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Topic Review
Techniques Related to Chinese Speech Emotion Recognition
The use of Artificial Intelligence for emotion recognition has attracted much attention. The industrial applicability of emotion recognition is quite comprehensive and has good development potential. 
  • 1.3K
  • 12 Jul 2022
Topic Review
Machine Learning-Based Application Life-Cycle
The application of emerging technologies, such as Artificial Intelligence (AI), entails risks that need to be addressed to ensure secure and trustworthy socio-technical infrastructures. Machine Learning (ML), the most developed subfield of AI, allows for improved decision-making processes. ML models exhibit specific vulnerabilities that conventional IT systems are not subject to. As systems incorporating ML components become increasingly pervasive, the need to provide security practitioners with threat modeling tailored to the specific AI-ML pipeline is of paramount importance.
  • 1.3K
  • 21 Sep 2022
Topic Review
Smart Farm and Forest Operations Needs Human-Centered AI
The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness.
  • 1.3K
  • 12 Jul 2022
Topic Review
IoT
This entry presents the problems of powering wireless sensor networks operating in the structures of the Internet of Things (IoT). This issue was discussed on the example of a universal end node in IoT technology containing RFID (Radio Frequency Identification) tags. The basic methods of signal transmission in these types of networks are discussed and their impact on the basic requirements such as range, transmission speed, low energy consumption, and the maximum number of devices that can simultaneously operate in the network. The issue of low power consumption of devices used in IoT solutions is one of the main research objects. The analysis of possible communication protocols has shown that there is a possibility of effective optimization in this area. The wide range of power sources available on the market, used in nodes of wireless sensor networks, was compared. The alternative possibilities of powering the network nodes from Energy Harvesting (EH) generators are presented.
  • 1.3K
  • 25 May 2021
Topic Review
Generative Adversarial Network in Amodal Completion
The generative adversarial network (GAN) is a structured probabilistic model that consists of two networks, a generator that captures the data distributions and a discriminator that decides whether the produced data come from the actual data distribution or from the generator. The two networks train in a two-player minimax game fashion until the generator can generate samples that are similar to the true samples, and the discriminator can no longer distinguish between the real and the fake samples. Although current computer vision systems are closer to the human intelligence when it comes to comprehending the visible world than previously, their performance is hindered when objects are partially occluded. Since we live in a dynamic and complex environment, we encounter more occluded objects than fully visible ones. Therefore, instilling the capability of amodal perception into those vision systems is crucial. However, overcoming occlusion is difficult and comes with its own challenges. GAN, on the other hand, is renowned for its generative power in producing data from a random noise distribution that approaches the samples that come from real data distributions.
  • 1.3K
  • 24 Apr 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.
  • 1.3K
  • 12 Dec 2023
Topic Review
NLP- and API-Sequence-Based Malware Detection and Classification Methodologies
The surge in malware threats propelled by the rapid evolution of the internet and smart device technology necessitates effective automatic malware classification for robust system security.
  • 1.3K
  • 24 Jan 2024
Topic Review
Ethnicity Classification
Ethnic conflicts frequently lead to violations of human rights, such as genocide and crimes against humanity, as well as economic collapse, governmental failure, environmental problems, and massive influxes of refugees. Many innocent people suffer as a result of violent ethnic conflict. People’s ethnicity can pose a threat to their safety. There have been many studies on the topic of how to categorize people by race.
  • 1.3K
  • 19 Sep 2023
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
Automatic Speech Recognition in Portuguese Language
Automatic speech recognition (ASR), commonly known as speech-to-text, is the process of transcribing audio recordings into text, i.e., transforming speech into the respective sequence of words. 
  • 1.3K
  • 15 Jun 2023
Topic Review
Reminiscence Therapy in Depression Treatment in the Elderly
Reminiscence therapy is a mechanism to help someone remember events from their life. It is often used as a therapy tool for reducing depression, calming behavioral and psychological symptoms of dementia, or affecting mood of the elderly. Although its most common use is for the elderly and people affected with dementia or depression, it has also been used with people of all ages, including children. The reminiscing process can take place in a group or individually or by using technological devices such as mobile devices or robots. It is marked by remembering notable events from the past.
  • 1.2K
  • 03 Mar 2022
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.
  • 1.2K
  • 21 Jun 2022
Topic Review
COVID-19 Fake News in Brazilian Portuguese Language
Public health interventions to counter the COVID-19 pandemic have accelerated and increased digital adoption and use of the Internet for sourcing health information. Unfortunately, there is evidence to suggest that it has also accelerated and increased the spread of false information relating to COVID-19. The consequences of misinformation, disinformation and misinterpretation of health information can interfere with attempts to curb the virus, delay or result in failure to seek or continue legitimate medical treatment and adherence to vaccination, as well as interfere with sound public health policy and attempts to disseminate public health messages. While there is a significant body of literature, datasets and tools to support countermeasures against the spread of false information online in resource-rich languages such as English and Chinese, there are few such resources to support Portuguese, and Brazilian Portuguese specifically.
  • 1.2K
  • 29 Apr 2022
Topic Review
Transformer Framework and YOLO Framework for Object Detection
Object detection for remote sensing is a fundamental task in image processing of remote sensing; as one of the core components, small or tiny object detection plays an important role. 
  • 1.2K
  • 25 Aug 2023
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.
  • 1.2K
  • 28 Oct 2020
Topic Review
Stream Classification Algorithms and Architectures
Areas of stream classification are diverse—ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. 
  • 1.2K
  • 30 Nov 2022
Topic Review
Intelligent Fault Diagnosis
For ensuring the safety and reliability of high-speed trains, fault diagnosis (FD) technique plays an important role. Benefiting from the rapid developments of artificial intelligence, intelligent FD (IFD) strategies have obtained much attention in the field of academics and applications, where the qualitative approach is an important branch.
  • 1.2K
  • 14 Jan 2021
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.
  • 1.2K
  • 28 Apr 2021
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
Artificial Neural Networks and Energy Forecasting
Load prediction with higher accuracy and less computing power has become an important problem in the smart grids domain in general and especially in demand-side management (DSM), as it can serve to minimize global warming and better integrate renewable energies. Indeed, artificial neural networks (ANN) are the most used methods in forecasting electrical load. They are widely employed in this field for their numerous advantages. In fact, the complexity of this task is considerable due to several factors/parameters, such as weather and holidays (linear and non-linear relationships), which is a well-suited problem for ANNs and their capacity to deal with non-linear relationships.
  • 1.2K
  • 21 Jun 2022
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