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Aspect Extraction in Sentiment Analysis Based on Emotional Affect Using Supervised Approach 4

Aspect Extraction in Sentiment Analysis Based on Emotional A...

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4th International Conference on Artificial Intelligence and Big Data, ICAIBD 2021

作者： Maitama, Jaafar Zubairu Idris, Norisma Abdi, Asad Bimba, Andrew Thomas Universiti Malaya Faculty of Computer Science and Information Technology Department of Artificial Intelligence Kuala Lumpur50603 Malaysia University of Twente Department of Industrial Engineering and Business Information System Netherlands

ISBN: (纸本)9780738131702

Aspect based sentiment analysis (ABSA) is regards as the most demanding area of research in the field of text mining and natural language processing. In the last decade, people tend to be more interested in consulting online media outlets to make decisions concerning products or events. However, the existing ABSA approaches alone are not sufficient in providing comprehensive information required to make an informed decision. Most of the early studies focused on the binary classification of 'positive' and 'negative' to predict an overall sentiment of a review, which reduced the effectiveness in decision making towards the entities. Even though earlier studies on SA are centered on aspect extraction, there are limited works that effectively combine aspect extraction with emotional affects such as surprise, sadness and happiness, which consequently leads to incomplete meaning of a content. In this paper, we investigate a problem involving aspects in relation to emotional affects. To propose a novel supervised approach for the aspect extraction with their associated emotion classes in sentiment analysis (SA), we conducted an analysis of some prominent ABSA approaches. This research work will lead to a more accurate results of SA and could assist both novice and prominent researchers in comprehending the relationship between aspects and emotions in SA considering its sematic role in business intelligence. © 2021 IEEE.

关键词： Sentiment analysis

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A Comparison Study of FC-RDVM and RIWM Router Placement Methods for WMNs: Performance Evaluation Results by WMN-PSOHC Simulation system Considering Chi-Square Distribution and Different Instances 1

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37th International Conference on Advanced information Networking and Applications, AINA 2023

作者： Sakamoto, Shinji Barolli, Admir Liu, Yi Kulla, Elis Barolli, Leonard Takizawa, Makoto Department of Information and Computer Science Kanazawa Institute of Technology 7-1 Ohgigaoka Ishikawa Nonoichi921-8501 Japan Department of Information Technology Aleksander Moisiu University of Durres L.1 Rruga e Currilave Durres Albania Department of Computer Science National Institute of Technology Oita College 1666 Maki Oita870-0152 Japan Department of System Management Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-Ku Fukuoka811-0295 Japan Department of Information and Communication Engineering Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-Ku Fukuoka811-0295 Japan Department of Advanced Sciences Faculty of Science and Engineering Hosei University Tokyo Kajino-Machi Koganei-Shi184-8584 Japan

ISBN: (数字)9783031290565

ISBN: (纸本)9783031290558

In this work, we deal with the node placement problem in Wireless Mesh Networks (WMNs). We present a hybrid intelligent simulation system called WMN-PSOHC, which combines Particle Swarm Optimization (PSO) and Hill Climbing (HC). We implement in WMN-PSOHC system the Fast Convergence Rational Decrement of Vmax Method (FC-RDVM) and Random Inertia Weight Method (RIWM) router replacement methods. By using WMN-PSOHC system, we carry out simulations the access the performance of these two methods considering Chi-Square distribution of mesh clients and two different instances. The simulation results show that FC-RDVM performs better then RIWM in the considered scenario. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Particle swarm optimization (PSO)

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification 19

Deep Learning in Smart Healthcare: A GAN-based Approach for ...

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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy information Technology, SEIT 2024

作者： Tufail, Hina Ahad, Abdul Puspitasari, Ira Shayea, Ibraheem Coelho, Paulo Jorge Pires, Ivan Miguel Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga Indonesia School of Software Northwestern Polytechnical University Shaanxi Xian China Turkey Polytechnic University of Leiria Leiria Portugal Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which affects patients' lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD. © 2024 The Authors. Published by Elsevier B.V.

关键词： Generative adversarial networks

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Driver Behavior Assessment with Different ML Models Using EEG and Physiological Data - A Comparative Study

Driver Behavior Assessment with Different ML Models Using EE...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Sama Hussein Al-Gburi Kanar Alaa Al-Sammak Khattab M. Ali Alheeti George Suciu Abedulqader Gailan Abdulqader Faculty of Electronics Telecommunications and Information Technology National University of Science and Technology Politehnica Bucharest (UNSTPB) Bucharest Romania Faculty of Department of Computer Networking System College of Computer Sciences and Information Technology University of Anbar Ramadi Iraq Faculty of Electronics Telecommunications and Information Technology National University of Science and Technology Politehnica Bucharest (UNSTPB) and BEIA Consult International Bucharest Romania Karkh Second Directorate of Education Ministry of Education Baghdad Iraq

