Healthcare sector is probably the most benefited from the applications of nanotechnology. The nanotechnology, in the forms of nanomedicine, nanoimplants, nanobiosensors along with the internet of nano things (IoNT), has the potential to bring a revolutionizing advancement in the field of medicine and healthcare services. The primary aim of this paper is to explore the clinical and medical possibilities of these different implementations of nanotechnology. This paper provides a comprehensive overview of nanotechnology, biosensors, nanobiosensors, and IoNT. Furthermore, multilevel taxonomies of nanotechnology, nanoparticles, biosensors, nanobiosensors, and nanozymes are presented. The potential medical and clinical applications of these technologies are discussed in details with several examples. This paper specifically focuses on IoNT and its role in healthcare. In addition to describing a general architecture of IoNT for healthcare, the communication architecture of the IoNT is also explained. The challenges in the successful realization of IoNT are also discussed critically, along with a special discussion on internet of bio-nano things (IoBNT) and its potential in making IoNT more compatible to human body.

The advancement and popularity of smartphones have made it an essential and all-purpose device. But lack of advancement in battery technology has held back its optimum potential. Therefore, considering its scarcity, optimal use and efficient management of energy are crucial in a smartphone. For that, a fair understanding of a smartphone’s energy consumption factors is necessary for both users and device manufacturers, along with other stakeholders in the smartphone ecosystem. It is important to assess how much of the device’s energy is consumed by which components and under what circumstances. This paper provides a generalized, but detailed analysis of the power consumption causes (internal and external) of a smartphone and also offers suggestive measures to minimize the consumption for each factor. The main contribution of this paper is four comprehensive literature reviews on: a) smartphone’s power consumption assessment and estimation (including power consumption analysis and modelling), b) power consumption management for smartphones (including energy-saving methods and techniques), c) state-of-the-art of the research and commercial developments of smartphone batteries (including alternative power sources), and d) mitigating the hazardous issues of smartphones’ batteries (with a details explanation of the issues). The research works are further subcategorized based on different research and solution approaches. A good number of recent empirical research works are considered for this comprehensive review, and each of them is succinctly analysed and discussed.

Introduction: Diabetes is considered one of the leading healthcare concerns affecting millions worldwide. Taking appropriate action at the earliest stages of the disease depends on early diabetes prediction and identification. To support healthcare providers for better diagnosis and prognosis of diseases, machine learning has been explored in the healthcare industry in recent years.Methods: To predict diabetes, this research has conducted experiments on five boosting algorithms on the Pima diabetes dataset. The dataset was obtained from the University of California, Irvine (UCI) machine learning repository, which contains several important clinical features. Exploratory data analysis was used to identify the characteristics of the dataset. Moreover, upsampling, normalisation, feature selection, and hyperparameter tuning were employed for predictive analytics.Results: The results were analysed using various statistical/machine learning metrics and k-fold cross-validation techniques. Gradient boosting achieved the greatest accuracy rate of 92.85% among all the classifiers. Precision, recall, f1-score, and receiver operating characteristic (ROC) curves were used to further validate the model.Discussion: The suggested model outperformed the current studies in terms of prediction accuracy, demonstrating its applicability to other diseases with similar predicate indications.

Heart disease is one of the leading causes of death worldwide. Predicting and detecting heart disease early is crucial, as it allows medical professionals to take appropriate and necessary actions at earlier stages. Healthcare professionals can diagnose cardiac conditions more accurately by applying machine learning technology. This study aimed to enhance heart disease prediction using stacking and voting ensemble methods. Fifteen base models were trained on two different heart disease datasets. After evaluating various combinations, six base models were pipelined to develop ensemble models employing a meta-model (stacking) and a majority vote (voting). The performance of the stacking and voting models was compared to that of the individual base models. To ensure the robustness of the performance evaluation, we conducted a statistical analysis using the Friedman aligned ranks test and Holm post-hoc pairwise comparisons. The results indicated that the developed ensemble models, particularly stacking, consistently outperformed the other models, achieving higher accuracy and improved predictive outcomes. This rigorous statistical validation emphasised the reliability of the proposed methods. Furthermore, we incorporated explainable AI (XAI) through SHAP analysis to interpret the model predictions, providing transparency and insight into how individual features influence heart disease prediction. These findings suggest that combining the predictions of multiple models through stacking or voting may enhance the performance of heart disease prediction and serve as a valuable tool in clinical decision-making.

In mobile crowd computing (MCC), smart mobile devices (SMDs) are utilized as computing resources. To achieve satisfactory performance and quality of service, selecting the most suitable resources (SMDs) is crucial. The selection is generally made based on the computing capability of an SMD, which is defined by its various fixed and variable resource parameters. As the selection is made on different criteria of varying significance, the resource selection problem can be duly represented as an MCDM problem. However, for the real-time implementation of MCC and considering its dynamicity, the resource selection algorithm should be time-efficient. In this paper, we aim to find out a suitable MCDM method for resource selection in such a dynamic and time-constraint environment. For this, we present a comparative analysis of various MCDM methods under asymmetric conditions with varying selection criteria and alternative sets. Various datasets of different sizes are used for evaluation. We execute each program on a Windows-based laptop and also on an Android-based smartphone to assess average runtimes. Besides time complexity analysis, we perform sensitivity analysis and ranking order comparison to check the correctness, stability, and reliability of the rankings generated by each method.

Background: Lung cancer is the second most common cancer worldwide, with over two million new cases per year. Early identification would allow healthcare practitioners to handle it more effectively. The advancement of computer-aided detection systems significantly impacted clinical analysis and decision-making on human disease. Towards this, machine learning and deep learning techniques are successfully being applied. Due to several advantages, transfer learning has become popular for disease detection based on image data. Methods: In this work, we build a novel transfer learning model (VER-Net) by stacking three different transfer learning models to detect lung cancer using lung CT scan images. The model is trained to map the CT scan images with four lung cancer classes. Various measures, such as image preprocessing, data augmentation, and hyperparameter tuning, are taken to improve the efficacy of VER-Net. All the models are trained and evaluated using multiclass classifications chest CT images. Results: The experimental results confirm that VER-Net outperformed the other eight transfer learning models compared with. VER-Net scored 91%, 92%, 91%, and 91.3% when tested for accuracy, precision, recall, and F1-score, respectively. Compared to the state-of-the-art, VER-Net has better accuracy. Conclusion: VER-Net is not only effectively used for lung cancer detection but may also be useful for other diseases for which CT scan images are available.