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The internet utilization has been developing quickly, mostly in previous eras. Though, by way of the internet develops an important section of the daily life routine, cybercrime is similarly on the grow. The prices of cybercrime will approximately five lakh crores dollars per year through 2022 according to cyber security endeavours details in 2021. The cyber attackers exploit some internet resources as a principal way of transformation through a victim system; therefore intruders generate benefit based on economic, promotional and many more through developing the susceptibilities over devices. The computing cybercrime threats and providing security procedures through physical schemes utilizing previous methodological techniques and moreover examinations have unsuccessful many times to govern cybercrime threats. The previous literature in field of cybercrime threats agonizes from absence of evaluation schemes to guess the cybercrimes, mainly on unstructured information. Hence, an Improved Grey-wolf Optimization based Classification Model (IGO-CM) is developed with the help of chaos system and information entropy utilizing machine learning schemes for cybercrime data analysis to compute the rate of cybercrime by classifying the cybercrime data. The protection examinations by means of the relationship of data analysis methodologies provide services to examine and classify crime data in unstructured form taking from India. The implementation of IGO-CM is performed on MATLAB 2021a tool for unstructured cybercrime data and the outcomes describe the superior performance of IGO-CM depending on accuracy, F-Measure, standard deviation, purity index, intra-cluster distance, root mean square error, and time complexity against a popular classification scheme K-Means, and some optimization schemes like ACO, ALO, PSO and GO. [ABSTRACT FROM AUTHOR]

The present study proposes a new chaos theory-based local approximation technique for the spatial downscaling of rainfall from GCM outputs. The approach involves: (1) reconstruction of relevant climate data in the state space using an embedding technique; and (2) use of a neighbor search procedure in the reconstructed space and local approximation for downscaling. The approach is implemented for downscaling of monthly rainfall at a spatial resolution of 0.25° × 0.25° across India. The effectiveness of the approach is first demonstrated using the NCEP/NCAR reanalysis dataset (1979–2014) and then tested using the outputs from five CMIP6 GCMs (CMCC-ESM2, CESM2-WACCM, CMCC-CM2-HR4, E3SM-1-1-ECA, and BCC-ESM1) (1961–2014). Eight climate variables (eastward wind, northward wind, relative humidity, specific humidity, surface temperature, air temperature, sea level pressure, and geopotential height) are considered as predictors, with the observed rainfall data from the India Meteorological Department (IMD) as the predictand. Three statistical measures, namely correlation coefficient (CC), Nash-Sutcliffe efficiency (NSE), and normalized root mean square error (NRMSE), are used for evaluation. The results indicate that the local approximation downscaling approach yields promising results for both reanalysis dataset and GCM outputs. Among the five GCMs, the CMCC-ESM2 model yields the best results. Comparison of the results from the chaos theory-based approach with those of the linear regression-based approach indicates better performance of the former. The proposed framework is general and can be applied for any climate variable, GCM, and region. • A chaos theory approach is proposed for spatial rainfall downscaling of GCM outputs. • It involves embedding of climate data, neighbor search, and local approximation. • It is implemented for spatial downscaling of monthly rainfall across India. • Performance is assessed for reanalysis dataset and outputs from five GCMs. • Chaos theory-based downscaling approach outperforms linear regression method. [ABSTRACT FROM AUTHOR]

In this paper we determine the extent of predictability of India's major spot exchange rates by using the Lyapunov exponent. We first determine whether the series is fractal (self-similar) in nature. If it is indeed so, then next we determine whether the underlying dynamics of the system is deterministic or stochastic. If the dynamics is found to be deterministic then we calculate the Largest Lyapunov Exponent (LLE) to determine whether the series has deterministic chaos. Finally we use the inverse of the Lyapunov exponent to estimate the time period for which out of sample predictions for the series make sense. We find that India's major spot exchange rates are: a) fractal in nature, b) chaotic with a high embedding dimension and c) The inverse of the LLE gives us a time frame in which any meaningful predictions can be made. These results are interpreted in two ways. First, exploiting the efficient market interpretation of randomness we conclude that since available information is fairly rapidly internalized, chaotic behaviour is mainly due to the unforeseen nature of the pool of new information affecting the systems at such short intervals of time. Second, anti-cyclical central bank interventions are conjectured to be the source of determinism in otherwise almost random movements. [ABSTRACT FROM AUTHOR]

