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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Hossain, Md Junayed Monir, Md Fahad Ahmed, Tarem Department of Computer Science & Engineering Bangladesh Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331504847

A global public health concern, Chronic Kidney Disease causes renal failure, cardiovascular disease, and premature death. Given the frequency of kidney diseases and their significant impact on public health, detection of any abnormalities in the kidney is crucial. Effective patient treatment and medical care planning depend on the early and precise diagnosis of a variety of kidney abnormalities. Early detection enable treatments that enhance patient outcomes, slow the progression of disease, and lessen the strain on healthcare systems. In order to detect anomalies in kidney, we utilized the Region of Interest (ROI) and Pixel Reduction techniques separately in this work. We have applied the Bicubic Interpolation method to reduce the size of individual pixels in an image while preserving a significant data of the original image data. A novel hybrid strategy that aims to preserve diagnostic data while reducing computing complexity uses the original images for ROI extraction. The deep learning method is then independently trained and tested using the pixel-reduced and localized dataset. In order to categorize the images, we use Inception V3 and EfficientNet B7 as Deep Learning models. Using the ROI-extracted data, we were able to achieve approximately 99.75% accuracy with the EfficientNet B7 model. Moreover, the same algorithm and dataset were used to obtain 98.45%, 99.02%, and 98.78% Precision, Recall, and F1-score, respectively. This framework provides medical professionals with a helpful prognostic tool to early detect identify various forms of kidney abnormalities. © 2025 IEEE.

关键词： Surgery

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Comparative Performance of Object Detection Algorithms Under Low Illumination Conditions

Comparative Performance of Object Detection Algorithms Under...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Hossain, Md Junayed Rahman, Shoptorshi Monir, Md Fahad Ahmed, Tarem Department of Computer Science & Engineering Bangladesh Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331504847

Real-time object detection is pivotal in various computer vision and robotics applications, such as security surveillance, autonomous navigation, and interactive systems. This study evaluates several object detection algorithms under low illumination conditions, comparing traditional methods including SIFT, SURF, ORB, BRISK, KAZE, and AKAZE with the deep learning model YOLOv5. To simulate these conditions, we utilized the COCO dataset and implemented a sequence of preprocessing procedures, such as adjusting the gamma correction throughout the dataset, introducing Gaussian noise, and equalizing the histogram to improve image contrast. Here, YOLOv5 exhibited superior performance, achieving a detection accuracy of 92.47% and a processing time of 45 milliseconds per image. Traditional algorithms like SURF and KAZE, although slower, demonstrated precision rates of 78.54% and 76.86%, respectively. YOLOv5 also identified the highest number of key points and matches. Our findings indicate that while YOLOv5 excels in real-time detection under low-light conditions, traditional methods are effective for specific tasks in resource-constrained environments. This study benchmarks these algorithms and proposes a hybrid approach to enhance real-time object detection systems under low illumination conditions. © 2025 IEEE.

关键词： Deep learning

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3D Vehicle Detection in Roadside Traffic Flow Using Complex-YOLO 23

3D Vehicle Detection in Roadside Traffic Flow Using Complex-...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Cordova, Jonathan Jiang, Xunfei Jia, Xudong Li, Bingbing California State University Department of Computer Science NorthridgeCA91330 United States College of Engineering and Computer Science California State University NorthridgeCA91330 United States California State University Department of Manufacturing Systems Engineering and Management NorthridgeCA91330 United States

ISBN: (纸本)9798350374889

Vehicle detection and classification are important for intelligent transportation planning, directly impacting traffic flow and management efficiency. There have been a lot of research on vehicle detection and classification for autonomous driving, but research on roadside traffic flow has not been fully investigated. This research explores various methods for vehicle detection and classification that could be applied to 3D roadside traffic flow analysis. Using data collected from a Velodyne 32C Lidar sensor 3D camera and a stereo-based depth 2D camera, this project implemented a Complex-YOLO projection-based model, by converting 3D point cloud data into a 2D bird-eye-view projection and applying the YOLO model for vehicle detection and classification. Our experimental results demonstrated that using transfer learning as a training technique, along with normalization of the rotation angle, enhanced the model performance for vehicle detection and classification. Without normalization of the rotation angle, the highest precision achieved was 89.71 %, with a recall of 85.15 %. With normalization of the rotation angle, the best model achieved a precision of 92.20 % with a recall of 84.90 %, demonstrating the effectiveness of both transfer learning and normalization in handling and aligning the dataset for more effective learning. © 2024 IEEE.

