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2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Katarya, Rahul Nath, G Amar Singhal, Divit Shukla, Aryaman Delhi Technological University Big Data Analytics and Web Intelligence Laboratory Department of Computer Science Engineering New Delhi India

ISBN: (纸本)9781665495295

The coronavirus emanated in Wuhan city of China, in the last month of 2019 and was even announced as a global threat. Social media could be an utterly noteworthy supply of facts during a time of crisis. User-generated texts yield perception into users' minds withinside the direction of such times, giving us insights into their critiques in addition to moods. This venture examines Twitter messages (tweets) regarding people's sentiment on the unconventional coronavirus. The essential aim of sentiment evaluation is the origin of human emotion from messages or tweets. This venture is geared toward using numerous gadgets studying type algorithms to expect the people's reception of the worldwide pandemic by reading their tweets on Twitter. In the course of this paper, we are testing our dataset on five different classifiers, namely Random Forest, Logistic regression, Multinomial naive Bayes, K-nearest neighbor, and Support vector machines classifiers. Together with precision rankings and balanced accuracy rankings, metrics are offered to gauge the fulfilment of the numerous algorithms implemented. The K-Nearest Neighbor classifier has given the highest precision score while the Logistic Regression classifier gives the highest recall, F1, accuracy and balanced accuracy scores. © 2022 IEEE.

关键词： Coronavirus

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Towards Graph-Based Semi-Supervised Learning on Audio Embeddings for Label Classification 22

Towards Graph-Based Semi-Supervised Learning on Audio Embedd...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Chandaliya, Rishabh Timilsina, Mohan Breslin, John Serrano, Martin University of Galway Ireland Insight Sfi Research Centre for Data Analytics Department of Computer Science Galway Ireland

ISBN: (纸本)9798350345346

The increasing importance of audio-based healthcare diagnostics, particularly in chronic respiratory problems, has prompted the development of novel approaches for disease classification using the full spectrum analysis of cough sounds. This paper presents an innovative study exploring the potential of employing supervised and semi-supervised learning methodologies for disease categorization based on cough audio samples. Specifically, this study focuses on scenarios characterized by a shortage of annotated data related to chronic diseases. The objectives of our study involved the utilization of standard machine learning algorithms for direct classification based on embeddings, as well as the integration of Graph Neural Networks (GNNs) on the KNN graph. Preliminary results indicate that GNN models consistently outperformed traditional classifiers. For instance, with just 1 % of the data, GAT and GCN achieved AUC PR values of 0.84 and 0.87, respectively, surpassing all traditional methods. The superiority mentioned above was sustained even when the data fraction was augmented to 3% and 5%. The usefulness of graph neural networks (GNNs) was further supported by comprehensive performance measures, wherein the graph convolutional network (GCN) exhibited exceptional preci-sion and PR (precision-recall). In summary, the AudioVecDiagnosis framework presents a promising opportunity for further investigation in audio-based healthcare diagnostics. It provides an optimum approach for situations with a limited availability of labeled data. © 2023 IEEE.

关键词： Audio-based medical diagnostics AudioVec-Diagnosis Cough noises Disease classification Semi-supervised learning Wav2Vec2.0 model

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Methane (CH4) Detection System Using the TGS2611 Sensor and MQ-4 Sensor 3

Methane (CH4) Detection System Using the TGS2611 Sensor and ...

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3rd International Symposium on Physics and Applications 2023, ISPA 2023

作者： Ainur Rahma, Mellynia Suweni Muntini, Melania Sugriwan, Iwan Department of Physics Faculty of Science and Data Analytics Sepuluh Nopember Institute of Technology Jl. Arief Rahman Hakim Surabaya60111 Indonesia

This research focused on cow dung digesters as measurement samples, utilizing the TGS2611 and MQ-4 sensors, both known for their affordability. The objective was to develop a low-cost gas sensor system for monitoring methane gas concentrations through a straightforward web-based data acquisition platform. The DHT11 sensor was employed to measure air temperature and humidity. The electronic sensor circuit in this research utilized a voltage divider circuit. Testing the performance of the low-cost gas sensor involved integrating it with an anaerobic digestion system. For both the TGS2611 and MQ-4 sensors, the graph illustrating the relationship between sensor voltage (volts) and methane gas concentration (ppm) formed a logarithmic or non-linear pattern. The measurement range for the system was around 4700 ppm to 6300 ppm for TGS2611 and 4000 ppm to 5100 ppm for MQ-4. The sensor sensitivity was determined as 0.5433 Volts/ppm for TGS2611 and 0.5639 Volt/ppm for MQ-4. The TGS2611 sensor exhibited a higher response to changes in methane gas concentration, while the MQ-4 sensor successfully detected methane gas within its designated range. Interestingly, the TGS2611 sensor accurately detected methane gas concentrations above its specified range. The gas sampling procedure involved automatic gas sampling in a vacuum chamber for measurement, a strategy potentially enhancing sensor robustness. This research successfully amalgamated sensor advantages, circuit control, and data analysis to realize an economical yet reliable monitoring system. © Published under licence by IOP Publishing Ltd.

关键词： data acquisition

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Classification of Sailboat Tell Tail Based on Deep Learning

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Journal of Ocean University of China 2024年第3期23卷 710-720页

作者： CHANG Xiaofeng YU Jintao GAO Ying DING Hongchen LIU Yulong YU Huaming Department of Physical Education Ocean University of ChinaQingdao 266100China College of Big Data Qingdao University of Science and TechnologyQingdao 266061China College of Oceanic and Atmospheric Sciences Ocean University of ChinaQingdao 266100China

The tell tail is usually placed on the triangular sail to display the running state of the air flow on the sail *** is of great significance to make accurate judgement on the drift of the tell tail of the sailboat during sailing for the best sailing *** it is difficult for sailors to keep an eye for a long time on the tell sail for accurate judging its changes,affected by strong sunlight and visual *** this case,we adopt computer vision technology in hope of helping the sailors judge the changes of the tell tail in ease with *** paper proposes for the first time a method to classify sailboat tell tails based on deep learning and an expert guidance system,supported by a sailboat tell tail classification data set on the expert guidance system of interpreting the tell tails states in different sea wind conditions,including the feature extraction *** the expression capabilities that vary with the computational features in different visual tasks,the paper focuses on five tell tail computing features,which are recoded by an automatic encoder and classified by a SVM *** experimental samples were randomly divided into five groups,and four groups were selected from each group as the training set to train the *** remaining one group was used as the test set for *** highest resolution value of the ResNet network was 80.26%.To achieve better operational results on the basis of deep computing features obtained through the ResNet network in the *** method can be used to assist the sailors in making better judgement about the tell tail changes during sailing.

关键词： tell tail sailboat classification deep learning

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Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems

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Computers, Materials & Continua 2024年第8期80卷 2721-2741页

作者： Tareq Hamadneh Khalid Kaabneh Ibraheem AbuFalahah Gulnara Bektemyssova Galymzhan Shaikemelev Dauren Umutkulov Sayan Omarov Zeinab Monrazeri Frank Werner Mohammad Dehghani Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Faculty of Information Technology Al-Ahliyya Amman UniversityAmman19328Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Department of Big Data Analytics and Data Science Software Development Company《QazCode》Almaty050063Kazakhstan Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany

This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization(MFO),inspired by the unique behaviors observed in magnificent frigatebirds in their natural *** foundation of MFO is based on the kleptoparasitic behavior of these birds,where they steal prey from other *** this process,a magnificent frigatebird targets a food-carrying seabird,aggressively pecking at it until the seabird drops its *** frigatebird then swiftly dives to capture the abandoned prey before it falls into the *** theoretical framework of MFO is thoroughly detailed and mathematically represented,mimicking the frigatebird’s kleptoparasitic behavior in two distinct phases:exploration and *** the exploration phase,the algorithm searches for new potential solutions across a broad area,akin to the frigatebird scouting for vulnerable *** the exploitation phase,the algorithm fine-tunes the solutions,similar to the frigatebird focusing on a single target to secure its *** evaluate MFO’s performance,the algorithm is tested on twenty-three standard benchmark functions,including unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** results from these evaluations highlight MFO’s proficiency in balancing exploration and exploitation throughout the optimization *** studies with twelve well-known metaheuristic algo-rithms demonstrate that MFO consistently achieves superior optimization results,outperforming its competitors across various *** addition,the implementation of MFO on four engineering design problems shows the effectiveness of the proposed approach in handling real-world applications,thereby validating its practical utility and robustness.

关键词： Optimization metaheuristic magnificent frigatebirds exploration exploitation

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Website-Based Monitoring System for Methane ( CH4) Concentration, Humidity, and Temperature 3

Website-Based Monitoring System for Methane ( CH4) Concentra...

