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OCEANS 2024 - Halifax, OCEANS 2024

作者： Chapuis, Lucille Burca, Mihai Gridley, Tess Mouy, Xavier Nath, Artash Nedelec, Sophie Seeyave, Sophie Theriault, Jim Urban, Ed Williams, Rob Zimmer, Walter M.X. School of Biological Sciences University of Bristol BristolBS8 1TQ United Kingdom National Institute of Oceanography and Applied Geophysics Ogs Sgonico Italy University of Cape Town Dept. of Statistical Science Cape Town South Africa Applied Ocean Physics And Engineering Woods Hole Oceanographic Institution Woods HoleMA United States Computer Science and Envoronment Stanford University StanfordCA United States Bioscience Hatherly Laboraties University of Exeter ExeterEX4 4PS United Kingdom Partnership for Observation of the Global Ocean Plymouth United Kingdom Ocean Environmental Consulting Theriault Environmental Consulting HalifaxNS Canada NewarkDE United States Oceans Intitiative SeattleWA United States Center for Maritime Research and Experimentation Nato Science and Technology Office La Spezia Italy

ISBN: (纸本)9798331540081

In an effort to support the widespread use of passive acoustic monitoring devices in marine environments, a Task Team on Low-Cost Hydrophones for Research, Education, and Community science was launched under the auspices of the Partnership for Observation of the Global Ocean and the International Quiet Ocean Experiment. To this aim, the Task Team is developing an open-source Low-Cost Marine Acoustic REcorder (LC-MARE), including hydrophone ceramics with a frequency response range of 10 Hz to 100 kHz, an effective sensitivity of at least -180 dB re V/µPa and an endurance exceeding one week of continuous recording. To minimise costs, the proposed deployment depth will be limited to 100 m and the pressure housing will be made of commercially available PVC tubes and fittings. The envisioned price tag of less than US$200 makes the suggested marine acoustic recorder affordable in low-budget educational and community science contexts, as well as scientific applications where finances are limiting (for example in developing countries and for entry-level researchers). © 2024 IEEE.

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Customer Churn Prediction in the Telecom Sector with Machine Learning and Adaptive k-Means Cluster using Imbalance Data

Customer Churn Prediction in the Telecom Sector with Machine...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： N L Aravinda R Archana Reddy Bala Dhandayuthapani V Jithendra P R Nayak Ahmad Husseen Electronics and Communication Engineering Department Mallareddy Engineering College for Women (Autonomous) Hyderabad Telangana India School of Computer Science and Engineering SR University Warangal Telangana India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas branch Oman Department of CSE Srinivas Institute of Technology Valachil Mangalore India The Islamic university Najaf Iraq

A crucial aspect of maintaining a customer-oriented business in the telecommunications sector with machine learning (ML) is understanding the reasons and factors that lead to customer churn. However, the dataset is difficult by noise, misclassifications, duplicated data, and imbalanced customer information complicating the process of identifying ways to split the dataset based on events. The under-sampling method address the imbalance data by decreasing the data quantity of majority class, thereby achieving a balanced dataset. This method, utilizing Adaptive k-means clustering, directly determines the reduction based on that data quality in each class. The datasets are combined with class labels to generate a new balanced dataset used for classification in the SVM algorithm, which distinguishes between customer churn and non-churn in the prediction for the telecom sector. The SVM algorithm can perform linear and non-linear classification with a kernel function to control overfitting improve generalization and handle high-dimensional data. The obtained results demonstrate that the proposed method achieves better accuracy of 95.70%, precision of 56.01%, Recall of 55.05%, and F1-score of 96.05% on the telecom churn dataset. These results ensure superior detection and classification performance compared to other existing methods, such as Random Forest (RF), Decision Tree (DT) and K-Nearest Neighbour (KNN).

