the frequency band which is not heavily occupied by licensed users hence improving the spectrum utilization. In this paper various spectrum sensing techniques, such as energy detection, cyclostationary detection, matched filter detection methods are discussed and compared with one another.
Low Power Op-Amp Design with Current Compensation Technique

The trends in electronic design field are growing day by day towards the low power chip design system. The need of smaller size chips with very small power dissipation increases the demand of low power designs. The supply voltage must be reduced to lower the overall power consumption of the system. In this paper ,we are presented a low power single output two stage CMOS operational amplifier (Op-Amp) with current buffer compensation technique operates in weak inversion region with a 1.8 V supply voltage. The two stage CMOS Op-Amp is designed in UMC 0. 18μm CMOS technology. Since MOS transistor in sub-threshold region allows to work at low input bias current and low voltage, so the presented Op-Amp has very low power consumption with a high driving capabilities. The proposed Op-Amp has open loop gain =73.57db, the gain bandwidth product (GBW) 1.094 and 4. 35μW power consumption.
Synthesis, thermal behavior and thermodynamic data of hafnium selenite tetraaqua complex dihydrate

Data are reported about the hydrothermal synthesis, thermal behavior and the thermodynamic properties of hafnium selenite tetraaqua complex dihydrate. The compound was subjected to infrared spectroscopic analysis and the experimental data were compared to the theoretically calculated ones by the density functional theory (DFT) and analogous experimental data on zirconium selenite. By thermal decomposition, the hydrate water is released up to 110°С, above which temperature the tetraaqua complex of the hafnium selenite remains stable up to 240°C. The anhydrous hafnium selenite decomposes in the temperature interval 580–700°С, probably forming dihafnyl selenite Hf2O3(SeO3). Using the experimental data from the differential scanning calorimetric analysis (DSC) the specific heat capacities (Cp,m) and the thermodynamic properties ( o ' m T T S  , o ' m T T H  , – o ' m T T G  /T) of the initial phase and the phases formed after the thermal decomposition of the compound synthesized were calculated. The temperature dependencies of their molar heat capacities were determined by regression analysis.
Optimizing Resource Allocation through Virtual Machines in Cloud Computing

--Cloud computing has emerged to be a modern computing model based on Internet. This technology can enable organizations and individuals to share state-of-the-art computing resources in pay as you use fashion. Virtualization is the technology that enabled cloud computing to be affordable. The cloud resources are to be utilized optimally. In order to achieve this dynamic resource allocation is the best solution. However, the dynamic resource allocation is a challenging problem to be addressed. In this paper we proposed an algorithm that takes care of dynamic resource allocation using two possible cases. In the first case capacity of a VM is increased at runtime while in the second case VM migration takes place to different physical machine. We built a prototype application to demonstrate the simulations for proof of concept. We considered memory as a resource. The experimental results are encouraging.
PRIVACY PRESERVING AUDITING OF ENCRYPTED CLOUD DATA BY USING THIRD PARTY AUDITOR

Cloud computing is an internet based computing. Using cloud computing, user can place their data remotely and enables sharing of services. Many users place their data on the cloud. Hence correctness and security of data is a prime concern. Physical possession of the outsourced data is restricted to users. It ensures the data integrity protection in Cloud Computing. Ensuring integrity is a difficult task for users with limited number of computing resources. With cloud computing user can use cloud storage like a local storage which verifies integrity. A third party auditor checks the integrity of a data stored on cloud and enables the public audit ability for cloud storage which is a crucial task. The auditing process does not bring any new vulnerability towards user data privacy and does not introduce any additional online burden to user to securely introduce an effective TPA. Here, proposed a secure cloud storage system using Third Party Auditor on encrypted cloud data which will preserve privacy. It is further extended to enable the TPA to perform audits for multiple users simultaneously and efficiently. Wide-spread security and performance analysis give an idea about the proposed scheme is secure and highly capable.
A Study on Travel Recommendation System

