Rajan Prasad

Work place: Artificial Intelligence Research Center, Department of Computer Science and Engineering, Babu Banarasi Das University, Lucknow, India

E-mail: rajan18781@gmail.com

Website: https://orcid.org/0000-0002-5238-9690

Research Interests: Operating Systems, Computer systems and computational processes


Rajan Prasad received the Bachelor of Technology in Computer Science & Engineering and Master of Technology in Software Engineering degrees from the Babu Banarasi Das University, Lucknow India. He is Research Scholar in Department of Computer Science and Engineering, Babu.

Banarasi Das University, Lucknow India. His research interest includes Fuzzy Systems, Machine learning and soft computing.

Author Articles
Interpretable Fuzzy System for Malicious Domain Classification Using Projection Neural Network

By Rajan Prasad Praveen Kumar Shukla

DOI: https://doi.org/10.5815/ijwmt.2023.06.01, Pub. Date: 8 Dec. 2023

In this study, we suggest an interpretable fuzzy system for the classification of malicious domains. The proposed system is integration of Sugeno type fuzzy system and projection neural network, the main advantage of interpretable fuzzy system is to classify the patterns and self-explainable capability. Whereas the projection network is used to exact mapped fuzzy inference rules to the network's projection layer. On the other hands, the system is able to deal with large amount of real-time data. The proposed model is tested malicious URL datasets collected from Alexa. The experimental results show that the system is able to classify malicious domain with high accuracy and interpretability as compared to existing methods. The proposed model is usefull to classify malicious attacks and explain the couses behind the decision. The evaluation of model based on confusion matrices, ROC and the nauck index is used for the interpretability assessments.

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Interpretable Fuzzy System for Early Detection Autism Spectrum Disorder

By Rajan Prasad Praveen Kumar Shukla

DOI: https://doi.org/10.5815/ijisa.2023.04.03, Pub. Date: 8 Aug. 2023

Autism spectrum disorder (ASD) is a chronic developmental impairment that impairs a person's ability to communicate and connect with others. In people with ASD, social contact and reciprocal communication are continually jeopardized. People with ASD may require varying degrees of psychological aid in order to gain greater independence, or they may require ongoing supervision and care. Early discovery of ASD results in more time allocated to individual rehabilitation. In this study, we proposed the fuzzy classifier for ASD classification and tested its interpretability with the fuzzy index and Nauck's index to ensure its reliability. Then, the rule base is created with the Gauje tool. The fuzzy rules were then applied to the fuzzy neural network to predict autism. The suggested model is built on the Mamdani rule set and optimized using the backpropagation algorithm. The proposed model uses a heuristic function and pattern evolution to classify dataset. The model is evaluated using the benchmark metrics accuracy and F-measure, and Nauck's index and fuzzy index are employed to quantify interpretability. The proposed model is superior in its ability to accurately detect ASD, with an average accuracy rate of 91% compared to other classifiers.

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Indeterminacy Handling of Adaptive Neuro-fuzzy Inference System Using Neutrosophic Set Theory: A Case Study for the Classification of Diabetes Mellitus

By Rajan Prasad Praveen Kumar Shukla

DOI: https://doi.org/10.5815/ijisa.2023.03.01, Pub. Date: 8 Jun. 2023

Early diabetes diagnosis allows patients to begin treatment on time, reducing or eliminating the risk of serious consequences. In this paper, we propose the Neutrosophic-Adaptive Neuro-Fuzzy Inference System (N-ANFIS) for the classification of diabetes. It is an extension of the generic ANFIS model. Neutrosophic logic is capable of handling the uncertain and imprecise information of the traditional fuzzy set. The suggested method begins with the conversion of crisp values to neutrosophic sets using a trapezoidal and triangular neutrosophic membership function. These values are fed into an inferential system, which compares the most impacted value to a diagnosis. The result demonstrates that the suggested model has successfully dealt with vague information. For practical implementation, a single-value neutrosophic number has been used; it is a special case of the neutrosophic set. To highlight the promising potential of the suggested technique, an experimental investigation of the well-known Pima Indian diabetes dataset is presented. The results of our trials show that the proposed technique attained a high degree of accuracy and produced a generic model capable of effectively classifying previously unknown data. It can also surpass some of the most advanced classification algorithms based on machine learning and fuzzy systems.

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