检索结果-内蒙古大学图书馆

24th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2023

作者： Zhai, Yu-Huan Hua, Qiang Wang, Xiao-Wen Dong, Chun-Ru Zhang, Feng Xu, Da- Chuan College of Mathematics and Information Science Hebei University Baoding071002 China Beijing Institute for Scientific and Engineering Computing Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789819982103

Studies in speech separation have achieved significant success in recent years. To correctly separate the mixture signals, it is critical to encode the signals into an appropriate latent space. Existing speech separation methods include transforming mixed signals into frequency domain space or time domain space. The frequency domain features (spectrogram) are generated by STFT, which is closely related to speech articulation and reflects the energy of speech directly. The time domain features are learned from a latent embedding space, and the separation effect is facilitated by the end-to-end structure. However, these methods are based on the representations from only one domain, which is insufficient for providing a speech separation encoding space that is completely separable. Therefore, a Triple-Path Recurrent Neural Network (TPRNN) that fuse features from two domains is proposed. It employs a spectrogram as auxiliary information to improve the performance of speech separation. Experimental results on the Wall Street Journal (WSJ0) dataset show that this approach is beneficial to improve speech separation performance. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

Development of A Multipurpose Healthcare Mobile App Using JAVA

Development of A Multipurpose Healthcare Mobile App Using JA...

引用

2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Pongiannan, R.K. Bushra, S. Nikkath Lathif, Syed Ismail Abdul Pradhan, Saahil Brindha, R. Franklin, J. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India Data Science and Business Systems School of Computing Srm Institute of Science and Technology Kattankulathur India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur India

ISBN: (纸本)9798350315127

Health care has become an essential part of present human life. The preliminary requirement to select the basic information about medical facilities through a mobile app will serve the needs of the common people. This paper presents an integrated mobile app developed using JAVA. This app is user-friendly in terms of login, registration, and bookings. This caters to the facility to select information regarding doctors, medical lab facilities, pharmacy details, and health articles. This app will provide communication. © 2023 IEEE.

关键词： Medicine

来源：评论

学校读者我要写书评

暂无评论

Methodology to Maximize the Output Voltage Harvested from Thermoelectric Generator by Using Luo Converter 4

Methodology to Maximize the Output Voltage Harvested from Th...

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Sivagami, P. Harikrishnan, R. Srideviponmalar, P. Deshpande, Shripad V. Sathyabama Institute of Science & Technology Department of Electrical and Electronics Engineering Chennai India Symbiosis Institute of Technology Symbiosis International Deemed University Pune Campus Pune India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9798331508845

In this technical epoch we are dealing with sensible issues such as global warming, pollution, depletion of available resources, increase in power cost etc. Researchers are focusing to reduce the effects due to sensible issues in present time by looking out for neoteric energy harvesters, that is power generators. A physical phenomenon that converts heat energy into electrical energy is thermoelectric effect. Thermal energy is transformed into electrical energy by thermoelectric generators (TEGs) using the Seebeck effect. Energy harvested from TEG have no moving parts, can be integrated on other devices, produces no noise, can be positioned anywhere that is position independent. The operating life time is also high and has no maintenance. TEG can harness energy for 1-2K thermal discrepancy in the environment. Using any wearable application in the body thermo electric generator can forge power in microwatts and voltage in mv. TEG has low conversion efficiency that it produces voltage in millivolt. To mold this trifling amount of voltage to some electrical appliances mv is boosted using LUO converter. Luo-converters boosts the voltage in geometric progression fed from the thermoelectric generator. Thermoelectric generator fed Luo converter is controlled by PID controller to proliferate output volt-age at the LUO converter side. Thus, TEG has raised as emerging green technology to power devices. This paper discusses about TEG application for charging devices. © 2024 IEEE.

关键词： Energy harvesting

来源：评论

学校读者我要写书评

暂无评论

AI-Based Autonomous Paddy Farm using Smart and Precise Irrigation System

AI-Based Autonomous Paddy Farm using Smart and Precise Irrig...

引用

2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Pongiannan, R.K. Brindha, R. Akbar, Sahil Ali Pravin, A Richard Franklin, J. Pemila, M. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur India

ISBN: (纸本)9798350315127

With the rampant growth of the global population and the increasing need for agricultural products, farmers confront a critical hurdle of ensuring an adequate supply of water resources for irrigation to meet these escalating demands. Amid limited water resources, farmers must embrace innovative solutions and efficient water management practices in the era of Agriculture 4.0 turning this concept into a tangible reality to meet the growing demand for efficient farming practices. By integrating Artificial Intelligence (AI) and the Internet of Things (IoT) to gather and analyze agricultural data, we now have the capability to make more accurate and informed decisions in agriculture. In this, an innovative and adaptable irrigation system is introduced which prioritizes efficiency and cost-effectiveness by minimizing resource consumption. This strategy leverages the capabilities of Machine Learning (ML) algorithms to elevate the level of agricultural intelligence. By employing a suite of sensors, including those for soil moisture, temperature, and rainfall, within an environment conducive to prolonged plant growth, the collected data to construct SVM models findings indicate that SVM outperforms other models (LR, NN, NB) with an impressive recognition rate of 98.5% and a minimal root mean square error (RMSE) of 0.10. Through this system, crop irrigation is achieved with a high level of precision, entirely automated, and without the need for human intervention. As a result, this technology leads to a substantial reduction in water consumption, up to 50%. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Depression Detection Using Twitter Data

