Transforming tabular data into images via enhanced spatial relationships for CNN processing by Hameedah A. Alenizy, Jawad Berri

“…Abstract Convolutional neural networks (CNNs), renowned for their efficiency in image analysis, have revolutionized pattern and structure recognition in visual data. Despite their success in image-based applications, CNNs face challenges when applied to tabular data due to the lack of inherent spatial relationships among features. …”
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A novel pedestrian detection algorithm based on data fusion of face images by Jianhu Zheng, Jinshuan Peng

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Continuous multimodal data supply chain and expandable clinical decision support for oncology by Jee Suk Chang, Hyunwook Kim, Eun Sil Baek, Jeong Eun Choi, Joon Seok Lim, Jin Sung Kim, Sang Joon Shin

“…We have developed the Yonsei Cancer Data Library (YCDL) data integration framework to continuously collect and update multimodal datasets comprising over 800 features per case. …”
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JPEG reversible data hiding method based on optimal DCT frequency embedding by Ziyang ZHOU, Yun TAN, Jiaohua QIN, Xuyu XIANG

“…Joint photographic experts group (JPEG) images are widely used on the Internet due to their ability to maintain good picture quality while requiring less storage space.Reversible data hiding (RDH) techniques for JPEG images enable the hiding and extraction of secret information while allowing for lossless restoration of the original image.RDH is of great significance in file management and image authentication.However, many existing RDH methods result in a significant increase in the storage space of the encoded image after embedding secret information.For methods based on discrete cosine transform (DCT) coefficient embedding, the key issue that affects the performance of reversible information hiding in JPEG images is how to select the frequency band position.A reversible information hiding method for JPEG images was proposed, based on optimal DCT frequency embedding.The entropy decoded JPEG image was divided into 8×8 non-overlapping DCT blocks.The average texture complexity of each DCT block was calculated and ordered in ascending order.Then the alternating current (AC) in adjacent DCT blocks was paired pairwise.By satisfying the capacity of the information to be embedded, the optimal AC coefficient frequency position set was obtained from the distortion cost function of the AC coefficient frequency.The secret information was embedded according to the designed two-dimensional histogram mapping strategy.Experimental results demonstrate that the proposed method can reduce the expansion of the loaded image file’s storage size compared to existing methods, while maintaining good visual quality of the cover image.…”
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Research trends in digital marketing and data-driven marketing: A bibliometric analysis by Tuğçe Acar Kara, Ferhat Orman

“…The results of keywords’ occurrence analysis were utilized to create thematic maps for visualizing motor, niche, basic and emerging/declining research areas in the field of digital and data driven marketing.…”
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Understanding the reliability of citizen science observational data using item response models by Edgar Santos‐Fernandez, Kerrie Mengersen

“…The identification of very capable versus less skilled participants can facilitate selective use of data in analyses and more targeted training programs for citizen scientists. …”
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Application of the β-substitution method for analyzing censored data on indoor radon concentrations by Dmitry V. Kononenko

“…This issue is usually referred to as the management of left-censored data. The paper presents the results of the practical application of one the most accurate and easy to calculate methods for analyzing censored data – the β-substitution method – to the analysis of an indoor radon concentrations dataset. …”
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API development for data collection automation on soil and climate conditions in grape growing by Oleinikov Nikolay, Grishin Yurij, Kazak Anatoliy, Donskaya Darya

“…The system can collect data in two versions: issuing API commands to an external meteorological system, or processing data received from microcontrollers. …”
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Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation by Polina Lemenkova

“…Technically, the advantages of applying Python programming language for oceanographic data sets were tested. The methodological approaches include GIS data collecting, data analysis, statistical modelling, plotting and visualizing. …”
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Automated Reconstruction of Parametric BIM for Bridge Based on Terrestrial Laser Scanning Data by Guocheng Qin, Yin Zhou, Kaixin Hu, Daguang Han, Chunli Ying

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Integration of data science with the intelligent IoT (IIoT): Current challenges and future perspectives by Inam Ullah, Deepak Adhikari, Xin Su, Francesco Palmieri, Celimuge Wu, Chang Choi

“…Extensive insights into IoT data security, privacy, and challenges are visualized in the context of data science for IoT. …”
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Inception networks, data augmentation and transfer learning in EEG-based photosensitivity diagnosis by Fernando Moncada Martins, Víctor M González, José R Villar, Beatriz García López, Ana Isabel Gómez-Menéndez

“…This research tackles this problem and proposes using Inception-based deep learning (DL) neural networks that, together with transfer learning, are trained in epilepsy seizure detection and tuned in the PPR automatic detection task. A data augmentation (DA) technique is also applied to balance the available data set, evaluating its effects on the DL models. …”
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Scalable earthquake magnitude prediction using spatio-temporal data and model versioning by Rahul Singh, Bholanath Roy

“…This study introduces a novel approach by using spatio-temporal data from seismic records obtained from the Indian government seismology department and weather data sourced via VisualCrossing to predict earthquake magnitudes. …”
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Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information by Sang-Il Kim, Donghyun Jin, Jiyeon Kim, Do-Seob Ahn, Kyung-Soo Han

“…We constructed a distributed processing system using Apache Spark, enabling the efficient processing of large-scale weather data. The performance evaluation demonstrated excellent scalability and efficiency in processing large-scale data. …”
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Robust Fusion of Time Series and Image Data for Improved Multimodal Clinical Prediction by Ali Rasekh, Reza Heidari, Amir Hosein Haji Mohammad Rezaie, Parsa Sharifi Sedeh, Zahra Ahmadi, Prasenjit Mitra, Wolfgang Nejdl

“…To tackle this challenge, we introduce a new method that uses two separate encoders, one for each type of data, allowing the model to understand complex patterns in both visual and time-based information. …”
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Building Text‐to‐Speech Models for Low‐Resourced Languages From Crowdsourced Data by Andrew Katumba, Sulaiman Kagumire, Joyce Nakatumba‐Nabende, John Quinn, Sudi Murindanyi

“…In this paper, we demonstrate that crowdsourced recordings can help overcome the lack of single‐speaker data by compensating with data from other speakers of similar intonation (how the voice rises and falls in speech). …”
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Effective deep learning aided vehicle classification approach using Seismic Data by Sherief Hashima, Mohamed H. Saad, Ahmad B. Ahmad, Takeshi Tsuji, Hamada Rizk

“…Traditional classification algorithms rely heavily on visual or sensor-based data (e.g., radar or image signals), often compromised by adverse weather, poor lighting, or occlusion. …”
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Evaluating Art Exhibition Spaces Through Space Syntax and Multimodal Physiological Data by Yunwan Dai, Yujie Ren, Hong Li, Meng Wang

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Identifying performance patterns in professional mixed martial arts: An exploratory data approach by Muhammad Qasthalani, Ahmad Maulana, Bonita Amelia

“…With the sport’s rapid professionalization, data-driven approaches have become increasingly important in evaluating fighter performance and informing strategy. …”
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Multimodal Learning for Traffic Risk Prediction: Combining Aerial Imagery With Contextual Data by Hanlin Tian, Yuxiang Feng, Mohammed Quddus, Yiannis Demiris, Panagiotis Angeloudis

“…Using DeepLabV3+, UNet++, and SegFormer as baseline models, we demonstrate that combining building and traffic data enhances prediction accuracy compared to models relying solely on visual data. …”
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