Miracle-dz/UniFAR-FTUs huggingface dataset:
Text Mining using Side Information from Twitter Tweets

 Abstract — Twitter is a popular social media application which allows users to interact with each other using short messages. These messages are called tweets, they are large repository of social data that is a great starting point for the process of text mining because of its public availability and openness. Side information in tweets such as username and time are useful for text mining process. Trending topics, user comments, updates, reviews all over the world from users having different background are collected from the Twitter Server using Twitter Application programming Interface. These tweets are processed using Java Script Object Notation format based serialization techniques and a live dataset is built. Frequent association technique is used to extract the most frequently associated tweets that contains words that are searched. Then sentiment analysis is performed on all tweets in the live dataset to determine the positivity of each tweet. Content based clustering is performed to classify the tweets based on side information such as username and positivity. The results of this text mining application can reveal interesting patterns, which are strongly related tweets according to the search words given as input to the mining program. Clustered tweets based on positivity can reveal unique relationships among desired tweets. This Twitter Tweets based mining application has wide usage in key decision making that can be implemented in various policy making fields where people feedbacks are primary factors .
Web Page Recommendation System using Biclustering with Greedy Search and Genetic Algorithm

-A Recommender system is one of the best web usage mining Application which reduces the difficulties faced by the users to meet their requirements. Web usage mining is the process of obtaining useful knowledge from the server logs. In this work, a new recommendation system is proposed to predict the user’s navigational behaviour. We are using the biclustering approach with greedy search and genetic algorithm to overcome the problems of grouping available in traditional clustering approach. The practical implementation of this algorithm shows the more accurate prediction of user’s intuition on web. The knowledge gained by the analysis is applied to target marketing and in the designing of web portals.
A Novel Video De-Noising Algorithm for Removing High Density Impulse Noise

Impulse noise is one of the prominent noise which affect the video as well as the image very badly rendering it look unclear and hazy. Median filter approach for removing such kind of noise proved very inefficient if the noise density is high. In this paper, a novel algorithm is proposed for removing the high density salt and pepper noise from the video sequence. This scheme incorporates noisy pixel detection algorithm prior to applying the filtering operation. a hybrid of median, min and max filter method is used for removing the noise from the video frames. Experimental results shows that the performance of this method is far better than the simple median filter approach.
Identification of currency denomination using image processing

This paper aims to solve the problem of detecting a paper currency and output the result as speech. The developed model accepts an image from a webcam (RasPi Camera). The Raspberry Pi’s image processing algorithm then extracts certain features from this image and matches them against a set of training data. When an appropriate match of the acceptable threshold is found, the device outputs the denomination value through speech synthesis. Moreover, this paper also compares two methods of image processing namely, Brute Force Matcher Algorithm and Convolution Neural Network Algorithm. Finally, the pros and cons of both the methods are evaluated and the best algorithm is ascertained.
Development of Lecture Attendance System for Staff Performance Rating in a Tertiary Institution using Fingerprint Technology

Managing time attendance of staff in organizations has proven to be a challenging endeavor. Manual methods have been proposed in capturing employees’ attendance but there are inadequacies in measuring the performance of employees. This paper aims at promoting staff efficiency at work by developing a secure attendance management system for use in tertiary institutions via two subsystems; Fingerprint biometrics as a method of identification and authentication and Question-Answering module for staff performance rating. The system uses staff fingerprints stored in the database upon registration for carrying out the process of attendance and validate staff lectures’ requirements. It is accentuated by creating a question-answering module that allows students to answer some questions on each available and registered staff, hence, giving a more concentrated content about staff’s activities within the school. The evaluation was carried out based on the matching efficiency and attendance accuracy of the proposed system. The proposed system performed excellently with 98.51% attendance accuracy based on the high successful staff identification recorded. Hence, staff performance rating was generated, thereby creating an avenue for determining promotion grounds.
Study of Texture features for Content Based Image Retrieval

