Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Sliding Variable on Robust Position Control of DC Motor

The conventional sliding mode control (SMC) is designed for position control of the DC motor, which makes the system highly robust against system parameter such as Coulomb frict ion torque and unknown payload variations. The main purpose of this work is to show the effect of slid ing surface coefficient matrix on the reaching t ime of motor position and speed. The proposed control technique also makes the system insensitive to payload variation and to any unknown change in the payload mass value .
Prediction of User Topic Opinions based on Social Media Analysis

Micro-blogs are common platforms for the people and organizations to create, share, or exchange information, experiences and ideas. Peoples conversations on social media portray their emotional status. Therefore, extensive research and analysis is performed on the data gathered from various sites, which includes user’s social and contextual information. A Challenging and most essential task is to use the learned knowledge in the prediction of user’s opinion towards specific topics which are not directly stated. Unlike previous works, we predict individual’s topic opinions on specific topics which are not given directly, using obtained knowledge
Content Based Image Retrieval Method using Fuzzy Heuristics

Content based image retrieval (CBIR) refers to image content that is retrieved directly, by which the images with features or containing certain contents will be searched in an image database. The main idea of CBIR is to analyze image information by low level features of an image, which includes color, texture, shape and space relationship of objects etc., and to set up feature vectors of an image as its index. A new CBIR search engine is proposed using three features and similarity is measured and controlled by fuzzy heuristics. CBIR Search Engine relies on the characterization of primitive features such as color, shape, and texture that are automatically extracted from the images. There are several techniques to deal with CBIR problems for retrieving the relevant images. CBIR proposed by using three methods. Colour feature is extracted by using histogram based method, texture feature is extracted by using Gabor filter and shape feature is by moment invariant algorithm. For searching the similar images with the database similarity measure is calculated and is controlled by using fuzzy. Fuzzy similarity measure is implemented by using mamdani fuzzy inference method. The use of these three algorithms ensures that the image retrieval approach produces images which are relevant to the content of an image query.
Bit Error Rate Improvement by using Active Constellation Extension and Neural Network in OFDM System

– The demand for multimedia data services has grown up rapidly. One of the most promising multi-carrier system, Orthogonal Frequency Division Multiplexing (OFDM) form basis for all 4G wireless communication systems due to its large capacity to allow the number of subcarriers, high data rate and ubiquitous coverage with high mobility. OFDM is drastically affected by peak-to-average-power ratio (PAPR). In this paper, an effort has been made to analyze how well an OFDM system can perform when a signal, of various PAPR reduction techniques, is transmitted over Additive White Gaussian Noise (AWGN) channel using 16-QAM modulation technique. The performances of PAPR reduction schemes have been evaluated in terms of the bit error rate (BER). Simulation results reveal that, the OFDM system with Active constellation extension (ACE) and Neural Network (NN) schemes improve the BER performance and robust to high PAPR.
Software Reliability Assessment Using High-Order Markov Chains

: The assumption of independent execution of the component in the software reliability prediction models using component-based approach is a simplification of the real work of the software. In this paper the method of software reliability prediction that takes into account dependencies between software components is described. It uses higher-order Markov processes and consists of the following steps: definition of each component’s failure rate, probability of transitions between components, the optimal order of Markov process and computation of the whole software system reliability itself.
Citrulline: Modulation on Protein Synthesis, Intestinal Homeostasis and Antioxidant Status

Citrulline, a nonessential amino acid, is an important precursor substance of arginine. It has been implicated in diverse biological and physiological events, such as nitrogen balance, growth and development, muscle performance, and intestinal homeostasis, but its underlying mechanism in intestinal function is unclear. This review discusses the modulation effects of citrulline on protein synthesis and intestinal homeostasis, as well as its potential mechanisms to broaden its application.
Use of Mind Maps for Understanding the Autistic Condition

The medical, psychological, psychiatric and neurological terminology is very abstract, so much that it can confuse patients and their families. With technological advances people have access to information but human contact and the effects of this have not yet been replaced, being able to receive an explanation by a person trained for assistance provides stability and calm. It will allow taking more satisfactory actions.
Comparative Evaluation of CFX96TM Real Time PCR with Conventional PCR for Rapid Diagnosis of Mycobacterium tuberculosis Complex in Clinical Isolates

Background: Globally, Tuberculosis (TB) still remains a major public health problem. In India, Pulmonary Tuberculosis (PTB) is the most common form of the disease; however, Extrapulmonary Tuberculosis (EPTB) comprises 10 to 20% of all TB cases. The diagnosis of EPTB cases is difficult because of paucibacillary nature and consequently is associated with low sensitivity of ZhielNeelson (ZN) smear and culture on Lowenstein-Jensen (LJ) media as gold standard. The present study comparatively evaluates the utility of real time quantitative PCR over nested PCR and other conventional techniques for the detection of M. tuberculosis Complex (Mtc) in clinical isolates of PTB and EPTB samples at a tertiary care centre, Shri Ram Murti Smarak Institute of Medical Sciences (SRMSIMS), Bareilly. Methods and Findings: In total, 205 both pulmonary (24) and extrapulmonary (181) specimens were processed for ZN smear, culture on LJ media, nested PCR and real time quantitative PCR using IS6110 as a gene target. Out of 205 samples, none of the sample was found to be smearpositive and only 28 (14%) samples were found to be culture positive. The nested PCR and real time quantitative PCR positivity was observed 100% in culture positive specimens. However in majority of culture negative specimens the sensitivity was 100% in both nested PCR and real time quantitative PCR assays for EPTB and PTB specimens. The specificity was better in case of real time quatitative PCR as compared to nested PCR, 45.8% and 20% in case of nested PCR for nonrespiratory and respiratory specimens respectively. The specificity was increased upto 86.1 and 100% with real time quantitative PCR for EPTB and PTB cases respectively. Conclusions: The combined analysis of nested PCR, real time quantitative PCR and other lab investigations can be very useful in the rapid diagnosis of M. tuberculosis in paucibacillary extrapulmonary tuberculosis samples in Indian scenario.
Relation Extraction for Massive News Texts

: With the development of information technology including Internet technologies, the amount of textual information that people need to process daily is increasing. In order to automatically obtain valuable and user-informed information from massive amounts of textual data, many researchers have conducted in-depth research in the area of entity relation extraction. Based on the existing research of word vector and the method of entity relation extraction, this paper designs and implements an method based on support vector machine (SVM) for extracting English entity relationships from massive news texts. The method converts sentences in natural language into a form of numerical matrix that can be understood and processed by computers through word embedding and position embedding. Then the key features are extracted, and feature vectors are constructed and sent to the SVM classifiers for relation classification. In the process of feature extraction, we had two different models to finish the job, one by Principal Component Analysis (PCA) and the other by Convolutional Neural Networks (CNN). We designed experiments to evaluate the algorithm.
Estimating the Genetic Capability of Different Phytoplankton Organisms to Adapt to Climate Warming

Current predictions of temperature increase in sea surface water estimate that extensive regions of the ocean will be warmer than at any time in the past million years as a consequence of the present trend of release the CO2 excess into the atmosphere. Studying the capacity of phytoplankton to adapt to warming has become a relevant issue because phytoplankton represents the basis of the aquatic food web supporting about half of the global primary production. Considering the complexity of the phytoplankton community in both taxonomic level and habitat preferences, different responses to increased temperature are expected.We experimentally estimate the potential of different phytoplanktonic populations of 15 species, belonging to different taxonomic groups (Cyanoprokaryota, Dinophyta, Chlorophyta, Haptophyta, Heterokontophyta) and habitat preferences(e.g. coastal waters, open ocean, coral symbiotic), to genetically adapt in an evolutionary sense to marine warming.Since genetic variance in fitness estimates capability for adaptation of a population (Fisher’s Fundamental Theorem of Natural Selection) we measured the heritability of fitness (i.e. proportion of variance in fitness that has genetic basis) under increasing temperatures, using an experimental quantitative genetic procedure suitable to phytoplankton populations. Our results reveal that there are interspecific differences in phytoplankton capability for adaptation under a gradual warming process and provides experimental evidences for assessing how phytoplanktonic organisms might evolve under climate warming in the near future.
HierarchalVariableSwitchingSetsofInteracting M ultipleM odel forTrackingM aneuvering Targets in SensorNetwork

