Miracle-dz/UniFAR-FTUs huggingface dataset:
MONITORING DATA IN DISTRIBUTED COMPUTING SYSTEM

: At present distributed computing is one of the most exploited computing platforms with the emerging techniques like cloud computing. Therefore it is becoming essential to understand all features of the distributed computing; by setting up the hardware to applications of several software’s on the distributed systems possibly the most significant factor is “monitoring service”. Distributed Computing System (DCS) aims at attaining higher execution speed than the one obtainable with uni-processor system by exploiting the collaboration of multiple computing nodes interconnected in some fashion. Present paper focused on the importance of DCS and its major role in monitoring data, earlier DCS models which has been developed to monitor data along with its advantages and limitations. Finally paper focuses on cost optimization using DCS in general. Monitoring is an important tool for program visualisation, debugging, testing, and development. Thus there is a need to develop the generic monitoring service to support all aspects of management in a distributed system.
Privacy Preserving in Database Service using Non-Colluding Cloud Architecture

In the present scenario, businesses and people are outsourcing database to accomplish helpful administrations and minimal effort applications. To provide sufficient functionality for SQL queries, many secure database schemes have been proposed. However, the proposed schemes are vulnerable to privacy leakage to cloud server. The main reason is that database is hosted and processed in cloud server, which is beyond the control of data owners. For the numerical range query (“>”, “<”, etc.), the schemes cannot provide sufficient privacy protection against the practical challenges. Portions of the difficulties faced are privacy leakage of statistical attributes and access patterns. Furthermore, increased number of queries will inevitably leak more information to the cloud server. In this paper, we propose a two-cloud architecture for secure database, with a series of intersection protocols that provide privacy preservation to various numeric-related range queries. Security analysis shows that privacy of numerical information is strongly protected against cloud providers in our proposed scheme.
INTERNET OF THINGS AND TRUST MANAGEMENT IN IOT – REVIEW

: The phrase Internet of Things (IOT) heralds a vision of the future Internet where connecting physical things, from bank notes to bicycles, through a network will let them take an active part in the Internet, exchanging information about themselves and their surroundings. This will give immediate access to information about the physical world and the objects in it—leading to innovative services and increase in efficiency and productivity. Trust management plays an important role in IOT for reliable data fusion and mining, qualified services with context-awareness, and enhanced user privacy and information security. This paper studies the state-of-the-art of trust management in IOT and presents, challenges, trust meaning, trust objectives.
Examining the Relationship between the Dark Triad Traits and Big Five Personality Dimensions

In this study it is aimed to analyze the relationship between the Dark Triad traits (i.e., narcissism, Machiavellianism and subclinical psychopathy) and five factor personality model dimensions (i.e., extraversion, neuroticism, openness to experience, conscientiousness and agreeableness). Since the dark side of personality is an interesting topic in both organizational and psychological studies, analyzing the relationships between the Dark Triad and one of the most used personality model might contribute to understand dynamics of the dark side of personality. Therefore we conducted a questionnaire to the sample group of 293 students. The results indicated that narcissism was positively related to extraversion, openness to experience and consciousness. Machiavellianism negatively related to agreeableness. Psychopathy was negatively related to agreeableness and consciousness and it was positively related to extraversion. ___________________________________________________________________________
Prevention of Spammers and Promoters in Video Social Networks using SVM-KNN

— As online social networks acquire larger user bases, they also become more interesting targets for spammers and promoters. Spam can take very different forms on social websites, especially in the form of videos and cannot always be detected by analyzing textual content. There are online video sharing systems that allow the users to post videos s response to any type of discussion topic. This feature encourages some of the users to post polluted content illegally as responses and there may be content promoters who try to promote them in the top listed search. Content pollution like spread advertise to generate sales, disseminate pornography, and compromise system reputation may threaten the trust of users on the system, thus weaken its success in promoting social interactions. As a solution for this problem, we classify the users as spammers, content promoters and legitimate users by building a test collection of real YouTube users using which we can provide a classification we use of content, individual and social attributes that help in characterizing each user class. For effective classification we use SVM-KNN which is an active learning approach. Our proposed approach poses a promising alternative to simply considering all users as legitimate or to randomly selecting users for manual inspection. In simple SVM training is very slow on whole dataset and not works very well on multiple classes. To overcome this problem and to provide efficient classification in fast manner we proposed new approach is SVM-KNN. Train a Support Vector Machine on K no of collections of nearest neighbours.
Intrusion Detection Based on Information Entropy of Multiple Support Vector Machine