To retrieve the required images from databaseshas become an area of wide interest in many applications. Relevant images can be retrieved using unique image features like texture, color or shape. This paper presents a extensive survey of the Content Based Image Retrieval (CBIR) techniques based on texture features. Paper presents through discussion on three texture features used for development of CBIR systems. These features includesGray-Level Co-Occurrence Matrix (GLCM), Gabor filterand Local Binary Pattern (LBP). Performance analysis of these features is compared to showcase effectiveness in retrieval problems.
DWT Based Image Fusion for Concealed Weapon Detection

: In this paper, the technology which predicts the suicide bombers and explosion of weapons through imaging for concealed weapon detection is described. The detection of weapons concealed under a person's cloth is an obstacle to the improvement of the security of the general public as well as the safety of public assets like railway station and airports. It is desirable to detect concealed weapons and explosive material from a standoff distance, especially when it is impossible to arrange the flow of people through a controlled process. The goal is the development of technology of automatic detection of concealed weap-ons. So fusion algorithm is proposed for early and automatic detection of weapon. Image fusion is a process of combining two or more images into an image. It can extract features from source images, and provide more information than one image. The feasibility of the proposed fusion technique is demonstrated by some experimental results.
MHD Free Convective Flow of a nanofluid over A Permeable Shrinking Sheet with Binary Chemical Reaction And Activation Energy

We have investigated a two dimensional unsteady free convective MHD flow and heat transfer of nanofluid over a permeable shrinking sheet with binary chemical reaction and activation energy. In this problem, mathematical formulation has designed for momentum, temperature and nanofluid solid volume profiles. Using similarity transformation, the coupled partial differential equations are non dimensionlized and then solved by using finite difference method. The results for the dimensionless velocity, temperature, and nanofluid solid volume profiles are discussed with the help of graphs.
Antimicrobial Activity of Bacillus cereus and Bacillus thuringiensis on Pathogenic Vibrio harveyi in Litopenaeus vannamei. J. Life Sci. Biomed. 6(1): 10-14

The bacterial strain of Bacillus cereus and Bacillus thuringiensis has been known to produce antimicrobial activity against pathogenic Vibrio harveyi. The effect of B. cereus and B. thuringiensis were tested by in vitro and in vivo. In vitro test was used to analyze antagonism characteristic of bacteria using the paper disc diffusion method. In vivo test was applied to evaluate antimicrobial activity of B. cereus and B. thuringiensis (105 CFU ml-1) on survival rate and histopathology of Litopenaeus vannamei challenged with V. harveyi. The results showed that B. thuringiensis had a greater inhibitory activity of 18.60–35.97 mm. Both Bacillus bacteria treatment resulted in survival rate of 100%, compared with 75% in the treatment without Bacillus. It can be concluded that B. cereus and B. thuringiensis have potential applications for controlling pathogenic V. harveyi in shrimp aquaculture.
An overview of Multi Task Clustering Techniques: a Survey

: Clustering is the process of grouping data points into a bunch such that data points that belong to one cluster are more similar to each other than data points that belong to other cluster. Lots of data is being collected and warehoused and due to emanation of data on web, many issue s had been hurled on classical clustering algorithms such as the correlations that exist among many clustering tasks. However traditional clustering algorithms fail to deal with related clustering tasks. Combining such multiple clustering tasks that are related to each other are simply termed as Multi Task Clustering (MTC). It is one such promising way to reinforce the clustering performance. In this paper an effort is made to address various multi task clustering techniques in the data mining environment.
Semi-Supervised Collaborative Image Retrieval using Relevance Feedback

--------------------------------------------------------------------- Abstract - In Digital Image Processing, Content-Based Image Retrieval has gained much popularity. However, the accuracy of CBIR system is low. To improve the performance of CBIR system, Relevance feedback system can be used. In relevance feedback system the user refines the search results, progressively by marking images in the results as ”relevant”, ”irrelevant”, or ”neutral” to the search query and then repeating the search with the new information. In many cases, there may be a large number of images to a label. Most of the times user would not like to label a large number of images. In this paper, a semisupervised method is used. This means the user needs to label only a few most informative images. These labeled images are then used as a training set for SVM classifier. The images in the database are resorted based on a new similarity metric. If the user is satisfied with the results, Relevance feedback is nolonger required and the system gives the final results. These results are most semantically relevant to the query image. Thus the image retrieval process is ended. Otherwise, Relevance Feedback will be performed iteratively.
Assessment of serum cortisol levels in oral submucous fibrosis patients

