检索结果-内蒙古大学图书馆

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Habelalmateen, Mohammed I. Kumar, Gotte Ranjith Nayana, B.P. Venkatramulu, S. Saranya, N. Naga Universiti Tun Hussein Onn Malaysia Electrical and Electronic Engineering Johor Malaysia College of Technical Engineering the Islamic University Department of Computer Technical Engineering Najaf Iraq SR University School of Computer Science and Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of ISE Bengaluru India Kakatiya Institute of Technology and Science CSE Dept Telangana Warangal India Saveetha College of Liberal Arts and Science SIMATS Department of Computer Applications Chennai India

ISBN: (纸本)9798350317558

Enhancing a network lifetime by balancing energy as well as minimizing delay is a significant aim of Wireless Sensor Network (WSN). Besides, number of clustering approaches are developed to this process through selecting the optimum Cluster Heads (CHs) in heterogeneous WSN, but it has a significant challenge. In this research, the Wild Horse Optimization (WHO)-based Optimized Clustering approach for single and multiple data sinks is proposed for optimizing CH selection through combining the parameters of sink distance, residual energy in its formulated fitness function. The proposed method achieves better results and it is evaluated by various performance metrices like residual energy, network lifetime, throughput, alive nodes, FND as well as LND and it achieves the values of about each as 61.37, 98.36, 7.8, 96.39, 6129 and 8347 respectively when compared to the existing methods such as Energy-Efficient CH selection utilizing Improved version of Grey Wolf Optimization-based (EECHIGWO) and Enhanced Pelican Optimization Algorithm for the CH Selection (EPOA-CHS). © 2024 IEEE.

关键词： Cluster Head selection Multiple data sinks Network lifetime Wild Horse Optimization and Wireless Sensor Network

来源：评论

学校读者我要写书评

暂无评论

Deep analysis of MFCC and MEL spectrogram features to recognize and classify stuttered speech

引用

Multimedia Tools and applications 2025年 1-20页

作者： Banerjee, Nilanjan Sethi, Nilambar Borah, Samarjeet Department of Computer Science and Engineering GIET University Odisha India Department of Computer Applications Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India

Human thoughts, feelings, and ideas are expressed through speech. Stuttering, also referred to as stammering, is an impediment to speech that affects millions of people across the globe. Stuttering speech recognition is a good deal of research within the realm of speech signal processing. Classification of eight different types of stuttering including fluent, interjections, broken words, prolongations, word repetitions, phrase–repetitions, part-word repetitions, and sound repetitions is the main motivation in this research. Stuttering speech recognition is becoming easier with the advancement of machine learning and deep learning. The present study focuses on the efficacy of stuttering speech recognition through the application of machine learning & deep learning. Analysis in this study began with traditional machine learning algorithms, such as Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). It is seen, feature extraction is an essential component in achieving accurate classification for machine learning algorithms. MFCC & MEL spectrogram features are extracted for this experiment. Furthermore, Delta2 MFCC and Delta2 Mel Spectrogram are derived by second-order derivatives on MFCC & Mel Spectrogram features. Therefore, a variety of these features are employed as input to the model. In comparison with all the aforesaid traditional machine learning models, the KNN model results in the highest accuracy of 69.1% based on MFCC-40 input features. To improve accuracy, we move to a deep learning approach. In this experiment, the convolutional neural network (CNN) model produces better outcomes than traditional machine learning. The study presents a comprehensive analysis of the CNN model with MFCC & MEL spectrogram features, as well as its efficiency. It is noted that MFCC-40 input features in CNN model results in the highest accuracy of 89% after 400 epochs. © The Author(s), under exclusive licence to Springer science+Bus

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Asthma Disease Risk Prediction Using Machine Learning Techniques

Asthma Disease Risk Prediction Using Machine Learning Techni...

