MULTI-CRITERIA EVALUATION OF ARTIFICIAL NEURAL NETWORK APPLICATIONS USING TOPSIS
DOI:
https://doi.org/10.34218/IJCET_16_02_034Keywords:
Artificial Neural Networks, Applications, Smart ANN Master, TOPSIS MethodAbstract
Artificial Neural Networks (ANNs) represent a category of machine learning models that draw inspiration from the arrangement and operation of the human brain. These models consist of interconnected units or neurons, which are grouped into layers. ANNs find extensive application in computer-related tasks, encompassing activities like identifying patterns, categorizing information, predicting outcomes, and beyond. Within the domain of artificial intelligence and machine learning, Artificial Neural Networks (ANNs) have surfaced as a crucial and transformative technology. Within this abstract, we delve into a range of computer applications utilizing Artificial Neural Networks (ANNs), with each application strategically designed to leverage the capabilities of neural networks for various objectives. The applications discussed include NeuroNetApp, DeepLearnPro, SmartANN Master, AIBrainWave, and CogniTech Learn. The accompanying table presents distinct metrics related to each application, namely "SI Plus," "SI Negative," and "Ci." Although the precise definitions of these metrics are not revealed, they presumably offer insights into the performance, effectiveness, or pertinent characteristics of the applications. The realm of artificial intelligence has undergone a profound shift due to the influence of applications based on Artificial Neural Networks (ANNs). These applications have ushered in revolutionary progress across domains like image recognition, natural language processing, pattern recognition, and predictive modeling. This abstract serve to illuminate the importance and potential influence of these ANN-driven applications. However, it's important to acknowledge that further exploration and examination are essential to fully uncover the extensive capabilities and contributions these applications bring to the field of artificial intelligence.
References
Khan, Zabir Haider, Tasnim Sharmin Alin, and Md Akter Hussain. "Price prediction of share market using artificial neural network (ANN)." International Journal of Computer Applications 22, no. 2 (2011): 42-47.
Khan, Zabir Haider, Tasnim Sharmin Alin, and Md Akter Hussain. "Price prediction of share market using artificial neural network (ANN)." International Journal of Computer Applications 22, no. 2 (2011): 42-47.
Ramancha, Nitesh Kumar. "Statistical Evaluation of Incident Resolution Time Distribution at SAP." Journal of Computer Science Applications and Information Technology, vol. 8, no.
, 2023, pp. 1–10. DOI: 10.15226/2474-9257/8/1/001533.
Ashenayi, K., G. A. Nazi, J. F. Lea, and Frank Kemp. "Application of artificial neural network to pump card diagnosis." SPE Computer Applications 6, no. 06 (1994): 9-14.
Dachepalli. V, “Chatbot for ERP User Support Using AI” International Journal of Robotics and Machine Learning Technologies., 2025, vol. 1, no. 1, pp. 1–10. doi: http://dx.doi.org/10.55124/jmms.v1i1.236
Ballamudi, S."Performance Analysis of Machine Learning Algorithms in SAP Extended Warehouse Management Using ARAS Methodology" International Journal of Computer Science and Data Engineering., 2025,vol. 2, no. 2, pp. 1-15. doi: https://doi.org/10.55124/csdb.v2i2.246
Dachepalli. V, “Intelligent Resource Allocation in ERP with Machine Learning” Journal of Artificial intelligence and Machine Learning., 2025, vol. 3, no. 2, pp. 1–18. doi: http://dx.doi.org/10.55124/jaim.v3i2.257
Ramancha, Nitesh Kumar, "Performance Evaluation of AI Nursing Coordinators Using TOPSIS Method." Advances in Computer Sciences, vol. 4, no. 1, Nov. 2022, p. 123. Boffin Access Limited, doi:10.31021/ACS224125.
Narvekar, Meera, and Priyanca Fargose. "Daily weather forecasting using artificial neural network." (2015).
