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共 265 条记录，以下是141-150 订阅

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6th international conference on Optimization and learning, OLA 2023

6th International Conference on Optimization and Learning, O...

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6th international conference on Optimization and learning, OLA 2023

ISBN: (纸本)9783031340192

the proceedings contain 32 papers. the special focus in this conference is on Optimization and learning. the topics include: We Won’t Get Fooled Again: When Performance Metric Malfunction Affects the Landscape of Hyperparameter Optimization Problems;condition-Based Maintenance Optimization Under Large Action Space with Deep Reinforcement learning Method;an Application of machine learning Tools to Predict the Number of Solutions for a Minimum Cardinality Set Covering Problem;adaptative Local Search for a Pickup and Delivery Problem applied to Large Parcel Distribution;GRAPH Reinforcement learning for Operator Selection in the ALNS Metaheuristic;multi-objective Optimization of Adhesive Bonding Process in Constrained and Noisy Settings;evaluating Surrogate Models for Robot Swarm Simulations;interactive Job Scheduling with Partially Known Personnel Availabilities;multi-armed Bandit-Based Metaheuristic Operator Selection: the Pendulum Algorithm Binarization Case;diagonal Barzilai-Borwein Rules in Stochastic Gradient-Like Methods;binary Black Widow with Hill Climbing Algorithm for Feature Selection;optimization of Fuzzy C-Means with Alternating Direction Method of Multipliers;partial K-Means with M Outliers: Mathematical Programs and Complexity Results;An Optimization Approach for Optimizing PRIM’s Randomly Generated Rules Using the Genetic Algorithm;A Fast Methodology to Find Decisively Strong Association Rules (DSR) by Mining datasets of Security Records;characterization and Categorization of Software Programs on X86 Architectures;robot-Assisted Delivery Problems and their Exact Solutions;modeling and Analysis of Organizational Network Analysis Graphs Based on Employee data;time Series Forecasting for Parking Occupancy: Case Study of Malaga and Birmingham Cities;e-scooters Routes Potential: Open data Analysis in Current Infrastructure. Malaga Case;algorithm Selection for Large-Scale Multi-objective Optimization;automatic Generation of Subtitles for Videos of the Governm

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EL-ID-BID: Ensemble Stacking-Based Intruder Detection in BoT-IoT data 6th

EL-ID-BID: Ensemble Stacking-Based Intruder Detection in BoT...

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6th international conference on Innovative Computing and Communication, ICICC 2023

作者： Rao, Cheruku Poorna Venkata Srinivasa Bhavani, Rudrarapu Indhumathi, Narala Raviteja, Gedela Department of Information Technology Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India

ISBN: (纸本)9789819940707

the Internet of things continues to grow in size, connection, and applicability. Just like other new technologies, this ecosystem affects every area of our daily existence (Kafle et al. in IEEE Commun Mag 54:43–49, 2016). Despite the many advantages of the Internet of things (IoT), the importance of securing its expanded attack surface has never been higher. there has been a recent increase in reports of botnet threats moving into the Internet of things (IoT) environment. As a result, finding effective methods to secure IoT systems is a critical and challenging area of study. Potentially useful alternatives include methods based on machine learning, which can identify suspicious activities and even uncover network attacks. Simply relying on one machine learning strategy may lead to inaccuracies in data collection, processing, and representation if applied in practice. this research uses stacked ensemble learning to detect attacks better than conventional learning, which uses one algorithm for intruder detection (ID). To evaluate how well the stacked ensemble system performs in comparison to other common machine learning algorithms like Decision Tree (DT), random forest (RF), Naive Bayes (NB), and support vector machine (SVM), the BoT-IoT benchmark dataset has been used. Based on the findings of the experiments, stacked ensemble learning is the best method for classifying attacks currently available. Our experimental outcomes were assessed for validation data set, accuracy, precision, recall, and F1-score. Our results were competitive with best accuracy and ROC values when benchmarked against existing research. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Internet of things

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Developing Area Real Estate Valuation Based on Linear Regression and KNN Algorithm 6

Developing Area Real Estate Valuation Based on Linear Regres...

