Multisource Accident Datasets-Driven Deep Learning-Based Traffic Accident Portrait for Accident Reasoning by Chun-Hao Wang, Yue-Tian-Si Ji, Li Ruan, Joshua Luhwago, Yin-Xuan Saw, Sokhey Kim, Tao Ruan, Li-Min Xiao, Rui-Jue Zhou

“…Moreover, to solve the problem of how to construct hidden relationship among the multisource accident data, a multisource accident data visualization method based on traffic accident knowledge graph where the accident relational inference algorithm is to complete the hidden relationship between traffic accident data labels is used and then data are visualized using the traffic accident knowledge graph. …”
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Outdoor Massive MIMO Testbed With Directional Beams: Design, Implementation, and Validation by Spandan Bisoyi, Pasupuleti Muralimohan, Harish Kumar Dureppagari, Pavan Reddy Manne, Kiran Kuchi

“…To address this, we introduced a fair scheduling algorithm alongside user pairing, which reduces the simultaneous number of paired users to 7 with QPSK modulation within the 30° sector, ensuring equal pairing opportunities for all users. …”
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A preoperative predictive model based on multi-modal features to predict pathological complete response after neoadjuvant chemoimmunotherapy in esophageal cancer patients by Yana Qi, Yanran Hu, Chengting Lin, Ge Song, Liting Shi, Hui Zhu

“…After feature selection, three single-modality prediction models and a combined multi-modality model integrating two radiomics features, 11 pathomics features, and two clinicopathological features were constructed using the support vector machine (SVM) algorithm. The performance of the models were evaluated using receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA). …”
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Adversarial measurements for convolutional neural network-based energy theft detection model in smart grid by Santosh Nirmal, Pramod Patil, Sagar Shinde

“…The attack comprises two methods: the first is we originally propose a novel Adversarial Data Generation Method (ADGM), which is an algorithm designed to generate adversarial data, and the other is Fast Gradient Sign Method (FGSM). …”
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Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach by Dongmei Pei, Chengpu Zhang, Yu Quan, Qiyong Guo

“…With a strict data filtration, 10,436 records from the eligible participants were utilized to develop a prediction model using the J48 decision tree algorithm. Nine variables, including age, gender, body mass index (BMI), hypertension, history of cardiovascular disease or stroke, family history of diabetes, physical activity, work-related stress, and salty food preference, were considered. …”
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Machine learning models reveal microbial signatures in healthy human tissues, challenging the sterility of human organs by Anargyros Skoulakis, Anargyros Skoulakis, Giorgos Skoufos, Giorgos Skoufos, Armen Ovsepian, Armen Ovsepian, Artemis G. Hatzigeorgiou, Artemis G. Hatzigeorgiou

“…Unaligned sequencing reads with the human genome were processed using AGAMEMNON, an algorithm for metagenomic microbial quantification, with a reference database comprising bacterial, archaeal, and viral genomes, alongside fungal transcriptomes. …”
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Identification of CD8+ T Cell-Related Genes: Correlations with Immune Phenotypes and Outcomes of Liver Cancer by Qi Pan, Ying Cheng, Donghua Cheng

“…The “estimate” package and the CIBERSORT algorithm were used to evaluate tumor purity and the proportion of CD8+ T lymphocytes in the liver cancer cohort. …”
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Pengembangan Sistem Pemantauan Sentimen Berita Berbahasa Indonesia Berdasarkan Konten dengan Long-Short-Term Memory by Dewi Yanti Liliana, Nadia Nurul Hikmah, Maykada Harjono

“…The data is then processed using the Long Short Term Memory (LSTM) algorithm. LSTM is commonly used to handle classification in various fields especially in a sequential input. …”
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Modeling Burst Error Processes in ITU-T G.hnem-Based PLC Systems: Noisy Indoor Environment Analysis Using a Generative Semi-Hidden Markov Model by Akintunde Oluremi Iyiola, Theo G. Swart, Ayokunle Damilola Familua, Thokozani Shongwe

