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Since the first fully homomorphic encryption scheme was published in 2009, many papers have been published on fully homomorphic encryption and its applications. Machine learning is one of the most interesting applications and has drawn a lot of attention from researchers. To better represent and understand the field of Homomorphic Encryption in Machine Learning (HEML), this paper utilizes automated citation and topic analysis to characterize the HEML research literature over the years and provide the bibliometrics assessments for this burgeoning field. This is conducted by using a bibliometric statistical analysis approach. We make use of web-based literature databases and automated tools to present the development of HEML. This allows us to target several popular topics for in-depth discussion. To achieve these goals, we have chosen the well-established Scopus literature database and analyzed them through keyword counts and Scopus relevance searches. The results show a relative increase in the number of papers published each year that involve both homomorphic cryptography and machine learning. Using text mining of articles titles, we have found that cloud computing is a popular topic in this field, which also includes neural networks, big data, and the Internet of Things. The analysis results show that China, the US, and India have generated almost half of all the research contributions in HEML. The citation statistics, keyword statistics, and topic analyses give us a quick overview of the development of the field, which can be of great help to new researchers. It is also possible to apply our methodology to other research areas, and we see great value in this approach. [ABSTRACT FROM AUTHOR]

This paper analyzes the time to event data in the presence of collinearity. To address collinearity, the ridge regression estimator was applied in multiple and logistic regression as an alternative to the maximum likelihood estimator (MLE), among others. It has a smaller mean square error (MSE) and is therefore more precise. This paper generalizes the approach to address collinearity in the frailty model, which is a random effect model for the time variable. A simulation study is conducted to evaluate its performance. Furthermore, the proposed method is applied on real life data taken from the largest sample survey of India, i.e., national family health survey (2005–2006) data to evaluate the association of different determinants on infant mortality in India. [ABSTRACT FROM AUTHOR]

The increased demand for this form of food delivery has been expected to drastically alter restaurant patrons' dining habits. As people have been forced to stay indoors to prevent the virus from spreading, food delivery services over the internet are in high demand. As established in this study, the planned ideal is a good executive implementation for online meal delivery services. Food delivery services are rapidly growing in India, opening up several opportunities for a wide range of online food delivery (OFD) platforms while also generating a competitive commercial sector. Following that, the fuzzy technique for order performance by similarity to ideal solution method (FTOPSIS) is used to rank online food delivery (OFD) enterprises based on the characteristics chosen. In this paper, we study the present multi-criteria decision-analysis (MCDA) paradigm based on the fuzzy analytic hierarchy process (FAHP) and the fuzzy technique for order performance by similarity to ideal solution (FTOPSIS) method to achieve the goal. After that, a hierarchy multiple criteria decision-analysis (MCDA) model based on fuzzy sets theory is introduced to deal with the online food delivery Service in the chain system. The fuzzy analytic hierarchy process (FAHP) is a fuzzy set theory technique for generating criteria weights, which are then used to interpret expert phonological evaluation statements. [ABSTRACT FROM AUTHOR]

The rapid growth of electric vehicles in India necessitates more power to energize such vehicles. Furthermore, the transport industry emits greenhouse gases, particularly SO2, CO2. The national grid has to supply an enormous amount of power on a daily basis due to the surplus power required to charge these electric vehicles. This paper presents the various hybrid energy system configurations to meet the power requirements of the electric vehicle charging station (EVCS) situated in the northwest region of Delhi, India. The three configurations are: (a) solar photovoltaic/diesel generator/battery-based EVCS, (b) solar photovoltaic/battery-based EVCS, and (c) grid-and-solar photovoltaic-based EVCS. The meta-heuristic techniques are implemented to analyze the technological, financial, and environmental feasibility of the three possible configurations. The optimization algorithm intends to reduce the total net present cost and levelized cost of energy while keeping the value of lack of power supply probability within limits. To confirm the solution quality obtained using modified salp swarm algorithm (MSSA), the popularly used HOMER software, salp swarm algorithm (SSA), and the gray wolf optimization are applied to the same problem, and their outcomes are equated to those attained by the MSSA. MSSA exhibits superior accuracy and robustness based on simulation outcomes. The MSSA performs much better in terms of computation time followed by the SSA and gray wolf optimization. MSSA results in reduced levelized cost of energy values in all three configurations, i.e., USD 0.482/kWh, USD 0.684/kWh, and USD 0.119/kWh in configurations 1, 2, and 3, respectively. Our findings will be useful for researchers in determining the best method for the sizing of energy system components. [ABSTRACT FROM AUTHOR]