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6th Annual international conference on data Science and Business analytics, ICDSBA 2022

作者： Sun, Yuandong Peng, Guanqiao Ucla Master of Applied Stats Department of Statistics Los AngelesCA90095-1554 United States Beijing 101 Middle School Haidian Dsitrict International Department Class2-21 Beijing100091 China

ISBN: (纸本)9781665454506

Investors not only finance real estate in well-developed regions but also developing countries and cities. We use Taiwan Real Estate data from UCI machine learning repository and set up linear regression models to predict a House Unit Price and a KNN classification model to categorize the ranks. the findings show that the distance to the nearest MRT station and the number of convenience stores within walking distance have major impacts on the House Unit Price. Governments within developing areas should focus on infrastructure and plan to create more business centers. this focus could increase the population and draw more investors to accelerate development. © 2022 IEEE.

关键词： Houses

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DL4HC: Deep learning for Healthcare 23

DL4HC: Deep Learning for Healthcare

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6th ACM India Joint international conference on data Science and Management of data, CODS-COMAD 2023

作者： Bhat, Raghavendra Mannarswamy, Sandya Shreyas, N.C. Intel India Karnataka Bangalore India

ISBN: (纸本)9781450397988

In Over the last few years, machine learning (ML), and particularly Deep learning (DL), has made great strides and has been successfully deployed in many real-world applications such as healthcare, customer care, finance, autonomous driving etc. One of the recent applications of deep learning has been in healthcare. ML/DL techniques have been applied to healthcare in medical imaging, clinical decision making, electronic healthcare records processing etc. In particular, Federated learning (FL), an important sub-area of DL has been considered as a critical part of advancing ML driven healthcare systems in a data siloed healthcare ecosystem. FL addresses the critical need to train ML/DL algorithms without needing to move data to a central location. this tutorial is intended to provide a high-level overview of DL4HC at a major data science/ML research venue such as CODS-COMAD to focus research attention in this emerging area. this tutorial provides a detailed overview of DL/FL techniques in healthcare. While there has been considerable progress in this area, several challenges remain, especially in applying probabilistic techniques to healthcare which often requires deterministic correctness, interpretability, and verifiability. We discuss the various challenges involved in applying DL/FL techniques to healthcare and outline some of the future research directions. We believe that having this tutorial at CODS-COMAD would be an important step in facilitating increased research focus from data science community and greater collaboration with mainstream healthcare community on this topic. © 2023 ACM.

关键词： Decision making

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Driving Style Classification Using Deep learning Techniques 6th

Driving Style Classification Using Deep Learning Techniques

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6th international conference on Intelligent Sustainable Systems, ICISS 2023

作者： Mohite, Apurva Ajay Patil, S.S. Mali, A.S. K. E. Society’ Shivaji University Kolhapur Rajarambapu Institute of Technology Islampur Maharashtra Sangli India

ISBN: (纸本)9789819917259

the increased interest in driving analysis is a result of growing worries about car safety. However, the lack of publicly accessible driving data currently hinders the development in this area. Since machine learning approaches require enormous volumes of data that are expensive and difficult to collect through Natu-ralistic Driving Studies, they are only partially accessible to the public research community. A cheap and simple platform for driver behavior detection has also been made available by the rise of smart phones;however, the data from these applications is not publicly accessible. Due to these factors, this study provides an available dataset UAH-Drive Set that enables deep driving. this dataset is an imbalanced dataset and a sampling technique must be used to make it a balanced dataset. By supplying a significant amount of data gathered by the system, the dataset is sampled first and after that, the machine learning algorithms are applied. the programmed system is handled by three distinct behavior, three different drivers and cars (slow, aggressive, and normal) on two different types of roads (a side road and a highway), resulting in more than8hofrealistic drivingalong with theassociated unprocessed data, the semantic data as well as the video recordings of the trips. Additionally, a tool that aids in plotting the display, the travel videos, and data are concurrent in order to easily analyze the data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Video recording

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An Explainable machine learning-based Prediction Model for In-hospital Mortality in Acute Myocardial Infarction Patients with Typical Chest Pain 23

An Explainable Machine Learning-based Prediction Model for I...

