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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

共 769 条记录，以下是391-400 订阅

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Role of Internet of things and machine learning in Precision Agriculture: A Short Review 6

Role of Internet of Things and Machine Learning in Precision...

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6th IEEE international conference on signal processing, Computing and Control, ISPCC 2021

作者： Singh, Dushyant Kumar Sobti, Rajeev Lovely Professional University School Of Electronics And Electrical Engineering Punjab India

ISBN: (纸本)9781665425520

Precision Agriculture deals with the use of technology for modernizing agriculture. this is required as many of the agriculture practices utilize agriculture resources inefficiently like conventional methods of irrigation utilizing irrigation water. Internet of things - IoT, Wireless Sensor Networks - WSN, and machine learning - ML are the technologies contributing and providing a lot of opportunities in Precision Agriculture. Precision Agriculture with IoT is also termed Agriculture 4.0. this article reviewed the various agricultural application makes the most use of these technologies. IoT with WSN nodes provides the mechanism for monitoring the agriculture variable along with the provision of control. IoT with ML helps in the monitoring and predicting the farmland or crop requirement such as irrigation requirement prediction. Various ML algorithms in the different agricultural applications are reviewed. the article also highlighted the challenges of using IoT and WSN for Precision Agriculture. © 2021 IEEE.

关键词： Precision agriculture

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Reinforcement learning in Natural Language processing: A Survey 23

Reinforcement Learning in Natural Language Processing: A Sur...

引用

Proceedings of the 2023 6th international conference on machine learning and Natural Language processing

作者： Yingli Shen Xiaobing Zhao School of Chinese Ethnic Minority Languages and Literatures Minzu University of China China School of Information Engineering Minzu University of China China

ISBN: (纸本)9798400709241

Reinforcement learning (RL) is a powerful technique for learning from data and feedback, but its effective application to natural language processing (NLP) tasks remains an open question. Consequently, this paper first introduces the general concepts of RL and the common approaches. Subsequently, we review the task construction settings and the application of RL for various NLP problems, such as machine translation, dialogue system, and text generation. Finally, we discuss some promising research directions and challenges of RL in NLP. We hope that our work can provide a comprehensive overview and inspire more research on this promising yet challenging topic.

关键词： Application Natural Language processing Reinforcement learning Survey

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Radar radiation source signal sorting method based on meta learning

Radar radiation source signal sorting method based on meta l...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Meibin Qi Dade Zhou Kunyuan Li School of Computer Science and Information Engineering HeFei University of Technology Hefei China

Radar radiation source sorting and identification methods based on in-depth learning have received considerable attention in recent years, but there are time and resource constraints in practice. To solve this problem, this paper presents a method of radar radiation source signal sorting based on meta-learning. Data samples for signal sorting are generated by acquiring the three-dimensional entropy, image coefficient, high-order cumulants, bandwidth and carrier frequency characteristics of the signal. A signal classification model based on adaptive channel attention is constructed. Model-Agnostic Meta-learning (MAML) meta-learning mechanism is used to enhance the generalization performance of the model to meet the task requirements of radar signal sorting under small sample conditions. the experimental results show that this method can effectively improve the low recognition rate of small sample radar radiation source signals, and achieve a good classification accuracy under the condition of a small sample of training data.

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Mongolian-Chinese machine Translation Based on Text Context Information

Mongolian-Chinese Machine Translation Based on Text Context ...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Junjin Zhang Yonghong Tian Zheyu Song Yufeng Hao College of Data Science and Application Inner Mongolia University of Technology Hohhot China

Mongolian-Chinese neural machine translation has the problem that it cannot make full use of context information for document-level translation. In order to solve this problem, a Mongolian-Chinese neural machine translation model using the context information of the passage is *** model makes better use of context information for document-level translation by introducing local encoders and global encoders in the encoder and caching mechanisms in the decoder. through experiments, the document-level Mongolian-Chinese machine translation model integrated with context information is compared with the sentence-level Mongolian-Chinese machine translation model based on Transformer. the experimental results verify the advantages of the document-level Mongolian-Chinese machine translation model integrated with context information in translation performance.

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Key Parameter Mapping Method of signal-To-Noise Ratio from Link Simulation to System Simulation

Key Parameter Mapping Method of Signal-To-Noise Ratio from L...

