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Dr. Amr Abdullatif
Department of Computer Science, University of Bradford, BD7 1DP, Bradford, UK

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0 Artificial Intelligence
0 Big Data
0 Deep Learning
0 Machine Learning
0 Clustering

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Journal article
Published: 13 November 2020 in Smart Cities
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Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results.

ACS Style

Dhavalkumar Thakker; Bhupesh Kumar Mishra; Amr Abdullatif; Suvodeep Mazumdar; Sydney Simpson. Explainable Artificial Intelligence for Developing Smart Cities Solutions. Smart Cities 2020, 3, 1353 -1382.

AMA Style

Dhavalkumar Thakker, Bhupesh Kumar Mishra, Amr Abdullatif, Suvodeep Mazumdar, Sydney Simpson. Explainable Artificial Intelligence for Developing Smart Cities Solutions. Smart Cities. 2020; 3 (4):1353-1382.

Chicago/Turabian Style

Dhavalkumar Thakker; Bhupesh Kumar Mishra; Amr Abdullatif; Suvodeep Mazumdar; Sydney Simpson. 2020. "Explainable Artificial Intelligence for Developing Smart Cities Solutions." Smart Cities 3, no. 4: 1353-1382.

Advanced review
Published: 17 April 2018 in WIREs Data Mining and Knowledge Discovery
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Data streams have arisen as a relevant research topic during the past decade. They are real‐time, incremental in nature, temporally ordered, massive, contain outliers, and the objects in a data stream may evolve over time (concept drift). Clustering is often one of the earliest and most important steps in the streaming data analysis workflow. A comprehensive literature is available about stream data clustering; however, less attention is devoted to the fuzzy clustering approach, even though the nonstationary nature of many data streams makes it especially appealing. This survey discusses relevant data stream clustering algorithms focusing mainly on fuzzy methods, including their treatment of outliers and concept drift and shift. This article is categorized under Technologies > Machine Learning Technologies > Computational Intelligence Fundamental Concepts of Data and Knowledge > Data Concepts

ACS Style

Amr Abdullatif; Francesco Masulli; Stefano Rovetta. Clustering of nonstationary data streams: A survey of fuzzy partitional methods. WIREs Data Mining and Knowledge Discovery 2018, 8, 1 .

AMA Style

Amr Abdullatif, Francesco Masulli, Stefano Rovetta. Clustering of nonstationary data streams: A survey of fuzzy partitional methods. WIREs Data Mining and Knowledge Discovery. 2018; 8 (4):1.

Chicago/Turabian Style

Amr Abdullatif; Francesco Masulli; Stefano Rovetta. 2018. "Clustering of nonstationary data streams: A survey of fuzzy partitional methods." WIREs Data Mining and Knowledge Discovery 8, no. 4: 1.

Article
Published: 01 September 2017 in Data Science and Engineering
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Data streams have arisen as a relevant topic during the last few years as an efficient method for extracting knowledge from big data. In the robust layered ensemble model (RLEM) proposed in this paper for short-term traffic flow forecasting, incoming traffic flow data of all connected road links are organized in chunks corresponding to an optimal time lag. The RLEM model is composed of two layers. In the first layer, we cluster the chunks by using the Graded Possibilistic c-Means method. The second layer is made up by an ensemble of forecasters, each of them trained for short-term traffic flow forecasting on the chunks belonging to a specific cluster. In the operational phase, as a new chunk of traffic flow data presented as input to the RLEM, its memberships to all clusters are evaluated, and if it is not recognized as an outlier, the outputs of all forecasters are combined in an ensemble, obtaining in this a way a forecasting of traffic flow for a short-term time horizon. The proposed RLEM model is evaluated on a synthetic data set, on a traffic flow data simulator and on two real-world traffic flow data sets. The model gives an accurate forecasting of the traffic flow rates with outlier detection and shows a good adaptation to non-stationary traffic regimes. Given its characteristics of outlier detection, accuracy, and robustness, RLEM can be fruitfully integrated in traffic flow management systems.

