Nikita Jindal
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Nikita Jindal has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Balwinder Kaur
School of Computer Science and Engineering, Lovely Professional University, Punjab 144411, India
1 shared publication - Changho Seo
Department of Convergence Science, Kongju National University, Gongju 32588, Republic of Korea
1 shared publication - Deepak Prashar
Department of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India
1 shared publication - Gyanendra Prasad Joshi
Department of Advanced AI Engineering, Kangwon National University, Samcheok, Republic of Korea
1 shared publication - Harsh Sadawarti
School of Engineering and Technology, CT University, Punjab 142024, India
1 shared publication - Jimmy Singla
Lovely Professional University, India
1 shared publication
Latest Publications
- Open Access
- 17 May 2020
- Published by MDPI AG in Applied Sciences
- Vol. 10 (10) , 3464
Fuzzy Logic Systems for Diagnosis of Renal Cancer
Renal cancer is a serious and common type of cancer affecting old ages. The growth of such type of cancer can be stopped by detecting it before it reaches advanced or end-stage. Hence, renal cancer must be identified and diagnosed in the initial stages. In this research paper, an intelligent medical diagnostic system to diagnose renal cancer is developed by using fuzzy and neuro-fuzzy techniques. Essentially, for a fuzzy inference system, two layers are used. The first layer gives the output about whether the patient is having renal cancer or not. Similarly, the second layer detects the current stage of suffering patients. While in the development of a medical diagnostic system by using a neuro-fuzzy technique, the Gaussian membership functions are used for all the input variables considered for the diagnosis. In this paper, the comparison between the performance of developed systems has been done by taking some suitable parameters. The results obtained from this comparison study show that the intelligent medical system developed by using a neuro-fuzzy model gives the more precise and accurate results than existing systems.
- 17 May 2020
- Published by MDPI AG in Applied Sciences
- Vol. 10 (10) , 3464
Open AccessFuzzy Logic Systems for Diagnosis of Renal Cancer
Renal cancer is a serious and common type of cancer affecting old ages. The growth of such type of cancer can be stopped by detecting it before it reaches advanced or end-stage. Hence, renal cancer must be identified and diagnosed in the initial stages. In this research paper, an intelligent medical diagnostic system to diagnose renal cancer is developed by using fuzzy and neuro-fuzzy techniques. Essentially, for a fuzzy inference system, two layers are used. The first layer gives the output about whether the patient is having renal cancer or not. Similarly, the second layer detects the current stage of suffering patients. While in the development of a medical diagnostic system by using a neuro-fuzzy technique, the Gaussian membership functions are used for all the input variables considered for the diagnosis. In this paper, the comparison between the performance of developed systems has been done by taking some suitable parameters. The results obtained from this comparison study show that the intelligent medical system developed by using a neuro-fuzzy model gives the more precise and accurate results than existing systems.