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1st International Conference on Artificial Intelligence and Data Analytics, CAIDA 2021

作者： Habib, Shaban Hussain, Altaf Islam, Muhammad Khan, Sheroz Albattah, Waleed Qassim University College of Computer Dept. of Information Technology Buraydah Saudi Arabia Islamia College University Dept. of Information Technology Dept. of Computer Science Peshawar Pakistan Onaizah College of Engineering and Information Technology Dept. of Electrical Engineering Onaizah Saudi Arabia

ISBN: (纸本)9780738131771

each year more than two million Muslims from around the world come to perform Hajj in Makah. It is considered the world's largest recorded human gathering during any worshiping event. Safety makes one of the main concerns with regards to managing such large crowds for ensuring that stampedes and other similar overcrowding accidents are avoided. For this purpose, 5000 cameras are installed around the holy sites for monitoring purposes. Due to the continuous nature of surveillance systems in generating video data, it is almost impossible to efficiently and accurately monitor an event of this size in real-time. Analyzing such huge data has required a lot of human resources. Therefore, there is a great need for advanced intelligent techniques to automatically count and manage such large crowds. In order to create an advanced intelligent system that contributes to crowds counting and managing through the surveillance system. In this paper, we propose an accurate computer vision-based approach to crowd management using Convolutional Neural Network (CNN). Our proposed framework is three folds. In the first fold, our own dataset for pilgrim detection is created, covering both sparse and dense crowds. In the second fold, a Faster-RCNN object detection model is trained to detect and count the number of pilgrims. In the third fold, utilizing the resources efficiently the surveillance system has used frame differencing technique to differentiate between motion and static video frames. Only in the case of some sort of motion, we will pass these frames to the pilgrims counting model to tell us about the number of pilgrims in the video. When the number of pilgrims counting is exceeded from the pre-defined threshold the system will automatically trigger the alarm pointing the camera to the location to inform the concerned authorities to take action appropriate measures. Along with that, only the dense crowd will be monitored by law enforcement and for better management. Our experim

关键词： Object detection

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Constrained clustering using Gaussian processes 28

Constrained clustering using Gaussian processes

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28th European Signal Processing Conference, EUSIPCO 2020

作者： Traganitis, Panagiotis A. Giannakis, Georgios B. Dept. of Electrical and Computer Engineering and Digital Technology Center University of Minnesota United States

ISBN: (纸本)9789082797053

Constrained clustering is an important machine learning, signal processing and data mining tool, for discovering clusters in data, in the presence of additional domain information. The present work introduces a probabilistic scheme for constrained clustering based on the popular Gaussian Process framework. The proposed scheme accommodates pairwise, must- and cannot-link constraints between data, does not require hyperparameter tuning, and enables assessment of the reliability of obtained results. Preliminary results on real data showcase the potential of the proposed approach. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Signal processing

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An Extensive Review of Intelligent Deep Learning Based Anomaly Detection and Classification of 3D MRI Brain Imaging Data

An Extensive Review of Intelligent Deep Learning Based Anoma...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： J.Hima Bindu M. Uma Devi Dept. of Computer Science and Engineering Vignan's Foundation for Science Technology& Research (Deemed to be University) Guntur Andhra Pradesh

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

MRI - Magnetic Resonance Imaging is one of the frequently employed imaging modalities for brain anomaly detection. MRI produces a massive quantity of data, which includes a consecutive set of scans taken at several time instants. Since the existence of brain anomalies that exists examined on every MR sequence, manual brain anomaly detection necessitates anatomical knowledge, costly, laborious, and inaccurate due to human error. Automatic brain anomaly segmentation from 3D Magnetic Resonance Image (MRI) is essential to perform proper diagnosis, monitoring, and treatment planning of the disease. However, owing to the structural difficulties such as hazy boundaries with uneven shapes, precise 3D brain tumor demarcation is interesting. With the recent developments of Deep Learning (DL) models, this research study reviews the recent brain anomaly detection and classification models based on DL approach with 3D MRI data. The existing techniques related to brain tumor segmentation, classification, and validations are reviewed. Every reviewed technique is investigated based on the aim, underlying technique used, dataset, and evaluation parameters. Besides, a comparison table is provided by summarizing the reviewed techniques under several aspects. Finally, a quick analysis of the evaluated techniques' results is conducted to gauge their effectiveness.

