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Hybrid Deep learning-Based Approach for Enhanced Multi-Label Emotion Classification 11

Hybrid Deep Learning-Based Approach for Enhanced Multi-Label...

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11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： Mohamed, Ebtesam Hussien Ismael, Osama El Kadi, Hatem Cairo University Faculty of Computers and Artificial Intelligence Information Systems Department Cairo Egypt

ISBN: (纸本)9798350322101

Nowadays, social networking is controlling our lives. Every passing second, millions of tweets are shared by individuals through online social media platforms like Twitter as a means to express their feelings and thoughts. Exploring and examining the emotions conveyed within social media content, along with extracting valuable insights from extensive data, categorizing it into various groups, and foreseeing end-user behavior or emotions, holds potential advantages across multiple domains such as public health, social welfare, commerce, etc. this paper offered a deep learning-based solution for addressing multiple emotion classification challenges on Twitter. the novel proposed presents an advanced deep learning system with a hybrid approach that harnesses the strengths of combining Bidirectional Long-Short Term Memory and Recurrent Neural Network with an additional attention mechanism. this model has been trained using the "SemEval 2018 Task1 (Twitter data)"to evaluate the performance of the suggested model. the experimental outcomes demonstrated that, compared to individual SVM, RNN, or LSTM models, as well as hybrid models, it produces classification results that are more accurate and have greater F1 scores and recall. Our model surpasses the existing cutting-edge systems, achieving a superior accuracy score of 0.90. the proposed model provides an alternate resolution to the issue of long-term dependency within current models while also addressing the challenge of data loss with growing training data sizes. the presented model achieved improved accuracy, which was amplified further with larger training data sizes as well as an increased number of training epochs. © 2023 IEEE.

关键词： Long short-term memory

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AI-Driven Diagnostics: Comparative Analysis of Machine learning and Deep learning Approaches in Multiple Sclerosis Diagnostics 11

AI-Driven Diagnostics: Comparative Analysis of Machine Learn...

引用

11th IEEE international conference on Advances in Computing and Communications, ICACC 2024

作者： Anitha, L. Narmadha, D. Naveen Sundar, G. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350379136

Multiple Sclerosis (MS) is a chronic autoimmune disorder that targets the central nervous system, resulting in considerable physical and cognitive disabilities. Timely and precise diagnosis is essential for the effective management and treatment of Multiple Sclerosis (MS). Predicting MS involves using various types of data to assess the risk and diagnose the disease. the data commonly used are clinical, imaging, speech and gene data. this work presents a review of machine learning and deep learning methods, encompassing a broad spectrum of algorithms such as Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Convolutional Neural Networks (CNNs), and Artificial Neural Networks (ANNs). Furthermore, the survey gives a comparative analysis of the dataset used, algorithms employed and the performance metrics. the survey also compares the implemented results with DL using the gene data. It also identifies current challenges in MS prediction, such as heterogeneity of MS, integration of multimodal data, validation and clinical implementation. Our goal is to offer a valuable resource for researchers and practitioners. © 2024 IEEE.

关键词： Convolutional neural networks

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Prediction of Diabetes using Machine learning Algorithms 11

Prediction of Diabetes using Machine Learning Algorithms

引用

11th international conference on Internet of Everything, Microwave engineering, Communication and Networks, IEMECON 2023

作者： Sidana, Khushi Narsee Monjee Institute of Management Studies Computer Science Mumbai India

ISBN: (纸本)9781665475129

Diabetes, a chronic and lethal disease is affecting individuals not only in India but all over the world at an exponential rate. this disease, if not predicted and left untreated can be very dangerous. Hence, with the rise in the prevalence of this disease, multiple researchers have been trying to find the most optimal methods for predicting diabetes using Machine learning algorithms (ML) and data Mining. In this paper, the objective is to predict diabetes using five supervised machine learning algorithms and compare their performance and accuracy. For the same, I compared the performance of Decision Tree, Logistic Regression, K Nearest Neighbours, Random Forest Tree and Support Vector Machine algorithms to get the best possible accuracy. Furthermore, owing to the fact that a larger and better dataset can improve the model's accuracy, I tested this theory by comparing the results of two separate datasets and achieved an accuracy of 97% using the Random Forest algorithm. this paper also discusses the impact overfitting and underfitting can have on the overall outcome. I hope this paper along with other such research works contributes to helping doctors and other personnel in the medical field to predict diabetes using machine learning algorithms. © 2023 IEEE.

