检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Roy, Ayush Bhattacharjee, Anurag Oliva, Diego Ramos-Soto, Oscar Alvarez-Padilla, Francisco J. Sarkar, Ram Dept. of Electrical Engineering Jadavpur University Kolkata India Dept. of Computer Science and Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Universidad de Guadalajara CUCEI Guadalajara Mexico Dept. of Computer Science and Engineering Jadavpur University Kolkata India

Pneumonia is a respiratory infection caused by bacteria, fungi, or viruses. It affects many people, particularly those in developing or underdeveloped nations with high pollution levels, unhygienic living conditions, overcrowding, and insufficient medical infrastructure. Pneumonia can cause pleural effusion, where fluids fill the lungs, leading to respiratory difficulty. Early diagnosis is crucial to ensure effective treatment and increase survival rates. Chest X-ray imaging is the most commonly used method for diagnosing pneumonia. However, visual examination of chest X-rays can be difficult and subjective. In this study, we have developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We have used DenseNet-121 and ResNet50 as the backbone for the binary class (pneumonia and normal) and multi-class (bacterial pneumonia, viral pneumonia, and normal) classification tasks, respectively. We have also implemented a channel-specific spatial attention mechanism, called Fuzzy Channel Selective Spatial Attention Module (FCSSAM), to highlight the specific spatial regions of relevant channels while removing the irrelevant channels of the extracted features by the backbone. We evaluated the proposed approach on a publicly available chest X-ray dataset, using binary and multi-class classification setups. Our proposed method achieves accuracy rates of 97.15% and 79.79% for the binary and multi-class classification setups, respectively. The results of our proposed method are superior to state-of-the-art (SOTA) methods. The code of the proposed model will be available at: https://***/AyushRoy2001/FA-Net. © 2024, CC BY.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Intelligent Resource Allocation for RIS-Assisted Cell-Free Massive MIMO Systems 22

Intelligent Resource Allocation for RIS-Assisted Cell-Free M...

引用

22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Mollah, Gloria Haider, Majumder Rawat, Danda B. Ahmed, Imtiaz Howard University Dept. of Electrical Engineering & Computer Science WashingtonDC United States

ISBN: (纸本)9798331508050

Cell-free massive multiple-input multiple-output (mMIMO) systems distribute a large number of access points (APs) over a wide geographic region, improving coverage, capacity, and user experiences notably. Similarly, reconfigurable intelligent surface (RIS) allows a significant leap forward in signal quality and energy efficiency by adding additional flexibility and intelligent control over the propagation environment of wireless systems. However, optimal resource allocation in RIS-integrated cell-free mMIMO systems faces substantial challenges due to the presence of a large number of APs and passive reflecting elements (PREs) at RIS while simultaneously providing network coverage to aerial and ground users. To efficiently navigate the high-dimensional optimization space, this paper proposes a novel framework for optimal resource allocation in RIS-assisted cell-free mMIMO systems employing deep learning (DL) techniques. In particular, we train a deep neural network offline to optimize the transmit powers at APs and the phase shift of PREs at RIS. The trained model is tested online to perform real-time optimization with low computational complexity. Simulation results demonstrate the effectiveness of the proposed data-driven resource allocation scheme while comparing it with the state-of-the-art baseline scheme. © 2025 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Human Safety Through IoT-Integrated Smart Helmet Technology

Enhancing Human Safety Through IoT-Integrated Smart Helmet T...

引用

Expert Clouds and Applications (ICOECA), International Conference on

作者： Venkatesh K Harshitha Jodu Sree Kanthi P Yatheesh Kumar Reddy K Sumalatha R Dept. Computer Science and Engineering (Data Science) Srinivasa Ramanujan Institute of Technology Anantapur India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

The smart helmet's characteristics are meant to identify and report whether or not a wearer has consumed alcohol while donning a helmet. Road accidents are increasing as a result of riders drinking alcohol and not wearing helmets. In the modern world, thousands of people lose their lives in auto accidents each year. Wearing a smart helmet can typically lower the number of accidents. The primary objective of this research study is to create a wearable helmet that can both detect alcohol and prevent accidents without the need for a helmet. Whether the user is wearing a helmet is determined by the limit switch. The rider's breath is tested by the Gas sensor to determine whether alcohol is present. If the rider has ingested alcohol or is not wearing a helmet, the bike will not start. Only when a helmet is on and there are no indications of intoxication can the bike be started. System construction is done by sensor actions.

关键词： Cloud computing Head Road accidents Switches Alcoholic beverages Position measurement Safety

来源：评论

学校读者我要写书评

暂无评论

Spam Email Detection using K-Nearest Neighbors: An Enhanced Approach

Spam Email Detection using K-Nearest Neighbors: An Enhanced ...

