Investigating car purchasing decision-making process using Multi- Objective Optimization Ratio Analysis based Analytical Hierarchy Process Model: An empirical case from Vietnam

This study aims to define and quantify the factors affecting selecting the best car among the market's available alternatives. Many criteria are involved while deciding to purchase the best car from various car models; therefore, car purchasing behaviour is a multi-criteria decision-making problem (MCDM). Proposed criteria are based on the customers’ survey when they are willing to purchase the cars, including Price, Branding, Safety, Performance, Exterior, Fuel efficiency, Maintainance cost, After-Sale Service, and Resale. In this study, the AHP technique calculates each criterion's weight, and then Multi-Objective Optimization Ratio Analysis (MOORA) is employed to rank the car models in a numerical example from Vietnam. The results show that this proposed model can minimize the consumer effort to select a car and make accurate decisions. Furthermore, this study's findings could provide car manufacturers with valuable insight into the criteria that reflect the customer's assessment of the car selection process. JEL: C02, C61, D53, Q1

Analyzing Business Leadership Skills Under Uncertain Environment Using Fuzzy AHP Approach.

Over the years, leaders affect followers' success and inspiration. Up to this point, research has concentrated on leader characteristics, attitudes, or followers' self-concepts and the related consequences. Dynamic leadership attracts attention because it leads to promoting future business leadership. This study aims to analyze the influencing factors of business leadership. This study proposes the Fuzzy Analytical Hierarchy Process Method (FAHP) to achieve a systematic weighting of critical factors affecting business leadership to overcome human expectations' vagueness. Group of critical criteria basing on experts' opinions are identified: Creative Thinking (A), Cause-Effect Analysis (B), Forecasting (C), Planning (D), Visioning (E), Problem Defining (F), Idea Evaluation (G), Wisdom (H), and Constraint Analysis (I). The results revealed that Visioning (E) is the most critical skill at a weight of 0.138, Forecasting (C) is the second position and Creative Thinking (A) is placed third with the weight of 0.126. The remaining skills’ rankings are Cause-Effect Analysis (B) > Planning (D)> Idea Evaluation (G)>Wisdom (H) Constraint Analysis (I)> Problem Defining(F), respectively. The implications of findings bearing on leader thinking skills for leader assessment and leader development are considered. Finally, sensitivity analysis is conducted to verify the robustness of the results

Fuzzy Q-Learning-Based Opportunistic Communication for MEC-Enhanced Vehicular Crowdsensing

This study focuses on MEC-enhanced, vehicle-based crowdsensing systems that rely on devices installed on automobiles. We investigate an opportunistic communication paradigm in which devices can transmit measured data directly to a crowdsensing server over a 4G communication channel or to nearby devices or so-called Road Side Units positioned along the road via Wi-Fi. We tackle a new problem that is how to reduce the cost of 4G while preserving the latency. We propose an offloading strategy that combines a reinforcement learning technique known as Q-learning with Fuzzy logic to accomplish the purpose. Q-learning assists devices in learning to decide the communication channel. Meanwhile, Fuzzy logic is used to optimize the reward function in Q-learning. The experiment results show that our offloading method significantly cuts down around 30-40% of the 4G communication cost while keeping the latency of 99% packets below the required threshold.Comment: IEEE Transactions on Network and Service Managemen

Contribution to the study on chemical constituents of Hedyotis auricularia L., (Rubiaceae)

From the whole plant of Hedyotis auricularia L., a new glycoside, 1’-deoxy-6’-O-(1-hydroxymethyl-2-hydroxy-1-methoxy)ethylglucopyranoside (1) was isolated along with 1’-O-ethyl-β-D-galactopyranoside (2), 2-formyl-5-hydroxymethylfuran (3), stigmasta-5,22-diene-3-O-β-D-glucopyranoside (4), ursolic acid (5) and oleanolic acid (6). Among them (1), (2), (3), (4)were the first time known to be present in this plant

Q-learning-based Opportunistic Communication for Real-time Mobile Air Quality Monitoring Systems

We focus on real-time air quality monitoring systems that rely on devices installed on automobiles in this research. We investigate an opportunistic communication model in which devices can send the measured data directly to the air quality server through a 4G communication channel or via Wi-Fi to adjacent devices or the so-called Road Side Units deployed along the road. We aim to reduce 4G costs while assuring data latency, where the data latency is defined as the amount of time it takes for data to reach the server. We propose an offloading scheme that leverages Q-learning to accomplish the purpose. The experiment results show that our offloading method significantly cuts down around 40-50% of the 4G communication cost while keeping the latency of 99.5% packets smaller than the required threshold.Comment: 2021 IEEE International Conference on Performance, Computing and Communications (IPCCC). arXiv admin note: substantial text overlap with arXiv:2405.0105

