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检索条件"任意字段=Proceedings of the 5th International Conference on Algorithms, Computing and Systems"

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BIDIRECTIONAL FEATURE PYRAMID FOR FACE DETECTION IN DENSE CROWD SCENES 18

BIDIRECTIONAL FEATURE PYRAMID FOR FACE DETECTION IN DENSE CR...

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18th international conference on Interfaces and Human Computer Interaction 2024, IHCI 2024, the 17th international conference on Game and Entertainment Technologies 2024, GET 2024 and the 18th international conference on Computer Graphics, Visualization, Computer Vision and Image Processing 2024, CGVCVIP 2024, Part of the 18th Multi conference on Computer Science and Information systems 2024, MCCSIS 2024

作者： Wei, Yajuan Xu, Zhiejie Dai, Chuan Zhang, Wanju Xi’an University of Posts and Telecommunications Xi’an China University of Huddersfield United Kingdom

ISBN: (纸本)9789898704580

Face detection algorithms based on deep learning approaches have made significant advancements in the last decade. However, recognising faces in dense crowd scenes remains challenging, particularly in situations with overlapping and obscured small targets. this paper introduces a lightweight model based on YOLOv5s, which replaces the Pyramid Attention Network (PANet) in the original network neck with a bidirectional feature pyramid network (BiFPN) for effective handling of varied feature sizes. the original YOLOv5s framework has a high down sampling ratio that leads to difficulties in learning the features of small objects. the devised model has deployed an additional output head to prioritise small object detection. Based on ablation and benchmarking experiments, the original network's loss function is substituted with the SIoU Loss Function, which not only aggregates regression metrics between predicted and ground truth bounding boxes but also identifies their mismatched orientations, hence enhancing the model's detection accuracy of overlapping targets. Experiments conducted on the COCO128 and WiderFace benchmarks validate the superiority of the proposed model, resulting significant mAP accuracy improvement. © 2024 Interfaces and Human Computer Interaction. All Rights Reserved.

关键词： Bidirectional Feature Pyramid Face Detection SIoU WiderFace YOLOv5s

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A Survey on Post-Quantum Cryptography for 5G/6G Communications 17

A Survey on Post-Quantum Cryptography for 5G/6G Communicatio...

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17th IEEE international conference on Service Operations and Logistics, and Informatics, SOLI 2023

作者： Zhou, Rongjie Guo, Huaqun Teo, Francis E.C. Bakiras, Spiridon Singapore Institute of Technology & WizVision Pte Ltd. Singapore Singapore Singapore Institute of Technology Singapore Singapore WizVision Pte. Ltd. Singapore Singapore

ISBN: (纸本)9798350396003

As quantum computing capabilities accelerate, together with the discovery of Shor's Algorithm, existing encryption protocols, such as TLS (Transport Layer Security) and IPsec (Internet Protocol Security), face the risk of becoming obsolete. this vulnerability poses a significant challenge for 5G and 6G mobile networks, which rely on these protocols for data transmission. this survey explores the potential of Post-Quantum Cryptography (PQC) algorithms as viable alternatives for securing mobile communications. through a comprehensive review of NIST-shortlisted PQC algorithms, this paper examines their relevance, efficiency, and adaptability in 5G and 6G architectures. Case studies, including real-world implementations by Amazon Web Services (AWS) and IBM, illustrate the growing acceptance and practical applicability of these quantum-resistant algorithms. © 2023 IEEE.

关键词： 5G mobile communication systems

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4th international conference on Advanced Informatics for computing Research, ICAICR 2020

4th International Conference on Advanced Informatics for Com...

