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Predictive Analytics for Breast Cancer Survival Rates Using Random Forest 10

Predictive Analytics for Breast Cancer Survival Rates Using ...

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Kumar, Rakesh Bordoloi, Dibyhash Michaelson, Jacob Laxminarayanamma, K. Kalra, Ravi Kumar, Anil Gla University Department of Computer Engineering & Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

The Random Forest algorithm was used in this study to build a prediction model for breast cancer survival rates. The study used a deductive methodology, a descriptive design, including secondary data collecting in accordance with the interpretivism philosophy. The dataset was painstakingly preprocessed, and numerous metrics were used to gauge the model's effectiveness. The significance of the model's features was investigated as well to offer insight into the factors affecting survival forecasts. The study emphasizes how the model's practical applications might improve patient care by guiding treatment choices. It assesses the model's resilience under various circumstances, admits its shortcomings, and suggests future study trajectories. The study demonstrates the way predictive models have the ability to greatly affect patient outcomes and breast cancer care. © 2023 IEEE.

关键词： Breast cancer clinical implications data preprocessing model robustness predictive modelling Random Forest algorithm

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PIVODL: Privacy-Preserving Vertical Federated Learning Over Distributed Labels

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 988-1001页

作者： Zhu, Hangyu Wang, Rui Jin, Yaochu Liang, Kaitai University of Surrey Department of Computer Science GuildfordGU27XH United Kingdom Delft University of Technology Department of Intelligent Systems Delft2628XE Netherlands Bielefeld University Nature Inspired Computing and Engineering Group Faculty of Technology BielefeldD-33619 Germany

Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data. Nonparametric models like gradient boosting decision trees (GBDTs) have been commonly used in FL for vertically partitioned data. However, all these studies assume that all the data labels are stored on only one client, which may be unrealistic for real-world applications. Therefore, in this article, we propose a secure vertical FL framework, named privacy-preserving vertical federated learning system over distributed labels (PIVODL), to train GBDTs with data labels distributed on multiple devices. Both homomorphic encryption and differential privacy are adopted to prevent label information from being leaked through transmitted gradients and leaf values. Our experimental results show that both information leakage and model performance degradation of the proposed PIVODL are negligible. Impact Statement - Federated learning is a distributed machine learning framework proposed for privacy preservation. Most federated learning algorithms work on horizontally partitioned data, with only a few exceptions considering vertically partitioned data that is widely seen in the real world. However, existing vertical federated learning makes an unrealistic assumption that data labels are distributed on only one device and no research has been reported so far that considers data labels distributed on multiple client devices. The PIVODL framework reported in this article allows us to build a secure vertical federated XGBoost system, in which the labels may distributed either on one device or on multiple devices, making it possible to apply federated learning to a wider range of real-world problems. © 2020 IEEE.

关键词： Decision trees

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Recent Trends on Blockchain for Internet of Things based Applications: Open Issues and Future Trends 2022

Recent Trends on Blockchain for Internet of Things based App...

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14th International Conference on Contemporary Computing, IC3 2022

作者： Deshmukh, Atharva Patil, Disha Tyagi, Amit Kumar Arumugam, S.S. Department of Computer Engineering Terna Engineering College Maharashtra Navi Mumbai400706 India Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Thandalam602105 India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai600127 India

ISBN: (纸本)9781450396752

Today Blockchain, a decentralized, and distributed ledger technology have started taking over almost all the applications from banking, agriculture, finance, transportation, etc. Such enhancements are possible when this technology is enabled with some other technology like the Internet of Things, cloud computing, machine learning, etc. This paper does a thorough evaluation of the literature concerning blockchain enabling IoT-based applications in diverse sectors. The goal is to look at the current state of blockchain based IoT systems and applications and to demonstrate how special qualities of this technology can transform the working methods in the industry. We analysed high-quality papers published in high-ranking scientific journals over the last years to write this paper. This work presents a comprehensive classification of IoT and Blockchain enabled applications across many sectors such as Agriculture, Business, Supply Chain, Data Management, Finance, Healthcare, IoT, and Privacy, and in the end, we have added recent trends, emerging areas, and open challenges, for future research across various sectors and industries, based on a structured, systematic review and thematic content analysis of the discovered literature. We feel that our work will be of tremendous use to both academics and practitioners because we have discovered such critical components for this technology. © 2022 ACM.

