An Adaptive Technique to Predict Heart Disease Using Hybrid Machine Learning Approach

cardiovascular disease is amongby far prevalent fatalities in today's society. Cardiovascular disease is extremely hard to predict using clinical data analysis. Machine learning (ML) hasproved to be useful for helping in judgement and predictions with the enormous amount data produced by the healthcare sectorbusiness. Furthermore, latest events in other IoT sectors have demonstrated that machine learning is used (IOT). Several studies have examined the use of MLa heart disease prediction. In this research, we describe a novel method that, by highlighting essential traits, can improvethe precision of heart disease prognosis. Numerous data combinations and well-known categorization algorithms are used to create the forecasting models. Using a decent accuracy of 88.7%, we raise the level of playusing a heart disease forecasting approach that incorporates a88.7% absolute certainty in a combination random forest and linear model. (HRFLM)

LSGDM Two Stage Consensus Reaching Process for Autocratic Decision Making using Group Recommendations

The decision making is a general and significant action in day-to-day life. In some cases, experts cannot express their preferences using precise value due to inherent unreliability. The utilization of linguistic labels creates expert judgement more informative and consistent for decision making. The group recommendation is considered as a significant factors of e-commerce domain due to their direct impact on profit. The personalized experiments improve the engagement and the count of purchases of the customer when the recommended products are matched to the current interest.In this paper, the Large-Scale Group Decision Making (LSGDM) two stage consensus reaching process is proposed by using three various Amazon real world dataset.This proposed method permits an autocratic decision maker to utilize a different group recommendation for a sequence of decisions at highest level of consensus. The performance of the model is estimated by applying parameters like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Precision and Recall. The obtained result shows that proposed methodology provides better result while comparing various other methods

A novel approach for iceberg query evaluation on multiple attributes using set representation

Iceberg query (IBQ) can be an really identifying kind of aggregation question that calculate aggregations up-on user given threshold(T). In data mining field, effective investigation of compounding queries was because of by the majority of investigators because the tremendous generation of information outside of industrial and businesses industries. Conclusion assist database and discovery of the majority of information connected systems largely calculate the worthiness of most fascinating features having an critical level of information from data foundations that may be tremendous. By means of the paper, we propose that an initial Manner of calculating IBQ, which builds a choice for every attribute nicely value, but additionally includes a One of a Kind events Inside the attribute column also plays specify operations for creating closing Outcomes. We formulated highly effective GUI software for just 2 characteristics, numerous traits employing egotistical prepare and several features utilizing lively plan. If data collection comprises two traits, then it truly is substantially more advanced than apply just two traits. In the event of information collection comprises multiple traits, predicated up on anyone choice suitable module could potentially be decided on. If characteristic uniqueness changes from characteristic in to the following characteristic, then vibrant variety approach is very powerful. This strategy somewhat reduces performance memory and time space contrast with additional processes. A experiment using artificial Statistics collection and actual info demonstrates our strategy will be considerably more effective compared to present apps for Nearly Every threshold

Violation of Traffic Rules and Detection of Sign Boards

Today's society has seen a sharp rise in the number of accidents caused by drivers failing to pay attention to traffic signals and regulations. Road accidents are increasing daily as the number of automobiles rises. By using synthesis data for training, which are produced from photos of road traffic signs, we are able to overcome the challenges of traffic sign identification and decrease violations of traffic laws by identifying triple-riding, no-helmet, and accidents, which vary for different nations and locations. This technique is used to create a database of synthetic images that may be used in conjunction with a convolution neural network (CNN) to identify traffic signs, triple riding, no helmet use, and accidents in a variety of view lighting situations. As a result, there will be fewer accidents, and the vehicle operator will be able to concentrate more on continuing to drive but instead of checking each individual road sign. Also, simplifies the process to recognize triple driving, accidents, but also incidents when a helmet was not used

An Adaptive Technique for Crime Rate Prediction using Machine Learning Algorithms

Any country must give the investigation and preventive of crime top priority. There are a rising amount of cases that are still pending due to the rapid increase in criminal cases in India and elsewhere. It is proving difficult to classify and address the rising number of criminal cases. Understanding a place's trends in criminal activity is essential to preventing it from occurring. Crime-solving organisations will be more effective if they have a clear awareness of the patterns of criminal behavior that are present in a particular area. Women's safety and protection are of highest importance despite the serious and persistent problem of crime against them. This study offers predictions about the kinds of crimes that might occur in a particular location using ensemble methods. This facilitates the categorization of criminal proceedings and subsequent action in a timely manner. We are applying machine learning methods like KNN, Linear regression, SVM, Lasso, Decision tree and Random forest in order to assess the highest accuracy

Design and Development IoT based Smart Energy Management Systems in Buildings through LoRa Communication Protocol

Energy management is a vital tool for reducing significant supply-side deficits and increasing the efficiency of power generation. The present energy system standard emphasizes lowering the total cost of power without limiting consumption by opting to lower electricity use during peak hours. The previous problem necessitates the development and growth of a flexible and mobile technology that meets the needs of a wide variety of customers while preserving the general energy balance. In order to replace a partial load decrease in a controlled manner, smart energy management systems are designed, according to the preferences of the user, for the situation of a full power loss in a particular region. Smart Energy Management Systems incorporate cost-optimization methods based on human satisfaction with sense input features and time of utilization. In addition to developing an Internet of Things (IoT) for data storage and analytics, reliable LoRa connectivity for residential area networks is also developed. The proposed method is named as LoRa_bidirectional gated recurrent neural network (LoRa_ BiGNN) model which achieves 0.11 and 0.13 of MAE, 0.21 and 0.23 of RMSE, 0.34 and 0.23 of MAPE for heating and cooling loads

Fabrication and surface functionalization of electrospun polystyrene submicron fibers with controllable surface roughness

Polystyrene (PS) submicron fibers of 14 wt% concentration were fabricated by electrospinning using dimethylformamide (DMF)–tetrahydrofuran (THF) solvent system. The surface morphology of PS fibers was modified from smooth to rough by changing the mixing ratio of DMF and THF in the spinning solution. The electrospun PS fibers with smooth and rough surfaces were then amidomethylated by treating with N-hydroxymethyl-2-chloroacetamide. PS fibers with higher roughness incorporated more amidomethyl functional groups on their surface, as confirmed by XPS analysis. This observation was further supported by BET adsorption isotherm results which showed a significant increase in specific surface area of rough PS electrospun fibers. Interestingly, amidomethylation has altered rough electrospun PS submicron fibers from extremely hydrophobic to hydrophilic. These chemically modified electrospun PS fibers with controllable surface roughness and wettability may be utilized as a carrier for proteins, mainly enzymes and antibodies, by covalent linkage through amino groups attached to their surface

Synthesis of aryl β-ketoesters by opening of aryl epoxides with ethyl diazoacetate catalyzed by BF₃OEt₂

356-358A facile synthesis of 4-aryl substituted β-ketoesters from the reaction aryl epoxides with ethyl diazoacetate, in presence of BF3OEt2 has been described with excellent yields