A digital twin-driven multi-agent deep reinforcement learning framework for synergistic resource scheduling in revolutionary heritage and sports tourism integration

An explainable AI framework integrating machine and deep learning models for multi-species DNA functional group classification

Pratik Chakraborty, P. B. Shanthi

Abstract DNA functional group classification across species plays a crucial role in understanding genetic diversity, evolutionary relationships and biological function. The increasing availability of genomic data has led to the use of machine learning and deep learning methods fo…Read more

Deep reinforcement learning-based resource recommendation system for ideological and political education

Zixuan Song, Lianxin Geng, Xiao Wang, Xiaoke Gong

In the context of developments in digital education and artificial intelligence technologies, ideological and political education (IPE) scenarios (e.g., university teaching, enterprise training, and community practice) generate growing demands for accurate resource recommendation…Read more

AI-driven digital twinning quantifies the trade-off between flood conveyance and riparian stability under climate extremes

Jae hun Shin, Seung Hwan Go, Jong Hwa Park

Anthropogenic climate change amplifies hydrological extremes, challenging flood models reliant on static hydraulic assumptions. Riparian vegetation is essential for ecological resilience and bank stability but is often removed to maximize flood conveyance. To quantify this trade-…Read more

Transparent deep learning and data-driven visual intelligence framework for robust crop recommendation in precision agriculture

B. Dhiyanesh, Parveen Begam Abdul Kareem, P. Shanmugaraja, V. Anusuya, A Baseera

A hybrid explainable deep learning framework for blood cancer classification using CNN-based feature embeddings and random forest decision models

Zulfikar Ali Ansari, Hemlata Pant, Nayancy, M. N. V. Kiranbabu, Sanjeet Kumar

The precision and early detection of subtypes of acute lymphoblastic leukaemia (ALL) in peripheral blood smear images are crucial for efficient clinical practice. Traditional deep learning methods tend to be challenging in terms of model interpretation and are often reliant on la…Read more

Hybrid deep learning framework for detection of lateral movement in advanced persistent threats

Balaganesh Bojarajulu, M. Sethumadhavan, Vasily Sachnev, P. P. Amritha

Advanced persistent threats often use stolen or legitimate credentials to move stealthily across networks, making them difficult to detect with traditional security systems. This study introduces a novel framework called dual classifier-based lateral movement detection (DC-LMD-AP…Read more