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dataset of Large Gathering Images for Person Identification and Tracking

computers, Materials & Continua 2023年第3期74卷 6065-6080页

作者： Adnan Nadeem Amir Mehmood Kashif Rizwan Muhammad Ashraf Nauman Qadeer Ali Alzahrani Qammer H.Abbasi Fazal Noor Majed Alhaisoni Nadeem Mahmood Faculty of Computer and Information System Islamic University of MadinahMadinah42351Saudi Arabia Department of Computer Science and Information Technology Sir Syed University of Engineering and TechnologyKarachi75300Pakistan Department of Computer Science Federal Urdu University of ArtsScience&TechnologyIslamabad45570Pakistan Department of Physics Federal Urdu University of ArtsScience&TechnologyKarachi75300Pakistan James Watt School of Engineering University of GlasgowGlasgowG128QQUK Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science University of KarachiKarachi75270Pakistan

This paper presents a large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed on the premises of Masjid Al-Nabvi,Madinah,Saudi *** dataset consists of raw and processed images reflecting a highly challenging and unconstraint *** methodology for building the dataset consists of four core phases;that include acquisition of videos,extraction of frames,localization of face regions,and cropping and resizing of detected face *** raw images in the dataset consist of a total of 4613 frames obtained fromvideo *** processed images in the dataset consist of the face regions of 250 persons extracted from raw data images to ensure the authenticity of the presented *** dataset further consists of 8 images corresponding to each of the 250 subjects(persons)for a total of 2000 *** portrays a highly unconstrained and challenging environment with human faces of varying sizes and pixel quality(resolution).Since the face regions in video sequences are severely degraded due to various unavoidable factors,it can be used as a benchmark to test and evaluate face detection and recognition algorithms for research *** have also gathered and displayed records of the presence of subjects who appear in presented frames;in a temporal *** can also be used as a temporal benchmark for tracking,finding persons,activity monitoring,and crowd counting in large crowd scenarios.

关键词： Large crowd gatherings a dataset of large crowd images highly uncontrolled environment tracking missing persons face recognition activity monitoring

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Software Testing Model by Measuring the Level of Accuracy Fault Output Using Neural Network Algorithm 7

Software Testing Model by Measuring the Level of Accuracy Fa...

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7th IEEE International Conference on Information Technology and Digital Applications, ICITDA 2022

作者： Zulkifli Gaol, Ford Lumban Trisetyarso, Agung Budiharto, Widodo Binus University Binus Graduate Program - Computer Science Computer Science Department Jakarta Indonesia Binus University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

The testing stage is essential in software development because it determines the quality level, which is indicated by minimal errors. Meanwhile, the error that is discovered by the tester is called a fault. Therefore, this research aims to create a Model-Based Testing (MBT) using the equivalence partitioning technique in the black-box method. The model is expected to log software errors such as Access, performance, initialization, a data structure or external database, incorrect or missing functions, to identify, categorize, and fix them methodically and appropriately. Based on the discoveries, the errors form a dataset that measures the accuracy of the fault output using a neural network algorithm. The result showed the formation of MBT can be developed using the equivalence partitioning technique by measuring the fault output accuracy level using a neural network algorithm, where the average accuracy rate is 80%. © 2022 IEEE.

关键词： Errors

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GAN-Alz: Synthetic data Generation for Multiclass Alzheimer's Classification

GAN-Alz: Synthetic Data Generation for Multiclass Alzheimer'...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Nair, Rekha R Babu, Tina Ramasamy, Gayatri Singh, Tripty Yuan, Xiaohui Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwavidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India University of North Texas Denton Department of Computer Science and Engineering TX United States

ISBN: (纸本)9798350385199

Accurate detection of skin cancer, particularly melanoma, is crucial for effective treatment and patient survival. This study explores the use of Convolutional Neural Networks (CNNs) enhanced by Generative Adversarial Networks (GANs) for skin cancer detection. CNNs have shown remarkable performance in image classification tasks but often require extensive labeled datasets, which are scarce in medical domains. To address this, GANs are employed to generate synthetic skin lesion images, augmenting the dataset and improving the model's generalization capability. The proposed approach integrates a pre-trained CNN architecture fine-tuned with a combination of real and GANgenerated images, enhancing the network's ability to distinguish between benign and malignant lesions. Experimental results demonstrate that the inclusion of GAN-augmented data improves the model's accuracy by 7%, achieving an overall accuracy of 94%, with precision and recall rates of 92% and 93%, respectively. These findings suggest that the GAN-augmented CNN model offers a promising solution for automated skin cancer screening © 2024 IEEE.

关键词： Generative adversarial networks

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SCTrans: Multi-scale scRNA-seq Sub-vector Completion Transformer for Gene-selective Cell Type Annotation 33

SCTrans: Multi-scale scRNA-seq Sub-vector Completion Transfo...

