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Topic Review
Scale-Arbitrary Super-Resolution for Satellite Images
The advancements in image super-resolution technology have led to its widespread use in remote sensing applications. The existing scale-arbitrary super-resolution methods are primarily predicated on learning either a discrete representation (DR) or a continuous representation (CR) of the image, with DR retaining the sensitivity to resolution and CR guaranteeing the generalization of the model.
  • 238
  • 23 Nov 2023
Topic Review
Brain Pathology Classification of MR Images
A brain tumor is essentially a collection of aberrant tissues, so it is crucial to classify tumors of the brain using MRI before beginning therapy. Tumor segmentation and classification from brain MRI scans using machine learning techniques are widely recognized as challenging and important tasks. The potential applications of machine learning in diagnostics, preoperative planning, and postoperative evaluations are substantial. Accurate determination of the tumor’s location on a brain MRI is of paramount importance. 
  • 237
  • 08 Sep 2023
Topic Review
Intrusion Detection and Datasets
With the significant increase in cyber-attacks and attempts to gain unauthorised access to systems and information, Network Intrusion-Detection Systems (NIDSs) have become essential detection tools. Anomaly-based systems use machine learning techniques to distinguish between normal and anomalous traffic. They do this by using training datasets that have been previously gathered and labelled, allowing them to learn to detect anomalies in future data. However, such datasets can be accidentally or deliberately contaminated, compromising the performance of NIDS.
  • 237
  • 30 Jan 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.
  • 236
  • 26 Feb 2024
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. 
  • 235
  • 15 Sep 2023
Topic Review
Image Fusion Methods
Image fusion is the generation of an informative image that contains complementary information from the original sensor images, such as texture details and attentional targets. Existing methods have designed a variety of feature extraction algorithms and fusion strategies to achieve image fusion. 
  • 235
  • 26 Jan 2024
Topic Review
Tasks for Multimodal Federated Learning
Multimodal federated learning (MFL) offers many advantages, such as privacy preservation and addressing the data silo problem. However, it also faces limitations such as communication costs, data heterogeneity, and hardware disparities compared to centralized multimodal learning. Therefore, in addition to the unique challenges of modal heterogeneity, the original multimodal learning tasks become more challenging when performed within a federated learning framework.
  • 233
  • 16 Aug 2023
Topic Review
Auction-based Learning for Knowledge Graph Question Answering
Knowledge graphs are graph-based data models which can represent real-time data that is constantly growing with the addition of new information. The question-answering systems over knowledge graphs (KGQA) retrieve answers to a natural language question from the knowledge graph. Most existing KGQA systems use static knowledge bases for offline training. After deployment, they fail to learn from unseen new entities added to the graph. There is a need for dynamic algorithms which can adapt to the evolving graphs and give interpretable results. The algorithms can adapt to changing environments in real-time, making them suitable for offline and online training. An auction algorithm computes paths connecting an origin node to one or more destination nodes in a directed graph and uses node prices to guide the search for the path. When new nodes and edges are dynamically added or removed in an evolving knowledge graph, the algorithm can adapt by reusing the prices of existing nodes and assigning arbitrary prices to the new nodes. For subsequent related searches, the “learned” prices provide the means to “transfer knowledge” and act as a “guide”: to steer it toward the lower-priced nodes.
  • 233
  • 15 Sep 2023
Topic Review
Machine Learning Techniques for Customer Churn Prediction
The application of various machine learning techniques for predicting customer churn in the telecommunications sector is explored. Researchers utilized a publicly accessible dataset and implemented several models, including Artificial Neural Networks, Decision Trees, Support Vector Machines, Random Forests, Logistic Regression, and gradient boosting techniques (XGBoost, LightGBM, and CatBoost). To mitigate the challenges posed by imbalanced datasets, researchers adopted different data sampling strategies, namely SMOTE, SMOTE combined with Tomek Links, and SMOTE combined with Edited Nearest Neighbors. Moreover, hyperparameter tuning was employed to enhance model performance. Resarchers' evaluation employed standard metrics, such as Precision, Recall, F1-score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). In terms of the F1-score metric, CatBoost demonstrates superior performance compared to other machine learning models, achieving an outstanding 93% following the application of Optuna hyperparameter optimization. In the context of the ROC AUC metric, both XGBoost and CatBoost exhibit exceptional performance, recording remarkable scores of 91%. This achievement for XGBoost is attained after implementing a combination of SMOTE with Tomek Links, while CatBoost reaches this level of performance after the application of Optuna hyperparameter optimization.