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3rd International Symposium on Physics and Applications 2023, ISPA 2023

作者： Isnaini, Nur Suweni Muntini, Melania Sugriwan, Iwan Department of Physics Faculty of Science and Data Analytics Sepuluh Nopember Institute of Technology Jl. Arief Rahman Hakim Surabaya60111 Indonesia

The research developed a monitoring system that focuses on the development of a system and the acquisition of its readings into a simple website. This research was conducted in an effort to obtain data that can be used for managing methane gas pollution in the environment. Methane gas (CH4) is a natural gas consisting of one carbon atom and four hydrogen atoms. Methane gas is one of the greenhouse gases that contribute to global warming, while humidity and temperature can increase or decrease methane gas productivity. Therefore, in this research, a monitoring system was created for methane gas concentration using TGS2611 sensor, humidity and temperature using DHT11 sensor through the creation of a chamber connected to test materials such as cow dung digesters and tofu wastewater as a source of methane gas production. The results obtained indicate that, in the development of the monitoring system as a measuring tool, it successfully detects the concentration of methane gas, humidity and temperature for each test material using calibration through characteristic equation V = 0,5591 ln(n) - 2,2657. For data acquisition in the form of methane gas concentration parameter readings by the TGS2611 sensor, humidity and temperature by the DHT11 sensor to the ESP8266 microcontroller to be displayed on the serial monitor, LCD, and the website that has been created in real-time. To display the data acquisition of the monitoring system, each component of the system is connected. Starting from software, hardware, and test materials. data acquisition begins with sensor readings by ESP8266 through a program from Arduino IDE displayed on the serial monitor and LCD. Once connected to wifi or a hotspot and the data is successfully sent, the data will be checked by notepad++ and entered into the phpMyAdmin website database for storage. From this database. The data will then be displayed on the website that has been created in real-time. Additionally, humidity and temperature affect the

关键词： data acquisition

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Strategic Blended Learning for Addressing Multiclass Imbalance in Classification

Strategic Blended Learning for Addressing Multiclass Imbalan...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Sridhar, S. Anusuya, S. SRM Institute of Science and Technology Department of CSE Emerging Technologies TN Chennai India Saveetha School of Engineering Department of Data Analytics SIMATS TN Chennai India

ISBN: (纸本)9798331512965

Numerous practical applications involve multiclass classification challenges, where the spread of data across the classes is frequently uneven. Due to this inherent data imbalance, supervised learning models often exhibit a bias toward classifying instances into the category with the highest number of data points, posing a significant problem that requires attention. In the realm of classification of data with multiple classes and imbalanced distributions, researchers in machine learning persistently endeavor to mitigate the classifier's inclination towards dominant class with more sample size. Existing studies aim to alleviate this inclination by alternatively adjusting the data for balance prior to the classifier's learning process or adjusting the classifier's training process to focus more on rare class having least number of samples, or employing hybrid strategies. Existing learning approaches encounter challenges in delineating clear boundaries between samples of classes resulting from an imbalanced class distribution and issues with class overlapping. Consequently, the recognition rate for minority classes is hindered. A novel Strategic Blended Learning (SBL) approach is introduced, employing dual decision trees. The first decision tree is utilized to generate an induced dataset through a grouping approach, while the second decision tree is employed to train and make decisions guided by the induced dataset. A noteworthy aspect of this algorithmic approach is its ability to identify rare data points without affecting the inherent distribution of data. To evaluate the proposed algorithm, various multiclass imbalanced datasets from UCI were employed for classification. The findings revealed that the Strategic Blended Learning method excels in detecting both rare and dominant category samples. © 2025 IEEE.

关键词： Contrastive Learning

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Multi-Agent RAG Chatbot Architecture for Decision Support in Net-Zero Emission Energy Systems 25

Multi-Agent RAG Chatbot Architecture for Decision Support in...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Gamage, Gihan Mills, Nishan De Silva, Daswin Manic, Milos Moraliyage, Harsha Jennings, Andrew Alahakoon, Damminda La Trobe University Centre For Data Analytics And Cognition Melbourne Australia Virginia Commonwealth University Department Of Computer Science Richmond United States

