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Innovations in Intelligent Systems and Applications Conference

作者： Rukiye Savran Kiziltepe Ercan Ezin Murat Karakus Dept. of Software Engineering Karadeniz Tech. University Trabzon Türkiye Dept. of Computer Engineering Harran University Sanliurfa Türkiye Dept. of Software Engineering Ankara University Ankara Türkiye

ISBN: (数字)9798350379433

ISBN: (纸本)9798350379440

This study investigates the performance of traditional Machine Learning (ML) models for Sentiment Analysis (SA) in the Turkish language, addressing the growing need for robust Natural Language Processing (NLP) techniques in low-resource languages. We compare the efficacy of Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) classifiers on two recently developed large-scale Turkish sentiment datasets: “Vitamins and Supplements Customer Review” (VSCR) and “Turkish Sentiment Analysis version I” (TRSAv1). Our research leverages advanced hyperparameter tuning techniques to optimize these models, providing benchmarks and practical insights for Turkish SA. The study contributes to the field by establishing performance baselines on new open-source datasets, enhancing understanding of NLP models for agglutinative languages, and analyzing factors affecting classifier performance in binary and multi-class scenarios. Our findings reveal the strengths and limitations of each model within the context of Turkish language SA, offering valuable guidance for future research and applications in this domain.

关键词： Support vector machines Analytical models Sentiment analysis Technological innovation Systematics Computational modeling Linguistics Benchmark testing Tuning Classification tree analysis

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Cloud-Based Framework for Data Exchange to Enhance Global Healthcare 19

Cloud-Based Framework for Data Exchange to Enhance Global He...

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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： Naz, Ammerha Ali, Muhammad Cheema, Sehrish Munawar Pires, Ivan Miguel Dept. of Computer Science University of Sialkot Punjab Sialkot Pakistan Dept. of Software Engineering Superior University Lahore Pakistan Dept. of Computer Science University of Management and Technology Lahore Pakistan Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Healthcare is a global pillar, with a surge in the adoption of information technology, particularly in hospital information systems (HIS). However, global protocols are needed to meet the growing demand for data interchange, practical implementations for sharing healthcare data among facilities, and a pressing need for processing and storage infrastructure to handle the escalating volume of healthcare data. This study proposes a solution for efficient data transmission using electronic health records (EHR) and Platform-as-a-Service (PaaS) to leverage cloud computing resources. This framework's architecture boasts robustness and adaptability, providing all registered software programs access to data interchange services. Through a comprehensive examination of the framework's structure, the essay also explores the most effective data-sharing methods. It identifies the healthcare system's optimal EHR data model. According to multiple healthcare experts, the operational building of this framework is expected to catalyze the growth of healthcare institutions both nationally and within specific industries. © 2024 The Authors. Published by Elsevier B.V.

关键词： Electronic health record

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A Review on Data Balancing Techniques and Machine Learning Methods 5

A Review on Data Balancing Techniques and Machine Learning M...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Parmar, Gaurav Gupta, Rimi Bhatt, Tejas Sahani, G.J. Panchal, Brijeshkumar Y. Patel, Hiren S.V.I.T Dept. of Computer Engineering Gujarat Vasad India Software Developer Tüv Süd South Asia Pvt. Ltd India

ISBN: (纸本)9781665474672

Researchers in the discipline of Data Mining (DM) occasionally disregard the need of ensuring a dataset is evenly distributed. As such, it may have a major impact on how things are ultimately sorted. Most classifiers function on the concept that the data is roughly normally distributed. Therefore, the categorization approach is no longer as successful as it once was and fixing this is essential. This research aims to construct an evaluation of several methods for producing labels for minority classes to prevent the model from developing bias toward protected attributes. In this study, different researcher's work learned to determine the most effective method for disseminating the data. There will be a discussion of ways to enhance categorization abilities in the conclusion. The future of data-balancing research is determined by weighing the relative merits of the several possible methodological amalgamations. © 2023 IEEE.

关键词： Data mining

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Investigating Effect of Optimization Algorithms on Chaos Based Image Encryption

Investigating Effect of Optimization Algorithms on Chaos Bas...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Cemile İnce Kenan İnce Computer Engineering Dept İnönü University Malatya Türkiye Software Engineering Dept İnönü University Malatya Türkiye

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

Image encryption is a specialized subfield of cryptography, distinguished by the high correlation between neighboring pixels, which necessitates unique approaches to disrupt this correlation. While all encryption approaches fundamentally involve confusion and diffusion operators, the specific implementations of these operators can vary significantly. In recent years, approaches that combine chaos theory with optimization techniques, which aim to find the optimal solution among numerous possibilities, have become prevalent. These studies, which aim to enhance the security and efficiency of the encryption process, such as by selecting the initial values of chaotic maps in regions with high chaotic behavior, show promise. This study examines the most commonly applied optimization methods in the field of cryptography within the context of image encryption, providing a comprehensive overview. Additionally, it empirically demonstrates that the optimization techniques employed in image encryption studies improve encryption quality.

