Genetic Circuits Combined with Machine Learning Provides Fast Responding Living Sensors

AbstractWhole cell biosensors (WCBs) have become prominent in many fields from environmental analysis to biomedical diagnostics thanks to advanced genetic circuit design principles. Despite increasing demand on cost effective and easy-to-use assessment methods, a considerable amount of WCBs retains certain drawbacks such as long response time, low precision and accuracy. Furthermore, the output signal level does not correspond to a specific analyte concentration value but shows comparative quantification. Here, we utilized a neural network-based architecture to improve the aforementioned features of WCBs and engineered a gold sensing WCB which has a long response time (18 h). Two Long-Short Term-Memory (LSTM)-based networks were integrated to assess both ON/OFF and concentration dependent states of the sensor output, respectively. We demonstrated that binary (ON/OFF) network was able to distinguish between ON/OFF states as early as 30 min with 78% accuracy and over 98% in 3 h. Furthermore, when analyzed in analog manner, we demonstrated that network can classify the raw fluorescence data into pre-defined analyte concentration groups with high precision (82%) in 3 h. This approach can be applied to a wide range of WCBs and improve rapidness, simplicity and accuracy which are the main challenges in synthetic biology enabled biosensing.

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Efficient and Cost-effective Service Broker Policy Based on User Priority in VIKOR for Cloud Computing

Basic and Applied Sciences - Scientific Journal of King Faisal University ◽

10.37575/b/cmp/210032 ◽

2021 ◽

pp. 1-8

Author(s):

Mohammed Radi ◽

Ali Alwan ◽

Abedallah Abualkishik ◽

Adam Marks ◽

Yonis Gulzar

Keyword(s):

Cloud Computing ◽

Response Time ◽

Service Providers ◽

Cost Effective ◽

Cloud Service ◽

User Preferences ◽

Cloud Environment ◽

Data Centre ◽

Wide Range ◽

Service Broker

Cloud computing has become a practical solution for processing big data. Cloud service providers have heterogeneous resources and offer a wide range of services with various processing capabilities. Typically, cloud users set preferences when working on a cloud platform. Some users tend to prefer the cheapest services for the given tasks, whereas other users prefer solutions that ensure the shortest response time or seek solutions that produce services ensuring an acceptable response time at a reasonable cost. The main responsibility of the cloud service broker is identifying the best data centre to be used for processing user requests. Therefore, to maintain a high level of quality of service, it is necessity to develop a service broker policy that is capable of selecting the best data centre, taking into consideration user preferences (e.g. cost, response time). This paper proposes an efficient and cost-effective plan for a service broker policy in a cloud environment based on the concept of VIKOR. The proposed solution relies on a multi-criteria decision-making technique aimed at generating an optimized solution that incorporates user preferences. The simulation results show that the proposed policy outperforms most recent policies designed for the cloud environment in many aspects, including processing time, response time, and processing cost. KEYWORDS Cloud computing, data centre selection, service broker, VIKOR, user priorities

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Biological and medicinal application of Cucumis sativus Linn. – review of current status with future possibilities

Journal of Complementary and Integrative Medicine ◽

10.1515/jcim-2020-0240 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Anayatullah Khan ◽

Anuradha Mishra ◽

Syed Misbahul Hasan ◽

Afreen Usmani ◽

Mohd Ubaid ◽

...

Keyword(s):

