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SSRN 2023年

作者： Abbas, Mohamed El-Zoghabi, Adel Shoukry, Amin Information Technology Institute of Graduate Studies and Research Alexandria Egypt Computer Science and Engineering Egypt-Japan University of Science and Technology New Borg-El-Arab City Alexandria Egypt Computer and Systems Engineering Dept Faculty of Engineering Alexandria Egypt

Many clustering algorithms fail when clusters are of arbitrary shapes, of varying densities, or the data classes are unbalanced and close to each other, even in two dimensions. A novel clustering algorithm "DenMune" is presented to meet this challenge. It is based on identifying dense regions using mutual nearest neighborhoods of size K, where K is the only parameter required from the user, besides obeying the mutual nearest neighbor consistency principle. The algorithm is stable for a wide range of values of K. Moreover, it is able to automatically detect and remove noise from the clustering process as well as detecting the target clusters. It produces robust results on various low and high-dimensional datasets relative to several known state-of-the-art clustering algorithms. © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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ResNet-based Deep Neural Network using Transfer Learning for Animal Activity Recognition 6

ResNet-based Deep Neural Network using Transfer Learning for...

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6th International Conference on information technology, InCIT 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665489126

Animal activity recognition (AAR) is a challenging and complex task for monitoring animal behavior to determine their health conditions and physical responses. To address this requirement, AAR research aims to develop an internet of things (IoT) platform that anticipates animal behavior based primarily on sensor data collected from wearable devices connected to animals. Because of its superior effectiveness on multi-dimensional sensor data, deep learning (DL) currently dominates sensor-based AAR applications. Nevertheless, DL techniques require significantly more training time than other learning-based strategies. This study employs transfer learning (TL) to generalize the DL model to overcome the issue of training time, indicating significant promise for future research on animal activity identification. Furthermore, we apply the TL to allow the reusability of a pre-trained DL model on entirely new data without the time-consuming operations of model training and data labeling. We conducted experiments on a publicly available AAR benchmark dataset (SAR) utilizing deep residual networks with automatically learned features. Utilizing source data, our technique achieved an overall accuracy of 96.46% (dataset collected by the first sensor) and 98.40% (dataset collected by the second sensor) based on evaluating findings. © 2022 IEEE.

关键词： Wearable sensors

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Virtual Laboratory Learning Experience in engineering: An Extended technology Acceptance Model (TAM)

Virtual Laboratory Learning Experience in Engineering: An Ex...

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Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Ahmed Jamah Ahmed Alnagrat Khamiss M. S. Ahmed Muhannad Ismail Alkhallas Osama Abulaqasim Ibrahim Almakhzoom Syed Zulkarnain Syed Idrus Rizalafande Che Ismail Department of Computer Science and Information Technology Higher Institute of Science and Technology Wadi al-Shati Wadi al-Shati Fezzan Libya Department of Computer Science Faculty of Information Technology Sebha University Sebha Libya Department of Software Engineering Faculty of Engineering Technology in Zuwarah Zuwarah Libya Department of Communication Faculty of Business & Communication (FPK) Universiti Malaysia Perlis(UniMAP) Kangar Perlis Malaysia Faculty of Electronic Engineering Technology (FTKEN) Universiti Malaysia Perlis (UniMAP) Arau Malaysia

This research presents an enhanced version of the technology Acceptance Model (TAM), which integrates usability and learning objectives to evaluate the effect of adopting virtual laboratories on student performance. Data from 97 participants, gathered via a survey, was used to examine the correlation between perceived usefulness (PU), perceived ease of use (PEOU), and intention to use (IU) concerning their experiences and outcomes with a simulation-based virtual laboratory. A custom questionnaire was designed to assess TAM components, namely PU, PEOU, and IU, specifically in the context of virtual laboratory learning environments. Findings indicate that the integration of specific laboratory learning objectives fosters the creation of a more credible and authentic simulation tool. This, in turn, optimizes the learning process and enhances students' learning outcomes. The refined TAM model provides valuable insights into the determinants of user acceptance of virtual laboratories and offers practical guidance for educators and instructional designers to construct more effective and efficient virtual laboratory environments in educational contexts.

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LSTM-XGB: A New Deep Learning Model for Human Activity Recognition based on LSTM and XGBoost 7

LSTM-XGB: A New Deep Learning Model for Human Activity Recog...

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7th International Conference on Digital Arts, Media and technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, NCON 2022

ISBN: (纸本)9781665495103

Recognition of human activity focuses on identifying various human motions and behaviors using data acquired from multiple types of sensors, a branch of computer science. Considering time series data, deep learning algorithms show promise compared to laborious handcrafted feature extraction methods, which are highly dependent on the quality of the used domain parameters. This paper proposes a novel deep learning model for HAR issues called the Long Short Term Memory Neural network with eXtreme Gradient Boosting (LSTM-XGB), developed on recurrent neural networks with eXtreme Gradient Boosting (XG-Boost). The LSTM-XGB comprises LSTM layers to understand the characteristics of the input and is capable of learning features automatically, followed by XGBoost in the final layer to forecast the class labels, which serves to enhance detection capability. When sufficient circumstances are satisfied, the LSTM-XGB model can be simpler by lowering the number of parameters. It is not essential to readjust the weighting factors throughout a backpropagation phase. Investigations using smartphone sensor data were carried out using a publicly available standardized HAR dataset called the UniMiB-SHAR dataset, which contains activities associated with everyday life and activities related to falls. The experiments show that the LSTM-XGB provides much superior identification capabilities than other standard deep learning techniques, with the highest accuracy of 92.59%. Furthermore, the LSTM-XGB models surpass the existing best-practice models on the same dataset. © 2022 IEEE.

关键词： Long short-term memory

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Mobile Deep Learning: Exploring Deep Neural Network for Predicting Context-Aware Smartphone Usage

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SN computer Science 2021年第3期2卷 1-12页

作者： Sarker, Iqbal H. Abushark, Yoosef B. Khan, Asif Irshad Alam, Md Mottahir Nowrozy, Raza Swinburne University of Technology Melbourne 3122 VIC Australia Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong 4349 Bangladesh Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah 21589 Saudi Arabia Victoria University Footscray 3011 VIC Australia

In this paper, we mainly formulate the problem of predicting smartphone usage based on contextual information, which involves both the user-centric and device-centric contexts. In the area of mobile analytics, traditional machine learning techniques, such as Decision Trees, Random Forests, Support Vector Machines, etc. are popular for building context-aware prediction models. However, real-life smartphone usage data may contain higher dimensions of contexts and can be huge in size considering the daily behavioral data of the users. Thus, the traditional machine learning models may not be effective to build the context-aware model. In this paper, we explore “Mobile Deep Learning”, an artificial neural network learning-based model considering multiple hidden layers for predicting context-aware smartphone usage. Our model first takes into account context correlation analysis to reduce the neurons as well as to simplify the network model through filtering the irrelevant or less significant contexts, and then build the deep learning model with the selected contexts. The experimental results on smartphone usage datasets show the effectiveness of the model. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Artificial neural network Context-aware computing Deep learning Intelligent applications Mobile data analytics Personalization Predictive analytics User behavior modeling

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A Web Attack Detection Method Based on DistilBERT and Feature Fusion for Power Micro-Application Server 2

A Web Attack Detection Method Based on DistilBERT and Featur...

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2nd International Conference on Advanced Electronics, Electrical and Green Energy, AEEGE 2023

作者： Nige, Li Lu, Chen Lei, Zhao Zhenning, Teng Zhiqiang, Wang Yiyang, Shao Xiaolin, Gui State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co.Ltd. Nanjing China Xi'an Jiaotong University Faculty of Electronic and Information Engineering Shaanxi Province Key Laboratory of Computer Network Xi'an China

ISBN: (纸本)9798350322866

The widespread use of power micro-applications has significantly expanded the attack surface of their server side, thus increasing the risk of vulnerability attacks such as SQL injection, XSS, and CRLF injection. To ensure the security of power systems, these attacks must be detected precisely and timely. Therefore, this paper proposes a server-side web attack detection method based on DistilBERT and feature fusion for HTTP and HTTPS requests. The method treats HTTP and HTTPS requests as text data, specifically, extracts deep semantic features using DistilBERT, and fuses them with well-designed empirical features to comprehensively characterize and classify the request. Consequently, anomalous requests can be detected. The experimental results show that the accuracy, precision, recall, and F1 score of the method on HTTP CSIC 2010 and FWAF datasets are close to or higher than 99%. Compared with other methods as far as we known, this method has better performance and efficiency. © 2023 IEEE.

关键词： Cybersecurity

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Sensors and healthcare 5.0:transformative shift in virtual care through emerging digital health technologies

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Global Health Journal 2021年第4期5卷 169-177页

作者： Elliot Mbunge Benhildah Muchemwa Sipho'esihle Jiyane John Batani Department of Computer Science Faculty of Science and EngineeringUniversity of EswatiniKwaluseniManziniEswatini Department of Information Technology Faculty of Accounting and InformaticsDurban University of TechnologyP.O.Box 1334Durban 4000South Africa Faculty of Engineering and Technology Botho UniversityMaseru 100Lesotho

