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Using machine-learning approaches to investigate the volatile-compound fingerprint of fishy off-flavour from beef with enhanced healthful fatty acids

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MEAT SCIENCE 2024年 218卷 109643页

作者： Barragan-Hernandez, W. Lopez-Campos, O. Aalhus, J. L. Prieto, N. Corp Colombiana Invest Agr AGROSAVIA Nus Res Ctr San Roque Antioquia Colombia Agr & Agri Food Canada Lacombe Res & Dev Ctr 6000 C&E Trail Lacombe AB T4L 1W1 Canada

machine learning classification approaches were used to discriminate a fishy off-flavour identified in beef with health-enhanced fatty acid profiles. The random forest approach outperformed (P < 0.001;receiver operating characteristic curve: 99.8 %, sensitivity: 99.9 % and specificity: 93.7 %) the logistic regression, partial leastsquares discrimination analysis and the support vector machine (linear and radial) approaches, correctly classifying 100 % and 82 % of the fishy and non-fishy meat samples, respectively. The random forest algorithm identified 20 volatile compounds responsible for the discrimination of fishy from non-fishy meat samples. Among those, seven volatile compounds (pentadecane, octadecane, gamma-dodecalactone, dodecanal, (E,E)-2,4-heptadienal, 2-heptanone, and ethylbenzene) were selected as significant contributors to the fishy off-flavour fingerprint, all being related to lipid oxidation. This fishy off-flavour fingerprint could facilitate the rapid monitoring of beef with enhanced healthy fatty acids to avoid consumer dissatisfaction due to fishy off-flavour.

关键词： Volatile compounds Fishy off-flavour Beef machine-learning algorithms

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Data exchanges based on blockchain in m-Health applications

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Procedia Computer Science 2019年 160卷 281-288页

作者： Antonio Clim Răzvan Daniel Zota Radu Constantinescu The Bucharest University of Economic Studies 15-17 Dorobanti Bucharest 010552 ROMANIA

The most important aspect of handling data in the healthcare industry is its seamless and secure transition across intercepting nodes. Effective elimination of third-party entities and ensuring direct links between patient and healthcare provider can result in the transmission of error-free, unduplicated data. The use of blockchains can open up opportunities to counter the current requirements due to their ability to safely share information across nodes and networks from the access point and secure the safety of transactions. Currently, sharing medical data is observed to be slow, incomplete, insecure, and provider-centric. These shortcomings prevent data interoperability and are a consequence of lack of foundational, structural, and semantic inoperability. By applying the blockchain technologies with appropriate markers, the safety of patient data can be ensured during data transmission. This paper evaluates the potential use of blockchain technology in association with mobile-based healthcare applications.

关键词： mobile healthcare blockchain technology data exchange machine-learning algorithms

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Attention-based LSTM for Remaining Useful Life Estimation of Aircraft Engines

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IFAC-PapersOnLine 2022年第12期55卷 450-455页

作者： Abdeltif Boujamza Saad Lissane Elhaq Team of production system optimization and energy Laboratory of Engineering research Hassan II University National Higher School of Electricity and Mechanics Casablanca Morocco

In a critical business sector such as the aviation industry, remaining useful life (RUL) prediction helps engineers schedule maintenance to avoid the risk of catastrophic failure in both the manufacturing and the servicing sectors. This paper attempts to review and evaluate various RUL predictive models for aircraft engines and compare their performance with a proposed Long-Short Term Memory (LSTM) method based on a data-driven machine learning approach. This study uses the C-MAPSS datasets in order to evaluate the performance and the results of each approach. The obtained outcomes show that the modified LSTM method with Attention mechanism improves the RUL prediction for aircraft engines and provides better performance.

关键词： Remaining useful life prognostic health management predictive maintenance aircraft Engines neural network machine-learning algorithms

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Comprehensive analysis of UK AADF traffic dataset set within four geographical regions of England

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EXPERT SYSTEMS 2023年第10期40卷 e13415-e13415页

作者： Chang, Victor Xu, Qianwen Ariel Hall, Karl Oluwaseyi, Olojede Theophilus Luo, Jiabin Aston Univ Aston Business Sch Dept Operat & Informat Management Birmingham England Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough England

