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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Chandra, Sanskriti Saxena, Shivam Kumar, Santosh Chaube, Mithilesh Kumar Srinivas, K.G. Alsamhi, Saeed Hamood Curry, Edward Saif, Abdu IIIT-Naya Raipur Department of Data Science and Artificial Intelligence Chhattisgarh India IIIT-Naya Raipur Department of Computer Science and Engineering Chhattisgarh India IIIT-Naya Raipur Department of Mathematical Sciences Chhattisgarh India University of Galway Insight Sfi Research Centre for Data Analytics Galway Ireland Faculty of Engineering & Information Technology Taiz Yemen

ISBN: (纸本)9798350321487

Digital face manipulation and classification have recently attracted the attention of academia and industry worldwide. Researchers have developed deep learning and computer vision techniques for detecting face manipulations, and it has become a challenging task to differentiate between authentic and manipulated face images manually. The challenge results in the decline of authenticity in digital media content. In this paper, we propose a framework for the classification of manipulating face images using the EfficientNet learning model. The proposed framework takes four digital facial forgeries: Face-Swap, Face-2-Face, DeepFakes, and neural textures. Multiple manipulation techniques are used to process manipulated faces, such as the Blaze-face tracking method, to determine the locations of the face images and pixel coordinates. The proposed framework is used first to identify the type of face manipulation and then to perform detection of the tampered regions in the face images. The proposed framework provided an automated benchmark that considers all four modification techniques in a realistic situation. The results show that the proposed framework outperforms existing approaches regarding accuracy and efficiency. Furthermore, the proposed framework is suitable for detecting digital face video manipulation in various applications, including forensics and security. © 2023 IEEE.

关键词： Textures

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Artificial Intelligence, VR, AR and Metaverse Technologies for Human Resources Management

arXiv

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arXiv 2024年

作者： Aydın, Ömer Narin, Nida Gökçe Karaarslan, Enis Department of Electrical and Electronics Engineering Faculty of Engineering and Natural Sciences Manisa Celal Bayar University Manisa Turkey Department of Statistics Faculty of Science Mugla Sitki Kocman University Mugla Turkey Department of Computer Engineering Faculty of Engineering Mugla Sitki Kocman University Mugla Turkey

Human Resources (HR) technology solutions encompass software and hardware tools designed to automate HR processes, gather, process, and analyze data, utilize it for strategic decision-making, and execute HR professionals’ tasks while prioritizing security and privacy considerations. As with numerous other domains, Digital Transformation and emerging technologies have commenced integration into HR processes. These technologies are utilized by HR professionals and various stakeholders involved in HR operations. This study evaluates the utilization of Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (VR), and the Metaverse within HR management, focusing on current trends and potential opportunities. A survey was conducted to gauge HR professionals’ perceptions and critiques regarding these technologies. Participants were the HR department officers, academicians who specialized in HR and staff who had courses at diverse levels about HR. The acquired results were subjected to comparative analysis within this article. © 2024, CC BY.

关键词： Augmented reality

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Smart Flood Detection and Communication in Hazardous Zones via MANET-Based IoT and Parallely Distributed Slimmable Neural Networks

Smart Flood Detection and Communication in Hazardous Zones v...

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Sustainable Expert Systems (ICSES), International Conference on

