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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Noble, Sandra-Carina Ward, Tomás Ringwood, John V. Maynooth University Department of Electronic Engineering Maynooth Ireland Dublin City University Insight SFI Research Centre for Data Analytics Dublin Ireland

ISBN: (纸本)9798350337020

The P300 speller is a widely used application in brain-computer interface research. It has been demonstrated that the P300 speller can serve as a neurofeedback training tool for attention enhancement by gradually increasing the difficulty of the spelling task. This adaptive approach makes it harder for users to spell words correctly, encouraging them to improve their attention to counteract the increasing difficulty. Therefore, the adaptive P300 speller has the potential to serve as a treatment option for children with ADHD, elderly patients with dementia, and as a cognitive enhancement tool for healthy adults. However, the training length, including setup time, needs to be quick to ensure user acceptability. This study investigates the effect of different electrode subsets on P300 speller performance, with and without the use of the xDAWN spatial filter. Results indicate that the xDAWN spatial filter can improve performance with many electrodes but can decrease results with fewer than eight electrodes. For scenarios where near-perfect performance is crucial and many electrodes are available, a set of 16 electrodes with the xDAWN spatial filter is recommended. For situations where cost and setup time are a concern and lower performances are acceptable, using six electrodes without the spatial filter were found to be sufficient. © 2023 IEEE.

关键词： BCI brain-computer interface EEG electroencephalography OpenViBE P300 speller xDAWN

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Massive data clustering by multi-scale psychological observations

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National Science Review 2022年第2期9卷 43-51页

作者： Shusen Yang Liwen Zhang Chen Xu Hanqiao Yu Jianqing Fan Zongben Xu National Engineering Laboratory of Big Data Analytics Xi'an Jiaotong University Industrial Artificial Intelligent Center Pazhou Laboratory Department of Mathematics and Statistics University of Ottawa Center for Statistics and Machine Learning Princeton University

Clustering is the discovery of latent group structure in data and is a fundamental problem in artificial intelligence,and a vital procedure in data-driven scientific research over all ***,existing methods have various limitations,especially we ak cognitive interpretability and poor computational scalability,when it comes to clustering massive datasets that are increasingly available in all ***,by simulating the multi-scale cognitive observation process of humans,we design a scalable algorithm to detect clusters hierarchically hidden in massive *** observation scale changes,following the Weber-Fechner law to capture the gradually emerging meaningful grouping *** validated our approach in real datasets with up to a billion records and 2000 dimensions,including taxi trajectories,single-cell gene expressions,face images,computer logs and *** approach outperformed popular methods in usability,efficiency,effectiveness and robustness across different domains.

关键词： massive data clustering psychological observation Weber–Fechner law cognitive interpretability computational scalability

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Cooperation and Contract Design in Project Management with Outsourcing

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Journal of Systems Science and Systems engineering 2023年第1期32卷 34-70页

作者： Xiaoqiang Cai Nicholas G.Hall Siyi Wang Feng Zhang School of Data Science The Chinese University of Hong KongShenzhen518172China Shenzhen Research Institute of Big Data Shenzhen518172China Department of Operations&Business Analytics Fisher College of BusinessOhio State UniversityColumbusOhioUSA School of Science and Engineering The Chinese University of Hong KongShenzhen518172China Suning Bank Nanjing210019China

We study a project management problem where the prime contractor needs to outsource tasks to subcontractors with the required *** execution of the project requires proper coordination among the subcontractors,as well as contract design by the prime contractor to incentivize the *** modeling the subcontractors’coordination problem as a cooperative game,we develop a profit sharing scheme to facilitate the subcontractors’*** consider two contract designs for the prime contractor:a uniform contract across all subcontractors,and a nonuniform one that customizes incentives for each *** propose efficient algorithms to solve the implicit optimization problems for optimal contract *** experiments show that the pooling effect of subcontractors’cooperation mitigates the negative impact of poor estimates about the crashing cost and resource *** observe three unexpected results through the randomized computation experiments:(i)the subcontractors’profits may decrease if they provide false information;(ii)it is safer for the prime contractor to overestimate subcontractors’crashing costs than underestimate them;and(iii)uniform contracts deliver more project profit for the subcontractors in the coalitions.

关键词： Project management with outsourcing subcontracting cooperative game contract design

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Influence of Span Length on Seismic Mainshock—Aftershock Response of RC Bridges Pre-Exposed to Scouring 2nd

Influence of Span Length on Seismic Mainshock—Aftershock Re...

