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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Akram Heidarizadeh Connor Hatfield Lorenzo Lazzarotto HanQin Cai George Atia Dept. of Electrical and Computer Engineering University of Central Florida Orlando FL USA School of Technology Pontifícia Universidade Católica do Rio Grande do Sul Porto Alegre Brazil Dept. of Statistics and Data Science Dept. of Computer Science University of Central Florida Orlando FL USA Dept. of Electrical and Computer Engineering Dept. of Computer Science University of Central Florida Orlando FL USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Traditional adversarial attacks typically aim to alter the predicted labels of input images by generating perturbations that are imperceptible to the human eye. However, these approaches often lack explainability. Moreover, most existing work on adversarial attacks focuses on single-stage classifiers, but multi-stage classifiers are largely unexplored. In this paper, we introduce instance-based adversarial attacks for multi-stage classifiers, leveraging Layer-wise Relevance Propagation (LRP), which assigns relevance scores to pixels based on their influence on classification outcomes. Our approach generates explainable adversarial perturbations by utilizing LRP to identify and target key features critical for both coarse and fine-grained classifications. Unlike conventional attacks, our method not only induces misclassification but also enhances the interpretability of the model’s behavior across classification stages, as demonstrated by experimental results.

关键词： Perturbation methods Decision making Signal processing Acoustics Speech processing

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YOLOv8-Based Pothole Detection for Autonomous Driving Safety

YOLOv8-Based Pothole Detection for Autonomous Driving Safety

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Poongothai P Sabareswari T C R Ramesh Veeramalai Sankaradass R. Ponnusamy Dept of Mathematics Chennai Institute of Technology Tamil Nadu India Dept of Electronics and Communication Engineering Chennai Institute of Technology Tamil Nadu India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Dept of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu India Dept. of Artificial Intelligence and Data Science Madha Engineering College Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Undeniably, autonomous vehicles are the most dramatic evolution of transportation yet. They offer new safety, efficiency, and convenience levels that have never been seen before. Still, some natural and unexpected conditions continually strike a blow against the safety of self-driving systems, such as potholes on roads. This paper introduces a new, cloud-based safety solution for autonomous vehicles through artificial intelligence and implementation with the YOLOv8 object detection model, coupled with the deep learning platform Weights & Biases, or wandb. As the real-time pothole-detecting system is developed, issues related to navigation appear while increasing the safety level. This work adopts the YOLOv8 object detection model in the use case due to its higher accuracy and much faster object detection rate. This can classify and predict the object boundaries of a single network, thus being a good fit for quick and accurate detection. Upgrades will be made in sync with new conditions in roads and types of potholes as the right solution gains ready to change the future of transportation about autonomous transportation, paving the path for safer and more reliable self-driving vehicles. It directly addresses the acute challenge of pothole-related hazards. It builds the way to an even safer and more efficient future for autonomous mobility, thus showing tremendous potential for transforming advanced AI technologies.

关键词： Accuracy Navigation Roads Transportation Object detection Real-time systems Safety Reliability Artificial intelligence Autonomous vehicles

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Integrating Spatial and Frequency Information for Under-Display Camera Image Restoration

arXiv

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arXiv 2025年

作者： Ahn, Kyusu Kim, Jinpyo Park, Chanwoo Kim, JiSoo Lee, Jaejin Dept. of Data Science Seoul National University Seoul Korea Republic of Dept. of Computer Science and Engineering Seoul National University Seoul Korea Republic of Research Center Samsung Display Co. Ltd. Yongin Korea Republic of

Under-Display Camera (UDC) houses a digital camera lens under a display panel. However, UDC introduces complex degradations such as noise, blur, decrease in transmittance, and flare. Despite the remarkable progress, previous research on UDC mainly focuses on eliminating diffraction in the spatial domain and rarely explores its potential in the frequency domain. It is essential to consider both the spatial and frequency domains effectively. For example, degradations, such as noise and blur, can be addressed by local information (e.g., CNN kernels in the spatial domain). At the same time, tackling flares may require leveraging global information (e.g., the frequency domain). In this paper, we revisit the UDC degradations in the Fourier space and figure out intrinsic frequency priors that imply the presence of the flares. Based on this observation, we propose a novel multi-level DNN architecture called SFIM. It efficiently restores UDC-distorted images by integrating local and global (the collective contribution of all points in the image) information. The architecture exploits CNNs to capture local information and FFT-based models to capture global information. SFIM comprises a spatial domain block (SDB), a Frequency Domain Block (FDB), and an Attention-based Multi-level Integration Block (AMIB). Specifically, SDB focuses more on detailed textures such as noise and blur, FDB emphasizes irregular texture loss in extensive areas such as flare, and AMIB enables effective cross-domain interaction. SFIM’s superior performance over state-of-the-art approaches is demonstrated through rigorous quantitative and qualitative assessments across three UDC benchmarks. © 2025, CC BY-NC-SA.

