Miracle-dz/UniFAR-FTUs huggingface dataset:
ALEAN STACKED ENSEMBLE MODEL(LSEM) TO ENHANCE THE EFFECTIVENESS OF CLASSIFYING DATA WITH HUGE IMBALANCE

: Knowledge discovery and analysis has become one of the major needs of the current information rich world. Effective information identification and prediction requires effective models. Several machine learning models are available for prediction. This paper concentrates on classification, a supervised machine learning model. An effective classifier can enable effective predictions. However, not all input data are perfect to enable highly accurate classification. Several factors such as data imbalance, noise and borderline entries affect the classifiers. This paper proposes a Lean SVM based Ensemble Model (LSEM) that enables effective classification of data without the need for pre-processing. A heterogeneous ensemble is created using Random Forest and One-Class SVM. The requirement of partial training data for SVM makes the model lean, enabling faster training. Experiment is conducted on data with varied imbalance levels and it is identified that the proposed LSEM operates better than state-of-the-art models and ensembles and hence enabling better predictions.
SINGLE MICROPHONE SPEECH ENHANCEMENT USING GENERAL KALMAN FILTER AND ESTIMATE MAXIMIZATION FRAME WORK

— The adverse effects of noise from various sources on speech signal are aimed to minimize in every speech enhancement algorithm. Non-stationary behavior of both speech and noise becomes a challenging problem. In this paper we present a method for speech enhancement which uses General Kalman Filter . GKF is one of the forms derived from popular kalman filter mostly used in echo cancellation application. An Estimate maximization (EM)frame work accompanied with GKF deals effectively with noise and its effects. The simulation results show that proposed method is dominant over some popular algorithm’s.
A Survey on Trust Management in Peer to Peer Systems

- Trust management in P2P system is used to detect malicious behaviors and to promote honest and cooperative interactions. The main goal of the earlier P2P systems is the capability of aggregating resources, which assumes certain honesty level of peers. However, as P2P systems grow tremendously in size, there will be a considerable number of malicious peers who bring security attacks and threats to the whole network. In a distributed infrastructure without centralized server for authority, providing security mechanism is more complicated than in server-centric solutions, as the existence of multiple sites increases the vulnerability and security efforts must be replicated at multiple sites. Therefore security issues are one of the major challenges that need to be carefully analyzed and addressed, especially for fully decentralized unstructured P2P systems. A lot of researches are being conducted to improve the trust management in peer to peer system. Several reputation-based trust management systems are analyzed here.
Enhanced SPRINT Algorithm based on SLIQ to Improve Attribute Classification

 Abstract — Classification is the most commonly applied data mining technique that had been the focus on research extensively in the past due to its significance. The classification algorithm is complex, and it is an open problem. Though classification is a well-studied problem for the past years, no such classifiers are perfect, and those drawbacks can be used as a basis for the researchers for the enhancement of such classifier. SPRINT is one of them, and the issue to be resolved are rewriting and resorting of attributes in classification. This paper presented the Enhanced SPRINT algorithm based on SLIQ pre-sorting technique to address the identified drawbacks in rewriting and resorting of attribute lists in each node to improve attribute classification. In experimental evaluation, it shows that the performance of the proposed algorithm had shortened the time taken for attributes classification as compared to SPRINT algorithm .
An Improved Particle Swarm Optimization Algorithm for Meta Task Scheduling In Cloud Environment

Task to resource mapping is a challenging issue in cloud computing environment. Computing resources are provided to user based on the demand of the user. The aim of user is to achieve maximum usage time. Task scheduling algorithms plays a vital role in this resource allocation based on the demand or supply of resources. The use of swarm algorithms in optimization problem helps to achieve better solution with global and fast convergence rate and also improves the efficiency. The aim of this paper is to come out with an Improved Particle Swarm Optimization (IPSO) algorithm for independent (meta) task scheduling in cloud computing with a proposed fitness function. The parameters considered are make span and resource utilization. Simulations are carried out using CloudSim simulator. Particle Swarm Optimization (PSO) is considered because of its simplicity, effectiveness and faster convergence. Simulation results show that the proposed IPSO algorithm minimizes make span when compared to standard PSO algorithm.
Design & Implementation an E-Commerce Sites Security System based on Local Binary Pattern Fingerprint Verification

