The dangerously contagious virus named “COVID-19” has struck the world strong and has locked down billions of people in their homes to stop the further spread. All the researchers and scientists in various fields are continually developing a vaccine and prevention methods to aid the world from this challenging situation. However, a reliable prediction of the epidemic may help control this contiguous disease until the cure is available. The machine learning techniques are one of the frontiers in predicting this outbreak’s future trend and behavior. Our research is focused on finding a suitable machine learning algorithm that can predict the COVID-19 daily new cases with higher accuracy. This research has used the adaptive neuro-fuzzy inference system (ANFIS) and the long short-term memory (LSTM) to foresee the newly infected cases in Bangladesh. We have compared both the experiments’ results, and it can be forenamed that LSTM has shown more satisfactory results. Upon study and testing on several models, we have shown that LSTM works better on a scenario-based model for Bangladesh with mean absolute percentage error (MAPE)—4.51, root-mean-square error (RMSE)—6.55, and correlation coefficient—0.75. This study is expected to shed light on COVID-19 prediction models for researchers working with machine learning techniques and avoid proven failures, especially for small imprecise datasets.

名为“COVID-19”的危险传染性病毒对世界造成了强烈打击，并已将数十亿人封锁在家中，以阻止进一步传播。各个领域的所有研究人员和科学家都在不断开发疫苗和预防方法，以帮助世界摆脱这一充满挑战的局面。然而，在治愈之前，对流行病的可靠预测可能有助于控制这种连续疾病。机器学习技术是预测此次疫情未来趋势和行为的前沿之一。我们的研究重点是寻找一种合适的机器学习算法，该算法可以更准确地预测 COVID-19 每日新增病例。该研究使用自适应神经模糊推理系统 (ANFIS) 和长短期记忆 (LSTM) 来预测孟加拉国的新感染病例。我们比较了两个实验的结果，可以说 LSTM 显示出更令人满意的结果。在对几个模型进行研究和测试后，我们发现 LSTM 在孟加拉国的基于场景的模型上效果更好，平均绝对百分比误差 (MAPE) — 4.51，均方根误差 (RMSE) — 6.55，相关系数 — 0.75。这项研究有望为使用机器学习技术的研究人员阐明 COVID-19 预测模型，并避免已证实的失败，尤其是对于不精确的小型数据集。均方根误差 (RMSE) — 6.55，相关系数 — 0.75。这项研究有望为使用机器学习技术的研究人员阐明 COVID-19 预测模型，并避免已证实的失败，尤其是对于不精确的小型数据集。均方根误差 (RMSE) — 6.55，相关系数 — 0.75。这项研究有望为使用机器学习技术的研究人员阐明 COVID-19 预测模型，并避免已证实的失败，尤其是对于不精确的小型数据集。