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Supervised Machine learning in Cardiology: A Predictive Model for Heart Disease 11

Supervised Machine Learning in Cardiology: A Predictive Mode...

引用

11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Mall, Anamika Singh, Ankita Bansal, Ayush Gupta, Hriday Kumar Kiet Group of Institutions Department of Computer Science and Engineering UP Ghaziabad India

ISBN: (纸本)9798350350357

One of the worst diseases in the world, heart disease takes a great toll on lives every year. In order to treat this potentially fatal illness, a tool or gadget must be practical, accurate, and dependable. this will allow for prompt detection or prediction, which will lead to efficient treatment and a decline in death rates. the heart is the most important organ in the human body, second only to the brain, and it is vital to the circulation of blood to all organs. A swift and accurate diagnostic method is imperative to mitigate the high mortality associated with cardiac disorders. Predicting the incidence of these disorders holds paramount importance in the medical domain. In the contemporary era, machine learning techniques prove invaluable for predicting and automating the interpretation of extensive and intricate datasets in various fields, including medicine. this research introduces a statistical model for heart disease, aiding medical examiners and cardiac practitioners in forecasting based on fundamental aspects of a patient's health history. the model, constructed using a Decision Tree, achieves an impressive accuracy of approximately 97%. the crucial functions of machine learning, data analytics, and data mining highlight how important these technologies are to improving the diagnosis and treatment of cardiac disorders. © 2024 IEEE.

关键词： Decision trees

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Leveraging Ensemble Deep learning Model for Fruit Image Recognition with Explainable AI 5

Leveraging Ensemble Deep Learning Model for Fruit Image Reco...

引用

5th international conference on Big data and Artificial Intelligence and Software engineering, ICBASE 2024

作者： Wang, Yaoqing Yu, Chang Lin, Xiyue Mgbejime, Goodness Temofe Nneji, Grace U. Monday, Happy N. University of Electronic Science and Technology School of Electronic Science and Engineering China Chengdu University of Technology Chengdu China Intelligent Computing Lab Hace Softtech Lagos102241 Nigeria

ISBN: (纸本)9798331506612

Precise classification of fruit images is crucial for a range of agricultural and retail purposes. Although there have been improvements, current methods have limits when it comes to dealing with complex feature extraction and ensuring model resilience. this paper introduces a new framework for categorizing fruits using a combination of advanced deep learning models, namely AlexNet, ResNet50, and EfficientNet. the collection of 43,912 photos across 44 fruit classifications underwent thorough pre-processing and data augmentation procedures. Grad-CAM, SHAP, and LIME were employed to achieve model interpretability, offering valuable insights into the decision-making process of the ensemble model. the experimental results indicate that the ensemble model surpasses the performance of individual models, obtaining 96.9% accuracy, 96.2% precision, 94.5% recall, and 95.4% F1-score. the proposed ensemble model provides a highly efficient solution for automated fruit classification, with substantial implications for agricultural technology and retail automation. © 2024 IEEE.

关键词： Convolutional neural networks

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intelligent Information Processing Technology for Digital Libraries Based on Deep learning 11th

Intelligent Information Processing Technology for Digital Li...

引用

11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Wang, Lichun Yu, Xiangning Jinan Preschool Education College Shandong Jinan250307 China

In the digital age, digital libraries have become an important channel for people to acquire knowledge and information. However, due to the vast amount and diverse types of information in digital libraries, traditional information processing methods are no longer able to meet user needs. this study aims to explore the intelligent information processing technology of digital libraries based on deep learning and propose effective methods. Firstly, it collects a large amount of digital library data, including various types of information such as text and audio. Next, the paper uses deep learning models to train these data and extract useful features and patterns. During the training process, convolutional neural networks (CNNs) are used to improve the accuracy and efficiency of information processing. through experiments and analysis, it was found that intelligent information processing technology based on deep learning has significant advantages in digital libraries. It can automatically recognize and classify different types of information, extract keywords and topics, and complete tasks such as information summarization and recommendation. In addition, deep learning models can continuously optimize and improve information processing performance through learning and knowledge transfer. © 2024 the Authors. Published by Elsevier B.V.

