检索结果-内蒙古大学图书馆

3rd International Conference on Flexible Electronics for Electrical Vehicle, FlexEV 2022

作者： Singh, Praveen Kumar Saraswat, Amit Gupta, Yogesh Goyal, Sunil Kumar Gupta, Yeshpal 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 Gurugram India Lincus Inc. TempeAZ United States

ISBN: (纸本)9789819947942

Energy and climate challenges have led to significant growth in solar power generation, and because of this, smart grids are increasingly utilizing solar power. The solar power is intermittent because solar energy is mainly dependent on radiation and other atmospheric factors. To ensure the reliable economic operation of micro grids and smart grids, precise forecasting of solar radiation is crucial. Deep learning techniques are proposed in this study to model the solar radiation production. Four artificial intelligence and deep learning-based forecasting models, the fully connected artificial neural network (ANN), the convolutional neural network (CNN), the long short-term memory network (LSTM), and the bidirectional neural network (Bi-LSTM) were examined for this study. To compare the prediction accuracy of all models, three performance evaluation metrics RSME, MAE, and R2 are used. The results obtained indicate that all four methods produce reasonable estimates of solar radiation generation. As a result of RMSE, MSE, and R2, the Bi-LSTM forecasting model offers the best estimate of forecasting accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024

关键词： Convolutional neural network Fully connected Artificial Neural Network Bi-Directional Neural Network Long short-term memory Solar power Solar radiation

来源：评论

学校读者我要写书评

暂无评论

Graph Encoding-Enhanced Transformer for Drug Recommendation

Graph Encoding-Enhanced Transformer for Drug Recommendation

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Cai, Xunsheng Thamrin, Syauki Aulia Chen, Arbee L.P. Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9798350324457

Doctors prescribe drugs for the patient with the objective of curing the patient. Some drugs cannot be consumed together since doing so may cause negative effects. This can be avoided by knowing the effects caused by consuming combinations of drugs. However, for complex cases of a patient, it can be difficult to decide the best combination of drugs. Therefore, automatic drug recommendation method was used to recommend drugs with minimal negative effects. It is performed by using a deep learning model which is trained on drug data. A graph called drug-drug interaction (DDI) is used to represent the drugs and effects of consuming one drug with other drugs. Additionally, information about the combination of drugs prescribed in the past for a patient is also important for drug recommendation. It can also be represented as a graph called drug concurrence relation (DCR). The DDI and DCR graphs can be input to the deep learning model through an encoding process. In this paper, we propose a graph encoding-enhanced transformer (GEET) to recommend drugs. The DDI and DCR graphs are encoded by using Graph Attention Network (GAT). The graph encoding model has multi-head attention, which makes the GEET model aware of the most important DDI and DCR from the graphs. Additionally, the encoding outputs are combined, and activation function and normalization methods are used to improve the performance. The model has been evaluated on the publicly available MIMIC-III dataset and has the best results on F1, Jaccard and PRAUC scores compared to the models proposed by the existing related research papers. © 2023 IEEE.

关键词： drug concurrence relation drug graph encoding Drug recommendation drug-drug interaction graph attention network transformer

来源：评论

学校读者我要写书评

暂无评论

Humanoid Robots that Move on Two Feet and Are Led by Vision Systems 3

Humanoid Robots that Move on Two Feet and Are Led by Vision ...

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

At the convergence of robotics and computer vision lies a crucial research field that focuses on the development of vision-guided walking for humanoid robots. The ability of humanoid robots to walk independently while simultaneously seeing and comprehending their surroundings via the use of visual inputs offers tremendous potential. Humanoid robots are developed to reproduce human-like qualities and skills. This abstract discusses current developments in the area of vision-guided walking for humanoid robots as well as the approaches that have been used. Through the use of computer vision methods in conjunction with walking algorithms, humanoid robots are given the capacity to traverse situations that are both complex and dynamic while exhibiting increased flexibility. For this, real-time sensing of the environment, object identification and avoidance of obstacles, as well as analysis of the terrain, are required in order to arrive at intelligent judgements while moving. Vision-guided walking not only makes robotic locomotion more secure and effective, but it also paves the way for a wide range of potential applications, including exploration, human-robot interaction, search and rescue, and many more. Visual sensing systems, sensor fusion procedures, gait generation methods, motion planning, and control strategies. These topics are discussed in relation to vision-guided walking. Different types of sensors, such as cameras, depth sensors, and LiDAR, are being investigated to see how they can best provide a holistic understanding of the surrounding environment. In addition, the abstract draws attention to the difficulties caused by sensor noise, occlusions, fluctuations in illumination, and the need for real-time processing. In places an emphasis on the role that methods of machine learning play in the improvement of vision-guided walking. The use of deep learning algorithms gives robots the ability to learn from data, hence enhancing their capacity to understand and r

