A hybrid intelligent system: genetic algorithm and rough set incorporated neural fuzzy inference network by Wang, Di

“…A committee of multiple GARSINFIS networks with the weighted voting scheme can be employed to increase the overall accuracy. …”
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KOMUNITAS SIPIL DAN PERILAKU MEMILIH DALAM PEMILU LEGISLATIF 2009 (Studi Tentang Pengaruh Komunitas Sipil Terhadap Perilaku Pemilih pada Pemilu Legislatif) by , Hendra Ginanjar Kurniawan, , Prof. Dr. Sunyoto Usman

“…The purposes of this study are about voting behavior of people on the elections legislative 2009, to discover about the process of mainstream civil society has a greater impact in mobilizing the support of voters, to obtain these tendencies in society and to find what the implications of these tendencies in political life are, especially political relations between political parties and the community. …”

An ensemble-based anomaly-behavioural crypto-ransomware pre-encryption detection model by Al-Rimy, Bander Ali Saleh

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Radiomics of pericardial fat: a new frontier in heart failure discrimination and prediction by Szabo, L, Salih, A, Pujadas, ER, Bard, A, McCracken, C, Ardissino, M, Antoniades, C, Vago, H, Maurovich-Horvat, P, Merkely, B, Neubauer, S, Lekadir, K, Petersen, SE, Raisi-Estabragh, Z

“…We achieved good discriminative performance for both prevalent (voting classifier; AUC: 0.76; F1 score: 0.70) and incident (light gradient boosting machine: AUC: 0.74; F1 score: 0.68) HF. …”

La oscilante (in)capacidad de la oposición venezolana en la disputa por el poder subnacional (2008-2022) by Jiménez, M, Vitale, S, Trak, JM, Aveledo, GT

“…Opposition parties have employed strategies that, at times, have brought them closer to propitiating greater electoral defeats to chavismo at the subnational level, demonstrating a key potential in terms of votes, but which has not yet translated into a greater number of governorships in their favor.…”

PARENTS 2 study: consensus report for parental engagement in the perinatal mortality review process by Bakhbakhi, D, Siassakos, D, Lynch, M, Timlin, L, Storey, C, Heazell, A, Burden, C, Parents Collaborative Group

“…Of the 25 participants, 96% agreed that a face‐to‐face explanation of the PNMR process was of critical importance, 72% considered that parents should be offered the opportunity to nominate a suitable advocate, 92% believed that responses to parents' comments should be formally documented, 96% indicated that it was vital for action plans to be translated into lessons learnt and that this process should be monitored, and 100% of stakeholders voted that a plain‐English summary should be produced for the parents following the meeting. …”

Confidential remote computing by Küçük, KA

“…Many-party applications can vary from smart-grid systems to electronic voting infrastructures and block-chain smart contracts to internet-of-things deployments. …”

Understanding the engagement and consequences of user generated online video and its virality towards generation y political inclination / Wan Hartini Wan Zainodin by Wan Zainodin, Wan Hartini

“…, (2) How does virality of User-generated online video contributes to voting decision among Gen Y?, 3) How does the User-generated online video and its virality contribute to the perception of Gen Y towards the Federal Government ruling parties?…”
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Development of a hybrid method by data envelopment analysis and analytic network process for project selection by Mojahed, Majid

“…To identify project selection criteria and the number of groups, voting method was used. The inputs of Cross Efficiency Matrix were provided by inputs and outputs of DEA method and finally, this hybrid method represents the project number 3 ‘Building construction of call Centre in Jajarm’ was selected as the profitable project and it was followed by project number 5’ Building construction of call Centre in Farooj’ and project number 4‘Building construction of call Centre in Esfaraeen’ and so on. …”
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A comparative study between rough and decision tree classifiers by Mohamad Mohsin, Mohamad Farhan

