Miracle-dz/UniFAR-FTUs huggingface dataset:
Region-Based Saliency Detection and its Application in Object Recognition

-This paper is used to improve saliency detection, in specific super pixels is more perceptually meaningful units are used to represent the input image, which could not detect large salient objects and tolerate the outliers in the messy background during saliency detection. This saliency detection aims at calculating saliency in the region level saliency inspired weighted sparse coding and saliency weighted max pooling by using the achieved saliency map for solving object recognition task. Such weighted sparse coding can learn a more discriminative codebook to favor the feature coding of those more salient/important features. Moreover, to further reduce the effect of noisy background and emphasize the importance of object regions, the saliency weighted max pooling for image representation can be implemented, which further improves the image representation ability.
Discovery of Ranking Fraud for Mobile Applications

— Ranking fraud in the mobile App market refers to fraudulent or misleading tricks which have a purpose of bumping up the Apps in the status list. Indeed, it becomes more and more frequent for App developers to use shady means, such as inflating their Apps‟ sales or posting phony App ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. To this end, in this paper, this provides a holistic view of ranking fraud and proposes a ranking fraud detection system for mobile Apps. Expressly, we first propose to accurately locate the ranking fraud by mining the active periods, namely leading sessions, of mobile Apps. Such leading sessions can be leveraged for detecting the local anomaly instead of global anomaly of App rankings. Furthermore, this investigate three types of evidences, i.e., ranking based evidences, rating based evidences and review based evidences, by modeling Apps‟ ranking, rating and review behaviors through statistical hypotheses tests. In addition, In this propose an optimization based aggregation method to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-world App data collected from the IOS App Store for a long time period. In the experiments, can validate the success of the planned system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.
An Optimization of Energy Saving in Cloud Environment

:- Cloud computing is a technology in distributed computing which facilitates pay per model based on user demand and requirement. Cloud can be defined as a collection of virtual machines. This includes both computational and storage facility. The goal of cloud computing is to provide efficient access to remote and geographically distributed resources. Cloud Computing is developing day by day and faces many challenges; one of them is i) Load Balancing and ii) Task scheduling. Load balancing is defined as division of the amount of work that a system has to do between two or more systems so that more work gets done in the same amount of time and all users get served faster. Load balancing can be implemented with hardware, software
Performance Evaluation of Extended AODV for Integrated MANET with Internet

— The integration of wired and wireless networks is gaining popularity due to its usefulness and practical use. This paper considers the problem of searching for a destination node in a large network, with the ability to conduct cost effective and fast searches. For this purpose, our attention is to the class of TTL based approaches that minimize the cost of such searches associated with the packet transmission, with the intention that they can be applied to wired cum wireless networks. The extended ad hoc on demand distance vector (AODV) routing protocol is used for interconnection of MANET with internet. The simulation is carried out for two different situations: when nodes are static and uniformly distributed along the square lattice and when nodes are mobile and randomly distributed. Simulation results and mathematical derivations used in this paper shows that the expected search cost is reduced in terms of route discovery delay, routing overhead and improved packet delivery ratio; with varying traffic sources. Thus, this is proved that the network wide search is the modification in TTL sequence values in Extended AODV routing protocol, provides the required outcome and more appropriate for integrated MANET with internet.
An Effective Approach for Facial Expression Recognition with Local Binary Pattern and Support Vector Machine

Many methods have been developed based on extracting Local Binary Pattern features associating different classifying techniques in order to get more and more better effects of facial expression recognition. In this work, we propose a novel method for recognizing facial expressions based on Local Binary Pattern features and Support Vector Machine with two effective improvements. First is the preprocessing step and second is the method of dividing face images into non-overlap square regions for extracting Local Binary Pattern features. The method has experimented on three typical kinds of database: small (213 images), medium (1386 images) and large (5130 images). Experimental results show the effectiveness of our method for obtaining remarkably better recognition rate in comparison with other methods. Besides, the proposed approach based on Local Binary Pattern features and Support Vector Machine is simple, fast and significant for real time applications.
Uterine Fibroids: Understanding their Origins to Better Understand their Future Treatments

