检索结果-内蒙古大学图书馆

8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Tummala, Bindu Madhavi Prem Kumar, Mandarapu Deepak Nagurbasha, Shaik Dept. of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha School of Engineering Siddhartha Academy of Higher Education Deemed to be University Vijayawada India Dept. of CSE (Artificial Intelligence and Machine Learning) Velagapudi Ramakrishna Siddhartha School of Engineering Siddhartha Academy of Higher Education Deemed to be University Vijayawada India

ISBN: (纸本)9798331512248

Rban parking systems face critical challenges, including inefficient slot detection, traffic congestion, and security vulnerabilities like unauthorized access. This paper proposes an IoT-based smart parking system that integrates infrared (IR) sensors and RFID technology to enable real-time parking slot detection and enhanced security. The system employs IR sensors to monitor slot occupancy with a response time of < 2 seconds, relaying data via NodeMCU ESP8266 to a Blynk mobile app for real-time user updates. Simultaneously, an EM18 RFID reader authenticates users, preventing unauthorized entry. Experimental results demonstrate 95% detection accuracy and 1.2-second authentication latency, while scalability tests confirm viability for deployments in malls, hospitals, and other high-demand areas. By automating slot detection and access control, the system reduces manual intervention, optimizes parking efficiency, and addresses key urban mobility challenges. Future work includes integrating GPS for dynamic slot navigation and pre-booking *** parking systems face critical challenges, including inefficient slot detection, traffic congestion, and security vulnerabilities like unauthorized access. This paper proposes an IoT-based smart parking system that integrates infrared (IR) sensors and RFID technology to enable real-time parking slot detection and enhanced security. The system employs IR sensors to monitor slot occupancy with a response time of < 2 seconds, relaying data via NodeMCU ESP8266 to a Blynk mobile app for real-time user updates. Simultaneously, an EM18 RFID reader authenticates users, preventing unauthorized entry. Experimental results demonstrate 95% detection accuracy and 1.2-second authentication latency, while scalability tests confirm viability for deployments in malls, hospitals, and other high-demand areas. By automating slot detection and access control, the system reduces manual intervention, optimizes parking efficiency, and address

关键词： Authentication Blynk EM18 reader IR sensor NODEMCU ESP8266 urban mobility vehicle detection

来源：评论

学校读者我要写书评

暂无评论

An Efficient Multiband Infrared Small Objects Detection Approach for Low-Altitude artificial intelligence of Things

引用

IEEE Internet of Things Journal 2025年第12期12卷 20757-20772页

作者： Peng, Yuhuai Wang, Jing Wang, Wenqian Liu, Lei Atiquzzaman, Mohammed Guizani, Mohsen Dustdar, Schahram Northeastern University School of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Strategic Research Department Hangzhou310024 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China Xi’an University of Posts and Telecommunications Shanxi Key Laboratory of Information Communication Network and Security Shanxi Xi’an710121 China University of Oklahoma School of Computer Science NormanOK73019 United States Mohamed bin Zayed University of Artificial Intelligence Department of Machine Learning Abu Dhabi United Arab Emirates Technische Universität Wien Distributed Systems Group Vienna1040 Austria Universitat Pompeu Fabra Barcelona ICREA Barcelona08002 Spain

As a cutting-edge technology of low-altitude artificial intelligence of Things (AIoT), autonomous aerial vehicle object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of object detection is exacerbated by the high proportion of small and obscure objects in the captured images. To address the mentioned challenges, we present an efficient multiband infrared small object detection approach for low-altitude intelligent surveillance services. First, we propose the multiband infrared image fusion algorithm based on cascade-GAN (MIF-CGAN), which produces fused images with high information entropy and high contrast. Then, the Transformer-based multiscale dense small object detection (MsDSOD) algorithm is proposed. The algorithm consists of the global-local object detection (G-LOD) network, the object dense area extraction (O-DAE) module, and the weighted boxes fusion (WBF) module. It extracts small objects features at different scales from infrared images and fuses the global and local detection results to accurately identify small objects in dense scenes. Furthermore, compared to the traditional algorithms, the mean average precision (mAP) of MsDSOD is improved by 0.80% and the average precision in small object detection (APs) is improved by 0.72%. The proposed algorithm is optimally suited to deal with complex scenes with dense small objects and background occlusion. © 2014 IEEE.

关键词： Infrared imaging

来源：评论

学校读者我要写书评

暂无评论

GroundingMate: Aiding Object Grounding for Goal-Oriented Vision-and-Language Navigation

GroundingMate: Aiding Object Grounding for Goal-Oriented Vis...

