International Journal of Computational Intelligence Systems

Exercise recommendation is an integral part of enabling personalized learning. Giving appropriate exercises can facilitate learning for learners. The programming problem recommendation is a specific application of the exercise recommendation. Therefore, an innovative recommendation framework for programming problems that integrate learners’ learning styles is proposed. In addition, there are some difficulties to be solved in this framework, such as quantifying learning behavior, representing programming problems, and quantifying learning strategies. For the difficulties in quantifying learning behavior and quantifying learning strategies, a programming problem recommendation algorithm based on deep reinforcement learning (DRLP) is proposed. DRLP includes the specific design of action space, action-value Q-network, and reward function. Learning style is embedded into DRLP through action space to make recommendations more personalized. To represent the programming problem in DRLP, a multi-dimensional integrated programming problem representation model is proposed to quantify the difficulty feature, knowledge point feature, text description, input description, and output description of programming problems. In particular, Bi-GRU is introduced to learn texts’ contextual semantic association information from both positive and negative directions. Finally, a simulation experiment is carried out with the actual learning behavior data of 47,147 learners in the LUOGU Online Judge system. Compared with the optimal baseline model, the recommendation effect of DRLP has improved (HR, MRR, and Novelty have increased by 4.35%, 1.15%, and 1.1%), which proves the rationality of the programming problem representation model and action-value Q-network.

Colorectal cancer ranks third in global malignancy incidence, and serrated adenoma is a precursor to colon cancer. However, current studies primarily focus on polyp detection, neglecting the crucial discrimination of polyp nature, hindering effective cancer prevention. This study established a static image dataset for serrated adenoma (SA) and developed a deep learning SA detection model. The proposed MSSDet (Multi-Scale Sub-pixel Detection) innovatively modifies each layer of the original feature pyramid’s structure to retain high-resolution polyp features. Additionally, feature fusion and optimization modules were incorporated to enhance multi-scale information utilization, leveraging the narrow-band imaging endoscope’s ability to provide clearer colonoscopy capillary and texture images. This paper utilized 639 cases of colonic NBI endoscopic images to construct the model, achieving a mean average precision (mAP) of 86.3% for SA in the test set. The SA detection rate via this approach has significantly surpassed conventional object detection methods.