检索结果-内蒙古大学图书馆

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Puri, Chetan Sharma, Mansi Reddy, K.T.V. Department of Computer Science and Design Wardha India Department of Artificial Intelligence and Data Science Wardha India

ISBN: (纸本)9798331523923

Lung cancer detection is the detection of tumors or cancerous cells in lung tissue. It is done using several medical imaging modalities, such as nuclear and genetic tests, magnetic resonance imaging (MRI), computed tomography (CT) scans, and X-rays. Detection of lung cancer at an early stage is very important as it increases the likelihood of successful treatment. For better diagnostic accuracy and patient outcomes, sophisticated detection methods now utilize regression models and machine learning algorithms. As one of the most common reasons for cancer fatalities globally, lung cancer highlights the urgent need for early and accurate diagnostic techniques. This research considers the use of regression-based strategies in lung cancer detection, suggesting their ability to improve diagnostic sensitivity and patient results. We created a strong predictive model that could effectively differentiate malignant nodules through sophisticated machine learning methods, such as support vector machines (SVM), decision trees, and linear regression. Regression analysis was used to assess how well benign and malignant lung lesions could be differentiated using a large clinical and medical imaging dastaset. Findings from research show that regression methods provide a sound method of enhancing early lung cancer detection, allowing for timely intervention and increased survival rates. The significance of machine learning in medical diagnosis is also illustrated through discussions on clinical implications and future research directions. The models that were tested, Random Forest had the best accuracy (94.6%), according to Stratified K-Fold cross-validation. The other models, including Gradient Boosting, Support Vector Classifier (SVC), and XGBoost, also showed high levels of accuracy, while the Multinomial Naïve Bayes model had the worst accuracy (75.7%). By reviewing clinical and imaging information and subjecting it to machine learning algorithms to identify patterns and associat

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Drug sales forecasting in the pharmaceutical market using deep neural network algorithms

International Journal of Systematic Innovation

引用

International Journal of Systematic Innovation 2024年第3期8卷 63-83页

作者： Qassrawi, Noura Azzeh, Mohammad Hijjawi, Mohammad Department of Computer Science Applied Science Private University Amman Jordan Department of Data Science Princess Sumaya University for Technology Amman Jordan

Drug sales and price forecasting have become an attractive investigation topic due to their important role in the pharmaceutical industry, A sales forecast helps every business to make better business decisions in overall business planning, budgeting, marketing, and risk management. The traditional forecasting method focuses on a conventional statistical model, which highly depends on the availability of historical sales data. However, for new drug entities, where not enough historical data is available, new methods of Machine Learning are applied. The aim of this paper is to identify an efficient Deep Neural Network algorithm suitable to forecast drug sales and pricing by applying Deep Neural Network Algorithms such as Multilayer Perceptron, Convolutional Neural Network, and Long Short-Term Memory, which are expected to perform well on this issue. The results are carried out to determine the efficiency of these algorithms by evaluating the performances of the models using MAE and RMSE performance metrics to identify the best algorithm for Drug Sales and Price Forecasting. The accepted accuracy should be more than 80\% of the actual value for quantity which is less than three thousand by unit and less than two dollars (USD) for price, Based on the results of the experiments Long Short Term Memory performed better than MLP and CNN for generating predictions with average Root Mean Square Error of for sales is 1.28(k) and Mean Absolute Error of about 0.85(k), and with average Root Mean Square Error for USD Prices is about 0.75, and Mean Absolute Error is about 0.44. The forecasts are then used to adjust stock levels according to the predictions. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Convolutional Neural Network Drug sales Forecasting Long Short-Term Memory Multilayer Perceptron

