Miracle-dz/UniFAR-FTUs huggingface dataset:
System biology : Mathematical modeling of biological systems

Biological systems have evolved optimally, which makes them highly complex. Advances in experimental techniques provide an opportunity for the development of mathematical models of biochemical networks, including signal transduction pathways and metabolic networks. Systems biology is an area of active research in which various modeling techniques have been proposed to analyze and identify a wide range of biological networks and that is achieved with the use of algorithms to aid in the task of integrating expert knowledge and get a synthetic view of the existing biological knowledge in a network and achieve the prediction of the dynamic behavior of a system. A review of the generalities of systems biology and mathematical modeling in the context of biological systems is presented.
Web- and text-based interventions for smoking cessation: Meta-analysis and meta-regression

Abstract Background: In the past decade, several smoking cessation interventions have been developed and implemented through information and communication technology (ICT). Evidence suggests that they might be suitable for large-scale public health interventions, based on updated communication media characteristics in terms of interplay between technology and graphical user interface, reaching high numbers of individuals. Objectives: We aimed at estimating web/text-based interventions effectiveness as compared with approaches routinely used for general population, that is smoking assessment or non-electronic self-help materials. Methods: A systematic review and meta-analysis was performed searching through PubMed, Embase and PsycInfo databases, as well as references of relevant papers. Heterogeneity and risk of bias were evaluated following standard methods. In addition, we performed meta-regression analyses testing if candidate covariates moderate the overall effect. Results: Slight but significant effectiveness was found for eHealth interventions over control conditions (RR = 1.28, 95% CI: 1.14–1.45). Meta-regressions showed similar findings for web- and text-based interventions. The effect seemed moderated by the follow-up period, being higher at 3 months and lower at 6/7 months follow-up. Conclusions: Our results outline moderate effectiveness of web/text-based interventions. However, a paucity of properly controlled studies and lack of information on several effect modifiers still hamper the development and implementation of smoking cessation interventions through ICT.
Performance Evaluation of Soft Computing using Clustering Techniques

Soft Computing can be considered as a tool to handle inexactness and uncertainty. The main concept of soft computing is to exploit the forbearance for inexactness, uncertainty, fractional truth and approximation to accomplish tractability, robustness and inexpensive solutions. It is used to solve real life problems full of uncomfortable features due to fractional, unclear, noisy and partial information. Clustering gets its name as a metaphor for the soft computing. Clustering aims to divide data groups into subgroups called clusters. Clustering is still searching for killer applications that not only takes advantage of its promise of "high performance and short development lead time". This paper focuses on the evaluation of the Genetic Algorithm concept of Soft Computing and measures its performance using various clustering techniques.
Three Stage Push Pull Inverters Based Transimpedance Amplifier

In this paper, a transimpedance amplifier is designed using 0. 18μm CMOS technology. In the proposed transimpedance amplifier (TIA), feedback resistor Rf of conventional transimpedance amplifier which was implemented using PMOS transistor has been replaced by NMOS transistor as an active feedback resistor. This circuit operates at 1.6 V power supply voltage and for a current of 3μA. The proposed transimpedance amplifier posses high gain, low noise, low power dissipation, and high bandwidth. The proposed Transimpedance Amplifier exhibits a power dissipation of 0.871mwatt, transimpedance gain of 54.55 dB, a bandwidth of 371.32MHz, an input-referred noise which is equal to 0.827×10A/√Hz.
DESIGN OF QOS BASED PRICE AND TIME SLOT NEGOTIATION MECHANISM FOR CLOUD SERVICE RESERVATIONS

— Cloud providers currently charge users for metered usage based on fixed and spot pricing. When making reservations for cloud services in automatic pricing mechanism, consumers and providers need to establish service-level agreements through Price and Time-slot Negotiations. Even though these issues are essential, mechanisms to automate the negotiation of price and time slot for cloud services using Quality of Service (QoS) have not been devised. Also existing PTN mechanisms have dealt with advance reservations considering bandwidth or time constraints and SLA negotiation, to date, there is no service reservation system that considers PTNs with QoS. QoS parameters such as service initiation time, penalty rate ratio is included in the PTN mechanism. The consumer can make multi issue negotiation and the provider offers concession for the requested PTNs to improve the resource utilization and acceptance rate. Another novelty of this work is formulating a novel time-slot utility function that characterizes preferences for different time slots. These ideas are implemented in an agent-based Cloud testbed. Empirical results show that PTN agents reach faster agreements and achieve higher utilities than other related approaches.
Content Based Image Retrieval Using the Low and Higher Order Moments of BDIP and BVLC

