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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Sylvia, Visca Lydia, Maya Silvi Nasution, Benny Benyamin Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia

ISBN: (纸本)9798350351613

The purpose of this study is to improve the performance of Support Vector Machine (SVM) algorithm in sentiment analysis of trainee reviews through parameter optimization using Grid Search. Trainee reviews were taken from the Vocational and Productivity Training Center (BBPVP) Medan, which is the context of this study. The method used includes collection and preprocessing of review data, followed by the application of SVM for sentiment analysis. Grid Search is used to optimize the cost and kernel parameters of SVM. The results show that after optimization, the resulting SVM model shows significant performance improvement compared to the model before optimization. Accuracy increased from 68.75% to 71.96%, with improvements in other metrics such as precision, recall, and F1 score. These findings suggest that Grid Search is an effective method to optimize SVM parameters in sentiment analysis, providing a more accurate evaluation of the effectiveness of training programs at BBPVP Medan. This research makes an important contribution by applying hyperparameter optimization to SVM and suggests the application of this method for similar analysis in the future. © 2024 IEEE.

关键词： Support vector machines

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Comparative Analysis of Edge Detection Algorithms with Particle Swarm Optimization for Enhanced Image Processing 11

Comparative Analysis of Edge Detection Algorithms with Parti...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Rahayu, Sri Nurtantio Andono, Pulung Pujiono Zainul Fanani, Ahmad Dian Nuswantoro University Faculty of Computer Science Department of Informatics Engineering Semarang Indonesia Institut Teknologi Garut Computer Science Department Garut Indonesia

ISBN: (纸本)9798350389654

Edge detection plays an important role in various fields by identifying object boundaries and supporting advanced image analysis, such as segmentation, recognition, and tracking. Many edge detection algorithms, such as Canny, Laplacian, and Prewitt, have been developed to address issues such as noise sensitivity, computational complexity, and detection accuracy. This research compares the optimized algorithms using Particle Swarm Optimization (PSO). The results of this study show that the optimized algorithm provides better performance based on the evaluation conducted using entropy, Mean Squared Error (MSE), and computation time, and has practical implications. This research also determines the most effective edge detection technique in various image processing scenarios, thus contributing to the optimization of image analysis workflows in real-world contexts. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Implementing a Digital Repository for the Cultural and Natural Heritage of the Island of Zakynthos 19

Implementing a Digital Repository for the Cultural and Natur...

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19th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2024

作者： Palamas, Stergios Voutos, Yorghos Kabassi, Katerina Mylonas, Phivos Ionian University Department of Informatics Corfu Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece Ionian University Department of Environment Zakynthos Greece

ISBN: (纸本)9798331504502

The present study discusses the development of a digital repository (ANTETI) for the preservation and dissemination of the Cultural and Natural Heritage of Zakynthos Island. The platform is based on a popular Content Management System (CMS) to provide the core functionality, extended with the use of the CMS's API, to provide additional personalisation functionality for the end-users. Additionally, the system features a web application, oriented mainly on users visiting the island of Zakynthos and developed exclusively with open web technologies and JavaScript frameworks. The web application is an alternative, map-centered, mobile optimised front-end, for the platform's content featured in the CMS. The system also provides a RESTful API, allowing integration with 3rd party systems and web applications, thereby expanding the repository's reach and capabilities. Content delivery is personalized based on user profiles, location, and preferences, enhancing engagement and usability. By integrating these features, the repository effectively preserves and makes accessible the unique cultural and natural heritage of Zakynthos to both local and global audiences. © 2024 IEEE.

关键词： Biodiversity Content Management Systems Cultural Heritage Multimedia Digital Repositories Natural Heritage Web Applications

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GAN-generated Synthetic Data and SVM-based Feature Selection for Improved Cardiovascular Disease Prediction

GAN-generated Synthetic Data and SVM-based Feature Selection...

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2023 Medical Technologies Congress, TIPTEKNO 2023

作者： Mandan, Amel Sulaiman Hazzaa, Nidhal Mohsin Yildiz, Oktay Informatics Institute Gazi University Computer Science Department Ankara Turkey Gazi University Faculty of Engineering Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350328967

Early diagnosis requires cardiovascular disease forecasting. Past patient interests can improve machine learning predictions. This study uses a Generative Adversarial Network (GAN) to augment data to address the issue of insufficient datasets. Our objective is to use the GAN and feature selection (FS) to tackle the issue of class imbalance. The study assesses the efficacy of three different machine learning (ML) algorithms in predicting coronary artery disease (CAD). The UCI repository provided the Z-Alizadeh sani dataset for our analysis. A GAN was used to balance an imbalanced dataset. We used GAN to augment data and synthesise data to improve the models. Support Vector Machine (SVM) as feature selection tool was used to extract more effective attributes. We trained and tested CAD prediction using machine learning algorithms using 5-fold cross validation. We compared the machine-learning algorithm's outcomes with and without GAN-based data enhancement and feature selection to evaluate its predicted accuracy. According to results GAN and feature selection improved accuracy, sensitivity, and specificity for all algorithms, suggesting that generated data and selected attributes can identify positive and negative cases. © 2023 IEEE.

