Purpose Since the emergence of a coronavirus disease (2019-nCoV) in December 2019, the whole world is in a state of chaos. Isolation strategy with quarantine is a useful model in controlling transmission and rapid spread. As a result, people remained at home and disrupted their outside daily activities. It led to the closure of educational institutes, which is a source of many students to cope with numerous personal and familial issues. This study aims to focus on exploring the relationships and potential mediational pathways between mental health problems, illness perception, anxiety and depression disorders. Design/methodology/approach The study incorporated snowball sampling techniques through a cross-sectional, Web-based survey and recruited 500 students from different universities of twin cities, Rawalpindi and Islamabad from March 23 to April 15, 2020, during the coronavirus outbreak lockdown. The study used four instruments, Beck Depression Scale, Beck Anxiety Inventory, Revised Illness Perception Questionnaire and The Warwick-Edinburgh Mental Well-being Scale for assessing depression, anxiety, illness perception and mental health disorders. Findings The findings indicated normal (43.2%), mild (20.5%), moderate (13.6%) and severe (22.7%) levels of anxiety prevalence in students. Results specified a normal (65.9%), mild (9.10%), moderate (9.12%) and severe (15.90%) depression prevalence and findings stipulated that anxiety disorder prevalence was higher than depression disorder. The correlational results specified a negative and significant relationship between mental health, illness perception, anxiety and depression symptoms. The multiple regression analysis stated that anxiety and depression disorders mediated the relationship between mental health and present illness perception. The perception of illness exhibited a relation to depression and anxiety disorders. Originality/value The study proposed a model to address mental health problems during the lockdown. The (2019-nCoV) illness perception developed mental disorders, including anxiety and depression, which has declined individuals’ mental health. There is an urgent need for ongoing clinical examination and management to address psychological disorders and findings suggest assessing mental health to combatting the pandemic worldwide. Findings recommend developing strategies to promote mental health-care facilities during COVID-19 wide-ranging disasters. These results highlight the impending importance of devising strategies to treat mental health problems.

The high data rates detail that internet-connected devices have been increasing exponentially. Cognitive radio (CR) is an auspicious technology used to address the resource shortage issue in wireless IoT networks. Resource optimization is considered a non-convex and nondeterministic polynomial (NP) complete problem within CR-based Internet of Things (IoT) networks (CR-IoT). Moreover, the combined optimization of conflicting objectives is a challenging issue in CR-IoT networks. In this paper, energy efficiency (EE) and spectral efficiency (SE) are considered as conflicting optimization objectives. This research work proposed a hybrid tabu search-based stimulated algorithm (HTSA) in order to achieve Pareto optimality between EE and SE. In addition, the fuzzy-based decision is employed to achieve better Pareto optimality. The performance of the proposed HTSA approach is analyzed using different resource allocation parameters and validated through simulation results.

Gastrointestinal diseases like ulcers, polyps', and bleeding are increasing rapidly in the world over the last decade. On average 0.7 million cases are reported worldwide every year. The main cause of gastrointestinal diseases is a Helicobacter Pylori (H. Pylori) bacterium that presents in more than 50% of people around the globe. Many researchers have proposed different methods for gastrointestinal disease using computer vision techniques. Few of them focused on the detection process and the rest of them performed classification. The major challenges that they faced are the similarity of infected and healthy regions that misleads the correct classification accuracy. In this work, we proposed a technique based on Mask Recurrent-Convolutional Neural Network (R-CNN) and fine-tuned pre-trained ResNet-50 and ResNet-152 networks for feature extraction. Initially, the region of interest is detected using Mask R-CNN which is later utilized for the training of fine-tuned models through transfer learning. Features are extracted from fine-tuned models that are later fused using a serial approach. Moreover, an Improved Ant Colony Optimization (ACO) algorithm has also opted for the best feature selection from the fused feature vector. The best-selected features are finally classified using machine learning techniques. The experimental process was conducted on the publicly available dataset and obtained an improved accuracy of 96.43%. In comparison with state-of-the-art techniques, it is observed that the proposed accuracy is improved.

Parkinson’s disease directly affects the nervous system are causes a change in voice, lower efficiency in daily routine tasks, failure of organs, and death. As an estimate, nearly ten million people are suffering from Parkinson’s disease worldwide, and this number is increasing day by day. The main cause of an increase in Parkinson’s disease patients is the unavailability of reliable procedures for diagnosing Parkinson’s disease. In the literature, we observed different methods for diagnosing Parkinson’s disease such as gait movement, voice signals, and handwriting tests. The detection of Parkinson’s disease is a difficult task because the important features that can help in detecting Parkinson’s disease are unknown. Our aim in this study is to extract those essential voice features which play a vital role in detecting Parkinson’s disease and develop a reliable model which can diagnose Parkinson’s disease at its early stages. Early diagnostic systems for the detection of Parkinson’s disease are needed to diagnose Parkinson’s disease early so that it can be controlled at the initial stages, but existing models have limitations that can lead to the misdiagnosing of the disease. Our proposed model can assist practitioners in continuously monitoring the Parkinson’s disease rating scale, known as the Total Unified Parkinson’s Disease Scale, which can help practitioners in treating their patients. The proposed model can detect Parkinson’s disease with an error of 0.10 RMSE, which is lower than that of existing models. The proposed model has the capability to extract vital voice features which can help detect Parkinson’s disease in its early stages.