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Virtual MOLLI Target: Generative Adversarial Networks Toward Improved Motion Correction in MRI Myocardial T1 Mapping

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Journal of Magnetic Resonance Imaging 2025年第1期61卷 209-219页

作者： Pan, Nai-Yu Huang, Teng-Yi Yu, Jui-Jung Peng, Hsu-Hsia Chuang, Tzu-Chao Lin, Yi-Ru Chung, Hsiao-Wen Wu, Ming-Ting Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Radiology Kaohsiung Veterans General Hospital Kaohsiung Taiwan School of Medicine College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan

Background: The modified Look-Locker inversion recovery (MOLLI) sequence is commonly used for myocardial T1 mapping. However, it acquires images with different inversion times, which causes difficulty in motion correction for respiratory-induced misregistration to a given target image. Hypothesis: Using a generative adversarial network (GAN) to produce virtual MOLLI images with consistent heart positions can reduce respiratory-induced misregistration of MOLLI datasets. Study Type: Retrospective. Population: 1071 MOLLI datasets from 392 human participants. Field Strength/Sequence: Modified Look-Locker inversion recovery sequence at 3 T. Assessment: A GAN model with a single inversion time image as input was trained to generate virtual MOLLI target (VMT) images at different inversion times which were subsequently used in an image registration algorithm. Four VMT models were investigated and the best performing model compared with the standard vendor-provided motion correction (MOCO) technique. Statistical Tests: The effectiveness of the motion correction technique was assessed using the fitting quality index (FQI), mutual information (MI), and Dice coefficients of motion-corrected images, plus subjective quality evaluation of T1 maps by three independent readers using Likert score. Wilcoxon signed-rank test with Bonferroni correction for multiple comparison. Significance levels were defined as P < 0.01 for highly significant differences and P < 0.05 for significant differences. Results: The best performing VMT model with iterative registration demonstrated significantly better performance (FQI 0.88 ± 0.03, MI 1.78 ± 0.20, Dice 0.84 ± 0.23, quality score 2.26 ± 0.95) compared to other approaches, including the vendor-provided MOCO method (FQI 0.86 ± 0.04, MI 1.69 ± 0.25, Dice 0.80 ± 0.27, quality score 2.16 ± 1.01). Data Conclusion: Our GAN model generating VMT images improved motion correction, which may assist reliable T1 mapping in the presence of respiratory motion

关键词： generative adversarial networks MOLLI registration

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Robust speech perception and classification-driven deep convolutional neural network with natural language processing

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Alexandria engineering Journal 2025年 123卷 358-368页

作者： Alshammari, Alya Alzaidi, Muhammad Swaileh A. Alrusaini, Othman Alshuhail, Asma Allafi, Randa Yafoz, Ayman Alsini, Raed Khadidos, Alaa O. Department of Applied Linguistics College of Languages Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Engineering and Applied Sciences Applied College Umm Al-Qura University Makkah Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Speech recognition systems use computer algorithms to interpret and process spoken words and translate them into text. Speech-to-text, or Speech detection, is the proficiency of a program or machine to find spoken words aloud and translate them into plain text. Speech detection systems are the most common complex systems that use wide-ranging research in computer engineering, linguistics, and computer science. In today's significant data era, several machine learning (ML) approaches for processing raw, unlabelled speech data still need to be improved. Deep learning (DL) models, on the other hand, with their powerful modelling ability for extensive data, are capable of directly processing unlabelled data and have become a crucial area of research in the domain of automated speech recognition. The DL model has recently gained popularity for voice disorders and automated speech detection. DL is an ML that exploits artificial neural networks (ANN) to gain meaningful insights from data. The DL approach demonstrated great success and achieved significant attention in different fields comprising speech processing, computer vision (CV), and natural language processing (NLP). Therefore, this article develops a Deep Learning with Natural Language Processing based Robust Speech Detection (DLNLP-RSD) technique. The DLNLP-RSD technique derives features from the mel-frequency cepstral coefficient (MFCC) technique. Followed by the DLNLP-RSD technique employs a one-dimensional deep convolutional neural network (CNN) approach for the detection and classification of speech. Moreover, the parameter tuning model uses the grasshopper fractals optimization algorithm (GOA) approach. The performance evaluation of the proposed technique highlights its significant performance over other models concerning distinct measures. An extensive simulation analysis is executed to examine the improved results of the DLNLP-RSD model. The experimental validation of the DLNLP-RSD technique portrayed a sup

