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Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

computer Systems science & engineering 2023年第10期47卷 873-889页

作者： Mohamed Yacin Sikkandar K.Hemalatha M.Subashree S.Srinivasan Seifedine Kadry Jungeun Kim Keejun Han Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Biomedical Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its *** researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their *** of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)*** the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical ***,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest *** the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly *** study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and *** major intention of the IFFO-DLEF model is to investigate the presence or absence of *** do so,the presented IFFODLEF model applies image pre-processing at the initial *** addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature ***,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify *** parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification *** experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches.

关键词： COVID-19 computer vision deep learning image classification fusion model

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Effectiveness of Using Deep Learning for Blister Blight Identification in Sri Lankan Tea

Effectiveness of Using Deep Learning for Blister Blight Iden...

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2023 International Research Conference on Smart Computing and Systems engineering, SCSE 2023

作者： Hewawitharana, G.H.A.U. Nawarathne, U.M.L.A. Hassan, A.S.F. Wijerathna, Lochana M. Sinniah, G.D. Vidhanaarachchi, Samitha P. Wickramarathne, Jagath Wijekoon, Janaka L. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Tea Research Institute of Sri Lanka Plant Pathology Division Talawakelle Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Military Technological College Systems Engineering Department Muscat Oman

ISBN: (纸本)9798350341454

Ceylon tea industry faces a major challenge in the form of pathogen-induced crop loss, with Blister Blight (BB) caused by Exobasidium vexans posing the greatest threat, leading to harvest losses of over 30%. This fungus attacks the tender tea shoots, resulting in a direct negative impact on the tea harvest. This paper presents a system to identify the suspicious tea leaves and BB disease at its early stages along with an assessment of severity, offering a potential solution to this critical issue. By utilizing real-time object detection, the system filters out non-tea leaves from the captured initial image of a segment of a tea plant. The identified tea leaves are then subjected to BB identification and severity assessment based on differing visual symptoms of the BB stages. This approach enables the system to accurately identify BB in the initial stage and severity stage, allowing for timely and targeted intervention to minimize crop losses. The YOLOv8 model has been able to correctly identify 98% of the objects it has detected as relevant (precision), and it has been able to correctly identify 96% of all the relevant objects present in the scene (recall). The Residual Network 50 (Resnet50) convolutional neural network (CNN) model was selected as the final model, achieving an accuracy of 89.90% during the training phase and an accuracy of 88.26% during the testing phase. © 2023 IEEE.

关键词： Convolutional neural networks

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AVENet: Disentangling Features by Approximating Average Features for Voice Conversion

arXiv

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arXiv 2025年

作者： Wang, Wenyu Zhou, Yiquan Zhu, Jihua Ding, Hongwu Xu, Jiacheng Li, Shihao School of Software Engineering Xi’an Jiaotong University Xi’an China School of Computer Science and Technology Anhui University Hefei China School of Software Engineering East China Normal University Shanghai China Division of Music and Audio Union Wheatland Culture and Media Ltd Chengdu China

Voice conversion (VC) has made progress in feature disentanglement, but it is still difficult to balance timbre and content information. This paper evaluates the pre-trained model features commonly used in voice conversion, and proposes an innovative method for disentangling speech feature representations. Specifically, we first propose an ideal content feature, referred to as the average feature, which is calculated by averaging the features within frame-level aligned parallel speech (FAPS) data. For generating FAPS data, we utilize a technique that involves freezing the duration predictor in a Text-to-Speech system and manipulating speaker embedding. To fit the average feature on traditional VC datasets, we then design the AVENet to take features as input and generate closely matching average features. Experiments are conducted on the performance of AVENet-extracted features within a VC system. The experimental results demonstrate its superiority over multiple current speech feature disentangling methods. These findings affirm the effectiveness of our disentanglement approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature extraction

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Towards Fully-Automated Materials Discovery via Large-Scale Synthesis Dataset and Expert-Level LLM-as-a-Judge

arXiv

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arXiv 2025年

作者： Kim, Heegyu Jeon, Taeyang Choi, Seungtaek Hong, Jihoon Jeon, Dongwon Baek, Ga-Yeon Kwak, Kyung-Won Lee, Dong-Hee Bae, Jisu Lee, Chihoon Kim, Yunseo Choi, Sun-Jin Park, Jinsung Cho, Sungbum Cho, Hyunsouk Department of Artificial Intelligence Korea Republic of Department of Software and Computer Engineering Korea Republic of Department of Materials Science and Engineering Department of Energy Systems Research Ajou University Suwon16499 Korea Republic of Division of Materials Science and Engineering Hanyang University Seoul04763 Korea Republic of

