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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Shujaaddeen, Abeer Abdullah Ba-Alwi, Fadl Mutaher Zahary, Ammar T. Alhegami, Ahmed Sultan Alsabry, Ayman Al-Badani, Abdulkader M. Faculty of Computer and Information Technology Sana’a University Sana’a Yemen International University of Technology Twintech Malaysia Department of Computer Science Faculty of Computing and Information Technology University of Science and Technology Sana’a University Sana’a Yemen Department of Computer Science International University of Technology Twintech Sana’a Yemen Faculty of Science and Engineering Department of Computers Aljazeera University Ibb Yemen

ISBN: (纸本)9798331533557

In this paper, a model was built to compare the performance of the following machine learning (ML) models: DT, RF, SVM, and MLP, using two types of classification: binary classification and multi classification. The researchers concluded that the MLP classifier was the most efficient using multi classifications, as the classifier gave an accuracy of 99.77%, a recall of 93.25%, a precision of 92.02%, and an F-score of 92.63%. Using the dataset provided by the Tax Authority of Yemen, which is related to the commercial and industrial profits tax explained in detail in other papers for the same authors, which consists of 1083 record, after the preprocessing of data. © 2024 IEEE.

关键词： Contrastive Learning

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On Thermal Decomposition of Biogas Digestate to Syngas

On Thermal Decomposition of Biogas Digestate to Syngas

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作者： Szwaja,S. Poskart,A. Zajemska,M. Faculty of Mechanical Engineering and Computer Science Czestochowa University of Technology 21 Armii Krajowej Avenue Czestochowa Poland Faculty of Production Engineering and Materials Technology Czestochowa University of Technology 21 Armii Krajowej Avenue Czestochowa Poland

The main target of the investigation was to introduce several issues concerning thermal decomposition of biogas digestate to syngas. Hence, the article presents results of experimental research and modeling high temperature thermal decomposition of biogas digestate in order to optimize gasification process. The biogas digestate is a by-product obtained from a biomass-to-biogas power plant. As known, there are hundreds tones digestate from a typical biogas agricultural power plant of 2 MWe daily, hence its environmentally friendly utilization is required. The digestate is post-fermented agricultural biomass which further fermentation potential expired. Digestate typically can be applied as soil fertilizer, but in some case it is not possible due to bacteria contamination. Hence, thermal processing for its utilization focused on heat and power generation is under investigation. Digestate LHV(lower heating value) was relatively high in comparison to other residues. Due to high content of moisture in the digestate, it can be applied for energy use after its dehydration and drying. Experimental investigation was focused on gasification of the digestate with particular interest on obtaining high calorific producer gas(commonly called syngas). Analysis of the data from both numerical simulation and experimental tests enables to determine the most favorable operating conditions for digestate gasification. On the basis of presented investigation, optimal process temperature was approximately 850-950°C for digestate with moisture content below 20%.

关键词： On Thermal Decomposition of Biogas Digestate to Syngas

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The Behavior Analysis of Pedestrian-cyclist Interaction at Non-signalized Intersection on Campus: Conflict and Interference

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Procedia Manufacturing 2015年 3卷 3345-3352页

作者： Li, Bixiang Xiong, Shengwu Li, Xiaohong Liu, Mianfang Zhang, Xiaohong School of Computer Science and Technology Wuhan University of Technology Wuhan 430070 China Faculty of Information Engineering City College of Wuhan University of Science and Technology Wuhan 430083 China