— Intrusion detection is essentially a classification problem. It is very important to increase the classification accuracy. Support vector machine (SVM) is a kind of very effective tool to solve classification problems, accuracy of SVM intrusion detection Based on information entropy is relatively high, but how to achieve higher accuracy is still a new issue. In this paper, we use multiple SVM intrusion detection algorithm based on information entropy to solve the problem, first, we use information entropy theory to optimize the SVM kernel function, then reduce and reclassify the Characteristic attributes of each Network connection, finally, training each Reclassification set of attributes with two Support vector machine, the experimental results show that the proposed method is an efficient network intrusion detection method.
Prevalence and Antimicrobial Susceptibility Patterns of Aerobic Bacterial Isolates from Pyogenic Wound Infections at a Tertiary Care Institute in Haryana, India

Pyogenic infection is characterized by several local inflammation, usually with pus formation, generally caused by one of the pyogenic bacteria, which can produce the accumulation of dead leukocytes and infectious agent commonly known as pus. Pyogenic infections may be endogenous or exogenous . A break or abrasion in the skin can provide an entryway for these surface bacteria into the body, and they stick very well to the moist edges of a cut. The bacteria begin to multiply and extend into the cut. The body’s defense mechanism includes bringing immune cells into the area to fight against the bacteria. Eventually, accumulation of International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 5 Number 2(2016) pp. 78-85 Journal homepage: http://www.ijcmas. com
An investigation into Centralized and Decentralized Micro-grid Systems with Synchronization Capability and Flywheel

Micro-grid system is currently a conceptual solution for the integration of renewable energy based power generation system to the grid. Micro-grids are meant to increase the reliability of power delivery. Renewable power sources such as wind and sun offer a good potential for emission free power. This paper investigates into centralized and decentralized micro-grid systems. A method for synchronizing micro-grid when diesel generator is present as a major source is investigated. The micro-grid systems considered are based on renewable energy resources like wind and solar along with an emergency diesel generator system to keep the voltage and frequency of the micro-grid at the desired level and storage systems to store excess power and to compensate for power shortages. A flywheel is also used to keep the frequency steady. Using MATLAB/SIMULINK, the system is modelled.
Data Relationship Query in Relational DB, NoSQL DB and Graph DB

Every nation has vast amount of census data and analysis of these data is the value for nation as source citations, correlating and corroborating sources, relevance or findings contradictions. These census data may relate in any form based on family group records, friendship, co-worker and etc. In this paper, our nation, Myanmar's census data is used as source citations for searching relationship between two distinct persons based on unique National Registration Card (NRC) Number. The Myanmar census data involve person name, date of birth, gender, occupation, parent names, relationship with householder, NRC number and detail parent’s family records including jobs and etc. NRC number is the unique identification number for every citizen in Myanmar. The aim of this paper is to observe the efficient data storage form for those related data among three types of database structure; relational DB, NoSQL DB, graph DB. The observation is done by retrieving data relationship from these databases using their query form. In relational database, personnel data is stored as table structure while in NoSQL databases like key-values store, column-family store and document store, personnel data is stored as key-values pair, column oriented and document oriented structure respectively. In graph database, personnel data is stored as graph structure with persons as nodes and relationship between them as edges. Then the query processing time is compared based on retrieving related data from those databases by using their relevant query system and find out which query process can produce the optimal running time. The experimental results show that graph database is more powerful in retrieving relationship over relational and NoSQL databases and it can provide better performance when handling in highly interconnected data compared to relational and NoSQL databases.
Detection of Defects in Printed Circuit Boards using Fuzzy Logic and Correlation Coefficient

A Printed Circuit Board (PCB) is a card made specifically for attaching electronic components on it. Inspection of PCB is necessary in order to reduce the defects otherwise it can lead to complete circuit failures. In this paper we have presented a PCB Inspection System which involves the use of correlation coefficient and fuzzy logic in order to detect the defects and the inspection system is applied at the time of manufacturing, i.e., the making of bare PCB. The inspection system also gives the degree of defectiveness present in the PCB. Typical defects that can be detected are over etchings (opens), under-etchings (shorts), holes etc.
A Critical Study Of Total Bed Material Load Predictors