关键词： Image reconstruction

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A Big data Analytic and IoT-Based Approach for Addressing Water Scarcity in Jordan

A Big Data Analytic and IoT-Based Approach for Addressing Wa...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Abdelraouf Ishtaiwi Nuha El-Khalili May Alnashashibi Mohammad Abu Arqoub Dept. of Data Science and Artificial Intelligence University of Petra Amman Jordan Department of Software Engineering University of Petra Amman Jordan Department of Computer Science University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Water scarcity critically threatens agricultural sus-tainability in Jordan, necessitating innovative technological solutions. This study presents a comprehensive framework that integrates Internet of Things (IoT) sensors with Big data Analytics to optimize water usage in agriculture. Employing a multi-stage approach, we conducted real-time data acquisition from diverse farms, processed the data using clustering algorithms, and implemented intelligent decision-making systems. Our analysis focused on key parameters such as soil moisture, temperature, rainfall, and wind speed across various farming locations. The methodology encompassed extensive field data collection, advanced analytics, and the deployment of smart irrigation systems. Results demonstrated a significant 25 % reduction in water consumption while maintaining optimal crop yields. Additionally, clustering identified three distinct farm categories, each requiring tailored water management strategies. This research contributes to the field of smart agriculture by providing practical, scalable solutions for regions facing severe water scarcity. The findings highlight the potential of advanced technologies to enhance agricultural sustainability and address critical water management challenges.

关键词： Temperature sensors Smart agriculture Irrigation Wind speed Soil moisture Big data Real-time systems Internet of Things Sustainable development Water resources

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Optimizing Production and Inventory Management for Defective Items in Manufacturing Systems 3

Optimizing Production and Inventory Management for Defective...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Ahmed, Khandakar Rabbi Hossain, Arafat Asif, MD Amaddus Bepary Mohammad, Mahathir Rahaman, Mustafizur Dewan, MD Arafat Miyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh Central Michigan University Dept. of Business Data Analytics Mt Pleasant United States Southern California State University Dept. of Business Administration and Management Los Angeles CA United States Southern California State University Dept. of Business Administration and Management United States Westcliff University Dept. of Cloud Engineering Irvine CA United States Los Angeles City College Dept. of Computer Science Los Angeles United States

ISBN: (纸本)9798331523893

Manufacturing defective items is one of the challenges for production efficiency and inventory control. Conventional approaches like Economic Order Quantity (EOQ) and Just-in-Time (JIT) often fall short of the flexibility needed to respond to quality variations in real time. It promotes defect detection and control by employing machine learning (ML) for inventory management. Four classifiers were tested using the UCI SECOM dataset (Bagging, AdaBoost, Gradient Boosting, and Random Forest) to identify defective items. We used effective preprocessing techniques to boost model performance, such as SMOTE for class balancing and PCA for reducing dimensionality and standardization. The bagging and random forest classifiers produced 96.6% and 96.9% test accuracies, with good precision and recall scores. Within this context, we propose an Adaptive Inventory Management Framework comprising machine learning algorithms incorporating predictive analytics to reduce waste, optimize production scheduling(s), and improve resource allocation. These results are consistent with defining Industry 4.0 strategies and show that ML has the potential to transform defect management and inventory optimization in manufacturing systems. © 2025 IEEE.