: This paper deals with efficient retrieval of images from massive databases based on texture, edges and valley contents in images. With the increasing necessity of the use of massive image databases in different applications, it becomes very essential to construct an automatic and efficient image retrieval system to search through the whole database. The proposed approach relies on the low order and higher order moments of visual cues that exploits edges and valleys, and textures using the BDIP (block difference of inverse probabilities) and BVLC (block variation of local correlation coefficients) respectively. Unlike the previous approach which concentrates only on extracting low order moments, the proposed work combines the both low order and higher order moments to characterize the BDIP and BVLC. Experimental results on a database of 1000 trademark images show that an integration of both low and higher order moments of BDIP and BVLC feature representation results in better accuracy. To access the performance of the proposed work precision, recall and average retrieval rate is incorporated.
Activity Recognition Using Filtering Algorithm

— This paper inspect the trouble in recognition of exercise and also discover the burned calories after exercise. Our topic and motive of research to detect a problem, how walking, jogging and running effect on the human body using personalize algorithms. Pedometer is a device which is used to count the step while walking. This device may be used as a hardware device which attached with the body or may hold on body like in hand and count the steps. Every person have different way of walking and also difference in step distance, an informal calibration, performed by the user. An accelerometer is a device that measures proper acceleration. It is not necessary that accelerometer always measure a proper acceleration for each coordinate acceleration But the benefit of this research, it remotely observe the moves, calories and find accurate results in jogging and running. There are specific algorithm used for finding the difference between the jogging and running, and detect the burned calories. Whole research performed in MATLAB platform. This platform is very reliable and flexible to use.
Comparative Evaluation of 0.2% Ropivacaine versus 0.125% Ropivacaine under Combined Spinal-epidural Technique for Labor Analgesia

Background: Both pharmacological and non-pharmacological strategies for pain relief in labor have been tried and tested since long. Combined spinal-epidural analgesia (CSEA) satisfies the basic requisites of labor analgesia. Various concentrations of local anesthetics along with the addition of opioids can be used. The objective of this study is to compare the quality of labor analgesia with two different concentrations of ropivacaine (0.2% vs. 0.125%) and assess fetomaternal outcome. Materials and Methods: A total of 60 primipara women with a singleton pregnancy in active labor were given CSEA after randomly allocating them in two groups of 30 each. Both Group A and Group B received intrathecal injection of 4 mg (2 ml) 0.2% ropivacaine + 25 μg (0.5 ml) fentanyl: Group A epidural dose of 15 ml of 0.2% ropivacaine solution + 2 μg/ml fentanyl and Group B epidural dose of 15 ml of 0.125% ropivacaine + 2 μg/ml fentanyl. Then, continuous epidural infusion was started at the rate of 10 ml/h which was continued until the end of delivery. Results: Group A showed better maintenance of analgesia and better maternal satisfaction while parturients in Group B needed rescue top-up analgesia due to breakthrough pain. Conclusions: It was concluded that ropivacaine in both concentrations (0.2% and 0.125%) with fentanyl is effective for initiation of labor analgesia. However, quality of analgesia with 0.2% ropivacaine concentration is superior to 0.125% concentration.
FRACTAL TRAFFIC MANAGEMENT IN WIRELESS NETWORK

Communication is significant way to move the data from one place to other place which is geographically located far away from each other. In early days telegraphy technique was used to send the information from place to other place with the help of wires. Telegraphy is very time consuming and non reliable method of data transmission. To overcome such type of problems engineers developed a new communication technology which is known as mobile communication. Mobile communication fulfils almost all the requirements of growing market and modern world. All business sectors are using mobile communication for data transmission. The new problem of fractal traffic is arrived because of limited channels and increase in fractal traffic to solve this problem traditional method is to improve the data transmission mechanism and quality of service.
An Efficient Algorithm for processing Top-k Spatial Preference Queries

A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example, using a real estate agency database of flats for sale, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, market, hospital, railway station, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different functions. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of our methods on both real and synthetic data reveals that an optimized branch-and-bound solution is efficient and robust with respect to different parameters.
Efficient Ways to Improve the Performance of HDFS for Small Files

Hadoop , an open-source implementation of MapReduce dealing with big data is widely used for short jobs that require low response time. Facebook, Yahoo, Google etc. makes use of Hadoop to process more than 15 terabytes of new data per day. MapReduce gathers the results across the multiple nodes and return a single result or set. The fault tolerance is offered by MapReduce platform and is entirely transparent to the programmers. HDFS (Hadoop Distributed File System), is a single master and multiple slave frameworks. It is one of the core component of Hadoop and it does not perform well for small files as huge numbers of small files pose a heavy burden on NameNode of HDFS and decreasing the performance of HDFS. HDFS is a distributed file system which can process large amounts of data. It is designed to handle large files and suffers performance penalty while dealing with large number of small files. This paper introduces about HDFS, small file problems and ways to deal with it. Keywords : Hadoop; Hadoop Distributed File System; MapReduce; small files
Analytical method development and validation by QbD approach – A review

