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Topic Review
Autonomous Approaches in Multi-Access Edge Computing Networks
The widespread use of technology has made communication technology an indispensable part of daily life. However, the present cloud infrastructure is insufficient to meet the industry’s growing demands, and multi-access edge computing (MEC) has emerged as a solution by providing real-time computation closer to the data source. Effective management of MEC is essential for providing high-quality services, and proactive self-healing is a promising approach that anticipates and executes remedial operations before faults occur.  The term self-healing (SH) is closely associated with autonomous computing (AC), which was introduced at IBM’s event in 2001. The event aimed to develop a system that could manage itself without human intervention to address the management issues arising from the growing computer systems and networks.
  • 448
  • 12 Oct 2023
Topic Review
Methods Based on Software-Defined Networks
With the rapid advancement of the Internet of Things (IoT), there is a global surge in network traffic. Software-Defined Networks (SDNs) provide a holistic network perspective, facilitating software-based traffic analysis, and are more suitable to handle dynamic loads than a traditional network. The standard SDN architecture control plane has been designed for a single controller or multiple distributed controllers; however, a logically centralized single controller faces severe bottleneck issues. 
  • 447
  • 04 Mar 2024
Topic Review
Personalized Oxygen Dosing System
Considering the prevalence of chronic obstructive pulmonary disease (COPD) and the limitations of traditional long-term oxygen therapy (LTOT) in meeting individual patient needs, a proactive and personalized oxygen dosing system is introduced. This system harnesses AI and edge-to-cloud technologies, distributed across the continuum and Its primary objective is to develop accurate, reliable, and efficient predictive SpO2 AI models for each enrolled patient.
  • 443
  • 26 Feb 2024
Topic Review
Efficient Thorax Disease Classification by DCNN
Thorax disease is a life-threatening disease caused by bacterial infections that occur in the lungs. It could be deadly if not treated at the right time, so early diagnosis of thoracic diseases is vital. Computer vision techniques using deep learning are being used specifically for categorizing medical and natural images. As a direct result of this endeavor’s success, many academics are presently using deep convolutional neural networks (DCNNs) to diagnose thoracic illnesses based on chest radiographs. 
  • 441
  • 27 Nov 2023
Topic Review
Lightweight Multi-Target Recognition Model for Live Streaming Scenes
The commercial potential of live e-commerce is being continuously explored, and machine vision algorithms are gradually attracting the attention of marketers and researchers. During live streaming, the visuals can be effectively captured by algorithms, thereby providing additional data support.
  • 440
  • 29 Dec 2023
Topic Review
Deep Learning-Based Diagnosis of Chest Diseases
Chest disease refers to a variety of lung disorders, including lung cancer (LC), COVID-19, pneumonia (PNEU), tuberculosis (TB), and numerous other respiratory disorders. The symptoms (i.e., fever, cough, sore throat, etc.) of these chest diseases are similar, which might mislead radiologists and health experts when classifying chest diseases. Chest X-rays (CXR), cough sounds, and computed tomography (CT) scans are utilized by researchers and doctors to identify chest diseases such as LC, COVID-19, PNEU, and TB. 
  • 439
  • 20 Sep 2023
Topic Review
Disruptive Business Model for Higher Education
This research presents a disruptive business model in higher education, centered on student-customized degree programs that meet the changing demands of today’s workforce. This model leverages emerging technologies such as AI (Artificial Intelligence), ChatGPT, and Gemini to create adaptable and accessible educational pathways, allowing students to personalize their learning based on individual goals. The COVID-19 pandemic, which accelerated the shift to online and hybrid learning models, highlights the need for flexible educational models like this one that prioritize student-centered approaches and operational efficiency. This study examines both the potential benefits and the challenges of implementing such a model.
  • 438
  • 12 Nov 2024
Topic Review
Confusion Analysis in Learning Based on EEG Signals
Human–computer interaction (HCI) plays a significant role in modern education, and emotion recognition is essential in the field of HCI. The potential of emotion recognition in education remains to be explored. Confusion is the primary cognitive emotion during learning and significantly affects student engagement. Electroencephalogram (EEG) signals, obtained through electrodes placed on the scalp, are valuable for studying brain activity and identifying emotions. 
  • 432
  • 08 Dec 2023
Topic Review
Deep Learning Stranded Neural Network Model
Maintenance processes are of high importance for industrial plants. They have to be performed regularly and uninterruptedly. To assist maintenance personnel, industrial sensors monitored by distributed control systems observe and collect several machinery parameters in the cloud. Then, machine learning algorithms try to match patterns and classify abnormal behaviors.
