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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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Why Should I Trust Your Explanation? An Evaluation Approach for XAI Methods Applied to Predictive Process Monitoring Results

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1458-1472页

作者： Elkhawaga, Ghada Elzeki, Omar M. Abu-Elkheir, Mervat Reichert, Manfred Ulm University Institute of Databases and Information Systems Ulm89081 Germany Mansoura University Faculty of Computers and Information Mansoura35516 Egypt New Mansoura University Faculty of Computer Science and Engineering Mansoura35516 Egypt German University in Cairo Faculty of Media Engineering and Technology New Cairo11835 Egypt

As a use case of process mining, predictive process monitoring (PPM) aims to provide information on the future course of running business process instances. A large number of available PPM approaches adopt predictive models based on machine learning (ML). With the improved efficiency and accuracy of ML models usually being coupled with increasing complexity, their understandability becomes compromised. Having the user at the center of attention, various eXplainable artificial intelligence (XAI) methods emerged to provide users with explanations of the reasoning process of an ML model. Though there is a growing interest in applying XAI methods to PPM results, various proposals have been made to evaluate explanations according to different criteria. In this article, we propose an approach to quantitatively evaluate XAI methods concerning their ability to reflect the facts learned from the underlying stores of business-related data, i.e., event logs. Our approach includes procedures to extract features that are crucial for generating predictions. Moreover, it computes ratios that have proven to be useful in differentiating XAI methods. We conduct experiments that produce useful insights into the effects of the various choices made through a PPM workflow. We can show that underlying data and model issues can be highlighted using the applied XAI methods. Furthermore, we could penalize and reward XAI methods for achieving certain levels of consistency with the facts learned about the underlying data. Our approach has been applied to different real-life event logs using different configurations of the PPM workflow. © 2020 IEEE.

关键词： Process monitoring

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PulmoSage Insight: An Integrated Deep Learning and Support Vector Machine (SVM) Framework for Precise Lung Cancer Histopathological Image Classification and Prognosis 15th

PulmoSage Insight: An Integrated Deep Learning and Support V...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Kashyap, Seema Shukla, Arvind Kumar Naim, Iram School of Computer Science and Engineering IFTM University Uttar Pradesh Moradabad India Faculty of Engineering and Technology MJP Rohilkhand University Bareilly India

ISBN: (纸本)9783031810794

The Lung and colon cancers are the top causes of death and illness, so early detection is very important. Conventional testing methods have flaws, such as the need for high-end tools and the chance of mistakes between observers. Histopathology is the diagnostic gold standard because it offers cell-level pictures of tissue, but it is labor-intensive and requires highly trained pathologists. This paper introduces a novel approach for classifying histopathological lung images in order to detect cancer using transfer learning techniques. Utilizing EfficientNetB0 neural network architecture models to analyze input images is the methodology. These models extract pertinent image features that are then transmitted to a multi-class Support Vector Machine (SVM) for classification. Training and evaluation are performed with the LC25000 histopathology images dataset. This enables the extraction of informative features from histopathological images of the lungs. The suggested method is compared with a number of other classification methods, such as Artificial Neural Networks (ANN), Random Forest, K-Nearest Neighbours, Adaboost, and XGBoost, to see how well it works compared to current techniques. The results indicate that the EfficientNetB0 and SVM combination is effective. Notably, the model obtains an accuracy of 100% during training, 99.02% accuracy during validation, and 99.33% accuracy during testing. Additional indicators of performance, such as F1-score, Cohen kappa score, recall, and precision, demonstrate that the proposed method outperforms existing approaches. This study has the potential to considerably improve the early diagnosis and treatment of lung cancer, a crucial area of medicine. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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A 69MHz-Bandwidth 40V/μ s-Slew-Rate 3n V/√Hz-Noise 4.5 μ V-Offset Chopper Operational Amplifier

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第7期71卷 2977-2988页

作者： Koolivand, Yarallah Rezaeiyan, Yasser Zamani, Milad Akbari, Meysam Shoaei, Omid Tang, Kea-Tiong Moradi, Farshad Khajeh Nasir Toosi University of Technology Faculty of Electrical Engineering Tehran16317-14191 Iran Aarhus University Department of Electrical and Computer Engineering Aarhus8000 Denmark University of Kurdistan Department of Engineering Sanandaj66177-15177 Iran University of Tehran Department of Electrical and Computer Engineering Tehran*** Iran National Tsing Hua University Faculty of Electrical Engineering Hsinchu300044 Taiwan

This paper presents a chopper-stabilized three-stage operational amplifier (OpAmp) with a unity gain bandwidth of 69 MHz and an input referred noise density of 3 nV√Hz. The proposed design achieves a stable unity gain by proposing a new pole and zero scheme with very low power consumption, drawing only 3.3 mA from a 1.8 V power supply while driving a load capacitor as large as 100pF. To achieve rail-to-rail input swing, the design uses both NMOS and PMOS differential pairs at the input and biases them in the subthreshold region to provide an identical net trans-conductance over the rail-to-rail input common mode. Furthermore, an adaptive biasing is employed and the current sources are kept ON during large signal transitions at the input, thus eliminating crossover distortion and providing a high slew rate of 40 V/μs at a 100 pF load capacitor. The design employs chopping at 2.5 MHz and is enhanced with a local ripple reduction loop, making the OpAmp suitable for high gain and wide bandwidth applications with less filtering required. The design also reduces the input bias current significantly from 500 nA to 1.5 nA by buffering the input and applying it to the modified bootstrap switches. The proposed OpAmp, fabricated in a 0.18μm CMOS process, exhibits a maximum offset of 4.5μV , a flicker noise corner frequency of 246 Hz, a DC gain of 146 dB, a power supply rejection ratio of 123 dB, and a common mode rejection ratio of 116 dB. © 2004-2012 IEEE.

