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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Faridoon, Fatima Ali, Raja Hashim Ul Abideen, Zain Shahzadi, Nazia Ijaz, Ali Zeeshan Arshad, Usama Ali, Nisar Imad, Muhammad Nabi, Said Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

PolyCystic Ovary Syndrome (PCOS) is a hormonal disorder frequently found in women of reproductive age having a significant impact on the cause of infertility. It is an endocrine condition characterized by abnormalities in female hormone levels and aberrant synthesis of male hormones. This syndrome causes ovarian malfunction, increasing the risk of miscarriage and infertility. PCOS has a wide range of symptoms, making a diagnosis difficult. In this study, we proposed a feature selection method based on genetic algorithm with logistic regression to increase the accuracy of early diagnosis. A dataset containing the clinical and biochemical characteristics of 109 patients, including 36 with PCOS, is used. The genetic algorithm is used to extract the most important features from the dataset. Then, a logistic regression model is used for classification. The proposed model outperformed the baseline model that used all features, which had an accuracy of 86.2%, and produced a markedly improved accuracy of 95.4%. These results show that the proposed model effectively locates key characteristics for PCOS diagnosis. © 2023 IEEE.

关键词： Feature Selection

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BuddyBot: AI Powered Chatbot for Enhancing English Language Learning

BuddyBot: AI Powered Chatbot for Enhancing English Language ...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Dhivvya J P Sai Bhargav Karnati Department of Computer Science and Engineering Amrita School of Computing Center for Wireless Networks and Applications Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Today, many students are having a hard time learning to speak the English language and the problem is even worse for those in rural areas, where both teachers and resources are rare. We are trying to break this disparity and provide better resources for those in need utilizing Artificial Intelligence and Natural Language Processing (NLP) to contribute to the SDG4 Quality and Inclusive Education. In this paper, we present the idea of how we developed Chatbot named BuddyBot Utilizing OpenAI Generative Pre-Trained Transformer 2 (GPT-2) and Google Text-to-Text Transfer Transformer (FLAN-T5) models. This system incorporates diverse datasets for fine-tuning and specialized tasks in English language learning. BuddyBot follows a five-phase architecture - User interface, Pre-processing, Natural Language Processing (NLP) stack, language generation engine and adaptive learning personalization. The E-Learn web-based interface offers interactive courses with BuddyBot assistance, emphasizing adaptive learning personalization. Dataset preprocessing includes tokenization, sentiment analysis, and specialized formatting. The NLP stack integrates GPT-2 and FLAN-T5 models into the framework, maintaining dialogue structure and personalization. Validation tests reveal model effectiveness, with reduced training loss (0.778 to 0.414 by epoch 8) and increased accuracy (0.810 training, 0.792 validation). Overall, BuddyBot's robust methodology and validation results showcase its efficacy in providing personalized English language learning experiences.

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Towards Automated Nanoenergetic Reaction Characterization with Computational Vision

Towards Automated Nanoenergetic Reaction Characterization wi...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Gaines, Timothy B. Hurt, J. Alex Keller, James M. Scott, Grant J. Boyle, Camden I. Maschmann, Matthew R. Price, Stanton R. University of Missouri Dept. of Electrical Engineering and Computer Science Columbia United States University of Missouri Dept. of Mechanical and Aerospace Engineering Columbia United States U.S. Army Engineer Research and Development Center VicksburgMS United States

ISBN: (纸本)9798350359527

In order to utilize aluminum-based nanoenergetic materials effectively, the mechanisms by which reactive aluminum fuel escapes a passivating aluminum shell must be better understood. These reactions can be classified based upon pre-and post-reaction imagery taken of the material. To aide in quantitatively understanding these reactions we investigate creating an improved reaction classifier through leveraging larger, state-of-The-Art deep learning models. Additionally, we experiment with various pre-Training methods and image analogs to enable effective training of large models even with a small dataset. Image features used from the past experimentation may be used in conjunction with reaction imagery to aid in reaction classification. © 2023 IEEE.

