Dr. Vinayak Dagadu Shinde
@slrtce.in
Associate Professor, Department of Computer Engineering
Shree L. R. Tiwari College of Engineering
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Hardware and Architecture, Software, Computer Networks and Communications
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Santosh Kumar, Vinayak Dagadu Shinde, Uma Bhavin Goradiya, Aabha Amey Patil, Sonu Prasad Verman, and Vilas Kisanrao Tembhurne
IEEE
Despite popular belief, agricultural research today is more based on hard evidence; exact; precise, and rigorous than ever before. Almost every industry has been disrupted by the spread of IoT-based technologies, including urban planning, healthcare, the electricity grid, the home, and agriculture, frequently referred to as “smart agriculture”. Machine learning (ML) and IoT data analytics in agriculture can boost crop yields to meet rising food demand. These revolutionary developments are upending standard agricultural practices and giving rise to new and finest opportunities, but with some drawbacks. Optimal agriculture output requires this research seeks to develop an effective and precise system that uses Crop selection choices made using algorithms that utilize Internet of Things (IoT) sensors and ML Therefore, this paper, proposed an ensemble model using machine learning for crop prediction based on IoT data which is collected from the IoT sensors using the PLX-DAQ tool. There are several suggested machines learning models, including “Naive Bayes, Decision Tree, Random Forest Support Vector Machine, and K-Nearest Neighbour,”. According to the experimental findings, ensemble learning had the greatest accuracy of 97.45% for predicting early crop yields. The results of this research will significantly increase the dependence on data for choices relating to climate change and agricultural practices.
Vilas Kisanrao Tembhurne, Vinayak Dagadu Shinde, Santosh Kumar, Manish Shrimali, Gunjan Chhabra, M N Quadri, and Vikram Rajpoot
IEEE
Air pollution (AP) is today’s most pressing issue. Particularly concerning are the potential adverse effects that an excessive amount of certain hazardous gases, such as CO, SO2, particulate matter, and several others, may have on human health. Other environmental gases are affected by temperature, humidity, etc., wind speed, and their causes and impacts. These weather factors include temperature, humidity, as well as wind speed.For this project, a centralized cloud-based system using sensors that monitor and analyze AP will be developed. The information gathered by each sensor node is uploaded to a cloud server, where it is stored and can be viewed through a web browser at any time and from any location. Because the environment is being monitored in real-time, prompt action may be performed in response to discovering a contaminant in the ecosystem. This project aims to monitor the AP of the surrounding area and ensure that data are kept up to date on the internet. Readings are conducted continuously throughout the day and in real-time. Many air pollutants like SO2, CO, PM10, humidity, and temperature are considered to measure air quality by IoT-based air pollution monitoring systems (APMS). We created graphics that simplify analyzing the proportion of pollutants in a certain location. The LCD can show the gas sensor’s real-time data constantly.
Akshata A. Churi and Vinayak D. Shinde
IEEE
As the demand of online data availability increases for sharing data, business analytics, security of available data becomes important issue, data needs to be protected from unauthorized access as well as it needs to provide authority that the data is received from a trusted owner. To provide owners identity digital watermarking technique is used since long time for multimedia data. This paper proposed a technique which supports watermarking on database as most of the data available today is in database format. The characters to be entered as watermark are converted into binary values; these binary values are hidden in the database using space character. Each bit is hidden in each tuple randomly. Ant colony optimization algorithm is proposed to select tuples where watermark bits are inserted. The proposed system is enhanced in terms of security due to use of ant colony optimization and resilient because even if some bits are modified the hidden text remains almost same.
Nishigandha Karbhari, Asmita Deshmukh, and Vinayak D. Shinde
IEEE
Recommender system known as information gathering system aims at creating an algorithm which, keeps in consideration the diverse needs and varying level of competence. It offers better opportunities in project development cycle under requirement phase and design phase. Social media and Ecommerce market has tapped in the recommender system to boost its growth by providing with precise results. It provides with either service or product recommendation using the information gathered in the software engineering process. It is broadly divided in three categories which are Collaborative, Content-based and Hybrid recommendation approach. This paper presents a model to generate recommendations based on marks of student. It discovers best solutions which would have otherwise remained hidden. The case study performed on the recommender system implementation in college campus will result a recommendation in placement of students (employee) to companies (employer) as per their requirements in shortest possible time. It can be expected as a situation where we have tried to achieve the results while keeping in mind the requirements of employer and employee.