A new and efficient intelligent collaboration scheme for fashion design

Technology-mediated collaboration process has been extensively studied for over a decade. Most applications with collaboration concepts reported in the literature focus on enhancing efficiency and effectiveness of the decision-making processes in objective and well-structured workflows. However, relatively few previous studies have investigated the applications of collaboration schemes to problems with subjective and unstructured nature. In this paper, we explore a new intelligent collaboration scheme for fashion design which, by nature, relies heavily on human judgment and creativity. Techniques such as multicriteria decision making, fuzzy logic, and artificial neural network (ANN) models are employed. Industrial data sets are used for the analysis. Our experimental results suggest that the proposed scheme exhibits significant improvement over the traditional method in terms of the time–cost effectiveness, and a company interview with design professionals has confirmed its effectiveness and significance

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Performance Evaluation of Mobile U-Navigation based on GPS/WLAN Hybridization

This paper present our mobile u-navigation system. This approach utilizes hybridization of wireless local area network and Global Positioning System internal sensor which to receive signal strength from access point and the same time retrieve Global Navigation System Satellite signal. This positioning information will be switched based on type of environment in order to ensure the ubiquity of positioning system. Finally we present our results to illustrate the performance of the localization system for an indoor/ outdoor environment set-up.Comment: Journal of Convergence Information Technology(JCIT

Efficient Optimization of Echo State Networks for Time Series Datasets

Echo State Networks (ESNs) are recurrent neural networks that only train their output layer, thereby precluding the need to backpropagate gradients through time, which leads to significant computational gains. Nevertheless, a common issue in ESNs is determining its hyperparameters, which are crucial in instantiating a well performing reservoir, but are often set manually or using heuristics. In this work we optimize the ESN hyperparameters using Bayesian optimization which, given a limited budget of function evaluations, outperforms a grid search strategy. In the context of large volumes of time series data, such as light curves in the field of astronomy, we can further reduce the optimization cost of ESNs. In particular, we wish to avoid tuning hyperparameters per individual time series as this is costly; instead, we want to find ESNs with hyperparameters that perform well not just on individual time series but rather on groups of similar time series without sacrificing predictive performance significantly. This naturally leads to a notion of clusters, where each cluster is represented by an ESN tuned to model a group of time series of similar temporal behavior. We demonstrate this approach both on synthetic datasets and real world light curves from the MACHO survey. We show that our approach results in a significant reduction in the number of ESN models required to model a whole dataset, while retaining predictive performance for the series in each cluster

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Self-growing neural network architecture using crisp and fuzzy entropy

The paper briefly describes the self-growing neural network algorithm, CID2, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results of a real-life recognition problem of distinguishing defects in a glass ribbon and of a benchmark problem of differentiating two spirals are shown and discussed

