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Use and Impact of Artificial Intelligence on Climate Change Adaptation in Africa [chapter]

Isaac Rutenberg, Arthur Gwagwa, Melissa Omino
2020 African Handbook of Climate Change Adaptation  
Use and Impact of Artificial Intelligence on Climate Change Adaptation in. . .  ...  The three factors of interest are interrelated, and it is difficult to assess any one factor in isolation.  ... 
doi:10.1007/978-3-030-42091-8_80-1 fatcat:6kijsqsxh5h25hogrsvngnyjiu

Intelligent Systems Using Sensors and/or Machine Learning to Mitigate Wildlife–Vehicle Collisions: A Review, Challenges, and New Perspectives

Irene Nandutu, Marcellin Atemkeng, Patrice Okouma
2022 Sensors  
We, therefore, argue that the use of the latest datasets and machine learning techniques will minimize false detection and improve model performance.  ...  We find that most animal detection systems (excluding autonomous vehicles) are relying neither on state-of-the-art datasets nor on recent breakthrough machine learning approaches.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s22072478 pmid:35408093 pmcid:PMC9003022 fatcat:rpin7w44bvbajj6n73v7x76o5q

Accelerating integration of immigrants using artificial intelligence-driven solutions: The panacea for integration gaps in Finland

Frank Ojwang
2022 Technium Social Sciences Journal  
This article uses grounded theory to test theories that underscore the role and impact of AI in accelerating integration.  ...  The algorithms are base on various taxonomies and topologies for individualized self-paced, life-long situation-specific integration support.  ...  Machine learning models outperform traditional human mobility models on a variety of evaluation metrics, both in the task of predicting migrations between regions as well as international migrations.  ... 
doi:10.47577/tssj.v33i1.6794 fatcat:mw35rkq2zfajtnnlrq3j6jldwm

Artificial Intelligence in Business: From Research and Innovation to Market Deployment

Neha Soni, Enakshi Khular Sharma, Narotam Singh, Amita Kapoor
2020 Procedia Computer Science  
The paper also contributes in investigating factors responsible for the advancement of AI.  ...  The paper also contributes in investigating factors responsible for the advancement of AI.  ...  Acknowledgements One of the authors, Ms. Neha Soni, wants to thank Department of Science & Technology, Ministry of Science & Technology, New Delhi, India for sponsoring this work.  ... 
doi:10.1016/j.procs.2020.03.272 fatcat:5rchhzimdzcdxn6fo7xxf6qqae

EiF: Toward an Elastic IoT Fog Framework for AI Services

JongGwan An, Wenbin Li, Franck Le Gall, Ernoe Kovac, Jaeho Kim, Tarik Taleb, JaeSeung Song
2019 IEEE Communications Magazine  
use in dynamic situations that require adaptive solutions.  ...  In this work, we introduce the Elastic-IoT-Fog (EiF), a flexible Fog computing framework that runs on IoT gateways with adaptive AI services fostered on the Cloud.  ...  Fig. 6: Machine learning latency cameras deployed on the road.  ... 
doi:10.1109/mcom.2019.1800215 fatcat:3agnvfekl5e7zgkktwaf32ltyq

Machine Learning for the Geosciences: Challenges and Opportunities [article]

Anuj Karpatne, Imme Ebert-Uphoff, Sai Ravela, Hassan Ali Babaie, and Vipin Kumar
2017 arXiv   pre-print
We first highlight typical sources of geoscience data and describe their properties that make it challenging to use traditional machine learning techniques.  ...  in machine learning.  ...  ACKNOWLEDGEMENTS The authors of this paper are supported by inter-disciplinary projects at the interface of machine learning and geoscience, including the NSF Expeditions in Computing grant on "Understanding  ... 
arXiv:1711.04708v1 fatcat:i7yjfka55fbn5biqcw7g2lafsa

Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques [article]

Zhiyuan Chen, Howe Seng Goh, Kai Ling Sin, Kelly Lim, Nicole Ka Hei Chung, Xin Yu Liew
2021 arXiv   pre-print
The intention of this research is to study and design an automated agriculture commodity price prediction system with novel machine learning techniques.  ...  On the other hand, when implementing machine learning techniques, finding a suitable model with optimal parameters for global solution, nonlinearity and avoiding curse of dimensionality are still biggest  ...  Later on several research studies have proposed to implement agriculture price prediction scheme using different machine learning algorithms [7] [8] [9] [10] .  ... 
arXiv:2106.12747v1 fatcat:25b5f2yyfbfslan2do6ed7mzga

Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review [article]

Sancho Salcedo-Sanz, Jorge Pérez-Aracil, Guido Ascenso, Javier Del Ser, David Casillas-Pérez, Christopher Kadow, Dusan Fister, David Barriopedro, Ricardo García-Herrera, Marcello Restelli, Mateo Giuliani, Andrea Castelletti
2022 arXiv   pre-print
Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs.  ...  A number of examples is discussed and perspectives and outlooks on the field are drawn.  ...  Acknowledgement This research has been partially supported by the European Union, through H2020 Project "CLIMATE INTELLIGENCE Extreme events detection, attribution and adaptation design using machine learning  ... 
arXiv:2207.07580v1 fatcat:ktzhrgnlcng55cdfdasynzfrxq

