Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Convolutional Neural Network (2)
- Machine learning (2)
- Random Forest (2)
- Apache Flink (1)
- Apache Kafka (1)
-
- Apache Samza (1)
- Apache Spark (1)
- Apache Storm (1)
- Artificial Neural Networks (1)
- BUNGEE (1)
- Benchmark (1)
- Bull and Bear Market (1)
- Cattle behaviour analysis (1)
- Class imbalance (1)
- Class imbalance learning (1)
- Cochran’s Q Test (1)
- Computer Vision (1)
- Computing (1)
- Containers (1)
- Convolutional Neural Networks (1)
- DNS tunnels (1)
- Data analytics (1)
- Decision Tree (1)
- Deep Learning (1)
- Deep learning (1)
- District heating network (1)
- ECS (1)
- Elastic Container Service (1)
- Elasticity (1)
- Exponential Linear Units (1)
Articles 1 - 15 of 15
Full-Text Articles in Engineering
Analysing Online User Activity To Implicitly Infer The Mental Workload Of Web-Based Tasks Using Defeasible Reasoning, Paul Mara
Dissertations
Mental workload can be considered the amount of cognitive load or effort used over time to complete a task in a complex system. Determining the limits of mental workload can assist in optimising designs and identify if user performance is affected by that design. Mental workload has also been presented as a defeasible concept, where one reason can defeat another and a 5-layer schema to represent domain knowledge to infer mental workload using defeasible reasoning has compared favourably to state-of-the-art inference techniques. Other previous work investigated using records of user activity for measuring mental workload at scale using web-based tasks …
Beef Cattle Instance Segmentation Using Mask R-Convolutional Neural Network, Mohammad Danish
Beef Cattle Instance Segmentation Using Mask R-Convolutional Neural Network, Mohammad Danish
Dissertations
Maintaining the cattle farm along with the wellbeing of every heifer has been the major concern in dairy farm. A robust system is required which can tackle the problem of continuous monitoring of cows. the computer vision techniques provide a new way to understand the challenges related to the identification and welfare of the cows. This paper presents a state-of-art instance segmentation mask RCNN algorithm to train and build a model on a very challenging cow dataset that is captured during the winter season. The dataset poses many challenges such as overlapping of cows, partial occlusion, similarity between cows and …
Spoilage Detection In Raspberry Fruit Based On Spectral Imaging Using Convolutional Neural Networks, Karthik Kuchangi Jothi Prakash
Spoilage Detection In Raspberry Fruit Based On Spectral Imaging Using Convolutional Neural Networks, Karthik Kuchangi Jothi Prakash
Dissertations
Effective spoilage detection of perishable food items like fruits and vegetables is essential for retailers who stock and sell large quantities of these items. This research is aimed at developing a non-destructive, rapid and accurate method which is based on Spectral Imaging (SI) used in tandem with Convolutional Neural Network (CNN) to predict whether the fruit is fresh or rotten. The study also aims to determine the number of days before which the fruit rots. This research employs a primary, quantitative and inductive methods to investigate the Deep Learning based approach to detect fruit spoilage. Raspberry fruit in particular has …
Use Of Hyperspectral Images (Hsi) And Convolutional Neural Network (Cnn) To Identify Normal, Precancerous And Cancerous Tissues, Pallavi Jain
Dissertations
Cancer detection has been a great topic of research for a long time, as early detection of cancer can help in increasing the survival rate of patients by providing on time better treatment. A robust system is required in order to detect early-stage cancer as its difficult to identify early-stage cancer from the normal clinical process. The computer vision techniques provide a new way to understand the challenges related to the medical image analysis. This thesis presents the medical image analysis using a combination of Convolutional Neural Network and Hyperspectral Images of cancer patient's tissues. The idea behind choosing the …
Can Threshold-Based Sensor Alerts Be Analysed To Detect Faults In A District Heating Network?, Liam Cantwell
Can Threshold-Based Sensor Alerts Be Analysed To Detect Faults In A District Heating Network?, Liam Cantwell
Dissertations
Older IoT “smart sensors” create system alerts from threshold rules on reading values. These simple thresholds are not very flexible to changes in the network. Due to the large number of false positives generated, these alerts are often ignored by network operators. Current state-of-the-art analytical models typically create alerts using raw sensor readings as the primary input. However, as greater numbers of sensors are being deployed, the growth in the number of readings that must be processed becomes problematic. The number of analytic models deployed to each of these systems is also increasing as analysis is broadened. This study aims …