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Non-sample fuzzy based convolutional neural network model for noise artifact in biomedical images

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Discover applied sciences 2024年第1期6卷 1-14页

作者： Byeon, Haewon Patel, Ruchi Kshatri Vidhate, Deepak A. Kiyosov, Sherzod Rahin, Saima Ahmed Keshta, Ismail Lakshmi, T. R. Vijaya Department of Digital Anti-Aging Healthcare Inje University Gimhae Republic of Korea Shri Ram Institute of Technology Jabalpur India Department of Information Technology Dr Vithalrao Vikhe Patil College of Engineering Ahmednagar India The Department of Tax and Taxation Tashkent Institute of Finance Tashkent Uzbekistan United International University Dhaka Bangladesh Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Mahatma Gandhi Institute of Technology Gandipet Hyderabad India

The use of a light-weight deep learning Convolutional Neural Network (CNN) augmented with the power of Fuzzy Non-Sample Shearlet Transformation (FNSST) has successfully solved the problem of reducing noise and artifacts in Low-Dose Computed Tomography (LDCT) pictures. Both the Normal-Dose Computed Tomography (NDCT) and the Low-Dose Computed Tomography (LDCT) images from the dataset are subjected to the FNSST decomposition procedure during the training phase, producing high-frequency sub-images that act as input for the CNN. The CNN creates a meaningful connection between the high-frequency sub-images from LDCT and their corresponding residual sub-images during the training operation. The CNN is given the capacity to distinguish between LDCT high-frequency sub-images and expected high-frequency sub-images, which frequently have varying levels of noise or artifacts, especially in a fuzzy setting. The FNSST-CNN then successfully distinguishes LDCT high-frequency sub-images from the expected high-frequency sub-images during the testing phase, thereby reducing noise and artifacts. When compared to other approaches like KSVD, BM3D, and conventional image domain CNNs, the performance of FNSST-CNN is impressive as shown by better peak signal-to-noise ratios, stronger structural similarity, and a closer likeness to NDCT pictures. 1. A CNN model has been proposed to reduce the noise and artifacts in Low-Dose Computed Tomography images. 2. During the testing phase, the proposed model successfully distinguishes between high frequency sub-images. 3. CNN performs better than KSVD, BM3D and conventional CNN models in terms of better Signal-to-noise ratio.

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Spam Text Detection using Machine Learning Model

Spam Text Detection using Machine Learning Model

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Mahasak Ketcham Thittaporn Ganokratanaa Patiyuth Pramkeaw Narumol Chumuang Dept. of Information Technology Management King Mongkut's University of Technology North Bangkok Bangkok Thailand Applied Computer Science Program King Mongkut's University of Technology Thonburi Bangkok Thailand Media Technology Program King Mongkut's University of Technology Thonburi Bangkok Thailand Dept. of Digital Media Technology Muban Chombueng Rajabhat University (MCRU) Thailand

This research presents a classification between spam and non-spam messages by removing duplicate sentences or words. meaningless word and various marks The data is then classified by machine learning techniques and compared by differences between the data sets. Quantitative transformations were performed on each model to find the most efficient model. which can filter spam messages efficiently and quickly By getting the best comparison results in terms of information. quantitative conversion and model use The experimental results showed that Of all the tests, the model that performed best was Random Forest, with an average accuracy of 97

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Contrastive learning for multi-layer network community detection via learnable network augmentation

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IEEE Transactions on Network science and Engineering 2025年

作者： Teng, Min Yin, Ze Huang, Jiajin Gao, Chao Li, Xianghua Nekorkin, Vladimir Wang, Zhen Northwestern Polytechnical University School of Cybersecurity Shaanxi Xi'an710072 China Hunan University College of Computer Science and Electronic Engineering Hunan Changsha410082 China Beijing University of Technology School of Information Science and Technology Beijing100124 China Shaanxi Xi'an710072 China Russian Academy of Sciences A.V. Gaponov-Grekhov Institute of Applied Physics Nizhny 603950 Russia

Community detection in multi-layer networks is crucial for revealing the functions of entities and understanding their connections across dimensions. However, existing semi-supervised methods often rely on manual labels, leading to the significant computational overhead in networks with complex structures. Moreover, unsupervised and self-supervised methods usually struggle to integrate the intra-layer and inter-layer features, as well as the local and global features of networks, resulting in the limited accuracy. To address these challenges, this paper proposes a self-supervised Network Augmentation Contrastive Constraint (NACC) method for multi-layer network community detection. Leveraging the ideas of network augmentation and contrastive learning, NACC detects the community structure based on the rich features contained in datasets. Specifically, NACC first integrates the intra-layer and inter-layer features of the multi-layer network to generate a learnable feature-augmented network. Then, it encodes the node and topology features, capturing both the local and global features, and generating the low-dimensional node representations for multi-layer and augmented networks. Moreover, the contrastive learning among different layers is proposed to train the above node representations, further enhancing the fusion of features. Finally, consense communities are detected based on the trained node representation. Extensive experiments demonstrate the performance of NACC in handling networks with numerous layers and complex structures, showcasing its reliability in real-world applications. © 2013 IEEE.

