Many elderly individuals live alone in their homes, which can lead to significant health and safety concerns due to the risk of falls. Falls not only cause physical injuries but also have social, psychological, and economic impacts that negatively affect the quality of life for older adults. In this context, early detection of falls and implementation of preventive measures are of great importance. Edge computing-based fall detection systems have been developed to effectively address the safety of older adults in such situations. In the present study, a fall detection system is proposed that utilizes edge computing and TinyML technologies, operating on an embedded platform. This system is designed for the interpretation of accelerometer sensor data and processes the data collected through sensors to obtain valuable information. The Edge Impulse platform is used for training an extensive dataset consisting of various fall examples for older adults, allowing the proposed system to achieve a 98.5% recognition accuracy. This cost-effective and user-friendly novel approach combines a portable accelerometer sensor and artificial intelligence software to target early detection and prevention of falls in older adults. This study contributes significantly to the field of edge computing and provides effective solutions to enhance the quality of life for elderly individuals.

Urban air quality has significant and far-reaching impacts on both human health and the broader environment. Pollutants like particulate matter (PM2.5 and PM10), for instance, are associated with a range of health issues including respiratory conditions, asthma, heart diseases, and even contribute to low birth weight in newborns. These health implications extend to larger environmental concerns such as air pollution, greenhouse gas emissions, and global climate change. Recognizing the urgent need for effective and dynamic air quality monitoring solutions, this paper explores the use of smart environmental drones as a promising approach. Our drone is equipped with a state-of-the-art, low-cost particulate matter sensor that can accurately measure PM2.5 and PM10 concentrations. Operating at a flight speed of 10 m/s and capable of covering a range of 5 km, the drone executes a pre-programmed flight plan to autonomously map pollution levels across urban areas. With a 95% accuracy rate in sensor readings, our model significantly minimizes potential errors commonly associated with traditional air quality monitoring methods. Furthermore, it simplifies maintenance procedures, reducing both time and financial costs. By employing drone technology in this innovative manner, our model offers a cost-effective, reliable, and dynamic solution for monitoring urban air quality. It provides real-time, actionable pollution indices that can inform public health decisions, regulatory policies, and community awareness, thereby contributing to the broader goal of improving air quality and public health.

This study aims to detect starch adulteration in dairy products utilizing an artificial neural network (ANN) model. Globally, milk fraud represents a significant challenge to food safety, posing substantial health risks to consumers. In this context, spectral data derived from milk samples with varying starch concentrations were processed using feature scaling and normalization techniques. The ANN model was rigorously trained and validated employing the stratified k-fold cross-validation method, demonstrating exceptional proficiency in detecting starch-adulterated milk samples and effectively differentiating among various starch concentrations. The principal findings indicate that the model achieved 100% accuracy, coupled with high levels of precision, sensitivity, and F1-scores. Future research should explore the application of this model to different types of adulteration and extend its validation on larger datasets. Furthermore, the potential adaptability of this method for other food products and field applications warrants investigation. This study offers valuable insights for milk producers, food safety professionals, and consumers, particularly highlighting the implications for small-scale rural farms, thereby enriching the discourse on food safety within short food supply chains.

In this comprehensive research, an in-depth evaluation of several machine learning algorithms, including Multilayer Perceptron, IBk, KStar, M5Rules, and RandomForest, is conducted to ascertain their effectiveness in detecting adulteration in milk products using spectroscopic data. The algorithms were rigorously deployed and assessed through a series of controlled experiments involving both raw and adulterated milk samples. Notably, IBk and KStar algorithms emerged with a perfect accuracy rate of 100% in identifying adulteration, highlighting their superior capability in this domain. Additionally, the Decision Table algorithm also performed exceptionally well, achieving a remarkable correlation coefficient of 0.9871. These promising results emphasize the undeniable potential of machine learning algorithms as reliable and precise tools for detecting adulteration in milk. Such technological interventions play a critical role in elevating the safety and quality standards of milk and milk-based products in the market. Moreover, the deployment of these advanced machine-learning techniques provides an invaluable layer of consumer protection, plays a significant role in combating widespread fraudulent practices in the milk industry, and ensures compliance with stringent food safety standards. These methodologies could be indispensable for both industry players and regulatory bodies, significantly contributing to the safeguarding of public health.

In this work, the use of courtesy lamps for communicating with visible light has been proposed for low cost indoor positioning. Modulated light emitting diode (LED) lights have been used for both positioning and lightening. The lamp ID information has been transmitted to three LED lamps. With the proposed system, the mobile receiver is provided with the location information and the environment on the screen is visualized. The proposed system consists of three components. These are the light transmission module, the light driver and the receiver circuit. The system is recommended for the display of works of art in indoor environments such as museums and art centers.