Miracle-dz/UniFAR-FTUs huggingface dataset:
Does a Heterochromatic variant affect the Human Reproductive outcome?

To study the association of chromosomal polymorphic variations with recurrent miscarriage. Recurrent miscarriage becomes a problem that affect an increasing number of couples with the frequency of about 1% in the couples who want to conceive. This study is based on comparison of chromosome Heteromorphism in the karyotypes of two groups. The first group with 400 individuals with the history of more than two miscarriages and no live birth and as control group 200 individuals with one or more than one normal child. The study revealed that the frequency of chromosomal abnormalities and variations leading to recurrent miscarriage in couples was 18% Chromosomal rearrangements constituted 27.78% of the cases while heterochromatic variations constituted 72.22% of the chromosomal cause for recurrent miscarriages. In the present study, pericentric inversion of chromosome 9 and heteromorphism of chromosomes 1 were the most common findings. Present study indicates that there is need to evaluate the known heterochromatic variants as these variants play an important role in pregnancy loss.
Indoor SLAM using Kinect Sensor

The field of automation has gained a lot of importance in recent years due to its ability to reduce human efforts. Today a number of systems hitherto operated manually have been automated and are now operated by robotic systems. In various military and industrial applications, automaton can help avoid loss of unnecessary human lives. A single robotic system can accomplish a task which otherwise required the efforts of a number of humans. Such robotic systems can be used to accomplish trivial tasks that do not require human instinct. The “Simultaneous Localization and Mapping” (SLAM) system will allow a user to map an unknown environment using a robotic system. In this project SLAM has been implemented using a Kinect sensor (mounted on a moving platform) to generate a 2-D /3-D map of an unknown indoor environment. This map can be viewed on various devices using a corresponding GUI and will allow users to understand the internal layout of any structure before actually entering it
AN IMPROVED APRIORI ‐ BASED ALGORITHM FOR ASSOCIATION RULE MINING

Association rule mining is an important part of the data mining process. It aims at finding some hidden information or relationships among the attributes of the database. Various algorithms have been proposed for this task. But there are certain limitations of each. In this paper, one of the most widely applicable algorithms i.e. Apriori algorithms has been considered. Firstly, the basic working of the algorithm has been given along with its limitations. Then the improvement in the algorithm has been suggested.
A Speech Analysis System Based on Vector Quantization Using the LBG Algorithm and Self-Organizing Maps

— Speech is the basic means of communication of people. In addition to having certain content, it also contains important acoustic information, which allows us to identify the speaker. The identification of persons has long been interesting for researchers. There is a whole set of methods aimed at this purpose, commonly called biometrics. However, if we want to identify a person by his/her voice, we have to analyze his/her speech and decide about the identity by means of specific properties of the voice. This article provides an overview of the possibilities of processing voice recordings and describes a speaker identification system using two different approaches. The first approach is based on vector quantization and the LBG algorithm, while the second approach uses self-organizing maps (SOM).
Low Noise Amplifier at 2.4 GHz for Zigbee inMOS 180nm Technology

The main focus of this paper is to design a “Low noise amplifier” for radio frequency applications using MOS 180nm technology. In modern RF electronics, we rarely design an LNA in isolation. Rather, we view and design the RF chain as one entity, performing many iterations among the stages. A low power MOS low noise amplifier (LNA) working at 2.4GHz for ZigBee communication is presented in this paper. The common source (CS) stage with inductive load and Cascode stage with inductive load is adapted and various parameters are compared like Gain, Input intercept point (IIP3) and Noise figure are compared between the two LNA architectures. The simulations are done and compared using H-SPICE.
Sixth Sense Technology Based Pointer Interface System

: As computers became more common in society, promoting natural human to computer interaction (HCI) will have a positive influence on their use. Hence, there has been growing interest in the development of new approaches and technologies for bridging the human–computer barrier. A Real Time Vision Based Pointer interface system is a new vision-based interface, which is sufficiently fast and effective to substitute a computer mouse. System is able to reduce hardware impact and also increases range of usage of physical word objects instead of digital word objects like mouse. The aim of a system is to bring HCI to a level where interactions with computers will be as natural as an interaction between humans
Analysis and Classification of Kidney Images Using Watershed Segmentation and Texture Properties

