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Topic Review
Facial Information for Healthcare Applications
The document is not limited to global face analysis but it also concentrates on methods related to local cues (e.g. the eyes). A research taxonomy is introduced by dividing the face in its main features: eyes, mouth, muscles, skin, and shape. For each facial feature, the computer vision-based tasks aiming at analyzing it and the related healthcare goals that could be pursued are detailed.
  • 1.3K
  • 28 Oct 2020
Topic Review
Lhia as A Chatbot for Breastfeeding Education
Human milk is the most important way to feed and protect newborns as it has the components to ensure human health. Human Milk Banks (HMBs) form a network that offers essential services to ensure that newborns and mothers can take advantage of the benefits of human milk.
  • 1.3K
  • 16 Jun 2023
Topic Review
Vehicular Ad hoc Networks (VANETs)
Vehicular ad hoc networks (VANETs) have become an essential part of the intelligent transportation system because they provide secure communication among vehicles, enhance vehicle safety, and improve the driving experience.
  • 1.3K
  • 03 Nov 2023
Topic Review
Artificial Intelligence (AI)-Empowered Echocardiography Interpretation
Echocardiography (Echo), a widely available, noninvasive, and portable bedside imaging tool, is the most frequently used imaging modality in assessing cardiac anatomy and function in clinical practice. Artificial-intelligence-empowered echo (AI-Echo) can potentially reduce inter-interpreter variability and indeterminate assessment and improve the detection of unique conditions as well as the management of various cardiac disorders.
  • 1.3K
  • 28 Apr 2021
Topic Review
Knee Injury Detection Using Deep Learning
Knee injuries account for the largest percentage of sport-related, severe injuries (i.e., injuries that cause more than 21 days of missed sport participation). The improved treatment of knee injuries critically relies on having an accurate and cost-effective detection. Deep-learning-based approaches have monopolized knee injury detection in MRI studies.
  • 1.3K
  • 20 May 2022
Topic Review
Emotion Recognition in Conversations
As a branch of sentiment analysis tasks, emotion recognition in conversation (ERC) aims to explore the hidden emotions of a speaker by analyzing the sentiments in utterance. In addition, emotion recognition in multimodal data from conversation includes the text of the utterance and its corresponding acoustic and visual data. By integrating features from various modalities, the emotion of utterance can be more accurately predicted.
  • 1.3K
  • 29 Dec 2023
Topic Review
Explainable AI (XAI) Explanation Techniques
Interest in artificial intelligence (AI) has been increasing rapidly over the past decade and has expanded to essentially all domains. Along with it grew the need to understand the predictions and suggestions provided by machine learning. Explanation techniques have been researched intensively in the context of explainable AI (XAI), with the goal of boosting confidence, trust, user satisfaction, and transparency.
  • 1.3K
  • 19 Jun 2023
Topic Review
Deepfake Identification and Traceability
Researchers and companies have released multiple datasets of face deepfakes labeled to indicate different methods of forgery. Naming these labels is often arbitrary and inconsistent. However, researchers must use multiple datasets in practical applications to conduct traceability research. The researchers utilize the K-means clustering method to identify datasets with similar feature values and analyze the feature values using the Calinski Harabasz Index method. Datasets with the same or similar labels in different deepfake datasets exhibit different forgery features. The KCE system can solve this problem, which combines multiple deepfake datasets according to feature similarity. In the model trained based on KCE combined data, the Calinski Harabasz scored 42.3% higher than the combined data by the same forgery name. It shows that this method improves the generalization ability of the model.