This paper deals with the mathematical modeling of the second wave of COVID-19 and verifies the current Omicron variant pandemic data in India. We also we discussed such as uniformly bounded of the system, Equilibrium analysis and basic reproduction number R 0 . We calculated the analytic solutions by HPM (homotopy perturbation method) and used Mathematica 12 software for numerical analysis up to 8th order approximation. It checked the error values of the approximation while the system has residual error, absolute error and h curve initial derivation of square error at up to 8th order approximation. The basic reproduction number ranges between 0.8454 and 2.0317 to form numerical simulation, it helps to identify the whole system fluctuations. Finally, our proposed model validated (from real life data) the highly affected five states of COVID-19 and the Omicron variant. The algorithm guidelines are used for international arrivals, with Omicron variant cases updated by the Union Health Ministry in January 2022. Right now, the third wave is underway in India, and we conclude that it may peak by the end of May 2022. [ABSTRACT FROM AUTHOR]

Neoteric biomedical, technological, and normative shifts have prompted care firms to establish clinical governance as a contrivance to assure high-quality service in an exceedingly intricate milieu. Web security is an epochal concern in the healthcare sector, although it has garnered scant attention since the inception of web applications. The necessity to provide adequate security for healthcare web applications (HWAs) cannot be exaggerated, as umpteen health agencies are contingent on them to carry out their operations. Every healthcare organization renders a humongous volume of data available online to practitioners, pharmacies, and patients. Researchers are continually endeavoring to ameliorate techniques to increase the security and longevity of HWAs. In this context, experts examined certain imperative security risks in HWAs to quantitatively evaluate them in the design phase and covered numerous facets of HWAs, along with their security attributes and risk factors. The authors have proposed a combined approach of fuzzy-based symmetric techniques, i.e., AHP-TOPSIS (Analytic Hierarchy Process–Technique for Order of Preference by Similarity to Ideal Solution), for the assessment of alternative HWAs, leveraging the multi-criteria decision-making (MCDM) approach. Ten consecutive HWAs from local hospitals in Uttar Pradesh, India, have been taken to estimate the security risk, incorporating this methodology to evaluate the priority of weightage and the impact of security attributes. Henceforth, the findings and methodology employed in this study can assist security practitioners in identifying and prioritizing the most influential risk factors to secure HWAs and encourage them to develop revamped or novel methods. [ABSTRACT FROM AUTHOR]

This work aims at providing a concise review of various agri-food models that employ fractional differential operators. In this context, various mathematical models based on fractional differential equations have been used to describe a wide range of problems in agri-food. As a result of this review, we found out that this new area of research is finding increased acceptance in recent years and that some reports have employed fractional operators successfully in order to model real-world data. Our results also show that the most commonly used differential operators in these problems are the Caputo, the Caputo–Fabrizio, the Atangana–Baleanu, and the Riemann–Liouville derivatives. Most of the authors in this field are predominantly from China and India. [ABSTRACT FROM AUTHOR]

Professional driving involves sitting in uncomfortable positions, navigating difficult terrain and roads, and occasionally conducting small repairs and other auxiliary transportation duties while at work for long periods. Drivers who engage in these activities may develop a variety of musculoskeletal disorders (MSDs). MSDs in professional drivers are accompanied by several risk factors. In this study, the various risk factors for MSD have been identified through the literature reviews, discussions with professional drivers, and consultations with ergonomics specialists. This study employed the ordinal priority approach (OPA), a multi-criteria decision-making (MCDM) technique, to rank the identified risk variables for MSD in order of importance. The same OPA method has also been used to identify the group of professional drivers who use eight different types of vehicles and are more likely to develop MSDs. The analyses findings show that the five main risk factors for MSDs among drivers are prolonged sitting, restricted posture, working hours, alcohol consumption, and uncomfortable seating. Additionally, among all drivers regarded as professionals, truck drivers are found to be the most at risk. For the study's conclusions to be validated, a sensitivity analysis was also carried out. The results of this study are anticipated to help formulate strategies for lowering these hazards through the ergonomic design of drivers' cabins by automobile OEMs (Original Equipment Manufacturers) and vehicle scheduling by concerned transportation organizations to reduce driver tiredness. [ABSTRACT FROM AUTHOR]

