scholarly journals Prediction of Approximate Multiplier for 16-Bit DICOM Image Contrast Scaling Using Classical Machine Learning Approach

2021 ◽  
Author(s):  
maria dominic ◽  
DEEPA T
Keyword(s):  
Machine Learning ◽  
Signal To Noise Ratio ◽  
Processing Application ◽  
Chip Area ◽  
Normalized Error ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
The Impact ◽  
Error Distance

Abstract The role of approximate arithmetic are involved when the processors are used for multimedia signal processing application. The impact of multiplier is very important in many processes done by these processors. The compressors are the core architecture for reduction stage if the multiplier width is increased. Later approximations are done in the compressor to limited error without affecting the signal standard. The design of scalable-split compressor is designed in this work and a counter matching method has been developed for approximation. The design of 32x32 and 16x16 multiplier with these new compressors are synthesised in 45nm Synopsis Design Compiler and shows an improvement of 25 % of Chip area and 27% power. The split-scalable architecture attempts to reduce the delay with trade-off in area and power. Mean Error Distance (MED) and Normalized Error Distance (NED) are the parameters that ensure the quality of any approximate arithmetic based design. 16-bit medical images are processed with both existing and proposed multipliers then the Peak Signal to Noise Ratio (PSNR) is compared. Finally with several input nature and targeted PSNR the best system is identified using classical machine learning model.

scholarly journals Café and Restaurant under My Home: Predicting Urban Commercialization through Machine Learning

2021 ◽  
Vol 13 (10) ◽  
pp. 5699
Author(s):  
Seung-Chul Noh ◽  
Jung-Ho Park
Keyword(s):  
Machine Learning ◽  
Warning System ◽  
Large Degree ◽  
Learning Model ◽  
Urban Housing ◽  
Residential Areas ◽  
Urban Context ◽  
Machine Learning Model ◽  
Machine Learning Approach

The small commercial stores opening in housing structures in Seoul have been soaring since the beginning of this century. While commercialization generally increases urban vitality and achieves land use mix, cafés and restaurants in low-rise residential areas may attract numerous passenger populations, with increased noise and crimes, in the residential area. The urban commercialization is so fast and prevalent that neither urban researchers nor policymakers can respond to it timely without a practical prediction tool. Focusing on cafés and restaurants, we propose an XGBoost machine learning model that can predict commercial store openings in urban residential areas and further play the role of an early warning system. Our findings highlight a large degree of difference in the predictor importance between the variables used in our machine learning model. The most important predictor relates to land price, indicating that economic motivation leads to the conversion of urban housing to small cafés and restaurants. The Mapo neighborhood is predicted to be the most prone to the commercialization of urban housing, therefore, its urgency to be prepared against expected commercialization deserves underscoring. Overall, our results show that the machine learning approach can be applied to predict changes in land uses and contribute to timely policy designs in rapidly changing urban context.

scholarly journals 180 The role of social constructs in placental insufficiency outcomes: an unsupervised machine learning approach

2021 ◽  
Vol 224 (2) ◽  
pp. S121-S122
Author(s):  
Ramamurthy Siripuram ◽  
Nathan R. Blue ◽  
Robert M. Silver ◽  
William A. Grobman ◽  
Uma M. Reddy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Placental Insufficiency ◽  
Learning Approach ◽  
Social Constructs ◽  
Unsupervised Machine Learning ◽  
Machine Learning Approach

scholarly journals Interphotoreceptor coupling: an evolutionary perspective

2021 ◽  
Author(s):  
Lorenzo Cangiano ◽  
Sabrina Asteriti
Keyword(s):  
Visual Information ◽  
Signal To Noise Ratio ◽  
Electrical Coupling ◽  
Physiological Role ◽  
Cellular Processes ◽  
Contact Points ◽  
Highly Sensitive ◽  
The Many ◽  
The Impact

AbstractIn the vertebrate retina, signals generated by cones of different spectral preference and by highly sensitive rod photoreceptors interact at various levels to extract salient visual information. The first opportunity for such interaction is offered by electrical coupling of the photoreceptors themselves, which is mediated by gap junctions located at the contact points of specialised cellular processes: synaptic terminals, telodendria and radial fins. Here, we examine the evolutionary pressures for and against interphotoreceptor coupling, which are likely to have shaped how coupling is deployed in different species. The impact of coupling on signal to noise ratio, spatial acuity, contrast sensitivity, absolute and increment threshold, retinal signal flow and colour discrimination is discussed while emphasising available data from a variety of vertebrate models spanning from lampreys to primates. We highlight the many gaps in our knowledge, persisting discrepancies in the literature, as well as some major unanswered questions on the actual extent and physiological role of cone-cone, rod-cone and rod-rod communication. Lastly, we point toward limited but intriguing evidence suggestive of the ancestral form of coupling among ciliary photoreceptors.

scholarly journals Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning

2021 ◽  
Vol 11 (10) ◽  
pp. 4602
Author(s):  
Farzin Piltan ◽  
Jong-Myon Kim
Keyword(s):  
Machine Learning ◽  
Variable Structure ◽  
Vibration Signal ◽  
Crack Size ◽  
Approximation Technique ◽  
Digital Twin ◽  
Average Accuracy ◽  
Machine Learning Approach ◽  
The Impact ◽  
Signal Approximation

