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共 1776 条记录，以下是51-60 订阅

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Electronic Music Signal Identification Model Based on machine learning Algorithm 4

Electronic Music Signal Identification Model Based on Machin...

引用

4th international conference for Emerging Technology, INCET 2023

作者： Dongfang, Li University of Jinan School of Music Jinan250002 China

ISBN: (纸本)9798350335750

the main idea of this model is to recognize sound signals and classify them into musical instruments. the working principle of the model is that different sounds have different characteristics and can be used for classification. the algorithm is based on machine learning algorithm, which can learn from data and improve performance over time. the concept of the model is to automatically identify music signals so that they can be used as inputs to other models. the model is based on machine learning algorithm and uses artificial neural network (ANN) and machine learning technology. Artificial neural network is a computer algorithm that simulates the working mode of human brain neurons. machine learning algorithms are more complex than artificial neural networks, which use different types of layers to process data and learn from it. they have been successfully applied in many fields, such as image recognition, speech recognition, text classification and so on. © 2023 IEEE.

关键词： Music

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machine learning Approach for Detection of Speech Emotions for RAVDESS Audio dataset 4

Machine Learning Approach for Detection of Speech Emotions f...

引用

4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Rochlani, Yogesh R. Raut, Anjali B. H. V. P. m'S College of Engineering and Technology Department of Computer Science and Engineering Maharashtra Amravati India

ISBN: (纸本)9798350343670

the most effective technique to convey one's thoughts and actions to another is through the use of emotion. Emotion recognition from one's own voice is the most urgently needed technology right now. Emotions are attached to everything that a person owns. Every customer's feelings might provide light on what they really need from the customer service professional. Since human communication relies heavily on the ability to read one another's emotions, speech emotion recognition is crucial for understanding another person's inner workings. the time and frequency-based acoustic characteristics integrated with machine learning classifiers that can categorize emotions to enhance the performance of the proposed intelligent system. this research makes use of the audio records available in the Ryerson Audio-Visual database of Emotional Speech and Song (RAVDESS). Emotion is gleaned from speech via temporal and spectral characteristics. three machine learning classifiers, the SVM, DT, and RF classifiers, are then trained on the retrieved data. the RF classifier achieved 88.78% higher accuracy rate than the state-of-the-art approaches. © 2024 IEEE.

关键词： Classification Decision Tree Emotion recognition Mel frequency cepstral coefficients Pitch Random Forest Classifier Short term energy Support Vector machine

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Zeroth Order GreedyLR: An Adaptive learning Rate Scheduler for Deep Neural Network Training 4

Zeroth Order GreedyLR: An Adaptive Learning Rate Scheduler f...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Subramanian, Shreyas Ganapathiraman, Vignesh Amazon Web Services Seattle United States

ISBN: (纸本)9798350324303

Deep neural networks are a powerful tool for a wide range of applications, including natural language processing (NLP) and computer vision (CV). However, training these networks can be a challenging task, as it requires careful selection of hyperparameters such as learning rates and scheduling strategies. Despite significant advances in designing dynamic (and adaptive) learning rate schedulers, choosing the right learning rate / schedule for a machine learning task is still more art than science. In this paper, we introduce Zeroth order GreedyLR, a novel scheduler that adaptively adjusts the learning rate during training based on the current loss and gradient information. To validate the effectiveness of our proposed method, we conduct experiments on several NLP and CV tasks. the results show that our approach outperforms several state-of-the-art schedulers in terms of accuracy, speed, and convergence. Furthermore, our method is easy to implement, computationally efficient, and requires minimal hyperparameter tuning. Overall, our study provides a useful tool for researchers and practitioners in the field of deep learning. © 2023 IEEE.

关键词： Deep neural networks

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Crops Analysis And Classification Using machine learning Techniques Based on Soil and Environmental Characteristics 4

Crops Analysis And Classification Using Machine Learning Tec...

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4th international conference on Communication, Computing and Industry 6.0, C216 2023

作者： Bharath, M. Gowtham, S. Rao, Trupthi Kodipalli, Ashwini Dept. of Artificial Intelligence & Data Science Global Academy of Technology Karnataka Bangalore India

ISBN: (纸本)9798350327328

In the evolving landscape of modern agriculture, the fusion of technology with traditional methods is essential. this study explores the transformative potential of machine learning in crop classification, leveraging soil and environmental data. the K-Nearest Neighbors (KNN) model achieved an impressive 97.5% accuracy in pattern recognition, while ensemble models, such as Random Forest, Naïve Bayes, and XGBoost, excelled with a remarkable 99% accuracy, surpassing conventional methods. this research underscores the profound impact of machine learning in advancing agriculture, facilitating precision farming, and addressing global concerns like food security and sustainability. Additionally, the practical implications of this research are significant for farmers, providing them with the means to select crops based on their environmental characteristics, optimizing resource allocation and crop management, and contributing to agricultural sustainability in an era of environmental unpredictability and climate change. © 2023 IEEE.

