Miracle-dz/UniFAR-FTUs huggingface dataset:
Location Tracking Method using Communication Signal Strength

 Abstract Background/Objectives: A location tracking system has been developed in order to tracks the location of a moving terminal and provide various tracking services based on the location. Methods/Statistical analysis: This paper presents a method to find a target using UAVs in LoRa communication environment. The proposed method proposes a UAV flight path where the target signal can be found without blind spots. The flight path was changed by using the intensity of the transmitted signal during the flight using the variation of the signal strength. Findings: We verified the proposed method by simulation and confirmed that it takes a long time to track the target, although the efficiency of the 3 flights is high. It is best to fly three UAVs to reduce time to find target and increase efficiency when the time is a critical factor. The LoRa communication lowers the production cost and makes it possible to construct the system with low cost. Improvements/Applications: The proposed method provides the location-based service collecting location target information on the terminal and provides various services such as route guidance, prevention of crime, or finding a missing child.
A Performance Evaluation of SMCA Using Similarity Association & Proximity Coefficient Relation For Hierarchical Clustering

— Clustering techniques have a wide use and importance nowadays. This importance tends to increase as the amount of data grows and the processing power of the computers increases. Clustering applications are used extensively in various fields such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing.There are several algorithms and methods have been developed for clustering problem. But problem are always arises for finding a new algorithm and process for extracting knowledge for improving accuracy and efficiency. This type of dilemma motivated us to develop new algorithm and process for clustering problems. There are several another issue are also exits like cluster analysis can contribute in compression of the information included in data. In several cases, the amount of available data is very large and its processing becomes very demanding. Clustering can be used to partition data set into a number of “interesting” clusters. Then, instead of processing the data set as an entity, we adopt the representatives of the defined clusters in our process. Thus, data compression is achieved. Cluster analysis is applied to the data set and the resulting clusters are characterized by the features of the patterns that belong to these clusters. Then, unknown patterns can be classified into specified clusters based on their similarity to the clusters’ features. Useful knowledge related to our data can be extracted [1].
PRODUCTION AND CHARACTERIZATION OF BIOSURFACTANTS BY ISOLATES FROM LUBRICATING OIL POLLUTED SOIL

The study intended to isolate, Produce and characterize biosurfactant producing isolates from spent lubricating oilpolluted soil. The isolated bacteria were identified as Acinectobacter iwoffii mpe25 and Micrococcus kristinea mpe12. Both bacteria exhibited the ability in producing biosurfactants as evidenced by blood haemolysis, oil displacement, and drop collapse and emulsification activities. After the fermentation for seven days, biosurfactant from the two bacteria was successfully extracted from the fermentation by centrifugation and purified by solvent extraction using acid precipitation method. Characterization of the biosurfactants was done by thin-layer chromatography (TLC), gas chromatography and mass spectroscopy (GC-MS), and Fourier transforms infrared (FTIR) spectroscopy. The bacteria lysed red blood cells by forming cleared transparent halos (beta haemolysis) around the colonies caused the wide diameter of oil displacement of 5.5cm, 6.10cm, and high emulsification capacity of 74.5% and 65.0 % respectively. The GC-MS revealed the presence of palmitic acid, 15-hydroxypentadecanoic acid, 2, 3dihydroxydodecanoic acid, sulfur-dodecyl-2-ethyl hexyl ester; the FTIR revealed important functional groups (S=O, C-O-C, C=O) that defined the biosurfactant to be a glycolipid with a unique sulfonyl group in both biosurfactants. These led to the assigned name of sulfoglycolipid Bios-25 and disulfoglycolipid Bios-12 to the biosurfactant produced by A. iwoffii mpe25 and M. kristinea mpe12 respectively. Due to the high emulsification capacity of these biosurfactants, it is recommended for use in bioremediation studies.
Novel Approach for Salient Region Detection Improved By Dynamic Region Merging and Boundary Analysis

: Detection of salient region detection is useful for several image processing applications such as security system for Airport, Medical imaging, Object reorganization, Criminal Investigation, Retrieval of image, filter design and Retargeting an Image. A novel approach to determine salient region of images is proposed in this paper. GMM if used for clustering the colour values in the L*a*b colour space into n classes and colour spatial variance is calculated. Then dynamic region merging segmentation algorithm is utilized to extract the high level information and to achieve full resolution saliency maps. Context-aware technique is used, which makes salient regions more brilliant in the saliency map. The segmented results of image are used to compute the saliency value based on the colour contrast computation and define the saliency for each region as the weighted sum of region contrast to all regions in the image to obtain better precision and recall. The results obtained from the three modules are combined to obtain the final saliency map which is then multiplied by center distance map.
Doble carga de malnutrición en escolares urbanos y rurales de Guatemala