关键词： Aluminum

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Integrating Big Data Analytics in Soil Quality Assessment: Techniques, Challenges, and applications

Integrating Big Data Analytics in Soil Quality Assessment: T...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Shehu Mohammed Arun Singh Md Abu Hanif Department of Computer Applications Lovely Professional University Phagwara India Department of Computer Science and Engineering Lovely Professional University Phagwara India Department of Computer Science Graduate Center The City of New York (CUNY) New York NY USA

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

This review paper investigates the application of Big Data analytics, focusing on soil quality assessment, with an emphasis on the innovative benefits offered by modern data-driven techniques for improved soil management. With world food demands rising, the importance for real-time and exact soil quality monitoring has increased. Although still useful, traditional approaches to modeling are limited in their ability to capture the spatial and temporal variability of soil properties. Big Data analytics sits as the answer to the above by facilitating extensive and large volume data processing resulting in better, on-demand and scalable soil quality assessment. In the paper, we describe machine learning algorithms, remote sensing and geospatial analysis as key techniques to mine soil data at scales and resolutions unprecedented so far. We also critically evaluate the challenge of data integration (heterogeneity, scalability and multi-disciplinarity). These analytics are scalable for assessment and decision-making in precision agriculture, sustainable land management and environmental conservation. Thus, this paper synthesizes current research and practical implications in support of the need for Big Data analytics within soils and soil sciences to cover also future challenges. Future research directions are also highlighted, including the importance of improved data acquisition and algorithmic advances as well greater synergy between Big Data and conventional soil science practices..

关键词： Precision agriculture Technological innovation Scalability Decision making Transforms Soil Big Data Quality assessment Environmental management Remote sensing

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Causal fMRI-Mamba: Causal State Space Model for Neural Decoding and Brain Task States Recognition

Causal fMRI-Mamba: Causal State Space Model for Neural Decod...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Weihao Deng Fei Han Qinghua Ling Qing Liu Henry Han School of Computer Science and Communication Engineering JiangSu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China School of Computer Jiangsu University of Science and Technology China School of Electronic and Information Engineering West Anhui University China School of Engineering and Computer Science Baylor University USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep learning advances neural decoding in functional magnetic resonance imaging (fMRI) tasks with convolution and attention-based methods. However, these methods struggle with capturing global spatiotemporal information due to high dimensionality, noise and inter-individual difference of fMRI, which also increase computational complexity and prior bias. To this end, a novel causal state space model, Causal fMRI-Mamba, is proposed for neural decoding and task state mapping. It effectively captures global spatiotemporal information via eliminating local redundancies and capturing long-distance dependencies. Meanwhile, a causal representation framework is designed to extract invariant high-order features and disentangle related causal features, enhancing model performance. Furthermore, a dense connection module is expanded to prevent significant causal information loss in hidden states of inter layers. On the HCP brain task state classification task, Causal fMRI-Mamba achieves better performance and generalization than comparison methods.

关键词： Deep learning Perturbation methods Redundancy Noise Functional magnetic resonance imaging Brain modeling Feature extraction Spatiotemporal phenomena Decoding Speech processing

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An efficient online histogram publication method for data streams with local differential privacy

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Frontiers of Information Technology & Electronic engineering 2024年第8期25卷 1096-1109页

作者： Tao TAO Funan ZHANG Xiujun WANG Xiao ZHENG Xin ZHAO School of Computer Science and Technology Anhui University of TechnologyMaanshan 243032China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Maanshan 243032China Engineering Research Institute Anhui University of TechnologyMaanshan 243032China Shengli No.1 Middle School of Dongying City Dongying 257000China

Many areas are now experiencing data streams that contain privacy-sensitive *** the sharing and release of these data are of great commercial value,if these data are released directly,the private user information in the data will be ***,how to continuously generate publishable histograms(meeting privacy protection requirements)based on sliding data stream windows has become a critical issue,especially when sending data to an untrusted third *** histogram publication methods are unsatisfactory in terms of time and storage costs,because they must cache all elements in the current sliding window(sW).Our work addresses this drawback by designing an efficient online histogram publication(EOHP)method for local differential privacy data ***,in the EOHP method,the data collector first crafts a histogram of the current SW using an approximate counting ***,the data collector reduces the privacy budget by using the optimized budget absorption mechanism and adds appropriate noise to the approximate histogram,making it possible to publish the histogram while retaining satisfactory data *** experimental results on two different real datasets show that the EOHP algorithm significantly reduces the time and storage costs and improves data utility compared to other existing algorithms.

关键词： Data stream Differential privacy Sliding windows Approximate counting

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Multi-level Distance Metric Learning for Few-shot Spoken term classification 7

Multi-level Distance Metric Learning for Few-shot Spoken ter...

