Computer prediction of biological activity of 2-methyl(phenyl)-6,9-epoksybenzo[g]quinoline-4,5,10-Trion and 5-methyl-(1,2,4-triazolo[4,3-a] quinoline) by Yu. V. Karpenko, , O. A. Brazhko, L. O. Omelyanchik

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Detection and Classification of Abnormal Power Load Data by Combining One-Hot Encoding and GAN–Transformer by Ting Yang, Hongyi Yu, Danhong Lu, Shengkui Bai, Yan Li, Wenyao Fan, Ketian Liu

“…Furthermore, it outperforms traditional methods such as LSTM-NDT, Transformer, OmniAnomaly and MAD-GAN in Overall Accuracy, Average Accuracy, and Kappa coefficient, thereby validating the effectiveness and superiority of the proposed anomaly detection and classification method.…”
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Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks by Ayan Chatterjee, Vajira Thambawita, Michael A. Riegler, Pal Halvorsen

“…In tests with physical activity data from Actigraph watches and MOX2-5 sensors, ADSiamNet achieved accuracies of 98.65% and 85.0%, respectively, outperforming other supervised anomaly detection methods. The model uses a contrastive loss function to compare input sequences and adjusts network weights iteratively during training to recognize intricate patterns. …”
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Detective Audit: Methodology for Assessing the Business Reliability of a Small and Medium-Sized Business Entity by A. E. Krioni

“…The purpose of the work is to develop methodological provisions for the detective form of the layout of the auditing. The offered method is steady in demand among customers of detectives as it opens new opportunities for the honest business executives. …”
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Nondestructive Detection of Rice Milling Quality Using Hyperspectral Imaging with Machine and Deep Learning Regression by Zhongjie Tang, Shanlin Ma, Hengnian Qi, Xincheng Zhang, Chu Zhang

“…This study confirmed that this nondestructive detection method for rice milling quality using hyperspectral imaging combined with machine learning and deep learning algorithms could effectively assess rice milling quality, thus contributing to breeding and growth management in the industry.…”
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Systematic review, Meta-analysis, and Pan-genome analytics predict the surging of Brucella melitensis by China and India-specific strains, elucidating the demand for enhanced prepa... by Ashwini Mallappa, Suresh Kuralayanapalya Puttahonnappa, Rajeshwari Shome, Sharanagouda S. Patil, Raghavendra G. Amachawadi, Kumar S.K. Mohan, Sudarshan Peramachanahalli Venkatesh, Varsha Ramesh, Yamini Sri Sekar, Hariprasad Thippeswamy, Archana Veeranagouda Patil

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A CRISPR/cas13a-assisted precise and portable test for Brucella nucleic acid detection by Haiwen Liu, Haiwen Liu, Ling Xu, Ying Xiu, Na Ta, Qingqing Xu, Yu Fan, Kun Li, Hongyan Zhao, Dongri Piao, Feng Ren, Hai Jiang

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PCA and PSO based optimized support vector machine for efficient intrusion detection in internet of things by Mutkule Prasad Raghunath, Shyam Deshmukh, Poonam Chaudhari, Sunil L. Bangare, Kishori Kasat, Mohan Awasthy, Batyrkhan Omarov, Rajesh R. Waghulde

“…The PSO-based SVM method is shown superior performance compared to random forest and linear regression methods in terms of precision, recall, and specificity.…”
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Toward Semi-Autonomous Robotic Arm Manipulation Operator Intention Detection From Force Data by Abdullah S. Alharthi, Ozan Tokatli, Erwin Lopez, Guido Herrmann

“…These results highlight the potential of our method to improve the safety, precision, and efficiency of robotic operations in hazardous environments, thereby significantly reducing human radiation exposure.…”
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A Novel Mechanism for Fire Detection in Subway Transportation Systems Based on Wireless Sensor Networks by Zhen-Jiang Zhang, Jun-Song Fu, Hua-Pei Chiang, Yueh-Min Huang

