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Journal articles on the topic 'Traditional Datasets'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Ma, Dong, and Haoyang Song. "Performance analysis and comparison of cat and dog image classification based on different models." Applied and Computational Engineering 41, no. 1 (2024): 197–201. http://dx.doi.org/10.54254/2755-2721/41/20230743.

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Image classification has widespread applications in computer vision, with significant advancements in performance due to deep learning models. Cat and dog image classification, as a classic problem, has attracted considerable research interest. This study aims to conduct a comprehensive analysis and comparison of deep learning models, including LeNet, ResNet, and VGG, in the context of cat and dog image classification. This paper employed two datasets: traditional cat and dog images and non-traditional, diverse images. Data preprocessing and augmentation were applied, and various model archite

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Masello, Leandro, Barry Sheehan, Finbarr Murphy, German Castignani, Kevin McDonnell, and Cian Ryan. "From Traditional to Autonomous Vehicles: A Systematic Review of Data Availability." Transportation Research Record: Journal of the Transportation Research Board 2676, no. 4 (2021): 161–93. http://dx.doi.org/10.1177/03611981211057532.

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The increasing accessibility of mobility datasets has enabled research in green mobility, road safety, vehicular automation, and transportation planning and optimization. Many stakeholders have leveraged vehicular datasets to study conventional driving characteristics and self-driving tasks. Notably, many of these datasets have been made publicly available, fostering collaboration, scientific comparability, and replication. As these datasets encompass several study domains and contain distinctive characteristics, selecting the appropriate dataset to investigate driving aspects might be challen

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Abu, Sarwar Zamani, Mobin Akhtar Md., and Ahamad Danish. "Concealment Conserving the Data Mining of Groups & Individual." Journal of Information Sciences and Computing Technologies 7, no. 1 (2018): 648–53. https://doi.org/10.5281/zenodo.3968147.

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We present an overview of privacy preserving data mining, one of the most popular directions in the data mining research community. In the first part of the chapter, we presented approaches that have been proposed for the protection of either the sensitive data itself in the course of data mining or the sensitive data mining results, in the context of traditional (relational) datasets. Following that, in the second part of the chapter, we focused our attention on one of the most recent as well as prominent directions in privacy preserving data mining: the mining of user mobility data. Although

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Sarma, Moumita Sen, Kaushik Deb, Pranab Kumar Dhar, and Takeshi Koshiba. "Traditional Bangladeshi Sports Video Classification Using Deep Learning Method." Applied Sciences 11, no. 5 (2021): 2149. http://dx.doi.org/10.3390/app11052149.

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Sports activities play a crucial role in preserving our health and mind. Due to the rapid growth of sports video repositories, automatized classification has become essential for easy access and retrieval, content-based recommendations, contextual advertising, etc. Traditional Bangladeshi sport is a genre of sports that bears the cultural significance of Bangladesh. Classification of this genre can act as a catalyst in reviving their lost dignity. In this paper, the Deep Learning method is utilized to classify traditional Bangladeshi sports videos by extracting both the spatial and temporal fe

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Seong, Teh Boon, Vasaki Ponnusamy, Noor Zaman Jhanjhi, Robithoh Annur, and M. N. Talib. "A comparative analysis on traditional wired datasets and the need for wireless datasets for IoT wireless intrusion detection." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 1165. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp1165-1176.

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<span>IoT networks mostly rely on wireless mediums for communication, and due to that, they are very susceptible to intrusions. And due to the tiny nature, processing complexity, and limited storage capacities, IoT networks require very reliable intrusion detection systems (IDS). Although there are many IDS types of research available in the literature, most of these systems are suitable for wired network environments, and the benchmark datasets used for these research works are mostly relying on wired datasets such as KDD Cup’99 and NSL-KDD. IoT and wireless networks are distinct in nat

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Seong, Teh Boon, Vasaki Ponnusamy, NZ Jhanjhi, Robithoh Annur, and M. N. Talib. "A comparative analysis on traditional wired datasets and the need for wireless datasets for IoT wireless intrusion detection." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 1165–76. https://doi.org/10.11591/ijeecs.v22.i2.pp1165-1176.

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IoT networks mostly rely on wireless mediums for communication, and due to that, they are very susceptible to intrusions. And due to the tiny nature, processing complexity, and limited storage capacities, IoT networks require very reliable intrusion detection systems (IDS). Although there are many IDS types of research available in the literature, most of these systems are suitable for wired network environments, and the benchmark datasets used for these research works are mostly relying on wired datasets such as KDD Cup’99 and NSL-KDD. IoT and wireless networks are distinct in nature as

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Wang, Hanqiu, Aybek Rehmetulla, Shanshan Guo, et al. "Machine learning based on structural and FTIR spectroscopic datasets for seed autoclassification." RSC Advances 12, no. 18 (2022): 11413–19. http://dx.doi.org/10.1039/d2ra00239f.