引用

IEEE Workshop on Applications of computer Vision (WACV)

作者： Qianyi Liu Siqi Zhang Yanyuan Qiao Junyou Zhu Xiang Li Longteng Guo Qunbo Wang Xingjian He Qi Wu Jing Liu Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Department of Computer Science and Technology Tongji University Australian Institute for Machine Learning The University of Adelaide

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Goal-Oriented Vision-and-Language Navigation (VLN) aims to enable agents to navigate to specified locations and identify designated target objects following natural language instruction. This approach has gained popularity due to its close alignment with real-world scenarios. However, existing studies have predominantly focused on enhancing navigation performance, neglecting the ability to locate objects at the navigation endpoint. This oversight has resulted in a significant discrepancy between the success rates of navigation and object grounding. The challenge is compounded by the complex reasoning required by the instructions and the necessity to synthesize multiperspective images of objects, which overwhelms traditional object grounding methods. We leverage the Multi-Modal Large Language Model (MLLM) to bridge this gap, allowing agents to seek assistance from these models when struggling to locate the target object. The agent conducts a multi-stage evaluation to discern the cause of its confusion and promptly extracts and updates the most relevant information for MLLM to assess. Our method is plug-and-play and model-agnostic, facilitating integration with numerous existing VLN strategies without the need for retraining. Implementing our approach across four distinct methods has improved performance on the REVERIE and SOON datasets, demonstrating the effectiveness and generalizability of our technique.

关键词： Bridges computer vision Navigation Grounding Large language models Computational modeling Natural languages Cognition Data mining Object recognition

来源：评论

学校读者我要写书评

暂无评论

Energy-Proficient Cluster Enrichment in Wireless Sensor Networks via Categorized Fuzzy Clustering and Multi-Hop Routing Optimization

引用

SN computer science 2025年第1期6卷 1-12页

作者： Shakya, Harish Kumar Chandanan, Amit Kumar Subbalakshmi, Chatti Khambra, Geetanjli Ansari, Mohd Shahnawaz Kushwaha, Ajay Roy, Vandana Artificial Intelligence and Machine Learning Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science and Engineering Guru Ghasidas Vishwavidyalaya (A Central University) Chhattisgarh Bilaspur India Department of Computer Science and Engineerings Guru Nanak Instructions Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telagana State Hyderabad 501506 India Department of Computer Science and Application The Bhopal School of Social Sciences (BSSS) College M.P. Bhopal India Department of Computer Science and Engineering Eklavya University Damoh MP Damoh India Computer Science and Engineering Rungta College of Engineering and Technology Bhilai India Department of Electronics Communication Gyan Ganga Institute of Technology and Sciences MP Jabalpur India

The WSN's lifetime can be extended by practicing energy saving. By using the appropriate routing mechanism when executing clustering, it can be done successfully. For improved energy conservation, clustering, and routing work together. Clustering lowers duplicate data transmission, while superior routing enables energy-saving travel along the quickest route to the destination. Nodes can be grouped via clustering, and each group will have CH. CH provides data to the BS either directly or via the intermediary nodes. By reducing the workload of resource-limited nodes, clustering chooses CH from resource-rich nodes. The entire network needs to undergo re-clustering in the traditional WSN clustering approach. The suggested protocol creates an energy-efficient cluster head selection approach applying FIS in hierarchical WSNs, which in turn increases sensing network lifetime, power consumption rate, and network communication performance. The proposed FBUCA and Modified minimum spanning tree with fuzzy logic can efficiently route data packets. The load-balancing among clusters and decreased energy usage demonstrated by the proposed model in MATLAB R2016 simulations are key to increasing throughput and extending the network's lifetime. The proposed approach takes into account six of the aforementioned use scenarios. The experimental results confirmed the adaptability and sturdiness of the proposed method, showing that it works well with a large number of WSNs. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CH FBUCA Fuzzy logic WSN

来源：评论

学校读者我要写书评

暂无评论

Personality Detection Using Context Based Emotions in Cognitive Agents

引用

computers, Materials & Continua 2022年第3期70卷 4947-4964页

作者： Nouh Sabri Elmitwally Asma Kanwal Sagheer Abbas Muhammad A.Khan Muhammad Adnan Khan Munir Ahmad Saad Alanazi College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egyp t 3School of Computer Science National College of Business administration and EconomicsLahore54000Pakistan Department of Computer Science GC UniversityLahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13120Korea

