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Method for Finding an Investment Strategy in the Case of a Sparse Covariance Matrix 17

Method for Finding an Investment Strategy in the Case of a S...

引用

17th International Conference on Management of Large-Scale System Development, MLSD 2024

作者： Gorelik, Victor Zolotova, Tatiana Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Simulation Systems and Operations Research Moscow Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

An optimality principle is proposed for making investment decisions based on efficiency and risk assessments with a sparse covariance matrix. The method is implemented as a program with a graphical interface and demon... 详细信息

ISBN: (纸本)9798350375718

关键词： Marketplaces

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Life Expectancy Prediction using machine learning Technique - KNN 3

Life Expectancy Prediction using Machine Learning Technique ...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Narooka, Preeti Vashistha, Aarushi Mitra, Priyanka Derashri, Gulisha School of Computer Science Engineering Manipal University Department of Artificial Intelligence and Machine Learning Rajasthan Jaipur303007 India Jaipur Engineering College and Research Centre Department of Computer Science and Engineering Jaipur303905 India Jaipur Engineering College and Research Centre Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350384598

In the modern world, life expectancy turns out to be the most valuable factor in determining population's health. Today, it is more crucial to predict the future because life is more precarious due to a variety of circumstances, including social, economic, environmental, educational, lifestyle, and health conditions. The machine learning algorithm KNN for life expectancy is presented in this work. The dataset gathered for this study is separated into a training set (70%) and a test set (30%), and the forecasts precision is examined. To avoid misclassification, KNN needs a larger sample size, and the best K are chosen more in accordance with cross-validation experience than with strong statistical theory. © 2024 IEEE.

关键词： Adversarial machine learning

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A Review on Implementation of Wearable Devices in Healthcare 7

A Review on Implementation of Wearable Devices in Healthcare

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Suman, Saurabh Choudhary, Vishal Kumar Pacharaney, Utkarsha Kumar, Abhishek Kumar, Dhiraj Raushan, Rahul Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India Faculty of Engineering And Technology Department of Artificial Intelligence And Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798350359299

The capacity to get additional patient data, including clinical, behavioral, and self-monitored data, has enhanced by the expanding usage of wearable technology. Large volumes of previously unobtainable data are now available, providing a chance to create new instruments with sophisticated analysis and decision support systems for application in clinical settings. Additionally, it gives patients additional options by arming them with personalized decision-support resources and information, enabling them to take charge of their own health care while maintaining their sense of autonomy. Especially well-suited for miniaturization and integration into microfluidic devices are biosensors with electrochemical sensors. Applications cover activities in the domains of diagnostics, food and environmental control, and the identification of complete cells, the parts of cells, proteins, and tiny compounds. Microfluidic technologies can be as basic as sample transport tubes or as sophisticated as platforms with extra components for sample preparation and sensing electrode integration. The kind of detection, application, and sample material all influence the microfluidics design. The next issue for the healthcare industry will be how to use newly acquired information in a way that benefits patients, healthcare organizations, and professionals all while reducing costs and workloads. Given the current limitations, the challenge for wearable technology designers is to provide solutions that healthcare practitioners can readily integrate and use. © 2024 IEEE.

关键词： Biosensors

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Kolmogorov Arnold Network for Hyperspectral Image Classification: A Detailed Explanation 2

Kolmogorov Arnold Network for Hyperspectral Image Classifica...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Suman, Saurabh Pacharaney, Utkarsha Choudhary, Vishal Kumar Raushan, Rahul Kumar, Abhishek Kumar, Gulshan Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331528713

Hyperspectral imaging (HSI) has been proved to be useful in numerous fields because of its ability to acquire the spectral information across the hundreds of contiguous bands. Nevertheless, the vast dimensionality of the hyperspectral data presents a great problem especially during data processing as well as during classification. Recently, one of the techniques that have attracted most interest for the classification of hyperspectral images is Kolmogorov Arnold Network (KAN), which is efficient with regard to those high-dimensional non- linear data sets found in hyperspectral imagery. This study focuses on how KAN can be utilized for classifying hyperspectral images, how B-spline curves can be used for augmenting the performance of the model, and what experimentation was done with the state-of-the-art hyperspectral data set. From these results, it was established that KAN plus B-spline curves was a good strategy for hyperspectral images classification compared to existing systems in both accuracy and speed. © 2024 IEEE.