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Nassiwa, Faith, and Jiahui Zeng. "Evaluating Traditional Machine Learning Models for Predicting Diabetes Onset Using the Pima Indians Dataset." Annals of Medical and Health Sciences Research 14, no. 7 (2024): 6. https://doi.org/10.5281/zenodo.14505301.

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Diabetes is a leading disease in the world. With the seriousness of diabetes and its complexity in diagnosis, we aimed to produce a model to help with prediction of onset of diabetes. Three models, logistic regression, gradient boosting and random forest were performed and evaluated to predict the onset of diabetes. A dataset of size 768 that includes information about some indian population were used. the population are specific to indian women that are at least 21 years old and of Pima Indian Heritage. Methods of standardizing including Synthetic Minority Oversampling Technique (SMOTE) and h

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Zou, Yajie. "Modeling highly dispersed crash data with sichel GAMLSS: An alternative approach to traditional methods." Multidisciplinary Science Journal 7, no. 8 (2025): 2025392. https://doi.org/10.31893/multiscience.2025392.

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This research examines the application of Sichel (SI) generalized additive models for location, scale, and shape (GAMLSS) in addressing the challenge of modeling highly dispersed crash data. The Sichel distribution, which combines the Poisson distribution with the generalized inverse Gaussian distribution, is particularly suited for modeling data with significant dispersion, where traditional models often prove inadequate. The primary objective of this study was to assess the performance of the Sichel GAMLSS in comparison with the widely-used Negative Binomial (NB) generalized linear model (GL

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J. Thaker, Dhaval, Hitesh R. Raval, and Juhi Khengar. "Leveraging AI for Enhanced Dataset Usability: Intelligent Summarization and Labeling for Academic-Industry Collaboration." Cuestiones de Fisioterapia 54, no. 2 (2025): 3867–77. https://doi.org/10.48047/3xjtdx63.

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In the era of digital transformation, artificial intelligence (AI) and cloud-based technologies arerevolutionizing university-industry collaboration by enhancing data accessibility, organization, andusability. Traditional data management approaches often suffer from inefficiencies, leading to fragmented,underutilized datasets. This research proposes an AI-powered framework that integrates intelligent labeling,automated dataset summarization, and vector-based retrieval to optimize dataset management. The systemefficiently categorizes and summarizes datasets by leveraging natural language proces

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Vanitha, G., and M. Kasthuri. "A robust feature selection approach for high-dimensional medical data classification using enhanced correlation attribute evaluation." Scientific Temper 16, no. 02 (2025): 3736–46. https://doi.org/10.58414/scientifictemper.2025.16.2.06.

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The challenge of high-dimensional feature spaces and redundant attributes significantly impacts classification performance in medical datasets. Addressing this, the proposed Enhanced Correlation Attribute Evaluation (E-CAE) method effectively integrates multiple correlation measures such as Pearson, Spearman, Kendall, Biweight Midcorrelation, and Distance Correlation to rank and select the most relevant features. This hybrid feature selection technique was rigorously tested on three datasets: the Darwin dataset, Parkinson’s speech dataset, and Dyslexia dataset. The E-CAE method demonstrated su

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Shaikh, Mohsin, Sabina Akram, Jawad Khan, Shah Khalid, and Youngmoon Lee. "DIAFM: An Improved and Novel Approach for Incremental Frequent Itemset Mining." Mathematics 12, no. 24 (2024): 3930. https://doi.org/10.3390/math12243930.

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Traditional approaches to data mining are generally designed for small, centralized, and static datasets. However, when a dataset grows at an enormous rate, the algorithms become infeasible in terms of huge consumption of computational and I/O resources. Frequent itemset mining (FIM) is one of the key algorithms in data mining and finds applications in a variety of domains; however, traditional algorithms do face problems in efficiently processing large and dynamic datasets. This research introduces a distributed incremental approximation frequent itemset mining (DIAFM) algorithm that tackles

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Tang, Wenkai, and Peiyong Zhang. "GPGCN: A General-Purpose Graph Convolution Neural Network Accelerator Based on RISC-V ISA Extension." Electronics 11, no. 22 (2022): 3833. http://dx.doi.org/10.3390/electronics11223833.

