Hypergraph Learning: From Algorithms to Applications

Graphs are a general language for describing and modeling interconnected systems. To learn graph data, Graph Neural Networks (GNNs) have been introduced. However, traditional graph data structures often fall short of describing the higher-order complex relationships within these systems. Hypergraphs, with their natural ability to capture such higher-order relations, offer a promising alternative. Despite their potential, GNNs are inherently designed for simple graphs and do not extend naturally to hypergraphs, leaving a gap in effectively leveraging hypergraph structures. To address this gap, Hypergraph Neural Networks (HyperGNNs) have been proposed. HyperGNNs offer enhanced capabilities to learn higher-order complex relationships beyond the scope of traditional GNNs. However, despite their potential, there remains a gap in effectively leveraging HyperGNNs for complex real-world problems due to limitations in current methodologies and applications. This dissertation aims to bridge this gap by developing and presenting new models for HyperGNN and examining their applications in real-world challenges. This work is built upon four pivotal studies, each emphasizing the development of novel HyperGNNs to tackle complex issues, especially in biomedical contexts, while also advancing methodologies to achieve superior outcomes

A systematic assertive wide-band routing using location and potential aware technique

Delays occur when packets must be routed over several paths in a wireless sensor network with multiple origins and destinations. There are several causes, delays may occur everywhere, even in a multi-hop wireless network. Due to the broadcast nature of wireless networks, opportunistic routing was able to circumvent these delays. To avoid unnecessary delays, wide-band routing may be used to calculate the smaller path between two nodes. In this case, we address the shortcomings of the standard approach by taking into account the node's power. Path routing as well as the broadcast nature of wireless signals help mitigate the effects of shoddy wireless connections. The results show that the suggested approach outperformed the baseline in both end-to-end latency and packet delivery ratio

Integrated energy-efficient and location-aware routing in wireless sensor networks

Sensor nodes in wireless sensor networks are commonly distributed randomly across a given landscape, and their placement may be randomized for specific applications, even extending to national deployments. The energy consumption associated with data transmission and reception by the cluster’s leader is notably higher compared to other nodes. To address this issue, it is recommended that wireless sensor networks adopt a more energy-efficient routing technique. This proposed technique assumes a spatial separation between different node types. Elevating the threshold enhances the likelihood that nodes with ample remaining power will endure as cluster leaders. Ultimately, a hybrid data transfer strategy is formulated, wherein data is directly exchanged between the base station and cluster heads among the super nodes containing advanced nodes. Most nodes employ a combination of single-hop and multi-hop approaches for data transport, aiming to minimize the power required for transmission between the cluster’s control node and the base station. According to simulation results, this proposed method surpasses the stable election protocol (SEP), demonstrating superiority over the improved threshold-sensitive stable election protocol in terms of the operational duration of a wireless sensor network

DDI Prediction via Heterogeneous Graph Attention Networks

Polypharmacy, defined as the use of multiple drugs together, is a standard treatment method, especially for severe and chronic diseases. However, using multiple drugs together may cause interactions between drugs. Drug-drug interaction (DDI) is the activity that occurs when the impact of one drug changes when combined with another. DDIs may obstruct, increase, or decrease the intended effect of either drug or, in the worst-case scenario, create adverse side effects. While it is critical to detect DDIs on time, it is timeconsuming and expensive to identify them in clinical trials due to their short duration and many possible drug pairs to be considered for testing. As a result, computational methods are needed for predicting DDIs. In this paper, we present a novel heterogeneous graph attention model, HAN-DDI to predict drug-drug interactions. We create a heterogeneous network of drugs with different biological entities. Then, we develop a heterogeneous graph attention network to learn DDIs using relations of drugs with other entities. It consists of an attention-based heterogeneous graph node encoder for obtaining drug node representations and a decoder for predicting drug-drug interactions. Further, we utilize comprehensive experiments to evaluate of our model and to compare it with state-of-the-art models. Experimental results show that our proposed method, HAN-DDI, outperforms the baselines significantly and accurately predicts DDIs, even for new drugs.Comment: 10 pages, 3 figures, 8 tables, accepted in BioKD

