System and Method for Truth Discovery in social media Big Data

Within the span of enormous information and the coming of numerous advancements in the communication technologies, at every tick of the clock, enormous sums of information is produced from different sources. One such source of data generation is social media. However, such data carries much of the noisy, uncertain, and untrustworthy data. In this way, finding independable information from loud information is one of the characteristic challenges of huge information focusing on the esteem characteristic of enormous information. Therefore, in this article, an attempt is made to target a few challenges arriving from “misinformation spread”, “data sparsity” or the “long-tail wonder” in the domain of social media data analytics. The study uses an instance from the Online Social Network (OSN) datasets to develop scalable to wide-range social sensing by consolidating Scalable Robust Trust Discovery (SRTD) plots to address the mentioned challenges utilizing the distributed parallel computing framework. The dataset picked for investigation includes 128,483 tweets which incorporates 20% deception, 80% retweets bringing about 0.05 milliseconds utilizing Spark parallel processing

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Not AvailableThe energy consumption pattern and greenhouse gas (GHG) emission of any rice production system is important to know the sustainability of varied cultivation and establishment technique. This study was conducted to determine the energy use pattern, GHG emission and efficiency of rice farms in puddled transplanted (PTR, rainfed) and direct-seeded rice (DSR, irrigated) production systems in Karnataka, India. The energy indices and GHG emission of different input and output in a rice production system were assessed by using energy and carbon equivalence. The efficiency of PTR and DSR farms were identified using data envelopment analysis (DEA) and energy optimization was ascertained. The key finding was excessive use of non-renewable energy inputs was observed for the PTR (92.4%) compare to DSR (60.3%) methods. The higher energy use efficiency (7.3), energy productivity (0.3 kg MJ−1) and energy profitability (6.3) were mainly attributed to the large decrease in energy inputs under DSR. The DEA showed efficiency for 26 PTR farms in comparison for 87 DSR farms. The mean technical efficiency value highlighted the scope for saving energy by 6% and 2% in PTR and DSR, respectively and showed an economic reduction of $405.5/ha with PTR versus$163.3/ha with the DSR method if these inefficient farms perform efficiently. The GHG emissions revealed that the total emissions for PTR versus DSR production caused by on-farm emissions were 86% and 65%, respectively. The DSR method also had a higher carbon efficiency ratio and carbon sustainability index (10.1 and 9.1, respectively). Thus, adoption of DSR method is imperative for reduction of energy consumption and GHG emissions to achieve the carbon sustainability.Not Availabl

Not Available

Not AvailableThe energy consumption pattern and greenhouse gas (GHG) emission of any rice production system is important to know the sustainability of varied cultivation and establishment technique. This study was conducted to determine the energy use pattern, GHG emission and efficiency of rice farms in puddled transplanted (PTR, rainfed) and direct-seeded rice (DSR, irrigated) production systems in Karnataka, India. The energy indices and GHG emission of different input and output in a rice production system were assessed by using energy and carbon equivalence. The efficiency of PTR and DSR farms were identified using data envelopment analysis (DEA) and energy optimization was ascertained. The key finding was excessive use of non-renewable energy inputs was observed for the PTR (92.4%) compare to DSR (60.3%) methods. The higher energy use efficiency (7.3), energy productivity (0.3 kg MJ−1) and energy profitability (6.3) were mainly attributed to the large decrease in energy inputs under DSR. The DEA showed efficiency for 26 PTR farms in comparison for 87DSR farms. The mean technical efficiency value highlighted the scope for saving energy by 6% and 2% in PTR and DSR, respectively and showed an economic reduction of $405.5/ha with PTR versus$163.3/ha with the DSR method if these inefficient farms perform efficiently. The GHG emissions revealed that the total emissions for PTR versus DSR production caused by on-farm emissions were 86% and 65%, respectively. The DSR method also had a higher carbon efficiency ratio and carbon sustainability index (10.1 and 9.1, respectively). Thus, adoption of DSR method is imperative for reduction of energy consumption and GHG emissions to achieve the carbon sustainability.Not Availabl