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

Ensuring road safety is becoming more and more significant for researchers and for all the society. One of the main issues in this area considers driver drowsiness. Therefore, the focus of this paper is the comprehensive analysis and comparison of four distinct machine learning models: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient Boosting. The ultimate goal is to gain insights into the intricate factors that shape driver behavior. When assessing the effectiveness of each model, four key metrics are taken into account: precision, recall, F1 score, and accuracy. In this study, we used a “TRYOUT” dataset. The study yielded several significant findings regarding the performance of the machine learning models employed. Remarkably, the accuracy scores for all models were notably high, falling within a range of 0.8651 to 0.9999. Among all the models, the Random Forest model stood out as the most effective, displaying an impressive accuracy score of 0.9999 on the merged data. The SVM model, while not as proficient as the Random Forest, still managed a respectable performance, particularly on the MPM39 dataset where it achieved an accuracy of $\mathbf{7 5. 0 6 \%}$. The Logistic Regression model demonstrated its strength on the CVD08 dataset, although its performance was comparatively weaker on the other datasets. Lastly, the Gradient Boosting model demonstrated consistent competency across all datasets, underscoring its reliability in a range of circumstances.

关键词： Support vector machines Logistic regression Accuracy Shape Computational modeling Brain modeling Boosting

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Multiple Instances Fingerprint Image Data Acquisition

Multiple Instances Fingerprint Image Data Acquisition

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Janet O. Jooda Roseline Oluwaseun Ogundokun Oluyinka T. Adedeji Sunday Adeola Ajagbe Matthew O. Adigun Alice O. Oke Elijah O. Omidiora Department of Computer Engineering Redeemer's University Ede Osun State Nigeria Department of Multimedia Engineering Kaunas University of Technology Kaunas Lithuania Department of Computer Science Landmark University Omu Aran Nigeria Department of Information System Science Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Computer Science University of Zululand Kwadlangezwa South Africa Department of Computer Engineering First Technical University Ibadan Nigeria Department of Computer Engineering Ladoke Akintola University of Technology Ogbomoso Nigeria

Using public fingerprint databases to validate the multi-instance fusion approach of a Multi-instance Biometric Authentication system (MBAS) for accurate authentication is a means of overcoming some of the limitations of a Unimodal Biometric system (UBS). Nevertheless, a significant portion of the web databases used for MBAS (Machine Learning-Based Automated systems) were obtained under controlled conditions, with the photos being curated to align with a certain algorithmic objective. The performance of biometric systems exhibits variability when the datasets used in the algorithms undergo modifications as a result of discrepancies in the settings under which the photos were obtained. In this study, a database containing multiple fingerprint instances was developed locally in an uncontrolled environment. The aim was to create a database containing numerous examples of fingerprints for the same person from one or multiple data collection sessions. The database was produced locally by acquiring six samples of ten fingerprint instances of 150 subjects in an uncontrolled environment using a Futronic fingerprint scanner. The created database and results will be helpful to all researchers in the biometrics field as a tool for improving multi-instance fusion methods and enabling an unbiased evaluation of algorithms.

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Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning

Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Agus Pratondo Astri Novianty Budi Laksono Putro Siska Komala Sari Department of Multimedia Engineering School of Applied Sciences Telkom University Bandung Indonesia Department of Computer Engineering School of Electrical Engineering Telkom University Bandung Indonesia Department of Computer Science Education Indonesia University of Education Bandung Indonesia Diploma Program of Information System School of Applied System Telkom University Bandung Indonesia

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice.

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Leveraging Adaptive Algorithms for Real-Time Data Analysis

Leveraging Adaptive Algorithms for Real-Time Data Analysis

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International Conference on Computing and Networking Technology (ICCNT)

作者： Girija Shankar Sahoo R Hannah Jessie Rani Manish Kumar Goyal Vazeer Ali Mohammed D Little Femilin Jana Namrata N. Wasatkar Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer & Information System Lewis University Illinois USA Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper proposes the use of adaptive algorithms for real-time data analysis. Adaptive algorithms are a set of practical methods used for acting information-driven optimization and studying. They have a wide variety of applications and may be implemented to a selection of facts analysis obligations. This paper offers an overview of existing adaptive algorithms and descriptions of several new programs in the area of actual-time facts analysis. In particular, the paper discusses the notion of adaptive thresholding, which allows for practical real-time evaluation of large datasets. The paper also provides a novel technique for making use of these algorithms in an online learning environment. Results from simulations illustrate the ability of the proposed method in real-international packages. Sooner or later, the paper presents numerous key lessons discovered and affords recommendations for future research and packages in this area.

