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Enhancing Real-Time Data Analysis Performance by Applying Adaptive Machine Learning Algorithms

Enhancing Real-Time Data Analysis Performance by Applying Ad...

2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Agarwal, Ankita Zaidi, Taskeen Mishra, Rahul Parmar, Yuvraj Jayasree, S. Karyakarte, Mandar S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350329773

This paper offers a way of enhancing real-time information evaluation overall performance by way of applying adaptive machine mastering algorithms. The proposed method specializes in refining the present algorithms to enhance the predictive accuracy of huge-scale datasets. Particularly, a singular set of rules, combining the help vector device (SVM) and random forest algorithms, is used to filter an exceptionally small subset of incoming facts for the motive of improving the general overall performance of actual-time information analysis. This technique is evaluated using a simulated real-international network dataset, the simulation outcomes showing that it could lessen the evaluation time through at least eighty two.9%. Moreover, the approach suggests that it is able to additionally improve the version accuracy of the random forest algorithm by means of as much as 7.39%. Thus, this paintings is able to provide a more green and correct technique for actual-time records analysis. © 2023 IEEE.

关键词： Information analysis

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DEBRA: On the Unsupervised Learning of Concept Hierarchies from (Literary) Text

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International Journal of Intelligence science 2023年第4期13卷 81-130页

作者： Peter J. Worth Domagoj Doresic Department of Electrical Engineering and Computer Science Florida Atlantic University Boca Raton USA Interdisciplinary Research Unit Mathematics and Life Sciences University of Bonn Bonn Germany

With this work, we introduce a novel method for the unsupervised learning of conceptual hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with literary texts, as such distinguishing itself from the majority of research literature on the topic which is primarily focused on building ontologies from a vast array of different types of data sources, both structured and unstructured, to support various forms of AI, in particular, the Semantic Web as envisioned by Tim Berners-Lee. We first elaborate on mutually informing disciplines of philosophy and computer science, or more specifically the relationship between metaphysics, epistemology, ontology, computing and AI, followed by a technically in-depth discussion of DEBRA, our dependency tree based concept hierarchy constructor, which as its name alludes to, constructs a conceptual map in the form of a directed graph which illustrates the concepts, their respective relations, and the implied ontological structure of the concepts as encoded in the text, decoded with standard Python NLP libraries such as spaCy and NLTK. With this work we hope to both augment the Knowledge Representation literature with opportunities for intellectual advancement in AI with more intuitive, less analytical, and well-known forms of knowledge representation from the cognitive science community, as well as open up new areas of research between computer science and the Humanities with respect to the application of the latest in NLP tools and techniques upon literature of cultural significance, shedding light on existing methods of computation with respect to documents in semantic space that effectively allows for, at the very least, the comparison and evolution of texts through time, using vector space math.

关键词： Ontology Learning Ontology engineering Concept Hierarchies Concept Mapping Concept Maps Artificial Intelligence Philosophy Natural Language Processing Knowledge Representation Knowledge Representation and Reasoning Machine Learning Natural Language Processing NLP computer science Theoretical computer science Epistemology Metaphysics Philosophy Logic Computing Ontology First Order Logic Predicate Calculus

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Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment

arXiv

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arXiv 2024年

作者： Yu, Sangwon Song, Jongyoon Hwang, Bongkyu Kang, Hoyoung Cho, Sooah Choi, Junhwa Joe, Seongho Lee, Taehee Gwon, Youngjune L. Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Samsung SDS Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

A binary decision task, like yes-no questions or answer verification, reflects a significant real-world scenario such as where users look for confirmation about the correctness of their decisions on specific issues. In this work, we observe that language models exhibit a negative bias in the binary decisions of complex reasoning tasks. Based on our observations and the rationale about attention-based model dynamics, we propose a negative attention score (NAS) to systematically and quantitatively formulate negative bias. Based on NAS, we identify attention heads that attend to negative tokens provided in the instructions as answer candidate of binary decisions, regardless of the question in the prompt, and validate their association with the negative bias. Additionally, we propose the negative attention score alignment (NASA) method, which is a parameter-efficient fine-tuning technique to address the extracted negatively biased attention heads. Experimental results from various domains of reasoning tasks and large model search space demonstrate that NASA significantly reduces the gap between precision and recall caused by negative bias while preserving their generalization abilities. Our codes are available at https://***/ysw1021/NASA. Copyright © 2024, The Authors. All rights reserved.

关键词： NASA

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DDD: A Perceptually Superior Low-Response-Time DNN-Based Declipper

DDD: A Perceptually Superior Low-Response-Time DNN-Based Dec...

