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The Modelling and Designing of Tourism Activity Locator Application 1

International Conference on Applied Informatics and Media Design, AIMD 2019

作者： Gaol, Ford Lumban Wijaya, Tommy Matsuo, Tokuro Computer Science Binus University Jakarta Indonesia Information System Binus University Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (数字)9783031307690

ISBN: (纸本)9783031307683

Despite having so much variety of beauty in natural, cultural and historical heritage, Indonesia still has not maximized its tourism sector. The business problem identified was the lack of exposure on advertisement. The purpose of this thesis is to gain an in depth understanding of customer needs in order to bring more customers into tourism industry especially for tourism activity. By providing what the foreign tourist needs when visiting a country at the same time, this application exposes Indonesian providers and tourism activities to the global tourists. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tourism

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Intent Classification of Nepali Factoid Questions

Intent Classification of Nepali Factoid Questions

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Timilsina, Sushil Khadka, Ashim Lin, Jianchu Faculty of Science and Technology Gandaki College of Engineering and Science Pokhara Nepal Faculty of Science and Technology Nepal College of Information Technology Lalitpur Nepal Faculty of Computer and Software Engineer Huaiyin Institute of Technology Jiangsu China

ISBN: (纸本)9798350306293

Question Classification plays a vital role in identifying the accurate answer to any question, and is considered as a core component of Question Answering systems. In Nepali Natural Language Processing, the area of Question Answering has not even been initiated due to a lack of dataset. The dataset of Nepali factual questions has been created by collecting questions from general knowledge books and labelling them with developed taxonomy. The problem of Nepali factoid question classification has been studied with a Support Vector Machine, which works as a baseline classification algorithm and obtained a 0.74 weighted F1-score in the imbalance dataset. The experimental results of the proposed model show the effectiveness of the approach in classifying factoid questions accurately compared with the state-of-the-art approaches. © 2023 IEEE.

关键词： Natural language processing systems

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Arabic Sign Language Gesture Classification Using Deer Hunting Optimization with Machine Learning Model

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computers, Materials & Continua 2023年第5期75卷 3413-3429页

作者： Badriyya B.Al-onazi Mohamed K.Nour Hussain Alshahran Mohamed Ahmed Elfaki Mrim M.Alnfiai Radwa Marzouk Mahmoud Othman Mahir M.Sharif Abdelwahed Motwakel Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science Faculty of Computer Science and Information TechnologyOmdurman Islamic UniversityOmdurman14415Sudan Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Sign language includes the motion of the arms and hands to communicate with people with hearing *** models have been available in the literature for sign language detection and classification for enhanced *** the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural *** paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning(ASLGC-DHOML)*** presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language *** presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network(DenseNet169)*** gesture recognition and classification,a multilayer perceptron(MLP)classifier is exploited to recognize and classify the existence of sign language ***,the DHO algorithm is utilized for parameter optimization of the MLP *** experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct *** comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced gesture classification results than other techniques with maximum accuracy of 92.88%.

关键词： Machine learning sign language recognition multilayer perceptron deer hunting optimization densenet

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Binary and Multi-Class Classification on the IoT-23 Dataset

Binary and Multi-Class Classification on the IoT-23 Dataset

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2023 Advances in Science and engineering technology International Conferences, ASET 2023

作者： Ahli, Alia Raza, Ayesha Akpinar, Kevser Ovaz Akpinar, Mustafa Rochester Institute of Technology Computing Security Department Dubai United Arab Emirates Higher Colleges of Technology Faculty of Computer Information System Sharjah United Arab Emirates

ISBN: (纸本)9781665454742

In the recent years, Internet of Things (IoT) technology has become widespread and is commonly used by organizations for many different purposes. With the increase in the demand for this technology, new security challenges arise. It becomes difficult for cybersecurity experts to administer security measures and prevent the IoT network from malicious attacks. This paper proposes the use of machine learning algorithms - Random Forest (RF), Multi- layer Perceptron (MLP) and Gradient Boosting (GB) to detect malicious traffic flows and anomalies in the IoT network. For this purpose, Aposemat IoT-23 dataset is used, and the models are built, trained and tested on it. Two types of classification are considered - binary and multi-label. Random Forest and Gradient Boosting classifier were able to achieve high accuracies of 98.6% and 97.7% respectively. © 2023 IEEE.

关键词： Internet of things

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Range-angle Imaging Based on the Prior of Target Scattering Characteristics in Distributed Arrays 2

Range-angle Imaging Based on the Prior of Target Scattering ...

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2nd IEEE International Conference on Signal, information and Data Processing, ICSIDP 2024

作者： Mei, Yongjie Zhang, Guangbin Huang, Tianyao Wang, Yan Beijng Institute of Technology School of Information and Electronics Beijing China Yangtze Delta Region Academy Beijing Institute of Technology Radar System and Intelligent Signal Processing Jiaxing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798331515669

We consider the problem of target range-angle imaging using distributed arrays. High resolution imaging requires a large array aperture. However, due to the limited number of array elements, the subarrays are usually sparsely located, which results in severe under-sampling and ambiguity problem, degrading the imaging performance. To this end, we propose a range-angle imaging method based on the prior of target scattering characteristic to resolve the ambiguity. The main idea is to use the information increment of priors to reduce the dependence on the measurements. Particularly, we first statistically extract the prior distribution of target scattering intensity by data analysis on the target images. Then we use these priors to design weighting parameters and achieve the ambiguity-free images by solving a weighted sparse recovery problem. Finally, numerical simulation results verify the feasibility of the proposed method. © 2024 IEEE.

