Abstract Short-term traffic parameter forecasting is critical to modern urban traffic management and control systems. Predictive accuracy in data-driven traffic models is reduced when exposed to non-recurring or non-routine traffic events, such as accidents, road closures, and extreme weather conditions. The analytical mining of data from social networks – specifically twitter – can improve urban traffic parameter prediction by complementing traffic data with data representing events capable of disrupting regular traffic patterns reported in social media posts. This paper proposes a deep learning urban traffic prediction model that combines information extracted from tweet messages with traffic and weather information. The predictive model adopts a deep Bi-directional Long Short-Term Memory (LSTM) stacked autoencoder (SAE) architecture for multi-step traffic flow prediction trained using tweets, traffic and weather datasets. The model is evaluated on an urban road network in Greater Manchester, United Kingdom. The findings from extensive empirical analysis using real-world data demonstrate the effectiveness of the approach in improving prediction accuracy when compared to other classical/statistical and machine learning (ML) state-of-the-art models. The improvement in predictive accuracy can lead to reduced frustration for road users, cost savings for businesses, and less harm to the environment.

This paper presents a proactive model and tool for urban traffic analysis and management that integrates deep learning for traffic parameter prediction with traffic microsimulation, providing traffic analysts with the ability to visualise the traffic network state ahead of time, generate traffic control measures, and analyse the consequences of the applied traffic control measure(s). The model adopts an integrated assess-forecast-simulate approach in which traffic flow characteristics are applied on deep Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) stacked autoencoders to forecast traffic flow and speed, which are subsequently passed on to a traffic microsimulation tool - Simulation of Urban Mobility (SUMO) - where the predicted parameters are used to generate a traffic future state simulation. The model is evaluated using sensor-collected historical traffic and weather data from Stretford, Greater Manchester in the United Kingdom. The results show the promise of the proposed model when applied towards urban traffic management and control.

The use of vision technology for quality testing of food production has the obvious advantage of being able to continuously monitor a production using non-destructive methods thus increasing the quality and minimizing cost. The performance of a multispectral imaging system has been evaluated in monitoring the spoilage of minced beef stored either aerobically or under modified atmosphere packaging (MAP), at different storage temperatures (0, 5, 10, and 15 °C). The detection system explores both qualitative and quantitative information extracted from spectral data with the aid of an advanced neuro-fuzzy identification model. The proposed model constructs its initial rules by clustering while the final fuzzy rule base is determined by competitive learning. Results indicated that multispectral information could be considered as an alternative methodology for the accurate evaluation of meat spoilage.

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid determination of meat spoilage, Fourier transform infrared (FTIR) spectroscopy technique, with the help of advanced learning-based methods, was attempted in this work. FTIR spectra were obtained from the surface of beef samples during aerobic storage at various temperatures, while a microbiological analysis had identified the population of Total viable counts. A fuzzy principal component algorithm has been also developed to reduce the dimensionality of the spectral data. The results confirmed the superiority of the adopted scheme compared to the partial least squares technique, currently used in food microbiology.

Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.

Web mining is an intensive task which may yield significant insights that may revolutionise business and marketing by using effective information distribution, entertainment and communication techniques. For instance, corporations can optimise their eCommerce websites to maximize impact and personalise the online content of their websites. Corporations can use web mining techniques for better understanding about their customers and thus will gain competitive edge by delivering the right information and services. This paper concentrates on Reconstruction of visited Session which was recorded in a web log server. Existing approaches for Reconstruction of users' session deal only with the time when a user accesses every page, whereas, in this paper, each visited page is incorporated with a pair of ordered time points, which we call biTemporal. Re-produced sessions can be used latter in different ways such as personalization, sequential pattern discovery, etc.

Web usage mining can contribute to finding significant educational knowledge. It can play a vital role in the personalisation aspects of any domain. We propose a framework for personalizing e-learning that necessitates careful attention towards individual learning styles. We focus on identifying learning patterns of learners and the sequence of choosing learning resources in relation to their learning styles. A prototype for an adaptive Web based course has been developed where the learning environment is modifying its behaviour to reflect learning styles.

