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International Conference on Advancements in Computational Sciences (ICACS)

作者： Tayyab Farooq Muhammad Nadeem Nadir Muhammad Hayat Asad Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Computer Science The University of Lahore Lahore Pakistan School of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan China

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Reputable international scientific societies recommend yearly awards for candidates based on qualitative evaluations. Over fifty quantitative research assessment measures, such as g-index, h-index, and its variations, publication count, citation count, etc., have been provided by scientific community. The most recent state-of-the-art in author’s ranking does not establish optimal criterion that correctly translates experts’ qualitative opinion. It might be quite difficult to determine importance of each metric relative to others in such situations. We propose to develop an efficient Bayesian Award Prediction (BAP) approach. BAP with improved Gaussian Naive Byes architecture outperforms the previous works in the researcher’s ranking. Also, it identifies the parameter that has more impact on the researcher’s ranking. The proposed (BAP) approach also introduced attribute optimization using correlation between parameters to enhance the performance. BAP achieves $76 \%$ accuracy on the civil engineering domain researcher’s dataset. Compared with previous work, this study achieves a $9 \%$ improvement over the logistic regression.

关键词： Training Measurement Logistic regression Accuracy Correlation Computational modeling Machine learning Predictive models Bayes methods Optimization

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Real-time face mask detector using convolutional neural networks amidst COVID-19 pandemic 1

Real-time face mask detector using convolutional neural netw...

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1st International Conference on Novelties in Intelligent Digital Systems, NIDS 2021

作者： Kontellis, Efstratios Troussas, Christos Krouska, Akrivi Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Greece

ISBN: (纸本)9781643682044

The COVID-19 pandemic provoked many changes in our everyday life. For instance, wearing protective face masks has become a new norm and is an essential measure, having been imposed by countries worldwide. As such, during these times, people must wear masks to enter buildings. In view of this compelling need, the objective of this paper is to create a real-time face mask detector that uses image recognition technology to identify: (i) if it can detect a human face in a video stream and (ii) if the human face, which was detected, was wearing an object that it looked like a face mask and if it was properly worn. Our face mask detection model is using OpenCV Deep Neural Network (DNN), TensorFlow and MobileNetV2 architecture as an image classifier and after training, achieved 99.64% of accuracy. © 2021 The authors and IOS Press.

关键词： COVID-19

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Interest Flooding Attack in Named Data Network: Case Study on Palapa Ring Topology 16

Interest Flooding Attack in Named Data Network: Case Study o...

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16th International Conference on Telecommunication Systems Services and Applications, TSSA 2022

作者： Jupriyadi Ahdan, Syaiful Sucipto, Adi Zaki Hamidi, Eki Ahmad Arifin, Hasan Nur Syambas, Nana Rachmana School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Uin Sunan Gunung Djati Bandung Electrical Engineering Department Bandung Indonesia School of Electrical and Informatics Engineering Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9781665455947

This research focuses on the interest flooding attack model and its impact on the consumer in the Named Data Networking (NDN) architecture. NDN is a future internet network architecture has advantages compared to the current internet architecture. The NDN communication model changes the communication paradigm from a packet delivery model based on IP addresses to names. Data content needed is not directly taken from the provider but stored in a distributed manner on the router. Other consumer request data can served by nearest router. It will increase the speed of data access and reduce delay. The changes communication model also have an impact on the existing security system. One attack that may occur is the threat of a denial of service (DoS) known as an interest flooding attack. This attack makes the network services are being unavailable. This paper discussed examining the interest flooding attack model that occurred and its impact on the performance of NDN. The result shows that interest flooding attacks can decrease consumer satisfied interest. © 2022 IEEE.

关键词： Network architecture

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Leveraging Social Media Posts for Natural Disaster Response Using CNN-BiLSTM Model

Leveraging Social Media Posts for Natural Disaster Response ...

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International Conference on informatics and Computing (ICIC)

作者： Muhammad Rafi Mohammad Reza Faisal Irwan Budiman Muliadi Dodon Turianto Nugrahadi Mera Kartika Delimayanti Department of Computer Science Lambung Mangkurat University Banjarbaru Indonesia Department Politeknik Negeri Jakarta Computer and Informatics Engineering Depok Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

In the contemporary digital era, social media, particularly Twitter, has become a vital channel for sharing realtime information on natural disasters like floods, forest fires, and earthquakes. This rapid dissemination enables swift responses to disasters. However, classification models relying solely on social media data face challenges, notably the lack of richness and diversity in word embedding vectors, limiting precise analysis. To address this, the research integrates data from Wikipedia to improve word embeddings. The classification model employs Word Embedding, Convolutional Neural Network (CNN) 1D, and Bidirectional Long Short-Term Memory (BiLSTM) techniques. By combining embedding methods such as Word2Vec, GloVe, and FastText, derived from both social media and Wikipedia Indonesia, the model enhances word representation and improves classification accuracy. Results demonstrate accuracies of 85.61% for floods, $92.56 \%$ for forest fires, and $84.11 \%$ for earthquakes. This research contributes to advancing natural disaster communication classification by utilizing more diverse data and proposing methodologies for unstructured social media content analysis.

