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An Enhanced Detection System of Autism Spectrum Disorder Using Thermal Imaging and deep learning

SN Computer Science 2025年第2期6卷 1-11页

作者： Jegan Amarnath, J. Meera, S. Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Tamil Nadu Chennai 600117 India

Autism Spectrum Disorder (ASD) is a brain-based condition characterized by social difficulties and repetitive behaviors. Traditional diagnostic methods can be subjective and time-consuming. Current diagnostic techniques for ASD often rely on behavioral assessments and clinical observations, which can be inconsistent and prone to subjectivity. Additionally, there is a need for more reliable and objective methods to differentiate between autistic and non-autistic individuals. Existing imaging techniques require further refinement to improve accuracy and efficiency in detecting ASD. The primary objective of this research is to develop and evaluate an enhanced detection system for ASD using thermal imaging and Improved Mask Faster Recurrent Convolutional Neural Network (IMFRCNN). This research focuses on enhancing the detection of ASD using deep learning (DL) methods and thermal imaging. Traditional diagnostic techniques for ASD often rely on subjective behavioral assessments, highlighting the need for more reliable methods. By examining cerebral imaging data from the thermal face image datasets collect facial thermal images using thermal cameras. Ensure the dataset is diverse, including varying expressions and environmental conditions. The study compares the effectiveness of IMFRCNN and the ResNet 50 system in classifying thermal images of 50 autistic and 50 non-autistic individuals. Emotions were evoked using audio-visual stimuli, and temperature variations in facial areas such as the nose, forehead, cheek, and eyes were measured. Anger showed the greatest temperature differences, with a 12.6% variation between autistic and non-autistic individuals. The IMFRCNN achieved a reliability of 96%, while the ResNet 50 achieved 90%. These findings indicate that computer-aided assessment techniques utilizing thermal imaging and DL can provide a consistent and objective method for identifying individuals with ASD. © The Author(s), under exclusive licence to Springer Nature Sing

关键词： deep learning algorithms Diagnostic tool Improved mask faster recurrent convolutional neural network Performance measures

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A Real-Time Multistage False Data Detection Method Based on deep learning and Semisupervised Scoring algorithms

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IEEE SYSTEMS JOURNAL 2023年第2期17卷 1753-1764页

作者： Parizad, Ali Hatziadoniu, Constantine J. Southern Illinois Univ Dept Elect & Comp Engn Carbondale IL 62901 USA Virginia Tech Adv Res Inst Blacksburg VA 24061 USA

This article proposes a real-time multistage data-driven method to detect false data injected by an intruder. The proposed method is based on an accurate load model constructed by deep neural networks (long short-term memory and gated recurrent unit) where historical demand along with extra weather features are employed to formulate the problem as a sequence of predictions and build an appropriate DNN model. Adistinct feature of the proposed method that dramatically improves accuracy is hyperparameter optimization (e.g., dropout rate, batch size, beta 1, beta 2, a) based on cross-validation. Since hyperparameter optimization consumes enormous resources to analyze the different combinations of hyperparameters and causes out-of-memory problems, we have implemented our framework based on a parallel distributed computing technique on high-performance computer clusters. A variety of case studies are examined to find the best model. At the end of this step, the best model is employed as a base reference to a semisupervised scoring algorithm for the purpose of finding sequence of injected false data. To improve the detection efficiency, inter-range, pruning, and minimum score are added to the proposed methodology that suppress high false-positive rates. Several scenarios from a real-world dataset have been studied in this article, and the results demonstrate that the proposed method can be used to detect various types of attacks, including scaling, ramping, professional ramping, and random attacks, with a good performance score (i.e., recall, precision, and F1 score) for all of them. Furthermore, it can mitigate false data with the forecasted load values.

关键词： Attack templates deep learning algorithms false data injection attack (FDIA) gated recurrent unit (GRU) hyperparameter optimization semisupervised learning detection method

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deep learning in industrial machinery: A critical review of bearing fault classification methods

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APPLIED SOFT COMPUTING 2025年 171卷

作者： Rehman, Attiq Ur Jiao, Weidong Jiang, Yonghua Wei, Jianan Sohaib, Muhammad Sun, Jianfeng E, Shiju Rehman, Khalil Ur Chi, Yongwei Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China Zhejiang Normal Univ Zhejiang Inst Photoelect Jinhua 321004 Peoples R China Zhejiang Normal Univ Sch Engn Jinhua 321004 Peoples R China Zhejiang Normal Univ Xingzhi Coll Lanxi 321100 Peoples R China Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Guizhou Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China Peking Univ Adv Inst Informat Technol AIIT Hangzhou 311215 Peoples R China JIE Holding Grp Co Ltd Hangzhou 311223 Peoples R China

