Osteoporosis: a problem still faulty addressed by the Romanian healthcare system. Results of a questionnaire survey of people aged 40 years and over by Narcisa Jianu, Narcisa Jianu, Narcisa Jianu, Valentina Oana Buda, Valentina Oana Buda, Denisa Căpățână, Călin Muntean, Teodor Nicolae Onea, Maria Anastasia Jivulescu, Ana Teodor, Ana Teodor, Mirabela Romanescu, Mirabela Romanescu, Lucreția Udrescu, Lucreția Udrescu, Vlad Groza, Mihai Udrescu, Alina Ramona Buzatu, Cristina Adriana Dehelean, Cristina Adriana Dehelean, Minodora Andor, Minodora Andor

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HaTS - Hanover Traffic Scenario for SUMO by Nico Ostendorf, Keno Garlichs, Lars C. Wolf

“…HaTS provides a detailed and accurate representation of the road network, traffic light systems, and buildings within the city center of Hanover, Germany. …”
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Dynamic Split Computing Framework for Multi-Task Learning Models: A Deep Reinforcement Learning Approach by Haneul Ko, Sangwon Seo, Sangheon Pack

“…These structural characteristics, combined with the variability of network conditions, require a more flexible and adaptive offloading strategy. …”
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Improving Circulating Tumor Cell Detection Using Image Synthesis and Transformer Models in Cancer Diagnostics by Shuang Liang, Xue Bai, Yu Gu

“…We develop a detection network based on the Swin Transformer, featuring a backbone network, scale adapter module, shape adapter module, and detection head, which enhances CTC localization and identification in images. …”
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IFI35 and IFIT3 are potentially important biomarkers for early diagnosis and treatment of esophageal squamous cell carcinoma: based on WGCNA and machine learning analysis by Hao Wu, Liang Yang, Xiaokun Weng

“…To characterize co-expression network, weighted gene co-expression network analysis (WGCNA) was performed, allowing for the identification of relevant co-expression modules. …”
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Synthesis of a reversible quantum Vedic multiplier on IBM quantum computers by Mojtaba Noorallahzadeh, Mohammad Mosleh

“…This Toffoli-based network is then optimized using various techniques, ultimately transforming it into a network of fundamental quantum gates. …”
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Analysis of the Impact of Electromobility on the Distribution Grid by Tomislav Kovačević, Ružica Kljajić, Hrvoje Glavaš, Milan Kljajin

“…However, achieving this target can be challenging due to the characteristics and features of the electric vehicle charging stations and the associated charging methods, which can lead to constraints within the network. …”
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Research on transformer operation state prediction based on comprehensive weights and BO-CNN-GRU by Ying Liu, Wenbin Cao, Xiaoming Zhang, Yuhang Sun, Xu Sun

“…Aiming at the problem that it is difficult to predict the future operating state of the transformer, this paper proposes a method for predicting the operating state of transformers based on comprehensive weight and BO-CNN-GRU (Bayes Optimization -Convolutional Neural Network- Gated Recurrent Unit). Firstly, 11 kinds of monitoring data in three categories including oil chromatography gas content, temperature, and electrical quantity are selected as feature parameters; Then, the game theory method is used to integrate the weight values of the three methods of G1 method, entropy weight method and CRITIC method to get the comprehensive weight value of each feature parameter, and the transformer operation state index is constructed based on the comprehensive weight; Finally, the BO-CNN-GRU combination prediction model is built, which solves the problem of difficulty in determining the hyperparameters of the model. …”
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Mapping rapeseed (Brassica napus L.) aboveground biomass in different periods using optical and phenotypic metrics derived from UAV hyperspectral and RGB imagery by Chuanliang Sun, Chuanliang Sun, Weixin Zhang, Genping Zhao, Qian Wu, Wanjie Liang, Ni Ren, Hongxin Cao, Lidong Zou, Lidong Zou

“…We compared the accuracy of various feature combinations and evaluated model performance at different growth stages. …”
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An Improved Phase Space Reconstruction Method-Based Hybrid Model for Chaotic Traffic Flow Prediction by Yue Hou, Da Li, Di Zhang, Zhiyuan Deng

“…Secondly, to address the problem of insufficient learning ability of traditional convolutional combinatorial modeling for complex phase space laws of chaotic traffic flow, the high-dimensional phase space features are extracted using the layer-by-layer pretraining mechanism of convolutional deep belief networks (CDBNs), and the temporal features are extracted by combining with long short-term memory (LSTM). …”
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Integrating Explanations into CNNs by Adopting Spiking Attention Block for Skin Cancer Detection by Inzamam Mashood Nasir, Sara Tehsin, Robertas Damaševičius, Rytis Maskeliūnas

