This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and transfer learning in the context of medical imaging. Medical imaging plays a critical role in the diagnosis and treatment of diseases, and CNN-based models have demonstrated significant improvements in image analysis and classification tasks. Transfer learning, which involves reusing pre-trained CNN models, has also shown promise in addressing challenges related to small datasets and limited computational resources. This paper reviews the advantages of CNN and transfer learning in medical imaging, including improved accuracy, reduced time and resource requirements, and the ability to address class imbalances. It also discusses challenges, such as the need for large and diverse datasets, and the limited interpretability of deep learning models. What factors contribute to the success of these networks? How are they fashioned, exactly? What motivated them to build the structures that they did? Finally, the paper presents current and future research directions and opportunities, including the development of specialized architectures and the exploration of new modalities and applications for medical imaging using CNN and transfer learning techniques. Overall, the paper highlights the significant potential of CNN and transfer learning in the field of medical imaging, while also acknowledging the need for continued research and development to overcome existing challenges and limitations.

The aedes mosquito-borne dengue viruses cause dengue fever, an arboviral disease (DENVs). In 2019, the World Health Organization forecasts a yearly occurrence of infections from 100 million to 400 million, the maximum number of dengue cases ever testified worldwide, prompting WHO to label the virus one of the world’s top ten public health risks. Dengue hemorrhagic fever can progress into dengue shock syndrome, which can be fatal. Dengue hemorrhagic fever can also advance into dengue shock syndrome. To provide accessible and timely supportive care and therapy, it is necessary to have indispensable practical instruments that accurately differentiate Dengue and its subcategories in the early stages of illness development. Dengue fever can be predicted in advance, saving one’s life by warning them to seek proper diagnosis and treatment. Predicting infectious diseases such as dengue is difficult, and most forecast systems are still in their primary stages. In developing dengue predictive models, data from microarrays and RNA-Seq have been used significantly. Bayesian inferences and support vector machine algorithms are two examples of statistical methods that can mine opinions and analyze sentiment from text. In general, these methods are not very strong semantically, and they only work effectively when the text passage inputs are at the level of the page or the paragraph; they are poor miners of sentiment at the level of the sentence or the phrase. In this research, we propose to construct a machine learning method to forecast dengue fever.

Because it is associated with most multifactorial inherited diseases like heart disease, hypertension, diabetes, and other serious medical conditions, obesity is a major global health concern. Obesity is caused by hereditary, physiological, and environmental factors, as well as poor nutrition and a lack of exercise. Weight loss can be difficult for various reasons, and it is diagnosed via BMI, which is used to estimate body fat for most people. Muscular athletes, for example, may have a BMI in the obesity range even when they are not obese. Researchers from a variety of backgrounds and institutions devised different hypotheses and models for the prediction and classification of obesity using different approaches and various machine learning techniques. In this study, a majority voting-based hybrid modeling approach using a gradient boosting classifier, extreme gradient boosting, and a multilayer perceptron was developed. Seven distinct machine learning algorithms were used on open datasets from the UCI machine learning repository, and their respective accuracy levels were compared before the combined approaches were chosen. The proposed majority voting-based hybrid model for prediction and classification of obesity that was achieved has an accuracy of 97.16%, which is greater than both the individual models and the other hybrid models that have been developed.

Advancements in smartphone sensor technologies have significantly enriched the field of human activity recognition, facilitating a wide array of applications from health monitoring to personal navigation. This study utilized such advancements to explore human locomotion and localization recognition using data from accelerometers, microphones, gyroscopes, magnetometers, and GPS, applying Deep Polynomial Neural Networks (DPNN) and Multilayer Perceptron (MLP) across three datasets: the Continuous In-The-Wild Smart Watch Activity Dataset, the Huawei Locomotion Dataset, and the Extra Sensory Dataset. We employ two distinct approaches for activity recognition: Deep Polynomial Neural Networks (DPNN) for deep learning-based feature extraction and Multilayer Perceptron (MLP) with manual feature extraction techniques, including Linear Predictive Coding Cepstral Coefficients (LPCC), step length, signal magnitude area, spectral, and sound features. Through rigorous experimentation, we achieved remarkable accuracy in recognizing both locomotion and localization activities, with DPNN consistently outperforming MLP in terms of accuracy. Specifically, for the Continuous In-The-Wild Dataset, DPNN achieved a 93% accuracy rate for localization activities and 95% for locomotion activities, while MLP recorded 86% and 91% in the respective categories. Similarly, on the Huawei Locomotion Dataset, DPNN attained 95% accuracy for localization and 97% for locomotion, with MLP achieving 88% and 91%, respectively. Furthermore, the application of these models to the Extra Sensory Dataset yielded 92% accuracy for both localization and locomotion activities with DPNN, and 90% and 89% with MLP. In our study, we observed that in terms of accuracy, DPNN emerges as the clear winner; however, it is computationally expensive. Conversely, MLP, while being less accurate, stands out for its computational efficiency. This study not only highlights the effectiveness of incorporating advanced machine learning techniques in interpreting sensor data but also emphasizes the trade-offs between computational demands and accuracy in the domain of human activity recognition. Through our comprehensive analysis, we contribute valuable insights into the potential of smartphone sensors in enhancing activity recognition systems, paving the way for future innovations in mobile sensing technology.

Object Classification in Remote Sensing Imagery holds paramount importance for extracting meaningful insights from complex aerial scenes. Conventional methods encounter challenges in achieving precision amidst diverse landscape features. This paper introduces an innovative hybrid model to enhance the accuracy of remote sensing multi-object classification. Incorporating a feature-level fusion approach inspired by successful methods, we leverage Adoptive Fuzzy C-means segmentation for precise object classification and Conditional Random Field labeling. Our model excels in capturing diverse features within remote sensing images using multiple feature extraction methods. The distinctive feature of our methodology lies in the thoughtful incorporation of a Deep Belief Network. Through rigorous experimental evaluations on two standard datasets, our proposed system demonstrates exceptional performance, emphasizing its significant potential for advancing methodologies in remote sensing multi-object classification. This tactful integration results in substantial improvements, yielding high accuracies of 97.24% (UCM) and 96.84% (RESISC45). The proposed model is methodologically novel and effective solution for advancing remote sensing image classification.

Cybersecurity challenges pose a significant threat to Healthcare Cyber Physical Systems (CPS) because they heavily rely on wireless communication. Particularly, jamming attacks can severely disrupt the integrity of these CPS networks. This research introduces a decentralized system to address this issue. Therefore, this paper suggested a system that leverages trust and blockchain technology to detect jamming attacks in healthcare CPS effectively. It proposes a layered model to improve CPS networks’ lifetime and performance. In smart healthcare environments, it ensures secure and reliable communication between sensor nodes, wearable sensors, medical devices, and monitoring systems. Results show that the suggested approach outperforms the baseline model in identifying and minimizing jamming assaults, with an average percentage difference of 15.71% more detection rate, 20.21% less packet loss rates, 16.65% less node-level energy consumption, reduced network latency of 8.29%, and 9.63% more network throughput.