Website: jprsolutions.info ISSN: 0975-7619 nut. [3] Areca nut has a high copper content. This acts as a mediator of OSMF by upregulating collagen production in oral fibroblasts. Areca nut is made up of alkaloid and flavonoid components. Four different alkaloids have been identified in areca nut, namely arecoline, arecaidine, guvacine, and guvacoline of which arecoline is the most potent agent and plays a major role in the pathogenesis of OSF by causing an abnormal increase in collagen production.
PHOTOGRAPH RETRIEVAL BASED ON FACE SKETCH USING SIFT WITH PCA

The problem of matching a sketch to a gallery of mug shot images is addressed. Previous research in sketch matching only offered solutions to matching highly accurate sketches that were drawn while looking at the subject (viewed sketches). To identify sketches much efficient algorithm is presented here. Both sketches and photos are considered for extracting feature descriptors using SIFT (Scale Invariant Feature Transform). Proposed method can be used to match a few sketches against a mug shot gallery containing several images. Its matching performance can be improved using race and gender information to reduce the target gallery size. The proposed project will lead to state-of-the-art accuracy when matching viewed sketches. The proposed project represent both sketches and photos using SIFT feature descriptors and multiscale local binary patterns(MLBP).
The Spoilage Characteristics of Ground Beef Packaged in High-Oxygen and Low-Oxygen Modified Atmosphere Packages

Spoilage is a subjective measurement and includes changes in color, texture, odor, taste, and microbial counts. Gill (1986) defined meat spoilage as any single symptom or group of symptoms of overt microbial activity, manifested by changes in meat odor, flavor or appearance . However, many factors including storage temperature, atmospheric oxygen, light, meat constituents, microorganisms, and indigenous enzymes can affect meat quality over an extended period of time (Lambert et al., 1991). Several scientists have concluded that microbial growth is the most important factor in controlling the spoilage of meat (Lambert et al., 1991; Nassos et al., 1983; Jay 1996). Bacterial numbers of 10 7 and 10 8 per cm 2 have been shown to cause noticeable changes in off-odor and slime, respectively (Ayers, 1960). Jay (1964) concluded that microbial populations must reach approximately 10 8 /g for raw beef to have a tackiness when touched while others claim that proteolytic changes in raw beef do not occur until bacterial populations greater than 3.2 x 10 9 /cm 2 (Dainty et al., 1975) are reached indicating there are discrepancies in the amount of bacteria that must be present before meat is considered spoiled. In addition, the length of time it takes to get an accurate number of bacteria present in the meat as well as disagreements about the correlation between bacteria counts and organoleptic properties of meat make it difficult to assess future shelf life of the meat (Nassos et al., 1983).
EVALUATION OF INTRAVARIETAL HOMOGENEITY WITHIN GRAPEVINE ROOTSTOCK CULTIVARS AS REVEALED BY SSR FINGERPRINTING

Traditional rootstock cultivars of importance for the Mid European viticulture originate mainly from Zsigmond Teleki‘s material developed at the end of the 19-th century. Nowadays most widespread varieties derived from Teleki‘s primary vine population could be denoted SO4, Kober 5BB, Teleki 5C and Kober 125AA. Recently SSR markers became one of the most suitable and reliable tools for cultivar identification, but some problems can appear if results obtained from two independent laboratories are compared. In order to overcome this problem, the system based on reference alleles was utilised in this work. Nine rootstock genotypes from 4 varieties collected in Czech Republic were genotyped by using 9 SSR loci and compared with published genotypes from Austria, Australia, France, Italy, Hungary and USA. The major SSR profile for all rootstocks was found by comparing obtained results of different laboratories. SO4 and 125AA with almost identical SSR profile for all genotypes appears as very stable. Contrarily, the analysis of T5C and Kober 5BB revealed a higher level of heterogeneity within genotypes belonging to these cultivars.
ELICITATION, BIOCONVERSION AND QUANTIFICATION OF BERBERINE FROM CISSAMPELOS PARIERA CALLUS CULTURES