— E-Commerce has played a major role in changing the global economic thought, but it still suffers from serious threats concerning the protection of e-commerce transactions. As a result, there is a great demand for advanced protection system to increase consumer confidence and the institution in the field of e-commerce. In this paper, an e-commerce system is presented, which is a web application designed for browsing and purchasing products from the net. This system provides improved security techniques which are password and fingerprint verification to complete the purchasing procedure and protect e-bank and the system from intruder. The verification algorithm in purchasing procedure includes a series of steps starting with using canny edge detection filter and then using Local Binary Pattern (LBP) and histogram properties as statistical approaches for feature extraction from fingerprint image. A fingerprint image is first divided into (3*3) regions from which LBP histograms are extracted and then converted into a single feature vector. This feature vector forms an efficient representation of the fingerprint and is used to measure similarities between images.The result of matching has been compared by using the Chi Square Statistic (X 2 ) that depend on the resulted feature vector which is a powerful information to prove identity of a person. ASP.NET 2013, SQL Server 2014, VB.NET 2013 and C# 2013 programming language has been used to execute the paper algorithms. Finally, it must be mentioned that an excellent results have been obtained using different fingerprint images.
An Innovative and Efficient Approach for Detecting Unknown Attacks Using Feature Extraction Scheme and Fuzzy-Neural Networks

— In the past two decades, internet have experienced tremendous growth that has sped up a shift in computing environments from centralized computer systems to network information systems. A large volume of valuable information such as personal profiles, banking details and other valuable information is distributed and transferred through internet. Hence, network security has become a major concern than ever. Intrusion Detection Systems (IDSs) play a crucial role in detecting various kinds of attacks and defend our computer and data from them. Therefore, intrusion detection systems are effectively used for detecting intrusion accesses. Intrusion Detection Systems have been evolved over decades and various types of systems are currently available to identify and eradicate attacks based on different system conditions and different aptitudes. Many researchers have felt the importance of new techniques other than the ones which are currently uses. Towards this direction, data mining is considered to be very handy in achieving the desired results. Among many techniques fuzzy-neural networks (FNN) are very promising in this field. In this paper we are proposing a novel approach that uses Feature Extraction Scheme, Fuzzy-Neural Networks, K-means clustering and Support Vector Machines (SVM) for better results using Kyoto2006+ dataset.
A Multi-stage Stochastic model for a multi-product closed-loop supply chain network design

In this research, amulti-stage stochastic model for multi-product closed-loop supply chain network design is developed. The proposed network structure consists of a forward directionwhich provides the first customer zones with virgin products, and a reverse direction which provides the second customer with refurbished products,the suppliers with the recyclable materials and the factories with the remanufacturable products. A multi-stage stochastic mixed integer linear programming (smilp) is used inthe model formulation. The objective of the model is to optimally configure the closed-loop supply chain network to maximize the total expected profit.
Study on Electrocoalescence process: Review

This paper reviews the current understanding of electrocoalescence of water droplets in oil, highlighting particularly the mechanisms proposed for droplet–droplet and droplet–interface coalescence under the influence of an applied electric field, as well as various factors influencing the electrocoalescence phenomenon. The type of electric field such as alternating, direct and pulsed direct current, plays a significant role, depending on the design and setup of the system. The concept of effect of shear, turbulence and viscosity ratio are highlighted in this paper. The integration of processes like microwave radiation and flotation with electrocoalecscence is also discussed. Theconceptof an optimum frequency is also discussed here, relating to the electrode design and coating. More investigations, both experimental and by computer simulation, should be carried out to study the electrocoalescence phenomenon and to contribute to the design and operation of new electrocoalescers.
Hepatoprotective activity and antioxidant effects of El Nabka (Zizyphus spina-christi) fruits on rats hepatotoxicity induced by carbon tetrachloride

The present study was designed to evaluate the protective effect of the El Nabka (Zizyphus spina-christi) fruits as an antioxidant against carbon tetrachloride (CCL4) induced oxidative stress and hepatotoxicity in Albino Wistar rats was investigated. Subcutaneous injection of CCL4, produced a marked elevation (P<0.05) in the serum levels of aspartate transaminase(AST), alanine transaminase(ALT) and alkaline phosphatase(ALP). Daily dietary containing powder of ZSCF at 2.5, 5, 10, and 15% of basal diet for 6 weeks produced a reduction in the serum levels of liver enzymes. ZSCF has also restored normal levels of malondialdehyde and retained control activities of endogenous antioxidants such as Superxide Dismutase (SOD), and Glutation Peroxidase (GSH). Therefore, it is concluded that ZSCF can protect the liver against CCL4-induce oxidative damage in rats, and the hepatoprotective effect might be correlated with its antioxidant and free radical scavenger effects. [Heba Ez. Youssif, Abeer A. Khedr and Mohamed Z. Mahran. Hepatoprotective and antioxidant effects of Zizyphus spina-christi fruits on carbon tetrachloride induced hepatotoxicity in rats. Nature and Science 2011;9(2):1-7]. (ISSN: 1545-0740). http://www.sciencepub. net.
177-183 Bilal Ahmed