  • 1.3K
  • 08 Jun 2023
Topic Review
Universal Intelligence for Sustainability
Artificial intelligence (AI), as a product of biological intelligence, is a technological tool based on data and the information-processing power of discrete machines that carry out a series of interdependent operations to generate and store discrete data and information, using discrete, finite, and closed algorithms. In turn, the concept of sustainability is increasingly considered an almost essential component of discourses designed to support and justify decision-making at all levels of human activities.  The strong and functional couplings among ecological, economic, social, and technological processes explain the complexification of human-made systems, and phenomena such as globalization, climate change, the increased urbanization and inequality of human societies, and the power of information, and the COVID-19 syndemic. Sustainability for complex systems implies enough efficiency to explore and exploit their dynamic phase spaces and enough flexibility to coevolve with their environments. This means solving intractable nonlinear semi-structured dynamic multi-objective optimization problems, with conflicting, incommensurable, non-cooperative objectives and purposes, under dynamic uncertainty, restricted access to materials, energy, and information, and a given time horizon. Given the high stakes; the need for effective, efficient, diverse solutions; their local and global, and present and future effects; and their unforeseen short-, medium-, and long-term impacts; achieving sustainable complex systems implies the need for Sustainability-designed Universal Intelligent Agents (SUIAs). The proposed philosophical and technological SUIAs will be heuristic devices for harnessing the strong functional coupling between human, artificial, and nonhuman biological intelligence in a non-zero-sum game to achieve sustainability.
  • 1.3K
  • 21 Jun 2022
Topic Review
GIS and Reinforcement Learning for Aquaculture Disease Transmission
Aquaculture, a critical domain in sustainable food production, is increasingly relying on Geographic Information Systems (GIS) for disease transmission analysis. GIS, an advanced digital mapping and analytical tool, not only helps visualize the geographical spread of diseases but also discerns patterns and offers predictive insights. By integrating a variety of datasets, from environmental to biological, GIS enables comprehensive analysis of disease risk zones, facilitating preemptive measures and efficient resource allocation.
  • 1.3K
  • 23 Nov 2023
Topic Review
Sign Language Recognition
Recognition of hand motion capture is an interesting topic. Hand motion can represent many gestures. In particular, sign language plays an important role in the daily lives of hearing-impaired people. Sign language recognition is essential in hearing-impaired people’s communication. Wearable data gloves and computer vision are partially complementary solutions. However, sign language recognition using a general monocular camera suffers from occlusion and recognition accuracy issues.
  • 1.3K
  • 08 Dec 2023
Topic Review
AI Advancements
Artificial intelligence (AI) has seen remarkable advancements, stretching the limits of what is possible and opening up new frontiers. Beginning with the fundamentals of AI, including traditional machine learning and the transition to data-driven approaches, the narrative progresses through core AI techniques such as reinforcement learning, generative adversarial networks, transfer learning, and neuroevolution.
  • 1.2K
  • 28 Dec 2023
Topic Review
Synthetic Image Data in Computer Vision
Many computer vision applications cannot rely on general image data provided in the available public datasets to train models, instead requiring labelled image data that is not readily available in the public domain on a large scale. At the same time, acquiring such data from the real world can be difficult, costly to obtain, and manual labour intensive to label in large quantities. Because of this, synthetic image data has been pushed to the forefront as a potentially faster and cheaper alternative to collecting and annotating real data.
  • 1.2K
  • 15 Dec 2022
Topic Review
Relationship of Artificial Intelligence, Advertising, and Generative Models
Although artificial intelligence technologies have provided valuable insights into the advertising industry, more comprehensive studies that properly examine the applications of AI in advertising using scientometric network analysis are needed.
  • 1.2K
  • 11 Mar 2024
Topic Review
IoT Sensor Data Processing
In IoT sensor networks, wireless communication protocols are popularly used for the information exchange process. These communication protocols work as unlicensed frequency bands that ease the flexibility and scalability of sensor deployments. However, the utilization of communication protocols for wireless sensor network (WSN) under unlicensed frequency bands causes uncontrollable interference. The interference signals may lead to improper data transmission and sensor data with noise, missing values, outliers and redundancy. 