关键词： Convolutional neural networks

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Hybrid Deep Learning Model for Prostate Cancer Detection Using LeNet and MobileNet V2 Algorithms

Hybrid Deep Learning Model for Prostate Cancer Detection Usi...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Sabiya Fatima Ramya I M Ridhi Mayura Shivani R Rajesh Kumar K Jenitha T Amulya M P Department of Computer Science & Engineering Axis Institute of Technology and Management Kanpur Uttar Pradesh India Department of CSE East West Institute of Technology Bangalore India Department Of Computer Science And Engineering Sathyabama Institute Of Science And Technology Chennai Tamil Nadu Dept of CSE (Cyber security) R.M.K. College of Engineering and Technology Chennai India Department of CSE St. Joseph's College of Engineering Chennai India Department of ISE Don Bosco Institute of Technology Bangalore Karnataka India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Cancer of the prostate is among the most common neoplasms worldwide and in men especially; therefore, early diagnosis enhances the chances of the patients. In this work, we propose a proposed deep-learning framework by integrating LeNet and MobileNet V2 architectures for early diagnosis of prostate cancer from medical images. The proposed model had an accuracy of 96.2% and precision of 97.9 % , a recall of 96.9% and F1-Score of 97.4% and surpassed LeNet, MobileNet V2 and ResNet-50 and DenseNet-121 models. The particularity of the test was confirmed with a confusion matrix that revealed 15 false negatives and 10 false positives. From the computational perspective, the model has an inference time of roughly 48 ms for per image and has a memory consumption of 60 MB, it is thus fast for real time applications in clinics. It adds more explainability techniques such as Grad-CAM, which improves interpretability, which is essential for clinician trust. This work shows that PFCLA can be used in early diagnosis of prostate cancer using enhanced deep learning hybrid model and future works that can be done include, Exploratory Study of More real world independent data set on PFCLA along with, Expanding the applicability of PFCLA to other medical imaging tasks.

关键词： Deep learning Performance evaluation Accuracy Computational modeling Memory management Transfer learning Real-time systems Reliability Prostate cancer Cancer

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Mathematical Modelling of Dengue Transmission with Adaptive Fuzzy Backstepping Control and RK-5 Numerical Method

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International Journal of Applied and Computational Mathematics 2025年第3期11卷 1-29页

作者： Vijayalakshmi, G.M. Ariyanatchi, M. Akgül, Ali Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Tamilnadu Avadi 600062 India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Mathematics Art and Science Faculty Siirt University Siirt 56100 Turkey Department of Computer Engineering Biruni University Istanbul Topkapı 34010 Turkey Department of Mathematics Mathematics Research Center Near East University Near East Boulevard Mersin 10 Nicosia 99138 Turkey

The aim of this research is to create an adaptive nonlinear fuzzy backstepping robust controller for a dengue disease outbreak model. We use an SIR-SI compartmental mathematical model to analyse the diffusion mechanism of dengue disease. A nonlinear adaptive controller regulates prevention strategies for susceptible human beings and enhances hospitalization-based treatment for the infected people. Besides, the spraying of chemical insecticides will reduce the infected mosquito population. Adaptation laws are used to derive fuzzy system parameters, enhancing the system's adaptability. Then we construct a suitable Lyapunov function, which guarantees convergence, robustness, and stability for a closed-loop system. Finally, we plot all the system's control variables graphically and calculate their numerical approximations using the Runge–Kutta technique of fifth order. These simulations demonstrate that the proposed adaptive controllers can effectively control the dengue disease and ensure the stability of the system. Hence, we conclude that adaptive backstepping control measures can protect more people from dengue infection. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2025.