关键词： Defective Items Industry 4.0 Inventory Management Machine Learning Manufacturing Systems SECOM dataset

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Sentiment Analysis of Movie Reviews: Optimizing Predictions Through TF-IDF Vectorization

Sentiment Analysis of Movie Reviews: Optimizing Predictions ...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Mohd Irfan Anne Sreyas Venkatesam Hariharan Shanmugasundaram Mahendra Kumar Gourisaria Subrata Chowdhury Sudhansu Shekhar Patra Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad Telangana India Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India School of Computer Engineering Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India Dept. of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies(A) Chittoor Andhra Pradesh India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Sentiment analysis model is aimed to accurately interpret movie reviews, addressing challenges like sarcasm and noisy data. By integrating multiple machine learning classifiers Naive Bayes, Random Forest, K-Nearest Neighbors, Logistic Regression, Decision Tree, and XGBoost—the model enhances sentiment prediction accuracy. The Term Frequency-Inverse Document Frequency (TF-IDF) method is used to extract features from a dataset of 50,000 movie reviews. Performance is evaluated using accuracy, precision, recall, and F1-score, with Logistic Regression achieving the best results, including an highest accuracy of 84.33 % and an F1-score of 0.847. In comparison to traditional methods, this multi-classifier approach offers a deeper understanding of movie reviews and its influence on audience decisions. The study demonstrates the potential of using these machine learning techniques to provide real-time, scalable sentiment analysis, which is crucial for filmmakers and marketers seeking insights into audience satisfaction and box office performance.

关键词： Sentiment analysis Logistic regression Accuracy Reviews Predictive models Motion pictures Feature extraction Real-time systems Noise measurement Random forests

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AI-Powered Personalized Deep Learning Framework for Early Detection of Glioblastoma Using Multi-Modal MRI data

AI-Powered Personalized Deep Learning Framework for Early De...

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Sunitha T Veeramalai Sankaradass V.K Manindra Manish Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India Bachelor of Medicine and Bachelor of Science Dhanalakshmi Srinivasan Institute of Medical Sciences and Hospital Perambalur India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The prognosis of glioblastoma, which stands as the most malignant variant of a brain tumour, is clearly unfavourable due to the late diagnosis and the severe heterogeneity it is known to possess. Standard diagnostic techniques nowadays that employ MRI data quite often have a low-level performance in terms of diagnosing glioblastoma during its early stages owing to the diverse manner in which the tumours may grow in various patients. In light of this background, we present a new deep learning framework suitable for multi-modal MRI data and aimed at improving glioblastoma prognosis at an early stage. The framework utilizes convolutional neural networks (CNNs) to obtain features from T1, T2, and FLAIR MRI images, and combines this with an AI-guided individualized diagnostic system. This model utilizes relevant clinical data such as age and genetic data in a more individualized manner to meet patient’s needs during diagnosis. One more novel feature of our approach is the ensemble learning that combines CNNs with RNNs to detect minor tumour deposits more effectively over time. The proposed framework delivered an AUC of 0.94 on large glioblastoma MRI databases, thus progressing the early-stage diagnosis of the tumour type which was the aim. This advanced personalized method not only allows for superior diagnosis but also improves on the treatment plans that are available for such patients. Further development will concern clinical verification as well as increasing the capacity of the framework.

关键词： Deep learning Accuracy Sensitivity Magnetic resonance imaging Genetics Feature extraction History Prognostics and health management Standards Tumors

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A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectives

arXiv

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arXiv 2025年

作者： Hossain, Delower Chen, Jake Y. Dept. of Computer Science The University of Alabama Birmingham United States Dept. of Genetic Biomedical Engineering Informatics and Data Science Heersink School of Medicine The University of Alabama Birmingham United States Systems Pharmacology AI Research Center The University of Alabama Birmingham United States

Over the last few decades, Artificial Intelligence (AI) scientists have been conducting investigations to attain human-level performance by a machine in accomplishing a cognitive task. Within machine learning, the ultimate aspiration is to attain Artificial General Intelligence (AGI) through a machine. This pursuit has led to the exploration of two distinct AI paradigms. Symbolic AI, also known as classical or GOFAI (Good Old-Fashioned AI) and Connectionist (Sub-symbolic) AI, represented by Neural Systems, are two mutually exclusive paradigms. Symbolic AI excels in reasoning, explainability, and knowledge representation but faces challenges in processing complex real-world data with noise. Conversely, deep learning (Black-Box systems) research breakthroughs in neural networks are notable, yet they lack reasoning and interpretability. Neuro-symbolic AI (NeSy), an emerging area of AI research, attempts to bridge this gap by integrating logical reasoning into neural networks, enabling them to learn and reason with symbolic representations. While a long path, this strategy has made significant progress towards achieving common sense reasoning by systems. This article conducts an extensive review of over 977 studies from prominent scientific databases (DBLP, ACL, IEEExplore, Scopus, PubMed, ICML, ICLR), thoroughly examining the multifaceted capabilities of Neuro-Symbolic AI, with a particular focus on its healthcare applications, particularly in drug discovery, and Protein engineering research. The survey addresses vital themes, including reasoning, explainability, integration strategies, 41 healthcare-related use cases, benchmarking, datasets, current approach limitations from both healthcare and broader perspectives, and proposed novel approaches for future experiments. Moreover, it identifies critical challenges in applying Neuro-Symbolic AI to medicine and represents a comprehensive exploration of its transformative potential in the biomedical field. © 2025, CC BY-NC