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2nd International Conference on Materials, Mechanics and Structures, ICMMS 2022

作者： Jithiya, K.K. Pandikkadavath, Muhamed Safeer Nagarajan, Praveen Mangalathu, Sujith Department of Civil Engineering National Institute of Technology Calicut Kerala India Data Analytics Division Equifax Inc AtlantaGA United States

ISBN: (纸本)9789811933707

Scouring around the bridge substructure resulting from a flood may cause loss of its all-around lateral support and may make the structure more vulnerable to *** are not standalone events, usually, several aftershocks follow the seismic mainshock, which makes the bridge structures to a higher level of damage/collapse *** such cases, the multi-hazard scenario of flood-induced scours and mainshock–aftershock effects on bridges need to be accounted ***, the span length may influence the seismic behaviour of the bridge structures as it contributes to their seismic *** present study attempts to assess the influence of span length on the seismic mainshock–aftershock response of a bridge structure that is pre-exposed to flood-induced *** the study, a suite of earthquake records that come from different hazard levels is *** nonlinear time history analyses for sequential mainshocks–aftershocks are carried out on the bridge structure for different span lengths incorporating the flood-induced scour *** seismic responses of the structures are assessed in terms of displacement ductility at the pier *** curves are developed and compared for different damage levels under the accounted for various multi-hazard cases. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Scour

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Enhancing Lane Detection Performance A Comprehensive Analysis of YOLOPv2 under Adverse Conditions

Enhancing Lane Detection Performance A Comprehensive Analysi...

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Mohamed, Nour Shehada, Dina Bouridane, Ahmed Electrical Engineering Department University of Sharjah Sharjah United Arab Emirates College of Engineering and IT University of Dubai Dubai United Arab Emirates Centre for Data Analytics and Cybersecurity University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798400716928

With the growing global interest in autonomous driving from big car companies and the research community, road lane detection has become a hot topic. In this paper, You Only Look Once for Panoptic Driving (YOLOPv2), which is a deep learning network that is designed to perform multitasks including lane detection, is studied and evaluated using three challenging datasets. The authors also collected a DubaiLanes dataset which consists of 70 images under various conditions. The YOLOPv2 model was evaluated using Pixel Accuracy, MSE, IoU, and F1-score. The model was found to perform well with different lighting conditions, multiple lanes, and different curves. However, it was also found that it misdetects tiny speed bumps for lanes and does not create continuous lanes when obstacles are observed. © 2024 ACM.

关键词： Convolutional neural networks

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Long Short-Term Memory (LSTM) model for Indian sign language recognition

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 11185-11203页

作者： Nihalani, Rahul Chouhan, Siddharth Singh Mittal, Devansh Vadula, Jai Thakur, Shwetank Chakraborty, Sandeepan Patel, Rajneesh Kumar Singh, Uday Pratap Ghosh, Rajdeep Singh, Pritpal Saxena, Akash School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh Sehore India Department of Mathematics Central University of Jammu UT of JK India Department of Data Science and Analytics Central University of Rajasthan Rajasthan Ajmer India School of Engineering and Technology Central University of Haryana Haryana Mahendragarh India

The human-computer interaction process is a vital task in attaining artificial intelligence, especially for a person suffering from hearing or speaking disabilities. Recognizing actions more traditionally known as sign language is a common way for them to interact. Computer vision and Deep learning models are capable of understanding these actions and can simulate them to build up a sustainable learning process. This sign language mechanism will be helpful for both the persons with disabilities and the machines to unbound the gap to achieve intelligence. Therefore, in the proposed work, a real-Time sign language system is introduced that is capable of identifying numbers ranging from 0 to 9. The database is acquired from the 8 different subjects respectively and processed to achieve approximately 200k amount of data. Further, a deep learning model named LSTM is used for sign recognition. The results were compared with different approaches and on distinct databases proving the supremacy of the proposed work with 91.50% accuracy. Collection of daily life useful signs and further improving the efficiency of the LSTM model is the research direction for future work. The code and data will be available at https://***/rahuln2002/Sign-Language-Recognition-using-LSTM-model. © 2024-IOS Press. All rights reserved.

关键词： Long short-term memory

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Research on Large-scale Structured and Unstructured data Processing based on Large Language Model 24

Research on Large-scale Structured and Unstructured Data Pro...

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2024 International Conference on Machine Learning, Pattern Recognition and Automation engineering, MLPRAE 2024

作者： Li, Bohang Jiang, Gaozhe Li, Ningxin Song, Chaoda Department of Data Science Shopee Pte. Ltd. Singapore Institute of Operations Research and Analytics National University of Singapore Singapore Fu Foundation School of Engineering and Applied Science Columbia University United States School of Engineering Department of Computer and Data Sciences United States

ISBN: (纸本)9798400709876

Since the beginning of the internet era, there has been an explosion of growth in structured data (such as numbers, symbols, and labels) as well as unstructured data (including images, videos, and text). Efficient and accurate mixed query of these two types of data is a key technology to achieve high-quality information retrieval, and it is also a major challenge that needs to be solved urgently in the industry. In this study, we employ an advanced Transformer model that combines strategies and fine-tuning techniques for multi-task learning. Specifically, the model is first pre-trained on a large-scale, general-purpose dataset to learn different types of data representations and basic language comprehension skills. After that, we fine-tuned the parameters of the model to better suit these specific data processing tasks for specific application scenarios, such as image annotation, video content analysis, and structured data query. At the heart of the model is the self-attention mechanism, which allows the model to automatically emphasize the important parts and ignore irrelevant information when processing the input data. In addition, we have introduced task-specific adaptation layers that are designed to add additional processing power to the original Transformer architecture, such as a semantic analysis layer for unstructured text data and a relational extraction layer for structured data. This combination of general pre-training and task-specific fine-tuning allows the model to flexibly process and integrate information from different data sources, improving processing efficiency and accuracy. Experimental results show that the model performs well in a variety of data processing tasks, significantly improves the accuracy and efficiency of information retrieval, and verifies the strong potential and adaptability of large language models in processing mixed data types. © 2024 ACM.