关键词： Adaptive back stepping Dengue model Fuzzy system Lyapunov function Runge–Kutta method

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A Hybrid Approach for Cardiovascular Disease Risk Evaluation

A Hybrid Approach for Cardiovascular Disease Risk Evaluation

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： U. Sesadri Shaik Jumlesha S.Hrushikesava Raju S. Adinarayna Nabanita Choudhury Vijaya Chandra Jadala Department of CSE Vardhaman College of Engineering Hyderabad Telangana Department of CSE Annamacharya Institute of Technology and Science Tirupati Chittoor(dt) AP Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Department of Computer Science and Systems Engineering College of Engineering Andhra University Visakhapatnam Faculty of Computer Technology Assam down town University Guwahati Assam School of Computer Science and Artificial Intelligence SR University Hanumakonda Warangal Telangana India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Nowadays, the risk estimators are to be applied based on the population characteristics of their country, which is termed as race attribute. There were specific tools to determine the risk of cardiovascular disease. Among many diseases, the mortality rate of the people is increasing due to a lack of quality lifestyle. In order to provide a guide to the individual based on their specific characteristics and lifestyle, would classify the risk into specific categories like severe (High), Moderate, and normal(Low) with pre-defined threshold constraints. The risk assessment estimators alert the individual to control certain modifiable attributes irrespective of their non-modifiable elements. The integration of random forests and intelligent rule mining would generate the CVD risk prediction. Efficiency as well as interpretability are important in this competitive hybrid model. The ultimate benefits assured are extending the lives of the patients by knowing the importance of daily work, habits, and their control food, presenting a powerful opportunity by practicing better personalization monitoring and improved patient care, and improved informed decisions for immediate action. The early prevention initiation by identifying the risk severity optimizes follow-up of better practices and treatment styles, resulting in improved decision making for better health care of the individual.

关键词： Measurement Calculators Prevention and mitigation Mortality Medical services Cardiovascular diseases Informatics Random forests Monitoring Mobile computing

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Cocoscan: AI-Powered Precision Diagnostics for Coconut Leaf Disease 4

Cocoscan: AI-Powered Precision Diagnostics for Coconut Leaf ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Santhi, S. Murugan, M. Nandhiha, V. Vivitha, S. Karthikeyan, D. KIT-Kalaignarkarunanidhi Institute of Technology Dept of Computer Science and Engineering Tamil Nadu India Centre for Plant Protection Studies Tamil Nadu Agricultural University Dept. of Agricultural Entomology Tamil Nadu Coimbatore India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

Coconut cultivation is essential in tropical areas, but diseases such as Leaf Spot, Leaf Blight, Grey Leaf Spot, Root Wilt, Stem Bleeding, and Basal Stem Rot can severely harm coconut trees. To reduce the effects of these illnesses and guarantee the highest possible crop yields, early disease detection is important. Conventional detection techniques, which depend on specialist knowledge and manual examination, are labor-intensive, prone to human error, and frequently out of reach for smallholder farmers. This research presents a web-based application that automatically detects and categories diseases of coconut leaves using deep learning, most especially the Inception V3 model. The number of epochs to guarantee comprehensive training, a batch size that strikes a compromise between training speed and memory consumption, and a learning rate optimised for efficient convergence are important model parameters. In order to accommodate the architecture of the model, the image input size is set at 299×299 pixels. Through the web interface, farmers and agricultural specialists can submit photos of coconut leaves. The Inception V3 model, which has been trained on a sizable dataset of both healthy and diseased leaves, analyses the photos and offers real-time feedback, including suggestions for treatment, early disease detection, and disease identification. The user-friendly design requires no specialized knowledge, allowing farmers to quickly receive accurate diagnoses and preventive measures, improving farm management. The system also integrates Explainable AI (XAI) techniques to provide visual explanations of predictions, ensuring transparency and trust in the model's outputs. © 2025 IEEE.

关键词： Information management

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Comparative analysis of carbon dioxide and hydrogen plume migration in aquifers inspired by the FluidFlower benchmark study

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International Journal of Hydrogen Energy 2025年 135卷 56-68页

作者： Misaghi Bonabi, Amin van Rooijen, Willemijn Al Kobaisi, Mohammed Vuik, Cornelis Hajibeygi, Hadi Department of Applied Mathematics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Department of Geoscience and Engineering Faculty of Civil Engineering and Geosciences Delft University of Technology Delft Netherlands

Large-scale geological storages of hydrogen (H2) and carbon dioxide (CO2) in saline aquifers present feasible options for a sustainable energy future. We compared the plume migration of CO2 and H2 in aquifers using the FluidFlower benchmark, incorporating the state-of-the-art thermophysical and petrophysical properties. The H2 plume, with its higher buoyancy and mobility compared to CO2, remains predominantly in the gas phase due to its lower solubility, increasing the chances of escaping through fractures or migration to distant regions. This additionally leads to a higher pressurized reservoir, which, along with higher buoyancy, increases the chance of caprock penetration. Dissolution trapping of CO2 into brine increases over time due to its fingering, while H2 does not show fingering. Our findings show that while geological carbon storage (GCS) benefits significantly from all structural, dissolution, and residual trapping, underground hydrogen storage (UHS) relies mainly on structural trapping, making the integrity of sealing elements of the system a key factor in its performance. © 2025 The Authors

