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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Joubbi, Sara Maccari, Giuseppe Ciano, Giorgio Micheli, Alessio Milazzo, Paolo Medini, Duccio Department of Computer Science University of Pisa Pisa Italy Data Science for Health Lab Fondazione Toscana Life Sciences Siena Italy

Antibodies are essential proteins in the immune system due to their capacity to bind to specific antigens. They also play a critical role in developing vaccines and treatments for infectious diseases. Their complex structure, with variable regions for antigen binding and flexible hinge regions, presents challenges for accurate computational modeling. Recent advancements in deep learning have revolutionized protein structure prediction. Despite these advancements, predicting interactions between antibodies and antigens remains challenging, mainly due to the flexibility of antibodies and the dynamic nature of binding events. This study uses fingerprint-based methodologies that incorporate ESMFold confidence scores as a flexibility feature to model Ab-Ag interactions. Our methodology shows how including flexibility has improved Ab-Ag interactions by 3%, achieving an AUC-ROC of 91%. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Vaccines

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Revolutionizing Influenza A Detection in Humans: Unleashing the Power of XGBoost Classification 2

Revolutionizing Influenza A Detection in Humans: Unleashing ...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Nikhil, V. Sahaaya Arul Mary, S.A. Natarajan, B. Bajulunisha, A. Anuradha, R.S. Venkatraman, K. Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India VIT University School of Computer Science Engineering Chennai India M.A.M College of Engineering and Technology Department of Artificial Intelligence & Data Science Tiruchirappalli621105 India Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350383287

Influenza A, a zoonotic virus potentially affecting and infecting humans, poses a significant global health threat. This research paper presents a comprehensive study on predicting Influenza A outbreaks by applying the XGBoost classification algorithm. The study utilizes two substantial datasets: one from the World Health Organization (WHO) containing global Influenza A epidemiological data from 2003 to 2023 and the other encompassing Influenza A infections in New York counties from 2009 to 2019. Both datasets underwent pre-processing, with essential features selected to develop an XGBoost Classification model for forecasting Influenza A cases in humans. The models exhibited impressive accuracy and performance metrics. Specifically, the WHO dataset demonstrated an AUC of 0.996 and an accuracy score of 0.99, while the New York dataset displayed an AUC of 0.992 and an accuracy score of 0.978. These outcomes underscore the model's capability to differentiate between positive and negative cases, showcasing high precision, accuracy, and F1 scores. The implications of this research extend to various domains, including the early detection of outbreaks, informed decision-making processes, and the implementation of public health interventions. The findings emphasize the pivotal role of high-quality and comprehensive datasets in training effective predictive models. Furthermore, the study points towards potential future avenues, such as personalized predictions, geo-spatial analysis, and integrating these predictive models with healthcare systems. © 2024 IEEE.

关键词： Classification (of information)

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Epidermis Bruise Analysis Using Deep Learning 6

Epidermis Bruise Analysis Using Deep Learning

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Sharma, Vansheta Rani, Rupa Pathak, Priya Chakraborty, Sudeshna Department of Computer Science & Engineering- Data Science ABES Institute of Technology U.P. Ghaziabad India Department of Computer Science & Engineering Ajay Kumar Garg Engineering College U.P. Ghaziabad India Depatment of Computer Science & Engineering Banasthali Vidyapith Rajasthan India Department School of Computer Application and Technology Galgotias University UP India

ISBN: (纸本)9798350374629

Among the most vital organs that protect the body of a human from the external environment is the skin. Early skin illness identification is essential for reducing mortality because it prevents skin cancer, and any other skin disease from developing. But because it resembles skin patches, insect bites etc in appearance, early detection that might lengthen life expectancy is a fantastic experiment. We would be working with different skin diseases namely ‘Acne’, ‘Eczema or Atopic Dermatitis’, ‘melanoma’. A working model that uses image analysis and machine learning to classify and identify skin conditions. A brand-new, cutting edge automated system that can swiftly and accurately identify skin lesions is needed to deal with biomedical pictures. There is growing curiosity concerning the application of deep learning to the diagnosis and management of different diseases. For the early detection of skin disease, we therefore suggested a deep learning model based on CNN and the merger of the U-Net and ResNet models is presented as a fusion model that has been used to perform encoding decoding of the disorders using skin scans. This model integrates image processing, image enhancement, machine learning and segmentation techniques. The model yielded an overall accuracy of 95.6%. Incorporate additional skin conditions in the future to strengthen our model's suitability for medical applications. ©2024 IEEE.

