A developed urban city is always accustomed to lots of cars on its roads but is alternatively not equipped with the required parking space. The increased scalability of the number of vehicles due to increase in population makes allotment of parking space challenging. The other difficulties faced are congestion created due to increased vehicles, wastage of space and time, traffic issues, difficulty in cruising etc. Every time the person driving a vehicle enters the parking lot, they attempt to find an empty space for the vehicle to be parked which leads to loss of time and energy, especially during peak hours. Smart Parking Assistant provides an optimized solution to overcome the above problems. It also provides a sophisticated parking assistance in economical cars. It mainly deals to avoid crash and confusion during car parking in unorganized and unknown parking areas.
In recent times, the healthcare industry has been generating a significant amount of data in distinct formats, such as electronic health records (EHR), clinical trials, genetic data, payments, scientific articles, wearables, and care management databases. Data science is useful for analysis (pattern recognition, hypothesis testing, risk valuation) and prediction. The major, primary usage of data science in the healthcare domain is in medical imaging. At the same time, lung cancer diagnosis has become a hot research topic, as automated disease detection poses numerous benefits. Although numerous approaches have existed in the literature for lung cancer diagnosis, the design of a novel model to automatically identify lung cancer is a challenging task. In this view, this paper designs an automated machine learning (ML) with data science-enabled lung cancer diagnosis and classification (MLDS-LCDC) using computed tomography (CT) images. The presented model initially employs Gaussian filtering (GF)-based pre-processing technique on the CT images collected from the lung cancer database. Besides, they are fed into the normalized cuts (Ncuts) technique where the nodule in the pre-processed image can be determined. Moreover, the oriented FAST and rotated BRIEF (ORB) technique is applied as a feature extractor. At last, sunflower optimization-based wavelet neural network (SFO-WNN) model is employed for the classification of lung cancer. In order to examine the diagnostic outcome of the MLDS-LCDC model, a set of experiments were carried out and the results are investigated in terms of different aspects. The resultant values demonstrated the effectiveness of the MLDS-LCDC model over the other state-of-the-art methods with the maximum sensitivity of 97.01%, specificity of 98.64%, and accuracy of 98.11%.
Foreseeing the AQI is vital for natural and open wellbeing surveillance. It figures the state of the discuss at a given put and time utilizing numerical models and information investigation. AQI can be anticipated employing a assortment of strategies, such as machine learning calculations, AI models, and measurable models. The amount and quality of the input information, as well as the complexity of the calculations utilized, decide how exact these models are. Promising strategies for AQI forecast are profound learning calculations, especially the bidirectional long short-term memory (BiLSTM) show. By deciding the perfect values for the hyperparameters, the Spider Monkey Optimization (SMO) algorithm is utilized to extend the precision of AQI expectation. With a normal supreme blunder of 1.05., the recommended method's performance using SMO is predominant to other models within the writing. Significant specialists can utilize this data to advise their choices around natural security, open wellbeing, and natural approach.
Introduction: Diabetes Mellitus (DM) can be characterized as a metabolic disorder, which results in the increase of blood sugar level in the body which might lead to macrovascular and microvascular complication. It is a non- communicable disease. Diabetes Mellitus has also become a major concern in public health and also has imposed an economic burden on the society.
Aim: The aim of the study is to assess the knowledge, attitude and practice of Diabetic Retinopathy in patients with Diabetes Mellitus of the south Indian population.
Materials and Methods: Patients who were diagnosed with Diabetes Mellitus admitted in a tertiary care government hospital located in Chennai was included in this study. A questionnaire (Bandar Krayem Al Zarea et al) was provided to the patient to analyze their knowledge, attitude and practices about Diabetic Retinopathy among patients diagnosed with Diabetes Mellitus. The data collected were coded and entered into SPSS (Statistical Package for the Social Science) version 20 and statistical analysis was done.
Result: This study incorporated 230 diabetic individuals out of which 112 (48.7%) were male patients and 118 (51.3%) were females. The majority of the diabetic patients 146 (63.5%) were aware that Diabetes can cause eye disorders, 127 (55.2%) of patients replied that diabetic individuals should go for regular eye checkups and 141 (61.3%) of patients were aware that they should visit an ophthalmologist in the event of eye problem. Out of 230 diabetic 174 patients 174 (75.7%) were aware that timely treatment can prevent or delay damage of eyes in diabetic patients and about 82% of all the participants went for regular ocular examinations.
Conclusion: The majority of the patient diagnosed with Diabetes Mellitus had knowledge about Diabetes can cause eye disease and it is essential for all patients diagnosed with Diabetes Mellitus to take the regular ocular examination.
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