检索结果-内蒙古大学图书馆

Mobile Computing and Sustainable informatics (ICMCSI), International Conference on

作者： U. Sesadri Shaik Jumlesha S.Hrushikesava Raju S. Adinarayna Nabanita Choudhury Vijaya Chandra Jadala Department of CSE Vardhaman College of Engineering Hyderabad Telangana Department of CSE Annamacharya Institute of Technology and Science Tirupati Chittoor(dt) AP Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Department of Computer Science and Systems Engineering College of Engineering Andhra University Visakhapatnam Faculty of Computer Technology Assam down town University Guwahati Assam School of Computer Science and Artificial Intelligence SR University Hanumakonda Warangal Telangana India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Nowadays, the risk estimators are to be applied based on the population characteristics of their country, which is termed as race attribute. There were specific tools to determine the risk of cardiovascular disease. Among many diseases, the mortality rate of the people is increasing due to a lack of quality lifestyle. In order to provide a guide to the individual based on their specific characteristics and lifestyle, would classify the risk into specific categories like severe (High), Moderate, and normal(Low) with pre-defined threshold constraints. The risk assessment estimators alert the individual to control certain modifiable attributes irrespective of their non-modifiable elements. The integration of random forests and intelligent rule mining would generate the CVD risk prediction. Efficiency as well as interpretability are important in this competitive hybrid model. The ultimate benefits assured are extending the lives of the patients by knowing the importance of daily work, habits, and their control food, presenting a powerful opportunity by practicing better personalization monitoring and improved patient care, and improved informed decisions for immediate action. The early prevention initiation by identifying the risk severity optimizes follow-up of better practices and treatment styles, resulting in improved decision making for better health care of the individual.

关键词： Measurement Calculators Prevention and mitigation Mortality Medical services Cardiovascular diseases informatics Random forests Monitoring Mobile computing

来源：评论

学校读者我要写书评

暂无评论

Fruit Ripeness Detection using RipeNet

Fruit Ripeness Detection using RipeNet

引用

Electronics and Renewable systems (ICEARS), International Conference on

作者： Kapa Bhavana R. Sumathi Kandregula Vanaja S Manjula T Srinivasulu Siripuri Divya Department of Computer Science and Engineering Kalasalingam Academy of Research and Education India Department of Computer Science and Business Systems Nehru Institute of Engineering and Technology Coimbatore Tamilnadu India Department of Information Technology Aditya College of Engineering and Technology(A) Surampalem Andhra Pradesh India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad Telangana India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Fruits are absolutely delicious for the most part, but more importantly, they are good for healthy life. Fruits are nature's candy and offer all sorts of health benefits besides the great taste. They provide necessary nutrients to the body so that it will not be susceptible to disease or other ailments. Fruits contain enough fiber to keep our digestive system in top health form. Fruits also helps to regulate the water level in body as well as your blood sugar levels, which means that will usually feel hungry less often if we consumes a lot of fruits. The easier it is for people to recognize that a fruit is ripe and ready for selling at the market allows the farmers, in some cases, to sell their crop early before it would normally spoil. If we look closely, we see that many times people don’t buy something because it appears rotten or way past its peak ripeness, which demonstrates how products spoil right on time. This study proposed a user-convenient system for fruit ripeness detection using the convolutional neural network (CNN) based on RipeNet structure.

关键词： Measurement Renewable energy sources Accuracy Digestive system Crops Optical fiber networks Nutrients Real-time systems Convolutional neural networks Diseases

来源：评论

学校读者我要写书评

暂无评论

Lightweight AI and Blockchain Optimization for Enhancing Consumer Electronics Decision-Making

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Byeon, Haewon Alsaadi, Mahmood Keshta, Ismail Ahanger, Tariq Ahamed Safarova, Nodira Aldawsari, Hamad Cascone, Lucia Shabaz, Mohammad Korea University of Technology and Education Convergence Department Cheonan Korea Republic of University of Al Maarif College of Sciences Department of Computer Sciences Al Anbar31001 Iraq AlMaarefa University College of Applied Sciences Computer Science and Information Systems Department Riyadh Saudi Arabia CoBA Prince Sattam Bin Abdulaziz University Department of Management Information Systems Saudi Arabia Tashkent State University of Economics Department of Finance and Financial technologies Tashkent Uzbekistan University of Tabuk Department of Computer Science Haql University College Saudi Arabia University of Salerno Dept. of Computer Science Fisciano Italy Model Institute of Engineering and Technology J and K Jammu India

