Controllability design of a memristive hyperchaotic map and the construction of optical fiber secure communication system by LI Chunbiao, LI Yongxin, ZHONG Qing, YANG Yong, AKGUL Akif

“…Based on the RISC-V microcontroller of CH32V307, the proposed controllable hyperchaotic map was realized, and National Institute of Standards and Technology (NIST) testing proves the pseudo-random feature of hyperchaotic sequences. Based on the above hyperchaotic map, a physical layer scheme of orthogonal chirp division multiplexing-non-orthogonal multiple access (OCDM-NOMA) with encryption was constructed. …”
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Generative learning of continuous data by tensor networks by Alex Meiburg, Jing Chen, Jacob Miller, Raphaëlle Tihon, Guillaume Rabusseau, Alejandro Perdomo-Ortiz

“…We develop methods for modeling different data domains, and introduce a trainable compression layer which is found to increase model performance given limited memory or computational resources. …”
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Social Factors Influencing Healthcare Expenditures: A Machine Learning Perspective on Australia’s Fiscal Challenges by Wei Gu, Zhantian Zhang, Ou Liu

“…Using advanced models, including Random Forest, XGBoost, and Multi-Layer Perceptron (MLP), along with SHAP (SHapley Additive exPlanations) analysis, we identify the most influential factors driving healthcare spending. …”
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Controllability design of a memristive hyperchaotic map and the construction of optical fiber secure communication system by LI Chunbiao, LI Yongxin, ZHONG Qing, YANG Yong, AKGUL Akif

“…Based on the RISC-V microcontroller of CH32V307, the proposed controllable hyperchaotic map was realized, and National Institute of Standards and Technology (NIST) testing proves the pseudo-random feature of hyperchaotic sequences. Based on the above hyperchaotic map, a physical layer scheme of orthogonal chirp division multiplexing-non-orthogonal multiple access (OCDM-NOMA) with encryption was constructed. …”
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Effects of Phosphorus-Free Conversion Process Parameters on the Adhesion Properties of Electrophoretic Paint Film by ZHANG Cheng

“…To address the issue of insufficient adhesion strength of electrophoretic paint film on the cold-rolled steel plates beneath the doors of white body vehicles， this study compared the effects of various process parameters， including phosphorus-free conversion treatment time， electrophoretic baking temperature， free fluoride ion concentration， and ferrous ion accumulation， on the adhesion of electrophoretic paint film.Through random on-site inspections of thickness and adhesion strength of the electrophoretic paint film， it was found that when the coating thickness of the phosphorus-free conversion layer exceeded 40 mg/m2， adhesion failure of the electrophoretic paint film on the cold-rolled steel beneath the doors of white body vehicles was likely to occur.Based on both on-site data and laboratory simulation tests， the following conclusions were drawn: only by strictly monitoring the non-phosphorus conversion bath in accordance with the on-site process parameters， controlling the ferrous ion content below 25 mg/L and the fluoride ion concentration at 20～40 mg/L，can the adhesion of the electrophoretic paint film on the plate at this position reach the standard and adapt to the mass production process on site.…”
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Survival of the Fittest: An Active Queue Management Technique for Noisy Packet Flows by Shirish S. Karande, Kiran Misra, Hayder Radha

“…On the basis of actual 802.11b measurements we show that such a side information (SI) aware processing within the network can provide significant performance benefits over an SI-unaware scheme, random queue management (RQM), which is forced to randomly discard packets. …”
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Partial relay selection for secure cooperative NOMA networks by Wided Hadj Alouane, Chan-Wang Park

“…In this paper, we address the physical layer security (PLS) of multi-relay non-orthogonal multiple access (NOMA) schemes with partial relay selection (PRS) using decode-and-forward (DF) and amplify-and-forward (AF) protocols. …”
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Development of High Accuracy Classifier for the Speaker Recognition System by Raghad Tariq Al-Hassani, Dogu Cagdas Atilla, Çağatay Aydin

“…In order to enhance noise immunity, we proposed a single hidden layer feed-forward neural network (FFNN) tuned by an optimized particle swarm optimization (OPSO) algorithm. …”
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Chinese medical named entity recognition based on multimodal information fusion and hybrid attention mechanism. by Zhen Luo, Jingping Che, Fan Ji