关键词： Vehicle detection

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SMART-WRITE: Adaptive Learning-Based Write Energy Optimization for Phase Change Memory 15

SMART-WRITE: Adaptive Learning-Based Write Energy Optimizati...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Desai, Mahek Quinn, Rowena Asadinia, Marjan California State University Computer Science Department Northridge United States Illinois Central College Computer Science Department Peoria United States

ISBN: (纸本)9798331507695

As dynamic random access memory (DRAM) and other current transistor-based memories approach their scalability limits, the search for alternative storage methods becomes increasingly urgent. Phase-change memory (PCM) emerges as a promising candidate due to its scalability, fast access time, and zero leakage power compared to many existing memory technologies. However, PCM has significant drawbacks that currently hinder its viability as a replacement. PCM cells suffer from a limited lifespan because write operations degrade the physical material, and these operations consume a considerable amount of energy. For PCM to be a practical option for data storage-which involves frequent write operations-its cell endurance must be enhanced, and write energy must be reduced. In this paper, we propose SMART-WRITE, a method that integrates neural networks (NN) and reinforcement learning (RL) to dynamically optimize write energy and improve performance. The NN model monitors real-time operating conditions and device characteristics to determine optimal write parameters, while the RL model dynamically adjusts these parameters to further optimize PCM's energy consumption. By continuously adjusting PCM write parameters based on real-time system conditions, SMART-WRITE reduces write energy consumption by up to 63% and improves performance by up to 51% compared to the baseline and previous models. © 2025 IEEE.

关键词： Scalability

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A Real-Time Traffic Monitoring System Based on YOLOv8 for Vehicle Detection and Classification

A Real-Time Traffic Monitoring System Based on YOLOv8 for Ve...

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2024 IEEE International Conference on Green Energy and Smart Systems, GESS 2024

作者： Castillo, Jimwell Rosculete, Robin Babayans, Raymond Gharapeti Merzoian, Anthony Groves, Kaylee Calva, Gisela Dollente, Alma Jia, Xudong Li, Bingbing Jiang, Xunfei California State University Department of Computer Science NorthridgeCA91330 United States College of Engineering and Computer Science California State University NorthridgeCA91330 United States California State University Department of Manufacturing Systems Engineering and Management NorthridgeCA91330 United States

ISBN: (纸本)9798350379617

Urban traffic congestion presents significant challenges that require efficient and intelligent management solutions. Traditional traffic monitoring methods lack precision and adapt-ability, especially under varying conditions. Our project aims to develop a real-time traffic monitoring system using 2D camera technology and machine learning algorithms to address these gaps. The system includes a camera array for data collection, a data processing unit, and a user interface for real-time monitoring. Our solution employs YOLOv8 for accurate vehicle detection and classification and is demonstrated through rigorous testing. The system offers a robust framework for enhancing urban traffic management, reducing congestion, and improving road safety. Experimental results highlight the system's effectiveness in data aggregation, storage, and real-time processing. © 2024 IEEE.

关键词： Traffic congestion

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Deep Learning Approach for Incongruous Data: Dataset Matters 23

Deep Learning Approach for Incongruous Data: Dataset Matters

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Torabinejad, Tina Sood, Kanika College of Engineering and Computer Science California State University FullertonCA92831 United States California State University Faculty of College of Engineering and Computer Science FullertonCA92831 United States

ISBN: (纸本)9798331528041

Today, humans seek opportunities to grow professionally and improve their mental health. As a result, anxiety and depression are now prominent to the point that people often accept them as part of their usual regular life routine. According to the World Health Organization (WHO), more than 300 million people around the world are suffering from depression and anxiety disorder. Therefore, being able to detect risk factors that lead to depression can be beneficial to the healthcare system. We use machine learning techniques to detect depression risk factors using a medical survey dataset, the Behavioral Risk Factor Surveillance System (BRFSS), available from the Centers for Disease Control and Prevention (CDC). This survey is obtained explicitly through telephone calls from adults 18 years and older who live in the United states of America. Oversampling and undersampling techniques and eight machine learning techniques are used to evaluate further and examine the data set. Among all, the SMOTE oversampling techniques show enhanced performance over the undersampling and no sampling techniques, and the deep neural network generates the best outcome compared to other techniques. © 2024 IEEE.

