Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things.

Industrial automation uses robotics and software to operate equipment and procedures across industries. Many applications integrate IoT, machine learning, and other technologies to provide smart features that improve the user experience. The use of such technology offers businesses and people tremendous assistance in successfully achieving commercial and noncommercial requirements. Organizations are expected to automate industrial processes owing to the significant risk management and inefficiency of conventional processes. Hence, we developed an elaborative stepwise stacked artificial neural network (ESSANN) algorithm to greatly improve automation industries in controlling and monitoring the industrial environment. Initially, an industrial dataset provided by KLEEMANN Greece was used. The collected data were then preprocessed. Principal component analysis (PCA) was used to extract features, and feature selection was based on least absolute shrinkage and selection operator (LASSO). Subsequently, the ESSANN approach is proposed to improve automation industries. The performance of the proposed algorithm was also examined and compared with that of existing algorithms. The key factors compared with existing technologies are delay, network bandwidth, scalability, computation time, packet loss, operational cost, accuracy, precision, recall, and mean absolute error (MAE). Compared to traditional algorithms for industrial automation, our proposed techniques achieved high results, such as a delay of approximately 52%, network bandwidth accomplished at 97%, scalability attained at 96%, computation time acquired at 59 s, packet loss achieved at a minimum level of approximately 53%, an operational cost of approximately 59%, accuracy of 98%, precision of 98.95%, recall of 95.02%, and MAE of 80%. By analyzing the results, it can be seen that the proposed system was effectively implemented.

A Clinical Tool to Identify Candidates for Stress-First Myocardial Perfusion Imaging.

OBJECTIVES: This study sought to develop a clinical model that identifies a lower-risk population for coronary artery disease that could benefit from stress-first myocardial perfusion imaging (MPI) protocols and that can be used at point of care to risk stratify patients. BACKGROUND: There is an increasing interest in stress-first and stress-only imaging to reduce patient radiation exposure and improve patient workflow and experience. METHODS: A secondary analysis was conducted on a single-center cohort of patients undergoing single-photon emission computed tomography (SPECT) and positron emission tomography (PET) studies. Normal MPI was defined by the absence of perfusion abnormalities and other ischemic markers and the presence of normal left ventricular wall motion and left ventricular ejection fraction. A model was derived using a cohort of 18,389 consecutive patients who underwent SPECT and was validated in a separate cohort of patients who underwent SPECT (n = 5,819), 1 internal cohort of patients who underwent PET (n=4,631), and 1 external PET cohort (n = 7,028). RESULTS: Final models were made for men and women and consisted of 9 variables including age, smoking, hypertension, diabetes, dyslipidemia, typical angina, prior percutaneous coronary intervention, prior coronary artery bypass graft, and prior myocardial infarction. Patients with a score ≤1 were stratified as low risk. The model was robust with areas under the curve of 0.684 (95% confidence interval [CI]: 0.674 to 0.694) and 0.681 (95% CI: 0.666 to 0.696) in the derivation cohort, 0.745 (95% CI: 0.728 to 0.762) and 0.701 (95% CI: 0.673 to 0.728) in the SPECT validation cohort, 0.672 (95% CI: 0.649 to 0.696) and 0.686 (95% CI: 0.663 to 0.710) in the internal PET validation cohort, and 0.756 (95% CI: 0.740 to 0.772) and 0.737 (95% CI: 0.716 to 0.757) in the external PET validation cohort in men and women, respectively. Men and women who scored ≤1 had negative likelihood ratios of 0.48 and 0.52, respectively. CONCLUSIONS: A novel model, based on easily obtained clinical variables, is proposed to identify patients with low probability of having abnormal MPI results. This point-of-care tool may be used to identify a population that might qualify for stress-first MPI protocols.

Old World cutaneous leishmaniasis treatment response varies depending on parasite species, geographical location and development of secondary infection.

BACKGROUND: In the Kingdom of Saudi Arabia (KSA), Leishmania major and L. tropica are the main causative agents of Old World cutaneous leishmaniasis (CL). The national CL treatment regimen consists of topical 1% clotrimazole/2% fusidic acid cream followed by 1-2 courses of intralesional sodium stibogluconate (SSG); however, treatment efficacy is highly variable and the reasons for this are not well understood. In this study, we present a complete epidemiological map of CL and determined the efficacy of the standard CL treatment regime in several endemic regions of KSA. RESULTS: Overall, three quarters of patients in all CL-endemic areas studied responded satisfactorily to the current treatment regime, with the remaining requiring only an extra course of SSG. The majority of unresponsive cases were infected with L. tropica. Furthermore, the development of secondary infections (SI) around or within the CL lesion significantly favoured the treatment response of L. major patients but had no effect on L. tropica cases. CONCLUSIONS: The response of CL patients to a national treatment protocol appears to depend on several factors, including Leishmania parasite species, geographical location and occurrences of SI. Our findings suggest there is a need to implement alternative CL treatment protocols based on these parameters.

Detection of high levels of anti-α-galactosyl antibodies in sera of patients with Old World cutaneous leishmaniasis: a possible tool for diagnosis and biomarker for cure in an elimination setting.

In the Kingdom of Saudi Arabia (KSA), Old World cutaneous leishmaniasis (CL) is mainly caused by Leishmania major and Leishmania tropica parasites. Diagnosis of CL is predominately made by clinicians, who at times fail to detect the disease and are unable to identify parasite species. Here, we report the development of a chemiluminescent enzyme-linked immunosorbent assay (CL-ELISA) to measure the levels of anti-α-galactosyl antibodies in human sera. Using this assay, we have found that individuals infected with either Leishmania spp. had significantly elevated levels (up to 9-fold higher) of anti-α-Gal IgG compared to healthy control individuals. The assay sensitivity was 96% for L. major (95% CI; 94-98%) and 91% for L. tropica (95% CI; 86-98%) infections and therefore equivalent to restriction fragment length polymorphism-polymerase chain reaction analysis of parasite ITS1 gene. In addition, the assay had higher sensitivity than microscopy analysis, which only detected 68 and 45% of the L. major and L. tropica infections, respectively. Interestingly, up to 2 years following confirmed CL cure individuals had 28-fold higher levels of anti-α-Gal IgG compared to healthy volunteers. Monitoring levels of anti-α-Gal antibodies can be exploited as both a diagnostic tool and as a biomarker of cure of Old World CL in disease elimination settings.

Isolation and genetic characterization of Rift Valley fever virus from Aedes vexans arabiensis, Kingdom of Saudi Arabia.

An outbreak of Rift Valley fever in the Kingdom of Saudi Arabia and Yemen in 2000 was the first recognized occurrence of the illness outside of Africa and Madagascar. An assessment of potential mosquito vectors in the region yielded an isolate from Aedes vexans arabiensis, most closely related to strains from Madagascar (1991) and Kenya (1997).