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In the past two years, various graph convolution neural networks (GCNs) accelerators have emerged, each with their own characteristics, but their common disadvantage is that the hardware architecture is not programmable and it is optimized for a specific network and dataset. They may not support acceleration for different GCNs and may not achieve optimal hardware resource utilization for datasets of different sizes. Therefore, given the above shortcomings, and according to the development trend of traditional neural network accelerators, this paper proposes and implements GPGCN: a general-purp

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Balla, Benedek, Atsuhiro Hibi, and Pascal N. Tyrrell. "Diffusion-Based Image Synthesis or Traditional Augmentation for Enriching Musculoskeletal Ultrasound Datasets." BioMedInformatics 4, no. 3 (2024): 1934–48. http://dx.doi.org/10.3390/biomedinformatics4030106.

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Background: Machine learning models can provide quick and reliable assessments in place of medical practitioners. With over 50 million adults in the United States suffering from osteoarthritis, there is a need for models capable of interpreting musculoskeletal ultrasound images. However, machine learning requires lots of data, which poses significant challenges in medical imaging. Therefore, we explore two strategies for enriching a musculoskeletal ultrasound dataset independent of these limitations: traditional augmentation and diffusion-based image synthesis. Methods: First, we generate augm

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Karayiğit, Habibe, Ali Akdagli, and Çiğdem İnan Acı. "BERT-based Transfer Learning Model for COVID-19 Sentiment Analysis on Turkish Instagram Comments." Information Technology and Control 51, no. 3 (2022): 409–28. http://dx.doi.org/10.5755/j01.itc.51.3.30276.

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First seen in Wuhan, China, the coronavirus disease (COVID-19) became a worldwide epidemic. Turkey’s first reported case was announced on March 11, 2020—the day the World Health Organization declared COVID-19 is a pandemic. Due to the intense and widespread use of social media during the pandemic, determining the role and effect (i.e., positive, negative, neutral) of social media gives us important information about society's perspective on events. In our study, two datasets (i.e. Dataset1, Dataset2) consisting of Instagram comments on COVID-19 were composed between different dates of the pand

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Kassab, Kenan, Nikolay Teslya, and Ekaterina Vozhik. "Automated Dataset-Creation and Evaluation Pipeline for NER in Russian Literary Heritage." Applied Sciences 15, no. 4 (2025): 2072. https://doi.org/10.3390/app15042072.

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Developing robust and reliable models for Named Entity Recognition (NER) in the Russian language presents significant challenges due to the linguistic complexity of Russian and the limited availability of suitable training datasets. This study introduces a semi-automated methodology for building a customized Russian dataset for NER specifically designed for literary purposes. The paper provides a detailed description of the methodology employed for collecting and proofreading the dataset, outlining the pipeline used for processing and annotating its contents. A comprehensive analysis highlight

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Budd, Ann F., and Nathan D. Smith. "Diversification of a New Atlantic Clade of Scleractinian Reef Corals: Insights from Phylogenetic Analysis of Morphologic and Molecular Data." Paleontological Society Papers 11 (October 2005): 103–28. http://dx.doi.org/10.1017/s1089332600001273.

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Recent molecular analyses of the traditional scleractinian suborder Faviina have revealed a new Atlantic clade of reef corals, which disagrees with traditional classification. The new clade contradicts long-held notions of Cenozoic diversification being concentrated in the Pacific, and of Atlantic species bearing close evolutionary relationships with Pacific species. In the present paper, we outline an approach for integrating molecular, morphologic, and fossil data, which will allow future examination of the timing and phylogenetic context of the divergence of the new Atlantic clade. Our anal

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Kapočiūtė-Dzikienė, Jurgita, Robertas Damaševičius, and Marcin Woźniak. "Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches." Computers 8, no. 1 (2019): 4. http://dx.doi.org/10.3390/computers8010004.

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We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both

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Ji, Tianchen, Jiantao Li, Huaiying Fang, RenCheng Zhang, Jianhong Yang, and Lulu Fan. "Rapid dataset generation methods for stacked construction solid waste based on machine vision and deep learning." PLOS ONE 19, no. 1 (2024): e0296666. http://dx.doi.org/10.1371/journal.pone.0296666.

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The development of urbanization has brought convenience to people, but it has also brought a lot of harmful construction solid waste. The machine vision detection algorithm is the crucial technology for finely sorting solid waste, which is faster and more stable than traditional methods. However, accurate identification relies on large datasets, while the datasets from the field working conditions are scarce, and the manual annotation cost of datasets is high. To rapidly and automatically generate datasets for stacked construction waste, an acquisition and detection platform was built to autom

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Feng, Qian, Shenglong Du, Wuzheng Tan, and Jian Weng. "Efficient Cryptographic Solutions for Unbalanced Private Set Intersection in Mobile Communication." Information 15, no. 9 (2024): 554. http://dx.doi.org/10.3390/info15090554.

