Tamuka and Sibanda 2023 K-Means-LSTM Hybrid Model Claim Fact-Checked
“Tamuka and Sibanda (2023) further illustrate the broader theme of pattern-based analysis in their evaluation of a K-Means-LSTM hybrid model for spectrum sensing optimization, demonstrating that combining structured rule-based approaches with data-driven methods yields superior results.”
Summary
No available source documents a 2023 study by Tamuka and Sibanda on a K‑Means‑LSTM hybrid for spectrum‑sensing optimization. Existing literature shows K‑Means‑LSTM applied to supply‑chain forecasting and separate spectrum‑sensing models using LSTM variants, but not the claimed combination or authors. Consequently, the statement lacks evidence.
Sources 58 searched
- A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning | Scientific Reports
As the results show, the Hybrid LSTM-CNN model outperforms other models in terms of the accuracy of 99.87%, the false positive rate of 0.13% and the adversarial robustness of 90.2%. Being different from the conventional models that do not ...
- A comparative analysis of LSTM models aided with attention and squeeze and excitation blocks for activity recognition | Scientific Reports
Scientific Reports - A comparative analysis of LSTM models aided with attention and squeeze and excitation blocks for activity recognition
- RNN-Bi-LSTM spectrum sensing algorithm for NOMA waveform with diverse channel conditions | Scientific Reports
Scientific Reports - RNN-Bi-LSTM spectrum sensing algorithm for NOMA waveform with diverse channel conditions
- LSTM-ARIMA as a hybrid approach in algorithmic investment strategies - ScienceDirect
∗)). The findings conclude that the LSTM-ARIMA algorithm outperforms all the other algorithms across all the equity indices which confirms the strong potential behind hybrid ML-TS (machine learning - time series) models in searching for the ...
- RNN-BIRNN-LSTM based spectrum sensing for proficient data transmission in cognitive radio - ScienceDirect
With the advancements that are taking place in the wireless communications field, the number of users who are utilizing resources is also increasing; …
- CM-LSTM Based Spectrum Sensing - PMC - PubMed Central
This paper presents spectrum sensing as a classification problem, and uses a spectrum-sensing algorithm based on a signal covariance matrix and long short-term memory network (CM-LSTM). We jointly exploited the spatial cross-correlation of multiple ...
- A Novel K-means-LSTM Hybrid Model of Supply Chain Inventory Management | Proceedings of the 5th International Conference on Computer Information and Big Data Applications
This study develops a hybrid model that combines the K-means clustering algorithm and Long Short-Term Memory (LSTM) networks, aiming to group the three-dimensional historical data of merchants, warehouses, and product demand volumes, thereby categorizing time series and improving the accuracy of demand forecasting. This paper utilizes the shipping volume of 1996 "merchant + warehouse + product" combinations provided by JD Group from November 1 to November 11, 2022, to predict promotional trends from June 1 to June 20, 2023.
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