关键词： Adaptation models Data analysis Real-time systems Optimization

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Risk Management Capability Level of Mail information system in Surabaya Government

Risk Management Capability Level of Mail Information System ...

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information Technology International Seminar (ITIS)

作者： Anita Wulansari Carena Learns Prasetyo Siti Mukaromah Asif Faroqi Eristya Maya Safitri Abdul Rezha Efrat Najaf Department of Information System Faculty of Computer Science University of Pembangunan Nasional Veteran Jawa Timur Surabaya Indonesia

ISBN: (纸本)9798350398205

ICT (information and Communication Technology) plays a strategic role in government institutions. The existence of ICT is considered capable of promoting the realization of good governance. Therefore, Dinkominfo Surabaya implemented a mail management information system called e-Surat. Dinkomifo Surabaya has established a standard operating procedure (SOP) for e-Surat. However, problems such as data loss and mail disposition errors still occurred. Since SOPs were not enough to minimize the occurrence of information security risks, Dinkominfo Surabaya needed to know its capability level of information security risk. This study aimed to help Dinkominfo Surabaya assess the extent to which risk optimization management in e-Surat using COBIT 5 and focusing on the APO12 domain. The results showed that the capability level of the risk management process on the APO12 domain was at level 1 (performed), two levels below the expected level, indicating that the risk management process has been done well, although it has not met the criteria of the practice itself. Recommendations to improve risk management-related processes to achieve the expected level 3 (established) include determining the objectives and limits of the risk management process carried out and providing work products for institution leaders so that the handling of problems related to risk management can involve policymakers.

关键词： Seminars information security Focusing Management information systems information and communication technology Risk management Standards

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Comparative analysis of network forensic tools and network forensics processes 2

Comparative analysis of network forensic tools and network f...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Ghabban, Fahad M Alfadli, Ibrahim M Ameerbakhsh, Omair Abuali, Amer Nizar Al-Dhaqm, Arafat Al-Khasawneh, Mahmoud Ahmad Taibah University College of Computer Science and Engineering Information System Department Madinah Saudi Arabia University Technology Malaysia Faculty of Engineering School of Computing Johor Malaysia Al-Madinah International University Faculty of Computer Information Technology Shah Alam Malaysia

ISBN: (纸本)9781665432221

Network Forensics (NFs) is a branch of digital forensics which used to detect and capture potential digital crimes over computer networked environments crime. Network Forensic Tools (NFTs) and Network Forensic Processes (NFPs) have abilities to examine networks, collect all normal and abnormal traffic/data, help in network incident analysis, and assist in creating an appropriate incident detection and reaction and also create a forensic hypothesis that can be used in a court of law. Also, it assists in examining the internal incidents and exploitation of assets, attack goals, executes threat evaluation, also by evaluating network performance. According to existing literature, there exist quite a number of NFTs and NTPs that are used for identification, collection, reconstruction, and analysing the chain of incidents that happen on networks. However, they were vary and differ in their roles and functionalities. The main objective of this paper, therefore, is to assess and see the distinction that exist between Network Forensic Tools (NFTs) and Network Forensic Processes (NFPs). Precisely, this paper focuses on comparing among four famous NFTs: Xplico, OmniPeek, NetDetector, and NetIetercept. The outputs of this paper show that the Xplico tool has abilities to identify, collect, reconstruct, and analyse the chain of incidents that happen on networks than other NF tools. © 2021 IEEE.

关键词： computer crime

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Digital forensics domain and metamodeling development approaches 2

Digital forensics domain and metamodeling development approa...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Ameerbakhsh, Omair Ghabban, Fahad M Alfadli, Ibrahim M Abuali, Amer Nizar Al-Dhaqm, Arafat Al-Khasawneh, Mahmoud Ahmad Taibah University College of Computer Science and Engineering Information System Department Madinah Saudi Arabia University Technology Malaysia School of Computing Faculty of Engineering Johor Malaysia Al-Madinah International University Faculty of Computer and Information Technology Shah Alam Malaysia

ISBN: (纸本)9781665432221

Metamodeling is used as a general technique for integrating and defining models from different domains. This technique can be used in diverse application domains, especially for purposes of standardization. Also, this process mainly has a focus on the identification of general concepts that exist in various problem domain and their relations and to solve complexity, interoperability, and heterogeneity aspects of different domains. Several diverse metamodeling development approaches have been proposed in the literature to develop metamodels. Each metamodeling development process has some advantages and disadvantages too. Therefore, the objective of this paper is to provide a comprehensive review of existing metamodeling development approaches and conduct a comparative study among them-eventually selecting the best approach for metamodel development in the perspective of digital forensics. © 2021 IEEE.

关键词： Digital forensics

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