— The detection and diagnosis of kidney cancer from CT images by using digital image processing depends on textural analysis to obtain an accurate kidney diagnosis. This method will make the surgeon able to detect the cancer and then easing treatment also it helps physicians and radiologists to identify the affected parts of the kidney in order to protect the normal parts as much as possible from exposure to radiation. In this study using Image segmentation is one of the most important categories of image processing. The purpose of image segmentation is to divide an original image into homogeneous regions. It can be applied as a pre-processing stage for other image processing methods. The watersheds transformation is studied in this report as a particular method of a region-based approach to the segment an image. Watershed Transform can be applied to gray scale images, textural images and binary images. The watershed transform has been widely used in many fields of image processing, including medical image segmentation. In this paper the kidney images is obtained by creating a mask to obtained the kidney from the rest CT Images which contained the other organs, as the kidney image is produced the watershed segmentation is applied to classify the region, then the gray level matrix which is the second order statistical matrix is applied to calculated the statistical features.
PCA Based Anomaly Detection

-Anomaly detection is the process of identifying unusual behavior. It is widely used in data mining, for example, to identify fraud, customer behavioral change, and manufacturing flaws. We discuss how a probabilistic framework can elegantly support methods to automatically explain why observations are anomalous, assign a degree of anomalies, visualize the normal and abnormal observations and automatically name the clusters. To our knowledge, interactive visualization of anomalies has not previously been addressed, nor automatic naming of clusters for verification in the anomaly detection field. We specifically discuss anomaly detection using mixture models and the EM algorithm, however our ideas can be generalized to anomaly detection in other probabilistic settings. We implement our ideas in the SGI MineSet product as a mining plug-in re-using the MineSet visualizers. In this paper, we propose an online over-sampling principal component analysis (osPCA) algorithm to address this problem, and we aim at detecting the presence of outliers from a large amount of data via an online updating technique. Unlike prior PCA based approaches, we do not store the entire data matrix or covariance matrix, and thus our approach is especially of interest in online or large-scale problems. By over-sampling the target instance and extracting the principal direction of the data, the proposed osPCA allows us to determine the anomaly of the target instance according to the variation of the resulting dominant eigenvector. Since our osPCA need not perform eigen analysis explicitly, the proposed framework is favored for online applications which have computation or memory limitations. Compared with the well-known power method for PCA and other popular anomaly detection algorithms, our experimental results verify the feasibility of our proposed method in terms of both accuracy and efficiency.
Enhancing the Effectiveness of clustering using User Preferences and Voting/Rating

Finding the similarity between two objects is the most important fundamental operation in database management as well as in web searching environment. The similarity between two objects is generally computed based on the attribute values of the objects. Traditional similarity measures using only attribute values. In the proposed method, similarity between two objects is the most effective and accurate when the similarity is computed based on the attribute values as well as the voting/rating/preferences of values of attributes. That is, the similarity between objects is not based only on attribute values but instead object similarity is computed based on some of the weighted values of voting/rating/preferences and values of attributes. A linear similarity function is the simplest model for finding weighted similarities between objects. Similarity measure techniques are very much useful in processing database queries such as top-k queries, reverse top-k queries, k-nearest neighbor queries and other different types of queries related to business sales activities. Sometimes there is a need to construct and use multidimensional indexing data structures for efficient search/access of data of very large database sizes and the effective execution of queries. Also sometimes different types of pruning techniques are required for scalability purpose. In general, linear function computations are scalable for similarity finding measurements between objects.
Blind Fake Image detection

With the great convenience of computer graphics and digital imaging, it becomes much easier to alter the content of images than before without any visually traces to catch these manipulations. Many fake images are produced whose content is feigned. Thus, the images cannot be judged whether they are real or not visually. In order to detect fake images, this paper proposes a blind detection uses singular value decomposition (SVD) as a classifier to make a binary decision on whether an image is fake or real. This work is an improvements process to an existence method to detect fake image using SVD. The experimental results prove the effectiveness of this algorithm to detect any small changes in image even with one dot of real image.
Data Decomposition Technique Proposed for Candidate Itemsets Generation of Association Rule Mining Algorithms on Heterogeneous Cluster

— Among various data mining task, association rule mining (ARM) is the major technique which is widely used in retail marketing, bioinformatics, website navigation analysis etc. It finds correlations among items in a given data sets and establishes an association between two non overlapping sets of frequently occurring values in a database. Various sequential and parallel ARM algorithms have been developed that differs in data layout, search technique, data structure, the number of database scans used and the system on which it is developed i.e. homogeneous or heterogeneous systems. This paper mainly put emphasis in the need of a candidate based ARM algorithm for heterogeneous PC cluster that reduces the database scans and time complexity. It also describe the design and functioning of the heterogeneous PC cluster i.e. MPICH2 and the data decomposition technique applied for candidate itemsets generation that plays important role in balancing workload as well as enhancing the performance of the algorithm on MPICH2 heterogeneous PC cluster .
An Analysis and Survey of Security Techniques for Cloud Computing

: The cloud computing is the type of network in which no central controller is present due to which security is the major issue of the network. The security attacks affect network performance in terms of certain parameters. This research work is related to detection of zombie attack in cloud computing. In the zombie attack, the clones of the virtual machine are created which interact with the cloud server. The various security enhancement techniques are reviewed in this paper in terms of certain parameters
MOVIE SUCCESS FORECAST AND REVENUE PREDICTION USING FUZZY SYSTEM

In recent years, social media has become omnipresent and essential for social networking and content sharing. And yet, the content that is generated from these websites remains largely untapped by official authorities. The present tutorial investigates techniques for social media modeling, analytics and optimization. First we present methods for collecting large scale social media data and then discuss techniques for coping with and correcting for the effects arising from missing and incomplete data. In this paper, we show how social media content can be used to predict real-world outcomes. In particular, we use the chatter from Twitter.com to forecast box-office revenues for movies. We show that a simple model built from the rate at which tweets are created about particular topics can outperform market-based calculations. We further demonstrate how sentiments extracted from Twitter can be further utilized to improve the forecasting power of social media. We summarize an effort to predict box office revenues using Twitter data. It is hypothesized that an increased number of tweets about a movie before its release will result in increased box office revenue.
USE OF TOTAL QUALITY MANAGEMENT ( TQM ) AND STATISTICAL PROCESS CONTROL ( SPC ) IN ENHANCING BUSINESS CIMPETITIVENESS-AN INVESTIGATION INTO ZIMBABWEAN MANUFACTRUING COMPANIES

Volume 2, Issue 6, June 2013 Page 171 Abstract The success of TQM and SPC as a boost to business competitiveness has had its own share of controversies. Many organizations all over the world have embarked on the TQM journey, with their process under statistical control, to achieve increased competitiveness, some recorded success while others regretted ever embarking on such a journey. The purpose of this research is to determine if the myth of TQM and SPC enhancing business competitiveness is true for Zimbabwean manufacturing companies.
BIOREMEDIATION OF STEEL INDUSTRIAL EFFLUENTS USING SOIL MICROORGANISMS

Bioremediation is treatment processes that uses nat urally occurring microorganisms as well as plants t o breakdown, or degrade hazardous substances into less toxic or non toxic substances. Steel Industry is one of nine e rgyand wasteintensive industries Major processes in the production of finished steel include coke productio n, sinter production, iron making, steel making, alloying, ca sting and shaping, and finishing. During these proc esses many contaminants like organics, in organics and heavy m etals will be released into the effluents that need to be tackled in a safe manner. Bioremediation can be applied on sit e (in situ) or off site (ex situ), mediated by mixed microbial consortia and/or pure microbial strains. There are many mechanical and chemical methods for treatment of s eel industrial effluents. Also some studies on the bior emediation of industrial effluents are available. T he objective of the present study is to conduct preliminary studies using mixed microbial consortia isolated from sewa ge drain for the bioremediation of steel industrial effluent. Th e steel industrial effluent collected from a Patanc heru Effluent Treatment Ltd., Hyderabad was used in the present s tudy. Physicochemical analysis of the effluent was c rried out using standard methods of APHA. Experiments reveale d that, upto 95% reduction of BOD and COD was obser ved by using these mixed microbial consortia. Prelimina ry isolation studies revealed the presence of four microorganisms namely Pseudomonas sps, Bacillus sps, Arthrobacter sps and Micrococcus sps. in the mixed microbial consortia. The study indicates that these microorganisms once characterized can be further u s d for the bioremediation of industrial effluents.
Effect of Protease Supplementation on the Performance of Laying Chickens Fed Marginally Deficient Protein Diets By