The review provides an overview of the state-of-the-art in deep learning (DL) algorithms for rolling bearing fault classification which remains vital in industrial sectors including transportation, energy, manufacturing, and so forth. Even though they experience a variety of faults, rolling bearings are very crucial in ensuring machine efficiency. This prompts the review of the DL application, which is continuously growing, for intelligently detecting these faults. It comprehensively analyses DL models including Convolutional Neural Networks (CNNs), Auto-Encoders (AEs), deep Belief Neural Networks (DBNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and some advanced networks i.e., Transfer learning (TL), Transformer Neural Network (TNN), Self-Supervised learning (SSL), Federated learning (FL), Meta-learning and Interpreting Neural Networks assessing their effectiveness and limitations in fault classification. Thus, the current review is unique among available literature at present since it bridges this crucial gap by including all forms of advanced networks and gives an insight into the potential and challenges. Besides, it emphasizes the importance of different sensing techniques and key datasets in the field to show the contribution towards advancements of DL applications. Finally, referring to current challenges and recommendations for future research directions encompassing environmental adaption, sensor deployment, data preprocessing, model training enhancements, algorithm selection, classifier development, and systematic documentation frame the conclusive part of the paper. This

关键词： Rolling Bearing Fault classification methods deep learning algorithms Industrial machinery

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Impact Analysis of Stacked Machine learning algorithms Based Feature Selections for deep learning Algorithm Applied to Regression Analysis

Impact Analysis of Stacked Machine Learning Algorithms Based...

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SoutheastCon Conference

作者： Kulkarni, Shrirang Ambaji Gurupur, Varadraj P. King, Christian Univ Cent Florida Sch Global Hlth Management & Informat Orlando FL 32816 USA

ISBN: (纸本)9781665406529

Ensemble learning algorithms have proved to be one of the best machine learning algorithms towards optimal performances in terms of regression and classification tasks for a variety of applications. When applied for small or medium structured datasets, eXtreme Gradient Boosting (XGBoost) has emerged as a popular ensemble learning technique based on its performance merits. In recent years Light Gradient Boosting Machine (LightGBM) has emerged as a promising ensemble strategy that is competing with XGBoost in terms of performance. Also, Lasso Regression has proven capabilities in terms of feature selections and applications for small datasets. This paper illustrates experimentation performed on a diabetes dataset where the authors tested the hypothesis that feature selection has relatively no impact on the performances of deep learning algorithms as they have built-in capabilities in terms of layers to perform feature selection on their own. Therefore, the hypothesis tested stacking using deep learning - Multi-Layer Perceptron (DMLP) with optimal algorithms like XGBoost, LightGBM, and Lasso Regression. In the present work, DMLP with all feature variables (DMLP-ALL) outscored DMLP with stacked selected features (DMLP-MS) by 8.78 % in terms of R-2. Also, DMLP-ALL outperformed the benchmarked algorithm Automated Machine learning (AML) by 10.25% in terms of R-2. The validation of the proposed stacking models by applying a moderate-sized dataset provides promising results for deep learning models stacked with a powerful Level-0 learner.

关键词： Stacking models Machine learning algorithms deep learning algorithms Regression metrics

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TO BE OR NOT TO BE STABLE, THAT IS THE QUESTION: UNDERSTANDING NEURAL NETWORKS FOR INVERSE PROBLEMS

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2025年第1期47卷 C77-C99页

作者： Evangelista, Davide Piccolomini, Elena loli Morotti, Elena Nagy, James g. Univ Bologna Dept Comp Sci & Engn Bologna Italy Univ Bologna Dept Polit & Social Sci Bologna Italy Emory Univ Dept Math Atlanta GA USA

The solution of linear inverse problems arising, for example, in signal and image processing is a challenging problem since the ill-conditioning amplifies, in the solution, the noise present in the data. Recently introduced algorithms based on deep learning overwhelm the more traditional model-based approaches in performance, but they typically suffer from instability with respect to data perturbation. In this paper, we theoretically analyze the trade-off between stability and accuracy of neural networks when used to solve linear imaging inverse problems for cases that are not underdetermined. Moreover, we propose different supervised and unsupervised solutions to increase the network stability and maintain a good accuracy, by means of regularization properties inherited from a model-based iterative scheme during the network training. Extensive numerical experiments on image deblurring confirm the theoretical results and the effectiveness of the proposed deep learning--based approaches to handle noise on the data.