Cucumis Sativus ◽

Cost Effective ◽

Pharmacological Action ◽

Modern Medicine ◽

Current Status ◽

Synthetic Drugs ◽

Wide Range ◽

Increasing Demand ◽

Calcium Iron ◽

Whole Plants

Abstract Objectives The increasing demand for herbal drugs for the human application is causing a growing demand for the cultivation of Medicinal Plants. This demand has developed because of cost-effective, plant-derived products rather than commercially available synthetic drugs. Cucumis sativus Linn. (Ver. Kheera) is a vegetable climber, species belongs to family Cucurbitaceae This species has a wide range of medicinal and biological applications thanks to its richness in carbohydrate, proteins, minerals (calcium, iron, magnesium, phosphorus, potassium, zinc) and secondary metabolites like alkaloids, tannins, flavonoids, saponins, and phenolic compounds These phytoconstituents may be responsible for allied therapeutic application. So, C. sativus possess wider applications for preventing certain ailments. Content The literature in various national and international journals and reports pertaining to the medicinal and nutritional uses were reviewed. The result revealed the current therapeutic applications of C. sativus whole plants other than the nutritional value. C. sativus pharmacological action includes antioxidant, anti-diabetic, UV protectant, hepatoprotective, gastroprotective, anti-helminthic, wound healing, antimicrobial, and anticancer. So, it could be useful for both preventive and additive therapy along with modern medicine for the better management of certain disorders. Summary and Outlook This review furnishes updated information about the phytoconstituents and their medicinal applications so that it can pose a path for the young researchers to do future findings.

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Executing CNN-LSTM Algorithm for Recognizable Proof of Cervical Spondylosis Infection on Spinal Cord MRI Image

Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-6870-5.ch032 ◽

2021 ◽

pp. 468-484

Author(s):

Sasank V. V. S. ◽

Kranthi Kumar Singamaneni ◽

A. Sampath Dakshina Murthy ◽

S. K. Hasane Ahammad

Keyword(s):

Neural Network ◽

Cervical Spondylosis ◽

Short Term Memory ◽

Life Experience ◽

Programming Approach ◽

Physical Test ◽

Term Memory ◽

Wide Range ◽

Long Short Term Memory ◽

Mri Image

Various estimating mechanisms are present for evaluating the regional agony, neck torment, neurologic deficiencies of the sphincters at the stage midlevel of cervical spondylosis. It is necessary for the cervical spondylosis that the survey necessitates wide range of learning skills about the systemized life, experience, and ability of the expertise for learning the capability, life system, and experience. Doctors check the analysis of situation through MRI and CT scan, but additional interesting facts have been discovered in the physical test. For this, a programming approach is not available. The authors thereby propose a novel framework that accordingly inspects and investigates the cervical spondylosis employing computation of CNN-LSTM. Machine learning methods such as long short-term memory (LSTM) in fusion with convolution neural networks (CNNs), a kind of neural network (NN), are applied to this strategy to evaluate for making the systematization in various applications.

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Local Translation Services for Neglected Languages

10.5121/csit.2021.110110 ◽

2021 ◽

Author(s):

David Noever ◽

Josh Kalin ◽

Matthew Ciolino ◽

Dom Hambrick ◽

Gerry Dozier

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Personal Data ◽

Cost Effective ◽

Leonardo Da Vinci ◽

Original Contribution ◽

Translation Services ◽

Long Short Term Memory ◽

Translation Service ◽

New Applications

Taking advantage of computationally lightweight, but high-quality translators prompt consideration of new applications that address neglected languages. For projects with protected or personal data, translators for less popular or low-resource languages require specific compliance checks before posting to a public translation API. In these cases, locally run translators can render reasonable, cost-effective solutions if done with an army of offline, smallscale pair translators. Like handling a specialist’s dialect, this research illustrates translating two historically interesting, but obfuscated languages: 1) hacker-speak (“l33t”) and 2) reverse (or “mirror”) writing as practiced by Leonardo da Vinci. The work generalizes a deep learning architecture to translatable variants of hacker-speak with lite, medium, and hard vocabularies. The original contribution highlights a fluent translator of hacker-speak in under 50 megabytes and demonstrates a companion text generator for augmenting future datasets with greater than a million bilingual sentence pairs. A primary motivation stems from the need to understand and archive the evolution of the international computer community, one that continuously enhances their talent for speaking openly but in hidden contexts. This training of bilingual sentences supports deep learning models using a long short-term memory, recurrent neural network (LSTM-RNN). It extends previous work demonstrating an English-to-foreign translation service built from as little as 10,000 bilingual sentence pairs. This work further solves the equivalent translation problem in twenty-six additional (non-obfuscated) languages and rank orders those models and their proficiency quantitatively with Italian as the most successful and Mandarin Chinese as the most challenging. For neglected languages, the method prototypes novel services for smaller niche translations such as Kabyle (Algerian dialect) which covers between 5-7 million speakers but one which for most enterprise translators, has not yet reached development. One anticipates the extension of this approach to other important dialects, such as translating technical (medical or legal) jargon and processing health records or handling many of the dialects collected from specialized domains (mixed languages like “Spanglish”, acronym-laden Twitter feeds, or urban slang).

