Designing a human-centred, mobile interface to support real-time flood forecasting and warning system

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThere is a demand for human-centred technology which improves the management of flood events. This thesis describes the development, design and evaluation of a mobile GIS-based hydrological model. The application provides hydrological forecasts and issues flood warnings. The thesis reports on the usability and practicality of the application. The application, a mobile-based hydrological modelling system, permits the integrated handling of real-time rainfall data from a wireless monitoring network. A spatially distributed GIS-based model integrates this incoming data, approximating real-time, to generate data on catchment hydrology and runoff. The data can be accessed from any android-based mobile computer or mobile phone. It may be further analysed online using several GIS and numerical functions. A human-centred approach to design was taken. Design guidelines for a user-centric application were developed and deployed in the first prototype. There was intensive consultation with potential users. Particular attention was paid to the ease of use of the mobile interface. Users’ needs and attitudes were relevant in the achievement of a highly functional but intuitive interface. The first prototype underwent intensive testing with users. After the initial testing of the first prototype an interactive approach was taken to development. This generated a high-fidelity prototype which was matched to the taxonomy from a user’s mental model. Users were interrogated under controlled laboratory conditions as they performed predefined tasks which were selected to generate data across all aspects of the system and to identify weaknesses. Subsequent to this work there was a major prototype re-design. User test data, identified issues and an improved mental taxonomy closer were used to further refine the application. Of particular note was new functionality which aligned with user expectations and enhanced the applications credibility. The final evaluation of the system was undertaken with diverse subjects. Overall, the subjects considered the system efficient and effective. Users said the system was easy to learn and integrate into their work. Task completion rates were satisfactory. The final interviews with users confirmed that the application was ready to proceed to the implementation phase

Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques

Both paper-based and computerized exams have a high level of cheating. It is, therefore, desirable to be able to detect cheating accurately. Keeping the academic integrity of student evaluations intact is one of the biggest issues in online education. There is a substantial possibility of academic dishonesty during final exams since teachers are not directly monitoring students. We suggest a novel method in this study for identifying possible exam-cheating incidents using Machine Learning (ML) approaches. The 7WiseUp behavior dataset compiles data from surveys, sensor data, and institutional records to improve student well-being and academic performance. It offers information on academic achievement, student attendance, and behavior in general. In order to build models for predicting academic accomplishment, identifying at-risk students, and detecting problematic behavior, the dataset is designed for use in research on student behavior and performance. Our model approach surpassed all prior three-reference efforts with an accuracy of 90% and used a long short-term memory (LSTM) technique with a dropout layer, dense layers, and an optimizer called Adam. Implementing a more intricate and optimized architecture and hyperparameters is credited with increased accuracy. In addition, the increased accuracy could have been caused by how we cleaned and prepared our data. More investigation and analysis are required to determine the precise elements that led to our model’s superior performance

Human–Computer Interaction with a Real-Time Speech Emotion Recognition with Ensembling Techniques 1D Convolution Neural Network and Attention

Emotions have a crucial function in the mental existence of humans. They are vital for identifying a person’s behaviour and mental condition. Speech Emotion Recognition (SER) is extracting a speaker’s emotional state from their speech signal. SER is a growing discipline in human–computer interaction, and it has recently attracted more significant interest. This is because there are not so many universal emotions; therefore, any intelligent system with enough computational capacity can educate itself to recognise them. However, the issue is that human speech is immensely diverse, making it difficult to create a single, standardised recipe for detecting hidden emotions. This work attempted to solve this research difficulty by combining a multilingual emotional dataset with building a more generalised and effective model for recognising human emotions. A two-step process was used to develop the model. The first stage involved the extraction of features, and the second stage involved the classification of the features that were extracted. ZCR, RMSE, and the renowned MFC coefficients were retrieved as features. Two proposed models, 1D CNN combined with LSTM and attention and a proprietary 2D CNN architecture, were used for classification. The outcomes demonstrated that the suggested 1D CNN with LSTM and attention performed better than the 2D CNN. For the EMO-DB, SAVEE, ANAD, and BAVED datasets, the model’s accuracy was 96.72%, 97.13%, 96.72%, and 88.39%, respectively. The model beat several earlier efforts on the same datasets, demonstrating the generality and efficacy of recognising multiple emotions from various languages