Despite the high prevalence and the enormous impact the uterine fibroids has on the healthcare system and the economy, there is no effective medical treatment available to eliminate fibroids. This is due in part to the fact that very little is known about their pathophysiology; in order to look for and potentially find treatments capable of eliminating fibroids; we must gain a deep understanding of the mechanisms behind their initiation, stimulation and growth. In this article, we comprehensively review current knowledge of the pathogenesis of uterine fibroids, with the aim of better understanding their origin, as well as discussing the action of already existing medical treatments in order to highlight ways that potential future therapies may be able to target the disease. A literature search was performed in PubMed to find articles related to the etiopathogenesis of fibroids and their treatments. Myomas comprise areas of disordered smooth muscle fascicles characterized by an excess of a cellular extracellular matrix (ECM) with a deregulated phenotype. Although the initial event is believed to be smooth muscle cell proliferation, it is thought that a complex signalling system is also necessary. Non-hormonal factors may be responsible for initiating the development of uterine fibroids, although hormonal stimulation is an essential factor for their growth. Recent studies have confirmed that the different cell types contained in fibroids are all clonally derived from a parental cell with implied multipotent stem cell properties. Ethnicity also strongly influences the development and clinical severity of fibroids. Many Growth factors and their respective receptors have been implicated in fibroid growth. The available evidence continues to reinforce the importance of progesterone in the development of fibroids. The Ulipristal Acetate mediated volume reduction is due to a series of multifactorial and successive events that lead to a low proliferation rate.
New drugs for the management of relapsed or refractory diffuse large B-cell lymphoma

Approximately 65% of patients with diffuse large B-cell lymphoma (DLBCL) can be cured with standard front-line therapy and achieve an overall survival comparable to the general population. Of the 35% of patients who fail front-line therapy, less than a quarter can be salvaged and cured by intensive chemotherapy followed by an autologous stem cell transplant. Patients who are transplant-ineligible (including elderly patients with co-morbidities, patients who are chemotherapy-refractory or those who have failed transplant) represent an unmet medical need population with a very poor outcome. While chimeric antigen receptor T-cell (CAR-T) therapy has shown promise in this setting, many patients will be unsuitable or relapse after CAR-T therapy. These patients are ideal candidates for less toxic novel therapies and a more tailored personalized approach, recognizing the biological heterogeneity of DLBCL. In this review, we will briefly summarize the standard management options for relapsed/refractory DLBCL and then focus on the novel therapies currently in development. We aim to discuss the biological rationale and available clinical data for the most promising agents, including monoclonal antibodies, antibody-drug conjugates (ADC), pathway inhibitors, immunomodulatory agents and epigenetic modifiers.
Ensemble Methods for Classification of Direct Marketing

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed using both homogeneous ensemble classifiers with bagging and heterogeneous ensemble classifiers with arcing. Classifier ensembles are designed using Radial Basis Function (RBF) and Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Multilayer Perceptron (MLP) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard data sets of direct marketing. A wide range of comparative experiments are conducted for standard data sets of direct marketing and the classification performance of the proposed homogeneous and heterogeneous ensemble classifiers are evaluated in terms of accuracy. The objective is to examine the performance of proposed ensemble methods on a real-world data of bank deposit subscription and labor relations data. The purpose is increasing the campaign effectiveness by identifying the main characteristics that affect a success. The experimental results demonstrate, with higher accuracies, the success of these models in predicting the best campaign contact with the clients for subscribing deposit and also labor resources management practices.
Understanding the Dynamics of Heart Rate Variability in Cardiovascular Disorders using Recurrence Quantification Analysis and Radial Basis Function Neural Network

: Heart diseases are a major health concern throughout the world. While ECG signal is of importance in diagnosis of the cardiac ailment, Heart rate variability (HRV) has been found to be of prognostic and diagnostic significance in a variety of heart conditions. HRV is derived from a large time series of ECG denoting peak to peak RR intervals. The seemingly noisy looking HRV signal is characteristically nonlinear and chaotic, therefore, a non-linear analysis method like Recurrence Plot (RP) is considered appropriate to its analysis. In this paper, we have used Recurrence Quantification Analysis (RQA) and have extracted 13 parameters i.e., recurrence rate (RR), determinism(DET), mean diagonal line length (L), maximum diagonal line length (Lmax), entropy (ENTR ), Laminarity (LAM), trapping time ( TT), maximum verticalline length (Vmax), recurrence time of 1st type (T1), recurrence time of 2nd type (T2), recurrence time entropy (RTE), clustering Co-efficient (Clust), transitivity (Trans) of the HRV data. The above parameters have been subjected to a Radial Basis Probabilistic Neural Network (RBPNN) for classification and clustering of different heart disorders. The method was successful in identifying the various class of disorders with 94.2% of accuracy. This result is of importance for the development of automated diagnosis, monitoring, prognostic and predictive devices for myocardial dysfunctions. It may have utility as a warning system in critical care units.
Postharvest Pathogen Interventions for Meat and Poultry

