Pub Date : 2025-03-26DOI: 10.1109/TAI.2025.3572849
Shivam Mishra;Amit Vishwakarma;Anil Kumar
An automated nuclei segmentation is an important technique for understanding and analyzing cellular characteristics that ease computer-aided digital pathology and are useful for disease diagnosis. However, this task is difficult because of the diversity in nuclei size, blurry boundaries, and several imaging modalities. A convolutional neural network (CNN)-based multiheaded U-Net (M-UNet) framework has been proposed to address such issues. This architecture uses filters of different kernel sizes for multiple heads to extract multiresolution features of an image. Shearlet-based unsharp masking (SBUM) method is proposed for preprocessing, which primarily emphasizes features like contours, boundaries, and minute details of the source image. In this article, a hybrid loss function is formulated, which includes intersection over union (IOU) loss and Dice loss along with binary cross entropy loss. The hybrid loss function is tried to be minimized by the optimization algorithm, and the higher metrics values during the testing phase represent better segmentation performance in the spatial domain. The proposed method yields superior segmentation images and quantitative findings as compared to the state-of-the-art nuclei segmentation techniques. The proposed technique attains IOU, F1Score, accuracy, and precision values of 0.8325, 0.9086, 0.9651, and 0.9001, respectively.
{"title":"Nuclei Segmentation Using Multiheaded U-Net and Shearlet-Based Unsharp Masking","authors":"Shivam Mishra;Amit Vishwakarma;Anil Kumar","doi":"10.1109/TAI.2025.3572849","DOIUrl":"https://doi.org/10.1109/TAI.2025.3572849","url":null,"abstract":"An automated nuclei segmentation is an important technique for understanding and analyzing cellular characteristics that ease computer-aided digital pathology and are useful for disease diagnosis. However, this task is difficult because of the diversity in nuclei size, blurry boundaries, and several imaging modalities. A convolutional neural network (CNN)-based multiheaded U-Net (M-UNet) framework has been proposed to address such issues. This architecture uses filters of different kernel sizes for multiple heads to extract multiresolution features of an image. Shearlet-based unsharp masking (SBUM) method is proposed for preprocessing, which primarily emphasizes features like contours, boundaries, and minute details of the source image. In this article, a hybrid loss function is formulated, which includes intersection over union (IOU) loss and Dice loss along with binary cross entropy loss. The hybrid loss function is tried to be minimized by the optimization algorithm, and the higher metrics values during the testing phase represent better segmentation performance in the spatial domain. The proposed method yields superior segmentation images and quantitative findings as compared to the state-of-the-art nuclei segmentation techniques. The proposed technique attains IOU, F1Score, accuracy, and precision values of 0.8325, 0.9086, 0.9651, and 0.9001, respectively.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"7 1","pages":"297-307"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-24DOI: 10.1109/TAI.2025.3554461
Md Abrar Jahin;Md. Akmol Masud;Md Wahiduzzaman Suva;M. F. Mridha;Nilanjan Dey
The rapid data surge from the high-luminosity Large Hadron Collider introduces critical computational challenges requiring novel approaches for efficient data processing in particle physics. Quantum machine learning, with its capability to leverage the extensive Hilbert space of quantum hardware, offers a promising solution. However, current quantum graph neural networks (GNNs) lack robustness to noise and are often constrained by fixed symmetry groups, limiting adaptability in complex particle interaction modeling. This article demonstrates that replacing the classical Lorentz group equivariant block modules in LorentzNet with a dressed quantum circuit significantly enhances performance despite using $approx 5.5$ times fewer parameters. Additionally, quantum circuits effectively replace MLPs by inherently preserving symmetries, with Lorentz symmetry integration ensuring robust handling of relativistic invariance. Our Lorentz-equivariant quantum graph neural network (Lorentz-EQGNN) achieved 74.00% test accuracy and an AUC of 87.38% on the Quark-Gluon jet tagging dataset, outperforming the classical and quantum GNNs with a reduced architecture using only 4 qubits. On the electron–photon dataset, Lorentz-EQGNN reached 67.00% test accuracy and an AUC of 68.20%, demonstrating competitive results with just 800 training samples. Evaluation of our model on generic MNIST and FashionMNIST datasets confirmed Lorentz-EQGNN’s efficiency, achieving 88.10% and 74.80% test accuracy, respectively. Ablation studies validated the impact of quantum components on performance, with notable improvements in background rejection rates over classical counterparts. These results highlight Lorentz-EQGNN’s potential for immediate applications in noise-resilient jet tagging, event classification, and broader data-scarce HEP tasks.
