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