Although progress has been made in regard to pathogen reduction technologies, the incidence and retardation of pathogens in the meat supply remains a high priority. As examples, on October 7, 2002, the Food Safety and Inspection Service (FSIS) issued a notice in the Federal Register mandating HACCP reassessments for E. coli O157:H7 and indicated that testing exemptions would furthermore be revoked. As promised, and due to the “E. coli summer season,” on April 18, 2003, the FSIS issued Notice 11-03 indicating that the FSIS will begin sampling raw ground products for E. coli O157:H7 at all grinding facilities, regardless if an establishment was previously exempt from routine sampling, in effect removing all exemptions. To this end, inplant inspectors will receive sampling request forms and draw samples regardless of the plants own testing program.
Smart Grid-An Approach of Integrating Conventional and Distributed Generation System

In this paper, the Smart-Grid model has been designed using MATLAB/SIMULINK, an approach for interconnection of conventional and distributed generation. The smart grid is regarded as the next generation power grid, uses two way flow of electricity and information to create a widely distributed automated energy delivery network. This paper presents a simple model for the proposed interconnection work using conventional generation and distributed generation. At each busbar and load locations the active power and voltage levels are monitored with and without interconnection of the sources. The Active powers and reactive powers are varied to observe the variations on the power and voltages of the systems. In this analysis the capacitive loads are kept constant. Simulation results are tabulated to show the effectiveness of the stability of the system.
EFFECTS OF OMEGA 3 AND VITAMIN E ON ANTIOXIDANT ENZYMES AND HSCRP IN CAD PATIENTS

Inflammation, as a consequence of oxidative stress, is a key factor in development of cardiovascular diseases. The aim of the current study was to assess the effects of omega 3 and vitamin E supplementation on the serum level of antioxidant enzymes and hsCRP levels in CAD patients. Participants of this RC T study included 60 non-smoker male CAD patients who received 4g/day of omega 3 and vitamin E placebo (OP), omega 3 (4g/day) and 400IU/day vitamin E (OE) or omega 3 and vitamin E placebos (PP) for two months. Serum antioxidant enzymes and hsCRP levels were measured at the baseline and at the end of the study and effects of the intervention on these parameters were compared between and within the groups. Serum levels of catalase increased significantly in OE and OP groups (P=0.002 and P= 0.015, respectively). TAC increased significantly in OE group (P=0.009) but not in OP and PP groups. Serum SOD and GPX did not change significantly in any of the groups or between the groups. Omega 3 alone and combined omega 3 and vitamin E supplementations decreased hsCRP significantly in OP and OE groups (P=0.008 and P= 0.050, respectively). Co-administration of omega 3 and vitamin E in CAD patients seems to include beneficial effects on inflammation by decreasing hsCRP levels and to improve oxidative stress by influencing antioxidant enzymes.
“A Review on Recommendation System and Web Usage Data Mining using K-Nearest Neighbor(KNN) method”

---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Data Mining is a extraction of knowledge from large amount of Observational datasets. In Web world, there is immense of information available on the internet but user is not capable to find relevant information in short period of time. Therefore, a system called recommendation system developed to assist user in their browsing activities. It analyzes users need and provides relevant information in shorter span. K-NN algorithm helps to know the users behavior and his interest. This paper focuses on recommendation systems based on the user’s navigational patterns and provides suitable recommendations to cater to the current needs of the user .
A Survey on Web Forum Crawling Techniques

: Web forum has become an important source on the Web due to its rich information contributed by millions of users. But still web forum crawling is a challenging task due to complex in-site link structures and login controls of most forum sites. The main goal of this paper is to focus on various web forum crawling techniques. Board Forum Crawling is a technique which exploits the organized characteristics of the Web forum sites and simulates human behaviour of visiting Web Forums. A recent and more comprehensive work on forum crawling is iRobot. iRobot aims to automatically learn a forum crawler with minimum human intervention by sampling pages, clustering them, selecting informative clusters via an informativeness measure, and finding a traversal path by a spanning tree algorithm. However, the traversal path selection procedure requires human inspection. Forum Crawler Under Supervision (FoCUS) is a supervised web-scale forum crawler. The goal of this technique is to crawl relevant forum content from the web with minimal overhead. Forum threads contain information content that is the target of forum crawlers.
AMPLIFICATION, CLONING AND SEQUENCING OF ANTHRANILATE SYNTHASE COMPONENT I (TrpE) FROM MYCOBACTERIUM TUBERCULOSIS