Data warehouses (DWH) have been established as the core of decision support systems. On top of a DWH, different applications can be realised with regard to conventional reporting. On line analytical processing (OLAP) has reached the maturity as an interactive and explorative way of analysing DWH data. However DWH are mostly organised as snapshot databases. For this reason important tasks like "how many times have product of a specific brand been sold in the past?" cannot be answered successfully - in order to control the success of reshuffling the product range it is necessary to compare the sales of old and new products. The same applies in cases where the seasonality aspect for a particular range of products has to be answered. On the other hand, temporal databases allow a valid time to be assigned to data. In this manner, a past state can be reconstructed during retrieval. In this paper, we address the integration of DWH and OLAP with temporal databases semantics.

With the rapid growing number of WWW users, hidden information becomes ever increasingly valuable. As a consequence of this phenomenon, mining Web data and analysing on-line users' behaviour and their on-line traversal pattern have emerged as a new area of research. Primarily based on the Web servers' log files, the main objective of traversal pattern mining is to discover the frequent patterns in users' browsing paths and behaviors. This paper presents a complete framework for Web mining, allowing users to pre-define physical constraints when analysing complex traversal patterns in order to improve the efficiency of algorithms and offer flexibility in producing the results.

Workflows are activities involving the coordinated execution of multiple tasks performed by entities, in the enterprise environment. Workflow systems support the encoding and execution of workflows. A workflow management system is proposed, in which a considerable attention is paid on the utilisation of the services of a flexible database formalism. Flexible database formalisms must permit the modelling of trends, seasonality (periodicity), cyclic variations of trends and irregular activities (no predictable patterns) in the enterprise environment Furthermore, the formalism must permit the, expression of enterprise concepts founded in various conceptual levels. This paper will be restricted in workflow management system design, and activity scheduling, in a post relational environment whereas relational operators utilise paths (links between domains), in order to express interdependencies between activities.

The increasing need for applications to utilise a community of information providers in their effort to answer an information request has been a lasting problem of research. In requesting a community of information providers two types of incompatibility may occur, namely structural and semantic. The former may be caused by differences in the attribute domain, format, units, and granularity (intentional inconsistencies). The latter can be caused by attribute value conflict, since similar defined attributes utilise different levels of a conceptual hierarchy (tree, lattice) in defining themselves (extensional inconsistencies).

The task of an information system is the representation and management of indicative information from multiple sources describing the state of some enterprise. Most snapshot database models represent enterprises that are crisp where all relationships are fixed, and all attributes are atomic valued. In temporal databases, approaches are dealing mainly with precise absolute times. Very few approaches deal with imprecise absolute times or with infinite absolute times. No consideration has been given to imprecise infinite times. Many algebraic models are dealing with temporal or value imperfection, with no description of the semantics of uncertain information. This paper suggests an application independent conceptual framework describing the semantics of temporal and incomplete information and translates this to database representation. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

An information system is used for representing and managing indicative information from multiple sources describing the state of an enterprise. Most information systems model enterprises that are crisp. A crisp enterprise is one that is highly quantifiable; relationships are fixed and attributes are atomic valued. The premises for this paper are precise enterprises, where data are imperfect and multiple conflicting sources of information do exist. In such cases, information can be imperfect and/or temporal, or any possible combination of each two of them. In domains where information is perfect, all information sources are absolutely reliable. In more speculative domains, like diagnosis, information may be asserted relatively to some time intervals in which it is possibly defined and probably believed. In such domains different sources of information may be assigned different degrees of reliability. Value imperfection and/or temporal indeterminacy may cause uncertainty. Temporal information may be recurring, periodical or definite.

Article Free Access Share on Development and evaluation of an application in a deductive environment Authors: Ilias Petrounias Information Systems Group, Department of Computation, UMIST, P.O. Box 88, Manchester M60 1QD Information Systems Group, Department of Computation, UMIST, P.O. Box 88, Manchester M60 1QDView Profile , Pericles Loucopoulos Information Systems Group, Department of Computation, UMIST, P.O. Box 88, Manchester M60 1QD Information Systems Group, Department of Computation, UMIST, P.O. Box 88, Manchester M60 1QDView Profile Authors Info & Claims CIKM '93: Proceedings of the second international conference on Information and knowledge managementDecember 1993 Pages 184–193https://doi.org/10.1145/170088.170126Published:01 December 1993Publication History 1citation218DownloadsMetricsTotal Citations1Total Downloads218Last 12 Months3Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF