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Insect classification and detection in field crops using modern machine learning techniques

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Information Processing in Agriculture 2021年第3期8卷 446-457页

作者： Thenmozhi Kasinathan Dakshayani Singaraju Srinivasulu Reddy Uyyala Machine learning and Data Analytics Lab Centre of Excellence in Artificial IntelligenceDepartment of Computer ApplicationsNational Institute of TechnologyTiruchirappalliTiruchirappalli 620015Tamil NaduIndia

The agriculture sector has an immense potential to improve the requirement of food and supplies healthy and nutritious *** insect detection is a challenging task for farmers as a significant portion of the crops are damaged,and the quality is degraded due to the pest *** insect identification has the drawback of requiring well-trained tax-onomists to identify insects based on morphological features *** were conducted for classification on nine and 24 insect classes of Wang and Xie dataset using the shape features and applying machine learning techniques such as artificial neural net-works(ANN),support vector machine(SVM),k-nearest neighbors(KNN),naive bayes(NB)and convolutional neural network(CNN)*** paper presents the insect pest detec-tion algorithm that consists of foreground extraction and contour identification to detect the insects for Wang,Xie,Deng,and IP102 datasets in a highly complex *** 9-fold cross-validation was applied to improve the performance of the classification *** highest classification rate of 91.5%and 90%was achieved for nine and 24 class insects using the CNN *** detection performance was accomplished with less com-putation time for Wang,Xie,Deng,and IP102 datasets using insect pest detection *** comparison results with the state-of-the-art classification algorithms exhibited considerable improvement in classification accuracy,computation time perfor-mance while apply more efficiently in field crops to recognize the *** results of classification accuracy are used to recognize the crop insects in the early stages and reduce the time to enhance the crop yield and crop quality in agriculture.

关键词： Crop pest classification Crop insect detection Image processing Machine learning Image segmentation

The impact of ChatGPT on foreign language teaching and learning: Opportunities in education and research

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教育技术与创新 2023年第1期5卷 37-45页

作者： Wilson Cheong Hin Hong centre for teaching and learning Enhancement Macao Institute for Tourism StudiesMacaoChina

The revolutionary online application ChatGPT has brought immense concerns to the education *** language teachers being some of those most reliant on writing assessments were among the most anxious,exacerbated by the extensive media coverage about the much-fantasized functionality of the ***,the article starts by elucidating the mechanisms,functions and common misconceptions about *** and risks associated with its usage are discussed,followed by an in-depth discussion of how the chatbot can be harnessed by learners and *** is argued that ChatGPT offers major opportunities for teachers and education institutes to improve second/foreign language teaching and assessments,which similarly provided researchers with an array of research opportunities,especially towards a more personalized learning experience.

关键词： Large Language Model second language education flip classroom personalized learning formative assessment

Wheat Ear Segmentation Based on a Multisensor System and Superpixel Classification

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Plant Phenomics 2022年第1期4卷 409-418页

作者： Alexis Carlier Sebastien Dandrifosse Benjamin Dumont Benoit Mercatoris Biosystems Dynamics and Exchanges TERRA Teaching and Research CentreGembloux Agro-Bio TechUniversity of Liège5030 GemblouxBelgium Plant Sciences TERRA Teaching and Research CentreGembloux Agro-Bio TechUniversity of Liège5030 GemblouxBelgium

The automatic segmentation of ears in wheat canopy images is an important step to measure ear density or extract relevant plant traits separately for the different *** deep learning algorithms appear as promising tools to accurately detect ears in a wide diversity of ***,they remain complicated to implement and necessitate a huge training *** paper is aimed at proposing an easy and quick to train and robust alternative to segment wheat ears from heading to maturity growth stage.

关键词： Wheat heading maturity

Application of machine learning and artificial intelligence in oil and gas industry

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Petroleum Research 2021年第4期6卷 379-391页

作者： Anirbid Sircar Kriti Yadav Kamakshi Rayavarapu Namrata Bist Hemangi Oza centre of excellence for Geothermal Energy Pandit Deendayal Petroleum UniversityGandhinagarGujaratIndia

Oil and gas industries are facing several challenges and issues in data processing and *** amount of data bank is generated with various techniques and *** proper technical analysis of this database is to be carried out to improve performance of oil and gas *** paper provides a comprehensive state-of-art review in the field of machine learning and artificial intelligence to solve oil and gas industry *** also narrates the various types of machine learning and artificial intelligence techniques which can be used for data processing and interpretation in different sectors of upstream oil and gas *** achievements and developments promise the benefits of machine learning and artificial intelligence techniques towards large data storage capabilities and high efficiency of numerical *** this paper a summary of various researchers work on machine learning and artificial intelligence applications and limitations is showcased for upstream and sectors of oil and gas *** existence of this extensive intelligent system could really eliminate the risk factor and cost of *** development and progress using this emerging technologies have become smart and makes the judgement procedure easy and *** study is useful to access intelligence of different machine learning methods to declare its application for distinct task in oil and gas sector.