关键词： Convolutional Neural Network Deep learning Digital Libraries intelligent Information Processing Technology

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Wireless Signal Recognition Based on ResNet-Transformer 11

Wireless Signal Recognition Based on ResNet-Transformer

引用

11th international conference on intelligent Computing and Wireless Optical Communications, ICWOC 2023

作者： Chen, Jianyong Zhao, Rui Yang, Chengyu Dai, Yaoyao Huaqiao University College of Information Science and Engineering Xiamen China

ISBN: (纸本)9798350321791

In the face of the rapid development of wireless technology in the Internet of things (IoT), the communication capacity that wireless spectrum can carry is increasing. this has led to wireless signal recognition becoming a key technology for intelligent spectrum management. this paper proposes a deep learning model for wireless signal recognition, called Hybrid net. this model consists of a residual block that extracts local information from the sample and a Transformer block that extracts global information from the sample. these two blocks are added together through a residual unit to form a more complete representation of the feature space. It also avoids the problem of high model complexity and long training time caused by using data in frequency domain format. In order to better simulate actual communication environments, in-phase and quadrature (IQ) and IQ with random phase offset datasets were constructed for training and testing. Experimental results show that the proposed Hybrid net can achieve recognition accuracy of over 80% at low signal to noise ratios (SNRs grater than -6 dB). the Hybrid net even reach 100% at 4 dB. In addition, Hybrid net also demonstrated good generalization performance on different datasets. © 2023 IEEE.

关键词： Internet of things

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Machine learning-Based Fall Detection Algorithm Using data Fusion Techniques 11

Machine Learning-Based Fall Detection Algorithm Using Data F...

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11th RSI international conference on Robotics and Mechatronics, ICRoM 2023

作者： Radmanesh, Elahe Iranfar, Arash Kalhor, Ahmad Moshiri, Behzad University College of Engineering University of Tehran School of Electrical and Computer Engineering Tehran Iran University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada

ISBN: (纸本)9798350308105

For} elderly people, accurate fall detection offers prompt medical assistance. this study describes a reliable fall detection system that combines multi-level data with machine learning. the SisFall dataset, which is open to the public, was chosen because of its benefits. Butterworth filtering and sliding window segmentation were used as preprocessing techniques. there were three degrees of data fusion used: data-level fusion (Kalman filtering) fused accelerometer and gyroscope signals;feature-level fusion (PCA) aggregated extracted features;and decision-level fusion (weighted majority vote bagging) combined classifier outputs. XGBoost, Naive Bayes, Random Forest, SVM, KNN, and logistic regression were among the machine learning models that were assessed. Accuracy and robustness were improved through the use of data fusion techniques and machine learning algorithms. the effectiveness of fusing complementary data at various levels was demonstrated by the final multi-level fusion technique, which had a fall detection accuracy of 99.0%. © 2023 IEEE.

关键词： Kalman filters

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Optimizing High-Frequency Radar data: A Study on Effective Imputation Methods for Ocean Current Analysis 11

Optimizing High-Frequency Radar Data: A Study on Effective I...

引用

11th international conference on Information Technology, Computer and Electrical engineering, ICITACEE 2024

作者： Hartika, Yuni Permanasari, Adhistya Erna Fauziati, Silmi Aldini, Ittaka Gadjah Mada University Department of Electrical Engineering and Information Engineering Yogyakarta Indonesia Indonesian Agency for Meteorology Climatology and Geophysics Jakarta Indonesia

ISBN: (纸本)9798350389289

this study addresses the issue of missing data in High-Frequency Radar (HFR) measurements, which are crucial for monitoring ocean surface currents and supporting maritime operations and environmental studies. However, the integrity of HFR data can be compromised by factors such as sensor malfunctions and environmental disturbances, creating a need for effective data imputation methods. the research focuses on evaluating and comparing the effectiveness of different statistical and machine learning techniques, including Seasonal-Trend Decomposition using Loess (STL) and Cubic Spline Interpolation, to accurately impute missing wind speed data. the findings of the study indicate that STL and Cubic Spline methods are superior in handling missing data, demonstrating high accuracy and strong correlation with actual data across scenarios with 5%, 10%, and 15% missing data. these methods outperform others like the Last Observation Carried Forward and Moving Average, especially when the percentage of missing data increases. the study highlights the potential of advanced imputation techniques to enhance the reliability of oceanographic data analysis, thereby improving the accuracy of ocean current models and forecasts. these advancements are crucial for enhancing maritime safety, resource management, and environmental monitoring, and they pave the way for further research into optimizing data imputation methodologies in related fields. © 2024 IEEE.

