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Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jaweria Kainat ◽  
Syed Sajid Ullah ◽  
Fahd S. Alharithi ◽  
Roobaea Alroobaea ◽  
Saddam Hussain ◽  
...  

Existing plant leaf disease detection approaches are based on features of extracting algorithms. These algorithms have some limits in feature selection for the diseased portion, but they can be used in conjunction with other image processing methods. Diseases of a plant can be classified from their symptoms. We proposed a cucumber leaf recognition approach, consisting of five steps: preprocessing, normalization, features extraction, features fusion, and classification. Otsu’s thresholding is implemented in preprocessing and Tan–Triggs normalization is applied for normalizing the dataset. During the features extraction step, texture and shape features are extracted. In addition, increasing the instances improves some characteristics. Through a principal component analysis approach, serial feature fusion is employed to provide a feature score. Fused features can be classified through a support vector machine. The accuracy of the Fine KNN is 94.30%, which is higher than the previous work in past papers.


2021 ◽  
Author(s):  
Qiyang Zhang ◽  
Amanda Jean Neitzel

In recent years, the increasing influence of evidence-based research and the rapid development of artificial intelligence have enabled the launch of many new reference screening software tools. Due to a dearth of research comparing different screening tools in educational research, researchers often choose the most convenient rather than the most suitable screening tool. This review aims to provide assistance for screening tool selection through a systematic narrative review and feature analysis of these tools’ functions and privacy policies. The current adoption rate of transparent tool reporting is low: by screening 191 studies published in the Review of Educational Research since 2015, we found that only eight (4.19%) studies reported screening tools. To locate available screening tools in the market, we consulted various sources and found 24 tools. Through citation search, we identified eight screening tools used by educational reviewers and ranked them in descending order of feature score: EPPI-Reviewer (tie), DistillerSR (tie), Covidence, Rayyan, Abstrackr, ASReview, RevMan, and Excel. For practitioners’ convenience, we concluded the paper with a decision tree to assist educational systematic reviewers in identifying suitable tools. This paper represents the first effort to provide educational researchers with guidance on how to navigate screening tools. Our results encourage researchers to report their tool usage in publications and select tools based on suitability instead of convenience.


2021 ◽  
Author(s):  
Ali M. S. Alfosool ◽  
Daniel Fuller ◽  
Yuanzhu Chen

Measuring environments around us (cities, roads, social environments) is crucial to understand human behaviour and help predict how aspects of environment influence behaviour and health. Walkability is one measure of environment used to predict health. Walkability combines aspects of environment (population, roads, amenities) into a single score. Existing measures are often one-size-fits-all with very limited personalization. In our previous work, we defined Active Living Feature Score, ALF-Score, a novel approach to measure network-based walkability. ALF-Score uses road network structures and points of interest to generate models capable of estimating walkability for any point on map. One of ALF-Score's contributions was the inclusion of user opinions to partially address the different perception among individuals and help derive a more personalized walkability score. Here, we take this personalization much further by introducing ALF-Score+ which uses individual user demographics (age, gender, ...) grouped using k-means and t-distributed stochastic neighbor embedding to create clusters based on individuals’ demographic characteristics. Each cluster is treated as a single profile representing a subset of users. Cluster profiles are added into our pipelines to generate profile-specific network-based walkability models. Results show strong variability among scores generated for each cluster profile with a clear variation in walkability generated for different users within same clusters. ALF-Score+ maintains an accuracy of 90.48% on average showing improvement compared to ALF-Score. We found strong association between cluster profiles' demographics and their scores. ALF-Score+ shows promising results providing personalized walkability based on cluster profiles, instead of a one-size-fits-all approach used by other walkability measures.


2021 ◽  
Author(s):  
Fumiaki Kondo ◽  
Takahiko Sugihara ◽  
Natsuka Umezawa ◽  
Hisanori Hasegawa ◽  
Tadashi Hosoya ◽  
...  

Abstract Background High-dose glucocorticoids (GC) are first-line treatment for adult onset Still’s disease (AOSD), however some of the patients remain refractory to initial GC therapy, or rapidly relapse. The aim of this study was to identify prognostic factors for poor treatment response to initial GC therapy for AOSD. Methods Data on newly-diagnosed AOSD patients were extracted from our database (n=71, mean age 51.6 years). The primary outcome was a poor treatment outcome at 4 weeks, which was defined as failure to achieve remission or relapse after achieving remission within 4 weeks, followed by administration of two or more rounds of GC pulse therapy or of any other immunosuppressive drugs. Results The initial mean dose ± standard deviation of prednisolone was 0.82 ± 0.23 mg/kg/day, and 34 (47.3%) patients received GC pulse therapy at week 0. Twenty-nine of 71 patients exhibited a poor treatment outcome at 4 weeks (40.8%). The second round of GC pulse therapy or immunosuppressive drugs was added in 17 or 24 of the 29 patients, respectively. These patients had higher baseline white blood cell (WBC) counts, serum ferritin levels, systemic feature score based on clinical symptoms (modified systemic feature score, mSFS), more hemophagocytic syndrome (HPS) over the 4 weeks, and the higher severity score based on modified Pouchot score or severity index of the Japanese Ministry of Health, Labour and Welfare, than the remaining 42 patients. Multivariable logistic regression model identified baseline WBC count as a prognostic factor for poor outcome (odds ratio per 1,000/µl increment: 1.12, 95% CI 1.04-1.29), while thrombocytopenia, hyperferritinemia, and mSFS at baseline did not achieve statistical significance. Receiver-Operating Characteristic curve analysis showed that the optimal cut-off for WBC count was 13,050/µl. The Kaplan-Meier method showed the cumulative rate of poor treatment outcome to be 60.0% in patients with WBC ≥13,050/µl and 23.5% in those with WBC <13,050/µl. Conclusions A higher WBC count but not thrombocytopenia, hyperferritinemia, and mSFS at baseline was a significant prognostic factor for poor treatment outcome at week 4 in this retrospective cohort of AOSD patients. Our findings provide important information for determining the initial treatment strategy of newly-diagnosed AOSD.