关键词： Artificial intelligence Machine learning Upstream Oil and gas industry Petroleum exploration

An Integrative Meta-analysis of MicroRNAs in Hepatocellular Carcinoma

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Genomics, Proteomics & Bioinformatics 2013年第6期11卷 354-367页

作者： Mahmoud ElHefnawi Bangli Soliman Nourhan Abu-Shahba Marwa Amer centre of excellence for Advanced Sciences Informatics and Systems DepartmentNational Research Centre Stem Cells Research Group Centre of Excellence for Advanced SciencesMedical Molecular Genetics DepartmentNational Research Centre Biology Department American University in Cairo (AUC) Faculty of Biotechnology Misr University for Science and Technology (MUST)

We aimed to shed new light on the roles of microRNAs （miRNAs） in liver cancer using an integrative in silico bioinformatics analysis. A new protocol for target prediction and functional analysis is presented and applied to the 26 highly differentially deregulated miRNAs in hepatocellular carcinoma. This framework comprises：（1） the overlap of prediction results by four out of five target prediction tools, including TargetScan, PicTar, miRanda, DIANA-microT and miRDB （combining machine-learning, alignment, interaction energy and statistical tests in order to minimize false positives）, （2） evidence from previous microarray analysis on the expression of these targets, （3） gene ontology （GO） and pathway enrichment analysis of the miRNA targets and their pathways and （4） linking these results to oncogenesis and cancer hallmarks. This yielded new insights into the roles of miRNAs in cancer hallmarks. Here we presented several key targets and hundreds of new targets that are significantly enriched in many new cancer-related hallmarks. In addition, we also revealed some known and new oncogenic pathways for liver cancer. These included the famous MAPK, TGFβ and cell cycle pathways. New insights were also provided into Wnt signaling, prostate cancer, axon guidance and oocyte meiosis pathways. These signaling and developmental pathways crosstalk to regulate stem cell transformation and implicate a role of miRNAs in hepatic stem cell deregulation and cancer development. By analyzing their complete interactome, we proposed new categorization for some of these miRNAs as either tumor-suppressors or oncomiRs with dual roles. Therefore some of these miRNAs may be addressed as therapeutic targets or used as therapeutic agents. Such dual roles thus expand the view of miRNAs as active maintainers of cellular homeostasis.

关键词： Hepatocellular carcinoma miRNA Target prediction Integrative bioinformatics Cancer hallmarks

Functional gastrointestinal disorders and gut-brain axis: What does the future hold?

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World Journal of Gastroenterology 2019年第5期25卷 552-566页

作者： Kashif Mukhtar Hasham Nawaz Shahab Abid centre of excellence in Women and Child Health Aga Khan University Department of Medicine Section of Gastroenterology Aga Khan University

Despite their high prevalence, lack of understanding of the exact pathophysiology of the functional gastrointestinal disorders has restricted us to symptomatic diagnostic tools and therapies. Complex mechanisms underlying the disturbances in the bidirectional communication between the gastrointestinal tract and the brain have a vital role in the pathogenesis and are key to our understanding of the disease phenomenon. Although we have come a long way in our understanding of these complex disorders with the help of studies on animals especially rodents, there need to be more studies in humans, especially to identify the therapeutic targets. This review study looks at the anatomical features of the gut-brain axis in order to discuss the different factors and underlying molecular mechanisms that may have a role in the pathogenesis of functional gastrointestinal disorders. These molecules and their receptors can be targeted in future for further studies and possible therapeutic interventions. The article also discusses the potential role of artificial intelligence and machine learning and its possible role in our understanding of these scientifically challenging disorders.

关键词： Functional gastrointestinal disorders Idiopathic bowel syndrome Gut-brain axis Microbiome-gut-brain axis Machine learning Artificial intelligence

Effect of structural parameters on the hydrodynamic performance of vertical curved V-Type otter board

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Aquaculture and Fisheries 2024年第6期9卷 989-999页

作者： Wenhua Chu Huiqin Guo Huaizhi Zhang Shulong Wu Senqi Cui Yibo Wang College of Marine Sciences Shanghai Ocean UniversityShanghai201306China National Distant-Water Fisheries Engineering Research Center Shanghai201306China Experimental teaching and learning Demonstration centre for Marine Science and Technology Shanghai Ocean UniversityShanghai201306China Shanghai Marine Renewable Energy Engineering and Technology Research centre Shanghai201306China