关键词： Decision trees

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An intelligent Feature Extraction and Multiple learning-Based Classification Techniques for Diagnosis of Breast Cancer 8th

An Intelligent Feature Extraction and Multiple Learning-Base...

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8th international conference on Internet of things and Connected Technologies, ICIoTCT 2023

作者： Patro, Satyabrata Mishra, Jyotirmaya Panda, Bhavani Sankar Department of Computer Science and Engineering GIET University Odisha Gunupur765022 India Department of Computer Science and Engineering Raghu Engineering College Andhra Pradesh Visakhapatnam531162 India

ISBN: (纸本)9789819757855

Breast cancer is often curable if discovered in its early stages. Machine learning algorithms are used to automate disease identification. To increase the likelihood of recognizing the condition at an early stage, an effective classifier for automated diagnosis of breast cancer is needed. this research attempts to introduce a mixed machine learning model for predicting breast cancer. To outperform other strategies, it requires enormous amounts of data. Moreover, the intricate data models increase the training cost. For enhancing the efficiency of basic classifiers, ensemble learning holds great promise. A rapid discrete wavelet transforms using the wrapping approach is then used to extract the characteristics from the Region of Interest (ROI) mammography pictures. In addition to being too high to be categorized, the extracted wavelet coefficients also have incredibly high run-time complexity. the modified whale optimization method, which employs swarm intelligence, has been presented to lower time complexity and select the significant characteristics. Ensemble learning has been applied for the requisite efficiency in the suggested technique. three machine learning (ML) classifiers—the extreme gradient boost classifier (XGB), support vector machine (SVM), and enhanced granular neural network (E-GNN)—form the ensemble voting system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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WHEN SOURCE-FREE DOMAIN ADAPTATION MEETS learning WIth NOISY LABELS 11

WHEN SOURCE-FREE DOMAIN ADAPTATION MEETS LEARNING WITH NOISY...

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11th international conference on learning Representations, ICLR 2023

作者： Yi, Li Xu, Gezheng Xu, Pengcheng Li, Jiaqi Pu, Ruizhi Ling, Charles McLeod, A. Ian Wang, Boyu Department of Statistical and Actuarial Sciences University of Western Ontario Canada Department of Computer Science University of Western Ontario Canada

Recent state-of-the-art source-free domain adaptation (SFDA) methods have focused on learning meaningful cluster structures in the feature space, which have succeeded in adapting the knowledge from source domain to unlabeled target domain without accessing the private source data. However, existing methods rely on the pseudo-labels generated by source models that can be noisy due to domain shift. In this paper, we study SFDA from the perspective of learning with label noise (LLN). Unlike the label noise in the conventional LLN scenario, we prove that the label noise in SFDA follows a different distribution assumption. We also prove that such a difference makes existing LLN methods that rely on their distribution assumptions unable to address the label noise in SFDA. Empirical evidence suggests that only marginal improvements are achieved when applying the existing LLN methods to solve the SFDA problem. On the other hand, although there exists a fundamental difference between the label noise in the two scenarios, we demonstrate theoretically that the early-time training phenomenon (ETP), which has been previously observed in conventional label noise settings, can also be observed in the SFDA problem. Extensive experiments demonstrate significant improvements to existing SFDA algorithms by leveraging ETP to address the label noise in SFDA. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

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Projecting the Needs of intelligent Transportation System (ITS) Based on Comfort and Security Views of Trans Jateng's Women Ridership 11

Projecting the Needs of Intelligent Transportation System (I...

