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Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

computer Systems Science & engineering 2023年第1期44卷 235-247页

作者： R.Bhaskaran S.Saravanan M.Kavitha C.Jeyalakshmi Seifedine Kadry Hafiz Tayyab Rauf Reem Alkhammash Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTrichy621112India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science Faculty of Engineering&InformaticsUniversity of BradfordBradfordUnited Kingdom English Department University CollegeTarabaTaif UniversityTaif21944Saudi Arabia

Sentiment Analysis(SA)is one of the subfields in Natural Language Processing(NLP)which focuses on identification and extraction of opinions that exist in the text provided across reviews,social media,blogs,news,and so *** has the ability to handle the drastically-increasing unstructured text by transform-ing them into structured data with the help of NLP and open source *** current research work designs a novel Modified Red Deer Algorithm(MRDA)Extreme Learning Machine Sparse Autoencoder(ELMSAE)model for SA and *** proposed MRDA-ELMSAE technique initially performs pre-processing to transform the data into a compatible ***,TF-IDF vec-torizer is employed in the extraction of features while ELMSAE model is applied in the classification of ***,optimal parameter tuning is done for ELMSAE model using MRDA technique.A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced effi-ciency of MRDA-ELMSAE technique against other recent techniques.

关键词： Sentiment analysis data classification machine learning red deer algorithm extreme learning machine natural language processing

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Comparative Evaluation between Accelerated RISC- V and ARM AI Inference Machines 6

Comparative Evaluation between Accelerated RISC- V and ARM A...

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6th World Symposium on Communication engineering, WSCE 2023

作者： Christofas, Vasileios Amanatidis, Petros Karampatzakis, Dimitris Lagkas, Thomas Goudos, Sotirios K. Psannis, Konstantinos E. Sarigiannidis, Panagiotis International Hellenic University Department of Computer Science Kavala Greece Aristotle University of Thessaloniki Department of Physics Thessaloniki Greece University of Macedonia Department of Applied Informatics Thessaloniki Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece

ISBN: (纸本)9798350339505

Embedded AI development has been rapidly im-proving for the past few years and has had a great impact on edge AI networks. However, as neural networks become deeper and deeper it becomes more difficult to execute complicated tasks without sacrificing a good amount of power and performance. In this paper, we make a comparative evaluation between two AI acceleration devices. The first one features a RISC- V 64-bit processor while the other one is ARM powered. These devices are combined with AI co-processors, or ASICs, with computer vision capabilities. Our benchmark consists of a simple classification task split into multiple versions. The results showed that the RISC- V inference machine had 4 times lower consumption while the ARM machine was up to 15 times faster in our largest network. We discuss the results in great detail while keeping our focus on all aspects equally. Finally, we make recommendations based on their usage and application. © 2023 IEEE.

关键词： computer vision

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Synergistic Swarm Optimization Algorithm

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computer Modeling in engineering & Sciences 2024年第6期139卷 2557-2604页

作者： Sharaf Alzoubi Laith Abualigah Mohamed Sharaf Mohammad Sh.Daoud Nima Khodadadi Heming Jia Faculty of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Al Al-Bayt UniversityMafraq25113Jordan Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Department of Industrial Engineering College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia College of Engineering Al Ain UniversityAbu Dhabi112612United Arab Emirates Department of Civil and Architectural Engineering University of MiamiCoral Gables1251USA School of Information Engineering Sanming UniversitySanming365004China

This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search *** cooperation enhances the exploitation of promising regions in the search space while maintaining exploration ***,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and *** leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization *** effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design *** experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and *** codes of SSOA are available at:https://***/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.

关键词： Synergistic swarm optimization algorithm optimization algorithm metaheuristic engineering problems benchmark functions

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Effective solutions to prevent digital fraud by introducing electronic signature of PDF files 24

Effective solutions to prevent digital fraud by introducing ...

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24th International Conference on Control Systems and computer Science, CSCS 2023

作者： Anghel, Ionut Petrisor Radulescu, Dragos Marinescu, Ion Alexandru Polytechnic University of Bucharest Computer Science and Engineering Department Bucharest Romania National Institute for Research and Development in Informatics Digital Transformation and Governance Bucharest Romania

ISBN: (纸本)9798350313390

With the exponential growth of digital transactions, the creation and verification of electronic signatures contributes decisively to the creation of a safer environment for business development and innovation, based on security factors such as confidentiality, integrity, authenticity or non-repudiation. Implementing electronic signatures in PDF files can improve the prevention of digital fraud by providing an additional level of security and authenticity. For this reason, in addition to choosing a reliable electronic signature provider, it is necessary to establish clear signing procedures in order to minimize the risk of fraud. For this purpose, a software solution intended mainly for the academic environment is presented, which facilitates the electronic signature with legal value of PDF documents using a digital certificate. This application offers the possibility of simultaneously signing of multiples files in a folder, thus streamlining the process of digital signature and avoiding the use of third-party applications. © 2023 IEEE.

