The implementation of IoT based smart refrigerator system

Wasted food due to spoilage is a critical resource issue. Food waste or food loss is food that is discarded or lost uneaten. Currently, in the world, according to the Food and Agriculture Organization of the United Nations (FAO), consumers waste about 1.3 billion tons of food annually and consumers in rich countries waste about 222 million tons of food products Once food products are purchased and set aside in a refrigerator, the users do not alert about their food items' expiration date and/or freshness unless they individually examine and track them. Moreover, for food products which are not labeled with an explicit expiration date may lead to significant food spoilage and additional expenditure for the users. However, with the latest trend technology of the Internet of Things (IoT), this problem can be resolved. Combining the idea of Internet of Things and smart kitchen evolution, the smart refrigerator system is developed. The system consists of three main parts which are sensing module, control module and transmission module. Sensing module consists of load cell and odour sensor while control module consists of Arduino UNO and power supply unit and last but not least, the transmission module consists of LCD module and Wi-Fi module. These modules work together to determine contents status inside the refrigerator and notify the user about the condition and quantity of the food via an SMS or an email

A review of techniques in automatic programming assessment for practical skill test

Computer programming ability is a challenging competency that requires several cognitive skills and extensive practice. The increased number of students enrolled in computer and engineering courses and also the increased of failure and drop rate in programming subject is the motivational factor to this research. Due to the importance of this skill, this paper intends to study the landscape of current scenario in assisted assessment for hands-on practical programming focusing on competency-based assessment. The Bloom Taxonomy is used as a competency-based assessment platform. The review showed to-date that there are several automatic assessments for programming skills. However, there is no common grading being applied. Thus, further research is required to propose an automatic assessment that grades the student achievement based on learning taxonomy such as Bloom Cognitive Competency model

Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm

The advancements in electronic devices have increased the demand for the internet of things (IoT) based smart homes, where the connecting devices are growing at a rapid pace. Connected electronic devices are more common in smart buildings, smart cities, smart grids, and smart homes. The advancements in smart grid technologies have enabled to monitor every moment of energy consumption in smart buildings. The issue with smart devices is more energy consumption as compared to ordinary buildings. Due to smart cities and smart homes’ growth rates, the demand for efficient resource management is also growing day by day. Energy is a vital resource, and its production cost is very high. Due to that, scientists and researchers are working on optimizing energy usage, especially in smart cities, besides providing a comfortable environment. The central focus of this paper is on energy consumption optimization in smart buildings or smart homes. For the comfort index (thermal, visual, and air quality), we have used three parameters, i.e., Temperature (◦F), illumination (lx), and CO2 (ppm). The major problem with the previous methods in the literature is the static user parameters (Temperature, illumination, and CO2); when they (parameters) are assigned at the beginning, they cannot be changed. In this paper, the Alpha Beta filter has been used to predict the indoor Temperature, illumination, and air quality and remove noise from the data. We applied a deep extreme learning machine approach to predict the user parameters. We have used the Bat algorithm and fuzzy logic to optimize energy consumption and comfort index management. The predicted user parameters have improved the system’s overall performance in terms of ease of use of smart systems, energy consumption, and comfort index management. The comfort index after optimization remained near to 1, which proves the significance of the system. After optimization, the power consumption also reduced and stayed around the maximum of 15-18w

An Efficient Classification of Emotions in Students\u27 Feedback using Deep Neural Network

Background and Objective: In both the corporate and academic worlds, the collection and analysis of feedback (product evaluation, social media debate, and student input) has long been a significant topic. The traditional approaches to collect student feedback focused on data collection and analysis via questionnaires. However, the student makes comments on social media sites that need to be looked at to improve educational standards at schools.Methods: The purpose of this work is to construct a deep neural network-based system to assess students\u27 feedback and emotions found in the reviews. Our approach applies a Deep Learning-based Bi-LSTM Model to a benchmark student input dataset. It would categorize students\u27 feedback about their instructors according to their emotional states, such as love, happiness, fury, and disdain.Results: The experimental findings demonstrate that the proposed approach outperforms both benchmark studies and state-of-the-art machine learning classifiers