Under the "dual carbon" background, consumerelectronics consumption has become deeply ingrained in people'sminds. However, consumers often distrust the sustainabilityclaims of consumer electronics products. Artificial intelligence(AI) and blockchain technology can address this trust deficitthrough transparency and traceability mechanisms. This studyintegrates blockchain technology into traditional consumerelectronics supply chains, considering consumers' preferences andtrust in these products. An AI-based game model is proposed toanalyze the interactions among supply chain members before andafter implementing blockchain technology, under varying EdgeComputing-based power structures. This model quantitativelyevaluates emission reduction and pricing strategies, aiming tooptimize consumer surplus and total social welfare. By leveragingLightweight AI and blockchain, smart wholesale and cost-sharingcontracts are designed to establish reasonable ranges for wholesaleprices and optimal cost-sharing ratios, enhancing enterpriseoperational efficiency and achieving supply chain *** demonstrate that when consumers exhibit a strongerpreference for consumer electronics products, the adoption ofLightweight AI and blockchain delivers greater benefits across thesupply chain. Furthermore, as consumer willingness to purchasethese products increases, the advantages become morepronounced. Numerical analysis highlights that smart contractscan better coordinate the supply chain, particularly in retailer-dominated scenarios. Finally, empirical cases validate theeffectiveness of the proposed strategies and models. © 1975-2011 IEEE.

关键词： Supply chain management

来源：评论

学校读者我要写书评

暂无评论

Secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks

引用

Alexandria Engineering Journal 2025年 128卷 127-143页

作者： Islam, Shayla Topazal, S.M. Hasan, Mohammad Kamrul Kolandaisamy, Raenu AL Abbas, Huda Saleh Vo, Nguyen Thakkar, Rahul Khan, Muhammad Attique Gupta, Deepak AlHammadi, Dina Abdulaziz Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Center for Cyber Security Faculty of Information Science and Technology the National University of Malaysia Selangor Bangi43600 Malaysia Department of computer science College of Computer Science and Engineering Taibah University Madina Saudi Arabia Victorian Institute of Technology Melbourne Australia Center of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Maharaja Agrasen Institute of Technology Delhi India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O.Box 84428 Riyadh11671 Saudi Arabia

The emergence of 5 G and 6 G networks promises significant advancements in the Internet of Medical Things (IoMT) by enabling real-time medical data transmission and continuous connectivity for mobile healthcare devices. However, traditional Handover (HO) mechanisms face latency, security, energy consumption, and scalability challenges when supporting fast-moving and dense IoMT environments. This paper presents a secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks, incorporating a predictive algorithm, Blockchain-Based (BC-B) decentralized authentication mechanism, and AI-driven anomaly detection. The predictive algorithm anticipates device movement and pre-allocates resources at the target Base Station (BS), reducing HO latency by 20–30 % in 5 G and up to 40 % in 6 G. Meanwhile, blockchain-based authentication ensures tamper-proof and decentralized security, reducing vulnerability to cyberattacks by 90 % compared to traditional methods. Additionally, the energy consumption during HO events is reduced by 20–35 %, dropping from 15 mJ in traditional schemes to 12 mJ in 5 G and further to 10 mJ in 6 G. The scheme also incorporates AI-driven anomaly detection, achieving an accuracy of 96.7 %, ensuring proactive identification and mitigation of potential security threats. The analyzed scheme maintains consistent throughput with less than 5 % reduction and scales effectively for up to 300 devices per cell. The simulation results demonstrate significant improvements across key performance metrics, including latency, security, energy efficiency, and scalability, making this approach highly suitable for critical IoMT applications such as telesurgery, remote patient monitoring, and innovative healthcare environments. These advancements position the analyzed framework as a cornerstone for next-generation (NG) healthcare networks. © 2025 The Authors

关键词： Remote patient monitoring

来源：评论

学校读者我要写书评

暂无评论

VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models

引用

Procedia computer science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Independent Researcher

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.

