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检索条件"任意字段=2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023"

共 718 条记录，以下是631-640 订阅

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YOLO-NAS-powered Forest Monitoring System: ANew Frontier in Forest Management

YOLO-NAS-powered Forest Monitoring System: ANew Frontier in ...

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international conference on Image Information Processing

作者： Aman Ibrahim Jyoti Saini Department of CSE Center of Excellence-Data Science and Cognitive Computing Chandigarh University Mohali Punjab

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Forests and natural preserves are vital biological riches, yet unauthorized invasions pose a serious threat to them. This study describes a revolutionary forest monitoring system that uses the cutting-edge and incredibly effective YOLO-NAS (You Only Look Once Neural Architecture Search) object recognition technology. In comparison to existing models, YOLO-NAS automates model design and offers quicker and more accurate results. The system can effectively identify and track unauthorized people, poachers, illicit loggers in real-time by deploying cellular cameras outfitted with YOLO-NAS. The suggested method has the power to transform forest management and advance conservation efforts.

关键词： YOLO Forests Biological system modeling Transforms Information processing Search problems Cameras

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Heart Disease Predictive Analysis Using Machine Learning Approaches

Heart Disease Predictive Analysis Using Machine Learning App...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Anamika Rana Sushma Malik Madhu Chauhan Prashant Kumar Department of Computer Application Maharaja Surajmal Institute New Delhi India Department of Computer Application Institute of Innovation in Technology & Management New Delhi India Jagannath International Management School New Delhi India

Machine Learning (ML) has found widespread applications in the healthcare sector worldwide, including the diagnosis and treatment of heart diseases, locomotor disorders, and various other medical conditions. ML methods have revolutionized healthcare by enabling the analysis of large and complex medical datasets, leading to valuable insights and predictions that aid healthcare professionals in providing better patient care. ML's ability to analyze vast amounts of healthcare data, uncover patterns, and make predictions has significant potential to improve patient outcomes, optimize medical workflows, and advance medical research. However, it's essential to address privacy and ethical considerations when using ML in healthcare, ensuring the responsible and secure use of sensitive patient information. Supervised Learning methods like SVM, Random Forest, and Logistic Regression are used for the analysis of the dataset downloaded from Kaggle. The various performance parameters such as Precision, F-1 score, Accuracy, and Recall were used to compare the performance of different ML classification techniques. Among the various methods evaluated, the Random Forest classification algorithm was found to outperform the other methods across the fourteen available parameters.

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Cloud-Native Mobile Application Applied for Law and Regulations Harmonization

Cloud-Native Mobile Application Applied for Law and Regulati...

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Intelligent Cybernetics Technology & Applications (ICICyTA), international conference on

作者： Muhammad Juan Al Qausar Yaya Heryadi Lukas Lukas Wayan Suparta Ilvico Sonata Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Navigating the complex legal and regulatory landscape requires a sophisticated platform that is not only comprehensive but also user-friendly and enables seamless analysis and document comparison in the legal realm. To address this challenging requirement, this research is dedicated to the user experience (UX) design of state-of the-art cloud-native mobile applications specifically designed for legal and regulatory harmonization. The methodology used is based on design thinking principles and takes advantage of the benefits of cloud computing. These include, but are not limited to, scalability, reliability, and security. The result is a solution that is not only powerful but also cost-effective. The core of this application follows a user-centered design philosophy and involves potential users closely in the design process. A comprehensive evaluation is performed using the System Usability Scale (SUS) methodology to measure the effectiveness and usability of the application. The results of this comprehensive approach are convincing. The application has an impressive 95.5% usage rate, indicating strong user engagement. Furthermore, user satisfaction ratings are particularly high and transformative impact of this application in a complex environment of legal harmonization. Through its innovative design, seamless functionality, and user-centric approach, this application has proven to be not only a technical solution, but also a catalyst for positive change in the legal and regulatory harmonization process.

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Analysis of Physiological Response of Attention and Stress States in Teleoperation Performance of Social Tasks

Analysis of Physiological Response of Attention and Stress S...