引用

4th international conference on Advanced Informatics for computing Research, ICAICR 2020

ISBN: (纸本)9789811636523

the proceedings contain 23 papers. the special focus in this conference is on Advanced Informatics for computing Research. the topics include: Internet of things in Healthcare: A Review;inter-Vehicular Communication for Intelligent Collision Avoidance Using Machine Learning: An Overview;wireless Sensor Networks Source Location Privacy Preservation Mechanism;a Study on Secure Data Aggregation and Routing for Wireless Sensor Networks;comparative Analysis of Internet of things (IoT) Security Models;study of Energy Efficient Routing Protocols for Adhoc-Wireless Sensor Networks;ioT Based Smart Transport Management System;vehicular Ad Hoc Network: Routing Protocols;a Reluctant Checksum Scheme Based on the Inherent Characteristic of Wireless Networks;Optimised Multi Energy System (OMES) for Electric Vehicle;anomaly Based Detection of Cross Site Scripting Attack in Web Applications Using Gradient Boosting Classifier;crypto Key Protection Generated from Images and Chaotic Logistic Maps;an Effıcıent Fault Management in Communication Networks Usıng On Demand Link State Algorıthm;application and Analysis of Phishing Website Detection in Machine Learning and Neural Networks;modeling of Microstrip Patch Antenna Using Artificial Neural Network algorithms;Smart PDM: A Novel Smart Meter for Design Measurement and Data Collection for Smart Grid;GSM Based Remote Distribution Transformer Condition Monitoring System;Performance Analysis and Classification of Node Capture Attack Models in WSN;an Efficient IoT Based Framework for Missed Call Messaging Services for Electricity Consumers;A Divide and Conquer Based Data Aggregation Technique for WBANs;open Research Challenges and Blockchain Based Security Solution for 5G Enabled IoT.

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Amplifying Object Tracking Performance on Edge Devices 5

Amplifying Object Tracking Performance on Edge Devices

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5th IEEE international conference on Cognitive Machine Intelligence, CogMI 2023

作者： Ganesh, Sanjana Vijay Wu, Yanzhao Liu, Gaowen Kompella, Ramana Liu, Ling Georgia Institute of Technology AtlantaGA United States Florida International University MiamiFL United States Cisco Systems Inc. San JoseCA United States

ISBN: (纸本)9798350323832

Object tracking is an important functionality of edge video analytic systems and services. Multi-object tracking (MOT) detects the moving objects and tracks their locations frame by frame as real scenes are being captured into a video. However, it is well known that real time object tracking at the edge poses critical technical challenges, especially with edge devices of heterogeneous computing resources. this paper examines the performance issues and edge-specific optimization opportunities for object tracking. We will show that even the well trained and optimized MOT model may still suffer from random frame dropping problems when edge devices have insufficient computation resources. We present several edge specific performance optimization strategies, collectively coined as EMO, to speed up the real time object tracking, ranging from window-based optimization to similarity based optimization. Extensive experiments on popular MOT benchmarks demonstrate that our EMO approach is competitive with respect to the representative methods for on-device object tracking techniques in terms of run-time performance and tracking accuracy. EMO is released on Github at https://***/git-disl/EMO. © 2023 IEEE.

关键词： Object detection

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4th international conference on Advanced Informatics for computing Research, ICAICR 2020

4th International Conference on Advanced Informatics for Com...

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4th international conference on Advanced Informatics for computing Research, ICAICR 2020

ISBN: (纸本)9789811636592

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A method for improving data processing efficiency of operators based on 5G edge computing technology 5

A method for improving data processing efficiency of operato...

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5th international conference on Computer Communication and Network Security, CCNS 2024

作者： Zheng, Min Chao, Yuling Mei, Hong Song, Guolin Guo, Mingjing Wang, Xinbo Li, Yuan Xu, Shougang Liu, Yajun Gao, Yaofei Kang, Zhiwen Ding, Haolun Chen, Lu Wang, Kang Hou, Jinfeng China Mobile Communications Group Beijing100032 China China Mobile Design Institute Beijing100080 China

