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Topic Review
Input Enhancement (Computer Science)
In computer science, input enhancement is the principle that processing a given input to a problem and altering it in a specific way will increase runtime efficiency or space efficiency, or both. The altered input is usually stored and accessed to simplify the problem. By exploiting the structure and properties of the inputs, input enhancement creates various speed-ups in the efficiency of the algorithm.
  • 618
  • 02 Dec 2022
Topic Review
Detection of Suspicious Behaviors from Movement Trajectory Data
Early detection of people’s suspicious behaviors can aid in the prevention of crimes and make the community safer. Existing methods are mostly focused on identifying abnormal behaviors from video surveillance that are based on computer vision, which are more suitable for detecting ongoing behaviors.  Due to advances in positioning technology and the increasing number of cameras, smart mobile terminals, and WLAN networks, large amounts of fine-grained personal trajectory data are collected. Such a large number of trajectories provide people with an unprecedented opportunity to automatically discover helpful knowledge, such as identifying suspicious movements and unusual activities. Therefore, crimes can be prevented if people’s suspicious behaviors can be automatically detected by mining the semantic information that is hidden in the trajectory data.
  • 617
  • 23 Sep 2022
Topic Review
Opium Poppy Detection in Unmanned Aerial Vehicle Imagery
Opium poppy is a medicinal plant, and its cultivation is illegal without legal approval in China. Unmanned aerial vehicle (UAV) is an effective tool for monitoring illegal poppy cultivation. Unmanned aerial vehicle (UAV) is more flexible and mobile than remote sensing satellite, and their high-resolution images can help to detect poppies in areas that are hard to see. 
  • 616
  • 18 Feb 2024
Topic Review
Healthcare Sustainability: Hospitalization Rate Forecasting
Monitoring and forecasting hospitalization rates are of essential significance to public health systems in understanding and managing overall healthcare deliveries and strategizing long-term sustainability. Early-stage prediction of hospitalization rates is crucial to meet the medical needs of numerous patients during emerging epidemic diseases such as COVID-19. Nevertheless, this is a challenging task due to insufficient data and experience. In addition, relevant existing work neglects or fails to exploit the extensive contribution of external factors such as news, policies, and geolocations. Herein, researchers demonstrate the significant relationship between hospitalization rates and COVID-19 infection cases. A transfer learning architecture with dynamic location-aware sentiment and semantic analysis (TLSS) is adapted to a new application scenario: hospitalization rate prediction during COVID-19. This architecture learns and transfers general transmission patterns of existing epidemic diseases to predict hospitalization rates during COVID-19. Researchers combine the learned knowledge with time series features and news sentiment and semantic features in a dynamic propagation process. Extensive experiments are conducted to compare the proposed approach with several state-of-the-art machine learning methods with different lead times of ground truth. The results show that TLSS exhibits outstanding predictive performance for hospitalization rates. Thus, it provides advanced artificial intelligence (AI) techniques for supporting decision-making in healthcare sustainability.
  • 615
  • 24 Nov 2023
Topic Review
Compensation of Pressure Sensor Drifts
Pressure sensor chips embodied in very tiny packages are deployed in a wide range of advanced applications. Examples of them range from industrial to altitude location services. They are also becoming increasingly pervasive in many other fields, ranging from industrial to military to consumer. However, these sensors, which are very cheap to manufacture in silicon, are strongly affected by thermal, mechanical and environmental stresses, which ultimately affect their measurement accuracy in the form of variations in gain, hysteresis, and nonlinear responses. To compensate induced drift in measurements, several neural networks were devised and be applied to stresses caused by two thermal cycles: 260 C for 10-40 seconds (JEDEC soldering procedure) and 100 C for two hours. These models were characterized in accuracy and deployability on tiny embedded devices and improved accuracy was observed.
  • 615
  • 08 Dec 2023
Topic Review
Types of Dimension Reduction Techniques
Hybridization is the most widely used modification technique for the dimension reduction problem. There are three types of hybridization: integrating a nature-inspired algorithm with another nature-inspired algorithm, integrating a nature-inspired algorithm with a classifier, and integrating a nature-inspired algorithm with filter or extraction techniques.
  • 614
  • 17 Mar 2023
Topic Review
Deep Learning Architectures for Multivariate Time-Series Forecasting
Deep learning algorithms, renowned for their ability to extract intricate patterns from complex datasets, have proven particularly adept at handling the multifaceted time-series data characteristic of smart city IoT applications. Deep learning architectures model complex relationships through a series of nonlinear layers—the set of nodes of each intermediate layer capturing the corresponding feature representation of the input.