“…This research makes four key contributions: (1) the development of a robust experimental testbed for real-time burst error analysis in NB-PLC systems, (2) the creation of a comprehensive dataset that characterizes noise-induced disruptions in real-world indoor scenarios, (3) a theoretical exploration and practical application of SHMMs for modeling error sequences in NB-PLC channels, and (4) the validation of SHMMs and a modified Baum-Welch algorithm (BWA) for efficient modeling and parameter estimation of burst error processes. …”
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Advanced microgrid optimization using price-elastic demand response and greedy rat swarm optimization for economic and environmental efficiency by Arvind R. Singh, Bishwajit Dey, Mohit Bajaj, Sahil Kadiwala, Rangu Seshu Kumar, Soham Dutta, Ievgen Zaitsev

“…Furthermore, limiting the grid’s upstream power capacity to 30 kW resulted in a 7% increase in generation cost across all cases, confirming the importance of grid participation in reducing operational costs. The GRSO algorithm outperformed traditional metaheuristics in terms of both execution time and convergence, making it a viable solution for real-time microgrid optimization. …”
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The Social Construction of Categorical Data: Mixed Methods Approach to Assessing Data Features in Publicly Available Datasets by Theresa Willem, Alessandro Wollek, Theodor Cheslerean-Boghiu, Martha Kenney, Alena Buyx

“…We first present an exploratory, quantitative study that assesses the effects when including or excluding each of the unique categorical data features of the PAD-UFES 20 dataset for training a transformer-based model using a data fusion algorithm. We then pair our quantitative analysis with a qualitative examination of the data categories based on interviews with the dataset authors. …”
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Minimizing Power Consumption and Interference Mitigation of Downlink NOMA HetNets by IRS-Supported Aerial Base Stations by Osamah Thamer Hassan Alzubaidi, Mhd Nour Hindia, Kaharudin Dimyati, Kamarul Ariffin Noordin, Faizan Qamar, Atef Abdrabou

“…To tackle this problem, a meta-heuristic algorithm based on the modified gray wolf optimization (MGWO) and an alternation max/min technique based on the developed Block Coordinate Descent (DBCD) are proposed. …”
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TLR7: A Key Prognostic Biomarker and Immunotherapeutic Target in Lung Adenocarcinoma by Feiming Hu, Chenchen Hu, Yuanli He, Yuanjie Sun, Chenying Han, Xiyang Zhang, Lingying Yu, Daimei Shi, Yubo Sun, Junqi Zhang, Dongbo Jiang, Shuya Yang, Kun Yang

“…<b>Methods:</b> We utilized the ESTIMATE algorithm to evaluate the immune and stromal components of the LUAD TME from the TCGA database. …”
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Spatiotemporal optimization of irrigation practices for winter wheat in China: Rationale, implications, and solutions by Luchen Zhang, Yuan Cao, Weihao Qian, Junning Tian, Shengshi Huang, Xiaolei Qiu, Bing Liu, Liang Tang, Liujun Xiao, Weixing Cao, Yan Zhu, Leilei Liu

“…Subsequently, a multi-model ensemble approach integrated with a multi-objective optimization algorithm was utilized to propose the optimized irrigation strategies, which could enhance water use efficiency while attaining high yields. …”
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Can primary care research be conducted more efficiently using routinely reported practice-level data: a cluster randomised controlled trial conducted in England? by Jeremy Horwood, Alastair D Hay, Patricia Jane Lucas, Clare Clement, Peter S Blair, Jodi Taylor, Christie Cabral, Martin C Gulliford, Jenny Ingram, Padraig Dixon, Nick Francis, Athene Lane, Grace Young, Elizabeth Beech, Penny Seume, Scott Bevan, Sam T Creavin

