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2021 International Seminar on Intelligent technology and Its Application, ISITIA 2021

作者： Suprapty, Bedi Wiguna, Anggri Sartika Wajiansyah, Agusma Malani, Rheo Politeknik Negeri Samarinda The Applied Modern Computing Robotic Systems Unit East Kalimantan Indonesia Universitas Pgri Kanjuruhan Malang Faculty of Science Technology Informatics Department East Java Indonesia

ISBN: (纸本)9781665428477

Clustering is categorized as unsupervised learning because there is no class label information available in grouping a dataset. For this reason, an assessment of the quality of the clustering results is critical. In general, two essential clustering parameters are the similarities between cluster members in a cluster and the cluster centers' separation. Various approaches can be used to improve a clustering algorithm's performance: raw data pre-processing, cluster center initialization techniques, objective function assignment, modification of specific steps, and others. The subject of this study is the pattern of rainfall every month during the year of the observation period obtained from the clustering process. This study aims to improve the performance of K-Mean Clustering through manipulation of raw data pre-processing into certain datasets. Image filtering technique is used to generate a dataset based on the relationship between neighboring rainfall values. Polar-based Cartesian data space transformation is used to generate a dataset based on a range of rainfall values for each month during the year of the observation period. Four scenarios have been used to test the performance of the proposed method. The study results show that the proposed method produces the highest performance ratio (54.79%) of all scenarios' total average GOS (Global Optimum Solution). Meanwhile, increasing GOS to the original method also resulted in the highest increase in GOS ratio (63.68%) compared to other methods. Further studies will focus on the application of the proposed methods for improving the performance of SOM and Fuzzy C-Mean Clustering. © 2021 IEEE.

关键词： K-means clustering

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Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

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Neural computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

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A Deep Learning Based Approach for Janya Raga Classification in Carnatic Music

A Deep Learning Based Approach for Janya Raga Classification...

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Artificial Intelligence (SLAAI-ICAI), SLAAI International Conference on

作者： P. Kavitha J. Charles L. S. Lekamge Department of Computing and Information Systems Faculty of Applied Sciences Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Physical Sciences and Technology Faculty of Applied Sciences Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

ISBN: (纸本)9781665476089

Janya Ragas are Carnatic music ragas derived from the fundamental ragas called Melakarta Raga, by the permutation and combination of various ascending and descending swaras. Raga recognition is an important task, essential to research in Music Information Retrieval in Indian Classical Music. Even though the automatic raga recognition methods have been widely adopted in Carnatic music, most of the existing methods are restricted only to Melakarta Ragas. It is an important task to identify the Melakarta Raga of a Janya Raga, especially for improved music recommendation performance. In this study, we used deep learning-based approach to address this problem. Music files were collected for 65 Janya Ragas belonging to seven Melakarta Ragas. Classification-based supervised deep learning models: 1D CNN, LSTM and 1D CNN-LSTM were used in the study. The models were implemented to learn similarities from 1–20 mean values of Mel-Frequency Cepstral Coefficients (MFCCs) features of audio samples, by using TensorFlow and Keras APIs. The results revealed that the 1D CNN-LSTM model outperformed the other models with an accuracy of 82.0%. In the future, 1D CNN-LSTM based Siamese neural networks can be introduced to reduce the dependence on large amounts of labeled audio data.

关键词： Deep learning Cepstral analysis Neural networks Convolutional neural networks Task analysis Music information retrieval Artificial intelligence

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Optimizing Routing Paths in Mobile Wireless Sensor Networks: A Sub-Flow Adaptive Multipath Approach for Energy Efficiency and Delay Sensitivity

Optimizing Routing Paths in Mobile Wireless Sensor Networks:...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： R. Vinston Raja Shavej Ali Siddiqui Avilasha BG L. Mohana Kannan Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Deparment of Applied Science and Humanities Faculty of Engineering and Technology Khwaja Moinuddin Chishti Language University Lucknow India Deparment of Mechanical Engineering Dayanandasagar College of Engineering Bangalore India Department of Biomedical Engineering Erode Sengunthar Engineering College Perundurai Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Wireless Sensor Networks (WSNs) have become crucial in various domains such as social applications, healthcare, and the military. However, their diversity makes them susceptible to a wide range of attacks, leading to challenges like poor throughput, high network latency, and excessive energy consumption. This study proposes a novel approach that combines residual Riemannian neural networks with the Sub-Flow Adaptive Multipath Routing algorithm for enhanced routing and attack detection in WSNs. By leveraging node position data, the Sub-Flow Adaptive Multipath Routing algorithm optimizes energy consumption and extends network lifespan. Simultaneously, residual Riemannian neural networks analyze residual patterns in network traffic data to accurately detect rogue nodes, thereby enhancing security. Blockchain technology is then employed to securely store classified data, ensuring data integrity and security. This integrated approach significantly improves network performance, maintains robust security against potential threats, and enhances the reliability of WSNs in demanding environments.