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

作者： Jayeon Yi Junghyun Koo Kyogu Lee Dept. of Electrical Engineering and Computer Science University of Michigan Dept. of Intelligence and Information Seoul National University Interdisciplinary Program in Artificial Intelligence Seoul National University Artificial Intelligence Institute Seoul National University

Clipping is a common nonlinear distortion that occurs whenever the input or output of an audio system exceeds the supported range. This phenomenon undermines not only the perception of speech quality but also downstream processes utilizing the disrupted signal. Therefore, a real-time-capable, robust, and low-response-time method for speech declipping (SD) is desired. In this work, we introduce DDD (Demucs-Discriminator-Declipper), a real-time-capable speech-declipping deep neural network (DNN) that requires less response time by design. We first observe that a previously untested real-time-capable DNN model, Demucs, exhibits a reasonable declipping performance. Then we utilize adversarial learning objectives to increase the perceptual quality of output speech without additional inference overhead. Subjective evaluations on harshly clipped speech shows that DDD outperforms the baselines by a wide margin in terms of speech quality. We perform detailed waveform and spectral analyses to gain an insight into the output behavior of DDD in comparison to the baselines. Finally, our streaming simulations also show that DDD is capable of sub-decisecond mean response times, outperforming the state-of-the-art DNN approach by a factor of six.

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Prospective evaluation of data-driven models to predict daily risk of Clostridioides difficile infection at 2 large academic health centers (vol 2, pg 1, 2022)

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INFECTION CONTROL & HOSPITAL EPIDEMIOLOGY 2023年第10期44卷 1705-1705页

作者： Kamineni, Meghana Otles, Erkin Oh, Jeeheh Rao, Krishna Young, Vincent B. Li, Benjamin Y. West, Lauren R. Hooper, David C. Shenoy, Erica S. Guttag, John G. Wiens, Jenna Makar, Maggie Electrical Engineering and Computer Science Department Massachusetts Institute of Technology Cambridge Massachusetts Medical Scientist Training Program University of Michigan Medical School Ann Arbor Michigan Department of Industrial and Operations Engineering University of Michigan College of Engineering Ann Arbor Michigan Division of Computer Science and Engineering University of Michigan College of Engineering Ann Arbor Michigan Department of Internal Medicine Division of Infectious Diseases University of Michigan Medical School Ann Arbor Michigan Infection Control Unit Massachusetts General Hospital Boston Massachusetts Division of Infectious Diseases Massachusetts General Hospital Boston Massachusetts Harvard Medical School Boston Massachusetts

Many data-driven patient risk stratification models have not been evaluated prospectively. We performed and compared the prospective and retrospective evaluations of 2 Clostridioides difficile infection (CDI) risk-prediction models at 2 large academic health centers, and we discuss the models’ robustness to data-set shifts.

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A 3D Dual-band electrically Small Monopole Antenna for Internet of Sea Applications

A 3D Dual-band Electrically Small Monopole Antenna for Inter...

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2021 IEEE International Symposium on Antennas and Propagation and North American Radio science Meeting, APS/URSI 2021

作者： Liao, Hanguang Bilal, Rana Muhammad Shamim, Atif Electrical and Computer Engineering Program King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (纸本)9781728146706

Sensing and retrieving data from ocean to land are challenging and expensive tasks, while the Internet of Sea (IoS) concept can help to monitor ocean environment in a low-cost way. In this paper, an electrically Small (ES) dual-band monopole antenna is proposed for the IoS application. Dual-band operation (GSM-900, LoRa, and BLE bands) and vertical polarization are maintained in a very compact size. The proposed antenna operates as a folded monopole antenna at 900MHz, and as a simple monopole antenna at 2.4 GHz, by combining the top-loaded folded monopole antenna and the 2-layer monopole feeding structure. The proposed antenna has ka of 0.45 at the LB, with a decent polarization purity and radiation efficiency of 54%. The proposed antenna is designed with a low-cost additive manufacturing material system. © 2021 IEEE.

关键词： Monopole antennas

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electrical Characteristic and Power Fluctuations of GAA Si NS CFETs by Simultaneously Considering Six Process Variation Factors

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 229-238页

作者： Kola, Sekhar Reddy Li, Yiming National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Biomedical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

Characteristic variability induced by process variation effect (PVE) is one of technological challenges in semiconductor industry. In this work, we computationally study electrical characteristic and power fluctuations induced by six factors of PVE of the gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field-effect-transistors (CFETs) which are formed by vertically stacking n-FET on top of p-FET. Among the six factors, NS thickness (TNS), NS width (WNS), and gate length (LG) are identified as crucial factors contributing to large variations in device characteristics. The p-FET exhibits substantial off-state current fluctuation (about 151%) due to the bottom parasitic channel leakages. Compared with the magnitudes of dynamic and short circuit powers, the static power is marginal, but it possesses the largest fluctuation (up to 148%). If we assume that each factor of PVE has the same probability distribution as the others and all are mutually independent, the statistical sum of their power fluctuations will exhibit more than 50% overestimations, compared with the results when all factors are considered simultaneously. © 2020 IEEE.

