Eyewitness accuracy rates in police showup and lineup presentations: A meta-analytic comparison.

Emotional intelligence (EI) refers to a set of competencies to process, understand, and reason with affective information. Recent studies suggest ability measures of experiential and strategic EI differentially predict performance on non-emotional and emotionally laden tasks. To explore cognitive processes underlying these abilities further, we varied the affective context of a traditional letter-based n-back working-memory task. In study 1, participants completed 0-, 2-, and 3-back tasks with flanking distractors that were either emotional (fearful or happy faces) or non-emotional (shapes or letters stimuli). Strategic EI, but not experiential EI, significantly influenced participants’ accuracy across all n-back levels, irrespective of flanker type. In Study 2, participants completed 1-, 2-, and 3-back levels. Experiential EI was positively associated with response times for emotional flankers at the 1-back level but not other levels or flanker types, suggesting those higher in experiential EI reacted slower on low-load trials with affective context. In Study 3, flankers were asynchronously presented either 300 ms or 1000 ms before probes. Results mirrored Study 1 for accuracy rates and Study 2 for response times. Our findings (a) provide experimental evidence for the distinctness of experiential and strategic EI and (b) suggest that each are related to different aspects of cognitive processes underlying working memory.

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Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

Electronics ◽

10.3390/electronics10121388 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1388

Author(s):

Sk Mahmudul Hassan ◽

Arnab Kumar Maji ◽

Michał Jasiński ◽

Zbigniew Leonowicz ◽

Elżbieta Jasińska

Keyword(s):

Disease Classification ◽

Batch Size ◽

Healthy Plant ◽

Plant Leaves ◽

Early Prevention ◽

Computation Cost ◽

Training Time ◽

Feature Based ◽

Deep Cnn ◽

Accuracy Rates

The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems.

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Oestrus Analysis of Sows Based on Bionic Boars and Machine Vision Technology

Animals ◽

10.3390/ani11061485 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1485

Author(s):

Kaidong Lei ◽

Chao Zong ◽

Xiaodong Du ◽

Guanghui Teng ◽

Feiqi Feng

Keyword(s):

Support Vector Machine ◽

Machine Vision ◽

Recognition Accuracy ◽

Average Duration ◽

Support Vector ◽

Contact Duration ◽

Innovative Design ◽

Sparse Autoencoder ◽

Accuracy Rates ◽

Interactive Behaviour

This study proposes a method and device for the intelligent mobile monitoring of oestrus on a sow farm, applied in the field of sow production. A bionic boar model that imitates the sounds, smells, and touch of real boars was built to detect the oestrus of sows after weaning. Machine vision technology was used to identify the interactive behaviour between empty sows and bionic boars and to establish deep belief network (DBN), sparse autoencoder (SAE), and support vector machine (SVM) models, and the resulting recognition accuracy rates were 96.12%, 98.25%, and 90.00%, respectively. The interaction times and frequencies between the sow and the bionic boar and the static behaviours of both ears during heat were further analysed. The results show that there is a strong correlation between the duration of contact between the oestrus sow and the bionic boar and the static behaviours of both ears. The average contact duration between the sows in oestrus and the bionic boars was 29.7 s/3 min, and the average duration in which the ears of the oestrus sows remained static was 41.3 s/3 min. The interactions between the sow and the bionic boar were used as the basis for judging the sow’s oestrus states. In contrast with the methods of other studies, the proposed innovative design for recyclable bionic boars can be used to check emotions, and machine vision technology can be used to quickly identify oestrus behaviours. This approach can more accurately obtain the oestrus duration of a sow and provide a scientific reference for a sow’s conception time.

