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Deep Pyramidal Residual Network for Indoor-Outdoor Activity Recognition Based on Wearable Sensor

Intelligent Automation & Soft Computing 2023年第9期37卷 2669-2686页

作者： Sakorn Mekruksavanich Narit Hnoohom Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of PhayaoPhayao56000Thailand Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North BangkokBangkok10800Thailand

Recognition of human activity is one of the most exciting aspects of time-series classification,with substantial practical and theoretical *** evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor ***,researchers have demon-strated considerable passion for developing cutting-edge deep learning sys-tems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many *** study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called Sen-PyramidNet and motion information from wearable sensors(accelerometer and gyroscope).The suggested technique develops a residual unit based on a deep pyramidal residual network and introduces the concept of a pyramidal residual unit to increase detection *** proposed deep learning-based model was assessed using the publicly available 19Nonsens dataset,which gathered motion signals from various indoor and outdoor activities,including practicing various body *** experimental findings demon-strate that the proposed approach can efficiently reuse characteristics and has achieved an identification accuracy of 96.37%for indoor and 97.25%for outdoor ***,comparison experiments demonstrate that the SenPyramidNet surpasses other cutting-edge deep learning models in terms of accuracy and ***,this study explores the influence of several wearable sensors on indoor and outdoor action recognition ability.

关键词： Human activity recognition deep learning wearable sensors indoor and outdoor activity deep pyramidal residual network

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Building Footprint Extraction in Dense Areas using Super Resolution and Frame Field Learning 12

Building Footprint Extraction in Dense Areas using Super Res...

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12th International Conference on Awareness science and technology, iCAST 2023

作者： Nguyen, Vuong Ho, Trong-Anh Vu, Duc-Anh Ngoc Anh, Nguyen Thi Thang, Tran Ngoc Department of Computer Science University of Houston HoustonTX United States Skymap Global Hanoi Viet Nam Nanyang Technological University Singapore School of Applied Mathematics and Informatics Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9798350324693

Despite notable results on standard aerial datasets, current state-of-the-arts fail to produce accurate building footprints in dense areas due to challenging properties posed by these areas and limited data availability. In this paper, we propose a framework to address such issues in polygonal building extraction. First, super resolution is employed to enhance the spatial resolution of aerial image, allowing for finer details to be captured. This enhanced imagery serves as input to a multitask learning module, which consists of a segmentation head and a frame field l earning h ead t o e ffectively h andle t he irregular building structures. Our model is supervised by adaptive loss weighting, enabling extraction of sharp edges and fine-grained polygons which is difficult due to overlapping buildings and low data quality. Extensive experiments on a slum area in India that mimics a dense area demonstrate that our proposed approach significantly outperforms the current state-of-the-art methods by a large margin. © 2023 IEEE.

关键词： Extraction

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Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

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computers, Materials & Continua 2023年第8期76卷 1463-1477页

作者： Ily s Abdullaev Natalia Prodanova KAruna Bhaskar ELaxmi Lydia Seifedine Kadry Jungeun Kim Dean of the Faculty of Economics Department of Management and MarketingFaculty of EconomicsUrgench State UniversityUrganch220100Uzbekistan Basic Department Financial Control Analysis and Audit of Moscow Main Control DepartmentPlekhanov Russian University of EconomicsMoscow117997Russia Department of Computer Science and Engineering KL Deemed to UniversityVaddeswaramGunturAndhra PradeshIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data *** city benefitted from offloading to edge *** a mobile edge computing(MEC)network in multiple *** comprise N MDs and many access points,in which everyMDhasM independent real-time *** study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)*** proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system *** addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted *** TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading ***,the SGO algorithm is used for the parameter tuning of the DBN *** simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.

关键词： Mobile edge computing seagull optimization deep belief network resource management parameter tuning

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A Cautionary Note on Poli's Stability Condition for Particle Swarm Optimization

A Cautionary Note on Poli's Stability Condition for Particle...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Von Eschwege, Daniel H. Engelbrecht, Andries P. Stellenbosch University Department of Industrial Engineering Stellenbosch South Africa Stellenbosch University Department of Industrial Engineering and Computer Science Division Stellenbosch South Africa Gulf University for Science and Technology Center for Applied Mathematics and Bioinformatics Kuwait Kuwait

ISBN: (纸本)9781665430654

Particle swarm optimization (PSO) is a swarm intelligence algorithm that finds candidate solutions by iteratively updating the positions of particles in a swarm. PSO performance depends on the use of a suitable control parameter (CP) configuration, which governs the trade-off between exploration and exploitation in the swarm. Various methods of adapting or tuning CPs exist, but many result in exploding particle velocities and an unstable search process. Poli's stability condition ensures convergence in the mathematical limit, and is often used to inform CP configuration. However, this study shows that since it does not place any practical convergence constraints, it cannot be used to guarantee a stable search process. Velocity explosion occurs nonetheless and can lead to floating-point overflow and numerical instability. The investigation into various CP configurations across diverse functions and measurements of particle velocities provides empirical evidence of velocity explosion, and cautions against the assumption that enforcing Poli's criterion guarantees stability. The findings underline the need for comprehensive understanding of CP tuning and stability conditions in PSO, as well as the crucial role of empirical evidence in evaluating the real-world performance of swarm intelligence algorithms. © 2023 IEEE.

