Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers

Biomass and Carbon Stock Estimation in Woody Grass (\u3cem\u3eDendrocalamus strictus\u3c/em\u3e L.) in Doon Valley, India

Bamboos commonly kown as woody grass are one of the most important species particularly in Asia, where it is frequently considered as the ―timber of the poor‖ (Rao et al., 1985). With about 23 genera and 136 species, India is the second largest reservoir of bamboos, next only to China (SFR, 2013 and Nath et al., 2009). Bamboos occur extensively in the managed ecosystems of India—both as plantations (and in agroforestry (scattered clumps, hedgerows on farm boundaries etc. Dendrocalamus strictus L. is most commonly found bamboo in India. It is widely distributed in dry deciduous forests and grows rapidly in all climatic conditions and occupies about 53 % of total bamboo area in India. It grows better in the drier parts and on sandstone, granite and coarse grained soils with low moisture- retaining capacity and soils with pH range 5.5–7.6. It grows more than 8 feet in 6–8 months. The species is used widely for as raw material in paper mills and also for variety of purposes such as construction, agricultural implements, musical instruments, furniture etc. The species is also suitable for reclamations of degraded and ravine lands. The accurate assessment of biomass estimates of a forest is important for many applications (Brown, 2002; Chave et al., 2004; Arora et al., 2014; Verma et al., 2014). In recent years, the carbon cycle has become an important issue in the world and plants play a major role in carbon storage. Biomass estimation enables us to estimate the amount of carbon dioxide that can be sequestered from the atmosphere. However, most of the carbon and biomass studies focus on assessing the capability of trees viz., poplar, eucalyptus, shisham, chir teak, subabul etc. The studies related to biomass and carbon stock estimation in bamboos is limited. The present study examine specifically the above ground stand biomass, biomass structure and C storage in D. strictus

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Not AvailableShelterbelt plantations are one of the biggest ecofriendly technologies to address wind erosion, control sand movement and related problems in the arid areas, which cover 12 per cent of India and more than half of Rajasthan. However, the role of shelterbelts in arid agriculture is changing. A survey was undertaken to investigate the farmers' perception toward field shelterbelts/windbreaks and its impact in farmers' fields in the arid district of Bikaner, Rajasthan. Questionnaire, group discussion and observations were used for data collection. The respondents of the study were the farmers having already established shelterbelts in their farms. The study revealed that farmers were using windbreaks on agricultural lands mainly to protect crops from strong winds, provide shade to livestock, reduce crop damage from frost and cold winds, reduce soil erosion etc.; while the biggest challenge was the competition by trees to the crops growing below them. The farmers' preference for tree species was also noted to have changed over the years. Though old shelterbelts were mainly of Acacia tortilis, farmers now prefer Dalbergia sissoo (45 per cent prevalence), since it does not affect crop growth and for its economic timber value. Other species like Zizyphus sp., Eucalyptus sp., Cordia myxa etc. when used as shelterbelts provide fruits and fuelwood which provide additional income to farmers.Not Availabl