Spartandark: Anonymity Model Integration With A Blockchain Network Using Spartangold,
2023
San Jose State University
Spartandark: Anonymity Model Integration With A Blockchain Network Using Spartangold, Nishanth Uchil
Master's Projects
Demand for blockchain ecosystems has seen exponential growth in recent times due to its decentralized nature and trustless verification process for the transactions involved. However, transaction data needs to be leveraged for verification, which coupled with the transparent nature of the blockchain ledger, provides sufficient data for malicious entities to reveal identities and even financial history of users. Data masking techniques have been employed over the years to make blockchain transactions anonymous, making them resistant to identity analysis, a key set of methods being zero-knowledge proof (zk-proof) protocols that guarantee zero data leak. In this research, we develop SpartanDark, a …
Advances In Robustness Of Image-Based Malware Detection,
2023
San Jose State University
Advances In Robustness Of Image-Based Malware Detection, Rishika Pamanji
Master's Projects
In recent years, deep learning has emerged as a powerful tool for image classification tasks. However, the performance of individual deep learning models can be limited by their architecture and training data. In this project, various Convolutional Neural Network (CNN) architectures are proposed to train the malware data for feature extraction for various color coordinates such as L, CMYK, RGB, RGBA, and YCbCr. Different optimization techniques like Stochastic Gradient Descent, Root Mean Square Propagation, Ada Delta, Adam, and Adaptive Gradient are used to minimize errors in the trained data, leading to enhanced accuracy. The proposed ensemble deep learning model for …
Pygrapherconnect,
2023
San Jose State University
Pygrapherconnect, Shubham Jain
Master's Projects
The evolving landscape of backend computational systems especially in biomedical research involving heavy data operations which have a gap of not being used properly. It is due to the lack of communication standard between the frontend and backend. This gap presents a problem to researchers who need to use the frontend for visualizing and manipulating their data but also want to do complex analysis. CAPRI a python-based backend system specializing in analyzing Evidential Reasoning data also has the same issue. This project offers a solution PyGrapherConnect module acting as a data conversion layer between CAPRI and PyGrapher, its frontend interface. …
Gesture Recognition Of Sign Language Alphabet Using Machine Learning Techniques,
2023
San Jose State University
Gesture Recognition Of Sign Language Alphabet Using Machine Learning Techniques, Gursimran Singh
Master's Projects
With the rising incidence of hearing loss, effective sign language recognition has become crucial for enhancing communication for individuals with hearing impairments. Traditional sensor-based recognition systems have been challenged by the complexities of realworld settings, prompting a shift toward more adaptable vision-based recognition systems. Distinct from previous studies, this work pioneers the use of ensemble methods with advanced filtering techniques on the Sign Language MNIST dataset, offering a novel perspective on sign language recognition. This research delves into the intersection of machine learning and image processing to develop a robust framework for sign language recognition. A range of filters, including …
Nuancenet: Comparative Analysis Of Ai In Complex Language Interpretation For Disaster Detection,
2023
San Jose State University
Nuancenet: Comparative Analysis Of Ai In Complex Language Interpretation For Disaster Detection, Pavan Koushik Kommuri
Master's Projects
Disaster Detection using Twitter content is critical for emergency response, but accurately identifying relevant tweets remains challenging due to nuances, informal language, and emotional expressions. This paper presents a comparative analysis between traditional Machine Learning models, Deep Learning models and Large Language Models (LLM) for classifying disaster vs. non-disaster tweets. While existing works have applied pattern recognition and dataset-specific learning, LLMs with their deeper understanding of linguistics and semantics can potentially handle the complexities of tweets more effectively. This study leverages LLMs including Llama2, Mistral, and Falcon, Open AI GPT 3.5, hypothesizing their superior contextual comprehension will excel in tweets …
Prediction Of 2024 Indian Pm Election Results Using Sentiment Analysis On Twitter Data,
