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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Anwar, Adeem Ali Younas, Irfan Liu, Guanfeng Zhang, Xuyun School of Computing Faculty of Science and Engineering Macquarie University SydneyNSW Australia Odyssey Analytics 5757 Woodway Drive HoustonTX77057 United States

ISBN: (纸本)9783031466601

Heuristics have been effective in solving computationally difficult optimization issues, but because they are often created for certain problem domains, they perform poorly when the challenges are significantly altered. The currently available techniques are either designed to address single- or multi-objective optimization issues solely, or they perform poorly with the same parameters. The multi-domain approach known as hyper-heuristics (HHs) can be used to solve optimization issues with minor variations. Motivated by the notion of utilizing the benefits of low-level heuristics (LLHs) in order to obtain well-distributed and convergent optimum solutions along with taking into account the shortcomings of the work completed in many-objective HHs. For many-objective optimization problems, this paper develops a high-level selection approach that employs indicators by preference and offers a unique selection hyper-heuristic called Preference-based Indicator Selection Hyper-heuristic (PBI-HH). In order to establish fairness between exploration and exploitation, the method makes use of a randomization mechanism and a greedy strategy to address a significant problem faced by HHs. Three well-known many-objective evolutionary algorithms are combined in the unique technique that is being proposed. The efficacy of the proposed strategy is assessed by contrasting it with cutting-edge HHs. PBI-HH performs better or equal to the state-of-the-art HHs on 155 out of 160 cases employing the HV indicator and has the optimal μ norm mean values across all datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Multiobjective optimization

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Machine Learning based Comparative Analysis of Cervical Cancer Risk Classifications Algorithms 2

Machine Learning based Comparative Analysis of Cervical Canc...

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2nd IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Ganguly, Tania Pati, Peeta Basa Deepa, K. Singh, Tripty Ozer, Tolga Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India Afyon Kocatepe University Technology Faculty Electric Electronic Engineering Afyonkarahisar Turkey

ISBN: (纸本)9798350315905

Cervical cancer is a major health concern for women worldwide, and early detection is essential for successful treatment. Since symptoms often do not appear until later stages, early screening is necessary. Machine learning can help classify cervical cancer risk by analyzing patient datasets and identifying the important factors that predict the likelihood of emerging cervical cancer. This paper evaluates six different machine-learning approaches for analyzing risk factors associated with cervical cancer using a dataset of 838 instances with 36 features. Results show that the SVM classifier performs the best, with an accuracy of 99.60% which emphasize the possibility of utilizing machine learning to enhance the precision of cervical cancer risk assessment. This can result in the development of better screening and prevention techniques for cervical cancer, which can be more effective in identifying and managing this disease. © 2023 IEEE.

关键词： Logistic regression

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Blockchain-Based Access Control with Decentralized Architecture for Data Storage and Transfer 1

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6th International Conference on Innovative computing and Communication, ICICC 2023

作者： Vanjipriya, V. Suresh, A. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu SRM Nagar Kattankulathur Chennai603203 India

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

When it comes to the security of data in the cloud storage model, access control is the most crucial factor. However, traditional methods of data sharing and access control present significant difficulties in the field of study due to issues like privacy data leakage and key abuse. The blockchain model also offers security because of the various encryption methods used for user authentication. Since this research focuses on the blockchain-based cloud, two major contributions are developed: one for effective access control and the other for privacy-based data sharing and retrieval. The first contribution implements a blockchain-based model for cloud-based access control and data sharing. This issue of a single point of failure in cloud architecture is effectively addressed by the developed blockchain-based access control and data sharing process. According to the credentials, the data user (DU) generates a registration request that is sent to the data owner (DO). In order to ensure safe transmission, an EHR application implements a data protection procedure. The cloud infrastructure incorporates components such as data consumers, data producers, blockchain transactions, smart contracts, and the Interplanetary File System (IPFS). To top it all off, DO includes a data protection algorithm for securing HER, wherein encrypted EHR is converted to IPFS before being shared with the data consumer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Blockchain

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Hybrid Music Recommendation System Utilizing Item Popularity and Content-Based Filtering Innovations in music

Hybrid Music Recommendation System Utilizing Item Popularity...

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2022 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2022

作者： Singh, Aditya Porwal, Utsav Selvaraj, P. SRM Institute of Science and Technology College of Engineering and Technology Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur603203 India

ISBN: (纸本)9798350333848

recommendation systems are increasing, making it viable to customize the listening trends and fine-tune the selections per the user's preferences. Over the years, every user's personalized music playlist can be primarily customized based totally on the statement of that user's behavior. In this work, we advocate a Hybrid Music advice machine using object recognition and content analysis primarily based on innovative filtering. We illustrated its implementation for virtual audio, a product class with swiftly increasing trends. The proposed machine routinely indicates customized playlists of MP3 songs for every user. The machine works in real-time and is scalable to the significant statistics generally encountered in personalization applications. We applied this personalization system on the 'Million-Song Dataset' and examined its overall performance with several users. Moreover, our proposed recommendation system performs well in recommending the music based on the item's popularity and user-centric content filtering. © 2022 IEEE.

