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Journal articles on the topic 'Datasety'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

Lingeswari Sivagnanam. "Enhanced Ensemble Machine Learning Technique to detect Bipolar Disorder." Journal of Information Systems Engineering and Management 10, no. 38s (2025): 610–19. https://doi.org/10.52783/jisem.v10i38s.6932.

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The study introduced a unique Enhanced Ensemble Machine Learning (EEML) method for the detection of bipolar disorder, a serious medical diagnoses problem that acquires an immediate and timely diagnosis for an effective and earlier treatment. The EEML approach combines several machine learning models with the data based on patient’s attitude on different perspectives such as real, signal based, textual and behavioural data and more. In order to train individual classifiers for identifying mental diseases, pertinent characteristics are taken from each data source using a thorough feature selecti

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Almeida, Daniela, Dany Domínguez-Pérez, Ana Matos, et al. "Data Employed in the Construction of a Composite Protein Database for Proteogenomic Analyses of Cephalopods Salivary Apparatus." Data 5, no. 4 (2020): 110. http://dx.doi.org/10.3390/data5040110.

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Here we provide all datasets and details applied in the construction of a composite protein database required for the proteogenomic analyses of the article “Putative Antimicrobial Peptides of the Posterior Salivary Glands from the Cephalopod Octopus vulgaris Revealed by Exploring a Composite Protein Database”. All data, subdivided into six datasets, are deposited at the Mendeley Data repository as follows. Dataset_1 provides our composite database “All_Databases_5950827_sequences.fasta” derived from six smaller databases composed of (i) protein sequences retrieved from public databases related

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Al-Zubaidi, Asst Prof Ehsan Ali, Furkan Rabee, and Ahmed H. Al-Sulttani. "Classification of Large-Scale Datasets of Landsat-8 Satellite Image Based on LIBLINEAR Library." Al-Salam Journal for Engineering and Technology 1, no. 2 (2022): 9–17. http://dx.doi.org/10.55145/ajest.2022.01.02.003.

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One of the most common machine learning approaches is linear classification. Multithreading used in LIBLINEAR Library has become an important research topic for processing large datasets. Working in a multithreaded environment accelerates the training of large datasets and increases classification accuracy. This study examines multiple linear support vector machine implementations for binary classification of Landsat-8 satellite image datasets to determine the optimal model. Four datasets have been created for different scenes in Iraq. Each dataset contains millions of pixels to be classified

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Kabir, Masud, Fatih Unal, Tahir Cetin Akinci, Alfredo A. Martinez-Morales, and Sami Ekici. "Revealing GLCM Metric Variations across a Plant Disease Dataset: A Comprehensive Examination and Future Prospects for Enhanced Deep Learning Applications." Electronics 13, no. 12 (2024): 2299. http://dx.doi.org/10.3390/electronics13122299.

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This study highlights the intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance in the context of plant disease identification. It emphasizes the importance of rigorous dataset evaluation and selection protocols to ensure reliable and generalizable classification outcomes. Through a comprehensive examination of publicly available plant disease datasets, focusing on their performance as measured by GLCM metrics, this research identified dataset_2 (D2), a database of leaf images, as the top performer across all GLCM analyses. These d

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Papadopoulos, Symeon, Giorgos Kordopatis-Zilos, Markos Zampoglou, and Olga Papadopoulou. "Dataset column: Datasets for online multimedia verification." ACM SIGMultimedia Records 11, no. 3 (2019): 1. http://dx.doi.org/10.1145/3524460.3524468.

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Li, Yiwen. "Elastic distributed dataset RDD (Resilient Distributed Datasets)." Applied and Computational Engineering 31, no. 1 (2024): 184–93. http://dx.doi.org/10.54254/2755-2721/31/20230149.

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In recent years, the amount of data to be processed has become larger and larger, and it is difficult for a single processor to handle data, so we need to work hard to develop the expansion mode of cluster mode. Mapreduce, Storm, etc. are a solution for clusters to process data. However, we still have parallel processing, user resource sharing and other issues to be addressed. RDD data model is the key to solve these problems. In this paper, RDD is applied to Spark for detailed expansion, and project experiments are carried out to study the RDD data model. The programming language used in this

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A. Adesokan, Ademola. "Covid-19 Control: Face Mask Detection Using Deep Learning for Balanced and Unbalanced Dataset." International Journal of Intelligent Systems and Applications 14, no. 6 (2022): 50–62. http://dx.doi.org/10.5815/ijisa.2022.06.05.