关键词： Quantum cryptography

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Ship Engine Data Analysis for the Application of Machine Learning Algorithms 8

Ship Engine Data Analysis for the Application of Machine Lea...

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8th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2023

作者： Dimitriou, Theodoros Skondras, Emmanouil Hitiris, Christos Gkola, Cleopatra Papapanagiotou, Ioannis S. Vergados, Dimitrios J. Vergopoulos, Constantinos Koumantakis, Stratos Michalas, Angelos Vergados, Dimitrios D. University of Piraeus Department of Informatics Piraeus Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece University of Western Macedonia Department of Informatics Kastoria Greece Internet Business Hellas Athens Greece Mas S.A. Athens Greece

ISBN: (纸本)9798350383676

In Machine Learning (ML) the analysis and the preparation of data before their use is considered an important task, in order to improve the performance of the ML algorithms. Techniques like clustering and dimensionality reduction are applied for the preparation of the data offering several advantages for the ML algorithms to which the data will be used. Some indicative advantages include the improved performance and the faster training of the ML algorithms, the handling of missing or corrupted data, the detection of data overfitting and the visualization of the data. In this paper, a dataset that contains plenty of ship engine's data is analyzed. Specifically, methodologies for density estimation, clustering and dimensionality reduction are studied. Subsequently, such methodologies are applied to the aforementioned dataset, providing useful results about the structure of the dataset, about the correlation of its data, as well as about the importance of each feature included in the dataset. © 2023 IEEE.

关键词： Machine learning

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AI and ML Applications Traffic: Designing Challenges for Performance Optimization of Optical Data Center Networks

AI and ML Applications Traffic: Designing Challenges for Per...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Peristera Baziana Georgios Drainakis David Georgantas Adonis Bogris Department of Informatics and Telecommunications University of Thessaly Greece School of Electrical and Computer Engineering National Technical University of Athens Greece Department of Informatics and Computer Engineering University of West Attica Greece

ISBN: (数字)9789532901382

ISBN: (纸本)9798350354614

Artificial intelligence (AI) and machine learning (ML) are vastly becoming key parts of today’s applications, supporting diverse human activities and decision making. New training models are used requiring an explosive traffic exchange among data centers (DCs) servers, putting tremendous pressure on DC network (DCN). Therefore, new DCN design challenges are required to efficiently handle the emerging AI/ML applications traffic. Contrary to existing works that do not account for these challenges, in this paper we introduce an optical intra-rack DCN architecture, as an optical local area network (LAN) paradigm, and an efficient wavelength division multiplexing access (WDMA) algorithm tailored to serve the AI/ML traffic. For the proposed WDMA scheme, we assume diverse AI/ML traffic classes according to the required service time and quality of service (QoS) delivery and we adopt appropriate access rules for each of them to achieve high DCN performance and access fairness. Simulation results show that our proposal reaches high bandwidth utilization ($90 \%$ under nominal load 400 Gbps) and low end-to-end latency (<300 ns across all loads for high/medium QoS traffic, and <100 $\mu \mathrm{s}$ under nominal load for low QoS traffic), contributing to the agenda of new design challenges for optical DCNs suitable for AI/ML traffic.

关键词： Training Data centers Protocols Optical design Quality of service computer architecture Traffic control Wavelength division multiplexing Servers Local area networks

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Annealing-Based Optimization for Selecting Training Space in Ensemble Learning 10

Annealing-Based Optimization for Selecting Training Space in...

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10th International Conference on Advanced informatics: Concept, Theory and Application, ICAICTA 2023

作者： Yulianti, Lenny Putri Trisetyarso, Agung Santoso, Judhi Surendro, Kridanto School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia School of Computer Science Bina Nusantara University Department of Mathematics Jakarta Indonesia

ISBN: (纸本)9798350329919

Ensemble learning integrates many learners to achieve better performance, however, it can increase the complexity dramatically. Redundant learners should be pruned to reduce ensemble complexity while allowing for increased accuracy. There are two common ways to achieve this, which include selecting the optimal training space and the optimal trained learners. This study contributes to reducing redundant learners by selecting the optimal training space using annealing-based optimization. The optimal training space was defined by 1) measuring the similarity between training spaces (clusters) and 2) searching for the best set of training spaces by minimizing the similarity between the selected training spaces. The proposed method was tested on 10 datasets from the UCI repository and compared to five state-of-The-Art random subspace methods. The results showed that the proposed method could reduce the size of the ensemble while significantly increasing its accuracy. © 2023 IEEE.