In this study, the application of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification will be observed. The intelligent digital twin has two main sections: signal approximation and intelligent signal estimation. The mathematical vibration bearing signal approximation is integrated with machine learning-based signal approximation to approximate the bearing vibration signal in normal conditions. After that, the combination of the Kalman filter, high-order variable structure technique, and adaptive neural-fuzzy technique is integrated with the proposed signal approximation technique to design an intelligent digital twin. Next, the residual signals will be generated using the proposed intelligent digital twin and the original RAW signals. The machine learning approach will be integrated with the proposed intelligent digital twin for the classification of the bearing anomaly and crack sizes. The Case Western Reserve University bearing dataset is used to test the impact of the proposed scheme. Regarding the experimental results, the average accuracy for the bearing fault pattern recognition and crack size identification will be, respectively, 99.5% and 99.6%.

scholarly journals Accelerating diagnosis of Parkinson’s disease through risk prediction

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
William Yuan ◽  
Brett Beaulieu-Jones ◽  
Richard Krolewski ◽  
Nathan Palmer ◽  
Christine Veyrat-Follet ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Subsequent Development ◽  
Time Compression ◽  
Disease Modifying Therapies ◽  
Machine Learning Approach ◽  
Diagnostic Window

Abstract Background Characterization of prediagnostic Parkinson’s Disease (PD) and early prediction of subsequent development are critical for preventive interventions, risk stratification and understanding of disease pathology. This study aims to characterize the role of the prediagnostic period in PD and, using selected features from this period as novel interception points, construct a prediction model to accelerate the diagnosis in a real-world setting. Methods We constructed two sets of machine learning models: a retrospective approach highlighting exposures up to 5 years prior to PD diagnosis, and an alternative model that prospectively predicted future PD diagnosis from all individuals at their first diagnosis of a gait or tremor disorder, these being features that appeared to represent the initiation of a differential diagnostic window. Results We found many novel features captured by the retrospective models; however, the high accuracy was primarily driven from surrogate diagnoses for PD, such as gait and tremor disorders, suggesting the presence of a distinctive differential diagnostic period when the clinician already suspected PD. The model utilizing a gait/tremor diagnosis as the interception point, achieved a validation AUC of 0.874 with potential time compression to a future PD diagnosis of more than 300 days. Comparisons of predictive diagnoses between the prospective and prediagnostic cohorts suggest the presence of distinctive trajectories of PD progression based on comorbidity profiles. Conclusions Overall, our machine learning approach allows for both guiding clinical decisions such as the initiation of neuroprotective interventions and importantly, the possibility of earlier diagnosis for clinical trials for disease modifying therapies.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

scholarly journals A machine learning approach to predicting short-term mortality risk in patients starting chemotherapy

2017 ◽  
Author(s):  
Aymen A. Elfiky ◽  
Maximilian J. Pany ◽  
Ravi B. Parikh ◽  
Ziad Obermeyer
Keyword(s):  
Machine Learning ◽  
Mortality Risk ◽  
Palliative Chemotherapy ◽  
Learning Algorithm ◽  
Cancer Center ◽  
Short Term ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Short Term Mortality ◽  
And Performance

ABSTRACTBackgroundCancer patients who die soon after starting chemotherapy incur costs of treatment without benefits. Accurately predicting mortality risk from chemotherapy is important, but few patient data-driven tools exist. We sought to create and validate a machine learning model predicting mortality for patients starting new chemotherapy.MethodsWe obtained electronic health records for patients treated at a large cancer center (26,946 patients; 51,774 new regimens) over 2004-14, linked to Social Security data for date of death. The model was derived using 2004-11 data, and performance measured on non-overlapping 2012-14 data.Findings30-day mortality from chemotherapy start was 2.1%. Common cancers included breast (21.1%), colorectal (19.3%), and lung (18.0%). Model predictions were accurate for all patients (AUC 0.94). Predictions for patients starting palliative chemotherapy (46.6% of regimens), for whom prognosis is particularly important, remained highly accurate (AUC 0.92). To illustrate model discrimination, we ranked patients initiating palliative chemotherapy by model-predicted mortality risk, and calculated observed mortality by risk decile. 30-day mortality in the highest-risk decile was 22.6%; in the lowest-risk decile, no patients died. Predictions remained accurate across all primary cancers, stages, and chemotherapies—even for clinical trial regimens that first appeared in years after the model was trained (AUC 0.94). The model also performed well for prediction of 180-day mortality (AUC 0.87; mortality 74.8% in the highest risk decile vs. 0.2% in the lowest). Predictions were more accurate than data from randomized trials of individual chemotherapies, or SEER estimates.InterpretationA machine learning algorithm accurately predicted short-term mortality in patients starting chemotherapy using EHR data. Further research is necessary to determine generalizability and the feasibility of applying this algorithm in clinical settings.

scholarly journals Airbnb Short-term Housing Rental Status Prediction Model Under the Impact of the COVID-19 Pandemic

2021 ◽  
Vol 251 ◽  
pp. 01017
Author(s):  
Zhixiang Lu
Keyword(s):  
Machine Learning ◽  
New York ◽  
Regional Differences ◽  
Prediction Accuracy ◽  
Visual Analysis ◽  
Sharing Economy ◽  
Short Term ◽  
Emerging Industries ◽  
Machine Learning Model ◽  
The Impact

With the vigorous development of the sharing economy, the short-term rental industry has also spawned many emerging industries that belong to the sharing economy. However, due to the impact of the COVID-19 pandemic in 2020, many sharing economy industries, including the short-term housing leasing industry, have been affected. This study takes the rental information of 1,004 short-term rental houses in New York in April 2020 as an example, through machine learning and quantitative analysis, we conducted statistical and visual analysis on the impact of different factors on the housing rental status. This project is based on the machine learning model to predict the changes in the rental status of the house on the time series. The results show that the prediction accuracy of the random forest model has reached more than 94%, and the prediction accuracy of the logistic model has reached more than 74%. At the same time, we have further explored the impact of time span differences and regional differences on the housing rental status.

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