Cissampelos pariera, an important medicinal plant was subjected to elicitation and bioconversion of precursors for the formation of Berberine, an important medicinal component. In vitro cultures of Cissampelos pariera, was successfully established using stem and leaf explants. MS Media fortified with IAA (2 mg/l) alone and MS media enriched with IAA (2mg/l) showed profuse callusing from leaf and stem explants respectively. Elicitation with yeast resulted in the highest percentage of berberine yield from leaf callus (0.055%). Elicitation with Bacillus subtilis showed the presence of 0.0294% berberine in leaf callus. Elicitation with Aspergillus niger showed the highest yield of 0.0019% from stem callus. Addition of precursor components Tryptophan and Phenylalanine did not increased the berberine from leaf callus. But addition of precursor components Tryptophan and Phenylalanine showed the bioconversion to 0.0019% and 0.0022% of berberine in stem callus. The study reports a standardized protocol for the efficient conversion of precursors to breberine and also to elicit the production using elicitors. INTRODUCTION: Tissue culture mediated production or enrichment of phytochemicals is one of the promising techniques used in the recent years. Plants are able to synthesis complex compounds that may be difficult to synthesize in a chemical laboratory 1 . But most of the time the extraction of these chemicals from plants leads to complete destruction of plants from the environment. This leads to the depletion of species. Thus plant cell cultures are found to be an attractive alternative to meet the demands, without destroying the plant species.
FORMULATION AND EVALUATION OF FAST DISSOLVING TABLETS OF CANDESARTAN USING SOLID DISPERSION TECHNIQUE

Recent developments in fast dissolving/disintegrating tablets have brought convenience in dosing to elderly and children who have trouble in swallowing tablets. The objective of the present study was to prepare the mouth disintegrating tablet of Candesartan. Solid dispersion of drug was prepared. The drug Candesartan is fused with carriers i.e; PEG, urea, cyclodextrin, PVP, mannitol. The carrier which gives the best release of Candesartan is selected to prepare the fast dissolving tablet. Different super disintegrants such as CCS, SSG, CP were used. The tablets were prepared by direct compression technique. Candesartan, Being a class II drug, it exhibits low solubility and high permeability. Present work involves attempts to improve bioavailability of Candesartan through solid dispersion technique and induce fast dissolving property using super disintegrants. In the formulation of Candesartan fast dissolving tablets, Candesartan fused with mannitol (optimised carrier), is used as API and super disintegrant CP (5%) were added to the formulation, results showed that 88% was released in 30min in formulation (CSF1) and 81% was released in 30min from the formulation(CSF2) containing super disintegrant CCS. In the formulation 3,(CSF3) the super disintegrant SSG (5%) was added and 93% was released in 30min. In formulation 4,(CSF4) the super disintegrant CP (7.5%) was added and 90% was released in 30min.In formulation 5,(CSF5) the super disintegrant CCS (7.5%) was added and 89% was released in 30min. In formulation 6, (CSF6) the super disintegrant SSG (7.5%) was added and 96% was released in 30min, which is the highest amongst all and hence this is finalised. All these tablets showed required hardness, limited friability and good disintegration time (with in IP limits). All the formulations were evaluated for drug content and results are obtained. Amongst all formulations, formulation CSF6 and SSG as super disintegrant showed the least disintegration time and faster dissolution. M.A.Hamid Mudabbir* et al. International Journal Of Pharmacy & Technology IJPT| Oct-2014 | Vol. 6 | Issue No.2 | 6723-6738 Page 6724
Comparison of Different Feature Selection Techniques in Attribute Selection of Learning Style Prediction

