Applying Data Augmentation to Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks

Handwritten character recognition has been the center of research and a benchmark problem in the sector of pattern recognition and artificial intelligence, and it continues to be a challenging research topic. Due to its enormous application many works have been done in this field focusing on different languages. Arabic, being a diversified language has a huge scope of research with potential challenges. A convolutional neural network model for recognizing handwritten numerals in Arabic language is proposed in this paper, where the dataset is subject to various augmentation in order to add robustness needed for deep learning approach. The proposed method is empowered by the presence of dropout regularization to do away with the problem of data overfitting. Moreover, suitable change is introduced in activation function to overcome the problem of vanishing gradient. With these modifications, the proposed system achieves an accuracy of 99.4\% which performs better than every previous work on the dataset.Comment: 5 pages, 6 figures, 3 table

Probabilistic Argumentation for Patient Decision Making

Medical drug reviews are increasingly commonplace on the web and have become an important source of information for patients undergoing medical treatment. Patients will look to these reviews in order to understand the impact the drugs have had on others who have experienced them. In short these reviews can be interpreted as a body of arguments and counterarguments for/against the drug being reviewed. One of the challenges of reading these reviews is drawing out the arguments easily and forming a final opinion; this is due to the number of reviews and the variety of arguments presented. This thesis explores the use of computational models of argumentation in order to extract structured argumentation data from the reviews and present them to the user. In particular I propose a pipeline that performs argument extraction, argument graph extraction and visualisation

Smart objects as building blocks for the internet of things

The combination of the Internet and emerging technologies such as nearfield communications, real-time localization, and embedded sensors lets us transform everyday objects into smart objects that can understand and react to their environment. Such objects are building blocks for the Internet of Things and enable novel computing applications. As a step toward design and architectural principles for smart objects, the authors introduce a hierarchy of architectures with increasing levels of real-world awareness and interactivity. In particular, they describe activity-, policy-, and process-aware smart objects and demonstrate how the respective architectural abstractions support increasingly complex application

Computational Approaches for Remote Monitoring of Symptoms and Activities

We now have a unique phenomenon where significant computational power, storage, connectivity, and built-in sensors are carried by many people willingly as part of their life style; two billion people now use smart phones. Unique and innovative solutions using smart phones are motivated by rising health care cost in both the developed and developing worlds. In this work, development of a methodology for building a remote symptom monitoring system for rural people in developing countries has been explored. Design, development, deployment, and evaluation of e-ESAS is described. The system’s performance was studied by analyzing feedback from users. A smart phone based prototype activity detection system that can detect basic human activities for monitoring by remote observers was developed and explored in this study. The majority voting fusion technique, along with decision tree learners were used to classify eight activities in a multi-sensor framework. This multimodal approach was examined in details and evaluated for both single and multi-subject cases. Time-delay embedding with expectation-maximization for Gaussian Mixture Model was explored as a way of developing activity detection system using reduced number of sensors, leading to a lower computational cost algorithm. The systems and algorithms developed in this work focus on means for remote monitoring using smart phones. The smart phone based remote symptom monitoring system called e-ESAS serves as a working tool to monitor essential symptoms of patients with breast cancer by doctors. The activity detection system allows a remote observer to monitor basic human activities. For the activity detection system, the majority voting fusion technique in multi-sensor architecture is evaluated for eight activities in both single and multiple subjects cases. Time-delay embedding with expectation-maximization algorithm for Gaussian Mixture Model was studied using data from multiple single sensor cases

Computational Approaches for Remote Monitoring of Symptoms and Activities

We now have a unique phenomenon where significant computational power, storage, connectivity, and built-in sensors are carried by many people willingly as part of their life style; two billion people now use smart phones. Unique and innovative solutions using smart phones are motivated by rising health care cost in both the developed and developing worlds. In this work, development of a methodology for building a remote symptom monitoring system for rural people in developing countries has been explored. Design, development, deployment, and evaluation of e-ESAS is described. The system’s performance was studied by analyzing feedback from users. A smart phone based prototype activity detection system that can detect basic human activities for monitoring by remote observers was developed and explored in this study. The majority voting fusion technique, along with decision tree learners were used to classify eight activities in a multi-sensor framework. This multimodal approach was examined in details and evaluated for both single and multi-subject cases. Time-delay embedding with expectation-maximization for Gaussian Mixture Model was explored as a way of developing activity detection system using reduced number of sensors, leading to a lower computational cost algorithm. The systems and algorithms developed in this work focus on means for remote monitoring using smart phones. The smart phone based remote symptom monitoring system called e-ESAS serves as a working tool to monitor essential symptoms of patients with breast cancer by doctors. The activity detection system allows a remote observer to monitor basic human activities. For the activity detection system, the majority voting fusion technique in multi-sensor architecture is evaluated for eight activities in both single and multiple subjects cases. Time-delay embedding with expectation-maximization algorithm for Gaussian Mixture Model was studied using data from multiple single sensor cases

