Impact of Mobile Health (mHealth) Use by Community Health Workers on the Utilization of Maternity Care in Rural Malawi: A Time Series Analysis by Kachimanga C, Ng’ambi WF, Kazinga D, Ndarama E, Amulele MA, Munyaneza F, Abejirinde IOO, van den Akker T, Kulinkina AV

“…For long-term effect, YendaNafe was associated with an increase in new ANC visits (aIRR 1.04, 95% CI 1.01– 1.07, p < 0.01) and ANC in the first trimester (aIRR 1.03,95% CI 1.00– 1.07 p=0.046), but not facility-based births (aIRR 1.01, 95% CI 0.99– 1.03, p=0.46) or PNC (aIRR 0.97 95% CI 0.93– 1.01, p=0.14).Conclusion: mHealth shows potential of increasing utilization of new ANC visits, ANC in the first trimester and facility-based births. Further research is needed to understand why mHealth did not have an effect on PNC.Keywords: antenatal care, facility-based births, postnatal care, maternity care…”
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Mitigating overconfidence bias: A cross-sectional pilot study of male maintenance workers in the engineering sector by Luca DI GIAMPAOLO, Federica MARINO, Claudia GIURGOLA, Piergiorgio ASTOLFI, Luca COPPETA, Simone DE SIO, Paola BORRELLI, Teresa GALANTI, Laura FORCELLA, Maria Elisa MAIOLO, Kavita BATRA, Murat YILDIRIM, Hicham KHABBACHE, Amelia RIZZO, Mahmood BAHRAMIZADEH, Hiroo WADA, Qiao NIU, Hector NIETO, Francesco CHIRICO

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Komparasi Machine Learning Berbasis Pso Untuk Prediksi Tingkat Keberhasilan Belajar Berbasis E-Learning by Elin Panca Saputra, Siti Nurajizah, Mawadatul Maulidah, Nadiyah Hidayati, Taufik Rahman

“…The problem in this study is that the number of attributes in the data can reduce accuracy, so from experiments with several yahoos on machine learning, we tried to apply Particle Swarm Optimizatio (PSO) to increase higher accuracy. …”
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