Periodicals of Engineering and Natural Sciences (PEN - International University of Sarajevo)
Not a member yet
1290 research outputs found
Sort by
Solid State Welding and Application in Aeronautical Industry
Get PDFIn this study solid state welding andapplication in aeronautic industryhave been researched. The solid state welding technicisused in the industrial production fields such as aircraft, nucleer, space industry, aeronautic industry, ect., actually solid state welding is a process by which similar and dissmilar metals can be bonded together. Hence a material can be created as not heavy but strong strength. Beside, advantages and disasvantages of solid state welding have been discussed. Also the diffusion welding and friction welding which belong to the solid state welding is obsevered in aeronautic industry
Metallographic Procedures and Analysis – A review
Get PDFThe purpose of this research is to give readers general insight in what metallography generally is, what are themetallographic preparation processes, and how to analyse the prepared specimens. 
Boosted Networks for the Diagnosis of Cardiovascular Diseases
Get PDFA boosting by filtering technique for neural network systems with back propagation together with a majorityvoting scheme is presented in this paper. Previous research with regards to predict the presence ofcardiovascular diseases has shown accuracy rates up to 72.9%. Using a boosting by filtering techniqueprediction accuracy increased over 80%. The designed neural network system in this article presents a significantincrease of robustness and it is shown that by majority voting of the parallel networks, recognition rates reach to> 90 in the V.A. Medical Center, Long Beach and Cleveland Clinic Foundation data set. 
Using machine learning for intelligent shard sizing on the cloud
Get PDFSharding implementations use conservative approximations for determiningthe number of cloud instances required and the size of the shards to be storedon each of them. Conservative approximations are often inaccurate and resultin overloaded deployments, which need reactive refinement. Reactiverefinement results in demand for additional resources from an alreadyoverloaded system and is counterproductive.This paper proposes an algorithm that eliminates the need for conservativeapproximations and reduces the need for reactive refinement. A multiplelinear regression based machine learning algorithm is used to predict thelatency of requests for a given application deployed on a cloud machine. Thepredicted latency helps to decide accurately and with certainty if the capacityof the cloud machine will satisfy the service level agreement for effectiveoperation of the application. Application of the proposed methods on apopular database schema on the cloud resulted in highly accurate predictions.The results of the deployment and the tests performed to establish theaccuracy have been presented in detail and are shown to establish theauthenticity of the claims
Emotions identification utilizing periodic handwriting on mobile surfaces
Get PDFThe purpose of this study is to between the learners’ emotional characteristics and styles in touch screen environments. We propose a method to identify the variety of learners\u27 boredom in the learning process utilising handwriting data. The novelty of the method is to avoid explicit polling of learners how do they feel. We use the recurring acquiring of personal handwriting data utilising the computational power of both a mobile device and cloud-based resources. Also, we use machine learning-based sentiment detection in the research. We smoothly inject periodic handwriting tests convolving them with learning objects to study the correlation between learners’ emotions dynamic, they demonstrate, and the ability to focus and think critically. With the help of machine-learning methods and new communication protocols, we can step up the student-centric mobile-based education process by taking advantage of the latest achievements in a big data analysis and cloud computing. Also, we clarify the conceptual model for the testbed used in the experiment. The findings may likely impact the future personalized e-learning systems
Intelligent control interfaces developed on Versatile Portable Intelligent Platform in order to improving autonomous navigation robots performances
Get PDFThe paper presents Intelligent Control Interfaces (ICIs) for real-time control for terrestrial mobile robots or unmanned aerial robots in order to improve the navigation performances. Intelligent control interfaces using advanced control strategies adapted to robot environment are presented, implemented through IT & C techniques with fast processing and real-time communications in order to develop a versatile, intelligent and portable VIPRO Platform with behavior of e-learning platform, which allows achievement inter-academic research networks and building new intelligent vectors robots. Implementation of ICIs laws in the intelligent real time control interfaces depends on the particular circumstances of the characteristics model used and the exact definition of optimization problem. The results led to the development of the ICI interfaces through image analysis using Images Operation Sampling & Quantization (IOSQ)
A cyber-physical systems approach to cognitive enterprise
Get PDFInternet of Things and Cyber-Physical Systems are paradigms that have an important influence on enterprise systems architecture and implementation. The Cognitive Manufacturing as well as the Cognitive Enterprise are emerging models, related to these paradigms, that intend to redesign in the Enterprise Information Model by integrating new information processing and problem-solving methods.
This paper intends to analyses and discuss Cognitive Enterprise enablers and principles considering an approach based on models of human brain perception-reasoning-learning processes
Finger Vein Recognition using Two Parallel Enhancement Approachs based Fuzzy Histogram Equalization
Get PDFThis paper evaluates a set of enhancement stages for finger vein enhancement that not only has low computational complexity but also high distinguishing power. This proposed set of enhancement stages is centered around fuzzy histogram equalization. Two sets of evaluation are carried out: one with the proposed approach and one with another unique approach that was formulated by rearranging and cropping down the preprocessing steps of the original proposed approach. To extract features, a combination of Hierarchical Centroid and Histogram of Gradients was used. Both enhancement stages were evaluated with K Nearest Neighbor and Deep Neural Networks using 6 fold stratified cross validation. Results showed improvement as compared to three latest benchmarks in this field that used 6-fold validation
Lung cancer classification using data mining and supervised learning algorithms on multi-dimensional data set
Get PDFThese With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about the cancer disease detection. Developing a proposed data mining model is useful to diagnose the cancer disease once the cancer detection is accomplished using data mining for the examination and classification of machine learning supervised algorithms
Agro-economic models: a review and directions for research
Get PDFThe article is devoted to reviewing of main 8 models, which are used to analyze the agriculture sector, medium, and long-term forecasts, as well as policy making. The review is based on comparative analysis of models conducted by the authors according to a number of criteria. On its basis, formed the distinctive features of modeling, which are realized in these models. The first distinctive feature is the problem of choosing the level of aggregation in models. This feature generates the direction of research about the effectiveness of the application of one or another aggregation level in modeling. The second distinctive feature of modeling is structurization models into two types: partial equilibrium and computable general equilibrium models. The method of choosing the type of model is one of the actual problems. The third distinctive feature is dominance of deterministic approaches in the construction of models. The use of stochastic analysis in models, in the opinion of the authors, does not yet have a system analysis. Based on the carried out analysis, the authors tried to form directions for the development of the agriculture sector modeling