A MOBILE BASED MICROSCOPE FOR SAMPLE RECOGNITION

This paper considers the process of developing a method to recognize the causes of plant growth deviations from normal using the advancements in artificial intelligence. The medicinal plant Aloe arborescens L. was chosen as the object of this research given that this plant had been for decades one of the best-selling new products in the world. Aloe arborescens L. is famous for its medicinal properties used in medicine, cosmetology, and even the food industry. Diagnosing the abnormalities in the plant development in a timely and accurate manner plays an important role in preventing the loss of crop production yields. The current study has built a method for recognizing the causes of abnormalities in the development of Aloe arborescens L. caused by a lack of watering or lighting, based on the use of transfer training of the VGG-16 convolutional neural network (United Kingdom). A given architecture is aimed at recognizing objects in images, which is the main reason for using it to achieve the goal set. The analysis of the quality metrics of the proposed image classification process by specified classes has revealed high recognition reliability (for a normally developing plant, 91 %; for a plant without proper watering, 89 %; and for a plant without proper lighting, 83 %). The analysis of the validity of test sample recognition has demonstrated a similar validity of the plant's classification to one of three classes: 92.6 %; 87.5 %; and 85.5 %, respectively. The results reported here make it possible to supplement the automated systems that control the mode parameters of hydroponic installations by the world's major producers with the main feedback on the deviation of the plant's development from the specified values

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Research on Recognition Method of COVID-19 Images Based on Deep Learning

10.1101/2020.12.09.20246371 ◽

2020 ◽

Author(s):

dongshen ji ◽

yanzhong zhao ◽

zhujun zhang ◽

qianchuan zhao

Keyword(s):

Deep Learning ◽

Image Recognition ◽

Feature Fusion ◽

Recognition Accuracy ◽

Recognition Performance ◽

Small Sample ◽

Experimental Results ◽

Ct Image ◽

Recognition Method ◽

Sample Recognition

In view of the large demand for new coronary pneumonia covid19 image recognition samples,the recognition accuracy is not ideal.In this paper,a new coronary pneumonia positive image recognition method proposed based on small sample recognition. First, the CT image pictures are preprocessed, and the pictures are converted into the picture formats which are required for transfer learning. Secondly, perform small-sample image enhancement and expansion on the converted picture, such as miscut transformation, random rotation and translation, etc.. Then, multiple migration models are used to extract features and then perform feature fusion. Finally,the model is adjusted by fine-tuning.Then train the model to obtain experimental results. The experimental results show that our method has excellent recognition performance in the recognition of new coronary pneumonia images,even with only a small number of CT image samples.

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Recognition of Melody, Harmonic Accompaniment, and Instrumentation: Musicians vs. Nonmusicians

Music Perception An Interdisciplinary Journal ◽

10.2307/40285487 ◽

1990 ◽

Vol 8 (1) ◽

pp. 95-105 ◽

Cited By ~ 40

Author(s):

Rita S. Wolpert

Keyword(s):

Western Music ◽

Cognitive Schemata ◽

Match To Sample ◽

The Third ◽

Sample Recognition

This study examined the salience of instrumentation over melody and harmonic accompaniment in identifying music excerpts. Musicians and nonmusicians were tested on match-to-sample recognition tasks. In the first task, subjects were asked to choose which resembled the model more: (a) identical melodies played with instrumentation different from the model, or (b) different melodies played with the same instrumentation as the model. In the second task, the same melodies were played, this time with harmonic accompaniments. Again, subjects were asked to choose between: (a) identical melodies played with different instrumentation, or (b) different melodies with the same instrumentation as the model. In the third task, subjects were asked to choose between: (a) a melody and its accompaniment played in a different instrumentation from that of the model, or (b) a melody and its accompaniment played in the same instrumentation as that in the model, but with the accompaniment played in the key that was dominant to the key of the melody. Ninety-five percent of the nonmusicians chose instrumentation over melody and harmonic accompaniment as the salient cue for recognition. Musicians always chose melody and harmonic accompaniment over instrumentation. These findings indicate that untrained listeners do not share or perhaps use the same cognitive schemata as trained listeners do. Further, the assumptions held by musicians that melody and harmonic accompaniment define the essential structure of much Western music, while instrumentation is cosmetic, may not be shared by nonmusicians.

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Textual Backdoor Defense via Poisoned Sample Recognition

Applied Sciences ◽

10.3390/app11219938 ◽

2021 ◽

Vol 11 (21) ◽

pp. 9938

Author(s):

Kun Shao ◽

Yu Zhang ◽

Junan Yang ◽

Hui Liu

Keyword(s):

Success Rate ◽

Language Processing ◽

Training Data ◽

Infection Model ◽

Search Range ◽

Word Level ◽

Sentence Level ◽

Preliminary Model ◽

Sample Recognition ◽

Better Than

Deep learning models are vulnerable to backdoor attacks. The success rate of textual backdoor attacks based on data poisoning in existing research is as high as 100%. In order to enhance the natural language processing model’s defense against backdoor attacks, we propose a textual backdoor defense method via poisoned sample recognition. Our method consists of two parts: the first step is to add a controlled noise layer after the model embedding layer, and to train a preliminary model with incomplete or no backdoor embedding, which reduces the effectiveness of poisoned samples. Then, we use the model to initially identify the poisoned samples in the training set so as to narrow the search range of the poisoned samples. The second step uses all the training data to train an infection model embedded in the backdoor, which is used to reclassify the samples selected in the first step, and finally identify the poisoned samples. Through detailed experiments, we have proved that our defense method can effectively defend against a variety of backdoor attacks (character-level, word-level and sentence-level backdoor attacks), and the experimental effect is better than the baseline method. For the BERT model trained by the IMDB dataset, this method can even reduce the success rate of word-level backdoor attacks to 0%.

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Research on Recognition Method of COVID-19 Images Based on Deep Learning

E3S Web of Conferences ◽

10.1051/e3sconf/202127101039 ◽

2021 ◽

Vol 271 ◽

pp. 01039

Author(s):

Dongsheng Ji ◽

Yanzhong Zhao ◽

Zhujun Zhang ◽

Qianchuan Zhao

Keyword(s):

Image Recognition ◽

Feature Fusion ◽

Recognition Accuracy ◽

Recognition Performance ◽

Small Sample ◽

Experimental Results ◽

Fine Tuning ◽

Ct Image ◽

Recognition Method ◽

Sample Recognition

In view of the large demand for new coronary pneumonia covid19 image recognition samples, the recognition accuracy is not ideal. In this paper, a new coronary pneumonia positive image recognition method proposed based on small sample recognition. First, the CT image pictures are preprocessed, and the pictures are converted into the picture formats which are required for transfer learning. Secondly, small-sample image enhancement and extension are performed on the transformed image, such as staggered transformation, random rotation and translation, etc.. Then, multiple migration models are used to extract features and then perform feature fusion. Finally,the model is adjusted by fine-tuning. Then train the model to obtain experimental results. The experimental results show that our method has excellent recognition performance in the recognition of new coronary pneumonia images, even with only a small number of CT image samples.

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