关键词： Disease control

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Spatio-temporal Graph-Based Generation and Detection of Adversarial False Data Injection Evasion Attacks in Smart Grids

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6601-6616页

作者： Takiddin, Abdulrahman Ismail, Muhammad Atat, Rachad Serpedin, Erchin Florida State University Department of Electrical and Computer Engineering FAMU-FSU College of Engineering TallahasseeFL32310 United States Tennessee Tech University Department of Computer Science CookevilleTN38505 United States Lebanese American University Department of Computer Science and Mathematics Beirut1102-2801 Lebanon Texas A&M University Department of Electrical and Computer Engineering College StationTX77843 United States

Smart power grids are vulnerable to security threats due to their cyber-physical nature. Existing data-driven detectors aim to address simple traditional false data injection attacks (FDIAs). However, adversarial false data injection evasion attacks (FDIEAs) present a more serious threat as adversaries, with different levels of knowledge about the system, inject adversarial samples to circumvent the grid's attack detection system. The robustness of state-of-the-art graph-based detectors has not been investigated against sophisticated FDIEAs. Hence, this article answers three research questions. 1) What is the impact of utilizing spatio-temporal features to craft adversarial samples and how to select attack nodes? 2) How can adversaries generate surrogate spatio-temporal data when they lack knowledge about the system topology? 3) What are the required model characteristics for a robust detection against adversarial FDIEAs? To answer the questions, we examine the robustness of several detectors against five attack cases and conclude the following: 1) Attack generation with full knowledge using spatio-temporal features leads to 5%-26% and 2%-5% higher degradation in detection rate (DR) compared to traditional FDIAs and using temporal features, respectively, whereas centrality analysis-based attack node selection leads to 3%-11% higher degradation in DR compared to a random selection;2) Stochastic geometry-based graph generation to create surrogate adversarial topologies and samples leads to 3%-13% higher degradation in DR compared to traditional FDIAs;and 3) Adopting an unsupervised spatio-temporal graph autoencoder (STGAE)-based detector enhances the DR by 5-53% compared to benchmark detectors against FDIEAs. © 2020 IEEE.

关键词： Cyber attacks

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Intelligent Clinical Assistant for Personalized Responses and Clinical Summaries 39

Intelligent Clinical Assistant for Personalized Responses an...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Neupane, Subash Department of Computer Science & Engineering Mississippi State University United States

ISBN: (纸本)157735897X

Healthcare information is scattered across heterogeneous data sources, such as patient medical records, clinical guidelines, research literature, and online knowledge bases. Segmented information, both structured and unstructured, when integrated together using context augmentation - a knowledge fusion technique, has the ability to contextualize broader medical context. Current approaches lack knowledge aggregation that is necessary to generate personalized healthcare recommendations. I propose novel AI frameworks that leverage language models and hybrid retrieval techniques to aggregate multi source knowledge, enabling the generation of contextual and accurate medical response. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge engineering

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Game Theory Meets Data Augmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6080-6094页

作者： Kang, Yuhan Zare, Samira Lin, Alex Tong Han, Zhu Osher, Stanley Nguyen, Hien Van Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States University of California Department of Mathematics Los AngelesCA90095 United States University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Data augmentation is a critical component in building modern deep-learning systems. In this article, we propose MFG Augment, a novel data augmentation method based on the mean-field game (MFG) theory that can synthesize a sequence of data between every two images or features. The central idea is to consider every image as a distribution over its pixel or feature space. Using MFG theory, we can generate a time-continuous "path"from one distribution to another so that the points along the "path"are augmented images or features. Empirically, the experiment results on MNIST, CIFAR-10, and ImageNet demonstrate that the proposed technology has better generalization ability and higher classification accuracy as compared to several benchmark methods. More importantly, our MFG Augment improves the test accuracy significantly when the dataset size is small. MFG Augment consistently shows better affinity and diversity scores, two important empirical metrics for evaluating the generalization of data augmentation techniques. © 2020 IEEE.

关键词： Game theory

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Transfer Learning Enhanced Deep Reinforcement Learning for Volt-Var Control in Smart Grids

Transfer Learning Enhanced Deep Reinforcement Learning for V...

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Kumar, Kundan Ravikumar, Gelli Iowa State University Department of Computer Science and Electrical and Computer Engineering IA50010 United States

ISBN: (纸本)9798350352528

The integration of renewable energy resources has made power system management increasingly complex. DRL is a potential solution to optimize power system operations, but it requires significant time and resources during training. The control policies developed using DRL are specific to a single grid and require retraining from scratch for other grids. Training the DRL model from scratch is computationally expensive. This paper proposes a novel TL with a DRL framework to optimize VV C across different grids. This framework significantly reduces training time and improves VVC control performance by fine-tuning pre-trained DRL models for various grids. We developed a policy reuse classifier that transfers the knowledge from the IEEE-123 Bus system to the IEEE-13 Bus system. We performed an impact analysis to determine the effectiveness of TL. Our results show that TL improves the VVC control policy by 69.51 %, achieves faster convergence, and reduces the training time by 98.14%. © 2025 IEEE.

关键词： Deep reinforcement learning

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