http://10.9.150.37:8080/dspace//handle/atmiyauni/768 2026-04-27T18:51:05Z 2026-04-27T18:51:05Z Dave, Krupa Kothari, Ashish M. http://10.9.150.37:8080/dspace//handle/atmiyauni/860 2023-05-03T09:52:11Z 2022-01-01T00:00:00Z

Title: A Survey of power allocation techniques in NOMA: Research challenges and Future directions Authors: Dave, Krupa; Kothari, Ashish M. Abstract: Non-orthogonal multiple access, often known as NOMA, is one of the viable ways to big capacity radio access. It provides a number of desired features, including better spectrum efficiency, making it an appealing choice. This piece places a focus on power-domain NOMA, in which successive interference cancellation (SIC) and superposition coding (SC) are the most essential functions at the transmitter and receiver, respectively. Following an analysis of many standard power allocation methods and the restrictions they impose, the authors of this article go on to describe a variety of innovative power distribution techniques that are based on machine learning. Approaches that are based on machine learning and deep learning produced performance that was considerably near to the optimal in terms of total capacity, although having significantly lower computing costs. Optimal performance would be attained by having the most overall capacity. Discussion of a number of potential future research avenues based on the use of deep learning in NOMA systems is the last step of the process.

2022-01-01T00:00:00Z Modha, Hiren J. Kothari, Ashish M. http://10.9.150.37:8080/dspace//handle/atmiyauni/859 2023-05-03T09:38:34Z 2022-06-01T00:00:00Z

Title: Crop Diseases Severity Identification by Deep Learning Approach Authors: Modha, Hiren J.; Kothari, Ashish M. Abstract: Improving yield and maintaining crop strength with optimization in use of resources are the major requirements in smart farming. To build a smart decision support system for improving production with flexibility, it requires Remote Sensing Systems. Now days with effective use of machine learning and deep learning techniques, it is possible to make the system flexible and cost effective. The deep learning based system has enormous potential, so that it can process a large number of input data and it can also control nonlinear functions. Here it should be discussed that from continuous monitoring of crop leaves images shall ensures the diseases identification. The research concludes that the quick advances in deep learning methodology will provide gainful and complete classification of crop with 98.7% to 99.9% accuracy. In this research, different crop diseases are classified based on image processing and Convolutional neural network method. For classification of maize crop diseases, different models have been developed, compared, and finally best one is found out. Also the finest model has been tested for different crop diseases to check its consistency.

2022-06-01T00:00:00Z Modha, Hiren J. Kothari, Ashish M. http://10.9.150.37:8080/dspace//handle/atmiyauni/858 2023-05-03T09:20:09Z 2022-07-01T00:00:00Z

Title: Soil Moisture Prediction using Deep Neural Network Approach Authors: Modha, Hiren J.; Kothari, Ashish M. Abstract: Soil moisture content is the most significant element in fit farming output and circulation of water, and its accurate forecast is critical regarding water resource management. Mostly soil moisture is complicated through structural features in addition to climatic complications. It’s tough to come up with an optimal mathematical model for soil moisture since there are so many variables for calculation. Presented forecasting models have issues with prediction accuracy, generality, plus other factors like Prediction performance, as well as multi-feature processing capabilities etc. considering all these factors taking Gallipoli, Turkey as a reference site for developing a deep neural network model to forecast moisture with good accuracy and minimum error. The dataset contains entities since 2008 to 2021. Doing quite a bit of mathematical analysis and establishing the correlation between selected features with the spearman coefficient, the appropriate weather data is able to give proper weight to forecast soil moisture. The output of the proposed method proves that the deep learning approach is realistic as well as efficient for the prediction of moisture. Also, deep learning technique is able to make model generalizations with excellent accuracy and minimum errors which is used to save irrigation water with controlling drought.

2022-07-01T00:00:00Z Dave, Krupa Kothari, Ashish http://10.9.150.37:8080/dspace//handle/atmiyauni/855 2023-05-03T07:35:00Z 2022-01-01T00:00:00Z

Title: Noma Design In The Sixth Generation: A New Frontier Authors: Dave, Krupa; Kothari, Ashish Abstract: The communication industry is making rapid strides toward the implementation of 5G and beyond 5G (B5G) wireless technology. New applications are being developed in order to fulfil the ever-increasing demand for faster data rates as well as increased service quality (Quality Of Service). These new applications require wireless connectivity that possesses a massive increase in data rates, a significant decrease in latency, and widespread public acceptance for a large number of devices. The non-orthogonal multiple access (NOMA) protocol, which is a key component of the family of protocols known as the next-generation multiple access (NGMA), has recently come to be acknowledged as a viable multiple access option for sixth-generation (6G) networks. In this article, we will discuss the different 6G dimensions that are available. The primary objective of this paper is to present fundamental ideas on multiple access that are necessary for B5G communication, to discuss recent research developments on significant technologies, and to provide research and development guidelines for NOMA in mobile communication systems that go beyond 5G communication networks.

2022-01-01T00:00:00Z