关键词： Blockchain

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Sub-region aware retrieval-based network with multimodal prior knowledge guidance for microvascular invasion prediction in PET/CT imaging

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Neural Computing and Applications 2024年第19期36卷 11623-11641页

作者： Wu, Nan Wang, Yutao Zhang, Jian Yu, Zhongfei Jin, Wei Faculty of Electrical Engineering and Computer Science Ningbo University Zhejiang Ningbo315211 China Ningbo Ninth Hospital Zhejiang Ningbo315020 China The First Affiliated Hospital of Ningbo University Zhejiang Ningbo315010 China Shanghai Universal Medical Imaging Diagnostic Center Shanghai University Shanghai201103 China

Accurate preoperative microvascular invasion (MVI) prediction using PET/CT imaging is important for guiding surgical plans and treatment strategies for hepatocellular carcinoma (HCC) patients. Deep learning (DL)-based approaches have shown effectiveness in medical imaging-based diagnostics. However, most DL-based methods for automatic MVI diagnosis depend on detailed annotations of HCC regions by clinicians, then perform feature extraction and directly output MVI risk scores, which can increase application complexity and lack transparency in decision-making. Therefore, we propose the sub-region aware retrieval-based network (SRANet), utilizing the prior knowledge of modality complementarity and localization of HCC in PET/CT images for tumor area location and feature extraction. Specifically, in the feature extraction stage, to better highlight lesion areas, we designed the prior knowledge guided fusion (PKGF) block. PKGF integrates knowledge from PET and CT through attention mechanism-based intermodal interaction and implicitly weights features to calibrate different modalities. Furthermore, we developed a sub-region aware module (SRAM) that employs class activation schemes and peak selection to identify and highlight sparse, salient sub-regions in tumor areas, relying solely on coarse-grained supervision. Additionally, we design a new loopback Siamese training strategy, guiding the model to focus on lesion areas through the semantic invariance of lesion features. Finally, we employ a retrieval strategy to present similar slices, providing clinicians with transparent references and enhancing the understandability of MVI predictions beyond simple risk scores. Experimental results show SRANet outperforms leading methods, achieving a slice-level AUC of 0.855 and an individual-level AUC of 0.796, highlighting its potential for clinical application. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Feature extraction

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Deep Learning Scheme for Modulation Classification under Fading Channels 7

Deep Learning Scheme for Modulation Classification under Fad...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Ghauri, Sajjad A. Sarfraz, Mubashar Chhabra, Gunjan Kaushik, Keshav Zafar, Asim Khan, Inam Ullah Isra University School of Engineering & Applied Sciences Islamabad Pakistan Numl University Faculty of Engineering and Computing Islamabad Pakistan Graphic Era Hill University Graphic Era Deemed to be University Department of Cse Uttarakhand Dehradun India Amity University Amity School of Engineering and Technology Punjab India National University of Technology Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350350067

The field of automatic modulation classification using deep neural networks has undergone significant development in recent years. In this research, M-QAM and N-PSK modulation formats have been considered for classification. The deep neural network architecture is directly fed with raw signals that are often corrupted with channel noise and fading effects such as Rayleigh and Nakagami. The proposed classifier structure also incorporates normalized cumulants of higher order from the received signal. The proposed DNN architecture's performance is evaluated by comparing its efficiency for raw data and extracted features-based classification. We have validated the classifier efficiency through three different cases. Our proposed classifier demonstrates remarkable capability in efficiently classifying modulation formats. © 2024 IEEE.

关键词： Signal modulation

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An Effective Customer Segmentation Approach to Improve E-Commerce Platform Quality Based on Intensive Learning Methodology

An Effective Customer Segmentation Approach to Improve E-Com...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Suresh, G.R. Vinoparkavi, D. Govindasamy, Suresh Rohini, S. Giri, Jayant Sabri, M. Omar Dhanalakshmi Srinivasan College of Engineering and Technology Department of Ece Mamallapuram Chennai India Nandha Engineering College Department of Cse Erode India Sona College of Technology Department of Electrical and Electronics Engineering Salem India R.M.K Engineering College Department of Computer Science Engineering Kavaraipettai Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Economic Department of Business Information Technology Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