关键词： Wireless sensor networks Energy consumption Adaptive systems Neural networks Routing Throughput Delays Blockchains Security Reliability

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Forecasting the load flow of Engine Driven Pump based on Light Gradient Boosting Machine Model

Forecasting the load flow of Engine Driven Pump based on Lig...

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2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control technology, CEI 2021

作者： Niu, Wei Song, Xiaogang Wang, Hao Chen, Juan Xi'an Aeronautics Computing Technique Research Institute AVIC Xi'an China Xi'an University of Technology Faculty of Computer Science and Engineering Xi'an China Xi'an Institute of Applied Optics Xi'an China

ISBN: (纸本)9780738146492

The flow rate of aircraft hydraulic system is one of the important parameters to measure the performance of aircraft. The stability of flow rate of hydraulic pipeline is related to the safety of hydraulic system and even aircraft flight. Aiming at the problem that it is difficult to measure the load flow of aircraft hydraulic system, a prediction method based on ensemble learning for the load flow of Engine Driven Pump (EDP) is proposed. According to the different working conditions of EDP, the Light Gradient Boosting Machine (LightGBM) model is established. Through Particle Swarm Optimization algorithm optimizes the super parameters of LightGBM algorithm, and realizes the regression prediction mode. Finally, compared with the multiple linear regression, random forest, experimental results indicate that the proposed model obviously can improve the prediction accuracy. © 2021 IEEE.

关键词： Forecasting

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***: A Comprehensive Genomic Visualisation Database for Malaysian Species

***: A Comprehensive Genomic Visualisation Database for Mala...

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2022 IEEE International Conference on computing, ICOCO 2022

作者： Chan, Vei Siang Mohamed, Farhan Bin Salleh, Faezah Mohd Iglesias, Andres Wagiran, Alina Binti Selamat, Ali Bin Universiti Teknologi Malaysia School of Computing Faculty of Engineering Johor Bahru Malaysia Universiti Teknologi Malaysia Media and Game Innovation Center of Excellence Johor Bahru Malaysia Universiti Teknologi Malaysia Dept. of Biosciences Faculty of Science Johor Bahru Malaysia University of Cantabria Dept. of Applied Mathematics and Computational Sciences Santander Spain University of Technology Malaysia Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia

ISBN: (纸本)9781665489966

Malaysia is one of the most megadiverse regions in the world. Therefore, it is important to record these rich biodiversity data to safeguard and improve national conservation efforts. However, the lack of nationwide genomic sequencing hampers a more sophisticated species identification, data management, and analytics activities. Thus, this project aims to design and implement ***, a comprehensive one-stop online DNA reference database with integrated DNA visualisation and digital references to allow the users to search, retrieve and analyse the collected DNA sequencing data interactively. This paper documented the system architecture and implementation methods to create ***. The results of *** can display taxonomy information of biodiversity in the web's user interfaces and visualise the complex set of DNA data to allow new insights. Lastly, the authors identified several future works to improve *** including improve user interaction, add visualisation methods to support more bioinformatics tasks, and conduct formal user evaluation. © 2022 IEEE.

关键词： Taxonomies

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Is Alice Really in Wonderland? UWB-Based Proof of Location for UAVs with Hyperledger Fabric Blockchain

arXiv

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arXiv 2023年

作者： Fu, Lei Morón, Paola Torrico Queralta, Jorge Peña Hästbacka, David Edelman, Harry Westerlund, Tomi Turku Intelligent and Embedded Robotic Systems University of Turku Finland Department of Engineering and Business Turku Univesity of Applied Sciences Finland Computing Sciences Faculty of Information Technology and Communication Sciences Tampere University Finland

Remote identification of Unmanned Aerial Vehicles (UAVs) is becoming increasingly important since more UAVs are being widely used for different needs in urban areas. For example, in the US and in the EU, identification and position broadcasting is already a requirement for the use of drones. However, the current solutions do not validate the position of the UAV but its identity, while trusting the given position. Therefore, a more advanced solution enabling the proof of location is needed to avoid spoofing. We propose the combination of a permissioned blockchain managed by public authorities together with UWB-based communication to approach this. Specifically, we leverage the identity management tools from Hyperledger Fabric, an open-source permissioned blockchain framework, and ultra-wideband (UWB) ranging, leading to situated communication (i.e., simultaneous communication and localization). This approach allows us to prove both the UAV identity and also the location it broadcasts through interaction with ground infrastructure in known locations. Our initial experiments show that the proposed approach is viable and UWB transceivers can be used for UAVs to validate both their identity and position with ground infrastructure deployed in known locations. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Travel Mode Choice Modeling: Predictive Efficacy between Machine Learning Models and Discrete Choice Model

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Open Transportation Journal 2021年第1期15卷 241-255页

作者： Ali, Nur Fahriza Mohd. Sadullah, Ahmad Farhan Mohd. Majeed, Anwar PP Abdul Razman, Mohd Azraai Mohd. Zakaria, Muhammad Aizzat Nasir, Ahmad Fakhri Ab. School of Civil Engineering Universiti Sains Malaysia Engineering Campus Pulau Pinang Nibong Tebal 14300 Malaysia Innovative Manufacturing Mechatronics and Sports Laboratory Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Pahang Pekan 26600 Malaysia Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pahang Pekan 26600 Malaysia