关键词： neural networks stability linear inverse problems deep learning algorithms image deblurring trade-off accuracy stability

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Enhancing surgical precision in squamous cell carcinoma of the head and neck: Hyperspectral imaging and artificial intelligence for improved margin assessment in an ex vivo setting

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2025年 332卷 125817页

作者： Boehm, Felix Alperovich, Anna Schwamborn, Carolin Mostafa, Mayar Giannantonio, Tommaso Lingl, Julia Lehner, Rene Zhang, Xiaohan Hoffmann, Thomas K. Schuler, Patrick J. Ulm Univ Med Ctr Dept Otorhinolaryngol Head & Neck Surg Frauensteige 12 D-89075 Ulm Germany i2SOUL Consortium Surg Oncol Ulm Ulm Germany Carl Zeiss Corp Res & Technol Carl Zeiss Str 22 D-73447 Oberkochen Germany Carl Zeiss Meditec AG Rudolf Eber Str 11 D-73447 Oberkochen Germany Heidelberg Univ Med Ctr Dept Otorhinolaryngol D-69120 Heidelberg Germany

Background: Head and neck cancers, constituting 3-5% of all cancer cases, often require surgical resection for optimal outcomes. Achieving complete resection (R0) is crucial, but current methods, relying on white light endoscopy and microscopy, have limitations. Hyperspectral imaging (HSI) offers potential benefits by capturing detailed spectral information beyond human vision. Material and methods: This study enrolled 32 patients with head and neck squamous cell carcinoma (HNSCC). Following surgical resection specimens underwent ex vivo HSI imaging. Annotated regions were utilized to train a Convolutional Neural Network (CNN) and Graph Neural Network (GNN). Imaging parameters were carefully optimized for efficiency. Results: Our HSI imaging setup required around 12 min per measurement and demonstrated feasibility with promising accuracy. The combination of HSI and artificial intelligence (AI) achieved an 86% accuracy in predicting tumor tissue. Challenges included data volume and extended capture times. Conclusion: Hyperspectral imaging, complemented by AI, shows promise in enhancing tissue differentiation for HNSCC. The study envisions real-time integration of HSI into surgery for margin assessment. Challenges such as data volume and capture times warrant further exploration, emphasizing the need for ongoing investigations to refine clinical applications.

关键词： Head and neck carcinomas Hyperspectral imaging Artificial intelligence Neural network deep learning algorithms Surgical resection Margin assessment Tumor differentiation Squamous cell carcinoma HNSCC

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Control Interface for Multi-User Video Games with Hand or Head Gestures in Directional Key-Based Games

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第1期16卷 49-60页

作者： Ramirez-Valdez, Oscar Baluarte-Araya, Cesar Castillo-Lazo, Rodrigo Ccoscco-Alvis, Italo Valdiviezo-Tovar, Alexander Villafuerte-Quispe, Alexander Zuniga-Huraca, Dylan Univ Nacl San Agustin Arequipa Arequipa Peru

This paper describes the development and implementation of a hand or head gesture-based control interface for video games, enhanced for games that use directional keys. The objective is to develop an adaptive control system for a multiplayer video game that allows users to choose between the use of traditional directional keys or a gesture-based interface. The methodology used follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) development model, which allows a structured integration of analysis, design, implementation and evaluation steps. Technologies such as OpenCV, MediaPipe and deep learning algorithms are used, translating hand movements into directional commands in real time. In addition, the system integrates a client-server architecture based on *** that supports multiple users, enabling an immersive gaming experience on PC and mobile platforms. The results highlight the accuracy of the system and its potential to improve accessibility, especially for users with motor disabilities by using their hands or head movements to control the directional keys. Concluding that the control interface for multi-user video games provides the necessary support to gamers in performing the task, promoting accessibility in the entertainment environment.

