Volume 3, Issue 10 (October 2024) - 00 Articles
Journal of Engineering Research and Sciences, Volume 3, Issue 8, Page # i-i, 2024
Journal of Engineering Research and Sciences, Volume 3, Issue 8, Page # i-i, 2024
by ABC and DEF
Journal of Engineering Research and Sciences, Volume 3, Issue 8, Page # i-i, 2024
Journal of Engineering Research and Sciences, Volume 3, Issue 8, Page # i-i, 2024
by Swarup Kumar Mondal and Anindya Sen
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 1-14, 2024; DOI: 10.55708/js0310001
Abstract: Imbalanced dataset handling in real time is one of the most challenging tasks in predictive modelling. This work handles the critical issues arising in imbalanced dataset with implementation of artificial neural network and deep neural network architecture. The usual machine learning algorithms fails to achieve desired throughput with certain input circumstances due to mismatched class ratios in the sample dataset. Dealing with imbalanced dataset leads to performance degradation and interpretability issue in traditional ML architectures. For regression tasks, where the target variable is continuous, the skewed data distribution is major issue. In this study, we have investigated a detailed comparison of traditional ML algorithms and neural networks with dimensionality reduction method to overcome this problem. Principle component analysis has been used for feature selection and analysis on real time satellite-based air pollution dataset. Five regression algorithms Multilinear, Ridge, Lasso, Elastic Net and SVM regression is combined with PCA and non PCA to interpret the outcome. To address unbalanced datasets in real-time, deep neural networks and artificial neural network architectures have been developed. Each model’s experiments and mathematical modelling is done independently. The Deep neural network is superior compared to other conventional models for performance measures of target variable in imbalanced datasets…. Read More
(This article belongs to the Special Issue Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 & Section Biochemical Research Methods (BRM))
by Haoyuan Huang, and Rongcheng Cui
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 15-20, 2024; DOI: 10.55708/js0308002
Abstract: Accurate gemstone classification is critical to the gemstone and jewelry industry, and the good performance of convolutional neural networks in image processing has received wide attention in recent years. In order to better extract image content information and improve image classification accuracy, a CNNs gemstone image classification algorithm based on deep multi-feature fusion is proposed… Read More
(This article belongs to the Special Issue Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 & Section Biochemical Research Methods (BRM))
by Vaibhavi Tiwari
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 21-36, 2024; DOI: 10.55708/js0310003
Abstract: The incorporation of cutting-edge technologies like sensor networks, artificial intelligence (AI), and vehicle-to-everything (V2X) communication has hastened the rollout of autonomous vehicles (AVs), offering significant possibilities for the future of transportation. This document offers an extensive overview of AV technology, covering essential elements such as technological infrastructure, degrees of automation, cybersecurity threats, societal impacts, regulatory structures, and emerging trends. This analysis emphasizes the existing obstacles and progress within the industry by examining the activities of key entities like Tesla, Waymo, and General Motors. Additionally, a comparative examination of autonomous vehicles and drones is performed, providing distinct perspectives on possible cybersecurity vulnerabilities shared by both technologies, including GPS spoofing, jamming, and unauthorized data interception. This multifaceted approach highlights not only the existing vulnerabilities but also proposes proactive measures that can be implemented to reduce comparable risks across various AV
platforms. The results highlight the necessity of establishing strong cybersecurity measures, overcoming regulatory challenges, and building public confidence to realize the complete promise of autonomous vehicles as secure, effective, and eco-friendly transportation options. This analysis provides an essential resource for comprehending the complex aspects of AV technology and its consequences, offering readers a comprehensive perspective on the challenges and opportunities within the autonomous vehicle sector….. Read More
(This article belongs to the Special Issue Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 & Section Biochemical Research Methods (BRM))
by Bernd-Jürgen Falkowski
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 37-43, 2024; DOI: 10.55708/js0310004