ACS Style

Amr Abdullatif; Francesco Masulli; Stefano Rovetta. Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting. Data Science and Engineering 2017, 2, 210 -223.

AMA Style

Amr Abdullatif, Francesco Masulli, Stefano Rovetta. Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting. Data Science and Engineering. 2017; 2 (3):210-223.

Chicago/Turabian Style

Amr Abdullatif; Francesco Masulli; Stefano Rovetta. 2017. "Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting." Data Science and Engineering 2, no. 3: 210-223.

Conference paper
Published: 07 February 2017 in Transactions on Petri Nets and Other Models of Concurrency XV
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Multidimensional data streams are a major paradigm in data science. This work focuses on possibilistic clustering algorithms as means to perform clustering of multidimensional streaming data. The proposed approach exploits fuzzy outlier analysis to provide good learning and tracking abilities in both concept shift and concept drift.

ACS Style

Amr Rashad Ahmed Abdullatif; F. Masulli; Stefano Rovetta; Alberto Cabri. Graded Possibilistic Clustering of Non-stationary Data Streams. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 139 -150.

AMA Style

Amr Rashad Ahmed Abdullatif, F. Masulli, Stefano Rovetta, Alberto Cabri. Graded Possibilistic Clustering of Non-stationary Data Streams. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; ():139-150.

Chicago/Turabian Style

Amr Rashad Ahmed Abdullatif; F. Masulli; Stefano Rovetta; Alberto Cabri. 2017. "Graded Possibilistic Clustering of Non-stationary Data Streams." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 139-150.

Conference paper
Published: 14 November 2016 in 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)
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Real time traffic flow forecasting is a necessary requirement for traffic management in order to be able to evaluate the effects of different available strategies or policies. This paper focuses on short-term traffic flow forecasting by taking into consideration both spatial (road links) and temporal (lag or past traffic flow values) information. We propose a Layered Ensemble Model (LEM) which combines Artificial Neural Networks and Graded Possibilistic Clustering obtaining an accurate forecast of the traffic flow rates with outlier detection. Experimentation has been carried out on two different data sets. The former was obtained from real UK motorway and the later was obtained from simulated traffic flow on a street network in Genoa (Italy). The proposed LEM model for short-term traffic forecasting provides promising results and given the ability for outlier detection, accuracy, robustness of the proposed approach, it can be fruitful integrated in traffic flow management systems.

ACS Style

Amr Rashad Ahmed Abdullatif; Stefano Rovetta; Francesco Masulli. Layered ensemble model for short-term traffic flow forecasting with outlier detection. 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI) 2016, 1 -6.

AMA Style

Amr Rashad Ahmed Abdullatif, Stefano Rovetta, Francesco Masulli. Layered ensemble model for short-term traffic flow forecasting with outlier detection. 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI). 2016; ():1-6.

Chicago/Turabian Style

Amr Rashad Ahmed Abdullatif; Stefano Rovetta; Francesco Masulli. 2016. "Layered ensemble model for short-term traffic flow forecasting with outlier detection." 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI) , no. : 1-6.

Conference paper
Published: 13 August 2016 in Computer Vision
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ACS Style

Hassan Mahmoud; Francesco Masulli; Stefano Rovetta; Amr Rashad Ahmed Abdullatif. Comparison of Methods for Community Detection in Networks. Computer Vision 2016, 216 -224.

AMA Style

Hassan Mahmoud, Francesco Masulli, Stefano Rovetta, Amr Rashad Ahmed Abdullatif. Comparison of Methods for Community Detection in Networks. Computer Vision. 2016; ():216-224.

Chicago/Turabian Style

Hassan Mahmoud; Francesco Masulli; Stefano Rovetta; Amr Rashad Ahmed Abdullatif. 2016. "Comparison of Methods for Community Detection in Networks." Computer Vision , no. : 216-224.