A 10 weeks experiment was conducted to investigate the effect of protease supplementation on the performance of 32 week’s old laying chickens fed marginally deficient protein diets in a completely randomized design. A total of 60 Isa Brown laying birds were allotted to 5 dietary treatments, six replicates of 2 birds each per replicate. The diets were formulated to contain 16% crude protein in Treatment 1 which was the control diet, T2- 5% CP reduction (15.2% CP), T3- 5% CP reduction (15.2% CP + 0.05% protease), T4- 10% CP reduction (14.4% CP) and T5- 10% CP reduction (14.4% CP + 0.05% protease). There were no significant differences (P>0.05) among all treatments on feed intake, haugh unit and hen-day egg production (HDEP) of the birds. The internal (albumen and yolk indices) and external (egg weight and length indices, shell thickness and weight) egg parameters showed significant increase in T3 and T5 (with protease inclusion at 5% and 10% CP respectively). It was concluded from the study that reduction of crude protein by 5% and 10% and inclusion of protease in the diet improved performance and egg quality parameters of Isa brown laying strains. It was however recommended that 10% CP reduction with protease inclusion gives optimum performance.
Exploring and validating the clinical risk factors for pancreatic cancer in chronic pancreatitis patients using electronic medical records datasets: three cohorts comprising 2,960 patients

Background Patients with chronic pancreatitis (CP) have an increased risk of developing pancreatic cancer (PC). The purpose of this study was to identify predictors of PC in CP patients. Methods Electronic medical records (EMRs) of CP patients from two cohorts were collected, and a logistic regression analysis was performed to investigate the risk factors for PC. Subsequently, we validated the value of the risk prediction model with the EMRs of a third cohort. Results The derivation cohort consisted of 2,545 CP patients, and among them, 14 patients developed PC 7 years after CP diagnosis. Cyst of the pancreas [COP; odds ratio (OR): 4.37, 95% confidence interval (CI): 1.11 to 18.40, P=0.033], loss of weight (LW; OR: 3.21, 95% CI: 0.76 to 12.91, P=0.096) and high platelet (PLT) count (OR: 1.01 per 1 increment, 95% CI: 1.00 to 1.01, P=0.042) were independent risk factors for PC among CP patients. A risk prediction equation was constructed as follows: ln[p/(1–p)] = –6.68 + 1.55COP + 1.23LW + 0.0046PLT. The areas under the receiver operating characteristic (ROC) curve of our risk score were 0.83 and 0.72 in the derivation and validation cohorts, respectively. A score >0.0128 and >0.0122 had the best balance between sensitivity and specificity in the derivation and validation cohorts, respectively. Conclusions In CP patients, LW, COP and high PLT count were identified as novel predictors of PC. A risk prediction model based on these factors exhibited moderate predictive value for CP patients.
The DNA and Spermatozoa Quality of Mice (Mus musculus albinus) after Administration Aqueous Leaves and Seeds Extract of Neem (Azadirachta indica A. Juss)

1 Doctoral Program at Reproductive Biology in Department of Veterinary Clinic, Reproduction and Pathology, Faculty of Veterinary Medicine, Bogor Agricultural University, Dramaga Campus, Bogor 16680, Indonesia 2 Department of Veterinary Clinic, Reproduction and Pathology, Faculty of Veterinary Medicine, Bogor Agricultural University, Dramaga Campus, Bogor 16680, Indonesia) 3 Departement Anatomy, Physiology, and Pharmacology, Faculty of Veterinary Medicine, Bogor Agricultural University, Dramaga Campus, Bogor 16680, Indonesia 4 Department of Biology, Faculty of Mathematics and Natural Sciences, State University of Jakarta 13220, Indonesia
COCHLEAR IMPLANT ACOUSTIC SIMULATION MODEL BASED ON CRITICAL BAND FILTERS

Cochlear Implants (CI) are surgically implanted biomedical devices that can provide hearing to profoundly deaf people by direct electrical stimulation of the auditory nerve. Choosing the optimal number of electrodes for the improved perception of speech is an especially difficult problem for users of CI, as the prostheses system depends on various parameters and processing strategies. It has widely been recognized that using the auditory models in cochlear prostheses might significantly improve the speech perception. In this paper, we propose a new CI acoustic simulation model based on a 16-channel Critical Band Finite-Impulse Response (CB-FIR) filter bank and its performance is discussed. The place of stimulation in the cochlear electrode array is identified through the computation and selection of significant spectral band energies. We also investigate the temporal information through the simulation model based algorithm. The amplitude levels of the pulses are identified by envelope detection method. The simulation model is tested with TIMIT database for 15 phonemes. The experimental results indicated that, a reduced number of channels are only required for stimulation of cochlear electrodes, one at a time, thereby minimizing channel interaction. The proposed model should be helpful for the development of novel speech processing strategies to improve the speech perception of cochlear implantees.