Every recognized hospital’s patient management unit (PMU) has focused its efforts on improving clinical patient care, with a process approach, analyzing it from adult emergency overcrowding to prolonged stays in clinical services. It is generating many patients waiting for beds in the emergency service. The PMU does not have a business intelligence (BI) platform that provides information in real-time, generating a blind browsing problem. The purpose is to demonstrate the need for a BI platform using Artificial Intelligence (AI) to analyze in real-time the relevant information for decision making. The methodology consists of analyzing qualitative and quantitatively the statistics of the last three years, both from the emergency service and from the clinical services. This study shows that the saturation of the emergency service responds to the number of patients waiting for beds, which interferes with outpatient care. The projections for 2020 underestimated the demand, and the efforts to open hospital beds and home hospitalization quotas allowed to shovel said excess demand. The average stay numbers continue to increase, as does the number of hospitalized patients for emergencies, generating a progressive growth in demand. It is necessary to have a BI system adapted with AI to perform real-time analysis of the GRD, to be able to act during hospitalization and not afterward.

Because of the high mortality rate, increased medical costs, and ongoing global growth in the incidence of this malignancy, early detection has become a top priority. Early detection and treatment of melanoma are critically important; the likelihood of a positive outcome rises dramatically. To address this issue, academic researchers plan to develop a prototype image analysis system based on deep learning to determine whether a lesion is malignant or benign based on dermatoscopy image databases. Pretrained convolutional networks with simple architectures were employed in this study to grasp their design better and to train the given dataset more quickly. Using convolutional neural networks as the basis, this research seeks to develop a deep learning system capable of classifying images. To train our model with the pretrained AlexNet, VGG, and ResNet networks, we will use the learning transfer methodology (or transfer learning), whose architecture we will outline so that it may subsequently be adjusted to our data. In this research work, fairly basic pretrained convolutional networks have been used to understand their architecture and efficiently train the given dataset. However, other networks have much more complex structures or even the same networks used, but with many more layers. For possible future work, it is proposed to use, for example, ResNet-152, Vgg-19, or other different networks such as DenseNet or Inception.

Neurodegenerative diseases drastically affect human beings without distinction; it does not matter if they are male or female. Sometimes, it is not clear why a person in their life developed a well-known disease in the world such as Parkinson’s disease (PD). Nowadays, various novel machine learning-based algorithms for evaluating Parkinson’s disease have been designed. The most recent strategy, which was developed using deep learning and can forecast the severity of Parkinson’s disease, is the one described here. To identify this disease, a thorough medical history, previous treatment history, physical examinations, and some blood tests and brain films must be completed. Diagnoses are more critical since they are less expensive and less time-consuming. Voice data from 253 people used in the current study corroborates the doctor’s diagnosis of Parkinson’s disease. To acquire the best results from the data, preprocessing is done. To perform the balancing procedure, a systematic sampling strategy was used to select the data that would be analyzed. Several data groups were constructed using a feature selection technique based on the label’s effect strength. Classification algorithms and performance evaluation criteria employ DT, SVM, and kNN. The classification algorithm and data group with the highest performance value were chosen, and the model was created due to this selection. The SVM approach was employed when constructing the model, and 45% of the original data set data were used. The data was sorted from most relevant to least important. 86% performance accuracy was achieved, in addition to excellent results in all other areas of the project. As a result, it has been established that medical decision support will be provided to the doctor with the assistance of the data set obtained from the speech recordings of the individual who may have Parkinson’s disease and the model that has been developed.

Alumina nanoparticles were prepared by sol-gel method, where the obtained nanosize was 35 nm, and the nanomaterial was coated with PVP polymer, where the nanomaterial was dispersed by ultrasonic waves for half an hour, and then, the polymer was added, and under high magnetic stirring for 24 hours, it was dried at a temperature of 60°C for 24 hours. Cadmium salt solutions were prepared with different concentrations of 10, 30, and 60 ppm, and the nanomaterial was immersed in the prepared solutions at different times of 10, 30, and 60 minutes, and the measurement was done by an atomic absorption device. By means of the electronic scanner, a difference appeared in the nanosize, and this indicates that the packaging has completely occurred.

A smart, environmentally friendly superabsorbent polymer was prepared using solvents. It was polymerization on the microwave rays at a medium capacity and for 25 minutes, where the yellow gel was obtained. The polymer was cut and washed with absolute ethanol and methanol and was dried at a temperature of 60°C. Then, the polymer was extracted and milled with ceramic fat until obtaining a very soft powder, and the tests were taken as a formula for a scanning electron microscope and infrared spectrum. The results showed that the absorption value of polymer is at the equivalent acid function, where the absorption capacity was 467.32 grams. At room temperature, the water retention rate was 71%, and at 50°C, it was 52%, and the gel content was 90%. The results showed an improvement in the properties of the gypsum soil in terms of virtual density, porous, and acidic function, reaching 7.3%. The proportion of significant elements (P, N, Ca, K, Na) and moisture content in the soil was 64%, the cumulative tip amount and the consistency of soil granules through wet and dry palm, penetration resistance, electrical conductivity 4 ms, and organic material content were as follows, and the results were very high.