The accurate estimation of sediment transport rate in alluvial channels is vital for safe and economical design of canals and other hydraulic structures. The total bed material load transport phenomenon depends on such a large variety of circumstances that it is difficult to define such definite laws that would suggest prediction of total bed material load transport rate at a particular location in a stream with 100% accuracy. Moreover, despite vast knowledge gained from the intensive research carried out on this phenomenon, no definite solution is yet available. There are several total bed material transport predictors available; however, these predictors produce wide range of total bed material load estimates transport rate for the same set of data. In this paper an effort has been made to ascertain which of the predictors produce a reasonable estimate of the total bed material load transport rate that can be used by field engineers and researchers in the designs of hydraulic structures Ten well known total bed material load predictors are testified against the reliable published field data. A new model for predicting total bed material load is also developed using the same set of data. The study reveals that authors predictor followed by Ackers and White, Karim & Kennedy and Engelund & Hansen predictors produce more reasonable estimate of total bed material transport rate.
Kinetic and thermodynamic study of acid red 131 dye decolourization by Aspergillus niger

The aim of this work was to determine the optimal conditions and to obtain thermodynamic data for the decolorization of Acid Red 131 using an enzymatic system. The effect of physic-chemical parameters on decolorization was studied. The enzyme (laccase) activity was found to be 2.277 U/ μg. The decolorization of Acid red 131 by Aspergillus niger were obtained at three different temperatures. The reaction followed first order kinetics. For enzyme catalyzed bio-degradation, reaction were found to be less enthalpy-driven (high positive ΔH values), with negative entropy (ΔS) indicating that in the transition state, more complex ternary dye structures were formed. The negative values of ΔG explain the fast reaction rates at the transition state during biodegradation of the Acid Red dye, which is exoergonic by nature.
Review of Applications of Deep Learning Methods for Classification of Bio Medical Images

Deep Learning methods for machine learning are applied in various domains. An attempt is made in this paper to explore pos - sibility of applying deep learning methods for analyzing radiographs in general and intra oral periapical radiographs in particular. Machine learning is becoming a pervasive technology. Application domains of machine learning range from production line to health informatics. Researchers all over the world are working on inventing methods for intelligent data processing in all walks of life. Health informatics is a branch buzzing with machine learning methods for data processing. Radiographs are a form of data which is generated in abundance, in health industry. Intelligent processing of this data is requirement of the hour
Survey paper on Hand Gesture Recognition

- Now–a-days, computer become more extensive in society, facilitating natural human-computer interaction (HCI) will have a positive impact on their use. Till now human can interact with computer through input devices like mouse and keyboard or technologies like Graphical User Interface, Command Line Interface, touch user interface, motion tracking interface. Gesture recognition system could be used for improving human-machine interaction, this will allow a human user to communicate non-verbally using different body parts like finger, hand, face, eye, arm, palm etc. Hand gesture recognition approach works on the three main phases i.e. object detection, tracking of object, and gesture recognition. Each phases have different techniques which has its own benefits and limitations. In this survey paper different hand gesture recognition methods and issues are explained.
Information Retrieval using Context Based Document Indexing

— Information retrieval is task of retrieving relevant information according to query of user. A brief idea is presented in this paper about document retrieval using context based indexing approach. Here lexical association is used to separate content carrying terms and background terms. Content carrying terms are used as they give idea about theme of the document. Indexing weight calculation is done for content carrying terms. Lexical association measure is used to calculate indexing weight of terms. The term having higher indexing weight is considered as important and sentence which contains these terms is also important. When user enters search query, the important terms are matched with the terms with higher weights in order to retrieve documents. The documents which are relevant are retrieved according to importance of sentences. Using this approach information
Analysis of LMS and MUSIC Algorithms for Adaptive Array Antenna System

– As technology developed this decade proves communication is the most important factor for the data interchange. Smart antennas have been considered to be one of the most demanding communication technologies. It adapted as most demanding technology because of high-bit rate or high quality in broadband commercial wireless communication. Many algorithms for smart antenna system have been developed as per requirement of the technology. This paper presents different methodologies of direction-of-arrival estimation and adaptive beam forming. Parameterized analysis of the MUSIC algorithm has been done using MATLAB. The results are useful for the smart antenna system design.
NEAREST NEIGHBOR SEARCH ON LARGE-SCALE DATA USING SKIP GRAPH