Detection of personality using emotions is a research domain in artificial *** present,some agents can keep the human’s profile for interaction and adapts themselves according to their ***,the effective method for interaction is to detect the person’s personality by understanding the emotions and context of the *** idea behind adding personality in cognitive agents begins an attempt to maximize adaptability on the basis of *** our daily life,humans socially interact with each other by analyzing the emotions and context of interaction from audio or visual *** paper presents a conceptual personality model in cognitive agents that can determine personality and behavior based on some text input,using the context subjectivity of the given data and emotions obtained from a particular situation/*** proposed work consists of Jumbo Chatbot,which can chat with *** this social interaction,the chatbot predicts human personality by understanding the emotions and context of interactive ***,the Jumbo chatbot is using the BFI technique to interact with a *** accuracy of proposed work varies and improve through getting more experiences of interaction.

关键词： Emotions fuzzy personality detection contextual analysis semantic analysis

来源：评论

学校读者我要写书评

暂无评论

Discriminative Score Suppression for Weakly Supervised Video Anomaly Detection

Discriminative Score Suppression for Weakly Supervised Video...

引用

IEEE Workshop on Applications of computer Vision (WACV)

作者： Chen Xu Chunguo Li Hongjie Xing Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China School of Cyber Security and Computer Hebei University Baoding China

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Weakly supervised video anomaly detection (WSVAD) often relies on Multiple Instance learning (MIL). However, selecting only the most discriminative segments for training limits the model's ability to comprehensively detect anomalous events, particularly hard anomalies. To overcome this limitation, we propose the Discriminative Score Suppression (DSS) module. This module suppresses the discriminative scores of the most prominent anomalies, shifting the model's attention to less obvious but important hard anomalies. This approach guides the model to learn the critical features of hard anomalies, enabling a more comprehensive detection of anomalous events. Additionally, the Anomaly Score Refinement (ASR) module constructs a dissimilarity-based classifier by storing normal patterns as prototypes, and integrates this with a neural network classifier. By combining the anomaly scores from both classifiers, more accurate detection of true hard anomalies is achieved. A score-sensitive inner-bag loss function not only adjusts penalties based on anomaly scores but also ensures that the model avoids erroneous selections. Our method accurately detects various anomalies, including challenging and multi-segment anomalies, while minimizing false positives for normal events. Extensive experiments show that the proposed framework outperforms state-of-the-art methods on the UCF-Crime and XD-Violence datasets.

关键词： Training computer vision Accuracy Neural networks Prototypes Benchmark testing Feature extraction Robustness Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Big Data Solutions for Predictive Healthcare Analytics: Enhancing Patient Outcomes and Operational Efficiency 3

Big Data Solutions for Predictive Healthcare Analytics: Enha...

引用

3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Kumar, Nishi Kant Nandhini, J. Vinitha, S. Madhubala, P. Trimukhe, Dipmala Bhagwat Salunkhe, Kalpana Birla Institute of Technology Department of Computer Science and Engineering Mesra Off Campus Jharkhand Deoghar India Jai Shriram Engineering College Department of Ece Tirupur India Jerusalem College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Bharathiyar Institute of Engineering for Women Department of Computer Science and Engineering Salem India Government College of Engineering & Research Department of Instrumentation Avasari Khurd India School of Computational Sciences Jspm University Wagholi Pune India

ISBN: (纸本)9798331542375

The embracing of Big Data solutions in healthcare comes with widely prospect to enhance patient satisfaction and increase organizational effectiveness. The research aims to use Predictive techniques and models coupled with modern data integration systems to improve the health care services. The components of the suggested methodology are deep neural networks for patient monitoring and prognosis, wearable devices, and operational prognosis models for resources use. From the practice of this approach, we have noticed marked increase in the patient monitoring frequency, efficiency of timely intervention, and generalization of health care organization productivity factors such as wait time and other resource utilization. In addition, the Onshape plan carried measurable benefits for the patient results such as satisfaction, reduced mortality, improved chronic disease, and the lesser hospital readmission. The results emphasize that the opportunities that Big Data and predictive analytics offer for healthcare can be indeed immense - offering improved care and more effective outcomes at a lower total cost. This research focuses on how Big Data solutions can help create a better future of healthcare improving the clinical and operational activities in this sphere and, thus, the population's health. © 2025 IEEE.

关键词： Patient treatment

来源：评论

学校读者我要写书评

暂无评论

The Accurate Prediction and Forecasting Strategy Implementation for Various Weather Conditions using Different Algorithms 4

The Accurate Prediction and Forecasting Strategy Implementat...