Super-resolution image reconstruction from low-resolution images

Strathclyde theses - ask staff. Thesis no. : T13127The thesis addresses the problem of obtaining high-resolution image from a set of one or more low-resolution images. The thesis focused on three building blocks of super-resolution algorithms i.e., image registration for super-resolution, image fusion for super-resolution and super-resolution image reconstruction. These three parts are addressed separately and singular value decomposition-based fusion is introduced before performing interpolation or single-image super-resolution. An accurate image registration is crucial for super-resolution. An image registration approach for super-resolution based on a combination of Scale Invariant Feature Transform (SIFT), Belief Propagation (BP) and Random Sampling Consensus (RANSAC) is described to automatically register the low-resolution images. The results have shown effective for the removal of the mismatched features in the image. A novel SVD-based image fusion for super-resolution is developed for integrating the significant features from low-resolution images. The SVD-based image fusion is shown to enhance the super-resolution results. The implementation of a novel interpolation method based on a linear combination of the bicubic interpolation and their first-order derivates and the use of first-order difference equation to extract the features from the low-resolution images are described and shown to improve the method of single image super-resolution using sparse representation. The proposed method has shown to reduces the computational time and enhance the prior estimation of the high-resolution image as well as the final super-resolution results. The performance of the algorithms is evaluated using synthetic sequences and also on real sequences subjectively and objectively.The thesis addresses the problem of obtaining high-resolution image from a set of one or more low-resolution images. The thesis focused on three building blocks of super-resolution algorithms i.e., image registration for super-resolution, image fusion for super-resolution and super-resolution image reconstruction. These three parts are addressed separately and singular value decomposition-based fusion is introduced before performing interpolation or single-image super-resolution. An accurate image registration is crucial for super-resolution. An image registration approach for super-resolution based on a combination of Scale Invariant Feature Transform (SIFT), Belief Propagation (BP) and Random Sampling Consensus (RANSAC) is described to automatically register the low-resolution images. The results have shown effective for the removal of the mismatched features in the image. A novel SVD-based image fusion for super-resolution is developed for integrating the significant features from low-resolution images. The SVD-based image fusion is shown to enhance the super-resolution results. The implementation of a novel interpolation method based on a linear combination of the bicubic interpolation and their first-order derivates and the use of first-order difference equation to extract the features from the low-resolution images are described and shown to improve the method of single image super-resolution using sparse representation. The proposed method has shown to reduces the computational time and enhance the prior estimation of the high-resolution image as well as the final super-resolution results. The performance of the algorithms is evaluated using synthetic sequences and also on real sequences subjectively and objectively

Proposed assessment framework based on bloom taxonomy cognitive competency: Introduction to programming

Programming is a difficult course and often the result of its assessment is not encouraging. Programming skill is very important to graduate in order to get job and to success in the industry. A careful assessment is required to help the student learning. It is proved that the assessment itself will improve the learning. The review showed to-date that there are several automatic assessment for programming skills, however, there is no common grading being applied. This paper, is proposing an assessment framework based on Bloom Taxonomy cognitive domain to assess students programming skills. The focus is on basic programming course which introduce the programming concept to students

Image registration for super resolution using scale invariant feature transform, belief propagation and random sampling consensus

Accurate image registration is crucial for the effectiveness of super resolution. In super resolution, image registration is used to find the disparity between low resolution images. In this paper an image registration approach based on a combination of Scale Invariant Feature Transform (SIFT), Belief Propagation (BP) and Random Sampling Consensus (RANSAC) is proposed for super resolution. The SIFT algorithm is used to detect and extract the local features in images, BP is used to match the features while RANSAC is adopted to filter out the mismatched points and then estimate the transformation matrix. The proposed method is compared with traditional SIFT to verify its accuracy and stability. Finally, the result of using the proposed approach in the super resolution application is given

Students' intention to use emotion-aware virtual learning environment: does a lecturer's interaction make a difference?

Purpose: This study explored students’ perspective of using emotion-aware Vertual Learning Environment (VLE) in Malaysia’s higher education institutions. The purpose is to investigate the relationships among dimensions of Technology Readiness Index (TRI), attitude, intention to use VLE, and lecturer interaction. The outcomes concerned the emotions involved in the educational process of Malaysia’s higher education institutions. Methodology: Quantitative data were collected via an online survey from 260 students. An empirical analysis was then conducted using structural equation modelling (Smart PLS) in two phases: (1) examining the direct effect of students’ attitude on VLE adoption intention and (2) examining the indirect effect of constructs using lecturer interaction as a mediator. Findings: The findings revealed a significant mediating role of lecturer interaction on the relationship between attitude and intention to use VLE across the student cohort. Inhibitors, such as insecurity and discomfort, were less significant in affecting students’ attitude towards emotion-aware VLE. The results indicate that students are motivated to use VLE when lecturers understand their emotions and react accordingly. Significance: This is one of the studies pertaining to emotions in VLE and lecturer interaction in higher education institutions. The results facilitate an understanding of the pedagogical role of lecturer interaction as a practical learning motivation. It is of particular interest to curriculum and e-learning stakeholders looking to improve students’ interactions with the VLE systems. Apart from extending the current literature, this study has significant practical implications for education management in higher learning institutions