“…Multi-Head Attention is employed to further enhance feature representation and improve the model's ability to delineate medical entity boundaries. The Conditional Random Field (CRF) layer is used for decoding, ensuring global consistency in entity predictions and thereby enhancing recognition accuracy and robustness. …”
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Study on spatial variability of soil nutrients in Taihu Lake region by LIU Xing-mei, XU Jian-min, ZHANG Ming-kui, SHI Zhou, SHI Jia-chun

“…Spatial variability of nutrients in the plough layer (0-20 cm) of paddy soils from Pinghu City in the Taihu lake region in China was studied using GIS technique and geostatistical analysis. …”
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Multiscale Feature Fusion Attention Lightweight Facial Expression Recognition by Jinyuan Ni, Xinyue Zhang, Jianxun Zhang

“…The network first uses an improved random erasing method to preprocess facial expression images, which improves the generalizability of the model. …”
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SDW2vec: learning structural representations of nodes in weighted networks by Shu Liu, Masaki Chujyo, Fujio Toriumi

“…We subsequently construct weighted multi-layer graphs based on these distance measurements, apply random walks to generate node sequences, and learn the embedding representations using the Skip-gram model. …”
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Anxiety Detection System Based on Galvanic Skin Response Signals by Abeer Al-Nafjan, Mashael Aldayel

“…We employed two feature extraction methods: traditional statistical feature extraction and an innovative automatic feature extraction approach utilizing a 14-layer autoencoder, aimed at improving classification performance. …”
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Optimization and prediction of corporate credit rating through advanced feature selection based on AI and deep learning by Jumanah Ahmed Darwish

“…This study offers a comprehensive evaluation of six machine learning algorithms—Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine One-vs-One (SVM OVO), Support Vector Machine One-vs-All (SVM OVA), and Multi-Layer Perceptron (MLP)—in the context of corporate credit rating classification. …”
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Machine learning for predicting earthquake magnitudes in the Central Himalaya by Ram Krishna Tiwari, Rudra Prasad Poudel, Harihar Paudyal

“…Recent development of machine learning models has increasingly developed interest in forecasting and predicting the magnitude of earthquakes. In this work, Random Forest Regressor (RFR), Multi-Layer Perceptron Regressor(MLPR), and Support Vector Regression (SVR) models were employed to predict the magnitude of greater than 6 mb earthquakes that occurred in the year 2015 in the central Himalaya. …”
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ECG filtering and QRS extraction under steep pulse interference by Xi-tong YAO, Yu DAI, Jian-xun ZHANG, Jin-tao GE, Tong CHEN, Hao YANG

“…To identify the random noise in the medical environment accompanied by the occurrence of steep pulses, we analyzed the characteristics of random noise in the sub-signal after VMD. …”
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Machine-learning-based spatial analysis of the spring states in the southernmost Eurasian permafrost, Hangai Mountains, central Mongolia by Mamoru Ishikawa, Azumi Okazaki, Avirmed Dashtseren, Khurelbaatar Temuujin, Tetsuya Hiyama

“…In transitional zones where permafrost and non-permafrost areas coexist, the thickening of active layer and increases in unfrozen water contents help to sustain spring discharge despite ongoing aridification.…”
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Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks by Muawia A. Elsadig, Abdelrahman Altigani, Yasir Mohamed, Abdul Hakim Mohamed, Akbar Kannan, Mohamed Bashir, Mousab A. E. Adiel

“…In other words, two layers of enhancements were applied—using a suitable feature selection technique and fixing the dataset imbalance problem. …”
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Machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materials by Khuram Rashid, Fatima Rafique, Zunaira Naseem, Fahad K. Alqahtani, Idrees Zafar, Minkwan Ju

“…In the initial phase, three machine learning models—Decision Tree, Random Forest, and Multi-layer Perceptron—were developed and trained on a dataset of 1030 records to predict sustainable concrete's compressive strength accurately. …”
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Machine learning frameworks to accurately predict coke reactivity index by Ayat Hussein Adhab, Morug Salih Mahdi, Krunal Vaghela, Anupam Yadav, Jayaprakash B, Mayank Kundlas, Ankur Srivastava, Jayant Jagtap, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd, Samim Sherzod

“…The findings indicate that maximum fluidity and mean maximum reflectance (MMR) exhibit a direct correlation with CRI while being indirectly relevant to moisture content, ash content, sulfur content, basicity index, plastic layer thickness, and MMR. Among the various predictive models evaluated, the random forest model emerged as the most accurate tool, according to the performance metrics of R -squared, mean square error, and average absolute relative error (%), with numerical values of 0.958, 3.718, and 2.545%, respectively, for the total datapoints. …”
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