Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of a Class of Quasi-Symmetric 2-Designs

In this article we study proper quasi-symmetric 2 − designs i.e. block designs having two intersection numbers and , where 0 < < . Also, we present a construction method for quasi-symmetric 2 − designs with block intersection numbers and + 1, where is a prime number, under certain conditions on the cardinality of point set.
EVALUATION OF ANTIMICROBIAL ACTIVITY AND PHYTOCHEMICALS ANALYSIS OF WHOLE PLANT EXTRACT OF VINCA ROSEA

Objectives: The aim of this research work was to find out the antibacterial activity and phytochemical analysis of whole plant extracts (shoot, flower, and root) of Vinca rosea. Methods: In recent work, phytochemicals were extracted from various parts of the plants using various solvents ethyl acetate (ETOAC), ethanol (ETOH), and dimethyl sulfoxide (DMSO). These phytochemicals contain alkaloids, terpenoids, tannins, flavonoids, aminoacids, saponins, aromatic acids, phenolic compounds, triterpenoids, xantho proteins, PHILOBATININS, carbohydrate, reducing sugar, and proteins; and they were separated by the standard methods. Moreover, antimicrobial activities of methanolic separation were determined by a different species of bacteria and fungi. Agar well-diffusion method was used for the antimicrobial activity, and also analyzed the zone of inhibition. Results: The evaluation of phytochemical screening of extracts indicated the existence of alkaloids, terpenoids, tannins, flavonoids, aminoacids, saponins, aromatic acids, phenolic compounds, triterpenoids, xantho proteins, philobatinins, carbohydrate, reducing sugar, and proteins. All the tested bacteria and fungi were controlled efficiently, and the activities of the shoot extracts were better compared to flower and root extracts. ETOH extract of the shoot exhibited highest antifungal activity against Candida albicans, (17.34 mm), followed by Candida krusei (16.12 mm), Aspergillus niger (15.76), Mucor sp. (14.67 mm), and Rhizopus oryzae (13.46 mm), ETOH extract of the shoot exhibited highest antibacterial activity), when compared to flower and root extracts against Staphylococcus aureus (12.77 mm), followed by Pseudomonas aeruginosa (11.67 mm), Salmonella abony (10.23 mm), Escherichia coli (9.65 mm), Micrococcus luteus (8.95 mm. V. rosea shoot extracts highlighted effective antifungal activities compared with flower and root extracts against all the tested bacteria. Conclusion: Current studies have shown that the V. rosea plant contains significant antimicrobial activities in the ETOH extracts. The strong antifungal and antibacterial activities of V. rosea are owing to the presence of saponin, tannins, and flavonoids present in.
Lung Cancer Recognition in CT Image Using Watershed Segmentation

: Lung cancer seems to be the common cause of death among people throughout the world. Survival from lung cancer is directly related to its growth at its detection time. The earlier the detection is, the higher the chances of successful treatment. To enhance cancer detection the radiologists, uses CT scan images for inspecting the interiors of the body.Image processing techniques provide a good quality tool for improving the manual analysis. Hence, a lung cancer detection system using image processing is used to classify the present of lung cancer in an CT-images. An automatic cancer detection system is proposed to distinguish cancerous tumor from the CT scan images. The cancer detection scheme consists of four stages. They are preprocessing, segmentation, feature extraction and classification. These four levels are used in image processing to enhance the tumor identification precision. The final outcome of this paper is to find cancer detection.
GENERALIZED METHOD FOR IMAGE DATA CLUSTERING

Clustering is considered aninteresting approach for finding similaritiesin data and putting similar data into groups.Clustering partitions a data set into several groupssuch that the similarity within a group is largerthan that among groups. In this paper various clustering approaches are discussed. Further the typical steps for clustering and methodology used for the execution of clustering steps are included.
Characterization of Monascus purpureus isolated from Red Yeast Rice and its Evaluation for the Production of Cholesterol Lowering Lovastatin

Red yeast is a bio-pigment producing fungi which contain natural form of statins. It is a red mold species which grows on starch containing substrates. Red yeast rice is used as a coloring and flavoring agent beside production of various types of statins. In the present study red yeast was isolated from red rice and screened for the production of lovastatin using solid state fermentation in Rose Bengal Agar after 7 days growth at 35C. Based on colony morphology, microscopic observations and genetic characterization it was identified as Monascus purpureus. Morphological characteristics of the red yeast showed the strain with cleistothecia having oval ascospores and aleiroconidia.
Mixed PCA and Wavelet Transform based Effective Feature Extraction for Efficient Tumor Classification using DNA Microarray Gene Expression Data