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Facemask wearing is becoming a norm in our daily lives to curb the spread of Covid-19. Ensuring facemasks are worn correctly is a topic of concern worldwide. It could go beyond manual human control and enforcement, leading to the spread of this deadly virus and many cases globally. The main aim of wearing a facemask is to curtail the spread of the covid-19 virus, but the biggest concern of most deep learning research is about who is wearing the mask or not, and not who is incorrectly wearing the facemask while the main objective of mask wearing is to prevent the spread of the covid-19 virus. T

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Khan, Somaiya, and Ali Khan. "SkinViT: A transformer based method for Melanoma and Nonmelanoma classification." PLOS ONE 18, no. 12 (2023): e0295151. http://dx.doi.org/10.1371/journal.pone.0295151.

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Over the past few decades, skin cancer has emerged as a major global health concern. The efficacy of skin cancer treatment greatly depends upon early diagnosis and effective treatment. The automated classification of Melanoma and Nonmelanoma is quite challenging task due to presence of high visual similarities across different classes and variabilities within each class. According to the best of our knowledge, this study represents the classification of Melanoma and Nonmelanoma utilising Basal Cell Carcinoma (BCC) and Squamous Cell Carcinoma (SCC) under the Nonmelanoma class for the first time

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Prithvi R. "Multi-Model Evaluation of Computer Vision Techniques for Fine-Grained Vehicle Recognition." Journal of Information Systems Engineering and Management 10, no. 44s (2025): 428–59. https://doi.org/10.52783/jisem.v10i44s.8612.

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Vehicle make and model recognition is a crucial component in applications such as intelligent transportation systems, law enforcement, and autonomous vehicles.This paper presents a comparative analysis of Machine Learning approaches — KNN, SVM and Decision Tree Classifier which are the first appraoch and three deep learning models—Convolutional Neural Network (CNN), YOLOv8, and Faster R-CNN— are the second approach for vehicle recognition tasks. The models were evaluated on a two datasets comprising 197 vehicle classes (dataset1) under various conditions and the other with was scrapped manuall

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Haider, S. A., and N. S. Patil. "Minimization of Datasets : Using a Master Interlinked Dataset." Indian Journal of Computer Science 3, no. 5 (2018): 20. http://dx.doi.org/10.17010/ijcs/2018/v3/i5/138778.

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Afshan, Noor, Zohaib Mushtaq, Faten S. Alamri, Muhammad Farrukh Qureshi, Nabeel Ahmed Khan, and Imran Siddique. "Efficient thyroid disorder identification with weighted voting ensemble of super learners by using adaptive synthetic sampling technique." AIMS Mathematics 8, no. 10 (2023): 24274–309. http://dx.doi.org/10.3934/math.20231238.

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<abstract><p>There are millions of people suffering from thyroid disease all over the world. For thyroid cancer to be effectively treated and managed, a correct diagnosis is necessary. In this article, we suggest an innovative approach for diagnosing thyroid disease that combines an adaptive synthetic sampling method with weighted average voting (WAV) ensemble of two distinct super learners (SLs). Resampling techniques are used in the suggested methodology to correct the class imbalance in the datasets and a group of two SLs made up of various base estimators and meta-estimators is

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Hendriko, Viky, and Dedy Hermanto. "Performance Comparison of YOLOv10, YOLOv11, and YOLOv12 Models on Human Detection Datasets." Brilliance: Research of Artificial Intelligence 5, no. 1 (2025): 440–50. https://doi.org/10.47709/brilliance.v5i1.6447.

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One popular of object detection model for object detection is You Only Look Once (YOLO) with humans are among the most often utilized for detection objects. Despite the various of human datasets, just a few research that compared the datasets performance against various versions of the YOLO algorithm. This study compares the performance of YOLOv10, YOLOv11, and YOLOv12 on eight different datasets, such as CrowdHuman, CityPersons, Wider Person, Mall Dataset, INRIA, Microsoft Common Object (MS COCO), PASCAL VOC, and MOT17. Precision, recall, mAP@50, and mAP@50-95 are used to measure the YOLO mod

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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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Scheuerman, Morgan Klaus, Alex Hanna, and Emily Denton. "Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development." Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021): 1–37. http://dx.doi.org/10.1145/3476058.