{"title":"Lorentz-Equivariant Quantum Graph Neural Network for High-Energy Physics","authors":"Md Abrar Jahin;Md. Akmol Masud;Md Wahiduzzaman Suva;M. F. Mridha;Nilanjan Dey","doi":"10.1109/TAI.2025.3554461","DOIUrl":"https://doi.org/10.1109/TAI.2025.3554461","url":null,"abstract":"The rapid data surge from the high-luminosity Large Hadron Collider introduces critical computational challenges requiring novel approaches for efficient data processing in particle physics. Quantum machine learning, with its capability to leverage the extensive Hilbert space of quantum hardware, offers a promising solution. However, current quantum graph neural networks (GNNs) lack robustness to noise and are often constrained by fixed symmetry groups, limiting adaptability in complex particle interaction modeling. This article demonstrates that replacing the classical Lorentz group equivariant block modules in LorentzNet with a dressed quantum circuit significantly enhances performance despite using <inline-formula><tex-math>$approx 5.5$</tex-math></inline-formula> times fewer parameters. Additionally, quantum circuits effectively replace MLPs by inherently preserving symmetries, with Lorentz symmetry integration ensuring robust handling of relativistic invariance. Our <underline>Lorentz</u>-<underline>e</u>quivariant <underline>q</u>uantum <underline>g</u>raph <underline>n</u>eural <underline>n</u>etwork (Lorentz-EQGNN) achieved 74.00% test accuracy and an AUC of 87.38% on the Quark-Gluon jet tagging dataset, outperforming the classical and quantum GNNs with a reduced architecture using only 4 qubits. On the electron–photon dataset, Lorentz-EQGNN reached 67.00% test accuracy and an AUC of 68.20%, demonstrating competitive results with just 800 training samples. Evaluation of our model on generic MNIST and FashionMNIST datasets confirmed Lorentz-EQGNN’s efficiency, achieving 88.10% and 74.80% test accuracy, respectively. Ablation studies validated the impact of quantum components on performance, with notable improvements in background rejection rates over classical counterparts. These results highlight Lorentz-EQGNN’s potential for immediate applications in noise-resilient jet tagging, event classification, and broader data-scarce HEP tasks.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 12","pages":"3195-3206"},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-23DOI: 10.1109/TAI.2025.3572852
Bikash K. Behera;Saif Al-Kuwari;Ahmed Farouk
A brain–computer interface (BCI) system enables direct communication between the brain and external devices, offering significant potential for assistive technologies and advanced human–computer interaction. Despite progress, BCI systems face persistent challenges, including signal variability, classification inefficiency, and difficulty adapting to individual users in real time. In this study, we propose a novel hybrid quantum learning model, termed QSVM-QNN, which integrates a quantum support vector machine (QSVM) with a quantum neural network (QNN), to improve classification accuracy and robustness in EEG-based BCI tasks. Unlike existing models, QSVM-QNN combines the decision boundary capabilities of QSVM with the expressive learning power of QNN, leading to superior generalization performance. The proposed model is evaluated on two benchmark EEG datasets, achieving high accuracies of 0.990 and 0.950, outperforming both classical and standalone quantum models. To demonstrate real-world viability, we further validated the robustness of QNN, QSVM, and QSVM-QNN against six realistic quantum noise models, including bit flip and phase damping. These experiments reveal that QSVM-QNN maintains stable performance under noisy conditions, establishing its applicability for deployment in practical, noisy quantum environments. Beyond BCI, the proposed hybrid quantum architecture is generalizable to other biomedical and time-series classification tasks, offering a scalable and noise-resilient solution for next-generation neurotechnological systems.
