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\"This reference text presents the state-of-the-art in edge computing, its primitives, devices and simulators, applications, and healthcare-based case studies. The text provides integration of blockchain with edge computing systems and integration of edge with Internet of Things (IoT) and cloud computing. It will facilitate the readers to setup edge-based environment and work with edge analytics. It covers important topics including cluster computing, fog computing, networking architecture, edge computing simulators, edge analytics, privacy-preserving schemes, edge computing with blockchain, autonomous vehicles, and cross-domain authentication. Aimed at senior undergraduate, graduate students and professionals in the fields of electrical engineering, electronics engineering, computer science, and information technology, this text: Discusses edge data storage security with case studies and blockchain integration with edge computing system. Covers theoretical methods with the help of applications, use cases, case studies, and examples. Provides healthcare real-time case studies are elaborated in detailed by utilizing the virtues of homomorphic encryption. Discusses real-time interfaces, devices, and simulators in detail\"-- Provided by publisher.

The book gives a comprehensive overview of the evolving landscape of cyber-physical systems (CPS) with a primary focus on security challenges and innovative solutions. CPS, encompassing a wide array of applications from e-Health to smart grid and industry automation, is explored in depth through eight edited reviews. The book starts with an exploration of various threat detection and prevention techniques in IoT environments, followed by discussions on security in smart grid cyber-physical systems, and the integration of cyber-physical systems with game theory. It also covers important topics such as cyber-physical systems in healthcare, augmented reality challenges, network and computer forensic frameworks, and a review of industrial critical infrastructure perspectives. The journey from traditional data warehouses to data lakes is thoroughly examined, shedding light on the evolution of data storage methods. The final chapter explains intrusion detection in industrial critical infrastructure, reviewing feature selection and classification models. By navigating through these topics, the book equips readers with a comprehensive understanding of cybersecurity challenges and solutions in an era of automation and IoT technologies. This book is intended for a diverse readership, including professionals, researchers, and technology enthusiasts keen on exploring the intricacies of CPS, IoT security, data storage evolution, and industrial infrastructure protection. Key Features: Analytical insights into cyber-physical systems security. Thorough exploration of threat detection and prevention techniques. Application-focused chapters covering smart grid, healthcare, and more. Integration of game theory and augmented reality in cyber-physical systems. Comprehensive overview on network and computer forensic frameworks. Readership Computer science students; Cybersecurity graduates and trainees; academics, researchers and industry professionals interested in understanding and utilizing cyber-physical systems.

Future rice (Oryza sativa) crops will likely experience a range of growth conditions, and root architectural plasticity will be an important characteristic to confer adaptability across variable environments. In this study, the relationship between root architectural plasticity and adaptability (i.e. yield stability) was evaluated in two traditional × improved rice populations (Aus 276 × MTU1010 and Kali Aus × MTU1010). Forty contrasting genotypes were grown in direct-seeded upland and transplanted lowland conditions with drought and drought + rewatered stress treatments in lysimeter and field studies and a low-phosphorus stress treatment in a Rhizoscope study. Relationships among root architectural plasticity for root dry weight, root length density, and percentage lateral roots with yield stability were identified. Selected genotypes that showed high yield stability also showed a high degree of root plasticity in response to both drought and low phosphorus. The two populations varied in the soil depth effect on root architectural plasticity traits, none of which resulted in reduced grain yield. Root architectural plasticity traits were related to 13 (Aus 276 population) and 21 (Kali Aus population) genetic loci, which were contributed by both the traditional donor parents and MTU1010. Three genomic loci were identified as hot spots with multiple root architectural plasticity traits in both populations, and one locus for both root architectural plasticity and grain yield was detected. These results suggest an important role of root architectural plasticity across future rice crop conditions and provide a starting point for marker-assisted selection for plasticity.