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Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： David Achanccaray Hidenobu Sumioka Deep Interaction Lab Hiroshi Ishiguro Laboratories Advanced Telecommunications Research Institute International (ATR) Kyoto Japan

Some studies addressed monitoring mental states by physiological responses analysis in robots’ teleoperation in traditional applications such as inspection and exploration; however, no study analyzed the physiological response during teleoperated social tasks to the best of our knowledge. We analyzed the physiological response of attention and stress mental states by computing the correlation between multimodal biomarkers and performance, pleasure-arousal scale, and workload. Physiological data were recorded during simulated teleoperated social tasks to induce mental states, such as normal, attention, and stress. The results showed that task performance and workload subscales achieved moderate correlations with some multimodal biomarkers. The correlations depended on the induced state. The cognitive workload was related to brain biomarkers of attention in the frontal and frontal-central regions. These regions were close to the frontopolar region, which is commonly reported in attentional studies. Thus, some multimodal biomarkers of attention and stress mental states could monitor or predict metrics related to the performance in teleoperation of social tasks.

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cognitive data fusion for improving flexibility in smart homes

Cognitive data fusion for improving flexibility in smart hom...

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27th international conference on Electricity Distribution (CIRED 2023)

作者： S. Redhu R. Raja B. A. Bremdal H. S. Nygård Smart Innovation Norway Norway NMBU Norway

With the advancements in information and communication technology, accurate monitoring of energy consumption in smarts home has become feasible. This results in improved flexibility at household level by optimizing the appliances' scheduling. However, this is challenging at the same time due to uncertainties in energy consumption, and therefore demands a highly accurate forecasting of appliance-level energy consumption. In this context, this work presents a scheme where the energy consumption patterns of multiple appliances are explored jointly. The energy consumption of several appliances which are recorded as a time-series data, are clustered based on a well-established algorithm named k-shape. Utilizing k-shape time-series clustering algorithm, the consumption relation between different appliances is observed by computing the cross-correlation phenomenon which provides several clusters of appliances. All the appliances of a particular cluster show similarity in their energy consumption pattern. Further, the output from k-shape algorithm is utilized to forecast the consumption of a particular appliance in a cluster by utilizing the time-series energy consumption of other appliances of that cluster. We performed the clustering and forecasting experiments on a real dataset of 43 appliances in a household. We observed improved day-ahead forecasting performance utilizing the proposed scheme which motivates us to implement it in practical scenarios to enhance flexibility in smart homes.

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Multi-branch Attention Fusion Network for Aspect Sentiment Classification

Multi-branch Attention Fusion Network for Aspect Sentiment C...

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6th international conference on Intelligent computing and Signal Processing (ICSP)

作者： HaiFeng Zhao Kun Yan MingWei Cao Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology & Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui University Hefei China

For specific aspects (aspect words) of the object described in a given sentence, the research goal of aspect sentiment classification is to predict the sentiment polarity expressed by different aspect words. At present, sentiment classification methods based on attention mechanism and graph neural networks use attention models to capture contextual information related to aspect words, but ignore the global information of the sentence where aspect words are located. The classification accuracy in dealing with complex sentences still needs to be further improved. To solve the above problems, we propose a novel syntax-aware method - Multi-branch Attention Fusion Network (MAFNet) for aspect sentiment classification. Firstly, we use graph attention networks to capture contextual information related to aspect words, while using the mean-pooling layer to capture global information of sentences. Secondly, we propose a novel attention fusion mechanism to integrate contextual information related to aspect words and global information of sentences, which can enrich the semantic relationship between aspect words and opinion words in the sentence. Then, we use orthogonal and differential regularization to constrain the attention weight of contextual information, which can accurately capture the semantic correlation between aspect words and opinion words in the sentence. Finally, we verify the effectiveness of the algorithm on three public datasets. The experimental results prove that our method can correctly establish the semantic relationship between aspect words and opinion words, which can achieve better classification accuracy than traditional methods, attention-based methods, and graph neural network-based methods.

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An Adaptive Service-Oriented Business Management Pattern Based on Machine Learning Rule ML

An Adaptive Service-Oriented Business Management Pattern Bas...

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Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Mohan N K D V Prasad K. Soujanya Dinesh Chandra Dobhal Mohammad Ali Mano Ashish Tripathi CMR Institute of Technology Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune Department of BBA Koneru Lakshmaiah Educational Foundation Vaddeswaram Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science Khazar University Baku Azerbaijan Department of Humanities and Social Sciences Motilal Nehru National Institute of Technology Allahabad

The significance of artificial intelligence (AI) and machine learning (ML) for businesses is briefly and comprehensively discussed in this study. AI is a new trend and technology of the modern era that offers numerous advantages to businesses. AI and machine learning save the company money by lowering over- all business operations costs. In addition, it enables businesses to effectively solve business issues and helps them make better decisions about their processes. Customers can communicate with the AI-based chat bots at any time, day or night, and they can answer their questions about any business or product. Based on business operations, ML creates opportunities for businesses and automates the process completely. In addition, the ML effectively enhances cognitive engagement between employees and customers and offers solutions to customer issues like password issues and many others. In addition, the study contains the strategies and procedures utilized in its completion. In this study, the researcher has used secondary qualitative and quantitative methods to collect data. Companies must comprehend the augmentation and automation process in order to implement AI and ML in business operations. In addition, the type of business is used to describe the Al’s market size in percentage and dollars.