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Data is a crucial component of machine learning. The field is reliant on data to train, validate, and test models. With increased technical capabilities, machine learning research has boomed in both academic and industry settings, and one major focus has been on computer vision. Computer vision is a popular domain of machine learning increasingly pertinent to real-world applications, from facial recognition in policing to object detection for autonomous vehicles. Given computer vision's propensity to shape machine learning research and impact human life, we seek to understand disciplinary prac

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Chang, Nai Chen, Elissa Aminoff, John Pyles, Michael Tarr, and Abhinav Gupta. "Scaling Up Neural Datasets: A public fMRI dataset of 5000 scenes." Journal of Vision 18, no. 10 (2018): 732. http://dx.doi.org/10.1167/18.10.732.

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Yoon, Seo-Hee, Go-Eun Ha, and Seok-Jong Yu. "Meta-Dataset Creation Method for Classification and Search of Open Datasets." Journal of Korean Institute of Information Technology 22, no. 4 (2024): 17–22. http://dx.doi.org/10.14801/jkiit.2024.22.4.17.

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Bogaardt, Laurens, Romulo Goncalves, Raul Zurita-Milla, and Emma Izquierdo-Verdiguier. "Dataset Reduction Techniques to Speed Up SVD Analyses on Big Geo-Datasets." ISPRS International Journal of Geo-Information 8, no. 2 (2019): 55. http://dx.doi.org/10.3390/ijgi8020055.

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The Singular Value Decomposition (SVD) is a mathematical procedure with multiple applications in the geosciences. For instance, it is used in dimensionality reduction and as a support operator for various analytical tasks applicable to spatio-temporal data. Performing SVD analyses on large datasets, however, can be computationally costly, time consuming, and sometimes practically infeasible. However, techniques exist to arrive at the same output, or at a close approximation, which requires far less effort. This article examines several such techniques in relation to the inherent scale of the s

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Zhang, Yulian, and Shigeyuki Hamori. "Forecasting Crude Oil Market Crashes Using Machine Learning Technologies." Energies 13, no. 10 (2020): 2440. http://dx.doi.org/10.3390/en13102440.

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To the best of our knowledge, this study provides new insight into the forecasting of crude oil futures price crashes in America, employing a moving window. One is the fixed-length window and the other is the expanding-length window, which has never been reported in the past. We aimed to investigate if there is any difference when historical data are discarded. As the explanatory variables, we adapted 13 variables to obtain two datasets, 16 explanatory variables for Dataset1 and 121 explanatory variables for Dataset2. We try to observe results from the different-sized sets of explanatory varia

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Harshal Paratwar, Vitthal Waghere, Chaitanya Ambekar, Deepak S. Uplaonkar, Santosh Sarvade,. "Curated Datasets for Use in Automated Media Monitoring and Feedback System: “News Classification System” Dataset, “Government News Classification” Dataset." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 1019–27. http://dx.doi.org/10.17762/ijritcc.v11i9.8993.

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Online journalism in India, a growing field that involves news websites and Digital media, connects with the Press Information Bureau (PIB), a government agency dedicated to sharing accurate information about government policies and initiatives with journalists. While various news outlets publish diverse articles and opinions on these topics, the government seeks to leverage Artificial Intelligence and Machine Learning for gathering feedback in multiple languages. To develop such a system, a notable obstacle is the lack of a readily accessible standard dataset is required. To address this, two

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Wang, Juan, Zhibin Zhang, and Yanjuan Li. "Constructing Phylogenetic Networks Based on the Isomorphism of Datasets." BioMed Research International 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/4236858.

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Constructing rooted phylogenetic networks from rooted phylogenetic trees has become an important problem in molecular evolution. So far, many methods have been presented in this area, in which most efficient methods are based on the incompatible graph, such as the CASS, the LNETWORK,and the BIMLR. This paper will research the commonness of the methods based on the incompatible graph, the relationship between incompatible graph and the phylogenetic network, and the topologies of incompatible graphs. We can find out all the simplest datasets for a topologyGand construct a network for every datas

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Xie, Yanqing, Zhengqiang Li, Weizhen Hou, et al. "Validation of FY-3D MERSI-2 Precipitable Water Vapor (PWV) Datasets Using Ground-Based PWV Data from AERONET." Remote Sensing 13, no. 16 (2021): 3246. http://dx.doi.org/10.3390/rs13163246.