In order to study the influence mechanism of structural type and size on the hydrodynamic performance of vertical curved V-type otter board,this paper based on the computational fluid dynamics method,a three-dimensional numerical model of vertical curved V-type otter board is established to calculate and analyze its hydrodynamic performance,and the validity of the numerical simulation results is verified by designing and constructing a scaled model for dynamic flume *** this basis,the effects of different structural parameters,such as the deflector angle,camber,and spacing,on the hydrodynamic performance of the vertical curved V-Type otter board are investigated and the optimal structural design scheme is *** results demonstrate that single or synchronous changes of the angle,camber,and spacing of the two deflectors have varying degrees of impact on the hydrodynamic performance of the vertical curved V-Type otter *** improve the expansion effect of the otter board,setting the angle of the deflector A to 40◦and that of the deflector B to 30◦,or setting the camber of deflectors A and B to 18%,or setting the spacing between deflectors A and B to 380 mm and that between the deflector and the main panel to 670 mm can increase the lift coefficient of the vertical curved V-Type otter board to its maximum *** the overall performance of the otter board,when the angle of the deflector A is set to 25◦and that of the deflector B is set to 30◦,or the camber of the deflector A is set to 6% and that of the deflector B is set to 9%,or the spacing between deflectors A and B is set to 340 mm and that between the deflector and the main panel is set to 610 mm,the lift-to-drag ratio of the otter board can be increased to its maximum value.

关键词： Vertical curved V-type otter board Hydrodynamic characteristics Structural parameters

Fundamental error in tree-based machine learning model selection for reservoir characterisation

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Energy Geoscience

Energy Geoscience 2024年第2期5卷 214-224页

作者： Daniel Asante Otchere centre of excellence in Subsurface Seismic Imaging and Hydrocarbon Prediction Universiti Teknologi PETRONAS32610Seri IskandarPerak Darul RidzuanMalaysia

Over the past two decades,machine learning techniques have been extensively used in predicting reservoir *** this approach has significantly contributed to the industry,selecting an appropriate model is still challenging for most *** solely on statistical metrics to select the best model for a particular problem may not always be the most effective *** study encourages researchers to incorporate data visualization in their analysis and model selection *** evaluate the suitability of different models in predicting horizontal permeability in the Volve field,wireline logs were used to train Extra-Trees,Ridge,Bagging,and XGBoost *** Random Forest feature selection technique was applied to select the relevant logs as inputs for the *** on statistical metrics,the Extra-Trees model achieved the highest test accuracy of 0.996,RMSE of 19.54 mD,and MAE of 3.18 mD,with XGBoost coming in ***,when the results were visualised,it was discovered that the XGBoost model was more suitable for the problem being *** XGBoost model was a better predictor within the sandstone interval,while the Extra-Trees model was more appropriate in non-sandstone *** this study aims to predict permeability in the reservoir interval,the XGBoost model is the most *** contrasting results demonstrate the importance of incorporating data visualisation techniques as an evaluation *** the heterogeneity of the subsurface,relying solely on statistical metrics may not be sufficient to determine which model is best suited for a particular problem.

关键词： Data visualisation Permeability Machine learning Statistical metrics

Feedforward Neural Network for joint inversion of geophysical data to identify geothermal sweet spots in Gandhar,Gujarat,India

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Energy Geoscience 2021年第3期2卷 189-200页

作者： Apurwa Yadav Kriti Yadav Anirbid Sircar Silver Oak College of Engineering and Technology S.G.RoadGujaratIndia centre of excellence for Geothermal Energy Pandit Deendayal Petroleum UniversityRaisanGandhinagar382007GujaratIndia DG GERMI and HeadCentre of Excellence for Geothermal EnergyPandit Deendayal Petroleum UniversityRaisanGandhinagar382007GujaratIndia

Artificial Neural Networks(ANNs)are used in numerous engineering and scientific disciplines as an automated approach to resolve a number of ***,to build an artificial neural network that is prudent enough to rely on,vast quantities of relevant data have to be *** this study,we analysed the scope of artificial neural networks in geothermal reservoir *** particular,we attempted to solve joint inversion problem through Feedforward Neural Network(FNN)*** order to identify geothermal sweet spots in the subsurface,an extensive geophysical studies were conducted in Gandhar area of Gujarat,*** data were acquired along six profile lines for gravity,magnetics and *** low velocity zone was identified using refraction seismic technique in order to set a common datum level for other potential *** depth of low velocity zone in Gandhar was identified at 11 *** FNN backpropagation method was applied to gain the global minima of the data space and model space as *** input dataset fed to the inversion algorithm in the form of gravity,magnetic susceptibility and resistivity helped to predict the suitable model after network training in multiple *** joint inversion of data is conducive to understanding the subsurface geological and lithological features along with probable geothermal sweet *** results of this study show the geothermal sweet spots at depth ranging from 200 m to 300 *** results from our study can be used for targeted zones for geothermal water exploitation.

关键词： Artificial neural network(ANN) Geotherm Feedforward neural network(FNN) Geophysics Machine learning(ML)