关键词： Chaos Measurement Correlation Metaheuristics Optimization methods Data processing Encryption Artificial intelligence Optimization

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Web Accessibility of the Cyprus Island Food Retailers' Websites 5

Web Accessibility of the Cyprus Island Food Retailers' Websi...

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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Iseri, Erkut Inan Uyar, Kaan Ilhan, Umit Near East University Dept. of Physics TRNC Mersin 10 Nicosia Turkey Near East University Dept. of Software Engineering TRNC Mersin 10 Nicosia Turkey Near East University Dept. of Computer Engineering TRNC Mersin 10 Nicosia Turkey

ISBN: (纸本)9798350337525

Accessibility is the process of making information and electronic communication environments meaningful and usable for most people including those with disabilities. It is not always an easy task to provide all user communities with different areas of interests and needs with exact styles and contents. It is a good practice to employ responsible designs and a certain degree of interaction that provide equitable conditions to suit the needs of the most user communities. It became evident that in the passed two years the world went thru a terrifying Coronavirus disease which made the accessibility of web sites of retailers even more challenging. Most of the food supplying companies introduced shop-to door deliveries of food products with web based applications. Although, technically speaking, these web sites accomplished the task for most of the people but with some accessibility issues. These issues can be identified and solved by following the international standards to gain world wide acceptance. This study covers the investigation of the accessibility of the Food Retailers' websites in the whole of Cyprus Island. Web Content Accessibility Guidelines 2.1 (WCAG 2.1) published by the World Wide Web Consortium (W3C) is followed in the process. Three testing software are used to determine the degree of compliance of the food retailers with the WCAG 2.1 in Cyprus Island. The aim of this study is to raise awareness about web accessibility among general public. The findings are not so promising and the sites examined are not in compliance with the standard of the guidelines defined in WCAG. © 2023 IEEE.

关键词： Websites

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Towards the Utilization of Parallel Programming to Speed Up RAM Forensics 10

Towards the Utilization of Parallel Programming to Speed Up ...

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10th International Conference on software Defined Systems, SDS 2023

作者： Al-Sharif, Ziad A. Alhamouri, Rahaf Jararweh, Yaser Lewis University Dept. of Engineering Computing and Mathematical Sciences Romeoville United States Jordan University of Sci. and Tech. Dept. of Software Engineering Irbid Jordan Jordan University of Sci. and Tech. Dept. of Computer Science Irbid Jordan

ISBN: (纸本)9798350382235

Memory forensics uses volatile digital artifacts as evidence about criminal activities. Analyzing captured memory dumps for volatile data requires time and effort. This paper studies the utilization of parallel programming to speed up RAM forensics. It presents a performance-based evaluation of parallel programming in the domain of memory forensics and compares sequential and parallel approaches to speed up the memory analysis process. First, it evaluates the sequential approach and uses it as a base case for further parallel approaches. Second, it evaluates two of the parallel approaches that can be performed on a typical user machine. Our experiments evaluate the use of two parallel programming paradigms: the in-process parallelization approach using OpenMP, and the inter-process parallelization approach using MPI. Our results compare the performance of the sequential approach, OpenMP thread-based approach, and MPI process-based approach. Experimentations compare the performance of three scenarios using six files of different sizes and various numbers of threads and/or processes. The results show that the use of MPI is slightly better than OpenMP approaches on the use of 2 and 4 processes/threads. However, when the number of processors/threads is increased to 8 and 16, OpenMP slightly outperforms the MPI approach. Additionally, the parallelization approach using OpenMP and MPI provides 3X to 5X speed up over the traditional sequential approach. Moreover, it is worth mentioning that this speed-up is achieved on traditional user machines without the use of HPC computers. © 2023 IEEE.

