检索结果-内蒙古大学图书馆

Towards Safer Roads: A Comprehensive Review of Object Detection Techniques for Autonomous Vehicles

SN computer science 2025年第5期6卷 1-20页

作者： Parisapogu, Samson Anosh Babu Narla, Nitya Juryala, Aarthi Ramavath, Siddhu Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amaravati Campus Andhra Pradesh Amaravati 522 503 India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India

Nowadays, people spend a lot of time in travel, and road safety has become an important area of research. As a result, self-driving cars have been gaining popularity in recent years because they have a lot of benefits in terms of reducing traffic congestion and improving road safety, thereby reducing accidents. They also have an environmental benefit as they reduce travel times and hence reduce the usage of fuel. Though a lot of research has already been done in this field, there are always new problems being created and new solutions being found. Apart from self-driving cars, automated navigation also has a huge number of applications. Therefore, it is important to stay up to date about the latest developments in this field. This research is a comprehensive survey that explores and evaluates the different object-detecting algorithms that have been used throughout the years, their advantages, disadvantages, and trade-offs. It also analyses the datasets and benchmarks that have been used for evaluating the models. It aims to identify the gaps in research in this growing field so that it can act as a roadmap for researchers to focus their study on. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning Object detection Road safety Self-driving YOLO

来源：评论

学校读者我要写书评

暂无评论

Unleashing the Power of Knowledge Graph for Recommendation via Invariant Learning 24

Unleashing the Power of Knowledge Graph for Recommendation v...

引用

33rd ACM Web Conference, WWW 2024

作者： Wang, Shuyao Sui, Yongduo Wang, Chao Xiong, Hui School of Data Science University of Science and Technology of China China Hong Kong The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou Hkust Fok Ying Tung Research Institute Hong Kong

ISBN: (纸本)9798400701719

Knowledge graph (KG) demonstrates substantial potential for enhancing the performance of recommender systems. Due to its rich semantic content and associations among interactive entities, it can effectively alleviate inherent limitations in collaborative filtering (CF), such as data sparsity or cold-start issues. However, most existing knowledge-aware recommendation models indiscriminately aggregate all information in KG, without considering information specifically relevant to the recommendation task. Such indiscriminate aggregation could introduce additional noisy knowledge into representation learning, which can distort the understanding of users' genuine preferences, thereby sacrificing the recommendation quality. In this paper, we introduce the principle of invariance to the knowledge-aware recommendation, culminating in our Knowledge Graph Invariant Learning (KGIL) framework. It aims to discern and harness the task-relevant knowledge connections within KG to enhance the recommendation models. Specifically, we employ multiple environment generators to simulate diverse noisy KG-environments. Then we devise a novel attention learning mechanism for KG and user-item interaction graph, aiming to learn environment-invariant subgraphs. Leveraging an adversarial optimization strategy, we enhance the diversity of the environments, meanwhile, promote invariant representation learning across environments. We conduct extensive experiments on three datasets and compare KGIL with state-of-the-art methods. The experimental results further demonstrate the superiority of our approach. © 2024 ACM.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

OUT-OF-DOMAIN UNLABELED data IMPROVES GENERALIZATION 12

OUT-OF-DOMAIN UNLABELED DATA IMPROVES GENERALIZATION

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Saberi, Amir Hossein Movasaghinia, Mohammad Hosein Najafi, Amir Motahari, Seyed Abolfazl Heidari, Alireza Khalaj, Babak H. Department of Electrical Engineering Department of Computer Engineering Sharif Center for Information Systems and Data Science Iran Sharif Institute for Convergence Science & Technology Sharif University of Technology Tehran Iran