来源：评论

学校读者我要写书评

暂无评论

Leveraging XGBoost and Clinical Attributes for Heart Disease Prediction 5

Leveraging XGBoost and Clinical Attributes for Heart Disease...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Susmitha, Kota Bhargavi, Maridu Sri, Achyuta Mohitha Sai Reddy, Bogala Devi Prasaad Satyanarayana, Paladugu Siva Rao, K V Ranga Vignan's Foundation for Science Technology and Research Department of Computer Science and Engineering Guntur India Department of Computer Science and Engineering-Data Science Hyderabad India

ISBN: (纸本)9798331530013

Despite the various advances in medical technology, heart disease continues to rank among the leading causes of mortality in the world, killing millions each year. There is hope that the risks involved with heart disease can be reversed in time and the outcomes of patients improved with earlier prediction and diagnosis. However, the use of conventional methods of diagnostic techniques tends to be very resource-intensive and inaccessible to other more disadvantaged areas. The present work defines a model for machine learning in predicting heart disease and illustrates a more efficient and accessible solution. There is a risk for heart disease that is determined using a set of various clinical features. The algorithm used in the prediction of heart disease is XG Boost that are applied to the dataset. The method provides a reliable tool for the early detection of heart disease, thus helping healthcare providers to improve preventive care and treatment strategies. The results of this study will be helpful in advancing research to identify other avenues for prediction of heart disease. © 2024 IEEE.

关键词： early diagnosis Heart disease UCI (University California Irvine) Heart Disease dataset XG Boost

来源：评论

学校读者我要写书评

暂无评论

NeuroProbe: A Performance Profiling Feed Forward Neural Network Simulator for Hardware Interaction Analysis

引用

SN computer science 2025年第5期6卷 1-22页

作者： Ayyappan, Binu Kumar, G. Santhosh Department of Computer Science Cochin University of Science and Technology Kochi India Department of Artificial Intelligence and Data Science Rajagiri School of Engineering & Technology Kochi India

NeuroProbe is a simple neural network simulator designed by authors specifically for educational purposes focusing on simulating inference phase on a computationally capable embedded hardware, aiming to provide a deeper understanding of hardware interactions and performance improvement. This simulator incorporates performance profiling capabilities, allowing students and educators to analyze and optimize the performance of a simulated trained neural network. By simulating the intricate hardware interactions, NeuroProbe enables users to gain insights into how various components impact the overall performance of neural networks. Using this simulator, learners can experiment with different configurations and architectures, identify potential bottlenecks, and explore optimization strategies to enhance neural network performance before deploying the model on an Edge Computing environment. It also enriches computer science education by enabling learners to gain deeper insights into hardware interactions and performance improvement. By identifying performance bottlenecks, learners can refine their understanding of neural networks and develop skills in optimizing their models for enhanced performance. In conclusion, NeuroProbe empowers users to bridge the gap between theoretical knowledge and practical implementation of neural network on an embedded device, fostering a more comprehensive understanding of neural networks and their hardware requirements. Currently, it is focused solely on single-threaded CPU-based computation enabling SSE, AVX. At present, NeuroProbe does not support SIMD extensions such as AVX-FMA, AVX-512, multi-threaded computation, and accelerators such as NPU, TPU, and GPU. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hardware–software interactions Neural network simulator NeuroProbe Performance bottlenecks Performance profiling Simulations

来源：评论

学校读者我要写书评

暂无评论

Bi-clustering of Binary data via Neurodynamics-driven Binary Matrix Factorization 13

Bi-clustering of Binary Data via Neurodynamics-driven Binary...

引用

13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Li, Hongzong Wang, Jun Department of Computer Science City University of HongKong Kowloon Hong Kong Department of Computer Science Department of Data Science City University of HongKong Kowloon Hong Kong

ISBN: (纸本)9798331516147

Bi-clustering, also known as co-clustering, is a powerful data analysis technique that simultaneously clusters rows and columns of a data matrix, revealing hidden patterns. In this paper, we propose a neurodynamics-driven binary matrix factorization approach for bi-clustering binary data. The proposed method utilizes multiple discrete Hopfield networks operating concurrently to explore local optimal solutions. Furthermore, a particle swarm optimization rule is iteratively applied to reinitialize the neuronal states for directing the search toward better solutions. Comparative evaluations across six benchmark datasets reveal that the proposed method outperforms five existing methods in terms of five internal and external indices. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

