Kishen Ajay kumar

Mr. Kishen Ajay kumar


Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2022 in Journal of Mathematics
Verified
Journal Article
Open access
PDF file
KKKishen Ajay kumar
Kishen Ajay kumar
MKM. Rudra Kumar
M. Rudra Kumar
NSN. Shribala
N. Shribala
NSNinni Singh
Ninni Singh
VGVinit Kumar Gunjan
Vinit Kumar Gunjan
MAMuhammad Arif
Muhammad Arif
The burst dropping ratio is witnessed in the contemporary literature as a considerable constraint of optical burst switching (OBS) networks that attained many researchers’ efforts in the recent past. Among the multiple practices endeavoring to reduce the burst drop ratio, the optimal burst scheduling is one dimension in this regard. The transmission channel scheduling and appropriate wavelength allocation are critical objectives to achieve optimal burst scheduling in regard to minimal burst drop ratio. Many of the scheduling models depicted in the contemporary literature aimed to achieve the optimum scheduling by electing the channels, which depend on optimum utilization of idle time. Some of the studies tried to select channels by any metrics of quality, and significantly minimal amount of studies focused on wavelength allocation for lowering BDR. Moreover, in regard to this, this study tried to achieve optimum wavelength allocation beneath manifold objective QoS metrics, which is identified as “multiobjective dynamic wavelength scheduling (DyWaS).” The experimental study carried through the simulations evinced that the proposed model DyWaS escalated the optimality of burst scheduling through wavelength allocation compared with other existing methods represented in the contemporary literature.

10 April 2019 in Wireless Personal Communications
Verified
Journal Article
Closed access
PDF file
KKKishen Ajay kumar
Kishen Ajay kumar
KRKatam Suresh Reddy
Katam Suresh Reddy
MPMahendra Giri Prasad
Mahendra Giri Prasad

25 April 2025
Verified
Book Chapter
Closed access
RGRamakrishna Rama Gandi
Ramakrishna Rama Gandi
GSG. Suseela
G. Suseela
SSSwati Singh
Swati Singh
LKLnc. Prakash K
Lnc. Prakash K
KKKishen Ajay kumar
Kishen Ajay kumar
MSMalla Sudhakara
Malla Sudhakara
Rehabilitation is undergoing a revolution with machine learning (ML) enabling personalized solutions based on patient-specific data. Unlike current rehabilitation practices that lack adaptability, ML optimizes physical, cognitive, and psychological aspects through real-time monitoring, modeling, and intervention. Computer vision enhances movement analysis, NLP supports psychological assessments, and predictive models optimize treatment plans. Applications such as wearable technology, telerehabilitation, and ML-driven robotics improve patient interaction and supervision, offering flexible and remote care. Integrating ML with technologies like VR and IoT creates patient-centric, accessible solutions with superior prognostic value. These advancements usher in a new era of algorithms designed for better patient care, safety, and personalized therapeutic processes.

9 March 2018 in Journal of Optics
Verified
Journal Article
Closed access
PDF file
KKKishen Ajay kumar
Kishen Ajay kumar
KRK. Suresh Reddy
K. Suresh Reddy
MPM. N. Giri Prasad
M. N. Giri Prasad

27 September 2022
Verified
Book Chapter
Closed access
PDF file
KKKishen Ajay kumar
Kishen Ajay kumar
KRK. Suresh Reddy
K. Suresh Reddy
APA. Prashanth
A. Prashanth
BKB. Ravi Kumar
B. Ravi Kumar

15 June 2026 in Internet Technology Letters
Verified
Journal Article
Closed access
P Kiran RaoP Kiran Rao
P Kiran Rao
PPP. Suman Prakash
P. Suman Prakash
ARA. Vishnuvardhan Reddy
A. Vishnuvardhan Reddy
KKKishen Ajay kumar
Kishen Ajay kumar
ASAfsha Jabeen Shaik
Afsha Jabeen Shaik