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Reseach Article

Analyzing the Impact of Artificial Intelligence Approaches on Sustainability

by Jaskirat Kaur
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 40
Year of Publication: 2024
Authors: Jaskirat Kaur
10.5120/ijca2024923991

Jaskirat Kaur . Analyzing the Impact of Artificial Intelligence Approaches on Sustainability. International Journal of Computer Applications. 186, 40 ( Sep 2024), 18-25. DOI=10.5120/ijca2024923991

@article{ 10.5120/ijca2024923991,
author = { Jaskirat Kaur },
title = { Analyzing the Impact of Artificial Intelligence Approaches on Sustainability },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2024 },
volume = { 186 },
number = { 40 },
month = { Sep },
year = { 2024 },
issn = { 0975-8887 },
pages = { 18-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number40/analyzing-the-impact-of-artificial-intelligence-approaches-on-sustainability/ },
doi = { 10.5120/ijca2024923991 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-09-27T00:46:19.255696+05:30
%A Jaskirat Kaur
%T Analyzing the Impact of Artificial Intelligence Approaches on Sustainability
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 40
%P 18-25
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Artificial Intelligence is the science and engineering of making intelligent machines, aimed at providing machines with the ability to think, reach, and surpass human-level intelligence. Three essential terms Automation, i.e., reducing human interaction in operations, Intelligent, i.e., ability to extract insights or usable knowledge from data, and smart computing, i.e., self-monitoring, analyzing, and reporting, known as self-awareness, have evolved into essential standards for creating modern systems and applications in all area of our life, as we live in a technologically dependent society. AI offers creative, scalable, and reasonably priced solutions; it enables underdeveloped nations to overcome conventional developmental obstacles. AI reduces inequality, increases resilience to environmental challenges, and promotes equitable growth. This paper highlights ideas and capabilities of potential AI techniques that can be applied to create intelligent and smart systems that improve productivity, make better decisions, manage resources, etc. in a range of real-world domains, including cyber security, smart cities, finance, business, and agriculture. This study examines the relationship between AI and the Sustainable Development Goals (SDGs), emphasizing how important AI is to advancing sustainability.