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Pyingkodi, M. Thenmozhi, K. Wilfred Blessing, N.R. Selvaraj, P.A. Kumar, Kamdev Aadarsh, V. Mariya Arockiya Akash, A. Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences IT Department Oman

ISBN: (纸本)9798350348217

The signs of asthma, a long-term inflammatory condition of the airways, include wheezing, throat tightness, coughing, and breathing difficulties. The attack of an asthma, which can be fatal, is the fast worsening of these symptoms. Another severe, irreversible airflow restriction in the lungs is caused by respiratory COPD, which encompasses emphysema and chronic bronchitis. In this project, a machine learning-based algorithm is for predicting asthma risk is presented (ML). PEFR, which are widely used external tools like peak flow meters and recognized asthma risk predictors, are frequently monitored. This study shows a relationship between the ambient particle matter(PM) and the weather outdoors. According to the best peak flow value each person was able to acquire, the results are divided into two groups: Safe and Risk circumstances. The link between indoor PM and weather data is mapped to the found values using a convolutional neural network (CNN) architecture. The suggested method's root mean square and mean absolute error accuracy metrics are contrasted with those of current deep neural network (DNN)-based methods. Additionally, the accuracy of the classification methods KNN and SVM are carried out. The new data set's asthma category may be predicted more accurately thanks to the application of SVM, KNN, and CNN classification. Python 3.7 is the coding language employed. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

An Autonomous Emotion Recognition Strategy Employing Deep Learning for Self-Learning 3

An Autonomous Emotion Recognition Strategy Employing Deep Le...

引用

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kumar, Santosh Yadav, Satya Prakash School of Computing Graphic Era Hill University Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Jaipuria Institute of Management Department of Management Jaipur302033 India Glbitm Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9798350342338

Because of their individuality and diversity, facial expressions are crucial to deciphering human communication and humour. Predicting a person's facial expression accurately not only helps you understand their emotional state but also gives you greater control over how you engage with them. Since personalised interactions are shown to enhance development and rapport., this ability to autonomously recognise human facial emotions has tremendous promise in fields like finance and education. In this study., we provide a new method for identifying human emotions based on Convolutional Neural Networks (CNNs). This method departs from conventional approaches by harnessing the potential of modern machine learning methods that make use of face datasets. When implemented in the TensorFlow framework., this method might significantly alter the way grammatical constructions are understood. Convolutional Neural Networks are at the core of our technique since they are a complicated design that can easily deal with complex picture data. We overcome the limits of previous methods by using CNNs to extract complex characteristics from face photos, which we access by abandoning traditional paradigms. To ensure the efficacy of our approach., we ran trials using the Facial Expression Recognition Challenge (FERC-2013) Dataset., a collection of 32 thousand different human faces. Surprisingly., our method achieved classification rates over 65%., demonstrating its efficiency in deciphering human emotions across a wide range of photos. Our approach is built on the incorporation of the AlexNet Architecture., a powerful algorithm that acts as the process's conductor. One interesting feature is that it can be trained in batches., which speeds up the learning process and requires less resources. This effectiveness not only aids in faster learning., but also makes the method more widely applicable. Our work essentially advances facial emotion identification into the cutting edge of machine learni

关键词： Affective computing Deep learning Emotion recognition Facial expression analysis Machine learning Neural networks Self-learning

来源：评论

学校读者我要写书评

暂无评论

Design and Analysis of Deep Learning based Water Potability Prediction

Design and Analysis of Deep Learning based Water Potability ...

引用

2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Arumugam, M. Vijayakumar, P. Kannan, A.C. Velmurugan, J. Arun, G. Rohith Bhat, C. Kongu Engineering College Department of Computer Technology - Pg Perundurai India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai638060 India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul624622 India Erode Sengunthar Engineering College Department of Artificial Intelligence and Data Science Thuduppathi638057 India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350300857