关键词： Generative adversarial networks

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A Business Plan for Data Security and Privacy Services 24

A Business Plan for Data Security and Privacy Services

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24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Zeng, Wen Smith, Abigail Carroll-Mayer, Moira Germanos, Vasileios Siewe, Francois School of Computer and Information Engineering Shanghai Polytechnic University Shanghai China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9798350376647

Cyber Security Academic Startup Programme (Cyber Asap) provides academics with the expertise, knowledge and training needed to convert their research into technologies, products and services. Due to the fast development of digital technologies, data security and privacy became an active research topic. However, we do not have business plan templates for data security and privacy. Therefore, it is necessary to develop a business plan for data security and privacy technologies, which can help the data security and privacy research out of the university lab and into the commercial market. In this study, technical and non-technical risks and related contingency plans will be addressed through consideration of risk management and secure system architecture. © 2024 IEEE.

关键词： Differential privacy

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A Multi-Class Classification Approach for Anemia Level Prediction with Machine Learning Models 8

A Multi-Class Classification Approach for Anemia Level Predi...

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

作者： Trigka, Maria Dritsas, Elias Mylonas, Phivos University of West Attica Department of Informatics and Computer Engineering Athens Egaleo Greece University of Patras Department of Electrical and Computer Engineering Patras Greece

ISBN: (纸本)9798350383676

It is a common belief that Artificial Intelligence (AI) and Machine Learning (ML) provide researchers and medical experts with concepts, tools, and techniques to build intelligent systems able to analyze, process and detect hidden patterns, in order to turn data into actionable knowledge for personalized medicine and decision-making (e.g., disease prevention via associated risk prediction, treatment, etc.). The present study seeks an ML solution that will facilitate the recognition of anemia level by investigating the performance of two well-established strategies for multi-class classification tasks;the One-Vs-All (OVA) and One-Vs-One (OVO). Under the specific strategies, two well-known ML models were assumed, namely, Logistic Regression (LR) and Support Vector Machines (SVM). The validation of both strategies was carried out in a publicly available dataset by measuring the weighted average performance in all involved classes that capture the anemia levels;accuracy, precision, recall and Area Under the ROC curve (AUC) were captured and compared for the identification of the dominant model and strategy. After the experimental evaluation, the OVO strategy with the LR model is the main suggestion of the current study achieving an accuracy of 95.05 %, precision and recall equal to 0.951 and an AUC of 0.990. © 2023 IEEE.

关键词： Forecasting

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A Hybrid Localization Algorithm of RSS and TOA Based on Adaptive Weight Errors 5

A Hybrid Localization Algorithm of RSS and TOA Based on Adap...

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5th International Conference on Information Technology, Applied Mathematics and Statistics, ICITAMS 2023

作者： Tariq, Suphian Mohammed Al-Mejibli, Intisar Shadeed Al-Iraqia University Computer Engineering Informatics Institute For Postgraduate Iraqi Commission For Computers & Informatics College Of Engineering Baghdad Iraq Biomedical Informatics College University Of Information Technology And Communications Baghdad Iraq

ISBN: (纸本)9798350335118

Specifying the location of wireless sensor devices is vital in many applications such as target tracking, remote environmental monitoring, and more. Time of arrival (TOA), the received signal strength indicator (RSSI) and range information are applied to locate the wireless sensor network (WSN) node's location. Still, they suffer inaccuracy because of environmental factors in the 2D region. A ranging method has been suggested, merging the TOA information domain with RSSI to accurately determine the distance between the unknown node and the anchor node. This approach relies on utilizing RSSI and TOA ranging techniques in the near and far endings. We combine received signal strength indication and arrival time in parallel style as major elements. The findings of the two methods are then combined with the error percentage to determine the final location using adaptive weight errors. The proposed range method is promoted in this work to enhance the accuracy of the positioning. The proposal has been implemented by MATLAB simulation and the results show improving positioning accuracy compared to the conventional TOA and RSSI ranging method. The mean square error in the proposed method is 0.5044, whereas it was 0.7032 and 0.8102 in RSSI, respectively. This indicates that the suggested approach is more efficient and performs better for localization than the other two methods. © 2023 IEEE.

