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Pests Prediction and Detection of Disease Spreading Frequency in Native crops using Machine Learning Technique

Disease and pest control models can generate information on agrochemical use only if necessary, reducing costs and environmental impacts. With machine learning algorithms, it is possible to develop models that will be used for diseases and pest warning systems to improve the effectiveness of chemical control over coffee tree pests. Therefore, infection rates are linked with climate change and measured and evaluated by machine learning algorithms for predicting the occurrence of diseases. Algorithms that are tested to predict incidence are (a)Multi-line regression (RLM); (b) K Neighbors Regressor (KNN); (c) Random Forest Regressor (RFT), and (d)Artificial Neural Networks. Pearson correlation analysis is to be considered under three different time periods,1-10 days (from 1-10 days before the incidence evaluation),11-20d, and 21-30d, and used to evaluate the unit correlations between the weather variables and infection rates. The number of days, maximum temperature, and relative humidity exceeding 80% are meteorological variables that show a significant correlation with this disease. There is a negative correlation with rainfall, and the severity of pests decreases with increasing rainfall. Machine learning algorithms can be used to predict diseases and pests.

Published by: Impana V., Vanishree K., Hemanth Kumar, Sumit Atram

Author: Impana V.

Paper ID: V8I3-1453

Paper Status: published

Published: July 6, 2022

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Research Paper

Resource demand prediction for minimizing power consumption at data centers

Technical progress in servers, systems, and capacity virtualization is empowering the production of resource pools of servers that allows various application workloads to share each server in the pool. This approach proposes and assesses the parts of a capacity management process for creating an efficient utilization of such pools and also facilitates a huge quantity of business administrations. The objective of our approach is to give a capacity management procedure to resource pools that let capacity organizers coordinate-free market activity for resource limits in a given interval of time. In this approach, we will describe the workloads of big business applications to pick up experiences for their conduct. In this paper, we will follow a trace-based approach for capacity management that relies upon the definition of required capacity and portrayal of workload request designs. The exactness of scope quantification expectations relies upon our capacity to describe workload request examples, to perceive patterns for expected changes in future requests, and reflect business conjectures for any sudden changes in future requests. A contextual analysis with 6 months of information that speaks to the asset utilization of 159 workloads in a venture server farm shows the adequacy of the proposed limit administration handle. Our results and conclusion will show that whenever we will use 8 processor systems, we will predict the exact future per-server required capacity to 1 processor every 98 percent of the time. This approach will enable a 38 percent reduction in processor utilization as compared to today’s current best practice for workload placement. This knowledge will help resource pool operators to decide on the best capacity for their server pools.

Published by: Gaurav Thakur

Author: Gaurav Thakur

Paper ID: V8I3-1459

Paper Status: published

Published: July 5, 2022

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Research Paper

Smart pothole detection system using Machine Learning

Roadways are considered the primary mode of transportation around the world. In recent years we have seen a major surge in the numbers of private as well as public vehicles traversing the road on a daily basis. As such it is very important to be cautious about potential mishaps due to road damages which can result in serious consequences. Heavy traffic, severe weather conditions, and many other things can be a major hindrance in the way of keeping the roads in good condition. Thus for traffic safety, it becomes very important to identify the various road damages, especially the hazardous ones, as quickly as possible. In this project, we are using machine learning to make a road damage identifier system that can work to detect the damage caused by various reasons.

Published by: Sayan Desarkar, Anik Basak, Ankita Ghosh, Rathindra Pramanick, Arnav Sarkar, Dr. Sangita Roy

Author: Sayan Desarkar

Paper ID: V8I3-1434

Paper Status: published

Published: July 5, 2022

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Review Paper

Seed quality assurance regulations and certification system in India: A review

The Certified seed being a soul of agriculture is scientifically produced from the seed of known genetic origin and genetic purity with controlled and tested production, processed, and declared in accordance with the Law on Seeds. Seed certification is a legally sanctioned system for quality control and seed multiplication and production as the quality seeds are inevitable to meet the challenges of the ever-increasing population by ensuring food security. Seed; being a carrier of technology, over a period of time emerged as a trade commodity. Seed quality assurance in India is subjected to the jurisdiction of the Seeds Act 1966, wherein quality seed must satisfy the requirements of Indian Minimum Seed Certification Standards (IMSCS), whereas; under the global scenario seed quality assurance system for seed export comes under the scope of Organization for Economic Cooperation and Development (OECD) standards and International Seed Testing Association (ISTA) methodology of seed testing. The present paper reviews the important stages of the seed certification procedure to be followed to ensure the quality of seed and related aspects of quality regulations in the Indian contest.

