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Topic Review
6-DoF Object Pose Estimation
Accurately estimating the six-degree-of-freedom (6-DoF) of objects is a critical task in various applications, including robotics, autonomous driving, and virtual reality. For instance, the precise estimation of spatial coordinates and rotational orientation of an object is essential for robotic tasks such as manipulation, navigation, and assembly.
  • 987
  • 13 Oct 2023
Topic Review
Recommendation Systems for e-Shopping
The interest in recommendation systems (RSs) has dramatically increased, as they have become main components of all online stores. The aims of an RS can be multifaceted, related not only to the increase in sales or the convenience of the customer, but may include the promotion of alternative environmentally friendly products or to strengthen policies and campaigns. In addition to accurate suggestions, important aspects of contemporary RSs are therefore to align with the particular marketing goals of the e-shop and with the stances of the targeted audience, ensuring user acceptance, satisfaction, high impact, and achieving sustained usage by customers.
  • 987
  • 21 Dec 2023
Topic Review
Path Planning for Agricultural Ground Robots
Ground robots have been developed for a variety of agricultural applications, with autonomous and safe navigation being one of the most difficult hurdles in this development. When a mobile platform moves autonomously, it must perform a variety of tasks, including localization, route planning, motion control, and mapping, which is a critical stage in autonomous operations. 
  • 985
  • 26 Sep 2023
Topic Review
Sleep Spindle
Sleep spindles are bursts of neural oscillatory activity that are generated by interplay of the thalamic reticular nucleus (TRN) and other thalamic nuclei during stage 2 NREM sleep in a frequency of ~10 –12 Hz for at least 0.5 seconds. After generation in the TRN, spindles are sustained and relayed to the cortex by a thalamo-thalamic and thalamo-cortical feedback loops regulated by both GABAergic and NMDA-receptor mediated glutamatergic neurotransmission. Sleep spindles have been found in all tested mammalian species and in vitro cells. Research supports that spindles (sometimes referred to as "sigma bands" or "sigma waves") play an essential role in both sensory processing and long term memory consolidation. Until recently, it was believed that each sleep spindle oscillation peaked at the same time throughout the neocortex. It was determined that oscillations sweep across the neocortex in circular patterns around the neocortex, peaking in one area, and then a few milliseconds later in an adjacent area. It has been suggested that this spindle organization allows for neurons to communicate across cortices. The time scale at which the waves travel at is the same speed it takes for neurons to communicate with each other.
  • 982
  • 12 Oct 2022
Topic Review
Convolution Neural Network  and Transformer-Based Human Pose Estimation
Human pose estimation is a complex detection task in which the network needs to capture the rich information contained in the images.
  • 982
  • 03 Aug 2023
Topic Review
Categorical Exploratory Data Analysis
Categorical exploratory data analysis (CEDA) is demonstrated to provide new resolutions for two topics: multiclass classification (MCC) with one single categorical response variable and response manifold analytics (RMA) with multiple response variables. 
  • 982
  • 08 Jul 2021
Topic Review
Conflict Prediction in Sub-Saharan Africa
This entry offers policymakers and researchers pragmatic and sustainable approaches to identify and mitigate conflict threats by looking beyond p-values and plausible instruments. We argue that predicting conflict successfully depends on the choice of algorithms, which, if chosen accurately, can reduce economic and social instabilities caused by post-conflict reconstruction. After collating data with variables linked to conflict, we used a grid level dataset of 5928 observations spanning 48 countries across sub-Saharan Africa to predict civil conflict. The goals of the study were to assess the performance of supervised classification machine learning (ML) algorithms in comparison with logistic model, assess the implication of selecting a specific performance metric on policy initiatives, and evaluate the value of interpretability of the selected model. After comparing class imbalance resampling methods, the synthetic minority over-sampling technique (SMOTE) was employed to improve out-of-sample prediction for the trained model. The results indicate that if our selected performance metric is recall, gradient tree boosting is the best algorithm; however, if precision or F1 score is the selected metric, then the multilayer perceptron algorithm produces the best model. 
  • 981
  • 09 Jul 2021
Topic Review
Recommender System for the Tourism Domain
Tourism is a widespread activity and a huge industry, in their travels, tourists have to select among numerous alternatives of landmarks, places and in general points of interest (POIs) they can visit. This can be challenging for travellers that have no prior experience of or “inside knowledge” regarding their destination, and even more so for short-term visits. As a result, a booming industry of travel-related recommender systems (RSs) has been developed, in order to provide users with recommendations most relevant to their interests. Recommender systems are considered to be personalized and non-personalized. Personalized recommender systems extrapolate user’s preferences to create efficient recommendations based on the users’ past interaction with the system. On the contrary, non-personalized recommenders suggest items that are most relevant and popular among all users.