ISBN: (纸本)9798350340266

Modern energy platforms are increasingly leveraging Artificial Intelligence (AI) for effective decision-making and efficient operations. This has led to the development of expansive data spaces that comprise both structured and unstructured energy data in various modalities. Conversational agents with the most recent advancements in Large Language Models (LLM) are primed to facilitate the efficient retrieval of this diverse information for decision support. In this paper, we propose a multi-agent chatbot architecture for decision support in net-zero emissions energy systems, leveraging LLMs and Retrieval-Augmented Generation (RAG). This architecture consists of a Chatbot User Interface (UI), an advanced Natural Language Understanding (NLU) module for precise entity and intent recognition, a robust Chatbot Core with four specialized agents: Observer, Knowledge Retriever, Behavior Analyzer, and Visualizer and Response Construction Module. These components work together to address diverse decision support needs in energy environments, specifically for net zero carbon emissions initiatives that need to consider diverse parameters and large volumes of data. We showcase the chatbot's successful integration and evaluation for decision support in the net-zero emissions energy system of a large tertiary education institution. © 2024 IEEE.

关键词： User interfaces

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Accounting For Product End-of-Life Status in Disassembly Time Estimation Using Modified Maynard Operation Sequences 30

Accounting For Product End-of-Life Status in Disassembly Tim...

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30th CIRP Life Cycle Engineering Conference, LCE 2023

作者： Formentini, Giovanni Ramanujan, Devarajan Department of Mechanical and Production Engineering - Design and Manufacturing Aarhus University Katrinebjergvej 89 G-F Aarhus N8200 Denmark Centre for Digitalisation Big Data and Data Analytics Aarhus University Aarhus8200 Denmark

Design for disassembly (DfD) approaches play a vital role in (re)designing products from improving their sustainability and circularity performance. This is particularly true in the case of complex resource-intensive products wherein material recovery and reutilization at their end-of-life (EoL) is dictated by the ease-of-disassembly. Various DfD approaches have been developed for increasing recovery of valuable materials by reducing the overall disassembly time (DT). However, such methods do not consider the actual EoL status of a product while estimating DT. The lack of a systematic approach for adjusting DT based on the EoL product status (e.g., fully operational product, presence of functional failures in specific parts, missing parts) prevents current DfD approaches from accurately assessing DT and can therefore lead designers towards solutions that reduce the overall sustainability and circularity performance of products. To address this gap, the proposed study develops an approach that can account for EoL product status during DT estimation of DfD scenarios during early design. The central idea of this approach is modifying standardized motion sequences for disassembly operations based on potential failure modes in a product's EoL. The proposed approach was tested on a real case study involving an electrical kettle. Results show the proposed approach can generate new insights regarding product disassembly performance. In particular, some Failure Modes can lead to a reduction of DT due to changes in disassembly action type and the corresponding product EoL scenario (i.e., from reuse to disposal). In such cases, a reduction in DT can be counterproductive from a circular economy perspective as the gains result from a diminished ability to recover/reuse products. © 2023 The Authors. Published by Elsevier B.V.

关键词： Sustainable development

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A structural variation genotyping algorithm enhanced by CNV quantitative transfer

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Frontiers of Computer science 2022年第6期16卷 159-165页

作者： Tian ZHENG Xinyang QIAN Jiayin WANG Department of Computer Science and Technology School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an710049China Institute of Data Science and Information Quality Shaanxi Engineering Research Center of Medical and Health Big DataXi’an Jiaotong UniversityXi’an710049China

Genotyping of structural variations considering copy number variations(CNVs)is an infancy and challenging ***,a prevalent form of critical genetic variations that cause abnormal copy numbers of large genomic regions in cells,often affect transcription and contribute to a variety of *** characteristics of CNVs often lead to the ambiguity and confusion of existing genotyping features and algorithms,which may cause heterozygous variations to be erroneously genotyped as homozygous variations and seriously affect the accuracy of downstream *** the allelic copy number increases,the error rate of genotyping increases *** instances with different copy numbers play an auxiliary role in the genotyping classification problem,but some will seriously interfere with the accuracy of the *** by these,we propose a transfer learning-based method to genotype structural variations accurately considering *** method first divides the instances with different allelic copy numbers and trains the basic machine learning framework with different genotype *** maximizes the weights of the instances that contribute to classification and minimizes the weights of the instances that hinder correct *** adjusting the weights of the instances with different allelic copy numbers,the contribution of all the instances to genotyping can be maximized,and the genotyping errors of heterozygote variations caused by CNVs can be *** applied the proposed method to both the simulated and real datasets,and compared it to some popular algorithms including GATK,Facets and *** experimental results demonstrate that the proposed method outperforms the others in terms of accuracy,stability and *** source codes have been uploaded at github/TrinaZ/CNVtransfer for academic use only.

关键词： genotyping copy number variations transfer earning

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