Traffic flow detection plays a significant part in freeway traffic surveillance systems. Currently, effective autonomous traffic analysis is a challenging task due to the complexity of traffic delays, despite the significant investment spent by authorities in monitoring and analysing traffic congestion. This study builds an intelligent analytic method based on machine-learning algorithms to investigate and predict road traffic flows in four locations in the United Kingdom (London, Yorkshire and the Humber, North East, and North West) with a range of relevant factors. While aiming to conduct the study, the dataset 'estimated annual average daily flows (AADFs) Data-major and minor roads' from the UK government was used. machine-learning algorithms are used for this research and classification applied consists of Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors, and Gradient Boosting. Each of these algorithms achieves an accuracy of over 93% and the F1 score of over 95%, with Random Forest outperforming the other algorithms. This analytical approach helps to focus attention on critical areas to reduce traffic flows on major and minor roads in the area. In summary, the findings on traffic analysis have been discussed in detail to demonstrate the practical insights of this study.

关键词： algorithms for traffic analysis machine-learning algorithms Random Forest traffic analysis traffic flow

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Mapping the spatial distribution of underutilised crop species under climate change using the MaxEnt model: A case of KwaZulu-Natal, South Africa

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CLIMATE SERVICES 2022年 28卷

作者： Mugiyo, H. Chimonyo, V. G. P. Kunz, R. Sibanda, M. Nhamo, L. Masemola, C. Ramakgahlele Modi, A. T. Mabhaudhi, T. Univ KwaZulu Natal Ctr Transformat Agr & Food Syst Sch Agr Earth & Environm Sci P Bag X01 ZA-3209 Pietermaritzburg South Africa Int Maize & Wheat Improvement Ctr CIMMYT Zimbabwe POB MP 163 Harare Zimbabwe Univ KwaZulu Natal Ctr Water Resources Res Sch Agr Earth & Environm Sci P Bag X01 Scottsville South Africa Univ Western Cape Fac Arts Dept Geog Environm Studies & Tourism Bellville South Africa Water Res Commiss South Africa 4 Daventry St ZA-0081 Pretoria South Africa CSIR Precis Agr Grp Pretoria South Africa Int Water Management Inst IWMI GH West Africa Reg Off Accra Ghana

Knowing the spatial and temporal suitability of neglected and underutilised crop species (NUS) is important for fitting them into marginal production areas and cropping systems under climate change. The current study used climate change scenarios to map the future distribution of selected NUS, namely, sorghum (Sorghum bicolor), cowpea (Vigna unguiculata), amaranth (Amaranthus) and taro (Colocasia esculenta) in the KwaZulu-Natal (KZN) province, South Africa. The future distribution of NUS was simulated using a maximum entropy (MaxEnt) model using regional circulation models (RCMs) from the CORDEX archive, each driven by a different global circulation model (GCM), for the years 2030 to 2070. The study showed an increase of 0.1-11.8% under highly suitable (S1), moderately suitable (S2), and marginally suitable (S3) for sorghum, cowpea, and amaranth growing areas from 2030 to 2070 across all RCPs. In contrast, the total highly suitable area for taro production is projected to decrease by 0.3-9.78% across all RCPs. The jack-knife tests of the MaxEnt model performed efficiently, with areas under the curve being more significant than 0.8. The study identified annual precipitation, length of the growing period, and minimum and maximum temperature as variables contributing significantly to model predictions. The developed maps indicate possible changes in the future suitability of NUS within the KZN province. Understanding the future distribution of NUS is useful for developing transformative climate change adaptation strategies that consider future crop distribution. It is recommended to develop regionally differen-tiated climate-smart agriculture production guidelines matched to spatial and temporal variability in crop suitability.

关键词： Food and nutrition security Land suitability analysis machine-learning algorithms Neglected and underutilised crop species

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Predictive Models of Recombination Rate Variation across the Drosophila melanogaster Genome

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GENOME BIOLOGY AND EVOLUTION 2016年第8期8卷 2597-2612页

作者： Adrian, Andrew B. Corchado, Johnny Cruz Comeron, Josep M. Univ Iowa Dept Biol Iowa City IA 52242 USA Univ Iowa Interdisciplinary Grad Program Genet Iowa City IA 52242 USA

