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共 11563 条记录，以下是411-420 订阅

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Sarcasm Detection in Newspaper Headlines 4

Sarcasm Detection in Newspaper Headlines

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Sreenivas Prasad, B. Akash Babu, N. Harithikeswar Reddy, T. Vishnu Vardhan Reddy, A. Vekkot, Susmitha Nair, Priyanka C. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India Amrita School of Engineering Department of Electronics & Communication Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350349900

In the current age of information saturation, where headlines serve as primary points of engagement, the pervasive use of sarcasm in news headlines poses a challenge for readers' comprehension, failing to discern sarcasm may lead to misunderstandings, misinformation dissemination, and harm journalistic integrity. this study addresses this critical issue and enhances the credibility of news sources by employing various ensemble learning algorithms and deep learning models with different types of word embedding, the evaluation includes various possible performance metrics and a complete correlation of the outcomes. XGBoost consistently outperforms other models with higher accuracy, precision, recall, and F 1-score in sarcasm detection. Among deep learning models, BiLSTM emerges as the top performer, achieving 0.86 accuracy, capturing nuanced language cues effectively and appears as the optimal model for sarcasm detection. BERT stands out with superior F1 scores, showcasing its exceptional ability in deciphering sarcastic language nuances. © 2024 IEEE.

关键词： Contrastive learning

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learning Different Separations in Branch and Cut: A Survey 4th

Learning Different Separations in Branch and Cut: A Survey

引用

4th International conference on intelligent Systems and Pattern Recognition-ISPR

作者： Amar, Chaima Ayachi Bouanane, Khadra Aiadi, Oussama LINATI Lab Ouargla Algeria Univ Kasdi Merbah Ouargla Ouargla 30000 Algeria

ISBN: (纸本)9783031821554;9783031821561

Selecting cutting planes for solving Mixed-Integer Linear Programming (MILP) is a critical task as it directly impacts the efficiency and effectiveness of the optimization process. By carefully choosing a set of cuts, it becomes possible to significantly reduce the size of the search space, thereby enhancing the algorithm's ability to find optimal or near-optimal solutions within a reasonable computational time frame. In this paper, we examine recent advancements in utilizing Machine learning (ML) techniques for the selection of cutting planes (separation) in MILP. Despite the existence of various types of separations, the task of selecting a set of cuts to augment the Linear Programming (LP) relaxation at a particular node of the Branch-and-Bound (B&B) tree remains a challenge, both in terms of formal methods and heuristic approaches. ML presents a promising direction for enhancing the cut selection process by leveraging data to identify promising cuts that expedite the resolution of MILP problems. Focusing on recent developments in research, we investigate common methodologies for data gathering, evaluation techniques, and ML model architectures. We review empirical findings in the literature to assess the extent of progress achieved and conclude by suggesting potential directions for future investigation. In conclusion, the integration of ML techniques holds great promise for advancing the field of MILP by improving the efficiency and effectiveness of cut selection processes, ultimately leading to better solutions for complex optimization problems

关键词： Combinatorial optimization Mixed Linear Integer Programming MILP Cutting plane Machine learning

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Real Time Pothole Detection System Using Deep learning Techniques: A Systematic Review 4

Real Time Pothole Detection System Using Deep Learning Techn...

引用

4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Saraswathi, S. Vigneshwari, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331529635

Real-time pothole detection serves a crucial role in maintaining road infrastructure and ensuring the safety of drivers. Traditional methods for identifying potholes are often labour-intensive and inefficient, prompting the exploration of automated techniques using deep learning. this comprehensive review aims to synthesize the current state of research on pothole detection systems leveraging deep learning algorithms. By examining a range of studies published over the past decade, the review highlights various methodologies, including You Only Look Once (YOLO), convolutional neural networks (CNNs), deep CNN, and hybrid models, as well as the different datasets and image processing techniques employed. the review categorizes the approaches based on their accuracy, computational efficiency, and practical implementation challenges. the review also identifies research gaps in the literature survey, such as the demand for standardized datasets and real-time processing capabilities, and suggests directions for future research. By providing a systematic overview of deep learning-based pothole detection systems, the purpose of this review is to help researchers and practitioners develop more reliable and scalable solutions for maintaining road infrastructure. © 2024 IEEE.

关键词： Convolutional neural networks

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Detection of Control Valve Diagnosis and Pipeline Using Machine learning 4

Detection of Control Valve Diagnosis and Pipeline Using Mach...