引用

11th international conference on ICT for Smart Society, ICISS 2024

作者： Romadlon, Fauzan Saintika, Yudha Aliyah, Khikmatul Hamid, Abdul Institut Teknologi Telkom Purwokerto Industrial Engineering Department Purwokerto Indonesia Institut Teknologi Telkom Purwokerto Information System Department Purwokerto Indonesia Universiti Tun Hussein Onn Technology Studies Department Johor Darul Ta'zim Malaysia

ISBN: (纸本)9798350389654

the ridership of the Bus Rapid Transit (BRT) Trans Jateng is dominated by women. the domination will lead to the suggestion of additional services to accommodate their needs. therefore, a service perception is needed to measure the potential for implementing the intelligent Transportation System (ITS) service. ITS is considered to facilitate Purwokerto and Purbalingga initiating smart city concepts. the methods used are quantitative and qualitative. the quantitative method is descriptive statistics and the Analysis of Variance (ANOVA) test, while the qualitative method emphasizes the analysis of the potential indicators of ITS implementation. the results show a significant correlation between women's ridership jobs and the presence of security officers at each bus stop to ensure a low crime rate. A significant correlation is also between the response to using priority seats according to their function and age. then, the potential needs for ITS products or services based on response mapping are pre-trip information, en-route driver information, route guidance, service information for women ridership, en-route transit, personalized public transit, and public travel security. the implementation of the potential of ITS services implementation needs to be applied to improve security and comfort effectively. © 2024 IEEE.

关键词： Rapid transit

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FLIP: A PROVABLE DEFENSE FRAMEWORK FOR BACKDOOR MITIGATION IN FEDERATED learning 11

FLIP: A PROVABLE DEFENSE FRAMEWORK FOR BACKDOOR MITIGATION I...

引用

11th international conference on learning Representations, ICLR 2023

作者： Zhang, Kaiyuan Tao, Guanhong Xu, Qiuling Cheng, Siyuan An, Shengwei Liu, Yingqi Feng, Shiwei Shen, Guangyu Chen, Pin-Yu Ma, Shiqing Zhang, Xiangyu Purdue University United States IBM Research United States Rutgers University United States

Federated learning (FL) is a distributed learning paradigm that enables different parties to train a model together for high quality and strong privacy *** this scenario, individual participants may get compromised and perform backdoor attacks by poisoning the data (or gradients).Existing work on robust aggregation and certified FL robustness does not study how hardening benign clients can affect the global model (and the malicious clients).In this work, we theoretically analyze the connection among cross-entropy loss, attack success rate, and clean accuracy in this ***, we propose a trigger reverse engineering based defense and show that our method can achieve robustness improvement with guarantee (i.e., reducing the attack success rate) without affecting benign *** conduct comprehensive experiments across different datasets and attack *** results on nine competing SOTA defense methods show the empirical superiority of our method on both single-shot and continuous FL backdoor *** is available at https://***/KaiyuanZh/FLIP. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Network security

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Predictive Analysis of Ship Heave Motion and Ensemble learning Prediction Strategy 11

Predictive Analysis of Ship Heave Motion and Ensemble Learni...

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11th IEEE international conference on Computer Science and Network Technology, ICCSNT 2023

作者： Qin, Zhang Daijing, He Xiong, Hu Logistics Engineering College Shanghai Maritime University Shanghai China

ISBN: (纸本)9798350311594

Prediction of ship heave motion is of great significance to wave compensation, and high-precision prediction is the most important prerequisite for wave compensation control. therefore, a prediction analysis of ship heave motion and ensemble learning prediction strategy are proposed in this paper. First, Hurst index is calculated to analyze the predictability of time series. Secondly, the one-dimensional sequence is reconstructed into a multidimensional phase space. Finally, the number of Long Short-term memory models in phase space involved in ensemble learning is determined, and multiple phase space LSTM models with different weights are used for AdaBoost ensemble prediction to obtain multi-step heave motion prediction data. the experimental results show that the data with large Hurst index under the same sea state level are more predictable, and the multi-step prediction effect of LSTM and AdaBoost ensemble learning through phase space reconstruction is better than ARIMA, SVM, BP, and LSTM models, which can provide high-precision prediction for wave compensation of ship heave motion. © 2023 IEEE.