关键词： Splines

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A Secure and Efficient Framework for Storing Medical Data using Blockchain and Cloud Servers 8

A Secure and Efficient Framework for Storing Medical Data us...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Lakshmanan, M. Mala, G.S. Anandha Sivakumar, G. Divya Sundar, V.S. Kesavan, Rupa Ajoe Sweetlin Jeena, A. Dayananda Sagar University Department of Artificial Intelligence and Machine Learning Bangalore India Easwari Engineering College Anna Univeristy Department of Computer Science and Engineering Chennai India Gnanamani College of Technology Anna University Department of Artificial Intelligence and Machine Learning India Anna Univeristy Saveetha Engineering College Chennai India Sri Venkateswara College of Engineering Computer Science and Engineering Sriperumbudur India

ISBN: (纸本)9798350367904

The digitization of Electronic Health Records (EHRs) has brought a revolution in health care delivery and even practices in reporting key organizational HR processes. However, these advancements have their merits and demerits particularly in regard to data security, the speed of data retrieval, and how data transactions are performed. This paper presents a solution to this problem in the medical world. It presents a unique architecture that connects cloud servers with Blockchain technology for better and secure medical databases. Using Merkle trees to protect health information and advanced heuristic algorithms to optimize operations, the system deals with main issues of transaction time, retrieval rate, and data privacy. The method is tested on a realized medical database and shows that the performance indicators increased substantially. It would seem that Blockchain assures that data cannot be altered once uploaded, while cloud servers provide flexibility to users and allow faster data retrieval. Proposed system improves the transaction time by 30 percent and the retrieval rate by 40 percent compared with traditional methods and supports strategic management of EHR systems. © 2024 IEEE.

关键词： Electronic health record

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Faster Algorithms for User-Level Private Stochastic Convex Optimization 38

Faster Algorithms for User-Level Private Stochastic Convex O...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lowy, Andrew Liu, Daogao Asi, Hilal Wisconsin Institute for Discovery University of Wisconsin-Madison United States Department of Computer Science University of Washington United States Apple Machine Learning Research United States

We study private stochastic convex optimization (SCO) under user-level differential privacy (DP) constraints. In this setting, there are n users (e.g., cell phones), each possessing m data items (e.g., text messages), and we need to protect the privacy of each user's entire collection of data items. Existing algorithms for user-level DP SCO are impractical in many large-scale machine learning scenarios because: (i) they make restrictive assumptions on the smoothness parameter of the loss function and require the number of users to grow polynomially with the dimension of the parameter space;or (ii) they are prohibitively slow, requiring at least (mn)3/2 gradient computations for smooth losses and (mn)3 computations for non-smooth losses. To address these limitations, we provide novel user-level DP algorithms with state-of-the-art excess risk and runtime guarantees, without stringent assumptions. First, we develop a linear-time algorithm with state-of-the-art excess risk (for a non-trivial linear-time algorithm) under a mild smoothness assumption. Our second algorithm applies to arbitrary smooth losses and achieves optimal excess risk in ≈ (mn)9/8 gradient computations. Third, for non-smooth loss functions, we obtain optimal excess risk in n11/8m5/4 gradient computations. Moreover, our algorithms do not require the number of users to grow polynomially with the dimension. © 2024 Neural information processing systems foundation. All rights reserved.

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Deep learning Approaches for Strengthening Network Intrusion Detection Systems 7

Deep Learning Approaches for Strengthening Network Intrusion...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Kaulgud, Sanjeev P Chempavathy, B. Tejeswar, Nama Sai Vigneshwar, Chekka Kesava Kumar, Akuthota Rakesh Eswar, Akula New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India New Horizon College of Engineering Department of Computer Science & Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331527822

Strengthening network security is a must in today's digital landscape. Existing Intrusion Detection Systems commonly make use of deep learning techniques such as Deep Neural Networks to identify malicious & anomaly activities. However, there are serious problems of overfitting and huge demand for computational resources in this system that hinder the efficiency and scalability of this system. It introduces an advanced form of an Intrusion Detection System (IDS) which integrates a hybrid machine learning algorithm with Recurrent Neural Network (RNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) along with other machine learning techniques to improve threat detection. This system would realize the recognition of established patterns as well as new anomalies about attacks by means of enhanced feature engineering and real-time AI-driven threat intelligence. It also addresses scalability for more extensive and complex configurations of the network as well as response mechanisms that automatically neutralize threats quickly. Then there is user behavioral analytics that helps in identifying insider threats. It is aimed at providing an all-embracing security solution that is strong and adaptable, greatly improving overall protection of the network. Hence, the effective execution of this project will thus provide a new benchmark for the effectiveness of IDS in delivering reliable security across different sectors as well as paving the way for further improvements in cybersecurity around the globe. © 2024 IEEE.

关键词： Network intrusion

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Cross Sequencing Integration of Compositional Microbiome Data in Cancer 19th

Cross Sequencing Integration of Compositional Microbiome Da...