Tracking maneuvering targets introduce two major directions to improve the Multiple Model (MM) approach: Develop a better MM algorithm and design a better model set. The Interacting Multiple Model (IMM) estimator is a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation schemes. The main feature of this algorithm is the ability to estimate the state of a dynamic system with several behavior modes which can “switch” from one to another. In particular, the use of too many models is performance-wise as bad as that of too few models. In this paper we show that the use of too many models is performance-wise as bad as that of too few models. To overcome this we divide the models into a small number of sets, tuning these sets during operation at the right operating set. We proposed Hierarchal Switching sets of IMM (HSIMM). The state space of the nonlinear variable is divided into sets each set has its own IMM. The connection between them is the switching algorithm which manages the activation and termination of sets. Also the re initialization process overcomes the error accumulation due to the targets changes from one model to another. This switching can introduce a number of different models while no restriction on their number. The activation of sets depends on the threshold value of set likely hood. As the likely hood of the set is higher than threshold it is active otherwise it is minimized. The result is the weighted sum of the output of active sets. The computational time is minimum than introduced by IMM and VIMM. HSIMM introduces less error as the noise increase and there is no need for re adjustment to the Covariance as the noise increase so it is more robust against noise and introduces minimum computational time.
Enhanced Churn Prediction on Huge Telecom Data using Particle Swarm Optimization

: Churn prediction in telecom has gained a huge prominence in the recent times due to the extensive interests exhibited by the stakeholders, large number of competitors and huge revenue losses incurred due to churn. Predicting telecom churn is challenging due to the voluminous and sparse nature of the data. This paper presents a technique for telecom churn prediction that employs PSO for churn prediction. Experiments reveal that the performance of metaheuristics was more efficient and they also exhibited better predictability levels.
Phytochemical analysis and DPPH scavenging activity of punctatum var. squamosum (Combretaceae), an ethnomedicinal plant of

1Regional Institute of Paramedical and Nursing Sciences India. 2Dibrugarh University, India. A b s t r a c t The main objective of the study is to determine the phytochemical constituents and the DPPH scavenging activity of the aqueous extract phytochemical screening revealed that the crude aqueous extract contained phenols, flavonoids, phytosterols and diterpenes while alkaloids, glycosides, tannins, carbohydrates, proteins and amino acids were found t the crude aqueous extract was found to be comparatively high, i.e. 111 mg/g GAE and 120.99 mg/g QE respectively. It was also found that the % DPPH scavenging activity was very high and 1.76
Storage Quality and Oxidative Stability Attributes of Almond (Prunus dulcis) Fortified Chevon Nuggets

