Dr. Yogita Dhanesh Patil
@ghrcemj.raisoni.net
Assistant Professor
G H Raisoni College of Engineering and Management
RESEARCH, TEACHING, or OTHER INTERESTS
Multidisciplinary, Organizational Behavior and Human Resource Management, Strategy and Management, Business and International Management
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Deepak Sharma, Pankajkumar Anawade, Shailesh Gahane, and Yogita Patil
IEEE
Real-time data gathering and remote livestock management have been made possible by the integration of Internet of Things (IoT) technology into livestock monitoring systems, which has completely changed the agricultural sector. IoT-based solutions provide significant security and privacy problems in addition to their many positive effects, such as improved animal care and better productivity. An overview of the main privacy and security issues pertaining to Internet of Things-based livestock monitoring systems is given in this abstract.The main security risks in IoT-based livestock monitoring are system flaws and unauthorized access to sensitive data. IoT devices are vulnerable to both physical manipulation and hacking, including sensors and gateways. To stop data breaches and eavesdropping, data must be protected both in transit and at rest.Additionally, ensuring the integrity of data is vital to maintaining the accuracy of livestock health and behavior information.Privacy issues arise from the collection and storage of extensive data about individual animals and farming practices. This data can potentially be misused or accessed without proper consent. Striking a balance between data granularity for effective monitoring and preserving livestock owners’ privacy rights is a significant challenge.This abstract discusses potential security measures for IoT-based livestock monitoring, including device authentication, encryption, and intrusion detection systems. Furthermore, it highlights privacy-preserving techniques like data anonymization and user consent mechanisms to address privacy concerns.
P Anusha, A. Balaji, B. Nithyasundari, Yogita Dayanand Patil, and S. Ravi
IEEE
In order to warn employees of dangerous gas levels in real time, this article intends to provide a mechanism for doing just that. The goal of this project is to develop an Internet of Things (IoT) system that can identify gas mixtures, monitor the levels of individual gases in real time, and record any dynamic changes in the aforementioned variables. Data about gas levels is sent to Firebase. If the levels rise over a certain point, it will notify the authorized individuals working remotely through their linked mobile devices. Officials can be notified by SMS message if the sewage is likely to overflow. This study developed a novel approach called the Internet of Things Powered Sewage Gas Monitor (IoTSGM) that utilizes the association of Artificial Intelligence (AI) and the Internet of Things (IoT). In order to determine how effective the suggested scheme is, this approach is compared to the traditional one, the Sensors based Sewage Gas Monitor (SSGM). A number of sensors in this system may detect harmful gasses, the amount of water in the manhole containing the sewage, and a worker-worn sensor that can monitor their surroundings. When it hits a specific mark, it notifies the proper authorities and the hospital of its whereabouts by GPS. For the purpose of monitoring, an android app was created and connected with the planned system.
Adithya Pothan Raj V, Yogita Dayanand Patil, Madhurikkha S, V. Srithar, and Vidhya
IEEE
This research article presents a revolutionary approach to address the challenges of reversible data embedding in encrypted images within cloud networks. Our pioneering algorithm combines the potency of the Discrete Cosine Transform (DCT) and Convolutional Neural Network (CNN) to introduce a novel DCT-CNN hybrid model, specifically designed for efficient and high-capacity reversible data hiding. Diverging from conventional methods, our approach eliminates the need for trial data embedding during predictor selection. The initial phase involves estimating the optimal predictor using CNN, where training labels are forged through a trial embedding procedure. The selected predictor, providing the highest embedding rate, is assigned as the class label for the corresponding encrypted image. Users secure their images through a three-tier encryption process before transferring them to the cloud. During the testing phase, the trained CNN model processes encrypted images uploaded by users to determine the predictor class with the maximum embedding capacity. Data embedding is then executed using the classified predictor. The proposed predictor storage demonstrates impressive results, achieving an accuracy, recall, and precision of $93.9 \\%, 92.6 \\%$, and $95.3 \\%$ in average, respectively, particularly for the BossBase dataset. Additionally, our reversible data embedding algorithm, employing the DCT-CNN paradigm, achieves a commendable embedding rate of 69.1bpp, along with an average PSNR of 38.8 dB and SSIM of 0.93 in average. Evaluations conducted on datasets, including BossBase and BOWS-2, convincingly demonstrate the stoutness and effectiveness of the proposed method. This research introduces a unique perspective to the realm of reversible data embedding in cloud networks, offering a promising solution for secure and high-capacity data concealment within encrypted images.
Dhanesh S. Patil, Yogita Patil, Yogesh Kirange, Nilesh S. Mahajan, and Rupesh S. Patil
IEEE
Air pollution is currently causing severe problems in India. According to a survey, certain cities have high levels of air contamination brought on by the transportation and industrial sectors. In India, the transport industry accounts for almost 18-19% of all energy consumption. Because of the constant exploitation and usage of natural resources, there is a need to protect renewable energy sources and environmentally friendly goods. Out of such a product, the electric vehicle is one such notion that is a successful creation that will displace traditional petroleum-based automobiles. Electric engines are used instead of internal combustion engines, which significantly cuts pollution. This paper provides the study of evolution of electric vehicles in India.