Pedestrian-cyclist interaction is closely relative with traffic safety on campus intersection. The relationships of flow, speed, Level of Service (LOS) with pedestrian-cyclist conflict and interference were studied. Studying pedestrian-cyclist interaction was using collected video data which recorded the behaviours of pedestrians and cyclists at a non-signalized intersection on campus. The pedestrians and cyclists’ flows were recorded by video statistic software, the collected videos were decoded into image frames, their trajectory and distance data were acquired by image processing software, the data was converted into numbers as a VCNXY(Video number, Current frame no, Pedno, Pedx, Pedy) database, then their speeds were computed by Euler's formula. The LOS is classified with three levels under different traffic density and road traffic capacity. Through the analysing of pedestrians and cyclists’ flows and their average speeds at different time, and analysing the relationships with pedestrian-cyclist interaction and their flow, speed, LOS. Results showed that campus’ transportation is much different from city transportation, the proportion of pedestrians and cyclists are much higher, the average speeds of pedestrians and cyclists are lower. Experimental results show that flow, speed and LOS are very strongly related to pedestrian-cyclist interaction. © 2015 The Authors

关键词： Conflict Interference Level of Service (LOS) Pedestrian-cyclist interaction

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Sentiment Analysis of Israeli-Palestinian Conflict on Indonesian Tweets Using Machine Learning 4

Sentiment Analysis of Israeli-Palestinian Conflict on Indone...

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4th International Conference on Electrical engineering and computer Science, ICECOS 2024

作者： Rahardi, Majid Arsya, Muhammad Rezzha Riamrizal Aminuddin, Afrig Abdulloh, Ferian Fauzi Miah, Mohammad Badrul Alam Pratikno, Heri Universitas Amikom Yogyakarta Faculty of Computer Science Sleman Indonesia Mawlana Bhashani Science and Technology University Faculty of Engineering Tangail Bangladesh Universitas Dinamika Faculty of Technology and Informatics Surabaya Indonesia

ISBN: (纸本)9798350368253

The social media of X has become a focal point for discussions on sensitive issues, including the Israeli-Palestinian conflict. This research analyzes sentiment toward tweets expressing support for Palestine on X, comparing the Support Vector Machine (SVM) and Naïve Bayes methods. Given the global attention to the conflict, sentiment analysis on this topic is crucial. The results reveal that the SVM model outperforms Naïve Bayes, achieving accuracy, precision, recall, and F1-score of 85%, 87%, 85%, and 85% for Naïve Bayes, and 91%, 92%, 90%, and 90% for SVM, respectively. It demonstrates the SVM model's capability to distinguish between positive and negative sentiments. Comparison with previous research indicates that this study surpasses the sentiment analysis results of previous SVM models, while Naïve Bayes excels in specific research. With superior performance, this research provides a new contribution to recognizing and classifying sentiments on a particular dataset related to the Israeli-Palestinian conflict on X social media. © 2024 IEEE.

关键词： Tweets

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MAP source-controlled channel decoding with interleavers for MPEG-4 image indoor wireless transmission systems

MAP source-controlled channel decoding with interleavers for...

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作者： Mahapakulchai, Srijidtra Charoenlarpnopparut, Chalie Electrical Engineering Department Faculty of Engineering Kasetsart University Thailand School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Thammasat University Thailand

In the modern day, MPEG-4 image compression technique have been commonly applied in various indoor wireless communication systems. The efficient system design mostly relies on the joint source channel coding algorithms, which aim to reduce the complexity of channel coding process, while maintaining the quality of the receiving images. In this paper, we design the MAP source-controlled channel decoders with both random and semirandom interleavers for Rician slow flat blockfading channels. The MAP-Viterbi decoder employs the residual redundancy from zerotree symbol sequences of MPEG-4 HFS packets. The interleaving processes are done after the overall channel coding process to combat the block-fading effects. The computer simulations summarize the system performance in terms of average WER and PSNR (dB). With the interleavers, the significant improvement in PSNR of about 15-17 dB is obtained for both ML and MAP decoding. Also in many cases, we obtain more improvement of about 0.2-0.4 dB for using MAP decoding with the interleavers. Copyright © 2009 The Institute of Electronics, Information and Communication Engineers.