Published by: Dr. Vaibhav V. Ujjainkar

Author: Dr. Vaibhav V. Ujjainkar

Paper ID: V8I3-1448

Paper Status: published

Published: July 5, 2022

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Research Paper

Technical Loss Reduction on Feeder-3 of an Electric Cooperative In Pampanga, Anao Substation by Feeder Reconductoring

A technical loss reduction program has been activated by distribution utilities due to the increasing electricity demand. This paper focuses on the analysis of reconductoring the existing Feeder 3 with 3-phase, 2/0 conductors, ACSR, and 19.6 km in length. Feeder reconductoring is a technique where the existing conductor of the feeder is replaced by a more prominent conductor of optimal size and length. Two alternatives had been proposed; 4/0 conductor size was proposed in Alternative 1, while 336 size conductor was proposed in Alternative 2 in reconductoring the existing feeder. Technical loss reduction analysis, voltage regulation analysis, and cost-benefit analysis were performed to determine what alternative reduce the technical loss significantly, improve the voltage regulation, increase the capacity of the wire and be more economical to the distribution utility. Considering the results, Alternative 2 accounts for the highest reduction in technical loss, with an average of 65.8% technical loss reduction from 2022 to 2023. Also, Alternative 2 has the most significant improvement in voltage regulation with an average of 1.39% regulation due to low voltage drop. In terms of economic factors, implementing Alternative 2 is more beneficial to the distribution utility and can save 1,532.54 MWh of energy from 2022 to 2031. If converted into a monetary unit, the distribution utility can save Php 14,620,462.32. Implementing Alternative 2 is attainable and justifiable since the Benefit/Cost ratio is greater than 1.

Published by: Russel M. Serna, Glennmar L. Dela Cruz, Jonathan D. Mercado, Arsenio B. Manlapaz Jr., Alma L. Tangcuangco, Edgardo M. Santos, Louie G. Serrano

Author: Russel M. Serna

Paper ID: V8I3-1409

Paper Status: published

Published: July 5, 2022

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Research Paper

Prevalence of Intestinal Helminths and Associated Factors among the Inhabitants of Selected Villages, around Usmanu Danfodiyo University Sokoto, North-Western Nigeria

A cross-sectional study was carried out from October 2021 to March 2022 to assess the prevalence of intestinal helminth infections and associated factors among the inhabitants of selected villages around Usman Danfodiyo University Sokoto, North-Western Nigeria. A structured questionnaire was used to gather data on the demographic and risk factors associated with the intestinal helminth infections, stool samples were collected and examined for helminth eggs using the formalin-ether concentration technique. The overall prevalence of intestinal helminth infection in the study area was Ascaris hembricoides (35.0%) Strongyloides Stercorarius (9.0%), Trichuris trichivra (7.5%), Schistosoma mansoni (3.5%) and Heokwom (3.0%). Out of the 200 subjects examined for infection, 119 (59.5%) were found to be positive. The prevalence of helminthic infection in relation to sex was recorded with males having comparatively more infections (52.3%) than females (37.8%). However, there was a strong negative correlation between helminth infection and sex ϒ = - 0.50. A weak negative correlation was also found between age and prevalence of intestinal helminths ϒ = - 0.15. Bush defaecation showed the highest infections of Ascaris infection than water closet and pit latrine. However, there was no significant association between prevalence and toilet facilities (P>0.05). The study demonstrates a significant burden of intestinal helminth infections in this part of Nigeria and highlights the need for intervention measures.

Published by: Bukar Usman, Babagana Zannah Audu

Author: Bukar Usman

Paper ID: V8I3-1368

Paper Status: published

Published: July 4, 2022

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