Cancer classification is an emerging research area in the field of bioinformatics. Gene expression profiles using microarray data play important role in accurate tumor diagnosis. Hence correlation between gene expression profiles to disease through microarray data and its analysis has been an intensive task in molecular biology. As the microarray data have thousands of genes and very few samples, it is crucial to develop techniques to effectively exploit the huge quantity of data produced. Thus efficient feature extraction and computational method development is indispensible for the analysis. In this paper a mixed feature extraction method by combining principal component analysis (PCA) and discrete wavelet transform (DWT) has been proposed to detect informative genes effectively. The PCA is a dimensionality reduction algorithm which aims to map high dimensional data to a lower dimensional space. The reduced data represents the most important variables underlying the original data. Further a feature extraction method based on the DWT is proposed. The approximation coefficients obtained by the decomposition at a particular level is used as the features for further study. Radial basis function neural network (RBFNN) classifier is used to efficiently predict the sample class which has a low complexity than other classifier. The potential of the proposed approach is evaluated through an exhaustive study by many benchmark datasets. The experimental results show that the proposed method can be a useful approach for cancer classification with low computational complexity and high accuracy.
Sensor and Vision Fusion for Virtual Reality

:This paper proposes an easy to employ and feasible solution for implementing interaction with virtual environments, pertaining to applications at educational training and product designing. Aiming at achieving better interaction with non-immersive Virtual Reality, this paper describes the fusion of two technologies. The first technology integrates the real time values from the Micro-electromechanical System (MEMS) based accelerometer, gyroscope and magnetometer sensors atop the Inertial Measurement Unit (IMU), to get the required degrees of freedom for real time pose estimation. The other technology is based on Computer Vision for tracking of region of interest using image processing through Webcam. Both the technologies can be implemented simultaneously to achieve efficient pose estimation and precise motion tracking which aid the interaction with virtual objects. With these two easy to employ technologies implemented at low cost, the possibilities can be immense, and the scope for applications can be expanded to domestic as well as industrial applications
INTERCOMMUNICATION INFORMATION SYSTEM USING ANDROID

Developing an intercommunication information system using android application is to give great facilities to the Head of the departments, to the staffs and to the students in the campus. Intercommunication information system using android provides a meaning of collaborative learning for the students. In recent years the Android Technology with web services has brought many extreme changes in the mobile application development field. Intercommunication information system using android provides a simple interface for better communication between head of the departments, staffs and students. It can be used by educational institutions and colleges to circulate messages very fast. It also spreads all the activities happening in the college.
Design of Ontology Learning Model Based on Text Classification for Domain Concept Taxonomy

In this paper we take the approach that constructed the Domain Concept Taxonomy which attempted to take a method that extremely beneficial for the knowledge acquisition task. This work is the integration of knowledge acquisition with machine learning techniques to increase the ontology creation effect, including taxonomy relation Generation, non-taxonomy relation Generation. In this work, the related techniques of machine learning and statistical natural language processing attempt to construct the Domain Concept Taxonomy.
CLUSTERING BASED INFREQUENT WEIGHTED ITEMSET MINING

Wireless sensor networks can be defined as a collection of devices, denoted as nodes, which can sense the environment and communicate the information gathered from the monitored field through wireless links. The data is forwarded through multiple hops to a sink which can use it locally and a gateway is used to get connected to networks. In WSNs where each device can sense, process and talk to its peers. WSNs have been used in many applications including military survey applications, health and wellness monitoring etc. In these applications Energy consumption is the major issue for past years, various techniques have been used for energy consumption as it had consumed only up to certain constraints. By using Zigbee-IRS technique, which is used to design nodes on WSNs to achieve a very low energy consumption and latency.
“ Financial Liberalization , Crisis and Restructuring : A Comparative study of bank performance and bank governance in South East Asia

This paper examines the impact of changes in bank governance on bank performance for a sample of commercial banks operating in South East Asia between 1990 and 2003. To meet their objective, authors collect data from Bankscope and IBCA databases for bank financial statements whilst macroeconomic data were obtained from the IMF International Financial Statistics and the Asian Development Bank. To qualify bank ownership, authors relied on the aforementioned databases, bank websites, and academic sources that have detailed changes in banking ownership including privatizations.
EOQ MODEL WITH PERMISSIBLE DELAY IN PAYMENTS UNDER PROGRESSIVE INTEREST SCHEME