Spatio-temporal analyses of the relationship between armed conflict and climate change in the eastern Africa

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Despite recent methodological improvements and higher data availability, the Climate Change (CC) and Armed Conflict (AC) studies are suffering from poor data and inappropriate research designs (e.g., Incompatibilities of scale). This study fills the gaps by taking the climate conflict analyses into a different scale (e.g., 55 km x 55 km sub-national cell/year) and uses high resolution Geo-referenced data sets. This study presents the results from 10 years (1991-2000) of observations and a rigorous modelling methodology to understand the effects of climate change on the conflict occurrence in the Eastern Africa. The main objective of the study is to identify and understand the conflict dynamics, verify the pattern of conflict distribution, possible interaction between the conflict sites and the influence of climatic covariates of conflict outbreak. We have found that if the climate related anomaly increases, the probability of armed conflict outbreak also increases significantly. To identify the effect of climate change on armed conflict we have modeled the relationship between them, using different kinds of point process models and Spatial Autoregressive (SAR) Lag models for both spatial and spatio-temporal cases. In modelling, we have introduced one new climate indicator, termed as Weighted Anomaly Soil Water Index (WASWI), which is a dimensionless measure of the relative severity of soil water containment indicating in the form of surplus or deficit. In all the models the coefficients of WASWI were found negative and to be significant, predicting armed conflict at 0.05 level of significance for the whole period. The conflicts were found to be clustered up to 200 kilometers and the local level negative relationship between conflict and climate suggests that change in WASWI impacts changes in AC by - 0.1981 or -0.1657. We have also found that the conflict in the own cell associated to a ( app. 0.7) increase in the probability of conflict occurances in the neighbouring cell and also to a (app. 0.6) increase of the following years (spatio-temporal). So, climate change indicators are a vital predictor of armed conflict and provides a proper predictive framework for conflict expectation. This study also provides a sound methodological framework for climate conflict research which encompasses two big approaches, point process modelling and lattice approach with careful modelling of spatial dependence, spatial and sptio-temporal autocorrelation, etc

Evaluation of contact and non-contact lap splices in concrete block masonry specimens

An experimental program was performed for qualitative and quantitative comparison of the maximum tensile resistance of contact and non-contact lap spliced bars in reinforced concrete block masonry using double pullout and wall splice specimens. A total of 32 specimens were tested, consisting of an equal number of double pullout specimens and full-scale wall splice specimens. Both specimen types had the identical cross-section. Eight replicate specimens for each specimen type were constructed with both contact and non-contact lap splice arrangements. Grade 400 deformed reinforcing bars with a 300 mm lap splice length were provided in all specimens. The double pullout specimens were tested applying direct tension to the lapped reinforcing bars. The splice resistance and displacement were recorded during testing. All double pullout specimens with contact lap splices developed, as a minimum, the yield strength of the reinforcing bars and generally displayed evidence of a yield plateau. In contrast, the double pullout specimens with non-contact lap splices failed when only 46.1% of the theoretical yield strength of the reinforcing bars was recorded as the maximum splice resistance. The difference between the average value of the tensile resistance in the contact and non-contact spliced bars was identified as being statistically significant at the 95% confidence level. Wall splice specimens were tested under a four-point loading arrangement with the lapped bars located in the constant moment region. The applied load and specimen deflection were recorded until failure occurred. A numerical analysis was then performed to calculate the maximum resistance of the spliced bars. The specimens with contact lap splices developed the theoretical yield capacity of the reinforcing bars. In contrast, the wall splice specimens with non-contact lap splices developed an average tensile resistance of 78% of the theoretical yield capacity. The difference between the average tensile resistances of the lapped bars in the two splice arrangements was identified as being statistically significant at the 95% confidence level. On average, the contact and non-contact lap spliced bars in the double pullout specimens developed 8.47% and 41.2% less tensile resistance, respectively, as compared to the wall splice specimens with the identical splice arrangement. Both differences were identified as being statistically significant at the 95% confidence level. Bond loss between the reinforcing bars and the surrounding grout was identified as the failure mode for both the double pullout and wall splice specimens with contact lap splices. In contrast, bond loss at the masonry block/grout interface was observed along the non-contact lapped bars in both specimen types, as identified by visual observations upon removal of the face shell and the surrounding grout. Based on the test results of the wall splice specimens with non-contact lap splices, a correction factor of 1.5 is suggested when calculating the effective splice length for the non-contact splice arrangement as tested