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Topic Review
Olive Leaf Disease Diagnosis
Artificial intelligence has many applications in various industries, including agriculture. It can help overcome challenges by providing efficient solutions, especially in the early stages of development. When working with tree leaves to identify the type of disease, diseases often show up through changes in leaf color. Therefore, it is crucial to improve the color brightness before using them in intelligent agricultural systems.
  • 222
  • 24 Nov 2023
Topic Review
Facial Expression Image Classification
As emotional states are diverse, simply classifying them through discrete facial expressions has its limitations. Therefore, to create a facial expression recognition system for practical applications, not only must facial expressions be classified, emotional changes must be measured as continuous values.
  • 220
  • 15 Aug 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.
  • 220
  • 11 Dec 2023
Topic Review
Memristor-Based Spiking Artificial Neural Networks
Arrays of memristive devices coupled with photosensors can be used for capturing and processing visual information, thereby realizing the concept of “in-sensor computing”. This is a promising concept associated with the development of compact and low-power machine vision devices, which is crucial important for bionic prostheses of eyes, on-board image recognition systems for unmanned vehicles, computer vision in robotics, etc. This concept can be applied for the creation of a memristor based neuromorphic analog machine vision systems.
  • 220
  • 21 Dec 2023
Topic Review
Software Agent Platforms Available in 2023
Agent systems provide a bottom-up approach to addressing complex tasks. Agents also provide a means of modeling and simulating phenomena that are difficult to model and understand via standard analytical methods. Although the main applications of agent systems pertain to computer science and are often related to artificial intelligence, there are increasingly numerous uses of agents in areas such as the life sciences, ecological sciences, and social sciences. In response to these interests, numerous platforms have been developed as general or application-focused tools. Over time, the field of agent platforms has evolved, with new platforms being developed and others being abandoned.
  • 219
  • 25 Sep 2023
Topic Review
Digital Twins for Industrie 4.0 in Urban Planning
Digital twins are emerging as a prime analysis, prediction, and control concepts for enabling the Industrie 4.0 vision of cyber-physical production systems (CPPSs) as well as for modeling complex tasks such as urban planning. Today’s growing complexity and volatility cannot be handled by monolithic digital twins but require a fundamentally decentralized paradigm of cooperating and competing digital twins.
  • 219
  • 13 Dec 2023
Topic Review
Anomaly Detection in Video Surveillance
The escalating use of security cameras has resulted in a surge in images requiring analysis, a task hindered by the inefficiency and error-prone nature of manual monitoring. Research on anomaly detection in CCTV videos is being actively conducted using various techniques.
  • 219
  • 22 Jan 2024
Topic Review Peer Reviewed
Nuclear Magnetic Resonance and Artificial Intelligence
This review explores the current applications of artificial intelligence (AI) in nuclear magnetic resonance (NMR) spectroscopy, with a particular emphasis on small molecule chemistry. Applications of AI techniques, especially machine learning (ML) and deep learning (DL) in the areas of shift prediction, spectral simulations, spectral processing, structure elucidation, mixture analysis, and metabolomics, are demonstrated. The review also shows where progress is limited.
  • 218
  • 18 Oct 2024
Topic Review
Deep Learning-Based Diagnosis of Chest Diseases
Chest disease refers to a variety of lung disorders, including lung cancer (LC), COVID-19, pneumonia (PNEU), tuberculosis (TB), and numerous other respiratory disorders. The symptoms (i.e., fever, cough, sore throat, etc.) of these chest diseases are similar, which might mislead radiologists and health experts when classifying chest diseases. Chest X-rays (CXR), cough sounds, and computed tomography (CT) scans are utilized by researchers and doctors to identify chest diseases such as LC, COVID-19, PNEU, and TB. 
  • 217
  • 20 Sep 2023
Topic Review
Air-to-Sea Integrated Maritime Internet of Things
Future generation communication systems are exemplified by 5G and 6G wireless technologies, and the utilization of integrated air-to-sea (A2S) communication infrastructure is employed to extend network coverage and enhance data throughput to support data-driven maritime applications. These ground-breaking techniques have promoted the rapid development of the maritime internet of things (MIoT). MIoT is a special kind of IoT system and is very different from the IoT on land in terms of connection methods, device types, data transmission, and so on. The complexity and variability in the marine environment make the construction and application of the MIoT more extensive and has far-reaching significance. 
  • 216
  • 10 Jan 2024
Topic Review
Explainable AI for Health Care
Artificial intelligence (AI) have facilitated its widespread adoption in primary medical services, addressing the demand–supply imbalance in healthcare. Vision Transformers (ViT) have emerged as state-of-the-art computer vision models, benefiting from self-attention modules. However, compared to traditional machine learning approaches, deep learning models are complex and are often treated as a “black box” that can cause uncertainty regarding how they operate. Explainable artificial intelligence (XAI) refers to methods that explain and interpret machine learning models’ inner workings and how they come to decisions, which is especially important in the medical domain to guide healthcare decision-making processes. 