引用

International Conference on ent Computing and Control Systems (ICICCS)

作者： Sumit Saklani Kalyan Thapa Devendra Singh Dept. of Computer Science & Engineering Graphic Era Hill University Dehradun India Dept. of Computer Application Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Spam emails represent a large portion of global email traffic and pose several risks, such as phishing, fraud, and malware attacks. Traditional spam filters are ineffective in combating spam that is ever-evolving, which makes the problems more complex in nature. Therefore, advanced machine learning techniques need to be adopted. This study attempts to integrate an optimized K-Nearest Neighbors (KNN) model for spam detection that implements Term Frequency-Inverse Document Frequency (TF-IDF) for text representation, along with Principal Component Analysis (PCA) for feature selection. Classification is performed with relevance to the neighbors with more distance using an adaptive distance weighting algorithm. Enhanced KNN techniques improve the metrics on Naive Bayes and Support Vector Machines (SVM) with respect to accuracy, precision, recall, and F1 score. Experimental results demonstrated the classification accuracy to amount 96.4 %, exceeds the existing systems. This study develops efficient spam detection systems having the capability of adapting novel spam methods.

关键词： Support vector machines Adaptation models Accuracy Computational modeling Unsolicited e-mail Heuristic algorithms Nearest neighbor methods Feature extraction Bayes methods Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Demystifying the Applications of Internet of Medical Things (IoMT) for smart health care system

Demystifying the Applications of Internet of Medical Things ...

引用

Data, Information and Computing science (CDICS), International Conference on

作者： Ruqqaiya Begum Polisetty Swetha Koti Tejasvi M. A. Jabbar Dept. of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Dept. of Computer Science and Engineering(AI&ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350382778

ISBN: (纸本)9798350382785

As Internet of Things (IoT) devices become more commonplace and are used in more aspects of daily life, a bright future for people, data, and processes is emerging. Because of its potential to enhance patient care as well as its capacity to deliver more trustworthy clinical data, boost productivity, and save costs, In recent years, the Internet of Medical Things (IoMT) has a strategic goal for future e-healthcare. It is understandable why so many healthcare organization’s nowadays want to take advantage of what the IoMT has to offer. Its infrastructure includes interconnected medical equipment, software, and care systems and services. For everyone to understand how IoMT is enhancing the health care system with the aid of other technologies, we have described the implementation of IoMT in detail in this paper. Along with this, we have also discussed the different security concerns and difficulties the healthcare system is experiencing due to the IoMT’s information-gathering shortcomings.

关键词： Productivity Biomedical equipment Costs Smart healthcare Internet of Medical Things software Security

来源：评论

学校读者我要写书评

暂无评论

FloraNet: An Approach to Prediction of Sea Buckthorn Disease based on a Convolutional Neural Network and a Random Forest Method

FloraNet: An Approach to Prediction of Sea Buckthorn Disease...

引用

Emerging Technology (INCET), International Conference for

作者： J. Pushpakumari Nuthanakanti Bhaskar G. Divya Murali Kanthi K. Srujan Raju Jonnadula Narasimharao Dept. of Computer Science and Engineering (AIML & IOT) VNRVJIET Hyderabad Telangana India Dept. of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Dept. of Information Technology CMR Technical Campus Hyderabad Telangana India Department of Computer Science & Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

In the current study, FloraNet was used to present an improved method for diagnosing leaf diseases in Sea Buckthorn. In the proposed model, Convolutional Neural Networks (CNN) and Random Forests (RF) are integrated. Essentially, CNN architecture is composed of a pair of convolutional layers, followed by a max-pooling layer; it is then followed by another pair of convolutional layers, before finishing off with a final pair of convolutional layers. Performance indicators such as Precision, Recall, F1-Score, or Accuracy are used to determine the performance of the model in terms of its performance. There were five disorders under investigation: Phakopsora, Erysiphe, Septoria, Colletotrichum, Phoma, as well as viral infections. Overall, the results show that the Micro Average accuracy for all classes is 95.11 %. According to the results, the individual accuracy ranges from 0.98 to 0.99 for each illness, while the F1-Scores range from 94.02% to 95.91% for each illness. This study concludes that the weighted average accuracy of 95.11 demonstrates a high level of proficiency of the model in identifying diseases, and indicates that the model is capable of identifying a wide variety of diseases. By using this integrated approach, agricultural scientists and researchers are able to detect Sea Buckthorn leaf diseases.

关键词： Measurement Accuracy Sea measurements Forestry Predictive models Solids Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Adaptive Weather Routing for Advanced Ship Navigation: A Comprehensive Review

Adaptive Weather Routing for Advanced Ship Navigation: A Com...