关键词： Artificial intelligence computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

来源：评论

学校读者我要写书评

暂无评论

Loss Functions in Deep Learning: A Comprehensive Review

arXiv

引用

arXiv 2025年

作者： Elharrouss, Omar Mahmood, Yasir Bechqito, Yassine Serhani, Mohamed Adel Badidi, Elarbi Riffi, Jamal Tairi, Hamid Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Informatics Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University Fez Morocco

Loss functions are at the heart of deep learning, shaping how models learn and perform across diverse tasks. They are used to quantify the difference between predicted outputs and ground truth labels, guiding the optimization process to minimize errors. Selecting the right loss function is critical, as it directly impacts model convergence, generalization, and overall performance across various applications, from computer vision to time series forecasting. This paper presents a comprehensive review of loss functions, covering fundamental metrics like Mean Squared Error and Cross-Entropy to advanced functions such as Adversarial and Diffusion losses. We explore their mathematical foundations, impact on model training, and strategic selection for various applications, including computer vision (Discriminative and generative), tabular data prediction, and time series forecasting. For each of these categories, we discuss the most used loss functions in the recent advancements of deep learning techniques. Also, this review explore the historical evolution, computational efficiency, and ongoing challenges in loss function design, underlining the need for more adaptive and robust solutions. Emphasis is placed on complex scenarios involving multi-modal data, class imbalances, and real-world constraints. Finally, we identify key future directions, advocating for loss functions that enhance interpretability, scalability, and generalization, leading to more effective and resilient deep learning models. © 2025, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Probabilistic Health Monitoring and Maintenance Scheduling for Nuclear Power systems

Probabilistic Health Monitoring and Maintenance Scheduling f...

引用

AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Amer, Ahmad Tsilifis, Panagiotis Saxena, Abhinav Baglietto, Emilio Khan, Genghis Probabilistic Design & Material Informatics GE Aerospace Research Niskayuna NY12309 United States Robotics AI & Computer Vision GE Vernova Advanced Research Niskayuna NY12309 United States AI & Computer Vision GE Aerospace Research San RamonCA94583 United States Department of Nuclear Science & Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Sustainable Systems GE Aerospace Research Niskayuna NY12309 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Operations and Maintenance (O&M) cost optimization in the nuclear energy industry is an imperative task for developing sustainable systems and efficient renewable technologies. We present a modular probabilistic framework for addressing maintenance scheduling challenges for small modular nuclear reactors using a Dynamic Bayesian Network (DBN) approach that monitors fatigue and performance in multiple subsystems of the reactors and provides a probabilistic assessment for potential failure events. The DBN framework fuses information from potential digital twins that assimilate real time data from several sensors with different time granularities in order to monitor and update thecurrentstate of knowledge aboutthestatus of several fatigue or performance criteria. Comprised of multi-modal digital twins (empirical, data- and physics-based twins), this DBN serves as a predictive framework forecasting the future state of specific reactor subsystems and enhancing decision-making capabilities associated with preventing system failure and minimizing the time and cost of maintenance events. The work presented in this paper is a holistic approach for monitoring and safeguarding complex, multi-component engineering systems where maintenance scheduling is critical and does not restrict itself to nuclear environments. However, our goal is to illustrate how such a framework can be adjusted to small modular water reactors. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Nuclear energy

来源：评论

学校读者我要写书评

暂无评论

Handling Imbalanced Data for Credit Card Fraudulent Detection: A Machine Learning Approach 1st

Handling Imbalanced Data for Credit Card Fraudulent Detectio...

引用

1st International Conference on Advances in Artificial Intelligence and Machine Learning in Big Data Processing, AAIMB 2023

作者： Sujatha, E. Umarani, V. Rekha, K.S. Gopirajan, P.V. Manickavasagan, V. Sriperumbudur India Department of Computational Intelligence SRM Institute of Science and Technology Chennai India Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai India