We propose a novel framework for incorporating unlabeled data into semi-supervised classification problems, where scenarios involving the minimization of either i) adversarially robust or ii) non-robust loss functions have been considered. Notably, we allow the unlabeled samples to deviate slightly (in total variation sense) from the in-domain distribution. The core idea behind our framework is to combine Distributionally Robust Optimization (DRO) with self-supervised training. As a result, we also leverage efficient polynomial-time algorithms for the training stage. From a theoretical standpoint, we apply our framework on the classification problem of a mixture of two Gaussians in Rd, where in addition to the m independent and labeled samples from the true distribution, a set of n (usually with n m) out of domain and unlabeled samples are given as well. Using only the labeled data, it is known that the generalization error can be bounded by ∝ (d/m)1/2. However, using our method on both isotropic and non-isotropic Gaussian mixture models, one can derive a new set of analytically explicit and non-asymptotic bounds which show substantial improvement on the generalization error compared to ERM. Our results underscore two significant insights: 1) out-of-domain samples, even when unlabeled, can be harnessed to narrow the generalization gap, provided that the true data distribution adheres to a form of the "cluster assumption", and 2) the semi-supervised learning paradigm can be regarded as a special case of our framework when there are no distributional shifts. We validate our claims through experiments conducted on a variety of synthetic and real-world datasets. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Polynomial approximation

来源：评论

学校读者我要写书评

暂无评论

Regression-Based Exploration of Factors Influencing Movie Profits

Regression-Based Exploration of Factors Influencing Movie Pr...

引用

International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Lindawaty Veronica Tifara Beata Wibowo Alexander Agung Santoso Gunawan Karli Eka Setiawan Computer Science Department Data Science Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350366648

ISBN: (纸本)9798350366655

Accurate box office prediction is crucial for managing financial risks in film production. The internet has transformed consumer behavior, affecting marketing strategies. Critical online reviews, more than early revenue, predict a film's profit, while establishing film critics as the predictors. Hence, this study investigates the intricate relationship between ratings and movies profit. While also finding the best model to predict those variables. This research was conducted through a comprehensive process encompassing data collection and processing, feature selection, prediction modeling, and a detailed discussion of the conducted analysis. Several manual interventions were applied to the data to support further discoveries and analysis in this research. These interventions were carried out based on a solid foundation to enhance the depth of exploration and understanding within the study. The findings reveal that the random forest regression emerged as the most effective in this study, achieving the Mean Squared Error (MSE) of 768.40. Leveraging a dataset of 500 movies from ***, incorporating critics' ratings from ***. Through this research, industry professionals not only enhance their understanding but also identify factors that can be considered to increase a movie’s revenue.

关键词： Seminars Runtime Reviews Profitability Production Predictive models Motion pictures Solids Robustness Random forests

来源：评论

学校读者我要写书评

暂无评论

Hybrid intelligent system for channel allocation and packet transmission in CR-IoT networks

引用

International Journal of Hybrid Intelligent Systems 2024年第2期20卷 101-117页

作者： Asuquo, Daniel E. Umoh, Uduak A. Robinson, Samuel A. Dan, Emmanuel A. Udoh, Samuel S. Attai, Kingsley F. Department of Information Systems Faculty of Computing University of Uyo TETFund Center of Excellence in Computational Intelligence Research University of Uyo Department of Cyber Security Faculty of Computing University of Uyo Department of Computer Science Faculty of Computing University of Uyo Department of Data Science Faculty of Computing University of Uyo Department of Mathematics and Computer Science Ritman University Ikot Ekpene

The proliferation of interconnected devices is driving a surge in the demand for wireless spectrum. Meeting the need for wireless channel access for every device, while also ensuring consistent quality of service (QoS), poses significant challenges. This is particularly true for resource-limited heterogeneous devices within Internet of Things (IoT) networks. Cognitive radio (CR) technology addresses the shortcomings of traditional fixed channel allocation policies by enabling unlicensed users to opportunistically access unused spectrum belonging to licensed users. This facilitates timely and reliable transmission of mission-critical data packets. A cognitive radio-enabled IoT (CR-IoT) network is poised to better accommodate the growing demands of diverse applications and services within the smart city framework, spanning areas such as healthcare, agriculture, manufacturing, logistics, transportation, environment, public safety, and pharmaceuticals. To minimize switching delays and ensure energy and spectral efficiency, this study proposes a hybrid intelligent system for efficient channel allocation and packet transmission in CR-IoT networks. Leveraging Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), the system dynamically manages spectrum resources to minimize handoffs while upholding QoS. A Java-based simulation integrates system outputs with interference temperature data to accommodate service demands across 2G-4G spectrums. Evaluation reveals SVM's 98.8% accuracy in detecting spectrum holes and ANFIS's 90.4% accuracy in channel allocation. These results demonstrate significant potential for enhancing spectrum utilization in various IoT applications. © 2024-IOS Press. All rights reserved.