A Comparison Of Real Time Stream Processing Frameworks, Jonathan Curtis
A Comparison Of Real Time Stream Processing Frameworks, Jonathan Curtis
Dissertations
The need to process the ever-expanding volumes of information being generated daily in the modern world is driving radical changes in traditional data analysis techniques. As a result of this, a number of open source tools for handling real-time data streams has become available in recent years. Four, in particular, have gained significant traction: Apache Flink, Apache Samza, Apache Spark and Apache Storm. Despite the rising popularity of these frameworks, however, there are few studies that analyse their performance in terms of important metrics, such as throughput and latency. This study aims to correct this, by running several benchmarks against …
Can Machine Learning Beat Physics At Modeling Car Crashes?, Gavin Byrne
Can Machine Learning Beat Physics At Modeling Car Crashes?, Gavin Byrne
Dissertations
This study aimed to look at a traditional method used for measuring the severity and principle direction of force of a car crash and see if it could be improved on using machine learning models. The data used was publicly available from the NHTSA database and included descriptions of the vehicle, test and sensors as well as the accelerometer data over the period of the crashes. The models built were SVM classifiers and multinomial regression models. Although the SVM and Regression models were built successfully and gave higher levels of accuracy than the momentum models in terms of the severity, …
An Application Of Natural Language Processing For Triangulation Of Cognitive Load Assessments In Third Level Education, Luis Alfredo Contreras
An Application Of Natural Language Processing For Triangulation Of Cognitive Load Assessments In Third Level Education, Luis Alfredo Contreras
Dissertations
Work has been done to measure Mental Workload based on applications mainly related to ergonomics, human factors, and Machine Learning. The influence of Machine Learning is a reflection of an increased use of new technologies applied to areas conventionally dominated by theoretical approaches. However, collaboration between MWL and Natural Language Processing techniques seems to happen rarely. In this sense, the objective of this research is to make use of Natural Languages Processing techniques to contribute to the analysis of the relationship between Mental Workload subjective measures and Relative Frequency Ratios of keywords gathered during pre-tasks and post-tasks of MWL activities …
Comparing The Effectiveness Of Different Classification Techniques In Predicting Dns Tunnels, Patrick Walsh
Comparing The Effectiveness Of Different Classification Techniques In Predicting Dns Tunnels, Patrick Walsh
Dissertations
DNS is one of the most widely used protocols on the internet and is used in the translation of domain names into IP address in order to correctly route messages between computers. It presents an attractive attack vector for criminals as the service is not as closely monitored by security experts as other protocols such as HTTP or FTP. Its use as a covert means of communication has increased with the availability of tools that allow for the creation of DNS tunnels using the protocol. One of the primary motivations for using DNS tunnels is the illegal extraction of information …
Elasticity Measurement In Caas Environments - Extending The Existing Bungee Elasticity Benchmark To Aws's Elastic Container Service, Nora Limbourg
Elasticity Measurement In Caas Environments - Extending The Existing Bungee Elasticity Benchmark To Aws's Elastic Container Service, Nora Limbourg
Dissertations
Rapid elasticity and automatic scaling are core concepts of most current cloud computing systems. Elasticity describes how well and how fast cloud systems adapt to increases and decreases in workload. In parallel, software architectures are moving towards employing containerised microservices running on systems managed by container orchestration platforms. Cloud users who employ such container-based systems may want to compare the elasticity of different systems or system settings to ensure rapid elasticity and maintain service level objectives while avoiding over-provisioning. Previous research has established a variety of metrics to measure elasticity. Some existing benchmark tools are designed to measure elasticity in …
From Business Understanding To Deployment: An Application Of Machine Learning Algorithms To Forecast Customer Visits Per Hour To A Fast-Casual Restaurant In Dublin, Odunayo David Adedeji
