Instructional practices have undergone a drastic change as a result of the development of new educational technology. Artificial intelligence (AI) as a teaching and learning technology will be examined in this theoretical review study. To enhance the quality of teaching and learning, the use of artificial intelligence approaches is being studied. Artificial intelligence integration in educational institutions has been addressed, though. Students’ assistance, teaching, learning, and administration are also addressed in the discussion of students’ adoption of artificial intelligence. Artificial intelligence has the potential to revolutionize our social interactions and generate new teaching and learning methods that may be evaluated in a variety of contexts. New educational technology can help students and teachers better accomplish and manage their educational objectives. Artificial intelligence algorithms are used in a hybrid teaching mode in this work to examine students’ attributes and introduce predictions of future learning success. The teaching process may be carried out in a more efficient manner using the hybrid mode. Educators and scientists alike will benefit from artificial intelligence algorithms that may be used to extract useful information from the vast amounts of data collected on human behavior.

There has been an increase in credit card fraud as e-commerce has become more widespread. Financial transactions are essential to our economy, so detecting bank fraud is essential. Experiments on automated and real-time fraud detection are needed here. There are numerous machine learning techniques for identifying credit card fraud, and the most prevalent are support vector machine (SVM), logic regression, and random forest. When models penalise all errors equally during training, the quality of these detection approaches becomes crucial. This paper uses an innovative sensing method to judge the classification algorithm by considering the misclassification cost and at the same time by employing SVM hyperparameter optimization using grid search cross-validation and separating the hyperplane using the theory of reproducing kernels like linear, Gaussian, and polynomial, and the robustness is maintained. Because of this, credit card fraud has been identified significantly more successful than in the past.

The goal of this study was to develop atorvastatin (ATN) calcium biloaded, i.e., immediate release (IR) and sustained release (SR) capsules that would promote the quick onset of action and a better dissolution profile on both the IR and SR aspects. The IR granules were prepared by the wet granulation method, and an aqueous solubility study proved that the IR granules released the ATN within 25min compared to the pure drug due to the addition of PEG and super disintegrants such as croscarmellose (CC) and crospovidone (CP). The sustained release nanoparticles (SR-NPs) were synthesized using a solvent evaporation technique and an optimal combination of hydrophilic and hydrophobic polymers. The addition of a hydrophobic polymer to a hydrophilic polymer delays drug release, resulting in a sustained and controlled release lasting up to 12 hours. The drug release of ATN from SR nanoparticles followed the Higuchi and KorsmeyerPeppas models and had first-order kinetics (r2=???). Fourier transform infrared spectrophotometry, powder X-ray diffraction, and differential scanning calorimetric analysis were used to test the prepared biloaded capsules, and the results showed that there was no significant interaction between the polymers, excipients, and drug. The SEM and DLS analysis clearly demonstrated that drug particles in a continuous layer were enclosed by polymers at the nanoscale. To summarise, integrating both layers into a single capsule resulted in a superior release profile and patient compliance. Finally, prepared biloaded capsules were discovered to exhibit both an IR and an SR profile.

Coronavirus took the world by surprise and caused a lot of trouble in all the important fields in life. The complexity of dealing with coronavirus lies in the fact that it is highly infectious and is a novel virus which is hard to detect with exact precision. The typical detection method for COVID-19 infection is the RT-PCR but it is a rather expensive method which is also invasive and has a high margin of error. Radiographies are a good alternative for COVID-19 detection given the experience of the radiologist and his learning capabilities. To make an accurate detection from chest X-Rays, deep learning technologies can be involved to analyze the radiographs, learn distinctive patterns of coronavirus presence, find these patterns in the tested radiograph, and determine whether the sample is actually COVID-19 positive or negative. In this study, we propose a model based on deep learning technology using Convolutional Neural Networks and training it on a dataset containing a total of over 35,000 chest X-Ray images, nearly 16,000 for COVID-19 positive images, 15,000 for normal images, and 5,000 for pneumonia-positive images. The models performance was assessed in terms of accuracy, precision, recall, and F1-score, and it achieved 99 accuracy, 0.98 precision, 1.02 recall, and 99.0 F1-score, thus outperforming other deep learning models from other studies.

The impact of a soluble complex (SC) of curcumin (CuR) synthesized using hot melt (HM) and hot-melt extrusion (HE) technologies on adenocarcinoma cells for the treatment of colorectal cancer by enhancing CuR solubility is investigated in this work. In silico molecular modelling, solubility, drug release, and physicochemical analysis were all part of the phase solubility (PS) study, which featured a novel dyeing test and a central composite design to optimize the best complex (CDD). The optimal HE-SC (1:5) enhances solubility (0.8521±0.016mg·mL⁻¹) and dissolution (91.87±0.208 at 30min), and it has an ideal stability constant (309 and 377M⁻¹) at 25 and 37°C and an A_L type of isotherm, implying 1:1 stoichiometry according to the findings. An intermolecular hydrogen bond that has not undergone any chemical change and has resulted in the complete conversion of the amorphous form aids in the creation of SC. In vitro cytotoxicity was measured at IC₅₀ on the SW480 (72M·mL⁻¹) and Caco-2 (40M·mL⁻¹) cells. According to apoptotic studies, apoptosis was responsible for the vast majority of cell death, with necrosis accounting for a small proportion of the total. In vivo toxicity was established using a zebrafish model, and a western blot examination revealed apoptosis at the molecular level. It was argued that the novel formulations developed using HE technology are more significant and effective than existing pure CuR formulations.