References
  1. Kanade V. What is artificial intelligence (AI)? Definition, types, goals, challenges, and trends in 2022. https://www.spiceworks.com/tech/artificial-intelligence/articles/what-is-ai
  2. Russell S, Norvig P. Artificial intelligence: a modern approach, global edition 4th. Foundations. 2021;19:23.
  3. Avrin G. Assessing artificial intelligence capabilities. Available online: https://www.oecd-ilibrary.org/education/ai-and-the-future-of-skills-volume-1_47d04fe3-en (accessed on 2 January 2024).
  4. Lammers, T., Rashid, L., Kratzer, J., Voinov, A., 2022. An analysis of the sustainability goals of digital technology start-ups in Berlin. Technol. Forecast. Soc. Chang. https://doi.org/10.1016/j.techfore.2022.122096.
  5. Gupta, B.B., Gaurav, A., Panigrahi, P.K., Arya, V., 2023. Analysis of artificial intelligence- based technologies and approaches on sustainable entrepreneurship. Technol. Forecast. Soc. Chang. 186, 122152 https://doi.org/10.1016/j. techfore.2022.122152.
  6. Di Vaio A, Palladino R, Hassan R, et al. Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research. 2020; 121: 283-314. doi: 10.1016/j.jbusres.2020.08.019
  7. Farahani MS, Esfahani A, Alipoor F. The Application of Machine Learning in the Corona Era, With an Emphasis on Economic Concepts and Sustainable Development Goals. International Journal of Mathematical, Engineering, Biological and Applied Computing. 2022; 1(2): 95-149. doi: 10.31586/ijmebac.2022.519.
  8. Sarker IH. Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Com- put Sci. 2021;2(6):1–20.
  9. Sarker Iqbal H. Data science and analytics: an overview from data-driven smart computing, decision-making and applications perspective. SN Comput Sci. 2021;20:21.
  10. Nadkarni, P.M., Ohno-Machado, L., and Chapman, W. (2011). Natural Language Processing: An Introduction, 18 (Journal of the American Medical Informatics Association Jamia), 544–551.
  11. Massaoudi M, Abu-Rub H, Refaat SS, Chihi I, Oueslati FS. Deep learning in smart grid technology: a review of recent advancements and future prospects. IEEE Access. 2021;9:54558–78.
  12. Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Dou- lamis, Eftychios Protopapadakis. Deep learning for computer vision: a brief review. Comput Intell Neurosci. 2018;20:18.
  13. Allahyari M, Pouriyeh S, Assefi M, Safaei S, Trippe ED, Gutier- rez JB, Kochut K. A brief survey of text mining: classification, clustering and extraction techniques. arXiv:1707.02919 (arXiv preprint), 2017.
  14. Deng L, Liu Y. Deep learning in natural language processing. Berlin: Springer; 2018.
  15. Singh, B.; Kumar, R.; Singh, V.P. Reinforcement Learning in Robotic Applications: A Comprehensive Survey. Artif. Intell. Rev. 2022, 55, 1–46.
  16. Padallan JO. Key Concepts in Artificial Intelligence. Arcler Press; 2022.
  17. DIXIT, N. S., HINGOLE, R. S., 2020. Review on Knowledge Based Expert System Applications in Metal Forming Processes. In: Journal of Xi'an University of Architecture & Technology. ISSN 1006-7930.
  18. Hamamoto AH, Carvalho LF, Sampaio LDH, Abrão T, Proença ML Jr. Network anomaly detection system using genetic algo- rithm and fuzzy logic. Expert Syst Appl. 2018;92:390–402.
  19. Lamy J-B, Sekar B, Guezennec G, Bouaud J, Séroussi B. Explainable artificial intelligence for breast cancer: a visual case- based reasoning approach. Artif Intell Med. 2019;94:42–53.
  20. Arora NK, Mishra I. United Nations Sustainable Development Goals 2030 and environmental sustainability: race against time. Environmental Sustainability. 2019; 2(4): 339-342. doi: 10.1007/s42398-019-00092-y
  21. THE 17 GOALS | Sustainable Development (un.org) https://sdgs.un.org/goals
  22. Vinuesa R, Azizpour H, Leite I, et al. The role of artificial intelligence in achieving the Sustainable Development Goals. Nature communications. 2020; 11(1): 1-10. doi: 10.1088/1757-899X/982/1/012063/meta
  23. Krishnan RS, Julie EG, Robinson YH, Raja S, Kumar R, Thong PH, et al. Fuzzy logic based smart irrigation system using inter net of things. J Clean Prod. 2020;252:119902.
  24. Schwalbe N, Wahl B. Artificial intelligence and the future of global health. The Lancet. 2020;395:1579–86.
  25. Leal Filho, W., Wall, T., Rui Mucova, S.A., Nagy, G.J., Balogun, A.-L., Luetz, J.M., Ng, A. W., Kovaleva, M., Safiul Azam, F.M., Alves, F., Guevara, Z., Matandirotya, N.R., Skouloudis, A., Tzachor, A., Malakar, K., Gandhi, O., 2022. Deploying artificial intelligence for climate change adaptation. Technol. Forecast. Soc. Chang. 180, 121662 https://doi.org/10.1016/j.techfore.2022.121662.
  26. Pilipczuk O. Sustainable smart cities and energy management: the labor market perspective. Energies. 2020;13:6084.
  27. Ebrahimi SH, Ossewaarde M, Need A. Smart fishery: a systematic review and research agenda for sustainable fisheries in the age of AI. Sustainability (Switzerland). 2021;13:6037
  28. Giannetti BF, Diaz Lopez FJ, Liu G, et al. A resilient and sustainable world: Contributions from cleaner production, circular economy, eco-innovation, responsible consumption, and cleaner waste systems. Journal of Cleaner Production. 2023; 384: 135465. doi: 10.1016/j.jclepro.2022.135465