作者： V. Sathya Shuchi Bhadula R. Srinivasan Bhargav H K Bhavani Sankar Panda Amit Barve Department of Computer Science Periyar Maniammai Institute of Science and Technology (Deemed to be University) Vallam Thanjavur Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Veltech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai Tiruvallur Tamil Nadu India Department of Computer Science and Engineering Industry Institute Interaction Cell (IIIC) and Associate Professor Shridevi Institute of Engineering and Technology Tumakuru Karnataka India Department of Computer Science and Engineering Raghu Engineering College Visakhapatnam Andhra Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Technology Parul University Limda Gujarat India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Disasters can be mitigated by an early warning signal and proper communication within the hazardous environment using the MANET technology. However, the exact prediction of disaster situation is needed for the timely disaster management. Hence in addition to MANET technology, deep learning algorithms and Inter of Things (IoT) can also be integrated into disaster detection and communication systems. The presented research developed a novel parallely distributed slimmable neural network with orchard algorithm for the accurate flood disaster prediction. The model processed the dataset containing various measurements such as temperature, humidity, precipitation, air, soil moisture and rainfall level that are collected by the IoT sensor deployed in the various location of the hazardous environment. The gathered data are initially pre-processed using the correlation coefficient min-max normalization approach. Further, the relevant characteristics of the flood disaster from the data are extracted through spike driven transformer process. These refined and dimensionality reduced data are then entered into the proposed framework for the disaster prediction. Here the integrated orchard algorithm increased the disaster monitoring accuracy on the basis of better optimized parameters. The model resulted 95% accuracy, 93% precision, 94% recall and 96% f-score. Therefore, the presented method is an effective prediction mechanism for the disaster management in IoT MANET.

关键词： Temperature sensors Temperature measurement Accuracy Disasters Neural networks Disaster management Prediction algorithms Transformers Mobile ad hoc networks Load modeling

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Automated Speech Recognition System to Detect Babies’ Feelings through Feature Analysis

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computers, Materials & Continua 2022年第11期73卷 4349-4367页

作者： Sana Yasin Umar Draz Tariq Ali Kashaf Shahid Amna Abid Rukhsana Bibi Muhammad Irfan Mohammed A.Huneif Sultan A.Almedhesh Seham M.Alqahtani Alqahtani Abdulwahab Mohammed Jamaan Alzahrani Dhafer Batti Alshehri Alshehri Ali Abdullah Saifur Rahman Faculty of Computing University of OkaraOkara57000Pakistan Department of Computer Science University of SahiwalSahiwal57000PunjabPakistan Computer Science Department COMSATS University IslamabadLahore Campus57000Pakistan Department of Computer Science CUISahiwal CampusSahiwal57000Pakistan Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Pediatric Department Medical CollegeNajran University Saudi ArabiaNajran61441Saudi Arabia Consultant of ORL&HNs and Facial Plastic Surgery College of MedicineNajran University Saudi ArabiaNajran61441Saudi Arabia

Diagnosing a baby’s feelings poses a challenge for both doctors and parents because babies cannot explain their feelings through expression or *** the emotions of babies and their associated expressions during different sensations such as hunger,pain,etc.,is a complicated *** infancy,all communication and feelings are propagated through cryspeech,which is a natural *** clinical methods can be used to diagnose a baby’s diseases,but nonclinical methods of diagnosing a baby’s feelings are *** such,in this study,we aimed to identify babies’feelings and emotions through their cry using a nonclinical *** in the cry sound can be identified using our method and used to assess the baby’s *** considered the frequency of the cries from the energy of the *** feelings represented by the infant’s cry are judged to represent certain sensations expressed by the child using the optimal frequency of the recognition of a real-world audio *** used machine learning and artificial intelligence to distinguish cry tones in real time through feature *** experimental group consisted of 50%each male and female babies,and we determined the relevancy of the results against different *** application produced real-time results after recognizing a child’s cry *** novelty of our work is that we,for the first time,successfully derived the feelings of young children through the cry-speech of the child,showing promise for end-user applications.

关键词： Cry-to-speak machine learning artificial intelligence cry speech detection babies

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Optimal Barcode Representation for NLP Embeddings

Optimal Barcode Representation for NLP Embeddings

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IEEE International Conference on Systems, Man and Cybernetics

作者： Soumen Sinha Azam Asilian Bidgoli Shahryar Rahnamayan Department of Computer Science Mahindra University India Faculty of Science Wilfrid Laurier University Canada Department of Engineering Brock Univeristy Canada