{"title":"QSVM-QNN: Quantum Support Vector Machine Based Quantum Neural Network Learning Algorithm for Brain–Computer Interfacing Systems","authors":"Bikash K. Behera;Saif Al-Kuwari;Ahmed Farouk","doi":"10.1109/TAI.2025.3572852","DOIUrl":"https://doi.org/10.1109/TAI.2025.3572852","url":null,"abstract":"A brain–computer interface (BCI) system enables direct communication between the brain and external devices, offering significant potential for assistive technologies and advanced human–computer interaction. Despite progress, BCI systems face persistent challenges, including signal variability, classification inefficiency, and difficulty adapting to individual users in real time. In this study, we propose a novel hybrid quantum learning model, termed QSVM-QNN, which integrates a quantum support vector machine (QSVM) with a quantum neural network (QNN), to improve classification accuracy and robustness in EEG-based BCI tasks. Unlike existing models, QSVM-QNN combines the decision boundary capabilities of QSVM with the expressive learning power of QNN, leading to superior generalization performance. The proposed model is evaluated on two benchmark EEG datasets, achieving high accuracies of 0.990 and 0.950, outperforming both classical and standalone quantum models. To demonstrate real-world viability, we further validated the robustness of QNN, QSVM, and QSVM-QNN against six realistic quantum noise models, including bit flip and phase damping. These experiments reveal that QSVM-QNN maintains stable performance under noisy conditions, establishing its applicability for deployment in practical, noisy quantum environments. Beyond BCI, the proposed hybrid quantum architecture is generalizable to other biomedical and time-series classification tasks, offering a scalable and noise-resilient solution for next-generation neurotechnological systems.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"7 1","pages":"308-320"},"PeriodicalIF":0.0,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145898244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-19DOI: 10.1109/TAI.2025.3552686
Hang Wang;David J. Miller;George Kesidis
Sources of overfitting in deep neural net (DNN) classifiers include: 1) large class imbalances; 2) insufficient training set diversity; and 3) over-training. Recently, it was shown that backdoor data-poisoning also induces overfitting, with unusually large maximum classification margins (MMs) to the attacker’s target class. This is enabled by (unbounded) ReLU activation functions, which allow large signals to propagate in the DNN. Thus, an effective posttraining backdoor mitigation approach (with no knowledge of the training set and no knowledge or control of the training process) was proposed, informed by a small, clean (poisoning-free) data set and choosing saturation levels on neural activations to limit the DNN’s MMs. Here, we show that nonmalicious sources of overfitting also exhibit unusually large MMs. Thus, we propose novel posttraining MM-based regularization that substantially mitigates nonmalicious overfitting due to class imbalances and overtraining. Whereas backdoor mitigation and other adversarial learning defenses often trade off a classifier’s accuracy to achieve robustness against attacks, our approach, inspired by ideas from adversarial learning, helps the classifier’s generalization accuracy: as shown for CIFAR-10 and CIFAR-100, our approach improves both the accuracy for rare categories as well as overall. Moreover, unlike other overfitting mitigation methods, it does so with no knowledge of class imbalances, no knowledge of the training set, and without control of the training process.
