Abhishek Kumar
@cuchd.in
Assistant Director
Chandigarh University
Dr. Abhishek Kumar is currently working as an Assistant director /Associate professor in Computer science & Engineering Department in Chandigarh University, Punjab, India .He is Doctorate in computer science from University of Madras and is doing Post-Doctoral Fellow in Ingenium Research Group Ingenium Research Group Lab, Universidad De Castilla-La Mancha, Ciudad Real, and Ciudad Real Spain. He has done M.Tech in Computer Sci. & Engineering and B.Tech in I.T. from, Rajasthan Technical University, Kota India. He has total Academic teaching experience of more than 11 years along with 2 years teaching assistantship. He is having more than 100 publications in reputed, peer reviewed National and International Journals, books & Conferences He has guided more than 30 M.Tech Projects at national and International level and guiding 6 PhD Scholar. His research area includes- Artificial intelligence, Renewable Energy Image processing, Computer Vision, Data Mining, Machine Learning. He has been Se
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Engineering, Health Information Management, Energy
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Pramod Singh Rathore, Abhishek Kumar, Amita Nandal, Arvind Dhaka, and Arpit Kumar Sharma
Springer Science and Business Media LLC
Abhishek Kumar, Priya Batta, Pramod Singh Rathore, and Sachin Ahuja
Springer Science and Business Media LLC
, Kruthika S. G, Trisiladevi C. Nagavi, P. Mahesha, and Abhishek Kumar
MECS Publisher
Benkhaddra Ilyas, Abhishek Kumar, Setitra Mohamed Ali, and Hang Lei
Springer Science and Business Media LLC
Mariya Ouaissa, Mariyam Ouaissa, Zakaria Boulouard, Abhishek Kumar, Vandana Sharma, and Keshav Kaushik
CRC Press
Ankita Parihar, Amit Kumar, and Abhishek
CRC Press
Hemant Kumar Saini and Abhishek Kumar
CRC Press
Abhishek Kumar, Hemant Kumar Saini, Ashutosh Kumar Dubey, and Vicente García Díaz
CRC Press
Priya Batta, Sachin Ahuja, and Abhishek Kumar
Springer Science and Business Media LLC
Abhishek Kumar, Ashutosh Kumar Dubey, Isaac Segovia Ramírez, Alba Muñoz del Río, and Fausto Pedro García Márquez
Springer Science and Business Media LLC
AbstractNovel algorithms and techniques are being developed for design, forecasting and maintenance in photovoltaic due to high computational costs and volume of data. Machine Learning, artificial intelligence techniques and algorithms provide automated, intelligent and history-based solutions for complex scenarios. This paper aims to identify through a systematic review and analysis the role of artificial intelligence algorithms in photovoltaic systems analysis and control. The main novelty of this work is the exploration of methodological insights in three different ways. The first approach is to investigate the applicability of artificial intelligence techniques in photovoltaic systems. The second approach is the computational study and analysis of data operations, failure predictors, maintenance assessment, safety response, photovoltaic installation issues, intelligent monitoring etc. All these factors are discussed along with the results after applying the artificial intelligence techniques on photovoltaic systems, exploring the challenges and limitations considering a wide variety of latest related manuscripts.
Maibam Naresh Singh, Abhishek Kumar, and Paurav Goel
IEEE
Biotic and abiotic stresses lower crop production in agriculture by 22 percent. These include diseases and environmental factors. Early identification of these stresses is very necessary for management purposes. Recently, computer vision techniques have turned out to be very effective tools in recognizing the first appearance of plant diseases. Deep learning techniques coupled with computer vision have given rise to recognition, either single or multiple, of the biotic stresses on a plant's leaf. It offers accurate segmentation of objects using a PlantVillage dataset. The architecture of a Hybrid-CNN is optimized for the segmentation of diseased plant leaves, having been trained with a combination of meticulous annotation and augmented data. Evaluations relative to traditional architectures like U-Net and Seg-Net imply that Hybrid-CNN models do better, much more so when it comes to instance segmentation. Semantic segmentation data help to identify and classify single leaf and multiple-leaf diseases with the highest level of accuracy. The newly proposed Hybrid-CNN model gives an impressive accuracy of 94 percent on the validation set, which consists of 10,861 images, thereby proving superior performance to modified segmentation models. These results strongly try to underline the efficacy of deep learning approaches in agricultural disease management.
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
Method Of Data Transmission In A Cluster Network
INDIAN PATENT OFFICE
System And Method For Cluster Head Selection And Cluster Formation For Improving Radio Frequency Identification
Network
INDIAN PATENT OFFICE
202111022269
Iot Enabled Wall Climbing Robot For Security
IP AUSTRALIA /GRANTED
2021101471
An Artificial Intelligence And IoT Based Method For Prevention Of Security Attack On Cloud Medical Data
IP AUSTRALIA/ GRANTED
2021102115
Iot Based Generic Framework For Computer Security Using Artificial Immune System
IP AUSTRALIA /GRANTED
2021102104
Podium with display facility, box and glass holder
INDIAN PATENT OFFICE/GRANTED
346057-001
Hexa Tube LED Bulb
INDIAN PATENT OFFICE/GRANTED
356883001
SMART SHOPPING CART
INDIAN PATENT OFFICE
202111061690
202111018897