Quality by Design is the modern approach for quality of pharmaceuticals. The aim of the pharmaceutical development is to design a quality product and its manufacturing process to consistently deliver the intended performance of the product. Quality cannot be tested into products but quality should be built in by design. It is an essential part of the modern approach to pharmaceutical quality. Under this concept of QbD throughout designing and development of a product, it is essential to define desire product performance profile [Target product Profile (TPP), Target Product Quality Profile (TPQP)] and identify critical quality attributed (CQA). On the basis of this we can design the product formulation and process to meet the product attributes. This leads to recognize the impact of raw materials [critical material attributes (CMA)], critical process parameters (CPP) on the CQAs and identification and control sources of variability.
A Critical Analysis of the Existing Ontology Evolution Approaches

: Because of the rapid improvements occurring in the dynamic environment of web applications, ontologies have to be modified to reflect the changes made to the applications. Management of the changes within ontologies is one of the most crucial tasks that needs to be resolved. Various approaches and frameworks have been devised by the researchers to handle it. Despite all the efforts made in this direction, the problem still requires to be researched. To address the problem, we have critically analyzed a number of existing ontology evolution approaches against a criterion we have defined in this paper. Having identified the limitations and weaknesses along with their strengths, we have proposed some requirements that must be incorporated in the design of ontology management approaches. [Khadim H. Asif
Survey on Anomaly Extraction System Using Featured Histogram and Mining Rules

— With the Progress in anomaly extraction in backbone network, the networking demand for finding out anomaly is growing that also increases demand for finding out root-cause analysis, network forensics, attack mitigation, and anomaly modeling especially in backbone network. Also when its scope enlarges to rich traffic and very small number of false positive there will be need to maintain best method for mining. Numerous Techniques have been developed for anomaly extraction and data mining and purpose of this paper is to categorize and evaluate these methods. For finding out one that is abnormal or exception the best method will be discussed. Paper Also Summarizes several methods to ensure highly extracted data which includes Apriori algorithm, Fp growth algorithm, Enhance FP growth algorithm such a mining methods , Also with the rich traffic data and false positive rate of methods with Several scenarios will discussed.
Rough Set Based Grid Computing Service in Wireless Network

- The computational grid is rapidly evolving into a service-oriented computing infrastructure that facilitates resource sharing and large-scale problem solving over the Internet. Service discovery becomes an issue of vital importance in utilizing grid facilities. In this paper presents ROSSE, a Rough sets-based search engine for grid service discovery. Building on the Rough sets theory, ROSSE is novel in its capability to deal with the uncertainty of properties when matching services. In this way, ROSSE can discover the services that are most relevant to a service query from a functional point of view. Since functionally matched services may have distinct nonfunctional properties related to the quality of service (QoS), ROSSE introduces a QoS model to further filter matched services with their QoS values to maximize user satisfaction in service discovery
Moving Objects Tracking with due Account for Motion Direction and Partial Occlusions

— In this paper we propose a method of detection partial occlusions of moving objects that is based on agglomerative hierarchical clustering of moving objects feature points and using cluster fragmentation method that is based on watershed transformation. The main advantage of this method is a minimization of hardware resources usage and an advanced and robust efficiency of edges estimation between moving objects regions. The practical results of software implementation have shown availability of an accurate functioning in real-time mode.
Estimation of workload using EEG data and classification using linear classifiers

—Cognitive workload is a subjective term operantly defined as a worker’s perception of a work performance and work difficulty. To estimate workload through Electroencephalogram (EEG) requires good algorithm with best features. Objective of this research study was to estimate workload using linear classifiers on non linear data. Workload was presented by varying levels of Multi Attribute Task Battery II (MATB-II task) . Two non linear features of Hurst exponent and Higuchi Fractal Dimension have been extracted from the data which was acquired from 28 subjects who were all male in the age group 25-40. The classification has been performed using three prominent classifiers i.e. K-Means, K Nearest Neighbor (KNN) and Support Vector Machine (SVM) to test their efficiency in the case of workload data. We have hypothesized SVM classifier to give best results out of the three classifiers. Comparing the performance accuracy of the selected classifiers, we propose a classifier that will give best results for workload classification
A Review on Hybrid Intrusion Detection System Using TAN & SVM