In all eukaryotic species examined, meiotic recombination, and crossovers in particular, occurnon-randomly along chromosomes. The cause for this non-random distribution remains poorly understood but some specific DNA sequence motifs have been shown to be enriched near crossover hotspots in a number of species. We present analyses using machine learning algorithms to investigate whether DNA motif distribution across the genome can be used to predict crossover variation in Drosophila melanogaster, a species without hotspots. Our study exposes a combinatorial non-linear influence of motif presence able to account for a significant fraction of the genome-wide variation in crossover rates at all genomic scales investigated, from 20% at 5-kbto almost 70% at 2,500-kb scale. The models are particularly predictive for regions with the highest and lowest crossover rates and remain highly informative after removing sub-telomeric and -centromeric regions known to have strongly reduced crossover rates. Transcriptional activity during early meiosis and differences in motif use between autosomes and the X chromosome add to the predictive power of the models. Moreover, we show that population-specific differences in crossover rates can be partly explained by differences in motif presence. Our results suggest that crossover distribution in Drosophila is influenced by both meiosis-specific chromatin dynamics and very local constitutive open chromatin associated with DNA motifs that prevent nucleosome stabilization. These findings provide new information on the genetic factors influencing variation in recombination rates and a baseline to study epigenetic mechanisms responsible for plastic recombination as response to different biotic and abiotic conditions and stresses.

关键词： recombination double strand break DNA motif analysis machine-learning algorithms

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Self-Healing in Cyber-Physical Systems Using machine learning: A Critical Analysis of Theories and Tools

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FUTURE INTERNET 2023年第7期15卷 244-244页

作者： Johnphill, Obinna Sadiq, Ali Safaa Al-Obeidat, Feras Al-Khateeb, Haider Taheir, Mohammed Adam Kaiwartya, Omprakash Ali, Mohammed Nottingham Trent Univ Dept Comp Sci Clifton Lane Nottingham NG11 8NS England Zayed Univ Coll Technol Innovat POB 144534 Abu Dhabi U Arab Emirates Aston Business Sch Cyber Secur Innovat CSI Res Ctr Aston St Birmingham B4 7ET England Zalingei Univ Fac Technol Sci POB 6 Zalingei Cent Darfur Sudan King Khalid Univ Dept Comp Sci Abha 61421 Saudi Arabia

The rapid advancement of networking, computing, sensing, and control systems has introduced a wide range of cyber threats, including those from new devices deployed during the development of scenarios. With recent advancements in automobiles, medical devices, smart industrial systems, and other technologies, system failures resulting from external attacks or internal process malfunctions are increasingly common. Restoring the system's stable state requires autonomous intervention through the self-healing process to maintain service quality. This paper, therefore, aims to analyse state of the art and identify where self-healing using machine learning can be applied to cyber-physical systems to enhance security and prevent failures within the system. The paper describes three key components of self-healing functionality in computer systems: anomaly detection, fault alert, and fault auto-remediation. The significance of these components is that self-healing functionality cannot be practical without considering all three. Understanding the self-healing theories that form the guiding principles for implementing these functionalities with real-life implications is crucial. There are strong indications that self-healing functionality in the cyber-physical system is an emerging area of research that holds great promise for the future of computing technology. It has the potential to provide seamless self-organising and self-restoration functionality to cyber-physical systems, leading to increased security of systems and improved user experience. For instance, a functional self-healing system implemented on a power grid will react autonomously when a threat or fault occurs, without requiring human intervention to restore power to communities and preserve critical services after power outages or defects. This paper presents the existing vulnerabilities, threats, and challenges and critically analyses the current self-healing theories and methods that use machine learning for c

关键词： cyber-physical system cybersecurity threat tolerance self-healing intrusion detection machine-learning algorithms

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A data-driven, kinematic feature-based, near real-time algorithm for injury severity prediction of vehicle occupants

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ACCIDENT ANALYSIS AND PREVENTION 2021年 156卷 106149-106149页

作者： Wang, Qingfan Gan, Shun Chen, Wentao Li, Quan Nie, Bingbing Tsinghua Univ Sch Vehicle & Mobil State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China

Accurate real-time prediction of occupant injury severity in unavoidable collision scenarios is a prerequisite for enhancing road traffic safety with the development of highly automated vehicles. Specifically, a safety prediction model provides a decision reference for the trajectory planning system in the pre-crash phase and the adaptive restraint system in the in-crash phase. The main goal of the current study is to construct a data-driven, vehicle kinematic feature-based model to realize accurate and near real-time prediction of in-vehicle occupant injury severity. A large-scale numerical database was established focusing on occupant kinetics. A first-step deeplearning model was established to predict occupant kinetics and injury severity using a convolutional neural network (CNN). To reduce the computational time for real-time application, the second step was to extract simplified kinematic features from vehicle crash pulses via a feature extraction method, which was inspired by a visualization approach applied to the CNN-based model. The features were incorporated with a low-complexity machine-learning algorithm and achieved satisfactory accuracy (85.4 % on the numerical database, 78.7 % on a 192-case real-world dataset) and decreased computational time (1.2 +/- 0.4 ms) on the prediction tasks. This study demonstrated the feasibility of using data-driven and feature-based approaches to achieve accurate injury risk estimation prior to collision. The proposed model is expected to provide a decision reference for integrated safety systems in the next generation of automated vehicles.