关键词： Particle swarm optimization Poli Stability condition Swarm intelligence

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Next Level of Human-Robot Collaboration by Utilizing AI Pose Estimation and Model Predictive Motion Planning Technologies

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IFAC-PapersOnLine 2023年第2期56卷 5313-5319页

作者： Esteban Pozo Bishoy Gerges Mohammed Nafea Frank Schrödel Automation Technology & Robotics Schmalkalden University of Applied Sciences Germany Engineering Mathematics and Computer Science University of Twente Netherlands

Over recent years, there has been a significant focus on Human Robot Interaction (HRI). The aim of this interaction is to create a mutually beneficial partnership between humans and robots, with robots contributing precision, speed, and force, while humans provide experience, intuition, and high-level management and control strategy understanding. One of the most crucial factors in achieving a successful HRI is the guidance of these robots. This paper proposes the use of reliable Hand Detection based on Long Short-Term Memory (LSTM) and intelligent robot motion planning based on Model Predictive Control Methods (MPC) to detect hand signals and communicate with the robot. By utilizing vision systems, a micro controller-based Edge AI approach, and a wired connection, the reaction speed of the robot can be optimized to maintain a safe separation distance between the human operator and the robot, thus enabling a collaborative and safe environment. The effectiveness and capabilities of this automation framework are validated through a case study.

关键词： Optimal Control Artificial Intelligenc Modeling Guidance navigation and control Robots manipulators

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Digital Transformation and its Impact on Operational Efficiency and Competitive Advantage in Islamic Banks 2

Digital Transformation and its Impact on Operational Efficie...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Shehadeh, Maha Almajali, Dmaithan Abu-Alsondos, Ibrahim A. Alkhwaldi, Abeer F. Al-Gasaymeh, Anwar S. Applied Science Private University Finance and Banking Science Department Amman Jordan Applied Science Private University Management Information System Department Amman Jordan American University in the Emirates College of Computer Information Technology Dubai United Arab Emirates Mutah University Department of Management Information Systems Karak Jordan

ISBN: (纸本)9798350335644

This study aimed to identify the impact of digital transformation on Islamic banks operating in Jordan by using the descriptive analytical method. Primary data were obtained through a questionnaire and distributed to the study sample, which consisted of 68 employees of 4 Islamic banks operating in Jordan. The structural equation model was used in the process of testing the study hypotheses. We obtained a number of results, the most important of which is the presence of a statistically significant effect at the significance level (α ≤ 0.05) of digital transformation in Islamic banks operating in Jordan in relation to operational efficiency and competitive advantage. We recommend that Islamic banks need to develop a strategy for digital transformation aimed at innovation and competition, without prejudice to the controls of Islamic finance. There is a need to focus on managing the risks associated with digital transformation, as well as treating the risks associated with modern technologies, especially those that threaten Islamic banks and financial stability. © 2023 IEEE.

关键词： Competition

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SSANet: Side-by-Side Additive Network for Knee Osteoarthritis Severity Detection from X-Ray Images 4th

SSANet: Side-by-Side Additive Network for Knee Osteoarthrit...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Tewari, Rakhi Bhattacharjee, Debotosh Roy, Hiranmoy Krejcar, Ondrej Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Department of Information Technology RCC Institute of Information Technology Canal South Road Beliaghata Kolkata700015 India Faculty of Informatics and Management Center for Basic and Applied Science University of Hradec Kralove Rokitanskeho 62 Hradec Kralove50003 Czech Republic

ISBN: (纸本)9789819726103

Knee Osteoarthritis (KOA) is becoming one of the most frequent degenerative and irreversible diseases in elderly people. Its early detection and diagnosis is also a difficult and time-intensive task. The recent advances in Machine Learning (ML) and computer Vision (CV) created new paths to automatically detect KOA in the early stages. Detecting and grading the severity of KOA requires a dedicated ML model. This paper proposes a Side-by-Side Additive Network (SSANet)-based model for detecting and grading the severity of KOA. Since KOA affects the knee joint space by narrowing the gap between the joints, we preprocess the X-ray images to detect the Region of Interest (ROI) before the training of the model. From experimental results, it is evidenced that the ROI selection is really improving the detection accuracy of the model. Unlike sequentially connected convolution layer-based deep learning models, the proposed SSANet is based on a parallel convolution layer to reduce the degradation of feature quality. Again, to enhance the edge feature maps, we apply an additive layer that couples the previous layers’ convolutional output. SSANet captures both high-level and low-level features to predict the KOA severity effectively. The presence of a smaller number of layers and fewer numbers of learnable parameters than the existing popular deep learning models, make SSANet a time and space-efficient network. Moreover, the performance of the proposed SSANet in detecting KOA severity is significantly superior to that of other popular state-of-the-art networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolution

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Machine Learning and AI based Robotic System for Archaeological Research 6

Machine Learning and AI based Robotic System for Archaeologi...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Singh, Manmohan Rai, Ashutosh Kumar Vamsi Krishna, G. Dileep Kumar, Y. Mishra, Rashmi Kothandaraman, D. Department of Computer Science and Engineering IES College of Technology Bhopal India VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India Department of Electronics and Instrumentation Engineering Sree Vidyanikethan Engineering College Mohan Babu University Andhra Pradesh Tirupati India Department of Applied Science & Humanities G L Bajaj Institute of Technology & Management Greater Noida India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India

ISBN: (纸本)9798350398496

The use of artificial intelligence (AI) in archaeology and its prospective use in the discipline are the primary focuses of this research study. Even if this issue may not fall under the major interests of certain people, the consequences of being ignorant of the most important aspects of the subject might be catastrophic. As AI is used in many different methods by archaeologists, including the creation of 3D representations of historical sites and the scanning of lands with a laser radar in order to identify old burials. In addition, freshly developed strategies have been put through rigorous testing, and they are almost ready to be put into action. This article provides insight into a branch of research that examines how the recent past and the established present interact. One last thing to mention is that AI is assisting archaeologists in their digs, which in turn makes artifacts and cultural treasures more accessible to the general public. Researchers believe that in the not-too-distant future, they will be able to appease members of the public by virtually demonstrating the significance of ancient sites. © 2023 IEEE.

关键词： Robotics

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Role of Data Mining Across Different Formats of Multimedia

Role of Data Mining Across Different Formats of Multimedia

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Velmurugan, A.K. Rajalingam, A. Dhawaleswar Rao, C.H. Balasubadra, K. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering andhra Pradesh Vijayawada India University of Technology and Applied Sciences-Shinas Department of Electronics and Communication Engineering Oman Centurion University of Technology and Management Department of Computer Science and Engineering Paralakhemundi Campus Alluri Sitapur Odisha Gajapati India R.M.D Engineering College Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350333602

In today's world, mining data from videos plays a significant role in the process of information retrieval. The generation of information pertaining to moving images is straightforward for many users. As a result of this, obtaining the required material, particularly the unstructured datasets is a challenging process. There are a number of factors it might contribute to this level of complication, including recordings captured by individuals without professional training, subpar cameras, inadequate lighting, and many more. There were several other algorithms it had been presented to recover useful video footage, but none of them achieved the results it was anticipated. It is, and will likely always be, the most difficult area for scholars to work in. A brand-new method for extracting video images is presented in this article, and it makes use of the image feature value as its primary tool. At first, the picture is grouped, and then the image input extract feature for particular information and then is used to get rid of undesirable image frames inside the grouped image. The results of the experiments demonstrate the procedure suitable for all kinds of moving pictures. © 2023 IEEE.

关键词： Data mining

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AI-Based Algorithm for Zero-Day Attack Detection Using Reinforcement Learning

AI-Based Algorithm for Zero-Day Attack Detection Using Reinf...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Ola Nasir Waleed Amer Ala Hamarsheh Ali Mahmoud Ali Amani Abu Zaid Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyber Secyrity Faculty of Information Technology Zarqa University Zarqa Jordan Department of computer science Faculty of Information Technology Arab American University Department of Computer Network Faculty of Engineering Technology Al-Balqa Applied University Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Zero-Day attacks represent one of the most challenging threats in modern cybersecurity, as they exploit unknown vulnerabilities and bypass traditional detection systems. In this paper, we propose a hybrid threat detection system that integrates three key components: a Random Forest-based supervised classification model for detecting known attack patterns, an autoencoder-based anomaly detection module to uncover novel threats, and a Reinforcement Learning (RL) agent to dynamically optimize response strategies in real-time. The proposed system achieves a detection Accuracy of 97.8% for known attacks. The autoencoder, trained exclusively on normal traffic, employs a dynamically determined threshold (set at 0.8409) to flag anomalous behavior indicative of Zero-Day attacks. Furthermore, the RL agent adapts quickly to evolving threats, effectively filtering out 53.2% of benign traffic while blocking 39.7% of attack traffic and quarantining 7.1% of ambiguous cases for further inspection. Experimental results demonstrate that this integrated approach not only enhances top-level threat prevention but also significantly reduces false alerts.

关键词： Heuristic algorithms Prevention and mitigation Autoencoders Reinforcement learning Threat assessment Real-time systems computer security Anomaly detection Random forests Testing

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