A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning

Quadratic Unconstrained Binary Optimization (QUBO) is a generic technique to model various NP-hard combinatorial optimization problems in the form of binary variables. The Hamiltonian function is often used to formulate QUBO problems where it is used as the objective function in the context of optimization. Recently, PI-GNN, a generic scalable framework, has been proposed to address the Combinatorial Optimization (CO) problems over graphs based on a simple Graph Neural Network (GNN) architecture. Their novel contribution was a generic QUBO-formulated Hamiltonian-inspired loss function that was optimized using GNN. In this study, we address a crucial issue related to the aforementioned setup especially observed in denser graphs. The reinforcement learning-based paradigm has also been widely used to address numerous CO problems. Here we also formulate and empirically evaluate the compatibility of the QUBO-formulated Hamiltonian as the generic reward function in the Reinforcement Learning paradigm to directly integrate the actual node projection status during training as the form of rewards. In our experiments, we observed up to 44% improvement in the RL-based setup compared to the PI-GNN algorithm. Our implementation can be found in https://github.com/rizveeredwan/learning-graph-structure

LeafNet: A proficient convolutional neural network for detecting seven prominent mango leaf diseases

Fruit production plays a significant role in meeting nutritional needs and contributing to the lessening of the global food crisis. Plant diseases are quite a common phenomenon that hampers gross production and causes huge losses for farmers in tropical South Asian weather conditions. In context, early-stage detection of plant disease is essential for healthy production. This research develops LeafNet, a convolutional neural network (CNN)-based approach to detect seven of the most common diseases of mango using images of the leaves. This model is trained specially for the pattern of mango diseases in Bangladesh using a novel dataset of region-specific images and is classified for almost all highly available mango diseases. The performance of LeafNet is evaluated with an average accuracy, precision, recall, F-score, and specificity of 98.55%, 99.508%, 99.45%, 99.47%, and 99.878%, respectively, in a 5-fold cross-validation that is higher than the state-of-the-art models like AlexNet and VGG16. LeafNet can be helpful in the detection of early symptoms of diseases, ultimately leading to a higher production of mangoes and contributing to the national economy