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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Mathur, Abeer Tejpal, Moulik Bhargava, Kshitiz Natarajan, Krishnaraj Singh, Manvendra Vellore Institute of Technology School of Computer Science & Engineering Department of Software Systems Vellore India Vellore Institute of Technology School of Computer Science & Engineering Department of Database Systems Vellore India

ISBN: (纸本)9798350369083

The extensive spread of DeepFake images on the internet has emerged as a significant challenge, with applications ranging from harmless entertainment to harmful acts like blackmail, misinformation, and spreading false propaganda. To tackle this issue, this paper introduces a sophisticated DeepFake detection model designed to identify and mitigate the increase of these deceptive images. The model architecture integrates an ensemble approach, combining the strengths of two pre-trained Convolutional Neural Network (CNN) models - MobileNet and Xception - with a novel CNN architecture, the Advanced CNN (ACNN). This rigorous validation process enabled the model to achieve a high accuracy rate of 97.89% in detecting DeepFakes. The successful implementation of this ensemble CNN approach demonstrates its effectiveness in distinguishing between real and fabricated imagery with high precision. This research makes a substantial contribution to the field of digital image forensics, offering a reliable tool for stakeholders across various sectors to identify and counteract the spread of DeepFake images online. © 2024 IEEE.

关键词： Generative adversarial networks

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Owl Ontology for Liver Diseases Using Protégé & WEB-V-OWL 16

Owl Ontology for Liver Diseases Using Protégé & WEB-V-OWL

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16th International Conference on Open Source Systems and Technologies, ICOSST 2022

作者： Anwaar, Zaeem Khan, Farasat Ullah Naz, Mehwish Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665464772

Science and health care systems are working hand in hand to cater and support each other in the current era. The liver is one of the important body parts that need to work appropriately for a human body. But sometimes the most hazardous reasons for liver problems are infections or diseases like Hepatitis because they remain for an extended time and direct to dangerous difficulties (liver swelling, liver cancer, etc.). Liver hepatitis contains a diversity of different types of viruses: hepatitis A, B, C, and D. This study presents the design and implementation of liver hepatitis ontology. Protege-Owl and WEB-V-OWL is used for implementing and its visualization. The proposed ontology is portable and can be edited for further addition of concepts in the future. Web ontology language is used to implement this proposed ontology. By developing the Hepatitis ontology, both Intelligent health care systems and physicians can share, reason, and exploit this knowledge in different ways. © 2022 IEEE.

关键词： Ontology

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UKF‐MOT:An unscented Kalman filter‐based 3D multi‐object tracker

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 1031-1041页

作者： Meng Liu Jianwei Niu Yu Liu Collective Intelligence&Collaboration Laboratory China North Artificial Intelligence and Innovation Research InstituteBeijingChina China North Vehicle Research Institute BeijingChina State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijingChina State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijingChina School of Computer Science and Engineering Beihang UniversityBeijingChina

Multi‐object tracking in autonomous driving is a non‐linear *** better address the tracking problem,this paper leveraged an unscented Kalman filter to predict the object's *** the association stage,the Mahalanobis distance was employed as an affinity metric,and a Non‐minimum Suppression method was designed for *** the detections fed into the tracker and continuous‘predicting‐matching’steps,the states of each object at different time steps were described as their own continuous *** conducted extensive experiments to evaluate tracking accuracy on three challenging datasets(KITTI,nuScenes and Waymo).The experimental results demon-strated that our method effectively achieved multi‐object tracking with satisfactory ac-curacy and real‐time efficiency.

关键词： autonomous vehicle transportation

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Performance Comparison of Deep and Machine Learning Approaches Toward COVID-19 Detection

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Intelligent Automation & Soft Computing 2023年第8期37卷 2247-2261页

作者： Amani Yahyaoui Jawad Rasheed Shtwai Alsubai Raed M.Shubair Abdullah Alqahtani Buket Isler Rana Zeeshan Haider Department of Software Engineering Istanbul Sabahattin Zaim UniversityIstanbul34303Turkey Department of Software Engineering Nisantasi UniversityIstanbul34398Turkey Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Electrical and Computer Engineering New York University(NYU)Abu Dhabi129188United Arab Emirates College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Engineering Istanbul Topkapi UniversityIstanbul34087Turkey Baqai Institute of Hematology Baqai Medical UniversityKarachi75340Pakistan