Mohaghegh, Shahab, Reza Arefi, Ilkin Bilgesu, Samuel Ameri, and Deanna Rose. "Design and development of an artificial neural network for estimation of formation permeability." SPE Computer Applications 7, no. 06 (1995): 151-154.
Ballamudi, S. "Interleaved Feature Extraction Model Bridging Multiple Techniques for Enhanced Object Identification" Journal of Artificial Intelligence and Machine Learning., 2023, vol. 1, no. 2, pp. 1-7. doi: https://doi.org/10.55124/ jbid.v1i2.253
Naik, Sapan, and Bankim Patel. "Machine vision based fruit classification and grading-a review." International Journal of Computer Applications 170, no. 9 (2017): 22-34.
Mittapally. R, “Optimizing Business Intelligence Solutions: A TOPSIS-based Assessment of Micro Strategy Implementation Alternatives” Journal of Business Intelligence and Data Analytics., 2025, vol. 2, no. 1, pp. 1–14. doi: https://dx.doi.org/10.55124/jbid.v2i1.237
Mittapally. R, “Security First: Evaluating Cloud Services for Data Protection and Compliance” International Journal of Computer Science and Data Engineering., Int. J. of. Comp. Sci. and Data Eng.2025, 2, 51-59. doi: https://dx.doi.org/10.55124/csdb.v1i1.240
Mittapally. R, “Predictive Modeling of Surface Roughness in Manufacturing A Study Using Multiple Machine Learning Techniques” International Journal of Robotics and Machine Learning Technologies., 2025, vol. 1, no. 1, pp. 19–33. doi: https://dx.doi.org/10.55124/jmms.v1i1.237
Mittapally. R, “Evaluating Machine Learning Techniques for Demand Forecasting in Supply Chains Using MOORA Method” Journal of Artificial Intelligence and Machine Learning., 2025, vol. 3, no. 1, pp. 1–13. doi: https://dx.doi.org/10.55124/jaim.v3i1.259
Nayak, Deepak Ranjan, Amitav Mahapatra, and Pranati Mishra. "A survey on rainfall prediction using artificial neural network." International journal of computer applications 72, no. 16 (2013).
Mangai, S. Alamelu, B. Ravi Sankar, and K. Alagarsamy. "Taylor series prediction of time series data with error propagated by artificial neural network." International Journal of Computer Applications 89, no. 1 (2014).
Ballamudi, S., “Comparative Analysis of Machine Learning Models for Laptop Price Prediction An Evaluation of Linear Regression, Histogram Gradient Boosting, and XG Boost Approaches” International Journal of Robotics and Machine Learning Technologies., 2025, vol. 1, no. 1, pp. 1–12. doi: https://doi.org/10.55124/jmms.v1i1.234
Mohammed, Nomaan Jaweed, and Mohamed Manzoor Ul Hassan. "Cryptosystem in artificial neural network in Internet of Medical Things in Unmanned Aerial Vehicle." Journal of Survey in Fisheries Sciences 10, no. 2S (2023): 2057-2072.
Veeresh. D, “AI-Driven Decision Support Systems in ERP” International Journal of Computer Science and Data Engineering., 2025, vol. 2, no. 2, pp. 1–7. doi: http://dx.doi.org/10.55124/csdb.v2i2.248
Dachepalli. V, “A Smarter ERP: How Artificial Intelligence is Reshaping Enterprise Workflows” Journal of Artificial intelligence and Machine Learning., 2024, vol. 2, no. 2, pp. 1–11. doi: http://dx.doi.org/10.55124/jaim.v2i2.265
Arashpour, Mehrdad, Emad M. Golafshani, Rajendran Parthiban, Julia Lamborn, Alireza Kashani, Heng Li, and Parisa Farzanehfar. "Predicting individual learning performance using machine‐learning hybridized with the teaching‐learning‐based optimization." Computer Applications in Engineering Education 31, no. 1 (2023): 83-99.
