ORCID Profile
0000-0003-0339-4474
Current Organisation
Birmingham City University
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Publisher: Auerbach Publications
Date: 25-06-2014
DOI: 10.1201/B17112
Publisher: IEEE
Date: 12-2014
DOI: 10.1109/UCC.2014.130
Publisher: IEEE
Date: 10-2010
Publisher: Elsevier BV
Date: 02-2015
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE
Date: 10-2010
Publisher: Springer London
Date: 2012
Publisher: MDPI AG
Date: 21-02-2022
DOI: 10.3390/BDCC6010021
Abstract: The study of the dynamics or the progress of science has been widely explored with descriptive and statistical analyses. Also this study has attracted several computational approaches that are labelled together as the Computational History of Science, especially with the rise of data science and the development of increasingly powerful computers. Among these approaches, some works have studied dynamism in scientific literature by employing text analysis techniques that rely on topic models to study the dynamics of research topics. Unlike topic models that do not delve deeper into the content of scientific publications, for the first time, this paper uses temporal word embeddings to automatically track the dynamics of scientific keywords over time. To this end, we propose Vec2Dynamics, a neural-based computational history approach that reports stability of k-nearest neighbors of scientific keywords over time the stability indicates whether the keywords are taking new neighborhood due to evolution of scientific literature. To evaluate how Vec2Dynamics models such relationships in the domain of Machine Learning (ML), we constructed scientific corpora from the papers published in the Neural Information Processing Systems (NIPS actually abbreviated NeurIPS) conference between 1987 and 2016. The descriptive analysis that we performed in this paper verify the efficacy of our proposed approach. In fact, we found a generally strong consistency between the obtained results and the Machine Learning timeline.
Publisher: Springer London
Date: 2013
Publisher: Informa UK Limited
Date: 06-10-2014
Publisher: IEEE
Date: 12-2014
Publisher: IEEE
Date: 2007
Publisher: Wiley
Date: 10-11-2020
DOI: 10.1002/WIDM.1395
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 2009
Publisher: IEEE
Date: 07-2011
Publisher: Elsevier BV
Date: 04-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Informa UK Limited
Date: 31-10-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2014
Publisher: IGI Global
Date: 2010
DOI: 10.4018/978-1-61520-967-5.CH055
Abstract: With the emergence of high-end smart phones/PDAs there is a growing opportunity to enrich mobile ervasive services with semantic reasoning. This article presents novel strategies for optimising semantic reasoning for realizing semantic applications and services on mobile devices. We have developed the mTableaux algorithm which optimizes the reasoning process to facilitate service selection. We present comparative experimental results which show that mTableaux improves the performance and scalability of semantic reasoning for mobile devices.
Publisher: IEEE
Date: 04-2011
Publisher: MDPI AG
Date: 30-06-2022
DOI: 10.3390/E24070910
Abstract: This paper presents a set of methods, jointly called PGraphD*, which includes two new methods (PGraphDD-QM and PGraphDD-SS) for drift detection and one new method (PGraphDL) for drift localisation in business processes. The methods are based on deep learning and graphs, with PGraphDD-QM and PGraphDD-SS employing a quality metric and a similarity score for detecting drifts, respectively. According to experimental results, PGraphDD-SS outperforms PGraphDD-QM in drift detection, achieving an accuracy score of 100% over the majority of synthetic logs and an accuracy score of 80% over a complex real-life log. Furthermore, PGraphDD-SS detects drifts with delays that are 59% shorter on average compared to the best performing state-of-the-art method.
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE
Date: 05-2017
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 04-12-2017
Publisher: Wiley
Date: 07-2013
DOI: 10.1002/WIDM.1093
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 07-2012
DOI: 10.1109/MDM.2012.33
Publisher: IEEE
Date: 07-2019
Publisher: IEEE
Date: 06-2011
DOI: 10.1109/MDM.2011.67
Publisher: Elsevier BV
Date: 12-2009
Publisher: IEEE
Date: 10-2013
DOI: 10.1109/SMC.2013.177
Publisher: Wiley
Date: 28-08-2008
Publisher: Springer International Publishing
Date: 2014
Publisher: IEEE
Date: 07-2012
DOI: 10.1109/MDM.2012.37
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer International Publishing
Date: 2014
Publisher: IGI Global
Date: 2011
DOI: 10.4018/978-1-60960-593-3.CH007
Abstract: With the emergence of high-end smart phones/PDAs there is a growing opportunity to enrich mobile ervasive services with semantic reasoning. This paper presents novel strategies for optimising semantic reasoning for realising semantic applications and services on mobile devices. We have developed the mTableaux algorithm which optimises the reasoning process to facilitate service selection. We present comparative experimental results which show that mTableaux improves the performance and scalability of semantic reasoning for mobile devices.