关键词： Recommender systems

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A logic for reasoning with inconsistent knowledge - A reformulation using nowadays terminology (2024)

arXiv

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arXiv 2024年

作者： Roos, Nico Department of Advanced Computing Sciences Faculty of Science and Engineering Maastricht University Netherlands

In many situations humans have to reason with inconsistent knowledge. These inconsistencies may occur due to not fully reliable sources of information. In order to reason with inconsistent knowledge, it is not possible to view a set of premisses as absolute truths as is done in predicate logic. Viewing the set of premisses as a set of assumptions, however, it is possible to deduce useful conclusions from an inconsistent set of premisses. In this paper a logic for reasoning with inconsistent knowledge is described. This logic is a generalization of the work of N. Rescher [15]. In the logic a reliability relation is used to choose between incompatible assumptions. These choices are only made when a contradiction is derived. As long as no contradiction is derived, the knowledge is assumed to be consistent. This makes it possible to define an argumentation-based deduction process for the logic. For the logic a semantics based on the ideas of Y. Shoham [22, 23], is defined. It turns out that the semantics for the logic is a preferential semantics according to the definition S. Kraus, D. Lehmann and M. Magidor [12]. Therefore the logic is a logic of system P and possesses all the properties of an ideal non-monotonic *** Codes 68T27, 68T30 © 2024, CC BY.

关键词： Semantics

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Enhancing Micro-Expression Recognition with Broadbent Attention Mechanism: A High-Performance Approach for Emotion Detection 21

Enhancing Micro-Expression Recognition with Broadbent Attent...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Ashraf, Imran Kehkashan, Tanzila Zaman, Muhammad Akhunzada, Adnan Talib, Hafiz Khizer Bin Cao, Yanlong University of Lahore Faculty of Computer Science Lahore Pakistan Universiti Teknologi Malaysia Faculty of Computing Johor Bahru Malaysia COMSATS University Islamabad Department of Computer Science Islamabad Pakistan University of Doha for Science and Technology Department of Data and Cybersecurity Doha24449 Qatar School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350378078

Micro-expression recognition (MER) is a critical task in affective computing, yet it remains challenging due to the subtle, brief, and involuntary nature of micro-expressions. Existing methods often find it difficult to spot these quick micro facial expressions (MFEs), especially when there are obstacles, background noise, or differences between people. In this study, we propose a novel approach that integrates the Broadbent Attention Mechanism into a Convolutional Neural Network (CNN) to enhance MER. This attention mechanism is designed to focus on the most informative regions of facial images, improving the model's ability to extract relevant features and discard irrelevant information. The model is evaluated on the SAMM dataset, covering various emotions. Extensive experiments are conducted using both K-fold and Stratified K-fold cross-validation techniques to ensure a robust evaluation process. The proposed method achieves high accuracy 99.72%, precision 99.73%, Recall 99.72% and f1-Score 99.72% with Stratified K-fold & accuracy 99.65%, precision 99.65%, Recall 99.66% and f'1-Score 99.65% with K-fold, outperforming baseline models and demonstrating its effectiveness in recognizing subtle micro-expressions. These results indicate that the Broadbent Attention Mechanism significantly enhances accuracy and robustness of MER systems, offering a promising solution for applications in human-computer interaction (HCI) and emotion analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

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Computers, Materials & Continua 2023年第3期74卷 5757-5776页

作者： Nebras M.Sobahi Ahteshamul Haque V S Bharath Kurukuru Md.Mottahir Alam Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew DelhiIndia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)*** light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated *** achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the *** localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the *** develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation ***,the developed algorithm is combined with the PMCU implemented with the experimental setup of *** results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

关键词： Condition monitoring anomaly detection performance evaluation fault classification optimization

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Sentiment Analysis of ChatGPT Healthcare Discourse: Insights from Twitter Data 15

Sentiment Analysis of ChatGPT Healthcare Discourse: Insights...

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15th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2023

作者： Kumar, Roshan Ayyasamy, Ramesh Kumar Sangodiah, Anbuselvan Krishnan, Kesavan Jebna, Abdulkarim Kanaan Theam, Lim Jit Universiti Tunku Abdul Rahman Faculty of Information and Comunication Technology Kampar Malaysia Quest International University Faculty of Computing and Engineering Ipoh Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350316551