关键词： Accuracy Social networking (online) Disasters Bidirectional long short term memory Encyclopedias Data models Vectors Internet Online services Convolutional neural networks

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A novel framework incorporating augmented reality and pedagogy for improving reading comprehension in special education 1

A novel framework incorporating augmented reality and pedago...

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1st International Conference on Novelties in Intelligent Digital Systems, NIDS 2021

作者： Kapetanaki, Andrianthi Krouska, Akrivi Troussas, Christos Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Greece

ISBN: (纸本)9781643682044

Augmented Reality (AR) is an emerging technology thriving in recent years. The implementation of AR in education offers great opportunities to enhance educational environments achieving better learning outcomes. As students with learning disabilities struggle with reading comprehension, an AR learning environment provides them support to better understand texts they actually read. Even though few studies have tried to explore the impact of AR technology to reading comprehension for students with learning disabilities in Secondary Education, there is a lack of research grounded in the incorporation of learning theories and personalization technologies. The goal of this paper is to present an AR educational environment capable of supporting meaningful learning outcomes by taking into consideration each student special educational needs and learning style. The novelty of this study lies in the student-centered and personalized design, which leads to improved understanding, student interaction and self-learning. © 2021 The authors and IOS Press.

关键词： Augmented reality

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Efficient Cross Dynamic Task Assignment in Spatial Crowdsourcing 39

Efficient Cross Dynamic Task Assignment in Spatial Crowdsour...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Ren, Tianyue Zhou, Xu Li, Kenli Gao, Yunjun Zhang, Ji Li, Keqin Hunan University College of Computer Science and Electronic Engineering China Zhejiang University College of Computer Science and Technology China Zhejiang Lab Zhejiang Hangzhou China Department of Computer Science State University of New York United States

ISBN: (纸本)9798350322279

As a novel intelligent sensing paradigm, spatial crowdsourcing has received extensive attention. Task assignment is a key issue in spatial crowdsourcing. In practice, tasks are unevenly distributed in time and space. Accordingly, the problem of cross task assignment attracts growing attention in both industry and academia. Although there has been a research on this problem, it focuses only on maximizing total revenues for inner platforms. Therefore, it can also be improved to bring a multi-win situation for outer workers and task requesters as well as the inner platform. Inspired by this, we first formulate a new cross dynamic task assignment (CDTA) problem by introducing the reputation scores of workers, and prove it to be NP-hard. For the CDTA problem, a hybrid batch-based framework is presented on the basis of a new cross-platform incentive mechanism and a hybrid batch processing strategy, which are efficient in solving the problem of uneven spatial and time distribution of tasks, respectively. After that, a KM-based algorithm and a density-aware greedy algorithm are proposed to gain an accurate assignment result of tasks in each batch and good performance, respectively. Furthermore, the CDTA problem is modeled as a potential game that is proven to have at least a pure Nash Equilibrium theoretically. Last but not least, a game-theoretic approach is developed to maximize the revenues of the inner platform and outer workers at the same time. Extensive experiments on both real and synthetic datasets are conducted to demonstrate the effectiveness and efficiency of the proposed algorithms. © 2023 IEEE.

关键词： Game theory

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Real-time rapid accident detection for optimizing road safety in Bangladesh

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Heliyon 2025年第4期11卷 e42432页

作者： Arefin, Md Shamsul Mahin, Md Ibrahim Shikder Mily, Farzana Akter Department of Electrical & Electronic Engineering BUBT Dhaka Bangladesh Department of Computer Science & Engineering BUBT Dhaka Bangladesh

Road traffic accidents in Dhaka are among the worst in the world, along with huge human fatalities and vast economic losses. An advanced car accident detecting system using a YOLOv11 model is proposed for highly efficient real-time detection to further upgrade the emergency response systems. Moreover, a comparative analysis has been made on YOLOv9, YOLOv10 and YOLOv11 on the same dataset to detect accident accurately. The pre-trained weights were used through state-of-the-art object detection techniques such as IoU and NMS, which are very accurate for the YOLOv11. The dataset of 9000 labelled images have been used for training for extremely accurate object detection and classification of on-road accidents. On a 0.8249 Recall, it does an exceptionally good job with a perfect Precision of 1.0000 and a mean Average Precision of 0.9940 on a 50 % IoU threshold. It also has very low latency, requiring only 19.93 ms per frame on a GPU, hence suitable for real-time applications. This may potentially improve the response time at an accident site, significantly reducing the risk of fatalities. These results point toward the great avenue that AI-driven systems can offer in the quest toward making roads safer and responses better. The integration of such traffic management within existing frameworks would ensure great monitoring and timely response actions to save a life. It also reduces the impact on society resulting from traffic accidents. Indeed, this is a giant step towards harnessing artificial intelligence potentials for public safety and infrastructural development. © 2025 The Authors

关键词： AI Image processing mAP Road accidents YOLO

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Extreme Learning Machine for Parking Slot Recognition in Smart Parking System 8

Extreme Learning Machine for Parking Slot Recognition in Sma...