Learning style of specific users on an online learning system is determined based on their interaction and behavior towards the system. The most common online learning theory used in determining the learning style is the Felder-Silverman’s Theory. Many researchers have proposed machine learning algorithms to establish learning style by using the log file attributes. Due to many attributes in predicting the learning style, the performance and efficiency of the classification and prediction are still poor; so far it is only between 58%-85%. This research is conducted to determine the most relevant attributes in predicting the learning style. First, three different feature selection methods are used to select the most relevant number of attributes, which are Rank by Importance (RbI), Recursive Feature Elimination (RFE) and Correlation. Next, five different classifiers are used to evaluate those selected feature selection methods. The classifiers are Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbour (KNN), Linear Discriminant Analysis (LDA) and Classification and Regression Tree (CART). From the experiments, RbI has proven to be the most effective feature selection method, with the accuracy improvement from 87% to 91%.
Improved Fault Tolerant Elastic Scheduling Algorithm (IFESTAL)

— The existing fault tolerant models focus on the failure of host. If a host fails, the copies of tasks on this host will fail to finish. At one time instant, at most one host may encounter a failure, that is to say, if the primaries of tasks fail, the backups can always finish successfully before another host fails. Failures can be either transient or permanent, and are independent, affecting only a single host. In this paper, an attempt will be made to propose an improved algorithm to tolerate multiple host failures which will be tested using guarantee ratio, hosts active time, ratio of task over host time.
Structure Elucidation of a Novel Pentasaccharide Meeniosefrom Babalus bubalis Colostrum

Oligosaccharides present in the milk of different origin have shown varied biological activities. Oligosaccharide isolated from Buffalo milk has shown encouraging immune stimulant activities. With a view to isolate more biologically active oligosaccharides from colostrum of buffalo (Babalus bubalis). Colostrum was collected and processed by modified method of Kobata and Ginsberg, involving deproteination, centrifugation and lyophilization followed by acetylation and silica gel column chromatography of derivatized oligosaccharides. The structure of isolated penta saccharide was elucidated by chemical transformation, chemical degradation, H NMR, C NMR, 2D NMR (COSY, TOCSY and HSQC) and mass spectrometry. The geometry of isolated compound Meeniose was optimized at B3LYP method and 6-311+G (d, p) basis set. In the light of results obtained from above experiments, the structure of novel pentasaccharides meeniose was confirmed as β-GalNAc (1→2)β-Gal(1→4)β-GlcNAc (1→3) -β-GalNAc (1→4)Glc.
Outlier Mining for Removing the Anomalies in High Dimensional Data Using ARVDH Algorithm

— In Data mining outliers are one of the main threats for efficient information retrieval from databases. Outliers are also known as Anomalies. Mining of outliers from the normal data is very important and scope of this is very high. Anomaly detection can be found in applications such as credit card fraud detection, intrusion and insider threat detection in cyber-security, detection of fault, or malignant diagnosis. Anomalous data present in database is harmful for the processing of information and usage of that information. Viscous data contain erroneous information and it may contain dangerous code for carking the whole system where it is stored. The main drawback of the existing system is, it does not support data with Multiclustering for removing viscous data. To avoid this problem we propose one algorithm which is Algorithm for Removing the Viscous data in High Dimensional data (ARVDH). Simple and efficient steps are used to remove outliers form information
FAIR RESOURCE ALLOCATION SCHEMES FOR RESOURCE CONSTRAINED CLOUD COMPUTING

Fair resource allocation mechanism allocates resources to different users or hosts in an efficient manner alleviating starvation of certain nodes. This ultimately utilizes the channel capacity effectively. The proposed mechanism uses priority of request and reduction ratio to reduce the number of resources required by the client. When a user requests for resources the threshold value is checked to allocate resources along with the priority of request to solve the starvation problem. When resources are scarce, the resources are allocated based on the reduction ratio. This study aims to analyze various fair resource allocation schemes to avoid congestion and improves the performance of network considerably.