Publisher: MDPI AG
Date: 28-11-2020
DOI: 10.3390/BDCC4040037
Abstract: To make healthcare available and easily accessible, the Internet of Things (IoT), which paved the way to the construction of smart cities, marked the birth of many smart applications in numerous areas, including healthcare. As a result, smart healthcare applications have been and are being developed to provide, using mobile and electronic technology, higher diagnosis quality of the diseases, better treatment of the patients, and improved quality of lives. Since smart healthcare applications that are mainly concerned with the prediction of healthcare data (like diseases for ex le) rely on predictive healthcare data analytics, it is imperative for such predictive healthcare data analytics to be as accurate as possible. In this paper, we will exploit supervised machine learning methods in classification and regression to improve the performance of the traditional Random Forest on healthcare datasets, both in terms of accuracy and classification/regression speed, in order to produce an effective and efficient smart healthcare application, which we have termed eGAP. eGAP uses the evolutionary game theoretic approach replicator dynamics to evolve a Random Forest ensemble. Trees of high resemblance in an initial Random Forest are clustered, and then clusters grow and shrink by adding and removing trees using replicator dynamics, according to the predictive accuracy of each subforest represented by a cluster of trees. All clusters have an initial number of trees that is equal to the number of trees in the smallest cluster. Cluster growth is performed using trees that are not initially s led. The speed and accuracy of the proposed method have been demonstrated by an experimental study on 10 classification and 10 regression medical datasets.
Publisher: Elsevier BV
Date: 02-2015
Publisher: IEEE
Date: 09-2013
Publisher: Springer International Publishing
Date: 2015
Publisher: Association for Computing Machinery (ACM)
Date: 06-2005
Abstract: The recent advances in hardware and software have enabled the capture of different measurements of data in a wide range of fields. These measurements are generated continuously and in a very high fluctuating data rates. Ex les include sensor networks, web logs, and computer network traffic. The storage, querying and mining of such data sets are highly computationally challenging tasks. Mining data streams is concerned with extracting knowledge structures represented in models and patterns in non stopping streams of information. The research in data stream mining has gained a high attraction due to the importance of its applications and the increasing generation of streaming information. Applications of data stream analysis can vary from critical scientific and astronomical applications to important business and financial ones. Algorithms, systems and frameworks that address streaming challenges have been developed over the past three years. In this review paper, we present the state-of-the-art in this growing vital field.
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE
Date: 05-2015
Publisher: IEEE
Date: 12-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 15-11-2017
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 04-06-2020
DOI: 10.1007/S10462-020-09833-6
Abstract: Modern machine learning methods typically produce “black box” models that are opaque to interpretation. Yet, their demand has been increasing in the Human-in-the-Loop processes, that is, those processes that require a human agent to verify, approve or reason about the automated decisions before they can be applied. To facilitate this interpretation, we propose Collection of High Importance Random Path Snippets (CHIRPS) a novel algorithm for explaining random forest classification per data instance . CHIRPS extracts a decision path from each tree in the forest that contributes to the majority classification, and then uses frequent pattern mining to identify the most commonly occurring split conditions. Then a simple, conjunctive form rule is constructed where the antecedent terms are derived from the attributes that had the most influence on the classification. This rule is returned alongside estimates of the rule’s precision and coverage on the training data along with counter-factual details. An experimental study involving nine data sets shows that classification rules returned by CHIRPS have a precision at least as high as the state of the art when evaluated on unseen data (0.91–0.99) and offer a much greater coverage (0.04–0.54). Furthermore, CHIRPS uniquely controls against under- and over-fitting solutions by maximising novel objective functions that are better suited to the local (per instance) explanation setting.