Abstract: TThis is the extended version of a paper presented at CISP-BMEI 2023. After a general introduction kernels are described by showing how they arise from considerations concerning elementary geometrical properties. They appear as generalizations of the scalarproduct that in turn is the algebraic version of length and angle. By introducing the Reproducing Kernel Hilbert Space it is shown how operations in a high dimensional feature space can be performed without explicitly using an embedding function (the “kernel trick”). The general section of the paper lists some kernels and sophisticated kernel clustering algorithms. Thus the continuing popularity of the k-means algorithm is probably due to its simplicity. This explains why an elegant version of a k-means iterative algorithm originally established by Duda is treated. This was extended to a kernel algorithm by the author. However, its performance still heavily depended on the initialization. In this paper previous results on the original k-means algorithm are transferred to the kernel version thus removing these setbacks. Moreover the algorithm is slightly modified to allow for an easy quantification of the improvements to the target function after initializaztion….. Read More
(This article belongs to the Special Issue Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 & Section Biochemical Research Methods (BRM))
by Renyan Jiang, Kunpeng Zhang, Xia Xu and Yu Cao
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 44-54, 2024; DOI: 10.55708/js0310005
Abstract: Research on repairable systems in a fleet is mainly concerned with modelling of the failure times using point processes. One important issue is to quantitatively evaluate the heterogeneity among systems, which is usually analyzed using frailty models. Recently, a fleet heterogeneity evaluation method is proposed in the literature. This method describes the heterogeneity with the relative dispersion of equivalent acceleration factors (EAFs) of systems, which is defined as the ratio of the mean times between failures (MTBFs) of a system and a reference system. A main drawback of this method is that the MTBFs of a specific system and the reference system are estimated at different times while the MTBF estimated at different time can be different. This paper aims to address this issue by proposing an improved method. The proposed method uses an “average process” as the reference process and estimates the MTBFs of systems and the reference system at a common time point. This leads to more robust MTBF estimates. Three datasets are analyzed to illustrate the proposed method and its superiority…… Read More
(This article belongs to the Special Issue Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 & Section Biochemical Research Methods (BRM))
by Haithem Aouabed, Mourad Elloumi and Fahad Algarni
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 55-68, 2024; DOI: 10.55708/js0310006
Abstract: Biclustering is a non-supervised data mining method used to analyze gene expression data by identifying groups of genes that exhibit similar patterns across specific groups of conditions. Discovering these co-expressed genes (called biclusters) can aid in understanding gene interactions in various biological contexts. Biclustering is characterized by its bi-dimensional nature, grouping both genes and conditions in the same bicluster and its overlapping property, allowing genes to belong to multiple biclusters. Biclustering algorithms often produce a large number of overlapping biclusters. Visualizing these results is not a straightforward task due to the specific characteristics of biclusters. In fact, biclustering results visualization is a crucial process to infer patterns from the expression data. In this paper, we explore the various techniques for visualizing multiple biclusters simultaneously and we evaluate them in order to help biologists to better choose their appropriate visualization techniques…… Read More
(This article belongs to the Special Issue Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 & Section Biochemical Research Methods (BRM))
by Yuexin Liu, Amir Tofighi Zavareh and Ben Zoghi
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 69-75, 2024; DOI: 10.55708/js0310007
Abstract: Mental health concerns are increasingly prevalent among university students, particularly in engineering programs where academic demands are high. This study builds upon previous work aimed at improving mental health support for engineering students through the use of machine learning (ML) and eye-tracking technology. A framework was developed to monitor mental health by analyzing eye movements and physiological data to provide personalized support based on student behavior. In this extended study, baseline data were analyzed to explore the correlations between emotions and physiological biomarkers. Key findings indicate that emotions such as Anger and Fear are positively correlated with increased physical activity, while Sadness is associated with elevated respiratory rates. A strong positive correlation between Electrodermal Activity (EDA) and Happiness was also identified, indicating physiological markers linked to positive emotional states. Temporal patterns were observed, with heightened emotional tagging occurring more frequently in the evening. These findings deepen the understanding of how emotional states manifest through physiological changes, providing a foundation for enhancing real-time, personalized mental health interventions. The results contribute to a more comprehensive framework for supporting student well-being and academic performance within engineering education….. Read More
(This article belongs to the Special Issue Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 & Section Biochemical Research Methods (BRM))
