In recent years Internet of things has gained much attention. Various techniques are being developed to acquire the data from human environment for various smart services and applications. Monitoring elderly people re...
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Optimization of APP interface design is challenging with dynamic user preferences, heterogeneous interaction patterns, and the subjective evaluation of usability. Conventional interface design methods depend on user f...
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ISBN:
(数字)9798331533663
ISBN:
(纸本)9798331533670
Optimization of APP interface design is challenging with dynamic user preferences, heterogeneous interaction patterns, and the subjective evaluation of usability. Conventional interface design methods depend on user feedback and manual analysis, which tend to be erratic and non-adjusting to changing user needs. Current automated design models mostly use rule-based and statistical models but are unable to adequately capture sophisticated user behavior and design flexibility. To overcome these disadvantages, this work introduces a future-proof swarm intelligence recommendation algorithm that dynamically optimizes APP interface design. Based on user interaction statistics, behavioral analysis, and situational preferences, the introduced mechanism maximizes usability, personalization, and usage engagement. Experiments conducted over actual application interface designs prove the introduced approach shows a 97.3% accuracy of user satisfaction and interaction efficiency rates against traditional models of design. this work offers an intelligent and scalable solution that helps develop adaptive APP interface design methodologies.
Background: the increasing prevalence of unethical practices in the financial sector has led to stricter regulatory frameworks, driving the demand for advanced Regulatory Technology (RegTech) solutions. Machine learni...
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ISBN:
(数字)9798331533663
ISBN:
(纸本)9798331533670
Background: the increasing prevalence of unethical practices in the financial sector has led to stricter regulatory frameworks, driving the demand for advanced Regulatory Technology (RegTech) solutions. Machine learning offers a promising approach to enhance compliance monitoring. Methods: this study applies RandomForest (RF), Gradient Boosting (GB), and AdaBoost (AB) to predict compliance risks using the bank marketing dataset. SMOTE and cost-sensitive learning addressed class imbalance, while Grid Search Cross-Validation optimized model performance. Accuracy, precision, recall, and F1-score were utilized for evaluation. Results: RF achieved the best performance with 93.98% accuracy, 92.03% precision, 96.42% recall, and a 94.17% F1-score, followed by GB and AB with slightly lower results. Traditional models like K-Nearest Neighbors (KNN) and Extreme Gradient Boosting (XGB) performed less effectively. Conclusion: the research shows that financial institutions may get a better handle on regulatory conformity withthe use of RegTech solutions based on ML, which greatly enhance compliance monitoring.
When faced with large language datasets, machine translation language models are difficult to process, resulting in low accuracy and efficiency of translation. therefore, this study constructs a framework based on Fir...
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ISBN:
(数字)9798331528348
ISBN:
(纸本)9798331528355
When faced with large language datasets, machine translation language models are difficult to process, resulting in low accuracy and efficiency of translation. therefore, this study constructs a framework based on Firstly, by installing Apache Spark and configuring its integration with Hadoop distributed File System (HDFS), a distributedcomputing environment capable of parallel data processing is established to support rapid processing of large-scale data. then, the distributed storage system HDFS is adopted to optimize data access efficiency and reduce IO (Input/Output) bottlenecks. Finally, a new iterative training strategy is implemented to gradually improve the translation accuracy of the model through incremental learning. Under the optimal configuration, the combination of a learning rate of 0.1 and batch processing of 2048 achieves a BLEU score of up to 30.8%, with training lasting only 4 hours. this study demonstrates the effectiveness of a large-scale machine translation language model training system based on Spark in improving translation efficiency and accuracy.
A wide range of IoT applications for the industry has been developed, in recent years has been expanded. To understand the development of the internet of things in the industry. Internet of things (IoT) is to build a ...
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Modern massive multiple-input multiple-output (MIMO) systems allow for a great increase in cell throughput by means of spatial multiplexing. However, urban environmental conditions, such as low signal-to-noise ratio (...
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Electric cars are evolving into a clean and stable environment. It has lower maintenance costs than fossil fuels, supports emerging mobility technologies and emits less air pollution. When it is combined with renewabl...
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the charging facilities should be available at appropriate locations to provide better services to the electric vehicle (EV) users. Also, optimally allocated charging stations are necessary in distribution network to ...
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Indonesia is a country that has a high level of motorcycle usage. this condition was not accompanied by adequate road conditions. therefore, to maintain safety and driving comfort, every rider is recommended to check ...
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Vehicles are equipped with various sensors such as LiDAR, which enable them to perceive the surrounding environment and enhance driver safety through advanced driver assistance systems. However, these sensors are limi...
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ISBN:
(数字)9798350386059
ISBN:
(纸本)9798350386066
Vehicles are equipped with various sensors such as LiDAR, which enable them to perceive the surrounding environment and enhance driver safety through advanced driver assistance systems. However, these sensors are limited by line-of-sight, preventing them from seeing beyond occlusions. One solution is to leverage the edge server which can collect and share perception data with other vehicles. Most existing research focuses on improve the performance of uploading perception data to the server, and the problem of perception dissemination remains largely unexplored, despite the challenges posed by the large volume of perception data and the limited wireless bandwidth. In this paper, we propose an edge-assisted relevance-aware perception dissemination system that collects perception data from multiple vehicles and selectively disseminates only the necessary data to appropriate vehicles. the necessity of dissem-ination is determined by evaluating the relevance of perception data, which quantifies the probability of potential collisions between corresponding objects. We then formulate and solve the relevance-aware perception dissemination problem whose goal is to maximize the relevance of disseminated data under bandwidth constraints. Extensive evaluation results demonstrate that our system can significantly enhance traffic safety while reducing the overall bandwidth consumption.
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