Software Effort estimation (SEE) is a vital task for project management as it is essential for resource allocation and project planning. Numerous algorithms have been investigated for forecasting software effort, yet achieving precise predictions remains a significant hurdle in the software industry. To achieve optimal accuracy, machine learning algorithms are employed. Remarkably, Random Forest (RF) algorithm produced better accuracy when compared with various algorithms. In this paper, the prediction is extended by increasing the number of trees and Improved Random Forest (IRF) is implemented by including three decision techniques such as residual analysis, partial dependence plots and feature engineering to improve prediction accuracy. To make improved random forest to be adaptive, it is further extended in this paper by integrating three techniques such as: Bayesian Optimization with Deep Kernel Learning (BO-DKL) to adaptively set hyperparameters, Time-Series Residual Analysis to detect autocorrelation patterns among model error, and Explainable AI techniques Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) to improve feature interpretability. This Improved Adaptive Random Forest (IARF) mutually contributes to a comprehensive evaluation and improvement of accuracy in prediction. Metrics used for evaluation are Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-Squared, Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE) and Prediction Interval Coverage Probability (PICP). Overall, the improved adaptive RF model had an average improvement ratio of 18.5% on MAE, 20.3% on RMSE, 3.8% on R 2 , 5.4% on MAPE, 7% reduction in MASE and a 3–5% improvement in PICP across all data sets compared to the Random Forest model, with much improved prediction accuracy. These findings validate that the combination of adaptive learning methods and explainability-based adjustments considerably improves accuracy of software effort estimation models and facilitates more trustworthy decision-making in software development projects.

Artificial Intelligence is a superset of Machine Learning and Deep learning, used to build intelligent systems that can solve problems. Software Effort Estimation is used to predict the number of hours of work required to complete the task. It is difficult and a challenging task to forecast Software Effort in the project during initial stages, due to several uncertainties. Software Effort Estimation helps in planning, scheduling, budgeting a project. Various experiments were proposed to predict effort alike expert judgment, analogy based estimations, regression estimations, classification approaches, deep learning algorithms. In this paper, comparison of deepnet, neuralnet, support vector machine and random forest algorithms were carried out and the results show that random forest outperforms other algorithms because of its robustness and capacity to handle large datasets. Evaluation metrics deliberated are Mean Absolute Error, Root Mean Squared Error, Mean Square Error and R-Squared.

Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

Piperazinium bis (trifluoroacetate) organic single crystal has been grown by solution growth method for the first-time reported in the literature. Transparent piperazinium bis (trifluoroacetate) single crystal with dimension of 11 mm × 9 mm × 5 mm was collected after a 20 days growth period. The new crystal structural parameters (unit cell & space group) were ensured by single crystal XRD. The crystalline perfection of PTFA crystal was characterized using powder XRD, UV–Visible-NIR, FTIR, TG–DTA, chemical etching, dielectric, Vickers microhardness, and Z -scan analysis. The chemical etching study investigation has shown that the PTFA crystal has less defects. The electrical properties of PTFA crystal have a low density of defects and a low value of dielectric loss found from the dielectric studies. Intermolecular interaction of PTFA is identified by the Hirshfeld surface analysis. The third harmonic generation study was carried out to investigate centrosymmetric nature of PTFA crystal. In order to find out the nonlinear susceptibility ( χ (3) ), Z -scan analysis was used.