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Private Set Intersection (PSI) is a cryptographic method in secure multi-party computation that allows entities to identify common elements in their datasets without revealing their private data. Traditional approaches assume similar-sized datasets and equal computational power, overlooking practical imbalances. In real-world applications, dataset sizes and computational capacities often vary, particularly in the Internet of Things and mobile scenarios where device limitations restrict computational types. Traditional PSI protocols are inefficient here, as computational and communication compl

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Shao, Ran, Xiao-Jun Bi, and Zheng Chen. "A novel hybrid transformer-CNN architecture for environmental microorganism classification." PLOS ONE 17, no. 11 (2022): e0277557. http://dx.doi.org/10.1371/journal.pone.0277557.

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The success of vision transformers (ViTs) has given rise to their application in classification tasks of small environmental microorganism (EM) datasets. However, due to the lack of multi-scale feature maps and local feature extraction capabilities, the pure transformer architecture cannot achieve good results on small EM datasets. In this work, a novel hybrid model is proposed by combining the transformer with a convolution neural network (CNN). Compared to traditional ViTs and CNNs, the proposed model achieves state-of-the-art performance when trained on small EM datasets. This is accomplish

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Chen, Kang, Yajing Zheng, Tiejun Huang, and Zhaofei Yu. "Rethinking High-speed Image Reconstruction Framework with Spike Camera." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 2 (2025): 2097–104. https://doi.org/10.1609/aaai.v39i2.32207.

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Spike cameras, as innovative neuromorphic devices, generate continuous spike streams to capture high-speed scenes with lower bandwidth and higher dynamic range than traditional RGB cameras. However, reconstructing high-quality images from the spike input under low-light conditions remains challenging. Conventional learning-based methods often rely on the synthetic dataset as the supervision for training. Still, these approaches falter when dealing with noisy spikes fired under the low-light environment, leading to further performance degradation in the real-world dataset. This phenomenon is pr

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Trutschl, Marjan, Tzvetanka D. Dinkova, and Robert E. Rhoads. "Application of machine learning and visualization of heterogeneous datasets to uncover relationships between translation and developmental stage expression ofC. elegansmRNAs." Physiological Genomics 21, no. 2 (2005): 264–73. http://dx.doi.org/10.1152/physiolgenomics.00307.2004.

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The relationships between genes in neighboring clusters in a self-organizing map (SOM) and properties attributed to them are sometimes difficult to discern, especially when heterogeneous datasets are used. We report a novel approach to identify correlations between heterogeneous datasets. One dataset, derived from microarray analysis of polysomal distribution, contained changes in the translational efficiency of Caenorhabditis elegans mRNAs resulting from loss of specific eIF4E isoform. The other dataset contained expression patterns of mRNAs across all developmental stages. Two algorithms wer

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Akgül, İsmail, Volkan Kaya, and Özge Zencir Tanır. "A novel hybrid system for automatic detection of fish quality from eye and gill color characteristics using transfer learning technique." PLOS ONE 18, no. 4 (2023): e0284804. http://dx.doi.org/10.1371/journal.pone.0284804.

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Fish remains popular among the body’s most essential nutrients, as it contains protein and polyunsaturated fatty acids. It is extremely important to choose the fish consumption according to the season and the freshness of the fish to be purchased. It is very difficult to distinguish between non-fresh fish and fresh fish mixed in the fish stalls. In addition to traditional methods used to determine meat freshness, significant success has been achieved in studies on fresh fish detection with artificial intelligence techniques. In this study, two different types of fish (anchovy and horse mackere

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Wild, Ashley, Zhi-Weng Chua, and Yuriy Kuleshov. "Triple Collocation Analysis of Satellite Precipitation Estimates over Australia." Remote Sensing 14, no. 11 (2022): 2724. http://dx.doi.org/10.3390/rs14112724.

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The validation of precipitation estimates is necessary for the selection of the most appropriate dataset, as well as for having confidence in its selection. Traditional validation against gauges or radars is much less effective when the quality of these references (which are considered the ‘truth’) degrades, such as in areas of poor coverage. In scenarios like this where the ‘truth’ is unreliable or unknown, triple collocation analysis (TCA) facilitates a relative ranking of independent datasets based on their similarity to each other. TCA has been successfully employed for precipitation error

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Tsai, Chi-Yi, Wei-Hsuan Shih, and Humaira Nisar. "Three-Stage Recursive Learning Technique for Face Mask Detection on Imbalanced Datasets." Mathematics 12, no. 19 (2024): 3104. http://dx.doi.org/10.3390/math12193104.

