Fruit Image Classification using Deep Learning

Gill, Harmandeep Singh; Khehra, Baljit Singh

doi:10.21203/rs.3.rs-574901/v1

Cited by 13 publications

(10 citation statements)

References 16 publications

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“…The inspection robot first explores aimlessly in a finite unknown space, where there are several detection points. The detection points will be designed as signal transmitters in the simulation experiment, which can emit a range signal of a certain radius, and the robot will no longer maintain interest in that point [21,22].…”

Section: Implementation Methods Of Inspection Path Planning Based On ...mentioning

confidence: 99%

Reinforcement Learning-Based Path Planning Algorithm for Mobile Robots

Liu

Wang

Yang³

2022

Wireless Communications and Mobile Computing

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A robot path planning algorithm based on reinforcement learning is proposed. The algorithm discretizes the information of obstacles around the mobile robot and the direction information of target points obtained by LiDAR into finite states, then reasonably designs the number of environment model and state space, and designs a continuous reward function, so that each action of the robot can be rewarded accordingly, which improves the algorithm and improves the training efficiency of the algorithm. Finally, the agent training simulation environment is built on gazebo, and the training results verify the effectiveness of the algorithm. At the same time, the navigation experiment is carried out on the actual robot. The experimental results show that the algorithm can also complete the navigation task in the real environment.

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Section: Implementation Methods Of Inspection Path Planning Based On ...mentioning

confidence: 99%

Reinforcement Learning-Based Path Planning Algorithm for Mobile Robots

Liu

Wang

Yang³

2022

Wireless Communications and Mobile Computing

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“…of Ideographic Characters. The survey was conducted on a total of international students in the College of International Cultural Education of Heilongjiang University [19]. The students of each level were basically the same in terms of study time, study conditions, and Chinese language level.…”

Section: A Survey and Analysis Of International Students' Perceptionmentioning

confidence: 99%

Teaching Morphophonetic Characters in Teaching Chinese as a Foreign Language Based on Internet of Things Assistance

Xiao

2022

Wireless Communications and Mobile Computing

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Teaching Chinese as a foreign language is a very important part of teaching Chinese as a foreign language, and morphemes account for a large proportion of Chinese characters. In this paper, a questionnaire survey on the cognition of morphemes was conducted for each grade of students in the School of International Cultural Education of Heilongjiang University. Through the survey, it was concluded that international students in the Chinese culture circle and non-Chinese culture circle affect the cognition of morphemes at the primary stage, and a study on the teaching of morphemes in teaching Chinese as a foreign language based on the assistance of Internet of Things was proposed to analyze that students learning Chinese no longer affect the cognition of morphemes. In teaching Chinese as a foreign language, the use of the Internet of Things system and the introduction of video sensors (mainly for the access and computer analysis of spoken Chinese as a foreign language learner model) and voice sensor (mainly for the Chinese as a foreign language learner voice access and computer analysis), with the standard spoken language pattern in the software and the comparison of standard pronunciation, the oral mode and pronunciation of TCSL learners can be corrected. The experiment knows the biases that international students are prone to when learning Chinese characters and summarizes the implications for teaching Chinese characters to foreigners; in addition, the higher the level of learning, the higher is the degree of cognition of morphophonetic characters.

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“…Since the ML algorithm does not work for textual data, the preprocessing needs to be applied to convert textual data into numerical vectors. The preprocessing is carried out with the help of the NLTK library in python [54][55][56][57][58][59][60][61][62]. The preprocessing module as shown in Fig.…”

Section: Preprocessing Modulementioning

confidence: 99%

Multi-Tier Sentiment Analysis of Social Media Text Using Supervised Machine Learning

Tariq¹,

Masood²,

Subahi³

et al. 2023

Computers, Materials &Amp; Continua

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Sentiment Analysis (SA) is often referred to as opinion mining. It is defined as the extraction, identification, or characterization of the sentiment from text. Generally, the sentiment of a textual document is classified into binary classes i.e., positive and negative. However, fine-grained classification provides a better insight into the sentiments. The downside is that fine-grained classification is more challenging as compared to binary. On the contrary, performance deteriorates significantly in the case of multi-class classification. In this study, pre-processing techniques and machine learning models for the multi-class classification of sentiments were explored. To augment the performance, a multi-layer classification model has been proposed. Owing to similitude with social media text, the movie reviews dataset has been used for the implementation. Supervised machine learning models namely Decision Tree, Support Vector Machine, and Naïve Bayes models have been implemented for the task of sentiment classification. We have compared the models of single-layer architecture with multi-tier model. The results of Multi-tier model have slight improvement over the single-layer architecture. Moreover, multi-tier models have better recall which allow our proposed model to learn more context. We have discussed certain shortcomings of the model that will help researchers to design multi-tier models with more contextual information.

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Fruit Image Classification using Deep Learning

Cited by 13 publications

References 16 publications

Reinforcement Learning-Based Path Planning Algorithm for Mobile Robots

Reinforcement Learning-Based Path Planning Algorithm for Mobile Robots

Teaching Morphophonetic Characters in Teaching Chinese as a Foreign Language Based on Internet of Things Assistance

Multi-Tier Sentiment Analysis of Social Media Text Using Supervised Machine Learning

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