Background: A complex travel behaviour among users is intertwined with many factors. Traditionally, the exploration in travel mode choice modeling has been dominated by the Discrete Choice model, nonetheless, owing to the advancement in computational techniques, machine learning has gained traction in understanding travel behavior. Aim: This study aims at predicting users’ travel model choice by means of machine learning models against a conventional Discrete Choice Model, i.e., Binary Logistic Regression. Objective: To investigate the comparison between machine learning models, namely Neural Network, Random Forest, Decision Tree, and Support Vector Machine against the Discrete Choice Model (Binary Logistic Regression) in the prediction of travel mode choice amongst Kuantan City. Methodology: The dataset was collected in Kuantan City, Malaysia, through the Revealed/Stated Preferences (RP/SP) Survey. The data collected was split into a ratio of 80:20 for training and testing before evaluating them between the aforesaid models. The hyperparameters of the models were set to default. The performance of the models is evaluated based on classification accuracy. Results: It was shown in the present study that the Neural Network Model is able to attain a higher prediction accuracy as compared to Binary Logistic Regression (Discrete Choice Model) in classifying mode choice of Kuantan users either to choose public transport or private vehicles as daily transportation. Feature importance technique is crucial for identifying the significant features in modelling travel mode choice. It is demonstrated that the Neural Network Model can yield exceptional classification of mode choice up to 73.4% and 72.4% of training and testing data, respectively, by considering the features identified via the feature importance technique, suggesting the viability of the proposed technique in supporting an informed decision. Conclusion: The findings highlight the strengths and limitations of the

关键词： Discrete choice model Machine learning technique Private vehicles Public transport Revealed/Stated preference survey Travel mode choice

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Breast-Cancer Detection using Thermal Images with Marine-Predators-Algorithm Selected Features 7

Breast-Cancer Detection using Thermal Images with Marine-Pre...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Rajinikanth, Venkatesan Kadry, Seifedine Taniar, David Damasevicius, Robertas Rauf, Hafiz Tayyab St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Monash University Faculty of Information Technology ClaytonVIC3800 Australia Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland Staffordshire University Centre for Smart Systems Ai and Cybersecurity Stoke-on-Trent United Kingdom

ISBN: (纸本)9781665441261

Breast-cancer (BC) is one of the major diseases in women group and the early diagnosis and treatment is necessary to cure the disease. Early detection of BC is very essential to implement appropriate treatment and the proposed research aims to develop an automated BC detection system using Breast-Thermal-Images (BTI). The executed approach is as follows;(i) Recording the image for various breast orientation, (ii) Extracting the healthy/DCIS image patches, (iii) Treating the patches with image processing scheme, (iv) Feature extraction, (v) Feature optimization with Marine-Predators-Algorithm (MPA), and (vi) Two-class classification and validation. In this work, the essential image features, such as GLCM and LBP with varied weights are considered to classify the clinically collected BTI into healthy/DCIS class using a chosen two-class classifier. The result of this study confirms that the Decision-Tree (DT) classifier helps to achieve enhanced accuracy (>92%) compared to other methods adopted in this research. © 2021 IEEE.

关键词： Diseases

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Intelligent Traffic Management System for Smart Cities Utilizing Reinforcement Learning Algorithm

Intelligent Traffic Management System for Smart Cities Utili...

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2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2022

作者： Rajan, Karthick Kumar, K Sampath Kannapiran, Thirunavukkarasu Khan, Shahnawaz Al-Dmour, Ayman Sharef, Baraa T. School of Computing Science and Engineering Galgotias University Greater Noida India Unitedworld School of Computational Intelligence Karnavati University Gujarat India Bahrain Polytechnic Faculty of Engineering Design and Information and Communications Technology Isa Town Bahrain College of Arts and Science Applied Science University Al Eker Bahrain College of Information Technology Ahlia University Manama Bahrain

ISBN: (数字)9781665469197

ISBN: (纸本)9781665469197

Traffic congestion on roadways or at intersections can occur for a variety of reasons, including increased vehicle queues, slow driving, and so on. When multiple emergencies occur at the same time, traffic gridlock may occur. An adaptive traffic control system is a traffic management method that allows signals to adapt to the current traffic demand in real time. Adaptive traffic signals work by synchronizing software and hardware. Q-learning necessitates the creation of accurate natural environment forms prior to taking action. Instead, a dynamic communication system was developed to explore the interplay between state, action, and rewards in that particular setting. The current traffic signal functions are based on predetermined traffic flow information to extract short time expectations, making it easier to calculate the signal management system's conclusion. If a certain model is used, then customizable traffic light management agents will have to collect photos of the current traffic situation and generate control signals at regular intervals. To improve the algorithm's stability, replay, occurrence, and ideal techniques have been implemented. The primary goal of the algorithm is to regulate congested traffic, which is accomplished by combining the dynamic network with the linear signal layout. © 2022 IEEE.

关键词： Traffic signals

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