Quercetin (QCT), a naturally occurring flavonoid has a wide array of pharmacological properties such as anticancer, antioxidant and anti-inflammatory activities. QCT has low solubility in water and poor bioavailability, which limited its use as a therapeutic molecule. Polymeric micelles (PMs) is a novel drug delivery system having characteristics like smaller particle size, higher drug loading, sustained drug release, high stability, increased cellular uptake and improved therapeutic potential. In the present study, we have formulated and characterized mixed PMs (MPMs) containing QCT for increasing its anticancer potential. The MPMs were prepared by thin film hydration method, and their physicochemical properties were characterized. The in vitro anticancer activity of the MPMs were tested in breast (MCF-7 and MDA-MB-231, epithelial and metastatic cancer cell lines, respectively), and ovarian (SKOV-3 and NCI/ADR, epithelial and multi-drug resistant cell lines, respectively) cancer. The optimal MPM formulations were obtained from Pluronic polymers, P123 and P407 with molar ratio of 7:3 (A16); and P123, P407 and TPGS in the molar ratio of 7:2:1 (A22). The size of the particles before lyophilization (24.83±0.44 nm) and after lyophilisation (37.10±4.23 nm), drug loading (8.75±0.41%), and encapsulation efficiency (87.48±4.15%) for formulation A16 were determined. For formulation A22, the particle size before lyophilization, after lyophilization, drug loading and encapsulation efficiency were 26.37±2.19 nm, 45.88±13.80 nm, 9.01±0.11% and 90.07±1.09%, respectively. The MPMs exhibited sustained release of QCT compared to free QCT as demonstrated from in vitro release experiments. The solubility of QCT was markedly improved compared to pure QCT. The MPMs were highly stable in aqueous media as demonstrated by their low critical micelle concentration. The concentration which inhibited 50% growth (IC ) values of both micellar preparations in all the cancer cell lines were significantly less compared to free QCT. Both the MPMs containing QCT could be used for effective delivery to different type of cancer and may be considered for further development.

1,2,3-Benzotriazole 4-chloro-2-Nitrobenzoic acid (BTPCZ) single crystal was successfully grown by conventional slow evaporation solution technique (SEST) using methanol as the solvent. BTPCZ single crystal was grown for the first time in the literature. The crystal system and space group of the BTPCZ single crystal were identified by single crystal X-ray diffraction (SXRD) analysis. The BTPCZ crystal was subjected to powder X-ray diffraction (PXRD) to endorse the crystalline quality of the material. The UV–Visible-NIR study exposes the transparency and cut-off wavelength of the BTPCZ crystal. The vibrational assignments and their functional groups were identified from the FTIR and FT-Raman analyses. The thermal properties such as thermal stability, melting point, and decomposing point were examined by Thermogravimetric/Differential thermal analysis (TG–DTA). To understand the intermolecular interaction of crystal packing, the BTPCZ material was investigated by Hirshfeld surface analysis and it was calculated that the molecular H–H interactions are of higher value compared to other interactions of the total molecules. The mechanical strength was investigated by Vickers microhardness tester. The frequency dependent dielectric properties of the crystals were investigated. To determine the appropriateness of the grown crystal for the high-power laser application, laser damage threshold (LDT) analysis has been carried out by Nd: YAG laser of wavelength of 1064 nm. The third-order nonlinear optical properties like refractive index ( n 2 ), absorption co-efficient ( β ) and susceptibility (χ (3) ) were studied using Z-scan technique at 632.8 nm of He–Ne laser.

Medical records generated in hospitals are treasures for academic research and future references. Medical Image Retrieval (MIR) Systems contribute significantly to locating the relevant records required for a particular diagnosis, analysis, and treatment. An efficient classifier and effective indexing technique are required for the storage and retrieval of medical images. In this paper, a retrieval framework is formulated by adopting a modified Local Binary Pattern feature (AvN-LBP) for indexing and an optimized Fuzzy Art Map (FAM) for classifying and searching medical images. The proposed indexing method extracts LBP considering information from neighborhood pixels and is robust to background noise. The FAM network is optimized using the Differential Evaluation (DE) algorithm (DEFAMNet) with a modified mutation operation to minimize the size of the network without compromising the classification accuracy. The performance of the proposed DEFAMNet is compared with that of other classifiers and descriptors; the classification accuracy of the proposed AvN-LBP operator with DEFAMNet is higher. The experimental results on three benchmark medical image datasets provide evidence that the proposed framework classifies the medical images faster and more efficiently with lesser computational cost.