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Topic Review
Food Image Dataset and Segmentation Model
The development of vision-based dietary assessment (VBDA) systems. These systems generally consist of three main stages: food image analysis, portion estimation, and nutrient derivation. The effectiveness of the initial step is highly dependent on the use of accurate segmentation and image recognition models and the availability of high-quality training datasets. Food image segmentation still faces various challenges, and most existing research focuses mainly on Asian and Western food images. 
  • 396
  • 09 Jan 2024
Topic Review
Attack Investigation
Attack investigation is an important research field in forensics analysis. Many existing supervised attack investigation methods rely on well-labeled data for effective training. While the unsupervised approach based on BERT can mitigate the issues, the high degree of similarity between certain real-world attacks and normal behaviors makes it challenging to accurately identify disguised attacks.
  • 110
  • 08 Jan 2024
Topic Review
Single-Agent Reinforcement Learning and Multi-Agent Reinforcement Learning
Flexible job shop scheduling (FJSP) is regarded as an effective measure to deal with the challenge of mass personalized and customized manufacturing in the era of Industry 4.0, and is widely extended to many real applications. Single-Agent Reinforcement Learning (SARL) is the algorithm only contains one agent that makes all the decisions for a control system. Multi-Agent Reinforcement Learning (MARL) is the algorithm comprises multiple agents that interact with the environment through their respective policies.
  • 234
  • 08 Jan 2024
Topic Review
Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
Software-defined networks (SDNs) are becoming increasingly popular due to their centralized control and flexibility, but this also makes them a target for cyberattacks. Detecting DoS/DDoS attacks in SDNs is a challenging task due to the complex nature of the network traffic.
  • 156
  • 08 Jan 2024
Topic Review
Breast Cancer Diagnosis Based on Deep Mutual Learning
Breast cancer (BC) is the most common kind of cancer in women, accounting for around 30% of all new cancer diagnoses; it is also the second most fatal malignancy after lung and bronchial cancers. Centered on deep convolutional neural networks, a new BC histopathological image category blind inpainting convolutional neural network (BiCNN) model has been developed. It was developed to cope with the two-class categorization of BC on the diagnostic image.
  • 165
  • 05 Jan 2024
Topic Review
Sustainability in the Textile and Clothing Value Chain
Textile and clothing is one of the most important industrial sectors, not only due to the significant number of jobs generated, but also because it addresses one of the people’s fundamental needs (clothing). It is, however, a sector with a huge global environmental impact, and also an important negative social impact, especially in developing countries. Sustainability in the textile and clothing value chain is a known issue, concerning both environmental and economic-social facets of sustainability. One way to improve sustainability in this sector is by measuring and monitoring the environmental, economic and social impacts of activities along the value chain and, ultimately, computing an environmental and circular score for each batch of textile and clothing product, and an economic and social score for each involved company, reflected in their products. The consumer will then have the opportunity and responsibility for selecting products with the least negative environmental, economic and social impact.
  • 243
  • 05 Jan 2024
Topic Review
LiDAR Local Domain Adaptation for Autonomous Vehicles
Perception algorithms for autonomous vehicles demand large, labeled datasets. Real-world data acquisition and annotation costs are high, making synthetic data from simulation a cost-effective option. However, training on one source domain and testing on a target domain can cause a domain shift attributed to local structure differences, resulting in a decrease in the model’s performance. Domain adaptation is a form of transfer learning that aims to minimize the domain shift between datasets.
  • 204
  • 05 Jan 2024
Topic Review
Blockchain-Based E-Voting Systems
The employment of blockchain technology in electronic voting (e-voting) systems is attracting significant attention due to its ability to enhance transparency, security, and integrity in digital voting. Blockchain technology has been recognized as a potential solution for secure and transparent e-voting systems. By leveraging the decentralization, immutability, and transparency of blockchain technology, e-voting systems can prevent fraud and manipulation, improve voter anonymity, and increase trust in the electoral process. Moreover, blockchain-based e-voting systems can reduce the cost and time associated with traditional voting systems.
  • 280
  • 03 Jan 2024
Topic Review
Cloud-Based Platforms for Health Monitoring
The main objective of the most popular healthcare platforms, including Apple Health, Google Fit, Samsung Health, and Fitbit, is to provide a well-coordinated, personalized, and satisfying user experience while reducing the overall cost of care. Health platforms can improve the health status of the population by fostering collaboration and integration among the various stakeholders in the health sector. Successful efforts to improve population health require that stakeholders are open and willing to leverage each other’s assets, such as data, skills, and resources.
  • 178
  • 03 Jan 2024
Topic Review
State-of the-Art Constraint-Based Modeling of Microbial Metabolism
Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. 
  • 272
  • 03 Jan 2024
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