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6th international conference on Software Engineering and Information Management, ICSIM 2023

作者： Zheng, Huilin Waqar, Malik Muhammad Arif, Saba Sherazi, Syed Waseem Abbas Son, Sang Hyeok Lee, Jong Yun Department of Computer Science Chungbuk National University Cheongju Chungbuk28644 Korea Republic of

ISBN: (纸本)9781450398237

Acute myocardial infarction (AMI) is the leading cause of hospital admissions and death all over the world and chest pain is the most common presenting complaint of AMI. therefore, this paper proposes a machine learning (ML)-based prediction model for the in-hospital mortality in AMI patients with typical chest pain. To understand the principle of the black-box prediction model, a Shapley additive explanations (SHAP) method is applied to the ML-based prediction model. the experimental framework mainly includes three steps. First, we extract the experimental data from the Korea Acute Myocardial Infarction Registry National Institutes of Health (KAMIR-NIH), and then preprocess the selected data with missing value imputation, data normalization, and splitting. thereafter, two kinds of data sampling methods such as synthetic minority oversampling techniques (SMOTE) and Adaptive Synthetic (ADASYN), are applied to handle the class imbalance problem on the experimental data. Second, different ML models such as decision tree, random forest, extreme gradient boosting (XGBoost), support vector machine, and logistic regression, are trained and evaluated on the preprocessed AMI patient data. Finally, the SHAP method is used to explain the best ML-based prediction model. the experimental results showed that the logistic regression with the ADASYN approach achieved the highest performance. Moreover, the SHAP technique enhanced the transparency of the ML model and can be a good reference for doctors to support their decisions in real life. © 2023 ACM.

关键词： Decision trees

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data Security Patterns for Critical Big data Systems 6

Data Security Patterns for Critical Big Data Systems

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6th IEEE international conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, CloudTech 2023

作者： Majdoubi, Chaymae El Mendili, Saida Gahi, Youssef National School of Applied Sciences Ibn Tofail University Kenitra Morocco

ISBN: (纸本)9798350303063

With the words' current growth in technologies and digitalization, protecting data in massive data-driven systems has become a new challenge to overcome. therefore, many measures, algorithms, and protocols are used and still under development to minimize risks of data loss, manipulation, sniffing, or spying... In order to gain more in terms of security, privacy, and integrity levels. As important as this issue is, combined efforts from different disciplines (Network Security, Security Operations Center Analysis (SOC Analysis), machine learning (ML), and Deep learning (DL) algorithms) are necessary to enhance data protection. In this paper, we will expose some natural threats of Big data, based on its definition, ethics, and suggestions to avoid these threats, or at least limit their effects. We will also show works related to big data security based on ML and shed light on Artificial Neural Networks (ANN) applications for data classification and threat detection. then focus on Convolutional Neural Networks (CNN) to optimize parameters. We will try to find solutions for CNN limitations of hyperparameters tuning and rotating image feature extraction. Furthermore, we will discuss state of art results to understand the matter in a deeper sense and expose high-accuracy solutions with improvement path suggestions. Our work is going to come up with some natural threats, compare some existing methods, expose, and discuss results, and then suggest optimal ways to help increase data security, especially through improving CNN limitations. © 2023 IEEE.

关键词： Big data

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A deep learning method trained on synthetic data for digital breast tomosynthesis reconstruction 6

A deep learning method trained on synthetic data for digital...

