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<p>Finding connectivity in graphs has numerous applications, such as social network analysis, data mining, intra-city or inter-cities connectivity, neural network, and many more. A deluge of graph applications makes graph connectivity problems extremely important and worthwhile to explore. Currently, there are many single-node algorithms for graph mining and analysis; however, those algorithms primarily apply to small graphs and are implemented on a single machine node. Finding 2-Edge Connected Components (2-ECCs) in massive graphs (billions of edges and vertices) is impractical and time-consuming, even with the best-known single-node algorithm. Processing a big graph in a parallel and distributed fashion will save considerable time to finish processing. Moreover, it enables stream data processing by allowing quick results for vast and continuous nature data sets. In this research, we propose a distributed and parallel algorithm for finding 2-ECCs in big undirected graphs (subsequently called ”BiECCA”) and present its time complexity analysis. The proposed algorithm is implemented on a MapReduce framework. The proposed algorithm uses an existing algorithm to find Connected Components (CCs) in a graph as a sub- step. Finally, we suggest a few novel ideas and approaches as extensions to our work. </p>

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<p>Crop analysis and prediction is a rapidly growing field that plays a vital role in optimizing agricultural practices. Crop recommendation plays a pivotal role in agriculture, empowering farmers to make informed decisions about the most suitable crops for their specific land and climate conditions. Traditionally, this process heavily relied on expert knowledge, which proved time-consuming and labor-intensive. Moreover, considering the projected global population of 9.7 billion by 2050, the need to produce more food sustainably becomes imperative. Machine learning techniques can play a crucial role in effectively automating crop recommendations, and detecting pests and diseases to enable farmers to optimize their yield from the land while simultaneously maintaining soil fertility and replenishing essential nutrients. This paper analyses the performance of crop recommendation across seven distinct machine-learning algorithms. The proposed system leverages various features, including soil composition and climate data, to accurately predict the most suitable crops for specific locations. This system has the potential to revolutionize crop recommendation, benefiting farmers of all scales by enhancing crop yields, sustainability, and overall profitability. Through extensive evaluation of a comprehensive historical data set, we have achieved near-perfect accuracy by training and testing models the machine learning algorithms with various configurations. We demonstrate accuracy consistently over 95% across all models, with the highest achieved accuracy reaching 99.5%.</p>

<p>Crop analysis and prediction is a rapidly growing field that plays a vital role in optimizing agricultural practices. Crop recommendation plays a pivotal role in agriculture, empowering farmers to make informed decisions about the most suitable crops for their specific land and climate conditions. Traditionally, this process heavily relied on expert knowledge, which proved time-consuming and labor-intensive. Moreover, considering the projected global population of 9.7 billion by 2050, the need to produce more food sustainably becomes imperative. Machine learning techniques can play a crucial role in effectively automating crop recommendations, and detecting pests and diseases to enable farmers to optimize their yield from the land while simultaneously maintaining soil fertility and replenishing essential nutrients. This paper analyses the performance of crop recommendation across seven distinct machine-learning algorithms. The proposed system leverages various features, including soil composition and climate data, to accurately predict the most suitable crops for specific locations. This system has the potential to revolutionize crop recommendation, benefiting farmers of all scales by enhancing crop yields, sustainability, and overall profitability. Through extensive evaluation of a comprehensive historical data set, we have achieved near-perfect accuracy by training and testing models the machine learning algorithms with various configurations. We demonstrate accuracy consistently over 95% across all models, with the highest achieved accuracy reaching 99.5%.</p>