The Internet of Things has been instrumental in bringing about several advancements and innovations in the domain of healthcare. Healthcare professionals are essentially life savers when it comes to handling emergency cases such as accidents, heart attacks, etc. Only the patient’s vital parameters generally characterize emergency cases, and the doctors must wait for additional details for a wholesome diagnosis. As a result, the treatment processes and procedures sometimes get hastened and, in turn, put the patients’ lives at risk. It would always be helpful for doctors to be equipped with the medical requirements in advance for deciding the right course of action, thereby increasing the scope and chances of recovery. In this work, Multi-Model IoT (MMIoT) devices are deployed to monitor and collect health data from different body parts simultaneously. The healthcare data comprises signals and imagery captured from the MMIoT devices. Both the U-Net model and LSTM model are used to analyze the data automatically. The data processing is carried out by the server connected to the MMIoT network. All the medical IoT devices experimented with in this work are interconnected using a potential 5G network for optimal data transmission. The output obtained from the U-Net and the LSTM are channelized through a dense layer to classify the health anomalies accurately. It would not only facilitate but also educate medical professionals to handle unseen and typical cases in the future confidently. It can improve the overall quality of treatment and save lives with the best available resources.

Every aspect of the 21st century has undergone a revolution because of the Internet of Things (IoT) and smart computing technologies. These technologies are applied in many different ways, from monitoring the state of crops and the moisture level of the soil in real-time to using drones to help with chores such as spraying pesticides. The extensive integration of both recent IT and conventional agriculture has brought in the phase of agriculture 4.0, often known as smart agriculture. Agriculture intelligence and automation are addressed by smart agriculture. However, with the advancement of agriculture brought about by recent digital technology, information security challenges cannot be overlooked. The article begins by providing an overview of the development of agriculture 4.0 with pros and cons. This study focused on layered architectural design, identified security issues, and presented security demands and upcoming prospects. In addition to that, we propose a security architectural framework for agriculture 4.0 that combines blockchain technology, fog computing, and software-defined networking. The suggested framework combines Ethereum blockchain and software-defined networking technologies on an open-source IoT platform. It is then tested with three different cases under a DDoS attack. The results of the performance analysis show that overall, the proposed security framework has performed well.

Brain tumors can cause serious health complications and lead to death if not detected accurately. Therefore, early-stage detection of brain tumors and accurate classification of types of brain tumors play a major role in diagnosis. Recently, deep convolutional neural network (DCNN) based approaches using brain magnetic resonance imaging (MRI) images have shown excellent performance in detection and classification tasks. However, the accuracy of DCNN architectures depends on the training of data samples since it requires more precise data for better output. Thus, we propose a transfer learning-based DCNN framework to classify brain tumors for example meningioma tumors, glioma tumors, and pituitary tumors. We use a pre-trained DCNN architecture VGGNet which is previously trained on huge datasets and used to transfer its learning parameters to the target dataset. Also, we employ transfer learning aspects such as fine-tune the convolutional network and freeze the layers of the convolutional network for better performance. Further, this proposed approach uses a Global Average Pooling (GAP) layer at the output to avoid overfitting issues and vanishing gradient problems. The proposed architecture is assessed and compared with competing deep learning based brain tumor classification approaches on the Figshare dataset. Our proposed approach produces 98.93% testing accuracy and outperforms the contemporary learning-based approaches.

Parkinson’s Disease (PD) is a neural system disorder that disturbs the mental activities and physical activities of human beings. Analyzing the symptoms and biosignal data of PD is crucially focused in medical research fields. The existing PD diagnosis models are limited to real-time issues, insufficient deep data extraction, and early monitoring problems. On the scope, the proposed Optimal Health Support and PD Analysis System (OHPAS) analyses the symptoms of PD using a deeply trained biosensors network environment. The novel system trains the biosensor network using complex Machine Learning (ML) and Deep Learning (DL) approaches. The environment of OHPAS sets up acoustic sensors (UT-PF), microphones (MC-1500 unit), and multimodal sensor units (MC-10 sensor). MC-10 is the sensor suite that has an accelerometer sensor, gyro sensor, and Electro Cardio Gram (ECG) sensor to observe the biosignals. For establishing the biodata analysis framework, OHPAS initiates the fusion of Variable Auto Encoder (VAER) and K-Means clustering techniques. This phase comprises dataset feature reduction, data regularization, and clustering operations to make the dataset effective for the training process. Finally, the Long Short Term Memory network (LSTM) uses the preprocessed dataset for computing the training dataset. The proposed OHPAS contributes novel features such as a real-time patient monitoring environment, effective sensor data reduction, distributed sensor data analysis, day-wise PD symptom prediction, reactive PD alerts, and accurate early detection solutions. Considering effective medical data analysis with minimal response time, the proposed model creates reactive body sensor network. Under this sensor platform, sensor modules contain proposed DL procedures in its internal memory for initiating data analysis practices. Consequently, the symptoms of PD are commendably detected and predicted with minimal response time. The experimental results indicate the proposed PD system outperforms the existing systems with 8% to 10% of better results.

Early detection and diagnosis of pneumonia play a significant role in saving human life. However, detection...