Small sample smart contract vulnerability detection method based on multi-layer feature fusion by Jinlin Fan, Yaqiong He, Huaiguang Wu

“…Therefore, to overcome the challenges posed by limited smart contract vulnerability datasets and high false positive rates, we introduce a data augmentation technique that incorporates function feature screening with those special smart contracts into the training set. …”
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Deep learning approach based on a patch residual for pediatric supracondylar subtle fracture detection by Qingming Ye, Zhilu Wang, Yi Lou, Yang Yang, Jue Hou, Zheng Liu, Weiguang Liu, Jiayu Li

“…Datasets from two different hospitals were used, with data augmentation techniques applied during both training and validation. …”
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High-Performance Data Acquisition for Fourier Transform Mass Spectrometry by Anton N. Kozhinov, Konstantin O. Nagornov, Yury O. Tsybin

“…As FTMS technologies advance with an increasing focus on acquiring and processing big data, FTMS Boosters, and other high-performance DAQ systems are set to become indispensable in addressing the growing demands of data-intensive scientific research and applications. …”
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Enhancing Fracture Detection in Remote Settings: Evaluating the Efficacy of FIXUS AI Deep Learning Algorithms in Identifying Fifth Metatarsal Fractures Using Mixed-Quality X-rays by Atta Taseh MD, Alireza Gholipour PhD, Mani Eftekhari, Alireza Ebrahimi MD, Alexandra F. Flaherty MD, MS, Alexandra F. Flaherty MD, MS, Sumner Jones, Varun Nukala, Gregory R. Waryasz MD, Daniel Guss MD, MBA, John Y. Kwon MD, Christopher W. DiGiovanni MD, Lorena Bejarano-Pineda MD, Soheil Ashkani-Esfahani MD

“…Category: Midfoot/Forefoot; Trauma Introduction/Purpose: The diagnosis of fractures can be challenging in specific medical settings due to limited expertise or time. While deep learning has shown promising results, its use is confined to the quality of images and the hassle of importing images to the models. …”
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Integrating anamnestic and lifestyle data with sphingolipid levels for risk-based prostate cancer screening by Caterina Peraldo-Neia, Paola Ostano, Melissa Savioli, Maurizia Mello-Grand, Ilaria Gregnanin, Francesca Guana, Francesca Crivelli, Francesco Montagnani, Michele Dei-Cas, Rita Paroni, Antonella Sinopoli, Francesco Ferranti, Nicolò Testino, Marco Oderda, Andrea Zitella, Chiara Fiameni, Amedeo Gagliardi, Alessio Naccarati, Luca Clivio, Paolo Gontero, Stefano Zaramella, Giovanna Chiorino

“…Our study aims to integrate anamnestic and lifestyle data with circulating biomarkers to minimize unnecessary second-level investigations (SLIs) for patients with suspected PCa, while improving the detection of clinically significant PCa (ISUP > 1). …”
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MQTTEEB-D: A Real-World IoT Cybersecurity Dataset for AI-Powered Threat Detection in MQTT NetworksMendeley Data by Abderrahmane Aqachtoul, Khaoula Karam, Abderrahmane Elamrani, Mehdi Najib, Najat Rafalia, Mohamed Bakhouya

“…In this paper, we introduce the framework and its experimental design, which was used to elaborate the MQTTEEB-D dataset and to execute real-time MQTT-based attacks while collecting traffic data. The MQTTEEB-D dataset is a practical real-world data set for intrusion detection improvement in MQTT-based IoT networks. …”
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The value of MRI-based radiomics and clinicoradiological data for the detection of forkhead box protein A1 gene mutated prostate cancer by Lin Deng, Ruchuan Chen, Bingni Zhou, Guoqing Hu, Hualei Gan, Ling Zhang, Liangping Zhou, Kefu Liu, Xiaohang Liu

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Automated Detection of Gibbon Calls From Passive Acoustic Monitoring Data Using Convolutional Neural Networks in the “Torch for R” Ecosystem by Dena J. Clink, Jinsung Kim, Hope Cross‐Jaya, Abdul Hamid Ahmad, Moeurk Hong, Roeun Sala, Hélène Birot, Cain Agger, Thinh Tien Vu, Hoa Nguyen Thi, Thanh Nguyen Chi, Holger Klinck

“…The fields of deep learning and automated detection are rapidly evolving, and we provide the methods and data sets as benchmarks for future work.…”
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Annotated image dataset with different stages of European pear rust for UAV-based automated symptom detection in orchardsMendeley Data by Virginia Maß, Pendar Alirezazadeh, Johannes Seidl-Schulz, Matthias Leipnitz, Eric Fritzsche, Rasheed Ali Adam Ibraheem, Martin Geyer, Michael Pflanz, Stefanie Reim

