An intelligent framework for monitoring student performance using fuzzy rule-based linguistic summarisation

Monitoring students' activity and performance is vital to enable educators to provide effective teaching and learning in order to better engage students with the subject and improve their understanding of the material being taught. We describe the use of a fuzzy Linguistic Summarisation (LS) technique for extracting linguistically interpretable scaled fuzzy weighted rules from student data describing prominent relationships between activity / engagement characteristics and achieved performance. We propose an intelligent framework for monitoring individual or group performance during activity and problem based learning tasks. The system can be used to more effectively evaluate new teaching approaches and methodologies, identify weaknesses and provide more personalised feedback on learner's progress. We present a case study and initial experiments in which we apply the fuzzy LS technique for analysing the effectiveness of using a Group Performance Model (GPM) to deploy Activity Led Learning (ALL) in a Master-level module. Results show that the fuzzy weighted rules can identify useful relationships between student engagement and performance providing a mechanism allowing educators to transparently evaluate teaching and factors effecting student performance, which can be incorporated as part of an automated intelligent analysis and feedback system

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

Hierarchical spatial-temporal state machine for vehicle instrument cluster manufacturing

The vehicle instrument cluster is one of the most advanced and complicated electronic embedded control systems used in modern vehicles providing a driver with an interface to control and determine the status of the vehicle. In this paper, we develop a novel hybrid approach called Hierarchical Spatial-Temporal State Machine (HSTSM). The approach addresses a problem of spatial-temporal inference in complex dynamic systems. It is based on a memory-prediction framework and Deep Neural Networks (DNN) which is used for fault detection and isolation in automatic inspection and manufacturing of vehicle instrument cluster. The technique has been compared with existing methods namely rule-based, template-based, Bayesian, restricted Boltzmann machine and hierarchical temporal memory methods. Results show that the proposed approach can successfully diagnose and locate multiple classes of faults under real-time working conditions

Smartphone-based Calorie Estimation From Food Image Using Distance Information

Personal assistive systems for diet control can play a vital role to combat obesity. As smartphones have become inseparable companions for a large number of people around the world, designing smartphone-based system is perhaps the best choice at the moment. Using this system people can take an image of their food right before eating, know the calorie content based on the food items on the plate. In this paper, we propose a simple method that ensures both user flexibility and high accuracy at the same time. The proposed system employs capturing food images with a fixed posture and estimating the volume of the food using simple geometry. The real world experiments on different food items chosen arbitrarily show that the proposed system can work well for both regular and liquid food items

Evaluating the Microstructure of Photoluminescent Concrete Pavement Containing Strontium-Aluminate, Acrylic and Recycled Waste Glass

This paper constructs the photoluminescent concrete pavement (PhotoCP) mixing the photoluminescent material (Strontium-Aluminate) with recycled waste glass and transparent acrylic to visible the neighbourhood streets without streetlights. The non-destructive analyses of photoluminescent materials were conducted using the X-Ray Fluorescence, X-Ray diffraction, and Scanning Electron Microscopy instruments to understand the behaviour of atoms in photoluminescent materials when they interact with radiation. The compressive strength test examined the load bearing capacity of PhotoCP. A 30cm x 230cm test bed was constructed at a neighbourhood street in Peshawar, Pakistan to assess the impact of photoluminescent materials on lighting the neighbourhood street. The non-destructive analyses and compressive strength test show that PCP specimens have good interlocking capability, structural strength and durability. The testbed experiment observed the illuminance of PhotoCP for a period of 6 to 8 hours with highest lumen intensity of 1-3 lux from sunset to 8:30 pm

Adolescent food insecurity in rural Sindh, Pakistan: A cross-sectional survey

Background: Food insecurity (FI) is alarmingly high in developing countries including Pakistan. A quarter of Pakistan\u27s population consists of adolescents yet there is no information on their experience of FI. FI at adolescent age have long term effect on mental and physical health hence we aimed to determine the prevalence of food insecurity (FI) among adolescents and compare it with household FI, and assess social determinants of adolescent FI.Methods: A cross-sectional survey on 799 households with unmarried adolescents was conducted from September 2015 to June 2016 in three union councils of Hyderabad, Pakistan. Unmarried 10-19 years old girls and boys were interviewed regarding their FI status using Household Food Insecurity Assessment Scale (HFIAS). Household-level FI was also assessed by interviewing mothers of adolescents, and it was compared with adolescent\u27s FI. Association of adolescent\u27s FI with socio-demographic determinants was explored through Cox regression using STATA version 14.0. and prevalence ratios were estimated.Results: FI was found among 52.4% of the adolescents compared to 39% of the households. Thirty percent of the adolescents were food insecure within the food secure households. Female adolescents were found to be less food insecure (Adjusted Prevalence Ratio (APR) 0.4 95% CI [0.3, 0.5]) compared to males. Social determinants like socioeconomic status (SES), crowding index or education of parents were not associated with adolescents\u27 FI.Conclusion: Half of the adolescents were found to be food insecure which raises concerns regarding their health in the long run. Gender is an important social determinant of FI among adolescents which suggests an in-depth exploration of social dynamics of adolescent FI. We recommend the mixed-methods study to develop contextually relevant interventions to reduce FI among this group and improve their health status