关键词： Parallel programming

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PRAGyan - Connecting the Dots in Tweets

arXiv

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arXiv 2024年

作者： Ravi, Rahul Ginde, Gouri Rokne, Jon Dept. of Computer Science University of Calgary Canada Dept. of Electrical and Software Engineering University of Calgary Canada

As social media platforms grow, understanding the underlying reasons behind events and statements becomes crucial for businesses, policymakers, and researchers. This research explores the integration of Knowledge Graphs (KGs) with Large Language Models (LLMs) to perform causal analysis of tweets dataset. The LLM aided analysis techniques often lack depth in uncovering the causes driving observed effects. By leveraging KGs and LLMs, which encode rich semantic relationships and temporal information, this study aims to uncover the complex interplay of factors influencing causal dynamics and compare the results obtained using GPT-3.5 Turbo. We employ a Retrieval-Augmented Generation (RAG) model, utilizing a KG stored in a Neo4j (a.k.a PRAGyan) data format, to retrieve relevant context for causal reasoning. Our approach demonstrates that the KG-enhanced LLM RAG can provide improved results when compared to the baseline LLM (GPT-3.5 Turbo) model as the source corpus increases in size. Our qualitative analysis highlights the advantages of combining KGs with LLMs for improved interpretability and actionable insights, facilitating informed decision-making across various domains. Whereas, quantitative analysis using metrics such as BLEU and cosine similarity show that our approach outperforms the baseline by 10%. © 2024, CC BY.

关键词： Knowledge graph

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CADS: Cyber-Attack Detection System Using Decision Tree for Automotive Cyber-Physical Systems

CADS: Cyber-Attack Detection System Using Decision Tree for ...

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International Conference on Electrical, Communication, and computer engineering (ICECCE)

作者： Seo-Hee Park Youngmi Baek Dept. of Computer Software Engineering Changshin University Changwon-si Korea

Modern vehicles are equipped with a large number of electronic control units (ECUs) to provide more convenient and safe services for drivers. These ECUs are required to share the various control data for driving using the CAN (Controller Area Network) protocol that is one of the communication protocols for in-vehicle networks. Recently, as vehicles become capable of real-time, two-way communication with various elements on the road, modern vehicles faces a wide range of security issues. The in-vehicle networks are already suffered from the cyber-attacks exploiting newly exposed security vulnerabilities. However, it cannot guarantee the safety and security of the vehicles from as the CAN is designed without considering security. Therefore, this paper proposes a machine learning-based cyber-attack detection system (CADS) in CAN to ensure driving safety. This CADS learns the data pattern of the CAN frames generated while the driving and is able to distinguish between regular messages and cyberattack messages. The performance of the fully trained detection model of the CADS is evaluated using a vast number of testing dataset. The results show that our CADS in terms of accuracy, precision, recall, and F1 score has 100% of the detection rate for regular messages, replay attacks, brute-force attacks except for modification messages.

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Explainable Multiclass Blood Cell Classification: Combining Custom CNNs with SHAP

Explainable Multiclass Blood Cell Classification: Combining ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md. Ismiel Hossen Abir Shantanu Dey Anik Hasibul Islam Peyal Dept. of Computer Science & Engineering International Standard University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Birulia Savar Dhaka Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Blood cell classification is a crucial task in medical diagnostics. This work can identify different blood cell types that play essential roles in the immune system and overall body function. This research introduces a custom Convolutional Neural Network (CNN) model with SHAP for explainable multiclass classification of blood cell images. We classify eight types of blood cells—basophil, eosinophil, erythroblast, immunoglobulin (Ig), lymphocyte, monocyte, neutrophil, and platelet. An ablation study was conducted to optimize the CNN architecture. We compared its performance with eleven transfer learning models, including VGG16, ResNet101, and EfficientNet variants. Our model achieved 92.60% accuracy while maintaining lower complexity. The use of SHAP provides transparency by highlighting crucial features in the model’s decision-making process.

关键词： Training Accuracy Microprocessors Transfer learning Lymphocytes computer architecture Convolutional neural networks Medical diagnosis Blood Testing

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Research Paper Classification and Recommendation System based-on Fine-Tuning BERT

Research Paper Classification and Recommendation System base...

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IEEE International Conference on Information Reuse and Integration (IRI)

作者： Dipto Biswas Joon-Min Gil Dept. of Computer Software Engineering Graduate School of Daegu Catholic University Gyeongsan-si South Korea School of Computer Software Engineering Gyeongsan-si South Korea

In this paper, we compare the performance of two popular NLP models, pre-train fine-tuned BERT and BiLSTM with combined CNN, in terms of the classification and recommendation tasks of research papers. We conduct the performance evaluation of these two models with research journal benchmark dataset. Performance results show that the pre-train fine-tuned BERT model is superior to CNN-BiLSTM combined model in terms of classification performance.

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