  • 233
  • 12 Dec 2023
Topic Review
Plant-Parasitic Nematode
Plant-parasitic nematodes (PPN), especially sedentary endoparasitic nematodes like root-knot nematodes (RKN), pose a significant threat to major crops and vegetables. They are responsible for causing substantial yield losses, leading to economic consequences, and impacting the global food supply. The identification of PPNs and the assessment of their population is a tedious and time-consuming task.
  • 230
  • 31 Jan 2024
Topic Review
Social Network Sentiment Analysis
The ever-increasing amount of information and opinions available on social networks has made it imperative to develop automatic methods for effective information classification and analysis. Sentiment analysis (SA) in social networks has, therefore, become a crucial process in numerous sectors at both social and business levels. 
  • 230
  • 06 Nov 2023
Topic Review
Contrast Enhancement-Based Preprocessing Process and Object Task Performance
Excessive lighting or sunlight can make it difficult to judge visually. The same goes for cameras that function like the human eye. In the field of computer vision, object tasks have a significant impact on performance depending on how much object information is provided. Light presents difficulties in recognizing objects, and recognition is not easy in shadows or dark areas. Light is one of the biggest factors that make it difficult to recognize the original shape of an object by lowering the object recognition rate.
  • 228
  • 13 Oct 2023
Topic Review
Efficient Detection of Forest Fire Smoke
Forest fires are a significant environmental threat, causing loss of biodiversity, alteration of ecosystems, and impacting human lives and properties. Early detection is critical for effective firefighting and minimizing damages. Smoke detection plays an indispensable role in the early monitoring of forest fires. Its rapid dispersion, visibility, and integration with contemporary sensor technologies render it not only an effective complement but also a potential substitute for flame monitoring. In this context, various forest fire smoke detection methods and systems have been developed. These methods include satellite-based smoke detection, ground-based sensors for smoke detection, and UAV-based detection, each with its unique approach, advantages, and limitations. Moreover, image processing technology occupies a crucial position in the detection of forest fire smoke.
  • 228
  • 27 Dec 2023
Topic Review
Quality of OpenStreetMap Data
OpenStreetMap (OSM) is a potential source of geospatial open data for monitoring sustainable development goals (SDG) indicators. Improving the quality of these crowdsourcing data has significant implications for monitoring and achieving SDGs, such as zero hunger, sustainable cities, ensuring tenure security, and preserving biodiversity. The quality of OpenStreetMap (OSM) has been widely concerned as a valuable source for monitoring some sustainable development goals (SDG) indicators. Improving its semantic quality is still challenging. As a kind of solution, road type prediction plays an important role. However, most existing algorithms show low accuracy, owing to data sparseness and inaccurate description. 
  • 226
  • 19 Dec 2023
Topic Review
Deep Learning in Neuro-Oncology Data Analysis
Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. 
  • 226
  • 19 Jan 2024
Topic Review
Graph Clustering Algorithms
Graph clustering has received considerable attention, and its applications are numerous, ranging from the detection of social communities to the clustering of computer networks. It is classified as an NP-class problem, and several algorithms have been proposed with specific objectives. There also exist various quality metrics for evaluating them. Having clusters with the required density can be beneficial because it permits the effective deployment of resources.
  • 226
  • 26 Jan 2024
Topic Review
Edge-Guided Multimodal Transformers Change Detection
Change detection from heterogeneous satellite and aerial images plays a progressively important role in many fields, including disaster assessment, urban construction, and land use monitoring. Researchers have mainly devoted their attention to change detection using homologous image pairs and achieved many remarkable results. It is sometimes necessary to use heterogeneous images for change detection in practical scenarios due to missing images, emergency situations, and cloud and fog occlusion.
  • 226
  • 05 Mar 2024
Topic Review
Natural Image Reconstruction from fMRI
Reconstructing natural stimulus images using functional magnetic resonance imaging (fMRI) is one of the most challenging problems in brain decoding and is also the crucial component of a brain–computer interface.
  • 224
  • 18 Mar 2024
Topic Review
Network Incident Identification through Genetic Algorithm-Driven Feature Selection
The cybersecurity landscape presents daunting challenges, particularly in the face of Denial of Service (DoS) attacks such as DoS Http Unbearable Load King (HULK) attacks and DoS GoldenEye attacks. These malicious tactics are designed to disrupt critical services by overwhelming web servers with malicious requests.
  • 224
  • 30 Jan 2024
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.
  • 223
  • 19 Oct 2023
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