Passenger Flow Prediction Based on Newly Adopted Algorithms

Passenger flow forecasting is an essential part of transportation systems. Neural networks in the transportation field have been applied to passenger demand prediction. In this paper, we developed two hybrid methods, known as parlimentary optimization algorithm-artificial neural network (POA-ANN), and intelligent water drops algorithm-ANN (IWD algorithm-ANN). In addition, we applied the proposed algorithms to illustrate the effect of precise prediction for passenger queues. We mainly focus on predicting passenger demand by comparing the genetic algorithm-ANN (GA-ANN) with POA-ANN and IWD-ANN. The results of prediction methods suggest that both POA-ANN and IWD-ANN provide a better forecasting performance, which is obtained via mean square error (MSE), than GA-ANN in the field of passenger flow prediction. This study illustrates that the newly adopted algorithms exhibit good performance for passenger prediction.     

Combating discrimination using Bayesian networks

Discrimination in decision making is prohibited on many attributes (religion, gender, etc…), but often present in historical decisions. Use of such discriminatory historical decision making as training data can perpetuate discrimination, even if the protected attributes are not directly present in the data. This work focuses on discovering discrimination in instances and preventing discrimination in classification. First, we propose a discrimination discovery method based on modeling the probability distribution of a class using Bayesian networks. This measures the effect of a protected attribute (e.g., gender) in a subset of the dataset using the estimated probability distribution (via a Bayesian network). Second, we propose a classification method that corrects for the discovered discrimination without using protected attributes in the decision process. We evaluate the discrimination discovery and discrimination prevention approaches on two different datasets. The empirical results show that a substantial amount of discrimination identified in instances is prevented in future decisions.     

A new model for the hazardous waste location-routing problem

Hazardous waste management involves the collection, transportation, treatment and disposal of hazardous wastes. In this paper a new multiobjective location-routing model is proposed. Our model also includes some constraints, which were observed in the literature but were not incorporated into previous models. The aim of the proposed model is to answer the following questions: where to open treatment centers and with which technologies, where to open disposal centers, how to route different types of hazardous waste to which of the compatible treatment technologies, and how to route waste residues to disposal centers. The model has the objective of minimizing the total cost and the transportation risk. A large-scale implementation of the model in the Central Anatolian region of Turkey is presented.     

Analysis of the human immune response to vaccinia by use of a novel protein microarray suggests that antibodies recognize less than 10% of the total viral proteome

Control of smallpox by mass vaccination was one of the most effective public health measures ever employed for eradicating a devastating infectious disease. However, new methods are needed for monitoring smallpox immunity within current vulnerable populations, and for the development of replacement vaccines for use by immunocompromized or low-responding individuals. As a measure for achieving this goal, we developed a protein microarray of the vaccinia virus proteome by using high-throughput baculovirus expression and purification of individual elements. The array was validated with therapeutic-grade, human hyperimmune sera, and these data were compared to results obtained from individuals vaccinated against smallpox using Dryvax. A high level of reproducibility with a very low background were apparent in repetitive assays that confirmed previously reported antigens and identified new proteins that may be important for neutralizing viral infection. Our results suggest that proteins recognized by antibodies from all vaccinees constituted <10% of the total vaccinia proteome.     

Communication patterns during synchronous Web-based military training in problem solving

The nature of communication among geographically dispersed groups of learners using text messaging in a military training environment was assessed. A total of 6601 acts of chat were coded into one of three interaction content categories (social, task, or technology-related) and analyzed for frequency and relative change over time. Results indicated shifting patterns of interaction over the 6-month course; while technology concerns gradually diminished, on task discussion peaked in the middle months and social interactions were higher at the start and end of the training. Overall, student chats were categorized as on-task 55%, social 30%, or technology-related 15%. Examples of chats and focus group data indicated that there was an emphasis on fostering student problem solving within the online course.     

Comparison of different classifier algorithms for diagnosing macular and optic nerve diseases

Abstract: The aim of this research was to compare classifier algorithms including the C4.5 decision tree classifier, the least squares support vector machine (LS-SVM) and the artificial immune recognition system (AIRS) for diagnosing macular and optic nerve diseases from pattern electroretinography signals. The pattern electroretinography signals were obtained by electrophysiological testing devices from 106 subjects who were optic nerve and macular disease subjects. In order to show the test performance of the classifier algorithms, the classification accuracy, receiver operating characteristic curves, sensitivity and specificity values, confusion matrix and 10-fold cross-validation have been used. The classification results obtained are 85.9%, 100% and 81.82% for the C4.5 decision tree classifier, the LS-SVM classifier and the AIRS classifier respectively using 10-fold cross-validation. It is shown that the LS-SVM classifier is a robust and effective classifier system for the determination of macular and optic nerve diseases.     

Hazardous waste management problem: The case for incineration

We define the hazardous waste management problem as the combined decisions of selecting the disposal method, siting the disposal plants and deciding on the waste flow structure. The hazardous waste management problem has additional requirements depending on the selected disposal method. In this paper we focus on incineration, for which the main additional requirement is to satisfy the air pollution standards imposed by the governmental restrictions. We propose a cost-based mathematical model in which the satisfaction of air pollution standards is also incorporated. We used the Gaussian Plume equation in measuring the air pollution concentrations at population centers. A large-scale implementation of the proposed model within Turkey is provided.     

From engineering diagrams to engineering models: Visual recognition and applications

We present a computational recognition approach to convert network-like, image-based engineering diagrams into engineering models with which computations of interests, such as CAD modeling, simulation, information retrieval and semantic-aware editing, are enabled. The proposed approach is designed to work on diagrams produced using computer-aided drawing tools or hand sketches, and does not rely on temporal information for recognition. Our approach leverages a Convolutional Neural Network (CNN) as a trainable engineering symbol recognizer. The CNN is capable of learning the visual features of the defined symbol categories from a few user-supplied prototypical diagrams and a set of synthetically generated training samples. When deployed, the trained CNN is applied either to the entire input diagram using a multi-scale sliding window or, where applicable, to each isolated pixel cluster obtained through Connected Component Analysis (CCA). Then the connectivity between the detected symbols are analyzed to obtain an attributed graph representing the engineering model conveyed by the diagram. We evaluate the performance of the approach with benchmark datasets and demonstrate its utility in different application scenarios, including the construction and simulation of control system or mechanical vibratory system models from hand-sketched or camera-captured images, content-based image retrieval for resonant circuits and sematic-aware image editing for floor plans.     

Supplier selection with an integrated methodology in unknown environment

This paper proposes an integrated methodology and its solution for supplier selection problem. A two-stage stochastic programming model and fuzzy TOPSIS methods are consolidated in this methodology. After pre-research phase, in the second part of the methodology, fuzzy TOPSIS is used for ranking potential suppliers considering qualitative data under fuzzy environment. In the third part of the methodology a group of ranked potential suppliers are included in a two-stage stochastic programming model for evaluation. In the two-stage stochastic programming model demand is assumed as uncertain and different scenarios are generated for this parameter. With this methodology, supplier evaluation procedure can be done in unknown environment. Our methodology is interested with a problem in multi-product, multi-period and multi-sourcing environment.