关键词： Operational amplifiers

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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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Behavior-based Feedback Loop for Attentive E-reading (BFLAe): A Real-Time computer Vision Approach 1

Behavior-based Feedback Loop for Attentive E-reading (BFLAe)...

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1st IJCAI Workshop and Challenge on Micro-Gesture Analysis for Hidden Emotion Understanding, MiGA 2023

作者： Lee, Yoon Migut, Gosia Specht, Marcus Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands

This study is built upon a behavior-based framework for real-time attention evaluation of higher education learners in e-reading. Significant challenges in AI model developments for learning analytics have been 1) defining valid indicators and 2) connecting the analytics results to interventions, balancing the generalization and personalization needs. To address this, we utilized a public multimodal WEDAR dataset and trained a neural network model based on real-time features of learners, aiming at predicting learners' moment-to-moment distractions. Real-time features for model training include 30 learners' attention regulation behaviors annotated every second, reaction times to blur stimuli, and page numbers indicating various reading phases. Our preliminary model based on a neural network has achieved 66.26% accuracy in predicting self-reported distractions. Based on the model, we suggest a framework of a Behavior-based Feedback Loop for Attentive e-reading (BFLAe). It has text blur as feedback, a mechanism responsive to learners' distractions that also works as data for next-round feedback. The general feedback implementation rules are established on a statistical analysis conducted on all learners. In addition, we propose a strategy for personalizing feedback using a quartile analysis of individual data, promoting learner-specific feedback. Our framework addresses the high demand for an automated e-learning assistant with non-intrusive data collection based on real-world settings and intuitive feedback provision. The feedback system aims to help learners with longer attention spans and less frequent distractions, leading to more engaging e-reading. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Modal analysis

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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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A new hybrid teaching learning based Optimization -Extreme learning Machine model based Intrusion-Detection system

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Materials Today: Proceedings 2023年 80卷 2701-2705页

作者： Qahatan Alsudani, Mustafa Abbdal Reflish, Salah H. Moorthy, Kohbalan Mundher Adnan, Myasar Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-sadiq University Najaf Iraq Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Kuantan Malaysia Islamic University Najaf Iraq Faculty of Engineering School of Computing Universiti Teknologi Malaysia Johor Bahru81310 Malaysia

Currently, effective Intrusion-detection systems (IDS) still represent one of the important security tools. However, hybrid models based on the IDS achieve better results compared with intrusion detection based on a single algorithm. But even so, the hybrid models based on traditional algorithms still face different limitations. This work is focused on providing two main goals;firstly, analysis based on the main methods and limitations of the most-recent hybrid model-based on intrusion detection, secondly, to propose a novel hybrid IDS model called TLBO-ELM based on the Firefly algorithm and Fast Learning Network. © 2021

关键词： Optimization

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Crack Detection for Non-Metallic Material Using Split Ring Resonator-based Chipless RFID Sensor 20

Crack Detection for Non-Metallic Material Using Split Ring R...

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20th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON 2023

作者： Nongpromma, Sirisak Janin, Suwan Kaemarungsi, Kamol Boonpoonga, Akkarat Torrungrueng, Danai Bannawat, Lakkhana King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand Rajamangala University of Technology Lanna Lampang Faculty of Engineering Department of Engineering and Technology Lampang52000 Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Department of Electrical and Computer Engineering Bangkok Thailand Department of Teacher Training in Electrical Engineering Bangkok Thailand Rajamangala University of Technology Rattanakosin Faculty of Engineering Department of Computer Engineering Nakhon Pathom Thailand

ISBN: (纸本)9798350310467

The study presented a novel chipless RFID sensor designed for the detection of cracks in non-metallic materials. The sensor utilized square coplanar split-ring resonators (SRR) and was mounted on the material under test (MUT), including materials such as Teflon (PTFE), Polycarbonate, and Kapton (Polyimide), for detecting cracks. To assess the effectiveness of the sensor in detecting cracks, simulation experiments were conducted using the CST Microwave Studio software, which analyzed shifts in the resonant frequency. The resulting RCS spectra demonstrated a clear correlation between the size of the crack and the resonance frequencies of the chipless RFID sensor, confirming the sensor's ability to detect cracks in nonmetallic materials. © 2023 IEEE.

关键词： Nonmetallic materials

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Dynamic Graph Convolution Recurrent Neural Network for Traffic Flow Prediction 3

Dynamic Graph Convolution Recurrent Neural Network for Traff...

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3rd International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Lou, Haijie Ma, Ying Li, Jianmin Faculty of Computer and Information Engineering Xiamen University of Technology Xiamen China Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9781510671485

To forecast the condition of traffic networks in the future, it is crucial to model the spatial and temporal correlation of traffic series. The majority of current research has been on creating complicated graph neural networks that can capture common patterns using preconfigured graphs. In this paper, we claim that predefined graphs may be avoided and that adaptive graphs can be used to capture spatial correlations between traffic series and improve the performance of graph neural networks. In order to capture the temporal relationships of sequences, we also aggregated gated recurrent neural networks. Then, we encode the relative time position of the sequence in order to fully extract the characteristics of the traffic sequence. Finally, we add the values of the previous day and the same day of the previous week as a reference in the final prediction to improve the accuracy of our prediction. Experimental results on two sets of real-word traffic data (PeMSD4 and PeMSD8) demonstrate that our method is better than the existing methods. © 2023 SPIE.

关键词： Graph neural networks

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