- The dramatically development of internet, Security of network traffic is becoming a major issue of computer network system. Attacks on the network are increasing day-by-day. The Hybrid framework would henceforth, will lead to effective, adaptive and intelligent intrusion detection. In this paper, We propose a hybrid fuzzy rough with Naive bayes classifier, Support Vector Machine and K-nearest neighbor (K-NN) based classifier (FRNN) to classify the patterns in the reduced datasets, obtained from the fuzzy rough bioinspired algorithm search. The proposed hybrid is subsequently validated using real-life datasets obtained from the University of California, Irvine machine learning repository. Simulation results demonstrate that the proposed hybrid produces good classification accuracy. Finally, parametric and nonparametric statistical tests of significance are carried out to observe consistency of the classifiers.
Automatic Speech Segmentation for Amharic Phonemes Using Hidden Markov Model Toolkit (HTK)

Speech segmentation is the process of identifying the boundaries between meaningful units like phonemes in a continuous speech. A need exists for reliable, automatic determination of phonemes boundaries in different speech research areas such as automatic speech recognition (ASR) to improve the performance of the recognizer, to improve the quality speech synthesis system through segmented database, and to improve performance of language identification and speaker verification system. In this study unsupervised method of automatic speech segmentation is proposed as a solution. Text corpus with size of 1000 Amharic sentences is recorded by one female in order to have parallel speech corpus. Both text and speech corpuses are split in to training (90%) and test (10%) data sets. Phoneme based speaker dependent Hidden Markov Model is preferred, being the one that is most widely used, and also due to the ready availability of the HTK software suite. HMM approach is used to model Amharic phonemes in individual HMM with three emitting and two non emitting states without skipping left to right HMM. MFCC feature vectors together with their first and second derivatives are selected for individual HMM models. Letter and phoneme were used as a basic unit to model the HMM in context independent, context dependent with single Gaussian mixture and context dependent with Multiple Gaussian mixtures. The system is also evaluated in terms of percentage of boundary deviations with 5ms, 10ms, 15ms and 20ms tolerance values with reference to manual s egmentation results. The evaluation of the experiments shows that best performance with Minimum percentage of time boundary deviations are achieved using phoneme based approach in context dependent environment with two Gaussian mixture.
Synthesis of Silver Nanoparticles from Edible Mushroom and Its Antimicrobial Activity against Human Pathogens

In the present study, we have reported the biological synthesis of silver nanoparticles (AgNPs) by using edible mushroom extract, Agaricus bisporus, as a bioreductant. The biosynthetic method developed in this study for producing silver nanoparticles has distinct advantages over chemical methods such as high biosafety and being ecofriendly and nontoxic to the environment. Furthermore, these functionalized silver nanoparticles showed a noticeable antimicrobial activity against different clinically important pathogenic microorganisms. Hence, such type of synthesis methods for the production of nanostructured materials at lower cost and with natural energy may encourage production of functionalized AgNPs on industrial scale. This is quite handy for using AgNPs on a wide range of applications in the field of Nano biotechnology.
COLORIZATION OF GRAYSCALE IMAGES USING MULTIPLE KERNEL FUZZY C-MEANS CLUSTERING

— Image processing is any form of signal processing for which the input is an image, the output may be either an image or a set of characteristics or parameters related to the image. The image segmentation is the process of partitioning a digital image in which it is divided into multiple segments. Here we are analyzing a set of sample color images, and coherent regions of homogeneous textures are extracted. A multi-channel filtering technique is used for texture-based image segmentation, combined with a modified Fuzzy C-means (MKFCM) clustering algorithm. For each area of interest, texture descriptors are computed and stored, along with corresponding color information which is used for colorization of a grayscale image with similar textures. The colorization process is performed by chroma replacement. This research finds numerous applications,from classic film restoration and enhancement, to adding valuable information into medical and satellite imaging. This can be used as an enhancement in the detection of objects from x-ray images at the airports. Since the textures extracted from each sample image have no preset size or shape, the texture retrieval methods need to be improved for scale and rotation invariance, which would lead to a better accuracy rate. This method could also be enhanced to store more complete color descriptors to accommodate more complex textures containing multiple colors.
Classification of High Dimensional Class Imbalanced Data Using Data mining Techniques

: As increase in data dimensionality classification of data increased. In industries or organizations fault detection is important task. Due to imbalanced of data classification process has problem. In standard algorithm of classification majority classes have priority for classification and minority classes have less priority for classification therefore it is not suitable for minority classes fault detection from data is applied for only majority classes and less for minority classes. Incremental clustering algorithm solved this problem but it reduced data attribute. To maximize the accuracy, time, and memory for this we proposed a feature selection algorithm for better performance of classification and fault detection.