{"title":"Maximum Margin-Based Activation Clipping for Posttraining Overfitting Mitigation in DNN Classifiers","authors":"Hang Wang;David J. Miller;George Kesidis","doi":"10.1109/TAI.2025.3552686","DOIUrl":"https://doi.org/10.1109/TAI.2025.3552686","url":null,"abstract":"Sources of overfitting in deep neural net (DNN) classifiers include: 1) large class imbalances; 2) insufficient training set diversity; and 3) over-training. Recently, it was shown that backdoor data-poisoning <italic>also</i> induces overfitting, with unusually large maximum classification margins (MMs) to the attacker’s target class. This is enabled by (unbounded) ReLU activation functions, which allow large signals to propagate in the DNN. Thus, an effective <italic>posttraining</i> backdoor mitigation approach (with no knowledge of the training set and no knowledge or control of the training process) was proposed, informed by a small, clean (poisoning-free) data set and choosing saturation levels on neural activations to limit the DNN’s MMs. Here, we show that nonmalicious sources of overfitting <italic>also</i> exhibit unusually large MMs. Thus, we propose novel posttraining MM-based regularization that substantially mitigates <italic>nonmalicious</i> overfitting due to class imbalances and overtraining. Whereas backdoor mitigation and other adversarial learning defenses often <italic>trade off</i> a classifier’s accuracy to achieve robustness against attacks, our approach, inspired by ideas from adversarial learning, <italic>helps</i> the classifier’s generalization accuracy: as shown for CIFAR-10 and CIFAR-100, our approach improves both the accuracy for rare categories as well as overall. Moreover, unlike other overfitting mitigation methods, it does so with no knowledge of class imbalances, no knowledge of the training set, and without control of the training process.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 10","pages":"2840-2847"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145196042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-18DOI: 10.1109/TAI.2025.3552687
Junpeng Li;Shuying Huang;Changchun Hua;Yana Yang
Learning from pairwise similarity and unlabeled data (SU) is a recently emerging weakly-supervised learning method, which learns a classifier from similar data pairs (two instances belonging to the same class) and unlabeled data. However, this framework is insoluble for triplet similarities and unlabeled data. To address this limitation, this article develops a framework for learning from triplet similarities (three instances belonging to the same class) and unlabeled data points, denoted as TSU. This framework not only showcases the feasibility of constructing a TSU classifier but also serves as an inspiration to explore the broader challenge of addressing N-tuple similarities (N ≥ 2) and unlabeled data points. To tackle this more generalized problem, the present article develops an advancing weakly-supervision framework of learning from N-tuple similarities (N instances belong to the same class) and unlabeled data points, named NSU. This framework provides a solid foundation for handling diverse similarity scenarios. Based on these findings, we propose empirical risk minimization estimators for both TSU and NSU classification. The estimation error bounds are also established for the proposed methods. Finally, experiments are performed to verify the effectiveness of the proposed algorithm.
{"title":"Learning From N-Tuple Similarities and Unlabeled Data","authors":"Junpeng Li;Shuying Huang;Changchun Hua;Yana Yang","doi":"10.1109/TAI.2025.3552687","DOIUrl":"https://doi.org/10.1109/TAI.2025.3552687","url":null,"abstract":"Learning from pairwise similarity and unlabeled data (SU) is a recently emerging weakly-supervised learning method, which learns a classifier from similar data pairs (two instances belonging to the same class) and unlabeled data. However, this framework is insoluble for triplet similarities and unlabeled data. To address this limitation, this article develops a framework for learning from triplet similarities (three instances belonging to the same class) and unlabeled data points, denoted as TSU. This framework not only showcases the feasibility of constructing a TSU classifier but also serves as an inspiration to explore the broader challenge of addressing N-tuple similarities (N ≥ 2) and unlabeled data points. To tackle this more generalized problem, the present article develops an advancing weakly-supervision framework of learning from N-tuple similarities (N instances belong to the same class) and unlabeled data points, named NSU. This framework provides a solid foundation for handling diverse similarity scenarios. Based on these findings, we propose empirical risk minimization estimators for both TSU and NSU classification. The estimation error bounds are also established for the proposed methods. Finally, experiments are performed to verify the effectiveness of the proposed algorithm.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 9","pages":"2542-2551"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-17DOI: 10.1109/TAI.2025.3550458
Xinge Zhao;Chien Chern Cheah
Recent advancements in learning from graph-structured data have highlighted the importance of graph convolutional networks (GCNs). Despite some research efforts on the theoretical aspects of GCNs, a gap remains in understanding their training process, especially concerning convergence analysis. This study introduces a two-stage training methodology for GCNs, incorporating both pretraining and fine-tuning phases. A two-layer GCN model is used for the convergence analysis and case studies. The convergence analysis that employs a Lyapunov-like approach is performed on the proposed learning algorithm, providing conditions to ensure the convergence of the model learning. Additionally, an automated learning rate scheduler is proposed based on the convergence conditions to prevent divergence and eliminate the need for manual tuning of the initial learning rate. The efficacy of the proposed method is demonstrated through case studies on the node classification problem. The results reveal that the proposed method outperforms gradient descent-based optimizers by achieving consistent training accuracies within a variation of 0.1% across various initial learning rates, without requiring manual tuning.
