Alzheimer’s disease (AD) is a neurological disorder that results in the death of brain cells, causing memory loss, behavioral changes, and cognitive impairment. It drastically affects the individual’s work and social life, often leading to death, and is now the sixth leading cause of mortality worldwide. AD patients have limited mobility, which restricts their movement outside their homes. Thankfully, new internet of things (IoT) applications have made it possible to monitor people with various illnesses in their everyday lives, providing valuable assistance to caregivers. This study aims to create an IoT prototype that can locate an AD patient in real time and remind them to take their medication on schedule via an alarm. The small, lightweight, portable patient carrier has a NodeMCU-23DSP board, a Neo-06 global positioning system (GPS) module, and a wireless modem/Wi-Fi router. Remote patient follow-up through the Blynk 2.0 application on computers and Android devices allows for monitoring of the patient’s medication regimen and daily activities. As AD patients struggle with memory and organization, the prototype’s design enables monitoring of a patient’s course of medication, making it easier for caregivers to provide the necessary assistance. The prototype was tested to demonstrate its efficiency and performance.

Earprint has interestingly been considered for recognition systems. It refers to the shape of ear, where each person has a unique shape of earprint. It is a strong biometric pattern and it can effectively be used for authentications. In this paper, an efficient deep learning (DL) model for earprint recognition is designed. This model is named the deep earprint learning (DEL). It is a deep network that carefully designed for segmented and normalized ear patterns. IIT Delhi ear database (IITDED) version 1.0 has been exploited in this study. The best obtaining accuracy of 94% is recorded for the proposed DEL.

Due to significant challenges faced by traditional methods of personal identification like fingerprinting, eye scanning, and voice recognition, new techniques are needed. One such approach involves the use of human nail images for identification and access to personal identification programs and electronic patient files. A novel algorithm, which consists of three stages, has been proposed utilizing the HSV color space detection algorithm, grayscale contrast optimization algorithm, nail segmentation, and image smoothing with a Gaussian filter. This method reduces tested image data and preserves the primary image structure, and has the potential to surpass the accuracy of traditional methods, providing an additional layer of security in personal identification programs and electronic patient files. Nail image detection can be conducted remotely and accessed through standard cameras or smartphones, making it a more hygienic and convenient option than physical contact methods such as fingerprinting or eye scanning. Moreover, the use of nail images for personal identification has several other benefits, especially in situations where traditional methods are not feasible, such as in individuals with skin conditions that prevent fingerprinting. The success of the proposed algorithm in detecting nail images for personal identification has implications beyond individual security and can be applied in different fields, including healthcare and forensic science, to improve identification accuracy and prevent fraud. For example, the use of nail images could help prevent identity theft in healthcare settings, where sensitive information is stored and exchanged.

A photovoltaic (PV) is a technical terminology that is used to generate electricity from sunlight. Solar energy is one of the solutions for solving the electricity needs in any area. Designing a grid-tie PV system based on real data is very important to utilize a great system. The case study was taken on a national thermal power corporation (NTPC) that lies at Gomti Nager in India. In this work, Newzealand mathematical calculation method is used to design a PV system and a comparison between conventional PV systems and nano PV systems is made. It has been concluded that the nano-PV system cost was lesser than the conventional PV system.