To accomplish company goals and reap advantages while satisfying and meeting consumer expectations, segmentation is an essential facilitator. Identifying subgroups of the target market that share common traits, requirements, and priorities is customer segmentation's primary goal. Various dataset dimensionalities are also considered, as are changes across time. In this study, we use time-based segmentation and clustering approaches to the e-commerce industry to conduct predictive neural network analysis using factors such as product reviews, goods, buying trend, watching pattern, and more. In this research, Intensive Neural Classification Learning (INCL) is presented as a novel deep learning technique. To assess its efficacy, it is cross-validated with the traditional Neural Network (NN) learning algorithm. We have evaluated unigrams, bigrams, and trigrams as part of the feature extraction process and identified the unique brands per consumer. In addition, the customer's chosen brands may be determined using a neural network, and the projected data can then be utilized for statistical analysis. We all know that sentiment analysis and categorization are huge help when trying to guess how customers feel about certain companies and items. Lastly, using the provided set of input data, we will choose the top brands by assessing the accuracy of the forecasts. © 2024 IEEE.

关键词： Sales

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High-Resolution Optical Imaging Incorporated Non-Destructive Morphological Boundary Detection of Fruit Disorders: A Pilot Study 6

High-Resolution Optical Imaging Incorporated Non-Destructive...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Hakmana, Nethmi Tharushi Kahatapitiya, Nipun Shantha Weerasinghe, Sasmitha Kalupahana, Deshan Silva, Bhagya Nathali Ravichandran, Naresh Kumar Seong, Daewoon Han, Sangyeob Lee, Seung Yeol June, Hee-Young Jeon, Mansik Kim, Jeehyun Wijenayake, Udaya Wijesinghe, Ruchire Eranga Faculty of Engineering Sri Lanka Institute of Information Technology Department of Mechanical Engineering Malabe Sri Lanka Faculty of Engineering University of Sri Jayewardenepura Department of Computer Engineering Nugegoda Sri Lanka Faculty of Computing Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Scientific Instrumentation Korea Basic Science Institute Daejeon Korea Republic of School of Electronic and Electrical Engineering College of IT Engineering Kyungpook National University Daegu Korea Republic of ICT Convergence Research Center Kyungpook National University Daegu Korea Republic of Kyungpook National University Department of Plant Medicine Daegu Korea Republic of Institute of Plant Medicine Kyungpook National University Daegu Korea Republic of Faculty of Engineering Sri Lanka Institute of Information Technology Department of Electrical and Electronic Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

Plant diseases caused by various pathogens, such as fungi, bacteria, and viruses, lead to significant reductions in crop yield and quality. Early detection of these diseases is critical for preventing their spread and optimizing crop yield. Traditional methods of disease detection, while effective, often involve invasive or time-consuming procedures that may not provide real-time insights. This study investigates the use of Swept-Source Optical Coherence Tomography (SS-OCT) for non-destructive, high-resolution imaging to identify morphological abnormalities in fruit specimens affected by diseases. The research focuses on differentiating healthy regions, healthy boundaries, infected boundaries, and fully infected regions in apples. Using a 1310 nm center wavelength laser, the SS-OCT system captured detailed cross-sectional images of fruit tissue, revealing microstructural changes associated with disease progression. A qualitative assessment of the two-dimensional (2D) OCT images highlighted distinct layers in healthy tissues, noticeable structural deformities, and the merging of microstructural layers in the boundary and infected regions. Quantitative depth-profile analysis of axial-direction A-scan profiles further demonstrated the reduction and merging of intensity peaks correlating with tissue abnormalities in infected fruit specimens. These findings underscore the potential of SS-OCT as an effective tool for early disease detection in agricultural products, providing a means for timely intervention and optimized postharvest management. © 2024 IEEE.

关键词： Fruits

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Deep Left Ventricular Motion Estimation Methods in Echocardiography: A Comparative Study

Deep Left Ventricular Motion Estimation Methods in Echocardi...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Sofia Ferraz Miguel Coimbra João Pedrosa Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Faculty of Engineering of the University of Porto (FEUP) Porto Portugal Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Faculty of Sciences of the University of Porto (FCUP) Porto Portugal

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Motion estimation in echocardiography is critical when assessing heart function and calculating myocardial deformation indices. Nevertheless, there are limitations in clinical practice, particularly with regard to the accuracy and reliability of measurements retrieved from images. In this study, deep learning-based motion estimation architectures were used to determine the left ventricular longitudinal strain in echocardiography. Three motion estimation approaches, pretrained on popular optical flow datasets, were applied to a simulated echocardiographic dataset. Results show that PWC-Net, RAFT and FlowFormer achieved an average end point error of 0.20, 0.11 and 0.09 mm per frame, respectively. Additionally, global longitudinal strain was calculated from the FlowFormer outputs to assess strain correlation. Notably, there is variability in strain accuracy among different vendors. Thus, optical flow-based motion estimation has the potential to facilitate the use of strain imaging in clinical practice.

