K. Rajesh

There are urgent problems in urban sustainability monitoring to process huge heterogeneous data streams of IoT sensors, building management systems, and environmental monitors and ensure analytical accuracy in real time. The proposed study presents Count-MinSketch with SHAP-Driven Adaptive Sketch Optimization (SDASO), which is a new probabilistic data structure that is augmented with explainable AI to support real-time smart city sustainability assessment. The suggested framework combines space-efficient approximation of Count-Min Sketch with SHAP (SHapley Additive exPlanations) feature attribution that dynamically chooses parameters of the sketch according to the features of data streams. When summarized using Count-Min Sketch, Heavy Hitters (Lossy Counting), the Sliding Window Algorithm, and ADWIN (Adaptive Windowing), it is seen that it performs better when compared to other algorithms in terms of computation and accuracy. The SDASO model had an outstanding approximation quality of 98% and a small error rate (δ=0.012) and approximation error of 0.003 with a root mean squared error of 1375.92. The SHAP integration produced impressive model explainability of R² = 0.9957 and an effectiveness score of 0.1223 and was capable of selecting adaptive features through 9 indicators of critical sustainability. Evaluation of the system performance indicated exceptional computational performance: throughput was 367.04 records/second, processing latency was 2.72 ms, CPU utilization was only 2%, and the total memory footprint was 0.095 MB operating with 2,673 building records and 8 partitions with a sketch utilization of 47.46%. The findings indicate that SDASO is able to deliver interpretable, resource-effective, and highly accurate real-time sustainability monitoring of large-scale smart city infrastructure to support the realization of data-driven urban environmental governance and policy optimization.

This study introduces a hybrid framework combining Betweenness Centrality (BC) and a Multi-Objective Evolutionary Algorithm based on Decomposition with Differential Evolution (MOEA/D-DE) for intelligent sustainable manufacturing monitoring and optimization. The presented model overcomes major issues in big data analytics of complex manufacturing networks by focusing on accuracy and performance of network analysis simultaneously. The conventional algorithms of BC have scaling issues when dealing with large manufacturing datasets; the hybrid architecture will eliminate those obstacles by using evolutionary multi-objective optimization. The model has outstanding performance with an approximation error at 0.012187 with a mean squared error at 0.0017, with a 3990.91% improvement in BC accuracy over baseline methods. The MOEA/D-DE has the best convergence (0.99) and optimized sampling rate (0.1549) and depth limit (2); the error rate is the lowest (0.0069). The efficiency of the approach is confirmed by the system-level measures: the CPU time of 0.0013 seconds, CPU utilization of 74 percent, processing time of 0.094 seconds, and ultra-low latency (16.87 ms). The platform has 10,119.46 records/second processing throughput, and memory consumption is relatively low (1255 MB). The model is highly fault tolerant, and its performance is equally scalable (8.2/10) with networks that have 110 nodes and 34 edges. These findings show that there is strong potential for real-time optimization of manufacturing networks to allow the use of data to make decisions that are sustainable in manufacturing. Combining evolutionary algorithms and centrality can present a powerful method of discovering the critical nodes and bottlenecks within the manufacturing systems to enable proactive sharing of resources and process optimization in Industry 4.0 systems.

Early prediction of cyclones helps reduce deaths and damage to properties around the world. With the advancement in satellite imaging technology, it is possible to obtain atmospheric images and remotely sensed objects such as cyclones using different modalities. Such images are very useful for weather prediction, specifically for forecasting cyclonic storms. However, it has a few limitations for achieving higher accuracy of prediction with minimal time. A novel technique known as proposed Czekanowsky Dice Hypergraphic Extended Kalman Momentum Filterization based Bivariate Correlative Deep Structure Learning classification (CDHEKMF-BCDSLC) is introduced to achieve better cyclone prediction accuracy. Initially, the multiple satellite images are gathered from cyclone datasets. The proposed technique comprised in four processing steps namely segmentation, preprocessing, feature extraction and classification. At first, Czekanowsky dice Intensity threshold- based Interval Hypergraph (CDIT-IH) model using Segmentation process. It minimized to perform cyclone prediction time. After that, the Preprocessing is applied to novel invariant extended Kalman momentum filter is designed for improved image contrast. Next, Bivariate correlative Spatio-temporal Feature extraction is performed on each pixel intensity of the image to extract features over time and location. Finally, multidimensional deep belief network classification model is applied for accurate cyclone prediction. A multidimensional deep belief network classification model is a machine learning technique that consists of multiple layers for learning the given input (i.e. Spatio-Temporal features). This process increases the prediction accuracy. Experimental results reveal that the proposed technique noticeably predicts cyclone conditional variants by means of prediction accuracy, precision, recall, F-measure and prediction time with respect to the number of cyclone images. The Quantitative results are obtained that proposed technique achieves 6% of better accuracy with 17% of minimum prediction time, 5% of improved precision and f- measure and 4% of recall while compared to the state-of-the-art methods.

Hypergraph-based Algorithm for Segmentation of Weather Satellite Imagery

A Novel Approach for Color Image, Steganography Using Nubasi and Randomized, Secret Sharing Algorithm

Medical monitoring system using the pervasive computing has the potential to improve the quality of health care of an individuals. It's repeatedly represented as one of the research areas. Our model provides an approach of mobile computing technologies for improving the communication among the patients, physicians and other health care organizations. The IRS will provide continuous and long term approach in-terms of monitoring and integrating the patients with their everyday activities and without restricting the mobility and also provides responses to the patients and is based on the presence sensor signal.