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A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

computers, Materials & Continua 2023年第4期75卷 697-714页

作者： Samra Rehman Muhammad Attique Khan Majed Alhaisoni Ammar Armghan Usman Tariq Fayadh Alenezi Ye Jin Kim Byoungchol Chang Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakahSaudi Arabia Department of Management Information Systems CoBAPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Identifying fruit disease manually is time-consuming, expertrequired,and expensive;thus, a computer-based automated system is widelyrequired. Fruit diseases affect not only the quality but also the *** a result, it is possible to detect the disease early on and cure the fruitsusing computer-based techniques. However, computer-based methods faceseveral challenges, including low contrast, a lack of dataset for training amodel, and inappropriate feature extraction for final classification. In thispaper, we proposed an automated framework for detecting apple fruit leafdiseases usingCNNand a hybrid optimization algorithm. data augmentationis performed initially to balance the selected apple dataset. After that, twopre-trained deep models are fine-tuning and trained using transfer ***, a fusion technique is proposed named Parallel Correlation Threshold(PCT). The fused feature vector is optimized in the next step using a hybridoptimization algorithm. The selected features are finally classified usingmachine learning algorithms. Four different experiments have been carriedout on the augmented Plant Village dataset and yielded the best accuracy of99.8%. The accuracy of the proposed framework is also compared to that ofseveral neural nets, and it outperforms them all.

关键词： Convolutional neural networks deep learning features fusion features optimization classification

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UATTA-QSM: Uncertainty-Aware Test Time Adaptation for Improved Quantitative Susceptibility Mapping 22

UATTA-QSM: Uncertainty-Aware Test Time Adaptation for Improv...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Ravishankar, Hariharan Paluru, Naveen Sudhakar, Prasad Yalavarthy, Phaneendra K. Indian Institute of Science Department of Computational and Data Sciences Bangalore India Ge HealthCare Science and Technology Organization Bangalore India

ISBN: (纸本)9798331520526

This work addresses the problem of deriving improved quantitative susceptibility mapping (QSM) from magnetic resonance (MR) acquisitions. Deep learning based models that map measured MR local phase field to QSM maps, have recently demonstrated satisfactory reconstruction quality. However, they suffer from poor generalization on acquisitions that deviate from training settings. To address this, this work proposes a patient-specific, test-time adaptation method for modifying pre-trained deep learning models, conditioned on individual subject data. The proposed method of uncertainty-aware test-time adaptation (UATTA-QSM), for the first-time introduces, lipschitz constraints on local phase field measurements and QSM reconstructions to jointly reduce uncertainty and adapt model weights during inference time with functional regularization. This framework was evaluated on multiple adaptation scenarios like changing field strength, limited data training and different architectures. On unseen datasets, the proposed method of UATTA-QSM consistently demonstrates performance improvement on all metrics of interest. © 2025 IEEE.

关键词： Inverse problems

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Inference Time Adaptation for Improved Retinal Disease Diagnosis Using Optical Coherence Tomography Images 22

Inference Time Adaptation for Improved Retinal Disease Diagn...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Ravishankar, Hariharan Paluru, Naveen Sudhakar, Prasad Yalavarthy, Phaneendra K. Indian Institute of Science Department of Computational and Data Sciences Bangalore India Science and Technology Organization Ge HealthCare Bangalore India

ISBN: (纸本)9798331520526

Spectral-Domain Optical Coherence Tomography (SDOCT) is the most common and cost-effective variant of OCT for diagnosing retinal diseases. Deep learning models that are utilized for automated diagnosis of these images often exhibit poor performance - across data-sets with varying acquisition settings, limiting their clinical utility. This work, for the first time proposes 'Inference Time Adaptation (ITA)' with lightweight deep learning models for SDOCT analysis, enhancing model deployability and generalizability. Furthermore, this work extends ITA, introducing 'Inference Time Adaptation with Domain Transforms (ITADT)' to improve the generalizability of deep learning models for retinal disease diagnosis. Domain specific augmentations are applied to test samples to enable regularized model adaptation, enhancing classification performance. The proposed approach is model agnostic and can be applied to any trained deep learning model. The proposed methods achieve significant performance improvement of > 10% on average in accuracy, compared with the un-adapted/original deep learning model on benchmark data-sets for classification of retinal diseases. © 2025 IEEE.

