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Articles 31 - 60 of 2694

Full-Text Articles in Computer Sciences

Understanding Patient Profiles In Sickle Cell Disease Using Unsupervised Machine Learning, Raj Kamal Somavarapu Jan 2024

Understanding Patient Profiles In Sickle Cell Disease Using Unsupervised Machine Learning, Raj Kamal Somavarapu

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Sickle Cell Disease (SCD) is one of the most prevalent genetic blood disorders affecting millions of people worldwide. It is often accompanied by acute and/or chronic pain leading to increased healthcare costs and adverse outcomes. Effective management of SCD requires an understanding of the diverse physiological profiles. This study employs unsupervised machine learning, specifically K-means clustering to categorize the patients suffering with SCD into different clusters based on their vital signs. The main aim is to identify the groups that reflect similarities in physiological and pain profiles, allowing an in-depth analysis to reveal distinctive features distinguishing patient clusters. The project …

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Enhancing Robustness Of Graph Neural Network Against Adversarial Attacks By Balancing Local And Global Perspectives, Bibek Raj Joshi Jan 2024

Enhancing Robustness Of Graph Neural Network Against Adversarial Attacks By Balancing Local And Global Perspectives, Bibek Raj Joshi

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Graph Neural Networks (GNNs) have increasingly gained popularity as tools for analyzing graph data in areas like biology, knowledge-graphs, social networks, biology, and recommendation systems. However, their vulnerability to adversarial attacks - small, targeted manipulations of graph structures or node features - raises serious concerns about their reliability in real-world applications. Existing defense strategies, such as adversarial training, edge filtering, low-rank approximations, and randomization-based methods, often suffer from high computational costs, scalability issues, or reduced clean-data performance. Unlike these methods, the proposed approach integrates multi-hop relationships, applies adaptive regularization, and maintains a balance between feature-based and structural embeddings, ensuring improved …

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Meta-Learning-Based Model Stacking Framework For Hardware Trojan Detection In Fpga Systems, Mani Rupak Gurram Jan 2024

Meta-Learning-Based Model Stacking Framework For Hardware Trojan Detection In Fpga Systems, Mani Rupak Gurram

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In today's technological landscape, hardware devices are integral to critical applications such as industrial automation, autonomous vehicles, and medical equipment, relying on advanced platforms like FPGAs for core functionalities. However, the multi-stage manufacturing process, often distributed across various foundries, introduces substantial security risks, notably the potential for hardware Trojan insertion. These malicious modifications compromise the reliability and safety of hardware systems. This research addresses the detection of hardware Trojans through side-channel analysis, utilizing power and electromagnetic signal data, combined with meta-learning techniques, specifically model stacking. By employing diverse base models and a meta-model to consolidate predictions, this non-invasive approach effectively …

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Evaluating The Deductive Competence Of Large Language Models, Spencer M. Seals, Valerie L. Shalin Jan 2024

Evaluating The Deductive Competence Of Large Language Models, Spencer M. Seals, Valerie L. Shalin

Psychology Faculty Publications

The development of highly fluent large language models (LLMs) has prompted increased interest in assessing their reasoning and problem-solving capabilities. We investigate whether several LLMs can solve a classic type of deductive reasoning problem from the cognitive science literature. The tested LLMs have limited abilities to solve these problems in their conventional form. We performed follow up experiments to investigate if changes to the presentation format and content improve model performance. We do find performance differences between conditions; however, they do not improve overall performance. Moreover, we find that performance interacts with presentation format and content in unexpected ways that …

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Enhancing Cross-Modal Contextual Congruence For Crowdfunding Success Using Knowledge-Infused Learning, Trilok Padhi, Ugur Kursuncu, Yaman Kumar, Valerie L. Shalin, Lane Peterson Fronczek Jan 2024

Enhancing Cross-Modal Contextual Congruence For Crowdfunding Success Using Knowledge-Infused Learning, Trilok Padhi, Ugur Kursuncu, Yaman Kumar, Valerie L. Shalin, Lane Peterson Fronczek

Psychology Faculty Publications

The digital landscape continually evolves with multimodality, enriching the online experience for users. Creators and marketers aim to weave subtle contextual cues from various modalities into congruent content to engage users with a harmonious message. This interplay of multimodal cues is often a crucial factor in attracting users' attention. However, this richness of multimodality presents a challenge to computational modeling, as the semantic contextual cues spanning across modalities need to be unified to capture the true holistic meaning of the multimodal content. This contextual meaning is critical in attracting user engagement as it conveys the intended message of the brand …

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The Easy-Ai Symbology, Alexis Ellis, Cogan Shimizu Jan 2024

The Easy-Ai Symbology, Alexis Ellis, Cogan Shimizu

Computer Science and Engineering Faculty Publications

As artificial intelligence (AI) surges into the forefront of research and the lives of everyday people, challenges in understanding and communicating how these systems operate are becoming more prevalent. The need for a common language for AI systems that allows for multidisciplinary understanding and communication is a prevalent topic within the field. In this work, we take the visual framework EASY-AI and create a symbolic system that overlays the framework’s ontology to facilitate such communication and understanding. Poster submission.

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An Ontology Design Pattern For Role-Dependent Names, Rushrukh Rayan, Cogan Shimizu, Pascal Hitzler Jan 2024

An Ontology Design Pattern For Role-Dependent Names, Rushrukh Rayan, Cogan Shimizu, Pascal Hitzler

Computer Science and Engineering Faculty Publications

We present an ontology design pattern for modeling Names as part of Roles, to capture scenarios where an Agent performs different Roles using different Names associated with the different Roles. Examples of an Agent performing a Role using different Names are rather ubiquitous, e.g., authors who write under different pseudonyms, or different legal names for citizens of more than one country. The proposed pattern is a modified merger of a standard Agent Role and a standard Name pattern stub.

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The Hip Ontology: A Formal Framework To Support Disaster Risk Reduction And Management, Shirly Stephen, Mark Schildhauer, Krzysztof Janowicz, Kitty Currier, Pascal Hitzler, Cogan Shimizu, Colby K. Fisher, Dean Rehberger Jan 2024

The Hip Ontology: A Formal Framework To Support Disaster Risk Reduction And Management, Shirly Stephen, Mark Schildhauer, Krzysztof Janowicz, Kitty Currier, Pascal Hitzler, Cogan Shimizu, Colby K. Fisher, Dean Rehberger

Computer Science and Engineering Faculty Publications

Open data initiatives and knowledge graphs, in synergy, have contributed to an increasing volume of disaster-related data in the Semantic Web. Synthesizing and enriching these data is critical to support all aspects of data-driven disaster risk reduction and management. A standard template that coherently defines, maps, and classifies the wide range of hazards to which communities are exposed is a key input for this task. The UNDRR-ISC Hazard Information Profiles (HIPs) provide evidence-informed standardization of hazard nomenclature and definitions and a “science-backed” classification. Unfortunately, they are not in a machine-readable format. This paper develops the HIP Ontology as its FAIR …

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An Efficient And Trusted Deep Learning Framework For Real-Time Ppe Detection In Secure Iomt Environment, Anusha Verma Jan 2024

An Efficient And Trusted Deep Learning Framework For Real-Time Ppe Detection In Secure Iomt Environment, Anusha Verma

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Occupationally-acquired infections impact thousands of healthcare workers (HCWs) in the U.S., with many cases preventable through proper use of personal protective equipment (PPE). This study seeks to develop a robust system to enhance PPE compliance and reduce infection risks among HCWs. The objectives of this thesis are twofold: (1) to create a hybrid machine learning model that combines object detection and keypoint detection to ensure correct donning and doffing of PPE, and (2) to design a real-time feedback system using LED indicators and a display interface to offer actionable guidance to HCWs during PPE usage. The goal is to optimize …

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An Enhanced Real-Time Object Detection Of Helmets And License Plates Using A Lightweight Yolov8 Deep Learning Model, Mounika Thatikonda Jan 2024

An Enhanced Real-Time Object Detection Of Helmets And License Plates Using A Lightweight Yolov8 Deep Learning Model, Mounika Thatikonda

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Traffic surveillance and enforcement heavily depend on the real-time detection of helmets and license plates, particularly in high-density urban environments. This study presents a dynamic and optimized lightweight model, the proposed G-YOLOv8n, designed for resource constrained edge devices like the Raspberry Pi. By integrating the GhostNet module into the YOLOv8n architecture, this research achieves a nearly 50% reduction in model size and computational load, while maintaining comparable detection accuracy to the original YOLOv8n. These enhancements enable real-time processing capabilities crucial for traffic monitoring operations. The growing demand for real-time, low-power solutions in intelligent transportation systems necessitates lightweight, efficient detection models. …

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Integrating Knowledge Graphs With Large Language Models For Natural Language Querying, Rakesh Kandula Jan 2024

Integrating Knowledge Graphs With Large Language Models For Natural Language Querying, Rakesh Kandula

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This research explores the integration of knowledge graphs with large language models that have already been trained on a vast pool of unstructured text data. Large language models trained on this type of data have a tendency to hallucinate and produce factually inaccurate results. This behavior is primarily due to the data being trained is unstructured and huge text corpus, and large language model uses predictive text analysis methods to obtain a response. These issues can be addressed by applying Retrieval Augmented Generation and Fine-tuning to large language models, employing an underlying domainspecific knowledge graph. Integrating knowledge graph and large …

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Prediction Interpretations Of Ensemble Models In Chronic Kidney Disease Using Explainable Ai, K M Tawsik Jawad Jan 2024

Prediction Interpretations Of Ensemble Models In Chronic Kidney Disease Using Explainable Ai, K M Tawsik Jawad

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Chronic Kidney Disease (CKD) poses significant health and financial threat to millions of patients all around the world. The irreversible nature of this disease not just leads to comorbid diseases like Diabetes Mellitus, Hypertension, Anemia, Bone Disease, Neurological Implants etc. It can permanently damage the kidney by progressing to Acute Kidney Injury (AKI) or End Stage Renal Diseases (ESRD). The risk factors of CKD become more dangerous as patients suffering from it have little to no idea about the presence of CKD in their body until it takes the shape of AKI or ESRD. There are severe economic burdens for …

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An Ml-Assisted Golden-Free Hardware Trojan Localization And Detection Approach For Trusted Microelectronics, Ashutosh Ghimire Jan 2024

An Ml-Assisted Golden-Free Hardware Trojan Localization And Detection Approach For Trusted Microelectronics, Ashutosh Ghimire

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Hardware Trojans are malicious circuits, hidden in integrated circuits (ICs) which pose a significant threat to security. Detection of hardware Trojans is important to build trust, verify, and make the semiconductor ICs process secure. The existing hardware Trojan detection methods are generally destructive, require intricate comparisons, or require a long time for reverse engineering. In the initial phase of this study, the substitution of supervised hardware Trojan detection methods in ASICs chips is explored with unsupervised approaches, thereby eliminating the dependence on golden references. The Trojan detection uses a ring oscillator (RO) based on NAND as the power monitor. Frequency …

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Understanding Impact Of Graph Structure On Knowledge Graph Embedding, Brandon Dave Jan 2024

Understanding Impact Of Graph Structure On Knowledge Graph Embedding, Brandon Dave

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The effectiveness of a deployed knowledge graph is commonly evaluated with defined use-cases from domain experts. This poses challenges during the development cycle in determining how to represent data. Developers of a knowledge graph can optionally include semantics into a knowledge graph by abstracting the data representation in such a way that mirrors information as it exists in the real world. Consequently, the abstraction is represented by additional layers, resulting in performant differences in knowledge graph embedding; such as, the embedded model's ability to infer facts between entities through link predictions. This thesis presents a comprehensive analysis of the performance …

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Ai-Enabled Hardware Security Approach For Aging Classification And Manufacturer Identification Of Sram Pufs, Harshdeep Singh Jan 2024

Ai-Enabled Hardware Security Approach For Aging Classification And Manufacturer Identification Of Sram Pufs, Harshdeep Singh

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Semiconductor microelectronics integrated circuits (ICs) are increasingly integrated into modern life-critical applications, from intelligent infrastructure and consumer electronics to the Internet of Things (IoT) and advanced military and medical systems. Unfortunately, these applications are vulnerable to new hardware security attacks, including microelectronics counterfeits and hardware modification attacks. Physical Unclonable Functions (PUFs) are state-of-the-art hardware security solutions that utilize process variations of integrated circuits for device authentication, secret key generation, and microelectronics counterfeit detection. The negative impact of aging on Static Random Access Memory Physical Unclonable Functions (SRAM PUFs) has significant consequences for microelectronics authentication, security, and reliability. This research thoroughly …

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Ml-Assisted Side Channel Security Approaches For Hardware Trojan Detection And Puf Modeling Attacks, Niraj Prasad Bhatta Jan 2024

Ml-Assisted Side Channel Security Approaches For Hardware Trojan Detection And Puf Modeling Attacks, Niraj Prasad Bhatta

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Hardware components are becoming prone to threats with increasing technological advances. Malicious modifications to such components are increasing and are known as hardware Trojans. Traditional approaches rely on functional assessments and are not sufficient to detect such malicious actions of Trojans. Machine learning (ML) assisted techniques play a vital role in the overall detection and improvement of Trojan. Our novel approach using various ML models brings an improvement in hardware Trojan identification with power signal side channel analysis. This study brings a paradigm shift in the improvement of Trojan detection in integrated circuits (ICs). In addition to this, our further …

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Multi-Semantic-Stage Neural Networks For Robust And Interpretable Deep Learning, Christopher J. Menart Jan 2024

Multi-Semantic-Stage Neural Networks For Robust And Interpretable Deep Learning, Christopher J. Menart

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Deep neural networks have great representational power. However, most deep neural nets today optimize directly for performance on a single task defined only by labeled training data. This excludes potential sources of knowledge and ways of learning which could improve their performance, and address challenges, such as explainability, which are pressing to the field. We propose a framework for neural network architecture which generalizes it to a graph of many semantically-meaningful variables. We call it the Multi-Semantic-Stage Neural Network (MSSNN). An MSSNN models its domain as a web of conditional probabilities, i.e. a collection of inter-related tasks which can learn …

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Pneumonia Detection With Limited And Imbalanced Data Using Energy-Based Out-Of-Distribution Technique, Jasbin Karki Jan 2024

Pneumonia Detection With Limited And Imbalanced Data Using Energy-Based Out-Of-Distribution Technique, Jasbin Karki

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The automated detection of pneumonia through chest X-ray presents a critical challenge in medical diagnostics, particularly due to the restrictions of limited and imbalanced chest X-ray data for training AI models. Traditional methods that depend on softmax confidence scores can be overconfident even when generating erroneous outputs especially when they are processing completely new inputs, leading to unreliable diagnostic results. This research addresses challenges in AI models which aim to develop a robust pneumonia detection system using an Energy-Based Out-of-Distribution (OOD) technique that can work effectively even with limited and imbalanced data. The study focused on creating a more reliable …

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Test-Time Backdoor Attack Using Universal Perturbation, Jesse Alexander Smith Jan 2024

Test-Time Backdoor Attack Using Universal Perturbation, Jesse Alexander Smith

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The rapid growth and widespread reliance on machine learning (ML) systems across critical applications such as healthcare, autonomous driving, and cybersecurity have un- derscored their transformative potential and heightened their susceptibility to adversarial attacks and vulnerabilities. This thesis investigates vulnerabilities in ML models, focusing on backdoor attacks, including naive backdoor attack, feature collision backdoor attack, hidden trigger backdoor attack, and test-time backdoor attack using universal perturbation technique. These methodologies demonstrate how adversaries can automate and conceal malicious behaviors to achieve specific objectives, posing significant challenges to ML model integrity and trustworthiness. The research provides a comprehensive analysis of the theoretical …

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Semantics-Aware Text-Guided Aerial Image Synthesis By Feature Augmented Diffusion Models, Douglas J. Townsell Jan 2024

Semantics-Aware Text-Guided Aerial Image Synthesis By Feature Augmented Diffusion Models, Douglas J. Townsell

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Aerial imagery provides crucial insights for various fields, including remote monitoring, environmental assessment, and autonomous navigation. However, the availability of aerial image datasets is limited due to privacy concerns and imbalanced data distribution, impeding the development of robust deep learning models. While recent text-guided generative models have shown promise in synthesizing high-quality images, they fall short in handling the unique challenges of aerial imagery, including densely packed objects, intricate spatial relationships, and the absence of paired text-aerial image datasets. To tackle these limitations, we propose STARS, a groundbreaking framework for Semantic-aware Text-guided Aerial image Refinement and Synthesis. STARS introduces a …

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Graph-Centric Bot Detection: Addressing Extreme Data Imbalances, Heterophily, And Scarcity, Bradley T. Ashmore Jan 2024

Graph-Centric Bot Detection: Addressing Extreme Data Imbalances, Heterophily, And Scarcity, Bradley T. Ashmore

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The digital landscape is ever-evolving. In recent years the amount of bot traffic, traffic generated by autonomous applications over the internet has increased significantly. Many bots perform useful and needed functions, however, malicious bots are known sources of both common and emerging security threats. Denial-of-Services (DoS), information theft, and credential stuffing have all been conducted by malicious software running on unknowingly infected machines. The dichotomy of useful bots operating in the same networks as malicious bots combined with novel bot attacks and an ever-increasing number of personal devices connecting to the Internet drives the need for continued advancement of malicious …

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A Trusted Adversarial Ml Countermeasure Approach For Secure And Resilient Ai-Driven Hardware Trojan Detection, Mohammed G M Alkurdi Jan 2024

A Trusted Adversarial Ml Countermeasure Approach For Secure And Resilient Ai-Driven Hardware Trojan Detection, Mohammed G M Alkurdi

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Semiconductor microelectronics Integrated Circuits (ICs) are increasingly integrated into critical life applications including medical, aerospace, and Internet of things. Their increasing importance as a technology gave rise to critical concerns regarding their security. This has led to the focus of the research community on hardware Trojans, which are malicious modifications to the ICs with undesirable outcomes. Their detection is becoming increasingly critical, with many researchers proposing methods to do so such as reverse engineering, logic testing, and side-channel analysis. Many of these proposals utilize machine learning methods to detect these malicious modifications with high accuracy and confidence. However, machine learning …

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Cvii: Enhancing Interpretability In Intelligent Sensor Systems Via Computer Vision Interpretability Index, Hossein Mohammadi, Krishnaprasad Thirunarayan, Lingwei Chen Dec 2023

Cvii: Enhancing Interpretability In Intelligent Sensor Systems Via Computer Vision Interpretability Index, Hossein Mohammadi, Krishnaprasad Thirunarayan, Lingwei Chen

Computer Science and Engineering Faculty Publications

In the realm of intelligent sensor systems, the dependence on Artificial Intelligence (AI) applications has heightened the importance of interpretability. This is particularly critical for opaque models such as Deep Neural Networks (DNN), as understanding their decisions is essential, not only for ethical and regulatory compliance, but also for fostering trust in AI-driven outcomes. This paper introduces the novel concept of a Computer Vision Interpretability Index (CVII). The CVII framework is designed to emulate human cognitive processes, specifically in tasks related to vision. It addresses the intricate challenge of quantifying interpretability, a task that is inherently subjective and varies across …

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Mining Themes In Clinical Notes To Identify Phenotypes And To Predict Length Of Stay In Patients Admitted With Heart Failure, Ankita Agarwal, Tanvi Banerjee, William Romine, Krishnaprasad Thirunarayan, Lingwei Chen, Mia Cajita May 2023

Mining Themes In Clinical Notes To Identify Phenotypes And To Predict Length Of Stay In Patients Admitted With Heart Failure, Ankita Agarwal, Tanvi Banerjee, William Romine, Krishnaprasad Thirunarayan, Lingwei Chen, Mia Cajita

Computer Science and Engineering Faculty Publications

Heart failure is a syndrome which occurs when the heart is not able to pump blood and oxygen to support other organs in the body. Identifying the underlying themes in the diagnostic codes and procedure reports of patients admitted for heart failure could reveal the clinical phenotypes associated with heart failure and to group patients based on their similar characteristics which could also help in predicting patient outcomes like length of stay. These clinical phenotypes usually have a probabilistic latent structure and hence, as there has been no previous work on identifying phenotypes in clinical notes of heart failure patients …

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Sustainable Grain Transportation In Ukraine Amidst War Utilizing Knarm And Knowwheregraph, Yinglun Zhang, Antonina Broyaka, Jude Kastens, Allen M. Featherstone, Cogan Shimizu, Pascal Hitzler, Hande Küçük Mcginty Apr 2023

Sustainable Grain Transportation In Ukraine Amidst War Utilizing Knarm And Knowwheregraph, Yinglun Zhang, Antonina Broyaka, Jude Kastens, Allen M. Featherstone, Cogan Shimizu, Pascal Hitzler, Hande Küçük Mcginty

Computer Science and Engineering Faculty Publications

In this work, we propose a sustainable path-finding application for grain transportation during the ongoing Russian military invasion in Ukraine. This application is to build a suite of algorithms to find possible optimal paths for transporting grain that remains in Ukraine. The application uses the KNowledge Acquisition and Representation Methodology(KNARM) and the KnowWhereGraph to achieve this goal. Currently, we are working towards creating an ontology that will allow for a more effective heuristic approach by incorporating the lessons learned from the KnowWhereGraph. The aim is to enhance the path-finding process and provide more accurate and efficient results. In the future, …

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A Preliminary Study Of The Efficacy Of Using A Wrist-Worn Multiparameter Sensor For The Prediction Of Cognitive Flow States In University-Level Students, Josephine Graft, William Romine, Brooklynn Watts, Noah Schroeder, Tawsik Jawad, Tanvi Banerjee Apr 2023

A Preliminary Study Of The Efficacy Of Using A Wrist-Worn Multiparameter Sensor For The Prediction Of Cognitive Flow States In University-Level Students, Josephine Graft, William Romine, Brooklynn Watts, Noah Schroeder, Tawsik Jawad, Tanvi Banerjee

Computer Science and Engineering Faculty Publications

Engagement is enhanced by the ability to access the state of flow during a task, which is described as a full immersion experience. We report two studies on the efficacy of using physiological data collected from a wearable sensor for the automated prediction of flow. Study 1 took a two-level block design where activities were nested within its participants. A total of five participants were asked to complete 12 tasks that aligned with their interests while wearing the Empatica E4 sensor. This yielded 60 total tasks across the five participants. In a second study representing daily use of the device, …

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Predicting Thermoelectric Power Factor Of Bismuth Telluride During Laser Powder Bed Fusion Additive Manufacturing, Ankita Agarwal, Tanvi Banerjee, Joy Gockel, Saniya Leblanc, Joe Walker, John Middendorf Mar 2023

Predicting Thermoelectric Power Factor Of Bismuth Telluride During Laser Powder Bed Fusion Additive Manufacturing, Ankita Agarwal, Tanvi Banerjee, Joy Gockel, Saniya Leblanc, Joe Walker, John Middendorf

Computer Science and Engineering Faculty Publications

An additive manufacturing (AM) process, like laser powder bed fusion, allows for the fabrication of objects by spreading and melting powder in layers until a freeform part shape is created. In order to improve the properties of the material involved in the AM process, it is important to predict the material characterization property as a function of the processing conditions. In thermoelectric materials, the power factor is a measure of how efficiently the material can convert heat to electricity. While earlier works have predicted the material characterization properties of different thermoelectric materials using various techniques, implementation of machine learning models …

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Overcoming Uncertainties In Molecular Visualization, Thomas Wischgoll Feb 2023

Overcoming Uncertainties In Molecular Visualization, Thomas Wischgoll

Computer Science and Engineering Faculty Publications

Uncertainties are difficult if not impossible to avoid. Capturing data from the analog world almost always results in some form of uncertainty. The amount of uncertainty depends on the method of measurement and its accuracy. When visualizing data that has some associated uncertainty, it is essential to properly process and convey such uncertainty and especially the amount of uncertainty keeping in mind that additional processing steps can amplify the uncertainty. There are various sources of uncertainty, such as numerical limitations or limitations of the capture device. However, there are other sources of uncertainty. Some of these uncertainties stem from model …

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Encryption And Compression Classification Of Internet Of Things Traffic, Mariam Najdat M Saleh Jan 2023

Encryption And Compression Classification Of Internet Of Things Traffic, Mariam Najdat M Saleh

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The Internet of Things (IoT) is used in many fields that generate sensitive data, such as healthcare and surveillance. Increased reliance on IoT raised serious information security concerns. This dissertation presents three systems for analyzing and classifying IoT traffic using Deep Learning (DL) models, and a large dataset is built for systems training and evaluation. The first system studies the effect of combining raw data and engineered features to optimize the classification of encrypted and compressed IoT traffic using Engineered Features Classification (EFC), Raw Data Classification (RDC), and combined Raw Data and Engineered Features Classification (RDEFC) approaches. Our results demonstrate …

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Efficient Cloud-Based Ml-Approach For Safe Smart Cities, Niveshitha Niveshitha Jan 2023

Efficient Cloud-Based Ml-Approach For Safe Smart Cities, Niveshitha Niveshitha

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Smart cities have emerged to tackle many critical problems that can thwart the overwhelming urbanization process, such as traffic jams, environmental pollution, expensive health care, and increasing energy demand. This Master thesis proposes efficient and high-quality cloud-based machine-learning solutions for efficient and sustainable smart cities environment. Different supervised machine-learning models for air quality predication (AQP) in efficient and sustainable smart cities environment is developed. For that, ML-based techniques are implemented using cloud-based solutions. For example, regression and classification methods are implemented using distributed cloud computing to forecast air execution time and accuracy of the implemented ML solution. These models are …

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