Multi-Agent Deep Reinforcement Learning With Trajectory Prediction for Task Migration-Assisted Computation Offloading

引用

IEEE Transactions on Mobile Computing 2025年第7期24卷 5839-5856页

作者： Zhang, Xinyi Wang, Chunyang Zhu, Yanmin Cao, Jian Liu, Tong Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China East China Normal University School of Data Science and Engineering Shanghai200050 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Multi-access edge computing has become an effective paradigm to provide offloading services for computation-intensive and delay-sensitive tasks on vehicles. However, high mobility of vehicles usually incurs spatio-temporal load-imbalances among edge servers. Therefore, task migration is employed to maintain dynamic workload balancing by transmitting excessive tasks from overloaded to underloaded servers. Recent studies adopt deep reinforcement learning approaches to generate offloading and migration decisions based on current observations of systems. However, we argue that the migration direction is highly dependent on vehicular movements, and task migration towards the wrong direction could lead to additional delays. Therefore, we emphasize the importance of guiding task migration via exploring prospective trajectories of vehicles. We propose a Mobility-Aware Cooperative Multi-Agent (MCMA) deep reinforcement learning approach to make vehicle-by-vehicle decisions in multi-edge computation offloading scenarios. A two-stage decision framework is designed to solve the joint optimization problem of computation offloading and resource allocation. Additionally, an Informer-based multi-step vehicular trajectory prediction module is incorporated to enhance the capability of forecasting vehicular movements. Extensive experiments and analysis are conducted on synthetic and realistic scenarios, showing that our approach consistently outperforms both heuristic and DRL-based methods. The simulation scenarios and source codes are publicly available here. © 2002-2012 IEEE.

关键词： Computation offloading

来源：评论

学校读者我要写书评

暂无评论

Ordinal classification for knee osteoarthritis x-rays using vision transformers

引用

Multimedia Tools and Applications 2025年 1-28页

作者： Tariq, Tayyaba Suhail, Zobia Nawaz, Zubair Department of Computer Science University of the Punjab Allama Iqbal Campus Punjab Lahore54000 Pakistan Department of Data Science University of the Punjab Allama Iqbal Campus Punjab Lahore54000 Pakistan

Knee Osteoarthritis is a progressive and chronic knee joint disease that shows symptoms of pain, stiffness, and swelling and is diagnosed by knee radiographs. These radiographs are evaluated by radiologists using Kellgren and Lawrence’s (KL) grading scheme. According to this scheme, the knee radiograph is graded from KL grade 0 (i.e., Normal) to KL grade 4 (i.e., Severe) to assess the KOA severity. For an automated system, the first step is to localize knee joints from knee radiographs and then classify them according to the KL grading system. In this work, we have proposed vision-based transformers to localize knee joints. We also proposed a single-stage model to localize the knee and classify KOA using a single model. We also explored and compared the performance of various transformers using a multiclass image classification strategy. Since KL grades maintain intrinsic ordinal information of KL grades, we employed an ordinal classification-based strategy to improve automated knee osteoarthritis classification performance. A total of 10 models are trained and validated on multiple datasets, and visualizations are generated for the best model for better comprehension. Our model obtained state-of-the-art results for knee detection and ordinal X-ray classification for KOA. Analysis of Variance (ANOVA) tests also indicate that the ordinal classification approach significantly improves the performance of models over traditional image classification approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Arthroplasty

来源：评论

学校读者我要写书评

暂无评论

Interpretable Machine Learning in Leveraging Customer Information for Success Direct Marketing Campaigns 10

Interpretable Machine Learning in Leveraging Customer Inform...

