Miracle-dz/UniFAR-FTUs huggingface dataset:
Diabetes and Obesity International Journal Diagnosis and Prevalence of Sarcopenic Obesity Associated with Low Cardiorespiratory Fitness and Insulin Resistance in Adults Clinically Selected for Lifestyle Modification Program Diabetes Obes Int J Diagnosis and Prevalence of Sarcopenic Obesity Associate

Recently, obesity and sarcopenia have been showing high prevalence among population and when these conditions are combined may increase damage to the individual's health. This study aimed to diagnose, assess prevalence, muscle mass function and metabolic variables related to sarcopenic obesity in adults. Were assessed 523 individuals, of both sexes, with higher prevalence in females (72.7%) with a mean age of 54.74 ± 10.18 years old, selected for Lifestyle Modification Program (MEV). The individuals were assessed for medical anamnese, blood pressure, cardiorespiratory fitness, body composition and anthropometry, physical fitness and biochemical analyzes, including analysis of insulin resistance (HOMA-IR), oxidative stress (malondialdehyde MDA) and inflammatory stress (CReactive Protein PCR). Statistical analyzes were performed by SAS software 9.2 (p <0.05). Obesity was defined by higher measures of waist circumference. It was verified that individuals with abdominal obesity compared to nonobese, presented higher BMI,% BF, MMI, HOMA-IR, CRP, uric acid, triglycerides and blood glucose and reduced levels of HDL-c, flexibility and VO2max. When assessed separately, high abdominal obesity was associated with low aerobic fitness, muscle strength, poor flexibility and insulin resistance, on the other hand, low handgrip strength, HDL-c and insulin resistance was associated with sarcopenia status. When combined, sarcopenic obesity differs mainly in physical fitness, with cardiorespiratory fitness as determinant of sarcopenic obesity discrimination. Thus, was verified that sarcopenic obesity, in this population, are related to changes on physical fitness and the cardiorrespiratory fitness is the major component associated with this pathology.
Generalization Of Selfmaps And Contraction Mapping Principle In D-Metric Space

Large number of fixed point results for selfmappings satisfying various types of contractive inequalities which in [ 5, 9 ,12]. In this paper, theorems on selfmaps and some fixed point theorems are proved in D-metric spaces. The Generalization of contraction mapping principle in D-metric space which include some fixed point results in U.P.Dolhare [1], B.C. Dhage, U.P.Dolhare and Adrian Petrusel [ 3 ] ,Rhoades [ 12 ] in D-metric spaces as special cases.
Review on Centralized Energy Efficient Clustering(CEEC) Routing protocol for Wireless Sensor Networks

— Due to limited energy of Wireless Sensor Networks (WSNs), routing plays a crucial role in improving energy efficiency. Energy efficient routing protocol for Wireless Sensor Networks (WSNs) is one of the most challenging task for researcher. We propose a Centralized Energy Efficient Clustering (CEEC) routing protocol. We will focus on protocols that use distributed clustering algorithm that increase computational overhead on all nodes. And other problem is that, optimum number of cluster-heads are also not guaranteed through distributed algorithm. We will simulate our proposed routing protocol using MATLAB or NS2. The results of simulations verify that our proposed model provide better network life time as compare to LEACH, SEP, E-SEP and DEEC.
COST SENSITIVE SOFTWARE DEFECT PREDICTION TECHNIQUE USING GENETIC ALGORITHM

Research has yielded approaches to predict future defects in software artifacts based on historical information, thus assisting companies in effectively allocating limited development resources and developers in reviewing each other’s' code reduces the cost of maintenance. Developers are unlikely to devote the same effort to inspect each software artifact predicted to contain defects, since the effort varies with the artifacts' size (the number of LOC and cost) of defects that it exhibits (effectiveness). We propose to use Genetic Algorithms (GAs) for training prediction models to maximize their cost-effectiveness. We evaluate the approach on two well-known models, Regression Tree and Generalized Linear Model, and predict defects between multiple releases of 5 open source projects. Our results show that regression models trained by GAs significantly outperform their conventional counterparts, improving the cost-effectiveness by up to 120 %.
NANOEMULSION-A METHOD TO IMPROVE THE SOLUBILITY OF LIPOPHILIC DRUGS