关键词： Optical coherence tomography

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EfficientNetB1 Deep Learning Model for Microscopic Lung Cancer Lesion Detection and Classification Using Histopathological Images

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computers, Materials & Continua 2024年第10期81卷 809-825页

作者： Rabia Javed Tanzila Saba Tahani Jaser Alahmadi Sarah Al-Otaibi Bayan AlGhofaily Amjad Rehman Department of Computer Science Lahore College for Women UniversityLahore54000Pakistan Artificial Intelligence&Data Analytics Lab. CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh145111Saudi Arabia

Cancer poses a significant threat due to its aggressive nature,potential for widespread metastasis,and inherent heterogeneity,which often leads to resistance to *** cancer ranks among the most prevalent forms of cancer worldwide,affecting individuals of all *** and accurate lung cancer detection is critical for improving cancer patients’treatment outcomes and survival *** examinations for lung cancer detection,however,frequently fall short of detecting small polyps and *** address these limitations,computer-aided techniques for lung cancer detection prove to be invaluable resources for both healthcare practitioners and patients *** research implements an enhanced EfficientNetB1 deep learning model for accurate detection and classification using histopathological *** proposed technique accurately classifies the histopathological images into three distinct classes:(1)no cancer(benign),(2)adenocarcinomas,and(3)squamous cell *** evaluated the performance of the proposed technique using the histopathological(LC25000)lung *** preprocessing steps,such as image resizing and augmentation,are followed by loading a pretrained model and applying transfer *** dataset is then split into training and validation sets,with fine-tuning and retraining performed on the training *** model’s performance is evaluated on the validation dataset,and the results of lung cancer detection and classification into three classes are *** study’s findings show that an enhanced model achieves exceptional classification accuracy of 99.8%.

关键词： Colon cancer EfficientNetB1 histopathological image processing transfer learning health risks

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Comparative Analysis of Traditional and Contextual Embedding for Bangla Sarcasm Detection in Natural Language Processing 12

Comparative Analysis of Traditional and Contextual Embedding...

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12th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2023

作者： Fahim, Kaji Mehedi Hasan Moontaha, Mithila Rahman, Mashrur Rhythm, Ehsanur Rahman Rasel, Annajiat Alim Brac University School of Data and Sciences Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341102

Sarcasm, a sort of sentiment characterized by a disparity between the apparent and intended meanings of the text, is a key component of sentiment analysis, opinion extraction, and social media analytics. However, sarcasm detection in Bangla has not received sufficient research attention yet. Moreover, there hasn't been a significant amount of study done comparing traditional and contextual word embeddings for the Bengali language. This study aims to address this gap by comparing traditional embedding by using the Bidirectional Gated Recurrent Unit - BiGRU model and contextual embedding by using Bidirectional Encoder Representations from Transformers - BERT for sarcasm detection in Bangla. The collection of the dataset of Bangla text was from social media platforms, containing labelled instances - whether it provides sarcasm or non-sarcasm. Pre-trained word embeddings i.e. GloVe and FastText are used as traditional embedding for this study. By using metrics like precision, recall and F1-score, the performances for both models have been obtained. When the two traditional word embedding approaches are compared, GloVe embedding with Bi-GRU has outperformed FastText embedding with a macro-averaged F1 score of 0.9395. On the other hand, contextual word embedding using BERT has outperformed both the traditional approaches having a better macro-averaged F1 score of 0.9572 and greater class-wise performance as compared with traditional embedding for both non-sarcastic (96%) and sarcastic (96%) text detection. In our findings, contextual word embedding i.e. BERT has performed better as compared with the two traditional word embeddings for this specific Bangla sarcasm detection binary classification task. © 2023 IEEE.