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In response to the COVID-19 pandemic, governments worldwide have implemented mandatory face mask regulations in crowded public spaces, making the development of automatic face mask detection systems critical. To achieve robust face mask detection performance, a high-quality and comprehensive face mask dataset is required. However, due to the difficulty in obtaining face samples with masks in the real-world, public face mask datasets are often imbalanced, leading to the data imbalance problem in model training and negatively impacting detection performance. To address this problem, this paper p

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Jin, Jun, and Zhongxun Zhao. "Composite Quantile Regression Neural Network for Massive Datasets." Mathematical Problems in Engineering 2021 (May 4, 2021): 1–10. http://dx.doi.org/10.1155/2021/6682793.

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Traditional statistical methods and machine learning on massive datasets are challenging owing to limitations of computer primary memory. Composite quantile regression neural network (CQRNN) is an efficient and robust estimation method. But most of existing computational algorithms cannot solve CQRNN for massive datasets reliably and efficiently. In this end, we propose a divide and conquer CQRNN (DC-CQRNN) method to extend CQRNN on massive datasets. The major idea is to divide the overall dataset into some subsets, applying the CQRNN for data within each subsets, and final results through com

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Wang, Ruiqi, Ning Cao, Yajie Guo, Shujuan Ji, and Sachin Kumar. "A Comparative Analysis of Fraudulent Recruitment Advertisement Detection Methods in the IoT Environment." Journal of Sensors 2022 (November 8, 2022): 1–11. http://dx.doi.org/10.1155/2022/4583512.

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The growth of the Internet of Things has changed the way of job hunting. Online recruitment has gradually replaced the traditional offline recruitment mode. Some unscrupulous people use online recruitment platforms to publish fraudulent recruitment advertisements, which not only bring financial and reputational losses to job seekers but also harm the sustainable development of society. However, previous studies have not used unified evaluation metrics and datasets, and detecting fraudulent recruitment advertisements lacks systematic research. To resolve this problem, this paper selects four re

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Feng, Xinjie, Hongxun Yao, and Shengping Zhang. "Focal CTC Loss for Chinese Optical Character Recognition on Unbalanced Datasets." Complexity 2019 (January 2, 2019): 1–11. http://dx.doi.org/10.1155/2019/9345861.

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In this paper, we propose a novel deep model for unbalanced distribution Character Recognition by employing focal loss based connectionist temporal classification (CTC) function. Previous works utilize Traditional CTC to compute prediction losses. However, some datasets may consist of extremely unbalanced samples, such as Chinese. In other words, both training and testing sets contain large amounts of low-frequent samples. The low-frequent samples have very limited influence on the model during training. To solve this issue, we modify the traditional CTC by fusing focal loss with it and thus m

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Paixao, Lucas de Almeida Gama, William Pratt Rogers, and Erisvaldo Bitencourt de Jesus. "A Novel Methodology to Develop Mining Stope Stability Graphs on Imbalanced Datasets Using Probabilistic Approaches." Mining 5, no. 2 (2025): 24. https://doi.org/10.3390/mining5020024.

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Predicting and analyzing the stability of underground stopes is critical for ensuring worker safety, reducing dilution, and maintaining operational efficiency in mining. Traditional stability graphs are widely used but often criticized for oversimplifying the stability phenomenon and relying on subjective classifications. Additionally, the imbalanced nature of stope stability datasets poses challenges for traditional machine learning and statistical models, which often bias predictions toward the majority class. This study proposes a novel methodology for developing site-specific stability gra

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Bondžulić, Boban, Boban Pavlović, Nenad Stojanović, and Vladimir Petrović. "Picture-wise just noticeable difference prediction model for JPEG image quality assessment." Vojnotehnicki glasnik 70, no. 1 (2022): 62–86. http://dx.doi.org/10.5937/vojtehg70-34739.

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Introduction/purpose: The paper presents interesting research related to the performance analysis of the picture-wise just noticeable difference (JND) prediction model and its application in the quality assessment of images with JPEG compression. Methods: The performance analysis of the JND model was conducted in an indirect way by using the publicly available results of subject-rated image datasets with the separation of images into two classes (above and below the threshold of visible differences). In the performance analysis of the JND prediction model and image quality assessment, five ima

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Ravaglia, Leonardo, Roberto Longo, Kaili Wang, et al. "RGB-to-Infrared Translation Using Ensemble Learning Applied to Driving Scenarios." Journal of Imaging 11, no. 7 (2025): 206. https://doi.org/10.3390/jimaging11070206.