“…Theoretically, a good set of knowledge should provide good accuracy when dealing with new cases.Besides accuracy, a good rule set must also has a minimum number of rules and each rule should be short as possible.It is often that a rule set contains smaller quantity of rules but they usually have more conditions.An ideal model should be able to produces fewer, shorter rule and classify new data with good accuracy.Consequently, the quality and compact knowledge will contribute manager with a good decision model.Because of that, the search for appropriate data mining approach which can provide quality knowledge is important.Rough classifier (RC) and decision tree classifier (DTC) are categorized as RBC.The purpose of this study is to investigate the capability of RC and DTC in generating quality knowledge which leads to the good accuracy.To achieve that, both classifiers are compared based on four measurements that are accuracy of the classification, the number of rule, the length of rule, and the coverage of rule.Five dataset from UCI Machine Learning namely United States Congressional Voting Records, Credit Approval, Wisconsin Diagnostic Breast Cancer, Pima Indians Diabetes Database, and Vehicle Silhouettes are chosen as data experiment.All datasets were mined using RC toolkit namely ROSETTA while C4.5 algorithm in WEKA application was chosen as DTC rule generator.The experimental results indicated that both classifiers produced good classification result and had generated quality rule in different types of model – higher accuracy, fewer rule, shorter rule, and higher coverage.In term of accuracy, RC obtained higher accuracy in average while DTC significantly generated lower number of rule than RC.In term of rule length, RC produced compact and shorter rule than DTC and the length is not significantly different.Meanwhile, RC has better coverage than DTC.Final conclusion can be decided as follows “If the user interested at a variety of rule pattern with a good accuracy and the number of rule is not important, RC is the best solution whereas if the user looks for fewer nr, DTC might be the best choice”…”
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Clinical Discussions in Antithrombotic Therapy Management in Patients With Atrial Fibrillation: A Delphi Consensus Panel by Nicola Mumoli, MD, Claudia Amellone, MD, Gianfranco Antonelli, MD, Giuseppe Augello, MD, Cosima Cloro, MD, Alberto D’Alleva, MD, Leonardo Di Ascenzo, MD, PhD, Egidio Imbalzano, MD, Renato Masala, MD, Graziano Riccioni, MD, Emanuele Romeo, MD, Luca Rossi, MD, Giosuè Santoro, MD, Edoardo Sciatti, MD, Antonio Tondo, MD, Elisabetta Toso, MD, Elio Venturini, MD, Enrico Vizzardi, MD, Giosuè Mascioli, MD

“…Results: A total of 101 physicians (cardiologists, internists, geriatricians, and hematologists) from Italy expressed their level of agreement on each statement by using a 5-point Likert scale (1 = strongly disagree; 2 = disagree; 3 = somewhat agree; 4 = agree; 5 = strongly agree). Votes 1-2 were considered to be disagreement; votes 3-5 were considered to be agreement. …”
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“Corona-Debriefing”: concept and pilot testing of a 90-minute workshop for undergraduate-education and specialist-training in family medicine by Klement, Andreas, Ibs, Torben, Longard, Sebastian, Klinkhart, Catarina, Frese, Thomas, Heise, Marcus

“…Therefore, we designed a 90-minute workshop “Corona-Debriefing” for students and physicians in specialist-training in family medicine (ÄiW) using three successive moderated interaction phases: Questionnaire survey via tele-dialogue voting (TED) with immediate presentation of results and discussion, moderated experience reports on the categories risk/assessment/support/coping and finally moderated group discussions in small groups to collect “best practice” examples of crisis management. …”
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Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer by Fatemeh Khojasteh-Leylakoohi, Reza Mohit, Nima Khalili-Tanha, Alireza Asadnia, Hamid Naderi, Ghazaleh Pourali, Zahra Yousefli, Ghazaleh Khalili-Tanha, Majid Khazaei, Mina Maftooh, Mohammadreza Nassiri, Seyed Mahdi Hassanian, Majid Ghayour-Mobarhan, Gordon A. Ferns, Soodabeh Shahidsales, Alfred King-yin Lam, Elisa Giovannetti, Elham Nazari, Jyotsna Batra, Amir Avan

“…Ensemble learning, Random Forest (RF), Max Voting, Adaboost, Gradient boosting machines (GBM), and Extreme Gradient Boosting (XGB) techniques were used, and Gradient boosting machines (GBM) were selected with 100% accuracy for analysis. …”
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Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data [version 2; peer review: 1 approved, 2 approved with reservations] by J. Javier Diaz-Mejia, Elaine C. Meng, Alexander R. Pico, Sonya A. MacParland, Troy Ketela, Trevor J. Pugh, Gary D. Bader, John H. Morris