HyGNN: Drug-Drug Interaction Prediction via Hypergraph Neural Network

Drug-Drug Interactions (DDIs) may hamper the functionalities of drugs, and in the worst scenario, they may lead to adverse drug reactions (ADRs). Predicting all DDIs is a challenging and critical problem. Most existing computational models integrate drug-centric information from different sources and leverage them as features in machine learning classifiers to predict DDIs. However, these models have a high chance of failure, especially for the new drugs when all the information is not available. This paper proposes a novel Hypergraph Neural Network (HyGNN) model based on only the SMILES string of drugs, available for any drug, for the DDI prediction problem. To capture the drug similarities, we create a hypergraph from drugs' chemical substructures extracted from the SMILES strings. Then, we develop HyGNN consisting of a novel attention-based hypergraph edge encoder to get the representation of drugs as hyperedges and a decoder to predict the interactions between drug pairs. Furthermore, we conduct extensive experiments to evaluate our model and compare it with several state-of-the-art methods. Experimental results demonstrate that our proposed HyGNN model effectively predicts DDIs and impressively outperforms the baselines with a maximum ROC-AUC and PR-AUC of 97.9% and 98.1%, respectively.Comment: Some new experiments have been added. One more dataset has been considered. Theoretical part has been updated to

Biodiesel production from waste cooking sunflower oil and environmental impact analysis

Waste cooking oil offers great potential as a low cost biodiesel feedstock. Several parameters were tested for the optimumproduction of biodiesel and these included varying the alcohol:oil molar ratios, different catalyst concentrations,temperatures and stirring speed. For the optimum production of biodiesel, the molar ratio of alcohol to oil used was 6:1.The fatty acid methyl esters identified in the biodiesel were methyl palmitate, methyl linoleate, methyloleate and methylstearate. The viscosity of the produced biodiesel was within the range of international ASTM standards. Engine exhaustemission tests of biodiesel showed that the carbon monoxide and unburned hydrocarbon emissions were lower than thatof petrodiesel. The nitrogenous oxides emission and specific fuel consumption were higher than that of conventionaldiesel fuel. It can be concluded that biodiesel produced from waste sunflower oil can be considered as a great potentialsource of commercial biodiesel

Analysis of a field oriented control based variable direct current link drive for electric vehicles

The performance and efficiency of electric vehicles (EVs) depend mostly on the EVs powertrains. Therefore, one of the most vital research areas is the efficiency analysis of the drivetrain parts in electric vehicles. In this work, two basic drivetrain configurations of EVs are considered and evaluated. Firstly, the one with constant direct current (DC) link voltage and second one having a bidirectional DC-DC converter which supplies variable DC link voltage to the inverter. In order to control a permanent magnet synchronous machine (PMSM), field oriented control (FOC) technique is performed. Furthermore, a bidirectional DC-DC converter with optimized parameters is designed and implemented with variable DC link voltage controller. Finally, a comparison is made taking into account both drivetrain configurations with details efficiency analysis using real time switches at wide operating points of the machine

HeTriNet: Heterogeneous Graph Triplet Attention Network for Drug-Target-Disease Interaction

Modeling the interactions between drugs, targets, and diseases is paramount in drug discovery and has significant implications for precision medicine and personalized treatments. Current approaches frequently consider drug-target or drug-disease interactions individually, ignoring the interdependencies among all three entities. Within human metabolic systems, drugs interact with protein targets in cells, influencing target activities and subsequently impacting biological pathways to promote healthy functions and treat diseases. Moving beyond binary relationships and exploring tighter triple relationships is essential to understanding drugs' mechanism of action (MoAs). Moreover, identifying the heterogeneity of drugs, targets, and diseases, along with their distinct characteristics, is critical to model these complex interactions appropriately. To address these challenges, we effectively model the interconnectedness of all entities in a heterogeneous graph and develop a novel Heterogeneous Graph Triplet Attention Network (\texttt{HeTriNet}). \texttt{HeTriNet} introduces a novel triplet attention mechanism within this heterogeneous graph structure. Beyond pairwise attention as the importance of an entity for the other one, we define triplet attention to model the importance of pairs for entities in the drug-target-disease triplet prediction problem. Experimental results on real-world datasets show that \texttt{HeTriNet} outperforms several baselines, demonstrating its remarkable proficiency in uncovering novel drug-target-disease relationships.Comment: 13 pages, 3 figures, 6 table