  29. Isabelle DA, Westerlund M. A Review and Categorization of Artificial Intelligence-Based Opportunities in Wildlife, Ocean and Land Conservation. Sustainability. 2022; 14(4): 1979. doi: 10.3390/su14041979
  30. Dasandi N, Mikhaylov SJ. AI for SDG 16 on Peace, Justice, and Strong Institutions: Tracking Progress and Assessing Impact. Available online: https://sjankin.com/assets/img/research/ijcai19-sdg16.pdf (accessed on 2 January 2024).
  31. de Lange DE. Responsible Artificial Intelligence and Partnerships for the Goals. In Partnerships for the Goals. Springer International Publishing; 2021.
  32. González-Briones A, Prieto J, De La Prieta F, Herrera-Viedma E, Corchado JM. Energy optimization using a case-based reasoning strategy. Sensors. 2018;18(3):865.
  33. Holmes J, Moraes OR, Rickards L, et al. Online learning and teaching for the SDGs–exploring emerging university strategies. International Journal of Sustainability in Higher Education. 2022; 23(3): 503-521. doi: 10.1108/IJSHE-07-2020-0278/full/html
  34. Noriega M. The application of artificial intelligence in police interrogations: An analysis addressing the proposed effect AI has on racial and gender bias, cooperation, and false confessions. Futures. 2020; 117: 102510. doi: 10.1016/j.futures.2019.102510
  35. Kulkov I, Kulkova J, Rohrbeck R, et al. Artificial intelligence-driven sustainable development: Examining organizational, technical, and processing approaches to achieving global goals. Sustainable Development. Published online October 6, 2023. doi: 10.1002/sd.2773
  36. Naudé W, Vinuesa R. Data deprivations, data gaps and digital divides: Lessons from the COVID-19 pandemic. Big Data &Society 2021. https://doi.org/10.1177/20539517211025545
  37. Blumenstock J. Machine learning can help get covid-19 aid to those who need it most. Nature. 2020;20:20.
  38. Sarker Iqbal H. Cyberlearning: effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi- attacks. Internet Things. 2021;100:393.
  39. Saharan S, Kumar N, Bawa S. An efficient smart parking pricing system for smart city environment: a machine-learning based approach. Future Gener Comput Syst. 2020;106:622–40.
  40. Ramzan B, Bajwa IS, Jamil N, Amin RU, Ramzan S, Mirza F, Sarwar N. An intelligent data analysis for recommendation sys- tems using machine learning. Sci Programm. 2019;20:19.
  41. Kim J-Y, Seok-Jun B, Cho S-B. Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders. Inf Sci. 2018;460:83–102.
  42. Piccialli F, Giampaolo F, Prezioso E, Crisci D, Cuomo S. Pre- dictive analytics for smart parking: a deep learning approach in forecasting of iot data. ACM Trans Internet Technol. 2021;21(3):1–21
  43. Ale L, Sheta A, Li L, Wang Y, Zhang N. Deep learning based plant disease detection for smart agriculture. In: 2019 IEEE globecom workshops (GC Wkshps), IEEE; 2019. p. 1–6
  44. Anuradha J, et al. Big data based stock trend prediction using deep cnn with reinforcement-lstm model. Int J Syst Assur Eng Manage. 2021;2:1–11.
  45. Dhyani M, Kumar R. An intelligent chatbot using deep learning with bidirectional rnn and attention model. Mater Today Proc. 2021;34:817–24.
  46. Reddy GT, Reddy MPK, Lakshmanna K, Rajput DS, Kaluri R, Srivastava G. Hybrid genetic algorithm and a fuzzy logic classi- fier for heart disease diagnosis. Evol Intel. 2020;13(2):185–96.
  47. Kang X, Porter CS, Bohemia E. Using the fuzzy weighted asso- ciation rule mining approach to develop a customer satisfaction product form. J Intell Fuzzy Syst. 2020;38(4):4343–57.
  48. Goel D, Pahal N, Jain P, Chaudhury S. An ontology-driven con- text aware framework for smart traffic monitoring. In: 2017 IEEE region 10 symposium (TENSYMP), IEEE; 2017. p. 1–5
  49. Kiran GM, Nalini N. Enhanced security-aware technique and ontology data access control in cloud computing. Int J Commun Syst. 2020;33(15):e4554.
  50. Syed R. Cybersecurity vulnerability management: a conceptual ontology and cyber intelligence alert system. Inform Manage. 2020;57(6):103334.
  51. Sarker IH, Khan AI, Abushark YB, Alsolami F. Mobile expert system: exploring context-aware machine learning rules for per- sonalized decision-making in mobile applications. Symmetry. 2021;13(10):1975.
  52. Khosravani MR, Nasiri S, Weinberg K. Application of case-based reasoning in a fault detection system on production of drippers. Appl Soft Comput. 2019;75:227–32.
  53. Corrales DC, Ledezma A, Corrales JC. A case-based reason- ing system for recommendation of data cleaning algorithms in classification and regression tasks. Appl Soft Comput. 2020;90:106180.
  54. Elakkiya R, Subramaniyaswamy V, Vijayakumar V, Aniket Mahanti. Cervical cancer diagnostics healthcare system using hybrid object detection adversarial networks. IEEE J Biomed Health Inform. 2021;20:20.
  55. Harrou F, Zerrouki N, Sun Y, Houacine A. An integrated vision- based approach for efficient human fall detection in a home envi- ronment. IEEE Access. 2019;7:114966–74.
  56. Pan M, Liu Y, Jiayi Cao Yu, Li CL, Chen C-H. Visual recognition based on deep learning for navigation mark classification. IEEE Access. 2020;8:32767–75.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial intelligence (AI); Sustainable development goals (SDGs) Machine learning (ML) · Deep Learning (DL). Natural Language Processing (NLP) Robotics Expert systems