关键词： Two phase flow

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II-NVM: Enhancing Map Accuracy and Consistency With Normal Vector-Assisted Mapping

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IEEE Robotics and Automation Letters 2025年第6期10卷 5465-5472页

作者： Zhao, Chengwei Li, Yixuan Jian, Yina Xu, Jie Wang, Linji Ma, Yongxin Jin, Xinglai Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China Hangzhou Qisheng Intelligent Techology Company Ltd Hangzhou311217 China Columbia University in the City of New York Department of Computer Science New YorkNY10027 United States Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin150001 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore

SLAM technology plays a crucial role in indoor mapping and localization. A common challenge in indoor environments is the "double-sided mapping issue", where closely positioned walls, doors, and other surfaces are mistakenly identified as a single plane, significantly hindering map accuracy and consistency. To addressing this issue this letter introduces a SLAM approach that ensures accurate mapping using normal vector consistency. We enhance the voxel map structure to store both point cloud data and normal vector information, enabling the system to evaluate consistency during nearest neighbor searches and map updates. This process distinguishes between the front and back sides of surfaces, preventing incorrect point-to-plane constraints. Moreover, we implement an adaptive radius KD-tree search method that dynamically adjusts the search radius based on the local density of the point cloud, thereby enhancing the accuracy of normal vector calculations. To further improve real-time performance and storage efficiency, we incorporate a Least Recently Used (LRU) cache strategy, which facilitates efficient incremental updates of the voxel map. The code is released as open-source and validated in both simulated environments and real indoor scenarios. Experimental results demonstrate that this approach effectively resolves the "double-sided mapping issue" and significantly improves mapping precision. Additionally, we have developed and open-sourced the first simulation and real-world dataset specifically tailored for the "double-sided mapping issue". © 2016 IEEE.

关键词： Mapping

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AI-enabled driver assistance: monitoring head and gaze movements for enhanced safety

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Complex & Intelligent Systems 2025年第7期11卷 1-28页

作者： Mudassar Shah, Sayyed Zengkang, Gan Sun, Zhaoyun Hussain, Tariq Zaman, Khalid Alwabli, Abdullah Jaffar, Amar Y. Ali, Farman School of Civil and Transportation Engineering Shenzhen University Guangdong China Institute of Intelligent Manufacturing Technology Shenzhen Polytechnic University Shenzhen China School of Computer Science and Technology Zhejiang Gongshang University Hangzhou China Department of Electrical Engineering College of Engineering and Computing in Alqunfudah Umm Al-Qura University Mecca Saudi Arabia Computer and Network Engineering Department College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science and Engineering School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul South Korea

This paper introduces a real-time head-pose detection and eye-gaze estimation system for Automatic Driver Assistance technology (ADAT) aimed at enhancing driver safety by accurately collecting and transmitting data on the driver’s head position and eye gaze to mitigate potential risks. Existing methods are constrained by significant limitations, including reduced accuracy under challenging conditions such as varying head orientations and lighting, higher latency in real-time applications (e.g., Faster-RCNN and TPH-YOLOv5), and computational inefficiency, which hinders their deployment in resource-constrained environments. To address these challenges, we propose a novel framework using the Transformer Detection of Gaze Head - YOLOv7 (TDGH-YOLOv7) object detector. The key contributions of this work include the development of a reference image dataset encompassing diverse vertical and horizontal gaze positions alongside the implementation of an optimized detection system that achieves state-of-the-art performance in terms of accuracy and latency. The proposed system achieves superior precision, with a weighted accuracy of 95.02% and Root Mean Square Errors of 2.23 and 1.68 for vertical and horizontal gaze estimation, respectively, validated on the MPII-Gaze and DG-Unicamp datasets. A comprehensive comparative analysis with existing models, such as CNN, SSD, Faster-RCNN, and YOLOv8, underscores the robustness and efficiency of the proposed approach. Finally, the implications of these findings are discussed, and potential avenues for future research are outlined.

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