关键词： Contrastive Learning

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Utilizing a machine learning algorithm to choose a significant traffic identification system

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International Journal of Information Management Data Insights 2024年第1期4卷

作者： Natarajan, Rajesh Ranjith, Christodoss Prasanna Mohideen, M．Syed Khaja Gururaj, H.L. Flammini, Francesco Thangarasu, N. Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Al-Aqr Shinas 324 Oman Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences- Salalah Post Box No 608 Salalah 211 Oman Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India IDSIA USI-SUPSI University of Applied Sciences and Arts of Southern Switzerland Switzerland Department of Computer Science Karpagam Academy of Higher Education Tamil Nadu Coimbatore India

The tremendous advancements in computing, sensing, and cognitive-based revolution have made way for critical infrastructure needed to improve the internet for more extensive applications. However, the computation performed by devices and the ability to communicate is limited due to the use of numerous devices for storage. Among the most complex model scenarios, a framework based on a Traffic Identification System (TIS) is a crucial answer. The Web-based brute force and botnet attacks are linked in this initial phase. To forecast the samples using the adaptive bootstrap method, the second phase entails creating a classifier on the training dataset. The normalization model and features are combined in the model, which then identifies traffic attacks on the network. Based on voting mechanisms from several decision trees utilized in the mode, an ensemble model using the Adaptive Boosting Classifier and Random Forest Algorithm (ABC-RFA) method is employed to construct the classification model. According to the overhead analysis of the dataset used for testing, the suggested model's overall performance is approximately 98.90 %, much better than the conventional models currently in use. Overall Precision, Specificity, Rate of Detection, And Accuracy Were All Attained. © 2024 The Author(s)

关键词： Adaptive bootstrap Classification Ensemble Model Random Forest (RF)

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Federated Learning for Distributed Optimal Power Flow Solutions in Smart Grids

Federated Learning for Distributed Optimal Power Flow Soluti...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： BK Aishwarya K Shekar Revathi V Jay Singh Navdeep Singh Hayder Saadoon Abdulaali Institute of Aeronautical Engineering Hyderabad Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Department of Applied Sciences New Horizon College of Engineering Bangalore Dept. of EEE GL Bajaj Institute of Technology & Management Greater Noida U.P. India Lovely Professional University Phagwara College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

The rapid evolution of smart grids has necessitated the development of advanced computational techniques to ensure efficient and reliable power distribution. This paper introduces a novel approach to solving the Optimal Power Flow (OPF) problem by leveraging Federated Learning (FL) - a decentralized machine learning paradigm. Traditional centralized methods for OPF solutions often face scalability issues and data privacy concerns when applied to large-scale, distributed smart grids. The proposed FL-based method addresses these challenges by allowing individual grid nodes to locally compute model updates without sharing raw data. This not only preserves data privacy but also reduces the communication overhead. The paper presents a comprehensive mathematical formulation of the FL-based OPF problem, followed by a detailed analysis of its convergence properties and computational efficiency. Comparative studies with conventional methods demonstrate the superiority of the proposed approach in terms of accuracy, speed, and scalability. The findings suggest that FL can serve as a robust framework for distributed OPF solutions, paving the way for more resilient and efficient smart grid operations.

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Importance of Isoleucine Residue in Ion Channel Formation Ability of 11-Residue Peptaibols

SSRN

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SSRN 2024年

作者： Nakatani, Taiki Goto, Shogo Inoue, Marina Seki, Kiyohiko Shigedomi, Keita Araki, Kaoru Taira, Junichi Kodama, Hiroaki Osada, Satoshi Department of Chemistry and Applied Chemistry Faculty of Science and Engineering Saga University 1 Honjo-machi Saga840–8502 Japan Department of Applied Biochemistry and Food Science Faculty of Agriculture Saga University 1 Honjo-machi Saga840-8502 Japan Health Care Center Saga University 1 Honjo-machi Saga840-8502 Japan Department of Bioscience and Bioinformatics Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka820-8502 Japan