Publisher: ACM
Date: 22-03-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Association for Computing Machinery (ACM)
Date: 31-03-2011
Abstract: Sensor data is being collected at an unprecedented rate across a variety of domains from a broad spectrum of sources, such as wide-area sensor infrastructures, remote sensing instruments, RFIDs, and wireless sensor networks. With the recent proliferation of smart-phones, and similar GPS enabled mobile devices, collection of sensor data is no longer limited to scientific communities, but has reached general public. With massive volumes of such disparate, dynamic, and geographically distributed data available, many high-priority applications have been identified that involve analysis of such data to solve real world problems such as understanding climate change and its impacts, electric grid monitoring, disaster preparedness and management, national or homeland security, and the management of critical infrastructures. Given the unique characteristics of sensor data, particularly its spatiotemporal nature and presence of constraints associated with the data collection and computational resources, there have been many research efforts to analyze the sensor data which build upon the general research in the data mining community but are significantly different in terms of how they address the specific challenges encountered when dealing with sensor data. In particular, the raw data from sensors needs to be efficiently managed and transformed to usable information through data fusion, which in turn must be converted to predictive insights via knowledge discovery, ultimately facilitating automated or humaninduced tactical decisions or strategic policy based on decision sciences and decision support systems. Keeping in view the requirements of the emerging field of knowledge discovery from sensor data, we took initiative to develop a community of researchers with common interests and scientific goals, which culminated into the organization of SensorKDD series of workshops in conjunction with the prestigious ACM SIGKDD International Conference of Knowledge Discovery and Data Mining. In this report, we summarize events at the Fourth ACM-SIGKDD International Workshop on Knowledge Discovery form Sensor Data (SensorKDD 2010).
Publisher: Springer Science and Business Media LLC
Date: 22-09-2015
Publisher: Elsevier BV
Date: 11-2017
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer US
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 12-2011
Publisher: ACM
Date: 24-10-2011
Publisher: ACM
Date: 13-07-2009
Publisher: MDPI AG
Date: 03-09-2018
DOI: 10.3390/BDCC2030026
Abstract: Machine learning has traditionally been solely performed on servers and high-performance machines. However, advances in chip technology have given us miniature libraries that fit in our pockets and mobile processors have vastly increased in capability narrowing the vast gap between the simple processors embedded in such things and their more complex cousins in personal computers. Thus, with the current advancement in these devices, in terms of processing power, energy storage and memory capacity, the opportunity has arisen to extract great value in having on-device machine learning for Internet of Things (IoT) devices. Implementing machine learning inference on edge devices has huge potential and is still in its early stages. However, it is already more powerful than most realise. In this paper, a step forward has been taken to understand the feasibility of running machine learning algorithms, both training and inference, on a Raspberry Pi, an embedded version of the Android operating system designed for IoT device development. Three different algorithms: Random Forests, Support Vector Machine (SVM) and Multi-Layer Perceptron, respectively, have been tested using ten erse data sets on the Raspberry Pi to profile their performance in terms of speed (training and inference), accuracy, and power consumption. As a result of the conducted tests, the SVM algorithm proved to be slightly faster in inference and more efficient in power consumption, but the Random Forest algorithm exhibited the highest accuracy. In addition to the performance results, we will discuss their usability scenarios and the idea of implementing more complex and taxing algorithms such as Deep Learning on these small devices in more details.
Publisher: Springer Science and Business Media LLC
Date: 13-02-2019
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: MDPI AG
Date: 09-01-2021
DOI: 10.3390/INFO12010024
Abstract: This paper introduces a new and expressive algorithm for inducing descriptive rule-sets from streaming data in real-time in order to describe frequent patterns explicitly encoded in the stream. Data Stream Mining (DSM) is concerned with the automatic analysis of data streams in real-time. Rapid flows of data challenge the state-of-the art processing and communication infrastructure, hence the motivation for research and innovation into real-time algorithms that analyse data streams on-the-fly and can automatically adapt to concept drifts. To date, DSM techniques have largely focused on predictive data mining applications that aim to forecast the value of a particular target feature of unseen data instances, answering questions such as whether a credit card transaction is fraudulent or not. A real-time, expressive and descriptive Data Mining technique for streaming data has not been previously established as part of the DSM toolkit. This has motivated the work reported in this paper, which has resulted in developing and validating a Generalised Rule Induction (GRI) tool, thus producing expressive rules as explanations that can be easily understood by human analysts. The expressiveness of decision models in data streams serves the objectives of transparency, underpinning the vision of ‘explainable AI’ and yet is an area of research that has attracted less attention despite being of high practical importance. The algorithm introduced and described in this paper is termed Fast Generalised Rule Induction (FGRI). FGRI is able to induce descriptive rules incrementally for raw data from both categorical and numerical features. FGRI is able to adapt rule-sets to changes of the pattern encoded in the data stream (concept drift) on the fly as new data arrives and can thus be applied continuously in real-time. The paper also provides a theoretical, qualitative and empirical evaluation of FGRI.
