In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects—voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds (575 h) if it was annotated manually. For the validation of the proposed technique, a sub-dataset—Voice, is annotated manually as well as with the proposed technique. Cohen's Kappa statistics is used to evaluate the degree of agreement between the two annotations. The high Kappa value (i.e., 0.9571%) shows the high level of agreement between the two. This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost. This research also contributes in consolidating the guidelines for the manual annotation process.

For conserving energy, duty cycle is defined by setting up the active and sleep periods of network nodes. In beacon enabled networks, to provide support for duty cycle, the IEEE 802.15.4 standard uses optional super-frame structure. This duty cycle is usually fixed and does not consider the topology changes that often occur in dynamic sensor networks. In this paper, existing energy conserving duty cycling approaches for 802.15.4 networks especially the adaptive duty cycling techniques for wireless sensor networks are summed up. Also, this paper highlights the shortcomings of the proposals in the literature, such as induced additional latency, so that they may not support the practical scenarios of Internet of Things (IoT). Further, this study highlights a gross shortcoming that relative performance comparison of RL-based proposals cannot be performed without using a benchmarking framework and real test-bed environment. In this paper, we have presented the future research directions that would lay the foundation for successful development of energy efficient RL-based duty-cycling techniques.

Heart attack and other heart-related diseases are among the main causes of fatalities in the world. These diseases and some other severe problems like kidney failure and paralysis are mainly caused by hypertension. Since bioactive peptides extracted from naturally existing food substances possess antihypertensive activity, these antihypertensive peptides (AHTP) can function as prospective replacements for existing pharmacological drugs with no or fewer side effects. Such naturally existing peptides can be identified using in-silico approaches. The in-silico methods have been proven to save huge amounts of time and money in the identification of effective peptides. The proposed methodology is a deep learning-based in-silico approach for the identification of antihypertensive peptides (AHTPs). An ensemble method is proposed that combines convolutional neural network (CNN) and support vector machine (SVM) classifiers. Amino acid composition (AAC) and g-gap dipeptide composition (DPC) techniques are used for feature extraction. The proposed methodology has been evaluated on two standard antihypertensive peptide sequence datasets. The model yields 95% accuracy on the benchmarking dataset and 88.9% accuracy on the independent dataset. Comparative analysis is provided to demonstrate that the proposed method outperforms existing state-of-the-art methods on both of the benchmarking and independent datasets.

Over the past decade, there has been an increase in hateful content on social media platforms, specifically in tweets. Hence, it brings a challenge to identify and classify tweets containing racism, discrimination, offense, toxicity, or abuse. This article proposes a novel hybrid approach that combines the power of Transformer-based language modeling with ensemble learning to classify offensive, toxic, and hateful tweets. Specifically, the robustly optimized bidirectional encoder representations from Transformers pretraining approach (RoBERTa)-large model is employed for feature extraction, followed by a hyperparameter-tuned extreme gradient boosting (XGBoost) classifier for final classification. The approach was evaluated on three widely used datasets ToxicTweets, Davidson, and HateSpeechDetection and compared against state-of-the-art methods, including deep architectures such as convolutional neural network (CNN), bidirectional encoder representations from Transformers (BERT), and AngryBERT; transformer models including DistilBERT, RoBERTa, A Lite BERT (ALBERT), and efficiently learning an encoder that classifies token replacements accurately (ELECTRA); and logistic regression. The experimental results demonstrate that the proposed hybrid model significantly outperforms existing approaches in terms of accuracy, precision, recall, and F1-score, achieving the highest accuracy of 97% on the HateSpeechDetection dataset and 92.42% on the Davidson dataset. Furthermore, the approach was compared with other ensemble methods, including Adaptive Boosting (AdaBoost), random forest, support vector classifier (SVC), light gradient boosting machine (LightGBM), and bagging, to highlight its superior performance. This study suggests that integrating RoBERTa-large with XGBoost is an effective approach for hate speech detection and provides a robust solution to the increasing problem of online hate and toxicity.