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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Oyeleye, Matthew Chen, Tianhua Su, Pan Antoniou, Grigoris School Of Computing And Engineering University Of Huddersfield Department Of Computer Science United Kingdom School Of Control And Computer Engineering North China Electric Power University China

ISBN: (纸本)9798350340266

Human activity recognition involves identifying the daily living activities of an individual through the utilization of sensor attributes and intelligent learning algorithms. The identification of intricate human activities proves to be a labo-rious task, given the inherent difficulty of capturing long-term dependencies and extracting efficient features from unprocessed sensor data. For this purpose, this study aims at recognizing and classifying human activities using physiological and biological sensor data generated by Actigraph, as they can accurately measure moderate-to-vigorous intensity physical which is mostly affected by body composition and also better suited for self-monitoring. We examined the effectiveness of these features by applying prevalent machine learning classifiers and long short-term memory (LSTM) networks on recently publicly available data, which includes accelerometer and heart rate recordings. The results from our experiments showed that LSTM models performed better than conventional ML classifiers with the best result achieving an accuracy of 86.5%. The findings also confirms the significance of the heart rate in accurately classifying and identification of human activity more. © 2024 IEEE.

关键词： Long short-term memory

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Predicting Lumpy Skin Disease using Various Machine Learning Models

Predicting Lumpy Skin Disease using Various Machine Learning...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Ashwani Kumar, Bhawnesh Negi, Harendra Singh Shri Ram Group of Colleges Department of Computer Science and Engineering Muzaffarnagar India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

As infection with the virus of lumpy skin disease, which causes acute or subacute sickness in cattle and the population of water buffalo, poses a serious threat to the cattle industry. Machine learning models gives the direction to make ease of testing the lumpy disease. To predict Lumpy skin disease various machine learning models are available. Various parameters are considered to find out the numerous values such as accuracy, MAE, MSE and RMSE. In this paper distinguish machine learning models are considered to make prediction level high. This paper works for the different ML models on the lumpy disease prediction to forecast the cattle disease. Random forest model gives the highest accuracy which is 97.10% and poly-F is at second position which is 96.77% accuracy score. Random forest gives MAE closet value to 0 which is 0.028 that indicates the best ML model with respect to error as well. The next ML model is decision tree, the value is 0.037. For MSE value, poly-F model gives remarkable output which is 0.025 and random forest with having 0.028 value. For RMSE, poly-F model gives remarkable output which is 0.16, random forest with having 0.017 value and decision tree is at 0.19. © 2023 IEEE.

关键词： Decision trees

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Latency and Cost-Aware Deployment of Dynamic Service Function Chains in 5G Networks

Latency and Cost-Aware Deployment of Dynamic Service Functio...

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Karim, Amrin Ema, Jannatul Yasmin, Tasnia Roy, Palash Razzaque, Md. Abdur Green University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh University of Dhaka Green Networking Research Group Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350335590

Efficient deployment of a service function chain (SFC) on virtual network functions (VNFs) and their mapping to virtual machines (VMs) in 5G networks is highly important to reduce user application service latency as well as to minimize the cost of VM migration. Existing works in the literature have either considered service latency or migration cost while deploying an SFC on VMs. In this paper, the problem of dynamically mapping VNFs running a SFC to different virtual machines in a 5G network has been formulated as a multi-objective linear Programming (MOLP) problem. The developed optimization model, namely Trade-Lcm, minimizes user application service latency while reducing the cost of migrating virtual network functions associated with an SFC. The numerical performance analysis results demonstrate a significant improvement in minimizing service latency of user applications and cost of VM migration as high as 30% and 10%, respectively, compared to the state-of-the-art work. © 2023 IEEE.

关键词： Multiobjective optimization

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User Quality of Experience and Profit Aware Task Allocation in Mobile Device Cloud

User Quality of Experience and Profit Aware Task Allocation ...