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A Cost-Effective CNN-LSTM-Based Solution for Predicting Faulty Remote Water Meter Reading Devices in AMI Systems

Sensors ◽

10.3390/s21186229 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6229

Author(s):

Jaeseung Lee ◽

Woojin Choi ◽

Jibum Kim

Keyword(s):

Real World ◽

Short Term Memory ◽

Cost Effective ◽

Recursive Feature Elimination ◽

Training Method ◽

Short Term ◽

Memory Network ◽

Long Short Term Memory ◽

The Cost ◽

Water Meter

Automatic meter infrastructure (AMI) systems using remote metering are being widely used to utilize water resources efficiently and minimize non-revenue water. We propose a convolutional neural network-long short-term memory network (CNN-LSTM)-based solution that can predict faulty remote water meter reading (RWMR) devices by analyzing approximately 2,850,000 AMI data collected from 2762 customers over 360 days in a small-sized city in South Korea. The AMI data used in this study is a challenging, highly unbalanced real-world dataset with limited features. First, we perform extensive preprocessing steps and extract meaningful features for handling this challenging dataset with limited features. Next, we select important features that have a higher influence on the classifier using a recursive feature elimination method. Finally, we apply the CNN-LSTM model for predicting faulty RWMR devices. We also propose an efficient training method for ML models to learn the unbalanced real-world AMI dataset. A cost-effective threshold for evaluating the performance of ML models is proposed by considering the mispredictions of ML models as well as the cost. Our experimental results show that an F-measure of 0.82 and MCC of 0.83 are obtained when the CNN-LSTM model is used for prediction.

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Prediction of gait trajectories based on the Long Short Term Memory neural networks

PLoS ONE ◽

10.1371/journal.pone.0255597 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0255597

Author(s):

Abdelrahman Zaroug ◽

Alessandro Garofolini ◽

Daniel T. H. Lai ◽

Kurt Mudie ◽

Rezaul Begg

Keyword(s):

Lower Limb ◽

Short Term Memory ◽

Series Data ◽

Short Term ◽

Term Memory ◽

Wide Range ◽

Limb Kinematics ◽

Long Short Term Memory ◽

Balance Loss ◽

Lower Limb Kinematics

The forecasting of lower limb trajectories can improve the operation of assistive devices and minimise the risk of tripping and balance loss. The aim of this work was to examine four Long Short Term Memory (LSTM) neural network architectures (Vanilla, Stacked, Bidirectional and Autoencoders) in predicting the future trajectories of lower limb kinematics, i.e. Angular Velocity (AV) and Linear Acceleration (LA). Kinematics data of foot, shank and thigh (LA and AV) were collected from 13 male and 3 female participants (28 ± 4 years old, 1.72 ± 0.07 m in height, 66 ± 10 kg in mass) who walked for 10 minutes at preferred walking speed (4.34 ± 0.43 km.h-1) and at an imposed speed (5km.h-1, 15.4% ± 7.6% faster) on a 0% gradient treadmill. The sliding window technique was adopted for training and testing the LSTM models with total kinematics time-series data of 10,500 strides. Results based on leave-one-out cross validation, suggested that the LSTM autoencoders is the top predictor of the lower limb kinematics trajectories (i.e. up to 0.1s). The normalised mean squared error was evaluated on trajectory predictions at each time-step and it obtained 2.82–5.31% for the LSTM autoencoders. The ability to predict future lower limb motions may have a wide range of applications including the design and control of bionics allowing improved human-machine interface and mitigating the risk of falls and balance loss.