Emerging digital technologies continues to evolve posing unprecedented opportunities in health systems globally to improve healthcare services *** has been significant progress in ***,the lack of emotive recognition coupled with a dearth of personalized and pervasive health applications and emotive smart devices calls for the integration of intelligent sensors health systems through emerging *** there has been significant progress in smart and connected health care,more research innovation,dissemination and technologies are needed to unbundle new opportunities and move towards healthcare *** is at the dawn of a paradigm change to reach the new era of smart disease control and detection,virtual care,smart health management,smart monitoring,and ***,this study discusses the roles and capacities of sensors,their capabilities and other emerging technologies such as nanotechnology,5 G technologies,drone technology,blockchain,robotics,big data,internet of things,artificial intelligence,and cloud *** 5.0 provides healthcare services including patient remote *** and virtual clinics,emotive telemedicine,ambient assisted living,smart self-management,wellness monitoring and control,smart treatment reminders,compliance and adherence,and personalized and connected health ***,building resilience and robust healthcare 5.0 is not immune to *** challenges,technological and infrastructural barriers,lack of legal and regulatory frameworks and e-health policies,individual perceptions,misalignment with hospitals'strategy,lack of funding,religious and cultural bariers are identified as potential barriers to the successful implementation of healthcare ***,there is a need for building resilient technology-driven healthcare *** achieve this,there is a need for expanding technological infrastructure,provision of budgetary support based o

关键词： Healthcare 5.0 Sensors Smart devices Virtual care Emerging technologies Coronavirus disease 2019(COVID-19)

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Multi-Expert Adaptive Selection: Task-Balancing for All-in-One Image Restoration

arXiv

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arXiv 2024年

作者： Yu, Xiaoyan Zhou, Shen Li, Huafeng Zhu, Liehuang School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China

The use of a single image restoration framework to achieve multi-task image restoration has garnered significant attention from researchers. However, several practical challenges remain, including meeting the specific and simultaneous demands of different tasks, balancing relationships between tasks, and effectively utilizing task correlations in model design. To address these challenges, this paper explores a multi-expert adaptive selection mechanism. We begin by designing a feature representation method that accounts for both the pixel channel level and the global level, encompassing low-frequency and high-frequency components of the image. Based on this method, we construct a multi-expert selection and ensemble scheme. This scheme adaptively selects the most suitable expert from the expert library according to the content of the input image and the prompts of the current task. It not only meets the individualized needs of different tasks but also achieves balance and optimization across tasks. By sharing experts, our design promotes interconnections between different tasks, thereby enhancing overall performance and resource utilization. Additionally, the multi-expert mechanism effectively eliminates irrelevant experts, reducing interference from them and further improving the effectiveness and accuracy of image restoration. Experimental results demonstrate that our proposed method is both effective and superior to existing approaches, highlighting its potential for practical applications in multi-task image restoration. The source code of the proposed method is available at https://***/zhoushen1/MEASNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Restoration

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Recognition of Complex Human Activities for Wellness Management from Smartwatch using Deep Residual Neural Network 7

Recognition of Complex Human Activities for Wellness Managem...

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ISBN: (纸本)9781665495103

Activity-based wellbeing administration integrating health apps on intelligent wearables is becoming increasingly prominent. The accessibility of multimodal sensors in everyday wearable devices makes it feasible to measure human behavior and provide wellness services that are aware of the user's environment. In smartphones, smartwatches, and many other wearables, integrated sensors provide a wealth of information that can be utilized to recognize human movement. This research shows how effective deep learning algorithms can extract abstract information for human movement categorization in daily life. We came up with an approach to deal with this issue is what we name one-dimensional deep residual neural network (1D-ResNet). With an overall accuracy of 94.15%, we observed that the proposed 1D-ResNet outperforms standard deep learning models in categorizing complicated human activities such as reading, writing, eating, and other hand-oriented tasks. © 2022 IEEE.

关键词： Deep neural networks

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Real-Time Violent Action Recognition Using Key Frames Extraction and Deep Learning

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computers, Materials & Continua 2021年第11期69卷 2217-2230页

作者： Muzamil Ahmed Muhammad Ramzan Hikmat Ullah Khan Saqib Iqbal Muhammad Attique Khan Jung-In Choi Yunyoung Nam Seifedine Kadry Deparment of Computer Science and Information Technology The University of LahoreSargodha CampusSargodha40100Pakistan Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan School of System and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan College of Engineering Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Science HITEC University TaxilaTaxilaPakistan Applied Artificial Intelligence Ajou UniversitySuwonKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab UniversityLebanon

Violence recognition is crucial because of its applications in activities related to security and law *** semi-automated systems have issues such as tedious manual surveillances,which causes human errors and makes these systems less *** approaches have been proposed using trajectory-based,non-object-centric,and deep-learning-based *** studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other ***,the their performance must be *** study explores the state-of-the-art deep learning architecture of convolutional neural networks(CNNs)and inception V4 to detect and recognize violence using video *** the proposed framework,the keyframe extraction technique eliminates duplicate consecutive *** keyframing phase reduces the training data size and hence decreases the computational cost by avoiding duplicate *** feature selection and classification tasks,the applied sequential CNN uses one kernel size,whereas the inception v4 CNN uses multiple kernels for different layers of the *** empirical analysis,four widely used standard datasets are used with diverse *** results confirm that the proposed approach attains 98%accuracy,reduces the computational cost,and outperforms the existing techniques of violence detection and recognition.

关键词： Violence detection violence recognition deep learning convolutional neural network inception v4 keyframe extraction

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