  • 214
  • 18 Jan 2024
Topic Review
Digital Twins for Access Networks
As the complexity and scale of modern networks continue to grow, the need for efficient, secure management, and optimization becomes increasingly vital. Digital twin (DT) technology has emerged as a promising approach to address these challenges by providing a virtual representation of the physical network, enabling analysis, diagnosis, emulation, and control. The emergence of Software-defined network (SDN) has facilitated a holistic view of the network topology, enabling the use of Graph neural network (GNN) as a data-driven technique to solve diverse problems in future networks.
  • 213
  • 06 Dec 2023
Topic Review
Persistence Landscapes for Clustering Noisy IoT Time Series
With the advancement of IoT technologies, there is a large amount of data available from wireless sensor networks (WSN), particularly for studying climate change. Clustering long and noisy time series has become an important research area for analyzing this data.
  • 210
  • 11 Sep 2023
Topic Review
Autonomous Approaches in Multi-Access Edge Computing Networks
The widespread use of technology has made communication technology an indispensable part of daily life. However, the present cloud infrastructure is insufficient to meet the industry’s growing demands, and multi-access edge computing (MEC) has emerged as a solution by providing real-time computation closer to the data source. Effective management of MEC is essential for providing high-quality services, and proactive self-healing is a promising approach that anticipates and executes remedial operations before faults occur.  The term self-healing (SH) is closely associated with autonomous computing (AC), which was introduced at IBM’s event in 2001. The event aimed to develop a system that could manage itself without human intervention to address the management issues arising from the growing computer systems and networks.
  • 209
  • 12 Oct 2023
Topic Review
Occluded Face Recognition
Occlusion in facial photos poses a significant challenge for machine detection and recognition. Consequently, occluded face recognition for camera-captured images has emerged as a prominent and widely discussed topic in computer vision. The standard face recognition methods have achieved remarkable performance in unoccluded face recognition but performed poorly when directly applied to occluded face datasets. The main reason lies in the absence of identity cues caused by occlusions. Therefore, a direct idea of recovering the occluded areas through an inpainting model has been proposed. 
  • 209
  • 26 Jan 2024
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. 
  • 208
  • 11 Dec 2023
Topic Review
Modelling and Measuring Trust in Human–Robot Collaboration
Human–Robot Collaboration (HRC) has emerged as a critical area in the engineering and social sciences domain. In any kind of collaboration, including HRC, trust has been identified as a significant factor that can either motivate or hinder cooperation, especially in scenarios characterized by incomplete or uncertain information.
  • 207
  • 08 Mar 2024
Topic Review
Green Energy Transportation Systems on Urban Air Quality
Transitioning to green energy transport systems, notably electric vehicles, is crucial to both combat climate change and enhance urban air quality in developing nations. Urban air quality is pivotal, given its impact on health, necessitating accurate pollutant forecasting and emission reduction strategies to ensure overall well-being. This study forecasts the influence of green energy transport systems on the air quality in Lahore and Islamabad, Pakistan, while noting the projected surge in electric vehicle adoption from less than 1% to 10% within three years. Predicting the impact of this change involves analyzing data before, during, and after the COVID-19 pandemic. The lockdown led to minimal fossil fuel vehicle usage, resembling a green energy transportation scenario. The novelty of this work is twofold. Firstly, remote sensing data from the Sentinel-5P satellite were utilized to predict air quality index (AQI) trends before, during, and after COVID-19. Secondly, deep learning models, including long short-term memory (LSTM) and bidirectional LSTM, and machine learning models, including decision tree and random forest regression, were utilized to forecast the levels of NO22, SO22, and CO in the atmosphere. Our results demonstrate that implementing green energy transportation systems in urban centers of developing countries can enhance air quality by approximately 98%. Notably, the bidirectional LSTM model outperformed others in predicting NO22 and SO22 concentrations, while the LSTM model excelled in forecasting CO concentration. These results offer valuable insights into predicting air pollution levels and guiding green energy policies to mitigate the adverse health effects of air pollution.
  • 206
  • 21 Sep 2023
Topic Review
Deep Learning and Neural Networks in Decision Making
The term “deep learning” encompasses the challenge of adjusting the link weights within a deep neural network to achieve a desired input–output mapping. Deep neural networks are computational systems comprising units that resemble neurons and are connected through synapse-like connections. These units transmit scalar values akin to spike rates, which are determined by the total of their inputs or the activity of preceding units multiplied by the strength of the transmitting synapse. One of the formidable challenges faced by scholars and professionals is making decisions based on massive data while considering multiple criteria. The integration of deep learning algorithms into current approaches for handling large datasets has enabled the development of more intelligent decision support systems.
  • 206
  • 07 Oct 2023
Topic Review
Sensor-Based Gesture Recognition and Algorithm
Traditional vision-based gesture recognition technology has matured, it has significant limitations in underwater environments. The cost of underwater cameras is high, the underwater shooting environment is complex, and it is very easy to be disturbed by water flow, water bubbles, etc., which hinder the line of sight and make shooting difficult. Sensor-based gesture recognition technology has become popular for underwater gesture recognition because of its lower cost and higher stability (not easily affected by the underwater environment).
  • 206
  • 12 Nov 2023
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