RMD Engineering College

In this paper, an sustainable imperfect production system for shortages and complementary products with advertisement-price- sales return and green degree dependent demand and carbon emission is considered. The green production lot size, shortages and optimal pricing are three decision variables in third order equation. A provision has been made to sell the defective items. In literature, only two decision variables are considered in imperfect production system. In this model, three decision variables that is green production lot size, shortages and pricing are considered in imperfect production system. Price-break-even point is determined, the law of demand is verified, and highest possible profit is identified from the three alternative prices. The price break-even of the product per unit is 3299.4834 and in this stage neither profit nor loss for the concern. Taking from a third-order equation including three price variables, optimum pricing solution shows that a price level of 4079.5108 yields the highest profit of 102184.49. The objective of this model is to determine the green production lot size, shortages, and pricing for overall maximum profitability. The mathematical formulation's validity is shown by a numerical experiment, and the effects of inventory components on overall profit and managerial insights are examined using sensitivity analysis. The validation of the model's results was programmed utilizing Microsoft Visual Basic 6.0.

The present study investigates the development and electromagnetic shielding performance of environmentally sustainable PVA-based hybrid composites reinforced with hennep microfibres and biocarbon particles. Composite films were fabricated by incorporating a constant volume fraction of hennep microfibre with varying biocarbon particle contents in a PVA matrix. The mechanical behaviour, dielectric characteristics, and electromagnetic interference (EMI) shielding effectiveness of the composites were systematically evaluated. Mechanical testing revealed a significant improvement in tensile strength from approximately 32 MPa for neat PVA (F1) to nearly 50 MPa for the F5 composite, indicating about 56

A Mobile Ad Hoc Network (MANET) decentralized network requires an effective packet-transmission routing protocol. In MANET, routing protocols are vital in managing numerous resource-constrained nodes. As the network grows, increased overhead and congestion cause unpredictable routing, packet loss, and fluctuations. In this case, ensuring secure routing and efficient data management remains a significant challenge. We proposed a Trust is Valued via Fuzzy Logic Optimization (TB-FL) to address this. The proposed TB-FL is designed with trust-based node evaluation, which works efficiently to handle dynamic MANET architecture by ensuring secure packet transmission. Fuzzy Logic Optimization assesses network path trust levels, enhancing reliability. The TB-FL model continuously maintains trust assessments, adapting to dynamic network conditions. To determine the performance of the proposed TB-FL, a comparison work is carried out with the existing Adaptive Beaconing Strategy Based on the Fuzzy Logic Scheme for Geographical Routing (AFB-GPSR), Enhanced Virtual Cord Protocol (EVCP) and Optimized Control Interval-Optimized Link State Routing-Based Efficient Routing (OCI-OLSR) Mechanisms. Compared to existing approaches, the proposed ABC performs better with 100 nodes, including 99

Diabetes mellitus is a chronic and progressive metabolic syndrome that affects millions worldwide, with growing prevalence due to sedentary lifestyles, poor dietary habits, and genetic predisposition. Early diagnosis and effective management are essential to minimizing the risks of complications, like neuropathy, cardiovascular diseases, and kidney failure. Nonetheless, conventional diagnostic approaches often fail to generalize dietary rules that may not meet the specific nutritional requirements of individual patients, leading to suboptimal results. Accordingly, this research develops a new deep learning (DL) framework for diabetic prediction and personalized diet recommendation. Primarily, the input data undergoes a normalization procedure, which is performed by employing Stopp Normalization to standardize the input. Next, an optimal feature subset is extracted employing the Spider-Tailed Kite Optimization Algorithm (Spi-TKOA), which is the fusion of Spider-Tailed Horned Viper Optimization (STHVO) and Black‑winged Kite Algorithm (BKA). Thereafter, data augmentation is done by exploiting Bootstrapping. Lastly, diabetic prediction is performed by utilizing the Transformer Squeeze-and-Excitation Residual Network (TransSEResNet), and the hyperparameters are tuned by exploiting Spi-TKOA. If diabetes is predicted, a balanced diet is recommended to support insulin sensitivity, control blood sugar levels, and prevent complications. The proposed Spi-TKOA_TransSEResNet attained an accuracy of 93.987

Nowadays, the Industrial Internet of Things (IIoT) is a quickly evolving advanced technology with the potential to digitalize and connect several sectors for enormous business prospects and the emergence of global networks. IIoT has been applied in various fields like transportation, logistics, energy utilities and aviation, oil and gas, manufacturing, mining, and metals. Though IIoT offers good opportunities for the growth of various industrial applications, they are expected to face cyberattacks and requires stronger security mechanisms. The intrusion detection system (IDS), which examines network traffic and recognizes network behaviour, serves as a primary security mechanism to secure IIoT applications from attacks. In recent times, the application of machine learning (ML) as well as deep learning (DL) have proven to reduce numerous security risks and improve the intrusion detection performance. This paper develops an Intrusion Detection System for Industrial Internet of Things Using Ensemble Learning and Bio-Inspired Optimization (IDSIIoT-ELBIO) Model. The paper aims to develop an efficient IDS for IIoT environments to enhance security, reliability, and real-time threat mitigation. Initially, the standard scaling method is leveraged in the data standardization step to transform the input data into beneficial formats. For the feature subset selection, the proposed IDSIIoT-ELBIO model designs a grasshopper optimization algorithm (GOA) to select the optimal features. Furthermore, the ensemble of three classifiers, such as variational autoencoder (VAE), temporal convolutional network (TCN), and deep Q-network (DQN), has been deployed for intrusion detection operation. Eventually, the parameter fine-tuning method is mainly implemented by a harris hawk optimizer (HHO) algorithm. The effectiveness of the IDSIIoT-ELBIO model has been validated through comprehensive experimental analysis utilizing the benchmark dataset. The numerical outcome displays that the IDSIIoT-ELBIO model has improved performance and scalability under different measures over the existing approaches.