关键词： Training Accuracy Echocardiography Motion estimation Pipelines Noise Strain measurement Reliability Optical flow Strain

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Potential mechanisms of non-coding RNA regulation in Alzheimer's disease

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Neural Regeneration Research 2026年第1期21卷 265-280页

作者： Yue Sun Xinping Pang Xudong Huang Dinglu Liu Jingyue Huang Pengtao Zheng Yanyu Wei Chaoyang Pang College of Computer Science Sichuan Normal UniversityChengduSichuan ProvinceChina School of Science Xi’an Jiaotong-Liverpool UniversitySuzhouJiangsu ProvinceChina Neurochemistry Laboratory Department of PsychiatryMassachusetts General Hospital and Harvard Medical SchoolCharlestownMAUSA National Key Laboratory of Science and Technology on Vacuum Electronics School of Electronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina

Alzheimer's disease,a progressively degenerative neurological disorder,is the most common cause of dementia in the *** its precise etiology remains unclear,researchers have identified diverse pathological characteristics and molecular pathways associated with its *** in scientific research have increasingly highlighted the crucial role of non-coding RNAs in the progression of Alzheimer's *** non-coding RNAs regulate several biological processes critical to the advancement of the disease,offering promising potential as therapeutic targets and diagnostic ***,this review aims to investigate the underlying mechanisms of Alzheimer's disease onset,with a particular focus on microRNAs,long non-coding RNAs,and circular RNAs associated with the *** review elucidates the potential pathogenic processes of Alzheimer's disease and provides a detailed description of the synthesis mechanisms of the three aforementioned non-coding *** comprehensively summarizes the various non-coding RNAs that have been identified to play key regulatory roles in Alzheimer's disease,as well as how these noncoding RNAs influence the disease's progression by regulating gene expression and protein *** example,miR-9 targets the UBE4B gene,promoting autophagy-mediated degradation of Tau protein,thereby reducing Tau accumulation and delaying Alzheimer's disease ***,the long non-coding RNA BACE1-AS stabilizes BACE1 mRNA,promoting the generation of amyloid-βand accelerating Alzheimer's disease ***,circular RNAs play significant roles in regulating neuroinflammatory *** integrating insights from these regulatory mechanisms,there is potential to discover new therapeutic targets and potential biomarkers for early detection and management of Alzheimer's *** review aims to enhance the understanding of the relationship between Alzheimer's disease and non-coding RNAs,potentially pav

关键词： Alzheimer's disease biomarkers circular RNA long non-coding RNA microRNA ncRNA regulation neurodegeneration non-coding RNA pathogenesis therapeutic targets

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A Multimodal Learning-based Approach for Autonomous Landing of UAV

A Multimodal Learning-based Approach for Autonomous Landing ...

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IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications, MESA

作者： Francisco S. Neves Luís M. Branco Maria I. Pereira Rafael M. Claro Andry M. Pinto Department of Electrical and Computer Engineering (DEEC) Faculty of Engineering of University of Porto (FEUP) Institute for Systems and Computer Engineering - Technology and Science (INESC TEC) Porto Portugal

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

In the field of autonomous Unmanned Aerial Vehicles (UAVs) landing, conventional approaches fall short in delivering not only the required precision but also the resilience against environmental disturbances. Yet, learning-based algorithms can offer promising solutions by leveraging their ability to learn the intelligent behaviour from data. On one hand, this paper introduces a novel multimodal transformer-based Deep Learning detector, that can provide reliable positioning for precise autonomous landing. It surpasses standard approaches by addressing individual sensor limitations, achieving high reliability even in diverse weather and sensor failure conditions. It was rigorously validated across varying environments, achieving optimal true positive rates and average precisions of up to 90%. On the other hand, it is proposed a Reinforcement Learning (RL) decision-making model, based on a Deep Q-Network (DQN) rationale. Initially trained in simulation, its adaptive behaviour is successfully transferred and validated in a real outdoor scenario. Furthermore, this approach demonstrates rapid inference times of approximately 5ms, validating its applicability on edge devices.

关键词： Training Wind Image edge detection Decision making Detectors Reinforcement learning Robot sensing systems Autonomous aerial vehicles Transformers Resilience

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