According to the United Nations, having access to clean water for consumption is a fundamental human right. General Assembly in 2010. The importance of safe drinking water cannot be overstated. Unsafe drinking water access is a significant public health concern, especially in poor nations. In many regions of the world, waterborne illnesses including cholera, typhoid fever, and dysentery are important causes of illness and death. These diseases are especially dangerous for children, who are more vulnerable to their effects. In addition, a lack of access to clean drinking water can result in other health issues like stunted growth and malnutrition. Access to safe drinking-water is also a key component of effective policy for health protection at all levels, whether it is at the national, regional, or local level. Policies aimed at improving access to safe drinking-water can include measures such as water quality testing, water treatment, and the provision of clean water sources. Investments in water supply and sanitation can have a positive economic impact, especially in areas with inadequate access to clean drinking water. The costs of implementing the treatments may be outweighed by the reductions in unfavorable health impacts and medical expenses that may result from these investments. In conclusion, having access to clean drinking water is crucial for encouraging wellness, reducing waterborne illnesses, and raising standard of living in general. The input to the DNN could be the various water quality parameters, such as pH, hardness, chloride content, and total dissolved solids, while the output would be a binary classification indicating whether the water is potable or not. The DNN would need to be trained on a large and diverse dataset of water samples to accurately classify new samples. © 2023 IEEE.

关键词： Potable water

来源：评论

学校读者我要写书评

暂无评论

Multi Task Learning for Plant Leaf Segmentation and Counting

Multi Task Learning for Plant Leaf Segmentation and Counting

引用

2023 IEEE Applied Sensing Conference, APSCON 2023

作者： Chaudhury, Bharathi Joshi, Vasudha Mitra, Pabitra Sahadevan, Anand S. Indian Institute of Technolog Kharagpur Department of Computer Science and Engineering Kharagpur India Indian Space Research Organization Space Applications Centre Ahmedabad India

ISBN: (纸本)9781665461634

In this paper, we propose an efficient method to segment the leaves and count the number of leaves in digital plant images, important tasks in plant phenotyping. However, for large cropping lands, automated leaf counting and segmentation are challenging and time-consuming due to the plant's rapid growth, varying size of leaves and shape, and presence of occlusions, which pose the task challenging to even human experts. Deep learning methods show considerable improvement when employed in image-based plant phenotyping tasks like plant leaf counting and leaf segmentation. However, most of the research related to plant phenotyping was designed to perform a single-task at a time. Therefore, a multi-task deep learning (MTL) framework is proposed to infer the two tasks simultaneously (i)leaf segmentation and (ii)leaf counting. Compared to single-task learning, MTL achieved the segmentation mean intersection over union score of 0.7478 (improved by 2%), and the difference in the count is decreased by two times when employed in agricultural field images. © 2023 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Neural Network Interpretability: Methods for Understanding and Visualizing Deep Learning Models 7

Neural Network Interpretability: Methods for Understanding a...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Sudhakar, Angatha Veera Venkata Sharma, M.K. Mohan, Chinnem Rama Singh, Suraj Chouhan, Kuldeep Geetha, B.T. Sr University School of Engineering Department of Eee Telangana Warangal India Chaudhary Charan Singh University Uttar Pradesh Meerut250004 India Narayana Engineering College Department of Computer Science and Engineering Andhra Pradesh Nellore524004 India Electrical Engineering Department Mullana Haryana Ambala133207 India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida201310 India Department of Ece Tamil Nadu India

ISBN: (纸本)9798350350067

Neural networks, which are a type of deep learning model, are massively criticized due to their 'black box' approach, which does not allow interpreting their decisions. In this research, several post-hoc interpretability techniques, which seek to explain the behavior of such models, are discussed. Specifically, this paper aims to evaluate the application of activation maps, feature importance, and attention mechanisms for explaining CNN and LSTM architectures within two distinct datasets namely Fashion MNIST and IMDB. Thus, the work has a focus on showing that these interpretability methods are not only useful for understanding deep learning models, as well as for reducing bias, fairness, and trust in AI systems. It emphasizes that the complexity and interpretability of models used in socially important tasks should be more thoroughly researched in order to develop effective means of improving artificial intelligence systems while maintaining explanatory power and non-discriminatory approach. © 2024 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Classification of EEG Signals on SEED Dataset Using Improved CNN 2

Classification of EEG Signals on SEED Dataset Using Improved...