关键词： Channel coding

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ThaiCoGen: Knowledge Graph Generation Enriching Suvarnabhumi's Historical Insights

ThaiCoGen: Knowledge Graph Generation Enriching Suvarnabhumi...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Takhom, Akkharawoot Sophaken, Chotanansub Vongpanich, Kantapong Utasri, Tharathon Saengthongpattana, Kanchana Supnithi, Thepchai Thammasat University Faculty Of Engineering Thammasat School Of Engineering Pathum Thani Thailand King Mongkut's University Of Technology Thonburi Faculty Of Engineering Bangkok Thailand National Electronics And Computer Technology Center Pathum Thani Thailand

ISBN: (纸本)9798350370096

Historical tourism documentation includes guidebooks, pamphlets, and digital resources, which are collections of materials that promote the exploration of historical sites, events, and cultural heritage. These materials aim to offer insights into the history and importance of particular locations, people, and items. Within these documents, nominal historical terminologies possess valuable information, serving as significant entities that can contribute to our understanding of history. This study introduces a method for knowledge graph (KG) generation through Thai co-occurrence network analysis, called ThaiCoGen. This work improves KG generation by using three important methods: (1) term frequency-inverse document frequency (TF-IDF) to find important terms and entities;(2) Thai co-occurrence network text analysis (ThaiCoNet) to connect semantic relationships;and (3) adding to KGs using linked open data from encyclopedias. Our contributions are the generation of KGs using extracted high-n-gram entities and the highlighting of semantic enrichment. Our contributions include (1) generating KGs by identifying the extracted height n-gram entities and (2) highlighting semantic enrichment. A case study was conducted on the Thai encyclopedia *** in the domain of the Suvarnabhumi Silk Road and demonstrated the efficacy of the ThaiCoGen methodology. Despite the substantial temporal gap between historical records from the Suvarnabhumi Silk Road era (100-700 BE) and contemporary tourist documentation from the modern era (2500 BE), ThaiCoGen successfully integrates these sources into enriched KGs. The discussion of this paper intends to elaborate on the historically accurate portrayal of the Suvarnabhumi Silk Road from the enriched KG, especially Thailand's tourism industry. Finally, this research provides the open KGs for Thailand's cultural heritage in order to build the robustness of semantic application development. © 2024 IEEE.

关键词： Knowledge graph

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Machine Learning based Fitness Landscape Smoothing for Guiding Evolutionary Optimization 17

Machine Learning based Fitness Landscape Smoothing for Guidi...

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17th International Conference on Computational Intelligence and Security, CIS 2021

作者： Lyu, Chao Sun, Lijun Shi, Yuhui Harbin Institute of Technology Harbin China University of Technology Sydney Faculty of Engineering and Information Technology Sydney Australia Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9781665494892

Simplifying optimization problems by smoothing their rugged and rough fitness landscapes is an effective approach to improve the performance of evolutionary algorithms (EA). However, due to the dimension curse, existing smoothing methods can only handle low dimensional fitness functions by traditional digital signal processing methods or smooth a very local region with special crossover operators. To solve this problem, we propose a methodology to smooth fitness landscapes by utilizing the low-pass filtering performance of machine learning (ML) models. Firstly, we design a framework called simplified landscape assisted evolutionary algorithm (SLEA) to utilize the fitness landscapes smoothed by ML models to guide evolutionary search. To verify its effectiveness, we design several example algorithms by combining variant ML models with the particle swarm optimization under the proposed framework and compare their optimization performances on benchmark functions. Experimental results show that by embedding an EA into the SLEA framework with suitable ML smoothing models, the global search ability of the EA in rugged and rough fitness landscapes can be improved without increasing the total computational cost. © 2021 IEEE.

关键词： Particle swarm optimization (PSO)

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Pre-trained CNN Based Deep Features with Hand-Crafted Features and Patient Data for Skin Lesion Classification 3rd

Pre-trained CNN Based Deep Features with Hand-Crafted Featur...