关键词： Head movement in robot Humanoid robots Planning and control Proprioceptive sensors Sensors Vision guided

来源：评论

学校读者我要写书评

暂无评论

The Efficient Traffic Sign Detection and Recognition for Taiwan Road Using YOLO Model with Hybrid Dataset 9

The Efficient Traffic Sign Detection and Recognition for Tai...

引用

9th International Conference on Applied System Innovation, ICASI 2023

作者： Suwattanapunkul, Taweelap Wang, Lung-Jen National Pingtung University International Master Program in Information Technology and Applications Taiwan National Pingtung University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350398380

According to the trend of worldwide car sales have grown up, this cause may increase accidents on the road due to human error. The self-driverless car has been developed to solve this problem. One task of the self-driverless car is traffic sign detection and recognition (TSDR), which will help drivers notify the traffic sign installed on the road in advance. Taiwan roads have specific traffic signs, and no Taiwan traffic sign public dataset is available. In this paper, our proposed object detection method was experimentally performed using YOLOv5s6 and YOLOv8s models on three different datasets, as Tsinghua-Tencent 100K (TT100k), the self-created Taiwan traffic sign (TWTS), and the hybrid dataset, which combine the traffic scenes between TT100k and TWTS dataset. The output results from each dataset and each model, which is trained on the same parameter, will be compared to validate the proposed method. The experiment results' comparison of the hybrid dataset between YOLOv5s6 and YOLOv8s models display the results of the mAP@.5 is about 65% and 76.2%, respectively, which means the performance of the YOLOv8s is higher than the YOLOv5s6 when using hybrid dataset. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Smart Antennas and Machine Learning: Enhancing Beamforming in Wireless Communications

Smart Antennas and Machine Learning: Enhancing Beamforming i...

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Sudhakar, A.V.V. Sharma, Upasana Visala, K. Kumar, K. Suresh Arora, Chirag Chouhan, Kuldeep Sr University School of Engineering Department of Eee Telangana Warangal India Abes Engineering College Department of Electronics and Communication Engineering UP Ghaziabad India Vignan's Institute of Information Technology Computer Science Engineering Andhra Pradesh Vishakapatnam India Panimalar Engineering College Mba Department Varadarajapuram Chennai600123 India Kiet Group of Institutions Department of Electronics and Communication Engineering U.P Ghaziabad India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida201310 India

ISBN: (纸本)9798331508685

One of the most effective and widely used technological advancements for increasing capacity, enhancing quality, and expanding coverage in wireless communications systems is the smart antenna. Smart antennas are essential for increasing the effectiveness and dependability of wireless communication systems because they make use of cutting-edge signal processing techniques. Smart antennas' beamforming capability, which concentrates signal transmission and reception in particular directions, increases signal strength, lowers interference, and expands network capacity. As wireless networks become more complicated, machine learning (ML) has become a potent tool for beamforming optimisation. ML algorithms can proactively adjust antenna characteristics by using real-time data, learning from fluctuations in the environment to optimise performance. In order to improve beamforming capabilities, this research investigates the incorporation of ML with smart antenna systems. We go over several ML techniques, such as supervised and reinforcement learning, for resource allocation, user localisation, and signal-to-noise ratio (SNR) optimisation. Additionally, we draw attention to how deep learning might enhance spatial filtering, facilitating more effective communication in 5G and beyond. The potential direction for next-generation wireless communication networks is presented by the convergence of ML and smart antennas. © 2025 IEEE.

关键词： Smart antennas

来源：评论

学校读者我要写书评

暂无评论

Cluster Head Selection for Single and Multiple data Sinks in Heterogeneous WSN using Wild Horse Optimization 2

Cluster Head Selection for Single and Multiple data Sinks in...

引用

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

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

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

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

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

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

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：