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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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Convergence of blockchain and Internet of Things:integration, security, and use cases

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Frontiers of Information Technology & Electronic Engineering 2024年第10期25卷 1295-1321页

作者： Robertas DAMASEVICIUS Sanjay MISRA Rytis MASKELIUNAS Anand NAYYAR Department of Applied Informatics Vytautas Magnus UniversityKaunas 44404Lithuania Department of Applied Data Science Institute for Energy TechnologyHalden 17Norway Department of Multimedia Engineering Kaunas University of TechnologyKaunas 51368Lithuania Graduate School Faculty of Information TechnologyDuy Tan UniversityDa Nang 550000Viet Nam

Internet of Things(IoT) devices are becoming increasingly ubiquitous, and their adoption is growing at an exponential rate. However, they are vulnerable to security breaches, and traditional security mechanisms are not enough to protect them. The massive amounts of data generated by IoT devices can be easily manipulated or stolen, posing significant privacy concerns. This paper is to provide a comprehensive overview of the integration of blockchain and IoT technologies and their potential to enhance the security and privacy of IoT systems. The paper examines various security issues and vulnerabilities in IoT and explores how blockchain-based solutions can be used to address them. It provides insights into the various security issues and vulnerabilities in IoT and explores how blockchain can be used to enhance security and privacy. The paper also discusses the potential applications of blockchain-based IoT(B-IoT) systems in various sectors, such as healthcare, transportation, and supply chain management. The paper reveals that the integration of blockchain and IoT has the potential to enhance the security,privacy, and trustworthiness of IoT systems. The multi-layered architecture of B-IoT, consisting of perception, network, data processing, and application layers, provides a comprehensive framework for the integration of blockchain and IoT *** study identifies various security solutions for B-IoT, including smart contracts, decentralized control, immutable data storage,identity and access management(IAM), and consensus mechanisms. The study also discusses the challenges and future research directions in the field of B-IoT.

关键词： Blockchain Internet of Things(loT) Blockchain-based IoT(B-IoT) Security Scalability Privacy

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Evaluating the Usability and Career Guidance Functionality of an Academic Performance Tracking Application Using the UEQ

Evaluating the Usability and Career Guidance Functionality o...

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Information Technology International Seminar (ITIS)

作者： Siti Mukaromah Arista Pratama Henni Endah Wahanani Mohammad Idhom Agussalim Eristya Maya Safitri Department of Information System Faculty of Computer Science University of Pembangunan Nasional "Veteran" Jawa Timur Surabaya Indonesia Department of Informatics Faculty of Computer Science University of Pembangunan Nasional "Veteran" Jawa Timur Surabaya Indonesia Department of Data Science Faculty of Computer Science University of Pembangunan Nasional "Veteran" Jawa Timur Surabaya Indonesia

ISBN: (数字)9798331521295

ISBN: (纸本)9798331521301

This article evaluates an academic application designed to provide career guidance using the User Experience Questionnaire (UEQ). A survey involving 255 respondents assessed the application across 6 UX dimensions: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. The results reveal the highest scores on Attractiveness 1.473 and Perspicuity 1.642, indicating that the platform is visually appealing and easy to navigate. However, lower scores on Novelty 0.774 and Dependability 1.243 highlight areas requiring improvement, particularly in building trust among users and introducing innovative features. The implications of these findings suggest that while the platform holds significant potential as a tool for academic monitoring and career guidance, enhancing user engagement and reliability will be critical to achieving broader acceptance. Addressing the gaps in Stimulation (1.135) and Novelty could lead to a more engaging and unique user experience, aligning with the evolving needs of students. This study provides actionable insights for the iterative development of user-centered educational tools, emphasizing the importance of UX in supporting informed decision-making and improving academic outcomes.

关键词： Surveys Seminars Engineering profession Navigation User experience Iterative methods Reliability Usability Information technology Monitoring

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Enhanced DCOPA with Security Features for WSN-Based IoT Clustering

Enhanced DCOPA with Security Features for WSN-Based IoT Clus...