The present study was conducted to evaluate the possibility of utilization of almonds in the development of value added healthier chevon nuggets and to study their physico-chemical, sensory and storage properties. Four levels of almonds (0, 5, 10 and 15%) were incorporated replacing lean meat in the formulation. The products developed were assessed for various physico-chemical and sensory parameters. Based on these parameters, chevon nuggets containing (5%) levels of almond were selected and further studied for its storage quality. The optimized nuggets were aerobically packaged in low density polyethylene pouches along with control and evaluated for storage quality (Oxidative stability, microbiological and sensory parameters) for 21 days under refrigerated conditions (4±1oC). Based on various parameters, a 5% level of almonds was adjudged as optimum for the preparation of almond enriched chevon nuggets. The storage quality parameters like Free fatty acid value, Thiobarbituric acid reactive substances value, Total plate count and Psychrophillic count increased signifi cantly (P<0.05) whereas all the sensory parameters decreased signifi cantly (P<0.05) with increasing days of storage. The product can be successfully stored for 14 days under refrigerated conditions (4±1oC) without marked loss in quality.
FERTILITY-ENHANCING POTENTIALS OF IPOMOEA BATATAS (SWEET POTATO) IN MALE ALBINO WISTAR RATS

This work is aimed at investigating the effects of Ipomoea batatas on fertility in male albino wistar rats. Fourteen adult male wistar rats weighing between 160g-200g were randomly assigned into two groups of 7 rats each. Group A (control) received 200g of animal feed per day, while Group B (Test group) received 120g animal feed thoroughly mixed with 80g dried ground Ipomoea batatas root tuber particles, daily for 60 days. The weights of the animals were taken weekly. Fertility test was done by the natural mating method by cohabiting two mature fertile female rats with each male rat during the last ten days of treatment. The females were sacrificed after 7 days of separation from the males, and fertility parameters determined. On the 61 st day, the male rats from each group were sacrificed and the testis and epididymis excised and weighed. Semen from the epididymis was analysed for sperm characteristics. The results in test group showed significant (P<0.05) increase in the following: weight of testis and epididymis, sperm motility, sperm count and sperm viability; with an insignificant (P>0.05) reduction in sperm abnormality. Also, there was a significant (P<0.05) increase in the number of pregnant female rats in test group, corresponding to 42.86% increase in fertility rate, compared to the control animals. It was concluded that prolong consumption of Ipomoea batatas enhances fertility in male wistar rats by improving the sperm quality.
Predictive Data Mining: Promising Future and Applications

: Predictive analytics is the branch of data mining concerned with the prediction of future probabilities and trends. The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other entity to predict future behavior. For example, an insurance company is likely to take into account potential driving safety predictors such as age, gender, and driving record when issuing car insurance policies. Multiple predictors are combined into a predictive model, which, when subjected to analysis, can be used to forecast future probabilities with an acceptable level of reliability. In predictive modeling, data is collected, a statistical model is formulated, predictions are made and the model is validated (or revised) as additional data becomes available. Predictive analytics are applied to many research areas, including meteorology, security, genetics, economics, and marketing. In this paper, we have done an extensive study on various predictive techniques with all its future directions and applications in various areas are being explained
SENTIMENT ANALYSIS ON TWITTER DATA

- Twitter is one of the most commonly used platforms for sharing opinions, expressing views. Sentiment Analysis on twitter can allow users to understand the opinions expressed in tweets and classifying them in positive or negative categories. The organizations can use sentiment analysis to get an idea of the customer reviews of their products, and subsequently try and improve their services based on the reviews.
An Improved Colour Texture Based Face Spoofing Detection

: Research on non-intrusive software based face spoofing detection schemes has mainly been focused on the analysis of the luminance information of the face images, hence discarding the chroma component which can be very useful for discriminating fake faces from genuine ones. This work introduces a novel and appealing approach for detecting face spoofing using colour texture analysis. We exploit the joint colour-texture information from the luminance and the chrominance channels by extracting complementary low-level feature descriptions from different colour spaces. The colour local binary patterns (LBP) descriptor, explore the facial colour texture content using four other descriptors: the local phase quantization (LPQ), the co-occurrence of adjacent local binary patterns (CoALBP), the binarized statistical image features (BSIF) and the scale-invariant descriptor (SID) that have shown to be effective in gray-scale texture based face anti-spoofing. Here by using these features the colour texture is analyzed and extracted by face descriptors from different colour bands . To gain insight into which colour spaces are most suitable for discriminating genuine faces from fake ones, considered three colour spaces, namely RGB, HSV and YCbCr. A new and appealing approach using colour texture analysis and demonstrate that the chroma co mponent can be very useful in discriminating fake faces from genuine ones. First, the face is detected, cropped and normalised into an M×N pixel image. Then, holistic texture descriptions are extracted from each colo ur channel and the resulting feature vectors are concatenated into an enhanced feature vector in order to get an overall representation of the facial colour texture. The final feature vector is fed to a binary classifier and the output score value describes whether it is a real or a fake image.
The evaluation of QTc prolongation and QT dispersion in type 2 diabetes mellitus as an indicator of cardiac autonomic neuropathy

