IEEE

Deep Learning Approaches for Solving Challenges in Computer Vision Applications and Exploring Evolving Patterns

Abstract: Deep learning has brought substantial advancements to computer vision in recent years by providing answers to challenging visual interpretation problems. It investigates several DL ...
IEEE

Malware Detection and Classification in Android Application Using Simhash-Based Feature Extraction and Machine Learning

Abstract: The need to detect and classify malware on Android devices has become crucial due to the widespread use of these devices daily. This paper presents a method for detecting and classifying ...
Scientific Research Publishing

Hastie, T., Tibshirani, R. and Friedman, J. (2009) The Elements of Statistical Learning Data Mining, Inference, and Prediction. 2nd Edition, Springer.

ABSTRACT: The accurate prediction of backbreak, a crucial parameter in mining operations, has a significant influence on safety and operational efficiency. The occurrence of this phenomenon is ...
IEEE

AI Applications and Simulation-Based Learning Integrating Future of Nursing Education

Abstract: Nurse educators are modifying their practices with the help of artificial intelligence. artificial intelligence helps nursing students gain their Goal facility arsenic good arsenic their ...
IEEE

Fusing Multimodality of Large Language Models and Satellite Imagery via Simplicial Contrastive Learning for Latent Urban Feature Identification and Environmental Application

Abstract: Satellite imagery is a readily available data source for monitoring a broad range of urban geographical contexts related to environmental, socio-demographic, and health disparities. To ...
IEEE

Innovative Applications of Unsupervised Learning in Uncertainty-Aware Pharmaceutical Supply Chain Planning

Abstract: The significance of resiliency, reliability, and equity in the pharmaceutical supply chain is often overlooked but becomes evident in the wake of disastrous events. Disruptive incidents ...
IEEE

A New Data Science Model With Supervised Learning and its Application on Pesticide Poisoning Diagnosis in Rural Workers

Abstract: In a Data Science project, it is essential to determine the relevance of the data and identify patterns that contribute to decision–making based on domain–specific knowledge. Furthermore, a ...
GitHub

Bayesian Statistical Modeling with Stan, R, and Python

Kentaro Matsuura (2023). Bayesian Statistical Modeling with Stan, R, and Python. Singapore: Springer. URL: https://link.springer.com/book/10.1007/978-981-19-4755-1 ...
IEEE

Decentralized Multi-Client Functional Encryption for Inner Product With Applications to Federated Learning

Abstract: Decentralized multi-client functional encryption for inner product (DMCFE-IP) enables efficient joint functional computation of private inputs in a secure manner without a trusted third ...
IEEE

Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models

Abstract: Federated learning (FL) is an approach to training machine learning models that takes advantage of multiple distributed datasets while maintaining data privacy and reducing communication ...
IEEE

Feature learning of Japanese pitch accents and applications to Japanese speech education

Abstract: We modeled pitch frequency by a sequential variational autoencoder to obtain the feature representations of the pitch accents of Japanese words for applications to Japanese speech education ...
IEEE

A Comprehensive Review of Machine Learning Applications in Laser Optics

Abstract: Machine learning (ML) has emerged as a transformative tool in laser-based optics, driving advancements in both fundamental research and industrial applications. This review paper provides a ...