“…Fire is a common and disastrous phenomenon in subway transportation systems because of closed environment and large passenger flow. Traditional methods detect and forecast fire incidents by fusing the data collected by wireless sensor networks and compare the fusion result with a threshold. …”
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Enhancing Kármán Vortex Street Detection via Auxiliary Networks Incorporating Key Atmospheric Parameters by Yihan Zhang, Zhi Zhang, Qiao Su, Chaoyue Wu, Yuqi Zhang, Daoyi Chen

“…Experimental results demonstrate that the integration of horizontal wind speed and vertical air velocity achieves the highest detection metrics (precision of 0.838, recall of 0.797, mAP50 of 0.865, and mAP50-95 of 0.413) in precision-critical scenarios, outperforming traditional image-only detection method (precision of 0.745, recall of 0.745, mAP50 of 0.759, and mAP50-95 of 0.372). …”
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Leveraging IoT-Enabled Sensor Networks and Machine Learning for Early Detection and Management of Wheat Rust by Adnan Myasar M., Almoussawi Zainab Abed, Anuradha Kodali

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Impact of Phenological and Lighting Conditions on Early Detection of Grapevine Inflorescences and Bunches Using Deep Learning by Rubén Íñiguez, Carlos Poblete-Echeverría, Ignacio Barrio, Inés Hernández, Salvador Gutiérrez, Eduardo Martínez-Cámara, Javier Tardáguila

“…Traditional yield prediction methods are labor-intensive, subjective, and often restricted to advanced phenological stages. …”
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Anomaly Detection Using Machine Learning in Hydrochemical Data From Hot Springs: Implications for Earthquake Prediction by Ruijie Zhu, Fengtian Yang, Xiaocheng Zhou, Jiao Tian, Yongxian Zhang, Miao He, Jingchao Li, Jinyuan Dong, Ying Li

“…Our comprehensive analysis conclusively demonstrates the superiority of machine learning algorithms over traditional statistical methods for earthquake prediction. Additionally, including sampling time in the data sets significantly improves the model's predictive performance. …”
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Unlocking Potato Phenology: Harnessing Sentinel-1 and Sentinel-2 Synergy for Precise Crop Stage Detection by Diego Gomez, Pablo Salvador, Jorge Gil, Juan Fernando Rodrigo

“…The study demonstrates the potential of combining SAR and optical data for post-season crop phenology analysis, providing insights that can inform the development of new methods and strategies to enhance on-season crop monitoring and yield forecasting.…”
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Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines by Cheng Yin, Le Thanh Binh, Duong Tran Anh, Son T. Mai, Anh Le, Van-Hau Nguyen, Van-Chien Nguyen, Nguyen Xuan Tinh, Hitoshi Tanaka, Nguyen Trung Viet, Long D. Nguyen, Trung Q. Duong

“…Compared to the Empirical Orthogonal Function (EOF), the most common method used for predicting shoreline changes from cameras, we demonstrate that the SARIMA, NNAR and LSTM models outperform the EOF model significantly in terms of prediction accuracy. …”
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Multicentral Agent-Based Model of Six Epidemic Waves of COVID-19 in the Nizhny Novgorod Region of Russian Federation by A. V. Hilov, N. V. Saperkin, O. V. Kovalishena, N. A. Sadykova, V. V. Perekatova, N. V. Perekhozheva, D. A. Kurakina, M. Ju. Kirillin

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A deep learning model for detection and classification of coffee-leaf diseases using the transfer-learning technique by Nabila Mansouri, Hanene Guessmi, Adel Alkhalil

“…Our method involves 195 different pre-trained deep learning models, including real-time models like MobileNet and dense ones like EfficientNet and ResNet for the detection of four different diseases. …”
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State-space modelling for infectious disease surveillance data: Stochastic simulation techniques and structural change detection by Christopher D. Prashad

“…Utilizing COVID-19 surveillance data from the province of Ontario, Canada, we employ Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) methods to detect structural changes and pre-dict future trends in case counts. …”
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Detecting Arctic Icebergs in Sea Ice in L-Band SAR Images Using a Multiscale CFAR Algorithm by Laust Faerch, Wolfgang Dierking, Nick Hughes, Anthony P. Doulgeris

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