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2022 7th International Conference on Electronic Technology and Information science, ICETIS 2022

作者： Wu, Yiqi Ma, Zhongchen Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University Jiangsu Zhenjiang212000 China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Jiangsu Zhenjiang212000 China

ISBN: (纸本)9783800757794

Spoken term classification has attracted great attention of researchers because of its wide application in real life. Traditional methods use a deep neural network trained with tremendous samples to classify a particular keyword which may not be applicable in case of insufficient samples and restrict the system from recognizing new or user-defined keywords. Few-shot learning algorithm is then applied to this speech task since it can use only a few examples to adapt to classifying new classes. Metric learning for few-shot learning aims to learn a metric in episode learning such that the distance between the positive pair of each episode is closed and the distance between the negative pair of each episode is far away. However, due to various pronunciation, speed or intonation, some spoken samples may be extremely dissimilar to its positive sample, but very similar to its negative sample, leading these samples to be ignored or overly influenced in metric learning. Therefore, to cope with this problem we propose a multi-level distance regularized metric learning method for few-shot spoken term classification. It explicitly disturbs a learning procedure by regularizing pairwise distances between embedding vectors into multiple levels. In this way, the degree of similarity between a pair can be fully utilized and the importance of each sample in metric learning is considered in a balanced manner. Multi-level Distance Regularization with simple Prototypical Network achieves the state-of-the-art performance in benchmark dataset: Google Speech Commands dataset. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Learning algorithms

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Soil Moisture During 2015 Spring Flood Events from the SMAP Radar Time-Series Ratio Algorithm

Soil Moisture During 2015 Spring Flood Events from the SMAP ...

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2024 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2024

作者： Horton, Dustin Johnson, Joel T. Al-Khaldi, Mohammad Baris, Ismail Park, Jeonghwan Bindlish, Rajat Department of Electrical and Computer Engineering ElectroScience Laboratory Ohio State University ColumbusOH43212 United States Microwave and Radar Institute German Aerospace Center Munich Germany NASA Goddard Space Flight Center GreenbeltMD United States Global Science and Technology Inc. GreenbeltMD20770 United States

ISBN: (纸本)9781946815194

The hydrologic cycle of the Earth is a global process that describes the exchange of water as it cycles between the land surface, sea, and atmosphere. One key variable in this cycle is surface soil moisture which influences a variety of different systems such as infiltration and runoff, terrestrial hydrology, and the evapotranspiration cycle. Soil moisture also impacts numerous ecological and climatological applications such as weather forecasting, water resource management, irrigation planning, crop yield prediction, food security, and flood dynamics. Methods to remotely sense surface soil moisture at fine spatial and temporal resolutions over extended geographic regions are therefore of interest. © 2024 IEEE. All rights reserved.

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Research on Thread Optimization and Opening Library Based on Parallel PVS Algorithm in Amazon Chess 33

Research on Thread Optimization and Opening Library Based on...

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第33届中国控制与决策会议

作者： Jingyuan Ju Hongkun Qiu FuShuai Wang XueJie Wang YaJie Wang School of Artificial Intelligence Academy Shenyang Aerospace University School of Computer Science Shenyang Aerospace University Engineering Training Center Shenyang Aerospace University College of Automation Shenyang Aerospace University

ISBN: (纸本)9781665431293

This paper introduces the main algorithms used in Amazon game system,and mainly studies the number of threads in parallel PVS algorithm based on *** view of the low utilization of computer hardware by the current algorithm,this paper improves the efficiency of the algorithm by changing the number of parallel algorithm threads to be equal to the number of CPU cores,and draws a conclusion that the multi-core resources can be maximized when the number of parallel algorithm threads is equal to the number of CPU *** paper studies the design of the opening Library of Amazon chess,puts forward the design idea of the opening Library of Amazon chess and the necessity of the opening library system.

关键词： computer Game Amazon Game Search algorithm Parallel efficiency Opening library

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Augmenting Context Representation with Triggers Knowledge for Relation Extraction 12th

Augmenting Context Representation with Triggers Knowledge fo...

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12th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2022

作者： Li, En Shi, Shumin Yang, Zhikun Huang, He Yan School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9783031039478

Relation Extraction (RE) requires the model to classify the correct relation from a set of relation candidates given the corresponding sentence and two entities. Recent work mainly studies how to utilize more data or incorporate extra context information especially with Pre-trained Language Models (PLMs). However, these models still face with the challenges of avoiding being affected by irrelevant or misleading words. In this paper, we propose a novel model to help alleviate such deficiency. Specifically, our model automatically mines the triggers of the sentence iteratively with the sentence itself from the previous iteration, and augment the semantics of the context representation from BERT with both entity pair and triggers skillfully. We conduct extensive experiments to evaluate the proposed model and effectively obtain empirical improvement in TACRED. © 2022, IFIP International Federation for Information Processing.

关键词： Semantics

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