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Bottleneck Based Gridlock Prediction in an Urban Road Network Using Long Short-Term Memory

Electronics ◽

10.3390/electronics9091412 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1412

Author(s):

Ei Ei Mon ◽

Hideya Ochiai ◽

Chaiyachet Saivichit ◽

Chaodit Aswakul

Keyword(s):

Road Network ◽

Short Term Memory ◽

Road Networks ◽

Sample Sizes ◽

Short Term ◽

Urban Road Network ◽

Term Memory ◽

Urban Road ◽

Wide Range ◽

Long Short Term Memory

The traffic bottlenecks in urban road networks are more challenging to investigate and discover than in freeways or simple arterial networks. A bottleneck indicates the congestion evolution and queue formation, which consequently disturb travel delay and degrade the urban traffic environment and safety. For urban road networks, sensors are needed to cover a wide range of areas, especially for bottleneck and gridlock analysis, requiring high installation and maintenance costs. The emerging widespread availability of GPS vehicles significantly helps to overcome the geographic coverage and spacing limitations of traditional fixed-location detector data. Therefore, this study investigated GPS vehicles that have passed through the links in the simulated gridlock-looped intersection area. The sample size estimation is fundamental to any traffic engineering analysis. Therefore, this study tried a different number of sample sizes to analyze the severe congestion state of gridlock. Traffic condition prediction is one of the primary components of intelligent transportation systems. In this study, the Long Short-Term Memory (LSTM) neural network was applied to predict gridlock based on bottleneck states of intersections in the simulated urban road network. This study chose to work on the Chula-Sathorn SUMO Simulator (Chula-SSS) dataset. It was calibrated with the past actual traffic data collection by using the Simulation of Urban MObility (SUMO) software. The experiments show that LSTM provides satisfactory results for gridlock prediction with temporal dependencies. The reported prediction error is based on long-range time dependencies on the respective sample sizes using the calibrated Chula-SSS dataset. On the other hand, the low sampling rate of GPS trajectories gives high RMSE and MAE error, but with reduced computation time. Analyzing the percentage of simulated GPS data with different random seed numbers suggests the possibility of gridlock identification and reports satisfying prediction errors.

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Time Series Segmentation Based on Stationarity Analysis to Improve New Samples Prediction

Sensors ◽

10.3390/s21217333 ◽

2021 ◽

Vol 21 (21) ◽

pp. 7333

Author(s):

Ricardo Petri Silva ◽

Bruno Bogaz Zarpelão ◽

Alberto Cano ◽

Sylvio Barbon Junior

Keyword(s):

Time Series ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Detection Methods ◽

Sequential Data ◽

Short Term ◽

Term Memory ◽

Wide Range ◽

Long Short Term Memory ◽

Adf Test

A wide range of applications based on sequential data, named time series, have become increasingly popular in recent years, mainly those based on the Internet of Things (IoT). Several different machine learning algorithms exploit the patterns extracted from sequential data to support multiple tasks. However, this data can suffer from unreliable readings that can lead to low accuracy models due to the low-quality training sets available. Detecting the change point between high representative segments is an important ally to find and thread biased subsequences. By constructing a framework based on the Augmented Dickey-Fuller (ADF) test for data stationarity, two proposals to automatically segment subsequences in a time series were developed. The former proposal, called Change Detector segmentation, relies on change detection methods of data stream mining. The latter, called ADF-based segmentation, is constructed on a new change detector derived from the ADF test only. Experiments over real-file IoT databases and benchmarks showed the improvement provided by our proposals for prediction tasks with traditional Autoregressive integrated moving average (ARIMA) and Deep Learning (Long short-term memory and Temporal Convolutional Networks) methods. Results obtained by the Long short-term memory predictive model reduced the relative prediction error from 1 to 0.67, compared to time series without segmentation.