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Sharmin, Zeneya Roy, Palash Razzaque, Md. Abdur Green University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Green Networking Research Group Department of Computer Science and Engineering University of Dhaka Bangladesh

ISBN: (纸本)9798350335590

Mobile Device Cloud (MDC) is a promising and lucrative cloud environment that uses the idle resources of nearby mobile devices to improve the performance of compute-intensive applications. By computing code on nearby devices instead of a distant master cloud, the MDC system can improve the performance of real-time applications. It is challenging for the MDC system to efficiently use the resources because of cost and QoE. While allocating high-quality resources to mobile applications might be effective in decreasing the latency, it will increase the cost, and the allocation of low-quality resources will increase execution latency, diminishing the quality of experience (QoE) of the users. The works in the literature didn't come up with methods of trading off these two conflicting objectives in a fair way. In this paper, we present an optimization framework, namely QCMDC, for allocating tasks in an MDC environment, which brings a trade-off between maximizing users' QoE and minimizing the execution cost of the worker devices. Empirical evaluations have been carried out in Python and the results demonstrate significant performance improvement in terms of QoE and execution cost compared to other state-of-the-art works. © 2023 IEEE.

关键词： Multiobjective optimization

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Energy Consumption Aware Delay Minimization for UAV Enabled Internet of Vehicle 5

Energy Consumption Aware Delay Minimization for UAV Enabled ...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Emon, Shariar Hossain Rahman, Md Lotifur Omi, Sumiya Siddika Roy, Palash Razzaque, Md. Abdur Shadman Wadith, K.M. Ahmed, Jargis Green University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh University of Dhaka Green Networking Research Group Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

Because of its adaptable nature and excellent maneuvering capabilities, the Unmanned Aerial Vehicle (UAV) can develop a potential solution for executing the Internet of Vehicles (IoV) applications. However, the limited battery capacity of the UAV devices imposes significant barriers to realtime task execution. Existing works are limited to either considering the executing tasks at the UAVs or Road Side Units (RSUs) without considering the collaborative relationship between them for minimizing service delay. In this work, we have presented an optimization framework for task execution of the Internet of Vehicles by exploiting Multi-Uav,Task Executing of Internet of Vehicle by exploiting Multi-UAV namely TEIMU, which minimizes the total average latency by considering energy consumption. The developed TEIMU framework is a combinatorial optimization problem that offloads the tasks by collaboration of RSUs and UAVs. According to the experimental findings, the developed system, TEIMU, surpasses state-of-the-art works in terms of overall average latency and energy usage by up to 10% and 25 %, respectively. © 2023 IEEE.

关键词： Energy utilization

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Using Recurrent Neural Networks to Forecast Climate Change: A Time Series Analysis of Global Temperature Variability 1

Using Recurrent Neural Networks to Forecast Climate Change: ...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Rathore, Nitasha Anubha Singh, Kuljeet Kaur, Gaganpreet Sharma, Chetna Sinha, Anurag Bharatiya Vidyapeeth College of Engineering Department of Computer Science and Engineering New Delhi India KIET Group of Institutions India School of Sciences Christ University Department of Computer Science Delhi NCR India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India IGNOU Department of Computer science New Delhi India

ISBN: (纸本)9798331518578

Predicting the upcoming weather instances is very crucial. It depends on different climatic parameters like humidity, pressure, temperature, etc. In this paper, the historical data of the weather in the India area is used for future weather instances of the India for farmers' convenience in terms of the agricultural instance which depends on the weather and, functioning according to that which will restore the energy. For weather forecasting we have use the machine learning algorithm and probabilistic predictions of the predictive analytics based on soft computing and. NGBoost algorithm and. Linear models of the machine learning for predictive. Comparative weather incidence spaced on the historical data. We have also used, sliding window algorithm of the statistics for predicting the ideology of the concept of different contrasted windows and year. We've also used utility thirds and machine learning algorithm, classified for predicting the weather based on different features The overall implementation in this paper, shows the accuracy which we have gathered from the data set. An implementation of algorithm which ranges between 80% to 90% and the entire algorithm have been compared based on the feature instances. Work can be concluded on the measurement of the algorithm, which we have got after the implementation of Models. Which rely upon the different data features and thus it can be beneficial for preserving the energy and materials in the India agriculture area and forecasting the weather as per day Agricultural conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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Nonlinear Dynamic Inversion-based Motion Planning of a Floating Satellite Platform