关键词： Control interface video games artificial vision gesture-based interface directional commands human-computer interaction deep learning algorithms accessibility real-time pattern recognition

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A hybrid method for enhancing aspect sentiment classification in the french language: combining rule-based and learning methods

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2025年第3期19卷 551-571页

作者： Hammi, Sarsabene Hammami, Souha Mezghani Belguith, Lamia Hadrich MIRACL Lab ANLP Res Grp Dept Comp Sci Route Tunis Sfax 3021 Tunisia

The aspect-based sentiment analysis (ABSA) task has become increasingly important to the scientific community with the growth of internet content. ABSA is a subfield of natural language processing (NLP) and it is principally divided into two sub-tasks: aspect extraction (AE) and the aspect sentiment classification (ASC). To accomplish this task, researchers first extract the aspect terms (e.g., camera, design, etc) of an entity (e.g., phone) and then classify them into positive, negative, or neutral. In this study, we focus only on the ASC task for the French language and we introduce a hybrid method that combines the rule-based method with the machine learning-based method. Firstly, we created a set of rules to annotate the dataset. These rules were constructed based on the opinion lexicon, dependency relations parser, and contextual information. Then, we introduced a new neural network architecture called CBGC (CNN-Bi-GRU-CRF), which combines deep learning models CNN (convolutional neural network) and Bi-GRU (bidirectional gated recurrent unit) with the classical machine learning model CRF (conditional random field). This architecture was trained on the annotated dataset to classify aspect terms into positive or negative. To evaluate the performance of the proposed hybrid method, we collected a dataset of 6000 French mobile phone comments from the Amazon website. The obtained results were very encouraging, with an F-measure value of 94.16% for the mobile phone domain and 84.38% for the SemEval-2016 restaurant dataset.

关键词： Aspect-based sentiment analysis task Aspect sentiment classification Rule-based method deep learning algorithms

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A Comparative Analysis of ResNet and MobileNet for Classifying MRI Images 5th

A Comparative Analysis of ResNet and MobileNet for Classifyi...

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5th International Conference on Data Science, Machine learning and Applications

作者： Padmaja, D. Lakshmi Nikhil, B. Akshaya, Banda Sai deepak, G. Surya Anurag Univ Hyderabad India North Carolina A&T State Univ Greensboro NC USA Cognizant Technol Solut Chennai India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

This work represents a comprehensive analysis of the performance of two popular deep learning architectures, ResNet and MobileNet, with particular attention to their use in the classification of magnetic resonance imaging (MRI) pictures. Healthcare professionals need to accurately classify medical images in order to make precise diagnosis and develop successful treatment plans. In this paper authors have done thorough comparative research to clarify the quantitative performance indicators while also exploring qualitative elements, such as the subtle differences between each model's strengths and weaknesses. Beyond the technical assessment, the study investigates ResNet's and MobileNet's computational effectiveness and flexibility in response to the various features present in medical imaging data. The project aims to provide a sophisticated understanding of these deep learning systems in order to make a significant addition to the medical image analysis field as a whole. The ultimate goal is to promote improvements in diagnostic accuracy, which will enable healthcare providers to make better judgments and provide better patient care. The results of this study will be crucial in determining the direction of future advancements in this important field as deep learning and medical imaging continue to cross paths. They provide insightful information that goes beyond ResNet and MobileNet to affect the larger field of deep learning applications in medical diagnostics and treatment planning. The aforementioned study highlights the profound potential of deep learning technology to enhance healthcare procedures and further advance medical science.

关键词： ResNet MobileNet Advancements deep learning algorithms Computer Vision

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deep learning for Structural Health Monitoring: Data, algorithms, Applications, Challenges, and Trends

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SENSORS 2023年第21期23卷 8824-8824页

作者： Jia, Jing Li, Ying Ocean Univ China Coll Engn Dept Civil Engn Qingdao 266100 Peoples R China

Environmental effects may lead to cracking, stiffness loss, brace damage, and other damages in bridges, frame structures, buildings, etc. Structural Health Monitoring (SHM) technology could prevent catastrophic events by detecting damage early. In recent years, deep learning (DL) has developed rapidly and has been applied to SHM to detect, localize, and evaluate diverse damages through efficient feature extraction. This paper analyzes 337 articles through a systematic literature review to investigate the application of DL for SHM in the operation and maintenance phase of facilities from three perspectives: data, DL algorithms, and applications. Firstly, the data types in SHM and the corresponding collection methods are summarized and analyzed. The most common data types are vibration signals and images, accounting for 80% of the literature studied. Secondly, the popular DL algorithm types and application areas are reviewed, of which CNN accounts for 60%. Then, this article carefully analyzes the specific functions of DL application for SHM based on the facility's characteristics. The most scrutinized study focused on cracks, accounting for 30 percent of research papers. Finally, challenges and trends in applying DL for SHM are discussed. Among the trends, the Structural Health Monitoring Digital Twin (SHMDT) model framework is suggested in response to the trend of strong coupling between SHM technology and Digital Twin (DT), which can advance the digitalization, visualization, and intelligent management of SHM.

关键词： structural health monitoring deep learning algorithms damage detection data acquisition facilities

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