引用

Intelligent Automation & Soft computing 2023年第5期36卷 1301-1313页

作者： Doaa Sami Khafaga Maheshwari Auvdaiappan KDeepa Mohamed Abouhawwash Faten Khalid Karim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering M.Kumarasamy College of EngineeringKarur639113India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI48824USA

Today social media became a communication line among people to share their happiness,sadness,and anger with their *** is necessary to know people’s emotions are very important to identify depressed people from their *** depression detection helps to save people’s lives and other dangerous mental *** are many intelligent algorithms for predicting depression with high accuracy,but they lack the deﬁnition of such *** machine learning methods help to identify depressed *** the accuracy of existing methods was not *** overcome this issue,the deep learning method is used in the proposed method for depression *** this paper,a novel Deep Learning Multi-Aspect Depression Detection with Hierarchical Atten-tion Network(MDHAN)is used for classifying the depression ***,the Twitter data was preprocessed by tokenization,punctuation mark removal,stop word removal,stemming,and *** Adaptive Particle and grey Wolf optimization methods are used for feature *** MDHAN classiﬁes the Twitter data and predicts the depressed and non-depressed ***,the proposed method is compared with existing methods such as Convolutional Neur-al Network(CNN),Support Vector Machine(SVM),Minimum Description Length(MDL),and *** suggested MDH-PWO architecture gains 99.86%accuracy,more signiﬁcant than frequency-based deep learning models,with a lower false-positive *** experimental result shows that the proposed method achieves better accuracy,precision,recall,and *** also mini-mizes the execution time.

关键词： Depression detection twitter data tweets deep learning swarm intelligence multi-aspect depression detection prediction

来源：评论

学校读者我要写书评

暂无评论

A NEW ADAPTIVE SUBSPACE MINIMIZATION THREE-TERM CONJUGATE GRADIENT ALGORITHM FOR UNCONSTRAINED OPTIMIZATION

引用

Journal of computational mathematics 2021年第2期39卷 159-177页

作者： Keke Zhang Hongwei Liu Zexian Liu School of Mathematics and Statistics Xidian UniversityXi’an 710126China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering computingAMSSChinese Academy of SciencesBeijing 100190China

A new adaptive subspace minimization three-term conjugate gradient algorithm with nonmonotone line search is introduced and analyzed in this *** search directions are computed by minimizing a quadratic approximation of the objective function on special subspaces,and we also proposed an adaptive rule for choosing different searching directions at each *** obtain a significant conclusion that the each choice of the search directions satisfies the sufficient descent *** the used nonmonotone line search,we prove that the new algorithm is globally convergent for general nonlinear functions under some mild *** experiments show that the proposed algorithm is promising for the given test problem set.

关键词： Conjugate gradient method Nonmonotone line search Subspace minimization Sufficient descent condition Global convergence

来源：评论

学校读者我要写书评

暂无评论

Modeling the dynamical behavior of the interaction of T-cells and human immunodeficiency virus with saturated incidence

引用

Communications in Theoretical Physics 2024年第3期76卷 1-14页

作者： Salah Boulaaras Rashid Jan Amin Khan Ali Allahem Imtiaz Ahmad Salma Bahramand Department of Mathematics College of ScienceQassim UniversityBuraydah51452Saudi Arabia Institute of Energy Infrastructure(IEI) Department of Civil EngineeringCollege of EngineeringUniversiti Tenaga Nasional(UNITEN)Putrajaya CampusJalan IKRAM-UNITEN43000 KajangSelangorMalaysia Department of Mathematics University of SwabiSwabi 23561Pakistan Department of Mathematics College of SciencesQassim UniversitySaudi Arabia Institute of Informatics and Computing in Energy(IICE) Universiti Tenaga NasionalKajangSelangorMalaysia Department of Political Science Bacha Khan University CharsaddaCharsadda 24420KPK Pakistan

In the last forty years,the rise of HIV has undoubtedly become a major concern in the field of public health,imposing significant economic burdens on affected ***,it becomes imperative to undertake comprehensive investigations into the mechanisms governing the dissemination of HIV within the human *** this work,we have devised a mathematical model that elucidates the intricate interplay between CD4^(+)T-cells and viruses of HIV,employing the principles of fractional *** production rate of CD4^(+)T-cells,like other immune cells depends on certain factors such as age,health status,and the presence of infections or ***,we incorporate a variable source term in the dynamics of HIV infection with a saturated incidence rate to enhance the precision of our *** introduce the fundamental concepts of fractional operators as a means of scrutinizing the proposed HIV *** facilitate a deeper understanding of our system,we present an iterative scheme that elucidates the trajectories of the solution pathways of the *** show the time series analysis of our model through numerical findings to conceptualize and understand the key factors of the *** addition to this,we present the phase portrait and the oscillatory behavior of the system with the variation of different input *** information can be utilized to predict the long-term behavior of the system,including whether it will converge to a steady state or exhibit periodic or chaotic oscillations.