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The utilization of binary representation of the embeddings over real valued features represents a promising avenue, in terms of memory savings and faster operations for various machine learning models. In this research paper, we delve into the exploration of barcode representation for text embeddings derived from BERT, which is optimized using Co-ordinate Search algorithm. These binary embeddings present a compact representation of text, thereby mitigating memory and computational demands, which is especially advantageous in the context of resource-intensive large-scale text processing tasks. In our study, we introduce a novel optimal threshold technique, coupled with the Coordinate Search algorithm to transform continuous BERT embeddings into binary barcodes thereby enabling effective Natural Language Processing while sustaining computational efficiency. The optimal barcode representations have been applied in Natural Language Processing applications, showcasing its innovative potential in revolutionizing text representation. Through an extensive series of experiments on various NLP task encompassing diverse datasets, we comprehensively evaluate our approach, comparing it against a spectrum of thresholding techniques. The binary embeddings achieved by optimal thresholds outperform traditional binarization methods in terms of accuracy. The proposed method for generating a binary representations is versatile, being independent of the model, data and task, making it applicable across various machine learning applications.

关键词： Accuracy Computational modeling Text categorization Machine learning Transforms Natural language processing Vectors Data models Real-time systems Text processing

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BigFeat: Scalable and Interpretable Automated Feature engineering Framework

BigFeat: Scalable and Interpretable Automated Feature Engine...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Eldeeb, Hassan Amashukeli, Shota Elshawi, Radwa University of Tartu Institute of Computer Science Tartu Estonia Tanta University Faculty of Engineering Computers and Automatic Control Department Tanta Egypt

ISBN: (纸本)9781665480451

Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the research area of automated feature engineering has attracted much interest lately, both in academia and industry, the scalability and efficiency of the existing systems and tools are still practically unsatisfactory. This paper presents a scalable and interpretable automated feature engineering framework, BigFeat, that optimizes input features' quality to maximize the predictive performance according to a user-defined metric. BigFeat employs a dynamic feature generation and selection mechanism that constructs a set of expressive features that improve the prediction performance while retaining interpretability. Extensive experiments are conducted, and the results show that BigFeat provides superior performance compared to the state-of-the-art automated feature engineering framework, AutoFeat, on a wide range of datasets. We show that BigFeat significantly improves the F1-Score of 8 classifiers by 4.59%, on average. In addition, the performance improvement achieved by integrating BigFeat into different AutoML frameworks is higher than that achieved by integrating AutoFeat into the same frameworks. Besides, the scalability of BigFeat is confirmed by its linear complexity, parallel design, and execution time which is, on average, 22x faster than AutoFeat. © 2022 IEEE.

关键词： Scalability

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Modelling of the Internet computer Protocol Architecture: The Next Generation Blockchain 4th

Modelling of the Internet Computer Protocol Architecture: ...

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4th International Congress on Blockchain and Applications, BLOCKCHAIN 2022

作者： Li, AoXuan Serena, Luca Zichichi, Mirko Tang, Su-Kit D’Angelo, Gabriele Ferretti, Stefano Faculty of Applied Sciences Macao Polytechnic University China Department of Computer Science and Engineering University of Bologna Bologna Italy Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Department of Pure and Applied Sciences University of Urbino "Carlo Bo" Urbino Italy

ISBN: (纸本)9783031212284

The Internet computer Protocol is described as a third-generation blockchain system that aims to provide secure and scalable distributed systems through blockchains and smart contracts. In this position paper, this innovative architecture is introduced and then discussed in view of its modeling and simulation aspects. In fact, a properly defined digital twin of the Internet computer Protocol could help its design, development, and evaluation in terms of performance and resilience to specific security attacks. To this extent, we propose a multi-level simulation model that follows an agent-based paradigm. The main issues of the modeling and simulation, and the main expected outcomes, are described and discussed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain Distributed Ledger Technology Internet computer Modelling and simulation

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Contrastive Learning at the Relation and Event Level for Rumor Detection 48

Contrastive Learning at the Relation and Event Level for Rum...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xu, Yingrui Hu, Jingyuan Ge, Jingguo Wu, Yulei Li, Tong Li, Hui Chinese Academy of Sciences Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China University of Exeter Faculty of Environment Science and Economy Department of Computer Science United Kingdom

ISBN: (纸本)9781728163277

Existing studies for rumor detection rely heavily on a large number of labeled data to operate in a fully-supervised manner. However, manual data annotation in realistic cases is very expensive and time-consuming. In this paper, we propose a novel self-supervised Relation-Event based Contrastive Learning (RECL) framework for rumor detection to address the above issue. Specifically, we present both the relation-level and event-level augmentation strategies to generate contrastive samples, which capture both the semantics revealed by repost relations and the structural features of rumor events. Moreover, contrastive learning tasks are devised to generate informative graph representations by utilizing self-supervision signals of unlabeled data. Extensive experimental results on real-world datasets demonstrate the effectiveness of our model, especially with limited labeled data. © 2023 IEEE.