引用

4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Selvakarthi, D. Hakeem, A. Abdul Akilesh, D. Dharshini, S. Amjath, M. Mohamed Kongu Engineering College Department of Electronics and Instrumentation Engineering Perundurai Tamilnadu Erode638060 India

ISBN: (纸本)9798331529635

Machine learning has the potential for transforming the workplace across various industries. It can streamline repetitive tasks, improve decisionmaking, enhance safety, and increase operational efficiency. Rather than relying on static images from multiple angles to train object detection models, using video footage can simplify the process and reduce the need for large image datasets. Amazon Rekognition, a service from Amazon Web Services (AWS), leverages deep learning algorithms to analyze both images and videos. Its features include facial recognition, object detection and tracking, text recognition, and content moderation. In this project, AWS Rekognition is utilized to assess control valves and detect pipeline leaks. It also distinguishes between different types of control valves, such as normally open, normally closed, and those with positioners. this approach illustrates the effectiveness of video-based learning for object detection and the utility of cloud-based AI services in industrial diagnostics and monitoring. © 2024 IEEE.

关键词： Safety valves

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Predicting the Onset of Lifestyle Diseases Using Machine learning Techniques 4

Predicting the Onset of Lifestyle Diseases Using Machine Lea...

引用

4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Prabagar, S. Babu, Tina Nair, Rekha R. Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331529635

Lifestyle diseases, including cardiovascular conditions, obesity, type 2 diabetes, and hypertension, are increasing global health concerns, particularly in developing countries. these non-communicable diseases are often attributed to daily habits such as poor diet, lack of exercise, smoking, and alcohol consumption. Early detection of these diseases is crucial due to their slow onset and the complexity of treatment once they manifest. this study explores the application of machine learning algorithms - such as decision trees, random forests, support vector machines (SVM), K-nearest neighbors (KNN), and Naive Bayes - in predicting the onset of lifestyle diseases. By utilizing key lifestyle factors as input data, the study evaluates the accuracy, precision, and recall of these models in diagnosing lifestyle diseases. the Random Forest algorithm emerged as the top performer with 88% accuracy. the results highlight the potential of machine learning to improve early disease detection, which could enable timely medical intervention and help reduce the long-term impact of lifestyle diseases on healthcare systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Reinforcement learning-Based optimization Strategy for Smart Building Operations 4

Deep Reinforcement Learning-Based Optimization Strategy for ...

引用

4th International Symposium on Artificial Intelligence and intelligent Manufacturing, AIIM 2024

作者： Sun, Xiaoke Lei, Xia Xihua University School of Electrical and Electronic Information Chengdu China

ISBN: (纸本)9798331541729

In recent years, with the increasing importance of building energy consumption and energy-saving management, building energy consumption and carbon emissions in China account for approximately 20% of the national total. the application of intelligent control systems to optimize building operational efficiency has gained attention. these systems improve building load demand response while reducing operational costs. this study designs an adjustable load control model for building operations based on deep reinforcement learning (DRL) technology, aiming to optimize both electricity cost and indoor temperature comfort. Using the Soft Actor-Critic (SAC) algorithm as a stable baseline model, the agent can intelligently adjust air conditioning loads under fluctuating electricity prices and temperature constraints to minimize building operating costs. To test the model's effectiveness, simulations were conducted on a test dataset containing actual load data. the results indicate that the model can effectively maintain indoor temperature within the target range over multiple periods while significantly reducing overall building load. this simulation validates the potential of deep reinforcement learning in complex building energy management and offers new insights for the development of building energy-saving control systems. © 2024 IEEE.

关键词： Demand response

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Research on intelligent scheduling optimization of unmanned tower based on genetic algorithm 4

Research on intelligent scheduling optimization of unmanned ...

引用

2024 4th International conference on intelligent Traffic Systems and Smart City, ITSSC 2024

作者： Li, Haocheng Shen, Di Chinese People's Liberation Army Air Force Engineering University Xi'an710100 China

ISBN: (纸本)9781510686304

In order to improve the efficiency and safety of airport unmanned tower scheduling system, genetic algorithm is used to design and optimize the intelligent scheduling system, integrating advanced sensors and data processing technology to achieve efficient management of airport ground traffic. the results show that the system can effectively reduce the total scheduling time and conflict rate, improve the execution efficiency of scheduling strategies, and demonstrate excellent environmental adaptability and reliability. © 2025 SPIE.

关键词： Genetic algorithms

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Robust optimization Strategy for Reserve Capacity via Source-Load-Storage Integration 4

Robust Optimization Strategy for Reserve Capacity via Source...

