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Energy Consumption Forecasting Using Machine Learning, Mahdi Mohammadigohari
Energy Consumption Forecasting Using Machine Learning, Mahdi Mohammadigohari
Theses
Forecasting electricity demand and consumption accurately is critical to the optimal and costeffective operation system, providing a competitive advantage to companies. In working with seasonal data and external variables, the traditional time-series forecasting methods cannot be applied to electricity consumption data. In energy planning for a generating company, accurate power forecasting for the electrical consumption prediction, as a technique, to understand and predict the market electricity demand is of paramount importance. Their power production can be adjusted accordingly in a deregulated market. As data type is seasonal, Persistence Models (Naïve Models), Seasonal AutoRegressive Integrated Moving Averages with eXogenous regressors (SARIMAX), …
Sentiment Analysis Of News Tweets, Haya Fathim
Sentiment Analysis Of News Tweets, Haya Fathim
Theses
Sentiment Analysis is a process of extracting information from a large amount of data and classifying them into different classes called sentiments. Python is a simple yet powerful, high-level, interpreted, and dynamic programming language, which is well known for its functionality of processing natural language data by using NLTK (Natural Language Toolkit). NLTK is a library of python, which provides a base for building programs and classification of data. NLTK also provides a graphical demonstration for representing various results or trends and it also provides sample data to train and test various classifiers respectively. Sentiment classification aims to automatically predict …
Correlating Sentiment In Reddit’S Wallstreetbets With The Stock Market Using Machine Learning Techniques, Sultan Ali Alzaabi
Correlating Sentiment In Reddit’S Wallstreetbets With The Stock Market Using Machine Learning Techniques, Sultan Ali Alzaabi
Theses
The issue that this study addresses is to observe whether there exists a statistical relation between the stock market and Reddit’s wallstreetbets. Previous research mainly focused on the relation between the stock market and Twitter. To gather data for the study, comments were scrapped from the subreddit wallstreetbets for a period of four months, Jan 1, 2021, till April 30, 2021. Different sentiment classifiers were, then, applied on a sample of the data to observe the most accurate classifier for the study. The study concluded that the most accurate sentiment classifier was an SVM classifier trained on 80% of Reddit …
Camera-Based Deep Learning Ai Assistant System For Basketball Training, Guangkun Zeng
Camera-Based Deep Learning Ai Assistant System For Basketball Training, Guangkun Zeng
Theses
The YOLO, a Computer Vision Algorithms, is brought out to analyze the basketball player’s status as a dataset. It can record the players’ behavior on the court including dribbling, shooting, and running. In this way, the app could collect the field goal you made and missed. First, you should use this app to record a video of your shoot training. After that, the AI would analyze and brings out a 3d virtual diagram to interpret your performance. This diagram will show the hot zone and cold zone for your field goal. Also, the track of your ball will be displayed …
Network Traffic Analysis Using Local Outlier Factor, Khalifa Almheiri
Network Traffic Analysis Using Local Outlier Factor, Khalifa Almheiri
Theses
The issue that this study addresses is the high rate of false positives, high maintenance, and lack of stability and precision that the existing network intrusion detection algorithm faces. To address this problem, we proposed a Local Outlier Factor (LOF) Algorithm that locates outliers and anomalies by comparing the deviation of one data point with respect to its neighbors. To gather data, we will use DARPA’s KDDCup99 as well as questions towards analysts. This data will help determine whether the LOF algorithm is more effective than existing solutions that are presented in the network intrusion detection space.