ISBN: (纸本)9781510681842

In the context of the continuous development of information technology, the 5G era has ushered in a new intuation, the rapid development of mobile Internet and the continuous richness of business types, the data generated by users in the process of using various types of business has multiplied, the emergence of massive data and the change of data structure, It brings great challenges to operators' data management, data collection, data statistical analysis, data processing and data operation and maintenance. How to deal with these data effectively and efficiently has become the key to operators' improvement of operational efficiency and service quality. Edge computing, as a new computing mode, assigns computing tasks to the edge of the network, making data processing and analysis more real-time and efficient. this paper provides strong technical support for the implementation of edge computing through the characteristics of 5G network, such as high speed, low latency and large connection. It proposes a method to improve the data processing efficiency of operators based on 5G edge computing technology. It adopts theoretical analysis and experiments to carry out comprehensive research, effectively improve the processing of massive data, reduce cost and increase efficiency. And realize the security and efficiency of data, enhance the market competitiveness, and thus improve the user experience. © 2024 SPIE.

关键词： Mobile edge computing

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6th international conference on Applied Informatics, ICAI 2023

6th International Conference on Applied Informatics, ICAI 20...

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6th international conference on Applied Informatics, ICAI 2023

ISBN: (纸本)9783031468124

the proceedings contain 30 papers. the special focus in this conference is on Applied Informatics. the topics include: A Bio-Inspired-Based Salp Swarm Algorithm Enabled with Deep Learning for Alzheimer’s Classification;a Scientometric Analysis of Virtual Tourism Technology Use in the Tourism Industry;a Tool to Predict Payment Default in Financial Institutions;prediction Value of a Real Estate in the City of Quito Post Pandemic;simulation Model to Assess Household Water Saving Devices in Bogota City;towards Reliable App Marketplaces: Machine Learning-Based Detection of Fraudulent Reviews;challenges to Use Role Playing in Software Engineering Education: A Rapid Review;team Productivity Factors in Agile Software Development: An Exploratory Survey with Practitioners;work-Life Interference on Employee Well-Being and Productivity. A Proof-of-Concept for Rapid and Continuous Population Analysis Using Evalu@;enhancing Face Anti-spoofing systems through Synthetic Image Generation;Comparative Analysis of Spatial and Environmental Data in Informal Settlements, from Point Clouds and RPAS Images;Prospects of UAVs in Agricultural Mapping;gross Motor Skills Development in Children with and Without Disabilities: A therapist’s Support System Based on Deep Learning and Adaboost Classifiers;TP53 Genetic Testing and Personalized Nutrition Service;changes in the Adaptive Capacity of Livelihood Vulnerability to Climate Change in Ecuador’s Tropical Commodity Crops: Banana and Cocoa;context and Characteristics of Software Related to Ecuadorian Scientific Production: A Bibliometric and Content Analysis Study;exploring the Potential of Genetic algorithms for Optimizing Academic Schedules at the School of Mechatronic Engineering: Preliminary Results;machine Masquerades a Poet: Using Unsupervised T5 Transformer for Semantic Style Transformation in Poetry Generation;Comparative Quality Analysis of GPT-Based Multiple Choice Question Generation.

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proceedings - 2023 international conference on computing, Networking, Telecommunications and Engineering Sciences Applications, CoNTESA 2023

Proceedings - 2023 International Conference on Computing, Ne...

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4th international conference on computing, Networking, Telecommunications and Engineering Sciences Applications, CoNTESA 2023

ISBN: (纸本)9798350382754

the proceedings contain 15 papers. the topics discussed include: application of LSTM network to predict InSAR-derived time-series deformation in the reclamation area of Busan, South Korea;generative AI: impactful considerations to responsible data practices in business execution;towards swarm intelligence ontology for formal description of metaheuristics algorithms;generating CGAN-based synthetic dermoscopy images and classifying with deep lightweight CNN;DesignsystemsJS - building a design systems API for aiding standardization and AI integration;availability of NETCONF training and documentation;teaching information literacy and critical thinking skills in ChatGPT time;six sigma: designing of a new product for an ISO 9001 certified company;and a comparative review of GPT-4’s applications in medicine and high decision making.

