Machine Learning-based Energy Management System for Prosumer

Knowledge Graph Embedding (KG Embed) has an important role in extracting the query and retrieving the required information in the technology specifically in scientific research. A surge is seen in the growth of knowledge graphs and also in knowledge representation and reasoning. This paper with the help of the knowledge graph creation helps in finding the hottest topic for research, Specifically, this study includes four research repositories that are Google Scholar (GS), IEEE Xplore, Science, and Worldwide Science (WWS). This study is based on keyword searching, which includes four keywords that are Knowledge graph (KG), Knowledge graph embedding (KG Embed), Recommendation system knowledge graph (RS KG), and Recommendation system in the education domain (RS in education). The data of several papers, articles, and books present in these four repositories from 2000 to 2022 is collected using these four keywords. After this, the paper visualizes this data using graphs and knowledge graphs with the help of parameters like centrality and clustering, which shows that Google Scholar contains more data than other repositories and the hottest topic in research is knowledge graph. It also gives an overview of how a knowledge graph is used in the recommendation system and how it is helpful. Based on the result that we got through the visualization of graphs and knowledge graphs; this study summarizes the suggestions and directions for researchers and practitioners to do future research on knowledge graphs by keeping all the related studies and points. This research takes into account the knowledge Graph research papers, articles, books, and theses from these four sources.

Section: Knowledge Graphmentioning

confidence: 99%

Graph Based Analytics and Review of Knowledge Graph Field by Using Two Decade Data- Finding Paradigms and Opportunities

Yadav,

Gopinathan

2024

“…Nowadays, machine learning and other approaches is widely utilized to solve a variety of difficult problems, such as making predictions and performing pattern recognition [5][6][7][8][9][10][11][12][13][14][15] . In modern era, there are others optimization techniques which is used in antenna structures like heuristic optimization techniques like genetic algorithms and particle swarm optimization [16][17][18][19][20] , but these algorithms examine for solution which is optimal by analyzing the output on individual data points and generating new and possibly improved search directions until a global maxima or minima is identified.…”

Section: Prerequisites For the Publicationmentioning

confidence: 99%

Design and Development of Machine Learning Assisted Cylindrical Dielectric Resonator Antenna

Ranjan¹,

Yadav²,

Gupta³

et al. 2023

The design of a Dielectric Resonator Antenna (DRA) utilizing machine learning approaches is presented in this study. Antennas are an essential part of a wireless network. An antenna with a decent design will reduce system requirements and improve overall system performance. Full-wave electromagnetic simulation is exact and necessary in antenna design. Still, it takes more time to perform, resulting in enormous challenges in designing, optimizing, and performing sensitivity analysis. The data in this study is subjected to several Machine Learning (ML) algorithms for optimizing antenna efficiency and S-parameter value. To expedite antenna design, machine learning-assisted optimization (MLAO) has proven to be the most effective method. A wide range of regression techniques, including as Support Vector Machine (SVM), Decision Tree Regression (DTR), and Random Forest Regression (RFR), have been used in machine learning (ML) techniques to develop antenna models, enabling quick response prediction and optimum S11 value. Multiple MLAO algorithms have been implemented using these machine-learning techniques for various applications out of which Decision Tree Regression and Random Forest Regression outperforms among all the above-mentioned algorithms with 99.98% and 99.99% of accuracy. First, a broad overview of recent advancements in ML approaches for antenna modelling is presented.

“…Robots are being created that can handle bulk harvesting with more accuracy and speed, allowing the fruit to reach your kitchen table faster. These kinds of tools boost the productivity and reduces the crop waste from the field 6) .…”

Section: Farm Harvesting Robotsmentioning

confidence: 99%

Modern-age Agriculture with Artificial Intelligence: A review emphasizing Crop Yield Prediction

Sharma,

Dadheech

2023

Agriculture is a key employment in several countries throughout the globe. AI is increasingly becoming a part of agriculture industry as traditional methods are insufficient to supply the massive survival needs of millions of people. AI, in form of machine learning and deep learning, is capable of providing a number of strategies that assist in the creation of more healthy seeds. This paper discusses significance of machine learning and deep learning that growers can use to gain access to increasingly sophisticated data and analytical tools, allowing them to make better decisions, improve efficiencies, and reduce wastes in food and bio-fuel production while minimizing negative environmental impacts. On the basis of critical parameters like temperature, rainfall, humidity, soil type, soil characteristics etc., ML and DL operate as recommenders and advise farmers to take the right action. Numerous AI applications in agriculture are addressed, with an emphasis on yield prediction. The article offers a comprehensive review of a variety of ML, DL and hybrid methodologies for correctly forecasting agricultural outputs that will promote the nation's economic growth.