“…Methods: In this study, we benchmarked five methods representing first-generation enrichment analysis (ORA), second-generation approaches (GSEA and GSVA), machine learning tools (CIBERSORT) and network-based neighbor voting (METANEIGHBOR), for the task of assigning cell type labels to cell clusters from scRNA-seq data. …”
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Study protocol for developing, piloting and disseminating the PRISMA-COSMIN guideline: a new reporting guideline for systematic reviews of outcome measurement instruments by Ellen B. M. Elsman, Nancy J. Butcher, Lidwine B. Mokkink, Caroline B. Terwee, Andrea Tricco, Joel J. Gagnier, Olalekan Lee Aiyegbusi, Carolina Barnett, Maureen Smith, David Moher, Martin Offringa

“…Fourth, a consensus meeting will be held to finalize the PRISMA-COSMIN guideline through roundtable discussions and voting. Last, a user manual will be developed and the final PRISMA-COSMIN guideline will be disseminated through publications, conferences, newsletters, and relevant websites. …”
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Using a priority setting exercise to identify priorities for guidelines on newborn and child health in South Africa, Malawi, and Nigeria by Solange Durão, Emmanuel Effa, Nyanyiwe Mbeye, Mashudu Mthethwa, Michael McCaul, Celeste Naude, Amanda Brand, Ntombifuthi Blose, Denny Mabetha, Moriam Chibuzor, Dachi Arikpo, Roselyn Chipojola, Gertrude Kunje, Per Olav Vandvik, Ekpereonne Esu, Simon Lewin, Tamara Kredo

“…Results Based on survey results, nine, 10 and 11 topics were identified in SA, Malawi, and Nigeria respectively, which informed consensus meetings. Through voting and discussion within meetings, and further engagement after the meetings, the top three priority topics were identified in each country. …”
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Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data [version 3; peer review: 2 approved, 1 approved with reservations] by J. Javier Diaz-Mejia, Elaine C. Meng, Alexander R. Pico, Sonya A. MacParland, Troy Ketela, Trevor J. Pugh, Gary D. Bader, John H. Morris

“…Methods: In this study, we benchmarked five methods representing first-generation enrichment analysis (ORA), second-generation approaches (GSEA and GSVA), machine learning tools (CIBERSORT) and network-based neighbor voting (METANEIGHBOR), for the task of assigning cell type labels to cell clusters from scRNA-seq data. …”
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Fully convolutional neural networks applied to large-scale marine morphology mapping by Riccardo Arosio, Riccardo Arosio, Brandon Hobley, Andrew J. Wheeler, Andrew J. Wheeler, Andrew J. Wheeler, Fabio Sacchetti, Luis A. Conti, Thomas Furey, Aaron Lim, Aaron Lim, Aaron Lim

“…A simple majority (modal) voting combining the ten best models produced an excellent map with overall F1 score of 0.96 and class precisions and recalls superior to 0.87. …”
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Prediction of posttraumatic functional recovery in middle-aged and older patients through dynamic ensemble selection modeling by Nguyen Thanh Nhu, Nguyen Thanh Nhu, Jiunn-Horng Kang, Jiunn-Horng Kang, Jiunn-Horng Kang, Jiunn-Horng Kang, Jiunn-Horng Kang, Tian-Shin Yeh, Tian-Shin Yeh, Tian-Shin Yeh, Tian-Shin Yeh, Chia-Chieh Wu, Chia-Chieh Wu, Cheng-Yu Tsai, Krisna Piravej, Krisna Piravej, Carlos Lam, Carlos Lam

“…The algorithms exhibiting satisfactory performance were subjected to bagging to construct stacking, voting, and dynamic ensemble selection models. The best model was evaluated on the test data set. …”
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Multidisciplinary Treatment of Non-Spine Bone Metastases: Results of a Modified Delphi Consensus Process by Erin F. Gillespie, Noah J. Mathis, Max Vaynrub, Ernesto Santos Martin, Rupesh Kotecha, Joseph Panoff, Andrew L. Salner, Alyson F. McIntosh, Ranju Gupta, Amitabh Gulati, Divya Yerramilli, Amy J. Xu, Meredith Bartelstein, David M. Guttmann, Yoshiya J. Yamada, Diana Lin, Kaitlyn Lapen, Deborah Korenstein, David G. Pfister, Allison Lipitz-Snyderman, Jonathan T. Yang

“…Consensus was reached for 16 of 17 answer statements (94%), of which 12 were approved after Round 1 and additional 4 approved after Round 2 of the modified Delphi voting process. Topics included indications for referral to surgery or interventional radiology, radiation fractionation and appropriate use of stereotactic approaches, and the handling of systemic therapies during radiation. …”
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