“…In this paper, multimodal Explainable Artificial Intelligence (XAI) is presented that offers explanations that (1) address a gap regarding interpretation by identifying specific dermoscopic features, thereby enabling (2) dermatologists to comprehend them during melanoma diagnosis and allowing for an (3) evaluation of the interaction between clinicians and XAI. …”
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Fish-aggregating-devices are viable for ocean model currents verification by Saima Aijaz, Gary B. Brassington, Lauriane Escalle

“…Abstract We have leveraged the rapid growth of satellite-tracked drifting fish-aggregating-devices used by the fishing industry to evaluate their potential as ocean observing systems. …”
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Low-hit frequency-hopping communication systems for power Internet of things random access by Shu DU, Mei MA, Bo ZHAO, Qi ZENG, Xing LIU

“…The communication network is an essential component of the data acquisition and information transmission in power Internet of things (IoT).To meet the development requirements of multiple power service in the future, the wireless communication technique with flexible-access and high scalability is one of the development directions for power IoT.Due to the features of large-scale access, random access time, high security and reliability for information transmission of power IoT, a frequency-hopping (FH) technique with low-hit rate for random access was proposed.The construction algorithm of such FH pattern was based upon the combination and shift operation of conventional FH sequences.By the theoretical arithmetic and numerical simulation, the properties of the proposed FH pattern and the error-rate of the FH-based power IoT were evaluated.The analysis reveals that the new class of FH sequences and the system can meet the requirements of large-scale access and highly reliable communication for the power IoT, which has good application prospects.…”
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Using Multioutput Learning to Diagnose Plant Disease and Stress Severity by Gianni Fenu, Francesca Maridina Malloci

“…The proposed model consists of a multioutput system based on convolutional neural networks. The deep learning approach considers five pretrained CNN architectures, namely, VGG-16, VGG-19, ResNet50, InceptionV3, MobileNetV2, and EfficientNetB0, as feature extractors to classify three diseases and six severity levels. …”
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Improving earthquake prediction accuracy in Los Angeles with machine learning by Cemil Emre Yavas, Lei Chen, Christopher Kadlec, Yiming Ji

“…Abstract This research breaks new ground in earthquake prediction for Los Angeles, California, by leveraging advanced machine learning and neural network models. We meticulously constructed a comprehensive feature matrix to maximize predictive accuracy. …”
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Advanced Hybrid Transformer-CNN Deep Learning Model for Effective Intrusion Detection Systems with Class Imbalance Mitigation Using Resampling Techniques by Hesham Kamal, Maggie Mashaly

“…The Transformer-CNN model focuses on three primary objectives to enhance detection accuracy and performance: (1) reducing false positives and false negatives, (2) enabling real-time intrusion detection in high-speed networks, and (3) detecting zero-day attacks. We evaluate our proposed model, Transformer-CNN, using the NF-UNSW-NB15-v2 and CICIDS2017 benchmark datasets, and assess its performance with metrics such as accuracy, precision, recall, and F1-score. …”
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Deep Learning-Based Medical Object Detection: A Survey by Mohammadreza Saraei, Mehrshad Lalinia, Eung-Joo Lee

“…These advancements leverage sophisticated features like Cross-Stage Partial (CSP) networks, Spatial Pyramid Pooling (SPP), and Bi-Directional Feature Pyramid Networks (BiFPN) to improve feature extraction and detection in medical images. …”
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A machine learning–based risk prediction model for atrial fibrillation in critically ill patients by Laith Alomari, MD, Yaman Jarrar, MD, Zaid Al-Fakhouri, MD, Emmanuel Otabor, MBBS, Justin Lam, MD, Jana Alomari

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Cotton transposon-related variome reveals roles of transposon-related variations in modern cotton cultivation by Shang Liu, Hailiang Cheng, Youping Zhang, Man He, Dongyun Zuo, Qiaolian Wang, Limin Lv, Zhongxv Lin, Ji Liu, Guoli Song

“…In addition, a convolutional neural network (CNN) model was constructed to evaluate epigenomic effects of transposon-related variations. …”
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A Simple but Effective Way to Handle Rotating Machine Fault Diagnosis With Imbalanced-Class Data: Repetitive Learning Using an Advanced Domain Adaptation Model by Donghwi Yoo, Minseok Choi, Hyunseok Oh, Bongtae Han

“…Deep convolutional domain adaptation networks are followed to extract features by minimizing different losses. …”
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