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Topic Review
Deep Reinforcement Learning for Vision-Based Navigation of UAVs
Unmanned Aerial Vehicles (UAVs), also known as drones, have advanced greatly in recent years. There are many ways in which drones can be used, including transportation, photography, climate monitoring, and disaster relief. The reason for this is their high level of efficiency and safety in all operations. While the design of drones strives for perfection, it is not yet flawless. When it comes to detecting and preventing collisions, drones still face many challenges. In this context, this research describes a methodology for developing a drone system that operates autonomously without the need for human intervention. This research applies reinforcement learning algorithms to train a drone to avoid obstacles autonomously in discrete and continuous action spaces based solely on image data. The research compare three different reinforcement learning strategies—namely, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC)—that can assist in avoiding obstacles, both stationary and moving The novelty of this research lies in its comprehensive assessment of the advantages, limitations, and future research directions of obstacle detection and avoidance for drones, using different reinforcement learning techniques. The findings could have practical implications for the development of safer and more efficient drones in the future.
  • 225
  • 13 Dec 2023
Topic Review
Computed Tomography Reconstruction
Computed tomography (CT) is a vital medical imaging technology that revolutionizes healthcare by providing high-resolution images of internal body structures, making it an essential tool in fields like radiology, oncology, and surgery. CT imaging uses X-ray technology to scan a patient. During the CT imaging process, the patient is positioned on a motorized examination table that passes through a CT scanner. The scanner emits narrow X-ray beams, which are measured by detectors on the opposite side of the patient. The data collected are X-ray projections or profiles. 
  • 291
  • 13 Dec 2023
Topic Review
Privacy and Security in Sustainable Smart City Applications
Smart city applications that request sensitive user information necessitate a comprehensive data privacy solution. Federated learning (FL), also known as privacy by design, is a new paradigm in machine learning (ML).
  • 161
  • 12 Dec 2023
Topic Review
Enhancing VNF Instantiation with Blockchain
In the realm of Network Function Virtualization (NFV), Virtual Network Functions (VNFs) are crucial software entities that require execution on virtualized hardware infrastructure. Deploying a Service Function Chain (SFC) requires multiple steps for instantiating VNFs to analyze, request, deploy, and monitor resources. It is well recognized that the sharing of infrastructure resources among different VNFs will enhance resource utilization. However, conventional mechanisms for VNF sharing often neglect the interests of both VNF instances and infrastructure providers.
  • 246
  • 12 Dec 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.
  • 147
  • 12 Dec 2023
Topic Review
Sensor Data Fusion Algorithms
Sensor Data Fusion (SDT) algorithms and methods have been utilised in many applications ranging from automobiles to healthcare systems. They can be used to design a redundant, reliable, and complementary system with the intent of enhancing the system’s performance. SDT can be multifaceted, involving many representations such as pixels, features, signals, and symbols.
  • 373
  • 12 Dec 2023
Topic Review
Cyber-Physical System Leveraging EFDPN for WSN-IoT Network Security
A wireless sensor network (WSN), which is made up of different kinds of sensors with limited resources, is an important part of monitoring an environment and sending important data to a designated node, also called a sink, through different communication protocols.
  • 146
  • 12 Dec 2023
Topic Review
Reinforcement Learning, Knowledge Distillation, and Channel Pruning
The methods used for model compression and acceleration are primarily divided into five categories—network pruning, parameter quantization, low-rank decomposition, lightweight network design, and knowledge distillation—such that the scope of actions and design ideas for each method are different.
  • 164
  • 12 Dec 2023
Topic Review
Microseismic Monitoring Signal Waveform Recognition and Classification
Microseismic event identification is of great significance for enhancing our understanding of underground phenomena and ensuring geological safety. Microseismic monitoring entails the continuous surveillance of minuscule seismic events during mining activities. These imperceptible events provide valuable information about evolving geological conditions. They serve as early warning signals, offering crucial insights into potential hazards and enabling timely preventive measures. This not only safeguards the well-being of miners but also enhances the overall efficiency and sustainability of mining practices.
  • 137
  • 11 Dec 2023
Topic Review
Visual Tracking Related to Age or Gender Information
Visual tracking of multiple targets, also referred to as multiple object tracking (MOT), since the target can be any moving object or entity, is a well-investigated computer vision task. Actually, the goal is to detect one or more targets in a time-variate scene and then obtain their trajectories in terms of following their tracklets, for a given video sequence. This is completed by associating newly detected instances with current ones. Typically, the association part assumes a prediction task whose aim is to favor the most possible correspondence among detections of consecutive frames for a given target. When the targets of interest are real people, resulting detections from this procedure are usually post-processed so as to extract useful information related, for instance, with their age or gender. 
  • 150
  • 11 Dec 2023
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