The state of cellulosic solid kraft paper (CSKP) insulation, to a large extent, is an indication of a transformer’s health. It not only reflects the condition of transformer but also diagnose its residual life. The quantity of 2-furfuraldehyde (2-FAL), carbon dioxide (CO₂), and carbon monoxide (CO) dissolved in the transformer oil are useful diagnostic indicators to predict the state of the CSKP insulation. In this work, the current physical state of the CSKP is determined with the help of easily measurable parameters, like temperature, moisture, and the aging time. Here, the degree of deterioration of CSKP insulation has been determined using an integrated insulation health assessment system. This technique integrates a two-stage system comprising of a neural network (NN) model followed by a Smart Life Prediction Approach (SLPA). A thermo-moisture-aging multi-layer feed-forward NN model has been developed to predict the concentrations of 2-FAL, CO₂, and CO, which are further correlated to estimate the Degree of Polymerization (DP) values adopting an SLPA. The advantage of the proposed integrated system is that it provides an alternative means of paper health assessment based on Dissolved Gas Analysis (DGA) without estimating dissolved gas concentrations in oil, thereby avoiding the use of sophisticated measuring instruments. The optimal configuration of the NN model has been achieved at minimum iterations with an average cross-validation mean square error of 3.78 × 10⁻⁷. The proposed system thereby avoids destructive and offline measurement of DP and facilitates real-time condition monitoring of oil-immersed transformers. The test results of the developed system show considerable reliability in determining insulation health using easily measurable parameters. Furthermore, the system’s performance is compared with reported work and has been found to provide encouraging outcomes.

Advancement in network technology has vastly increased the usage of the Internet. Consequently, there has been a rise in traffic volume and data sharing. This has made securing a network from sophisticated intrusion attacks very important to preserve users’ information and privacy. Our research focuses on combating and detecting intrusion attacks and preserving the integrity of online systems. In our research we first create a benchmark model for detecting intrusions and then employ various combinations of feature selection techniques based upon ensemble machine learning algorithms to improve the performance of the intrusion detection system. The performance of our model was investigated using three evaluation metrics namely: elimination time, accuracy and F1-score. The results of the experiment indicated that the random forest feature selection technique had the minimum elimination time, whereas the support vector machine model had the best accuracy and F1-score. Therefore, conclusive evidence could be drawn that the combination of random forest and support vector machine is suitable for low latency and highly accurate intrusion detection systems.

The rapid growth of technologies like the Internet of Things (IoT) has thrown newer challenges to resource management, making it one of the crucial tasks in dynamic application scenarios of IoT. Service-Oriented Architecture (SOA) plays an important role in this respect by proffering reference architecture towards better positioning of infrastructural software and hardware components, mostly comprehending from a service-oriented perspective. In this direction, we propose Cone Model that deals with the performance and handling issues with SOA system. Service integration is an all-important feature of SOA that supports communication and interaction between multiple service components (Service Containers). In addition to discussing the related work for critical resource allocation algorithms, this paper proposes Cone Model for assigning an available set of resources to requested services, explained through mathematical expressions and algorithms of the model. These algorithms justify how different services from various devices in IoT can be provisioned on the adoption of this proposed cone model. It is illustrated through the estimated utilization of resources by the services. The proposed model aligns with the applications in multifarious domains, primarily in the IoT and in areas of resource sharing for cloud computing and massive data centers.

Accurate assessment of transformer insulating paper is vital for reliable operation and optimal transformer management, with the Degree of Polymerization (DP) serving as a primary indicator of insulation health. Direct DP measurement is often impractical, prompting this study to explore machine learning models for predicting DP using 2-Furfuraldehyde (2-FAL), a cellulose degradation byproduct measurable in transformer oil. This approach classifies insulation into four categories—Fresh (DP: 700–1200), Lightly Aged (DP: 450–700), Moderately Aged (DP: 250–450), and Worstly Aged (DP < 250)—based on DP values, offering a streamlined alternative to conventional multi-gas diagnostic methods. Supervised machine learning algorithms were developed using IEEE C57.104-2019 standard data, employing regression (Linear Regression, Polynomial Regression, Random Forest Regressor) to predict continuous DP and classification (Logistic Regression, Support Vector Machine with RBF kernel, Random Forest Classifier) to categorize insulation condition. Model performance was evaluated using regression metrics (Mean Squared Error, Mean Absolute Error, R² Score) and classification metrics (accuracy, precision, recall, F1-score). The Random Forest Regressor (R²: 0.894) and Classifier (accuracy: 0.925) demonstrated superior performance, enabling precise, non-invasive DP estimation and condition assessment. These findings highlight the efficacy of 2-FAL-based machine learning models for transformer health monitoring, facilitating predictive maintenance and enhancing operational reliability.