关键词： Sensor nodes

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Optical Intra- and Inter-Rack Switching Architecture for Scalable, Low-Latency Data Center Networks 28

Optical Intra- and Inter-Rack Switching Architecture for Sca...

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28th IEEE Symposium on computers and Communications, ISCC 2023

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

ISBN: (纸本)9798350300482

In this paper we propose a DC network (DCN) architecture that interconnects servers in the intra-rack and inter-rack domain, utilizing optical switching at each domain. The proposed interconnection techniques are studied as an intermediate step before migrating the entire DCN to all-optical schemes. Unlike other studies, we study the server-to-server communication across the whole DCN. For the performance evaluation we produce numerical results for throughput and end-to-end delay for three traffic classes co-existing in DCN s. The numerical analysis reveals that bandwidth utilization reaches 90% and 100% in the intra- and inter- domain respectively. Meanwhile, the maximum end-to-end delay for the highest priority packets under congested load is lower than 0.56 and 0.41 μs for the two examined intra-rack capacity scenarios of 400 and 600 Gbps respectively. A comparative study shows that our solution can effectively interconnect up to 10000 servers with lower environmental footprint and end-to-end delay than other DCN s. © 2023 IEEE.

关键词： Network architecture

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Enhanced Water Quality Control Based on Predictive Optimization for Smart Fish Farming

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computers, Materials & Continua 2023年第6期75卷 5471-5499页

作者： Azimbek Khudoyberdiev Mohammed Abdul Jaleel Israr Ullah DoHyeun Kim Department of Applied Informatics Kimyo International University in TashkentTashkent100121Uzbekistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Computer Engineering Jeju National UniversityJeju63243Korea

The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource *** of Things(IoT)based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish *** objective requires intensive monitoring,prediction,and control by optimizing leading factors that impact fish growth,including temperature,the potential of hydrogen(pH),water level,and feeding *** paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish *** proposed fish farm control mechanism has a predictive optimization to deal with water quality control and efficient energy consumption *** farm indoor and outdoor values are applied to predict the water quality parameters,whereas a novel objective function is proposed to achieve an optimal fish growth environment based on predicted *** logic control is utilized to calculate control parameters for IoT actuators based on predictive optimal water quality parameters by minimizing energy *** evaluate the efficiency of the proposed system,the overall approach has been deployed to the fish tank as a case study,and a number of experiments have been carried *** results show that the predictive optimization module allowed the water quality parameters to be maintained at the optimal level with nearly 30%of energy efficiency at the maximum actuator control rate compared with other control levels.

关键词： Smart fish farming internet of things(IoT) predictive optimization objective function fuzzy logic control(FLC)

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Model-to-Circuit Cross-Approximation for Printed Machine Learning Classifiers

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2023年第11期42卷 3532-3544页

作者： Armeniakos, Giorgos Zervakis, Georgios Soudris, Dimitrios Tahoori, Mehdi B. Henkel, Jorg Karlsruhe Institute of Technology Karlsruhe76131 Germany National Technical University of Athens School of Electrical and Computer Engineering Athens15780 Greece University of Patras Computer Engineering and Informatics Department Patras26504 Greece Karlsruhe Institute of Technology Department of Computer Science Karlsruhe76131 Germany

Printed electronics (PEs) promises on-demand fabrication, low nonrecurring engineering costs, and subcent fabrication costs. It also allows for high customization that would be infeasible in silicon, and bespoke architectures prevail to improve the efficiency of emerging PE machine learning (ML) applications. Nevertheless, large feature sizes in PE prohibit the realization of complex ML models in PE, even with bespoke architectures. In this work, we present an automated, cross-layer approximation framework tailored to bespoke architectures that enable complex ML models, such as multilayer perceptrons (MLPs) and support vector machines (SVMs), in PE. Our framework adopts cooperatively a hardware-driven coefficient approximation of the ML model at algorithmic level, a netlist pruning at logic level, and a voltage overscaling at the circuit level. Extensive experimental evaluation on 12 MLPs and 12 SVMs and more than 6000 approximate and exact designs demonstrates that our model-to-circuit cross-approximation delivers power and area optimal designs that, compared to the state-of-the-art exact designs, feature on average 51% and 66% area and power reduction, respectively, for less than 5% accuracy loss. Finally, we demonstrate that our framework enables 80% of the examined classifiers to be battery-powered with almost identical accuracy with the exact designs, paving thus the way toward smart complex printed applications. © 1982-2012 IEEE.

关键词： Timing circuits

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