: Recommendation is a part of information filtering system which is used to predict the rating or preferences of the user in general. Considering the travel recommendation system, it predicts the user preferences or rating of a particular tourist place. Using that predicted value; a user can understand that the particular place is worth seeing. To make this prediction, we use this information filtering systems. Many filtering techniques exist, but we look into content based filtering, collaborative filtering, and Hybrid systems. This paper makes survey on different filtering systems used for recommendation systems. We also discuss about the factors for increasing the performance of recommendation system which paves the way for further research.
Scene Synthesis and Camera Calibration Techniques

: The fundamental concepts of how the camera calibration technique and pose estimation of the calibration objects is done as a series of tasks and experiments are explained in the paper. The brief about the proposed tasks and experiments is mentioned in the paper. In all the tasks, the basic concepts are studied, the applications to concepts are searched and some of the concepts are practically executed. Camera calibration is done with the help of camera calibration tool boxes (open source and free toolbox by Bouguetj and commercial package provided in Matlab2014a). A 3D model in Matlab is drawn using matlab functions and then visualizing and viewing the 3D model from different locations and positions. The graphic models of cameras are placed at different positions and the scene is captured. The main objective of this work is to estimate the scene that is taken by the camera. Here the projections involved like perspective, orthographic projections of the 3D model are obtained which is the main part of scene synthesis. Some of the main steps in camera calibration like computation of homography matrix and optimization procedures like Levenberg-Marquardt method are explored in the tasks performed. „Perspective-n-Point‟ for estimating the pose of a calibrated camera given a set of n 3D points and their corresponding 2D projections is use combination of linear methods like DLT algorithm and non-linear methods for refinement of the results and these concepts are explored. Here, feature matching in images by extracting feature descriptors from image pairs and matching them which is helpful in extracting the camera paramters and studying the concept of generic calibration and implementing it is explored. Reprojection errors, Sampson errors and algebraic errors are seen from the calibration packages being implemented.
Development of Software for Flower Recognition Using Image Processing Technique

: Prototype of desktop based software is developed for flower recognition and is explained in this research paper. We tried to help common people to recognize every specie of flower and have their specification including their scientific name given to them with a digital image of that flower with white background in their hand. Only a digital image is required in this software and it compares the uploaded image with the images already present in data folder and provides you with appropriate result .
Improving Performance of a Face Liveness Detection System Using Soft Biometrics

: A biometric system identifies an individual based on physiological or behavioral characteristics. Face is one of the most commonly used biometric in authentication systems but nowadays there are a lot of security threats due to spoofing. A face recognition system can easily be spoofed with a photograph or a video of the legitimate user . Liveness detection is a technique to make sure that the data is being provided by a live user and not from artificial sources. Although soft biometric traits such as eye color, age, gender, ethnicity, etc. lack the ability to uniquely identify an individual, yet they provide some additional information about the user and may improve the efficiency of the system. Here in this paper, we have proposed a technique for integrating liveness detection with soft biometrics to improve the performance of the authentication system.
Clustering Techniques Analysis for Microarray Data

: Microarray data is gene expression data which consists of the protein level of various genes for some samples. It is a high dimensional data. High dimensionality is a curse for the analysis of gene expression data. Thus gene selection process is used in which most informative genes are selected from the pool of gene expression data set. All the genes are not relevant in each case. First we need to select those genes which are relevant as well as there should be least redundancy among them. For this purpose various approaches can be used such as: Filter methods, wrapper methods, embedded approach and clustering. In this paper embedded approach for gene selection and clustering method will be used for performing the sample clustering to refine the classification and will be compared with each other on the basis of various parameters.
WEB SERVICES USAGE MONITORING FOR FUTURE USAGE AND FAILURE PREDICTION

Wider use of web services has dominated the software industry. Providing web services to its consumers, according to Service Level Agreement (SLA) is complex and difficult task. Failure to provide web services against agreed quality of service (QoS) parameters, i.e., unavailability of web services and slow response time will cause increase in penalties. This may lead to unsatisfied consumers that can be devastating for business goals and revenue of service providers. In order to ensure the satisfaction of consumers and avoid any violations of SLA, monitoring is required on the service provider side. However, monitoring against SLA is not enough - service providers must be able to analyse the past and current usage in order to predict future challenges. This research aims to provide a monitoring framework which empowers the service providers to monitor web services usage and failures. And analyse the monitoring data to predict future usage and failure patterns.
MIMO-OFDM in Rayleigh Fading Channel with LDPC

