检索结果-内蒙古大学图书馆

2nd Al-Muthanna International Conference on engineering science and Technology, MICEST 2022

作者： Jabbar, Ismael AbdulSattar Abdullh, Mohammed Najm Alhamdani, Rafah Shihab College of Sciences Mustansiriyah University Dept. Computer Science Baghdad Iraq University of Technology Dept. Computer Engineering Sciences Baghdad Iraq Al-Nahrain University Dept. Economic Relations Baghdad Iraq

ISBN: (纸本)9781665472135

This paper finds the mechanism to obtain learning rate value for training restricted Boltzmann artificial neural network that used for build recommender systems. One of the important problem in training the artificial neural network model is finding the appropriate learning rate values for making designed model reach the optimal result for learning process. The proposed model analyzes the behavior of the recommender system in context of mean squared error (MSE), mean absolute error (MAE), precision and recall as well as the free energy function. Proposed model © 2022 IEEE.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Load Balancing Mechanism in Cloud Computing

Performance Analysis of Load Balancing Mechanism in Cloud Co...

引用

Applied Intelligence and Sustainable Computing (ICAISC), International Conference on

作者： Annaiah Halappa A. Rajesh Dept. of Computer Science & Engineering Government Engineering College Hassan India Dept. of Computer Science & Engineering Jain Technological University Bengaluru India

The advancement of Information Technology has made cloud computing technology an innovative model for offering its consumers services on a rental basis at any time or location. Numerous firms converted to cloud technology by establishing new data centers because of the flexibility of cloud services. However, it has become necessary to ensure successful job execution and effective resource usage. Load balancing (LB) in cloud computing remains a complex challenge, specifically in the Infrastructure as a Service (IaaS) cloud architecture. A server being overloaded or underloaded is a problem that mustn’t happen in the process of cloud access because it would slow down processing or causes a system crash. Hence, to ignore these problems, a suitable resource schedule must be taken, so that system can load balance tasks over all accessible assists. This research suggests effective load-balancing approaches by analyzing the advantages, applications, and disadvantages of conventional LB techniques. The conclusions show that this research provides an exceptional path for researchers to overcome major drawbacks of existing LB techniques and achieves greater efficiency based on makespan, execution and response time, resource usage, efficiency, load balancing and throughput.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Environmental Sound Classification Using Feature Fusion of MFCCs, Mel-spectrogram, and Chroma

Environmental Sound Classification Using Feature Fusion of M...

引用

International Conference on computer and Information Technology (ICCIT)

作者： Mainul Islam Md. Nawab Yousuf Ali Dept. of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Environmental sound recognition enables automated systems to interpret and respond to diverse acoustic environments, enhancing applications in safety, monitoring, and smart technologies. However, recognizing environmental sounds is challenging due to their diverse types and complex acoustic properties, which encompass a wide range of natural and human-made sounds. To capture the subtleties in different sounds, a robust feature representation is crucial. In this study, we propose a feature fusion-based model for environmental sound classification. For an effective representation of the sounds, we combined three features: MFCCs, mel spectrogram, and chroma STFT. We experimented with several classification algorithms: SVM, LSTM, and BiLSTM. To assess the performance of our proposed model, we utilized the UrbanSound8k dataset. Our study suggests that integrating multiple features can significantly enhance the results compared to relying on individual features. The BiLSTM algorithm enabled our proposed model to achieve a remarkable accuracy of 93.81% on the UrbanSound8k dataset.

关键词： Support vector machines Accuracy Computational modeling Wildlife Bidirectional long short term memory Speech recognition Classification algorithms Speaker recognition Monitoring Spectrogram

来源：评论

学校读者我要写书评

暂无评论

Exploring the Efficacy of Deep Learning and Statistical Approaches in Temperature Forecasting 1

Exploring the Efficacy of Deep Learning and Statistical Appr...