关键词： ambiguity distributed array Range-angle imaging the prior of target scattering characteristics weight

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AI-Based Child Care Parental Control system 4

AI-Based Child Care Parental Control System

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Jayasekara, Udara Maniyangama, Hansindu Vithana, Kalhan Weerasinghe, Tharana Wijekoon, Janaka Panchendrarajan, Rrubaa Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka Department of Computer System Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398090

Due to the prevalence of the COVID-19 epidemic around the globe, children were compelled to engage in remote learning through online platforms, hence mobile phone has become one of their predominant devices. Mobile device with Internet access offers a major outlet for education, entertainment, and social connection, but this combination can lead to several significant bad sequences such as online exploitation, harmful addictions, and other negative impacts of online social networking. To address harmful effects, parental controls are becoming more crucial, yet Sri Lankan parents are less aware of this. Consequently, this study proposes a parental control system to monitor their child's activities. Android, Microsoft Azure, Java, Python, OpenCV, MySQL, and FastAPI are among the most prominent technologies utilized in the proposed application's development. The suggested approach focuses primarily on the Sri Lankan context and aims to enhance parental digital literacy while safeguarding children from cyber threats. Yielded results showed the proposed mobile application for the identification of toxic words, drugs & alcohol content, game character images, and Instagram Sinhala comments severity as 94%, 95%, 97%, and 55% respectively in controlled experiments. © 2022 IEEE.

关键词： Control systems

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Brain tumor classification model of ResNet-50 network based on different data enhancement algorithms 8

Brain tumor classification model of ResNet-50 network based ...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Zou, Menghan Ma, Mingze Wang, Anan Li, Yujie Toe, Teoh Teik Harbin Institute of Technology Weihai School of Computer Science and Technology WeiHai China Harbin Institute of Technology Weihai Welding Technology and Engineering WeiHai China Harbin Institute of Technology Weihai Marine Information Engineering WeiHai China Harbin Institute of Technology Weihai Information Management and Information System WeiHai China Nanyang Technological University Ntu Business Ai Lab Singapore

ISBN: (纸本)9798350302455

The accuracy and stability of brain tumor MRI image classification is significant for the healthcare system, but the traditional models have the defects of difficulty in handling complex features and unstable classification. In this paper, we propose a novel brain tumor classification model based on residual neural network, and use three different data enhancement algorithms: geometric transformation, mixup, and SamplePairing to process a dataset containing four different types of brain tumor images, and finally evaluate the classification effect of our proposed model. Among them, the best results were achieved by using geometric transformations for data enhancement. © 2023 IEEE.

关键词： Image classification

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Deep Learning-Based Real-Time Face Mask Detection for Human Using Novel YOLOv2 with Higher Accuracy 5th

Deep Learning-Based Real-Time Face Mask Detection for Human ...

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Tun, Nay Kyi Myat, Aye Min Oo, Htet Ne Thu, Amar Myo Win, Kyaw Thu Faculty of Computer System and Technology Myanmar Information Institute of Technology Mandalay Myanmar Pyin Oo Lwin Myanmar Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Electronic Department Defence Services Academy Pyin Oo Lwin Myanmar

ISBN: (纸本)9789819997060

A novel deep learning-based face mask detection system is designed to enhance public safety in various environments. With the ongoing global health concerns, the need for efficient and accurate methods to identify individuals wearing or not wearing face masks has become crucial. By utilizing convolutional neural networks (CNNs) and transfer learning techniques, proposed model achieves impressive accuracy while maintaining high-speed processing capabilities. This paper outlines the architecture, training process, and performance evaluation of the proposed deep learning-based face mask detection system, highlighting its promising role in contributing to a safer and healthier society. The development of a vision-based safety system, the transfer of a small YOLO object detection model, and the creation of a CNN-based classification model are the key objectives of this study. According to experimental findings, proposed system is capable of real-time face mask detection and classification with an accuracy of over 98%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

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computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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Secure-by-Construction Synthesis for Control systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 4170-4177页

作者： Zhong, Bingzhuo Liu, Siyuan Caccamo, Marco Zamani, Majid Thrust of Artificial Intelligence Information Hub The Thrust of Intelligent Transportation System Hub China KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden Technical University of Munich TUM School of Engineering and Design Germany University of Colorado Boulder Department of Computer Science United States Ludwig Maximilian University Department of Computer Science Munich Germany

In this note, we present the synthesis of secure-by-construction controllers that address safety and security properties simultaneously in cyber-physical systems. Our focus is on studying a specific security property called opacity, which characterizes the system's ability to maintain plausible deniability of its secret behavior in the presence of an intruder. These controllers are synthesized based on a concept of so-called (augmented) control barrier functions. We propose conditions that facilitate the construction of the desired (augmented) control barrier functions and their corresponding secure-by-construction controllers. To compute these functions, we propose an iterative scheme that leverages iterative sum-of-square programming techniques. This approach enables efficient computation of these functions, particularly for polynomial systems. Finally, we validate the effectiveness of our results through a case study of a vehicle. © 1963-2012 IEEE.

关键词： Iterative methods

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