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Multimodal sensing is essential in order to reach the robustness required of autonomous vehicle perception systems. Infrared (IR) imaging is of particular interest due to its low cost and complementarity with traditional RGB sensors. However, the lack of IR data in many datasets and simulation tools limits the development and validation of sensor fusion algorithms that exploit this complementarity. To address this, we propose an augmentation method that synthesizes realistic IR data from RGB images using gradient-boosting decision trees. We demonstrate that this method is an effective alternat

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Liu, Keliang, Yantao Xi, Junrong Liu, Wangyan Zhou, and Yidan Zhang. "MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data." Applied Sciences 13, no. 24 (2023): 13067. http://dx.doi.org/10.3390/app132413067.

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The use of deep learning methods to extract buildings from remote sensing images is a key contemporary research focus, and traditional deep convolutional networks continue to exhibit limitations in this regard. This study introduces a novel multi-feature fusion network (MFFNet), with the aim of enhancing the accuracy of building extraction from high-resolution remote sensing images of various sources. MFFNet improves feature capture for building targets by integrating deep semantic information from various attention mechanisms with multi-scale spatial information from a spatial pyramid module,

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Heenaye-Mamode Khan, Maleika, Nuzhah Gooda Sahib-Kaudeer, Motean Dayalen, et al. "Multi-Class Skin Problem Classification Using Deep Generative Adversarial Network (DGAN)." Computational Intelligence and Neuroscience 2022 (March 24, 2022): 1–13. http://dx.doi.org/10.1155/2022/1797471.

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The lack of annotated datasets makes the automatic detection of skin problems very difficult, which is also the case for most other medical applications. The outstanding results achieved by deep learning techniques in developing such applications have improved the diagnostic accuracy. Nevertheless, the performance of these models is heavily dependent on the volume of labelled data used for training, which is unfortunately not available. To address this problem, traditional data augmentation is usually adopted. Recently, the emergence of a generative adversarial network (GAN) seems a more plaus

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Goglia, Diletta, Laura Pollacci, and Alina Sîrbu. "Dataset of Multi-Aspect Integrated Migration Indicators." Data 8, no. 9 (2023): 139. http://dx.doi.org/10.3390/data8090139.

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Nowadays, new branches of research are proposing the use of non-traditional data sources for the study of migration trends in order to find an original methodology to answer open questions about cross-border human mobility. New knowledge extracted from these data must be validated using traditional data, which are however distributed across different sources and difficult to integrate. In this context we present the Multi-aspect Integrated Migration Indicators (MIMI) dataset, a new dataset of migration indicators (flows and stocks) and possible migration drivers (cultural, economic, demographi

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ZENG, MENG-YAO, JIN-LIAO CHEN, MING-ZHONG HUANG, and MING-HE LI. "Phylogenetic position of Vanda coelestis (Rchb.f.) Motes (Orchidaceae; Aeridinae): evidence from complete plastome and combined traditional sequences." Phytotaxa 638, no. 3 (2024): 285–93. http://dx.doi.org/10.11646/phytotaxa.638.3.8.

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The taxonomic status of Vanda coelestis (Rchb.f.) Motes remains a matter of discussion. So far, molecular systematic work has not been used to investigate. Here, we conducted the phylogenetic analyses using two datasets, the complete plastome and a combination of traditional molecular sequences (nrITS, matK, psbA-trnH, and trnL-trnF). Plastid-based phylogenetic trees indicated that this species was deeply embedded within the genus Vanda. In the analysis based on combined traditional sequences dataset, this species was further supported as sister to V. vietnamica with strong support.

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Phung and Rhee. "A High-Accuracy Model Average Ensemble of Convolutional Neural Networks for Classification of Cloud Image Patches on Small Datasets." Applied Sciences 9, no. 21 (2019): 4500. http://dx.doi.org/10.3390/app9214500.

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Research on clouds has an enormous influence on sky sciences and related applications, and cloud classification plays an essential role in it. Much research has been conducted which includes both traditional machine learning approaches and deep learning approaches. Compared with traditional machine learning approaches, deep learning approaches achieved better results. However, most deep learning models need large data to train due to the large number of parameters. Therefore, they cannot get high accuracy in case of small datasets. In this paper, we propose a complete solution for high accurac

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Chen, Runzi, Shuliang Zhao, and Meishe Liang. "A Fast Multiscale Clustering Approach Based on DBSCAN." Wireless Communications and Mobile Computing 2021 (July 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/4071177.

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Multiscale brings great benefits for people to observe objects or problems from different perspectives. It has practical significance for clustering on multiscale data. At present, there is a lack of research on the clustering of large-scale data under the premise that clustering results of small-scale datasets have been obtained. If one does cluster on large-scale datasets by using traditional methods, two disadvantages are as follows: (1) Clustering results of small-scale datasets are not utilized. (2) Traditional method will cause more running overhead. Aims at these shortcomings, this pape

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Fauzy, Che Yayah, Imran Ghauth Khairil, and Ting Choo-Yee. "Parallel classification and optimization of telco trouble ticket dataset." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 3 (2021): 872–85. https://doi.org/10.12928/telkomnika.v19i3.18159.

