4 research outputs found
A Survey on Binary Tree-Based Approaches for Data Transmission in Mobile Ad Hoc Networks
No full textA thorough analysis of the current binary tree-based data distribution techniques in MANETs is the goal of this paper. MANET communication is highly dynamic, necessitating effective data transmission methods to improve network stability while also saving energy. Binary tree topologies work in tandem with routing and data aggregation to improve scalability, reduce latency, and increase energy economy. The paper investigates several binary tree algorithms that are appropriate for data and security structures, as well as routing techniques. Similar to previous MANETs, the network has three main issues: security threats, energy constraints, and mobility issues. Key features of modern algorithms are briefly discussed in the study, along with the benefits and drawbacks of tree-based systems. The study itself outlines the goals and directions for further investigation just to find the best network throughputs while staying within the constraints of dynamism\u27s limited computing and energy resources
Blood Pressure Prediction Using Deep Learning
No full textAlthough traditional cuff-based blood pressure (BP) monitoring is sporadic and laborious, BP is essential for cardiovascular health. We review deep learning approaches for cuff-less blood pressure estimation, such as CNNs, RNNs, Transformer models, and attention processes, and provide two new PPG-to-ABP waveform synthesis methods. The first (ASBP) maps one-dimensional PPG signals into arterial blood pressure waveforms using a VGG-16 encoder-decoder, while the second (SEANet) uses causal dilated convolutions in a calibration-free framework for continuous blood pressure estimation. Using correlation coefficient (CC), mean absolute error (MAE), and mean absolute percentage error (MAPE) measures, both models are trained and assessed on the UCI cuff-less BP dataset. The results have near-normal residual distributions and satisfy AAMI/BHS clinical criteria. An organized comparison of twenty cutting-edge studies demonstrates the variety of datasets, methodological advancements, and clinical usefulness. We present future work for reliable, generalized blood pressure monitoring with wearable PPG sensors and talk about challenges, including dataset heterogeneity and real-time deployment
Overview of Algorithms for Image Recognition
No full textThe significance of image recognition technology is highlighted by its wide applications in fields such as security, medical image analysis, and data analysis. Its growing popularity reflects advancements in research. Traditional machine learning methods have markedly improved feature extraction, while deep learning techniques have advanced significantly due to the application of various neural networks. This paper reviews algorithms and systems for image recognition, covering both traditional and deep learning methods. It provides extensive descriptions of classification and object detection techniques involving feature extraction, convolutional neural network designs, and neuron activation functions. The focus extends to traditional algorithms like k-nearest neighbor, support vector machine, Naive Bayes, and parallel cascade selection. Additionally, it explores various deep learning approaches for image interpretation, detailing different convolutional network dimensions and neuron model constructions. The paper concludes by illustrating algorithms with application examples and clarifying the differences between traditional methods and deep learning
The Role of Machine Learning in Enhancing Marketing Strategies within Cloud-based Enterprise Systems
No full textCombining cloud computing, digital technologies, and machine learning is changing organizational systems and marketing techniques. This paper explores how this is happening. Switching to cloud-based systems improves operational efficiency and collaboration by increasing scalability, lowering expenses, and enabling real-time data access. More individualized and focused marketing strategies are made possible by machine learning approaches, such as consumer segmentation and predictive analytics, improving decision-making and customer interaction. However, there are still issues with managing computing resources, guaranteeing strong data security, and offering enough staff training for smooth integration. New developments like edge computing and federated learning are emphasized as possible directions for future research. These convergent technologies\u27 digital transformation allows companies to stay flexible and competitive in a changing market
