Edge detection determines and localizes unique breaks or continuity breaks in a picture. This may be accomplished by recognizing and localizing different interv

The most prominent source of drinking water is groundwater, followed by lakes and reservoirs. Hydrological parameters like temperature, dissolved oxygen, pH, conductivity, ORP, and turbidity often change due to waste dumping into natural drinking water sources, particularly in densely populated areas. As a result, the water quality must be tested before public consumption to ensure healthy living in society. This research collected water samples from 129 wells in the Kanchipuram district in Tamil Nadu, India. An efficient integrated machine-learning-based prediction model has been proposed and modeled to determine the groundwater quality index (GQI). Several machine learning models were used to predict the water’s quality, including the naïve Bayes model, the KNN classifier, and the XGBoost classifier. Water quality predictions in 2024 were made using a combination of classification algorithms and models based on long short-term memory (LSTM) neural networks. The projected water quality characteristics were analyzed using geographical information system (GIS) technology to better understand and visualize the results. The XGBoost classifier model outperforms prior findings in the literature, with an accuracy of roughly 94.6%. The classification and prediction model was validated using collected and tested current data samples from a selected well. The findings were accurate within the 5% error range, promoting sustainability.

In this article, a novel technique has been developed to measure the flank wear in turning nickel based super alloy Nimonic C263. The MSER (maximally stable extremal regions) and deep pattern network (DPN) has been applied on the worn out insert for the measurement of flank wear. The combination of MSER algorithm along with DPN is termed out as FlankNet in this article. The flank wear measured by tool maker’s microscope was compared with the values measured by FlankNet and the average percentage error of 3.03% was noted. As the Nimonic C263 is highly difficult-to-machine materials owing to high work hardening tendency and lower thermal conductivity, rapid tool wear was found and it would affect the dimensional and geometrical accuracy of machined parts. Therefore, the influences of the machining parameters on tool wear were also studied. The experimental trails were conducted based on L9 orthogonal array.

In this present paper, the machine learning approach is used to optimize, model, and predict the factors during drilling Nimonic C263 under dry mode. Nimonic C263 is tough to machine aero alloys, and it is required to find a predictive model and to optimize the factors in drilling this alloy before the actual machining process. It helps to avoid the actual machining cost and material cost. Experimental trails are planned based on Taguchi analysis, and L₂₇ orthogonal array was chosen. Speed, feed, and approach angle of drill were considered as controlling factors, and cutting force and surface roughness were considered as responses. The feed forward neural network (FFNN) was used to develop a predictive model. The prediction capability was validated with a predictive model developed by Taguchi analysis. Furthermore, ANOVA (analysis of variance) analysis was done to find out the most influence factor on the responses.

Surface roughness of specimens is an important area of research since it influences the performance of machined parts. Meanwhile, employing a vision system to judge the roughness of the machined surface of specimens via captured images acquired from the specimen is an innovative and extensively used method. In this investigation, a vision system is used to capture the SEM images of the machined surface. The two‐dimensional images of the machined surface of the Nimonic263 alloy are used to approximate the profile of the surface of specimens in finish turning. Surface roughness was detected in simulated images of specimens in a variety of machining conditions using the imaging technology. In this research work, the surface texture is extracted using a technique that combines 2D surface images and wavelet transform approach. The 2D wavelet transform has the capability to disintegrate a machined surface image into multiresolution depiction for several surface characteristics and can be utilized for surface evaluation. The difference in the histogram frequency of an illuminated region of interest (ROI) from turned surface images was analyzed to aid in the evaluation of surface roughness with an average prediction error of less than 3.2%.

This paper focuses on the initial assessment of cardiac abnormalities with heart murmurs. At times monitoring the patient Heart auscultations and murmur using a conventional stethoscope may lead to false diagnosis since they are low frequency and amplitude signal, as this procedure requires highly skilled expertise. This paper provides a solution by detecting the abnormality of heart sound earlier and easier by medical expertise. In this paper, an embedded acoustic stethoscope has been designed and implemented to provide a platform for the detection of cardiac disorders using computer aided diagnosis (CAD) of cardiac sound. This handheld device has the capability to diagnose heart beats in real-time which employs signal conversion of analog acoustic signals into digital signal and at the same time can be displayed on a PC using MATLAB GUI for visual elucidation thereby enabling critical analysis of the interpreted data. In addition, a pulse rate monitoring circuit is also incorporated to ensure proper blood circulation.