The design of effective formulations for drugs has long beena major challenge, because drug efficacy can be severelylimited by instability or poor solubility in the vehicle. One ofthe most promising technologies is the nanoemulsion drugdelivery system, which is being applied to enhance the solu-bility and bioavailability of lipophilic drugs. Interest in Lipid Based Drug Delivery (LBDD) is relatively recent and relates to the developments in the past 10 to 15 years. The primary application of such type of drug delivery system is to enhance bioavailability by increased solubility and targeting lymphatic transport. As pharmaceutical drug delivery systems, nanoemulsions have many advantages, including clarity, high stability, and ease of preparation. Among these carriers, nanoemulsion has been the most extensively investigated. The present review outlines the advantages and disadvantages of nanoemulsion with its preparation methods, and therapeutic applications published over the past decade. From the literature survey, it is realized that research activities on nanoemulsion systems containing various drugs for different therapeutic applications have increased at the rapid rate. Hence, it may be used as a new alternative and cheaper carrier in therapy for increased bioavailability, reduction in dose and thereby dose related systemic toxicities.
Changed Leadership -From Wise to Compromise: The Decline of Leadership Wisdom in Modern Organizations

This paper explores leadership challenges and problems as stemming from what is called “changed leadership” in describing the major difference existing between leadership of past and present societies. Leadership in modern society is seen as engendering more of compromise rather than wisdom, and this fundamentally constitutes a leadership challenge and can theoretically and hypothetically explain leadership problems across the board. The author believes that while leaders must lead in ways conducive to follower well-being and success, the leader’s position and roles are archetypically determined according to ideas of leadership and those who become leaders having, possessing, or acquiring more than ordinary significance through their impact and influence on others. The major differences between leadership of the past and present are explored from the perspective of leadership competences centered on responses to leadership situations and followers and the leader’s role relative to definition of what is called “wise leadership” or leadership based on the leader’s knowledge and understanding of right and wrong rather than on followers’ demands and expectations relative to fear of losing popularity (compromise leadership), or an interchanged idea of leader-follower and follower-leader roles bound in one individual as typical of leadership that demands leaders’ authentic power and knowledge relative to followers’ dictates. Citing examples of modern leaders considered to be “wise” in their positions as leaders, the author makes a philosophical comparison to the patristic conceptualization of leadership to demonstrate that wisdom in leadership is valued and constitutes the difference between leadership that transcends and leadership that only seeks to satisfy.
Review of Software Reuse Processes

Clients expect that enterprise software systems, which are continually growing in complexity, are of high quality, with reasonable price and have a short time to market. To find ways to meet with the increasing demands of business, software engineers are forced to find ways to streamline development process. Software reuse is believed to be one such approach. Companies that have adopted its techniques to their development process have reported useful improvements in development productivity and quality. The practices of reuse have proven not to be simple and there are many misconceptions about how to implement and gain benefits out of it. Effective reuse is not a simple addition to existing software development processes; it puts strong demands on development methods in order to be successful. Our research, based on the literature and empirical results, presents basic principles of software reuse. Driving factors that facilitates reuse are also presented together with potential benefits and key issues to consider in order to successfully adapting this approach.
Face Recognition and Retrieval Using Cross Age Reference Coding

: This paper introduces an approach for face cognizance throughout age and in addition a dataset containing variations of age in the wild. We use a data-driven system to deal with the go-age face realization challenge, known as cross-age reference coding (CARC). By using leveraging a colossal-scale snapshot dataset freely available on the web as a reference set, CARC can encode the low-degree feature of a face image with an age-invariant reference area. In the retrieval segment, our method most effective requires a linear projection to encode the feature and for that reason it's incredibly scalable. To evaluate our system, we introduce a tremendous-scale dataset known as cross-age dataset. To understand the difficulties of face awareness across age dataset involves 2,000 constructive pairs and a terrible pairs and is cautiously annotated by way of checking each the related photograph and net contents. Our endorse process show that although ultra-modern approaches can gain competitive efficiency compared to normal human efficiency, majority votes of a couple of humans can achieve much higher efficiency on this challenge. The gap between computer and human would imply feasible instructional materials for additional development of move-age face awareness someday.
Is ORB Efficient Over SURF for Object Recognition?

— Machine vision systems have fascinated humans since the emergence of Computing. Technological advancements, both in terms of hardware and software have led to development and deployment of numerous Machine Vision systems. In the modern era of computing, object recognition form a deeply entrenched and omnipresent component of intelligent social systems. Extensive research carried out by the community from past five decades have paved path for advancements in factories, offices, industrial inspection and defect identification systems, medical imaging, defence and biometrics. Accuracy of these applications depends on the robustness and efficiency of the feature detection, feature extraction and matching techniques deployed. In this direction, a wide range of algorithms for object recognition have been devised. Two buzz words of the day are SURF (Speed Up Robust Feature) which rely on Integral images and Hessian Matrix for feature detection and ORB (Oriented Fast and Rotated BREIF) which is a combination of two major techniques: FAST (Features from Accelerated Segment Test) and BRIEF (Binary Robust Independent Elementary Features). Keeping, this as a focal point, the work proposed aims at investigating and performing a comparative study of both the approaches for object recognition.
Fusion of Statistic, Data Mining and Genetic Algorithm for feature selection in Intrusion Detection