关键词： Motor vehicle crashes Occupant protection Injury risk Prediction models machine-learning algorithms

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Optimizing machine learning-based site-specific nitrogen application recommendations for canola production

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FIELD CROPS RESEARCH 2022年第0期288卷

作者： Wen, Guoqi Ma, Bao-Luo Vanasse, Anne Caldwell, Claude D. Smith, Donald L. Ottawa Res & Dev Ctr Agr & Agrifood Canada Ottawa ON K1A 0C6 Canada Laval Univ Quebec City PQ G1V 0A6 Canada Dalhousie Univ Truro NS B2N 5E3 Canada McGill Univ Sainte Anne de bellevue PQ H9X 3V9 Canada Ottawa Res & Dev Ctr Agr & Agrifood Canada 960 Carling Ave Ottawa ON K1A 0C6 Canada

Context or problem: machine learning approaches are attracting more attention in predicting the economic optimum nitrogen rate (EONR) for field crop production. However, the robustness in fertilizer recommendations appeared to be less responsive to extreme abiotic stress conditions, due to the lack of post-fertilization seasonal weather information. Objective or research question: This study employed four machine learning models, namely, support vector machine (SVM), gradient boosting (GB), random forest (RF), and ridge regression (RR) to predict the site-specific EONR values of canola crops, based on a 22-site-year field study across eastern Canada. Methods: Model performance was assessed using the 'leave-one-out' approach under three scenarios, i.e., different combinations of input variables, including current seasonal weather data before N topdressing, 10-year historical weather records, and field management and crop traits. Result: Results of this study showed that including historical weather data highly improved prediction accuracy for EONR in a variety of canola growing environments. The RF model outperformed other models in predicting site-specific EONRs, with a Pearson correlation coefficient of 0.9, a standard deviation of 23 kg N ha-1 and a relative error of 2%. For 16 of the 22 test environments, the EONR predicted using RF fell within its confidence range, indicating that the prediction has a 73% chance of being an acceptable recommendation. Overall, incorporating historical weather data is essential for successfully predicting crop N requirements under normal and stressful growing conditions. Conclusions: The results indicate that with a base fertilizer of 50 kg N ha-1, the recommended topdressing application rate varied from 50 to 110 kg N ha-1 in moist seasons, but decreased to 20-50 kg N ha-1 in hot and dry years, for sustainable canola production in eastern Canada. Implications: Appropriately incorporating historical weather data and soil propertie

关键词： Brassica napus L Drought stress machine-learning algorithms N recommendation Vegetation index

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Neurological disease prediction using impaired gait analysis for foot position in cerebellar ataxia by ensemble approach

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AUTOMATIKA 2023年第3期64卷 541-550页

作者： Sundari, M. Shanmuga Jadala, Vijaya Chandra Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswarm Andhra Pradesh India

In neurological field, Cerebellar Ataxia (CA) prediction is done with Gait values of human actions. The Analysis of Gait (AoG) may lead the good treatment. The goal of this work was to develop a machine-learning-based model for predicting AoG using the poor gait patterns that occur before AoG. While executing designed AoG-provoking walking tasks, an accelerometer was connected to the lower back of 21 subjects with 12 different walking positions to gather acceleration impulses. The exercise was walking for one minute at each of 12 varied walking speeds on a split-belt treadmill in the range [0.6, 1.7] m/s in 0.1 m/s increments. To reduce the effects of weariness, the speed sequence was randomized and kept a secret from the subjects. machine-learning algorithms like support vector machine (SVM) and k-nearest neighbours (KNN) have been tested in existing research studies. These algorithms perform well when the amount of data is little and the classification is binary. SVM, KNN, decision trees, and XGBoost algorithms have all been used in the proposed study on the CA data set. We discovered that the AdaBoost algorithm provides a more accurate categorization of the severity of CA disease.

关键词： Adaboost cerebellar ataxia neurological disease machine-learning algorithms

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