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Porting the Variant Calling Pipeline for NGS data in cloud-HPC environment

Porting the Variant Calling Pipeline for NGS data in cloud-H...

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IEEE Annual international Computer Software and Applications conference (COMPSAC)

作者： Alberto Mulone Sherine Awad Davide Chiarugi Marco Aldinucci Department of Computer Science University of Turin Turin Italy Institute of Metabolic Science University of Cambridge Cambridge United Kingdom Research and Development Group - Computing and Databases Services Max Planck Institute for Human Cognitive and Brain Science Leipzig Germany

In recent years we have understood the importance of analyzing and sequencing human genetic variation. A relevant aspect that emerged from the Covid-19 pandemic was the need to obtain results very quickly; this involved using High-Performance computing (HPC) environments to execute the Next Generation Sequencing (NGS) pipeline. However, HPC is not always the most suitable environment for the entire execution of a pipeline, especially when it involves many heterogeneous tools. The ability to execute parts of the pipeline on different environments can lead to higher performance but also cheaper executions. This work shows the design and optimization process that led us to a state-of-the-art Variant Calling hybrid workflow based on the StreamFlow Workflow Management System (WfMS). We also compare StreamFlow with Snakemake, an established WfMS targeting HPC facilities, observing comparable performance on single environments and satisfactory improvements with a hybrid cloud-HPC configuration.

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Privacy-Preserving Oriented Design for Multi-Modality Models using FL

Privacy-Preserving Oriented Design for Multi-Modality Models...

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IEEE international conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Mohammed Alduniawi Kemal Akkaya Ruimin Sun Knight Foundation School of Computing and Information Sciences Florida International University Miami United States

Mobile apps increasingly use federated learning for the convenience of training, user privacy preservation, and ease to regulation compliance. Recent models consider multi- modality, including text, image, and audio, for its better understanding of complex data and potential to improve data availability and model performance. However, multi-modality also brings security challenges to protect user sensitive information. Sensitive information may leak to unauthorized channels through inter process communication and runtime memory extraction. When devices interact with the cloud in federated learning, other malicious clients (or devices) may pollute models or reveal membership information. This paper aims to prevent the above attacks by proposing a privacy- preserving architecture design, called MuIMPC, for ML models using multi-modality input in FL. MulMPC includes reverse engineering techniques for mobile apps, homomorphic encryption for modality fusion, and multi-party computations for privacy preserving in FL. MuIMPC's design is generic and is promising in applying to different application domains.

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Adversarial Domain Adaptation Network with Enhanced Feature Discriminability for Thyroid Ultrasound Images

Adversarial Domain Adaptation Network with Enhanced Feature ...

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international Joint conference on Neural Networks (IJCNN)

作者： Xuewei Li Pinjie Li Ruixuan Zhang Chenhan Wang Xi Wei Mankun Zhao College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin Key Laboratory of Advanced Networking Tianjin China Tianjin International Engineering Institute Tianjin University Tianjin China OpenBayes (Tianjin) IT Co. Ltd. Tianjin China Department of Diagnostic and Therapeutic Ultrasonography Tianjin Medical University Cancer Institute and Hospital Tianjin China

In recent years, computer-aided diagnosis technology has made breakthroughs in clinical medicine. However, due to the different models and configurations of ultrasound instruments, the thyroid ultrasound images collected by different medical centers have different visual characteristics. It leads to domain shift in multi-center data and the lower generalization of computer-aided diagnosis models. We consider this limitation may attribute to the minor inter-class differences in thyroid ultrasound images, resulting in confusion space before and after domain feature alignment. Therefore, we propose an adversarial domain adaptation network with enhanced feature discriminability method. Among them, the discriminative feature learning module strengthens the discriminability of the learned features, so that the class distribution in each domain presents a high cohesion and low coupling effect; the class alignment module reduces the distance between the same class samples across domains to achieve class alignment and further strengthen the discriminability of each class. Experiments show that our method outperforms other state-of-the-art algorithms on the internal thyroid ultrasound image dataset. The method can effectively improve the model's generalization ability on multi-center thyroid ultrasound images.

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