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Topic Review
Edge-Guided Multimodal Transformers Change Detection
Change detection from heterogeneous satellite and aerial images plays a progressively important role in many fields, including disaster assessment, urban construction, and land use monitoring. Researchers have mainly devoted their attention to change detection using homologous image pairs and achieved many remarkable results. It is sometimes necessary to use heterogeneous images for change detection in practical scenarios due to missing images, emergency situations, and cloud and fog occlusion.
  • 103
  • 05 Mar 2024
Topic Review
Artificial Intelligence and Sustainability
Artificial intelligence has undergone transformative advancements, reshaping diverse sectors such as healthcare, transport, agriculture, energy, and the media. Despite the enthusiasm surrounding AI’s potential, concerns persist about its potential negative impacts, including substantial energy consumption and ethical challenges. This Systematic Mapping Study (SMS) study accomplishes a comprehensive analysis of "AI Sustainability," integrating both the sustainability of AI and AI for sustainability across environmental, social, and economic dimensions. The field exhibits a dynamic landscape, maturing significantly since 2019 with a surge in publications and diverse contributions. The study reveals a balanced perspective, emphasizing both sustainability perspectives equally. Recent papers indicate a trend towards holistic studies, yet the economic dimension remains relatively underexplored. Future research is encouraged to delve into the economic dimension, align with the United Nations’ Sustainable Development Goals (SDGs), and address stakeholder influence, ensuring a sustainable and inclusive AI future.
  • 158
  • 05 Mar 2024
Topic Review
Merging Ontologies and Data from Electronic Health Records
The Electronic Health Record (EHR) is a system for collecting and storing patient medical records as data that can be mechanically accessed, hence facilitating and assisting the medical decision-making process. EHRs exist in several formats, and each format lists thousands of keywords to classify patients data. The keywords are specific and are medical jargon; hence, data classification is very accurate. As the keywords constituting the formats of medical records express concepts by means of specific jargon without definitions or references, their proper use is left to clinicians and could be affected by their background, hence the interpretation of data could become slow or less accurate than that desired.
  • 150
  • 05 Mar 2024
Topic Review
Deep Learning for Alzheimer’s Disease Detection
Deep learning has become a prominent approach in Alzheimer’s disease (AD) detection using medical image data, incorporating modalities like positron emission tomography (PET) and magnetic resonance imaging (MRI). These advances in deep learning and multimodal imaging have improved AD detection accuracy and effectiveness, leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative modelling techniques. 
  • 101
  • 05 Mar 2024
Topic Review
Brain Tumor  Segmentation
Brain tumor segmentation plays a crucial role in the diagnosis, treatment planning, and monitoring of brain tumors. Accurate segmentation of brain tumor regions from multi-sequence magnetic resonance imaging (MRI) data is of paramount importance for precise tumor analysis and subsequent clinical decision making. The ability to delineate tumor boundaries in MRI scans enables radiologists and clinicians to assess tumor size, location, and heterogeneity, facilitating treatment planning and evaluating treatment response. Traditional manual segmentation methods are time-consuming, subjective, and prone to inter-observer variability. Therefore, the automatic segmentation algorithm has received widespread attention as an alternative solution. For instance, the self-organizing map (SOM) is an unsupervised exploratory data analysis tool that leverages principles of vector quantization and similarity measurement to automatically partition images into self-similar regions or clusters. Segmentation methods based on SOM have demonstrated the ability to distinguish high-level and low-level features of tumors, edema, necrosis, cerebrospinal fluid, and healthy tissue.
  • 145
  • 04 Mar 2024
Topic Review
Decision Support Systems in Forestry and Tree-Planting Practices
Using deep neural networks (DNNs), a decision support system (DSS) can be trained to learn from a large dataset of tree data, including information about tree species, climate, soil conditions, and other factors that influence tree growth and survival. This is because the use of neural networks was proposed three decades ago to solve forest management problems by integrating forest knowledge with artificial intelligence (AI). AI greatly benefits sustainability and the preservation of ecosystem values, as increasing disruptions in a changing world can only be managed beyond human intelligence. Furthermore, despite the various DSSs and AI systems used, the appointment of appropriate project managers is crucial to the execution and subsequent success of a project.
  • 281
  • 04 Mar 2024
Topic Review
INtra-INter Spectral Attention Network for Pedestrian Detection
Pedestrian detection is a critical task for safety-critical systems, but detecting pedestrians is challenging in low-light and adverse weather conditions. Thermal images can be used to improve robustness by providing complementary information to RGB images.
  • 131
  • 04 Mar 2024
Topic Review
Methods Based on Software-Defined Networks
With the rapid advancement of the Internet of Things (IoT), there is a global surge in network traffic. Software-Defined Networks (SDNs) provide a holistic network perspective, facilitating software-based traffic analysis, and are more suitable to handle dynamic loads than a traditional network. The standard SDN architecture control plane has been designed for a single controller or multiple distributed controllers; however, a logically centralized single controller faces severe bottleneck issues. 
  • 119
  • 04 Mar 2024
Topic Review
OptiDJS+
The continuously evolving world of cloud computing presents new challenges in resource allocation as dispersed systems struggle with overloaded conditions. In this regard, OptiDJS+ is a cutting-edge enhanced dynamic Johnson sequencing algorithm made to successfully handle resource scheduling challenges in cloud computing settings. With a solid foundation in the dynamic Johnson sequencing algorithm, OptiDJS+ builds upon it to suit the demands of modern cloud infrastructures. OptiDJS+ makes use of sophisticated optimization algorithms, heuristic approaches, and adaptive mechanisms to improve resource allocation, workload distribution, and task scheduling. To obtain the best performance, this strategy uses historical data, dynamic resource reconfiguration, and adaptation to changing workloads. 
  • 234
  • 01 Mar 2024
Topic Review
New Technologies-Based Physical Unclonable Functions
A physical unclonable function (PUF) is a technology designed to safeguard sensitive information and ensure data security. PUFs generate unique responses for each challenge by leveraging random deviations in the physical microstructures of integrated circuits (ICs), making it incredibly difficult to replicate them. However, traditional silicon PUFs are now susceptible to various attacks, such as modeling attacks using conventional machine learning techniques and reverse engineering strategies. As a result, PUFs based on new materials or methods are being developed to enhance their security.
  • 161
  • 01 Mar 2024
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