关键词： Embeddings

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Extraction and Classification of Medicines from Handwritten Medical Prescriptions 18

Extraction and Classification of Medicines from Handwritten ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Zia, Javeria Habib, Usman Naeem, Muhammad Asif National University of Computer and Emerging Sciences Department of Ai & Data Science Islamabad Pakistan

ISBN: (纸本)9798350328172

In the modern world of rapid technological progress and digital transformation, industries are swiftly shifting from paper-based to digital systems. Document digitization, especially for image-based documents lacking machine readability, is gaining global attention. Sectors like real estate, finance, law, healthcare, and education are actively adopting digital strategies. In healthcare, where handwritten prescriptions lead to inefficiencies such as errors in medicine name by pharmacist due to doctor's hand writing lead to catastrophic failure and delays in various processes. Therefore, digital solutions are being developed to enhance patient care. This study proposes an approach to extract and categorize medicines from handwritten prescriptions, improving accuracy and communication with pharmacists. We proposed an approach that uses stroke enhancement apart from pre-processing with comprehensive multi-modal architecture, including a transformer based deep neural network and a sequence-to-sequence model such as DONUT and layoutLMv2 model. Our approach achieves a mean accuracy of about 0.85%. It surpasses models like LayoutLMv2 and DONUT by providing a balance in accuracy and consistency. Furthermore, the extracted features are passed to the various state of art machine learning classification algorithms upon which Gradient Boosting, Random forest and Ridge classifier produced the promising results for medicine type classification. © 2023 IEEE.

关键词： Deep neural networks

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Text Description to Facial Sketch Generation using GANs 18

Text Description to Facial Sketch Generation using GANs

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Tanveer, Mohsin Habib, Usman National University of Computer and Emerging Sciences Department of Ai & Data Science Islamabad Pakistan

ISBN: (纸本)9798350328172

Text-to-face generation is an exciting area of research focused on generating human faces based on textual descriptions, presenting unique challenges that have primarily been explored in academia. The advancement of generative adversarial networks (GANs) has played a crucial role in implementing text-to-face generation. This study aims to develop a framework that allows users to express their desired human face characteristics through textual descriptions, with the tool generating lifelike human face images in response. The approach utilizes a Deep Fusion generative adversarial network (DFGAN) architecture, incorporating deep convolutional neural networks (CNNs), trained on the CelebA dataset, which provides a diverse collection of labeled face images for training. The two model's performance are evaluated using the Fréchet Inception Distance (FID) score. DFGAN has achieved a FID score of 100 after evaluating 15k images, surpassing the results obtained by the DCGAN model. © 2023 IEEE.

关键词： Generative adversarial networks

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Toward Optimal Periodic Crowd Tracking via Unmanned Aerial Vehicle

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computer Modeling in Engineering & sciences 2023年第10期137卷 233-263页

作者： Khalil Chebil Skander Htiouech Mahdi Khemakhem Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj11942Saudi Arabia Data Engineering and Semantics Research Unit Faculty of Sciences of SfaxUniversity of SfaxSfaxTunisia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Mathematics and Business Intelligence College of Electronics and Telecommunications Engineering of SfaxUniversity of SfaxSfaxTunisia