  • 611
  • 27 Oct 2023
Topic Review
Deep Learning Models in Video Deepfake Detection
The increasing use of deep learning techniques to manipulate images and videos, commonly referred to as “deepfakes”, is making it more challenging to differentiate between real and fake content, while various deepfake detection systems have been developed, they often struggle to detect deepfakes in real-world situations. In particular, these methods are often unable to effectively distinguish images or videos when these are modified using novel techniques which have not been used in the training set.
  • 611
  • 22 May 2023
Topic Review
Intelligence Edge Computing
Intelligence Edge Computing (IEC) is the key enabler of emerging 5G technologies networks and beyond. IEC is considered to be a promising backbone of future services and wireless communication systems in 5G integration. In addition, IEC enables various use cases and applications, including autonomous vehicles, augmented and virtual reality, big data analytic, and other customer-oriented services. 
  • 610
  • 23 Jun 2021
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. 
  • 610
  • 26 Sep 2023
Topic Review
Vision-Based Pose Estimation of Non-Cooperative Target
In the realm of non-cooperative space security and on-orbit service, a significant challenge is accurately determining the pose of abandoned satellites using imaging sensors. Traditional methods for estimating the position of the target encounter problems with stray light interference in space, leading to inaccurate results.
  • 610
  • 18 Dec 2023
Topic Review
Remote Sensing Applications in Almond Orchards
Almond cultivation is of great socio-economic importance worldwide. With the demand for almonds steadily increasing due to their nutritional value and versatility, optimizing the management of almond orchards becomes crucial to promote sustainable agriculture and ensure food security.
  • 610
  • 29 Feb 2024
Topic Review
Network Slicing
5G networks have been experiencing challenges in handling the heterogeneity and influx of user requests brought upon by the constant emergence of various services. As such, network slicing is considered one of the critical technologies for improving the performance of 5G networks. This technology has shown great potential for enhancing network scalability and dynamic service provisioning through the effective allocation of network resources. 
  • 609
  • 07 Jul 2022
Topic Review
Knowledge Graph Entity Alignment
The objective of the entity alignment (EA) task is to identify entities with identical semantics across distinct knowledge graphs (KGs) situated in the real world, which has garnered extensive recognition in both academic and industrial circles.
  • 609
  • 31 May 2023
Topic Review
Deep-Learning-Based Approach to Keystroke-Injection Payload Generation
USB-based keystroke-injection attacks involve manipulating USB devices to inject malicious keystrokes into the target system. These attacks exploit the trust of USB devices and can bypass traditional security measures if they are taken into account and adapted to the systems designed to prevent such attacks. By impersonating a keyboard or using programmable USB devices, attackers can execute unauthorized commands or gain unauthorized access to sensitive information mimicking legitimate user keystrokes. Different attack vectors, such as BadUSB and rogue device attacks, have drawn attention to the potential risks and ramifications involved in these types of attacks. However, the emergence of advanced attack methods necessitates the development of more sophisticated countermeasures. These attacks pose a significant security risk and highlight the importance of implementing strong defenses to mitigate the potential impact of such exploits, especially those that can bypass keystroke dynamics systems using rogue USB devices with implants.
  • 609
  • 17 Jul 2023
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.
  • 609
  • 29 Dec 2023
Topic Review
Development of AI in Surgery after SARS-CoV-2 Pandemic
SARS-CoV-2 has significantly transformed the healthcare environment, and it has triggered the development of electronic health and artificial intelligence mechanisms, for instance. 
  • 608
  • 04 Nov 2021
Topic Review
AI and Neural Network Algorithms
Al increases the potential of Micro-Electro-Mechanical System biosensors and opens up new opportunities for automation, consumer electronics, industrial manufacturing, defense, medical equipment, etc. Micro-Electro-Mechanical System microcantilever biosensors are currently making their way into the daily lives and playing a significant role in the advancement of social technology. Micro-Electro-Mechanical System biosensors with microcantilever structures have a num- ber of benefits over conventional biosensors, including small size, high sensitivity, mass production, simple arraying, integration, etc. These advantages have made them one of the development avenues for high-sensitivity sensors. The next generation of sensors will exhibit an intelligent development trajectory and aid people in interacting with other objects in a variety of scenario applications as a result of the active development of artificial intelligence (AI) and neural networks. A neural algorithm application in Micro-Electro-Mechanical System microcantilever biosensors is anticipated through the associated application of the principal com-ponent analysis approach. Researchers investigation has more scientific study value, because there are currently no favorable reports on the market regarding the use of AI with Micro-Electro-Mechanical System microcantilever sensors.
  • 607
  • 13 Sep 2022
Topic Review
Simulate Gene Expression and Infer Gene Regulatory Networks
The ability to simulate gene expression and infer gene regulatory networks has vast potential applications in various fields, including medicine, agriculture, and environmental science. Machine learning approaches to simulate gene expression and infer gene regulatory networks have gained significant attention as a promising area of research.
  • 606
  • 30 Aug 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.
  • 602
  • 15 Aug 2023
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