Publisher: Wiley
Date: 24-02-2014
DOI: 10.1002/WIDM.1115
Publisher: ACM
Date: 22-03-2010
Publisher: IEEE
Date: 2008
Publisher: CRC Press
Date: 10-12-2008
Publisher: Cambridge University Press (CUP)
Date: 02-2014
DOI: 10.1017/S0140525X13001854
Abstract: We are sympathetic with Bentley et al.’s attempt to encompass the wisdom of crowds in a generative model, but posit that a successful attempt at using big data will include more sensitive measurements, more varied sources of information, and will also build from the indirect information available through technology, from ancillary technical features to data from brain–computer interfaces.
Publisher: MDPI AG
Date: 16-05-2021
DOI: 10.3390/E23050620
Abstract: Automated grading systems using deep convolution neural networks (DCNNs) have proven their capability and potential to distinguish between different breast cancer grades using digitized histopathological images. In digital breast pathology, it is vital to measure how confident a DCNN is in grading using a machine-confidence metric, especially with the presence of major computer vision challenging problems such as the high visual variability of the images. Such a quantitative metric can be employed not only to improve the robustness of automated systems, but also to assist medical professionals in identifying complex cases. In this paper, we propose Entropy-based Elastic Ensemble of DCNN models (3E-Net) for grading invasive breast carcinoma microscopy images which provides an initial stage of explainability (using an uncertainty-aware mechanism adopting entropy). Our proposed model has been designed in a way to (1) exclude images that are less sensitive and highly uncertain to our ensemble model and (2) dynamically grade the non-excluded images using the certain models in the ensemble architecture. We evaluated two variations of 3E-Net on an invasive breast carcinoma dataset and we achieved grading accuracy of 96.15% and 99.50%.
Publisher: MDPI AG
Date: 27-01-2022
DOI: 10.3390/EN15030914
Abstract: Water Distribution System (WDS) threats have significantly grown following the Maroochy shire incident, as evidenced by proofed attacks on water premises. As a result, in addition to traditional solutions (e.g., data encryption and authentication), attack detection is being proposed in WDS to reduce disruption cases. The attack detection system must meet two critical requirements: high accuracy and near real-time detection. This drives us to propose a two-stage detection system that uses self-supervised and unsupervised algorithms to detect Cyber-Physical (CP) attacks. Stage 1 uses heuristic adaptive self-supervised algorithms to achieve near real-time decision-making and detection sensitivity of 66% utilizing Boss. Stage 2 attempts to validate the detection of attacks using an unsupervised algorithm to maintain a detection accuracy of 94% utilizing Isolation Forest. Both stages are examined against time granularity and are empirically analyzed against a variety of performance evaluation indicators. Our findings demonstrate that the algorithms in stage 1 are less favored than those in the literature, but their existence enables near real-time decision-making and detection reliability. In stage 2, the isolation Forest algorithm, in contrast, gives excellent accuracy. As a result, both stages can collaborate to maximize accuracy in a near real-time attack detection system.
Publisher: Springer International Publishing
Date: 29-07-2018
Publisher: ACM
Date: 21-03-2011
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: ACM Press
Date: 2014
Publisher: Wiley
Date: 27-07-2022
DOI: 10.1002/WIDM.1474
Abstract: The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning‐based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field. This article is categorized under: Application Areas Health Care
Publisher: IEEE
Date: 2008
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer International Publishing
Date: 2014
Publisher: Elsevier BV
Date: 05-2013
Publisher: Springer International Publishing
Date: 2014
Publisher: IEEE
Date: 11-2017
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer International Publishing
Date: 2014
Publisher: IEEE
Date: 07-2018
Publisher: ACM
Date: 16-03-2008
Publisher: Informa UK Limited
Date: 06-10-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Publisher: Elsevier BV
Date: 08-2016
Publisher: Unpublished
Date: 2015
Publisher: World Scientific Pub Co Pte Lt
Date: 12-2006
DOI: 10.1142/S0219622006002179
Abstract: Data stream mining has attracted considerable attention over the past few years owing to the significance of its applications. Streaming data is often evolving over time. Capturing changes could be used for detecting an event or a phenomenon in various applications. Weather conditions, economical changes, astronomical, and scientific phenomena are among a wide range of applications. Because of the high volume and speed of data streams, it is computationally hard to capture these changes from raw data in real-time. In this paper, we propose a novel algorithm that we term as STREAM-DETECT to capture these changes in data stream distribution and/or domain using clustering result deviation. STREAM-DETECT is followed by a process of offline classification CHANGE-CLASS. This classification is concerned with the association of the history of change characteristics with the observed event or phenomenon. Experimental results show the efficiency of the proposed framework in both detecting the changes and classification accuracy.