关键词： Cognitive radio network intelligent whitespace detection internet of things optimized channel allocation packet transmission

来源：评论

学校读者我要写书评

暂无评论

Heart Disease Diagnosis Using the Brute Force Algorithm and Machine Learning Techniques

引用

computers, Materials & Continua 2022年第8期72卷 3195-3211页

作者： Junaid Rashid Samina Kanwal Jungeun Kim Muhammad Wasif Nisar Usman Naseem Amir Hussain Department of Computer Science and Engineering Kongju National UniversityCheonan31080Korea Department of Computer Science COMSATS University IslamabadWah Campus47040Pakistan School of Computer Science University of SydneySydneyNSW 2006Australia Centre of AI and Data Science Edinburgh Napier UniversityEdinburghEH114DYUK

Heart disease is one of the leading causes of death in the world *** of heart disease is a prominent topic in the clinical data *** increase patient survival rates,early diagnosis of heart disease is an important field of research in the medical *** are many studies on the prediction of heart disease,but limited work is done on the selection of *** selection of features is one of the best techniques for the diagnosis of heart *** this research paper,we find optimal features using the brute-force algorithm,and machine learning techniques are used to improve the accuracy of heart disease *** performance evaluation,accuracy,sensitivity,and specificity are used with split and cross-validation *** results of the proposed technique are evaluated in three different heart disease datasets with a different number of records,and the proposed technique is found to have superior *** selection of optimized features generated by the brute force algorithm is used as input to machine learning algorithms such as Support Vector Machine(SVM),Random Forest(RF),K Nearest Neighbor(KNN),and Naive Bayes(NB).The proposed technique achieved 97%accuracy with Naive Bayes through split validation and 95%accuracy with Random Forest through *** Bayes and Random Forest are found to outperform other classification approaches when accurately *** results of the proposed technique are compared with the results of the existing study,and the results of the proposed technique are found to be better than other ***,our proposed approach plays an important role in the selection of important features and the automatic detection of heart disease.

关键词： Heart disease brute force machine learning feature selection

来源：评论

学校读者我要写书评

暂无评论

Enhanced Diabetes Detection with Deep Learning: An Iris Image Analysis Approach 5

Enhanced Diabetes Detection with Deep Learning: An Iris Imag...

引用

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

作者： Rajarajeswari, T.S. Snehanjali, Beeram Rizwana, Rapthadu Kasi Vardhan, Bethi Phanindra Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798331530013

Diabetes is a chronic and progressive condition that, if not diagnosed early, can lead to serious health complications, often we will see that patients learn they have diabetes only years after its emergence this points out the significance of precise and early identification. Thus, diabetes is a global health issue that requires timely detection. Traditional methods for diagnosing diabetes often involve expensive procedures, which can lead to delays in diagnosis. Using iris images for diabetes detection through deep learning algorithms offers a more accurate and cost-effective alternative. In general, a person with diabetes may experience changes in the iris, such as alterations in pattern, texture, or pigmentation. To identify these altered features, high-resolution observation is required. For this purpose, we use advanced imaging techniques and deep learning algorithms to analyze and interpret the changes in iris characteristics. CNN's, known for their powerful feature extraction capabilities, effectively captured and analyzed complex details in iris patterns. This approach utilizes several types of algorithms available in deep learning like Convolutional Neural Networks (CNNs), MobileNetV2, InceptionV3, ResNet50 and CNNs along with transformers. All these algorithms are widely known for their efficiency in image analyzing applications. Thus, we have chosen to implement the same procedural approach but for different algorithms. Finally, we had found an efficient algorithm in such a way that it gives the highest accuracy for the same dataset as compared with the other algorithms. It proved that CNNs are effective in identifying features and patterns in images and it gave the accuracy of about 93.91 % showing its effectiveness. Thus, ultimate aim of this approach is to provide an accurate, non-invasive and cost-effective way of diabetes disease diagnosis. © 2024 IEEE.