关键词： Soils

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An analytical review of Email Spam recognition Using Deep learning 4

An analytical review of Email Spam Recognition Using Deep Le...

引用

4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Juneja, Sonam Devi, Rajni Bhati, Bhoopesh Singh Tiwari, Dimple Chandigarh University Mohali India Indian Institute of Information Technology Sonepat India Ggsipu Vivekananda Institute of Professional Studies Technical Campus Delhi India

ISBN: (纸本)9798350364729

Email spam detection has become a serious issue in contemporary communication systems as a result of the proliferation of unwanted emails. Traditional methods often fall short of the ever- evolving tactics employed by spammers. Deep learning methods provide promising avenues to raise the effectiveness and precision of spam identi- fication systems. this paper looks at the current advancements in the usage of deep learning algo- rithms for email spam identification. We provide experimental data, review relevant literature, dis- cuss techniques, and render conclusions about the efficacy of deep learning in reducing unsolicited emails. © 2024 IEEE.

关键词： Adversarial machine learning

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A Novel Approach using machine learning and NLP for Revolutionizing Pali Manuscript Conservation 4

A Novel Approach using Machine Learning and NLP for Revoluti...

引用

4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Gudadhe, Sangita R. Bardekar, Aashish A. Ranit, Amitkumar B. Sipna COET Amravati Dept. of Computer Science & Engineering Amravati India Prof. Ram Meghe Cem Dept.of Civil Engineering Badanera Amravati India

ISBN: (纸本)9798331540364

the preservation and analysis of ancient Pali manuscripts are crucial for understanding India's cultural heritage. However, the unique shapes of Pali characters present significant challenges for accurate identification and classification. this research proposes a novel system that utilizes machine learning, image processing, and natural language processing (NLP) to convert Pali characters into understandable formats and decipher the Pali Prakrit language. By combining computer science and linguistics, the proposed model aims to effectively extract data from Pali literature. the proposed model was validated using the Government of India dataset, demonstrating improved character recognition accuracy and reduced memory footprint. this research contributes to the preservation of ancient Pali manuscripts and facilitates the study and understanding of this valuable cultural heritage. © 2024 IEEE.

关键词： Adversarial machine learning

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IoT-Enabled WBAN and machine learning for Emotion recognition 4

IoT-Enabled WBAN and Machine Learning for Emotion Recognitio...

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4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Mathur, Hritesh Panwar, Harsh Karki, Rahul Jangra, Vivek Yadav, Ramnaresh Kapoor, Anjali Amity University Department of Information Technology Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350387490

All industries, including computer science and health care and customer service are in dire need of efficient recognition of human feelings. this work is a script for a novel approach to affect recognition which involves both vocal emotion analysis and face emotion detection. Voice sentiment analysis project used machine learning technique to identify the prospects of emotional content in voice data. the model also assists in the classification of emotions with reference to the speech emotional states based on added features of MFCC and chroma. On the other hand, face emotion recognition project by applying deep learning algorithms identifies emotions by face expressions. With the help of convolutional, neural networks (CNNs) determining the happiness, sadness, and anger it analyses the facial features correctly. Combining all of these results in a coherent system that allows for the recognition of feelings through voice and or facial signals. Consequently, as demonstrated in the experimental results provided hereinafter, the accuracy of the presented system in correctly identifying human emotions is significantly higher than when using single models on their own. therefore this combination creates space for other uses such as healthcare marketing among other fields hence improving user experience while at the same time providing unforeseen analysis of human conduct during an interaction with intelligent devices. © 2024 IEEE.

关键词： Convolutional neural networks

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Early recognition of Sudden Cardiac Death Combined with Deep Transfer learning and Superficial ECG Features 4

Early Recognition of Sudden Cardiac Death Combined with Deep...