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The medium resolution spectral imager-2 (MERSI-2) is one of the most important sensors onboard China’s latest polar-orbiting meteorological satellite, Fengyun-3D (FY-3D). The National Satellite Meteorological Center of China Meteorological Administration has developed four precipitable water vapor (PWV) datasets using five near-infrared bands of MERSI-2, including the P905 dataset, P936 dataset, P940 dataset and the fusion dataset of the above three datasets. For the convenience of users, we comprehensively evaluate the quality of these PWV datasets with the ground-based PWV data derived from

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M.Lenin Babu, Y. Manjula Brahmeswari, N.Anusha, M. Vijaya Lakshmi, and M.Srikanth. "Knife Detection System Using CLIP-Based Vision-Language Model." international journal of engineering technology and management sciences 9, no. 2 (2025): 190–99. https://doi.org/10.46647/ijetms.2025.v09i02.026.

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Knife detection in restricted areas is essential to ensure security and preventing harm. In institutions such as schools, airports, prisons, or public transport, restricted areas can easily turn into a threat if someone carries a knife. Knifes are detected to prevent possible threats, eliminate injuries, and provide a safe environment for all. AI based Knife detection ensures safety, preventing harm, saves lives, promotes safe public spaces. In this work a machine learning algorithm known as ViT B/32 a Contrastive Language–Image Pretraining (CLIP) based model is presented for efficient detecti

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KAZI TANI, Lamia Fatiha, Mohammed Yassine KAZI TANI, and Benamar KADRI. "Gas-Net: A deep neural network for gastric tumor semantic segmentation." AIMS Bioengineering 9, no. 3 (2022): 266–82. http://dx.doi.org/10.3934/bioeng.2022018.

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<abstract> <p>Currently, the gastric cancer is the source of the high mortality rate where it is diagnoses from the stomach and esophagus tests. To this end, the whole of studies in the analysis of cancer are built on AI (artificial intelligence) to develop the analysis accuracy and decrease the danger of death. Mostly, deep learning methods in images processing has made remarkable advancement. In this paper, we present a method for detection, recognition and segmentation of gastric cancer in endoscopic images. To this end, we propose a deep learning method named GAS-Net to detect

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Majidifard, Hamed, Peng Jin, Yaw Adu-Gyamfi, and William G. Buttlar. "Pavement Image Datasets: A New Benchmark Dataset to Classify and Densify Pavement Distresses." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 2 (2020): 328–39. http://dx.doi.org/10.1177/0361198120907283.

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Automated pavement distresses detection using road images remains a challenging topic in the computer vision research community. Recent developments in deep learning have led to considerable research activity directed towards improving the efficacy of automated pavement distress identification and rating. Deep learning models require a large ground truth data set, which is often not readily available in the case of pavements. In this study, a labeled dataset approach is introduced as a first step towards a more robust, easy-to-deploy pavement condition assessment system. The technique is terme

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Nam, Ki Hyun. "Application of Serial Crystallography for Merging Incomplete Macromolecular Crystallography Datasets." Crystals 14, no. 12 (2024): 1012. http://dx.doi.org/10.3390/cryst14121012.

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In macromolecular crystallography (MX), a complete diffraction dataset is essential for determining the three-dimensional structure. However, collecting a complete experimental dataset using a single crystal is frequently unsuccessful due to poor crystal quality or radiation damage, resulting in the collection of multiple incomplete datasets. This issue can be solved by merging incomplete diffraction datasets to generate a complete dataset. This study introduced a new approach for merging incomplete datasets from MX to generate a complete dataset using serial crystallography (SX). Six incomple

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Li Yunpeng, 李云鹏, 葛宝臻 Ge Baozhen, 田庆国 Tian Qingguo та 吕且妮 Qieni Lü. "基于立体像对数据集的非对称离焦数据集构建". Acta Optica Sinica 42, № 14 (2022): 1415001. http://dx.doi.org/10.3788/aos202242.1415001.