关键词： Graph neural networks

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Effects of the Flatness Network Parameter Threshold on the Performance of the Rectified Linear Unit Memristor-Like Activation Function in Deep Learning

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SN computer science 2024年第8期5卷 1-10页

作者： Tchepgoua Mbakop, Marcelle Mboupda Pone, Justin Roger Chassem Kamdem, Priva Kengne, Romanic Research Unit of Condensed Matter Electronics and Signal Processing (RU-MACETS) Department of Physics Faculty of Science University of Dschang P. O. Box 67 Dschang Cameroon Research Unit of Automation and Applied Computer (RU-AIA) Electrical Engineering Department of IUT-FV University of Dschang P.O. Box 134 Bandjoun Cameroon Research Unit of Fundamental Computer Sciences and Artificial Intelligence (RU-FCSAI) Department of Computer Sciences Faculty of Science University of Dschang P. O. Box 67 Dschang Cameroon

In this contribution, we improve of the performance of the Rectified Linear Unit Memristor Like Activation Function with the implication to help training process of CNN without a lot of epochs by computing the best value of the flatness network parameter (p). In this regards the flatness network parameter threshold (p) has been investigated and a good performance of the activation function has been discovered at p = 4.5. We firstly used the MNIST and the CIFAR-10 datasets to trained and test the Alex-Net architecture model of convolutional neural network (CNN) and we showed better performances of Rectified Linear Unit Memristor-like Activation Function compared to those of the literature. We noticed that the performance of Alex-Net also improved and the better performance was recorded when p = 4.5 with 99.50%, 99.25%, 98.81% for training, validation and testing accuracy respectively when using the MNIST. These results open the outcome to reduce the training time of neural networks when this activation function is used. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Activation function Artificial intelligence Deep learning Flatness network parameter threshold Memristive windows function Neural network

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Modified Dragonfly Optimization with Machine Learning Based Arabic Text Recognition

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computers, Materials & Continua 2023年第8期76卷 1537-1554页

作者： Badriyya BAl-onazi Najm Alotaibi Jaber SAlzahrani Hussain Alshahrani Mohamed Ahmed Elfaki Radwa Marzouk Mahmoud Othman Abdelwahed Motwakel Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or *** the number of labels is limited to one,the task becomes single-label text *** Arabic texts include unstructured information also like English texts,and that is understandable for machine learning(ML)techniques,the text is changed and demonstrated by numerical *** recent times,the dominant method for natural language processing(NLP)tasks is recurrent neural network(RNN),in general,long short termmemory(LSTM)and convolutional neural network(CNN).Deep learning(DL)models are currently presented for deriving a massive amount of text deep features to an optimum performance from distinct domains such as text detection,medical image analysis,and so *** paper introduces aModified Dragonfly Optimization with Extreme Learning Machine for Text Representation and Recognition(MDFO-EMTRR)model *** presentedMDFO-EMTRR technique mainly concentrates on the recognition and classification of the Arabic *** achieve this,theMDFO-EMTRRtechnique encompasses data pre-processing to transform the input data into compatible ***,the ELM model is utilized for the representation and recognition of the Arabic *** last,the MDFO algorithm was exploited for optimal tuning of the parameters related to the ELM method and thereby accomplish enhanced classifier *** experimental result analysis of the MDFO-EMTRR system was performed on benchmark datasets and attained maximum accuracy of 99.74%.

关键词： Arabic corpus dragonfly algorithm machine learning text mining extreme learning machine

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