Publisher: Springer International Publishing
Date: 2013
Publisher: Springer Science and Business Media LLC
Date: 11-2006
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Association for Computing Machinery (ACM)
Date: 06-04-2017
DOI: 10.1145/3054912
Abstract: Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations, without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction, and computer games, to name a few. However, specialized algorithms are needed to effectively and robustly learn models as learning by imitation poses its own set of challenges. In this article, we survey imitation learning methods and present design options in different steps of the learning process. We introduce a background and motivation for the field as well as highlight challenges specific to the imitation problem. Methods for designing and evaluating imitation learning tasks are categorized and reviewed. Special attention is given to learning methods in robotics and games as these domains are the most popular in the literature and provide a wide array of problems and methodologies. We extensively discuss combining imitation learning approaches using different sources and methods, as well as incorporating other motion learning methods to enhance imitation. We also discuss the potential impact on industry, present major applications, and highlight current and future research directions.
Publisher: IEEE
Date: 12-2008
DOI: 10.1109/ICDM.2008.58
Publisher: Springer International Publishing
Date: 28-10-2018
Publisher: Association for Computing Machinery (ACM)
Date: 06-07-2018
DOI: 10.1145/3158645
Abstract: Activity recognition aims to provide accurate and opportune information on people’s activities by leveraging sensory data available in today’s sensory rich environments. Nowadays, activity recognition has become an emerging field in the areas of pervasive and ubiquitous computing. A typical activity recognition technique processes data streams that evolve from sensing platforms such as mobile sensors, on body sensors, and/or ambient sensors. This article surveys the two overlapped areas of research of activity recognition and data stream mining. The perspective of this article is to review the adaptation capabilities of activity recognition techniques in streaming environment. Categories of techniques are identified based on different features in both data streams and activity recognition. The pros and cons of the algorithms in each category are analysed, and the possible directions of future research are indicated.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Wiley
Date: 09-11-2018
DOI: 10.1002/WIDM.1292
Abstract: The Internet of Things (IoT) is the result of the convergence of sensing, computing, and networking technologies, allowing devices of varying sizes and computational capabilities (things) to intercommunicate. This communication can be achieved locally enabling what is known as edge and fog computing , or through the well‐established Internet infrastructure, exploiting the computational resources in the cloud. The IoT paradigm enables a new breed of applications in various areas including health care, energy management and smart cities. This paper starts off with reviewing these applications and their potential benefits. Challenges facing the realization of such applications are then discussed. The sheer amount of data stemmed from devices forming the IoT requires new data mining systems and techniques that are discussed and categorized later in this paper. Finally, the paper is concluded with future research directions. This article is categorized under: Fundamental Concepts of Data and Knowledge Big Data Mining Application Areas Health Care Application Areas Industry Specific Applications
Publisher: Springer International Publishing
Date: 2014
Publisher: Association for Computing Machinery (ACM)
Date: 31-03-2011
Abstract: Sensor data is being collected at an unprecedented rate across a variety of domains from a broad spectrum of sources, such as wide-area sensor infrastructures, remote sensing instruments, RFIDs, and wireless sensor networks. With the recent proliferation of smart-phones, and similar GPS enabled mobile devices, collection of sensor data is no longer limited to scientific communities, but has reached general public. With massive volumes of such disparate, dynamic, and geographically distributed data available, many high-priority applications have been identified that involve analysis of such data to solve real world problems such as understanding climate change and its impacts, electric grid monitoring, disaster preparedness and management, national or homeland security, and the management of critical infrastructures. Given the unique characteristics of sensor data, particularly its spatiotemporal nature and presence of constraints associated with the data collection and computational resources, there have been many research efforts to analyze the sensor data which build upon the general research in the data mining community but are significantly different in terms of how they address the specific challenges encountered when dealing with sensor data. In particular, the raw data from sensors needs to be efficiently managed and transformed to usable information through data fusion, which in turn must be converted to predictive insights via knowledge discovery, ultimately facilitating automated or humaninduced tactical decisions or strategic policy based on decision sciences and decision support systems. Keeping in view the requirements of the emerging field of knowledge discovery from sensor data, we took initiative to develop a community of researchers with common interests and scientific goals, which culminated into the organization of SensorKDD series of workshops in conjunction with the prestigious ACM SIGKDD International Conference of Knowledge Discovery and Data Mining. In this report, we summarize events at the Fourth ACM-SIGKDD International Workshop on Knowledge Discovery form Sensor Data (SensorKDD 2010).