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Iyyappan Venkatesan Dharanish M.B S. Muthumari Lakshmi S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Maritime navigation offers a comprehensive approach to optimising ship navigation, in such a way that guarantees the safety and effectiveness of the journey through multiple techniques, like the adaptive weather routing technique, which uses the predictive model and the current weather conditions for efficient navigation. The review mainly emphasizes the basic principles and computational simulation for sustainable navigation in advancing the route defining algorithm. Discusses about the conventional route defining algorithms and dynamic route defining algorithm by adjusting for wind, waves, and currents. A comparison analysis shows how well different algorithms work in various weather conditions, and case studies show how they are used in the actual world, while new trends are being discussed, major problems also concern the need for accurate climate predictions, high-speed data processing, and an efficient response plan. The Advancements in artificial intelligence and composite path forecasting also address issues related to major needs, such as accurate weather forecasts, high-speed data processing, and effective reaction tactics. Through this insights of this review provides on future directions for adaptive weather routing research and development that the sustainable growth of intelligent naval navigation is supported.

关键词： Accuracy Reviews Heuristic algorithms Predictive models Routing Prediction algorithms Satellite navigation systems Real-time systems Safety Meteorology

来源：评论

学校读者我要写书评

暂无评论

LiDAR-Based Elderly Fall Detection System for Indoor Environments Using Neural Network Algorithms

LiDAR-Based Elderly Fall Detection System for Indoor Environ...

引用

Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Puthucode Ganesh Vignesh Nithin Kumar S Senthil Kumar T Jeyakumar G Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Fall detection systems are critical in elderly care and healthcare monitoring, but traditional camera-based solutions raise significant privacy concerns. This research presents a novel approach utilizing LiDAR (Light Detection and Ranging) sensors for fall detection in indoor environments, addressing privacy issues while maintaining high accuracy. We introduce LiFall, a new dataset captured using a Velodyne VLP 16 Puck LiDAR sensor, comprising 12 scenes and over 500 frames simulating various fall scenarios (including back falls, bed falls, and walking falls), contributing to the scarcity of LiDAR-based fall detection datasets. Our methodology employs two advanced deep learning architectures: Annularly Convolutional Neural Networks (ACNN) and Dynamic Graph Convolutional Neural Networks (DGCNN). Evaluating these models on both the ModelNet40 dataset and our custom LiFall dataset, results show DGCNN outperforms ACNN, achieving an accuracy of 84% on the ModelNet40 dataset with a 70:30 train-test split. On the LiFall dataset, despite challenges with dataset imbalance and achieving lower detection accuracy even after data augmentation, our approach demonstrates the potential of LiDAR-based fall detection systems. Compared to other fall detection methods, our LiDAR-based system offers significant advantages beyond privacy preservation, such as robust performance in low-light conditions and the ability to capture detailed 3D spatial information. While our current implementation is limited to laboratory settings, future work will focus on real-world deployments and further addressing dataset imbalance issues to enhance overall system performance. This research contributes to the field by providing a privacy-preserving, high-performance alternative to camera-based fall detection systems, paving the way for more acceptable monitoring solutions in sensitive indoor environments.

关键词： Privacy Accuracy Laser radar Three-dimensional displays Sensors Indoor environment Convolutional neural networks Fall detection Older adults Monitoring

来源：评论

学校读者我要写书评

暂无评论

Detection of Sybil Attacks in Vanet using Transformer With CNN Hybrid Architecture and Optimized by AdamW Optimizer

Detection of Sybil Attacks in Vanet using Transformer With C...

引用

Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Abishek M Benny Israel J Adlin Sheeba Sundarababu Maddu Dept. of Computer Science and Engineering St.Joseph’s Institute of Technology Chennai

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Vehicular Ad Hoc Networks (VANETs) help improve road safety by letting cars talk to each other but they still face Sybil attacks. In a Sybil attack, bad nodes claim many identities and weaken the network. This paper offers a Transformer-CNN model that uses the AdamW optimizer to spot Sybil attacks in VANETs. It uses spatial as well as time data from vehicular communications. The model works best at finding these attacks. The system is measured with metrics like accuracy, precision, and it shows big gains compared to older methods.

关键词： Adaptation models Accuracy Image edge detection Vehicular ad hoc networks Gain measurement Transformers Feature extraction Road safety Real-time systems Overfitting

来源：评论

学校读者我要写书评

暂无评论

Movie Genre Prediction Using AI-Generated Data and Natural Language Processing

Movie Genre Prediction Using AI-Generated Data and Natural L...

引用

International Conference on computer and Information Technology (ICCIT)

作者： Sifat Tanvir Ayesha Parpia Abontee Md. Abu Rayhan Md. Fuadur Rahman Tasnim Sultana Ajwa Ahmed Dept. of Computer Science and Engineering Brac University Dhaka Bangladesh Dept. of Computer Science Brac University Dhaka Bangladesh

Every movie belongs to one or more specific genre. In this work, a dataset was prepared consisting of AI-generated movie titles and plots of 10 different genres. Text vectorization was used to convert every single word of the titles and plots into vectors. These vectors were used to predict which genre each movie belongs to. For training, a sequential neural network model was built and used, and an accuracy of almost 80% was achieved.

关键词：

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：