This research paper investigates the Ergodic capacity of MIMO-OFDM system in Rayleigh Fading Channel to increase the Ergodic Capacity of the system. Orthogonal Frequency Division Multiplexing (OFDM) is multicarrier modulation technique for transmission of signals over wireless channel. OFDM, using multiple sub carriers divides the available spectrum into number of sub orthogonal sub channels. Multiple input multiple outputs (MIMO) using multiple transmitting and receiving antennas for high data rate transmission with limited bandwidth are used. OFDM is combined with MIMO to increases the capacity of the system. MIMO-OFDM is popular technique for high data transmission because of usages of multiple carriers. One of the challenging problems of OFDM is Peak to Average Power Ratio (PAPR). In this it has been attempted to minimize the value of PAPR so that Ergodic Capacity of the system increases. For Reducing the value of PAPR we have used Coding Technique Low Density Parity Check Coding (LDPC). Further analysis by using 64 QAM with LDPC, using this Quardature Amplitude Modulation Spectral Efficiency increases. For investigating the systems performance Matlab has been used, mostly focusing on four Parameters Ergodic capacities, Signal to Noise Ratio, Bit Error Rate, Number of Transmitting Antennas. This will allow future wireless system to have high data rates. By reducing the number of guard bands the spectral efficiency can be increased in future.
An Efficient Technique for Indexing Temporal Databases

Temporal databases added a new dimension to traditional transaction databases. This dimension is the life time of each item, i.e. exhibition period, starting from the partition when this item appears in the transaction database to the partition when this item no longer exists. Mining temporal association rules became very interesting topic in many applications nowadays. In this paper, an efficient technique is proposed for indexing temporal databases in order to facilitate support counting process during mining operation. Some experiments were conducted using well-known real datasets to show the performance of the proposed indexing technique with respect to index size and running time of the mining algorithm. The results show that the proposed indexing technique saves a lot of running time and works efficiently with different databases characteristics.
A Survey Paper on the Feature Extraction Module of Offline Handwriting Character Recognition

Offline Character Recognition Is the process in image processing where a document is fed into the system in the form of an image for it to be recognized and classified. The feature extraction module is the basis for this paper and we give the different algorithms used in the feature extraction module after giving a brief overview of what a character recognition system is.
Unsupervised Image Segmentation Using Truncated Log Normal Distribution

: Color image processing plays a vital role in the perception of the images by the humans. Among the color models YIQ model is considered in this article. The benefit beyond the consideration of this model is due to the fact that, more bandwidth is allocated to the luminance or Y-component, and which helps the human eye to catch sensible color information. In order to acquire any object of interest it is customary to segment the image into homogeneous regions such that the object of interest can be easily retrieved. In order to speed up the retrievals or to identify the object of interest this article presents a methodology using Truncated Log Normal Distribution. The developed model is presented using a bench mark data set and performance evaluation is carried out to evaluate the efficiency of the model.
A review on markov models and web page prediction

A Markov process is a stochastic process in which the probability distribution of the current state is conditionally independent of the path of past states, a characteristic called the Markov property. Markov chain is a discrete-time stochastic process with the Markov property. In this paper we have to use the Markov model for the prediction of the different web pages based on the sequence of previously accessed page
A Review on Automatic Face-Name Association for Web Videos

- Celebrity face labeling in web videos is huge and challenging task because of large deviation in the appearance of person or celebrity in the web videos. This work explores the problem of missing name and missing faces in unconstrained videos with user created metadata. Instead of depending upon supervised learning, a better relationship built from content of video, those relationship includes arrival of faces in different spatio-temporal contexts and visual similarities between faces. The knowledge base consist of weakly tagged images along with set of names and celebrity social networks. Merging of relationship along with knowledge base is carried out via conditional random field. Two types of face name association are investigated: within video face labeling and between video face labeling. The within video labeling takes care of noisy as well as incomplete labels in metadata, where null assignments for the names is permitted. Furthermore Between video face labeling addresses the flaws in metadata, particularly to correct incorrect names and label faces with missing names in the metadata of a video, by considering a gathering of socially associated videos