Nonlinear Dynamic Inversion-based Motion Planning of a Float...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Banerjee, Avijit Tevetzidis, Ilias Satpute, Sumeet Gajanan Nikolakopoulos, George Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents a nonlinear dynamic inversion-based motion plan for a levitating robotic satellite emulation platform. The frictionless motion of such a levitating platform has dynamic equivalency with a satellite’s realistic movement in space, thus evolving as an efficient demonstration tool as a hardware-in-loop satellite motion simulator. A brief design description of the emulation platform unified with various sensor and actuator units is presented, and an associated nonlinear mathematical model representing its coupled dynamic motion is described. The proposed control design architecture manipulates the associated nonlinear dynamics, which appear due to the coupling of translational and rotational movement of the robotic platform, thereby establishing an efficient trajectory tracking capability for various operating regions without the additional requirement of gain scheduling. In order to realize the control requirement through a set of ON-OFF thruster-based actuation units, which closely resembles the reaction control system of realistic satellite design, an optimal thrust allocation logic is proposed that accounts for various physical limitations of thrusters such as thrusters as its magnitude constraint and minimum on time. The efficacy of the proposed modelling and control scheme is experimentally validated in different realistic motion planning scenarios, such as point-to-point manoeuvres with terminal objectives and a superior path-following capability. The experimental studies are supported with related results. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Motion planning

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Machine Learning and Artificial Intelligence in Cybersecurity: Innovations and Challenges 2

Machine Learning and Artificial Intelligence in Cybersecurit...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Khatri, Vinayak Agarwal, Gaurav Gupta, Amit Kumar Sanghi, Akash Invertis University Department of Computer Science and Engineering Bareilly India KIET Group of Institutions Department of Computer Applications Ghaziabad India Invertis University Department of Computer Applications Bareilly India

ISBN: (纸本)9798331519056

Rapid developments in machine learning (ML) and artificial intelligence (AI) have had a big impact on cyber security by providing creative answers to challenging security problems. This analysis examines how AI and ML are integrated into cyber security, emphasizing the uses, advantages, and drawbacks of each technology. To improve threat detection, malware categorization, phishing prevention, and network security, this study analyses many AI and ML methods, such as reinforcement learning, deep learning, supervised learning, and unsupervised learning. According to recent study findings, AI-driven intrusion detection systems can detect intrusions with up to 99% accuracy, which greatly lowers the number of false positives. Comparatively, compared to conventional techniques, machine learning algorithms for malware detection have increased detection rates by 20-30%. However, several technological obstacles stand in the way of AI's smooth integration with cyber security. These obstacles include problems with data quality, adversarial assaults, interpretability of models, and the computing demands of implementing AI at scale. There are other major obstacles, such as privacy and ethical considerations. Nonetheless, issues including adversarial attacks, ethical dilemmas, and poor data quality persist. This paper illustrates the usefulness of AI and ML technologies in cyber security through real-world case studies. Lastly, it emphasizes how AI and ML have the ability to revolutionize cyber security procedures by highlighting upcoming trends and research paths. This review attempts to provide useful insights for researchers, practitioners, and policymakers to harness AI and ML for a safer digital environment by providing a thorough overview and highlighting important discoveries. © 2024 IEEE.

关键词： Phishing

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Quadtree-based Free-space Cell-decomposition for Pathplanning with RRT* Implementation

Quadtree-based Free-space Cell-decomposition for Pathplannin...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Otte, Noah Satpute, Sumeet Gajanan Banerjee, Avijit Nikolakopoulos, George Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper introduces a novel, computationally efficient, random-search based path-planning algorithm specifically designed to enhance the dexterous accessibility of autonomous lunar rovers operating on highly cluttered obstacle-dense planetary surfaces. The proposed pathplanning framework employs a Quadtree-based cell decomposition to enhance the performance of sampling-based path-planning algorithms, specifically those in the category of Rapidlyexploring Random Trees (RRT) and its optimized variant, RRT*. In the process of generating the path through an occupancy grid map, the obstacle-free cells are utilized as discrete sampling points for the RRT* algorithm. A unique non-uniform sampling approach is employed to favour larger cells with a higher degree of safety, thereby enabling seamless navigation through the obstacle-dense map. Additionally, the non-uniform sampling approach enhances the overall computational efficiency of the framework, making it suitable for fast, lightweight onboard implementation. The efficacy of the proposed planning framework is demonstrated through numerous simulation scenarios. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Decision trees

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