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In the big data age, extracting applicable information using traditional machine learning methodology is very challenging. This problem emerges from the restricted design of existing traditional machine learning algorithms, which do not entirely support large datasets and distributed processing. The large volume of data nowadays demands an efficient method of building machinelearning classifiers to classify big data. New research is proposed to solve problems by converting traditional machine learning classification into a parallel capable. Apache Spark is recommended as the primary data proce

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Babu, R. Anand, Vishwa Priya V, Manoj Kumar Mishra, Inakoti Ramesh Raja, Surya Kiran Chebrolu, and B. Swarna. "Transformer-Based Tabular Foundation Models: Outperforming Traditional Methods with TabPFN." International Journal of Engineering, Science and Information Technology 5, no. 3 (2025): 448–55. https://doi.org/10.52088/ijesty.v5i3.1146.

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Scientific research and commercial applications rely heavily on tabular data, yet efficiently modelling this data has constantly been a problem. For over twenty years, the standard method for machine learning has been based on traditional models, with gradient-boosted decision trees (GBDTs). Despite recent advancements in deep learning, neural networks often fail to provide satisfactory results on compact tabular datasets due to factors such as overfitting, insufficient data intricate feature relationships. The study offers a Tabular Prior data Fitted Network, a foundation model developed by m

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Deng, Miaolei, Chuanchuan Sun, Yupei Kan, Haihang Xu, Xin Zhou, and Shaojun Fan. "Network Intrusion Detection Based on Deep Belief Network Broad Equalization Learning System." Electronics 13, no. 15 (2024): 3014. http://dx.doi.org/10.3390/electronics13153014.

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Network intrusion detection systems are an important defense technology to guarantee information security and protect a network from attacks. In recent years, the broad learning system has attracted much attention and has been introduced into intrusion detection systems with some success. However, since the traditional broad learning system is a simple linear structure, when dealing with imbalanced datasets, it often ignores the feature learning of minority class samples, leading to a poorer recognition rate of minority class samples. Secondly, the high dimensionality and redundant features in

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Di Mauro, Anna, Andrea Cominola, Andrea Castelletti, and Armando Di Nardo. "Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets." Water 13, no. 1 (2020): 36. http://dx.doi.org/10.3390/w13010036.

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Over the last three decades, the increasing development of smart water meter trials and the rise of demand management has fostered the collection of water demand data at increasingly higher spatial and temporal resolutions. Counting these new datasets and more traditional aggregate water demand data, the literature is rich with heterogeneous urban water demand datasets. They are characterized by heterogeneous spatial scales—from urban districts, to households or individual water fixtures—and temporal sampling frequencies—from seasonal/monthly up to sub-daily (minutes or seconds). Motivated by

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Rakhi A. Kalantri. "Advancing Cyber Threat Detection with Ai: Cutting-Edge Techniques and Future Trends." Journal of Information Systems Engineering and Management 10, no. 14s (2025): 338–52. https://doi.org/10.52783/jisem.v10i14s.2301.

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The digital age has made cyberspace indispensable for economic, social, and governmental functions, thus intensifying the critical need for robust cybersecurity. Our increasing dependence on digital platforms has exposed systems to a wide array of sophisticated cyber threats, including malware, phishing, distributed denial-of-service (DDoS) attacks, ransomware, and insider threats, often motivated by financial gain, political agendas, or espionage. These challenges underscore the urgent requirement for flexible and resilient cybersecurity strategies. Traditional signature-based and rule-based

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Abidoye, Itunuoluwa, Frances Ikeji, Charlie A. Coupland, Simon D. J. Calaminus, Nick Sander, and Eva Sousa. "Platelets Image Classification Through Data Augmentation: A Comparative Study of Traditional Imaging Augmentation and GAN-Based Synthetic Data Generation Techniques Using CNNs." Journal of Imaging 11, no. 6 (2025): 183. https://doi.org/10.3390/jimaging11060183.

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Platelets play a crucial role in diagnosing and detecting various diseases, influencing the progression of conditions and guiding treatment options. Accurate identification and classification of platelets are essential for these purposes. The present study aims to create a synthetic database of platelet images using Generative Adversarial Networks (GANs) and validate its effectiveness by comparing it with datasets of increasing sizes generated through traditional augmentation techniques. Starting from an initial dataset of 71 platelet images, the dataset was expanded to 141 images (Level 1) us

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SULLIVAN, DERRY O', BARRY SMYTH, and DAVID WILSON. "PRESERVING RECOMMENDER ACCURACY AND DIVERSITY IN SPARSE DATASETS." International Journal on Artificial Intelligence Tools 13, no. 01 (2004): 219–35. http://dx.doi.org/10.1142/s0218213004001491.