A Novel Approach for Pill-Prescription Matching with GNN Assistance and Contrastive Learning

Medication mistaking is one of the risks that can result in unpredictable consequences for patients. To mitigate this risk, we develop an automatic system that correctly identifies pill-prescription from mobile images. Specifically, we define a so-called pill-prescription matching task, which attempts to match the images of the pills taken with the pills' names in the prescription. We then propose PIMA, a novel approach using Graph Neural Network (GNN) and contrastive learning to address the targeted problem. In particular, GNN is used to learn the spatial correlation between the text boxes in the prescription and thereby highlight the text boxes carrying the pill names. In addition, contrastive learning is employed to facilitate the modeling of cross-modal similarity between textual representations of pill names and visual representations of pill images. We conducted extensive experiments and demonstrated that PIMA outperforms baseline models on a real-world dataset of pill and prescription images that we constructed. Specifically, PIMA improves the accuracy from 19.09% to 46.95% compared to other baselines. We believe our work can open up new opportunities to build new clinical applications and improve medication safety and patient care.Comment: Accepted for publication and presentation at the 19th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2022

Comparative Study of Image Denoise Algorithms

Denoising is a pre-processing step in digital image processing system. It is also typical image processing challenges. Many works proposed to solve problem with new approaching. They can be divided into two main categories: spatial-based or transform-based. Some denoising methods apply in both spatial and transform domains. The goal of this paper focuses on reviewing denoise methods, classifying them into different categories, and identifying new trends. Moreover, we do experiments to compare pros, cons of methods in survey

Numerical and Experimental Study on the Grinding Performance of Ti-Based Super-Alloy

The experiments of the surface grinding of Ti-6Al-4V grade 5 alloy (Ti-64) with a resin-bonded cubic Boron Nitride (cBN) grinding wheel are performed in this research to estimate the influence of cutting parameters named workpiece infeed speed, Depth of Cut (DOC), cooling condition on the grinding force, force ratio, and specific energy. A finite element simulation model of single-grain grinding of Ti-64 is also implemented in order to predict the values of grinding forces and temperature. The experimental results show that an increase of workpiece infeed speed creates higher intensified cutting forces than the DOC. The grinding experiments under wet conditions present slightly lower tangential forces, force ratio, and specific energy than those in dry grinding. The simulation outcomes exhibit that the relative deviation of simulated and experimental forces is in the range of 1-15%. The increase in feed rate considerably reduces grinding temperature, while enhancement of DOC elevates the heat generation in the cutting zone

IncepSE: Leveraging InceptionTime's performance with Squeeze and Excitation mechanism in ECG analysis

Our study focuses on the potential for modifications of Inception-like architecture within the electrocardiogram (ECG) domain. To this end, we introduce IncepSE, a novel network characterized by strategic architectural incorporation that leverages the strengths of both InceptionTime and channel attention mechanisms. Furthermore, we propose a training setup that employs stabilization techniques that are aimed at tackling the formidable challenges of severe imbalance dataset PTB-XL and gradient corruption. By this means, we manage to set a new height for deep learning model in a supervised learning manner across the majority of tasks. Our model consistently surpasses InceptionTime by substantial margins compared to other state-of-the-arts in this domain, noticeably 0.013 AUROC score improvement in the "all" task, while also mitigating the inherent dataset fluctuations during training

FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning

The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solutions attempted to achieve more fairness by weighted aggregating deep learning models across clients. This work introduces a novel non-IID type encountered in real-world datasets, namely cluster-skew, in which groups of clients have local data with similar distributions, causing the global model to converge to an over-fitted solution. To deal with non-IID data, particularly the cluster-skewed data, we propose FedDRL, a novel FL model that employs deep reinforcement learning to adaptively determine each client's impact factor (which will be used as the weights in the aggregation process). Extensive experiments on a suite of federated datasets confirm that the proposed FedDRL improves favorably against FedAvg and FedProx methods, e.g., up to 4.05% and 2.17% on average for the CIFAR-100 dataset, respectively.Comment: Accepted for presentation at the 51st International Conference on Parallel Processin