An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation.

With the increase in urbanization and smart cities initiatives, the management of waste generation has become a fundamental task. Recent studies have started applying machine learning techniques to prognosticate solid waste generation to assist authorities in the efficient planning of waste management processes, including collection, sorting, disposal, and recycling. However, identifying the best machine learning model to predict solid waste generation is a challenging endeavor, especially in view of the limited datasets and lack of important predictive features. In this research, we developed an ensemble learning technique that combines the advantages of (1) a hyperparameter optimization and (2) a meta regressor model to accurately predict the weekly waste generation of households within urban cities. The hyperparameter optimization of the models is achieved using the Optuna algorithm, while the outputs of the optimized single machine learning models are used to train the meta linear regressor. The ensemble model consists of an optimized mixture of machine learning models with different learning strategies. The proposed ensemble method achieved an R2 score of 0.8 and a mean percentage error of 0.26, outperforming the existing state-of-the-art approaches, including SARIMA, NARX, LightGBM, KNN, SVR, ETS, RF, XGBoosting, and ANN, in predicting future waste generation. Not only did our model outperform the optimized single machine learning models, but it also surpassed the average ensemble results of the machine learning models. Our findings suggest that using the proposed ensemble learning technique, even in the case of a feature-limited dataset, can significantly boost the model performance in predicting future household waste generation compared to individual learners. Moreover, the practical implications for the research community and respective city authorities are discussed.

Moisture Computing-Based Internet of Vehicles (IoV) Architecture for Smart Cities.

Recently, the concept of combining 'things' on the Internet to provide various services has gained tremendous momentum. Such a concept has also impacted the automotive industry, giving rise to the Internet of Vehicles (IoV). IoV enables Internet connectivity and communication between smart vehicles and other devices on the network. Shifting the computing towards the edge of the network reduces communication delays and provides various services instantly. However, both distributed (i.e., edge computing) and central computing (i.e., cloud computing) architectures suffer from several inherent issues, such as high latency, high infrastructure cost, and performance degradation. We propose a novel concept of computation, which we call moisture computing (MC) to be deployed slightly away from the edge of the network but below the cloud infrastructure. The MC-based IoV architecture can be used to assist smart vehicles in collaborating to solve traffic monitoring, road safety, and management issues. Moreover, the MC can be used to dispatch emergency and roadside assistance in case of incidents and accidents. In contrast to the cloud which covers a broader area, the MC provides smart vehicles with critical information with fewer delays. We argue that the MC can help reduce infrastructure costs efficiently since it requires a medium-scale data center with moderate resources to cover a wider area compared to small-scale data centers in edge computing and large-scale data centers in cloud computing. We performed mathematical analyses to demonstrate that the MC reduces network delays and enhances the response time in contrast to the edge and cloud infrastructure. Moreover, we present a simulation-based implementation to evaluate the computational performance of the MC. Our simulation results show that the total processing time (computation delay and communication delay) is optimized, and delays are minimized in the MC as apposed to the traditional approaches.

Awareness of Diabetic Retinopathy Among Patients With Type 2 Diabetes Mellitus in Primary Healthcare Centers in Madinah, Saudi Arabia: A Cross-Sectional Study.

Background The prevalence of diabetes mellitus (DM) in Saudi Arabia is among the highest in the Middle East and North Africa (MENA) regions. Various complications of DM can cause problems in the long term. One of the most prevalent microvascular problems and the primary cause of blindness is diabetic retinopathy (DR), and a significant proportion of the population with diabetes eventually develop diabetes retinopathy. Recognizing and understanding DR may be crucial for patients in identifying and averting this complication.  Objectives The objective of this atudy is to assess the awareness of DR among patients with type 2 DM at primary healthcare centers in Madinah, Saudi Arabia.  Methods This cross-sectional study involved a survey of patients with diabetes who attended Madinah primary care clinics between August and September 2023. The study was conducted in Madinah, Saudi Arabia, from May to November 2023.  Results A total of 240 patients participated with a median age of 49.7 years and a gender distribution of 121 (50.4%) men. Overall, less than half of patients had a fair level of knowledge (47.1%) and a good level of knowledge (42.1%) about DR, whereas 10.8% had poor knowledge. Physicians were the primary source of information for patients, followed by the internet, family, and friends. Higher levels of education, diabetes that had been present for a longer period, and regular eye exams were associated with better understanding. This study emphasizes the importance of improving patient knowledge and awareness of DR.  Conclusions We observed a high level of awareness of DR among participants. Furthermore, higher awareness was associated with longer disease duration and compliance with diabetes treatment.

Prevalence of Myopia and Its Related Factors Among University Students in Madinah, Saudi Arabia.

BACKGROUND: Myopia, a common refractive error, is a growing global health burden influenced by both genetic and environmental factors. Despite its high prevalence, studies on its prevalence and risk factors among university students are lacking. OBJECTIVES: The objective of this study is to investigate the prevalence of myopia and its associated factors among college students in Saudi Arabia's Madinah region. METHODS: A cross-sectional study was conducted in Al-Madinah, Saudi Arabia, from February to June 2023, utilizing a survey that was distributed to college students through a social media application. RESULTS: A total of 433 university students from Al-Madinah province were enrolled in this study; 66.3% were females and 33.7% were males. Participants' ages ranged from 18 to 33 years with a mean of 21.3 ± 2.0 years. The prevalence of myopia among college students in Al-Madinah and its provinces was 57.3%, and 87.9% of them had myopia in both eyes. Respondents with an electronic screen time of more than three hours and a reading distance of less than 15cm were at significant risk of myopia with p-values of 0.037 and 0.019, respectively. CONCLUSIONS: A significant prevalence of myopia has been observed among university students in Madinah. Studying in scientific and medical fields, having eye diseases, prolonged use of digital devices, limiting daily outdoor activities to one hour, and having a reading distance of less than 15 cm significantly increased the risk of myopia. Encouraging education and screening programs for myopia prevention and control is crucial.