EXPERIMENTAL AND MATHEMATICAL INVESTIGATION ON PERFORMANCE AND EMISSION CHARACTERISTICS OF OXYGEN ENRICHED AIR IN INTAKE OF A SINGLE CYLINDER DIESEL ENGINE

This  research revealed  that the  single cylinder diesel  engine performance and  emission   characteristics are improved by the oxygen content enriched  intake air and was varied between 21% to 27 % (ie., 21,23,25,27%  by the volume). The effects of enriched oxygen with different loads are analyzed in terms of brake thermal efficiency, specific fuel consumption, and also the environmental pollutant like NOx, CO, HC and Smoke. The   mathematical experiment were designed using a statistical tool know as design expert based on response surface modeling. Using RSM to predict the response parameter like brake thermal efficiency, brake specific fuel consumption, carbon monoxide, hydrocarbon, nitrogen oxides and smoke. Optimization of the input and response parameters is also done using desirability approach. Finally a software tool is developed using LabVIEW software for predicting engine parameters when the engine input parameters are given

IoT based smart surgical management platform for hospitals to enhance safety in medical treatment

An automated surgical monitoring system was developed and put into use depending mostly on sophisticated IoT. The use of a system for medical professionals to enter and access data reduces errors and saves time needed filling out paperwork, which improves clinical outcomes as well as the standard of care. Additionally, this technique makes it possible to precisely retain and share all the data that was obtained during process. This method may be used as a knowledge basis for new treatments and also decreased the cost to produce surgery document pages and health information pages. The surgical information could save clinical staff effort through routinely storing their private information. We also have implemented voice-based access to information to make it easier for people to access the data. Additionally, a chatbot that serves as a level of understanding for the procedures is educated on the medical data

Evolution of IOT in health care by protecting and safeguarding private security in healthcare

Machines are being linked together in order to lessen the burden of medics in the near future while also offering effective services to patients. Furthermore, these gadgets generate a large amount of data during transmission. A clinical IoT based network is a networked system that must be protected from beginning to finish in order to function and communicate. To avoid sensitive data leakage from such devices/systems, connection should be encrypted. We are proposing dynamic encryption model which works well on health records generated from IoT machines.  Similarly, we ensure the security of the data through a two-way authentication system with the approval from agent/patient to access and share the data

IoT based smart surgical management platform for hospitals to enhance safety in medical treatment

An automated surgical monitoring system was developed and put into use depending mostly on sophisticated IoT. The use of a system for medical professionals to enter and access data reduces errors and saves time needed filling out paperwork, which improves clinical outcomes as well as the standard of care. Additionally, this technique makes it possible to precisely retain and share all the data that was obtained during process. This method may be used as a knowledge basis for new treatments and also decreased the cost to produce surgery document pages and health information pages. The surgical information could save clinical staff effort through routinely storing their private information. We also have implemented voice-based access to information to make it easier for people to access the data. Additionally, a chatbot that serves as a level of understanding for the procedures is educated on the medical data

Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2.

Diabetes prediction is an ongoing study topic in which medical specialists are attempting to forecast the condition with greater precision. Diabetes typically stays lethargic, and on the off chance that patients are determined to have another illness, like harm to the kidney vessels, issues with the retina of the eye, or a heart issue, it can cause metabolic problems and various complexities in the body. Various worldwide learning procedures, including casting a ballot, supporting, and sacking, have been applied in this review. The Engineered Minority Oversampling Procedure (Destroyed), along with the K-overlay cross-approval approach, was utilized to achieve class evening out and approve the discoveries. Pima Indian Diabetes (PID) dataset is accumulated from the UCI Machine Learning (UCI ML) store for this review, and this dataset was picked. A highlighted engineering technique was used to calculate the influence of lifestyle factors. A two-phase classification model has been developed to predict insulin resistance using the Sequential Minimal Optimisation (SMO) and SMOTE approaches together. The SMOTE technique is used to preprocess data in the model's first phase, while SMO classes are used in the second phase. All other categorization techniques were outperformed by bagging decision trees in terms of Misclassification Error rate, Accuracy, Specificity, Precision, Recall, F1 measures, and ROC curve. The model was created using a combined SMOTE and SMO strategy, which achieved 99.07% correction with 0.1 ms of runtime. The suggested system's result is to enhance the classifier's performance in spotting illness early

Performance measure.

Diabetes prediction is an ongoing study topic in which medical specialists are attempting to forecast the condition with greater precision. Diabetes typically stays lethargic, and on the off chance that patients are determined to have another illness, like harm to the kidney vessels, issues with the retina of the eye, or a heart issue, it can cause metabolic problems and various complexities in the body. Various worldwide learning procedures, including casting a ballot, supporting, and sacking, have been applied in this review. The Engineered Minority Oversampling Procedure (Destroyed), along with the K-overlay cross-approval approach, was utilized to achieve class evening out and approve the discoveries. Pima Indian Diabetes (PID) dataset is accumulated from the UCI Machine Learning (UCI ML) store for this review, and this dataset was picked. A highlighted engineering technique was used to calculate the influence of lifestyle factors. A two-phase classification model has been developed to predict insulin resistance using the Sequential Minimal Optimisation (SMO) and SMOTE approaches together. The SMOTE technique is used to preprocess data in the model’s first phase, while SMO classes are used in the second phase. All other categorization techniques were outperformed by bagging decision trees in terms of Misclassification Error rate, Accuracy, Specificity, Precision, Recall, F1 measures, and ROC curve. The model was created using a combined SMOTE and SMO strategy, which achieved 99.07% correction with 0.1 ms of runtime. The suggested system’s result is to enhance the classifier’s performance in spotting illness early.</div

Description of parameters used in the dataset.

Diabetes prediction is an ongoing study topic in which medical specialists are attempting to forecast the condition with greater precision. Diabetes typically stays lethargic, and on the off chance that patients are determined to have another illness, like harm to the kidney vessels, issues with the retina of the eye, or a heart issue, it can cause metabolic problems and various complexities in the body. Various worldwide learning procedures, including casting a ballot, supporting, and sacking, have been applied in this review. The Engineered Minority Oversampling Procedure (Destroyed), along with the K-overlay cross-approval approach, was utilized to achieve class evening out and approve the discoveries. Pima Indian Diabetes (PID) dataset is accumulated from the UCI Machine Learning (UCI ML) store for this review, and this dataset was picked. A highlighted engineering technique was used to calculate the influence of lifestyle factors. A two-phase classification model has been developed to predict insulin resistance using the Sequential Minimal Optimisation (SMO) and SMOTE approaches together. The SMOTE technique is used to preprocess data in the model’s first phase, while SMO classes are used in the second phase. All other categorization techniques were outperformed by bagging decision trees in terms of Misclassification Error rate, Accuracy, Specificity, Precision, Recall, F1 measures, and ROC curve. The model was created using a combined SMOTE and SMO strategy, which achieved 99.07% correction with 0.1 ms of runtime. The suggested system’s result is to enhance the classifier’s performance in spotting illness early.</div