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33rd International Joint Conference on Artificial Intelligence, IJCAI 2024

作者： Lin, Lu Xue, Wen Wei, Xindian Shen, Wenjun Liu, Cheng Wu, Si Wong, Hau San School of Computer Science and Engineering South China University of Technology China Department of Computer Science City University of Hong Kong Hong Kong Department of Bioinformatics Shantou University Medical College China Department of Computer Science Shantou University China

ISBN: (纸本)9781956792041

Cell type annotation is pivotal to single-cell RNA sequencing data (scRNA-seq)-based biological and medical analysis, e.g., identifying biomarkers, exploring cellular heterogeneity, and understanding disease mechanisms. The previous annotation methods typically learn a nonlinear mapping to infer cell type from gene expression vectors, and thus fall short in discovering and associating salient genes with specific cell types. To address this issue, we propose a multi-scale scRNA-seq Sub-vector Completion Transformer, and our model is referred to as SCTrans. Considering that the expressiveness of gene sub-vectors is richer than that of individual genes, we perform multi-scale partitioning on gene vectors followed by masked sub-vector completion, conditioned on unmasked ones. Toward this end, the multi-scale sub-vectors are tokenized, and the intrinsic contextual relationships are modeled via self-attention computation and conditional contrastive regularization imposed on an encoding transformer. By performing mutual learning between the encoder and an additional lightweight counterpart, the salient tokens can be distinguished from the others. As a result, we can perform gene-selective cell type annotation, which contributes to our superior performance over state-of-the-art annotation methods. © 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.

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An Efficient Approach to data Clustering Using the K-Means Algorithm in Big data Analytics 3

An Efficient Approach to Data Clustering Using the K-Means A...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Vani, S. Sandhiya Ramya, P. Nagendar, Y. Prakash, P. Arun Chanti, Malladi Atheeswaran, Athiraja Rajalakshmi Engineering College Department Of Computer Science and Engineering Thandalam Chennai India Anna University University College of Engineering Department of Computer Science and Engineering Dindigul India Sr University Department of School of Computer Science and Artificial Intelligence Warangal Telangana Hyderabad India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering Maisammaguda Telangana Hyderabad India Telangana Hyderabad India

ISBN: (纸本)9798331515911

The large-scale, high-dimensionality of datasets poses major obstacles to effective and precise data clustering in the field of big data analytics. This work focuses on a well-known technique for dividing huge datasets into informative clusters: the K-Means algorithm. This approach to data clustering is efficient. However, because of scalability and computational complexity problems, K-Means is not as effective when managing large amounts of data. In order to reduce processing cost and improve clustering accuracy, this work provides an improved version of the K-Means algorithm that blends dimensionality reduction approaches and efficient initialization methods. Principal Component Analysis (PCA) is used as a preprocessing step in the methodology to reduce the dimensionality of huge datasets. An optimized K-Means algorithm with improved centroid initialization is then applied. These changes lead to shorter iteration durations and higher-quality clusters. Comparing the results to standard K-Means, experiments on many big data benchmarks showed a 15% gain in cluster accuracy and a 25% reduction in execution time. To sum up, the incorporation of optimized initialization techniques and dimensionality reduction greatly improves the scalability and performance of K-Means in big data analytics, with useful implications for sectors that depend on effective data analysis. © 2025 IEEE.

关键词： data accuracy

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Mobile Application as Community Social Media 6

Mobile Application as Community Social Media

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Pahlevi, Teuku Ryan Haryanto, Johannes Adha, Rahmat Alfian, Bayu Warnars, Harco Leslie Hendric Spits Widiono, Rahayu Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Binus Online Learning Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350398496

Today's society is different from the past, where the speed of the internet is getting more sophisticated, and the existing gadget technology is getting faster, and of course, this is also changing the lifestyle of today's people, which of course almost half of their daily life is spent in front of gadgets. On the one hand, this is undoubtedly very good and supports humans in carrying out their daily life activities, but on the other hand, this raises new problems where humans start to lack time to communicate and socialize with each other and often making them stressed and anxious because they are already tired and do not know how to socialize anymore. This paper begins with a model and implementation of a very simple mobile application that behaves as a social media inspired by *** as a social media for academics. The design of this mobile application is done by using use case diagrams to describe existing processes and class diagrams to describe class relationships. © 2023 IEEE.

关键词： Social networking (online)

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Ultra-Wideband Technology for Healthcare Monitoring: A Machine Learning Approach

Ultra-Wideband Technology for Healthcare Monitoring: A Machi...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Raj, Raunak Awasthi, Avinash Arora, Komal Singh, Shreshth Miranka, Hemant Rakhra, Manik School of Computer Science Engineering Lovely Professional Univeristy Punjab India Malviya National Institute of Technology Department of Computer Science and Engineering Rajasthan India Visvesaraya National Institute of Technology Department of Computer Science and Engineering Maharashtra India Lakshmi Niwas Mittal Institute of Information Technology Department of Computer Science and Engineering Rajasthan India

ISBN: (纸本)9798331542344

Ultra-Wideband (UWB) technology has emerged as an emerging solution for a variety of applications in clinical establishments., offering significant advantages such as precise localization capabilities., high data rates., and low power consumption. The paper systematically examines the utilization of UWB technology within clinical settings., focusing on its applications in health care monitoring., medical imaging., indoor positioning., and wireless communication. By leveraging a comprehensive dataset from the UCI Machine Learning Repository., the authors provide detailed empirical insights into the performance and effectiveness of VWB-based systems in enhancing healthcare outcomes. The authors also identify and discuss specific challenges., such as integration complexities and regulatory hurdles., as well as opportunities., including potential advancements in patient care and health care delivery through the adoption of UWB technology. Additionally., the authors have built a model using a Random Forest classifier and achieved an accuracy of 91.8%. © 2024 IEEE.