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Recommender systems combine research from user profiling, information filtering and artificial intelligence to provide users with more intelligent information access. They have proven to be useful in a range of Internet and e-commerce applications. Recent research has shown that a content-based (or case-based) perspective on collaborative filtering for recommendation can provide significant benefits in decision support accuracy over traditional collaborative techniques, particularly as dataset sparsity increases. These benefits derive both from the use of more sophisticated case-based similari

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Nguyen, Tuong Thanh, Van-Hung Le, Duy-Long Duong, Thanh-Cong Pham, and Dung Le. "3D Human Pose Estimation in Vietnamese Traditional Martial Art Videos." Journal of Advanced Engineering and Computation 3, no. 3 (2019): 471. http://dx.doi.org/10.25073/jaec.201933.252.

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Preserving, maintaining and teaching traditional martial arts are very important activities in social life. That helps preserve national culture, exercise and self-defense for practitioners. However, traditional martial arts have many different postures and activities of the body and body parts are diverse. The problem of estimating the actions of the human body still has many challenges, such as accuracy, obscurity, etc. In this paper, we survey several strong studies in the recent years for 3-D human pose estimation. Statistical tables have been compiled for years, typical results of these s

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Karia, Adrian Jackob, Juma Said Ally, and Stanley Leonard. "Enhancing Coffee Leaf Rust Detection Using DenseNet201: A Comprehensive Analysis of the Mbozi and Public Datasets in Songwe, Tanzania." African Journal of Empirical Research 6, no. 1 (2025): 171–88. https://doi.org/10.51867/ajernet.6.1.17.

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Coffee Leaf Rust (CLR) is a worldwide devastating fungal disease that threatens coffee production, upsetting economic and farmers' livelihoods. Traditional methods of detecting CLR heavily rely on using machine-learning (ML) models trained through weakly collected datasets and physical inspection which is tedious, time-consuming, and subject to human error. This study explores the performance of the DenseNet201 model using three datasets: Mbozi, Public, and Combined (a merger of Mbozi and Public datasets). Machine Learning Theory guided this research. The study objective is to assess the influ

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Chen, Zikang, Changli Zhou, and Zhenyu Jiang. "One-Shot Federated Learning with Label Differential Privacy." Electronics 13, no. 10 (2024): 1815. http://dx.doi.org/10.3390/electronics13101815.

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Federated learning (FL) has emerged as an extremely effective strategy for dismantling data silos and has attracted significant interest from both industry and academia in recent years. However, existing iterative FL approaches often require a large number of communication rounds and struggle to perform well on unbalanced datasets. Furthermore, the increased complexity of networks makes the application of traditional differential privacy to protect client privacy expensive. In this context, the authors introduce FedGM: a method designed to reduce communication overhead and achieve outstanding

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Sebakara, Emmanuel, and Dr K. N. Jonathan. "Encrypted Remote Access Trojan Detection: A Machine Learning Approach with Real-World and Open Datasets." Journal of Information and Technology 5, no. 3 (2025): 30–42. https://doi.org/10.70619/vol5iss3pp30-42.

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The increasing use of encryption by cyber attackers to conceal Remote Access Trojans (RATs) challenges traditional signature-based detection systems, which struggle with encrypted traffic and leave security gaps. In this study, we propose a privacy-preserving, machine-learning-based framework that detects encrypted RATs without decrypting traffic. Instead, it analyzes behavioral indicators and metadata, including packet timing anomalies, TLS handshake irregularities, and persistent unidirectional flows. We evaluated our approach using two datasets: a public Kaggle dataset (177,482 labeled reco

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Anwar, Saeed, Nick Barnes, and Lars Petersson. "A Systematic Evaluation: Fine-Grained CNN vs. Traditional CNN Classifiers." Electronics 12, no. 23 (2023): 4877. http://dx.doi.org/10.3390/electronics12234877.

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Fine-grained classifiers collect information about inter-class variations to best use the underlying minute and subtle differences. The task is challenging due to the minor differences between the colors, viewpoints, and structure in the same class entities. The classification becomes difficult and challenging due to the similarities between the differences in viewpoint with other classes and its own. This work investigates the performance of landmark traditional CNN classifiers, presenting top-notch results on large-scale classification datasets and comparing them against state-of-the-art fin

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