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2021 international conference on signal processing and machine learning, CONF-spml 2021

作者： Lei, Jizhao Li, Minghui Jin, Yihui Zhao, Di Wang, Huijun Liu, Yun Song, Ruiliang Lin, Wenliang Wang, Ke Macro Net Communication Co. Ltd Beijing China Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Shijiazhuang China The Chinese Institute of Electronics Beijing China The 54th Research Institute of CETC Shijiazhuang China

ISBN: (纸本)9781665417341

Research institutions around the world plan to build a simulation platform that links constellation configuration and protocols together to support the design and verification of key technologies in 6G satellite communication systems. An important issue to improve the reliability and efficiency of simulation verification is how to effectively transfer the parameters of link-level simulation and system-level simulation. In this paper, by optimizing the traditional ground-based EESM algorithm, 12 kinds of channels are fitted, and all L2S interface parameter $\beta$ values under MCS are obtained, and a MCS is proposed for $\beta$ fitting verification, and the fitting effect meets the specified error Required by 3GPP. the wideband CQI value is obtained through the simulation results of all links and based on the subband weighted CQI method. then specify the MCS-CQI mapping table according to the CQI value, and simulate the link adaptive adjustment process through the mapping table. Finally, the feedback SNR and CQI are used to verify whether the corresponding BLER value meets the requirement of less than 10%. © 2021 IEEE.

关键词： Mapping

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machine learning music emotion recognition based on audio features

Machine learning music emotion recognition based on audio fe...

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international conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Keju Wang Cheng Qian Lijun Zhang Orchestra Department Xi’an Conservatory of Music Humanities School Xi’an Jiaotong University Xi’an China School of Mechanical and Electrical Engineering Xi’an University of Architecture and Technology Xi’an China

Music is a kind of language expressing emotion, different structures and content contain different emotions. For the music emotion classification problem, a novel music emotion recognition strategy is proposed using audio features and machine learning. the developed scheme includes four stages: preprocessing, feature extraction, feature selection, and classification. As a first step, the signals in the data set are stably continuous by framing and windowing in processing. Secondly, a 1x140 size feature is derived from signal to profile the detailed music emotion. In the third step, the extracted features are selected and pre-weighted based on the feature weighting algorithms (ReliefF), max-relevance and min-redundancy (mRMR). In the final step, the data are led to the convolutional neural network and the long short-term memory (CNN-LSTM) combination model to recognize music emotions. the experimental results approve that the proposed CNN-LSTM based on ReliefF & mRMR (RM-CNN-LSTM) feature processing method could obtain higher identification accuracy, and all indicators of the proposed model improve significantly.

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machine learning-Assisted Partially Blind Handover Prediction in 5G Network Systems

Machine Learning-Assisted Partially Blind Handover Predictio...

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： İbrahim Yazici Emre Gures Türk Telekom R&D Department Acıbadem Istanbul Turkey Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

Handover is the process of transferring a cellular call or data session from one base station to another. this process aims to establish reliable and uninterrupted connection, thereby providing satisfactory Quality of Service (QoS) and Quality of Experience (QoE) for users. 5G networks will use millimetre wave (mmWave) frequencies in addition to sub-6 GHz bands, which will make handover more challenging. this paper focuses on the problem of partially blind handovers which is a novel handover type. In this sense, we modify an extant algorithm used for the partially blind handovers [1] which the algorithm is based on machine learning. In our modified algorithm, we use the extant algorithm with a powerful boosting method that is Categorical Boosting (CatBoost). We compare our modified algorithm with a baseline algorithm, the originally proposed algorithm, Support Vector machine integrated original algorithm. Different settings of simulation time and number of users are considered in comparing the algorithms, and our modified algorithm outperforms the rest of the algorithms in majority of the settings with higher handover prediction rates as per obtained results. the obtained results clearly indicate that the integration of machine learning with partially blind handovers enables accurate predictions whether handover execution will be successful in collocated cells in a network in the real-world case. A noteworthy takeaway from the obtained results is that machine learning deployment with partially blind handovers will likely contribute to self-organizing networks (SONs) in 5G communication systems.

