Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Security with Cryptography

: Network Security & Cryptography is a concept to protect network and data transmission over wireless network. Data Security is the main aspect of secure data transmission over unreliable network. Network security involves the authorization of access to data in a network, which is controlled by the network administrator. Users choose or are assigned an ID and password or other authenticating information that allows them access to information and programs within their authority. Network security covers a variety of computer networks, both public and private, that are used in everyday jobs conducting transactions and communications among businesses, government agencies and individuals. Networks can be private, such as within a company, and others which might be open to public access. Network security is involved in organizations, enterprises, and other types of institutions. In this paper we also studied cryptography along with its principles. Cryptographic systems with ciphers are described. The cryptographic models and algorithms are outlined.
Analysis of Students Performance Using Data Mining

: Students’ informal conversations on social media (e.g. Twitter, Facebook) shed light into their educational experiences opinions, feelings, and concerns about the learning process. Data from such un-instrumented environments can provide valuable knowledge to inform student learning. Analyzing such data, however, can be challenging. The complexity of students’ experiences reflected from social media content requires human interpretation. However, the growing scale of data demands automatic data analysis techniques. In this paper, we developed a workflow to integrate both qualitative analysis and large-scale data mining techniques. We focused on engineering students’ Twitter posts to understand issues and problems in their educational experiences. We first conducted a qualitative analysis on samples taken from about 25,000 tweets related to engineering students’ college life. We found engineering students encounter problems such as heavy study load, lack of social engagement, and sleep deprivation. Based on these results, we implemented a multi-label classification algorithm to classify tweets reflecting students’ problems. We then used the algorithm to train a detector of student problems from about 35,000 tweets streamed at the geo-location of Purdue University. In the proposed work we include two different algorithms such as c4.5 and Support Vector Machine (SVM). C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. [1] C4.5 is an extension of Quinlan's earlier ID3 algorithm. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier. A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. This work, for the first time, presents a methodology and results that show how informal social media data can provide insights into students’ experiences.
The Relationship between Forgiveness and Marital Satisfaction

The study examined the relationship between forgiveness and marital satisfaction in BandAbbas married women. The sample included 200 persons who were selected through stratified sampling. Data collection tools, including 3 measure of Demographic Questionnaire, The Enright Forgiveness Inventory [EFI], was translated and adopted into Persian by Ghobari et al and the ENRICH Marital Satisfaction scale [EMS], was translated and adopted into Persian by Soleymanian. This type Pearson correlation and multiple regression analysis of data (P<0.001) saying that a significant positive relationship between marital satisfaction and forgiveness. Results of multiple correlation analysis also suggests the existence of multiple relationships between forgiveness marital satisfaction and forgiveness was the best predictor of marital satisfaction
Success Factors for Enterprise Systems in the Higher Education Sector: A Case Study

Many large organisations have moved to Enterprise System solutions in recent years, including the higher education sector (HES). Whilst the benefits of Enterprise systems are well known, the sector has a social mission and characteristics that do not necessarily map to a commercially-focused corporate conceptualization, and assessing the suitability of any particular enterprise solution requires a qualified set of criteria to be applied. This paper looks at an “essential set” of critical success factors (CSFs) relevant to enterprise systems in the HES and applies them in a case study of a large Australian University. The CSFs found to be most relevant to successful ES deployment show differences from CSFs reported in other studies, mainly those in commercial sectors, suggesting a sector based approach be taken to evaluating ES success. We generalise our practical findings to theory, and propose further theory development and validation through confirmatory case studies and specific hypothesis testing. KEYWORDS-Critical Success Factors, CSFs, Enterprise Systems, Higher Education
Optimization of Parallel Aho-Corasick Multipattern Matching Algorithm on GPU

: Network Intrusion Detection Systems (NIDS) need to handle computationally intensive operations like pattern matching, where huge amount of data needs to be matched against the known patterns. Storage capacity and link speed has increased with the advent in technology. Therefore there has been an increase in the amount of data that needs to be matched against the known patterns. The traditional algorithms cannot handle this increased amount of data. Therefore, we need such a hardware and software solution that would help to handle this increased amount of incoming data in NIDS to match it with the known patterns. We have used a parallel multipattern matching algorithm that matches an input string with the known patterns (attack patterns or virus signatures) to check for the presence of any pattern in an input string and would return the state in the DFA and position in an input string where the pattern was found. We have run this algorithm on NVIDIA Geforce GTX 680 GPU with CUDA 6.5 programming model. We have also introduced several optimization techniques for the Parallel Aho-Corasick algorithm that would result in the reduction of memory usage, time and cost required to execute Parallel AC algorithm on GPU. Our results show that Optimized Parallel Aho-Corasick algorithm on GPU takes very less time for execution as compared to running the Serial Aho-Corasick algorithm on CPU, Parallel Aho-Corasick algorithm on CPU and Unoptimized Parallel Aho-Corasick algorithm on GPU.
Hierarchical Classification with Convolutional Neural Networks for Biomedical Literature