关键词： knowledge representation

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FLODA: Harnessing Vision-Language Models for Deepfake Assessment

FLODA: Harnessing Vision-Language Models for Deepfake Assess...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Seunghyeon Park Youngho Bae Gunhui Han Alexander W Olson School of Integrated Technology Yonsei University Incheon Republic of Korea dept. Data Science Hanyang University Seoul Republic of Korea dept. Mechanical Engineering Yonsei University Seoul Republic of Korea Centre for Analytics and Artificial Intelligence Engineering University of Toronto Toronto Canada

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

The proliferation of advanced generative AI models has ushered in a new era of image creation, where the distinction between real and synthetic content has become increasingly blurred. This phenomenon poses significant challenges to the integrity and trustworthiness of digital content and AI systems. Recently, Vision-Language Models (VLMs) have shown great potential in addressing these challenges due to their superior performance in vision-language tasks. In this paper, we propose FLODA (FLorence-2 Optimized for Deepfake Assessment), an advanced method designed to surpass existing deepfake detection models. FLODA leverages the VLM model, Florence-2, and extends it by integrating image captioning and authenticity assessment into a single end-to-end architecture. By utilizing caption information, FLODA enhances visual analysis with contextual details, streamlining both caption generation and deepfake detection. To ensure FLODA's efficacy, we performed an ablation study to identify the optimal model configuration and demonstrated its contributions to performance improvements. We also compared our best FLODA model against existing benchmarks through rigorous evaluation. Notably, FLODA demonstrates strong generalization, showcasing its robustness across diverse scenarios with an average accuracy of 97.14%. This research advances digital content integrity and AI trustworthiness, providing a promising direction for future VLM applications. Code and models can be found at https://***/byh711/FLODA.

关键词： Training Deepfakes Visualization Accuracy Generative AI Streaming media Robustness Faces Resilience Context modeling

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A Real Time approach for detecting and counting Vehicles using OpenCV and NumPy

A Real Time approach for detecting and counting Vehicles usi...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Suneetha, Irala Prathima, Ch. Sirisala, Srinivasulu Mahalakshmi, S.S.D.K. Balaji Nanda Kumar Reddy, K. Sambana, Bosubabu Annamacharya Institute of Technology and Sciences Ece Department Tirupati India Mohan Babu University Dept. of Ds Tirupati India Cvr College of Engineering Department of Cse Telagana India Lendi Institute of Engineering and Technology Department of Cse Vizianagaram India Annamacharya Institute of Technology and Sciences Eee Department Tirupati India Mohan Babu University School of Computing Department of Data Science Andhra Pradesh Tirupathi India

ISBN: (纸本)9798331508685

Vehicle detection and counting using OpenCV is a crucial aspect of modern traffic management systems. This mini project aims to develop a robust solution for detecting vehicles in real-time video streams and accurately counting them as they pass through a designated area. OpenCV, a popular computer vision library, is utilized for its extensive functionality and ease of implementation. First, the video stream is captured and processed to extract individual frames. Subsequently, each frame undergoes preprocessing techniques such as resizing, grayscale conversion, and noise reduction to enhance vehicle detection accuracy. Vehicle detection is achieved through the implementation of a deep learning- based object detection algorithm, such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector). These algorithms are trained on large datasets to recognize vehicles with high precision. Once vehicles are detected, a tracking algorithm is employed to maintain continuity between frames and accurately count the number of vehicles passing through the specified region of interest. This tracking algorithm ensures that vehicles are not double-counted and provides a reliable count for traffic analysis purposes. © 2025 IEEE.

关键词： Noise abatement

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