2023
San Jose State University
Prediction Of 2024 Indian Pm Election Results Using Sentiment Analysis On Twitter Data, Surabhi Gupta
Master's Projects
This sentiments analysis study presents a methodical approach to predict the 2024 Indian Prime Minister Election. Data collected spanning from 2020 to 2023 from Twitter using hashtags such as IndianPMElection2024 and on topics such as the revocation of the special status of Jammu and Kashmir, the Farm Bill, and the Digital India initiative, form the core of this research. We utilized a combination of sentiment extraction tools-namely, the NLP Town's Bidirectional Encoder Representations from Transformers (BERT)-based multilingual uncased sentiment model, Valance Aware Dictionary for Sentiment Reasoning (VADER), and TextBlob. Additionally, we used a well-established machine learning model Naive Bayes, deep …
Serverless Architecture For Machine Learning,
2023
San Jose State University
Serverless Architecture For Machine Learning, Ikshaku Goswami
Master's Projects
Serverless computing is an area under cloud computing which does not require individual management of cloud infrastructure and services. It is the groundwork behind Function as a Service or FaaS cloud computing technique. FaaS provides a stateless event-driven orchestration of functions and services for applications deployed in the cloud, without having to manage the servers and other infrastructure resources. This event driven architecture is being well utilized to manage different web-applications and services. Machine learning can bring a unique challenge to serverless computing, as it involves high-intensive tasks which requires voluminous data. In such a scenario it becomes essential to …
Uncertainty-Aware And Explainable Artificial Intelligence For Identification Of Human Errors In Nuclear Power Plants,
2023
San Jose State University
Uncertainty-Aware And Explainable Artificial Intelligence For Identification Of Human Errors In Nuclear Power Plants, Bhavya Reddy Kotla
Master's Projects
Nuclear Power Plants (NPPs) can face challenges in maintaining standard operations due to a range of issues, including human mistakes, mechanical breakdowns, electrical problems, measurement errors, and external influences. Swift and precise detection of these issues is crucial for stabilizing the NPPs. Identifying such operational anomalies is complex due to the numerous potential scenarios. Additionally, operators need to promptly discern the nature of an incident by tracking various indicators, a process that can be mentally taxing and increase the likelihood of human errors. Inaccurate identification of problems leads to inappropriate corrective actions, adversely affecting the safety and efficiency of NPPs. …
Multimap Implementation In Openjdk,
2023
San Jose State University
Multimap Implementation In Openjdk, Nishant Yadav
Master's Projects
A key-value pair is an elementary data model in which a unique key is associated with a given value. This association between the key and the value allows for a quick lookup of data based on the key and hence is extensively used in programming languages, NoSQL databases, caches, session management, etc. In Java OpenJDK, this elementary data model is implemented by the interface Map, which allows efficient storage and retrieval of data but can only store a single value against each key. In this project, we have implemented a MultiMap data structure in OpenJDK which allows associating multiple values …
Depression Classification On Privacy Protected Facial Features Data,
2023
Faculty of Engineering
Depression Classification On Privacy Protected Facial Features Data, Yanisa Mahayossanunt
Chulalongkorn University Theses and Dissertations (Chula ETD)
This thesis presents depression classification on privacy protected facial features data. Fast depression classification to help patients receive proper treatment is a method that can prevent the damage of depression. However, fast and effective depression classification is difficult because medical personnel are adequate and the time to analyze depression is long per patient. Applied artificial intelligence in the medical field can help reduce the workload of medical personnel. It is also difficult because of privacy protection. Therefore, we utilize extracted facial features from facial expressions in clinical interview videos to develop a machine learning model. The model utilizes LSTM, attention …
Graphical User Interface For Evidential Reasoning Models,
2023
San Jose State University