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6th international conference on Medical Imaging with Deep learning (MIDL)

作者： Quillent, Arnaud Bismuth, Vincent Bloch, Isabelle Kervazo, Christophe Ladjal, Said GE HealthCare Buc France Inst Polytechn Paris LTCI Telecom Paris Palaiseau France Sorbonne Univ CNRS LIP6 Paris France

Digital Breast Tomosynthesis (DBT) is an X-ray imaging modality enabling the reconstruction of 3D volumes of breasts. DBT is mainly used for cancer screening, and is intended to replace conventional mammography in the coming years. However, DBT reconstructions are impeded by several types of artefacts induced by the geometry of the device itself, degrading the image quality and limiting its resolution along the thickness of the compressed breast. In this study, we propose a deep-learning-based pipeline to address the DBT reconstruction problem, focusing on the removal of sparse-view and limited-angle artefacts. Specifically, this procedure is composed of two steps: a classic reconstruction algorithm is first applied on normalised projections, then a deep neural network is tasked with erasing the artefacts present in the obtained volumes. A major difficulty to solve our problem is the lack of real conditions artefact-free data. To overcome this complication, we resort to a new dataset comprised of synthetic breast texture phantoms. We then show that our training method and database strategy are promising to tackle the problem as they improve the informational value of planes orthogonal to the detector, which are not currently used by radiologists due to their poor quality. Eventually, we assess the impact of removing the bias components from the network and using stacks of slices as inputs, with regard to the generalisation ability of our approach on both synthetic and clinical data.

关键词： DBT reconstruction inverse problem deep learning limited angle sparse view synthetic phantoms 2.5D

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machine Masquerades a Poet: Using Unsupervised T5 Transformer for Semantic Style Transformation in Poetry Generation 6th

Machine Masquerades a Poet: Using Unsupervised T5 Transform...

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6th international conference on applied Informatics, ICAI 2023

作者： Moitra, Agnij Amity International School M Block Saket New Delhi110017 India

ISBN: (纸本)9783031468124

this paper presents a novel approach to automatically capturing the unique style of various poets and using it to convert given poems into the styles of those poets. the method combines the power of web scraping, and T5 transformer (11 Billion Parameters). A dataset of poems was collected by web scraping popular online libraries, such as Project Gutenberg and Open Library. these poems were then pre-processed to remove using HTML tags and meta data. the pre-trained T5 Transformer-11b was fine tuned on this corpus of text. the results of this study were highly promising. the proposed method accurately captured the styles of various poets, effectively capturing their overall tone, ideologies, and poetic style. By providing a starting poem, the model generated new poems in the style of a specific poet, successfully mimicking their unique writing characteristics. these findings highlight the potential of machine learning algorithms in understanding and reproducing the intricate nuances of poetic styles. this work opens avenues for automated poem generation, enabling individuals to experience the styles and voices of renowned poets in a novel way. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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data Engineering: An Overview from a Future Perspective 6

Data Engineering: An Overview from a Future Perspective

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Jain, Atishay Sumit Monga, Chetna Mittal, Shikha Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350304480

data engineering is now an essential subject for handling, processing, and analysing big data as the amount of data collected is increasing exponentially. this paper gives a future-focused overview of data engineering. the creation, building, upkeep, and optimization of data architecture, infrastructure, and pipelines are all essential components of data engineering, a field within data science. data pipelines that can effectively process massive volumes of data from diverse sources and formats must be built using data engineering techniques that are reliable, scalable, and flexible. data warehousing, data modelling, SQL databases, NoSQL databases, and Apache Hadoop and Spark are just a few examples of the tools and technologies that fall under the broad umbrella of data engineering. data scientists, analysts, and business users should have rapid access to correct data, which is the main objective of data engineering. In addition to optimizing data storage and performance, data engineering also entails guaranteeing the quality, integrity, and security of the data. data engineering is an essential phase in the workflow for data scientists since it enables them to carry out advanced analytics, machine learning, and other data-driven operations. In conclusion, data engineering is a vital part of the ecosystem for data science, which offers the tools and infrastructure required for data processing and analysis. Companies may build strong, scalable data pipelines that foster business insights and innovation by utilizing best practices in data engineering. data engineering is an important field that will continue to grow and transform in the coming years. data engineers and organizations that can adapt to change and take advantage of emerging technologies and trends will be in the best position to succeed in a data-driven future. © 2023 IEEE.

关键词： data -Scientists data Architecture data Engineering data Science Pipelines

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