Developing Risk Assessment Tool For Patients’ In-Hospital Falls Using Predictive Modeling, Rasika Patil
Developing Risk Assessment Tool For Patients’ In-Hospital Falls Using Predictive Modeling, Rasika Patil
Theses
Inpatient falls are a serious cause of fatal and non-fatal injuries among patients of all ages leading to disability and stillness. The post-fall treatment comes with rising medical costs and a stressful recovery phase. The present assessment tools align with analyzing causes of falls from historical data instead of present conditions. The key focus area of this research is to develop general-purpose fall risk assessment tools using machine learning-based predictive modeling. We used performance metrics to compare the accuracy and suggest the best suitable model for each shift. This general-purpose fall risk assessment tool can be used for all age …
Reducing Catastrophic Forgetting In Self-Organizing Maps, Hitesh Ulhas Mangala Vaidya
Reducing Catastrophic Forgetting In Self-Organizing Maps, Hitesh Ulhas Mangala Vaidya
Theses
An agent that is capable of continual or lifelong learning is able to continuously learn from potentially infinite streams of pattern sensory data. One major historic difficulty in building agents capable of such learning is that neural systems struggle to retain previously-acquired knowledge when learning from new data samples. This problem is known as catastrophic forgetting and remains an unsolved problem in the domain of machine learning to this day. To overcome catastrophic forgetting, different approaches have been proposed. One major line of thought advocates the use of memory buffers to store data where the stored data is then used …
Semantic Segmentation And Change Detection In Satellite Imagery, Raaga Madappa
Semantic Segmentation And Change Detection In Satellite Imagery, Raaga Madappa
Theses
Processing of satellite images using deep learning and computer vision methods is needed for urban planning, crop assessments, disaster management, and rescue and recovery operations. Deep learning methods which are trained on ground-based imagery do not translate well to satellite imagery. In this thesis, we focus on the tasks of semantic segmentation and change detection in satellite imagery. A segmentation framework is presented based on existing waterfall-based modules. The proposed framework, called PyramidWASP, or PyWASP for short, can be used with two modules. PyWASP with the Waterfall Atrous Spatial Pooling (WASP) module investigates the effects of adding a feature pyramid …
The Use Of Machine Learning In Assessing Suicide Risk: A Meta-Analysis, Rachael Kang
The Use Of Machine Learning In Assessing Suicide Risk: A Meta-Analysis, Rachael Kang
Theses
Suicide is a devastating act in which a person takes their own life. Decades of research into suicide have identified a myriad of risk factors that have been used to create assessments of suicide risk and suicidality. However, more recent research has suggested that these identified risk factors may have no better predictive ability than chance, perhaps because suicide is actually a multi-dimensional, multi-faceted construct that has been viewed too simplistically for prediction’s sake. To try and better appreciate the complex nature of suicide while also increasing prediction accuracy, researchers have turned to machine learning. This study sought to meta-analyze …
State Of Refactoring Adoption: Towards Better Understanding Developer Perception Of Refactoring, Eman Abdullah Alomar
State Of Refactoring Adoption: Towards Better Understanding Developer Perception Of Refactoring, Eman Abdullah Alomar
Theses
Context: Refactoring is the art of improving the structural design of a software system without altering its external behavior. Today, refactoring has become a well-established and disciplined software engineering practice that has attracted a significant amount of research presuming that refactoring is primarily motivated by the need to improve system structures. However, recent studies have shown that developers may incorporate refactoring strategies in other development-related activities that go beyond improving the design especially with the emerging challenges in contemporary software engineering. Unfortunately, these studies are limited to developer interviews and a reduced set of projects. Objective: We aim at exploring …
Esports Game Skill Analysis & Prediction: League Of Legends, Abedalaziz Zandaki
Esports Game Skill Analysis & Prediction: League Of Legends, Abedalaziz Zandaki
Theses
Esports has been an explosive business especially with the current pandemic situation, it is rising to unparalleled levels of popularity as everything is going digital, Riot Games’ League of legends, which is considered one the most played video games right now with 27+ Million players per day and 115 Million over a month, checks many boxes where value can be obtained.[12] The general idea is that the game has a ranked ladder system, where players are evaluated by their win to lose ratio which is influenced by their skills in-game, each individual game performance counts towards the win or lose …
Oit: A Ux Design Project For Better Decision Making In Generative Design, Yunsheng Zhou
Oit: A Ux Design Project For Better Decision Making In Generative Design, Yunsheng Zhou
Theses