关键词： Support vector machines machine learning algorithms 5G mobile communication signal processing algorithms Quality of service Handover signal processing Prediction algorithms Boosting Quality of experience

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Feature Analysis and Automatic Word Segmentation of Natural Language processing Using Deep learning

Feature Analysis and Automatic Word Segmentation of Natural ...

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international conference on Recent Trends in Advance Computing (ICRTAC)

作者： G. Sugin Lal R. Porkodi Department of Computer Science Bharathiar University Coimbatore-46 Tamilnadu India

Natural Language processing (NLP) based automatic paper evaluation using ensemble deep learning algorithms involves leveraging advanced machine learning techniques to assess and evaluate academic papers automatically. Ensemble techniques combine the predictions of various kinds of deep learning models to improve overall performance and robustness. the proposed method includes feature extraction, segmentation and classification process. the feature extraction is done using multi modal neural network. this helps the features to convert into same modal features from different modes. then word segmentation process is carried using Recurrent Neural Network of Long Short Term Memory with Conditional Random Field (RNN-LSTM-CRF) and Gated Recurrent Unit with Conditional Random Field (RNN-GRU-CRF). Simulation results prove the RNN-GRUCRF provides better accuracy of word processing compared to the existing models.

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Local Vegetable Freshness Classification Based on Chronological Monitoring Using Transfer learning Approach 1

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Proceedings of the 6th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2023

作者： Abeer, Mahedi Hasan Lamia, Meherun Nessa Nafsi, Jannatul Feardous Chowdhury, Fabia Anol, Mahi Sarwar Ahamed, Md. Sazzadur Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9783031382963

ISBN: (纸本)9783031382956

Ensuring good quality vegetables in the diet is one of the primary challenges when it comes to establishing food security. With the advancement of scientific technologies, manual identification for freshness of vegetables is not practical at all as it is time consuming and inaccurate. To solve this issue Implementing a machine vision-based system that can quickly discover the freshness of vegetables through images makes the accuracy level according to our aspiration. Dataset was collected by manually clicking pictures of our selected vegetables. To create a customized dataset, we carefully chose new conditions for three selected vegetables, ‘eggplant’, ‘okra’ and ‘cauliflower’, from the market and prepared the dataset. After collecting six thousand of basic images, we sorted them into 3 categories: fresh, aged, rotten which was decided by physical attributes and image capture time (Day). Association between raw dataset and augmentation, we tried to increase the amount of dataset where we will reach an effortless position to train a dataset. Transfer learning models that are used in the preliminary stage were InceptionV3, Xception, MobileNetV-2, DenseNet201. After practical implementation we got our desired result from DenseNet201. though all these models give us decent accuracy level but after training DenseNet201 we were able to achieve the accuracy of 97.72%. this study will tremendously diminish the labor cost, and vegetables identification could also be made spontaneously. © 2023, IFIP international Federation for Information processing.

关键词： Vegetables

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Future Directions of Artificial Intelligence and machine learning in Healthcare: A Systematic Analysis and Mapping Study

Future Directions of Artificial Intelligence and Machine Lea...

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international conference on Information Systems and Computer Networks (ISCON)

作者： Hari Mohan Mishra Bilal Ahmed Mirza Shuja Ahmed Qtaishat Mukesh Kumar School of Computer Application Lovely Professional University Phagwara India Department of Information Technology Sohar University Sohar Sultanate of Oman

In order to organise previously conducted research on using machine learning in medical contexts, the authors of this paper employ a method known as systematic mapping. In order to do this, we looked through the abstracts of a variety of publications, including medical journals, healthcare periodicals, and conference proceedings, looking for the phrase “use of artificial intelligence and machine learning in healthcare.” After doing a search on Google Scholar, which resulted in the retrieval of 500 papers, we classified these studies in accordance with their objectives, approaches, primary concerns, and illnesses. With the use of this technique, we were able to organise our findings into the five categories that are as follows: privacy and security; a framework for privacy and security; interpretable machine learning; medical image assessment; electronic health record processing; and transfer learning. In addition, we found that the evaluation of medical images is the topic that receives the most research, that interpretable machine learning and explainable artificial intelligence are becoming increasingly popular, and that most authors are primarily interested in cancer research. To restate our mission, we want to provide future generations of scholars with an accurate picture of where the field is now and where it is headed.

关键词： Privacy Systematics Roads Transfer learning machine learning Medical services Search problems

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