Improving IPC in simultaneous multi-threading (SMT) processors by capping IQ utilization according to dispatched memory instructions

Sahba, Amin; Sahba, Ramin; Lin, Wei-Ming

doi:10.1109/wac.2014.6936190

Cited by 21 publications

(4 citation statements)

References 18 publications

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“…This integration allows us to enhance accuracy, efficiency, and effectiveness in managing energy resources and communication systems within smart cities. The significance of our research lies in its potential to make a substantial contribution to the field of sustainable energy systems [37]. By seamlessly integrating advanced methodologies, we seek to provide valuable insights that can guide the development and implementation of sustainable energy solutions in smart cities.…”

Section: Literature Analysismentioning

confidence: 99%

Hybrid Machine Learning and Modified Teaching Learning-Based English Optimization Algorithm for Smart City Communication

2023

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This paper introduces a hybrid algorithm that combines machine learning and modified teaching learning-based optimization (TLBO) for enhancing smart city communication and energy management. The primary objective is to optimize the modified systems, which face challenges due to their high population density. The proposed algorithm integrates the strengths of machine learning techniques, more specifically, the long short-term memory (LSTM) technique, with teaching learning-based optimization algorithms. To achieve optimization, the algorithm learns from historical data on energy consumption and communication patterns specific to the modeled system. By leveraging these insights, it can predict future energy consumption and communication patterns accurately. Additionally, the algorithm incorporates a modified teaching learning-based optimization approach inspired by the teaching and learning process in classrooms. It adjusts the system parameters based on feedback received from the system, thereby optimizing both energy consumption and communication systems. The effectiveness of the proposed algorithm is evaluated through a case study conducted on the test system, where historical data on energy consumption and communication patterns are analyzed. The results demonstrate that the algorithm efficiently optimizes the communication and energy management systems, leading to substantial energy savings and improved communication efficiency within the test system. In conclusion, this study presents a hybrid machine learning and modified teaching learning-based optimization algorithm that effectively addresses the communication and energy management challenges in the test system. Moreover, this algorithm holds the potential for application in various smart city domains beyond the test system. The findings of this research contribute to the advancement of smart city technologies and offer valuable insights into reducing energy consumption in densely populated urban areas.

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Section: Literature Analysismentioning

confidence: 99%

Hybrid Machine Learning and Modified Teaching Learning-Based English Optimization Algorithm for Smart City Communication

2023

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“…The improvement of the latest strategies is permanently required to support to clear up problems [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] . Some unique performances have been planned for automatic text summarization that deeds a spread of different strategies.…”

Section: Related Workmentioning

confidence: 99%

Automatic Text Summarization for Urdu Roman Language by Using Fuzzy Logic

Ali¹

2021

J Autonom Intell

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In the new era of technology, there is the redundancy of information in the internet world, which gives a hard time for users to contain the willed outcome it, to crack this hardship we need an automated process that riddle and search the obtained facts. Text summarization is one of the normal methods to solve problems. The target of the single document epitome is to raise the possibilities of data. we have worked mostly on extractive stationed text summarization. Sentence scoring is the method usually used for extractive text summarization. In this paper, we built an Urdu Roman Language Dataset which has thirty thousand articles. We follow the Fuzzy good judgment technique to clear up the hassle of text summarization. The fuzzy logic approach model delivers Fuzzy rules which have uncertain property weight and produce an acceptable outline. Our approach is to use Cosine similarity with Fuzzy logic to suppress the extra data from the summary to boost the proposed work. We used the standard Testing Method for Fuzzy Logic Urdu Roman Text Summarization and then compared our Machine-generated summary with the help of ROUGE and BLEU Score Method. The result shows that the Fuzzy Logic approach is better than the preceding avenue by a meaningful edge.

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“…The application system provides users with personalized services for different scenarios in real time by sensing the users' current location, historical space-time context, and other information. The value of an LBS lies in using information and communication technologies to build a bridge between the real and virtual worlds for users through various intelligent terminal devices (Sahba et al, 2014). With its computing transparency, seamless mobility, universality of information access, and intelligence based on context awareness, an LBS realizes user-centric information services (Sahba et al, 2018;Wen et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Attention-Based Time Sequence and Distance Contexts Gated Recurrent Unit for Personalized POI Recommendation

Yan-li

2023

International Journal of Information Technologies and Systems Approach

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Aiming at the problems resulting from the fact that the existing point of interest (POI) recommendation methods cannot effectively consider the personalized differences of users' mobile behavior in space and time, the author proposes a personalized POI recommendation method using attention-based time sequence and distance contexts gated recurrent unit (ATSD-GRU). First, the author combined the time sequence and distance context with the GRU to extract useful information from users, effectively alleviating the data sparsity. Second, inspired by the attention mechanism, the author introduced the attention model further into the neural network to capture the user's main mobile behavior intention. Finally, the author studied the ATSD-GRU and trained through Bayesian personalized sorting framework and back propagation algorithm. Experiments imply that the proposed method outperforms the comparison method in terms of the F1 index for any recommended number. When the recommendation list length is 15, the proposed algorithm exhibits an accuracy of 9.23% and a recall rate of 14.65%, both higher than the compared algorithm.

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Improving IPC in simultaneous multi-threading (SMT) processors by capping IQ utilization according to dispatched memory instructions

Cited by 21 publications

References 18 publications

Hybrid Machine Learning and Modified Teaching Learning-Based English Optimization Algorithm for Smart City Communication

Hybrid Machine Learning and Modified Teaching Learning-Based English Optimization Algorithm for Smart City Communication

Automatic Text Summarization for Urdu Roman Language by Using Fuzzy Logic

Attention-Based Time Sequence and Distance Contexts Gated Recurrent Unit for Personalized POI Recommendation

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