Publisher: Springer Science and Business Media LLC
Date: 02-2017
Publisher: Unpublished
Date: 2015
Publisher: IGI Global
Date: 31-07-2014
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: World Scientific Pub Co Pte Lt
Date: 11-2013
DOI: 10.1142/S0219622013500375
Abstract: In ubiquitous data stream mining, different devices often aim to learn concepts that are similar to some extent. In many applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy. Coll-Stream integrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluate Coll-Stream classification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show that Coll-Stream resultant model achieves stability and accuracy in a variety of situations using both synthetic and real-world datasets.
Publisher: Springer Science and Business Media LLC
Date: 03-02-2011
Publisher: Springer International Publishing
Date: 2014
Publisher: IEEE
Date: 11-2012
Publisher: MDPI AG
Date: 15-08-2017
DOI: 10.3390/JSAN6030017
Publisher: Association for Computing Machinery (ACM)
Date: 20-12-2008
Abstract: Extracting knowledge and emerging patterns from sensor data is a nontrivial task. The challenges for the knowledge discovery community are expected to be immense. On one hand, dynamic data streams or events require real-time analysis methodologies and systems, while on the other hand centralized processing through high end computing is also required for generating offline predictive insights, which in turn can facilitate real-time analysis. In addition, emerging societal problems require knowledge discovery solutions that are designed to investigate anomalies, changes, extremes and nonlinear processes, and departures from the normal. Keeping in view the requirements of the emerging field of knowledge discovery from sensor data, we took initiative to develop a community of researchers with common interests and scientific goals, which culminated into the organization of Sensor-KDD series of workshops in conjunction with the prestigious ACM SIGKDD International Conference of Knowledge Discovery and Data Mining. In this report, we summarize the events of the Second ACM-SIGKDD International Workshop on Knowledge Discovery form Sensor Data (Sensor-KDD 2008).
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Association for Computing Machinery (ACM)
Date: 20-12-2008
Abstract: Extracting knowledge and emerging patterns from sensor data is a nontrivial task. The challenges for the knowledge discovery community are expected to be immense. On one hand, dynamic data streams or events require real-time analysis methodologies and systems, while on the other hand centralized processing through high end computing is also required for generating offline predictive insights, which in turn can facilitate real-time analysis. In addition, emerging societal problems require knowledge discovery solutions that are designed to investigate anomalies, changes, extremes and nonlinear processes, and departures from the normal. Keeping in view the requirements of the emerging field of knowledge discovery from sensor data, we took initiative to develop a community of researchers with common interests and scientific goals, which culminated into the organization of Sensor-KDD series of workshops in conjunction with the prestigious ACM SIGKDD International Conference of Knowledge Discovery and Data Mining. In this report, we summarize the events of the Second ACM-SIGKDD International Workshop on Knowledge Discovery form Sensor Data (Sensor-KDD 2008).
Publisher: Springer Science and Business Media LLC
Date: 14-03-2016
Publisher: Springer Science and Business Media LLC
Date: 09-01-2020
DOI: 10.1007/S00607-019-00785-6
Abstract: In predictive healthcare data analytics, high accuracy is both vital and paramount as low accuracy can lead to misdiagnosis, which is known to cause serious health consequences or death. Fast prediction is also considered an important desideratum particularly for machines and mobile devices with limited memory and processing power. For real-time health care analytics applications, particularly the ones that run on mobile devices, such traits (high accuracy and fast prediction) are highly desirable. In this paper, we propose to use an ensemble regression technique based on CLUB-DRF , which is a pruned Random Forest that possesses these features. The speed and accuracy of the method have been demonstrated by an experimental study on three medical data sets of three different diseases.
Publisher: Conference and Custom Publishing
Date: 05-2007
DOI: 10.1109/MDM.2007.70
Publisher: IEEE
Date: 12-2010
Publisher: Informa UK Limited
Date: 03-07-2013
Publisher: Elsevier BV
Date: 2020
Publisher: IEEE
Date: 11-2012
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 12-2016
Publisher: ACM
Date: 10-09-2023
Publisher: Unpublished
Date: 2015
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Mohamed Gaber.