Ballamudi, S., “Evaluating IoT Platforms: An Approach Using the COPRAS Method” Journal of Data Science and Information Technology., 2025, vol. 2, no. 1, pp. 55–65. doi: https://doi.org/10.55124/jdit.v2i1.243
Mahadeva, Rajesh, Mahendra Kumar, Anubhav Goel, Shashikant P. Patole, and Gaurav Manik. "A novel AGPSO3-based ANN prediction approach: application to the RO desalination plant." Arabian Journal for Science and Engineering (2023): 1-12.
Zahmatkesh, Sasan, Yousof Rezakhani, Abdoulmohammad Gholamzadeh Chofreh, Melika Karimian, Chongqing Wang, Iman Ghodrati, Mudassir Hasan, Mika Sillanpaa, Hitesh Panchal, and Ramsha Khan. "SARS-CoV-2 removal by mix matrix membrane: A novel application of artificial neural network based simulation in MATLAB for evaluating wastewater reuse risks." Chemosphere 310
(2023): 136837.
Hephzipah, J. Jasmine, Ranadheer Reddy Vallem, M. Sahaya Sheela, and G. Dhanalakshmi. "An efficient cyber security system based on flow-based anomaly detection using Artificial neural network." Mesopotamian Journal of Cybersecurity 2023 (2023): 48-56.
Mittapally. R, “Redefining Business Intelligence Architecture with the EDAS Optimization Model” Journal of Business Intelligence and Data Analytics., 2025, vol. 3, no. 1, pp. 1–13. doi: https://dx.doi.org/10.55124/jbid.v1i2.249
Fiyadha, Seef Saadi, Saja Mohsen Alardhi, Mohamed Al Omar, Mustafa M. Aljumaily, Mohammed Abdulhakim Al Saadic, Sabah Saadi Fayaedd, Sulaiman Nayef Ahmede et al. "A comprehensive review on modelling the adsorption process for heavy metal removal from water using artificial neural network technique." Heliyon (2023).
Udaya Mohanan, Kannan, Seongjae Cho, and Byung-Gook Park. "Optimization of the structural complexity of artificial neural network for hardware-driven neuromorphic computing application." Applied Intelligence 53, no. 6 (2023): 6288-6306.
Dachepalli. V, “OPTIMIZED CLOUD SECURITY ECC-ENHANCED HOMOMORPHIC PAILLIER RE-ENCRYPTION” International Journal of Interpreting Enigma Engineers (IJIEE)., 2024, vol. 1, no. 2, pp. 1–7. doi: https://doi.org/10.62674/ijiee.2024.v1i02.001
Mousavi-Nasab, Seyed Hadi, and Alireza Sotoudeh-Anvari. "A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems." Materials & Design 121 (2017): 237-253.
Ballamudi, S. "Interleaved Feature Extraction Model Bridging Multiple Techniques for Enhanced Object Identification" Journal of Artificial Intelligence and Machine Learning., 2023, vol. 1, no. 2, pp. 1-7. doi: https://doi.org/10.55124/jbid.v1i2.253
Ozkaya, Gokhan, and Ceren Erdin. "Evaluation of smart and sustainable cities through a hybrid MCDM approach based on ANP and TOPSIS technique." Heliyon 6, no. 10 (2020).
Wang, Jue, San-Yang Liu, and Jie Zhang. "An extension of TOPSIS for fuzzy MCDM based on vague set theory." Journal of systems science and systems engineering 14 (2005): 73-84.
Li, Ming, Lei Jin, and Jun Wang. "A new MCDM method combining QFD with TOPSIS for knowledge management system selection from the user's perspective in intuitionistic fuzzy environment." Applied soft computing 21 (2014): 28-37.
Ramancha, N. K. "Evaluating the Role of Artificial Intelligence in Solar Energy Optimization Using the COPRAS Method." Advances in Computer Sciences, vol. 4, no. 1, Nov. 2022, p. 123. Boffin Access Limited, doi:10.31021/ACS224123.