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Waliser, Duane, Peter J. Gleckler, Robert Ferraro, et al. "Observations for Model Intercomparison Project (Obs4MIPs): status for CMIP6." Geoscientific Model Development 13, no. 7 (2020): 2945–58. http://dx.doi.org/10.5194/gmd-13-2945-2020.

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Abstract. The Observations for Model Intercomparison Project (Obs4MIPs) was initiated in 2010 to facilitate the use of observations in climate model evaluation and research, with a particular target being the Coupled Model Intercomparison Project (CMIP), a major initiative of the World Climate Research Programme (WCRP). To this end, Obs4MIPs (1) targets observed variables that can be compared to CMIP model variables; (2) utilizes dataset formatting specifications and metadata requirements closely aligned with CMIP model output; (3) provides brief technical documentation for each dataset, desig

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Chen, Zhiyu. "Dataset Search and Augmentation." ACM SIGIR Forum 56, no. 1 (2022): 1–2. http://dx.doi.org/10.1145/3582524.3582544.

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Data has become an indispensable part of our life. However, current mainstream commercial search engines do not support specialized functions for dataset search. A dataset usually consists of both metadata and data content. Existing information retrieval models designed for Web search cannot efficiently extract semantic information inside structured datasets, even when they contain textual content. Developing new algorithms for next-generation search engines to efficiently find datasets can benefit data practitioners in their data discovery experience. In this dissertation, we consider how to

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Putri, Pramaishella Ardiani Regita, Sri Suryani Prasetiyowati, and Yuliant Sibaroni. "The Performance of the Equal-Width and Equal-Frequency Discretization Methods on Data Features in Classification Process." sinkron 8, no. 4 (2023): 2082–98. http://dx.doi.org/10.33395/sinkron.v8i4.12730.

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The classification process often needs help with suboptimal accuracy values, which can be attributed to various factors, including the dataset's wide range of attribute values. Discretization methods offer a solution to address these issues. This study aims to compare the effectiveness of Equal-Width and Equal-Frequency discretization methods in enhancing accuracy during the classification process using datasets with varying sizes. The research employs Naïve Bayes, Decision Tree, and Support Vector Machine as classification models, with three datasets utilized: Bandung City Traffic data (3804

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Roy, Sudipto, and Jigyasu Dubey. "Optimizing Mental Health Detection in Indian Armed Forces Personnel through Feature Engineering Driven Dataset Reduction, Addressing Suicide, Depression, and Stress." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 2 (2024): 70–81. http://dx.doi.org/10.32628/cseit241026.

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Within the realm of machine learning, the construction of high-quality datasets stands as a crucial factor profoundly influencing model performance. This research aims to furnish a comprehensive guide for enhancing the accuracy and efficiency of dataset construction. It achieves this by integrating multi-variate reduction techniques and innovative feature engineering strategies, implemented within the Python programming ecosystem. As the landscape of datasets becomes increasingly diverse and complex, the imperative to optimize precision grows more critical. This study explores the judicious ap

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Kusetogullari, Huseyin, Amir Yavariabdi, Abbas Cheddad, Håkan Grahn, and Johan Hall. "ARDIS: a Swedish historical handwritten digit dataset." Neural Computing and Applications 32, no. 21 (2019): 16505–18. http://dx.doi.org/10.1007/s00521-019-04163-3.

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Abstract This paper introduces a new image-based handwritten historical digit dataset named Arkiv Digital Sweden (ARDIS). The images in ARDIS dataset are extracted from 15,000 Swedish church records which were written by different priests with various handwriting styles in the nineteenth and twentieth centuries. The constructed dataset consists of three single-digit datasets and one-digit string dataset. The digit string dataset includes 10,000 samples in red–green–blue color space, whereas the other datasets contain 7600 single-digit images in different color spaces. An extensive analysis of

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Eum, Hyung-Il, and Anil Gupta. "Hybrid climate datasets from a climate data evaluation system and their impacts on hydrologic simulations for the Athabasca River basin in Canada." Hydrology and Earth System Sciences 23, no. 12 (2019): 5151–73. http://dx.doi.org/10.5194/hess-23-5151-2019.

