288 research outputs found
An Intrusion Detection Using Machine Learning Algorithm Multi-Layer Perceptron (MlP): A Classification Enhancement in Wireless Sensor Network (WSN)
During several decades, there has been a meteoric rise in the development and use of cutting-edge technology. The Wireless Sensor Network (WSN) is a groundbreaking innovation that relies on a vast network of individual sensor nodes. The sensor nodes in the network are responsible for collecting data and uploading it to the cloud. When networks with little resources are deployed harshly and without regulation, security risks occur. Since the rate at which new information is being generated is increasing at an exponential rate, WSN communication has become the most challenging and complex aspect of the field. Therefore, WSNs are insecure because of this. With so much riding on WSN applications, accuracy in replies is paramount. Technology that can swiftly and continually analyse internet data streams is essential for spotting breaches and assaults. Without categorization, it is hard to simultaneously reduce processing time while maintaining a high level of detection accuracy. This paper proposed using a Multi-Layer Perceptron (MLP) to enhance the classification accuracy of a system. The proposed method utilises a feed-forward ANN model to generate a mapping for the training and testing datasets using backpropagation. Experiments are performed to determine how well the proposed MLP works. Then, the results are compared to those obtained by using the Hoeffding adaptive tree method and the Restricted Boltzmann Machine-based Clustered-Introduction Detection System. The proposed MLP achieves 98% accuracy, which is higher than the 96.33% achieved by the RBMC-IDS and the 97% accuracy achieved by the Hoeffding adaptive tree
Pilot studies on GP Crop yield estimation using Technology (Kharif 2019) using SENTINEL- 2 satellite data (in Andhra Pradesh, Telangana and Odisha States (Five Districts)) for Groundnut, Chickpea, Maize and Rice
The Government of India plans to optimize Crop Cutting Experiments (CCEs) using different technologies including satellite derived metrics on crop performance and spatial variability to guide the selection and number of ground data sites. This requires the development of an approach for different crops for the different agro-climatic regions of India. The present study plans to develop an approach for following crops viz., Groundnut, Chickpea, Rice and Maize. The above crops will be studied in five districts of three states viz. Andhra Pradesh, Telangana and Odisha. The study will use comprehensive and existing environmental, weather and management data along with satellite derived crop spatial data. This information will be modelled using statistical optimization techniques to assess the optimal numbers of CCE’s that can be undertaken
Dynamic modeling of mean-reverting spreads for statistical arbitrage
Statistical arbitrage strategies, such as pairs trading and its
generalizations, rely on the construction of mean-reverting spreads enjoying a
certain degree of predictability. Gaussian linear state-space processes have
recently been proposed as a model for such spreads under the assumption that
the observed process is a noisy realization of some hidden states. Real-time
estimation of the unobserved spread process can reveal temporary market
inefficiencies which can then be exploited to generate excess returns. Building
on previous work, we embrace the state-space framework for modeling spread
processes and extend this methodology along three different directions. First,
we introduce time-dependency in the model parameters, which allows for quick
adaptation to changes in the data generating process. Second, we provide an
on-line estimation algorithm that can be constantly run in real-time. Being
computationally fast, the algorithm is particularly suitable for building
aggressive trading strategies based on high-frequency data and may be used as a
monitoring device for mean-reversion. Finally, our framework naturally provides
informative uncertainty measures of all the estimated parameters. Experimental
results based on Monte Carlo simulations and historical equity data are
discussed, including a co-integration relationship involving two
exchange-traded funds.Comment: 34 pages, 6 figures. Submitte
A Multi-Model Systems Approach for Identifying Low Emissions Development Pathways– Analyzing Synergies and Trade-offs in Semiarid Agriculture in India
Food security in the face of changing climate has pushed governments and development actors to focus efforts on improving adaptation, however, going forward besides adaptation, mitigation efforts to reduce GHG emissions are of global significance and calls for triple-win solutions with positive contributions to productivity, adaptation and mitigation. An emerging paradigm for promoting mitigation in agriculture is the Low-Emissions Development (LED) strategies. LED acknowledge broader sustainable development goals and identify mitigation practices compatible with these goals. This case study examines the opportunities for obtaining synergies between agricultural productivity, whole-farm profitability and GHG mitigation and highlights where trade-offs exist and explores how agricultural practices and systems can be designed and managed to balance the synergies and trade-offs for small-holder farmers in semi-arid India. We used data from 100 farm-households of Telangana state, India on farm-household characteristics and agricultural practices. Quantifying synergies and trade-offs between profitability, adaptation and mitigation we employed simulation modelling- crop, livestock and whole-farm simulation models, and Cool Farm Tool to estimate net GHG emissions. Our analysis reveals that specific plot-level crop management strategies and farm-level enterprise interventions can increase profitability as well as benefit climate change mitigation. It depict how farming systems can be managed to achieve synergies between profitability and mitigation outcomes and where, if any trade-offs exist. Combinations of reduced tillage, retaining crop-residue, improved nitrogen management, utilizing organic manure, improved livestock feeding practices, introducing agro-forestry could contribute to GHG abatement and improved profitability at our study site. Such multi-model systems analysis using participatory design and tools could help practitioners and policymakers to identify and promote use of management practices that can help achieve multiple objectives and guide investments towards synergistic climate smart agriculture strategies. Our study contributes empirical evidence to the debate surrounding integrated approaches to sustainable development goals and adaptation and mitigation objectives
How to design a complex behaviour change intervention: experiences from a nutrition-sensitive agriculture trial in rural India
Many public health interventions aim to promote healthful
behaviours, with varying degrees of success. With a lack
of existing empirical evidence on the optimal number or
combination of behaviours to promote to achieve a given
health outcome, a key challenge in intervention design
lies in deciding what behaviours to prioritise, and how
best to promote them. We describe how key behaviours
were selected and promoted within a multisectoral
nutrition-sensitive agriculture intervention that aimed to
address maternal and child undernutrition in rural India.
First, we formulated a Theory of Change, which outlined
our hypothesised impact pathways. To do this, we used
the following inputs: existing conceptual frameworks,
published empirical evidence, a feasibility study, formative
research and the intervention team’s local knowledge.
Then, we selected specific behaviours to address within
each impact pathway, based on our formative research,
behaviour change models, local knowledge and community
feedback. As the intervention progressed, we mapped each
of the behaviours against our impact pathways and the
transtheoretical model of behaviour change, to monitor the
balance of behaviours across pathways and along stages
of behaviour change. By collectively agreeing on definitions
of complex concepts and hypothesised impact pathways,
implementing partners were able to communicate clearly
between each other and with intervention participants.
Our intervention was iteratively informed by continuous
review, by monitoring implementation against targets
and by integrating community feedback. Impact and
process evaluations will reveal whether these approaches
are effective for improving maternal and child nutrition,
and what the effects are on each hypothesised impact
pathway
Climate Risk Management in Smallholder Farming Systems in the Semiarid Tropics
Climate risk management in the semi-arid tropics (SAT) is one of the major
challenges to achieving food security and development in India and large parts of
sub-Saharan Africa and also in the case of Australia. Climate-induced production
risk associated with the current season-to-season variability of rainfall is a major
barrier in making rainfed agriculture sustainable and viable farm business. Since
season outcomes are uncertain, even with the best climate information, farmers
have limited flexibility in applying management with confidence. In fact in risky
environments, farmers most often respond by adapting a risk averse strategy and
are reluctant to invest in even risk reducing measures (Leathers and Quiggin
1991). In the SAT agro-ecologies, there are a limited range of enterprise or crop
options to consider which may be further restricted by cultural traditions, food
preferences or market opportunities.While there are fundamental differences
between large scale commercial farms in Australia compared to the predominantly
smallholder resource poor farms found in India, when it comes to climate risk
management in the SAT, there are many commonalities. The purpose of this
paper is therefore to (i) establish a framework for managing climate variability
and transforming farming systems to be more resilient and sustainable for future
climates; and (ii) provide some case study examples from climate risk management
in low rainfall cropping system in Australia and consider how they may be applied
in smallholder systems of the SAT..
An integrated crop model and GIS decision support system for assisting agronomic decision making under climate change
The semi-arid tropical (SAT) regions of India are suffering from low productivity which may be further aggravated by anticipated climate change. The present study analyzes the spatial variability of climate change impacts on groundnut yields in the Anantapur district of India and examines the relative contribution of adaptation strategies. For this purpose, a web based decision support tool that integrates crop simulation model and Geographical Information System (GIS) was developed to assist agronomic decision making and this tool can be scalable to any location and crop. The climate change projections of five global climate models (GCMs) relative to the 1980–2010 baseline for Anantapur district indicates an increase in rainfall activity to the tune of 10.6 to 25% during Mid-century period (2040–69) with RCP 8.5. The GCMs also predict warming exceeding 1.4 to 2.4 °C by 2069 in the study region. The spatial crop responses to the projected climate indicate a decrease in groundnut yields with four GCMs (MPI-ESM-MR, MIROC5, CCSM4 and HadGEM2-ES) and a contrasting 6.3% increase with the GCM, GFDL-ESM2M. The simulation studies using CROPGRO-Peanut model reveals that groundnut yields can be increased on average by 1.0%, 5.0%, 14.4%, and 20.2%, by adopting adaptation options of heat tolerance, drought tolerant cultivars, supplemental irrigation and a combination of drought tolerance cultivar and supplemental irrigation respectively. The spatial patterns of relative benefits of adaptation options were geographically different and the greatest benefits can be achieved by adopting new cultivars having drought tolerance and with the application of one supplemental irrigation at 60 days after sowing
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