2026 · 10 articles
Original Research Pages 1–20
The following limitations exist in the traditional crop stress detection techniques in the background of smart farming applications: low detection accuracy, inefficient feature extraction, poor adaptability to the environment, and high computational complexity. Stress detection u...
Original Research Pages 21–40
ASR systems have been highly successful in high-resource languages yet less effective in low-resource languages due to limited labeled speech data, speaker variability, background noise, and variable acoustic conditions. The significant barrier to accessing and deploying speech-e...
Original Research Pages 41–65
The current conventional traffic flow prediction methods adopted in Intelligent Traffic Systems (ITS) are severely frustrated by their static model architectures, fixed hyperparameter values, and restricted spatiotemporal learning capabilities, which are unable to model the nonli...
Original Research Pages 66–82
Social media websites generate high-volume, high-velocity data streams that are hard to deal with using traditional data-mining techniques. Most methods for mining frequent and sequential patterns of data currently in use require the whole data set to be reprocessed each time a n...
Original Research Pages 83–101
The classification of brain tumors with traditional deep learning methods is challenging due to its lack of interpretability, computational complexity and its limited classification ability for multimodal medical image data. The influence of clinical diagnosis can impede early de...
Original Research Pages 102–118
In medical imaging, there are many challenges associated with diagnostic systems to detect tumors earlier: the integration of different imaging modalities. Due to the multifactorial nature of tumors, the lack of representation of features and the high proportion of false positive...
Original Research Pages 119–136
The energy consumption is one of the significant challenges of smart grid networks, since it has nonlinear and changing trends, high computational complexity of the conventional forecasting systems, and uncertainty in the data. The existing machine learning and statistical foreca...
Original Research Pages 137–154
Smart grids collect a tremendous amount of energy-use data in various formats from smart meters, distributed sensors, renewable energy systems, and IoT monitoring systems. However, existing pattern mining techniques using deep neural networks are complex, and feature optimization...
Original Research Pages 155–174
The fast pace of the evolution of smart education platforms, it is possible to leverage the interaction data of the learner to create more advanced personalized learning analytics. Current approaches to prediction of student performance and the adaptivity of learning systems are...
Original Research Pages 175–194
Internet of Things (IoT) systems are a major source of vulnerability for large networks, leaving them particularly susceptible to distributed denial of service (DDoS) attacks, botnet growth, spoofing and zero-day attacks. The existing centralized intrusion detection systems (IDSs...