The main purpose of this paper is to investigate the retailer’s optimal replenishment policy under permissible delay in payments within the economic order quantity (EOQ) framework. Previously published articles dealing with optimal order quantity with permissible delay in payments assumed that the supplier only offers the retailer single trade credit period. In fact, most suppliers frequently offer retailers (or customers) a two trade credit period to attract new retailers (customers) from their competitors. In this paper, the retailer faces a progressive interest charge from the supplier. If the retailer pays the outstanding balance by the grace period (say, M), the supplier does not charge any interest to the retailer but if the outstanding amount is paid after M, but by N (with N > M), then the supplier charges the retailer the lower interest rate of Ic 1 on the unpaid balance. If the retailer pays the outstanding amount after N, the supplier charges the regular interest rate of Ic 2 (with Ic 2 > Ic 1 ). We first establish an appropriate EOQ model for a retailer when the supplier offers a progressive interest charge, and then provide an easy solution procedure to the problem. We also provide numerical example to illustrate the proposed model and sensitivity analysis is also carried out.
Online Hybrid Tweet Summarization and Query Reformation for Linguistic features using Hadoop

: Tweet Segmentation is usually constructed to cluster the tweets with predefined filtering rules and criteria’s but normal hybrid segmentation fails during tweets are being created and shared at an unprecedented rate with enormous amount of data noises and redundancy. Hence, an efficient tweet stream clustering algorithm named as an online algorithm allowing for effective clustering of tweets with only one pass over the data. This algorithm employs two data structures to keep important tweet information in clusters. The first one is a novel compressed structure called the Tweet Cluster Vector (TCV). TCVs are considered as potential sub-topic delegates and maintained dynamically in memory during stream processing. The second structure is the Pyramidal Time Frame (PTF) which is used to store and organize cluster data at different moments. Given a timeline range, the summarization system may produce a sequence of time stamped summaries to highlight points where the topic/subtopics evolved in the stream. Summarization represents a set of documents by a summary consisting of several sentences. Intuitively, a good summary should cover the main topics (or subtopics) and have diversity among the sentences to reduce redundancy. In general, tweet summarization has taken the temporal feature of the arriving tweets into consideration and the TCV-Rank summarization algorithm is applied to generate summaries. Experimental on the synthesis dataset demonstrated the efficiency, flexibility and cluster formation for topic evolution.
A High Dimensional Color Transform and Learning Based Approach for Dominant Area Detection

: An approach is described that significantly detect dominant region in an image. This approach considers global and local features, which is used to compute a dominancy map. First a dominancy map of an image is created by using a linear combination of colors in a high dimensional color space. This is based on observation that dominant regions have distinguishing colors compared to backgrounds in human vision. The small-dimensional red, green, and blue color are mapped to a feature vector in a high dimensional color space, an detailed dominancy map is composite by finding the excellent linear combination of color coefficients in the high dimensional color space. Secondly, the performance of dominancy estimation is raised by applying relative location and color contrast between superpixels to from a trimap via regression algorithm. The test results of two benchmark datasets found to be effective.
To Detect Terror-Related Activities on the Web using Data Mining Techniques

: An innovative knowledge-based methodology for terrorist detection by using Web traffic content as the audit information is presented. The proposed methodology learns the typical behavior (‘profile’) of terrorists by applying a data mining algorithm to the textual content of terror-related Web sites. The resulting profile is used by the system to perform real-time detection of users suspected of being engaged in terrorist activities. The Receiver-Operator Characteristic (ROC) analysis shows that this methodology can outperform a command-based intrusion detection system .
International Journal of Scientific Research in Computer Science, Engineering and Information Technology

290 Predicting Early Reviews for Effective Product Marketing on E-Commerce Websites F. Femila1, S. Janakipriya2, R. B. Nivetha Sruthi2, S. Rohini2 1Assistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India 2UG Scholar, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India ABSTRACT
Aspect - Based Sentiment Analysis Using Context - Aware Learning for Online Reviews

: To process the billions of daily social conversations across millions of sources require sophisticated streaming big data processing to churn through the huge volume of content. In order to filter the noise and spam and classify the information and intelligence, in turn, requires contextual semantic sentiment modeling to capture the intelligence through the complexity of online social discussions. Sentiment analysis is one of the techniques to capture the intelligence from Social Networks based on the user generated content. There are more and more researches doing about sentiment classification. Comparing researches about the document and sentence-level sentiment classification, aspect-based sentiment analysis researches are less. The aspect-based sentiment analysis is a fine - grained approach. The ultimate goal is to be able to generate summaries listing all the aspects and their overall polarity. The novel idea of this paper is to generate Context-Aware lexicon dictionary to capture contextual semantics polarity and highly relevant aspects are captured using Latent Semantic Analysis, which will improve the accuracy of aspect-based sentiment analysis
Continuum-Level Simulations of Biopolymer Networks: A Minireview