关键词： III-V semiconductors

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ConvNeXt Model for Breast Cancer Image Classification

ConvNeXt Model for Breast Cancer Image Classification

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Devin Setiawan Andrea Stevens Karnyoto Indo Intan Bens Pardamean Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83.

关键词： Support vector machines Accuracy Head Computational modeling Transfer learning Breast cancer Magnetic heads Data models Reliability Image classification

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Enhanced Tuberculosis Treatment and Prevention Using Genetic Algorithm Based Control Techniques

Enhanced Tuberculosis Treatment and Prevention Using Genetic...

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International Bhurban Conference on Applied sciences & Technology, IBCAST

作者： Atif Rehman Syed Hassan Ahmed Shah Iftikhar Ahmad Absaar ul Jabbar School of Interdisciplinary Engineering and Sciences (SINES) National University of Sciences and Technology (NUST) Islamabad Pakistan School of Electrical Engineering and Computer Science (SEECS)

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

Tuberculosis (TB) is a highly perilous infectious disease triggered by the bacteria known as mycobacterium tuberculosis (MTB). It primarily impacts the lungs but can also invade and affect various other organs and body parts. Researchers have extensively utilized the susceptible, exposed, infected, recovered, and resistant (SEIRS) model to enhance their understanding of TB transmission dynamics. This paper introduces a novel approach utilizing the controllers, like proportional integral derivative (PID) and sliding mode control (SMC). The objective is to devise effective treatment strategies to reduce the number of infected individuals while simultaneously employing vaccination to prevent disease transmission among susceptible and exposed individuals. A genetic algorithm (GA) is employed to optimize the gains of the proposed controller, utilizing the integral time absolute error as the objective function. The stability of the proposed controllers is demonstrated through mathematical analysis using the Lyapunov stability criteria. Performance comparisons of the controllers are conducted using MATLAB/Simulink simulations, while a hardware-in-loop experimental setup involving the C2000 Delfino and MCU F28379D Launchpad is utilized for real-time scenario validation.

关键词： Asymptotic stability Tuberculosis Stability criteria Manuals Real-time systems Vaccines MATLAB Sliding mode control Genetic algorithms Convergence

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Deep Learning Based Uplink Precoding for High Speed Train Communications in FD-RAN

Deep Learning Based Uplink Precoding for High Speed Train Co...

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IEEE Conference on Vehicular Technology (VTC)

作者： Zeyu Sun Jiwei Zhao Yunting Xu Jiacheng Chen Haibo Zhou Lian Zhao School of Electronic Science and Engineering Nanjing University Information Science and Electronic Engineering Zhejiang University Department of Strategic and Advanced Interdisciplinary Research Peng Cheng Laboratory Department of Electrical Computer Biomedical Engineering Toronto Metropolitian University

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

High speed train (HST) communications with multi-user multiple-input multiple-output (MU-MIMO) techniques have shown great potential in system performance improvements. However, the challenges caused by higher pilot overhead and the belated channel state information (CSI) feedback in such high mobility scenarios still need to be further addressed. To this end, fully-decoupled radio access network (FD-RAN) with novel location-based feedback-free transmission scheme and cooperative transmission/reception in separated downlink/uplink networks is regarded as a promising solution. In this paper, we study the uplink precoding design in FD-RAN for HST communications. To capture the inherent relation between location and precoding, the line-of-sight (LoS) channel is derived from location and then fed into a proposed precoding design neural network (PDNN) which learns to jointly optimize the precoding scheme for all the multi-antenna mobile relays (MRs) in uplink MU-MIMO. We adopt a custom loss function to optimize the spectrum efficiency (SE). Moreover, a joint signal reception method is given, also based solely on the LoS channel derived from location, so as to avoid frequent pilot transmission. Simulation results show the advantages of FD-RAN against other architectures and demonstrate that our proposed PDNN achieves better performance than traditional precoding scheme in high mobility scenarios.

关键词： Vehicular and wireless technologies Precoding OFDM System performance Simulation Neural networks Rician channels

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