The emergence of multi-drug resistant (MDR) strains of Mycobacterium tuberculosis is the main reason why tuberculosis (TB) continues to be a major health problem worldwide. It is urgent to discover novel anti-mycobacterial agents based on new drug targets for the treatment of TB, especially MDR-TB. Tryptophan biosynthetic pathway, which is essential for the survival of M. tuberculosis and meanwhile absent in mammals, provides potential anti-TB drug targets. One of the promising drug targets in this pathway is anthranilate synthase component I (TrpE), whose role is to catalyze the conversion of chorismate to anthranilate using ammonia as amino source. In order to gets a deep understanding of TrpE, a study on Molecular cloning and identification of TrpE is required. The aim of this study was to amplify the trpE gene (TrpE)) from M. tuberculosis MTCC 400 bacteria and clone it into a vector for future use. Using M13 specific primers the trpE was amplified and ligated into the cloning vector pRSET and then transformed into E.coli. The recombinant plasmids were identified using restriction enzyme digestion. The sequence of cloned trpE gene from M. tuberculosis MTCC 400 was confirmed by DNA sequencing. The sequence was submitted to NCBI GenBank Accession no is JQ934965 and version JQ934965.1 GI: 397310105.
Alpha BrainWave Neurofeedback : Social and Economic Impact on Community , Propagation of Benefits Beyond Those Trained

The purpose of this retrospective survey study was to assess the effectiveness of alpha brain wave training in making positive social and economic differences in the trainees’ community. Trainees and their circle of contacts (family, friends and co-workers) were solicited to respond to an online survey that asked the respondents to rate and provide comments about the trainees’ changes in financial, physical, emotional and psychological and lifestyle factors. Results indicated that the trainees improved in significant areas of their lives, both at home and at work, and their self-observations were corroborated by their family, friends and co-workers. Furthermore, some of the trainees’ family, friends and co-workers reported that they made changes in their own behaviour as a result of the changes they observed in the trainee. The conclusion from this study is that alpha brain wave training has positive impact on the social and economic well-being of the trainees as well as the social and economic fabric of the community.
Dynamic Search Algorithm in Unstructured Peer-to-Peer Networks

Designing efficient search algorithms is a key challenge in unstructured peer-to-peer networks. Flooding and random walk (RW) are two typical search algorithms. Flooding searches aggressively and covers the most nodes. However, it generates a large amount of query messages and, thus, does not scale. On the contrary, RW searches conservatively. It only generates a fixed amount of query messages at each hop but would take longer search time. We propose the dynamic search (DS) algorithm, which is a generalization of flooding and RW. DS takes advantage of various contexts under which each previous search algorithm performs well. It resembles flooding for short-term search and RW for long-term search. Moreover, DS could be further combined with knowledge-based search mechanisms to improve the search performance. We analyze the performance of DS based on some performance metrics including the success rate, search time, query hits, query messages, query efficiency, and search efficiency. Numerical results show that DS provides a good tradeoff between search performance and cost. On average, DS performs about 25 times better than flooding and 58 times better than RW in power-law graphs, and about 186 times better than flooding and 120 times better than RW in bimodal topologies.
A Review of MIMO Antennas With Various Mutual Coupling Reduction Techniques

The high demand of wide bandwidth, high channel capacity and high data rate in wireless communication is achieved with the help of MIMO Antennas. The level of performance of the MIMO antenna is very high and steep if represented graphically but due to the use of many antennas the issue of mutual coupling exists which deteriorates the performance of MIMO antennas. The problem of multipath propagation can be solved using MIMO systems. Here in this paper we discussed the various types of monopole, fractal and hybrid antennas which can be chosen to make a MIMO system. The requirements such as high bandwidth, high data rate, omnidirectional characteristics, etc are achieved with the various types and designs of the antennas.
A Comparative Analysis of Knowledge Management in Banking Sector: An Empirical Research

Knowledge management is defined as the process of creating, storing, transferring, applying and saving organizational knowledge. Especially, in an environment of intensive competition increased with globalization; creating knowledge has been a vital factor for organizations, which want to gain advantage by innovative production. The development of information and communication technologies has accelerated this process. All of these developments attracted attention to the concept of knowledge, and a new period, in which the organizations that can create knowledge and use this acquired knowledge effectively and efficiently, have competitive advantage started. In this process, technology, organizational culture, leadership and measurement became important as basic components to provide effectiveness and efficiency in knowledge management. The main purpose of this study is to examine the basic components of knowledge management in banking sector with an empirical analysis and also to determine whether there is a difference between the private and state banks in the context of knowledge management practices. Key Words: Knowledge Management, Knowledge Management Components, Knowledge Management Process, Information Technologies, Banking Sector
Multipath Global Rerouting for Fault Tolerant Overlay Network (FTON) in Labeled Optical Burst-Switching

In this paper, mainly concentrate on reduce the burst loss and Non-availability of wavelength in network; proposed an overlay model known as Fault Tolerant Overlay Network (FTON) which improves the performance of a regular IP electronic network. An FTON is created over the existing network; an FTON node collects the information about the underlying network periodically. Whenever a fault occurs in the underlying network, and in case of non availability of required wavelength, FTON nodes generate an alternate path to route the bursts. This proposed work extends the resilience concept to the Optical Burst Switched Network