Graphical User Interface For Evidential Reasoning Models, Rohin Gopalakrishnan
Master's Projects
The Capri system is an evidential reasoning system based on the belief function calculus to support automated reasoning and decision making in uncertain environments. Example domains of application include, medical diagnosis, as well as identifying biological biomarkers. The purpose of this project is to build a Python web-based and app-based Graphical User Interface (GUI), called PyGrapher, that facilitates building graphical evidential reasoning models. The graphical models built using PyGrapher will then be converted to a form that is suitable for input to the Capri system. The PyGrapher system provides an intuitive means to build and manipulate evidential reasoning models as …
Chatgpt: Open Possibilities,
2023
Al Iraqia University
Chatgpt: Open Possibilities, Mohammad Aljanabi, Mohanad Ghazi, Ahmed Hussein Ali, Saad Abas Abed
Iraqi Journal for Computer Science and Mathematics
ChatGPT-3 is a powerful language model developed by OpenAI that has the potential to revolutionize the way we interact with technology. This model has been trained on a massive amount of data, allowing it to understand and generate human-like text with remarkable accuracy.One of the most exciting possibilities of ChatGPT-3 is its potential to improve natural language processing (NLP) and natural language understanding (NLU) in a wide range of applications. In particular, ChatGPT-3 can be used to power chatbots, virtual assistants, and other conversational interfaces. These types of systems are becoming increasingly important as more and more people use voice …
Cslinc - Development Of A National Outreach Vle,
2023
Technological University Dublin
Cslinc - Development Of A National Outreach Vle, Keith Nolan, Keith Quille
Conference Papers
Over the last year an online learning platform has been developed and piloted to the Irish second level education system allowing both students and teachers to participate in introductory computing modules. This poster will outline the development of the registration process of a system that is capable of managing potentially 728 schools, 1000+ classrooms and one million students (the entire Irish second level school system). CSLINC is an online student virtual learning environment for computing consisting of several modules built by academics and industry leaders and disseminated to schools through Moodle, our selected virtual learning environment. While Moodle has a …
A New Approach To Linear Displacement Measurements Based On Hall Effect Sensors,
2023
TÜBİTAK
A New Approach To Linear Displacement Measurements Based On Hall Effect Sensors, İsmai̇l Yari̇çi̇, Yavuz Öztürk
Turkish Journal of Electrical Engineering and Computer Sciences
Since displacement is a vital variable to be considered in many industrial applications, displacement sensing devices have been extensively studied both theoretically and experimentally. There have been also many studies on Hall effect-based displacement measurement, but for many systems linearity still remains a problem. This paper discusses different approaches to calculate the magnetic field due to a cylindrical permanent magnet and proposes a new setup geometry with 2-Hall effect sensors and a permanent magnet between them to overcome the linearity problems. Furthermore, theoretical and experimental studies of the discussed displacement sensor were presented by focusing on the linear range and …
Optimising Electric Vehicle Charging Infrastructure In Dublin Using Geecharge,
2023
Technological University Dublin
Optimising Electric Vehicle Charging Infrastructure In Dublin Using Geecharge, Alexander Mutua Mutiso, Ruairí De Fréin, Ali Malik, Eliel Kibanza, Marco Sahbane, Maxime Pantel
Conference papers
Range anxiety poses a hurdle to the adoption of Electric Vehicles (EVs), as drivers worry about running out of charge without timely access to a Charging Point (CP). We present novel methods for optimising the distribution of CPs, namely, EV portacharge and GEECharge. These solutions distribute CPs in Dublin, in this paper, by considering the population density and Points Of Interest (POIs) or road traffic. The object of this paper is to (1) develop and evaluate methods to distribute CPs in Dublin city; (2) optimise CP allocation; (3) visualise paths in the graph network to show the most used roads …
An Assessment Of The Effectiveness Of Using Data Analytics To Predict Death Claim Seasonality And Protection Policy Review Lapses In A Life Insurance Company,
2023
CCT College Dublin
An Assessment Of The Effectiveness Of Using Data Analytics To Predict Death Claim Seasonality And Protection Policy Review Lapses In A Life Insurance Company, Jennifer Loftus
ICT
Data analytics tools are becoming increasingly common in the life insurance industry. This research considers two use cases for predictive analytics in a life insurance company based in Ireland. The first case study relates to the use of time series models to forecast the seasonality of death claim notifications. The baseline model predicted no seasonal variation in death claim notifications over a calendar year. This reflects the life insurance company’s current approach, whereby it is assumed that claims are notified linearly over a calendar year. More accurate forecasting of death claims seasonality would enhance the life insurance company’s cashflow planning …
Pronostic Of Colo-Rectal Cancer (Crc) Using Machine Learning Models On Organoids Derived Of Patient,
2023
CCT College Dublin
Pronostic Of Colo-Rectal Cancer (Crc) Using Machine Learning Models On Organoids Derived Of Patient, Claudia Andrea Leiva Acevedo
ICT
Colorectal Cancer (CRC) is a globally prevalent and deadly carcinoma, necessitating advanced treatment approaches. Despite ongoing advancements, the mortality rate remains high. Various biological models, including animal studies, cell lines, and the emerging organoid model, contribute to understanding molecular mechanisms. Organoids, 3D cultures derived from tumor epithelial cells, offer advantages such as enhanced diversity, genetic modification, and extended culture capabilities. Recent applications of machine learning (ML) in predicting CRC treatment responses using organoids and tissue data indicate a promising avenue for advancing personalized therapies.
An Adaptive Image Restoration Algorithm Based On Hybrid Total Variation Regularization,
2023
TÜBİTAK
An Adaptive Image Restoration Algorithm Based On Hybrid Total Variation Regularization, Cong Thang Pham, Thi Thu Thao Tran, Hung Vi Dang, Hoai Phuong Dang
Turkish Journal of Electrical Engineering and Computer Sciences
In imaging systems, the mixed Poisson-Gaussian noise (MPGN) model can accurately describe the noise present. Total variation (TV) regularization-based methods have been widely utilized for Poisson-Gaussian removal with edge-preserving. However, TV regularization sometimes causes staircase artifacts with piecewise constants. To overcome this issue, we propose a new model in which the regularization term is represented by a combination of total variation and high-order total variation. We study the existence and uniqueness of the minimizer for the considered model. Numerically, the minimization problem can be efficiently solved by the alternating minimization method. Furthermore, we give rigorous convergence analyses of our algorithm. …
Deep Learning-Based Classification Of Chaotic Systems Over Phase Portraits,
2023
TÜBİTAK
Deep Learning-Based Classification Of Chaotic Systems Over Phase Portraits, Sezgi̇n Kaçar, Süleyman Uzun, Burak Aricioğlu
Turkish Journal of Electrical Engineering and Computer Sciences
This study performed a deep learning-based classification of chaotic systems over their phase portraits. To the best of the authors' knowledge, such classification studies over phase portraits have not been conducted in the literature. To that end, a dataset consisting of the phase portraits of the most known two chaotic systems, namely Lorenz and Chen, is generated for different values of the parameters, initial conditions, step size, and time length. Then, a classification with high accuracy is carried out employing transfer learning methods. The transfer learning methods used in the study are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet, and …
A Type-2 Fuzzy Rule-Based Model For Diagnosis Of Covid-19,
2023
TÜBİTAK
A Type-2 Fuzzy Rule-Based Model For Diagnosis Of Covid-19, İhsan Şahi̇n, Erhan Akdoğan, Mehmet Emi̇n Aktan
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, a type-2 fuzzy logic-based decision support system comprising clinical examination and blood test results that health professionals can use in addition to existing methods in the diagnosis of COVID-19 has been developed. The developed system consists of three fuzzy units. The first fuzzy unit produces COVID-19 positivity as a percentage according to the respiratory rate, loss of smell, and body temperature values, and the second fuzzy unit according to the C-reactive protein, lymphocyte, and D-dimer values obtained as a result of the blood tests. In the third fuzzy unit, the COVID-19 positivity risks according to the clinical …