The security of information and data is a critical issue in a computer networked environment. In our society computer networks are used to store proprietary information and to provide services for organizations and society. So in order to secure this valuable information from unknown attacks (intrusions) need of intrusion detection system arises. There are many intrusion detection approaches focused on the issues of feature reduction as some of the features are irrelevant or redundant which results in lengthy detection process and degrading the performance of an IDS. So in order to design lightweight IDS we investigate the performance of three feature selection approaches CFS, Information Gain and Gain Ratio. In this paper we propose a fusion model by making use of the three standard algorithms and finally applying genetic algorithm that identify important reduced input features. We apply Naive Bayes classifier on the dataset for evaluating the performance of the proposed method over the standard ones. The reduced attributes shows that proposed algorithm give better performance that is efficient and effective for detecting intrusions.
An Improved Multilayer Perceptron for BCI with Evolutionary Optimization

Brain Computer Interface (BCI) research is based on recording and analyzing electroencephalographic (EEG) data and recognizing EEG patterns associated with various mental states. BCIs had become an active research area in the last decade. The ability of The Multilayer Perceptron Neural Networks (MLPNN) to model non-linear relationship is appropriate to model the complex nature of EEG signals. MLPNN’s convergence rate is relatively slow and it often yields sub-optimal solutions. In this paper, it is proposed to enhance the classification ability of the MLPNN incorporating Particle Swarm Optimization (PSO). This optimization helps to overcome the slow convergence rate and to avoid local minimum. The features of motor imagery in the frequency domain are extracted using Hilbert transform, and Principal Component Analysis is used for feature reduction. This is classified by MLPNN modified with PSO.
Efficient Multicasting Algorithm Using SDN

Many group communication applications require multipoint communication, in order to reduce network traffic rates. Multicast technology has been used as an efficient and scalable technology for data distribution. However, in IP network, the responsibility for management of multicast groups is distributed among network routers and routing rules calculated based on local view resulting from sharing neighbor’s routers its information. Distributed calculation and local view of IP network causes some limitations, such as delays resulting from processing group events, and failed for calculating optimum solutions that required a global view of the network. Software Defined Networking (SDN) represented by OpenFlow presented as a solution for many problems, in SDN the control plane and data plane are separated by shifting the control and management to a remote centralized controller with a global view of the network, and the routers are used as a forwarder only. Recently, several researchers have been proposed multicast routing algorithms for solving the problem of shortest path tree (SPT) and Minimum Steiner tree (MST) in SDN. SPT can calculate multicast tree faster than MST. However, MST can generate solutions optimum than SPT. In this paper we take the advantage of OpenFlow to propose and implement multicasting OpenFlow controller, this centralized controller is a core part of our multicasting approach. For constructing the multicast tree, we proposed a new algorithm that combines both of Dijkstra shortest path algorithm and Tabu search (TS) algorithm. In the proposed algorithm, Dijkstra algorithm and TS work respectively for fast start-up multicast session and optimum solution. Proposed algorithm take the advantages of both algorithms such as fast convergence time of Dijkstra algorithm and optimum solution of TS and avoid the shortages of both algorithms. We validate our approach using the popular Mininet network emulation environment with Pox controller. The results prove that our approach can improve start-up time for initialization multicast session. Also, can minimize constructed the multicast tree.
Mac Architecture – Accumulator Based on Booth Encoding Parallel Multiplier

-The MAC provides high speed multiplication with accumulative addition. In this paper, we study the various parallel MAC architectures and then implement a design of parallel MAC based on some booth encodings such as radix-4 booth encoder and some final adders such as CLA, Kogge stone adder and then compare their performance characteristics. The one most effective way to increase the speed of a multiplier is to reduce the number of the partial products. Although the number of partial products can be reduced with a higher radix booth encoder, but the number of hard multiples that are expensive to generate also increases simultaneously. To increase the speed and performance, many parallel MAC architectures have been proposed. Parallelism in obtaining partial products is the most common technique used in this implemented architecture.
Video Based Traffic Sign Detection by Scale Based Frame Fusion Technique

- Many Research shown good performance can be obtained from intelligent transport systems. Some of the main components for intelligent transport systems are detection,recognition and tracking,however many Deep learning methods are required for high performance.In this approach we mostly concentrate on the single image detectionand task recognition , which is not implemented in Real world applications. [1] We introduce incremental framwork for Detecting,tracking and RecognizingTraffic sign using the camera mounted on moving vehicle over the nonstationary environment. The main work of this paper is : [2] To improve detection of traffic sign by utilizing information from spatial distributed trafiic signs [3] to enhance the location accuracy and tracking performance under moving environments[4] To obtain a stable classification result, we proposed a scale based Intra frame Fusion method
Advances in Atherosclerotic Plaque Targeting , Imaging and Treatment Using Nanotechnology and Stem Cells Research