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3rd International Conference on Intelligent Technologies and Applications, INTAP 2020

作者： Yildirim-Yayilgan, Sule Arifaj, Blend Rahimpour, Masoomeh Hardeberg, Jon Yngve Ahmedi, Lule Faculty of Information Technology and Electrical Engineering NTNU – Norwegian University of Science and Technology Gjøvik Norway Faculty of Electrical and Computer Engineering University of Prishtina Prishtina Catholic University of Leuven Leuven Belgium

ISBN: (纸本)9783030717100

Skin cancer is a major public health problem, with millions newly diagnosed cases each year. Melanoma is the deadliest form of skin cancer, responsible for the most over 6500 deaths each year in the US, and the rates have been rising rapidly over years. Because of this, a lot of research is being done in automated image-based systems for skin lesion classification. In our paper we propose an automated melanoma and seborrheic keratosis recognition system, which is based on pre-trained deep network combined with structural features. We compare using different pre-trained deep networks, analyze the impact of using patient data in our approach, and evaluate our system performance with different datasets. Our results shown us that patient data has impact on characteristic curve metric value with around 2–6% and different algorithm in final classification layer has impact with around 1–4%. © 2021, Springer Nature Switzerland AG.

关键词： Dermatology

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CBAR-UNet: A novel methodology for segmentation of cardiac magnetic resonance images using block attention-based deep residual neural network

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Multimedia Tools and Applications 2024年第37期83卷 85047-85063页

作者： Kumar, Rakesh Gupta, Meenu Agarwal, Aman Nayyar, Anand Department of Computer Science and Engineering Chandigarh University Punjab140413 India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam

computer-aided Medical Image Segmentation (MIS) plays a leading role in diagnosing diseases automatically. MIS is used extensively in diagnosing medical ailments to obtain clinically relevant information of the shapes and volumes of the target organs and tissues. In order to facilitate accurate segmentation, a Block Attention Based Deep Residual Neural Network, i.e., CBAR-UNet model, is proposed to perform cardiac image segmentation on short axis Magnetic Resonance Images (MRI) stacks from top to bottom slice. The Automated Cardiac Diagnosis Challenge (ACDC) dataset is used in the proposed work, which comprise of a 3D MRI of 100 patients (i.e., 200 MRI stacks). Further, 3D MRIs are converted into 2D by slicing each image to train the model, and Contrast Limited Adaptive Histogram Equalization (CLAHE) is performed on the 2D dataset. Residual connections are introduced that aid in training deeper models. Convolutional Block Attention Module (CBAM) is also added in the proposed network, which consists of Spatial and Channel attention that enhances the spatial and channel-wise features in the image by providing information like ‘what’ and ‘where’ to pay attention respectively. To test and validate the proposed methodology, extensive experimentations were conducted and it was observed that proposed image segmentation model delivers better results by 2.47% when compared to both 2D and 3D State-Of-The-Art (SOTA) methods with a Dice Score of 0.9428. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image segmentation

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Breast Cancer Detection Based Microwave Imaging Using Single Antipodal Vivaldi Antenna

Breast Cancer Detection Based Microwave Imaging Using Single...

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2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Tangwachirapan, Shalermchon Sawangsri, Panupong Pinitkiatisakul, Pitchvara Thongdit, Preecha Thaiwirot, Wanwisa Nakhon Pathom Rajabhat University Faculty of Science and Technology Department of Telecommunication Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

This paper presents breast cancer detection based on microwave imaging using single antipodal Vivaldi antenna. The breast cancer detection system mainly consists of antipodal Vivaldi antenna, vector network analyzer, breast phantom, and rotation platform. The artificial breast phantom is fabricated by using gelatin-oil mixture technology as fat and cancerous cell. The difference for relative permittivity and conductivity between cancerous cell and normal tissue is around 4:1 and 6:1, respectively. The process of breast cancer detection and imaging are presented. The measured results show that the cancerous cell embedded inside the breast phantom can be detected. In addition, the graphical user interface (GUI) for creating the 2D MWI is developed using MATLAB. © 2022 IEEE.

关键词： Graphical user interfaces

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