The mechanical properties of living cells are governed by the cytoskeleton, a network consisting of many different biopolymers together with transient linker proteins [1]. Biopolymers, especially those composed of globular proteins, are known to be semiflexible, that is the persistence length of these filaments is often comparable to their contour length, leading to small, yet significant, thermal fluctuations around a straight conformation and ultimately resulting in mechanical properties far from those exhibited by synthetic materials made of highly flexible polymers [2]. Theoretically, it remains a great challenge to understand/predict how a semiflexible filament network behave because of its many-body nature as well as the coupled elastic and entropic effects involved. For this reason, there has been a growing interest in the direct simulation on computer-generated networks in recent years. Here, we briefly review various continuum-level simulation approaches that have been developed in the past few decades.
Relevance Feedback for Content-Based Visual Information Retrieval using Active Learning Method

-- In content-based Visual Information retrieval, relevance feedback has been introduced to reduce the gap between low-level image properties and high-level properties. Furthermore, to speed up the converging to the query, several active learning methods have been proposed including random filtering to select images for labeling by the user. relevance feedback (RF) schemes improves the performance of content-based Visual Information retrieval (CBVIR) requiring the user to describe a large number of images. To reduce the labeling effort of the user, this paper presents a novel active learning (AL) method to drive RF for retrieving remote sensing images or sattelite images from large archives in the framework of the support vector machine classifier. The proposed AL method is specifically designed for CBVIR and defines an effective and as small as possible set of related and irrelated images with regard to a general query image by jointly evaluating three criteria: unreliabillity ; diversity; and density of images in the archive. The unreliabillity and diversity mainly aims at selecting the most informative images in the collection of images, whereas the density criterion goal is to choose the images that are representative of the underlying distribution of data in the collection. In first Step the most uncertain images are selected from the collection on the basis of margin sampling strategy. In second Step the images that are both diverse to each other and associated to high-density regions of the image feature space in the archive are chosen from most uncertain images. This step is achieved by a novel clustering-based strategy which contributes to solve the problems of unbalanced and biased set of related and irrelated images.
The Optimization Methodology for Large Scale Multi-User MIMO-OFDM Systems with PAPR Reduction

-Wireless communication is one of the important vital aspects of life. The growth of wireless communication technologies has been producing the enormous demand for high-speed, efficient, reliable voice & data communication. For better transmission, replaced single – carrier waves by multi – carrier waves. Hence, we proposed a novel technique which efficiently suited for PAR reduction in OFDM. This paper discussed about the PAR reduction aiming to achieve higher throughput, higher data rate and better performance. The use of OFDM causes a high peak to-average (power) ratio (PAR), which necessitate expensive and power-inefficient radio-frequency (RF) components at the base station. In this paper, we present a novel data transmission scheme, which exploits the massive tone reservation available in large-scale MU-MIMO-OFDM systems to achieve low PAR. In previous algorithm, we are applying FITRA for PMP . Here to reduce computational complexity of FITRA, we proposed a novel scheme based on sparse for PMP. We developed a corresponding algorithm is fast iterative truncation algorithm (FITRA) with sparse representation and show numerical results to demonstrate tremendous PAR-reduction capability. Experimental results show that our method will achieve better results compared to state-of-art criteria like FITRA in terms of PAR and computational complexity.
Hand Posture Detection: Principle, Need and Algorithms

Hand posture identification research has gained much attention because of its applications for interactive human–machine interface and virtual environments. Hand postures are frequently used as intuitive and convenient communications in our daily life, and the recognition of hand postures can be widely applied in human computer interfaces, robot control, and augmented reality, etc. Therefore an efficient, fast and reliable hand posture system is the need for today. In this paper we present the study, need type and various algorithms of a hand posture identification system
(AUTOMATIC DROWNING RESCUE SYSTEM FOR HUMAN USING RFID)

– It is very difficult and takes time to detect the person in water by usual ways like with the help of fire force or divers. Human sensor in the proposed system can detect the presence of human under water. RFID is used as the human sensor in the system. GPS and GSM can be used for identifying the location of the person and to send the message of person’s location to the receiver on the boat. The RFID consist of two parts RFID tag and reader. The RFID reader is placed at the dangerous locations of the water body and the RFID tag is attached to the person’s hand. The RFID reader will produce RF signals at all the time. When the tag is nearer to the reader it will be active using the RF signals from reader and send a unique serial number to the reader using its antenna. By programming the PIC microcontroller the tag will be identified and the GPS will be active at the same time. The GPS signal will be received at the PIC MC and send the location to the receiver on the boat using GSM technique. The boat will reach the location and save the person.