Sarcasm is a mode of expression whereby individuals communicate their positive or negative sentiments through words contrary to their intent. This communication style is prevalent in news headlines and social media platforms, making it increasingly challenging for individuals to detect sarcasm accurately. To mitigate this challenge, developing an intelligent system that can detect sarcasm in headlines and news is imperative. This research paper proposes a deep learning architecture-based model for sarcasm identification in news headlines. The proposed model has three main objectives: (1) to comprehend the original meaning of the text or headlines, (2) to learn the nature of sarcasm, and (3) to detect sarcasm in the text or headlines. Previous studies on sarcasm detection have utilized datasets of tweets and employed hashtags to differentiate between ordinary and sarcastic tweets depending on the limited dataset. However, these datasets were prone to noise regarding language and tags. In contrast, using multiple datasets in this study provides a comprehensive understanding of sarcasm detection in online communication. By incorporating different types of sarcasm from the Sarcasm Corpus V2 from Baskin Engineering and sarcastic news headlines from The Onion and HuffPost, the study aims to develop a model that can generalize well across different contexts. The proposed model uses LSTM to capture temporal dependencies, while the proposed model utilizes a GlobalMaxPool1D layer for better feature extraction. The model was evaluated on training and test data with an accuracy score of 0.999 and 0.925, respectively.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by significant challenges in social interaction, communication, and repetitive behaviors. Timely and precise ASD detection is crucial, particularly in regions with limited diagnostic resources like Pakistan. This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context. The research involves experimentation with VGG16 and MobileNet models, exploring different batch sizes, optimizers, and learning rate schedulers. In addition, the “Orange” machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool. The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach. Specifically, VGG16, with a batch size of 2 and the Adam optimizer, trained for 100 epochs, achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%. Furthermore, the model achieves an F1 score of 88%, precision of 85%, and recall of 90% on test images. To validate the practical applicability of the VGG16 model with 5-fold cross-validation, the study conducts further testing on a dataset sourced from autism centers in Pakistan, resulting in an accuracy rate of 85%. This reaffirms the model’s suitability for real-world ASD detection. This research offers valuable insights into classifier performance, emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder leading to an inability to socially communicate and in extreme cases individuals are completely dependent on caregivers. ASD detection at early ages is crucial as early detection can reduce the effect on social impairment. Deep learning models have shown capability to detect ASD earlier compared to traditional detection methods used by clinics and experts. Ensemble models, renowned for their ability to enhance predictive performance by combining multiple models, have emerged as a powerful tool in machine learning. This study harnesses the strength of ensemble learning to address the critical challenge of ASD diagnosis. This study proposed a deep ensemble model leveraging the strengths of VGG16 and Xception net trained on Facial Images for ASD detection overcoming limitations in existing datasets through extensive preprocessing. Proposed model preprocessed the training dataset of facial images by converting side posed images into frontal face images, using Histogram Equalization (HE) to enhance colors, data augmentation techniques application, and using the Hue Saturation Value (HSV) color model. By integrating the feature extraction strengths of VGG16 and Xception with fully connected layers, our model has achieved a notable 97% accuracy on the Kaggle ASD Face Image Dataset. This approach supports early detection of ASD and aligns with Sustainable Development Goal 3, which focuses on improving health and well-being.

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by heterogeneous behavioral and neurological patterns, complicating timely and accurate diagnosis. Behavioral datasets are commonly used to diagnose ASD. In clinical practice, it is difficult to identify ASD because of the complexity of the behavioral symptoms, overlap of neurological disorders, and individual heterogeneity. Correct and timely identification is dependent on the presence of skilled professionals to perform thorough neurological examinations. Nevertheless, with developments in deep learning techniques, the diagnostic process can be significantly improved by automatically identifying and automatically classifying patterns of ASD-related behaviors and neuroimaging features. Method: This study introduces a novel multimodal diagnostic paradigm that combines structured behavioral phenotypes and structural magnetic resonance imaging (sMRI) into an interpretable and personalized framework. A Generalized Additive Model with Interactions (GAMI-Net) is used to process behavioral data for transparent embedding of clinical phenotypes. Structural brain characteristics are extracted via a hybrid CNN–GNN model, which retains voxel-level patterns and region-based connectivity through the Harvard–Oxford atlas. The embeddings are then fused using an Autoencoder, compressing cross-modal data into a common latent space. A Hyper Network-based MLP classifier produces subject-specific weights to make the final classification. Results: On the held-out test set drawn from the ABIDE-I dataset, a 20% split with about 247 subjects, the constructed system achieved an accuracy of 99.40%, precision of 100%, recall of 98.84%, an F1-score of 99.42%, and an ROC-AUC of 99.99%. For another test of generalizability, five-fold stratified cross-validation on the entire dataset yielded a mean accuracy of 98.56%, an F1-score of 98.61%, precision of 98.13%, recall of 99.12%, and an ROC-AUC of 99.62%. Conclusions: These results suggest that interpretable and personalized multimodal fusion can be useful in aiding practitioners in performing effective and accurate ASD diagnosis. Nevertheless, as the test was performed on stratified cross-validation and a single held-out split, future research should seek to validate the framework on larger, multi-site datasets and different partitioning schemes to guarantee robustness over heterogeneous populations.

Specification of customer's need as software requirements in natural language create ambiguities in requirements and may also lead to failure of the software project. Generally, customers are unable to define their needs due to lack of domain understanding, technological constraints and knowledge gap between stakeholders and requirements analysts. One of the most effective approaches to minimize these gaps and ambiguities is the usage of ontologies for requirements specification and validation. However, the current approaches are mostly limited only to the translation of ambiguous software requirements. In this paper, we have discussed, analyzed and compared the current usage of these ontologies and found that these approaches are time-consuming and create complexities in the overall development process. We have presented a requirements specification ontology (ReqSpecOnto), bypassing the need for creating an ambiguous Software Requirement Specification (SRS). The upper software requirements ontology is defined in Ontology Web Language (OWL) that can be applied in different software scenarios. A case study of budget and planning system for a state physics lab is selected to specify its requirements as derived ontology from the upper ontology created. Results are validated through HermiT and Pellet reasoners to verify defined relationships and constraints. Finally, SPARQL queries are used to obtain the necessary requirements.