The traditional method of containing forex volatility is by proper controlling of domestic interest rate and foreign exchange reserves. Such instruments, however, have often failed raising doubts about the efficacy of central bank intervention in forex markets. Recent literature tries to rescue the policy makers by pointing toward the intrinsic dynamics or more specifically, the possible chaotic nature of forex markets and its policy implications. Since volatility is endogenous and no long-run predictions are possible in a chaotic market, traditional methods of interventions are likely to fail. This paper explores this issue in context of the Rupee-Dollar exchange rate. It finds that the central bank intervention to be indeed ineffective in an inherently chaotic market. Thus, inefficacy of intervention in forex market could possibly be explained in terms of its intrinsic dynamics. These findings call for a changed policy outlook that could consider the non-linear nature of the forex rates to successfully control its volatility. [ABSTRACT FROM AUTHOR]

The water sharing dispute in a multi-reservoir river basin forces the water resources planners to have an integrated operation of multi-reservoir system rather than considering them as a single reservoir system. Thus, optimizing the operations of a multi-reservoir system for an integrated operation is gaining importance, especially in India. Recently, evolutionary algorithms have been successfully applied for optimizing the multi-reservoir system operations. The evolutionary optimization algorithms start its search from a randomly generated initial population to attain the global optimal solution. However, simple evolutionary algorithms are slower in convergence and also results in sub-optimal solutions for complex problems with hardbound variables. Hence, in the present study, chaotic technique is introduced to generate the initial population and also in other search steps to enhance the performance of the evolutionary algorithms and applied for the optimization of a multi-reservoir system. The results are compared with that of a simple GA and DE algorithm. From the study, it is found that the chaotic algorithm with the general optimizer has produced the global optimal solution (optimal hydropower production in the present case) within lesser generations. This shows that coupling the chaotic algorithm with evolutionary algorithm will enrich the search technique by having better initial population and also converges quickly. Further, the performances of the developed policies are evaluated for longer run using a simulation model to assess the irrigation deficits. The simulation results show that the model satisfactorily meets the irrigation demand in most of the time periods and the deficit is very less. [ABSTRACT FROM AUTHOR]

The geographical entity now called West Bengal was once the seat of an ancient civilization going back a millennia. More recently, it has been the cradle of the Indian Renaissance, going back a few hundred years. Presently, it has turned into a land of chaos with daily street demonstrations, constant state-sponsored strikes, and enormous political meetings, along with corruption (with the highest black to white money ratio inside India, bribery at all levels, most intensively in the real estate and mining sectors), and criminalization (unruly, jobless, and drunk youth gangs, hundreds killed, inspired by political parties, extortion, kidnapping, and Maoist violence). This has been and still is a land with great promise and potential—a land of natural beauty (the Himalayas, dense mangrove forests with Royal Bengal tigers, a river-rich and very green landscape), mineral resources, and most importantly rich and diverse genetic material (from the synthesis of three main ethnic groups—Negroid, Mongoloid, Caucasoid). Throughout the millennia, the migration of different races, different religions, and different cultures into this area has been a dominant phenomenon. It is perhaps more extensive and varied here than anywhere else in the world. We need to find a way out of this mess and into a period of creativity. [ABSTRACT FROM AUTHOR]

In this article we first consider the importance of prediction of the monsoon, and events such as the intense rainfall event over Mumbai in July 2005. We then discuss how meteorologists make short-,medium-, and long-range forecasts and the concept of the limit of predictability in a chaotic system such as the atmosphere. Problems and prospects of prediction on different time-scales are discussed by using one example of short-range forecasts and the prediction of the monsoon by dynamical and statistical methods. Finally we consider measures of the skill of a forecast and how high the skill has to be for it to be useful for applications. [ABSTRACT FROM AUTHOR]

An evaluation of disasters in the form of landslides and floods is presented. The study has been carried out in the perspective of nonlinearity, chaos and the complexity of their management. The devastating landslide at Ambootia Tea Estate in the Himalayan Darjeeling District, West Bengal (a vulnerable earthquake zone) which took place in 1966, killing about ten thousand people is discussed in detail and mathematically analysed using the fractal concepts. It is suggested that the Ambootia landslide might be an instance in point when ∥δ(t)∥ exceeded a, a measure of tolerance that such landslides cannot be predicted longer than a few multiples of 1/λ, where λ is an averaged Liapunov constant. [ABSTRACT FROM AUTHOR]

Rainfall, like all other natural phenomena is highly unpredictable. Traditionally, principal component analysis and spectral analysis are used to understand trends in rainfall over long periods. In this paper, we present a new method that appears to be better than existing methods to understand the long-term behavior of rainfall phenomena. Using a case study on the rainfall in Kerala State, the southern part of Indian Peninsula, we show that a new kind of neural network known as the adaptive basis function network is a promising tool for climatic studies, especially rainfall analysis. The paper also reveals that in spite of the fluctuations resulting from the nonlinearity in the system, the trends in the rainfall pattern in Kerala state have remained unaffected over the past 87 years from 1893 to 1980. We also successfully filter out the chaotic part of the system and illustrate that its effects are marginal over long-term predictions. [Copyright &y& Elsevier]