Comparison of Option Pricing with Stochastic Volatility in Heston and Heston Nandi Model by Mohammad Reza Haddadi, Hossein Nasrollahi

“…The Heston-Nandy model considers the non-normal distribution of returns and random fluctuations more realistically. Since the Heston model is one of the effective models among the random turbulence models, in this study, the option pricing under Heston and Heston Nandi random stochastic is discussed, which has been investigated considering the non-normality of the data distribution.   …”
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Association between neutrophil-albumin ratio and ultrasound-defined metabolic dysfunction-associated fatty liver disease in U.S. adults: evidence from NHANES 2017–2018 by Ming-yu He, Xin-jie Du, Yi-ming Liu

“…CAP-related variables were ranked using XG Boost and random forest algorithms, and predictive models were developed and evaluated. …”
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Predicting pregnancy loss and its determinants among reproductive-aged women using supervised machine learning algorithms in Sub-Saharan Africa by Tirualem Zeleke Yehuala, Sara Beyene Mengesha, Nebebe Demis Baykemagn

“…Python software was used to process the data, and machine learning techniques such as Random Forest, Decision Tree, Logistic Regression, Extreme Gradient Boosting, and Gaussian were applied. …”
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Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques by Luis Mariano Esteban, Ángel Borque-Fernando, Maria Etelvina Escorihuela, Javier Esteban-Escaño, Jose María Abascal, Pol Servian, Juan Morote

“…When considering a sensitivity of 0.9, in the validation set, the XGBoost model outperforms others with a specificity of 0.640, followed closely by random forest (0.638), neural network (0.634), and logistic regression (0.620). …”
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Research on Quantization Error Influence of Millimeter-Wave Phased Array Antenna by Haigen Yang, Linqun Zhu, Zhun Xia, Yanqing Chen, Luohao Dai, Ruotian Xu, YuanHao Chen, Luyang Li, GuiYing Sun, Hongyang Yu, Wenting Xu

“…To reduce the error factor, it is necessary to ensure that the random phase and amplitude error should not exceed 10°,0.5 dB. …”
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Internal Benchmarking of Thermal Power Plants оf Electric Power Systems by E. M. Farhadzadeh, A. Z. Muradaliyev, Y. Z. Farzaliyev, U. K. Ashurova

“…Existing methods for calculating integrated performance indicators do not fully take into account the random nature of technical and economic indicators (TEI). …”
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Electricity Theft Detection Using Machine Learning in Traditional Meter Postpaid Residential Customers: A Case Study on State Electricity Company (PLN) Indonesia by Alief Pascal Taruna, Galih Arisona, Dwi Irwanto, Arif Bijak Bestari, Wildan Juniawan

“…Various classification models, including Decision Tree, Naive Bayes, Random Forest, K-Nearest Neighbors, Logistic Regression, and Deep Neural Network, were evaluated, with Random Forest achieving the best performance across simulations. …”
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ŠARL DE GOL: POSLEDNJI VELIKI FRANCUZ by Aleksandra Kolaković

“…A Certain Idea of France The life of Charles de Gaulle. London: Penguin Random House UK, pp. 887.…”

Co-infection of SARS‐CoV‐2 and influenza A/B among patients with COVID-19: a systematic review and meta-analysis by Monireh Golpour, Hossein Jalali, Reza Alizadeh-Navaei, Masoumeh Rezaei Talarposhti, Tahoora Mousavi, Ali Asghar Nadi Ghara

“…Result The analysis found that the prevalence of influenza in co-infected patients at 95% confidence interval using a random effect model was 14% (95% CI: 8–20%). Significant heterogeneity was observed in the random-effects model for influenza A, 11% (95% CI: 5-18%) and B, 4% (95% CI: 2-7%) in co-infected patients. …”
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Sparse signal transmission under lossy wireless links based on double process of compressive sensing by Peng SUN, Gui-nan LI, Lian-tao WU, Zhi WANG