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Improving Fine-Grained Opinion Mining Approach with a Deep Constituency Tree-Long Short Term Memory Network and Word Embedding

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200922142212 ◽

2020 ◽

Vol 13 ◽

Author(s):

Dalila Bouras ◽

Mohamed Amroune ◽

Hakim Bendjenna ◽

Issam Bendib

Keyword(s):

Opinion Mining ◽

Short Term Memory ◽

State Of The Art ◽

Syntactic Structure ◽

Product Reviews ◽

Short Term ◽

Term Memory ◽

Fine Grained ◽

Wide Range ◽

Long Short Term Memory

Objective: One key task of fine-grained opinion mining on product review is to extract product aspects and their corresponding opinion expressed by users. Previous work has demonstrated that precise modeling of opinion targets within the surrounding context can improve performances. However, how to effectively and efficiently learn hidden word semantics and better represent targets and the context still needs to be further studied. Recent years have seen a revival of the long short-term memory (LSTM), with its effectiveness being demonstrated on a wide range of problems. However, LSTM based approaches are still limited to linear data processing since it processes the information sequentially. As a result, they may perform poorly on user-generated texts, such as product reviews, tweets, etc., whose syntactic structure is not precise.To tackle this challenge, Methods: In this research paper, we propose a constituency tree long short term memory neural network-based approach. We compare our model with state-of-the-art baselines on SemEval 2014 datasets. Results: Experiment results show that our models obtain competitive performances compared to various supervised LSTM architectures. Conclusion: Our work contributes to the improvement of state-of-the-art aspect-level opinion mining methods and offers a new approach to support human decision-making process based on opinion mining results.

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Predicting energy and carbon fluxes using LSTM networks

10.5194/egusphere-egu21-14781 ◽

2021 ◽

Author(s):

Claire Brenner ◽

Jonathan Frame ◽

Grey Nearing ◽

Karsten Schulz

Keyword(s):

Short Term Memory ◽

Model Performance ◽

Carbon Fluxes ◽

Reanalysis Data ◽

Scientific Data ◽

Short Term ◽

Term Memory ◽

Carbon Cycles ◽

Wide Range ◽

Long Short Term Memory

Global land-atmosphere energy and carbon fluxes are key drivers of the Earth&#8217;s climate system. Their assessment over a wide range of climates and biomes is therefore essential (i) for a better understanding and characterization of land-atmosphere exchanges and feedbacks and (ii) for examining the effect of climate change on the global water, energy and carbon cycles.&#160;Large-sample datasets such as the FLUXNET2015 dataset (Pastorello et al., 2020) foster the use of machine learning (ML) techniques as a powerful addition to existing physically-based modelling approaches. Several studies have investigated ML techniques for assessing energy and carbon fluxes, and while across-site variability and the mean seasonal cycle are typically well predicted, deviations from mean seasonal behaviour remains challenging (Tramontana et al., 2016).&#160;In this study we examine the importance of memory effects for predicting energy and carbon fluxes at half-hourly and daily temporal resolutions. To this end, we train a Long Short-Term Memory (LSTM, Hochreiter and Schmidthuber, 1997), a recurrent neural network with explicit memory, that is particularly suited for time series predictions due to its capability to store information over longer (time) sequences. We train the LSTM on a large number of FLUXNET sites part of the FLUXNET2015 dataset using local meteorological forcings and static site attributes derived from remote sensing and reanalysis data.&#160;We evaluate model performance out-of-sample (leaving out individual sites) in a 10-fold cross-validation. Additionally, we compare results from the LSTM with results from another ML technique, XGBoost (Chen and Guestrin, 2016), that does not contain system memory. By analysing the differences in model performances of both approaches across various biomes, we investigate under which conditions the inclusion of memory might be beneficial for modelling energy and carbon fluxes.&#160;References:Chen, Tianqi, and Carlos Guestrin. "Xgboost: A scalable tree boosting system." Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016.Hochreiter, Sepp, and J&#252;rgen Schmidhuber. "Long short-term memory." Neural computation 9.8 (1997): 1735-1780.Pastorello, Gilberto, et al. "The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data." Scientific data 7.1 (2020): 1-27Tramontana, Gianluca, et al. "Predicting carbon dioxide and energy fluxes across global &#160; FLUXNET sites with regression algorithms." Biogeosciences 13.14 (2016): 4291-4313.

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