Multi-label document classification is a challenge task in many real-world applications. Recently, hierarchical classification methods have been widely used in document classification. However, at each layer of the hierarchical architecture, a classifier is trained independently, ignoring the relations between the other layers. In addition, compared with general documents, the biomedical literature only consists of the title and abstract information instead of the whole context. To overcome this problem, in this paper, we propose a novel hierarchical indexing method with Convolutional Neural Networks (CNNs) to tackle with the biomedical abstract document collections. First, we construct a hierarchical CNN indexing architecture which adaptively groups word2vec categories into (coarse) subsets by clustering. Next, a suitable loss function is designed for CNN training, where multi-label classification is actually performed in a coarse-to-fine learning style. Thereafter, a high-dimensional space representation is generated with feature extension by word sequence embedding, which contains more semantic information than bag-of-words. Experimental results show that our CNN model achieves an impressed performance.
EFFECT OF BRAND IMAGE ON CUSTOMER SATISFACTION & LOYALTY INTENTION AND THE ROLE OF CUSTOMER SATISFACTION BETWEEN BRAND IMAGE AND LOYALTY INTENTION

The Purpose of the study was to investigate the effect of brand image benefit on customer satisfaction and Loyalty intention directly and indirectly based upon hypothetical model in the current study for a cosmetic brand (Fair lovely) at Gwalior (M.P) in India. The measures were reconstructed and re-standardized to make it suitable for the purpose of the study. Numbers of factors were identified through exploratory factor analysis for all the variables. The results of multiple regression revealed that there is a strong positive relationship between brand image and loyalty intention, While, the relationship between customer satisfaction and loyalty intention was found to be less weak. This indicates that there might be a mediation effect of customer satisfaction between brand image and loyalty intention. For evaluating the mediation effect Sobel test was applied and the result of the Sobel test was found to be positive. Hence, a mediating effect of customer satisfaction was found between Brand Image and Loyalty Intention. The measure of brand image was constituted of Functional, Social, Symbolic, experiential and appearance enhance. A survey was carried out on 200 respondents. The results also indicated that overall satisfaction does influence customers' loyalty which implies that marketers should focus on brand image benefits to achieve customer loyalty.
A Survey on Emotion Recognition from Speech Signal

: Emotion recognition has been studied in recent past with great interest, out of various modalities from which emotions can be extracted, speech is the most natural and fastest of all the modalities. The major challenges for making a Speech emotion recognition system are finding and preparing database, selecting the most suitable features and designing appropriate classification scheme. The common approach is to extract a very large set of features over a generally long analysis time window and perform machine learning methods for classification. Speech emotion recognition increases the naturalness in Human Computer Interaction and can be used in wide variety of application in our day to day life. This paper surveys the three main building blocks of speech emotion recognition system, first part is survey of existing databases, the second part surveys most widely used features and the third part discuss various classification techniques.
A Review On Inadequacies To Overcome By Commercial Unmanned Aerial Vehicles In India

This paper explains the challenges faced by the drones utilized for commercial purposes and the solutions to overcome the problems. This is an attempt to find the problems faced in India and about the drawbacks of existing commercial drones all over the world. The paper has listed down the challenges and the causes are keenly listed down across the different possibilities of logical and technical issues faced by an UAV while delivering the cargo to the peers till the comeback of drone to the hangar and the moral issues that may arise where the drone owner and operators would be facing and how the drone is expected to function in the future.
Performance Analysis of Supervised Techniques for Review Spam Detection

---------------------------------------------------------------------- ABSTRACT ----------------------------------------------------------- - Nowadays, millions of products and services are available online. Searching for the best products which targets the individuals’ requirements would be difficult as the result of the existence of too many offers. One of the most useful approaches to choose a product or service is to use the reviews of the others who have already tried them. A reviewing system is a place where individuals write their reviews on their experienced products and services, and also benefit from others’ reviews. Moreover, companies utilize reviewing systems to apply opinion mining techniques in order to improve their goods or services and to watch their competitors. However, the popularity of the reviewing systems ignites this motivation for some people to enter fake review to promote some products or defame competitors products. These review spam should get detected and eliminated in order to prevent misleading potential customers. Opinion mining techniques should use to locate and eliminate potential spam reviews. The objective of this paper is to discover the concept of Review spam detection in the field of opinion mining, and presents a performance analysis of its techniques in this field.
Comparison between Structured and Unstructured Grid Generation on Two Dimensional Flows Based on Finite Volume Method (FVM)