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Innovative and Intelligent Information Technologies (IC3IT), International Conference on

作者： Foudil Mir Zoubeyr Farah Farid Meziane Faculty of Exact Sciences Laboratory of Medical Informatics and Intelligent and Dynamic Environments (LIMED) University of Bejaia Bejaia Algeria Data Science Research Centre University of Derby Derby UK

ISBN: (数字)9798331541064

ISBN: (纸本)9798331541071

In the context of Wireless Sensor Networks applied to the Internet of Things (WSN-Based IoT), security is a major concern due to the vulnerability of these networks to various threats and attacks. While the DCOPA protocol is efficient in terms of energy management and clustering, its security aspects have not been addressed. To mitigate security risks such as Sybil attacks, identity spoofing, and replay attacks, this paper proposes an enhancement to the DCOPA protocol, called Secured DCOPA (SECDCOPA), by integrating a robust and lightweight security mechanism into its operation. The main objective is to enhance the security of the network constructed using DCOPA to mitigate security threats while preserving energy efficiency. The proposed mechanism ensures that only authenticated entities can join the established cluster network, guaranteeing the authenticity of communicating entities and providing complete control over the integrity of exchanged information. The integration of this security mechanism into DCOPA is of paramount importance in IoT environments, where security is under constant threat. By employing a distributed hash-based security approach, we en-hance the security features of DCOPA while preserving its energy efficiency. This ensures the network's resilience against attacks and maintains its stability.

关键词： Wireless sensor networks Protocols Stability criteria Authentication Energy efficiency Energy states Security Internet of Things Information technology Resilience

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CFPChain: An Optimization of Research Funding Selection System Employing A Consortium Blockchain Based Approach 10

CFPChain: An Optimization of Research Funding Selection Syst...

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10th International Conference on Computer, Control, informatics and its Applications, IC3INA 2023

作者： Ramdhani, Taufik Iqbal Basuki, Akbari Indra Susanto, Heru Umbara, Raditya Panji Arifin, Samsul Faiza, Ninon Nurul Research Center for Artificial Intelligence and Cyber Security National Research and Innovation Agency Bandung Indonesia Research Center for Geological Disaster National Research and Innovation Agency Tangerang Selatan Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Data and Information Center National Research and Innovation Agency Jakarta Indonesia Faculty Computer Science Institut Teknologi Tangerang Selatan Indonesia Department of Informatics Faculty of Science Nad Technology Universitas Al Azhar Indonesia Jakarta Indonesia Department of Informatics Universitas Bunda Mulia Tangerang Selatan Indonesia

ISBN: (纸本)9798350394870

This paper addresses the challenges in the current centralized research funding selection system and proposes a solution using permissioned blockchain technology. The existing system suffers from drawbacks such as concealed data modification and lack of interoperability. The Blockchain approach, based on the principles of Bitcoin technology, offers high security, auditability, traceability, and integrity. By utilizing interconnected blocks and cryptography, the proposed system ensures the authenticity and consistency of data over time. The chosen blockchain platform, Hyperledger Fabric, provides crash-fault-tolerance consensus and private communication options for enhanced security and efficiency. The objective of this study is to develop a consortium blockchain-based research funding platform that enables collaboration among researchers from public agencies, universities, and companies, with funding provided by financial organizations. The proposed architecture model ensures tamper-proof data, accountability, and interoperability, benefiting researchers, reviewers, auditors, funders, and companies. © 2023 IEEE.

关键词： Smart contract

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Need Analysis of a Rewarding System Based on Design Thinking in Respect of Waste Sorting Behavior : Case Study: Saensook City

Need Analysis of a Rewarding System Based on Design Thinking...

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International Conference on Information Technology (INCIT)

作者： Benchaporn Jantarakongkul Kanuengnij Kubola Thatsanee Charoenporn Prawit Boonmee Prajaks Jitngernmadan Faculty of Informatics Burapha University Chon Buri Thailand Faculty of Data Science Musashino University Tokyo Japan

Designing and developing a sustainable rewarding system needs users’ involvement, especially when it comes to a public information system used by a community. In Saensook municipality, a solid waste management is one of the most important problems. We attempted to design and develop a sustainable rewarding system to encourage the people both residents and visitors (tourists and excursionists) to separate their own produced solid waste. This rewarding system is based on Design Thinking framework. In this stage, we did the need analysis and present the key factors affecting design and development of such a sustainable rewarding system for solid waste separation.

关键词： Waste materials Behavioral sciences Information technology Sorting Information systems

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Continual Domain Incremental Learning for Privacy-aware Digital Pathology

arXiv

引用

arXiv 2024年

作者： Kumari, Pratibha Reisenbüchler, Daniel Luttner, Lucas Schaadt, Nadine S. Feuerhake, Friedrich Merhof, Dorit Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Institute of Pathology Hannover Medical School Hannover Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

In recent years, there has been remarkable progress in the field of digital pathology, driven by the ability to model complex tissue patterns using advanced deep-learning algorithms. However, the robustness of these models is often severely compromised in the presence of data shifts (e.g., different stains, organs, centers, etc.). Alternatively, continual learning (CL) techniques aim to reduce the forgetting of past data when learning new data with distributional shift conditions. Specifically, rehearsal-based CL techniques, which store some past data in a buffer and then replay it with new data, have proven effective in medical image analysis tasks. However, privacy concerns arise as these approaches store past data, prompting the development of our novel Generative Latent Replay-based CL (GLRCL) approach. GLRCL captures the previous distribution through Gaussian Mixture Models instead of storing past samples, which are then utilized to generate features and perform latent replay with new data. We systematically evaluate our proposed framework under different shift conditions in histopathology data, including stain and organ shift. Our approach significantly outperforms popular buffer-free CL approaches and performs similarly to rehearsal-based CL approaches that require large buffers causing serious privacy violations. © 2024, CC BY.

