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IEEE International Conference on Networking, Sensing and Control

作者： Jianyun Lu Quanwang Wu Chunling Wu School of Artificial Intelligence and Big Data Chongqing College of Electronic Engineering Chongqing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665472449

Density-based clustering is an important research direction of data mining because density-based clustering algorithms can find clusters with arbitrary shape and are robust to noises. Local density estimation is widely used in the task of clustering and outlier detection. Traditionally, distance-based and statistic-based local density estimation methods are usually adopted for data mining. However, the effectiveness and stability of local density estimation models for clustering are still to be improved. In this paper, we propose a novel density clustering method based on dynamic local density estimation. First, we show Poisson distribution to fit the distribution of reverse $k$ nearest neighbor counts and describe the model of dynamic local density estimation. Second, we construct a cluster order based on dynamic local density. Finally, decision graph is developed for density clustering and the identified break points partition the cluster order into clusters. Experiment results show that the new dynamic local density estimation model is effective and can help improve the performance of density clustering.

关键词： Shape Clustering methods Estimation Clustering algorithms Stability analysis Sensors Task analysis

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Advancements in Artificial Intelligence for Better Image Analysis, Recognition, and Interpretation in the Field of Image Processing

Advancements in Artificial Intelligence for Better Image Ana...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： V. Saravanan A. Adaikkammai G. Sasikala Iyswariya A A. Babisha M. Latha Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Computer Science and Business Systems R.M.D. Engineering College Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of ECE R.M.K. Engineering College Chennai Department of Artificial Intelligence and Data Science Panimalar Engineering College Department of IT SRM Institute of Science and Technology

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The rapid advancement of artificial intelligence (AI) has brought about a significant revolution in image processing. This revolutionary technology has enabled the analysis, recognition, and interpretation of images in ways that were previously inconceivable. An investigation into the most recent developments in artificial intelligence techniques that have improved the efficiency and precision of image processing tasks is presented in this study. We survey the emergence of transformer architectures and the integration of deep learning models, namely Convolutional Neural Networks (CNNs), to explore how these technologies have transformed the understanding and application of images in several industries. An overview of existing image processing methods and the limits of those approaches is presented at the beginning of our research. This highlights the necessity of more advanced AI-driven solutions. After that, we examine the advancements that have been made in artificial intelligence, such as the incorporation of attention processes, the development of more effective CNN architectures, and the utilisation of generative adversarial networks (GANs) for the purpose of picture synthesis and augmentation. The purpose of this research is to investigate the impact that artificial intelligence has on particular applications of image processing, such as autonomous car navigation systems, facial recognition, medical imaging, and satellite images analysis. Specifically, we focus on the enhancements in diagnostic accuracy, environmental monitoring, security, and safety that arose as a result of the adoption of artificial intelligence in these sectors through the use of case studies. Furthermore, we discuss the issues that are associated with artificial intelligence in image processing. These challenges include the necessity for explainable AI models, the requirement for computational resources, and the protection of data privacy. A number of potential solutions to these problems

关键词： Industries Image recognition Image analysis Transformers Satellite navigation systems Generative adversarial networks Satellite images Convolutional neural networks Security Artificial intelligence

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

Classification of Breast Thermal Images into Healthy/Cancer ...

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2022 International Conference on Machine Learning and data Engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores. © 2023 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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Consumer Behavior in the Post-Pandemic Era: Insights from Machine Learning and AI-Driven Analytics

Consumer Behavior in the Post-Pandemic Era: Insights from Ma...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Divya Nimma Ch. Anudeep Tripti Sahu Nusrat Hamid Shah Lubna Hamid Shah B Kiran Bala PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of Management studies Shri Vishnu Engineering college for women (A) Bhimavaram India MIMA Institute of Management Pune Maharashtra India College of Engineering and Computer Science Jazan University Kingdom of Saudi Arabia Department of Management and Marketing College of Business Jazan University Kingdom of Saudi Arabia Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