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Abstract. A reliable climate dataset is the backbone for modelling the essential processes of the water cycle and predicting future conditions. Although a number of gridded climate datasets are available for the North American content which provide reasonable estimates of climatic conditions in the region, there are inherent inconsistencies in these available climate datasets (e.g., spatially and temporally varying data accuracies, meteorological parameters, lengths of records, spatial coverage, temporal resolution, etc.). These inconsistencies raise questions as to which datasets are the most

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Meier, Deborah, and Wolfgang Tschacher. "Beyond Dyadic Coupling: The Method of Multivariate Surrogate Synchrony (mv-SUSY)." Entropy 23, no. 11 (2021): 1385. http://dx.doi.org/10.3390/e23111385.

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Measuring interpersonal synchrony is a promising approach to assess the complexity of social interaction, which however has been mostly limited to dyads. In this study, we introduce multivariate Surrogate Synchrony (mv-SUSY) to extend the current set of computational methods. Methods: mv-SUSY was applied to eight datasets consisting of 10 time series each, all with n = 9600 observations. Datasets 1 to 5 consist of simulated time series with the following characteristics: white noise (dataset 1), non-stationarity with linear time trends (dataset 2), autocorrelation (dataset 3), oscillation (dat

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Chaudhary, Archana, Savita Kolhe, and Raj Kamal. "A hybrid ensemble for classification in multiclass datasets: An application to oilseed disease dataset." Computers and Electronics in Agriculture 124 (June 2016): 65–72. http://dx.doi.org/10.1016/j.compag.2016.03.026.

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Orr, Will, and Kate Crawford. "The social construction of datasets: On the practices, processes, and challenges of dataset creation for machine learning." New Media & Society 26, no. 9 (2024): 4955–72. http://dx.doi.org/10.1177/14614448241251797.

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Despite the critical role that datasets play in how systems make predictions and interpret the world, the dynamics of their construction are not well understood. Drawing on a corpus of interviews with dataset creators, we uncover the messy and contingent realities of dataset preparation. We identify four key challenges in constructing datasets, including balancing the benefits and costs of increasing dataset scale, limited access to resources, a reliance on shortcuts for compiling datasets and evaluating their quality, and ambivalence regarding accountability for a dataset. These themes illust

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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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Hu, Yuhuang, Hongjie Liu, Michael Pfeiffer, and Tobi Delbruck. "DVS Benchmark Datasets for Object Tracking, Action Recognition, and Object Recognition." Frontiers in Neuroscience 10 (August 31, 2016): 405. https://doi.org/10.3389/fnins.2016.00405.

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This data report summarizes a new benchmark dataset in which we converted established visual video benchmarks for object tracking, action recognition and object recognition into spiking neuromorphic datasets, recorded with the DVS output (Lichtsteiner et al., 2008) of a DAVIS camera (Berner et al., 2013; Brandli et al., 2014). This report presents our approach for sensor calibration and capture of frame-based videos into neuromorphic vision datasets with minimal human intervention. We converted four widely used dynamic datasets: the VOT Challenge 2015 Dataset (Kristan et al., 2016), TrackingDa

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Bhalekar, M., and M. Bedekar. "The New Dataset MITWPU-1K for Object Recognition and Image Captioning Tasks." Engineering, Technology & Applied Science Research 12, no. 4 (2022): 8803–8. http://dx.doi.org/10.48084/etasr.5039.

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In the domain of image captioning, many pre-trained datasets are available. Using these datasets, models can be trained to automatically generate image descriptions regarding the contents of an image. Researchers usually do not spend much time in creating and training the new dataset before using it for a specific application, instead, they simply use existing pre-trained datasets. MS COCO, ImageNet, Flicker, and Pascal VOC, are well-known datasets that are widely used in the task of generating image captions. In most available image captioning datasets, image textual information, which can pl

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Yu, Kristen K., Matthew Guzdial, and Nathan R. Sturtevant. "The FarmQuest Player Telemetry Dataset: Playthrough Data of a Cozy Farming Game." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 20, no. 1 (2024): 263–68. http://dx.doi.org/10.1609/aiide.v20i1.31889.

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Open datasets are the foundation of many types of academic research. One field that requires datasets is data-driven player modeling, but there is a lack of variety in existing datasets. We introduce the FarmQuest Player Telemetry (FPT) dataset, a new playthrough dataset of a cozy farming game. We envision this dataset will be used for reproducing prior results, evaluating initial ideas, and evaluating the generalizability of new and existing algorithms.