---- Finite volume methods (FVM) had been recognized as one of numerical has proven highly successful in solving problem of fluid mechanics, meteorology, and many other engineering areas. The implementation of the finite volume required spatial discretization of domain of the problem in hand. In this respect one may adopt structured or unstructured grid approach. However their implementation will generate different algorithm although they are used the same Finite Volume scheme. The present work develop two computer codes both used a cell centered Finite volume scheme with different of term of spatial discretization. The first computer code used a structured grid and the second one used unstructured grid. Both computer codes applied for solving two internal flow problems and one external flow problem of flow past through airfoil NACA 0012. Each of flow problems are solved for three different flow condition and their result presented in term of pressure and Mach number distribution along their geometry surfaces. Their comparison result indicates the cell centered Finite volume scheme is a robust scheme, since their capability to produce same result are achievable without strongly influenced by manner how the mesh flow domain created.
The molecular and analytical methods reveal aflatoxin B1-producing Aspergillus flavus isolated from ready-to-eat peanut samples are resistant to the antifungal agent methyl-thiophanate

The peanut is a product of high nutritive value and an economically important crop. However, peanuts can be contaminated during storage with Aspergillus flavus, which produces aflatoxins. In humans, aflatoxins can cause hepatocellular carcinoma. Exposure of animals to aflatoxins can result in weight loss and generate residues in meat and milk. The objective of this study was to isolate and identify aflatoxigenic fungi, to quantify the production of aflatoxin B1, and to correlate its production with resistance to the antifungal agent methyl-thiophanate (MT) in ready-to-eat peanuts samples. Nine of the 50 samples selected were found to be contaminated with A. flavus by PCR amplification of the ITS region. In addition, three genes of the aflatoxin B1 biosynthesis pathway were amplified. HPLC analysis revealed that the isolates produced high levels of aflatoxins ranging from 4 to 285 μg/mL. Furthermore, the isolate producing the highest concentration of aflatoxin (285 μg/mL) exhibited the highest resistance to antifungal MT (MIC ≥ 250 μg/mL), factors that might be related. The present results confirm the presence of aflatoxin-producing fungi resistant to MT in peanut samples, which may pose health risks to consumers.
Patient-level meta-analysis of randomized trials of aromatase inhibitors (AI) versus tamoxifen (Tam).

529 Background: The optimal way to schedule AIs and/or Tam in the adjuvant treatment of early breast cancer remains uncertain. Methods: ITT meta-analysis of individual patient data on 36 889 post-menopausal women with ER-positive invasive breast cancers in randomised trials of [A] Continuous AI (5yrs) vs Tam (5yrs); [B] Sequential Tam then AI (2-3yrs of Tam then 2-3 yrs AI) vs Tam (5yrs); [C] Continuous AI (5yrs) vs Sequential Tam then AI (5yrs). Results: [A] Fewer women had breast cancer recurrence with Continuous AI than Tam (827/4,970 vs 964/4,915, p<0.0001) and fewer died of breast cancer: 504 vs 562; rate ratio (RR) 0.86 [0.76-0.97], p=0.014. Recurrence RRs were: 0.66 during yrs 0-1 [95%CI 0.54-0.80], 0.75 during yrs 2-4 [0.64-0.88] and 0.90 in yrs 5+ [0.79-1.04]. [B] Recurrence was also lower with Sequential Tam then AI than with Tam alone (753/5,909 vs 863/5,889, p=0.0001) as was breast cancer mortality (361 vs 428 deaths; RR 0.84 [0.73-0.97], p=0.015). Recurrence RRs were 0.56 during yrs 2-4 [0.46.
Comparative Analysis of Algorithm for Cluster Head Selection in Wireless Sensor Network

— One of the challenging issues to be studied in WSN is its energy saving so as to extend lifetime. The primary goal of node clustering is network preprocessing that is used to obtain information and limit energy consumed. To support high adaptability and better accumulation of information data, sensor nodes are often grouped into disconnected, non overlapping batches, groups of nodes called clusters. Clusters design hierarchical WSNs which incorporate adequate performance of finite reserves of sensor nodes and thus enhance network lifetime. In this paper different clustering algorithm are compared having different cluster head selection approach. Our paper presents review of different energy efficient cluster head selection algorithms in WSNs.
OPEN LOOP CONTROL OF NPC MULTILEVEL INVERTER USING SVPWM INDUCTION MOTOR DRIVE FOR ELECTRIC VEHICLE APPLICATION