Free radicals are implicated for more than 80 diseases including diabetes mellitus, arthritis, cancer, ageing, etc. In treatment of these diseases, antioxidant therapy has gained an utmost importance and research is now directed towards finding naturally occurring antioxidant of plant origin. In a step in this direction we have evaluated antioxidant potency of a plant extract Helicteres isora of four different solvent extracts. But only hexane extract was investigated for its free radical scavenging action towards 1, 1-Diphenyl–2picryl hydrazyl, nitric oxide and superoxides and Reducing assay, FRAP, Total phenolic content etc and found that the hexane extract is having promising free radical scavenging activity in dose dependent manner. This antioxidant potency may be related to the presence of antioxidant vitamins and other compounds present in the extract. These results clearly indicate that it is effective against free radical mediated diseases.
1-D Cursor Movement using Brain Computer Interface

: The main idea of the current work is to use a wireless Electroencephalography (EEG) signal as a remote control for the mouse cursor of a personal computer. The proposed system uses EEG signals as a communication link between brains and computers. Signal records obtained from the PhysioNet EEG dataset were analyzed using the Coif lets wavelets and many features were extracted using PSD. The extracted features were inputted into machine learning algorithms to generate the decision rules required for our application. The suggested online system was tested and very good performance was achieved. This system could be helpful for disabled people as they can control computer applications via the imagination of 1D-cursor movement.
The Impact of Organizational Learning on Organizational Corruption and the Deviant Workplace Behavior: The Case Study of Public Sector Organizations in Pakistan

This study aimed to analyze the impact of organizational learning on corruption and deviant workplace behavior with moral disengagement as a mediator in public sector organizations of Pakistan. It is an accepted opinion that organizational corruption and workplace deviant behavior are challenging and complicated issues that are embedded in the structure of public organizations which negatively affects organizational reputation. This necessitates incorporating the economic and moral perspective of organizations’ learning that dedicatedly minimize the chances of employees being morally disengaged and that in turn restrict corruption and workplace deviant behavior. Through a paper-pen survey technique supervised by the first author, data were collected from 350 respondents working in different departments of public sector organizations located in Rawalpindi and Islamabad. Regression analysis was performed to statistically confirm the relationships hypothesized in this study. Findings support the historical studies and give significant support for accepting the proposed conceptualized model. Practical implications for the managers/ policy makers, certain limitations and future research directions are discussed.
Selective Marketing for Retailers to promote Stock using improved Ant Colony Algorithm

— Data mining is a knowledge discovery process which deals with analysing large storage of data in order to identify the relevant data. It is a powerful tool to uncover relationships within the data. Association rule mining is an important data mining model to mine frequent items in huge repository of data. It frames out association rules with the help of minimum support and confidence value which in turns paves way to identify the occurrence of frequent item sets. Frequent pattern mining starts from analysis of customers buying habits. From which various associations between the different items that the customers purchase are identified. With the help of such associations retailers perform selective marketing to promote their business. Biologically inspired algorithms have their process observed in nature as their origin. The best feature of Ant colony algorithm, which is a bio inspired algorithm based on the behaviour of natural ant colonies, is its parallel search over the problem data and previously obtained results from it. Dynamic memory management is done by pheromone updating operation. During each cycle, solutions are constructed by evaluation of the transition probability through pheromone level modification. An improved pheromone updating rule is used to find out all the frequent items. The proposed approach was tested using MATLAB along with WEKA toolkit. The experimental results prove that the stigmeric communication of improved ant colony algorithm helps in mining the frequent items faster and effectively than the existing algorithms.
H-EAACK-An Intrusion Detection System Using Hybrid Cryptography for MANET

- The migration to wireless network from wired network has been adopted in the past few decades. MANET, mobile ad-hoc network is one of the most important applications of wireless network. MANET is a continuously self-configuring, infrastructure-less network of mobile devices connected without wires. MANETs are used in applications like military and in natural disasters. Security measures play an important role in all these applications . Hence it is necessary to include intrusion-detection system for MANETs. There are various IDS proposed by researchers. One of them is EAACK (Enhanced Adaptive Acknowledgement) which demonstrates higher malicious behavior detection rates while does not greatly affect the network performances. EAACK scheme has used digital signatures for authentication process. All the acknowledgements are digitally signed. EAACK worked by implementing both DSA and RSA algorithm. But EAACK does not provide encryption to the data packets. It causes the network overhead when the malicious nodes are increased. To improve EAACK, we propose a hybrid cryptography scheme which uses symmetric as well as asymmetric cryptographic techniques. Hybrid scheme is implemented using symmetric cipher Triple DES and public key cryptography RSA with hash function MD5. The Triple DES algorithm provides confidentiality, the hash function provides the integrity and RSA will ensure the authentication.
Implementation of AES-192 Algorithm to Overt Fake Keys against Side Channel Attacks