  • 1.2K
  • 28 Oct 2022
Topic Review
Extended Reality Technology for Teaching New Languages
Much attention has been given to the use of extended reality (XR) technology in educationalinstitutions due to its flexibility, effectiveness, and attractiveness. However, there is a limited study of the application of XR technology for teaching and learning languages in schools. Thus, this paper presents a systematic review to identify the potential benefits and challenges of using XR technology for teaching new languages. This review provides a basis for adopting XR technology for teaching languages in schools. This research also provides recommendations to successfully implement the XR technology and ways to improve motivation, engagement, and enhanced accessibility of learning and teaching resources for both students and teachers. To fulfil the aims of this research, previous studies from 2011 to 2021 are collected from various academic databases. This study finds that there is still aneed to develop appropriate strategies for the development and implementation of XR technology for teaching new languages to school students.
  • 1.2K
  • 16 Dec 2021
Topic Review
Arabic Sentiment Analysis of YouTube Comments
Arabic sentiment analysis is a challenging task due to a variety of challenges with the language. In Arabic, the same word might have a variety of meanings depending on the context. Arabic also has a rich morphology, with verb forms that are difficult to understand and elaborate syntactic patterns. The wide range of dialects spoken in Arabic is a significant barrier to sentiment analysis. In the region of the Middle East and North Africa, Arabic is spoken in a number of dialects, with substantial variations in vocabulary, syntax, and pronunciation. These factors make it challenging to develop accurate sentiment analysis models for Arabic texts. Despite the challenges, there have been successful research studies within the framework of sentiment analysis applied to the Arabic language.
  • 1.2K
  • 28 Jul 2023
Topic Review
Radiomics of Liver Metastases
Multidisciplinary management of patients with liver metastases (LM) requires a precision medicine approach, based on adequate profiling of tumor biology and robust biomarkers. Radiomics, defined as the high-throughput identification, analysis, and translational applications of radiological textural features, could fulfill this need. The present review aims to elucidate the contribution of radiomic analyses to the management of patients with LM. We performed a systematic review of the literature through the most relevant databases and web sources. English language original articles published before June 2020 and concerning radiomics of LM extracted from CT, MRI, or PET-CT were considered. Thirty-two papers were identified. Baseline higher entropy and lower homogeneity of LM were associated with better survival and higher chemotherapy response rates. A decrease in entropy and an increase in homogeneity after chemotherapy correlated with radiological tumor response. Entropy and homogeneity were also highly predictive of tumor regression grade. In comparison with RECIST criteria, radiomic features provided an earlier prediction of response to chemotherapy. Lastly, texture analyses could differentiate LM from other liver tumors. The commonest limitations of studies were small sample size, retrospective design, lack of validation datasets, and unavailability of univocal cut-off values of radiomic features. In conclusion, radiomics can potentially contribute to the precision medicine approach to patients with LM, but interdisciplinarity, standardization, and adequate software tools are needed to translate the anticipated potentialities into clinical practice.
  • 1.2K
  • 06 Nov 2020
Topic Review
Hierarchical Clustering Algorithms
In the field of data mining, clustering has shown to be an important technique. Hierarchical clustering algorithms are a type of clustering algorithm where data items will be divided into sections in a hierarchical form. To create a dendrogram that shows the formulated cluster’s hierarchical structure, in a top-down or bottom-up way clusters are created iteratively. 
  • 1.2K
  • 15 May 2023
Topic Review
Intrusion Detection System in IoT Wi-Fi Networks
The Internet of Things (IoT) is a network of billions of interconnected devices embedded with sensors, software, and communication technologies. Wi-Fi is one of the main wireless communication technologies essential for establishing connections and facilitating communication in IoT environments. However, IoT networks are facing major security challenges due to various vulnerabilities, including de-authentication and disassociation DoS attacks that exploit IoT Wi-Fi network vulnerabilities. Traditional intrusion detection systems (IDSs) improved their cyberattack detection capabilities by adapting machine learning approaches, especially deep learning (DL). However, DL-based IDSs still need improvements in their accuracy, efficiency, and scalability to properly address the security challenges including de-authentication and disassociation DoS attacks tailored to suit IoT environments. The main purpose of this research was to overcome these limitations by designing a transfer learning (TL) and convolutional neural network (CNN)-based IDS for deauthentication and disassociation DoS attack detection with better overall accuracy compared to various current solutions. 
  • 1.2K
  • 15 Oct 2023
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