Rivers play a major role within ecosystems and society, including for domestic, industrial, and agricultural uses, and in power generation. Forecasting of suspended sediment yield (SSY) is critical for design, management, planning, and disaster prevention in river basin systems. It is difficult to forecast the SSY using conventional methods because these approaches cannot handle complicated non-stationarity and non-linearity. Artificial intelligence techniques have gained popularity in water resources due to handling complex problems of SSY. In this study, a fully automated generalized single hybrid intelligent artificial neural network (ANN)-based genetic algorithm (GA) forecasting model was developed using water discharge, temperature, rainfall, SSY, rock type, relief, and catchment area data of eleven gauging stations for forecasting the SSY. It is applied at individual gauging stations for SSY forecasting in the Mahanadi River which is one of India's largest peninsular rivers. All parameters of the ANN are optimized automatically and simultaneously using the GA. The multi-objective algorithm was applied to optimize the two conflicting objective functions (error variance and bias). The mean square error objective function was considered for the single-objective optimization model. Single and multi-objective GA-based ANN, autoregressive and multivariate autoregressive models were compared to each other. It was found that the single-objective GA-based ANN model provided the best accuracy among all comparative models, and it is the most suitable substitute for forecasting SSY. If the measurement of SSY is unavailable, then single-objective GA-based ANN modeling approaches can be recommended for forecasting SSY due to comparatively superior performance and simplicity of implementation. [ABSTRACT FROM AUTHOR]

Conventional Noise Clustering (NC) algorithms do not consider any spatial information in the image. In this study, three algorithms have been presented, Noise Local Information c-means (NLICM) and Adaptive Noise Local Information c-Means (ADNLICM), which use NC as the base classifier, and Noise Clustering with constraints (NC_S), which incorporates spatial information into the objective function of the NC classifier. These algorithms enhance the performance of classification by minimizing the effect of noise and outliers. The algorithms were tested on two study areas, Haridwar (Uttarakhand) and Banasthali (Rajasthan) in India. All three algorithms were examined using different parameters (distance measures, fuzziness factor, and δ). An analysis determined that the ADNLICM algorithm with Bray–Curtis distance measures, fuzziness factor m = 1.1, and δ = 106, outperformed the other algorithm and achieved 91.53% overall accuracy. The optimized algorithm returned the lowest variance and RMSE for both study areas, demonstrating that the optimized algorithm works for different satellite images. The optimized technique can be used to categorize images with noisy pixels and heterogeneity for various applications, such as mapping, change detection, area estimation, feature recognition, and classification. [ABSTRACT FROM AUTHOR]

Due to advances in information and communication technology, e-teaching has become increasingly popular and is in high demand by educational organizations. During the lockdown period of COVID-19 especially, e-teaching provided prior solutions to address the pressing need for monitoring students' learning progress. However, in many developing countries, it is apparent that a wide variety of issues are related to e-teaching adoption. Although the implementation issues associated with e-teaching have been addressed in the existing research literature and in practice for many years, from the available research, the evaluation of e-teaching adoption criteria and ranking using fuzzy theory has been ignored. Therefore, the present research aims to evaluate and rank the criteria for e-teaching adoption through Fuzzy Delphi and Fuzzy TOPSIS. A total of four criteria and twelve sub-criteria for e-teaching adoption were determined based on a systematic literature review and professors' opinions in India. In addition, the Fuzzy Delphi method was employed to finalize the criteria, and the Fuzzy TOPSIS method was employed for ranking the alternatives. The assessment results showed that among the identified alternatives, the "share the technology with other organizations" and "course integration with technology" were the top-ranked alternatives for improving e-teaching adoption. An understanding of these conceptual alternatives can encourage the adoption of e-teaching in educational organizations. [ABSTRACT FROM AUTHOR]

This study examines the dynamic interaction between oil, natural gas, and prices with Indian economic policy uncertainty (EPU). The study finds that gold prices and industrial production are fundamental drivers of Indian economic policy uncertainty in both the short and long runs, using a dynamic autoregressive distributed lag (ARDL) model with monthly data ranging from January 2003 to July 2020. Gold prices are positively related to the Indian EPU, while industrial production is negatively related to it. Thus, investors in the Indian economy should use gold as a hedge for portfolio diversification and as a safe haven during an economic crisis. We also find a significant positive interconnection between gold prices and crude oil prices in both the short run and the long run, while the significant positive impact of natural gas prices on crude oil prices manifests only in the long run. The evidence also indicates that the EPUs of the US and Europe positively affect the Indian EPU, while the EPU of China does not have a significant effect. Higher crude oil prices are associated with higher gas prices, whereas higher gold prices are negatively associated with the natural gas price and vice versa. Furthermore, the evidence shows that the Indian EPU does not have a significant effect on the changes in the prices of goods. [ABSTRACT FROM AUTHOR]