Introduccion: Guatemala ha estado experimentando una transicion en sus estilos de vida. Mientras que la desnutricion cronica sigue siendo prevalente, se ha experimentado un aumento sustancial de los casos de obesidad. Objetivo: proporcionar informacion actualizada sobre las nuevas tendencias en cuanto al estado nutricional en Guatemala. Metodos: se diseno un estudio transversal con una muestra de 392 ninos y adolescentes (5-18 anos) seleccionados de manera aleatoria. El muestreo fue estratificado segun su ubicacion geografica (rural y urbana), sexo y edad. Resultados: alrededor del 62% de la muestra urbana presento problemas de sobrepeso y un 13,8 de obesidad. Entre los participantes urbanos, la prevalencia de sobrepeso/obesidad fue mayor en el grupo de 11-14 anos. El grupo urbano presento una mayor prevalencia de sobrepeso/obesidad que el grupo rural para todos los grupos de edad (excepto en el grupo de 15-18 anos que presento unos indices de obesidad ligeramente mas elevados en el grupo rural). Se observo una mayor prevalencia de retraso en el crecimiento en el grupo rural, excepto para el grupo de edad de 15-18 anos. La prevalencia de bajo peso fue significativamente mayor en el grupo rural (25% vs. 1,2%) asi como la prevalencia del delgadez, excepto para el grupo de edad de 5-10 anos. Conclusion: la alta prevalencia de bajo peso y la de sobrepeso/obesidad coexisten en los ninos y adolescentes en Guatemala.
Hybridization of Cuckoo Search & Artificial Bee Colony Optimization for Satellite Image Classification

: In last few decades, Image classification has become one of the extensively used research area due to its wide range of applications like ground water exploration, environmental disaster assessment, terrain feature extraction, urban planning, & land use monitoring etc. There exist various traditional, nature inspired and other classification approaches that are being used for the satellite image classification. But the results obtained from the existing algorithms are nor much efficient to use for any real life application. In this paper, we are applying the swarm intelligence based hybrid approach to classify the land cover features. The algorithms considered for the hybridization are Cuckoo Search (CS) and Artificial Bee Colony Optimization (ABC). As individual algorithm are not much efficient for classification, so hybrid concept is considered to make the classification more efficient and accurate. The output results obtained from the hybrid concept are compared with the individual CS & ABC algorithms. Also some other algorithms like Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO), hybrid ACO/BBO, hybrid CS/PSO, hybrid CS/ACO and hybrid ABC/BBO are considered for the comparison purpose.
Fingerprint Based Gender Estimation using 2D Discrete Wavelet Transform and Independent Component Analysis

: Fingerprints are used for over a decade, and moreover it has been generally accepted form of biometric identiﬁ cation. Estimating the Gender of the ﬁ ngerprints is a developing ﬁ eld and numerous methods using the ﬁ ngerprint physical features like the ridge count and the ridge breadth have been used so far. In this paper, a novel technique for gender classiﬁ cation using ﬁ ngerprints has been implemented. The ﬁ rst method is the discrete wavelet transformation (DWT) which is employed to extract ﬁ ngerprint, and the second method Independent Component Analysis (ICA) is used to estimate a Gender using an extracted ﬁ ngerprint. The above said methods are combined and used for gender classiﬁ cations. The Multi Class Support Vector Machine (Multi-class SVM) was used as a classiﬁ er. The method is experimented with the internal database of 500 ﬁ ngerprints of left hand index ﬁ nger, 250 males & 250 females belonging to same age group. The experimental results show good for trained list of ﬁ ngerprints. It was found that increasing the Quality of the image improves the performance of the system.
Distance Based Fault Detection in Wireless Sensor Networks: A Survey

The wireless sensor network takes part in area monitoring, environmental monitoring, Medical application, structural monitoring, traffic monitoring and habitat monitoring. The wireless sensor network consists of large number of sensor nodes. They become failure due to hardware failure, communication error, energy depletion and malicious attack. Fault detection is one of the techniques in fault tolerance. Fault detection is to collect the information of possible faults. There are many mechanisms proposed to solve this issue, Distance based fault mechanisms is a method proposed to detect the faulty node. The aim of this paper is to survey the various Fault detection mechanisms. KeywordsWireless Sensor Network (WSN); Sensor Fault Models; Fault Tolerance; Distance Based Fault Detection.
Determinants of Dental Health Behaviors of Iranian Students Based on the Health Belief Model (HBM)

