• Application of machine learning to predict visitors’ green behaviours in marine protected areas: evidence from Cyprus

      Rezapouraghdam, Hamed; Akshiq, Arash; Ramkissoon, Haywantee; Cyprus University, Lefkosa, Turkey; Jagiellonian University, Gronostajowa 7, Krakow, Poland; The Arctic University of Norway, Tromsø, Norway; University of Derby; University of Johannesburg, Johannesburg, South Africa (Taylor & Francis, 2021-03-10)
      Interpretive marine turtle tours in Cyprus yields an alluring ground to unfold the complex nature of pro-environmental behavior among travelers in nature-based destinations. Framing on Collins (2004) interaction ritual concept and the complexity theory, the current study proposes a configurational model and probes the interactional effect of visitors’ memorable experiences with environmental passion and their demographics to identify the causal recipes leading to travelers’ sustainable behaviors. Data was collected from tourists in the marine protected areas located in Cyprus. Such destinations are highly valuable not only for their function as an economic source for locals but also as a significant habitat for biodiversity preservation. Using fuzzy-set Qualitative Comparative Analysis (fsQCA), this empirical study revealed that three recipes predict the high score level of visitors’ environmentally friendly behavior. Additionally, an adaptive neuro-fuzzy inference system (ANFIS) method was applied to train and test the patterns of visitors’ pro-environmental behavior in a machine learning environment to come up with a model which can best predict the outcome variable. The unprecedented implications on the use of technology to simulate and encourage pro-environmental behaviors in sensitive protected areas are discussed accordingly.
    • A conceptual framework for combining artificial neural networks with computational aeroacoustics for design development.

      McKee, Claire; Harmanto, Dani; Whitbrook, Amanda; University of Derby (Industrial Engineering and Operations Management Society (IEOM), 2018-03)
      This paper presents a preliminary method for improving the design and development process in a way that combines engineering design approaches based on learning algorithms and computational aeroacoustics. It is proposed that machine learning can effectively predict the noise generated by a coaxial jet exhaust by utilizing a database of computational experiments that cover a variety of flow and geometric configurations. A conceptual framework has been outlined for the development of a practical design tool to predict the changes in jet acoustics imparted by varying the fan nozzle geometry and engine cycle of a coaxial jet. It is proposed that computational aeroacoustic analysis is used to generate a training and validation database for an artificial neural network. The trained network can then predict noise data for any operational configuration. This method allows for the exploration of noise emissions from a variety of fan nozzle areas, engine cycles and flight conditions. It is intended that this be used as a design tool in order to reduce the design cycle time of new engine configurations and provide engineers with insight into the relationship between jet noise and the input variables.
    • A deep-learning-based emergency alert system

      Kang, Byungseok; Choo, Hyunseung; Sungkyunkwan University (Elsevier, 2016-05-21)
      Emergency alert systems serve as a critical link in the chain of crisis communication, and they are essential to minimize loss during emergencies. Acts of terrorism and violence, chemical spills, amber alerts, nuclear facility problems, weather-related emergencies, flu pandemics, and other emergencies all require those responsible such as government officials, building managers, and university administrators to be able to quickly and reliably distribute emergency information to the public. This paper presents our design of a deep-learning-based emergency warning system. The proposed system is considered suitable for application in existing infrastructure such as closed-circuit television and other monitoring devices. The experimental results show that in most cases, our system immediately detects emergencies such as car accidents and natural disasters.
    • Edge enhanced deep learning system for large-scale video stream analytics.

      Ali, Muhammad; Anjum, Ashiq; Yaseen, M. Usman; Zamani, A. Reza; Balouek-Thomert, Daniel; Rana, Omer; Parashar, Manish; University of Derby; Rutgers University; University of Cardiff (IEEE, 2018-05-14)
      Applying deep learning models to large-scale IoT data is a compute-intensive task and needs significant computational resources. Existing approaches transfer this big data from IoT devices to a central cloud where inference is performed using a machine learning model. However, the network connecting the data capture source and the cloud platform can become a bottleneck. We address this problem by distributing the deep learning pipeline across edge and cloudlet/fog resources. The basic processing stages and trained models are distributed towards the edge of the network and on in-transit and cloud resources. The proposed approach performs initial processing of the data close to the data source at edge and fog nodes, resulting in significant reduction in the data that is transferred and stored in the cloud. Results on an object recognition scenario show 71\% efficiency gain in the throughput of the system by employing a combination of edge, in-transit and cloud resources when compared to a cloud-only approach.
    • Intelligent grid enabled services for neuroimaging analysis