关键词： Image enhancement

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See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation Learning

See Detail Say Clear: Towards Brain CT Report Generation via...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zheng, Chengxin Ji, Junzhong Shi, Yanzhao Zhang, Xiaodan Qu, Liangqiong College of Computer Science Beijing University of Technology Beijing China Department of Statistics and Actuarial Science School of Computing and Data Science The University of Hong Kong Hong Kong

ISBN: (纸本)9798891761681

Brain CT report generation is significant to aid physicians in diagnosing cranial diseases. Recent studies concentrate on handling the consistency between visual and textual pathological features to improve the coherence of report. However, there exist some challenges: 1) Redundant visual representing: Massive irrelevant areas in 3D scans distract models from representing salient visual contexts. 2) Shifted semantic representing: Limited medical corpus causes difficulties for models to transfer the learned textual representations to generative layers. This study introduces a Pathological Clue-driven Representation Learning (PCRL) model to build cross-modal representations based on pathological clues and naturally adapt them for accurate report generation. Specifically, we construct pathological clues from perspectives of segmented regions, pathological entities, and report themes, to fully grasp visual pathological patterns and learn cross-modal feature representations. To adapt the representations for the text generation task, we bridge the gap between representation learning and report generation by using a unified large language model (LLM) with task-tailored instructions. These crafted instructions enable the LLM to be flexibly fine-tuned across tasks and smoothly transfer the semantic representation for report generation. Experiments demonstrate that our method outperforms previous methods and achieves SoTA performance. Our code is available at https://***/ChaunceyJheng/PCRL-MRG. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Accelerating local SGD for non-IID data using variance reduction

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Frontiers of computer science 2023年第2期17卷 73-89页

作者： Xianfeng LIANG Shuheng SHEN Enhong CHEN Jinchang LIU Qi LIU Yifei CHENG Zhen PAN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China Ant Financial Services Group Hangzhou 310000China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China

Distributed stochastic gradient descent and its variants have been widely adopted in the training of machine learning models,which apply multiple workers in *** them,local-based algorithms,including Local SGD and FedAvg,have gained much attention due to their superior properties,such as low communication cost and ***,when the data distribution on workers is non-identical,local-based algorithms would encounter a significant degradation in the convergence *** this paper,we propose Variance Reduced Local SGD(VRL-SGD)to deal with the heterogeneous *** extra communication cost,VRL-SGD can reduce the gradient variance among workers caused by the heterogeneous data,and thus it prevents local-based algorithms from slow convergence ***,we present VRL-SGD-W with an effectivewarm-up mechanism for the scenarios,where the data among workers are quite *** from eliminating the impact of such heterogeneous data,we theoretically prove that VRL-SGD achieves a linear iteration speedup with lower communication complexity even if workers access non-identical *** conduct experiments on three machine learning *** experimental results demonstrate that VRL-SGD performs impressively better than Local SGD for the heterogeneous data and VRL-SGD-W is much robust under high data variance among workers.

关键词： distributed optimization variance reduction local SGD federated learning non-IID data

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Optimizing Diabetic Foot Ulcer Detection: Leveraging SSD and YOLO Architectures 21

Optimizing Diabetic Foot Ulcer Detection: Leveraging SSD and...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Azeem, Muhammad Zaman, Muhammad Akhunzada, Adnan Kehkashan, Tanzila Ashraf, Imran Rehman, Abdul The University of Lahore Department of Computer Science Sargodha Pakistan COMSATS University Islamabad Department of Computer Science Islamabad Pakistan University of Doha for Science and Technology Department of Data and Cybersecurity Doha24449 Qatar Universiti Teknologi Malaysia Faculty of Computing Johor Bahru Malaysia The University of Lahore Faculty of Computer Science Sargodha Pakistan

ISBN: (纸本)9798350378078

Diabetic foot ulcer (DFU) are a severe complication in diabetic patients, requiring accurate and early diagnosis to prevent further complications such as infection or amputation. To address this, Research employed YOLO and SSD models for automated DFU detection from medical images, comparing their performance based on key evaluation metrics, including accuracy, precision, recall, and F1-score. Results demonstrate that both models perform exceptionally well, with SSD achieving an accuracy of 98.7%, surpassing YOLO's 97.5%. Additionally, SSD exhibited superior precision (99%), recall (98.9%), and F1-score (99.1%), indicating its robustness in detecting ulcerated regions of varying sizes and stages. These results were further contextualized through a comparison with previous studies, where SSD outperformed several CNN-based models, which achieved accuracy ranging from 93.7% to 97.0%. The SSD model's multi-scale object detection capabilities, combined with transfer learning and data augmentation techniques, contributed to its high performance. Overall, this study demonstrates the potential of deploying SSD and YOLO models in clinical settings for early and accurate DFU detection, paving the way for more reliable and timely interventions in diabetic care. The findings underline the significance of advanced object detection methods, offering promising avenues for improving patient outcomes in medical imaging © 2024 IEEE.