Background: Several factors affect oral and dental hygiene and therefore general health. Objectives: This study used the health belief model (HBM) to explore factors influencing dental health behaviors of students in Teheran, Iran. Methods: A cross-sectional study design was used. The participants were 400 male high-school students in district 5 in Tehran selected through multistage stratified random sampling. A self-administered questionnaire was used to collect the data, which included the following measures of the HBM: perceived severity, perceived susceptibility, perceived benefits, perceived barriers, cues to action, and self-efficacy. A multiple regression analysis and Pearson’s correlation were run to detect potential correlations between the independent variables and dependent variable. Results: All 400 students completed the study. The results of the linear regression analysis showed that perceived barriers, self-efficacy, and cues to action accounted for the highest percent of total variance observed in dental health behaviors. The total HBM constructs and demographic variables explained 29% of the variance in dental health behaviors. Conclusions: The current study provides evidence that oral and dental hygiene can be promoted by reducing perceived barriers and enhancing oral and dental hygiene self-efficacy.
Online Recommendation System Based on Reviews and Ratings

---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Now a days customers express their opinions and reviews of various online shopping products by the means of ratings and number of varied comments, which becomes tedious to understand the actual quality of the product. How to mine these review data to understand customers choice and make recommendations is crucial to merchants and researchers. Companies like Amazon use their large amounts of data to give recommendation to users. Based on the similarities among items ,systems can give prediction for a new items ratings. Recommender systems uses the user, item, and ratings information to predict how other users will like a particular item. To reach this goal, systems require to parse a lot of data and collect information, sometimes from different resources, and predicts how the user will like the product or item. The proposed system will identify and analyze orientation of product reviews and according to that system will display the rating of particular product. The orientation of product reviews collected by crawler from e-commerce websites is determine using Maximum Entropy Algorithm. Most systems like Amazon, Flipkart, Snapdeal, and others suggest things to users based on similarities among users, products, or both. This will make those systems more efficient from a users perspective. Commercial and trading systems gain trust and benefits using such systems if they successfully know what users want at what time and where. The system will collect information from different websites and then use the stored data in the Maximum Entropy algorithm for text classification providing more accurate results.
Effect of potent ethyl acetate extract of Amaranthus hybridus instreptozotocin-induced diabetic rats

The present study was aimed to evaluate ethyl acetate extract of Amaranthus hybridus leaves for antihyperglycemic effect in streptozotocin (STZ)-induced diabetic rats by acute and sub-acute models.In this research, various partitioned extracts of crude ethanol extract from Amaranthus hybridus were prepared and their effects on blood glucose levels in STZ-induced diabetic rats were studied after a single oral administration (200 mg/kg). The STZ induced diabetic rats were also treated orally with ethyl acetate extract of Amaranthus hybridusfor 14 days once daily at the dose of 200 mg/kg. The parameters such as the fasting blood glucose and hepatic antioxidant levels were monitored. Effects of the extract on the pancreatic and hepatic histopathological profile in diabetic rats were also evaluated. The acute study investigation showed that the ethyl acetate fraction is the most potent in reducing the fasting serum glucose levels of the diabetic rats. Administration of the ethyl acetate fraction for 14 days significantly (P< 0.001) reduced the fasting blood glucose and hepatic TBARS level and significantly (P< 0.001) increased the liver superoxide dismutase and catalyses activities as well as reduced glutathione levels in diabetic rats. The histopathological study of liver and pancreas in drug treated rats shows significant protective effect against STZ oxidative stress. Our study concludes that the ethyl acetate fraction of Amaranthus hybridus possesses significant anti-hyperglycemic and antioxidant effects. Hence, ethyl acetate fraction could be considered as a potent phytochemical source for development as a safe and effective new antidiabetic drug.
Isolation and Expression Analysis of a β-Ketoacyl-Acyl Carrier Protein Syn- thase I gene from Arachis hypogaea L

β-ketoacyl-acyl carrier protein synthase (KAS) plays a pivotal role in de novo fatty acid biosynthesis, joining short carbon units to construct fatty acyl chains by a three-step Claisen condensation reaction in plants and bacteria. A putative KASI cDNAs was isolated from peanut by searching a peanut seedling full-length cDNA library. The AhKASI contains four strictly conserved residues Cys221–His361–Lys392–His397 in the active site, which is an important characteristic of KAS Is in plants and bacteria. The AhKASI gene containing 1 912 bp cDNA sequence with a 1 413 bp ORF, encodes 470 amino acids. The predicted amino acid sequences translated from the AhKASI gene shared 90.2% sequence identity to the corresponding protein in Glycine max. The cDNA was cloned into plasmid pET-28a and expressed in Escherichia coli BL21. Quantitative real-time PCR analysis suggested AhKASI was expressed with higher levels in leaf and seed than those in other tissues. In addition, AhKASI RNA was found in high abundance at 45 and 65 DAP (days after pegging) during seed development. This work may serve as a foundation for further studies on the mechanisms regulating the expression of KASI gene and provide candidate genes for modifying oil quality via transgenic plants.
Variable Truncated Multiplier with Low Power