This paper explores the application of Chat Generative Pretrained Transformer (ChatGPT) in the healthcare domain, introducing a sentiment analysis model to evaluate ChatGPT-related tweets in healthcare contexts. The study aims to uncover predominant sentiments, thematic content, and diverse perspectives concerning ChatGPT's integration into healthcare, utilizing an extensive dataset from Twitter comprising 10,330 healthcare-related tweets. Leveraging advanced Natural Language Processing (NLP) techniques, we systematically categorized topics and emotional content within these tweets. Additionally, we conducted a comprehensive analysis of frequently occurring words in tweets expressing positive and negative sentiments. The findings reveal that the majority of healthcare-related ChatGPT tweets express either positive or negative sentiments, with a minor proportion conveying neutral viewpoints. Furthermore, to enhance our comprehension of sentiment dynamics in healthcare discussions involving ChatGPT, we applied four machine learning classifiers Support Vector Machine, K-Nearest Neighbors, Naive Bayes and Random Forest. Remarkably, the SVM classifier demonstrated the highest accuracy at 85.6%, affirming its efficacy in healthcare sentiment analysis. In summary, this research sheds light on prevailing sentiments and perspectives regarding ChatGPT in the healthcare sector, highlighting its predominantly positive and neutral reception on platforms like Twitter. Additionally, the success of SVM as a sentiment analysis tool underscores its potential for discerning sentiments in healthcare-related ChatGPT discussions, contributing to ongoing debates on AI integration in healthcare and guiding future endeavors in this evolving field. © 2023 IEEE.

关键词： Sentiment analysis

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Investigation of the Effect of Polyvinyl Acetate Coating on Multiple U‐shaped Fibre Optic Sensors in Different Solution Conditions

Investigation of the Effect of Polyvinyl Acetate Coating on ...

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ASEAN Australian engineering Congress, AAEC 2022

作者： Yek, Alan Wen Zheng Tan, Alfred Jia Yee Ng, Sing Muk Chua, Hong Siang Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Kuching Malaysia Research and Development Department Sarawak Energy Berhad Sarawak Kuching93050 Malaysia

ISBN: (纸本)9789819955466

U-shaped fibre optic sensors (FOSs) have been developed by many researchers in view of the low-cost and miniaturised size. The sensitivity of the FOS towards refractive index (RI) changes is affected by the fibre core diameter, bending radius and numerical aperture of the fibre. However, there are limited resources available to prove that the sensitivity of the sensor can be improved by a coating layer and can perform well in different types of solutions. Therefore, this project proposes the development of three U-shaped probes that can perform well in solutions with normal and viscous conditions. Glycerol and honey solutions were used to represent the normal and viscous conditions, respectively. To increase the sensitivity of the proposed sensor, fibre cladding removal and chemical material coating on the fibres were implemented. From the literature, the polyvinyl acetate (PVAc) coating material was proposed as the fabrication process of PVAc coating was simpler compared to other coating materials such as polyimide and Norland Optical Adhesive 61 (NOA61). By conducting some coating experiments, the coating thicknesses proposed in this project are 10 and 20 μm for 2 different fibre probes through the dripping method while another probe remained uncoated with the cladding removed. All the results are repeatable, and the accuracy of the FOS is 99.49%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coatings

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Phishing Website Detection with and Without Proper Feature Selection Techniques: Machine Learning Approach

Phishing Website Detection with and Without Proper Feature S...

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作者： Adane, Kibreab Beyene, Berhanu Faculty of Computing and Software Engineering Arba Minch University Arba Minch Ethiopia Computer Science Department Ethiopian Civil Service University Addis Ababa Ethiopia

Indeed, successful phishing website attempts could result in catastrophic data loss, login credential compromise, ransomware infection, and financial loss. It also significantly hampers the competitiveness and productivity of online users and internet-dependent organizations unless an intelligent anti-phishing solution is devised. Due to detecting fresh phishing website attacks with maximum accuracy by discovering hidden patterns from complex datasets is shown to be an intrinsic property of M- Learning approaches, the study conducted rigorous experiments on four purposely selected efficient supervised M-Learning algorithms before and after applying five widely used proper feature selection techniques such as Recursive Feature Elimination, Pierson Correlation Coefficient, Principal Component Analysis, Uni-variate Feature Selection, and Mutual Information. The proposed study was conducted to balance the research gaps and scientific disputes in the rigorously reviewed studies. The study’s final outcome is a proposal for an intelligent phishing website model that yields higher accuracy, faster response times, and fewer average misclassification rates. The study also explored the feature selection techniques that had more, less, and no contributions to enhancing each classifier's accuracy. As compared to the remaining classifiers, the Cat-Boost Classifier attained superior phishing website detection accuracy (97.46%), F1-score (97.49%), a lower average misclassification rate (2.54%), and acceptable train-test computational time (7 s) after using the UFS technique. On the other hand, the PCA technique failed to enhance the accuracy of the Cat-Boost, Gradient-Boost, and Random Forest Classifiers due to scoring less accuracy than the accuracy reached before using proper feature selection techniques. To obtain more promising results, in future work, phishing website detection is expected to be carried out using a Hybrid proper feature selection technique, huge datasets, prop

关键词： Feature Selection

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