关键词： Convolutional Neural Networks Disease diagnosis InceptionV3 Iris MobileNetV2 ResNet50 Transformer Networks

来源：评论

学校读者我要写书评

暂无评论

Detection of Plant Disease Using Machine Learning and Deep Learning Algorithms 8

Detection of Plant Disease Using Machine Learning and Deep L...

引用

8th IEEE International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Gollapudi, Kalyani Varigonda, Preethi Jancy, S. Pushpalatha, S. Marya, Viji Amutha Helen, Suji Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Simats Department of Data Analytics Chennai602105 India

ISBN: (纸本)9798350347791

Agriculture performs an critical position in India's economic system. Early detection of plant illnesses is critical to save you crop damage and similarly spread of diseases. Most plants, along with apple, tomato, cherry, grape, show symptoms of leaf ailment. These visible patterns may be found to correctly predict the disorder and take early movement to save you it. This can be triumph over with system getting to know and deep getting to know algorithms. We therefore recommend a method that determines tomato plant disease from pix of leaves. This method is performed with aid vector device (SVM), random woodland gadget studying algorithm, and look at algorithms Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and ResNet that is one of the switch learning techniques. Snoring. After the facts set is processed by way of the algorithms, the accuracy of the algorithms is compared and the snapshots are categorized. © 2023 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Predicting Employee Attrition: A Comparative Analysis of Machine Learning Models Using the IBM Human Resource Analytics dataset

引用

Procedia computer science 2025年 258卷 4084-4093页

作者： Rajkumar Govindarajan N. Komal Kumar Sudhakar Reddy P Sai Pravallika E Dhatri B Pavan Kumar G Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh-517

In employee turnover research and workforce management, addressing the impacts of suboptimal employee performance is crucial for organizations of all sizes and industries. Utilizing advanced machine learning classification models to predict potential employee resignations can enhance human resource departments’ intervention strategies, effectively mitigating attrition challenges. This research investigates the performance of various machine learning algorithms in classification tasks, focusing on their accuracy in predicting outcomes from a given dataset. Five models were evaluated: Random Forest, Support Vector Machine (SVM), Decision Tree, Gradient Boosting, and a Hybrid Model that integrates multiple algorithms. The goal was to identify which model yields the highest accuracy and to understand the strengths and weaknesses of each approach. Results showed that the Hybrid Model achieved the highest accuracy at 95.0%, suggesting that combining different algorithms effectively harnesses their strengths while mitigating individual weaknesses. The SVM accurately classified the instance with 88.6%, demonstrating its capability to manage complex decision boundaries in high-dimensional spaces. Both Random Forest and Gradient Boosting attained an accuracy of 87.3%, reflecting their ensemble techniques that enhance predictive performance by reducing overfitting and optimizing error reduction. In comparison, the Decision Tree classifier exhibited the least accuracy at 80.5%, highlighting its susceptibility to overfitting and limited generalizability. The superior performance of the Hybrid Model indicates a promising direction for future research, where integrating diverse algorithms could lead to more robust predictions. Overall, this study provides valuable insights for practitioners and researchers seeking to optimize model selection and improve predictive accuracy in their domains.

关键词： Employee turnover Workforce Predictive Human Resourse Intervention

来源：评论

学校读者我要写书评

暂无评论

引用

2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Rácz, Miklós Z. Sridhar, Anirudh Northwestern University Department of Statistics and Data Science Department of Computer Science EvanstonIL United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ United States

ISBN: (纸本)9781665475549

We consider the task of estimating the latent vertex correspondence between two edge-correlated random graphs with generic, inhomogeneous structure. We study the so-called k-core estimator, which outputs a vertex correspondence that induces a large, common subgraph of both graphs which has minimum degree at least k. We derive sufficient conditions under which the k-core estimator exactly or partially recovers the latent vertex correspondence. Finally, we specialize our general framework to derive new results on exact and partial recovery in correlated stochastic block models, correlated Chung-Lu graphs, and correlated random geometric graphs. © 2023 IEEE.

关键词： Stochastic systems

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：