关键词： Forecasting

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Assistive Multimodal Wearable for Open Air Digit Recognition Using Machine learning 11

Assistive Multimodal Wearable for Open Air Digit Recognition...

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11th international IEEE EMBS conference on Neural engineering (IEEE/EMBS NER)

作者： Rattray, John M. Ujhazy, Maxwell Stevens, Robert Etienne-Cummings, Ralph Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA Duquesne Univ Sch Hlth Sci Pittsburgh PA USA Johns Hopkins Univ Lab Computat Intens Care Med Baltimore MD USA

ISBN: (纸本)9781665462921

To increase access to digital systems for populations suffering from upper limb motor impairment we present an assistive wearable device to capture gestures performed in air. these open air gestures provide an interface for users who are unable to exhibit the fine motor control needed for standardized human computer interfaces utilizing miniature button input such as keyboards and keypads. By capturing the motion performed at the wrist by an accelerometer as well as the muscle activation signatures using surface electromyography, we improve the classification accuracy as compared to using either modality alone. Twelve features were extracted from the multimodal time series data in both the time and frequency domain and used as input to a collection 4 machine learning models for classification, Fine Tree, K-Nearest Neighbor, Support Vector Machine, and Artificial Neural Network. One subject performed the task of writing single digits in free space and after post-processing and feature extraction we achieved a classification accuracy of 96.2% for binary discrimination of digits zero and one using a support vector machine model and an accuracy of 71% when classifying all 10 digits using an artificial neural network. Our findings indicate the feasibility of a wearable multimodal human computer interface to relieve the burden conventional interfaces present to motor impaired users.

关键词： electromyography human computer interface assistive technology wearables gesture recognition upper limb motor impairment open air gestures

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Requirements of Machine learning and Semantic Enrichment for BIM-Based automated Code Compliance Checking: A Focus Group Study 19th

Requirements of Machine Learning and Semantic Enrichment for...

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19th international conference on Computing in Civil and Building engineering, ICCCBE 2022

作者： Karmakar, Ankan Delhi, Venkata Santosh Kumar Indian Institute of Technology Bombay Mumbai400076 India

ISBN: (纸本)9783031353987

Several regulations, standards, and requirements govern the lifecycle of the built environment. Such instances include legislations, government development control rules, environmental compliances, project and contractual requirements, and performance standards to be followed during construction. Compliance checking is a complex task that is often conducted manually, making it a resource-intensive, error-prone, and time-consuming affair. Past researchers have worked on methods and processes of automated compliance checking systems (ACCS);however, there has been a negligible adaptation in the industry. To this end, this study tries to recognize the reasons for the gap in the implementation of the pre-construction permit compliance systems in the Indian construction industry. the study understands the reasons from the end-user’s perspective through the means of a focus group study. Key findings indicate manual pre-processing of data is a significant hurdle in Building Information Models (BIM) for application in ACCS. ACCS applications developed are restricted in their area of usage due to the limitation in applicability beyond explicit building code clauses. Rule-based validation of regulation requires enriched structured data, which is generally absent from the models developed by architects in the design phase. the study indicates the necessity of automated data pre-processing step that includes intelligent model filling suggestions and semantic enrichment to increase the adoption of ACCS. this study points out that automatic semantic enrichment (SE) can be achieved by applying machine learning (ML). Areas of application of SE in ACCS are identified in the study, which can enhance the industry’s user experience and adaptation of ACCS. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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