引用

4th International conference on intelligent Power and Systems, ICIPS 2024

作者： Fang, Yuxuan Zhai, Haoyu Ma, Xurui Hu, Junjie North China Electric Power University State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources Beijing China

ISBN: (纸本)9798331532765

Due to the influence of external power supply environments and the frequent occurrence of extreme weather events, the power supply security of receiving-end power systems faces significant challenges, necessitating source-load-storage resources to provide more stable and reliable reserve capabilities. To address this issue, we propose a robust optimization dispatching strategy for source-load-storage resources that accommodates diverse system regulation demands. First, adjustable thermal power units are classified into cold, hot, and spinning reserve states based on response delay and regulation speed, and the adjustable characteristics of multiple resources, including various types of thermal units, electric vehicles, and distributed battery energy storage, are analyzed. Next, a robust optimization model for reserve capacity distribution of adjustable resources is developed, minimizing load shedding based on representative scenarios obtained through clustering. An iterative solution method is constructed to determine the optimal number of representative scenarios and the robust optimization strategy for reserve capacity. Finally, case studies validate the adaptability and effectiveness of the proposed robust optimization dispatching strategy in diverse regulation scenarios, supporting the adequate operation of power systems. © 2024 IEEE.

关键词： adjustable resource pool component receiving-end power system reserve capacity robust optimization

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Highway traffic parameter prediction model based on the combined deep learning model 4

Highway traffic parameter prediction model based on the comb...

引用

4th International conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Jiang, Li Duan, Chenggang Li, Zhongqiu Jiao, Yanli Ge, Jiqiang Chen, Yusen Hebei Expressway Yanchong Preparation Office Hebei Zhangjiakou075499 China Hebei Provincial Communications Planning and Design Institute Co. Ltd Hebei Shijiazhuang050021 China Beijing Jiaotong University School of Traffic and Transportation Beijing100044 China Beijing Waystone Information Technology Co. Ltd Beijing China

ISBN: (纸本)9798350394375

Traffic flow time series prediction is developing very rapidly. the research methods of prediction models mainly focus on model parameter optimization and data feature analysis to improve prediction accuracy. Based on this, this article adopts the form of model optimization and uses ETC gantry data to conduct optimal combination and applicability analysis of time series prediction *** paper builds two deep learning time series combination prediction models based on the Internet of Vehicles and Internet of things environments and combines them with current research results. Model 1 (INFO-LSTM) is an LSTM prediction model based on the vector-weighted optimization algorithm. Model 2 (ICSSA-GRU-ATT) is a GRU model based on the improved Sparrow Search Algorithm (ICSSA) under the attention mechanism. Short-term prediction was carried out using the ETC data of Shanlin Expressway in Gansu Province as the experimental content. the results showed that the data training and prediction capabilities of INFO-LSTM and ICSSA-GRU-ATT were ideal in the test. Model 1 performs very well in the prediction results of the training set and has strong data memory capabilities in data analysis. Model 2 is also able to meet the stability requirements. © 2024 IEEE.

关键词： Internet of things

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Caption Driven Video Event Detection 4

Caption Driven Video Event Detection

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Bhadane, Prathamesh Bhil, Pratik Dhup, Atharv Patel, Ankita K. Electronics and Telecommunications Engineering Pune India

ISBN: (纸本)9798350349900

Video activity detection is used in a wide range of applications, such as healthcare, video surveillance, intelligent vehicle systems and more. Recently, the emphasis has shifted to using the power of neural networks to improve the accuracy and effectiveness of activity detection. this paper provides an indepth review of the latest techniques for using neural networks for video activity detection. the paper examines the development of activity detection methods, ranging from traditional statistical approaches to the recent explosion of supervised learning techniques. It focuses on transfer learning, multi instance learning and deep learning methods with a particular focus on convolutions neural network (CNN's). CNN's are widely used for their ability to segment humans, to classify and to recognize activity within video sequences or static images. the review covers the entire video activity detection pipeline, from prepossessing and human segmentation to feature extraction and classification. It explains the challenges associated with each step and provides innovative solutions. the paper also discusses the critical role of benchmark data sets as well as simulation tools for evaluating activity detection models. In addition, the paper discusses the challenges that are still present in the field of video activity detection and suggests directions for future research. It emphasises the importance of this field for ensuring public safety and advancing applications in healthcare and surveillance. By summarising the most recent advances and research trends, the purpose of this review is to help researchers and practitioners to develop more efficient video activity detection solutions with neural networks. © 2024 IEEE.

关键词： Active learning

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