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8th International Conference on Electrical, Telecommunication and computer engineering, ELTICOM 2024

作者： Rahmat, Romi Fadillah Faza, Sharfina Syahputra, Ilham Nainggolan, Pauzi Ibrahim Sulaiman, Riza Rahman, Mohd Amiruddin Abd Universitas Sumatera Utara Department of Information Technology Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia Universitas Sumatera Utara Department of Computer Science Medan Indonesia Institute of IR 4.0 Universiti Kebangsaan Malaysia Bangi Malaysia Universiti Putra Malaysia Faculty of Science Selangor Malaysia

ISBN: (纸本)9798331531249

In the modern era, technology has become an integral part of human life, particularly in image processing. This advanced technology is now applied to parking areas to identify vacant parking spaces. The application is designed to detect empty parking slots in real time using image data captured by a camera as input. The system then automatically scans for available parking spaces, displaying the results on the interface with green indicators for empty slots and red for occupied ones. The process involves several key stages, such as image pre-processing, image segmentation, and object detection. The system employs the Extreme Learning Machine (ELM) method to enhance its object detection capabilities. This system achieves a high accuracy rate of 94%under well-lit conditions. This novel technology optimizes parking management, providing an effective and dependable method to monitor parking availability in real time. © 2024 IEEE.

关键词： Adversarial machine learning

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Design and Analysis of a Cross Split Ring Frequency Sensitive Surface Based EM Shield in Wireless Communication

Design and Analysis of a Cross Split Ring Frequency Sensitiv...

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Advances in Computing,Communication, Electrical, and Smart Systems (iCACCESS), International Conference on

作者： Syed Rakib Hasan Md. Saiam Nafiz Arman Saifur Rahman Sabuj Department of Electrical and Electronic Engineering Brac University Dhaka Bangladesh Department of Electrical and Electronic Engineering CCN University of Science & Technology Cumilla Bangladesh Department of Electrical and Computer Engineering University of Calgary Calgary Alberta Canada

Due to the growing operational frequency of wireless devices and systems, electromagnetic (EM) interference has escalated into a severe problem. As a result, EM shielding became crucial for safeguarding extremely sensitive radio frequency (RF) electronics. For mobile communication, a frequency selective surface (FSS) based $EM$ shield is presented in this study due to its benefits, including its compact size, frequency selectivity, and high stability. Two exterior square loops and a cross split ring loop make up the FSS unit cell, which was created using geometry. The suggested structure has an overall dimension of (50x50x2) mm and was created using COMSOL software RF module. The findings indicate that the FSS shield resonates with a center frequency of 5 GHz, respectively, spanning the 5G frequency ranges. At this band frequency, the suggested design offers shielding efficacy of roughly -30 dB, which is better than cross design. Moreover, this study shows better electric field norm and contour.

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Classification of Physical Activity from Electrocardiogram Signals Using Deep Learning

Classification of Physical Activity from Electrocardiogram S...

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International Conference on computer and Information Technology (ICCIT)

作者： Sadman Sakib Sanzida Anjum Kanta Md. Nuhi-Alamin Kusum Tara Department of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Accurate classification of physical activity based on ECG signals is essential for monitoring health and improving intervention strategies. This study focuses on utilizing ECG signals for the classification of physical activities through deep learning techniques. The analysis involves processing ECG time series data into Continuous Wavelet Transform (CWT) based scalogram images. ECG recordings are sourced from a dataset that represents various activity states: during mathematical problem-solving (M), while sitting (S), during hand-biking (H), and during walking (W). Convolutional neural network (CNN) architectures such as MobileNetV2, EfficientNetB0, DenseNet201, VGG16, and ResNet152 use CWT-based scalogram images as their input. MobileNetV2 shows the most effective performance in classifying these activity states, achieving testing, and validation accuracies of 97.07%, and 97.83%, respectively. The average of the performance parameters: Precision, Sensitivity, Specificity, and F1-scores are 97.75%, 97.75%, 99.25%, and 97.75% respectively. The results demonstrate the potential of deep learning in enhancing the classification of physical activities based on ECG data.

关键词： Deep learning Electric potential Continuous wavelet transforms Accuracy Time series analysis computer architecture Electrocardiography Wavelet analysis Convolutional neural networks Monitoring

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