关键词： Diagnosis

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A novel Approach for optimizing the crop yield by considering climatic and soil features through fusion of Machine Learning and Deep Learning algorithms 15

A novel Approach for optimizing the crop yield by considerin...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Suganthavalli, K. Meenakshi, C. Parvathy, K. Department of Computer Science Chennai Tamil Nadu Pallavaram India Department of Computer Application Chennai Tamil Nadu Pallavaram India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350370249

The agricultural sector is the scientific and artistic cultivation of plants and animals. Agriculture has an important role in India's economy, which covers a significant 60.45% of the country's land and stands as the world's second-largest agricultural sector, driving economic growth through its diverse produce and associated industries. Soil composition really matters for growing stuff. Elements like Nitrogen, Phosphorous, & Potassium play a big role. The way you rotate crops, soil moisture, and even temperature all affect how well things grow. Rainfall is important too. Research has shown that using machine learning can help figure out the best crops to grow. They utilize methods that include Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN) for this. The study also looks into deep learning methods to make the model even better. In addition to predicting crop yields, it also provides detailed information on the required amounts of soil elements and their associated costs. The new model offers superior precision compared to the current one. The system examines the provided data and assists farmers in forecasting a crop, which subsequently aids in achieving financial gains. The prediction of a suitable yield takes into account the meteorological and soil parameters of the area. This Python-based system utilizes advanced algorithms to optimize decision-making, forecasting the most lucrative outcomes in various situations while streamlining costs and boosting productivity. The study employs Support Vector Machines (SVM) as an example of Machine Learning (ML) method, while Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) are used as Deep Learning (DL) techniques. © 2024 IEEE.

关键词： Agriculture crop Prediction DL LSTM and RNN ML SVM

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CS2023: computer science Curriculum Guidelines: A New Liberal Arts Perspective

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ACM Inroads 2025年第1期16卷 40-52页

作者： Holland-Minkley, Amanda Barnard, Jakob E. Barr, Valerie Braught, Grant Davis, Janet Reed, David Schmitt, Karl Tartaro, Andrea Teresco, James D. Department of Computing and Information Studies Washington & Jefferson College 60 S. Lincoln St. WashingtonPA15301 United States Department of Computer Science and Technology University of Jamestown 6401 College Ln. JamestownND58405 United States Department of Computer Science Bard College 30 Campus Road Annandale-on-HudsonNY12504 United States Department of Computer Science Dickinson College P.O. Box 1773 CarlislePA17013 United States Department of Computer Science Whitman College 345 Boyer Ave. Walla WallaWA99362 United States Department of Computer Science Design & Journalism Creighton University Hitchcock 209 OmahaNE68178 United States Department of Computing and Data Analytics Trinity Christian College 6601 West College Drive Palos HeightsIL60463 United States Department of Computer Science Furman University 3300 Poinsett Hwy GreenvilleSC29690 United States Department of Computer Science Siena College 515 Loudon Rd. LoudonvilleNY12211 United States

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Hybrid RNN Based Text Classification Model for Unstructured data

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SN computer science 2024年第6期5卷 726页

作者： Sunagar, Pramod Sowmya, B.J. Pruthviraja, Dayananda Supreeth, S. Mathew, Jimpson Rohith, S. Shruthi, G. Cloudthat Technologies Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education School of Computer Science and Engineering REVA University Department of Computer Science and Engineering Indian Institute of Technology Patna Department of ECE Nagarjuna College of Engineering and Technology

The volume of social media posts is on the rise as the number of social media users expands. It is imperative that these data be analyzed using cutting-edge algorithms. This goal is handled by the many techniques used in text categorization. There are a variety of text categorization techniques available, ranging from machine learning to deep learning. Numerical crunching has become easier with less processing time since the emergence of high-end computer facilities. This has led to the development of sophisticated network architectures that can be trained to achieve higher precision and recall. The performance of neural network models which was evaluated by the F1 score is affected by cumulative performance in precision and recall. The current study intends to analyze and compare the performance of the neural network proposed, A Hybrid RNN model that has two layers of BiLSTM and two layers of GRU to that of previous hybrid models. GloVE dataset is used to train the models and their accuracy, precision, recall, and F1 score are used to assess performance. Except for the RNN + GRU model, the RNN + BILSTM + GRU model has a precision of 0.767, a recall of 0.759, and an F1-score of 0.7585. This hybrid model outperforms the others. © The Author(s) 2024.

关键词： Bidirectional long-short-term memory (BiLSTM) Gated recurrent units (GRU) Recurrent neural networks (RNN) Region-based convolutional neural networks (RCNN) Text classification

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3D E-commerce Using AI Depth Algorithm Augmented Reality 8th

3D E-commerce Using AI Depth Algorithm Augmented Reality

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Silviya, S. H. Annie Devi, S. Renuka Sriman, B. Aswinrani, M. Raja, S. Sunitha, T. Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9789819784219

In today's dynamic world of online shopping, augmented reality (AR) integration has emerged as a game-changing innovation. It transcends the limitations of traditional online shopping by harnessing AR technologies, such as Google's ARCore, to create immersive 3D product models. These models empower customers to virtually interact with and visualize products in their own environment, effectively bridging the experiential gap with in-store shopping. Employing AR software development kits and game engines, these 3D models seamlessly come to life on customers’ devices, offering a more engaging and informative shopping experience. This not only bolsters customer satisfaction but also helps online retailers manage operational costs more efficiently. With AR’s ability to let customers virtually try on clothes, visualize furniture placements, and inspect products from the comfort of their homes, it's poised to reshape the e-commerce landscape. The ongoing technological revolution is characterized by increasingly immersive, personalized online shopping, setting new benchmarks for customer expectations and retail success. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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