Cricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions based on ball speed, trajectory, fielder positions, etc. Recently, computer vision and machine learning techniques have gained attention as potential tools to predict cricket strokes played by batters. This study presents a cutting-edge approach to predicting batsman strokes using computer vision and machine learning. The study analyzes eight strokes: pull, cut, cover drive, straight drive, backfoot punch, on drive, flick, and sweep. The study uses the MediaPipe library to extract features from videos and several machine learning and deep learning algorithms, including random forest (RF), support vector machine, k-nearest neighbors, decision tree, linear regression, and long short-term memory to predict the strokes. The study achieves an outstanding accuracy of 99.77% using the RF algorithm, outperforming the other algorithms used in the study. The k-fold validation of the RF model is 95.0% with a standard deviation of 0.07, highlighting the potential of computer vision and machine learning techniques for predicting batsman strokes in cricket. The study’s results could help improve coaching techniques and enhance batsmen’s performance in cricket, ultimately improving the game’s overall quality.

Deepfake text known as synthetic text, involves using artificial intelligence (AI)-generated text to create fabricated information or imitate actual individuals. Twitter tweets related to deepfake can be used for many malicious intents, including impersonation, creating fake news, and spreading misinformation. The main goal of this investigation is to detect people’s sentiments related to deepfake technology with an advanced technique. A novel sentiment majority voting classifier (SMVC) is proposed for the sentiment labeling of collected tweets. The proposed SMVC selects the final sentiment from three lexicon-based models TextBlob, valence-aware dictionary and sentiment reasoner (VADER), and AFINN using a majority voting mechanism. For sentiment classification, we propose a novel transfer feature where embedding features are fed to a long short-term memory (LSTM), and decision tree (DT) models and outputs are combined into a single feature set. Extensive experiments show that transfer learning-based feature engineering results in the highest performance. The logistic regression outperforms with the highest accuracy of 98.9% with minimum computational complexity. The sentiment classification performance of each applied model is validated using the k-fold cross-validations. Moreover, performance assessment with existing state-of-the-art models is also carried out to show the robustness of the proposed technique.