引用

4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Sivapriyan, R. Vidya Prabha, C.V. Santhakumar, D. Rao, Kolli Himantha Mohammed, Safaa Hallol Abdtawfeeq, Tareq Hafdhi Sir Mvit R & D Centre Department of Eee Bengaluru India Karpagam College of Engineering Department of Computer Science and Engineering Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science Engineering Chennai India Institute of Cse Saveetha Engineering College Department of Artificial Intelligence and Machine Learning India National University of Science and Technology Collage of Pharmacy Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

this study presents a new regression method for forecasting the occurrence of rainfall. The method includes three main steps: feature extraction, classification, and preprocessing. In the feature extraction step, all weather samples are normalized for a uniform sampling frequency, after which signal noise is removed to improve data quality and each signal is then converted to a 1 × 34 feature vector, capturing subtle emotional nuances. Notably, normalization precedes classifier training to improve model performance. Performance metrics including f-score, sensitivity, precision, and accuracy are used to evaluate the efficiency of the model. The proposed methodology has the potential to advance our understanding of emotional responses to music, providing a promising avenue for future research in predicting rainfall and other natural phenomenon. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Financial Cloud Services: AI and Blockchain-driven Resource Allocation for Maximized Transaction Speed and Security 3

Revolutionizing Financial Cloud Services: AI and Blockchain-...

引用

3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Kumar, M. Lenin Amanullah, M. Krishnamurthy, Revatthy Suganthi, N. Mohana Kalaiselvan, S.A. Vignan Institute of Technology and Sciences Department of Electronics Communication and Engineering Telangana India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Chennai Tamil Nadu Avadi India

ISBN: (纸本)9798350369083

In response to the evolving challenges of real-time monitoring in financial cloud environments, we propose a comprehensive solution integrating cutting-edge technologies. Our system employs AI-driven decision-making (ADDM) with a Dynamic Nested Neural Network (DNN) for adaptive resource allocation. This is coupled with a Markov Decision Process (MDP) for probabilistic decision-making, optimizing computational power, storage, and network resources dynamically. Blockchain technology fortifies transaction verification through a decentralized consensus mechanism and cryptographic techniques like RSA. The collaborative synergy of optimized resource allocation and Blockchain security ensures near-instantaneous processing, meeting the demands of a dynamic financial landscape. Our proposed system transforms the financial cloud environment into an adaptive and intelligent system, enhancing efficiency, security, and responsiveness for seamless and reliable real-time transaction processing. The results demonstrate accelerated transaction speeds, resource efficiency, and a robust security framework, addressing critical concerns in the realm of financial transactions. © 2024 IEEE.

关键词： Decentralized finance

来源：评论

学校读者我要写书评

暂无评论

Wireless Capsule Endoscopy for Crohn's Disease Automated Image Classification using Deep learning 2

Wireless Capsule Endoscopy for Crohn's Disease Automated Ima...

引用

2nd IEEE International Conference on Integrated intelligence and Communication Systems, ICIICS 2024

作者： Nalayini, P. Jayasankari, S. Gunasundari, C. Elumalai, G. Kumar, Bharathi Ramesh Balamurugan, E. School of Computing Sastra Deemed to be University Thanjavur India School of Computing CSE Sastra Deemed to be University Thanjavur India School of Computing SRM Institute of Science and Technology Tiruchirappalli India Department of ECE Panimalar Engineering College Chennai India Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial intelligence and Machine Learning Bannari Amman Institute of Technology Sathyamangalam India

ISBN: (纸本)9798331504960

Despite the diagnosis of Crohn's Disease now transformed through wireless capsule endoscopy (WCE), traditional manual interpretation by gastroenterologists contains substantial restrictions such as high inter-observer inconsistency, time dedication, and potential oversight of important clinical signs. The paper proposes an automated image classification system founded on deep learning methods specifically, Convolutional Neural Network (CNN) for feature extraction and Long Short-Term Memory networks (LSTM) for temporal sequence examination. The technique applies Transfer learning to improve model performance with minimal labeled data, thus facilitating precise identification of CD-related abnormalities. Strong testing on an endoscopist-annotated dataset indicates the proposed system substantially surpasses existing systems about accuracy of 94.5%, sensitivity of 92.0%, and specificity of 96.0%. Promising enhanced patient outcomes in the therapy of Crohn's Disease, the automated method significantly decreases interpretation time, minimizes inter-observer inconsistency, and increases diagnostic accuracy. © 2024 IEEE.

关键词： Endoscopy

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：