Platforms and viability of mobile GIS in real‐time hydrological models

Purpose – The purpose of this paper is to examine the practicality of an application called the mobile geographic information system (GIS). The authors' purpose was to focus specifically on the mobile GIS application in a prototype, mobile‐based model that is utilized for detecting flood warnings and issuing forecasts. At the end of this research project, a usability study was carried out in a test‐lab environment. Design/methodology/approach – In this paper, research is presented regarding the architecture of a structure that has been built on practicality. Readers will learn about a system that is applicable within a vast array of turning‐point situations where rainfall data are communicated to the system in real time. Findings – It has been revealed that traditional GIS and remote sensing software packages are not as cost‐effective as GIS services that are mobile. Mobile GIS systems have the capability to combine GIS, global positioning system, and remote sensing abilities for retrieving geospatial data sets at costs that are not as pricey as the traditional systems. As time moves on, the need for reliable real‐time data sets is increasing. Additionally, flood management examination provides a valid debate for the combining of mobile GISs within the realm of hydrology. Empirical evidence insinuates and illustrates reliability of GIS and the enhancement in the utilization and creation of devices that are offer mobile capabilities. The usability study revealed that the slope, aspect, watershed, and flow direction functions were not easy to comprehend. It was also discovered during the usability study that the word arrangement, radio button arrangement, and dropdown list caused confusion amongst users. The issue that was deemed as most severe, that was discovered during the usability study, was the blurred comprehension that users experienced regarding the digital elevation model. Research limitations/implications – Before the wisest solution can be pinpointed, all of the associated constraints of mobile GIS mapping application need to be identified; however, enough constraints have already been identified to bring to a close that a basic mobile GIS mapping application could created and triumphantly used. There are many platforms to choose from in regards to providing a solution to a feasible incorporation of the mobile GISs into the playing field. It should be decided which browser‐based strategy would serve as the highest of benefit based on characteristics that are important to consumers, such as affordability, ease‐of‐use, user‐friendly coding, and acceptability by users. Originality/value – This research is highly indicative that mobile GIS would be of great benefit for future studies within the realm of disaster monitoring management. The research presented in this paper can be deemed as original due to the fact that it is a study about the utilization of mobile technologically‐advanced gadgets that provide data analysis for flooding in real time. Moreover, these highly technologically‐advanced devices are cost‐effective compared to those in the past

ECG-Based Subject Identification Using Common Spatial Pattern and SVM

In this paper, a nonfiducial electrocardiogram (ECG, the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin) identification system based on the common spatial pattern (CSP) feature extraction technique is presented. The single- and multilead ECG signals of each subject are divided into nonoverlapping segments, and different segment lengths (1, 3, 5, 7, 10, or 15 seconds) are investigated. Features are extracted from each signal segment through projection on a CSP projection matrix. The extracted features are then used to train a radial basis function kernel-based Support Vector Machine (SVM) classifier, which is then employed in the identification phase. The proposed identification system was evaluated on 10, 20, …, 200 reference subjects of the Physikalisch-Technische Bundesanstalt (PTB) ECG database. Using a single limb-based lead (I) with 200 reference subjects, the system achieved an identification rate of 95.15% and equal error rate of 0.1. The use of a single chest-based lead (V3) for 200 reference subjects resulted in an identification rate of 98.92% and equal error rate of 0.08

Epileptic MEG Spike Detection Using Statistical Features and Genetic Programming with KNN

Epilepsy is a neurological disorder that affects millions of people worldwide. Monitoring the brain activities and identifying the seizure source which starts with spike detection are important steps for epilepsy treatment. Magnetoencephalography (MEG) is an emerging epileptic diagnostic tool with high-density sensors; this makes manual analysis a challenging task due to the vast amount of MEG data. This paper explores the use of eight statistical features and genetic programing (GP) with the K-nearest neighbor (KNN) for interictal spike detection. The proposed method is comprised of three stages: preprocessing, genetic programming-based feature generation, and classification. The effectiveness of the proposed approach has been evaluated using real MEG data obtained from 28 epileptic patients. It has achieved a 91.75% average sensitivity and 92.99% average specificity