Dachepalli. V, “DATA ANALYSIS FOR STUDENTS BASED ON GEOLOCATION APPROACH” International Journal of Interpreting Enigma Engineers (IJIEE)., 2024, vol. 1, no. 3, pp. 33–41. doi: https://doi.org/10.62674/ijiee.2024.v1i03.005
Nagababu. K, “Evolution and Impact of Data Warehousing in Modern Business and Decision Support Systems” International Journal of Computer Science and Data Engineering., 2025, vol. 2, no. 2, pp. 1–11.doi: https://dx.doi.org/10.55124/jdit.v2i1.249
Nagababu. K, “Optimizing Image Processing in OmniView with EDAS Decision-Making” Journal of Business Intelligence and Data Analytics., 2025, vol. 2, no. 2, pp. 1–12.doi: https://dx.doi.org/10.55124/jbid.v2i2.248
Nagababu. K, “Innovative Fabrication of Advanced Robots Using the Waspas Method A New Era in Robotics Engineering” International Journal of Robotics and Machine Learning Technologies., 2025, vol. 1, no. 1, pp. 1–12. doi: http://dx.doi.org/10.55124/jmms.v1i1.235
Alrababah, Saif A. Ahmad, Keng Hoon Gan, and Tien-Ping Tan. "Comparative analysis of MCDM methods for product aspect ranking: TOPSIS and VIKOR." In 2017 8th International Conference on Information and Communication Systems (ICICS), pp. 76-81. IEEE, 2017.
Wang, Peng, Yang Li, Yong-Hu Wang, and Zhou-Quan Zhu. "A new method based on TOPSIS and response surface method for MCDM problems with interval numbers." Mathematical Problems in Engineering 2015 (2015).
Chen, Jui-Kuei, and I-Shuo Chen. "Using a novel conjunctive MCDM approach based on DEMATEL, fuzzy ANP, and TOPSIS as an innovation support system for Taiwanese higher education." Expert Systems with Applications 37, no. 3 (2010): 1981-1990.
Nagababu. K, “Machine Learning Approaches to Predict Tensile Strength in Nanocomposite Materials a Comparative Analysis” Journal of Artificial intelligence and Machine Learning., 2024, vol. 2, no. 1, pp. 1–16. doi: http://dx.doi.org/10.55124/jaim.v2i1.255
Nagababu. K, “Machine Learning Techniques in Fracture Mechanics a Comparative Study of Linear Regression, Random Forest, and Ada Boost Model” Journal of Artificial intelligence and Machine Learning., 2024, vol. 2, no. 2, pp. 1–13. doi: http://dx.doi.org/10.55124/jaim.v2i2.257
Khan, Akhtar, and Kalipada Maity. "Application of MCDM-based TOPSIS method for the selection of optimal process parameter in turning of pure titanium." Benchmarking: An International Journal 24, no. 7 (2017): 2009-2021.
Khan, Akhtar, and Kali Pada Maity. "Application of MCDM-based TOPSIS method for the optimization of multi quality characteristics of modern manufacturing processes." International Journal of Engineering Research in Africa 23 (2016): 33-51.
Raju, Siva S., Gunji B. Murali, and Prabina K. Patnaik. "Ranking of Al-CSA composite by MCDM approach using AHP–TOPSIS and MOORA methods." Journal of Reinforced Plastics and Composites 39, no. 19-20 (2020): 721-732.
Goswami, Shankha Shubhra, Dhiren Kumar Behera, Asif Afzal, Abdul Razak Kaladgi, Sher Afghan Khan, Parvathy Rajendran, Ram Subbiah, and Mohammad Asif. "Analysis of a robot selection problem using two newly developed hybrid MCDM models of TOPSIS-ARAS and COPRAS-ARAS." Symmetry 13, no. 8 (2021): 1331.
Wang, Chia-Nan, Ying-Fang Huang, I-Fang Cheng, and Van Thanh Nguyen. "A multi-criteria decision-making (MCDM) approach using hybrid SCOR metrics, AHP, and TOPSIS for supplier evaluation and selection in the gas and oil industry." Processes 6, no. 12 (2018): 252.
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