  • 978
  • 23 Oct 2023
Topic Review
Graph Neural Networks for Parkinson’s Disease
Graph neural networks (GNNs) have been increasingly employed in the field of Parkinson’s disease (PD) research. The use of GNNs provides a promising approach to address the complex relationship between various clinical and non-clinical factors that contribute to the progression of PD. 
  • 978
  • 13 Nov 2023
Topic Review
Transporting Blood Using Unmanned Aerial Vehicles
Unmanned Aerial Vehicles (UAVs) play crucial roles in numerous applications, such as healthcare services. For example, UAVs can help in disaster relief and rescue missions, such as by delivering blood samples and medical supplies.
  • 977
  • 21 Nov 2022
Topic Review
Electroencephalogram-Based Emotion Classification
Rapid advancements in the medical field have drawn much attention to automatic emotion classification from EEG data. People’s emotional states are crucial factors in how they behave and interact physiologically. The diagnosis of patients’ mental disorders is one potential medical use. When feeling well, people work and communicate more effectively. Negative emotions can be detrimental to both physical and mental health. Many earlier studies that investigated the use of the electroencephalogram (EEG) for emotion classification have focused on collecting data from the whole brain because of the rapidly developing science of machine learning.
  • 977
  • 09 Jan 2023
Topic Review
The Synergistic Relationship between AI and the Economy
Artificial intelligence (AI) is transforming various aspects of the economy, including manufacturing, healthcare, finance, and transportation. AI-powered systems are augmenting human decision-making, reducing operational costs, enhancing productivity, and creating new business models. However, the integration of AI into the economy also poses several challenges, such as job displacement, economic inequality, and ethical concerns. This research explores the complex relationship between AI and the economy, highlighting the opportunities and challenges that arise from their synergy.
  • 977
  • 18 May 2023
Topic Review
Equilibrium Optimizer Algorithm
The equilibrium optimizer (EO) is a recently developed physics-based optimization technique for complex optimization problems.
  • 975
  • 04 Sep 2023
Topic Review
Hesitant Fuzzy Graph Neural Network-Based Prototypical Network
Few-shot text classification aims to recognize new classes with only a few labeled text instances. Previous studies mainly utilized text semantic features to model the instance-level relation among partial samples. However, the single relation information makes it difficult for many models to address complicated natural language tasks. A novel hesitant fuzzy graph neural network (HFGNN) model that explores the multi-attribute relations between samples is proposed. HFGNN is combined with the Prototypical Network (HFGNN-Proto) to achieve few-shot text classification.
  • 973
  • 20 Dec 2022
Topic Review
Green Space Quality Analysis Using Machine Learning Approaches
Green space is any green infrastructure consisting of vegetation. Green space is linked with improving mental and physical health, providing opportunities for social interactions and physical activities, and aiding the environment. The quality of green space refers to the condition of the green space.
  • 972
  • 22 May 2023
Topic Review
Mental Fatigue Detection Using Physiological Signals
Fatigue is a state characterized by both physical and mental exhaustion, resulting from prolonged activity, inadequate rest, or excessive cognitive demands. Physiological signals offer a valuable insight into the body’s internal state. Monitoring and interpreting these signals provide real-time information about an individual’s physical and mental condition, enabling early fatigue detection. 
  • 972
  • 10 Nov 2023
Topic Review
Cardiac Failure Forecasting
Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation.
  • 971
  • 15 Aug 2023
Topic Review
Data-Driven Methods in Power Grids
Applications of data-driven methods in power grids are motivated by the need to predict and mitigate intermittency in a (future) grid that is expected to lean heavily on renewables.
  • 969
  • 22 Jun 2022
Topic Review
Computational Intelligence in Stock Portfolio Management
Stock portfolio management consists of defining how some investment resources should be allocated to a set of stocks. It is an important component in the functioning of modern societies throughout the world. However, it faces important theoretical and practical challenges. ANNs have high accuracy, fast prediction speed and clear superiority in predictions related to financial markets.
  • 968
  • 16 May 2022
Topic Review
ChatGPT in Teaching Practice
The emergence of new tools, especially those based on AI, raises concerns that technology may replace the teacher in the classroom. ChatGPT can support and automate the activities of educators, but their role as mentors who provide guidance and more profound assessment of learner abilities and role models cannot be entirely replaced by technology. ChatGPT, a generative artificial intelligence (GAI) representative, can create quizzes and assignments that are automatically checked and graded, generate feedback, and provide personalized learning content depending on the learners’ results.
  • 968
  • 06 Nov 2023
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