{"title":"Ensuring Reliable Learning in Graph Convolutional Networks: Convergence Analysis and Training Methodology","authors":"Xinge Zhao;Chien Chern Cheah","doi":"10.1109/TAI.2025.3550458","DOIUrl":"https://doi.org/10.1109/TAI.2025.3550458","url":null,"abstract":"Recent advancements in learning from graph-structured data have highlighted the importance of graph convolutional networks (GCNs). Despite some research efforts on the theoretical aspects of GCNs, a gap remains in understanding their training process, especially concerning convergence analysis. This study introduces a two-stage training methodology for GCNs, incorporating both pretraining and fine-tuning phases. A two-layer GCN model is used for the convergence analysis and case studies. The convergence analysis that employs a Lyapunov-like approach is performed on the proposed learning algorithm, providing conditions to ensure the convergence of the model learning. Additionally, an automated learning rate scheduler is proposed based on the convergence conditions to prevent divergence and eliminate the need for manual tuning of the initial learning rate. The efficacy of the proposed method is demonstrated through case studies on the node classification problem. The results reveal that the proposed method outperforms gradient descent-based optimizers by achieving consistent training accuracies within a variation of 0.1% across various initial learning rates, without requiring manual tuning.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 9","pages":"2510-2525"},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-17DOI: 10.1109/TAI.2025.3550913
Aranyak Maity;Ayan Banerjee;Sandeep K. S. Gupta
Errors in artificial intelligence (AI)-enabled autonomous systems (AASs) where both the cause and effect are unknown to the human operator at the time they occur are referred to as “unknown-unknown” errors. This article introduces a methodology for preemptively identifying “unknown-unknown” errors in AAS that arise due to unpredictable human interactions and complex real-world usage scenarios, potentially leading to critical safety incidents through unsafe shifts in operational data distributions. We posit that AAS functioning in human-in-the-loop and human-in-the-plant modes must adhere to established physical laws, even when unknown-unknown errors occur. Our approach employs constructing physics-guided models from operational data, coupled with conformal inference for assessing structural breaks in the underlying model caused by violations of physical laws, thereby facilitating early detection of such errors before unsafe shifts in operational data distribution occur. Validation across diverse contexts—zero-day vulnerabilities in autonomous vehicles, hardware failures in artificial pancreas systems, and design deficiencies in aircraft in maneuvering characteristics augmentation systems (MCASs)—demonstrates our framework's efficacy in preempting unsafe data distribution shifts due to unknown-unknowns. This methodology not only advances unknown-unknown error detection in AAS but also sets a new benchmark for integrating physics-guided models and machine learning to ensure system safety.