  • 430
  • 19 Oct 2023
Topic Review
Human Activities Recognition Based on Wrist-Worn Wearable Devices
The proposed system consists of a real-time activity recognition system based on a common wearable device: a smartwatch. The machine learning component recognizes activity based on plot images generated from raw sensor data and is exposed as a Web Application Programming Interface (API) microservice.
  • 429
  • 19 Jan 2024
Topic Review
Deep Convolutional Neural Networks and Retinal Vessels
Accurate segmentation of retinal vessels is an essential prerequisite for the subsequent analysis of fundus images. Recently, a number of methods based on deep learning have been proposed and shown to demonstrate promising segmentation performance, especially U-Net and its variants. However, tiny vessels and low-contrast vessels are hard to detect due to the issues of a loss of spatial details caused by consecutive down-sample operations and inadequate fusion of multi-level features caused by vanilla skip connections. 
  • 423
  • 16 Nov 2023
Topic Review
Business Purchase Prediction Based on Artificial Intelligence
An architecture of a machine learning time series prediction system for business purchase prediction based on neural networks and enhanced with Explainable artificial intelligence (XAI) techniques is proposed. The architecture is implemented on an example of a system for predicting the following purchases for time series using Long short-term memory (LSTM) neural networks and Shapley additive explanations (SHAP) values. Explanations generated by the XAI module are provided with the prediction results to the user to allow them to understand the system’s decisions.
  • 422
  • 15 Nov 2023
Topic Review
Persistence Landscapes for Clustering Noisy IoT Time Series
With the advancement of IoT technologies, there is a large amount of data available from wireless sensor networks (WSN), particularly for studying climate change. Clustering long and noisy time series has become an important research area for analyzing this data.
  • 418
  • 11 Sep 2023
Topic Review
Deep Learning in Distinguishing Bladder Cancer from Cystitis
Urinary tract cancers are considered life-threatening conditions worldwide, and Bladder Cancer is one of the most malignant urinary tract tumors. Bladder Cancer is a heterogeneous disease; the main symptom is painless hematuria. However, patients with Bladder Cancer may initially be misdiagnosed as Cystitis or infection, and cystoscopy alone may sometimes be misdiagnosed as urolithiasis or Cystitis, thereby delaying medical attention. Early diagnosis of Bladder Cancer is the key to successful treatment.
  • 418
  • 13 Nov 2023
Topic Review
Dimensionality Reduction Techniques for Heart Rate Variability Analysis
Heart rate variability (HRV) has emerged as an essential non-invasive tool for understanding cardiac autonomic function over the last few decades. This can be attributed to the direct connection between the heart’s rhythm and the activity of the sympathetic and parasympathetic nervous systems. Different researchers have employed various dimensionality reduction methods to decrease the feature dimension of the HRV features.
  • 409
  • 24 Nov 2023
Topic Review
Optimized Downlink Scheduling over Long-Term Evolution Network
Long-Term Evolution (LTE) technology is utilized efficiently for wireless broadband communication for mobile devices. It provides flexible bandwidth and frequency with high speed and peak data rates. Optimizing resource allocation is vital for improving the performance of the Long-Term Evolution (LTE) system and meeting the user’s quality of service (QoS) needs. The resource distribution in video streaming affects the LTE network performance, reducing network fairness and causing increased delay and lower data throughput. 
  • 406
  • 15 Sep 2023
Topic Review
Health Transport Demand
Efficient planning and management of health transport services are crucial for improving accessibility and enhancing the quality of healthcare. Specifically, health transport services data from Asturias, spanning a seven-year period, are analyzed with the aim of developing accurate predictive models.
  • 403
  • 31 Jan 2024
Topic Review
Medthods to Enhance Internet of Things Network Security
The internet of things (IoT) has ushered in a new era of connectivity, transforming various industries by enabling faster sensor and data access. The widespread adoption of the IoT is not without challenges. Devices often grapple with limited battery lifetimes, the need to function in remote locations, and demanding transceiver operations. Among these challenges, security stands out as the most daunting.
  • 397
  • 22 Dec 2023
Topic Review
Identifying Key Issues in Climate Change Litigation
As climate change, environmental, social, and governance (ESG), along with sustainability, become increasingly crucial for businesses and society, there is a noticeable scarcity of information and transparency regarding corporate practices. Often, government agency enforcement actions lead to litigation and are ultimately resolved by court decisions. Moreover, in instances when there is perceived inadequacy in government enforcement, citizens frequently turn to the courts for preventive judgments against businesses or agencies.
  • 390
  • 21 Dec 2023
Topic Review
CISA: Context Substitution for Image Semantics Augmentation
Context is essential for generic neural networks. In Computer Vision background defines context. Therefore, smart background substitution can improve the generalization capability of a trained model. 
  • 377
  • 18 Oct 2023
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