From Business Understanding To Deployment: An Application Of Machine Learning Algorithms To Forecast Customer Visits Per Hour To A Fast-Casual Restaurant In Dublin, Odunayo David Adedeji
Dissertations
This research project identifies the significant factors that affects the number of customer visits to a fast-casual restaurant every hour and proceeds to develop several machine learning models to forecast customer visits. The core value proposition of fast-casual restaurants is quality food delivered at speed which means they have to prepare meals in advance of customers visit but the problem with this approach is in forecasting future demand, under estimating demand could lead to inadequate meal preparation which would leave customers unsatisfied while over estimation of demand could lead to wastage especially with restaurants having to comply with food safety …
Application Of Synthetic Informative Minority Over-Sampling (Simo) Algorithm Leveraging Support Vector Machine (Svm) On Small Datasets With Class Imbalance, Akshatha Fakkeriah Kallappanamatt
Application Of Synthetic Informative Minority Over-Sampling (Simo) Algorithm Leveraging Support Vector Machine (Svm) On Small Datasets With Class Imbalance, Akshatha Fakkeriah Kallappanamatt
Dissertations
Developing predictive models for classification problems considering imbalanced datasets is one of the basic difficulties in data mining and decision-analytics. A classifier’s performance will decline dramatically when applied to an imbalanced dataset. Standard classifiers such as logistic regression, Support Vector Machine (SVM) are appropriate for balanced training sets whereas provides suboptimal classification results when used on unbalanced dataset. Performance metric with prediction accuracy encourages a bias towards the majority class, while the rare instances remain unknown though the model contributes a high overall precision. There are chances where minority instances might be treated as noise and vice versa. (Haixiang et …
Identifying Expert Investors On Financial Microblog Via Artificial Neural Networks, Pierluca Del Buono
Identifying Expert Investors On Financial Microblog Via Artificial Neural Networks, Pierluca Del Buono
Dissertations
In the recent years, thanks to social media platform, a plethora of information has been available to financial investors, that were traditionally dependent from financial institutions advisors. Strategies are now shared among web users, performances of stocks are commented in web communities and hints and suggestions are travelling on the internet with a fast pace, in a way that was unthinkable few years before. Several attempts have been made in the recent past, to predict Market movements and trends from activity of Financial Social Networks participants, and to evaluate if contributions from individuals with high level of expertise distinguish themselves …
Supervised Learning Models To Predict Stock Direction Within Different Sectors In A Bull And Bear Market, Tiffany Razy
Supervised Learning Models To Predict Stock Direction Within Different Sectors In A Bull And Bear Market, Tiffany Razy
Dissertations
Forecasting stock market price movement is a well researched and an alluring topic within the machine learning and financial realm. Supervised machine learning algorithms such as Random Forest (RF) and Support Vector Machines (SVM) have been used independently to gain insight on the market. With such volatility in the market the scope of this study will utilized the RF and SVM in a very volatility market to determine if these models will perform at a high level or outperform each other in both markets. This relative study is performed on 16 stocks in 4 different sectors over the bear market …
Investigating The Application Of Deep Convolutional Neural Networks In Semi-Supervised Video Object Segmentation, Jayadeep Sasikumar
Investigating The Application Of Deep Convolutional Neural Networks In Semi-Supervised Video Object Segmentation, Jayadeep Sasikumar
Dissertations
This thesis investigates the different approaches to video object segmentation and the current state-of-the-art in the discipline, focusing on the different deep learning techniques used to solve the problem. The primary contribution of the thesis is the investigation of usefulness of Exponential Linear Units as activation functions for deep convolutional neural architectures trained to perform object semi-supervised segmentation in videos. Mask R-CNN was chosen as the base convolutional neural architecture, with the view of extending the image segmentation algorithm to videos. Two models were created, one with Rectified Linear Units and the other with Exponential Linear Units as the respective …