Системный подход к прогнозированию

Проблематика. Для подальшого підвищення якості прогнозування динаміки розвитку фінансово-економічних процесів (ФЕП) необхідно розробляти нові методи та підходи в межах сучасних концепцій створення інформаційних систем підтримки прийняття рішень (СППР). Мета дослідження. Головна мета дослідження: розглянути принципи системного аналізу, що можуть бути використані для розв’язання задачі короткострокового прогнозування; розробити ефективну систему обробки даних, яка ґрунтується на принципах системного аналізу, реалізованих у СППР; проаналізувати можливі типи невизначеностей, що трапляються при побудові математичних моделей та оцінюванні прогнозів, а також запропонувати методи їх опису і врахування у процесі обробки даних. Методика реалізації. При створенні СППР для прогнозування ФЕП та оцінювання фінансових ризиків використано такі принципи системного аналізу: ієрархічність архітектури системи; ідентифікація та обробка можливих невизначеностей; обчислення альтернативних рішень і супроводження обчислювальних процедур на всіх етапах виконання обчислень. СППР має модульну архітектуру, яку можна розширювати новими функціями, що стосуються попередньої обробки даних, оцінювання параметрів моделей, обчислення оцінок прогнозів та можливих ризиків фінансових втрат. Результати дослідження. Основним результатом дослідження є системна методологія моделювання ФЕП, яка реалізована в межах запропонованої СППР. Висока якість остаточних результатів досягається завдяки супроводженню обчислювальних процесів за допомогою належних множин статистичних критеріїв. Наведено приклад математичного моделювання й оцінювання фінансового ризику. Отримані результати свідчать, що запропонований системний підхід має хороші перспективи для практичного використання. Висновки. Запропоновано системний підхід до математичного моделювання і прогнозування ФЕП та оцінювання фінансового ризику. Застосування цього підходу дає можливість отримувати високоякісні оцінки прогнозів на основі статистичних даних.Background. Further enhancement of forecasts quality for dynamics of financial and economic processes requires development of new techniques and approaches in the frames of modern concepts for constructing informational decision support systems (DSS). Objective. The main purpose of the study is as follows: to consider system analysis principles that are suitable for solving the problem of short-term forecasting; to develop effective data processing system that implements the system analysis principles selected in the frames of DSS; to analyze possible types of uncertainties that are encountered in model constructing and forecasts estimating, and to propose the methods for their description and taking into consideration. Methods. To develop DSS for forecasting financial and economic processes and estimation of financial risks the following system analysis principles were hired: hierarchical architecture, the possibilities for identification and processing possible uncertainties, alternatives computing, and tracking the computational procedures for all stages of data processing. The system developed provides possibilities for taking into consideration statistical and parametric uncertainties. The DSS proposed has a modular architecture that could be easily expanded with new functions like preliminary data processing, model parameters estimation, and procedures for computing forecasts and financial risks. Results. The main result of the study is systemic methodology of mathematical modeling financial and economic processes, that has been implemented in the frames of the DSS proposed. High quality of final results is achieved thanks to appropriate tracking of all computations using several sets of statistical quality criteria. An example is given for mathematical modeling, estimation and forecasting of financial risk. The results of estimation show that the systemic approach proposed has good perspectives for its practical use. Conclusions. Thus, we proposed a systemic approach to mathematical modeling and forecasting financial and economic processes as well as estimation of financial risk. The use of the approach provides possibilities for computing estimate forecasts of high quality using statistical data.Проблематика. Для дальнейшего повышения качества прогнозирования динамики развития финансово-экономических процессов (ФЭП) необходимо разрабатывать новые методы и подходы в рамках современных концепций создания информационных систем поддержки принятия решений (СППР). Цель исследования. Главная цель исследования: рассмотреть принципы системного анализа, которые могут быть использованы для решения задачи краткосрочного прогнозирования; разработать эффективную систему обработки данных, которая базируется на принципах системного анализа, реализованных в СППР; проанализировать возможные типы неопределенностей, встречающихся при построении математических моделей и оценивании прогнозов, а также предложить методы их описания и учета в процессе обработки данных. Методика реализации. При создании СППР для прогнозирования ФЭП и оценивания финансовых рисков использованы такие принципы системного анализа: иерархичность архитектуры системы, идентификация и обработка возможных неопределенностей, получение альтернативных решений и сопровождение вычислительных процедур на всех этапах выполнения вычислений. СППР имеет модульную архитектуру, которая легко расширяется новыми функциями предварительной обработки данных, оценивания параметров, а также процедурами вычисления оценок прогнозов и возможных рисков финансовых потерь. Результаты исследования. Основным результатом исследования является системная методология моделирования ФЭП, которая реализована в рамках предложенной СППР. Высокая точность окончательных результатов достигается благодаря сопровождению вычислительных процессов с помощью соответствующих множеств статистических критериев. Приведен пример математического моделирования и оценивания финансового риска. Полученные результаты свидетельствуют о том, что предложенный системный поход имеет хорошие перспективы для практического использования. Выводы. Предложен системный подход к математическому моделированию и прогнозированию ФЭП, а также к оцениванию финансовых рисков. Применение этого подхода дает возможность получать высококачественные оценки прогнозов на основе статистических данных

Human facial neural activities and gesture recognition for machine-interfacing applications

The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human–machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2–11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers