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Articles 1 - 30 of 382

Full-Text Articles in Computer Sciences

Context-Regulated Q-Neutrosophic Soft Model Selection Under Input Perturbation: Evidence From Three Classification Benchmarks, Poom Kumam, Supak Phiangsungnoen Oct 2026

Context-Regulated Q-Neutrosophic Soft Model Selection Under Input Perturbation: Evidence From Three Classification Benchmarks, Poom Kumam, Supak Phiangsungnoen

Neutrosophic Systems with Applications

Selecting a classifier using a single average metric can conceal instability across validation splits, poor lower-tail behavior, and sensitivity to input perturbations. This study formulates a context-regulated Q-neutrosophic soft model-selection procedure with two fixed evaluation conditions. For each classifier and performance criterion, mean attainment (truth), across-split dispersion (indeterminacy), and the complement of a lower empirical quantile (falsity) form an auditable three-component descriptor. A bounded lower-context regulator aggregates their penalized utilities; monotonicity and perturbation bounds follow directly from its convex representation. Four fixed classifiers are compared using five benefit-oriented criteria, three repetitions of stratified five-fold cross-validation, and paired test-feature perturbations. Three …

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Closing The Interpretability Gap: Explainable Ml-Based Malware Detection For Defensive Cyberspace Operations, Tashi Stirewalt, Sean Hodgson, Puumaaya Tahiru, Assefaw Gebremedhin Sep 2026

Closing The Interpretability Gap: Explainable Ml-Based Malware Detection For Defensive Cyberspace Operations, Tashi Stirewalt, Sean Hodgson, Puumaaya Tahiru, Assefaw Gebremedhin

Military Cyber Affairs

This paper presents an end-to-end, explainable malware triage pipeline designed for defense-oriented cyber operations. It combines high-performance static detection methods with analyst-centered interpretability. Utilizing the EMBER 2024 Windows PE subset, we train and evaluate four classifiers and select LightGBM as the production model based on its predictive performance, inference efficiency, and compatibility with exact tree-based attribution. The deployed system consists of four sequential components: PE feature extraction, malware probability scoring, dual explainability (using SHAP and LIME), and large language model (LLM) report generation, all integrated within a Flask web interface. On a temporal test set of 1,080,000 samples, LightGBM achieves …

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Autoencoders As Classifiers Trained On Single Sources Of Radiographic Images For Generalizability Across Unseen Sources, Ryan M. Putney Jun 2026

Autoencoders As Classifiers Trained On Single Sources Of Radiographic Images For Generalizability Across Unseen Sources, Ryan M. Putney

USF Tampa Graduate Theses and Dissertations

Artificial neural networks trained to classify X-ray images according to disease state will learn to distinguish between technical or procedural variations in the images rather than features relevant to the disease. For example, the model might learn to recognize the machine that captured the image or to distinguish an image taken while the patient is lying supine or standing upright. The present work is aimed at using autoencoders as a generalizable classifier to detect COVID vs pneumonia (PNA) X-ray images. The first experiment was to find an architecture for a fully convolutional autoencoder (CAE) and for a convolutional autoencoder with …

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Skin Lesion Classification Using Cnn Model And Augmented Dataset, Mohammed Nawzad Mohammed-Ramzi, Aso M. Aladdin Jun 2026

Skin Lesion Classification Using Cnn Model And Augmented Dataset, Mohammed Nawzad Mohammed-Ramzi, Aso M. Aladdin

Journal of Soft Computing and Computer Applications

Skin cancer is a deadly disease. Skin lesion classification is a critical challenge due to its prevalent and deadly nature. Skin lesions are difficult for dermatologists to detect using eye examination, which is time-consuming and variable. A deep learning model of skin lesions classification has been proposed using a Convolutional Neural Network (CNN) trained on the HAM10000 dataset of 10,015 dermatoscopies. To improve resilience and address the dataset's extreme class imbalance, data augmentation techniques such as geometric transformations, brightness/contrast adjustments, blurring, noise addition, histogram equalization, color space alterations, and elastic deformations are used. With a carefully balanced 10% test set, …

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Optimizing Gated Rnns, Joshua Paul Fechete May 2026

Optimizing Gated Rnns, Joshua Paul Fechete

Honors Projects

Gated recurrent neural networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) help fix instability present in normal recurrent neural networks. This allows them to be used for various real-world tasks, and due to their architecture, they are uniquely qualified to handle variable sized input such as text. However, even before training can begin on a machine learning model, various hyperparameters must be chosen to decide how the model will be architectured. Choosing good hyperparameters is vital for creating a model that performs well but is not larger and more computationally expensive to run than it needs …

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Enhanced Generative Convolutional Networks: A Hybrid Algorithm For Refinement Video Classification, Dalal Thair Mahjoub, Hala Bahjat Abdulwahab, Kesra Nermend Dec 2025

Enhanced Generative Convolutional Networks: A Hybrid Algorithm For Refinement Video Classification, Dalal Thair Mahjoub, Hala Bahjat Abdulwahab, Kesra Nermend

Journal of Soft Computing and Computer Applications

Video classification is a vital area of research due to the growing volume of video content in various applications. Accurate category across various resolutions poses challenges, which include adapting to scaling, resizing, and compression. Therefore, this paper introduces an innovative Generative Convolutional Network (GCN) set of rules tailored for multi-resolution video classes. The proposed GCN model utilizes Convolutional Neural Networks (CNNs) combined with generative modeling to enhance the extraction of functions across varying video resolutions, which is crucial for maintaining class robustness in the face of common video adjustments, such as scaling, resizing, and compression. In contrast, traditional fashions frequently …

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Understanding Bias And Fairness In Large Language Models: An Empirical Study, Joshua Johnson Dec 2025

Understanding Bias And Fairness In Large Language Models: An Empirical Study, Joshua Johnson

Electrical Engineering and Computer Science Undergraduate Honors Theses

This thesis investigates demographic bias in large language models (LLMs) through the use of evaluating outcome disparities when utilized in decision making tasks as well as underlying associations that could contribute to furthering these disparities. Using profiles from the Adult dataset, we analyze how Gemini 2.0 Flash performs in an income prediction task using zero-shot and few-shot prompting methods. Our findings show that models exhibit measurable differences in demographic parity and false positive rates, with the use of few-shot prompting reducing these disparities. Alongside this line of testing, we tested associational bias in Qwen 2.5 using probability based association tests …

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Predicting Stock Price Movement With Llm-Enhanced Tweet Emotion Analysis, An Vuong Dec 2025

Predicting Stock Price Movement With Llm-Enhanced Tweet Emotion Analysis, An Vuong

Graduate Theses and Dissertations

Accurately predicting short-term stock price movement remains a challenging task due to the market’s inherent volatility and sensitivity to investor sentiment. In this thesis, we present a published paper that discusses a deep learning framework integrating emo- tion features extracted from tweet data with historical stock price information to forecast significant price changes on the following day. We utilize Meta’s LLaMA 3.1-8B-Instruct model to preprocess tweet data, thereby enhancing the quality of emotion features derived from three emotion analysis approaches: a transformer-based DistilRoBERTa classifier from the Hugging Face library and two lexicon-based methods using National Research Council Canada (NRC) resources. …

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Enhancing Imbalanced Data Augmentation: A Comparative Study Of Ganified-Smote And Latent Factor Integration, Rusma Anieza Ruslan, Nureize Arbaiy, Pei-Chun Lin Oct 2025

Enhancing Imbalanced Data Augmentation: A Comparative Study Of Ganified-Smote And Latent Factor Integration, Rusma Anieza Ruslan, Nureize Arbaiy, Pei-Chun Lin

Journal of Informatics and Web Engineering

One such serious problem in machine learning (ML) is imbalanced datasets. Minority class samples are usually sparse but hold significant meaning. The model can become biased toward the majority class due to unbalanced class distribution. This results in fraudulently high accuracy without being able to detect minority cases. This bias is also most perilous in critical applications, where ignoring minority cases can be highly destructive. To overcome this problem, the Synthetic Minority Oversampling Technique (SMOTE) is one of the most widely used. SMOTE creates balanced class distribution by interpolating between existing minority samples. It creates samples that are too close …

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Skin Cancer Image Classification Using Deep Learning With Data Segmentation Technique, Akhilesh Kumar Shrivas, Hema Vastrakar Oct 2025

Skin Cancer Image Classification Using Deep Learning With Data Segmentation Technique, Akhilesh Kumar Shrivas, Hema Vastrakar

Karbala International Journal of Modern Science

The human skin is an impressive organ and structural element often impacted by a diverse range of recognized and unknown diseases. Diagnosing disorders that affect the outermost layer of the body is the most uncertain and difficult component in the scientific field. Dermatological diseases are one of the most significant health concerns in the 21st century since their identification is challenging and costly, plagued with challenges and the subjectivity that comes with human interpretation. The main objective of this piece of research work is to develop a robust model for the classification of skin cancer diseases using deep convolution neural …

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Deep Learning For Land Use Classification: A Systematic Review Of Hs-Lidar Imagery, Muhammad Zia Ur Rehman, Syed Mohammed Shamsul Islam, David Blake, Anwaar Ulhaq, Naeem Janjua Sep 2025

Deep Learning For Land Use Classification: A Systematic Review Of Hs-Lidar Imagery, Muhammad Zia Ur Rehman, Syed Mohammed Shamsul Islam, David Blake, Anwaar Ulhaq, Naeem Janjua

Research outputs 2022 to 2026

Remote sensing (RS) technologies have significantly advanced Earth observation capabilities, enhancing the characterization and identification of surface materials through both spaceborne and airborne systems. These advancements are crucial for improving environmental monitoring and urban planning. As RS datasets have become more accessible, their increased complexity has necessitated a shift from traditional machine learning techniques to more robust deep learning approaches, particularly convolutional neural networks (CNNs) and transformer-based models known for their superior feature extraction capabilities. This systematic review focuses on the application of these deep learning techniques in land use classification, emphasizing the fusion of hyperspectral (HS) and LiDAR data. …

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Ed-Filter: Dynamic Feature Filtering For Eating Disorder Classification, Mehdi Naseriparsa, Suku Sukunesan, Zhen Cai, Osama Alfarraj, Amr Tolba, Saba Fathi Rabooki, Feng Xia Aug 2025

Ed-Filter: Dynamic Feature Filtering For Eating Disorder Classification, Mehdi Naseriparsa, Suku Sukunesan, Zhen Cai, Osama Alfarraj, Amr Tolba, Saba Fathi Rabooki, Feng Xia

Research outputs 2022 to 2026

Eating disorders (ED) are critical psychiatric problems that have alarmed the mental health community. Mental health professionals are increasingly recognizing the utility of data derived from social media platforms such as Twitter. However, high dimensionality and extensive feature sets of Twitter data present remarkable challenges for ED classification. To overcome these hurdles, we introduce a novel method, an informed branch and bound search technique known as ED-Filter. This strategy significantly improves the drawbacks of conventional feature selection algorithms such as filters and wrappers. ED-Filter iteratively identifies an optimal set of promising features that maximize the eating disorder classification accuracy. In …

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Assessing The Adversarial Robustness Of Multimodal Medical Ai Systems: Insights Into Vulnerabilities And Modality Interactions, Ekaterina Mozhegova, Asad Masood Khattak, Adil Khan, Roman Garaev, Bader Rasheed, Muhammad Shahid Anwar Jul 2025

Assessing The Adversarial Robustness Of Multimodal Medical Ai Systems: Insights Into Vulnerabilities And Modality Interactions, Ekaterina Mozhegova, Asad Masood Khattak, Adil Khan, Roman Garaev, Bader Rasheed, Muhammad Shahid Anwar

All Works

The emergence of both task-specific single-modality models and general-purpose multimodal large models presents new opportunities, but also introduces challenges, particularly regarding adversarial attacks. In high-stakes domains like healthcare, these attacks can severely undermine model reliability and their applicability in real-world scenarios, highlighting the critical need for research focused on adversarial robustness. This study investigates the behavior of multimodal models under various adversarial attack scenarios. We conducted experiments involving two modalities: images and texts. Our findings indicate that multimodal models exhibit enhanced resilience against adversarial attacks compared to their single-modality counterparts. This supports our hypothesis that the integration of multiple modalities …

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Dynamic Job Recommendation By Profiling Undergraduates Academic Performances, Bao-Ling Foo, Choo-Yee Ting, Hui-Ngo Goh, Albert Quek, Chin-Leei Cham Jun 2025

Dynamic Job Recommendation By Profiling Undergraduates Academic Performances, Bao-Ling Foo, Choo-Yee Ting, Hui-Ngo Goh, Albert Quek, Chin-Leei Cham

Journal of Informatics and Web Engineering

Job-seeking tasks are always challenging. Often, job recommendation systems require human intervention in the job-seeking process. Therefore, the study focuses on recommendation of most relevant job sectors and prioritizing companies based on a student’s profile. The objectives of this study are: (i) to identify important features that optimize job recommendation, (ii) to construct a predictive model that recommends most relevant job sectors, and (iii) to recommend companies by computing the similarity between student and job profiles. In this study, the dataset was collected from Graduate Tracer Study from a university. Additionally, a job dataset was collected to extend the training …

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Hidden Layer Reshaping On Convolutional Neural Network, Alan Delgado May 2025

Hidden Layer Reshaping On Convolutional Neural Network, Alan Delgado

Open Access Theses & Dissertations

Artificial Intelligence (AI) technologies have become really popular in recent years. From ChatGPT to Tesla cars, many applications can benefit from these type of technologies. Automotive, healthcare, biomedical, cybersecurity, finances, and retail are some of the fields that take advantage of it. It has been seen that AI can solve complex problems, but there is still work to be done to optimize it. A deep learning neural network (DLNN) tries to simulate how a human brain operates. These DLNNs are made up of artificial neurons which are connected by weight that are modified when the network is trained. These networks …

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Improved Accuracy For Heart Disease Diagnosis Using Machine Learning Techniques, Neeraja Joshi, Tejal Dave Feb 2025

Improved Accuracy For Heart Disease Diagnosis Using Machine Learning Techniques, Neeraja Joshi, Tejal Dave

Journal of Informatics and Web Engineering

This work primarily focuses on diagnosis of heart disease before explicit visit to the expert doctor. Machine learning based systems have been found useful in medical diagnosis applications because of their ability to learn human like expertise and to utilize acquired knowledge for diagnosis. This work is performs classification of heart disease utilizing subject’s vital parameters. Pathological laboratory results available after testing are not understood by common people and patients have to wait till they visit expert doctors for inference. In this paper, traditional methods like linear regression  to various machine learning based systems including back propagation neural network, support …

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Classifying Items With The Rating Values 3 Using Text Reviews To Improve The Recommendation Accuracy In The Collaborative Filtering Approach, Ali Mohsin Ahmed Al-Sabaawi, Mohsin Hasan Hussein, Muyassar Dalli Jan 2025

Classifying Items With The Rating Values 3 Using Text Reviews To Improve The Recommendation Accuracy In The Collaborative Filtering Approach, Ali Mohsin Ahmed Al-Sabaawi, Mohsin Hasan Hussein, Muyassar Dalli

Karbala International Journal of Modern Science

Collaborative filtering is a common aspect recently used in e-commerce to increase sales and overcome information overload. One significant limitation in collaborative filtering is data sparseness. Several studies have proposed alleviating this issue by utilizing extra information such as users’ reviews. However, the researchers have been concerned with using entire reviews irrespective of the users’ ratings. This requires extra processing time and might perplex the recommendation decision. In this study, after analyzing the users’ ratings and reviews, it was noted that when the rating values are 4 or 5, most of the reviews accompanying these ratings are positive. Otherwise, when …

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Deep Learning-Based Ensemble Two-Step Classification Of Medical Images Using Cnn Architectures And Ensemble Methods, Noreliz Alorico Jan 2025

Deep Learning-Based Ensemble Two-Step Classification Of Medical Images Using Cnn Architectures And Ensemble Methods, Noreliz Alorico

Master's Theses or Doctor of Nursing Practice

Breast cancer remains one of the most common cancers amongst women globally. Early detection is crucial for improving survival rates. While mammography is widely used and an effective imaging technique, it can sometimes yield false positive or false negatives. Mammogram interpretation is highly operator-dependent, introducing variability and the potential for diagnostic errors. Additionally, mammographic images have limitations, such as low contrast in breast tissue and overlapping structures that can obscure lesions or mimic abnormalities. These limitations can lead to unnecessary biopsies or delayed diagnosis. These challenges highlight the needs for advanced and data driven diagnostic tools to support and enhance …

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Applying Machine Learning And Optimization Algorithms To Perform Feature Selection, Shizhao Yu Jan 2025

Applying Machine Learning And Optimization Algorithms To Perform Feature Selection, Shizhao Yu

Theses and Dissertations (Comprehensive)

The objective of feature selection in the realms of machine learning and data mining is integral, serving as an efficient mechanism to eradicate redundant or irrelevant features, and subsequently augmenting the performance of predictive models. In the contemporary landscape of big data, with the escalating dimensionality of datasets, the efficacy of traditional feature selection methodologies is compromised, due to their computational complexity and ineptitude in addressing the curse of dimensionality. This thesis posits a pioneering feature selection framework that amalgamates machine learning with advanced optimization algorithms. The methodology employs a Support Vector Machine (SVM), in conjunction with a cutting-edge metaheuristic …

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Differentiating Opioid Use Disorder From Healthy Controls Via Ml Analysis Of Rs-Fmri Networks, Ahmed Temtam, Megan A. Witherow, Liangsuo Ma, M. Shibly Sadique, F. Gerard Moeller, C. Kenneth, Dianne Wright, Khan M. Iftekharuddin Jan 2025

Differentiating Opioid Use Disorder From Healthy Controls Via Ml Analysis Of Rs-Fmri Networks, Ahmed Temtam, Megan A. Witherow, Liangsuo Ma, M. Shibly Sadique, F. Gerard Moeller, C. Kenneth, Dianne Wright, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Objectives/Goals: This work aims to identify functional brain networks that differentiate opioid use disorder (OUD) subjects from healthy controls (HC) using machine learning (ML) analysis of resting-state fMRI (rs-fMRI). We investigate the default mode network (DMN), salience network (SN), and executive control network (ECN), as well as demographic features. Methods/Study Population: This work uses high-resolution rs-fMRI data from a National Institute on Drug Abuse study (IRB #HM20023630) with 31 OUD and 45 HC subjects. We extract rs-fMRI blood oxygenation level-dependent (BOLD) features from the DMN, SN, and ECN. The Boruta ML algorithm identifies statistically significant features and brain activity mapping …

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Optiselect And Enshap: Integrating Machine Learning And Game Theory For Ischemic Stroke Prediction, Pritam Chakraborty, Anjan Bandyopadhyay, Sricheta Parul, Sujata Swain, Partha Sarathy Banerjee, Tapas Si, Hong Qin, Saurav Mallik Jan 2025

Optiselect And Enshap: Integrating Machine Learning And Game Theory For Ischemic Stroke Prediction, Pritam Chakraborty, Anjan Bandyopadhyay, Sricheta Parul, Sujata Swain, Partha Sarathy Banerjee, Tapas Si, Hong Qin, Saurav Mallik

Computer Science Faculty Publications

Stroke analysis using game theory and machine learning techniques. The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. For each sample, the top 3, 4, and 5 features are evaluated and selected to evaluate their performance. The Shapley value method was used to rank the models using their best four features based on their predictive capabilities. As a result, better-performing …

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A Review Of Image Steganography Based On Metaheuristic Optimization Algorithms, Fatima Abdulhussain Khalil, Ammar Ali Neamah, Hasanen Alyasiri Jan 2025

A Review Of Image Steganography Based On Metaheuristic Optimization Algorithms, Fatima Abdulhussain Khalil, Ammar Ali Neamah, Hasanen Alyasiri

Mesopotamian Journal of Computer Science

Due to the widespread popularity of digital images on the Internet, image-based steganography has become a widely adopted technique for embedding secret information into everyday visual content. In parallel, steganalysis plays a vital role in digital forensics and information security by seeking to uncover hidden content within these images. Although steganographic techniques—particularly those employing adaptive embedding strategies—have made significant progress, many steganalysis approaches still struggle to generalize effectively across different image types and embedding methods. This contrast highlights the need for more intelligent, flexible, and robust analysis frameworks. This review examines steganographic techniques for digital images and the application of …

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An Extensive Analysis Of Match-Tracking Methods For Artmap, Niklas M. Melton, Leonardo Enzo Brito Da Silva, Donald C. Wunsch Jan 2025

An Extensive Analysis Of Match-Tracking Methods For Artmap, Niklas M. Melton, Leonardo Enzo Brito Da Silva, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper identifies and studies five match-tracking (MT) methods in the adaptive resonance theory (ART) literature and conducts a detailed comparative analysis of these in ARTMAP applications. We focus on model performance for each MT method with respect to time and space efficiency as well as classification accuracy. Experimental results indicate that one MT variant, used in ARTMAP applications for the first time in this work, provides significant improvements in computational efficiency: depending on the ARTMAP variant, it was able to achieve up to one order of magnitude reduction in both time and space requirements, albeit with a compromise in …

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From Data To Insight: A Machine Learning Approach In Classifying Dairy Cow Productivity Level And Identifying Important Influencing Variables, Fatkhurokhman Fauzi, Achmad Fauzan, Rhendy K P Widiyanto, Khairil Anwar Notodiputro, Bagus Sartono Jan 2025

From Data To Insight: A Machine Learning Approach In Classifying Dairy Cow Productivity Level And Identifying Important Influencing Variables, Fatkhurokhman Fauzi, Achmad Fauzan, Rhendy K P Widiyanto, Khairil Anwar Notodiputro, Bagus Sartono

Knowledge Engineering and Data Science

Identifying influential predictor variables is crucial for enhancing model interpretability in supervised classification. This study applies Permutation Variable Importance (PVI), a model-agnostic approach, to evaluate variable relevance after model fitting. Using data from the 2024 Indonesia Dairy Cow Productivity Survey, this research investigates five classification techniques: (1) Support Vector Machine (SVM), (2) Neural Network (NN), (3) k-Nearest Neighbors (kNN), (4) Naïve Bayes Classifier (NB), and (5) Logistic Regression (LR), to identify which method(s) yield the best performance based on evaluation metrics such as accuracy, sensitivity, and specificity. PVI is employed to identify the most influential predictor variables within the best-performing …

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Balancing Performance And Efficiency: An Autoencoder Approach To Api-Based Malware Detection, Selma Bouraoui Jan 2025

Balancing Performance And Efficiency: An Autoencoder Approach To Api-Based Malware Detection, Selma Bouraoui

Graduate Theses/Dissertations

The constant evolution of malware presents a critical challenge to today's interconnected world. It poses an increasing threat on different scales, spanning from individuals, organizations to critical infrastructures such as government’s security. Hackers continuously develop new techniques to evade detection methods. When confronted with the high volume and variety of malware, conventional approaches tend to struggle to perform in robust, accurate and timely manner. This thesis explores the application of deep learning methods to improve malware detection and classification techniques. By analyzing API call sequences, the proposed approach leverages Autoencoders to compress high-dimensional malware data into more optimized representations that …

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A Fast Framework For Generating Radioactive Mixture Spectra And Its Application To Remote High-Performance Mixture Identification, Chiman Kwan, Bulent Ayhan, Adam Stavola, Kazi Aminul Islam, Hongfang Zhang, Jiang Li Jan 2025

A Fast Framework For Generating Radioactive Mixture Spectra And Its Application To Remote High-Performance Mixture Identification, Chiman Kwan, Bulent Ayhan, Adam Stavola, Kazi Aminul Islam, Hongfang Zhang, Jiang Li

Electrical & Computer Engineering Faculty Publications

Remote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present mixture isotope identification using data generated by the fast framework. Researchers have examined a range of conventional and recent algorithms within the fields of machine learning and deep learning. An application to uranium enrichment-level prediction has been included. Extensive simulation experiments validated the efficacy of the proposed framework.

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Land Target Detection Algorithm In Remote Sensing Images Based On Deep Learning, Wenyi Hu, Xiaomeng Jiang, Jiawei Tian, Shitong Ye, Shan Liu Jan 2025

Land Target Detection Algorithm In Remote Sensing Images Based On Deep Learning, Wenyi Hu, Xiaomeng Jiang, Jiawei Tian, Shitong Ye, Shan Liu

Electrical & Computer Engineering Faculty Publications

Remote sensing technology plays a crucial role across various sectors, such as meteorological monitoring, city planning, and natural resource exploration. A critical aspect of remote sensing image analysis is land target detection, which involves identifying and classifying land-based objects within satellite or aerial imagery. However, despite advancements in both traditional detection methods and deep-learning-based approaches, detecting land targets remains challenging, especially when dealing with small and rotated objects that are difficult to distinguish. To address these challenges, this study introduces an enhanced model, YOLOv5s-CACSD, which builds upon the YOLOv5s framework. Our model integrates the channel attention (CA) mechanism, CARAFE, and …

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Development Of Robot Feature For Stunting Analysis Using Long-Short Term Memory (Lstm) Algorithm, Muhammad Rahadian Abdurrahman, Halim Al Aziz, Farras Adhani Zayn, Muhammad Agus Purnomo, Heru Agus Santoso Oct 2024

Development Of Robot Feature For Stunting Analysis Using Long-Short Term Memory (Lstm) Algorithm, Muhammad Rahadian Abdurrahman, Halim Al Aziz, Farras Adhani Zayn, Muhammad Agus Purnomo, Heru Agus Santoso

Journal of Informatics and Web Engineering

Stunting prevalence in Indonesia persists as a significant challenge necessitating concerted efforts from all stakeholders. We developed robot for stunting analysis using deep learning algorithm. It aligns with the Sustainable Development Goal (SDG) agenda, specifically targeting SDG 3, which focuses on ensuring good health and well-being for all. Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) designed to overcome the vanishing gradient problem in traditional RNNs. In general, either LSTM can be used in analysis. This study aims to classify stunting based on age and height using LSTM. The LSTM model was trained with 50 epochs …

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A Generalized Machine Learning Model For Long-Term Coral Reef Monitoring In The Red Sea, Justin J. Gapper, Surendra Maharjan, Wenzhao Li, Erik Linstead, Surya Prakash Tiwari, Mohamed A. Qurban, Hesham El-Askary Sep 2024

A Generalized Machine Learning Model For Long-Term Coral Reef Monitoring In The Red Sea, Justin J. Gapper, Surendra Maharjan, Wenzhao Li, Erik Linstead, Surya Prakash Tiwari, Mohamed A. Qurban, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Coral reefs, despite covering less than 0.2 % of the ocean floor, harbor approximately 35 % of all known marine species, making their conservation critical. However, coral bleaching, exacerbated by climate change and phenomena such as El Niño, poses a significant threat to these ecosystems. This study focuses on the Red Sea, proposing a generalized machine learning approach to detect and monitor changes in coral reef cover over an 18-year period (2000–2018). Using Landsat 7 and 8 data, a Support Vector Machine (SVM) classifier was trained on depth-invariant indices (DII) derived from the Gulf of Aqaba and validated against ground …

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Putting Gpt-4o To The Sword: A Comprehensive Evaluation Of Language, Vision, Speech, And Multimodal Proficiency, Sakib Shahriar, Brady D. Lund, Nishith Reddy Mannuru, Muhammad Arbab Arshad, Kadhim Hayawi, Ravi Varma Kumar Bevara, Aashrith Mannuru, Laiba Batool Sep 2024

Putting Gpt-4o To The Sword: A Comprehensive Evaluation Of Language, Vision, Speech, And Multimodal Proficiency, Sakib Shahriar, Brady D. Lund, Nishith Reddy Mannuru, Muhammad Arbab Arshad, Kadhim Hayawi, Ravi Varma Kumar Bevara, Aashrith Mannuru, Laiba Batool

All Works

As large language models (LLMs) continue to advance, evaluating their comprehensive capabilities becomes significant for their application in various fields. This research study comprehensively evaluates the language, vision, speech, and multimodal capabilities of GPT-4o. The study employs standardized exam questions, reasoning tasks, and translation assessments to assess the model’s language capability. Additionally, GPT-4o’s vision and speech capabilities are tested through image classification and object-recognition tasks, as well as accent classification. The multimodal evaluation assesses the model’s performance in integrating visual and linguistic data. Our findings reveal that GPT-4o demonstrates high accuracy and efficiency across multiple domains in language and reasoning …

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