Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population’s health. It is a metabolic disorder that leads to high blood sugar levels and many other problems such as stroke, kidney failure, and heart and nerve problems. Several researchers have attempted to construct an accurate diabetes prediction model over the years. However, this subject still faces significant open research issues due to a lack of appropriate data sets and prediction approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study’s primary goal was to see how big data analytics and machine learning-based techniques may be used in diabetes. The examination of the results shows that the suggested ML-based framework may achieve a score of 86. Health experts and other stakeholders are working to develop categorization models that will aid in the prediction of diabetes and the formulation of preventative initiatives. The authors perform a review of the literature on machine models and suggest an intelligent framework for diabetes prediction based on their findings. Machine learning models are critically examined, and an intelligent machine learning-based architecture for diabetes prediction is proposed and evaluated by the authors. In this study, the authors utilize our framework to develop and assess decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction, which are the most widely used techniques in the literature at the time of writing. It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning. According to the framework, it was developed after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes. Using the framework, the authors describe the training procedures, model assessment strategies, and issues associated with diabetes prediction, as well as solutions they provide. The findings of this study may be utilized by health professionals, stakeholders, students, and researchers who are involved in diabetes prediction research and development. The proposed work gives 83% accuracy with the minimum error rate.

A difficult challenge in the realm of biomedical engineering is the detection of physiological changes occurring inside the human body, which is a difficult undertaking. At the moment, these irregularities are graded manually, which is very difficult, time-consuming, and tiresome due to the many complexities associated with the methods involved in their identification. In order to identify illnesses at an early stage, the use of computer-assisted diagnostics has acquired increased attention as a result of the requirement of a disease detection system. The major goal of this proposed work is to build a computer-aided design (CAD) system to help in the early identification of glaucoma as well as the screening and treatment of the disease. The fundus camera is the most affordable image analysis modality available, and it meets the financial needs of the general public. The extraction of structural characteristics from the segmented optic disc and the segmented optic cup may be used to characterize glaucoma and determine its severity. For this study, the primary goal is to estimate the potential of the image analysis model for the early identification and diagnosis of glaucoma, as well as for the evaluation of ocular disorders. The suggested CAD system would aid the ophthalmologist in the diagnosis of ocular illnesses by providing a second opinion as a judgment made by human specialists in a controlled environment. An ensemble-based deep learning model for the identification and diagnosis of glaucoma is in its early stages now. This method’s initial module is an ensemble-based deep learning model for glaucoma diagnosis, which is the first of its kind ever developed. It was decided to use three pretrained convolutional neural networks for the categorization of glaucoma. These networks included the residual network (ResNet), the visual geometry group network (VGGNet), and the GoogLeNet. It was necessary to use five different data sets in order to determine how well the proposed algorithm performed. These data sets included the DRISHTI-GS, the Optic Nerve Segmentation Database (DRIONS-DB), and the High-Resolution Fundus (HRF). Accuracy of 91.11% for the PSGIMSR data set and the sensitivity of 85.55% and specificity of 95.20% for the suggested ensemble architecture on the PSGIMSR data set were achieved. Similarly, accuracy rates of 95.63%, 98.67%, 95.64%, and 88.96% were achieved using the DRIONS-DB, HRF, DRISHTI-GS, and combined data sets, respectively.

Cancer is one of the top causes of mortality, and it arises when cells in the body grow abnormally, like in the case of breast cancer. For people all around the world, it has now become a huge issue and a threat to their safety and wellbeing. Breast cancer is one of the major causes of death among females all over the globe, and it is particularly prevalent in the United States. It is possible to diagnose breast cancer using a variety of imaging modalities including mammography, computerized tomography (CT), magnetic resonance imaging (MRI), ultrasound, and biopsies, among others. To analyze the picture, a histopathology study (biopsy) is often performed, which assists in the diagnosis of breast cancer. The goal of this study is to develop improved strategies for various CAD phases that will play a critical role in minimizing the variability gap between and among observers. It created an automatic segmentation approach that is then followed by self‐driven post‐processing activities to successfully identify the Fourier Transform based Segmentation in the CAD system to improve its performance. When compared to existing techniques, the proposed segmentation technique has several advantages: spatial information is incorporated, there is no need to set any initial parameters beforehand, it is independent of magnification, it automatically determines the inputs for morphological operations to enhance segmented images so that pathologists can analyze the image with greater clarity, and it is fast. Extensive tests were conducted to determine the most effective feature extraction techniques and to investigate how textural, morphological, and graph characteristics impact the accuracy of categorization classification. In addition, a classification strategy for breast cancer detection has been developed that is based on weighted feature selection and uses an upgraded version of the Genetic Algorithm in conjunction with a Convolutional Neural Network Classifier. The practical application of the suggested improved segmentation and classification algorithms for the CAD framework may reduce the number of incorrect diagnoses and increase the accuracy of classification. So, it may serve as a second opinion tool for pathologists and aid in the early detection of diseases.