关键词： Deep learning Solid modeling Three-dimensional displays Reviews Shape Magnetic resonance imaging Brain modeling

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Transformer-Based Ensemble Model for Binary and Multiclass Oral Cancer Segmentation

Transformer-Based Ensemble Model for Binary and Multiclass O...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Amira Hossain Anamul Sakib Al Shahriar Uddin Khondakar Pranta Jesika Debnath Md Tanvir Rahman Tarafder Sazzadul Islam Shamim Forhad Md. Redwan Ahmed Rezaul Haque Shafiur Rahman Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Business Administration International American University CA USA Dept. of Computer Science Wright State University Dayton OH USA Dept. of Information Technology Westcliff University Irvine California Dept. of Computer Science and Engineering BRAC University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Early and accurate segmentation of oral cancer is essential for timely diagnosis and treatment. Traditional methods like visual inspections and biopsies are often subjective and costly, which can hinder early detection. To improve segmentation accuracy for binary and multiclass classification, we propose a transformer-based ensemble model that combines Vision Transformer (ViT), Data-efficient Image Transformer (DeiT), Swin Transformer, and BEiT. This ensemble utilizes self-attention mechanisms for better feature extraction and spatial representation. Our study employs two datasets: the MOD dataset (463 images of oral diseases) and a histopathological dataset (1,224 images of oral squamous cell carcinoma and normal epithelium). We applied extensive preprocessing and augmentation techniques, such as grayscale conversion, binary thresholding, and Contrast Limited Adaptive Histogram Equalization (CLAHE), to enhance image quality and model generalization. The performance evaluation showed that our ensemble model outperformed individual architectures, achieving an Intersection over Union (IoU) of 0.9601 and a Dice Coefficient of 0.9598 for binary segmentation, and IoU of 0.9587 and Dice Coefficient of 0.9575 for multiclass segmentation. A comparative analysis with state-of-the-art models confirmed the effectiveness of our approach. These results demonstrate the potential of transformer-based ensemble learning for oral cancer diagnosis, presenting a scalable tool for clinical applications. Future work will focus on expanding dataset diversity, optimizing computational efficiency, and integrating real-time inference for improved usability in healthcare.

关键词： Image segmentation computer vision Adaptation models Visualization Accuracy Computational modeling Transformers Cancer detection Real-time systems Cancer

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Kernel-based multi-step predictors for data-driven analysis and control of nonlinear systems through the velocity form

arXiv

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arXiv 2024年

作者： Verhoek, Chris Tóth, Roland The Control Systems group The Dept. of Electrical Engineering The Eindhoven University of Technology Netherlands The Institute for Computer Science and Control Budapest Hungary

We propose kernel-based approaches for the construction of a single-step and multi-step predictor of the velocity form of nonlinear (NL) systems, which describes the time-difference dynamics of the corresponding NL system and admits a highly structured representation. The predictors in turn allow to formulate completely data-driven representations of the velocity form. The kernel-based formulation that we derive, inherently respects the structured quasi-linear and specific time-dependent relationship of the velocity form. This results in an efficient multi-step predictor for the velocity form and hence for nonlinear systems. Moreover, by using the velocity form, our methods open the door for data-driven behavioral analysis and control of nonlinear systems with global stability and performance guarantees. © 2024, CC BY-NC-SA.

关键词： Nonlinear simulations

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Security and Privacy Concerns in 'Communication Systems' Blockchain technology and the Internet of Things 3

Security and Privacy Concerns in 'Communication Systems' Blo...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Prakash, R.Om Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Noida Instue Engineering and Technology U.P Greator Noida201306 India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

This article examines the security and privacy concerns associated with the integration of blockchain technology and the Internet of Things (IoT) in communication systems. As the IoT continues to grow and evolve, there is an increasing need to ensure the security and privacy of the vast amounts of data generated and exchanged between connected devices. Blockchain technology offers promising solutions for enhancing security and privacy in communication systems;however, it also presents its own set of challenges and vulnerabilities. This article explores the existing literature on the topic and analyzes the materials and methods used in previous studies. The results and discussions delve into the key security and privacy concerns in communication systems with blockchain and IoT, including data integrity, authentication, and confidentiality, scalability, and consensus mechanisms. Finally, the article concludes with recommendations and future directions for addressing these concerns and advancing the security and privacy of communication systems in the context of blockchain technology and the IoT. © 2023 IEEE.

关键词： Internet of things

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A Real Time ML Technique for the Manufacturing and Image Classification System

A Real Time ML Technique for the Manufacturing and Image Cla...