COVID-19 has been the disruptor in the world, and the society has been changed from one perspective of the consumer behavior in terms of digital platforms and their preferences for safety, convenience and sustainability. It is therefore very important for any business to acquaint itself with these changes so that may be in a position to effectively compete for the market within the current ever-changing business environment. Conventional consumer behavior models, on the other hand, rely on fragmented data sources and analytical tools that may not reflect the current post-pandemic consumption patterns. Such models are also incapable of incorporating multiple data types and can also fail to address changing social factors. In response, a Multi-Modal Consumer Behavior Prediction Framework (MCBP) that considers a vast array of data sources is introduced. BERT and GNNs are used to give a detailed, real-time interpretation of consumer behaviour accomplished by the MCBP framework. It uses BERT to extract and analyze sentiment and topics of the textual data and GNNs to capture influence and peer effects on purchase decisions. The findings of the study show the effectiveness of the proposed framework with an accuracy of 99%. 5% and the implementation is done using Python. The high level of accuracy shown assures the success of the MCBP model in measuring and forecasting consumers' behaviors in the post-pandemic world.

关键词： Consumer behavior Accuracy Soft sensors Predictive models data models Social factors Real-time systems Safety Sustainable development Business

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Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models

arXiv

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arXiv 2023年

作者： Bai, Guangji Chai, Zheng Ling, Chen Wang, Shiyu Lu, Jiaying Zhang, Nan Shi, Tingwei Yu, Ziyang Zhu, Mengdan Zhang, Yifei Yang, Carl Cheng, Yue Zhao, Liang Department of Computer Science Emory University 201 Dowman Dr AtlantaGA30322 United States School of Data Science Department of Computer Science University of Virginia 1827 University Avenue CharlottesvilleVA22904 United States College of Information Sciences and Technology Pennsylvania State University 201 Old Main University ParkPA16802 United States

The burgeoning field of Large Language Models (LLMs), exemplified by sophisticated models like OpenAI’s ChatGPT, represents a significant advancement in artificial intelligence. These models, however, bring forth substantial challenges in high consumption of computational, memory, energy, and financial resources, especially in environments with limited resource capabilities. This survey aims to systematically address these challenges by reviewing a broad spectrum of techniques designed to enhance the resource efficiency of LLMs. We categorize methods based on their optimization focus—covering computational, memory, energy, financial, and network resources—and their applicability across various stages of an LLM’s lifecycle, including architecture design, pre-training, fine-tuning, and system design. Additionally, the survey introduces a nuanced categorization of resource efficiency techniques by their specific resource types, which uncovers the intricate relationships and mappings between various resources and corresponding optimization techniques. A standardized set of evaluation metrics and datasets is also presented to facilitate consistent and fair comparisons across different models and techniques. By offering a comprehensive overview of the current state-of-the-art and identifying open research avenues, this survey serves as a foundational reference for researchers and practitioners, aiding them in developing more sustainable and efficient LLMs in a rapidly evolving landscape. To make the field more accessible to interested beginners, we not only make a systematic review of existing works and a highly structured taxonomy of resource-efficient LLMs but also release a website including a constantly-updated paper list https://***/tiingweii-shii/Awesome-Resource-Efficient-LLMPapers. Copyright © 2023, The Authors. All rights reserved.

关键词： Web Design

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Deep Learning Driven - Pest Classification and Sustainable Agriculture

Deep Learning Driven - Pest Classification and Sustainable A...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： M Sirish Kumar R Usha B Rama Kantha Reddy S Ramachandra P Jaya Prakash B Swetha Dept. of Data Science School of Computing Mohan Babu University India CSE Madanapalle Institute of Technology and Science Madanapalle CSE (AI and ML) Sri Venkateswara College of Engineering Tirupati CSE Mother Theresa Institute of Engineering and Technology Palamaner Department of IT Sri Venkateswara College of Engineering Tirupati Dept. of Master of Computer Applications Mohan Babu University India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices.