“…In resource-limited wireless sensor networks,links with poor quality hinder its large-scale applications seriously.Thanks to the inherent sparse property of signals in WSN,the framework of sparse signal transmission based on double process of compressive sensing was proposed,providing an insight into a new way of real-time,accurate and energy-efficient sparse signal transmission.Firstly,the random packet loss during transmission under lossy wireless links was modeled as a linear dimension-reduced measurement process of CS (a passive process of CS).Then,considering that a large packet was often adopted in WSN for higher transmission efficiency,a random linear dimension-reduced projection (a simple source coding operation) was employed at the sender node (an active process of CS) to prevent block data loss.Now,the raw signal could be recovered from the lossy data at the receiver node using CS reconstruction algorithms.Furtherly,according to the theory of CS reconstruction and the formula of packet reception rate in wireless communication,the minimum compression ratio and the maximum packet length allowed were obtained.Extensive simulations demonstrate that the reliability of data transmission and its accuracy,the data transmission volume,the transmission delay and energy consumption could be greatly optimized by means of proposed method.…”
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Post-processing methods for delay embedding and feature scaling of reservoir computers by Jonnel Jaurigue, Joshua Robertson, Antonio Hurtado, Lina Jaurigue, Kathy Lüdge

“…Here we introduce the multi-random-timeshifting method that randomly recalls previous states of reservoir nodes. …”
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A Throughput Analysis Using a Non-Saturated Markov Chain Model for LTE-LAA and WLAN Coexistence by Mun-Suk Kim

“…The LTE-LAA system ensures efficient coexistence with other existing unlicensed systems by incorporating listen-before-talk functionality and conducting random backoff operations similar to those in the IEEE 802.11 distributed coordination function. …”
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Evaluating the effectiveness of prompt engineering for knowledge graph question answering by Catherine Kosten, Farhad Nooralahzadeh, Kurt Stockinger

“…Our experiments find that the most successful prompting framework for KGQA is a simple prompt combined with an ontology and five random shots.…”
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Multiorder Fusion Data Privacy-Preserving Scheme for Wireless Sensor Networks by Mingshan Xie, Yong Bai, Mengxing Huang, Zhuhua Hu

“…In this paper, multiorder fusion data privacy-preserving scheme (MOFDAP) is proposed. Random interference code, random decomposition of function library, and cryptographic vector are introduced for our proposed scheme. …”
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Faking the News: Intentional Guided Variation Reflects Cognitive Biases in Transmission Chains Without Recall by Stubbersfield Joseph, Tehrani Jamshid, Flynn Emma

“…Two potential forms of mutation in cultural evolution have been identified: ‘copying error’, where learners make random modifications to a behaviour and ‘guided variation’ where learners makes non-random modifications. …”
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Modeling and Simulation of the Hydrogenation of α-Methylstyrene on Catalytically Active Metal Foams as Tubular Reactor Packing by Farzad Lali, Felix-Aron Pahner, Rüdiger Lange

“…The obtained results were also compared in terms of space time yield and catalytic activity with experimental results and stirred tank and also with random packed bed reactor. The comparison shows that the application of solid foams as reactor packing is advantageous compared to the monolithic honeycombs and random packed beds.…”
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Identification on the structures of block ciphers using machine learning by Ruiqi XIA, Manman LI, Shaozhen CHEN

“…Cryptographic identification is a critical aspect of cryptanalysis and a fundamental premise for key recovery.With the advancement of artificial intelligence, cryptanalysis based on machine learning has become increasingly mature, providing more effective methods and valuable insights for cryptographic identification.The distinguishability experiments were performed based on the Machine Learning to identify the structures of block ciphers in conditions of random keys.The identification of two structures of block ciphers from theoretical and experimental angles was studied.The differences of features in two structures’ cipher texts have been deduced by introducing the runs distribution index, feature distribution functions, KL-divergence, etc.After completing the feasibility research, experiments to identify the structures of two block ciphers using two Machine Learning models and the runs distribution index were conducted.The experiments were divided into two groups: single algorithm group and mixture algorithms group.It is found that the accuracy of both groups are more than 80%, which is around 40% higher than former work.The problem of identifying the structures of Block Ciphers in the conditions of random keys is solved in detail.Meanwhile, differences between the two structures of block ciphers are verified, which can serve as a reference for the design of cryptography algorithms.…”
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