关键词： Annealing

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Towards an Automatic Extracting UML Class Diagram from System's Textual Specification 24

Towards an Automatic Extracting UML Class Diagram from Syste...

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7th International Conference on Networking, Intelligent Systems and Security, NISS 2024

作者： Babaalla, Zakaria Jakimi, Abdeslam Oualla, Mohamed Saadane, Rachid Chehri, Abdellah GL-ISI Team Department of Informatics Faculty of Sciences and Techniques Errachidia Morocco Electrical Engineering Department Hassania School of Public Works Casablanca Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada

ISBN: (纸本)9798400709296

Developing a software system from natural language requirements is a complex and delicate task that requires a high level of design and programming expertise. Increasing the level of abstraction used to describe these requirements is the most natural solution. Model-Driven engineering (MDE) also takes this route, using abstract models as primary entities to generate source code automatically or semi-automatically. Among these models, the UML class diagram occupies a privileged place in object-oriented systems because it not only serves as a basis for communication between developers but also provides a closely aligned static representation of the system implementation. However, creating a UML class diagram from a textual system specification poses a significant challenge due to the inherent imprecision and ambiguity commonly found in natural language expressions. In this paper, we propose a model-centric approach based on deep learning for the automatic extraction of UML class diagrams from textual requirements. © 2024 ACM.

关键词： Object oriented programming

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A Comparative Analysis of Tweet Analysis Algorithms Using Natural Language Processing and Machine Learning Models 18

A Comparative Analysis of Tweet Analysis Algorithms Using Na...

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18th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2023

作者： Kanavos, Andreas Antonopoulos, Nikos Karamitsos, Ioannis Mylonas, Phivos Ionian University Department of Informatics Corfu Greece Ionian University Department of Digital Media and Communication NeMeCULAB Argostoli Greece Rochester Institute of Technology Department of Graduate and Research Dubai United Arab Emirates University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798350327717

Online Social Networks (OSNs) have become integral platforms for information sharing, attracting both legitimate users and spammers. Detecting and mitigating spam within these networks pose significant challenges due to the dynamic nature of content and user behavior. In this paper, we present a comprehensive comparative analysis of algorithms for tweet analysis, focusing on Natural Language Processing (NLP) and Machine Learning (ML) models. We evaluate these algorithms through sentiment analysis and multiple attribute analysis, utilizing diverse methodologies and datasets. Our study explores feed-forward neural networks, Bayesian classifiers, and transformer-based models for NLP tasks, achieving high prediction accuracy and insightful metrics such as precision, recall, and F1 score. Furthermore, we delve into multiple attribute analysis using Random Forest, Logistic Regression, and Gradient Boosting algorithms. Through a systematic exploration of various approaches, this work contributes to a deeper understanding of spam detection and sentiment analysis within the context of OSNs, paving the way for enhanced social network security and content analysis. © 2023 IEEE.

关键词： Social networking (online)

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Automated User Experience (UX) Testing for Mobile Application: Strengths and Limitations

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第4期16卷 30-45页

作者： Abuaddous, Hayfa. Y. Saleh, Ashraf Mousa Enaizan, Odai Ghabban, Fahad Al-Badareen, Anas Bassam Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan Department of Management Information System University of Tabuk Tabuk Saudi Arabia College of Computer Science and Engineering Taibah University Medina Saudi Arabia Department of Software Engineering Aqaba University of Technology Aqaba Jordan

User Experience (UX) evaluation has a significant importance for any interactive application. Mobile device applications have additional limitations to convey good user experiences (UX) due to the usage and features of mobile devices in ubiquitous computing contexts. There are a lot of automated tools available for evaluating, such applications particularly developed for Android and iOS. Automated UX evaluation provides a number of strengths which are able to overcome the limitations of non-automated (moderated) counterparts. Automated UX evaluation is regarded as unexplored field, especially when it is adopted for the UX evaluation of complex mobile application. Despite these, automated UX evaluation tools are unexplored and should only be used in coupling with traditional UX evaluation techniques. Hence, this study aims to discuss the literature in automated UX evaluation and to highlight the UX measurements that merit further investigation. This paper presents the most fre-quently used automated UX evaluation tools for mobile applications. Therefore, the study analyzes existing automated UX tools and frameworks and identifies which UX attributes and measurements of automated UX evaluation are likely to be of use in future research. Moreover, we discuss the strengths and limitations of automated UX evaluation methods for mobile applications. Finally, the paper provides an overview of automated UX evaluation techniques and tools. It also presents the strengths and limitations for comparing various evaluation methods. © 2022,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Automation

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