Peptaibols are a class of short peptides, typically 7 to 20 amino acids long, characterized by noncanonical amino acid residues such as aminoisobutyric acid (Aib). Although the helix length is shorter than the membrane thickness, the 11-residue peptaibol trichorovin-XII (TV-XII) can form ion channels in membranes. Assuming that a higher proportion of isoleucine (Ile) relative to leucine (Leu) residues is crucial for maintaining the biological activity of TV-XII, peptide analogues of TV-XII with varying Ile content were designed, synthesized, and evaluated. The secondary structure of all derivatives under hydrophobic conditions was confirmed by CD measurement as an α-helix-like β-bend ribbon spiral structure. The most stable ion channel activity was found in compound 4a with maximum Ile. Furthermore, the C-terminal Ile analog showed greater ion channel activity compared to the Leu analog. This suggests that the choice between Leu and Ile can influence the expression of biological activity, which will be crucial for the de novo designed functional peptides. © 2024, The Authors. All rights reserved.

关键词： Bioactivity

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Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries

Hybrid genetic-discretized algorithm to handle data uncertai...

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作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Abdar, Moloud Beykikhoshk, Adham Khosravi, Abbas Nahavandi, Saeid Plawiak, Pawel Tan, Ru San Acharya, U Rajendra Deakin University GeelongVIC Australia Department of Engineering Fasa Branch Islamic Azad University Fars Fasa Iran Centre for Pattern Recognition and Data Analytics Deakin University GeelongVIC Australia Department of Information and Communications Technology Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 st. F-3 31-155 Krakow Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland National Heart Centre Singapore Singapore Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Coronary artery disease (CAD) is the leading cause of morbidity and death worldwide. Invasive coronary angiography is the most accurate technique for diagnosing CAD, but is invasive and costly. Hence, analytical methods such as machine learning and data mining techniques are becoming increasingly more popular. Although physicians need to know which arteries are stenotic, most of the researchers focus only on CAD detection and few studies have investigated stenosis of the right coronary artery (RCA), left circumflex (LCX) artery and left anterior descending (LAD) artery separately. Meanwhile, most of the datasets in this field are noisy (data uncertainty). However, to the best of our knowledge, there is no study conducted to address this important problem. This study uses the extension of the Z-Alizadeh Sani dataset, containing 303 records with 54 features. A new feature selection algorithm is proposed in this work. Meanwhile, by discretization of data, we also handle the uncertainty in CAD prediction. To the best of our knowledge, this is the first study attempted to handle uncertainty in CAD prediction. Finally, the genetic algorithm (GA) is used to determine the hyper-parameters of the support vector machine (SVM) kernels. We have achieved high accuracy for the stenosis diagnosis of each main coronary artery. The results of this study can aid the clinicians to validate their manual stenosis diagnosis of RCA, LCX and LAD coronary arteries. © 2020 John Wiley & Sons, Ltd.

关键词： Feature Selection

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Resource-Mediated Consensus Formation

arXiv

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arXiv 2022年

作者： Malik, Omar Flamino, James Szymanski, Boleslaw K. Department of Physics Applied Physics and Astronomy The Network Science and Technology Center Rensselaer Polytechnic Institute TroyNY United States Network Science and Technology Center Rensselaer Polytechnic Institute TroyNY United States Department of Computer Science The Network Science and Technology Center Rensselaer Polytechnic Institute TroyNY United States

In social sciences, simulating opinion dynamics to study the interplay between homophily and influence, and the subsequent formation of echo chambers, is of great importance. As such, in this paper we investigate echo chambers by implementing a unique social game in which we spawn in a large number of agents, each assigned one of the two opinions on an issue and a finite amount of influence in the form of a game currency. Agents attempt to have an opinion that is a majority at the end of the game, to obtain a reward also paid in the game currency. At the beginning of each round, a randomly selected agent is selected, referred to as a speaker. The second agent is selected in the radius of speaker influence (which is a set subset of the speaker's neighbors) to interact with the speaker as a listener. In this interaction, the speaker proposes a payoff in the game currency from their personal influence budget to persuade the listener to hold the speaker's opinion in future rounds until chosen listener again. The listener can either choose to accept or reject this payoff to hold the speaker's opinion for future rounds. The listener's choice is informed only by their estimate of global majority opinion through a limited view of the opinions of their neighboring agents. We show that the influence game leads to the formation of "echo chambers," or homogeneous clusters of opinions. We also investigate various scenarios to disrupt the creation of such echo chambers, including the introduction of resource disparity between agents with different opinions, initially preferentially assigning opinions to agents, and the introduction of committed agents, who never change their initial opinion. © 2022, CC BY.

关键词： Budget control

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