The Internet of Things (IoT) is a new revolution defined by heterogeneous devices made up of intelligent, omnipresent items that are all hooked up to The internet. These devices are frequently implemented in different areas to offer innovative programs in various industrial applications, including intelligent urban, medicine, and societies. Such Internet of Things (IoT) equipment generates a large volume of private and safety information. Because IoT systems are resource‐constrained in terms of operation, memory, and communication capability, safeguarding accessibility to them is a difficult task. In the blockchain concept, the majority, or even all network nodes, check the validity and accuracy of exchanged data before accepting and recording it, whether this data is related to financial transactions, measurements of a sensor, or an authentication message. In evaluating the validity of exchanged data, nodes must reach a consensus in order to perform a special action, in which case the opportunity to enter and record transactions and unreliable interactions with the system is significantly reduced. Recently, in order to share and access management of IoT devices’ information with a distributed attitude, a new authentication protocol based on blockchain has been proposed, and it is claimed that this protocol satisfies user privacy while preserving security. Today’s identification and authentication techniques have substantial shortcomings due to rapidly growing prevalence and implementation. As a result, the protection of such gadgets is critical to guarantee the program’s efficacy and safety. A decentralized authentication and access control method for lightweight IoT systems are proposed in this work and a blockchain‐based system that enables identification and secures messaging with IoT nodes. The technique is built on fog information systems and the idea of a blockchain system; when contrasted to something like a blockchain‐based verification system, the testing findings show that the suggested mechanism outperforms it. The authentication and verification system undergoes using the blockchain technique. Our method takes advantage of blockchain’s inherent advantages while also associated with development authentication systems. Our suggested blockchain‐based approach, structure, and layout, in particular, provide for transparency, consistency, and provenance while also providing tamper‐proof records. The article describes the general systems architectural style and the analysis and execution of a real scenario as just a prototype system. The authentication included give as protected prototype that can transmit data with secured protocol and achieves minimum error rate.

Mesenchymal stem cells (MSCs) have newly developed as a potential drug delivery system. MSC-based drug delivery systems (MSCs-DDS) have made significant strides in the treatment of several illnesses, as shown by a plethora of research. However, as this area of research rapidly develops, several issues with this delivery technique have emerged, most often as a result of its intrinsic limits. To increase the effectiveness and security of this system, several cutting-edge technologies are being developed concurrently. However, the advancement of MSC applicability in clinical practice is severely hampered by the absence of standardized methodologies for assessing cell safety, effectiveness, and biodistribution. In this work, the biodistribution and systemic safety of MSCs are highlighted as we assess the status of MSC-based cell therapy at this time. We also examine the underlying mechanisms of MSCs to better understand the risks of tumor initiation and propagation. Methods for MSC biodistribution are explored, as well as the pharmacokinetics and pharmacodynamics of cell therapies. We also highlight various promising technologies, such as nanotechnology, genome engineering technology, and biomimetic technology, to enhance MSC-DDS. For statistical analysis, we used analysis of variance (ANOVA), Kaplan Meier, and log-rank tests. In this work, we created a shared DDS medication distribution network using an extended enhanced optimization approach called enhanced particle swarm optimization (E-PSO). To identify the considerable untapped potential and highlight promising future research paths, we highlight the use of MSCs in gene delivery and medication, also membrane-coated MSC nanoparticles, for treatment and drug delivery.