{"title":"Detection of Unknown-Unknowns in Human-in-Loop Human-in-Plant Safety Critical Systems","authors":"Aranyak Maity;Ayan Banerjee;Sandeep K. S. Gupta","doi":"10.1109/TAI.2025.3550913","DOIUrl":"https://doi.org/10.1109/TAI.2025.3550913","url":null,"abstract":"Errors in artificial intelligence (AI)-enabled autonomous systems (AASs) where both the cause and effect are unknown to the human operator at the time they occur are referred to as “unknown-unknown” errors. This article introduces a methodology for preemptively identifying “unknown-unknown” errors in AAS that arise due to unpredictable human interactions and complex real-world usage scenarios, potentially leading to critical safety incidents through unsafe shifts in operational data distributions. We posit that AAS functioning in human-in-the-loop and human-in-the-plant modes must adhere to established physical laws, even when unknown-unknown errors occur. Our approach employs constructing physics-guided models from operational data, coupled with conformal inference for assessing structural breaks in the underlying model caused by violations of physical laws, thereby facilitating early detection of such errors before unsafe shifts in operational data distribution occur. Validation across diverse contexts—zero-day vulnerabilities in autonomous vehicles, hardware failures in artificial pancreas systems, and design deficiencies in aircraft in maneuvering characteristics augmentation systems (MCASs)—demonstrates our framework's efficacy in preempting unsafe data distribution shifts due to unknown-unknowns. This methodology not only advances unknown-unknown error detection in AAS but also sets a new benchmark for integrating physics-guided models and machine learning to ensure system safety.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 9","pages":"2526-2541"},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-15DOI: 10.1109/TAI.2025.3567434
Md. Ashikur Rahman;Md. Mamun Ali;Kawsar Ahmed;Imran Mahmud;Francis M. Bui;Li Chen;Mohammad Ali Moni
To prevent different chemicals from entering the brain, the blood–brain barrier penetrating peptide (3BPP) acts as a vital barrier between the bloodstream and the central nervous system (CNS). This barrier significantly hinders the treatment of neurological and CNS disorders. 3BPP can get beyond this barrier, making it easier to enter the brain and essential for treating CNS and neurological diseases and disorders. Computational techniques are being explored because traditional laboratory tests for 3BPP identification are costly and time-consuming. In this work, we introduced a novel technique for 3BPP prediction with a hybrid deep learning model. Our proposed model, Deep3BPP, leverages the LSA, a word embedding method for peptide sequence extraction, and integrates CNN with LSTM (CNN-LSTM) for the final prediction model. Deep3BPP performance metrics show a remarkable accuracy of 97.42%, a Kappa value of 0.9257, and an MCC of 0.9362. These findings indicate a more efficient and cost-effective method of identifying 3BPP, which has important implications for researchers in the pharmaceutical and medical industries. Thus, this work offers insightful information that can advance both scientific research and the well-being of people overall.
{"title":"Deep3BPP: Identification of Blood–Brain Barrier Penetrating Peptides Using Word Embedding Feature Extraction Method and CNN-LSTM","authors":"Md. Ashikur Rahman;Md. Mamun Ali;Kawsar Ahmed;Imran Mahmud;Francis M. Bui;Li Chen;Mohammad Ali Moni","doi":"10.1109/TAI.2025.3567434","DOIUrl":"https://doi.org/10.1109/TAI.2025.3567434","url":null,"abstract":"To prevent different chemicals from entering the brain, the blood–brain barrier penetrating peptide (3BPP) acts as a vital barrier between the bloodstream and the central nervous system (CNS). This barrier significantly hinders the treatment of neurological and CNS disorders. 3BPP can get beyond this barrier, making it easier to enter the brain and essential for treating CNS and neurological diseases and disorders. Computational techniques are being explored because traditional laboratory tests for 3BPP identification are costly and time-consuming. In this work, we introduced a novel technique for 3BPP prediction with a hybrid deep learning model. Our proposed model, Deep3BPP, leverages the LSA, a word embedding method for peptide sequence extraction, and integrates CNN with LSTM (CNN-LSTM) for the final prediction model. Deep3BPP performance metrics show a remarkable accuracy of 97.42%, a Kappa value of 0.9257, and an MCC of 0.9362. These findings indicate a more efficient and cost-effective method of identifying 3BPP, which has important implications for researchers in the pharmaceutical and medical industries. Thus, this work offers insightful information that can advance both scientific research and the well-being of people overall.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"7 1","pages":"562-570"},"PeriodicalIF":0.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145898221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-15DOI: 10.1109/TAI.2025.3570282
H. M. Dipu Kabir
Multitask learning is a popular approach to training high-performing neural networks with improved generalization. In this article, we propose a background class to achieve improved generalization at a lower computation compared to multitask learning to help researchers and organizations with limited computation power. We also present a methodology for selecting background images and discuss potential future improvements. We apply our approach to several datasets and achieve improved generalization with much lower computation. Through the class activation mappings (CAMs) of the trained models, we observed the tendency toward looking at a bigger picture with the proposed model training methodology. Applying the vision transformer with the proposed background class, we receive state-of-the-art (SOTA) performance on CIFAR-10C, Caltech-101, and CINIC-10 datasets.