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Advances in Modern Age Technologies for Health and engineering Science (AMATHE), International Conference on

作者： Thoutireddy Shilpa Nagendar Yamsani Bura Vijay Kumar Shaik Mabasha Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Medak Telangana School of Computer Science and Artificial Intelligence SR UNIVERSITY Warangal School of Computer Science and Artificial Intelligence SR University Warangal India Dept. of CSE (AIML & IoT) VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Manufacturing analysis at many facilities is of fundamental significance nowadays and is increasing in the present setting of Big Data in Industry. The chemometrics, machine learning and statistics are the areas were projected to offer methods can successfully manage numerous industrial data features. The undertaking of picture based item characterization is investigated in this examination. This is a supervised learning issue when the info is a picture, and the result is an extraordinary name relegated to the picture from a limited arrangement of marks which relate to the item classes accessible. This is a major challenge for the industry and recent deep learning technologies have proven beneficial in enhancing the accuracy of the image classification and in offering cutting edge outcomes. In this work, we hence utilize the capacity of profound deep neural networks(DNNs) to gain highlights from photographs consequently and test their exhibition for the expectation of the pellet structure in a certifiable modern climate. Transfer learning is used to speed the training of DNN, and previously a network is established for single process was designed to forecast the pellet form. In addition, the benefit of adoption of DNN in comparison to current classifiers is also examined in other less sophisticated techniques, such as partially less square discrimination analysis (PLS-DA) and random forests (RF).

关键词： Radio frequency Training Deep learning Filters Transfer learning Artificial neural networks Feature extraction

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Methods, Models and Hardware-Software Complex of Distributed Monitoring Based on Iot and Blockchain technology

Methods, Models and Hardware-Software Complex of Distributed...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Dmitry V. Goncharov Vyacheslav I. Fedorov Oleg O. Ivashchuk Dept of Information and Robotic Systems Institute of Engineering Technology and Natural Sciences Belgorod State National Research University Belgorod Russia Dept of Computer science Caspian University of Technology and Engineering named after Sh. Yessenov Aktau Kazakhstan

This research work explores the possibility of simultaneous use of Industry 4.0 technologies, namely the Internet of Things (IoT) and distributed systems (Blockchain) for collecting, processing, storing and confirming the adequacy of environmental data characterizing the state of sources of anthropogenic impact significantly distributed throughout the study area. It also describes the dynamics of the quality of natural environments during the dispersion and accumulation of pollutants. This approach is especially important for the implementation of market relations in the field of carbon quotas giving the possibility of redistributing the area of responsibility of production enterprises. The authors have developed a subsystem of an automated environmental safety management system, including a hardware and software complex that, based on Internet of Things technology, allows for the collection and integration of heterogeneous environmental information received from various sensors with its further storage and processing in a cloud system. The proposed subsystem, based on distributed registry technology, ensures openness and accessibility of the environmental data. It suggests decentralization of subsequent management decisions, so that it includes a specialized digital platform developed by the authors. The corresponding models and algorithms are presented in the research work, and their software implementation is developed.

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Video Analytics-based Ingredient Detection for Smart Chef using YOLOv3 and ImageAI 6

Video Analytics-based Ingredient Detection for Smart Chef us...

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6th International Conference on Recent Trends on Electronics, Information, Communication and technology, RTEICT 2021

作者： Aparna, R. Chaudhari, Shilpa Singh, Kapsha Roshan Mohammad, Fayaz Ahmed Padmannavar, Manish Charishma, Varra M S Ramaiah Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665435598

With the boom in internet usage every aspect of our daily lives has experienced a drastic change over a period of time. Searching the internet for food recipe videos is one of the most common searches around the world. Search results from internet are shown based on the keywords the video is tagged with and often it is observed that the videos are not much relevant and does not grab user's immediate attention. Our work is concerned with analyzing the objects and contents of videos to find the actual ingredients involved in them. The objective is to provide a list of the objects involved in the video. In this work, we have used YOLOv3 and ImageAI to detect objects from the video files. We consider using the COCO dataset for the analysis. So, the project is concerned about identifying relevant recipe videos conveniently using video analysis. © 2021 IEEE.

关键词： Object detection

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An automated IOT based fire detection & safety control for garments industry in Bangladesh: A-study

An automated IOT based fire detection & safety control for g...

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2021 International Conference on Automation, Control and Mechatronics for Industry 4.0, ACMI 2021

作者： Ahmed, Khandakar Rabbi Alomgir Hossain, Md International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665438438

Detection of Fire can end up being vital as it could mean the distinction between life and passing. A few groups don't see the need to have an identification framework. They simply expect that they could smell the fire and run out on schedule. The normal time for a piece of clothing to torch these days is only 60 seconds. Along these lines, when you smell the fire and attempt to flee, the fire has likely immersed the house. The web of things (IoT) is an arrangement of gadgets associated and open through the web. The "Thing"in IoT could allude to any actual gadget, changing from a toaster oven to an auto. These gadgets can be associated with the web and assist us with controlling or gather information from them. In this paper, we will utilize a wide assortment of sensors to identify the presence of fire and ready its essence to the administrator and fire authorities. It examines exhaustively the capacity of every module and it's anything but an intricate way. It additionally talks about the utilization of IoT innovation according to fire location advances. © 2021 IEEE.

关键词： Fires

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