GenAI-Based Jamming and Spoofing Attacks on UAVs by Burcu Sonmez Sarikaya, Serif Bahtiyar

“…Creating effective intrusion detection systems against such attacks has been a significant challenge since there is a lack of sufficient attack data that can be used to design an intrusion detection system with advanced computing algorithms. In this research, we propose a novel framework to create attacks data for UAVs by using generative artificial intelligence algorithms. …”
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Elemental numerical descriptions to enhance classification and regression model performance for high-entropy alloys by Yan Zhang, Cheng Wen, Pengfei Dang, Xue Jiang, Dezhen Xue, Yanjing Su

“…Moreover, these new numerical descriptions for phase classification can be directly applied to regression model predictions of HEAs, reducing the error by 22% and improving the R 2 value from 0.79 to 0.88 in hardness prediction. …”
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No-Reference Stereoscopic IQA Approach: From Nonlinear Effect to Parallax Compensation by Ke Gu, Guangtao Zhai, Xiaokang Yang, Wenjun Zhang

“…Third, the saliency based parallax compensation, resulted from different stereoscopic image contents, is considerably valid to improve the prediction performance of image quality metrics. Experimental results confirm that our proposed stereoscopic image quality assessment paradigm has superior prediction accuracy as compared to state-of-the-art competitors.…”
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Multiobjective Optimization of Milling Parameters for Ultrahigh-Strength Steel AF1410 Based on the NSGA-II Method by Jin Xu, Fuwu Yan, Yan Li, Zhenchao Yang, Long Li

“…The influence of milling parameters (milling speed, each tooth feed, radial depth of cut, and axial depth of cut) on milling force and surface roughness is studied by ANOVA and established prediction model. Multiobjective optimization of milling parameters is accomplished based on nondominated sorting genetic algorithm II (NSGA-II) with milling force, surface roughness, and material removal rate as optimization objectives. …”
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Editorial by Bulent Cavas

“…The eighth article, “Prediction of Middle School Students’ Recycling Behaviors with Machine Learning Algorithms,” by Mustafaoğlu and Alkan (Türkiye), applies machine learning techniques to identify factors predicting students' recycling behavior. …”
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The design of copper flotation process based on multi-label classification and regression by Haipei Dong, Fuli Wang, Dakuo He, Yan Liu

“…Moreover, further improves the prediction effect through the following methods: (1) Referencing adaboost algorithm, the training set samples with large prediction error in the previous iteration are set with higher weight; (2) To enhance robustness, the label uncertainty coefficient is introduced; (3) To alleviate the over fitting of small sample machine learning, bootstrap aggregating is introduced for each sub label. …”
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Field Test and Numerical Research of Blast-Induced Liquefaction in Calcareous Sand by Changchun Li, Yumin Chen, Yingkang Yao, Yonggang Gou, Qiongting Wang, Junwei Guo, Xiao Xie

“…The research results can provide a theoretical reference for the prediction of blast-induced liquefaction in saturated calcareous sand foundations.…”
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Risk assessment of tunnel water inrush based on Delphi method and machine learning by Leizhi Dong, Qingsong Wang, Weiguo Zhang, Yongjun Zhang, Xiaoshuang Li, Fei Liu

“…Then, the Radial Basis Function (RBF) network, improved by the Locally Linear Embedding (LLE) algorithm and the Particle Swarm Optimization (PSO), is applied to predict the risk level. …”
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Wound imaging software and digital platform to assist review of surgical wounds using patient smartphones: The development and evaluation of artificial intelligence (WISDOM AI stud... by Melissa Rochon, Judith Tanner, James Jurkiewicz, Jacqueline Beckhelling, Akuha Aondoakaa, Keith Wilson, Luxmi Dhoonmoon, Max Underwood, Lara Mason, Roy Harris, Karen Cariaga

“…Secondary objectives focused on specificity, positive predictive value (PPV), and negative predictive value (NPV) for various wound features.…”
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Robust techno-economic optimization of energy hubs under uncertainty using active learning with artificial neural networks by Aya M. A. Heikal, Shady H. E. Abdel Aleem, Ragab A. El-Sehiemy, Almoataz Y. Abdelaziz

“…Results demonstrate significant improvements in system reliability, cost efficiency, and flexible operation, validating the effectiveness of ANN-based AL to optimize EHs management and ensure sustainable operation complexities. The AL algorithm enhances the ANN model’s predictive ability, resulting in a 57.9% decrease in operating costs and a 0.010682 loss of energy supply probability (LESP) value. …”
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Developing an Artificial Neural Network-Based Grading Model for Energy Consumption in Residential Buildings by Yaser Shahbazi, Sahar Hosseinpour, Mohsen Mokhtari Kashavar, Mohammad Fotouhi, Siamak Pedrammehr