Materials synthesis is vital for innovations such as energy storage, catalysis, electronics, and biomedical devices. Yet, the process relies heavily on empirical, trial-and-error methods guided by expert intuition. Our work aims to support the materials science community by providing a practical, data-driven resource. We have curated a comprehensive dataset of 17K expert-verified synthesis recipes from open-access literature, which forms the basis of our newly developed benchmark, AlchemyBench. AlchemyBench offers an end-to-end framework that supports research in large language models applied to synthesis prediction. It encompasses key tasks, including raw materials and equipment prediction, synthesis procedure generation, and characterization outcome forecasting. We propose an LLM-as-a-Judge framework that leverages large language models for automated evaluation, demonstrating strong statistical agreement with expert assessments. Overall, our contributions offer a supportive foundation for exploring the capabilities of LLMs in predicting and guiding materials synthesis, ultimately paving the way for more efficient experimental design and accelerated innovation in materials science. © 2025, CC BY-SA.

关键词： Characterization (materials science)

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Poisoning Actuation Attacks Against the Learning of an Optimal Controller

Poisoning Actuation Attacks Against the Learning of an Optim...

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American Control Conference (ACC)

作者： Filippos Fotiadis Aris Kanellopoulos Kyriakos G. Vamvoudakis Jérôme Hugues Daniel Guggenheim School of Aerospace Engineering Georgia Institute of Technology Atlanta GA USA Division of Information Science and Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Carnegie Mellon University/Software Engineering Institute Pittsburgh PA USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

In this paper, we study the problem of poisoning the learning of an optimal controller by means of an actuation attack. We specifically consider a user who is gathering data from a linear system in the form of input and state measurements, and who uses these data to learn an optimal controller. Nevertheless, these measurements are corrupted by an attacker who has access to the system's actuators, and who is using them to launch an actuation attack during the learning process. We design this actuation attack so that it optimally corrupts the data used by the user: it forces the user to learn as closely as possible a gain that the attacker has selected, and which is unrelated to the actual optimal control gain. We prove that this poisoning actuation attack design boils down to the solution of certain coupled matrix equations, which we solve using the block successive over-relaxation (SOR) iterative procedure. Simulations on an aircraft model demonstrate theoretical findings, showing how the poisoning attack is effective in misleading the user towards learning an incorrect gain for the system.

关键词： Linear systems Actuators Atmospheric modeling Optimal control Mathematical models Iterative methods Aircraft

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Delay-optimized Topology Management of a Wirelessly Networked Disaster Area

Delay-optimized Topology Management of a Wirelessly Networke...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Michael Conrad Meyer Yu Wang Department of Computer Science and Electrical Engineering Eastern Washington University Spokane WA USA Software Research and Development Division TriOrb Inc Kitakyushu Japan

Major disasters can often destroy communication networks. To address this issue, companies such as NTT have developed movable base stations (MBSs), which can be easily deployed to construct an emergency communication network. A sample of how MBSs can be connected to form a network can be seen in Fig. 1. However, these networks have performance drawbacks. In order to improve their performance, we propose an algorithm that determines the best way for the MBSs to be connected. The proposed model and algorithm resulted in a reduction in the overall delay and average delay of the system compared to the greedy algorithm. For the realistic constraints, the genetic algorithm (GA) was so successful that its worst-case delay almost matched the average delay of the greedy algorithm. The simulations did show that for extremely high communication rates or extremely slow processing rates, the impact of the topology on the system delay was minimal, and thus both algorithms had similar results. This is because the system bottleneck in these cases is the processing times. However, for the realistic network constraints, the GA outperforms the greedy algorithm by approximately 40% in the fog system and 90% in the cloud system.

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Integrating Design Thinking into software Process Improvement 4

Integrating Design Thinking into Software Process Improvemen...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Khalid, Anum Hashmi, Amber Sarwar Kiani, Azaz Ahmed Muzammal, Syeda Mariam Jhanjhi, N.Z. Bibi, Ruqia Kumar, Sandeep Foundation University Department of Software Engineering Islamabad Pakistan Rawalpindi Women University Department of Software Engineering Rawalpindi Pakistan National University of Modern Languages Rawalpindi Pakistan School of Computer Science Taylor's University Faculty of Innovation and Technology Subang Jaya Malaysia University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture University Rawalpindi Pakistan Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

The need for software improvement is growing widely due to the demand for technology development. The issues that arise by expanding the software processes in the workplace are expense, lack of understanding, time constrained, and lack user experience. This research paper focuses on proposing a framework for integrating design thinking into software process improvement. The secret to project success may lie in fusing methodological and reusable approaches with human-centric techniques. A mixed-methods research strategy is proposed to compile thorough information and reach conclusions. In addition, it incorporates components of both qualitative and quantitative research (secondary research and descriptive analysis). The assumption is that the existing model significantly affects the user satisfaction variable. This indicates a statistically significant association between the individual's experience (number of years) and design thinking integration into the software process improvement. The variables in the proposed model and the dependent variable user satisfaction accounts for 6.1 © 2024 IEEE.

