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Archives: Paper Submission

The Impact of Greenwashing on Job Applicant’s Choice of Company

The purpose of this study is to present a theoretical framework for identifying the impact of greenwashing on job applicants’ choice of company and to test its usefulness in a laboratory experiment using eye tracking. The objective of this paper is to elucidate the influence of greenwashing on decision-making processes by delineating the cognitive mechanisms underlying the evaluation of corporate information by job applicants when selecting a prospective employer. The findings of this study indicate that the presentation of environmentally-oriented information can influence the selection of prospective employers by job applicants.

Predict diagnose diabetes using four algorithms in machine learning

Machine learning in artificial intelligence plays a very important role in various fields of life, along with data
science and data analysis. Among these roles through which machine learning can play an important role is the
role of human health in how to predict incurable diseases, such as diabetes. When learning the machine with
real data, it becomes possible to predict highly accurate results through which preventive measures can be
taken to avoid falling into such chronic diseases, and thus many people can be prevented from contracting such
chronic diseases. Future plans can also be made to avoid the spread of the disease and appropriate plans can
be made for that. By making the right decisions. Machine learning and artificial intelligence have algorithms
with specific characteristics that have the ability to predict results in advance. An example of these algorithms
is what is known as Logistic Regression, SVC, Random Forest Classifier and Gradient Boosting
Classifier.

Image Recognition Applied to Insulator Detection and Classification for Asset Management

This paper’s significant research contribution lies in the development of an original machine learning-based approach to automate insulator detection and management for overhead transmission lines. By leveraging retrained convolutional neural networks (YOLO), the study addresses challenges such as diverse image conditions and unbalanced datasets, achieving an f1-score of 97.5%. In addition to enhance the insulator detection and classification performance, we integrate our original approach seamlessly with existing asset management systems, improving real-time decision-making and reducing reliance on manual audits. This innovation significantly streamlines the maintenance and reliability of power transmission networks.

Predicting the Stay Length of Patients in Hospitals using Convolutional Gated Recurrent Deep Learning Model

Predicting hospital length of stay (LoS) stands as a critical factor in shaping public health strategies. This data serves as a cornerstone for governments to discern trends, patterns, and avenues for enhancing healthcare delivery. In this study, we introduce a robust hybrid deep learning model, a combination of Multi-layer Convolutional (CNNs) deep learning, Gated Recurrent Units (GRU), and Dense neural networks, that outperforms 11 conventional and state-of-the-art Machine Learning (ML) and Deep Learning (DL) methodologies in accurately forecasting inpatient hospital stay duration. Our investigation delves into the implementation of this hybrid model, scrutinising variables like geographic indicators tied to caregiving institutions, demographic markers encompassing patient ethnicity, race, and age, as well as medical attributes such as the CCS diagnosis code, APR DRG code, illness severity metrics, and hospital stay duration. Statistical evaluations reveal the pinnacle LoS accuracy achieved by our proposed model (CNN-GRU-DNN), which averages at 89% across a 10-fold cross-validation test, surpassing LSTM, BiLSTM, GRU, and Convolutional Neural Networks (CNNs) by 19%, 18.2%, 18.6%, and 7%, respectively.

Semiconductor Manufacturing Industry: Assessment, Challenges, and Future Trends

This is a review article on the state of the semiconductor industry, challenges, and future trends.

Detection of In-Vehicle Data Falsification: A Deep Learning-Based Approach

This study’s key contributions include:

Novel application of deep learning models (DNNs and RNNs) for in-vehicle data falsification detection.
Utilization of the new CICIoV2024 dataset, providing insights into latest IoV attack scenarios.

Enhancing Video Compression Efficiency for Low-Bandwidth Environments with H.265/HEVC

This paper explores the functionality of the H.265/HEVC (High Efficiency Video Coding) standard in low-bandwidth scenarios. We provide an overview of H.265’s key features and mechanisms that make it suitable for lower bandwidth environments. H.265, also known as High-Efficiency Video Coding (HEVC), is renowned for delivering superior video quality at lower bitrates. We investigate the critical features of H.265 and its application in low-bandwidth scenarios, providing insights into its efficiency, performance, and practical implementation. We present experimental results
demonstrating the performance improvements and benefits of H.265 regarding video quality and bandwidth utilisation. The paper discusses potential applications and directions for optimising video compression in constrained network conditions.

Environmental Monitoring in Industry: Leveraging AI and IoT for Sustainable Solutions

1. Environmental gases monitoring in Industry
2. Levaragimg of AI and IoT Technologies
3. Deployed IoT solutions

A Fast Multi-Threshold Image Segmentation Method Using a Bayesian Forecasting Evolutionary Algorithm

The main contributions of this paper are as follows.
1. First Application of BFEA in Image Thresholding: While the Bayesian Forecasting Evolutionary Algorithm (BFEA) was originally proposed in 2014, this paper marks the first time it has been employed in the field of image thresholding. By applying BFEA to image segmentation, we introduce a novel approach that leverages the advantages of this algorithm in handling complex optimization problems within the context of image processing.
2. Adaptation from Continuous to Discrete Optimization: In its original formulation, BFEA was primarily utilized for continuous function optimization. This paper simplifies and adapts BFEA to address discrete combinatorial optimization problems. By modifying the algorithm to suit multilevel thresholding tasks, we demonstrate its versatility and ability to solve a wide range of optimization problems beyond its initial scope.
3. Improved Solution Quality through Population Initialization: One of the key enhancements in this work is the integration of a population initialization strategy with BFEA. This strategy helps prevent the algorithm from becoming trapped in local optima, thereby increasing its robustness and ensuring a more thorough exploration of the solution space. As a result, the algorithm is able to achieve more accurate and reliable results, even in complex image segmentation tasks.

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