Energy efficiency is an existing research challenge in wireless sensor networks (WSNs); to make WSNs energy efficient, many researchers moved to low-duty cycle WSNs with different data forwarding schemes. Low-duty cycle is considered to be a promising approach to design routing protocols for resource-constrained WSNs. Many applications have real-time constraints, which requires an event must be reported to the sink before deadline. Furthermore, wireless links between low-power radios are highly unreliable, and end-to-end latency requirements are challenging due to multiple transmissions for a single packet delivery. By considering probabilistic delay (i.e., delay bounded data delivery with reliability constraints) and energy efficiency issues, little research has been done using different data forwarding schemes, but still, the problem exists. In this work, two strategies are proposed for data forwarding: an energy-optimal path and a delay-optimal path. The early-arrived packet follows an optimal energy route and a diverse path to achieve a delay guarantee with minimum transmission cost. The proposed scheme improves the performance of low-duty cycle WSNs in terms of delay, reliability, stability, and transmission cost. It is observed that under different low-duty cycles, the proposed scheme achieves up to 25% energy conservation compared with both MinEEC and MinEED. At last, undirected spanning trees are considered to balance communication costs. The simulation results of the proposed scheme are compared with the existing methods.

The coronavirus is considered this century's most disruptive catastrophe and global concern. This disease has prompted extreme social, psychological and economic impacts affecting millions of people around the globe. COVID-19 is transmitted from one infected person's body to another through respiratory droplets. This virus proliferates when people breathe in air-contaminated space with droplets and microscopic airborne particles. This research aims to analyze automatic COVID-19 detection using machine learning techniques to build an intelligent web application. The dataset has been preprocessed by dropping null values, feature engineering, and synthetic oversampling (SMOTE) techniques. Next, we trained and evaluated different classifiers, i.e., logistic regression, random forest, decision tree, k-nearest neighbor, support vector machine (SVM), ensemble models (adaptive boosting and extreme gradient boosting) and deep learning (artificial neural network, convolutional neural network and long short-term memory) techniques. Explainable AI with the LIME framework has been applied to interpret the prediction results. The hybrid CNN-LSTM algorithm with the SMOTE approach performed better than the other models on the employed open-source dataset obtained from the Israeli Ministry of Health website, with 96.34% accuracy and a 0.98 F1 score. Finally, this model was chosen to deploy the proposed prediction system to a website, where users may acquire an instantaneous COVID-19 prognosis based on their symptoms.

Multi-class Sentiment Analysis (SA) is an important field of computational linguistics that extracts multiple opinions expressed in a text using NLP and text-mining techniques. Existing research on multi-class SA in the Bengali language is directed towards ternary classification with unsatisfactory classification performance. Moreover, obtaining a higher performance score is challenging due to the peculiarities of Bengali text, lack of ground truth datasets, and low resources of preprocessing tools. Moreover, no research has shown that deep learning algorithms perform higher on four types of sentiments. Therefore, we proposed a supervised deep learning classifier based on CNN and LSTM to conduct multi-class SA on Bengali social media comments labelled as sexual, religious, political, and acceptable. The study aims to achieve maximum accuracy using the proposed model and provide a comparative analysis with the baseline models. Six machine learning models with two different feature extraction techniques were considered baseline models. The performance of our proposed CLSTM architecture can greatly improve the performance of SA with 85.8% accuracy and 0.86 F1 scores on a labelled dataset of 42,036 Facebook comments. A web application based on the proposed model and the highest-performing baseline model was built to detect the real-life sentiment of social media comments.