In today’s world, mental health diseases have become highly prevalent, and depression is one of the mental health problems that has become widespread. According to WHO reports, depression is the second-leading cause of the global burden of diseases. In the proliferation of such issues, social media has proven to be a great platform for people to express themselves. Thus, a user’s social media can speak a great deal about his/her emotional state and mental health. Considering the high pervasiveness of the disease, this paper presents a novel framework for depression detection from textual data, employing Natural Language Processing and deep learning techniques. For this purpose, a dataset consisting of tweets was created, which were then manually annotated by the domain experts to capture the implicit and explicit depression context. Two variations of the dataset were created, on having binary and one ternary labels, respectively. Ultimately, a deep-learning-based hybrid Sequence, Semantic, Context Learning (SSCL) classification framework with a self-attention mechanism is proposed that utilizes GloVe (pre-trained word embeddings) for feature extraction; LSTM and CNN were used to capture the sequence and semantics of tweets; finally, the GRUs and self-attention mechanism were used, which focus on contextual and implicit information in the tweets. The framework outperformed the existing techniques in detecting the explicit and implicit context, with an accuracy of 97.4 for binary labeled data and 82.9 for ternary labeled data. We further tested our proposed SSCL framework on unseen data (random tweets), for which an F1-score of 94.4 was achieved. Furthermore, in order to showcase the strengths of the proposed framework, we validated it on the “News Headline Data set” for sarcasm detection, considering a dataset from a different domain. It also outmatched the performance of existing techniques in cross-domain validation.

In developing countries, child health and restraining under-five child mortality are one of the fundamental concerns. UNICEF adopted sustainable development goal 3 (SDG3) to reduce the under-five child mortality rate globally to 25 deaths per 1,000 live births. The under-five mortality rate is 69 deaths per 1,000 live child-births in Pakistan as reported by the Demographic and Health Survey (2018). Predictive analytics has the power to transform the healthcare industry, personalizing care for every individual. Pakistan Demographic Health Survey (2017–2018), the publicly available dataset, is used in this study and multiple imputation methods are adopted for the treatment of missing values. The information gain, a feature selection method, ranked the information-rich features and examine their impact on child mortality prediction. The synthetic minority over-sampling method (SMOTE) balanced the training dataset, and four supervised machine learning classifiers have been used, namely the decision tree classifier, random forest classifier, naive Bayes classifier, and extreme gradient boosting classifier. For comparative analysis, accuracy, precision, recall, and F1-score have been used. Eventually, a predictive analytics framework is built that predicts whether the child is alive or dead. The number under-five children in a household, preceding birth interval, family members, mother age, age of mother at first birth, antenatal care visits, breastfeeding, child size at birth, and place of delivery were found to be critical risk factors for child mortality. The random forest classifier performed efficiently and predicted under-five child mortality with accuracy (93.8%), precision (0.964), recall (0.971), and F1-score (0.967). The findings could greatly assist child health intervention programs in decision-making.

High‐resolution images are crucial for many applications, but factors such as environmental conditions can reduce image quality. Super‐resolution (SR) techniques address this by generating high‐resolution images from low‐resolution inputs. While deep learning SR models have made significant progress, they can be computationally expensive and struggle with differentiating between various image scales. Lightweight SR methods, suitable for resource‐constrained devices, often compromise image quality. This study introduces a multi‐stage holistic attention‐based network, using Gaussian Laplacian pyramids to decompose images and apply holistic attention modules at each level. This approach reduces parameters and computational costs while maintaining image quality, achieving a PSNR score of 28 and SSIM of 0.91 with only 29,000 parameters. The model demonstrates the potential for efficient and high‐quality image reconstruction. Future work will focus on improving quality while minimizing costs and exploring other advanced techniques. The code will be made available upon request

Face recognition and person re‐identification in video remains a challenging area in computer vision community. A variety of texture feature sets are evaluated for better recognition of individuals in video for varying pose and illumination conditions—as found in real world video surveillance. This paper focuses on face recognition in video based changing environment using Fuzzy‐ARTMAP (FAM) neural network classifier. Individual specific facial model of each target subject is structured using two‐class classification to solve a multi‐class classification problem. Two lower dimensional linear subspace techniques, PCA and LDA, are applied. All evaluations are carried out by executing 10 iterations and average results are presented. We split dataset randomly in each permutation: two‐thirds of the data model is reserved for training of the classifier, and rest for testing. Experimental results of our study are determined on two publicly available databases: the ChokePoint database and the Extended YaleB database. Our findings show that FAM classifier performs better with the feature set “Pixel intensity combined with Local Binary Patterns (LBP) and Local Phase Quantization (LPQ)” in varying illumination and pose scenarios during face recognition problem.

The Internet of Things (IoT) constitutes a digitally integrated network of intelligent devices equipped with sensors, software, and communication capabilities, facilitating data exchange among a multitude of digital systems via the Internet. Despite its pivotal role in the software development life-cycle (SDLC) for ensuring software quality in terms of both functional and non-functional aspects, testing within this intricate software–hardware ecosystem has been somewhat overlooked. To address this, various testing techniques are applied for real-time minimization of failure rates in IoT applications. However, the execution of a comprehensive test suite for specific IoT software remains a complex undertaking. This paper proposes a holistic framework aimed at aiding quality assurance engineers in delineating essential testing methods across different testing levels within the IoT. This delineation is crucial for effective quality assurance, ultimately reducing failure rates in real-time scenarios. Furthermore, the paper offers a mapping of these identified tests to each layer within the layered framework of the IoT. This comprehensive approach seeks to enhance the reliability and performance of IoT-based applications.