关键词： HIV infection fractional-calculus dynamics of HIV iterative scheme dynamical behaviour mathematical model fractional derivatives

来源：评论

学校读者我要写书评

暂无评论

Graph Neural Networks for Optimizing Reliability in Vehicle-to-Infrastructure Communication in Autonomous Systems 9

Graph Neural Networks for Optimizing Reliability in Vehicle-...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Neelamegam, G. Revathy, J. Kannukkiniyal, M. Devendran, M. Akshya, J. Choudhry, Mani Deepak Vsb College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Christ the King Engineering College Department of Artificial Intelligence and Data Science Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India Hindustan institute of technology Department of Computer Science and Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350377972

High reliance on autonomous systems necessitates efficient and reliable data exchange through Vehicle-to-Infrastructure (V2I) communication to have appropriate and stable network performance in dynamic vehicular environments. With heuristic approaches, Dijkstra's algorithm, and Deep Q-Networks (DQN), several methods are proposed for optimizing latency reduction, packet delivery rate improvement, and power efficiency in the direction of V2I communications. However, these approaches suffer from important challenges: low adaptability of the network to real-time dynamics, suboptimal resource usage, and difficulties in scalability towards complex large-scale networks. In this scenario, we propose a framework that incorporates a GNN inside autonomous systems for V2I communication to optimize performance. The GNN will process real-time graph features, including signal strength, packet loss, and mobility speed, as it dynamically changes in adaptation to the evolution of network behaviour and other functions. This work shows the superiority of the proposed GNN model for the efficiency of experimentally conducted simulations compared to the current models;it achieved latency reduced by up to 28%, 5-10% PDR, and 20% energy efficiency across the various scenarios with urban, rural, highway, and suburban networks. The results validate robustness and scalability for practical implementations by the proposed approach in real-world autonomous systems. Further development is possible with this framework: for example, the use of multi-agent reinforcement learning techniques within cooperative decision-making and the enhancement through blockchain-based security mechanisms to ensure the integrity of the data. © 2024 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Stock Price Prediction Using Gradient Boosting Machine with Technical Indicators 3

Stock Price Prediction Using Gradient Boosting Machine with ...

引用

3rd International Conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Vinston Raja, R. Jayanthi, G. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India Saveetha Nagar Tamil Nadu Chennai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

This research endeavors to design a predictive model to forecast stock prices using a Gradient Boosting Machine (GBM) regressor, specifically emphasizing the adjusted closing price to mitigate the autocorrelation issues intrinsic to time series data. The investigation encompasses a 12-month duration of historical stock data, integrating two technical indicators, namely Exponential Moving Averages (EMA-5 and EMA-10), to augment predictive precision. Gradient Boosting, recognized for its proficiency in managing intricate and non-linear data patterns, was employed to forecast forthcoming stock prices. Critical performance indicators, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2), played a role in evaluating model accuracy, attaining an R2 score of 0.99, thereby signifying an almost perfect correlation between actual and predicted prices. Beyond numerical assessments, visual examinations through scatter plots, residual plots, and prediction error distribution further elucidated the model's efficiency. The findings reveal that the amalgamation of Gradient Boosting with Technical Indicators such as EMA-5 and EMA-10, coupled with an emphasis on the adjusted closing price, proves exceptionally effective for stock price forecasting. This methodology achieved an accuracy rate nearing 99%, illustrating its substantial promise for stock market prediction and its proficiency in alleviating autocorrelation challenges within time series datasets. © 2025 IEEE.

关键词： Autocorrelation

来源：评论

学校读者我要写书评

暂无评论

On the Attractive and Repulsive Forces of Generalized Stochastic Neighbor Embedding With Alpha-Divergence

引用

IEEE Access 2024年 12卷 90380-90394页

作者： Lin, Hsin-Yi Tseng, Huan-Hsin Chien, Jen-Tzung Seton Hall University Department Of Mathematics And Computer Science South OrangeNJ07079 United States Brookhaven National Laboratory Computational Science Initiative UptonNY11973 United States Institute Of Electrical And Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

Stochastic neighbor embedding (SNE) performs nonlinear transformation from high-dimensional observation space to low-dimensional latent space which preserves neighbor affinities. Data pairs in latent space tend to be crowded due to the dimensionality reduction. To mitigate the crowding problem, certain characteristics are favorable in the design of the SNE setting. This study presents a fundamental analysis of SNE that not only generalizes the previous SNEs but also provides a systematic way to understand the intrinsic properties. From the perspective of theoretical connection, we are able to conceive a new generalized SNE (g-SNE) by introducing a regularized power-law distribution with the α-divergence for manifold learning. The proposed method generalizes and incorporates various favorable features for the clustering process. In addition, the proposed method provides high flexibility, admitting tailored realizations to properly reflect the similarity between original and dimension-reduced samples. Experiments are performed to analyze the proposed method, and its effectiveness is demonstrated with several learning tasks. © 2024 The Authors.

关键词： Data visualization

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：