      McClatchey, Richard; Habib, Irfan; Anjum, Ashiq; Munir, Kamran; Branson, Andrew; Bloodsworth, Peter; Kiani, Saad Liaquat; University of West England; University of Derby; National University of Science and Technology (Elsevier, 2013-12)
      This paper reports our work in the context of the neuGRID project in the development of intelligent services for a robust and efficient Neuroimaging analysis environment. neuGRID is an EC-funded project driven by the needs of the Alzheimer's disease research community that aims to facilitate the collection and archiving of large amounts of imaging data coupled with a set of services and algorithms. By taking Alzheimer's disease as an exemplar, the neuGRID project has developed a set of intelligent services and a Grid infrastructure to enable the European neuroscience community to carry out research required for the study of degenerative brain diseases. We have investigated the use of machine learning approaches, especially evolutionary multi-objective meta-heuristics for optimising scientific analysis on distributed infrastructures. The salient features of the services and the functionality of a planning and execution architecture based on an evolutionary multi-objective meta-heuristics to achieve analysis efficiency are presented. We also describe implementation details of the services that will form an intelligent analysis environment and present results on the optimisation that has been achieved as a result of this investigation.
    • Smart anomaly detection in sensor systems: A multi-perspective review

      Erhan, L.; Ndubuaku, M.; Di Mauro, M.; Song, W.; Chen, M.; Fortino, G.; Bagdasar, Ovidiu; Liotta, A.; University of Derby; University of Salerno, Italy; et al. (Elsevier, 2020-10-15)
      Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behavior. This is an important research problem, due to its broad set of application domains, from data analysis to e-health, cybersecurity, predictive maintenance, fault prevention, and industrial automation. Herein, we review state-of-the-art methods that may be employed to detect anomalies in the specific area of sensor systems, which poses hard challenges in terms of information fusion, data volumes, data speed, and network/energy efficiency, to mention but the most pressing ones. In this context, anomaly detection is a particularly hard problem, given the need to find computing-energy-accuracy trade-offs in a constrained environment. We taxonomize methods ranging from conventional techniques (statistical methods, time-series analysis, signal processing, etc.) to data-driven techniques (supervised learning, reinforcement learning, deep learning, etc.). We also look at the impact that different architectural environments (Cloud, Fog, Edge) can have on the sensors ecosystem. The review points to the most promising intelligent-sensing methods, and pinpoints a set of interesting open issues and challenges.
    • A systematic literature review on machine learning applications for sustainable agriculture supply chain performance

      Sharma, Rohit; Kamble, Sachin; Kumar, Vikas; Gunasekaran, Angappa; Kumar, Anil; National Institute of Industrial Engineering; University of the West of England; California State University, Bakersfield; University of Derby (Elsevier, 2020-02-24)
      Agriculture plays an important role in sustaining all human activities. Major challenges such as overpopulation, competition for resources poses a threat to the food security of the planet. In order to tackle the ever-increasing complex problems in agricultural production systems, advancements in smart farming and precision agriculture offers important tools to address agricultural sustainability challenges. Data analytics hold the key to ensure future food security, food safety, and ecological sustainability. Disruptive information and communication technologies such as machine learning, big data analytics, cloud computing, and blockchain can address several problems such as productivity and yield improvement, water conservation, ensuring soil and plant health, and enhance environmental stewardship. The current study presents a systematic review of machine learning (ML) applications in agricultural supply chains (ASCs). Ninety three research papers were reviewed based on the applications of different ML algorithms in different phases of the ASCs. The study highlights how ASCs can benefit from ML techniques and lead to ASC sustainability. Based on the study findings an ML applications framework for sustainable ASC is proposed. The framework identifies the role of ML algorithms in providing real-time analytic insights for pro-active data-driven decision-making in the ASCs and provides the researchers, practitioners, and policymakers with guidelines on the successful management of ASCs for improved agricultural productivity and sustainability.
    • Traffic monitoring using video analytics in clouds

      Abdullah, Tariq; Anjum, Ashiq; Tariq, M. Fahim; Baltaci, Yusuf; Antonopoulos, Nikolaos; University of Derby (IEEE, 2014-12-12)
      Traffic monitoring is a challenging task on crowded roads. Traditional traffic monitoring procedures are manual, expensive, time consuming and involve human operators. They are subjective due to the very involvement of human factor and sometimes provide inaccurate/incomplete monitoring results. Large scale storage and analysis of video streams were not possible due to limited availability of storage and compute resources in the past. Recent advances in data storage, processing and communications have made it possible to store and process huge volumes of video data and develop applications that are neither subjective nor limited in feature sets. It is now possible to implement object detection and tracking, behavioural analysis of traffic patterns, number plate recognition and automate security and surveillance on video streams produced by traffic monitoring and surveillance cameras. In this paper, we present a video stream acquisition, processing and analytics framework in the clouds to address some of the traffic monitoring challenges mentioned above. This framework provides an end-to-end solution for video stream capture, storage and analysis using a cloud based GPU cluster. The framework empowers traffic control room operators by automating the process of vehicle identification and finding events of interest from the recorded video streams. An operator only specifies the analysis criteria and the duration of video streams to analyse. The video streams are then automatically fetched from the cloud storage, decoded and analysed on a Hadoop based GPU cluster without operator intervention in our framework. It reduces the latencies in video analysis process by porting its compute intensive parts to the GPU cluster. The framework is evaluated with one month of recorded video streams data on a cloud based GPU cluster. The results show a speedup of 14 times on a GPU and 4 times on a CPU when compared with one human operator analysing the same amount of video streams data.