关键词： Electronic health record

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Sentiment analysis in social internet of things using contextual representations and dilated convolution neural network

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Neural Computing and Applications 2024年第20期36卷 12357-12370页

作者： Abid, Fazeel Rasheed, Jawad Hamdi, Mohammed Alshahrani, Hani Al Reshan, Mana Saleh Shaikh, Asadullah Department of Information Systems Dr. Hassan Murad School of Management University of Management and Technology Lahore Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul34303 Turkey Department of Software Engineering Istanbul Nisantasi University Istanbul34394 Turkey Department Computer Science College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia Department Information Systems College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia

The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social media sentiment analysis, significant insights can produce efficient and intelligent applications. Neural networks such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) have been considered widely in many text classification tasks. However, RNNs are computationally expensive and require complex training to capture contextual information and long-term dependencies. Similarly, traditional CNNs must stack multiple convolutional layers, requiring massive computations and additional parameters. To address these problems, this work initialized the novel architecture, in which contextual representations (CRs) based on the textual framework are proposed at the initial step. In CRs, state-of-the-art word representation models, such as GloVe (global vectors) and FastText (subword information), collectively produce word representations upon the input sequence using a weight mechanism. Secondly, a unique way is introduced: a three-parallel layered dilated convolutional network with global mean pooling. The experimental results show that the proposed methods when compared with baseline methods, the dilation in CNNs following CRs significantly increases the accuracy from 72.45 to 98.98% and reduces computational resources. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Sentiment analysis

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A review on the energy efficient resource allocation schemes with improved quality of services in green cloud computing

A review on the energy efficient resource allocation schemes...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Fiaz, A.S. Syed Sonai, Veeramani Computer Science and Engineering Amrita Viswa Vidhyapeetham School of Computing Chennai India Department of Computer Science and Engineering School of Engineering Shiv Nadar University Chennai India

ISBN: (纸本)9781032669663

The vast changes in the computing utilization have created a need with the improvised resource availability and reliability. The virtualization of servers and data centers to increase their efficiency has transformed how enormous data is stored and handled. Cloud computing has created a supreme and impressive approach to virtualize servers and data centers and to make them energy efficient in order to leverage on diverse IT resources. Massive power and energy consumption by IT resources leads to an energy crisis and a change in the planet’s climate. But because these IT resources use so much energy and power, they end up being a significant source of CO2 emissions. Due to this, green cloud computing is now required in order to make IT resources cost-and energy-effective. It is necessary to do a thorough analysis of the cloud’s power efficiency in order to arrive at the best options for green cloud computing. Green Cloud Computing is well known to be a broad domain and a popular field for research because of the increasing rise of enormous data storage and computational demand. It can generate solutions that not only make IT resources energy efficient but also reduce operational expenses. This review’s objective is to highlight some of the best practices for achieving green cloud computing, including workload clustering, Auction-Based Resource Optimization techniques, Hardware and Software controlled solutions, Minimize cooling requirements scheduling, clustering, virtualization and other energy-saving techniques. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Carbon footprint

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Recovering Packet Collisions below the Noise Floor in Multi-gateway LoRa Networks 42

Recovering Packet Collisions below the Noise Floor in Multi-...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Mao, Wenliang Zhao, Zhiwei Zheng, Kaiwen Min, Geyong University of Electronic Science and Technology of China School of Computer Science and Engineering China University of Exeter Department of Computer Science United Kingdom

ISBN: (纸本)9798350334142

LoRa has been widely applied in various vertical areas such as smart grids, smart cities, etc. Packet collisions caused by concurrent transmissions have become one of the major limitations of LoRa networks due to the ALOHA MAC protocol and dense deployment. The existing studies on packet recovery usually assume that the collided packet signals are above the noise floor. However, considering the large-scale deployment and low-power nature of LoRa communications, many collided packets are below the noise floor. Consequently, the existing schemes will suffer from significant performance degradation in practical LoRa networks. To address this issue, we propose CPR, a Cooperative Packet Recovery mechanism aiming at recovering the collided packets below the noise floor. CPR firstly employs the incoherence of signals and noises at multiple gateways to detect and extract the frequency features of the collided packets hidden in the noise. Then, CPR adopts a novel gateway selection strategy to select the most appropriate gateways based on their packet power domain features extracted from collision detection, such that the interference can be eliminated and the original packets can be recovered. Extensive experimental results demonstrate that CPR can significantly increase the symbol recovery ratio in low-SNR scenarios. © 2023 IEEE.

关键词： Recovery

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