引用

2nd International Conference on Edge Computing and applications, ICECAA 2023

作者： Ramar, B. Ramalakshmi, R. Gandhi, Vaibhav Pandiselvam, P. Kalasalingam Academy of Research and Education Department of Computer Applications Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India Middlesex University Department of Design Engineering and Maths Education London United Kingdom

ISBN: (纸本)9798350347579

The proposed research introduces an Improved Convolutional Neural Network (ICNN) to construct EEG-based emotion detection models. This study has utilized an EEG dataset of 15 subjects available from a BCMI laboratory. In our work, differential entropy characteristics obtained from multichannel EEG data are used to train the Improved CNN. The best classification accuracy is 95.67% which is significantly higher than that of the original 62 channels. The most important channels and frequency bands are identified by Improved CNN. The outcomes of our study also demonstrate the existence of neuronal signatures linked to various emotions, which are consistent between sessions and people. Finally, the effectiveness of deep and shallow models are compared and also the performance of improved CNN is compared with benchmark algorithms. © 2023 IEEE.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Prediction of Short-Term Solar Radiation Using Machine Learning Methods 2nd

Prediction of Short-Term Solar Radiation Using Machine Learn...

引用

2nd Conference on Flexible Electronics for Electric Vehicles, FlexEV 2021

作者： Singh, Praveen Kumar Saraswat, Amit Gupta, Yogesh Goyal, Sunil Kumar Department of Computer Applications Manipal University Jaipur Rajasthan Jaipur India Department of Electrical Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science BML Munjal University Haryana Gurugram India

ISBN: (纸本)9789811905872

The renewable energy such as wind energy and solar energy has been established to offer benefits of sustainable energy sources and lowered environmental pollution levels. In addition to the benefits they offer, factors such as global warming and the growing energy crisis have led to a progressive increase in demand for renewable energy. To meet such a growing demand, there is a need for an efficient energy management system that promotes more accurate forecasting techniques. Forecasting to solar system output is mainly focused on the prediction of solar radiation. Numerous forecasting methods have been applied for achieving short-term solar radiation forecasting. This paper presents a comparative analysis of forecasting performance of three machine learning methods such as support vector machine, multiple linear regression, and artificial neural network. It is investigated that the prediction accuracy based on the multiple linear regression yields better results than that of the artificial neural network and support vector regression. Subsequent paragraphs, however, are indented. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multiple linear regression

来源：评论

学校读者我要写书评

暂无评论

ReFuSeg: Regularized Multi-modal Fusion for Precise Brain Tumour Segmentation 9th

ReFuSeg: Regularized Multi-modal Fusion for Precise Brain T...

引用

9th International Workshop on Brain Lesion workshop, BrainLes 2023 and 3rd Stroke Workshop on Imaging and Treatment CHallenges, SWITCH 2023 Held in Conjunction with 26th Medical Image Computing and computer Assisted Intervention, MICCAI 2023

作者： Kasliwal, Aditya Sagaram, Sankarshanaa Srivastava, Laven Seth, Pratinav Khan, Adil Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Mechanical Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031761591

Semantic segmentation of brain tumours is a fundamental task in medical image analysis that can help clinicians in diagnosing the patient and tracking the progression of any malignant entities. Accurate segmentation of brain lesions is essential for medical diagnosis and treatment planning. However, failure to acquire specific MRI imaging modalities can prevent applications from operating in critical situations, raising concerns about their reliability and overall trustworthiness. This paper presents a novel multi-modal approach for brain lesion segmentation that leverages information from four distinct imaging modalities while being robust to real-world scenarios of missing modalities, such as T1, T1c, T2, and FLAIR MRI of brains. Our proposed method can help address the challenges posed by artifacts in medical imagery due to data acquisition errors (such as patient motion) or a reconstruction algorithm’s inability to represent the anatomy while ensuring a trade-off in accuracy. Our proposed regularization module makes it robust to these scenarios and ensures the reliability of lesion segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：