The trend of writing fake reviews has recently increased with the rapid growth of e-commerce websites. Fake reviews are usually written to promote or demote the targeted products to affect the customer's decision and thus achieve a competitive advantage. Several techniques have been proposed to detect fake reviews written in English, and promising results have been obtained in the literature. Nevertheless, detecting fake reviews for low-resource languages (such as Roman Urdu) is still in the infancy stage and suffers from low classification results for two main reasons. Firstly, the existing studies mostly worked on textual features or lingual features. Secondly, the datasets used in existing studies are highly imbalanced, and proper attention to this issue may further enhance the performance. Therefore, to address these weaknesses and further enhance the performance, we have identified three types of discriminative features: review textual features, review lingual features, and review behavioral features using the Daraz 1 dataset. Moreover, we evaluated LSTM-based text generation techniques for textual features and the random undersampling and oversampling for behavioral and lingual features to deal with class imbalance problems. Finally, we empirically evaluated the performance of machine learning and deep learning algorithms in classifying fake reviews written in the Roman Urdu language. The experimental results show that user behavioral features play a vital role in detecting fake reviews. Moreover, it was found that text generation is ineffective for balancing the textual data because the informative feature for fake review detection depends on the user behavioral features compared to textual features. Finally, the experimental results show that gradient boosting (GB) outperformed other models and improved 3% accuracy from the baseline study.

In this paper, we introduced a novel Facial Emotion Recognition (FER) framework that utilizes a diffusion-based approach and an attention mechanism. The model is efficiently trained through self-supervised learning, leveraging labeled and unlabelled data. The proposed framework has been rigorously tested on the FER2013 and AffectNet datasets, achieving promising accuracies of $67.2 \%$ and $68.1 \%$, respectively. The quantitative results not only surpass the performance of existing state-of-the-art FER models but also demonstrate the synergistic effect of combining diffusion-based modeling with self-supervised learning and attention mechanisms within a solid architectural framework. Our approach sets a new benchmark in the field, offering a significant step forward in the accurate and efficient recognition of facial expressions.

Research on Speech Emotion Recognition is becoming more mature day by day, and a lot of research is being carried out on Speech Emotion Recognition in resource-rich languages like English, German, French, and Chinese. Urdu is among the top 10 languages spoken worldwide. Despite its importance, few studies have worked on Urdu Speech emotion as Urdu is recognized as a resource-poor language. The Urdu language lacks publicly available datasets, and for this reason, few researchers have worked on Urdu Speech Emotion Recognition. To the best of our knowledge, no review has been found on Urdu Speech Emotion recognition. This study is the first systematic literature review on Urdu Speech Emotion Recognition, and the primary goal of this study is to provide a detailed analysis of the literature on Urdu Speech Emotion Recognition which includes the datasets, features, pre-processing, approaches, performance metrics, and validation methods used for Urdu Speech Emotion Recognition. This study also highlights the challenges and future directions for Urdu Speech Emotion Recognition.

Forensic autopsy focuses on revealing the cause of death (CoD) by examination of a dead body. In this research study, various feature extraction schemes, feature value representation schemes and text classification algorithms have been applied on forensic autopsy reports to discover the suitable feature extraction approach, feature value representation approach and text classification approach. From experimental results, it was found that the unigram features outperformed bigram, trigram and hybrids of unigram, bigram and trigram features. Moreover, TF and TFiDF feature value representation schemes were proven more suitable than binary representation and normalized TFiDF schemes. Finally, SVM decision models outperformed RF and NB.