引用

1st International Conference on Emerging Technologies and Innovation for Sustainability, EmergIN 2024

作者： Tarunkumar, K. Umesh, Aditya Humbarwadi, Maithri Sohan, B.S. Bhargavi, M.S. Dept. of Computer Science and Engineering Bangalore Institute of Technology Bengaluru India

ISBN: (纸本)9798350391268

Temperature forecasting has become increasingly critical due to the rapidly changing climate, especially in urban environments where temperature fluctuations impact public health, infrastructure, and overall quality of life. Accurately predicting temperature patterns is difficult owing to the complexity of climate systems and the increasing variability caused by global climate change. This study addresses the issue by incorporating key weather features to forecast temperatures. The study investigates conventional statistical models, such as ARIMA and SARIMA, and cutting-edge methodologies for deep learning, such as Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Transformers. Among these, the Transformer model stands out because it uses its attention mechanism to detect precise trends in longitudinal data, making it ideal for high-precision temperature forecasting. By enhancing the accuracy of these predictions, the system provides an optimal tool for mitigating the impacts of climate change, helping urban areas better prepare for and adapt to temperature fluctuations. The study highlights the importance of advanced forecasting models in addressing the growing challenge of climate variability, contributing to more effective climate adaptation strategies. © 2024 IEEE.

关键词： Global warming

来源：评论

学校读者我要写书评

暂无评论

Detection of Dark Web Threats Using Machine Learning and Image Processing

arXiv

引用

arXiv 2024年

作者： Medipelly, Swetha Abosata, Nasr Dept. of Computer Science and Engineering Northumbria University London United Kingdom

kThis paper aimed to discover the risks associated with the dark web and to detect the threats related to human trafficking using image processing with OpenCV and Python. Apart from that, a development environment was set up by installing TensorFlow, OpenCV and Python. Through exploratory data analysis (EDA), significant insights into the distribution and interactions of dataset features were obtained, which are crucial for evaluating various cyberthreats. The construction and evaluation of logistic regression and support vector machine (SVM) models revealed that the SVM model outperforms logistic regression in accuracy. The paper delves into the intricacies of data preprocessing, EDA, and model development, offering valuable insights into network protection and cyberthreat response. © 2024, CC BY-NC-ND.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

Enhancing Supply Chain Efficiency through IoT Integration: A Detailed Examination of the Chances and Difficulties

Enhancing Supply Chain Efficiency through IoT Integration: A...

引用

Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Chaitanya Mehra Shaifali Sharma Jayant Arora Aakanksha Sharma Pranaya Mahajan Aryan Manhotra Dept. of Computer Science Engineering Chandigarh University Mohali Punjab India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

The adoption of Internet of Things (IoT) technology in supply chain management has brought about noteworthy transformations, boosting productivity, visibility, and evidence-based decision-making. This research investigates the primary reasons for implementing IoT in contemporary supply chains, tackling issues such as limited real-time monitoring, suboptimal inventory control, and the necessity for preemptive risk management. The investigation delves into the capabilities of IoT-driven solutions, including sensor technology, RFID systems, and data analysis tools, to confront these obstacles by enabling constant surveillance and anticipatory maintenance, taking into account the impact of innovative technologies such as blockchain and artificial intelligence. The results underscore the considerable effect of IoT in establishing robust, flexible, and eco-friendly supply chains, providing valuable insights for businesses aiming to improve their operational effectiveness in an increasingly digital landscape.

关键词： Supply chain management Data security Supply chains Inventory control Blockchains Maintenance Internet of Things Artificial intelligence Radiofrequency identification Edge computing

来源：评论

学校读者我要写书评

暂无评论

Distinguish Between Obese and Normal Body Types Through Gait Analysis Using Classification Models

Distinguish Between Obese and Normal Body Types Through Gait...

引用

International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Jae-Sik Yi Ji Hun Han Min Jeong Kim Youn-Sik Hong Dept. of Computer Science and Engineering Incheon National University Incheon Korea

For gait analysis, an IMU sensor was mounted on the knee and gait related data was collected. Various gait parameters such as gait time, stance swing ratio, heel strike, and toe off can be extracted from the dataset. To explore the relationship between gait parameters and individual gait characteristics, we analyzed the gait patterns of normal and obese people were analyzed based on BMI (Body Mass Index). To apply it to a classification model of machine learning, different gait cycles between subjects were normalized. Gait data was collected from eight subjects in their 20s. Using this dataset, we applied a logistic regression model, and obtained the classification accuracy of 92%. We also investigated the correlation between BMI and gait parameters and found that, the correlation between BMI and cadence was -0.66.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Practical Challenges and Methodologies in Next Basket Recommendation (NBR)

Practical Challenges and Methodologies in Next Basket Recomm...