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Marcoulides, Katerina, Jia Quan, and Eric Wright. "The Impact of Sample Size on Exchangeability in the Bayesian Synthesis Approach to Data Fusion." Journal of Behavioral Data Science 2, no. 1 (2022): 75–105. http://dx.doi.org/10.35566/jbds/v2n1/p5.

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Data fusion approaches have been adopted to facilitate more complex analyses and produce more accurate results. Bayesian Synthesis is a relatively new approach to data fusion where results from the analysis of one dataset are used as prior information for the analysis of the next dataset. Datasets of interest are sequentially analyzed until a final posterior distribution is created, incorporating information from all candidate datasets, rather than simply combining the datasets into one large dataset and analyzing them simultaneously. One concern with this approach lies in the sequence of data

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Ambachew, Daniel, Jorge Mario Londoño, Nohra Rodriguez Castillo, Asrat Asfaw, and Matthew Wohlgemuth Blair. "Genetic Diversity, Linkage Disequilibrium, and Population Structure in a Common Bean Reference Collection." Agronomy 14, no. 5 (2024): 985. http://dx.doi.org/10.3390/agronomy14050985.

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An in-depth understanding of the extent and pattern of genetic diversity and population structure in crop populations is of paramount importance for any crop improvement program to efficiently promote the translation of genetic diversity into genetic gain. A reference collection of 150 common bean genotypes selected from the International Center for Tropical Agriculture’s global core collection was evaluated using single-nucleotide polymorphism (SNP) markers to quantify the amount of genetic diversity, linkage disequilibrium, and population structure. The cultivars and landraces of the collect

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Kim, Doyoung, Inwoong Lee, Dohyung Kim, and Sanghoon Lee. "Action Recognition Using Close-Up of Maximum Activation and ETRI-Activity3D LivingLab Dataset." Sensors 21, no. 20 (2021): 6774. http://dx.doi.org/10.3390/s21206774.

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The development of action recognition models has shown great performance on various video datasets. Nevertheless, because there is no rich data on target actions in existing datasets, it is insufficient to perform action recognition applications required by industries. To satisfy this requirement, datasets composed of target actions with high availability have been created, but it is difficult to capture various characteristics in actual environments because video data are generated in a specific environment. In this paper, we introduce a new ETRI-Activity3D-LivingLab dataset, which provides a

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Arief, Muhammad, Made Gunawan, Agung Septiadi, et al. "A novel framework for analyzing internet of things datasets for machine learning and deep learning-based intrusion detection systems." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1574. http://dx.doi.org/10.11591/ijai.v13.i2.pp1574-1584.

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To generate a machine learning (ML) and deep learning (DL) architecture with good performance, we need a decent dataset for the training and testing phases of the development process. Starting with the knowledge discovery and data mining (KDD) Cup 99 dataset, numerous datasets have been produced since 1998 to be utilized in the ML and DL-based intrusion detection systems (IDS) training and testing process. Because there are so many datasets accessible, it might be challenging for researchers to choose which dataset to employ. Therefore, a framework for evaluating dataset appropriateness with t

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Muhammad, Arief, Gunawan Made, Septiadi Agung, et al. "A novel framework for analyzing internet of things datasets for machine learning and deep learning-based intrusion detection systems." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1574–84. https://doi.org/10.11591/ijai.v13.i2.pp1574-1584.

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To generate a machine learning (ML) and deep learning (DL) architecture with good performance, we need a decent dataset for the training and testing phases of the development process. Starting with the knowledge discovery and data mining (KDD) Cup 99 dataset, numerous datasets have been produced since 1998 to be utilized in the ML and DL-based intrusion detection systems (IDS) training and testing process. Because there are so many datasets accessible, it might be challenging for researchers to choose which dataset to employ. Therefore, a framework for evaluating dataset appropriateness with t

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Gao, Chenxi. "Generative Adversarial Networks-based solution for improving medical data quality and insufficiency." Applied and Computational Engineering 49, no. 1 (2024): 167–75. http://dx.doi.org/10.54254/2755-2721/49/20241086.