Nearest neighbor search is one of the most fundamental queries on massive datasets, and it has extensive applications such as pattern recognition, statistical classification, graph algorithms, Location-Based Services and online recommendations. In this report, we propose a new searchable scheme, which can efficiently and securely enable nearest neighbor search over encrypted data on untrusted networks. Specifically, we modify the search algorithm of nearest neighbors with hybrid structures i.e Skip Graph-KD Tree, where the modified algorithm adapts to lightweight cryptographic primitives (e.g., Order-Preserving Encryption) without affecting the original faster-than-linear search complexity. We address all the limitations in the previous works while still maintaining correctness and security. Moreover, our design is general, which can be used for secure k - nearest neighbor search, and it is compatible with other similar tree structures.
A Review on Software Performance Analysis for Early Detection of Latent Faults in Design Models

Product-Forms. These models can be used to derive performance models that make use of LQN, MSC, QNM and so on. The design models are to be mapped to performance models in order to predict performance of system early and render valuable feedback for improving quality of the system. Due to emerging distributed technologies such as EJB, CORBA, DCOM and SOA applications became very complex with collaboration with other software. The component based software systems, software systems that are embedded, distributed likely need more systematic performance models that can leverage the quality of such systems. Towards this end many techniques came into existence. This paper throws light into software performance analysis and its present state-of-the-art. It reviews different design models and performance models that provide valuable insights to make well informed decisions.
Web Recommendation System Based on Approach of Mining Frequent Sequential Patterns

: Today the internet users are increasing day by day so too the websites are also in the increased number. Analyzing and modeling web navigation behavior is helpful in understanding demands of online users. New viewers must follow the sequence path of top viewed websites instead of surfing randomly. Recommendation is a process to analyze users past behavior to recommend new user for purchasing products or viewing certain websites. The main aim of the proposed system is to achieve this feature so this system is designed which recommends the new user to visit the most frequently visited websites. With the help of recommendation process the time of user is saved as the top visited frequently accessed websites are shown in different manner. Also for online purchasing this system provides association between various products. For identifying the patterns the Apriori algorithm is used which shows the frequent items and is designed to operate on databases containing transactions.
Empirical Validation of Test Case Generation based on All-edge Coverage Criteria

Software testing assesses the functionality and correctness of the software through analysis and execution. It is done by exercising appropriate number of test cases so that no part of the program is left untested. Presence of multiple loops in a program makes it unlikely or impossible to test all paths. Therefore researchers try to find the subsets of the test cases, which when tested give confidence of complete testing. However, the subsets of paths are based on some testing criteria. In this research paper GA approach has been used to find out the subset of paths of the test program that fulfills all edge coverage criteria. The Genetic Algorithm for Test Case Generation (GATCG) proposed in this work generates reduced number of paths for a test program. These paths are termed as prime paths. The proposed GATCG technique makes use of the concept of prime paths to reduce the cost of testing. The efficiency of proposed algorithm is established from the results, in terms of number of iterations and time consumed in generating the prime paths for test programs.
ON : IN-SITU NASAL GEL

Recently, controlled and sustained drug delivery has become the standard in modern Pharmaceutical design and an intensive research have been undertaken in achieving much better drug product effectiveness, reliability and safety. This interest has been sparked by the advantages shown by in situ forming polymeric delivery systems such as ease of administration and reduced frequency of administration, improved patient compliance and comfort. The formation of gels depends on factors like temperature modulation, pH change, presence of ions and ultra violet irradiation, from which the drug gets released in a sustained and controlled manner. Various biodegradable polymers that are used for the formulation of in situ gels include gellan gum, alginic acid, xyloglucan, pectin, chitosan, poly(DLlactic acid), poly(DL-lactide-co-glycolide) and polycaprolactone. Mainly in situ gels are administered by oral, ocular, rectal, vaginal, injectable and intraperitoneal routes. The in situ gel forming polymeric formulations offer several advantages like sustained and prolonged action in comparison to conventional drug delivery systems. From a manufacturing point of view, the production of such devices is less complex and thus lowers the investment and manufacturing cost.
QUESTIONING DEFINITION OF RESEARCH IN INDIA

In India, there is misunderstanding and confusion about meaning of research and category of original research paper. This discussion is important because in India, research papers are made compulsory for promotion of medical teachers.There is no uniform policy regarding publications. All medical journals have different pattern regarding titles and publications. There should be uniform pattern of all medical journals. Innovative thoughts, new ideas,and arguments which prevent pollution in the field of research work should be promoted by the editors. Thanks to editor, WJPR, who really understands what research, is? All editors of different medical journals should follow policy of WJPR.