The coronavirus(COVID-19)is a disease declared a global pan-demic that threatens the whole *** then,research has accelerated and varied to find practical solutions for the early detection and correct identification of this *** researchers have focused on using the potential of Artificial Intelligence(AI)techniques in disease diagnosis to diagnose and detect the *** paper developed deep learning(DL)and machine learning(ML)-based models using laboratory findings to diagnose *** different methods are used in this study:K-nearest neighbor(KNN),Decision Tree(DT)and Naive Bayes(NB)as a machine learning method,and Deep Neural Network(DNN),Convolutional Neural Network(CNN),and Long-term memory(LSTM)as DL *** approaches are evaluated using a dataset obtained from the Israelita Albert Einstein Hospital in Sao Paulo,*** data consists of 5644 laboratory results from different patients,with 10%being Covid-19 positive *** dataset includes 18 attributes that characterize *** used accuracy,f1-score,recall and precision to evaluate the different developed *** obtained results confirmed these approaches’effectiveness in identifying COVID-19,However,ML-based classifiers couldn’t perform up to the standards achieved by DL-based *** all,NB performed worst by hardly achieving accuracy above 76%,Whereas KNN and DT compete by securing 84.56%and 85%accuracies,*** these,DL models attained better performance as CNN,DNN and LSTM secured more than 90%*** LTSM outperformed all by achieving an accuracy of 96.78%and an F1-score of 96.58%.

关键词： Artificial intelligence COVID-19 deep learning diagnosis machine learning

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Dysarthric Speech Recognition using Depthwise Separable Convolutions: Preliminary Study 12

Dysarthric Speech Recognition using Depthwise Separable Conv...

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12th International Conference on Speech Technology and Human-computer Dialogue, SpeD 2023

作者： Shahamiri, Seyed Reza Mandal, Krishnendu Sarkar, Sudeshna The University of Auckland Faulty of Engineering Department of Electrical Computer and Software Engineering Auckland New Zealand Indian Institute of Technology Computer Science & Engineering Department Kharagpur India

ISBN: (纸本)9798350327977

As a neurological disability that affects muscles involved in articulation, dysarthria is a speech impairment that leads to reduced speech intelligibility. In severe cases, these individuals could also be handicapped and unable to interact with digital devices. For such individuals, Automatic Speech Recognition (ASR) technologies could be life changing by enabling them to communicate with others as well as computing devices via voice commands. Nonetheless, ASR systems designed to recognize healthy speech have shown very poor performance to transcribe dysarthric speech, signaling the need to design ASR specifically tailored for dysarthria. Dysarthric Speech Recognition (DRS) research has progressed gradually because of the challenges the research community faces such as the scarcity of dysarthric speech that does not allow the researchers to design deeper acoustic models needed to better learn dysarthric speech variations. In this paper we report on our preliminary findings to improve our previous DSR called Speech Vision and study the effects of Separable Convolutional neurons to improve its acoustic model. Speech Vision is a novel Dysarthric Speech Recognition system that learns to recognize the shape of the words uttered by dysarthric speakers instead of recognizing phone sequences and then mapping them to words. Experiments conducted on the utterances provided by all UA-Speech dysarthric speakers indicate the proposed Depthwise separable architecture provided better word recognition accuracies compared to the original Speech Vision's architecture across all dysarthric speech intelligibility classes. © 2023 IEEE.

关键词： Convolution

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Global video object segmentation with spatial constraint module

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Computational Visual Media 2023年第2期9卷 385-400页

作者： Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory *** some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past *** algorithm uses a global context(GC)module to achieve highperformance,real-time *** GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real ***,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current *** SCM effectively alleviates mismatching of similar targets yet consumes few additional *** added a refinement module to the decoder to improve boundary *** model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

关键词： video object segmentation semantic segmentation global context(GC)module spatial constraint

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Deep Learning-Based APT Malware and Variants Detection with Attribution Analysis 6

Deep Learning-Based APT Malware and Variants Detection with ...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Tang, Binhui Leng, Peiyun Shen, Xuxiang Wei, Yuhang Sichuan University Chengdu School of Computer and Software Chengdu Jincheng College School of Cyber Science and Engineering China Chengdu Jincheng College School of Computer and Software Chengdu China

ISBN: (纸本)9798350325485

Malware has become one of the most severe security threats in cyber security, among which APT malware attacks are more threatening than advanced sustainable threat attacks. In this paper, we perform APT malware and variant detection based on deep learning and analyze the results by detecting and attribution. The variant detection of APT malware can inhibit the spread of malicious code, which is important for network security detection and defense. This paper proposes a detection and classification method for APT malware and its variants based on deep learning. Firstly, the feature representation method of APT malware based on RGB images is proposed to solve the gradient explosion and gradient disappearance of grayscale map feature extraction and generate images with rich texture information, which can mine deeper features of APT malware attacks. Secondly, this paper also improves the convolutional neural network model by combining the Self-Attention mechanism with the spatial pyramid pool SPP-net to solve the problem of image input of different sizes, as the accuracy is above 92.14%. Then, the experiment results prove the predictive analysis of the model to understand the organization to which the APT attack belongs and the way of its specific attack, providing the possibility of tracing the source. Finally, the visual analysis of APT threat characteristics is presented. © 2023 IEEE.