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NanoCore: core-genome-based bacterial genomic surveillance and outbreak detection in healthcare facilities from Nanopore and Illumina data by Sebastian A. Fuchs, Lisanna Hülse, Teresa Tamayo, Susanne Kolbe-Busch, Klaus Pfeffer, Alexander T. Dilthey

“…NanoCore implements a mapping, variant calling, and multilevel filtering strategy and also supports the analysis of Illumina data. We validated NanoCore on two 24-isolate data sets of methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecium (VRE). …”
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Unknown power quality disturbances classification based on transfer learning approach with imbalanced data considerations by Mohammad Mosayebi, Sasan Azad, Mohammad Taghi Ameli

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Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments by Małgorzata Przybyła-Kasperek, Jakub Sacewicz

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A Method of Simplified Synthetic Objects Creation for Detection of Underwater Objects from Remote Sensing Data Using YOLO Networks by Daniel Klukowski, Jacek Lubczonek, Pawel Adamski

“…The number of CNN application areas is growing, which leads to the need for training data. The research conducted in this work aimed to obtain effective detection models trained only using simplified synthetic objects (SSOs). …”
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Detection of X-Ray Polarization in the Hard State of IGR J17091-3624: Spectropolarimetric Study with IXPE and NuSTAR Data by Dipak Debnath, Subham Srimani, Hsiang-Kuang Chang

“…The joint spectral analysis using IXPE and NuSTAR data in the 2–70 keV band was performed with four different sets of phenomenological and physical models. …”
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Detection of carbapenemase-producing carbapenem resistant Enterobacterales (CP-CRE): A preliminary data from a tertiary hospital in Malaysia by Dr Fairuz Abdul Rashid, Dr Noraziah Sahlan, Associate Professor Dr Navindra Kumari Palanisamy, Dr Siti Farah Nawi, Associate Professor Dr Fadzilah Mohd Nor

“…Conclusion: The blaNDM is the most predominant CP-CRE gene mainly isolated from Klebsiella spp. with diverse AST profile remains evident. These preliminary data may serve as an initial guide towards a tailored management of CP-CRE infections in our setting.…”
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Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis by Hafiz Muhammad Raza Ur Rehman, Mahpara Saleem, Muhammad Zeeshan Jhandir, Eduardo Silva Alvarado, Helena Garay, Imran Ashraf

“…A thorough examination of hate speech detection methods in a variety of settings, such as code-mixed, multilingual, visual, audio, and textual scenarios, is presented in this paper. …”
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Enhancing needle puncture detection using high-pass filtering and diffuse reflectance by Rachael L’Orsa, Rachael L’Orsa, Rachael L’Orsa, Rachael L’Orsa, Anupam Bisht, Linhui Yu, Kartikeya Murari, Kartikeya Murari, Kartikeya Murari, Garnette R. Sutherland, Garnette R. Sutherland, David T. Westwick, Katherine J. Kuchenbecker

“…Four data-driven puncture-detection (DDPD) algorithms from the literature, which are appropriate for use with the variable tool velocities produced by manual insertions, were applied to the resulting data set offline. …”
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High-dimensional outlier detection based on deep belief network and linear one-class SVM by Haoqi LI, Na YING, Chunsheng GUO, Jinhua WANG

“…Aiming at the difficulties in high-dimensional outlier detection at present,an algorithm of high-dimensional outlier detection based on deep belief network and linear one-class SVM was proposed.The algorithm firstly used the deep belief network which had a good performance in the feature extraction to realize the dimensionality reduction of high-dimensional data,and then the outlier detection was achieved based on a one-class SVM with the linear kernel function.High-dimensional data sets in UCI machine learning repository were selected to experiment,result shows that the algorithm has obvious advantages in detection accuracy and computational complexity.Compared with the PCA-SVDD algorithm,the detection accuracy is improved by 4.65%.Compared with the automatic encoder algorithm,its training time and testing time decrease significantly.…”
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Privacy-Preserving Emotion Detection: Evaluating the Trade-Off Between K-Anonymity and Model Performance by Alejandro de Leon Langure, Mahdi Zareei

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WSN intrusion detection method using improved spatiotemporal ResNet and GAN by Yang Jing

“…A network intrusion detection method that integrates improved spatiotemporal residual network and generative adversarial network (GAN) in a big data environment is proposed to address the issues of poor feature extraction and significant impact from data imbalance in most existing intrusion detection methods. …”
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