关键词： software design

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Adaptive sampling for efficient softmax approximation 24

Adaptive sampling for efficient softmax approximation

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Tavor Z. Baharav Ryan Kang Colin Sullivan Mo Tiwari Eric Luxenberg David Tse Mert Pilanci Eric and Wendy Schmidt Center Broad Institute Cambridge MA Department of Computer Science Stanford University Stanford CA AI Division Software Engineering Institute Pittsburgh PA Department of Computer Science Stanford University Stanford CA Gridmatic Cupertino CA Department of Electrical Engineering Stanford University Stanford CA

ISBN: (纸本)9798331314385

The softmax function is ubiquitous in machine learning and optimization applications. Computing the full softmax evaluation of a matrix-vector product can be computationally expensive in high-dimensional settings. In many applications, however, it is sufficient to calculate only the top few outputs of the softmax function. In this work, we present an algorithm, dubbed AdaptiveSoftmax, that adaptively computes the top k softmax values more efficiently than the full softmax computation, with probabilistic guarantees. We demonstrate the sample efficiency improvements afforded by AdaptiveSoftmax on real and synthetic data to corroborate our theoretical results. AdaptiveSoftmax yields > 10x gain over full softmax computation on most datasets, yielding up to 30x improvement for Mistral7B evaluated on the Wikitext dataset. The adaptive method we propose for estimating the partition function (the softmax denominator) is of independent interest and can be used in other applications such as kernel density estimation.

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AI-based Bonsai Species Classification and Care Recommendation System using YOLO

AI-based Bonsai Species Classification and Care Recommendati...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Derry Jeswyn S K Ramalakshmi S. Shirly R Venkatesan G Naveen Sundar Saravana Kumar C S Division of CSE Karunya Institute of Technology and Sciences Coimbatore Department of CSE & IT Alliance University Bangalore Computer Science and Engineering Karunya University Division of CSE Karunya University Coimbatore Global Tech Park Bosch Global Software Technologies Coimbatore

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Bonsai, an ancient art form that blends horticultural precision with artistic expression, poses unique challenges to both beginners and seasoned practitioners due to its species-specific care requirements and intricate maintenance processes. This research introduces an innovative AI-based solution combining YOLOv8, an advanced object detection model, and Google Gemini LLM, to simplify bonsai species classification and care recommendations. The system is designed to accurately identify bonsai species and provide personalized, context-aware guidance on essential care aspects such as watering, pruning, and pest management, while dynamically adapting to environmental and seasonal variations. Featuring an intuitive interface with sub-100ms response times, the proposed solution enhances accessibility and engagement for users of all expertise levels. Testing affirms its capability to improve understanding, usability, and appreciation of bonsai cultivation. Beyond its application in bonsai care, this hybrid AI integration demonstrates the potential for broader adoption in fields requiring precise classification and tailored insights, showcasing a seamless blend of tradition and technology.

关键词： YOLO Accuracy Scalability Horticulture Natural language processing Internet Time factors Usability Recommender systems Testing

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Recognition and Diagnosis of Computed Tomography Images Using Reconstructive Techniques 10th

Recognition and Diagnosis of Computed Tomography Images Usin...

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10th International Conference on Frontier Computing, FC 2020

作者： Li, Pengzhi Li, Jianqiang Xie, Haihua Pei, Yan Feng, Hui School of Software Engineering Beijing University of Technology Beijing100124 China Computer Science Division University of Aizu Aizuwakamatsu965-8580 Japan

ISBN: (纸本)9789811601149

In this paper, we propose a diagnosis and classification method of colonography CT Images based on deep learning and image reconstruction, which is processed to increase the difference between pathological features and other healthy tissues and improve the accuracy of pathological images classification. It is difficult to accurately distinguish polyps features owing to the variability of appearances, fuzzy boundaries, heterogeneous densities, shapes and sizes of lesions. Some study results show that the ResNet network has a better performance than other methods and has broad application prospects in the identification of CT images. We used the improved ResNet network as the classification model. We completed the classification experiment on the colonography datasets. Through comparative experiments, we found gradient features play an important role in the classification of colonography CT images. More unexpectedly, for CT images of colonography, the classification effect of CT images with small information entropy is excellent. A new "RGB" image containing two channels of gradient features and one channel of LBP features is very effective for classification, which may be a new attempt. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image reconstruction

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