关键词： Convolutional neural networks

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Energy-Efficient Multi-UAV Collaborative Reliable Storage: A Deep Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年第12期12卷 20913-20926页

作者： Huang, Zhaoxiang Yu, Zhiwen Huang, Zhijie Zhou, Huan Yang, Erhe Yu, Ziyue Xu, Jiangyan Guo, Bin Northwestern Polytechnical University School of Computer Science Xi'an710129 China Vice President of Harbin Engineering University Harbin150001 China Northwestern Polytechnical University School of Software Xi'an710129 China

Unmanned Aerial Vehicle (UAV) crowdsensing, as a complement to Mobile Crowdsensing (MCS), can provide ubiquitous sensing in extreme environments and has gathered significant attention in recent years. In this paper, we investigate the issue of sensing data storage in UAV crowdsensing without edge assistance, where sensing data is stored locally in the UAVs. In this scenario, replication scheme is usually adopted to ensure data availability, and our objective is to find an optimal replica distribution scheme to maximize data availability while minimizing system energy consumption. Given the NP-hard nature of the optimization problem, traditional methods cannot achieve optimal solutions within limited timeframes. Therefore, we propose a centralized training and decentralized execution Deep Reinforcement Learning (DRL) algorithm based on Actor-Critic (AC), named 'MUCRS-DRL". Specifically, this method derives the optimal replica placement scheme based on UAV state information and data file information. Simulation results show that compared to the baseline methods, the proposed algorithm reduces data loss rate, time consumption, and energy consumption by up to 88%, 11%, and 11%, respectively. © 2014 IEEE.

关键词： Deep reinforcement learning

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Develop an Ontology for E-Commerce based on a Web Application to Assist Color-blind people 2

Develop an Ontology for E-Commerce based on a Web Applicatio...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Farooq, Muhammad Usman Qamar, Usman Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665498197

Color-blindness is a genetic eye disease. The person who is suffering from this disease cannot see the correct color of life. The human eye consists of rods and cones. Rods are responsible for black and white vision while cones are responsible for color vision. Colorblindness is arisen due to a deficiency of few cones of the eye. E-Commerce application plays an important role in the current era. But most known applications do not handle colorblind people. In the existing research, there is no such solution suggested however in this research an Ontology is proposed for E-commerce applications that assist color-blind people. The proposed ontology provides an understanding of the domain of color-blind disease and the E-commerce domain. The proposed ontology can be visualized by using the OWLViz tool. Validation of this ontology is provided by using a case study. This ontology can be further expanded by adding more concepts to it. This ontology can also be used by any GUI-based application by applying minor changes in concepts. © 2022 IEEE.

关键词： Color

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A Pretraining Strategy to Improve Faithfulness in Abstractive Text Summarization 7

A Pretraining Strategy to Improve Faithfulness in Abstractiv...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Alrefaai, Mohanad Akgun, Devrim Sakarya University Computer Engineering Department Sakarya Turkey Sakarya University Software Engineering Department Sakarya Turkey

ISBN: (纸本)9798350383065

A core dilemma of abstractive text summarization is to ensure that generated summaries are faithful to the relevant source documents, that is the degree to which the generated summary accurately reflects the content of the original text. Recent studies addressed the problem of faithfulness in abstractive text summarization from various aspects and they achieved significant progress. However, those studies lack a critical consideration of how pretraining strategies can affect and refine faithfulness in abstractive text summarization models. To address this issue, we proposed a novel pretraining method and analyzed its impact on both faithfulness and summarization. The proposed method stimulates the model to attend more for the more important sections that correlate with faithfulness in the source text. Our experiment and analysis illustrate that the proposed method can achieve better results on various faithfulness metrics than the baseline model. We also discussed that the proposed pretraining strategies can even refine the generated summaries in terms of summarization metrics. © 2023 IEEE.

关键词： Text processing

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