引用

10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Octavia Tjandra, Clarissa Shirley Cam, Grace Agung Santoso Gunawan, Alexander Eka Setiawan, Karli School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363104

A bank's marketing campaign execution is very important. Undoubtedly, a well-crafted marketing plan will contribute to the bank's increased revenue. Marketing is crucial because it can be used to connect with consumers and inform them of the advantages of making a deposit with the bank, which will increase the number of deposits the bank receives. Thus, the goal of this research is to determine the best customer information that affects their decision on subscribing a term deposit and providing the best model for predicting the outcome of future marketing campaign. By determining the significance of each feature, this research constructed machine learning models using methods like Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Logistic Regression (LR) as our research contribution. According to the research's findings, out of the three machine learning models, Random Forest (RF) has the highest accuracy scores. Additionally, the feature importance model reveals that a customer's decision to make a deposit or not is greatly influenced by factors such as the age of the customer, how much does the customer has in their bank account, did the bank have contacts with longer call durations, and did it has a positive outcomes on the previous marketing campaigns. The customers who are contacted more frequently and have subscribed before are more likely to subscribe in the future marketing campaign. © 2024 IEEE.

关键词： bank marketing classification direct marketing machine learning predictive analysis

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Imbalanced data in Telecom Churn Classification

Machine Learning for Imbalanced Data in Telecom Churn Classi...

引用

2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Dewi, Olivia Intan Permata Santiko, Vina Nathania Safa, Snow White Putri Gunawan, Alexander Agung Santoso Setiawan, Karli Eka Bina Nusantara University School of Computer Science Data Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350376210

In the highly competitive telecommunications industry, customer churn poses a significant threat to revenue and long-term sustainability. To proactively address this challenge, machine learning (ML) techniques offer promising solutions for predicting and mitigating customer churn. This study applies ML algorithms to churn prediction, focusing on the challenges of imbalanced data. Using a publicly available Telecom dataset from Kaggle, the data was meticulously prepared through a multi-stage process, including Random Under-Sampling (RUS) to address class imbalance. The study employed a range of ML algorithms, evaluating their performance using key metrics such as recall, precision, F1score, and accuracy. The findings reveal valuable insights into how RUS can enhance model performance by improving the prediction of the minority class. XGBoost outperformed Logistic Regression and Random Forest in accurately identifying positive outcomes, suggesting its effectiveness in recognizing true instances of customer churn. The confusion matrix supports this observation, showing that the recall value produced by XGBoost slightly exceeded those for Logistic Regression and Random Forest. © 2024 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Image-Based Natural Disaster Classification with Deep Learning: A Comparative Analysis

Image-Based Natural Disaster Classification with Deep Learni...

引用

2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Alamsyah, Samuel Eka Djamhari, Simen Ferdinand Sudjono, Salomo Hendrian Gunawan, Alexander A.S. Setiawan, Karli Eka Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350353464

Natural disasters, including earthquakes, cyclones, floods, and wildfires, cause significant environmental damage and have emerged as a major global issue. These events can result in loss of life and disrupt communities and economy. Effective disaster management relies on timely and accurate information. With social media becoming a primary source of real-time information during such events, many images of these disasters are shared, though some may not be accurately labeled. Therefore, the purpose of this study is classifying natural disaster images, mainly cyclone, wildfire, flood, earthquake from social media images. This paper is to conduct a comparative analysis over 4 models namely VGG16, VGG19, EfficientNetB0, and ResNet-50. This paper uses a dataset that contains 4428 human-annotated images including cyclones, earthquakes, floods, and wildfires. Results concluded that ResNet-50 and EfficientNetB0 give the best result with both got over 96% average accuracy. ResNet-50 delivers more stable and more convergent results compared to EfficientNetB0, but it delivers slightly less classification accuracy. VGG16 and VGG19 are both outclassed in terms of accuracy, but better in training time, with VGG19 having a more convergent result. ©2024 IEEE.

关键词： Convolutional neural networks

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：