New technology, such as AI and Machine learning, grows fast and plays a crucial role in improving efficiency and reducing the repeated working load in our daily lives. In the Architecture and Engineering area, AI is widely used in generative design. Generative design is a design exploration process. The software explores all the possible permutations of a solution, quickly generating design alternatives. However, the problem is that the intelligence systems can generate thousands of solutions that difficult for users to distill. This situation even reduces the efficiency of the working flow. The project's approach is to help users make better …
A Programmable Processing-In-Memory Architecture For Memory Intensive Applications, Mark Connolly
A Programmable Processing-In-Memory Architecture For Memory Intensive Applications, Mark Connolly
Theses
While both processing and memory architectures are rapidly improving in performance, memory architecture is lagging behind. As performance of processing architecture continues to eclipse that of memory, the memory architecture continues to become an increasingly unavoidable bottleneck in computer architecture. There are two drawbacks that are commonly associated with memory accesses: i) large delays causing the processor to remain idle waiting on data to become available and ii) the power consumption required to transfer the data. These performance issues are especially notable in research and enterprise computing applications such as deep learning models. Even when data for an application such …
Forensics Writer Identification Using Text Mining And Machine Learning, Saif Ali Alawar
Forensics Writer Identification Using Text Mining And Machine Learning, Saif Ali Alawar
Theses
Constant technological growth has resulted in the danger and seriousness of cyber-attacks, which has recently unmistakably developed in various institutions that have complex Information Technology (IT) infrastructure. For instance, for the last three (3) years, the most horrendous instances of cybercrimes were perceived globally with enormous information breaks, fake news spreading, cyberbullying, crypto-jacking, and cloud computing services. To this end, various agencies improvised techniques to curb this vice and bring perpetrators, both real and perceived, to book in relation to such serious cybersecurity issues. Consequently, Forensic Writer Identification was introduced as one of the most effective remedies to the concerned …
Using Advanced Analytics To Assist Stakeholders In Higher Education Institutions, Preethi Dsouza
Using Advanced Analytics To Assist Stakeholders In Higher Education Institutions, Preethi Dsouza
Theses
Higher education institutions have access to a vast amount of student data that is recorded during the process of admissions and throughout the course of the program. After the outbreak of the COVID 19 pandemic most universities around the world had to either adopt a blended or a complete online mode of teaching. Students were required to access resources through a virtual learning platform and interaction with this environment also left an important trail of valuable information. Data analysis and machine learning can provide insights and predictions about students’ performances, online activities and academic progress. Advanced analytics can provide necessary …
Can Feature Requests Reveal The Refactoring Types?, Sultan Fahad Almassari
Can Feature Requests Reveal The Refactoring Types?, Sultan Fahad Almassari
Theses
Software refactoring is the process of improving the design of a software system while preserving its external behavior. In recent years, refactoring research has been growing as a response to the degradation of software quality. Recent studies performed an in-depth investigation in (1) how refactoring practices are taking place during the software evolution, (2) how to recommend refactoring to improve the design of software, and (3) what type of refactoring operations can be implemented. However, there is a lack of support when it comes to developers’ typical programming tasks, including feature updates and bug fixes. The goal of this thesis …
Daniel: Towards Automated Bug Discovery By Black Box Test Case Generation & Recommendation, Michael G. Peechatt
Daniel: Towards Automated Bug Discovery By Black Box Test Case Generation & Recommendation, Michael G. Peechatt
Theses
Finding and documenting bugs in software systems is an essential component of the software development process. A bug is defined as a series of steps that produces behavior which differs from the software specification and requirements. Finding steps to produce such behavior requires expert knowledge of the possible operations of the software in development as well as intuition and creativity. This thesis proposes the Directed Action Node Input Execution Language (DANIEL), a language that represents test cases as directed graphs, where each node represents an action, and possible input arguments for each action are represented along the incoming directed edges. …
Towards Effective Detection Of Botnet Attacks Using Bot-Iot Dataset, Subiksha Srinivasa Gopalan
Towards Effective Detection Of Botnet Attacks Using Bot-Iot Dataset, Subiksha Srinivasa Gopalan
Theses
In the world of cybersecurity, intrusion detection systems (IDS) have leveraged the power of artificial intelligence for the efficient detection of attacks. This is done by applying supervised machine learning (ML) techniques on labeled datasets. A growing body of literature has been devoted to the use of BoT-IoT dataset for IDS based ML frameworks. A few number of related works have recognized the need for a balanced dataset and applied techniques to alleviate the issue of imbalance. However, a significant amount of related research works failed to treat the imbalance in the BoT-IoT dataset. A lack of unanimity was observed …