Dr. Babasaheb Ambedkar Technological University (DBATU) is a unitary, Maharashtra state Technological University in Lonere, Maharashtra, India. It is named after Babasaheb Ambedkar, a prominent Indian jurist, economist, politician and social reformer.
Using several industrial components as samples, this article attempts to 3D print finished products rather than merely initial versions—in every relevant field. Motor holder for CNC machines produced by additive manufacturing with PLA material. This paper describes design principles and approaches for fabricating Motor holder for CNC machine using 3D printing, an increasingly common place rapid prototyping technology. A simple fabrication technique of 3D-printed part has been used. This study gives an overview of the current methods for producing plastic parts using 3D printing techniques and validates the potential and difficulties of particle 3D printing techniques in the building of a plastic motor holder. This study outlines the potential for substituting the Ferrous or non-ferrous products by polymer products that are adequate in quality and have a reduced cost.
Due to the rapidly growing number of internet users, security continues to play a crucial role in today’s online world. To detect and identify intruders, many researchers have developed a variety of intrusion detection techniques. In the meantime, the detection accuracy of the current technologies was not acceptable. Here, Namib Beetle Migration Optimization-based Deep Stacked Auto Encoder (NBMO_DSAE) is introduced for Intrusion Detection (ID). Initially, input data is processed. Then Z-score Normalization (ZN) is utilized for normalizing input data and thereafter, feature fusion (FF) is conducted by Deep Belief Network (DBN).) with Jeffrey similarity. After that, the oversampling approach is exploited to carry out data augmentation (DA). At last, the network ID is accomplished using a Deep Stacked Auto Encoder (DSAE), which is trained by NBMO. NBMO is an assimilation of Namib beetle optimization (NBO) with Wild Geese Migration Optimization (GMO). Furthermore, in UNSWNB15, NBMO_DSAE acquired accuracy of 0.952, precision of 0.931, Recall of 0.943 and F1-Score using 0.938. For CICIDS, NBMO_DSAE achieved accuracy of 0.941, precision of 0.921, Recall of 0.931 and F1-Score 0.925 This method is scalable and can handle large volumes of data, making it suitable for Network Intrusion Detection.
Urban environmental management has come to regard air quality prediction as a pressing need, especially in Tier-2 cities that experience rapid growth, increasing traffic congestion, dust-based emissions, and scarce monitoring infrastructure, leading to high fluctuations in pollution levels. The complex interactions of pollutants in cities like Solapur cannot be effectively described using traditional statistical models due to their nonlinear nature, while sophisticated deep learning methods are often constrained by insufficient data availability and high computational requirements. This paper employs an Artificial Neural Network (ANN)-based model to forecast the Air Quality Index (AQI) of Solapur city using routinely measured pollutants, namely sulfur dioxide (SO2), nitrogen oxides (NOx), suspended particulate matter (SPM), and respirable suspended particulate matter (RSPM). Daily pollutant data spanning five years were preprocessed in accordance with Central Pollution Control Board (CPCB) guidelines and converted into AQI values. A multilayer perceptron ANN was trained and evaluated using standard performance measures, including mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), and the coefficient of determination (R2). The results show a strong agreement between observed and predicted AQI values across all monitoring stations, with MAE ranging from 0.136 to 3.7 and R2 values exceeding 0.98, indicating robust model performance under varying urban pollution conditions. The proposed framework emphasizes the role of dominant particulate pollutants, particularly SPM and RSPM, and aligns model predictions with CPCB AQI categories, providing a valid, intuitive, and computationally feasible approach to AQI prediction in data-limited urban environments to support timely public-health alerts and cost-effective air-quality management.
