Deep Neural Networks for Curbing Climate Change-Induced Farmers-Herdsmen Clashes in a Sustainable Social Inclusion Initiative

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Date

2019

Authors

Mısra, Sanjay
Misra, Sanjay
Fernandez Sanz, Luis
Ayeni, Foluso
Mbarika, Victor
Damasevicius, Robertas

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Politechnika Lubelska

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Computer Engineering
(1998)
The Atılım University Department of Computer Engineering was founded in 1998. The department curriculum is prepared in a way that meets the demands for knowledge and skills after graduation, and is subject to periodical reviews and updates in line with international standards. Our Department offers education in many fields of expertise, such as software development, hardware systems, data structures, computer networks, artificial intelligence, machine learning, image processing, natural language processing, object based design, information security, and cloud computing. The education offered by our department is based on practical approaches, with modern laboratories, projects and internship programs. The undergraduate program at our department was accredited in 2014 by the Association of Evaluation and Accreditation of Engineering Programs (MÜDEK) and was granted the label EUR-ACE, valid through Europe. In addition to the undergraduate program, our department offers thesis or non-thesis graduate degree programs (MS).

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Abstract

Peaceful coexistence of farmers and pastoralists is becoming increasingly elusive and has adverse impact on agricultural revolution and global food security. The targets of Sustainable Development Goal 16 (SDG 16) include promoting peaceful and inclusive societies for sustainable development, providing access to justice for all and building effective, accountable and inclusive institutions at all levels. As a soft approach and long term solution to the perennial farmers herdsmen clashes with attendant humanitarian crisis, this study proposes a social inclusion architecture using deep neural network (DNN). This is against the backdrop that formulating policies and implementing programmes based on unbiased information obtained from historical agricultural data using intelligent technology like deep neural network (DNN) can be handy in managing emotions. In this vision paper, a DNN-based Farmers-Herdsmen Expert System (FHES) is proposed based on data obtained from the Nigerian National Bureau of Statistics for tackling the incessant climate change induced farmers-herdsmen clashes, with particular reference to Nigeria. So far, many lives have been lost. FHES is modelled as a deep neural network and trained using farmers-herdsmen historical data. Input variables used include land, water, vegetation, and implements while the output is farmers/herders disposition to peace. Regression analysis and pattern recognition performed by the DNN on the farmers-herdsmen data will enrich the inference engine of FHES with extracted rules (knowledge base). This knowledge base is then relied upon to classify future behaviours of herdsmen/farmers as well as predict their dispositions to violence. Critical stakeholders like governments, service providers and researchers can leverage on such advisory to initiate proactive and socially inclusive conflict prevention measures such as, people-friendly policies, programmes and legislations. This way, conflicts can be averted, national security challenges tackled, and peaceful atmosphere guaranteed for sustainable development.

Description

Fernandez Sanz, Luis/0000-0003-0778-0073; AYENI, FOLUSO/0000-0003-0989-7056; Misra, Sanjay/0000-0002-3556-9331

Keywords

climate change, deep neural network, farmers-herdsmen clashes, policies and programmes, social inclusion

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Citation

11

WoS Q

Q4

Scopus Q

Q3

Source

Volume

14

Issue

2

Start Page

143

End Page

155

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