ISBN: (纸本)9783031730672

Imbalanced data is crucial for contemporary machine learning models. Evaluating the effectiveness of machine learning classifiers under an unfair dataset is problematic. Several resampling techniques were applied to the unwarranted dataset and pre-processing stages to increase the accuracy of prognosis classifiers. This proposed work discusses the basics of resampling techniques such as Random under-sampling, Random oversampling, SMOTE (Synthetic Minority Over-sampling Technique), SMOTE-ENN (Synthetic minority over-sampling technique and Edited nearest neighbor), ADASYN (Adaptive Synthetic), and SMOTE-Tomek to handle this Weakness with unbalanced data. The proposed system uses an unstable credit card fraudulent dataset collected from Kaggle, which contains 21 columns and 28382 rows, of which 18497 negative samples and 4208 positive samples. The balanced dataset got by applying resampling methods SMOTE, SMOTE-ENN, ADASYN, and SMOTE-Tomek. Afterward, it uses machine learning techniques such as Logistic Regression (LR), Decision tree (DT), and Random Forest (RF) for classification. From this analysis, it selects the optimal scheme and classifier. The grey wolf optimization technique improves the classifier’s performance, which optimizes the samples after sampling. This hybrid method’s effectiveness was evaluated with standard evaluation parameters. The hybrid Random Forest technique achieved 92% accuracy from this analysis compared to hybrid models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Relip: Reliable in-Band Parallel Communication for Magnetic MIMO Wireless Power Transfer System

引用

IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xinyu Zhou, Wangqiu Zhou, Hao Jiang, Shenyao Liu, Zhi Wang, Xiaoyan Ji, Yusheng Song, Qi City University of Hong Kong Department of Computer Science Hong Kong University of Science and Technology of China School of Computer Science and Technology Hefei230027 China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan Ibaraki University Graduate School of Science and Engineering Ibaraki310-8512 Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-0003 Japan

In magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems, receiver (RX) feedback communication is promising to enhance the capability and efficiency of the system. Although some studies have explored in-band implementations with low overhead costs, it has not been comprehensively investigated. In this paper, we propose Relip, a Reliable layer-level in-band parallel feedback communication mechanism for MIMO MRC-WPT systems, which addresses the impact of RX-RX couplings (i.e., non-negligible interference from strong couplings and positive effects of relay phenomenon), and provides a theoretical analysis of communication reliability. Technically, we first devise an On-Off based two-phase modulation mechanism to achieve RX identification and dependency detection under relay phenomenon. Then, we utilize observed channel decomposability to collect group-level power transfer channel conditions for eliminating the interference caused by strong RX-RX couplings. Furthermore, we perform RX selection to optimize the trade-off between communication reliability and time overhead. We design and implement the Relip prototype and conduct extensive experiments. The results validate the effectiveness of our mechanism, i.e., Relip can provide ≥99% average decoding accuracy for concurrent feedback communication of 14 devices, achieving an 18.31% improvement compared to the state-of-the-art solution. © 2002-2012 IEEE.

关键词： Inductive power transmission

来源：评论

学校读者我要写书评

暂无评论

AI Based Adaptive QoS Optimization for IoT Devices in Healthcare Networks

AI Based Adaptive QoS Optimization for IoT Devices in Health...

引用

Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： S. Karthika K. Juliana Gnanaselvi K. Palanisamy S. Umamaheswari Department of Computer Science& Business Systems Dr. N. G. P Institute of Technology Coimbatore India Department of Computer Science Rathinam College of Arts and Science Coimbatore India Department of CSE Karpagam Academy of Higher Education Coimbatore India Department of ECE Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This paper proposes an artificial intelligence-based adaptive Quality of Service (QoS) optimization framework for Internet of Things (IoT) devices in healthcare networks. IoT devices, such wearable monitoring and remote diagnostic tools, struggle to maintain optimal performance due to fluctuating network circumstances and hardware limitations. The suggested system uses machine learning and deep learning techniques to dynamically update QoS settings in order to achieve consistent, low-latency, and energy-efficient data transport. The AI model forecasts congestion, distributes resources optimally, and enhances network performance by continuously learning from real-time data and network conditions. This customisable solution improves healthcare delivery by providing timely, continuous services, resulting in better patient outcomes and ensuring the long-term viability of IoT healthcare networks. The framework's adaptive nature addresses scalability and flexibility challenges in healthcare IoT systems by constantly learning and optimising performance as network conditions change. The simulation results demonstrate how well the proposed solution works to improve QoS, ensure seamless healthcare delivery, and improve patient outcomes in dynamic network situations. This study paves the way for AI-powered healthcare IoT networks that are more reliable, responsive, and intelligent.

关键词： Performance evaluation Adaptive systems Medical services Quality of service Throughput Real-time systems Pattern recognition Internet of Things Convolutional neural networks Optimization

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：