Machine Learning and Cognitive Ergonomics in Air Traffic Management: Recent Developments and Considerations for Certification

Trevor Kistan, Alessandro Gardi, Roberto Sabatini
2018 Aerospace (Basel)  
By considering a novel cognitive human–machine interface (HMI), configured via machine learning, we examined the requirements for such techniques to be deployed operationally in an ATM system, exploring  ...  aspects of vendor verification, regulatory certification, and end-user acceptance.  ...  Impact of UAS on ATM Seasoned industry practitioners observed that systems developed for autonomous low-level operations may soon migrate to higher levels [44] .  ... 
doi:10.3390/aerospace5040103 fatcat:vjyg3cwkyrag7p35olkyzf4i74

Shazam for bats: Internet of Things for continuous real‐time biodiversity monitoring

Sarah Gallacher, Duncan Wilson, Alison Fairbrass, Daniyar Turmukhambetov, Michael Firman, Stefan Kreitmayer, Oisin Mac Aodha, Gabriel Brostow, Kate Jones
2021 IET Smart Cities  
Current methods rely on significant human input and therefore present an opportunity for continuous monitoring and in situ machine learning detection of bat calls in the field.  ...  Bats are important biodiversity indicators of the wider health of the environment and activity surveys of bat species are used to report on the performance of mitigation actions.  ...  One of the most common was the question of data accuracy given the potential 'black box' nature of the bat sensing prototype incorporating machine learning algorithms, and how details of algorithmic confidence  ... 
doi:10.1049/smc2.12016 fatcat:b4hbutbkzndcrf7tgtyrgrkuri

Driver Behavior Modeling: Developments and Future Directions

Najah AbuAli, Hatem Abou-zeid
2016 International Journal of Vehicular Technology  
Thus a more general review of the diverse aspects of DBM, with an emphasis on the most recent developments, is needed.  ...  Despite the progressive advancements in various aspects of driver behavior modeling (DBM), only limited work can be found that reviews the growing body of literature, which only targets a subset of DBM  ...  Algorithms and Approaches. Algorithms and approaches for DBM encompass a broad range of statistical, machine learning, and pattern recognition techniques, among others.  ... 
doi:10.1155/2016/6952791 fatcat:brcfhn3zh5a5tgxodilshrbweq

Assessment of Three Machine Learning Techniques with Open-Access Geographic Data for Forest Fire Susceptibility Monitoring—Evidence from Southern Ecuador

Fabián Reyes-Bueno, Julia Loján-Córdova
2022 Forests  
In the training of the machine learning models, a multitemporal database with 1436 points was used, fed with the information from seven variables related to fuel moisture, proximity to anthropic activities  ...  This study compares three machine learning techniques: logistic regression, logistic decision tree, and multivariate adaptive regression spline to identify areas susceptible to forest fires in the Loja  ...  no need to use software programs specialized in machine learning.  ... 
doi:10.3390/f13030474 fatcat:rz3f56i5zbg4tj5hktoh7akrzy

A Systematic Review of Autonomous Emergency Braking System: Impact Factor, Technology, and Performance Evaluation

Lan Yang, Yipeng Yang, Guoyuan Wu, Xiangmo Zhao, Shan Fang, Xishun Liao, Runmin Wang, Mengxiao Zhang
2022 Journal of Advanced Transportation  
In order to track the research progress of AEB-related technologies, this paper makes a systematic analysis and research on the impact factors, key technologies, and effect evaluation of AEB.  ...  First, the paper deeply analyzes the three levels of factors affecting the performance of AEB, which are vehicle factors, driver factors, and environmental factors.  ...  Impact Factors e AEB system performance is affected by both the intrinsic and extrinsic factors of the equipped vehicle while driving. e intrinsic factors cover on-board sensing, decision-making as well  ... 
doi:10.1155/2022/1188089 doaj:ba6ca96049d846d3ab2b2f79b07c87f2 fatcat:unbfwtslejhuxnywaushmsoimm

Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases [chapter]

Rajesh Singh, Anita Gehlot, Dharam Buddhi
2022 Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases  
A comparative study of different machine learning classifiers for chronic disease prediction viz Heart Disease & Diabetes Disease is done in this paper.  ...  Precise diagnosis of these diseases on time is very significant for maintaining a healthy life.  ...  The region-based segmentation will segment the data dependent on the taken-out features using GLCM algorithm.  ... 
doi:10.13052/rp-9788770227667 fatcat:da47mjbbyzfwnbpde7rgbrlppe

The Application of Information Classification in Agricultural Production based on Internet of Things and Deep Learning

Suwei Gao
2022 IEEE Access  
Using various agricultural economic development theories, analyzation is made on the present situation of domestic agricultural informatization development.  ...  At first, the study introduces Deep Learning (DL) technology and the Internet of Things (IoT) and their advantages.  ...  To improve the learning ability of plant pathological features, the deep CNN was used for migration learning, and the network structure was modified [11] .  ... 
doi:10.1109/access.2022.3154607 fatcat:cv3xc7njdre3dh3guocgenxc64
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