Obesity is a global epidemic, often considered an impending disaster for the world’s population. Healthcare organizations and professionals repeatedly emphasize the negative impacts on obesity in the development of cardiovascular diseases, hypertension and diabetes. The continuous monitoring of physiological parameters; namely SpO2, blood pressure, body temperature and pulse rate are imperative for obese adult patients. IoT is a dynamic field, used extensively in all fields: agriculture, automobile, manufacturing and retail industry primarily for automated remote real-time monitoring. This paper focuses on the implementation of IoT in the healthcare industry for monitoring and evaluating health conditions of obese adults, along with emphasis on the importance of medical data storage. Furthermore, a device is developed with a novel design and system, which not only allows real-time monitoring but also the storage of medical records for multiple patients simultaneously. The device facilitates measurements of these parameters using an Arduino environment and then transmits the data onto an IoT dashboard using a Wi-fi module for remote monitoring for healthcare professionals. The main aim is to provide a suitable device recommended by doctors for patients suffering from obesity, such that doctors can examine patient’s health trends over a period from the stored data for monitoring any changes that could be a symptom of an underlying unnoticed health condition.

Recent researchers have been drawn to the analysis of electroencephalogram (EEG) signals in order to confirm the disease and severity range by viewing the EEG signal which has complicated the dataset. The conventional models such as machine learning, classifiers, and other mathematical models achieved the lowest classification score. The current study proposes to implement a novel deep feature with the best solution for EEG signal analysis and severity specification. A greedy sandpiper‐based recurrent neural system (SbRNS) model for predicting Alzheimer’s disease (AD) severity has been proposed. The filtered data are used as input for the feature analysis and the severity range is divided into three classes: low, medium, and high. The designed approach was then implemented in the matrix laboratory (MATLAB) system, and the effectiveness score was calculated using key metrics such as precision, recall, specificity, accuracy, and misclassification score. The validation results show that the proposed scheme achieved the best classification outcome.

The current scenario of society is to produce fuel from renewable energy resources. The purpose of this research work is to develop an integrated approach for glycerol valorization and biodiesel production. Employing a range of methodologies widely used in the industry, technical analysis and assessments of the process’s applicability in real-world situations are also made. The integrated process plant is simulated using Aspen Plus^®. Several different sensitivity analyses are carried out to describe the process that improves efficiency and are designed to maximize hydrogen recovery from the reforming section. The integrated process results are compared with several existing standalone biodiesel production processes. Additionally, the results are verified with the theoretical studies on glycerol valorization. The outcomes of the process plant simulation reveal coherent results with the current industrial standards for the two processes. The results show that the amount of glycerol produced (stream 7) is 60.72 kmol/h in mass flow rate, this translates to 7272.74 kg/h. The hydrogen produced is 488.76 kmol/h and, in mass flow rate, this translates to 985.3 kg/h. The total yield of hydrogen produced is around 13%. The biodiesel yield is at 92.5%. It shows a realistic recovery that would be attained if the process is implemented, contrary to theoretical studies.

Aims: Analysis of the vital signs of patients can aid in early disease diagnosis and care. There are many illnesses which can be diagnosed and managed by monitoring this medical information periodically. Background: Detection of various early-stage medical diseases can be simply done by monitoring Human vital signs, as they show the standard body's essential functions, indicating the status of an individual's health condition. Objective: In many cases it so happens that patients do not receive appropriate medical treatment on time, as a result of which unexpected incidents happen due to ignorance of one’s health status. Since pulse rate and vital sign area unit are the foremost crucial parameters, an affordable device to detect such parameters is useful for human health. Methods: Photoplethysmography (PPG) is a photosensitive technique that measures difference in blood volume of the pulse (usually in the body's soft tissues) by specifically interfering with the differences in the photo-emitter's absorption, reflection, volume and dispersion of light, and then it is registered by the photoreceptor. Then, the PPG waveform reflects shifts in arterial blood supply. The waveform obtained with a pulse oximeter illuminates skin and processes variations in the absorption of light. Results: The embedded systems-Arduino, GSM module and the various sensors used in this research provide a simple monitoring method which does not require a smartphone or internet connectivity. Conclusion: This paper aims at illustrating the significance of constantly monitoring vital signs. The device proposed has been developed as an Arduino program that is straightforward and inexpensive, a conveyable system that acquires the vital signs data and sends a text message as warning messages during a health emergency.