Analyzing Instagram Engagement to Forecast Domestic Tourist Trips in Lake Toba and North Sumatra by Erna Nurmawati, Teguh Sugiyarto, Navika Artiari, Adelina Rahmawati

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Analyzing GHG emission forecasting in Korea's semiconductor and display industries using grey model by Inkyung Cho, Soohyeon Kim, Miyeon Yoo

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Analyzing the compressive performance of lightweight foamcrete and parameter interdependencies using machine intelligence strategies by Wang Guoyuan, Fan Wenbo, Shi Qingbin, Luo Yingqi

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Mixed Sequence Reader: A Program for Analyzing DNA Sequences with Heterozygous Base Calling by Chun-Tien Chang, Chi-Neu Tsai, Chuan Yi Tang, Chun-Houh Chen, Jang-Hau Lian, Chi-Yu Hu, Chia-Lung Tsai, Angel Chao, Chyong-Huey Lai, Tzu-Hao Wang, Yun-Shien Lee

“…In this study, we developed a free web-based program, Mixed Sequence Reader (MSR), which can directly analyze heterozygous base-calling fluorescence chromatogram data in .abi file format using comparisons with reference sequences. …”
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Deep learning for air pollution detection: Analyzing scots pine needles with SEM/EDS by Mirosław Szwed, Witold Żukowski, Dariusz Pasieka

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Analyzing the Mediterranean Tropical-like Cyclone Ianos Using the Moist Static Energy Budget by Miriam Saraceni, Lorenzo Silvestri, Paolina Bongioannini Cerlini

“…Medicanes are hybrid cyclonic systems that share characteristics of both extratropical and tropical cyclones, making their classification and prediction challenging. Using high-resolution ERA5 reanalysis data, we analyzed the life cycle of Ianos, which is one of the strongest recorded medicanes, employing the vertically integrated MSE spatial variance budget to quantify the contributions of different energy sources to the cyclone’s development. …”
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The cost of green: Analyzing the economic feasibility of hydrogen production from offshore wind power by Jun-Wei Ding, Yuan-Shin Fu, I-Yun Lisa Hsieh

“…While substantial research has analyzed the economic dynamics of offshore wind and green hydrogen, the impact of offshore distances on hydrogen production costs remains underexplored. …”
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Analyzing water footprint and water resources sustainability in China’s arid Northwest with Bayesian network by Lingyun Zhang, Lingxiao Sun, Yang Yu, Xiaoyun Ding, Zengkun Guo, Ruide Yu

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Analyzing Patient Experience on Weibo: Machine Learning Approach to Topic Modeling and Sentiment Analysis by Xiao Chen, Zhiyun Shen, Tingyu Guan, Yuchen Tao, Yichen Kang, Yuxia Zhang

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Analyzing the predictors of relapse in drug-induced headache: a prospective clinical and psychological study by E. A. Guziy, A. V. Sergeev, G. R. Tabeeva

“…The prevalence of drug-induced headache (DIHA) in the population is about 7%, and the relapse rate of DIHA after successful treatment is about 40% within 5 years.Objective: to analyze the risk factors for recurrence of DIHA and to develop a prognostic model for the probability of relapse after treatment.Material and methods. …”
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Portable lactate analyzer for measuring lactate in cerebrospinal fluid (CSF) and plasma ? method-comparison evaluations by Sérgio Monteiro de Almeida, Jennifer Marquie-Beck, Archana Bhatt, Scott Letendre, Allen McCutchan, Ron Ellis

“…For CSF the specificity was 0.95; the sensitivity 0.33; the negative predictive value was 95% and the positive predictive value 33%. …”
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Analyzing High-Speed Rail’s Transformative Impact on Public Transport in Thailand Using Machine Learning by Chinnakrit Banyong, Natthaporn Hantanong, Panuwat Wisutwattanasak, Thanapong Champahom, Kestsirin Theerathitichaipa, Rattanaporn Kasemsri, Manlika Seefong, Vatanavongs Ratanavaraha, Sajjakaj Jomnonkwao

“…The dataset, consisting of 38,400 observations, was analyzed using the CatBoost model and the multinomial logit (MNL) model. …”
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Analyzing the Implementation of PLS-SEM in Educational Technology Research: A Review of the Past 10 Years by Servet Demir, Muhammet Uşak

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Pyramidal Predictive Network V2: An Improved Predictive Architecture and Training Strategies for Future Perception Prediction by Chaofan Ling, Junpei Zhong, Weihua Li, Ran Dong, Mingjun Dai

“…In this paper, we propose an improved version of the Pyramidal Predictive Network (PPNV2), a theoretical framework inspired by predictive coding, which addresses the limitations of its predecessor (PPNV1) in the task of future perception prediction. …”
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Application of Random Forest Algorithm to Analyze the Confidence Level of Forest Fire Hotspots in Riau Peatland by Mitra Unik, Imas Sukaesih Sitanggang, Lailan Syaufina, I Nengah Surati Jaya

“…This study employs the Random Forest (RF) algorithm to analyze the confidence levels of hotspots, aiming to predict potential fire occurrences and improve fire management strategies. …”
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Analyzing Decision-Making in Cognitive Agent Simulations Using Generalized Linear Mixed-Effects Models by Shengkun Xie, Chong Gan, Anna T. Lawniczak

“…Enhancing model interpretability remains an ongoing challenge in predictive modelling, especially when applied to simulation data from complex systems. …”
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ANALYZING SOCIAL MEDIA SENTIMENT TOWARD SPECIFIC COMMODITIES FOR FORECASTING PRICE MOVEMENTS IN COMMODITY MARKETS by Mariono Mariono, Syaharuddin Syaharuddin, Sameer Ashraf, Sunday Emmanuel Fadugba

“…This study adopts a systematic literature review to analyze social media sentiment towards specific commodities to enhance the accuracy of price movement forecasts in commodity markets. …”
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The BVAR Model for Analyzing CO2 Emissions on Renewable Energy, Economic Growth, and Forest Area by Dodi Devianto, Ridho Saputra, Mutia Yollanda, Maiyastri Maiyastri, Yudiantri Asdi, Dony Permana, Erna Tri Herdiani

“…The BVAR model demonstrated substantial predictive accuracy, highlighting its suitability for analyzing environmental and economic data in resource-constrained scenarios. …”
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Generative AI for Industry 5.0: Analyzing the Impact of ChatGPT, DALLE, and Other Models by Siva Sai, Revant Sai, Vinay Chamola

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Models for analyzing the complexity of English words in the text on the scale from A1 to C2 by М. Бєліков, Т. Ліхоузова, Ю. Олійник

“…An application in the Python programming language is proposed, which receives a sample of data from the created storage, displays them graphically, performs intellectual analysis, trains and compares models according to accuracy, precision, recall and f1-score metrics. To analyze the data and predict the difficulty level of English words according to the CEFR scale from A1 to C2 based on their frequency of occurrence in the English language, the following models were used: PchipInterpolator, logarithmic model, Gradient Boosting Regressor, Random Forest Regressor and XGB regressor. …”
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