关键词： Deep learning Accuracy Insects System performance Transfer learning Crops Pesticides Agriculture Convolutional neural networks Testing

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Anomaly Detection in Pedestrian Walkways using Imperialist Competitive Algorithm with Deep Learning on Remote Sensing Images

Anomaly Detection in Pedestrian Walkways using Imperialist C...

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Innovations in Communications, Electrical and computer Engineering (ICICEC), International Conference on

作者： Ch. Gangadhar K.Swathine E.Kamalanaban J. Senthil Murugan Swagata Sarkar Sangeethaa SN Department of Electronics & Communication Engineering Prasad V. Potluri Siddhartha Institute of Technology Vijayawada Andhra Pradesh India Computing - Artificial Intelligence Coimbatore Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi India Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai India Computer Science & Engineering Bannari Amman Institute of Technology Erode India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Remote sensing images (RSI) describe images captured by sensors aboard satellites, drones, aircraft, or alternative platforms from a large distance above the Earth's surface. These images are useful for numerous applications, comprising agriculture, ecological monitoring, urban development, disaster management, and land use planning. Anomaly Detection (AD) in pedestrian walkways is a significant research domain, generally employed to increase the security of pedestrians. The conventional manual analysis of labeling abnormal actions is a tedious task owing to the extensive application of video surveillance systems and the increased quantity of captured videos. Currently, the expansion of deep learning (DL) methods are gained great interest in various methods of CV such as object classification and object detection, and such applications are dependent upon supervised learning that needs labels. This manuscript devises an Anomaly Detection in Pedestrian Walkways using Imperialist Competitive Algorithm with Deep Learning (ADPW-ICADL) technique on Remote Sensing Images. The ADPW-ICADL technique starts with Wiener filter (WF) scheme to develop the quality of the images by decreasing the noises. To derive features, the ADPW-ICADL technique applies a DenseNet100 architecture for capturing complicated patterns in pedestrian walkways. Also, the Imperialist Competitive Algorithm (ICA) is utilized for the hyperparameter tuning process to assure the optimal parameter selection of the DenseNet model. At last, Gradient Boosting Machine (GBM) is applied for proper identification of the anomalies in the walkway patterns. By wide-ranging experimental results on RSIs, the ADPW-ICADL technique demonstrates higher AD performance, emphasizing its impending for improving the dependability and productivity of pedestrian walkway investigation in several geographic settings.

关键词： Deep learning Productivity Pedestrians Noise computer architecture Classification algorithms Sensors Tuning Remote sensing Anomaly detection

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Prediction of Stock Market Price using Bi-directional Recurrent Neural Network

Prediction of Stock Market Price using Bi-directional Recurr...

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Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： R Archana Reddy Kassem Al-Attabi Bollampelly Chandana Ibraheem Hatem Mohammed Bhuvaneswary School of Computer Science and Artificial Intelligence SR University Warangal India The Islamic university Najaf Iraq Department of CSE (Data Science) Hyderabad India Department of Computer Communications Engineering Al Rafidain University College Bagdad Iraq Department of ECE Kalasalingam Academy of Research and Education Krishnankoil India

Recently, an accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. The advent of artificial intelligence and enhanced processing power has led to the realisation that preprogrammed techniques are more effective at forecasting stock values. The stock market is currently one of the most researched and fastest developing subj ects, and predicting its behaviour is crucial. The primary challenge in this area of research remains improving forecast precision. Strong shareholder returns are made possible by stock price forecasting. Despite this, researchers use these algorithms to predict continuing market trends based on Stock Technical Indicators (STIs), due to recent advancements in machine learning techniques. This study analyses Yahoo Finance data from STIs over a decade to predict stock prices using a Bi- directional Recurrent Neural Network (BI-RNN). Initially, the analysed STIs were used as input for an autoencoder during the dimensionality reduction procedure, which reduced the correlation between the STIs. These STIs and the Yahoo Finance data were then provided to BI-RNN. Next, in order to forecast stock prices, the BI-RNN's outcome attributes were fed into the soft max layer. From the results, it clearly shows that proposed Bi-RNN shown better results in terms of a minimal MAPE value of 0.41, MAE of 4.42, RMSE of 0.10, and MSE of 212.25 in the experiments, the proposed strategy surpassed traditional methods.