This paper presents an efficient AC Drive for an Electric Vehicle (EV) using an induction motor. In this the use of induction motor requires implementing of multilevel inverter supplied by DC voltage source and switched using three level space vector PWM. In this paper a technique is implemented for a better control for electric vehicle using induction motor drives. Multilevel inverter have less distortion and Total Harmonic Distortion (THD) of output voltages compared to other inverters. SVPWM generates the required gate drive waveform for each PWM cycle. The inverter is treated as one single unit and can combine different switching states (number of switching states depends on levels). The model of the multilevel inverter system is fed with SVM method to control the induction motor for electric vehicle applications.The simulations are performed using MATLAB/SIMULINK software and the results are presented.
Finite ElementAnalysis of Doubly Curved Thin Concrete Shells

A thin shell is a “Three-dimensional spatial structure made up of one or more curved surfaces whose thickness is small compared to their other dimensions”. Shells belong to the class of stressed skin structures which, because of their geometry and small flexural rigidity of the skin, tend to carry loads primarily by direct stresses acting in their plane. The shells are subjected to pure membrane state of stress, under appropriate loading and boundary condition the resulting bending and twisting moments are either zero or small which may be neglected. The coordinates of funicular shells are determined by masonry mould method by developing a computer program. In this study doubly curved thin shells are analysed using finite element software SAP 2000. Doubly curved shells which are in square plan having 10mX10m and 15mX15m are considered and shells in rectangular plan having dimensions 10mX15m and 15mX20m are considered. The behavior of shells under self-weight, live load varying from 0-20KN/m (UDL) is obtained. In this case study deflection curves, membrane stress and stress contour diagram are obtained. It is observed that with the increase in rise and thickness of funicular shell the deflection are reduced. The membrane stresses decreases with the increase in rise and thickness of concrete funicular shell. The aim of this study is to develop shells of different sizes and investigation is done on the shells by finite element analysis under given uniformly distributed load, to find out the behavior of shells in various cases using standard software, Structural Analysis Package (SAP 2000).
A Framework for Big Data Analytics as a Scalable Systems

------------------------------------------------------------------- ABSTRACT ------------------------------------------------------------- Rapid technological advancements in recent technologies have led to inundation of data from diverse domains like scientific generated data, web, business intelligence and medical science over the past few years. The concept of big data was put to capture the meaning of this emerging vision. In comparison to the traditional data, big data exposes other unique characteristics apart from its massive volume. For instance, big data is fully distributed and unstructured. This emerging trend forces to create a new architecture for data acquisition, transmission, storage and analytics. In this paper, we present a framework for big data analytics in the form of a scalable systems aiming to provide an overall dimensions of big data system and thus to advance the big data challenges. First, we present a definition and background of big data. Next, we present a framework to define the architecture of big data. Finally, we delineate the challenges and future work to be carried out for big data systems.
Software Project Management Training through game like simulation and Virtual Reality

Presently, we have observed that we are lacking with tools for teaching and assessing Software Project Management. We need to develop some tools to educate capacity of Software Project Management and to allow learners to check their skills and involve the required educational purpose in safe surroundings by using a gamification like methodology. In this paper, an Interactive Gaming System to enrich the fundamentals of Software Project Management using a gamut of techniques & technologies is designed with Gesture Recognition to increase player indulgence in the game. The proposed game also involves multiplayer participation for a common objective over TCP/IP or Internet resulting in increasing team effort. The proposed game is implemented in MATLAB.
A Comparative Analysis of Multilevel Inverter: A Theoretical Review

This paper presents a theoretical analysis and review of multilevel inverter (MLI) topologies. Multilevel inverters are getting popular in applications like solar PV cell, fuel cell, uninterruptible power system (UPS) systems, motor drives, etc.The three most important topologies like diode-clamped inverter (neutral-point clamped), flying capacitor (capacitor clamped) and cascaded H bridge topologies have been discussed in this paper. Here different inverter topologies and most preferable modulation PWM techniques preferred for the particular topology, advantages and drawbacks are discussed. Also the conventionally available three level NPC (3L NPC) inverter and one new proposed NPC inverter topologies have been compared.It is observed that the topologies are closely related to each particular application, depending on their unique features and limitations like power or voltage level, performance, reliability, count of component used, costs and other technical specifications.