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19th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2024

作者： Fernández-Edreira, Diego Liñares-Blanco, Jose Fernandez-Lozano, Carlos Machine Learning in Life Sciences Laboratory Department of Computer Science and Information Technologies Universidade da Coruña (CITIC) A Coruña Spain

ISBN: (纸本)9783031897030

High-throughput sequencing has revolutionized our understanding of the human microbiome, providing detailed insights into microbial communities under various health and disease conditions. Among the most common strategies for studying the microbiome are 16S rRNA amplicon sequencing and whole genome shotgun sequencing (WGS), each with its own advantages and limitations. However, integrating and comparing results from data obtained through these two sequencing techniques presents a challenge due to the inherent differences in methods and discrepancies among datasets and their sources. This work evaluates batch effect removal (BER) methods for integrating microbiome composition data from different sequencing platforms. Using data from ten different cohorts, we applied BER methods such as Combat, Limma, FAbatch, MMUPHin, and Percentile-normalization. Our results demonstrate the effectiveness of these methods in reducing batch effects. However, it remains unclear whether the remaining biological signal is reliable, which is critical. Additionally, we compared GG2 with standard databases (SILVA for 16S and WoL for WGS), showing that GG2 enables more unified analysis (increasing the number of taxa shared among cohorts from 94 genera and 58 species to 215 and 210, respectively). In conclusion, our findings suggest that appropriate BER methods can harmonize microbiome data from diverse sequencing platforms, but further experiments are needed to reliably understand how the biological signal is modulated in the process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 16S rRNA Batch effect Bioinformatics machine learning WGS

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Predicting the Spread of Covid-19 Using machine as a Service (MaaS) Through Modified Apriori Algorithm

Predicting the Spread of Covid-19 Using Machine as a Service...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Sett, Samir Kumar Sett, Aishik Ghosh, Anupam Department of Computer Application Techno India Kolkata India ITC Infotech Kolkata India Department of Computer Science and Engineering and Department of Artificial Intelligence & Machine Learning Netaji Subhash Engineering College Kolkata India

ISBN: (纸本)9789819613472

The cloud computing and machine learning is one of growing technology in the field of computer science. In recent years, the word "Cloud" is like a cup of tea in our daily lives. Every day, a significant volume of data is produced by different IT sectors, banking sectors, e-commerce websites like flipkart. It provides the organization with consistency, scalability, and strong enumerating power. It also delivers eminence service by generating proper outcomes in a brief amount of time. The objective of this paper is to predict the growth of Covid-19 virus in near future using machine as a Service (MaaS) through modified Apriori algorithm. In this paper, we will obtain the working graph of the algorithm. The result is also analyzed by obtaining the graphs for confidence against lift, support against confidence. At last, a running time comparative graph for modified apriori and apriori is shown for a certain duration of time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Apriori Cloud computing Confidence Covid-19 MaaS Support

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Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?

Medical Adaptation of Large Language and Vision-Language Mod...

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

作者： Jeong, Daniel P. Garg, Saurabh Lipton, Zachary C. Oberst, Michael Machine Learning Department Carnegie Mellon University United States Mistral AI France Department of Computer Science Johns Hopkins University United States Abridge AI United States

ISBN: (纸本)9798891761643

Several recent works seek to develop foundation models specifically for medical applications, adapting general-purpose large language models (LLMs) and vision-language models (VLMs) via continued pretraining on publicly available biomedical corpora. These works typically claim that such domain-adaptive pretraining (DAPT) improves performance on downstream medical tasks, such as answering medical licensing exam questions. In this paper, we compare seven public "medical" LLMs and two VLMs against their corresponding base models, arriving at a different conclusion: all medical VLMs and nearly all medical LLMs fail to consistently improve over their base models in the zero-/few-shot prompting regime for medical question-answering (QA) tasks. For instance, across the tasks and model pairs we consider in the 3-shot setting, medical LLMs only outperform their base models in 12.1% of cases, reach a (statistical) tie in 49.8% of cases, and are significantly worse than their base models in the remaining 38.2% of cases. Our conclusions are based on (i) comparing each medical model head-to-head, directly against the corresponding base model;(ii) optimizing the prompts for each model separately;and (iii) accounting for statistical uncertainty in comparisons. While these basic practices are not consistently adopted in the literature, our ablations show that they substantially impact conclusions. Our findings suggest that state-of-the-art general-domain models may already exhibit strong medical knowledge and reasoning capabilities, and offer recommendations to strengthen the conclusions of future studies. © 2024 Association for Computational Linguistics.

关键词： Visual languages

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