Email spam detection has become a serious issue in contemporary communication systems as a result of the proliferation of unwanted emails. Traditional methods often fall short of the ever- evolving tactics employed by...
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Analyzing digital devices to generate digital evidence relevant to incidents is essential in modern digital investigations. machinelearning models and patternrecognition capabilities can be used in forensic analysis...
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In the age of digital urban transformation, smart cities are emerging as ecosystems where technology, infrastructure and data converge to improve quality of life, sustainability and economic prosperity. In this contex...
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ISBN:
(纸本)9783031821523;9783031821530
In the age of digital urban transformation, smart cities are emerging as ecosystems where technology, infrastructure and data converge to improve quality of life, sustainability and economic prosperity. In this context, e-commerce plays a central role, providing platforms for businesses to thrive by enabling seamless transactions, personalised shopping experiences and greater market reach. However, the dynamic and evolving nature of user preferences presents a significant challenge, requiring more adaptive and smart recommendation systems. this paper presents an approach by integrating Deep Q-Network (DQN), a reinforcement learning technique, into recommendation systems for e-commerce in smart cities. By comparing the proposed DQN model based recommender system with traditional models such as MLP, DeepFM, LSTM and CNN using metrics such as MSE, RMSE and NDCG@5, we demonstrate its superior performance in predicting user preferences and dynamically adapting to changes in user behaviour. the results highlight the potential of DQN models to revolutionise e-commerce recommender systems, delivering more personalised and adaptive user experiences in the interconnected environments of smart cities.
the agricultural sector is complex, and farmers and agri-businesses face numerous decisions every day, influenced by various factors. Precise yield estimation is critical for effective agricultural planning. data mini...
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the preservation and analysis of ancient Pali manuscripts are crucial for understanding India's cultural heritage. However, the unique shapes of Pali characters present significant challenges for accurate identifi...
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the goal of this academic research is to improve the accuracy and timeliness of the description of corporate operating conditions. By integrating in-depth modeling technology of label stratification, as well as cuttin...
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All industries, including computer science and health care and customer service are in dire need of efficient recognition of human feelings. this work is a script for a novel approach to affect recognition which invol...
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machinelearning has emerged as a powerful tool in Earth System Science offering new avenues for data analysis and predictive modeling. this paper deep dive into the application of machinelearning techniques to under...
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Biomedical named entity recognition is of great significance in the field of natural language *** learning approaches are mainly used at present, and the BERT-BiLSTM-CRF model is one of them. Although the BiLSTM struc...
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machinelearning has proven to be an effective method for quantifying the costs of mechanical parts early in the design process. One of the most complex aspects remains the application of the costing method in real in...
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ISBN:
(纸本)9783031765964;9783031765971
machinelearning has proven to be an effective method for quantifying the costs of mechanical parts early in the design process. One of the most complex aspects remains the application of the costing method in real industrial contexts, such as made-to-order manufacturing. this paper introduces a novel cost modelling method based on machinelearning for the early design phase. the training dataset is generated using an automatic and analytic 3D-based software tool for process planning, time, cost and resource estimation. Subsequently, the CRISP-DM (Cross-Industry Standard Process for datamining) methodology is applied to preprocess the data. CRISP-DM is a data science process model that outlines the datamining lifecycle and offers flexibility for tailoring the model to specific project goals. the proposed approach has been effectively applied to develop resource prediction models for manufacturing sheet metals (i.e., cutting and bending) that can be used during early design. these resource prediction models serve as the foundation for cost calculations. the mean accuracy of the obtained cost models is lower than 10%, a value accepted by design engineers during preliminary design and feasibility studies.
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