引用

IEEE International Conference on Electro-Information Technology

作者： Somayeh Zamani Johannes Zenkert Madjid Fathi Dept. of Electrical Engineering & Computer Science University of Siegen Siegen Germany

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

Recommendation systems are evolving rapidly, with increasing emphasis on deep and sophisticated model architectures to handle complex user-item interactions, resulting in larger models for better performance. However, the deployment of these models faces challenges due to high computing requirements and latency issues. To address this, a General Bidirectional framework (BiDiGen) is proposed, integrating user preferences and item similarities for personalized and context-aware recommendations. BiDiGen employs knowledge distillation to transfer pre-trained knowledge from large teacher models to smaller student models, striking a balance between efficiency and effectiveness. The framework utilizes a bidirectional approach where teachers and students mutually learn from each other, along with hypergraph construction to capture higher-order correlations efficiently. Additionally, a denoising generator is proposed for identifying relevant items, and a sampling scheme is suggested to determine knowledge transfer between teachers and students, ultimately training them with distillation and collaborative filtering losses.

关键词： Training Correlation Computational modeling Noise reduction computer architecture Generators Information technology

来源：评论

学校读者我要写书评

暂无评论

Neural Harvest : Advancements in Crop Yield Forecasting

Neural Harvest : Advancements in Crop Yield Forecasting

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： Srinivasa Reddy Seelam Charitha Sai Thivari Bhagath Parabattina Narendra Kumar G Sumanth K Dept. of Computer Science and Engineering Lakireddy Bali Reddy College of Engineering Mylavaram India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Crop yield forecasting involves predicting the amount of crops that a farmer can expect from their field. This prediction is based on several factors, including soil type and environmental conditions. It is a crucial issue for the farming industry, which is the backbone of any nation’s economy. Thanks to advancements in Artificial Intelligence, farmers can benefit from accurate crop yield estimations. This research study presents a Feed Forward Neural Network architecture that enables farmers to get accurate crop yield estimates and finally discusses about the implementation of the procedure and its outcome.

关键词： Computational modeling Neural networks Estimation computer architecture Production Soil Predictive models

来源：评论

学校读者我要写书评

暂无评论

Multivariate Analysis of Urban Air Pollution: Clustering and Patterns Across Major Asian Cities

Multivariate Analysis of Urban Air Pollution: Clustering and...

引用

Future Machine Learning and Data science (FMLDS), IEEE International Conference on

作者： Anika Rahman Mst. Taskia Khatun Dept. of Computer Science & Engineering Stamford University Bangladesh Dhaka Bangladesh

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391220

This study analyzes air pollution in Asian cities using the Global Air Pollution Data, consisting of 6,196 entries from 31 countries. Our primary goal is to identify pollution patterns through multivariate analysis and evaluate the effectiveness of six clustering algorithms: K-Means, Hierarchical Clustering, DBSCAN, Gaussian Mixture Models (GMM), Agglomerative Clustering, and Spectral Clustering. Performance was assessed using Silhouette Score, Davies-Bouldin Index, Calinski-Harabasz Index, WCSS, Cohesion, and Separation. The novelty of this work lies in the comparative analysis of these clustering methods on air pollution data, providing new insights into pollution dynamics across Asian cities. The analysis identified four distinct clusters- ‘High Pollution’, ‘Moderate Pollution’, ‘Ozone-Dominated Pollution’, and ‘Low Pollution’- with K-Means proving to be the most effective. Significant disparities were found, particularly in South and East Asia, where countries like India, China, and Pakistan exhibited the highest pollution levels. Additionally, an examination of capital cities revealed specific pollution patterns and the primary pollutants-PM2.5, NO2, CO, and Ozone-aiding in identifying sources and affected regions. These findings underscore the need for targeted regional pollution control strategies.

关键词： Gases Environmentally friendly manufacturing techniques Asia Urban areas Pollution control Clustering algorithms Transportation Air pollution Indexes Public healthcare

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：