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Lattice mismatches caused by intrinsic stacking faults promote cracks initiation in Ni–Cu alloys

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Journal of Materials Research and Technology 2025年 36卷 9504-9517页

作者： Yu Zhou Yongchao Liang Yuanwei Pu Lili Zhou Zean Tian Qian Chen Yunfei Mo Institute of Advanced Optoelectronic Materials and Technology College of Big Data and Information Engineering Guizhou University Guiyang 550025 China Department of Information Engineering Gannan Medical University Ganzhou 341000 China College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China School of Electronic Information and Electrical Engineering Changsha University Changsha 410003 China

Metals are prone to cracking during processing, and the existence of cracks reduces the mechanical properties and service life. Therefore, molecular dynamics simulation method in this study is used to explore the initiation and propagation mechanism of Ni–Cu alloy cracks, so as to improve their mechanical properties. The results show that the intrinsic stacking faults (ISFs) were generated at the torsional end, resulting in the emission of dislocations and the initiation of shear transition zones (STZs). In the yielding stage, the rapidly growing ISFs led to the initiation of a large number of dislocations. At this time, STZs formed shear bands which caused the model to yield. The ISFs led severe lattice mismatch, increased dislocation density, and formed a large number of dislocation entanglements, thereby promoting the formation of cracks. The number and area of cracks rapidly increased during the plastic deformation stage, which eventually led to irrecoverable fracture of the material. The number and area of cracks are the largest at a high twisting rate. High Ni content can increase the yield strength and reduce the number of cracks. The study employed the construct surface mesh method to establish the relationship between shear bands, ISFs, dislocations, and cracks. The study investigates the mechanisms of crack initiation and propagation, providing a theoretical basis for optimizing the preparation process of Ni–Cu alloy and enhancing its mechanical properties.

关键词： Ni–Cu alloy Molecular dynamics simulation Intrinsic stacking faults Crack initiation Lattice mismatch

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Risk Probability Assessment and Prediction Based on K-means and CNN

Risk Probability Assessment and Prediction Based on K-means ...

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Digital Analysis and Processing, Intelligent Computation (DAPIC), International Conference on

作者： Wan Wang Luran Cao Zixuan Wang Wenqi Zhang Zijian Wei China North Industries Corporation Beijing China College of Computer and Information Science School of Software Southwest University Chongqing China School of Artificial Intelligence and Software JiangSu Normal University KeWen College Xuzhou China SWUFE-UD Institute of Data Science Southwestern University of Finance and Economics Chengdu China School of Mathematics and Information Sciences Guangzhou University Guangzhou China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

This paper presents a comprehensive framework for risk probability assessment and prediction, integrating K-means clustering, Critic-Topsis methodology, and Convolutional Neural Networks (CNNs). Initially, a risk evaluation model is established by leveraging K-means clustering to categorize probability data, followed by selecting the top seven most relevant features for each cluster. Objective weights for these features are calculated using the Entropy Weight Method (EWM). Subsequently, the Critic-Topsis approach is utilized to compute comprehensive evaluation scores, which, when combined with EWM weights, yield risk warning values for individual events within each risk category. Furthermore, a CNN-based occurrence probability prediction model is developed, utilizing the top ten most correlated indicators and probability data. Through convolutional and pooling operations, a regression model is trained and evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Comparative analysis with Random Forest and Support Vector Machine models demonstrates the superiority of the CNN model, achieving an R 2 of 98.6% and maintaining a high R 2 of 91.5% even with five features.

关键词： Support vector machines Analytical models Accuracy Sensitivity analysis Computational modeling Soft sensors Predictive models Robustness Convolutional neural networks Random forests

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