关键词： Differential privacy

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Multimodal E-Commerce Product Classification Using Hierarchical Fusion 2

Multimodal E-Commerce Product Classification Using Hierarchi...

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2nd IEEE Conference on Information Technology and data science, CITDS 2022

作者： Tashu, Tsegaye Misikir Fattouh, Sara Kiss, Peter Horvath, Tomas Elte - Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Pázmány Péter sétány 1/C Budapest1117 Hungary Faculty of Science Institute of Computer Science Pavol Jozef Šafárik University Jesenná5 Košice040 01 Slovakia College of Informatics Kombolcha Institute of Technology Wollo University Kombolcha208 Ethiopia

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

In this work, we present a multi-modal model for commercial product classification, that combines features extracted by multiple neural network models from textual (Camem-BERT and FlauBERT) and visual data (SE-ResNeXt-50), using simple fusion techniques. The proposed method significantly outperformed the performance of the unimodal models, as well as the reported performance of similar models on our specific task. We made experiments with multiple fusing techniques, and found, that the best preforming technique to combine the individual embedding of the unimodal network is based on the combination of concatenation and averaging the feature vectors. Each modality complemented the shortcomings of the other modalities, demonstrating that increasing the number of modalities can be an effective method for improving the performance of multi-label and multimodal classification problems. © 2022 IEEE.

关键词： Neural network models

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Multimodal Feature Extraction for Memes Sentiment Classification 2

Multimodal Feature Extraction for Memes Sentiment Classifica...

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2nd IEEE Conference on Information Technology and data science, CITDS 2022

作者： Ouaari, Sofiane Tashu, Tsegaye Misikir Horvath, Tomas Elte - Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Pázmány Péter sétány 1/C Budapest1117 Hungary Institute of Computer Science Faculty of Science Pavol Jozef Šafárik University Jesenná 5 Košice040 01 Slovakia College of Informatics Kombolcha Institute of Technology Wollo University Kombolcha208 Ethiopia

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

In this study, we propose feature extraction for multimodal meme classification using Deep Learning approaches. A meme is usually a photo or video with text shared by the young generation on social media platforms that expresses a culturally relevant idea. Since they are an efficient way to express emotions and feelings, a good classifier that can classify the sentiment behind the meme is important. To make the learning process more efficient, reduce the likelihood of overfitting, and improve the generalizability of the model, one needs a good approach for joint feature extraction from all modalities. In this work, we proposed to use different multimodal neural network approaches for multimodal feature extraction and use the extracted features to train a classifier to identify the sentiment in a meme. © 2022 IEEE.

关键词： Computer vision

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Investigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systems

arXiv

引用

arXiv 2024年

作者： Lakatos, Róbert Pollner, Péter Hajdu, András Joó, Tamás Data-Driven Health Division of National Laboratory for Health Security Health Services Management Training Centre Semmelweis University Hungary Department of Data Science and Visualization Faculty of Informatics University of Debrecen Hungary Doctoral School of Informatics University of Debrecen Hungary Neumann Technology Platform Neumann Nonprofit Ltd

The development of generative large language models (G-LLM) opened up new opportunities for the development of new types of knowledge-based systems similar to ChatGPT, Bing, or Gemini. Fine-tuning (FN) and Retrieval-Augmented Generation (RAG) are the techniques that can be used to implement domain adaptation for the development of G-LLM-based knowledge systems. In our study, using ROUGE, BLEU, METEOR scores, and cosine similarity, we compare and examine the performance of RAG and FN for the GPT-J-6B, OPT-6.7B, LlaMA, LlaMA-2 language models. Based on measurements shown on different datasets, we demonstrate that RAG-based constructions are more efficient than models produced with FN. We point out that connecting RAG and FN is not trivial, because connecting FN models with RAG can cause a decrease in performance. Furthermore, we outline a simple RAG-based architecture which, on average, outperforms the FN models by 16% in terms of the ROGUE score, 15% in the case of the BLEU score, and 53% based on the cosine similarity. This shows the significant advantage of RAG over FN in terms of hallucination, which is not offset by the fact that the average 8% better METEOR score of FN models indicates greater creativity compared to RAG. © 2024, CC BY.

关键词： Knowledge based systems

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