Deep Learning-Driven Predictive Modelling for Optimizing Stingless Beekeeping Yields by Noor Hafizah Khairul Anuar, Mohd Amri Md Yunus, Muhammad Ariff Baharudin, Sallehuddin Ibrahim, Shafishuhaza Sahlan

“…The dataset extracted from the 6th of January 2024 to the 5th of February 2024, at a 15-minute time interval comprising a total of 2577 data points was analyzed using various deep learning approaches for best RMSE performance. A single-layer LSTM model with 50 units produced the best RMSE performance of 0.039, representing that the beehive weight was accurately predicted. …”
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Respiratory Rate Sensing for a Non-Stationary Human Assisted by Motion Detection by Hsi-Chou Hsu, Wei-Hsin Chen, Yi-Wen Lin, Yung-Fa Huang

“…Experimental results are compared using alternative schemes, including fast Fourier transform (FFT), short-time Fourier transform (STFT), and RGB-D camera-assisted methods, in terms of root mean square error (RMSE) performance.…”
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NH4 Modelling with ARIMA and LSTM by Hanna Arini Parhusip, Suryasatriya Trihandaru, Johanes Dian Kurniawan

“…Despite its use, ARIMA's Root Mean Square Error (RMSE) performance was found lacking compared to more advanced methods. …”
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CDMA/OTFS Sensing Outperforms Pure OTFS at the Same Communication Throughput by Hugo Hawkins, Chao Xu, Lie-Liang Yang, Lajos Hanzo

“…Hence, this work characterises both the communication Bit Error Rate (BER) and sensing Root Mean Square Error (RMSE) performance of Code Division Multiple Access OTFS (CDMA/OTFS), and contrasts them to pure OTFS. …”
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Comparative Analysis of Attention Mechanisms in Densely Connected Network for Network Traffic Prediction by Myeongjun Oh, Sung Oh, Jongkyung Im, Myungho Kim, Joung-Sik Kim, Ji-Yeon Park, Na-Rae Yi, Sung-Ho Bae

“…The experimental results show that, compared to STDenseNet, our ADNet improved RMSE performance by 3.72%, 2.84%, and 5.87% in call service (Call), short message service (SMS), and Internet access (Internet) sub-datasets, respectively.…”
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Experimental analysis and gene expression programming optimization of sustainable concrete containing mineral fillers by Ayesha Rauf, Usama Asif, Kennedy Onyelowe, Muhammad Faisal Javed, Hisham Alabduljabbar

“…The outcomes of the GEP models were validated by comparing them with multi-linear regression (MLR) models and using various statistical metrics such as root mean squared error (RMSE), performance index (PI), correlation coefficient (R), and external validation methods. …”
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Source Localization via Doppler Shifts Using Mobile Sensors in ICNets Within Industry 5.0 by Lina Wang, Xiaoting Mao, Kai Fang, Ali Kashif Bashir, Marwan Omar, Xiaoping Wu, Wei Wang

“…Subsequently, the root mean square error (RMSE) performance is improved in the stage-two WLS solution, and we design the bias-compensated two-stage WLS (BCTSWLS) solution. …”
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Optimising air quality prediction in smart cities with hybrid particle swarm optimization‐long‐short term memory‐recurrent neural network model by Surjeet Dalal, Umesh Kumar Lilhore, Neetu Faujdar, Sarita Samiya, Vivek Jaglan, Roobaea Alroobaea, Momina Shaheen, Faizan Ahmad

“…The experimental findings demonstrate that the proposed LSTM model had RMSE performance in the prescribed dataset and statistically significant superior outcomes compared to existing methods.…”
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Energy storage efficiency modeling of high-entropy dielectric capacitors using extreme learning machine and swarm-based hybrid support vector regression computational methods by Yas Al-Hadeethi, Taoreed O. Owolabi, Mouftahou B. Latif, Bahaaudin M. Raffah, Ahmad H. Milyani, Saheed A. Tijani

“…The developed sigmoid (SG) activation function-based ELM (SG-ELM) shows performance improvement over sine (SI) function-based ELM (SI-ELM) model and PS-SVR model with an improvement of 79.25 % and 89.4 % using root mean square error (RMSE) performance measuring parameter. The dependency of energy storage efficiency on coercive energy and concentration of dopants in A and B-sites of the perovskite was established using the developed SG-ELM model. …”
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