“…The collected data informed the ANN model, enabling accurate predictions for existing and future constructions. The results demonstrate that the model achieves a remarkable prediction error of just 0.001, facilitating efficient energy assessments without requiring extensive modeling expertise. …”
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Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening. by Michael Phillips, Thomas L Bauer, Renee N Cataneo, Cassie Lebauer, Mayur Mundada, Harvey I Pass, Naren Ramakrishna, William N Rom, Eric Vallières

“…The algorithm predicted discriminant function (DF) values in blinded replicate breath VOC samples analyzed independently at two laboratories (A and B). …”
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Hybrid Artificial Neural Network Activation Function to Reduce Water Wastage in Agricultural Irrigation by Baraa H. Jawad, Ola A. Alwesabi, Nibras Abdullah, Ahmed Abed Mohammed

“…The study proposes a hybrid activation function based on the Artificial Neural Network algorithm. This function is used to classify the need for irrigation in various crops and predict the best time of the day for watering. …”
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Explainable artificial intelligence visions on incident duration using eXtreme Gradient Boosting and SHapley Additive exPlanations by Khaled Hamad, Emran Alotaibi, Waleed Zeiada, Ghazi Al-Khateeb, Saleh Abu Dabous, Maher Omar, Bharadwaj R.K. Mantha, Mohamed G. Arab, Tarek Merabtene

“…This study introduces an application of Explainable Artificial Intelligence (XAI) using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to analyze the complexities of traffic incident duration prediction. Utilizing a substantial dataset of over 366,000 records from the Houston traffic management center, the study innovates in the domain of traffic analytics by predicting incident durations and revealing the contribution of each predictive variable. …”
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A new method for determining factors Influencing productivity of deep coalbed methane vertical cluster wells by HUANG Li, XIONG Xianyue, WANG Feng, SUN Xiongwei, ZHANG Yixin, ZHAO Longmei, SHI Shi, ZHANG Wen, ZHAO Haoyang, JI Liang, DENG Lin

“…This method leverages the advantages of multiple machine-learning algorithms, demonstrating strong operability and improving the accuracy of CBM dynamic predictions. …”
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An AI-Based Approach for Developing a Recommendation System for Underground Mining Methods Pre-Selection by Elsa Pansilvania Andre Manjate, Natsuo Okada, Yoko Ohtomo, Tsuyoshi Adachi, Bernardo Miguel Bene, Takahiko Arima, Youhei Kawamura

“…The study integrates and evaluates the capability of two approaches for mining methods selection (MMS): the memory-based collaborative filtering (CF) approach aided by the UBC-MMS system to predict the top-3 relevant mining methods and supervised machine learning (ML) classification algorithms to enhance the effectiveness and novelty of the AI-MMRS, addressing the limitations of the CF approach. …”
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Research on leaf identification of table grape varieties based on deep learning by PAN Bowen, LIN Meiling, JU Yanlun, SU Baofeng, SUN Lei, FAN Xiucai, ZHANG Ying, ZHANG Yonghui, LIU Chonghuai, JIANG Jianfu, FANG Yulin

“…Among the 68 varieties identified by the ResNet-101 model, the prediction accuracy of the 23 varieties was 100%, and the average recognition accuracy of the 68 varieties reached 94.90%; The prediction accuracy of the ResNet-50 for the 13 varieties was 100%, and the average recognition accuracy of the 68 varieties reached 90.38%; The prediction accuracy of the VGG-16 for the 11 varieties was 100%, and the average recognition accuracy of the 68 varieties was 85.45%; The prediction accuracy of the GoogleNet model was 100% for only 5 varieties, and the average recognition accuracy of the 68 varieties was 78.79%. …”
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Development of a Japanese polygenic risk score model for amyloid-β PET imaging in Alzheimer’s disease by Misato Kaishima, Junichi Ito, Kentaro Takahashi, Kenji Tai, Junro Kuromitsu, Shogyoku Bun, Daisuke Ito

“…Abstract Background The use of polygenic risk scores (PRS) for predicting disease risk in Japanese populations, particularly for dementia and related phenotypes, remains markedly unexplored. …”
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Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients by Farnaz Khodami, Amanda S. Mahoney, James L. Coyle, Ervin Sejdic

“…The hybrid model employed for the second task successfully predicted the onset of laryngeal vestibule closure and reopening for 79.62% and 75.80% of patients, respectively, with the same error margin. …”
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How large is the universe of RNA-like motifs? A clustering analysis of RNA graph motifs using topological descriptors. by Rui Wang, Tamar Schlick

“…Furthermore, 46.017% of the hypothetical RNAs are predicted to be RNA-like. Among the top 15 graphs identified as high-likelihood candidates for novel RNA motifs, 4 were confirmed from the RNA dataset collected in 2022. …”
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