关键词： data accuracy

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Maize Plant Disease Prediction of UAV Images for Precision Agriculture Using Fusion of Multimodal 3

Maize Plant Disease Prediction of UAV Images for Precision A...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Mishra, Vinay Naik, Nenavath Srinivas Kumar, Santosh Alsamhi, Saeed Hamood Saif, Abdu Curry, Edward -Naya Raipur Department of Computer Science and Engineering Chhattisgarh India University of Galway Insight Centre for Data Analytics Galway Ireland Faculty of Engineering & Information Technology Taiz Yemen

ISBN: (纸本)9798350321487

The agriculture sector is vital to a country's economic and productive development. With technological advancements, we can identify plant diseases in their early stages, similar to how human diseases are diagnosed, and avoid significant financial loss. For example, a new technique has been developed to detect diseases in maize crops, a widely used raw product in the food industry and precision agriculture. Furthermore, farmers ensure the fields do not remain wet for extended periods to prevent fungus infection. Currently, machine learning and deep learning approaches are being utilized, making agriculture one of the most researched topics in AI. This study proposes a novel framework for predicting maize plant disease using a fusion of multi-deep learning models. The proposed framework uses a four-stage pipeline for detecting disease in maize plants by fusion of Xception, DenseNet-121, and ensembling of Xception and DenseNet-121 models to achieve excellent results on the OSF drone image dataset, which consists of 1821 high-resolution images of maize plants taken from unmanned aerial vehicles (UAVs). The accuracy of our proposed approach is 96.95%, which is highly effective, considering the challenges posed by drone images. © 2023 IEEE.

关键词： Drones

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Monitoring Recombinant Protein Titer in Escherichia Coli Fermentations Using Filtering Techniques

Monitoring Recombinant Protein Titer in Escherichia Coli Fer...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Okafor, Paul Siddique, Zahed Razzaghi, Talayeh University of Oklahoma Data Science and Analytics Institute Norman United States University of Oklahoma School of Aerospace and Mechanical Engineering Norman United States University of Oklahoma School of Industrial and Systems Engineering Norman United States

ISBN: (纸本)9798350362480

Recombinant protein production in Escherichia coli (E. coli) is crucial for biotechnological applications, but optimizing protein titer is challenging and often requires iterative experimentation and expert guidance. This study utilizes advanced filtering techniques - Kalman, particle, bilateral, and extended Kalman filters - to enhance titer estimation accuracy using fermentation data provided by Cytovance Biologics. The proposed techniques effectively impute missing titer values and are validated on a secondary dataset from Chinese Hamster Ovary (CHO) cell lines, demonstrating robustness and accuracy. By reducing the need for extensive sampling and iterative experimentation, these filtering techniques enable predictive control over fermentation conditions. One significant cost-saving advantage is identifying the protein titer plateau, allowing lab scientists to conclude the fermentation process at the optimal time. This approach ensures maximum protein production while preventing unnecessary resource consumption. As a result, this method significantly lowers the overall cost of recombinant protein production in E. coli while enhancing process efficiency. © 2024 IEEE.

关键词： Wiener filtering

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Advanced VANET Technology for Emergency Vehicle Safety in Time-Critical Scenario 8

Advanced VANET Technology for Emergency Vehicle Safety in Ti...

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8th IEEE International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Murali, N. Beulah David, D. Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamilnadu Chennai India Institute of Information Technology Saveetha School of Engineering Simats Department of Data Analytics Tamilnadu Chennai India

ISBN: (纸本)9798350347791

The most pressing issue with ITS is minimal latency in communication between vehicles, especially given that unstable traffic flow and road safety are two of their main concerns. Because to these problems, I have concentrated my study on employing a vehicular ad hoc network to track speedy object detection and provide the quickest warning to drivers. These systems require ongoing monitoring to function properly, making it possible to use machine learning techniques on vast amounts of data produced by various VANET applications. Machine learning is a technique used to prevent accidents for emergency vehicles by automatically learning and improving the system's rapid object detection stage in situations with high traffic. Our methods effectively implement the VANET aim by providing efficient unsupervised learning of the collected data. In this research, we suggested a unique real-time (DT-YOLO) algorithm-based automatic discovering vehicle gap approach for detecting and identifying objects in congested metropolitan regions. By using VANET technologies, we were able to address traffic, communication, and safety concerns in metropolitan areas. We investigated, using DT-YOLO technology, how these problems may be resolved in a transparent object-tracking system via machine learning techniques. Fast-moving EVs have a high probability of finding a gap to let the driver to make a decision. Moreover, a case study demonstrating a VANET-based scenario of nearest-object and gap-finding to flee from congested traffic with the ideal vehicle moment is shown. The paper examined current challenges and future directions. © 2023 IEEE.

关键词： Object detection

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