Background: Diabetes mellitus refers to group of metabolic disorders characterized by hyperglycemia due to an absolute or relative deficit in insulin production or action. Diabetes mellitus produces pathological changes in most organs of the body including heart, blood vessels, kidneys, nerves and eyes. Cardiovascular autonomic neuropathy (CAN) is a severely debilitating yet underdiagnosed complication of diabetes. Diabetes-associated cardiovascular autonomic neuropathy damages autonomic nerve fibers that innervate the heart and blood vessels causing abnormalities in heart rate and vascular dynamics. Methods: Total 80 cases of diabetes mellitus were selected. Cardiac autonomic neuropathy in them was diagnosed by a series of tests recommended by Ewing et al, which include - Valsalva ratio, Deep Breath Test, Heart rate response to standing, Postural Hypotension, SHGT Increase in diastolic BP on sustained hand grip. They were divided into 2 groups A and B depending on presence or absence of cardiac autonomic neuropathy. ECG was done to calculate QTc and QTd. Results: In group A mean QTc was 0.344 sec and in group B in patients with mild CAN mean QTc was 0.432, moderate CAN mean QTc was 0.444, and in patients of severe CAN mean QTc was 0.481. p value was 0.001 that it is highly significant. Means more was degree of CAN more was prolongation of QT and similarly more the degree of CAN more was QTd. Conclusions: Diagnosis of cardiac autonomic neuropathy by battery of cardiac autonomic function tests is a comlex procedure. The prolongation of QTc interval and more specifically QTd interval on ECG is a marker in diagnosis of cardiac autonomic neuropathy which can be easily evaluated.
Comparison of Feature Selection Techniques for Spam Email Classification

: Dimension reduction is problem pointing towards reducing high number of features in the dataset. Some of these features are irrelevant or redundant, may leads to inefficient model for classification. Dimension reduction using feature selection allo ws selecting minimal subset of features that are relevant to target concept for building efficient learning models. Email message is semi-structure, multidimensional in nature and represented by a large number of features. Directly applying classification makes learning intractable. This paper aims to target comparison of various feature selection techniques for email data. In our approach we have used only email header to find best subset of features. The resultant subsets of headers are used for email classification.
EFFICIENCY IN ORGANIZING TRANSPORT ROUTES AS PART OF THE CITY WASTE MANAGEMENT: PROPOSAL FOR INNOVATIVE WAY OF TRANSPORT

Research presented in this paper is primarily focused on the theoretical model of transportation routes' optimization, by means of using an innovative approach, projected to countries in transition. This paper presents part of the research, whose main parameters are directly related to basic research hypothesis, and are result of author's stay in vocational training in the Japanese city of Osaka, in the period 12.01. 07.03. 2015. Based on the transport routes' calculations, we came to an answer set in the research problem of this paper, as well as to the optimization of costs and avoiding a "downtime" during the collection of waste conducted by utility companies, who will be the beneficiaries of this model. The primary goal of the work is to, on the base of knowledge and experience acquired in Japan, create an Optimization Model of Transport Routes adjusted to countries in transition (Bosnia and Herzegovina and others), which will, in the long run, be of benefit to these countries, in terms of a sustainable waste management.