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An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination

Financial Innovation ◽

10.1186/s40854-021-00243-3 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Hakan Gunduz

Keyword(s):

Reduction Method ◽

Feature Reduction ◽

The Other ◽

Recursive Feature Elimination ◽

Accuracy Rate ◽

Stock Performance ◽

Attention Model ◽

Feature Sets ◽

Accuracy Rates ◽

F Measure

AbstractIn this study, the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models. These models were trained with four different feature sets and their performances were evaluated in terms of accuracy and F-measure metrics. While the first experiments directly used the own stock features as the model inputs, the second experiments utilized reduced stock features through Variational AutoEncoders (VAE). In the last experiments, in order to grasp the effects of the other banking stocks on individual stock performance, the features belonging to other stocks were also given as inputs to our models. While combining other stock features was done for both own (named as allstock_own) and VAE-reduced (named as allstock_VAE) stock features, the expanded dimensions of the feature sets were reduced by Recursive Feature Elimination. As the highest success rate increased up to 0.685 with allstock_own and LSTM with attention model, the combination of allstock_VAE and LSTM with the attention model obtained an accuracy rate of 0.675. Although the classification results achieved with both feature types was close, allstock_VAE achieved these results using nearly 16.67% less features compared to allstock_own. When all experimental results were examined, it was found out that the models trained with allstock_own and allstock_VAE achieved higher accuracy rates than those using individual stock features. It was also concluded that the results obtained with the VAE-reduced stock features were similar to those obtained by own stock features.

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Diversity Adversarial Training against Adversarial Attack on Deep Neural Networks

Symmetry ◽

10.3390/sym13030428 ◽

2021 ◽

Vol 13 (3) ◽

pp. 428

Author(s):

Hyun Kwon ◽

Jun Lee

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Diversity Training ◽

Original Data ◽

Training Method ◽

Learning Framework ◽

Adversarial Examples ◽

Adversarial Training ◽

Adversarial Attack ◽

Accuracy Rates

This paper presents research focusing on visualization and pattern recognition based on computer science. Although deep neural networks demonstrate satisfactory performance regarding image and voice recognition, as well as pattern analysis and intrusion detection, they exhibit inferior performance towards adversarial examples. Noise introduction, to some degree, to the original data could lead adversarial examples to be misclassified by deep neural networks, even though they can still be deemed as normal by humans. In this paper, a robust diversity adversarial training method against adversarial attacks was demonstrated. In this approach, the target model is more robust to unknown adversarial examples, as it trains various adversarial samples. During the experiment, Tensorflow was employed as our deep learning framework, while MNIST and Fashion-MNIST were used as experimental datasets. Results revealed that the diversity training method has lowered the attack success rate by an average of 27.2 and 24.3% for various adversarial examples, while maintaining the 98.7 and 91.5% accuracy rates regarding the original data of MNIST and Fashion-MNIST.

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Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network

Mathematical Problems in Engineering ◽

10.1155/2020/5795976 ◽

2020 ◽

Vol 2020 ◽

pp. 1-6

Author(s):

Jian-ye Yuan ◽

Xin-yuan Nan ◽

Cheng-rong Li ◽

Le-le Sun

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real Time ◽

Environmental Science ◽

Fine Tuning ◽

Classification Model ◽

Classification Models ◽

Data Set ◽

Classification Tasks ◽

Accuracy Rates

Considering that the garbage classification is urgent, a 23-layer convolutional neural network (CNN) model is designed in this paper, with the emphasis on the real-time garbage classification, to solve the low accuracy of garbage classification and recycling and difficulty in manual recycling. Firstly, the depthwise separable convolution was used to reduce the Params of the model. Then, the attention mechanism was used to improve the accuracy of the garbage classification model. Finally, the model fine-tuning method was used to further improve the performance of the garbage classification model. Besides, we compared the model with classic image classification models including AlexNet, VGG16, and ResNet18 and lightweight classification models including MobileNetV2 and SuffleNetV2 and found that the model GAF_dense has a higher accuracy rate, fewer Params, and FLOPs. To further check the performance of the model, we tested the CIFAR-10 data set and found the accuracy rates of the model (GAF_dense) are 0.018 and 0.03 higher than ResNet18 and SufflenetV2, respectively. In the ImageNet data set, the accuracy rates of the model (GAF_dense) are 0.225 and 0.146 higher than Resnet18 and SufflenetV2, respectively. Therefore, the garbage classification model proposed in this paper is suitable for garbage classification and other classification tasks to protect the ecological environment, which can be applied to classification tasks such as environmental science, children’s education, and environmental protection.

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