DSDL KIET

Hackthon Victories

Innotech-22 Winner

Rohit and Vartul, with Rohit being a member of the DSDL Club, showcased their prowess at Innotech-22, winning accolades for their innovative contributions. Their victory underscores the impact of collaboration and the cutting-edge insights fostered within technology-focused clubs, reaffirming their commitment to excellence in the field.

TechHacks 3.0

Kanisk Jaiswal, a key member of the DSDL Club, achieved the 1st Runner-Up position with cash prize of 10k at Techacks 3.0, Chitkara University held on 5-6 January 2023. Collaborating with the talented team of Piyush Sharma, Khushi Sachdev, and Sampada, they showcased exceptional skills and innovation. Congratulations to the DSDL Club for yet another remarkable triumph in the dynamic world of technology.

NASA Space Apps

Kanisk Jaiswal, a standout member of the DSDL Club, led the team to become the global nominee and secure an impressive 3rd position in the Greater Noida region with cash prize of 5K held on 2nd-3rd October 2022 . Collaborating with the talented team of Shivansh Pandey, Garima Shukla , Manas and their innovative prowess shone brightly.

Ideathon ISABVP

Anuj Gupta, Manisha Maurya a standout members of the DSDL Club, led the team to secure the 3rd position in the ideathon organized by ISABVP. Alongside the talented team of Akshat Srivastava and Ira Nafees for their innovative performance.

Publications

A Novel Model for Stress Detection and Management using Machine Learning

Explore a pioneering stress detection and management model by Aditi Agarwal, Astha Goel, Shristy Sharma, Soniya Jain, Mani Dwivedi, and Deep Kumar, published in IEEE Xplore, revolutionizing well-being through advanced machine learning techniques.

A comprehensive review on the advancement of high-dimensional neural networks in quaternionic domain with relevant applications.

The neurocomputing communities have focused much interest on quaternionic-valued neural networks (QVNNs) due to the natural extension in quaternionic signals, learning of inter and spatial relationships between the features, and remarkable improvement against real-valued neural networks (RVNNs) and complex-valued neural networks (CVNNs).