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As big data brings intelligent solutions and innovations to various fields, the goal of this research is to solve the problem of poor-quality and insufficient datasets in the medical field, to help poor areas can access to high quality and rich medical datasets as well. This study focuses on solving the current problem by utilizing variants of generative adversarial network, Super Resolution Generative Adversarial Network (SRGAN) and Deep Convolutional Generative Adversarial Network (DCGAN). In this study, OpenCV is employed to introduce fuzziness to the Brain Tumor MRI Dataset, resulting in a

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Dhadhal, Hema, and Paresh Kotak. "Leveraging datasets for effective mitigation of DDoS attacks in software-defined networking: significance and challenges." Radioelectronic and Computer Systems 2024, no. 2 (2024): 136–46. http://dx.doi.org/10.32620/reks.2024.2.11.

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Software-Defined Networking (SDN) has emerged as a transformative paradigm for network management, offering centralized control and programmability. However, with the proliferation of Distributed Denial of Service (DDoS) attacks that pose significant threats to network infrastructures, effective mitigation strategies are needed. The subject matter of this study is to explore the importance of datasets in the mitigation of DDoS attacks in SDN environments. The paper discusses the significance of datasets for training machine learning models, evaluating detection mechanisms, and enhancing the re

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Wang, Xufei, Penghui Wang, Jeongyoung Song, Taotao Hao, and Xinlu Duan. "A TEDE Algorithm Studies the Effect of Dataset Grouping on Supervised Learning Accuracy." Electronics 12, no. 11 (2023): 2546. http://dx.doi.org/10.3390/electronics12112546.

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Datasets are the basis for research on deep learning methods in computer vision. The impact of the percentage of training sets in a dataset on the performance of neural network models needs to be further explored. In this paper, a twice equal difference enumeration (TEDE) algorithm is proposed to investigate the effect of different training set percentages in the dataset on the performance of the network model, and the optimal training set percentage is determined. By selecting the Pascal VOC dataset and dividing it into six different datasets from largest to smallest, and then dividing each d

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Li, Jiajia, Zikai Wang, Longxuan Yu, Hui Liu, and Haitao Song. "Synthetic Data-Driven Approaches for Chinese Medical Abstract Sentence Classification: Computational Study." JMIR Formative Research 9 (March 19, 2025): e54803-e54803. https://doi.org/10.2196/54803.

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Abstract Background Medical abstract sentence classification is crucial for enhancing medical database searches, literature reviews, and generating new abstracts. However, Chinese medical abstract classification research is hindered by a lack of suitable datasets. Given the vastness of Chinese medical literature and the unique value of traditional Chinese medicine, precise classification of these abstracts is vital for advancing global medical research. Objective This study aims to address the data scarcity issue by generating a large volume of labeled Chinese abstract sentences without manual

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Zhang, Zongxi, Kejian Zhou, Xiaohu Wang, et al. "A Similarity-based Time Series Source Dataset Selection Method for Transfer Learning." Journal of Physics: Conference Series 2428, no. 1 (2023): 012038. http://dx.doi.org/10.1088/1742-6596/2428/1/012038.

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Abstract Source datasets are selected with high similarity to target datasets to improve the effect of transfer learning. The low similarity between the source and target datasets may lead to negative transfer. This paper proposes a similarity-based time series source dataset selection method for transfer learning. First, to reduce the complexity of the operation, we use Dynamic time-warped barycentric averaging to obtain the prototype signals of each dataset and convert the dataset similarity calculation into the similarity calculation between the prototype signals. The dynamic time warping (

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Xu, Xiaolin, Yuan Zong, Cheng Lu, and Xingxun Jiang. "Enhanced Sample Self-Revised Network for Cross-Dataset Facial Expression Recognition." Entropy 24, no. 10 (2022): 1475. http://dx.doi.org/10.3390/e24101475.

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Recently, cross-dataset facial expression recognition (FER) has obtained wide attention from researchers. Thanks to the emergence of large-scale facial expression datasets, cross-dataset FER has made great progress. Nevertheless, facial images in large-scale datasets with low quality, subjective annotation, severe occlusion, and rare subject identity can lead to the existence of outlier samples in facial expression datasets. These outlier samples are usually far from the clustering center of the dataset in the feature space, thus resulting in considerable differences in feature distribution, w

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