关键词： Ocean currents

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RIEMANNIAN METRIC learning VIA OPTIMAL TRANSPORT 11

RIEMANNIAN METRIC LEARNING VIA OPTIMAL TRANSPORT

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11th international conference on learning Representations, ICLR 2023

作者： Scarvelis, Christopher Solomon, Justin MIT CSAIL United States

We introduce an optimal transport-based model for learning a metric tensor from cross-sectional samples of evolving probability measures on a common Riemannian manifold. We neurally parametrize the metric as a spatially-varying matrix field and efficiently optimize our model's objective using a simple alternating scheme. Using this learned metric, we can nonlinearly interpolate between probability measures and compute geodesics on the manifold. We show that metrics learned using our method improve the quality of trajectory inference on scRNA and bird migration data at the cost of little additional cross-sectional data. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

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CONTINUAL TRANSFORMERS: REDUNDANCY-FREE ATTENTION FOR ONLINE INFERENCE 11

CONTINUAL TRANSFORMERS: REDUNDANCY-FREE ATTENTION FOR ONLINE...

引用

11th international conference on learning Representations, ICLR 2023

作者： Hedegaard, Lukas Bakhtiarnia, Arian Iosifidis, Alexandros Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark

Transformers in their common form are inherently limited to operate on whole token sequences rather than on one token at a ***, their use during online inference on time-series data entails considerable redundancy due to the overlap in successive token *** this work, we propose novel formulations of the Scaled Dot-Product Attention, which enable Transformers to perform efficient online token-by-token inference on a continual input ***, our modifications are purely to the order of computations, while the outputs and learned weights are identical to those of the original Transformer *** validate our Continual Transformer Encoder with experiments on the thUMOS14, TVSeries and GTZAN datasets with remarkable results: Our Continual one-and two-block architectures reduce the floating point operations per prediction by up to 63× and 2.6×, respectively, while retaining predictive performance. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Redundancy

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Development of Longitudinal and Transversal Crack Detection Feature in automated Road Pavement Condition Survey System Using YOLO Algorithm 11

Development of Longitudinal and Transversal Crack Detection ...

引用

11th international conference on ICT for Smart Society, ICISS 2024

作者： Jauhar Sidqi, Akmal Agil Alunjati, Figo Elviani, Ulva Hidayat, Fadhil School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350389654

Roads are critical infrastructure for economic growth and mobility in Indonesia. To ensure effective maintenance, an application was created to help government conduct road condition surveys automatically using machine learning by detecting road damage. However, the existing one are still insufficient to meet the stakeholder's needs, as it can only detect two types of damages. the aim of this paper is to improve the app's capabilities by increasing the number of damage types that can be detected from previously 2 types (pothole and alligator crack) to 4 types (adding longitudinal and transversal crack). the writers adopts V model system development methodology, a variation of the waterfall model, which consists of stages ranging from requirement modeling to acceptance testing. the result is that the system succeeded in adding two new detection features without reducing the precision of the previous application. the precision obtained is 77% for potholes, 67% for alligator cracks, 64% for longitudinal cracks, and 51 % for transversal cracks. Future work will focus on refining these technologies and expanding the types of detectable pavement damages. © 2024 IEEE.

关键词： Acceptance tests

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Survey on Use of AI/ML Algorithms in Enhancing Filesystem Performance and Efficiency 11

Survey on Use of AI/ML Algorithms in Enhancing Filesystem Pe...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Olekar, Vijay Patil, Yash Pawar, Vishvam Pitale, Shubham Jaiswal, V.R. Naranje, Vishal Pune Institute of Computer Technology Pune Dept. of Information Technology Pune India School of Engineering Architecture and Interior Design Amity University Dubai United Arab Emirates

ISBN: (纸本)9798350350357

the processing power of CPUs is increasing day by day, but systems often struggle to fully utilize this power due to the bottleneck of file access time from the disk. Over the years, various techniques have been devised to reduce I/O latency while considering various file parameters. Our survey paper aims to shed light on these techniques, efficiently comparing them and highlighting the benefits and drawbacks of each approach. For our evaluation, we have considered various file parameters, such as access time, file size, directory structure, and kernel execution flow. We have combined these parameters with a range of supervised and unsupervised machine learning algorithms, including Decision Tree, ABLE, RNN, GNN, CART, XGBoost, CNN, and more. this survey aims to highlight the use of ML/AI algorithms in the intelligent detection of file access and I/O patterns to optimize the performance of modern applications. © 2024 IEEE.

关键词： Cyber Physical System

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