: Multipliers play a significant part in the DSP architecture. Truncation helps in the reduction of power consumption by disabling a portion of the partial product. In this paper different multipliers are made to truncate the partial products variably using a control bit and then compared for power and delay. The comparison results show that the Dadda multiplier shows a small improvement in power and Baugh Wooley is better in terms of delay.
Approaching Machine Learning Using H2O and Inspecting it on Apparel Industry Using D3 Visualization

-Implementing machine learning algorithms was never a easy task, Although there are several approach to do so, but the procedure followed is always exasperating. In order to simplify the task of analyses with same accuracy we give a new approach of H2O.H2O is fast ,scalable, distributed, machine learning and deep learning application . It is a smarter Which implements many generalized linear models like linear regression, logistic regression ,Naive Bayes, K-means clustering, Naive Bayes algorithms . Here in this paper we are trying to approach H2O machine learning with more complex algorithms with an effective solution which can be which can be implemented on various real time problems, one such among them is apparel industry which still follows a traditional inspection and analysis system so using machine learning and D3 visualization we are trying to give better analyses and recommendation to the system.
MapReduce Based Implementation of Aggregate Functions on Cassandra

— MapReduce is a simple and powerful processing model that allows parallel scalable programs to run on large volume of data on massive cluster of computers. Besides, Cassandra is a popular database of NoSQL solutions. According to scientific knowledge, still there are no general suitable procedures to perform arbitrary calculations in this database on MapReduce model. So, in this paper we propose some procedures based on MapReduce model that are needed generally to perform variety of aggregate operations on Cassandra. Our evaluation, compare with the most common methods shows significant improvement in performance on multi-core computers and a set of peer machines .
ENERGY EFFICIENT DISTRIBUTED DETECTION OF NODE CLONES IN WIRELESS SENSOR NETWORK

: Wireless Sensor Network (WSN) consists of tiny sensing devices which co-operate and communicate with each other to perform certain tasks. These are generally deployed in hostile environment where an adversary may capture nodes, replicate them and use it for its own purpose. If the clone nodes remain undetected, it can disrupt the network functions making it vulnerable to attacks. Hence false data can be injected or the legitimate data can be taken out. Thus to detect the clone nodes is the fundamental problem in WSN. The objective of distributed node clone detection is to detect clone nodes with minimum communication and storage overhead. The proposed project is a node clone detection system for sensor networks, which will be capable of detecting the node clone attack in a distributed way.
An Epistemological and Pattern Analysis of Empirical Data that Influences Emergency Loan Need Among Graduate Students

Emergency loan need signal that domestic and international students may suffer sufficient economic distress due to shifts in the global economy and prohibitive historical social factors. Korn [1] reports that most low income students who pursue degrees fail to make it to graduation. This contributes to deepening economic calamity noted by Frolich [2] in the study that examined the earnings gap between black and white families. Debt among domestic and international students is an incurable ailment: a quagmire more tangled than the challenges of health insurance, tuition, and technology and resource fees [3-5]. The loan and financial aid theory assesses the economics of the debt service revenue bubble that impacts participation in contributing to the expansion of goods and services productivity [3,4]. Whether students are completers or noncompleters, emergency student loan need signals financial distress with a high likelihood that debt will restrict future participation in the market economy. Non-completers are burdened with self-acknowledgement that they accept less future earning power stepping out of their academic program. Korn [6] reports that more than half of public colleges in the U.S. are failing to bring in sufficient tuition revenue to keep up with inflation, and nearly as many private schools are facing a similar financial crisis. This assessment indicates that universities must borrow or pass cost directly to students in the form of tuition and/or fee increases. Domestic and international students must weigh the ethical quotient of acceptance against financial sustainability to completion, thus requiring a more careful analysis prior to enrollment in an academic institution.
A Technical Survey on Complementary Aspect-Based Opinion Mining Techniques

: Web content and applications from those data increase day by day. Online media platforms are the most important topic in the research field due to its popularity and necessity. Mining user opinions from that web content are more challenging. There are several methods available to detect and analyze the opinion from the huge web content. Aspect based opinion mining is a more popular and promising technique on opinion mining over online social media. In the literature, there are several promising approaches focused either on the topic or opinion modeling. However, the integrated part is missing to handle both. So, further innovation is still needed for developing an integrated aspect-based opinion mining model. To achieve the above many researchers found in the recent temporal gap. Many techniques are based on Latent Dirichlet Allocation (LDA) and few used word embedding techniques. Before developing a new Technique or approach for fining aspect-based opinion mining, the problem and real-time challenges on existing techniques should be examined. This survey brings a summary of those issues and gives a valid solution for the development.