Crowd management and analysis(CMA)systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles(UAVs)*** tracking using UAVs is among the most important services provided by a *** this paper,we studied the periodic crowd-tracking(PCT)*** consists in usingUAVs to follow-up crowds,during the life-cycle of an open crowded area(OCA).Two criteria were considered for this *** first is related to the CMA initial investment,while the second is to guarantee the quality of service(QoS).The existing works focus on very specified assumptions that are highly committed to CMAs applications *** study outlined a new binary linear programming(BLP)model to optimally solve the PCT motivated by a real-world application study taking into consideration the high level of *** closely approach different real-world contexts,we carefully defined and investigated a set of parameters related to the OCA characteristics,behaviors,and theCMAinitial infrastructure investment(e.g.,UAVs,charging stations(CSs)).In order to periodically update theUAVs/crowds andUAVs/CSs assignments,the proposed BLP was integrated into a linear algorithm called PCTs *** main objective was to study the PCT problem fromboth theoretical and numerical *** prove the PCTs solver effectiveness,we generated a diversified set of PCTs instances with different scenarios for simulation *** empirical results analysis enabled us to validate the BLPmodel and the PCTs solver,and to point out a set of new challenges for future research directions.

关键词： Unmanned aerial vehicles periodic crowd-tracking problem open crowded area optimization binary linear programming crowd management and analysis system

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Display Field-of-View Agnostic Robust Ct Kernel Synthesis Using Model-Based Deep Learning 22

Display Field-of-View Agnostic Robust Ct Kernel Synthesis Us...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Aggarwal, Hemant Kumar Jerald, Antony Yalavarthy, Phaneendra K. Langoju, Rajesh Das, Bipul Science and Technology Organization Ge HealthCare Bangalore India Indian Institute of Science Department of Computational and Data Sciences Bangalore India

ISBN: (纸本)9798331520526

In X-ray computed tomography (CT) imaging, the choice of reconstruction kernel is crucial as it significantly impacts the quality of clinical images. Different kernels influence spatial resolution, image noise, and contrast in various ways. Clinical applications involving lung imaging often require images reconstructed with both soft and sharp kernels. The reconstruction of images with different kernels require raw sinogram data and storing images for all kernels increases processing time and storage requirements. The Display Field-of-View (DFOV) adds complexity to kernel synthesis, as data acquired at different DFOVs exhibit varying levels of sharpness and details. This work introduces an efficient, DFOV-agnostic solution for image-based kernel synthesis using model-based deep learning. The proposed method explicitly integrates CT kernel and DFOV characteristics into the forward model. Experimental results on clinical data, along with quantitative analysis of the estimated modulation transfer function using wire phantom data, clearly demonstrate the utility of the proposed method in real time. Additionally, a comparative study with a direct learning network, that lacks forward model information, shows that the proposed method is more robust to DFOV variations. © 2025 IEEE.

关键词： Deep learning

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VulnFusion: Exploiting Multimodal Representations for Advanced Smart Contract Vulnerability Detection 6

VulnFusion: Exploiting Multimodal Representations for Advanc...

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6th International Conference on Blockchain Computing and Applications, BCCA 2024

作者： Upadhya, Jiblal Sainju, Arpan Upadhyay, Kritagya Poudel, Samir Hasan, Md Nahid Poudel, Khem Ranganathan, Jaishree Department of Computer Science Middle Tennessee State University MurfreesboroTN37132 United States Department of Computational and Data Science Middle Tennessee State University MurfreesboroTN37132 United States

ISBN: (纸本)9798350351538

In the fast-evolving domain of blockchain technology, smart contracts have become instrumental, facilitating billions of dollars in transactions daily. However, the increasing prevalence of smart contract vulnerabilities poses significant risks, potentially undermining trust in this innovative technology. To address these security challenges, our research introduces a novel approach for detecting and mitigating vulnerabilities through a comprehensive analysis of four principal modalities: Solidity source code, bytecode, opcode, and intermediate representations. This multimodal analysis employs a Transformer architecture, VulnFusion, which uniquely integrates these modalities to provide a deeper understanding of both the semantic and syntactical nuances of smart contracts. We propose an interleaving-based data fusion technique that outperforms traditional fusion methods such as concatenation and cross-attention. Experimental evaluations demonstrate that our interleaving-based fusion technique achieves an impressive average F1 score of 89% across source code, opcode, and intermediate representations, demonstrating the effectiveness of our approach. © 2024 IEEE.

关键词： Smart contract

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