{"title":"Reduction of Class Activation Uncertainty With Background Information","authors":"H. M. Dipu Kabir","doi":"10.1109/TAI.2025.3570282","DOIUrl":"https://doi.org/10.1109/TAI.2025.3570282","url":null,"abstract":"Multitask learning is a popular approach to training high-performing neural networks with improved generalization. In this article, we propose a background class to achieve improved generalization at a lower computation compared to multitask learning to help researchers and organizations with limited computation power. We also present a methodology for selecting background images and discuss potential future improvements. We apply our approach to several datasets and achieve improved generalization with much lower computation. Through the class activation mappings (CAMs) of the trained models, we observed the tendency toward looking at a bigger picture with the proposed model training methodology. Applying the vision transformer with the proposed background class, we receive state-of-the-art (SOTA) performance on CIFAR-10C, Caltech-101, and CINIC-10 datasets.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"7 1","pages":"571-585"},"PeriodicalIF":0.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145898263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-13DOI: 10.1109/TAI.2025.3551669
Anindita Mohanta;Sourav Dey Roy;Niharika Nath;Abhijit Datta;Mrinal Kanti Bhowmik
Cancer is one of the most severe diseases, affecting the lives of many people in the modern world. Among the various types of cancer, cervical cancer is one of the most frequently occurring cancers in the female population. In most cases, doctors and practitioners can typically only identify cervical cancer in its latter stages. Planning cancer therapy and increasing patient survival rates become very difficult as the disease progresses. As a result, diagnosing cervical cancer in its initial stages has become imperative to arrange proper therapy and surgery. In this article, we present a survey of automatic computerized methods for diagnosing cervical abnormalities based on microscopic imaging modalities. The present survey was conducted by defining a novel taxonomy of the surveyed techniques based on the approaches they used. We also discuss the challenges and subchallenges associated with an automatic cervical cancer diagnosis based on microscopic imaging modalities. Additionally, surveys on various public and private datasets used by the research community for developing new methods are presented. In this article, the performances of published papers are compared. The article concludes by suggesting possible research directions in these fields.
{"title":"A Comprehensive Survey on Diagnostic Microscopic Imaging Modalities, Challenges, Taxonomy, and Future Directions for Cervical Abnormality Detection and Grading","authors":"Anindita Mohanta;Sourav Dey Roy;Niharika Nath;Abhijit Datta;Mrinal Kanti Bhowmik","doi":"10.1109/TAI.2025.3551669","DOIUrl":"https://doi.org/10.1109/TAI.2025.3551669","url":null,"abstract":"Cancer is one of the most severe diseases, affecting the lives of many people in the modern world. Among the various types of cancer, cervical cancer is one of the most frequently occurring cancers in the female population. In most cases, doctors and practitioners can typically only identify cervical cancer in its latter stages. Planning cancer therapy and increasing patient survival rates become very difficult as the disease progresses. As a result, diagnosing cervical cancer in its initial stages has become imperative to arrange proper therapy and surgery. In this article, we present a survey of automatic computerized methods for diagnosing cervical abnormalities based on microscopic imaging modalities. The present survey was conducted by defining a novel taxonomy of the surveyed techniques based on the approaches they used. We also discuss the challenges and subchallenges associated with an automatic cervical cancer diagnosis based on microscopic imaging modalities. Additionally, surveys on various public and private datasets used by the research community for developing new methods are presented. In this article, the performances of published papers are compared. The article concludes by suggesting possible research directions in these fields.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 9","pages":"2354-2383"},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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