Hyperparameter tuning plays a crucial role in optimizing the performance of machine learningalgorithms. this study explores the effectiveness of Particle Swarm optimization (PSO) in fine-Tuning the hyperparameters of...
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In the context of effective resource management and ensuring nutritional stability, precise forecasting of crop yields becomes essential. the development of artificial intelligence methodologies, coupled with satellit...
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ISBN:
(纸本)9783031686740;9783031686757
In the context of effective resource management and ensuring nutritional stability, precise forecasting of crop yields becomes essential. the development of artificial intelligence methodologies, coupled with satellite imagery, has emerged as a powerful strategy for predicting crop yields in modern times. In this study, deep learningalgorithms based on LSTM (Long Short-Term Memory) were developed to efficiently optimize and extract from Sentinel-2 data spatiotemporal information of wheat yield. To estimate accurately wheat yield in Morocco, several machine learning and deep learning techniques such as Random Forest, LSTM, Bi-LSTM (Bidirectional LSTM), stacked LSTM, etc. were used and compared. the optimized Bi-LSTM model accurately estimates wheat yield based on NDVI (normalized difference vegetation index) data and weather data (temperature, precipitation). three datasets gathered from satellite imagery were used which are temperature data, precipitation data and NDVI data combined for training and testing the proposed model. After data processing, different machine learning and deep learningalgorithms were compared, and the result showed that Bi-LSTM estimates wheat yield accurately. the proposed and optimized Bi-LSTM model reached a satisfactory accuracy at the sizable regional scale. the obtained result demonstrates that the RMSE (Root Mean Square Error) score was 6.22 and the loss was 6.61 center dot 10(-4) after 20 epochs of training the proposed model, which overcomes most of the existing methods.
the notable expansion of technologies related to automated processes has been observed in recent years, largely driven by the significant advantages they provide across diverse industries. Concurrently, there has been...
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ISBN:
(纸本)9783031530357;9783031530364
the notable expansion of technologies related to automated processes has been observed in recent years, largely driven by the significant advantages they provide across diverse industries. Concurrently, there has been a rise in simulation technologies aimed at replicating these complex systems. Nevertheless, in order to fully leverage the potential of these technologies, it is crucial to ensure the highest possible resemblance of simulations to real-world scenarios. In brief, this work consists of the development of a data acquisition and processing pipeline allowing a posterior search for the optimal physical parameters in MuJoCo simulator to obtain a more accurate simulation of a dexterous robotic hand. In the end, a Random Search optimization algorithm was used to validate this same pipeline.
Cataract, a common eye disease characterized by clouding of the natural lens of the eye, is a serious threat to visual health. If left untreated, they can lead to blurred vision and even blindness, underscoring the im...
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Foreign exchange trading basically bridges a gap between buyer and seller to transact at a set of prices of the currencies to make profit out of it by the traders and investors. In this paper, foreign exchange predict...
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In the realm of cybersecurity, Distributed Denial of Service (DDoS) attacks remain a continuous threat, particularly TCP SYN flood attacks due to their stealthiness and potential for disruption. In this paper, we prop...
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the integration of IoT and ML brought forth new prospective solutions in menstrual health management, pertaining to continuous monitoring of physiological signals and personalization of insights for women. this articl...
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In this paper Deep learning (DL) based techniques for filtering YouTube comments are explored. this work primarily focuses on the applications of Feedforward Neural Networks (FNNs) and Recurrent Neural Networks (RNNs)...
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the proceedings contain 81 papers. the topics discussed include: imaging modalities in brain cancer detection and diagnosis;exploring metaverse dynamics in supply chain: a bibliometric analysis;towards a lightweight d...
ISBN:
(纸本)9798350354133
the proceedings contain 81 papers. the topics discussed include: imaging modalities in brain cancer detection and diagnosis;exploring metaverse dynamics in supply chain: a bibliometric analysis;towards a lightweight detection system leveraging ranking techniques with wrapper feature selection algorithm for selective forwarding attacks in low power and lossy networks of IoTs;kidney disease classification and diagnosis: a comprehensive review of current AI techniques;sensorless direct torque control in brushless DC motor using sliding mode observer;real-time diabetes detection using machine learning and Apache spark;agile ontology: a dynamic framework for e-business evolution;the role of YOLOv8 in enhancing strategic military equipment detection;and evaluating artificial intelligence bias in answering religious questions.
the quest for retrieving relevant media for a given query is well-studied and has various applications. Modern publicly available media collections provide diverse modalities of the same objects, which can enhance sea...
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ISBN:
(纸本)9798400706028
the quest for retrieving relevant media for a given query is well-studied and has various applications. Modern publicly available media collections provide diverse modalities of the same objects, which can enhance search. Our research delves into enhancing media retrieval by effectively representing and querying multimodal data. In the retrieval methods' ranking procedure, we examine efficiency through techniques like approximate nearest neighbor (ANN) indexing and high-performance computing (HPC). Our method, MuseHash, is proposed for single media object retrieval and is applied to images and 3D objects, outperforming existing methods on diverse datasets. Moreover, it significantly reduces execution times with ANN and HPC. Future plans include considering multimodality in the video retrieval domain.
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