Harnessing the synergy of statistics and deep learning for BCI competition 4 dataset 4: a novel approach by Gauttam Jangir, Nisheeth Joshi, Gaurav Purohit

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Swin-transformer for weak feature matching by Yuan Guo, Wenpeng Li, Ping Zhai

“…To address the issue of incorrect matches in scenes with repetitive patterns, we use a cross-attention and positional encoding mechanism to learn the correct matches of repetitive patterns in two scenes, achieving higher matching precision. …”
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MATHEMATICAL MODELING OF EPILEPTOGENESIS PROCESS IN DIFFERENT AGE GROUPS Kalinin V. A. by V. A. Kalinin, I. A. Poverennova, E. M. Beketova, M. A. Gromakovskaya

“…Materialy and  methods.  It  conducts   research  on  visual  evoked  potentials  facing checkerboard  pattern in patients  with various forms  of epilepsy  in the young  and the elderly. …”
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Restoring flowcell type and basecaller configuration from FASTQ files of nanopore sequencing data by Jun Mencius, Wenjun Chen, Youqi Zheng, Tingyi An, Yongguo Yu, Kun Sun, Huijuan Feng, Zhixing Feng

“…While these data hold immense potential for advancing genomic research, their utility is compromised by the absence of flowcell type and basecaller configuration in about 85% of the data and associated publications. …”
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Intelligent approach to detecting online fraudulent trading with solution for imbalanced data in fintech forensics by Saad M. Darwish, Amr Ibrahim Salama, Adel A. Elzoghabi

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Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance by Firgiawan Faira, Dandy Pramana Hostiadi

“…Industry 4.0 requires secure networks as the advancements in IoT and AI exacerbate the challenges and vulnerabilities in data security. This research focuses on detecting Bot-IoT activity using the Bot-IoT UNSW Canberra 2018 dataset. …”
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Clinical Characterization of Patients with Syncope of Unclear Cause Using Unsupervised Machine-Learning Tools: A Pilot Study by María-José Muñoz-Martínez, Manuel Casal-Guisande, María Torres-Durán, Bernardo Sopeña, Alberto Fernández-Villar

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Analyzing the performance of biomedical time-series segmentation with electrophysiology data by Richard Redina, Jakub Hejc, Marina Filipenska, Zdenek Starek

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Melanoma Skin Lesion Classification Using Neural Networks: A systematic review by ahmed hammo, Mohammed Younis

“…Finally, a research agenda was highlighted to advance the field towards the new trends.…”
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Development of an Optimal Machine Learning Model to Predict CO₂ Emissions at the Building Demolition Stage by Gi-Wook Cha, Choon-Wook Park

“…Systematic studies on the demolition stage, however, are still lacking. In this study, research on the development of optimal machine learning (ML) models was conducted to predict CO₂ emissions at the demolition stage. …”
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Utilization of Deep Learning YOLO V9 for Identification and Classification of Toraja Buffalo Breeds by Abdul Rachman Manga', Herawati Herawati, Purnawansyah Purnawansyah

“…This system can assist in maintaining the accuracy and consistency of buffalo classification according to local customs. Future research is recommended to explore broader datasets, compare alternative object detection algorithms, and develop an integrated application for practical field use.…”
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Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Farman Ali, Biswajeet Pradhan, Soo-Mi Choi

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Exploration of Energy-Saving Chilling Landscape Design Based on Algo for Group Intelligence by Zhuo Li

“…Due to the influence of regional climatic conditions, the development of cold ecological tourism agricultural park in the north is slow, its landscape planning and design research lags behind, and there are still some problems. …”
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The development of the Polish version of the Compassionate Engagement and Action Scales. by Mariusz Zięba, Julia E Wahl, Mateusz P Zatorski, Paul Gilbert

“…Compassion has been a subject of extensive scientific research for over two decades. There is clear evidence that our capacity for compassion evolved out of care motivation. …”
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Performance Estimation of Low Power and Area-Efficient Parallel Pipelined FFT by Surya P, Arunachalaperumal C, Dhilipkumar S

“…The new FFT model implements a data-inverted Vedic multiplier in the FFT architecture, which reduces data switching activity in the input patterns to minimize dynamic power consumption and computational delay. …”
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Klasifikasi Metode Data Mining untuk Prediksi Kelulusan Tepat Waktu Mahasiswa dengan Algoritma Naïve Bayes, Random Forest, Support Vector Machine (SVM) dan Artificial Neural Nerwor... by Satrio Junaidi, Rani Valicia Anggela, Delsi Kariman

“…This research can yield new information to help colleges anticipate student graduations that are not on time. …”
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Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. by Hussein, Alkattan, Alhumaima, Ali Subhi, Oluwaseun, Adelaja A., Abotaleb, Mostafa, Mijwil, Maad M., Pradeep, Mishra, Sekiwu, Denis, Bamwerinde, Wilson, Turyasingura, Benson

“…The study deals with the use of data mining techniques to build a classification model to predict students' academic performance. The research indicates that the use of machine learning models and data mining methods can reveal hidden patterns and relationships in big data, making them indispensable tools in the field of education analysis. …”
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Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms by Eva Guttmann-Flury, Xinjun Sheng, Xiangyang Zhu

“…Abstract In Brain-Computer Interface (BCI) research, the detailed study of blinks is crucial. …”
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A Comprehensive Vector Dataset of Bus Networks Across China for the Year 2024 by Shiguang Wang, Jinyu He, Rui Ma, Zeyang Cheng, Heng Ding

“…The dataset offers valuable insights for studying network patterns in China’s bus systems, supporting international comparative analyses of public transportation systems. …”
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Comparison of Random Forest, XGBoost, and LightGBM Methods for the Human Development Index Classification by Yunna Mentari Indah, Rafika Aristawidya, Anwar Fitrianto, Erfiani Erfiani, L.M. Risman Dwi Jumansyah

“…Machine learning classification is an effective tool for categorizing data based on patterns, which is particularly useful in analyzing the Human Development Index (HDI) in Indonesia. …”
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