2021 ◽  
Author(s):  
Ali M. S. Alfosool ◽  
Yuanzhu Chen ◽  
Daniel Fuller

Abstract Walkability is a term that describes various aspects of the built and social environment and has been associated with physical activity and public health. Walkability is subjective and although multiple definitions of walkability exist, there is no single agreed upon definition. Road networks are integral parts of mobility and should be an important part of walkability. However, using the road structure as nodes is not widely discussed in existing methods. Most walkability measures only provide area-based scores with low spatial resolution, have a one-size-fits-all approach, and do not consider individuals opinion. Active Living Feature Score (ALF-Score) is a network-based walkability measure that incorporates road network structures as a core component. It also utilizes user opinion to build a high-confidence ground-truth that is used in our machine learning pipeline to generate models capable of estimating walkability. We found combination of network features with road embedding and points of interest features creates a complimentary feature set enabling us to train our models with an accuracy of over 87% while maintaining a conversion consistency of over 98%. Our proposed approach outperforms existing measures by introducing a novel method to estimate walkability scores that are representative of users opinion with a high spatial resolution, for any point on the map.


2021 ◽  
Author(s):  
Ali M. S. Alfosool ◽  
Yuanzhu Chen ◽  
Daniel Fuller

Abstract Walkability is a term that describes various aspects of the built and social environment and has been associated with physical activity and public health. Walkability is subjective and although multiple definitions of walkability exist, there is no single agreed upon definition. Road networks are integral parts of mobility and should be an important part of walkability. However, they are missing from existing methods. Most walkability measures only provide area-based scores with low spatial resolution, have a one-size-fits-all approach, and do not consider individuals opinion. Active Living Feature Score (ALF-Score) is a network-based walkability measure that incorporates road network structures as a core component. It also utilizes user opinion to build a high-confidence ground-truth that is used in our machine learning pipeline to generate models capable of estimating walkability. We found combination of network features with road embedding and POI features creates a complimentary feature set enabling us to train our models with an accuracy of over 87% while maintaining a conversion consistency of over 98%. Our proposed approach outperforms existing measures by introducing a novel method to estimate walkability scores that are representative of users opinion with a high spatial resolution, for any point on the map.


Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1752
Author(s):  
Linna Ji ◽  
Fengbao Yang ◽  
Xiaoming Guo

Aiming at addressing the problem whereby existing image fusion models cannot reflect the demand of diverse attributes (e.g., type or amplitude) of difference features on algorithms, leading to poor or invalid fusion effect, this paper puts forward the construction and combination of difference features fusion validity distribution based on intuition-possible sets to deal with the selection of algorithms with better fusion effect in dual mode infrared images. Firstly, the distances of the amplitudes of difference features between fused images and source images are calculated. The distances can be divided into three levels according to the fusion result of each algorithm, which are regarded as intuition-possible sets of fusion validity of difference features, and a novel construction method of fusion validity distribution based on intuition-possible sets is proposed. Secondly, in view of multiple amplitude intervals of each difference feature, this paper proposes a distribution combination method based on intuition-possible set ordering. Difference feature score results are aggregated by a fuzzy operator. Joint drop shadows of difference feature score results are obtained. Finally, the experimental results indicate that our proposed method can achieve optimal selection of algorithms that has relatively better effect on the fusion of difference features according to the varied feature amplitudes.


2021 ◽  
Author(s):  
Ali M. S. Alfosool ◽  
Yuanzhu Chen ◽  
Daniel Fuller

Walkability is a term that describes aspects of the built and social environment. Previous studies have shown that different operationalisations of walkability are associated with physical activity and health. Walkability can be subjective and although multiple operational definitions and walkability measurement exist, there is no single agreed upon conceptual definition. Despite lack of consensus of a walkability definition, typical operational definitions include measures of population density, destinations, and the road network. Network science approaches such centralities and network embedding are missing from existing methods, yet they are integral parts of our mobility and should be an important part of how walkability is measured. Furthermore, most walkability measures have a one-size-fits-all approach and do not take into account individual user’s characteristics or walking preferences. To address some limitations of previous works, we developed the Active Living Feature Score (ALF-Score). ALF-Score is a network-based walkability measure that incorporates the road network structures as a core component. It also utilizes user data to build high-confidence ground truth that are used in conjunction with our machine learning pipeline to generate models capable of estimating walkability scores that address existing gaps in the walkability literature. We find, relying on road structure alone, we are able to train our models to estimate walkability scores with an accuracy of over 86% while maintaining a consistency of over 98% over collected user data. Our proposed approach outperforms existing measures by providing a walkability data at a much higher resolution as well as a user-derived result.


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