“…By using aggregate level, routinely collected data for the coprimary outcomes, we removed the need to recruit individual participants.Setting Primary care.Participants Baseline data from general practitioner practices and interviews with individuals from Clinical Research Networks (CRNs) in England who helped recruit practices and Clinical Commission Groups (CCGs) who collected outcome data.Intervention The intervention included: (1) explicit elicitation of parental concerns, (2) a prognostic algorithm to identify children at low risk of hospitalisation and (3) provision of a printout for carers including safety-netting advice.Coprimary outcomes For 0–9 years old—(1) Dispensing data for amoxicillin and macrolide antibiotics and (2) hospital admission rate for RTI.Results We recruited 294 of the intended 310 practices (95%) representing 336 496 registered 0–9 years old (5% of all 0–9 years old children). …”
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Hierarchizing multi-scale environmental effects on agricultural pest population dynamics: a case study on the annual onset of Bactrocera dorsalis population growth in Senegalese or... by Caumette, Cécile, Diatta, Paterne, Piry, Sylvain, Chapuis, Marie-Pierre, Faye, Emile, Sigrist, Fabio, Martin, Olivier, Papaïx, Julien, Brévault, Thierry, Berthier, Karine

“…We then developed a flexible analysis pipeline centred on a recent machine learning algorithm, which allows the combination of gradient boosting and grouped random effects models or Gaussian processes, to hierarchize the effects of multi-scale environmental variables on the onset of annual BD population growth in orchards. …”
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Role of the Bronchoalveolar Lavage in Noncritically Ill Patients during the SARS-CoV-2 Epidemic by Olivier Taton, Emmanuelle Papleux, Benjamin Bondue, Christiane Knoop, Sébastien Van Laethem, Alain Bauler, Delphine Martiny, Isabel Montesinos, Marie-Luce Delforge, Kahina Elmaouhab, Dimitri Leduc

“…BAL was performed according to a predefined decisional algorithm based on their state of immunocompetence, the chest CT scan features, and their respiratory status. …”
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Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals by Gulay Tasci, Prabal Datta Barua, Dahiru Tanko, Tugce Keles, Suat Tas, Ilknur Sercek, Suheda Kaya, Kubra Yildirim, Yunus Talu, Burak Tasci, Filiz Ozsoy, Nida Gonen, Irem Tasci, Sengul Dogan, Turker Tuncer

“…In the classification phase, we employed an ensemble and iterative distance-based classifier to achieve high classification performance. Therefore, a t-algorithm-based k-nearest neighbors (tkNN) classifier was used to obtain classification results. …”
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Predicted indirectly recognizable HLA epitopes scores and clinical outcomes after haploidentical stem cell transplantation in pediatric patients with relapsed neuroblastoma by Eun Seop Seo, Eun Seop Seo, In Hwa Jeong, In Hwa Jeong, Hee Young Ju, Ju Kyung Hyun, Ji Won Lee, Keon Hee Yoo, Won Young Heo, Ki Woong Sung, Hee Won Cho, Eun-Suk Kang

“…IntroductionThe Predicted Indirectly ReCognizable HLA Epitopes (PIRCHE) model is a recently developed algorithm that predicts indirect T-cell recognition by calculating the number of such epitopes in donor-recipient pairs.MethodsIn this study, the clinical significance of PIRCHE was evaluated in pediatric patients with relapsed/progressed neuroblastoma undergoing haploidentical stem cell transplantation (haplo-SCT).ResultsA higher PIRCHE-I score was associated with faster platelet recovery (P = 0.007) and lower incidence of hemorrhagic cystitis (13% vs. 41%, P = 0.028) and invasive fungal infections (0% vs. 18%, P = 0.045). …”
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Screening high-risk Veterans for cirrhosis: taking a stepwise population health approach by Jonathan Dounel, Carolyn Lamorte, Heather Patton, Ponni Perumalswami, Heather McCurdy, Nicole J. Kim, Lauren A. Beste, Dawn Scott, Jessimarie Casey, Patrick Spoutz, Linda Chia, Yiwen Yao, Elliott Lowy, Sandra Gibson, Timothy R. Morgan, Shari S. Rogal

“…Abstract Background Because cirrhosis is often unrecognized, we aimed to develop a stepwise screening algorithm for cirrhosis in the Veterans Health Administration (VHA) and assess this approach’s feasibility and acceptability. …”
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