Coronary artery disease (CAD) emerges from atherosclerosis and is the most common myocardial infarction-induced death in the western world. Most research data suggest that nanotechnologies could play a major role in the development of novel imagistic and therapeutic methods against atherosclerotic plaque. Here we present the latest achievements in atherosclerotic plaque targeting, imaging and treatment using nanotechnologies. Keywords— atherosclerosis, targeting, imaging, nanoparticles, plaque, therapy
Blind Image Quality Assessment without Considering Human Scores

: General purpose blind image quality assessment (BIQA) has been recently attracting significant attention in the fields of image processing, vision and machine learning. State of-the-art BIQA methods usually learn to evaluate the image quality by regression from human subjective scores of the training samples. However, these methods need a large number of human scored images for training, and lack an explicit explanation of how the image quality is affected by image local features. An interesting question is then: can we learn for effective BIQA without using human scored images? This paper makes a good effort to answer this question. We partition the distorted images into overlapped patches, and use a percentile pooling strategy to estimate the local quality of each patch. Then a quality-aware clustering (QAC) method is proposed to learn a set of centroids on each quality level. These centroids are then used as a codebook to infer the quality of each patch in a given image, and subsequently a perceptual quality score of the whole image can be obtained. The proposed QAC based BIQA method is simple yet effective. It not only has comparable accuracy to those methods using human scored images in learning, but also has merits such as high linearity to human perception of image quality, real-time implementation and availability of image local quality map.
Face Recognition Based Attendance Management System Using Machine Learning

- The most arduous task in any organization is attendance marking. In this paper we proposed an automated attendance management system which tackles the predicament of recognition of faces in biometric systems subject to different real time scenarios such as illumination, rotation and scaling. This model incorporates a camera that captures input image, an algorithm to detect a face from the input image, encode it and recognize the face and mark the attendance in a spreadsheet and convert it into PDF file. The system camera of an android phone captures the image and sends it to the server where faces are recognized from the database and attendance is calculated on basis of it.
PRIVACY PROTECTION FOR USER GROUPS IN PERSONALIZED WEB SEARCH WITH SEARCH EFFICIENCY

: Personalized web search (PWS) refers to search experiences that are adopted specifically for individual’s interests by incorporating information about an individual beyond specific query provided, but at the same time this will raises new privacy challenges. User’s hesitation to disclose their private information during search has become major issue on personalization technologies. To overcome this problem privacy protection is required. Personal user information is used to create user profiles, which is a critical data. In this system user profile is modeled as a hierarchical structure, which is created from a taxonomy repository. For privacy protection, Privacy protected PWS framework is proposed. An online profiler is designed in this system, which can adaptively generalize profiles by queries while respecting user specified privacy requirements. Runtime generalization aims at providing search efficiency along with privacy protection of user profiles. To prevent the information loss while performing runtime generalization, MinimizeIL algorithm is proposed. Location based search results can be integrated along with the profile based personalization to retrieve more relevant results, based on a location without specifying the same in the query. A group level privacy can be achieved so that user profiles in a group are secured, that are stored at the client side. With this system, even at offline phase also users can get the viewed results of their friends. Thus search efficiency of the system can also increase by reducing the execution time significantly.
The Impact Of Social Network Analysis On Telecommunication Customers

: Telecommunication industry has proven itself as a chain of socio - economic impact. Market leaders should take advantage of data analysis consecutively as the need to sustain customers the longest possible time. This research is based on community detection analysis through identifying group members and influencers in the telecommunication industry. It is motivated by two major research questions: Who are the influencers in subscribers for churn and new products adoptions? (2) Who are subscribers whose loyalty is threatened by churn around them? The use of data analysis and data mining algorithms and standard process in today‘s challenges was set to increase the efficiency of solving not only technical problems but business as to move toward insights and analysis based exercises. This research proposes a model based on the Similarity, Authority and Hub algorithms used in Social Network Analysis (SNA) to group members who share same patterns together, to identify network leaders in terms of authority and dissemination leaders and to extract group level insights that can be used in understanding subscribers‘ patterns and ways of communications (in - degrees and out - degrees) between them using IBM SPSS Modeler as the base of data mining process and Oracle as the base of corporate data warehouse. This study estimated how much reliable is to use SNA in order to shift from the traditional ways of targeting subscribers, understanding their pattern, enhancing offering efficiency and improving the process of launching new product by selecting the right target. It has contributed to shifting marketing leaders‘ way of thinking from individual view to network view. It has also contributed to providing decision makers with a systematic and scientific approach as to focus on network influencers, to save as much as they can and to build their offers based on it.