A Literature Review of Cuckoo Search Algorithm

Optimization techniques play key role in real world problems. In many situations where decisions are taken based on random search they are used. But choosing optimal Optimization algorithm is a major challenge to the user. This paper presents a review on Cuckoo Search Algorithm which can replace many traditionally used techniques. Cuckoo search uses Levi flight strategy based on Egg laying Radius in deriving the solution specific to problem. CS optimization algorithm increases the efficiency, accuracy, and convergence rate. Different categories of the cuckoo search and several applications of the cuckoo search are reviewed. Keywords: Cuckoo Search Optimization, Applications , Levy Flight DOI: 10.7176/JEP/11-8-01 Publication date:March 31st 202

CLOUD COMPUTING BASED INFRASTRUCTURE IN EDUCATION: INVESTING INTO THE PRIVATE INFRASTRUCTURE OR USING OUTSOURCING SERVICES

The main challenge for higher education in the nearest future is the global competitiveness, as the present global market requires the higher schools to present their courses in the international level. Thus, traditional universities must adapt educational infrastructures in response with the global requirement. Outsourcing of cloud computing based services increased over the years. The solutions allow not only create more effective teaching methods and new communication chances for the whole education society, but also to reduce costs of installing and maintaining programs.Aim of the article is to prepare a cost/benefit case analysis between investing into the private information and communication technology infrastructure and using outsourcing services. This paper focuses attention on cloud computing based information and communication technology infrastructures of two higher education institutions.Research methods include analysis of scientific literature, review of legal institutional documentation and reports and interviews with institutional representatives about education practices

Synchronizing inventory and transport within supply chain management

The problem considers synchronized optimization of inventory and transport, and focuses on producer-distributor relations. Particular attention is paid to developing a mathematical model and an optimization problem that can be used to minimize the overall distribution cost by an appropriate placement of warehouses and cross-docking points. Solutions to this problem are explored using genetic algorithms and ideas from graph/network theory. Note: there are three separate reports contained within the uploaded .pdf file

Selecting Statistical Characteristics of Brain Signals to Detect Epileptic Seizures using Discrete Wavelet Transform and Perceptron Neural Network

Electroencephalogram signals (EEG) have always been used in medical diagnosis. Evaluation of the statistical characteristics of EEG signals is actually the foundation of all brain signal processing methods. Since the correct prediction of disease status is of utmost importance, the goal is to use those models that have minimum error and maximum reliability. In anautomatic epileptic seizure detection system, we should be able to distinguish between EEG signals before, during and after seizure. Extracting useful characteristics from EEG data can greatly increase the classification accuracy. In this new approach, we first parse EEG signals to sub-bands in different categories with the help of discrete wavelet transform(DWT) and then we derive statistical characteristics such as maximum, minimum, average and standard deviation for each sub-band. A multilayer perceptron (MLP)neural network was used to assess the different scenarios of healthy and seizure among the collected signal sets. In order to assess the success and effectiveness of the proposed method, the confusion matrix was used and its accuracy was achieved98.33 percent. Due to the limitations and obstacles in analyzing EEG signals, the proposed method can greatly help professionals experimentally and visually in the classification and diagnosis of epileptic seizures

System reliability using generalized intuitionistic fuzzy Rayleigh lifetime distribution

Reliability analysis as one of the important research topics in engineering has been researched by a number of authors. Reliability in classical distributions is based on precise parameters. It is usually assumed that parameters of distributions are precise real numbers. However, in the real world, the data sometimes cannot be measured and recorded precisely. In this paper, the concept of fuzzy reliability is extended by the idea of generalized intuitionistic fuzzy reliability. We investigate the reliability characteristics of systems using Rayleigh lifetime distribution, in which the lifetime parameter is assumed to be generalized intuitionistic fuzzy number. Generalized intuitionistic fuzzy reliability, generalized intuitionistic fuzzy hazard function, generalized intuitionistic fuzzy mean time to failure and their cut sets are discussed when the systems follow generalized intuitionistic fuzzy Rayleigh lifetime distribution. In this approach, for every special cut set, reliability curve and hazard curve are like a band with upper and lower bound. A numerical example is given to illustrate the proposed approach. Further, reliability analysis of the series and parallel systems are done

A Novel Approach for Sustainable Supplier Selection Using Differential Evolution: A Case on Pulp and Paper Industry

Abstract. Diverse sustainable supplier selection (SSS) methodologies have been suggested by the practitioners in earlier, to find a solution to the SSS prob-lem. A SSS problem fundamentally is a multi-criteria practice. It is a judgment of tactical significance to enterprises. The nature of this decision usually is dif-ficult and unstructured. Optimization practices might be useful tools for these types of decision-making difficulties. During last few years, Differential Evolu-tion has arisen as a dominating tool used for solving a variety of problems aris-ing in numerous fields. In the current study, we present an approach to find a solution to the SSS problem using Differential Evolution in pulp and paper in-dustry. Hence this paper presents a novel approach is to practice Differential Evolution to select the efficient sustainable suppliers providing the maximum fulfillment for the sustainable criteria determined. Finally, an illustrative exam-ple on pulp and paper industry validates the application of the present ap-proach

Sickle cell identification using image processing and red blood cell morphological characteristics

Blood is the most vital liquid that helps sustain the healthiness and life of the human body. Biologically, normal red blood cells have circular shapes and play a key role in transporting oxygen and nutrient to tissues. Red blood cells are bendable, which allows them to pass through the veins and arteries smoothly. Sadly, there are exceptional individuals with abnormal blood cells call sickle cell disease. The physical shape of the abnormal blood cells is in sickle/crescent form. Sickle cell disease is hereditary, and a person becomes affected if at least one of the parents has the abnormal haemoglobin S gene. The danger of sickle cells is that they inflict many severe health conditions such as pain, tiredness, jaundice, kidney problem, and other critical illnesses. For many years, managing and diagnosing sickle patients is performed by collecting blood samples to manually observe the irregular shapes of the red blood cells using a microscope. This process is time-consuming and results in errors for large samples of blood. In this thesis, a compelling image processing method is proposed to optimize the detection of abnormality in human blood cells with the deep learning technique. Ten images of red blood cells were randomly collected from the online source using the Google search engine. Each image was analyzed using MATLAB codes for image processing using the blood cell area, eccentricity, diameter, extension, and form factor as input parameters. The study results show that the proposed technique has 71 – 100 percent accuracy, far higher than what is obtainable in the manual method. This technique can serve and enhance the current manual method of sickle cell segmentation because it is faster and more accurate