Smart farming leverages Artificial Intelligence (AI) to address modern agricultural sustainability challenges. This study investigates the application of machine learning (ML), deep learning (DL), and time series analysis in agriculture through a systematic literature review following the PRISMA methodology. The review highlights the critical roles of ML and DL techniques in optimizing agricultural processes, such as crop selection, yield prediction, soil compatibility classification, and water management. ML algorithms facilitate tasks like crop selection and soil fertility classification, while DL techniques contribute to forecasting crop production and commodity prices. Additionally, time series analysis is employed for demand forecasting of crops, commodity price prediction, and forecasting crop yield production. The focus of this article is to provide a comprehensive overview of ML and DL techniques within the farming industry. Utilizing crop datasets, ML algorithms are instrumental in classifying soil fertility, crop selection, and various other aspects. DL algorithms, when applied to farming data, enable effective time series analysis and crop selection. By synthesizing the integration of these technologies, this review underscores their potential to enhance decision-making in agriculture and mitigate food scarcity challenges in the future.

Movie recommendation algorithms play an important role in assisting consumers in identifying films that match their own likes. Deep Learning, particularly Long Short-Term Memory (LSTM) networks, have showed substantial promise in collecting sequential patterns to improve movie recommendations among the different techniques used for this purpose. Long Short-Term Memory-Inter Intra Metapath Aggregation (LSTM-IIMA) in movie recommendation systems is proposed in this study, with a specific focus on incorporating intra and inter metapath analysis. The intra metapath analysis investigates interactions within a single metapath, whereas the inter metapath analysis investigates links between numerous metapaths. Intra and inter metapath analyses are used in the LSTM-based movie recommendation system LSTM-IIMA to capitalise on these rich linkages. Each metapath sequence records the dependencies of a user’s interactions with films and other things. The LSTM architecture has been modified to handle these metapath sequences, processing them to record temporal dependencies and entity interactions. To optimize the parameters and minimize prediction errors, the model is trained using supervised learning techniques. To measure the quality and usefulness of the recommendations, the LSTM-IIMA evaluation incorporates metrics such as precision, recall, ablation analysis, time efficiency and Area Under the Curve (AUC). The performance of the system is compared to that of alternative recommendation techniques HAN and MAGNN. Overall, incorporating intra and inter metapath analysis into the LSTM-IIMA improves its ability to capture complex linkages and dependencies between movies, users, and other things.

Stream processing engines (SPEs) allow applications to process a large amount of data in real-time. However, to schedule big data applications; the SPEs create several challenges regarding resource utilisation, dynamic configurations, heterogeneous environment, resource awareness, load balancing, etc. As the volume of data increases over time, it also poses a challenge to predict the resource and application requirements for processing. All these factors play an important role, they can cause problems in achieving maximum throughput due to inefficiency in any of them. Most SPEs ignore the topology’s structure, which may minimise throughput during scheduling and may increase network latency. In this article, a topology-aware and resource-aware scheduler (named WG-Storm) is proposed based on a directed acyclic graph (DAG) that enhances resource usage and overall throughput using efficient task assignment. WG-Storm is built on Apache Storm. Results are generated using the two linear topologies and compared with the five state-of-art schedulers including A3-Storm, Default, Isolation, Multi-tenant, and Resource-aware. The experimental results show up to 30% increased throughput using the least required computing resources in a heterogeneous cluster.

Background: A scheduling algorithm tries to schedule multiple computational tasks on a cluster of multiple computing nodes to maximize throughput with optimal utilization of computational and communicational resources. A Stream Processing Engine (SPE) is deployed to run streaming applications (computational tasks) on a computational cluster which helps execution and coordination of these applications. It is observed that there is a gap in the optimal mapping of a computational and communicational load of a streaming application on the underlying computational and communication power of the resources (cluster). Frequently communicated tasks are scheduled at different processing nodes with relatively slow communicating links. This increases network latency with a decrease in resource utilization. Hence, reduces the achieved throughput of the cluster significantly. Methods: MF-Storm, a max-flow min-cut based job scheduler is presented to achieve a near-optimum schedule to maximize throughput. It schedules a streaming application by considering the processing, communication demands, available computational and communicational resources in a heterogeneous cluster, dynamically with minimized scheduling cost. To keep the scheduling cost minimum, the scheduler is built in a pipeline with two major stages: in the first stage, the application’s tasks graph is partitioned using the max-flow min-cut algorithm to minimize inter-partition traffic, and in the second stage, these partitions are assigned to computing nodes according to the computational power of the cluster’s nodes. Results: Extensive experiments were done to evaluate the performance of MF-Storm using different topologies with multiple scenarios on a physical cluster implementation. Results showed on average 148% improvement in throughput with 30% less computational resources as compared to different state-of-the-art schedulers.

Stream processing engines (SPEs) allow applications to process a large amount of data in real-time. However, to schedule big data applications; the SPEs create several challenges regarding resource utilisation, dynamic configurations, heterogeneous environment, resource awareness, load balancing, etc. As the volume of data increases over time, it also poses a challenge to predict the resource and application requirements for processing. All these factors play an important role, they can cause problems in achieving maximum throughput due to inefficiency in any of them. Most SPEs ignore the topology’s structure, which may minimise throughput during scheduling and may increase network latency. In this article, a topology-aware and resource-aware scheduler (named WG-Storm) is proposed based on a directed acyclic graph (DAG) that enhances resource usage and overall throughput using efficient task assignment. WG-Storm is built on Apache Storm. Results are generated using the two linear topologies and compared with the five state-of-art schedulers including A3-Storm, Default, Isolation, Multi-tenant, and Resource-aware. The experimental results show up to 30% increased throughput using the least required computing resources in a heterogeneous cluster.