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Topic Review
Machine Learning for Crop Diseases and Pests
Rapid population growth has resulted in an increased demand for agricultural goods. Pests and diseases are major obstacles to achieving this productivity outcome. Therefore, it is very important to develop efficient methods for the automatic detection, identification, and prediction of pests and diseases in agricultural crops. To perform such automation, Machine Learning (ML) techniques can be used to derive knowledge and relationships from the data that is being worked on. 
  • 706
  • 16 Sep 2022
Topic Review
Hybrid Digital Food Twin
Food production is highly complex due to the various chemo-physical and biological processes that must be controlled to transform ingredients into final products. Further, production processes must be adapted to the variability of the ingredients, e.g., due to seasonal fluctuations of raw material quality. Digital twins are known from Industry 4.0 as a method to model, simulate, and optimize processes. Such a digital food twin has to consider the changes within the food due to micro-biological, chemical, and physical processes. Consequently, researchers propose the concept of a hybrid digital twin, which integrates simulation and data science (i.e., machine learning) to combine a data-driven perspective, simulations, and scientific models to describe the food product and the food processing process. 
  • 475
  • 16 Sep 2022
Topic Review
Segmentation of Liver Tumor in Computed Tomography Scan
Segmentation of images is a common task within medical image analysis and a necessary component of medical image segmentation. The segmentation of the liver and liver tumors is an important but challenging stage in screening and diagnosing liver diseases. Many automated techniques have been developed for liver and tumor segmentation; however, segmentation of the liver is still challenging due to the fuzzy & complex background of the liver position with other organs. As a result, creating a considerable automated liver and tumour division from computed tomography (CT) scans is critical for identifying liver cancer.
  • 659
  • 15 Sep 2022
Topic Review
YOLOv5-AC
Attention Mechanism-Based Lightweight YOLOv5 for Track Pedestrian Detection. In response to the dangerous behavior of pedestrians roaming freely on unsupervised train tracks, the real-time detection of pedestrians is urgently required to ensure the safety of trains and people. Aiming to improve the low accuracy of railway pedestrian detection, the high missed-detection rate of target pedestrians, and the poor retention of non-redundant boxes, YOLOv5 is adopted as the baseline to improve the effectiveness of pedestrian detection by model pruning, improving attention mechanism, etc.
  • 2.5K
  • 15 Sep 2022
Topic Review
Path-Planning Approaches for Multiple Mobile Robots
Numerous path-planning studies have been conducted due to the challenges of obtaining optimal solutions. The multi-robot path-planning approaches have been classified as classical approaches, heuristic algorithms, bio-inspired techniques, and artificial intelligence approaches. Bio-inspired techniques are the most employed approaches, and artificial intelligence approaches have gained more attention. 
  • 708
  • 15 Sep 2022
Topic Review
MAC-Based Physical Layer Security over Wireless Sensor Network
Physical layer security for wireless sensor networks (WSNs) is a laborious and highly critical issue in the world. Wireless sensor networks have great importance in civil and military fields or applications. Security of data/information through wireless medium remains a challenge. The data that transmit wirelessly has increased the speed of transmission rate. In physical layer security, the data transfer between source and destination is not confidential, and thus the user has privacy issues, which is why improving the security of wireless sensor networks is a prime concern. The loss of physical security causes a great threat to a network. 
  • 712
  • 15 Sep 2022
Topic Review
Smart City Infrastructure Threat Modelling Methodologies
Smart city infrastructure and the related theme of critical national infrastructure have attracted growing interest in recent years in academic literature, notably how cyber-security can be effectively applied within the environment, which involves using cyber-physical systems. These operate cross-domain and have massively improved functionality and complexity, especially in threat modelling cyber-security analysis—the disparity between current cyber-security proficiency and the requirements for an effective cyber-security systems implementation.
  • 357
  • 14 Sep 2022
Topic Review
Surface Defect Detection of Strip-Steel
Surface-defect detection is crucial for assuring the quality of strip-steel manufacturing. Strip-steel surface-defect detection requires defect classification and precision localization, which is a challenge in real-world applications.
  • 530
  • 14 Sep 2022
Topic Review
The Taxonomy of Malware Analysis and Detection Approaches
The evolution of recent malicious software with the rising use of digital services has increased the probability of corrupting data, stealing information, or other cybercrimes by malware attacks. Therefore, malicious software must be detected before it impacts a large number of computers. While malware analysis is taxonomy and linked to the data types that are used with each analysis approach, malware detection is introduced with a deep taxonomy where each known detection approach is presented in subcategories and the relationship between each introduced detection subcategory and the data types that are utilized is determined. 
  • 1.5K
  • 14 Sep 2022
Topic Review
Cancer Metastasis Detection via Effective Contrastive Learning
The metastasis detection in lymph nodes via microscopic examination of H&E stained histopathological images is one of the most crucial diagnostic procedures for breast cancer staging. The manual analysis is extremely labor-intensive and time-consuming because of complexities and diversities of histopathological images. Deep learning has been utilized in automatic cancer metastasis detection in recent years. The success of supervised deep learning is credited to a large labeled dataset, which is hard to obtain in medical image analysis. Contrastive learning, a branch of self-supervised learning, can help in this aspect through introducing an advanced strategy to learn discriminative feature representations from unlabeled images.
  • 438
  • 13 Sep 2022
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