Choudhary Satya Kumar

EDUCATION

M.Tech : Computer Science & Engineering

RESEARCH INTERESTS

MANETS

Scopus Publications

Vehicle light communication design using an efficient learning model
C. Sathish kumar and R.K. Jeyachitra
Elsevier BV



Empowering Agriculture: Classification of Diseases Affecting the Leaves and Spikes of Wheat Crop using Hybrid Deep Learning Methodology
C. Sathish Kumar, Vijay Anand Kandaswamy, P. Sundara Bala Murugan, Srividhya S, P. Arivazhagi, and R. Lathamanju
IEEE
Worldwide, wheat ranks third in both harvesting and consumption of grains. Nevertheless, illnesses ruin a significant portion of wheat crops. Wheat crops are vulnerable to more than twenty different diseases. Consequently, it becomes exceedingly difficult to manually diagnose these disorders. Increases in both production and quality can be achieved via the use of automated disease categorization in wheat. Moreover, it has the potential to be a valuable tool for evaluating crop quality and setting prices. Disease diagnosis and categorization can benefit from image analysis based on deep learning. Among wheat plants, the spike and leaves take the biggest hit. These features are diagnostic for the vast majority of illnesses. This is due to a combination of reasons, including the fact that farm laborers are often illiterate and the wide variety of agricultural goods available. There have been a number of different models put out there as possible answers to the problem of wheat harvest disease detection. In order to detect and classify illnesses that impact wheat harvests, this study presented a new approach called Hybrid Learning for Wheat Crop Disease Detection (HLWCDD). This methodology combines Convolutional Neural Network (CNN) and Random Forest (RF) algorithms. Deep Classification Learning Model (DCLM), an existing deep learning model, is essentially a hybrid of Neural Networks and Support Vector Machines (SVMs). The suggested model is compared to this. In this study, we assess both models and demonstrate how effective the suggested method is. The programme makes use of trained models to detect important features in the images. Using the main criteria mentioned earlier, the proposed approach can distinguish between wheat harvests that have been impacted by disease and those that have not. After collecting 3,200 photos for this investigation, the dependability of the findings was determined to be 97.29%. Out of the total number of photos, eleven classes showed sick crops and one showed healthy crops. The photos that make up the collection were rotated at different angles so that the proposed model could detect and categorize illnesses from multiple viewpoints.


Indistinguishability Obfuscation: A Key Enabler for Lightweight Provable Data Possession in Cloud Storage
P. Arivazhagi, C. Sathish Kumar, Vijay Anand Kandaswamy, P. Sundara Bala Murugan, T. Sripriya, and D. Kanchana
IEEE
Storage external outsourcing, with its emphasis on data from outside distribution, has been growing in popularity in recent years. A number of intriguing issues regarding security and privacy are raised by this development. The lack of responsibility from external storage of information suppliers is a major worry. In order to help consumers verify the availability and integrity of their outsourcing data on insecure storage devices, this paper provides a comprehensive analysis of two schemas/algorithms. The schemas that have been examined are Provable-Data-Possession (PDP) and Proof-of-Retrievability (POR, which stand for indications of accessibility. In order to reassure customers that their data is safe on untrusted data storages, both protocols use cryptography. Lightweight Cipher Policy Assisted Cloud Data Handler (LCPCDH) is a new model for managing highly secure, lightweight cloud storage. It is cross-validated with the traditional RSA based Cloud Storage Scheme (RSACSS) to assess its performance. A hybrid of RSA with a standard cloud storage solution is the only tried-and-true approach here. According to this new model, the public verifier and the cloud provider neither have access to the actual signatures that authenticate cloud data. However, there are still reliable methods for checking the legitimacy of cloud-based data. For certain unique cases, such as outsourcing electronic checks and contracts, it might be helpful. In order to safeguard the confidentiality of the authentication system and enable blockless confirmation, we have developed a new authenticator named LCPCDH. In this paper, we build the first remote information ownership verification system for cloud storage using privacy-preserving authenticators, based on LCPCDH. Our suggested approach is tested through theoretical analysis and simulated tests to ensure its security and efficiency. The outcomes validate the safety and efficacy of our suggested paradigm.


A mechanism for efficient and secure data storage in cloud

RECENT SCHOLAR PUBLICATIONS

Health For You
P Surapur, VP Reddy, M Hussain, CS Kumar
IOP Conference Series: Materials Science and Engineering 1042 (1), 012023 2021



Chi Square Test and its Implementation in Making Associations between Attributes – State of Art
CSK Phani Prasad J
International Journal of Computer and Mathematical Sciences 6 (10), 144-147 2017



Hybrid Cuckoo search—ABC algorithm based vulnerabilities mapping and security in clouds
SK Prashanth, NS Rao, CS Kumar
2016 international conference on electrical, electronics, and optimization 2016



A Comparative Study of Proactive and Reactive Routing Protocols in Mobile Ad hoc Networks
CSK V Srinivas
International Journal of Advanced and Innovative Research 5 (1), 169-172 2015



Secure Classification as a Service Delegate Record Managing to the Cloud
CSK S. Ranjith Reddy
International Journal of Computer Science and Telecommunications 5 (3), 271-275 2014



Dynamic Resource Allocation in Cloud Computing Using Virtual Machines
CSK Nagendra Reddy. K
INTERNATIONAL JOURNAL OF COMPUTER SCIENCE & TECHNOLOGY (IJCST) 5 (3), 308-310 2014



Data sharing using cloud information accountability framework
C Chavali, LP Koyi, CS Kumar, NR Rao
International Journal of Engineering Research and Applications 4 (2), 43-39 2014



Comparison of remaining root dentine thickness after three rotary instrumentation techniques by cone beam computerised tomography-an in vitro study
KS Reddy, SD Prasad, CS Kumar, VR Reddy, M Hemadri, BS Karteek
J Res Adv Dent 3 (3), 32-39 2014



Identification of outliers by cook’s distance in agriculture datasets
T Jagadeeswari, N Harini, C Satya Kumar, M Tech
Int. J. Eng. Comput. Sci 2, 2319-7242 2013

MOST CITED SCHOLAR PUBLICATIONS

Identification of outliers by cook’s distance in agriculture datasets
T Jagadeeswari, N Harini, C Satya Kumar, M Tech
Int. J. Eng. Comput. Sci 2, 2319-7242 2013
Citations: 12



Hybrid Cuckoo search—ABC algorithm based vulnerabilities mapping and security in clouds
SK Prashanth, NS Rao, CS Kumar
2016 international conference on electrical, electronics, and optimization 2016
Citations: 7



Comparison of remaining root dentine thickness after three rotary instrumentation techniques by cone beam computerised tomography-an in vitro study
KS Reddy, SD Prasad, CS Kumar, VR Reddy, M Hemadri, BS Karteek
J Res Adv Dent 3 (3), 32-39 2014
Citations: 7



Data sharing using cloud information accountability framework
C Chavali, LP Koyi, CS Kumar, NR Rao
International Journal of Engineering Research and Applications 4 (2), 43-39 2014
Citations: 2



Health For You
P Surapur, VP Reddy, M Hussain, CS Kumar
IOP Conference Series: Materials Science and Engineering 1042 (1), 012023 2021
Citations: 1