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Optimizing Real-Time Medical Diagnostics with Adaptive AI Algorithm 7

Optimizing Real-Time Medical Diagnostics with Adaptive AI Al...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Ahmed, Zahid Pandey, Abha Sharma, Ashish Tandon, Anisha Teena Kumar, Sujeet Vivekananda Global University Department of Computer Science and Application Rajasthan Jaipur India Jagannath International Management School Vasant Kunj New Delhi India Jiet Department Of Technology Rajasthan Jodhpur India Uttar Pradesh Sector 110 Noida India Gl Bajaj Institute of Management Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

Early diagnosis and treatment of disease are key in concentrations done for medical diagnostics. Technology is moving fast and with that, there are always new methods for diagnosing patients being created, the improvement of accuracy and speed even in medical diagnostics. Unfortunately, the abundance of data collected by these tools makes it prohibitively difficult to accurately interpret and analyse real-time results. this is where Adaptive AI (Artificial Intelligence) algorithms are useful. this flexibility of adaptive AI systems makes them well-suited to the real-time medical diagnostics that are required. In addition, through the use of machine learning techniques these algorithms are capable to process and analyse large scale datasets at a speed that is orders faster than any human expert. they can also detect patterns and feature outliers within the data that traditional diagnostic methods may overlook. Using adaptive AI algorithms is anticipated to optimize the diagnostic process of medical conditions and increase accuracy thus allowing faster diagnosis. It could mean finding diseases earlier, and therefore treating they better as well as costing healthcare less. these algorithms are able to constantly learn from new data which allow them not only to improve with time, but also adapt in order effectively combat changing disease patterns. © 2024 IEEE.

关键词： Adversarial machine learning

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Weapon Detection Identification and Classification for DCNN and YOLO-V5 Techniques 5

Weapon Detection Identification and Classification for DCNN ...

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5th international conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Ashok Kumar, J. Dhivya, G. Selvaraj, P. Karthikeyan, V. Ganesh Ramesh, S.P. Lakshmi, S. Jeya Saravanakumar, R. Karpagam Academy of Higher Education Department of Computer Applications Tamil Nadu Coimbatore India School of Computer Science and Engineering Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India School of Computing SASTRA Deemed to Be University Thanjavur Tamil Nadu Thirumalaisamudram India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331518097

Over 850,000 people die every year as a direct result of gun violence, yet civilians hold more than 85% of the world's weapons. Detecting weapons via manual surveillance has not been successful. It is critical to automate a system that detects weapons. A large number of convolution neural network CNN-based automated weapon identification systems have shown promising results. these methods are crucial for weapon identification but expensive. these devices produce a large amount of false negatives since monitoring videos are not high enough quality or visible enough to identify weapons. Finding the sweet spot between fast detection and a few false positives and negatives in weapon detection is the main goal of this research. In addition to defining an Area of Interest, the suggested structure uses YOLO. the models use previously processed images with backgrounds that have been blurred using a median filter. Metrics such as F1 scores, accuracy, recall, False Positive, and False Negative will be used to assess the suggested architecture. this investigation shows that YOLO-v5 can quickly identify and has a high recall rate. the deep convolution neural network DCNN required 0.17 ms for each frame, but this one only required 0.010 ms, which would indicate that security and weapon detection have good promise. Starting with a labeled collection of weapon and non-weapon photos is the initial stage in DCNN and YOLOv5 weapon detection. Use YOLOv5's ability to recognize multiple things within frames to train a real-time object localization and identification model. DCNNs categorize weapons and extract complex data effectively. DCNN classifies objects, whereas YOLOv5 detects, bounds, and tracks them. Optimize both models' hyperparameters and apply data augmentation for better performance. Install the integrated system on cutting-edge equipment for real-time weapon identification and classification. © 2024 IEEE.

关键词： Median filters

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