— Cryptography is the study of mathematical techniques related to aspects of information security such as confidentiality, data integrity, entity authentication and data origin authentication. In data and telecommunications, cryptography is necessary when communicating over any unreliable medium, which includes any network particularly the internet. In this paper, an approach to overt the cryptographic key with a fake key, when there happens any counter attack so-called side-channel attack (SCAs) is applied to break the security of AES-192. SCAs happens based on the correlation produced by the data provided as input and operations performed by the device for the data provided and its actual power consumption for the operation. The proposed approach revealing false key introduces few modifications in the existing AES algorithm which aims at masking the true key by reinforcing the correlation coefficient in such a way that the attack leads to a false key which misleads the attacker. The algorithm is coded in Verilog and simulated using Modelsim DE6.5e. The synthesis process is done using Xilinx ISE8.1i and implemented in Xilinx Spartan 3E device. Experimental Matlab R2014a results show the strength of the proposed system, which is capable of successfully hiding the true cryptographic key.
Comparative Modeling and Functional annotation of Streptococcus pyogenes M1 GAS hypothetical protein SPy

In the Drug discovery process, identification of the target protein is an important step. In this study, potential target identification was done on the Streptococcus pyogenesM1GAS which is a human pathogenic organism. The availability of complete genome of S.pyogenes provides a wide opportunity to screen the drug targets. Even if the genome of an organism is completely sequenced, some of the protein sequences are not annotated completely. The proper and complete annotation of proteins is required to understand the pathogenicity and resistance function. The genome of S.pyogenes consists of 1801 genes representing 1693 proteins and many were described as hypothetical proteins. In the present work, the structural and functional annotation was predicted for Streptococcus pyogenes M1 GAS hypothetical protein SPy _ 0012 by using various bioinformatics tools. Analysis for homology and sub cellular localization was done by Blast p and PSORTb tool which showed that this protein is organism specific and not having any homology with the Homo sapiens and it may be localized in the cytoplasmic region . Then, the pathway analysis with KASS tool from KEGG server reveals that it is involved in the betalactam resistance activity. Finally, the primary, secondary structures were analyzed and tertiary structure was predicted by the different tools available in the Expasy server. Validation was done for the predicted 3D structure and it was submitted in the Protein Modeling Data Base and received PMDB ID of PM0080771. Further, putative binding region for the modeled protein was also identified using COACH server. Based on the nature of amino acids present in the ligand binding site, an effective beta lactamase inhibitor could be developed in future to enhance the spectrum of beta lactam antibiotics against S.pyogenes.
Image Segmentation for Pattern Recognition in Surveillance

-Movement control and vehicle proprietor recognizable proof has turned out to be real issue in each nation. Now and then it winds up noticeably very hard to recognize vehicle’s proprietor who disregards movement standards and drives too quick. In this manner, it isn't conceivable to get and rebuff those sorts of individuals in light of the fact that the activity individual won't not have the capacity to recover number from the vehicle compared with speed of distinctive vehicle. Hence, we create Automatic Number Plate Recognition (ANPR) framework is one of the answers for the problem. Robotized Number Plate Recognition framework would significantly upgrade the capacity of police to identify criminal action that includes the utilization of engine vehicles.
Proposed Technique to Improve VANET’s Vehicle Localization Accuracy in Multipath Environment

Localization (location estimation) of a vehicle in Vehicular Ad-hoc Network (VANET) has been studied in many fields since it has the ability to provide a variety of services like navigation, vehicle tracking and collision detection etc. Global Positioning System (GPS) and Inertial Navigation System (INS) both are very useful method of localization. By using Kalman Filter it is possible to combine these two systems to get better accuracy of localization. Now day’s typical localization techniques combines GPS receiver measurement and measurements of the vehicle’s motion by INS. However, when the vehicle traveling through an environment that creates a multipath effect, these techniques fail to produce the high localization accuracy that they attain in an open environments because of loss of satellite signal in a multipath area, such as areas with high buildings, trees, or tunnels. In this new advance localization technique is proposed to improve localization accuracy. Also Artificial Neural Network is used to detect multipath environment and then by using Nelder Mead Optimization method we can reduce the localization error of a vehicle when it travelling through multipath environment.
Review of Review of Review of Review of Code Code Code Code Clone Detection Clone Detection Clone Detection Clone Detection

- Now a days Copy and Paste of code fragments has been regularly practiced in development of software. Because of limitations of time and lack of knowledge programmers use this code strategy. This strategy is known as code cloning. Clones may cause many problems. Probability of errors and the maintenance cost is increased. Modification would be difficult because of clones. So it needs to detect clones and remove them.