A hybrid approach to recognising activities of daily living from object use in the home environment

Accurate recognition of Activities of Daily Living (ADL) plays an important role in providing assistance and support to the elderly and cognitively impaired. Current knowledge-driven and ontology-based techniques model object concepts from assumptions and everyday common knowledge of object use for routine activities. Modelling activities from such information can lead to incorrect recognition of particular routine activities resulting in possible failure to detect abnormal activity trends. In cases where such prior knowledge are not available, such techniques become virtually unemployable. A significant step in the recognition of activities is the accurate discovery of the object usage for specific routine activities. This paper presents a hybrid framework for automatic consumption of sensor data and associating object usage to routine activities using Latent Dirichlet Allocation (LDA) topic modelling. This process enables the recognition of simple activities of daily living from object usage and interactions in the home environment. The evaluation of the proposed framework on the Kasteren and Ordonez datasets show that it yields better results compared to existing techniques

TANGENT BUNDLES OF LP-SASAKIAN MANIFOLD ENDOWED WITH GENERALIZED SYMMETRIC METRIC CONNECTION

The aim of the present work is to study and establish conditions for anLP-Sasakian manifold on the tangent bundle $TM$. An LP-Sasakian manifold with the generalized symmetric metric connection on $TM$ is investigated. Next, the curvature tensor and the Ricci tensor of an LP-Sasakian manifold with respect to the generalized symmetric metric connection on $TM$ are calculated. Moreover, the projective curvature tensor with respect to the generalized symmetric metric connection on $TM$ is studied and showed that $TM$ is not $\hat{\xi}^C$-projectively flat. In particular, if $\alpha=0$ and $\beta=1$ then $TM$ is $\hat{\xi}^C$-projectively flat

The effect of variation of molarity of alkali activator and fine aggregate content on the compressive strength of the fly ash: Palm oil fuel ash based geopolymer mortar

The effect of molarity of alkali activator, manufactured sand (M-sand), and quarry dust (QD) on the compressive strength of palm oil fuel ash (POFA) and fly ash (FA) based geopolymermortar was investigated and reported. The variable investigated includes the quantities of replacement levels of M-sand, QD, and conventional mining sand (N-sand) in two concentrated alkaline solutions; the contents of alkaline solution, water, POFA/FA ratio, and curing condition remained constant. The results show that an average of 76% of the 28-day compressive strength was found at the age of 3 days. The rate of strength development from 3 to 7 days was found between 12 and 16% and it was found much less beyond this period. The addition of 100% M-sand and QD shows insignificant strength reduction compared to mixtures with 100% N-sand. The particle angularity and texture of fine aggregates played a significant role in the strength development due to the filling and packing ability. The rough texture and surface of QD enables stronger bond between the paste and the fine aggregate.The concentration of alkaline solution increased the reaction rate and thus enhanced the development of early age strength. The use of M-sand and QD in the development of geopolymer concrete is recommended as the strength variation between these waste materials and conventional sand is not high.Iftekhair Ibnul Bashar, U. Johnson Alengaram, Mohd Zamin Jumaat, and Azizul Isla

Fabrication and Characterization of Graphene-Barium Titanate-Graphene layered capacitors by spin coating at low processing temperatures

Barium titanate, BaTiO3 (BT), materials have been synthesized by two different routes: one ball-mill-derived (BMD) nanopowder and another precursor-derived (PCD) BT synthesis method were used separately to fabricate BT thin films on stainless steel (SS) and quartz substrates by spin coating. Then thin films from both synthesis routes were characterized by Ultraviolet-Visible-Near Infrared (UV-Vis-NIR) Spectroscopy, Field-Emission Scanning Electron Microscopy (FE-SEM), X-ray Diffractometry (XRD), Raman Spectroscopy, and Four-point collinear probe; all carried out at room temperature. Our studies revealed that the PCD synthesis process did not produce the BT phase even under the 900^0C air-annealing condition. In contrast, a homogeneous BT thin film has been formed from the BMD-BT nanopowder. The optical band gap of BMD-BT thin films was found in the 3.10 - 3.28 eV range. Finally, a Graphene-Barium Titanate-Graphene (G-BT-G) structure was fabricated on a SS substrate by spin coating at processing temperatures below 100^0C and characterized by two different pieces of equipment: a Potentiostat/Galvanostat (PG-STAT) and a Precision Impedance Analyzer (PIA). The G-BT-G structure exhibited a capacitance of 8 nF and 7.15 nF, a highest dielectric constant of 800 and 790, and a low dielectric loss of 4.5 and 5, investigated by PG-STAT and PIA equipment, respectively.Comment: 25 pages, 11 Figure

Predicting Bitcoin and Ethereum prices using long short-term memory and gated recurrent unit

Predicting future prices of cryptocurrencies, including Bitcoin and Ethereum, presents a formidable challenge owing to their inherent volatility. This study applies Long Short-Term Memory (LSTM), a well-established recurrent neural network for time series forecasting, to predict Bitcoin and Ethereum values. Historical price data for both cryptocurrencies, sourced from Yahoo Finance, serves as the basis for analysis. The dataset undergoes an 80% training and 20% testing partition. Subsequently, LSTM models are developed and trained on both datasets. In parallel, the gated recurrent unit (GRU), recognized as an advanced variant of the LSTM model, is explored for comparative purposes. Performance evaluation utilizes fundamental metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results reveal an intriguing trend: both models exhibit superior performance when applied to the Ethereum dataset compared to the Bitcoin dataset. This observation suggests the potential presence of Ethereum-specific features or patterns that align more effectively with deep learning model architectures. Notably, the GRU model consistently outperforms the LSTM model across RMSE, MAE, and MAPE. These outcomes underscore the GRU model’s capacity as a robust tool for cryptocurrency value prediction. In summary, this study tackles the challenge of cryptocurrency price prediction while emphasizing the promising role of advanced neural network architectures, such as GRU, in enhancing prediction accuracy, thus offering valuable insights into financial forecasting