An empirical comparison of algorithmic, instruction, and architectural power prediction models for high performance embedded DSP processors

ObjectivesTo measure the metrics of glioma pre-operative MRI reports and build IDH prediction models.MethodsPre-operative MRI reports of 144 glioma patients in a single institution were collected retrospectively. Words were transformed to lowercase letters. White spaces, punctuations, and stop words were removed. Stemming was performed. A word cloud method applied to processed text matrix visualized language behavior. Spearman’s rank correlation assessed the correlation between the subjective descriptions of the enhancement pattern. The T1-contrast images associated with enhancement descriptions were selected. The keywords associated with IDH status were evaluated by χ2 value ranking. Random forest, k-nearest neighbors and Support Vector Machine algorithms were used to train models based on report features and age. All statistical analysis used two-tailed test with significance at p <.05.ResultsLonger word counts occurred in reports of older patients, higher grade gliomas, and wild type IDH gliomas. We identified 30 glioma enhancement descriptions, eight of which were commonly used: peripheral, heterogeneous, irregular, nodular, thick, rim, large, and ring. Five of eight patterns were correlated. IDH mutant tumors were characterized by words related to normal, symmetric or negative findings. IDH wild type tumors were characterized words by related to pathological MR findings like enhancement, necrosis and FLAIR foci. An integrated KNN model based on report features and age demonstrated high-performance (AUC: 0.89, 95% CI: 0.88–0.90).ConclusionReport length depended on age, glioma grade, and IDH status. Description of glioma enhancement was varied. Report descriptions differed for IDH wild and mutant gliomas. Report features can be used to predict glioma IDH status.

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Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar

Energies ◽

10.3390/en12142782 ◽

2019 ◽

Vol 12 (14) ◽

pp. 2782 ◽

Cited By ~ 19

Author(s):

Amith Khandakar ◽

Muhammad E. H. Chowdhury ◽

Monzure- Khoda Kazi ◽

Kamel Benhmed ◽

Farid Touati ◽

...

Keyword(s):

Feature Selection ◽

Output Power ◽

Regression Models ◽

Prediction Models ◽

Gaussian Process Regression ◽

Environmental Parameters ◽

Ann Model ◽

Power Prediction ◽

Pv System ◽

Energy Evaluation

Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m2 in the Arabian Peninsula is an exploitable wealth of energy source. These countries plan to increase the contribution of power from renewable energy (RE) over the years. Due to its abundance, the focus of RE is on solar energy. Evaluation and analysis of PV performance in terms of predicting the output PV power with less error demands investigation of the effects of relevant environmental parameters on its performance. In this paper, the authors have studied the effects of the relevant environmental parameters, such as irradiance, relative humidity, ambient temperature, wind speed, PV surface temperature and accumulated dust on the output power of the PV panel. Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. The ANN models with all the features and features selected using correlation feature selection (CFS) and relief feature selection (ReliefF) techniques were found to successfully predict PV output power with Root Mean Square Error (RMSE) of 2.1436, 6.1555, and 5.5351, respectively. Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. This will have a noteworthy contribution in scaling the PV deployment in countries like Qatar and increase the share of PV power in the national power production.

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Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library

Molecules ◽

10.3390/molecules25122764 ◽

2020 ◽

Vol 25 (12) ◽

pp. 2764

Author(s):

Yasunari Matsuzaka ◽

Yoshihiro Uesawa

Keyword(s):

Deep Learning ◽

Signaling Pathways ◽

High Performance ◽

Prediction Models ◽

Environmental Chemicals ◽

Adverse Outcome Pathway ◽

Toxicological Evaluation ◽

Chemical Structures ◽

Molecular Image ◽

Wide Range

The interaction of nuclear receptors (NRs) with chemical compounds can cause dysregulation of endocrine signaling pathways, leading to adverse health outcomes due to the disruption of natural hormones. Thus, identifying possible ligands of NRs is a crucial task for understanding the adverse outcome pathway (AOP) for human toxicity as well as the development of novel drugs. However, the experimental assessment of novel ligands remains expensive and time-consuming. Therefore, an in silico approach with a wide range of applications instead of experimental examination is highly desirable. The recently developed novel molecular image-based deep learning (DL) method, DeepSnap-DL, can produce multiple snapshots from three-dimensional (3D) chemical structures and has achieved high performance in the prediction of chemicals for toxicological evaluation. In this study, we used DeepSnap-DL to construct prediction models of 35 agonist and antagonist allosteric modulators of NRs for chemicals derived from the Tox21 10K library. We demonstrate the high performance of DeepSnap-DL in constructing prediction models. These findings may aid in interpreting the key molecular events of toxicity and support the development of new fields of machine learning to identify environmental chemicals with the potential to interact with NR signaling pathways.

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