引用

4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Zhang, Yue Cha, Xingzeng Cui, Bingqian Lai, Dakun University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu China

ISBN: (纸本)9798350324303

Sudden cardiac death (SCD) is a global public safety issue that poses a serious threat to human life and health. Moreover, the clinical records of SCD data are extremely limited. this paper proposed an early prediction algorithm for SCD based on deep transfer learning. the algorithm used a two-stage training approach, which included pre-training and fine-tuning of convolutional neural networks (CNNs) to automatically capture the deep heart rate features (HRFs) in high-risk electrocardiogram (ECG) signals before SCDs. the aim is to explore automatic early prediction of SCD under limited ECG data training. this paper extracted three HRFs as input sets from 30 second single lead ECG signals of 18 patients with malignant arrhythmia and 18 healthy subjects, including RR interval, dRR interval, and turbulence onset (TO). then, the ten-fold crossover method was used to evaluate the classification performance of this model. Finally, the results showed that the average accuracy (Acc), sensitivity (Se), and specificity (Sp) of predicting SCD within 30 minutes before onset were 91.79%, 87.00%, and 96.63%, respectively. the transfer learning algorithm proposed in this paper effectively addresses the issue of insufficient data in the early prediction models of SCD. It also achieves early and accurate prediction of high-risk signs of SCD by using ECG. this is expected to form an intelligent and effective monitoring and early warning technology for high-risk patients of SCD. © 2023 IEEE.

关键词： Electrocardiograms

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Tunisian Sign Language recognition and Translation Using Deep learning 4th

Tunisian Sign Language Recognition and Translation Using Dee...

引用

4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： El Askri, Marah Basly, Hend Bchir, Riadh Zayene, Mohamed Amine Sayadi, Fatma Ezzahra Univ Sousse Natl Engn Sch Sousse Lab Networked Objects Control & Commun Syst NOCCS Eniso Sousse Tunisia Univ Monastir Fac Sci Monastir Monastir Tunisia

ISBN: (纸本)9783031821554;9783031821561

Human activity recognition, a dynamic field within computer vision and machine learning, aims to accurately identify and interpret human actions or activities using video data. this paper delves deeper into the domain by concentrating on Sign Language (SL) as a unique and intricate form of human activity. Sign Language (SL) is a vital communication tool, used by individuals who are deaf or hard of hearing, facilitating their interaction and engagement with others in a visually expressive manner. A lot of research and projects in SL are being done, and it has been observed that most of them are done in American Sign Language (ASL) or Indian Sign Language (ISL), but no such progress has been carried out for the other SLs. therefore, this paper aims to present a work done in Arabic sign language (ArSL), especially Tunisian SL. the main idea is to recognize Tunisian SL and then translate it into spoken language using artificial intelligence (AI) methods. the purpose of this work is to ease the communication between individuals who are deaf and those who are not. the proposed method uses holistic landmarks with sequence key points for Tunisian sign language and trains it with a Long Short-Term Memory (LSTM) model. the results show that the proposed model can be used for real-time SL estimation, providing a better interpretation method for the deaf community. As a result, our model achieved a validation accuracy of 98% with a cross-validation technique.

关键词： Deep learning LSTM Sign Language recognition

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Applying machine learning Approaches with Integrated Internet of things for Water Management System 4th

Applying Machine Learning Approaches with Integrated Interne...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Belmir, Meroua Difallah, Wafa Ghazli, Abdelkader Tahri Mohammed Univ Bechar Fac Exact Sci Dept Math & Comp Sci Bechar Algeria Coding & Informat Secur Lab LACOSI Oran Algeria Lab Energet Arid Zones ENERGARID Bechar Algeria

ISBN: (纸本)9783031821523;9783031821530

the challenges of water loss and inadequate scheduling in traditional irrigation methods hinder the ability to meet the increasing demand for food production while also conserving valuable water resources. In order to overcome these issues, this study introduces an advanced smart irrigation system that harnesses the capabilities of the Internet of things (IoT), data analysis, and machine learning. the primary objective is to determine the most effective method for applying and scheduling irrigation, thus optimizing resource usage. this innovative system relies on a meticulously curated dataset, "INTELLIGENT IRRIGATION SYSTEM" obtained from Kaggle. the dataset encompasses four essential attributes, extensively detailed in the metadata, providing foundational information crucial for precise irrigation management. this paper proposes an intelligent irrigation approach that can be deployed in different contexts. this approach is based on machine learning algorithms. Multiple models, including K-Nearest Neighbors (KNN), Logistic Regression (LR), and Gaussian process (GP) classifier, were employed in the development of this approach. the results showed that LR and GP are better with an accuracy of 0.9833, precision of 0.9782, F1 score of 0.9890, specificity of 0.9333, and sensitivity of 1.0 compared to KNN model.

关键词： Smart irrigation system machine learning smart agriculture Internet of things

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