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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Fouda, Mona A. El-Shafai, Walid El-Rabaie, El-Sayed M. Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt Prince Sultan University Security Engineering Lab Computer Science Department Riyadh11586 Saudi Arabia

ISBN: (纸本)9798350323511

With the rise in both the quantity and sophistication of deepfake videos, the need for robust detection systems to identify potentially misleading content on social media and the internet has become paramount. However, current automated face forgery detection systems still face limitations, often demonstrating bias towards the training dataset. This research paper addresses this issue by proposing a novel approach for detecting deepfake media. We introduce a custom Visual Geometry Group (VGG16) deepfake detection method that leverages convolutional neural network architectures. To evaluate the effectiveness of our approach, we utilize the deepfake detection challenge (DFDC) dataset on Kaggle to build network models and compare the performance of our custom VGG16 method against the standard VGG16. Additionally, we investigate the impact of data augmentation techniques on the performance of Convolutional Neural Network (CNN)-based deepfake detectors, examining their effect on both VGG16 and our custom VGG16 approach using the DFDC dataset. Our results demonstrate a high level of accuracy, with precision, recall, and f1-score values of 0.983, 0.975, and 0.979, respectively, and an overall accuracy of 0.986 for deepfake detection. This study presents a promising approach to enhance the accuracy of deepfake video detection, representing a crucial step towards mitigating the potential negative impacts of deepfake technology. © 2023 IEEE.

关键词： Convolutional neural networks

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Proposed Different Signal Processing Tools for Efficient Optical Wireless Communications

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computers, Materials & Continua 2022年第5期71卷 3293-3318页

作者： Hend Ibrahim Abeer D.Algarni Mahmoud Abdalla Walid El-Shafai Fathi E.Abd El-Samie Naglaa F.Soliman Department of Electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh 11586Saudi Arabia

Optical Wireless Communication(OWC)is a new trend in communication systems to achieve large bandwidth,high bit rate,high security,fast deployment,and low *** basic idea of the OWC is to transmit data on unguided media with *** system requires multi-carrier modulation such as Orthogonal Frequency Division Multiplexing(OFDM).This paper studies optical OFDM performance based on Intensity Modulation with Direct Detection(IM/DD)*** system requires a non-negativity *** paper presents a framework for wireless optical OFDM system that comprises(IM/DD)with different forms,Direct Current biased Optical OFDM(DCO-OFDM),Asymmetrically Clipped Optical OFDM(ACO-OFDM),Asymmetrically DC-biased Optical OFDM(ADO-OFDM),and *** also considers channel coding as a tool for error control,channel equalization for reducing deterioration due to channel effects,and investigation of the turbulence *** evaluation results of the proposed framework reveal enhancement of *** performance of the IM/DD-OFDM system is investigated over different channel models such as AWGN,log-normal turbulence channel model,and ceiling bounce channel *** simulation results show that the BER performance of the IM/DD-OFDM communication system is enhanced while the fading strength is *** results reveal also that Hamming codes,BCH codes,and convolutional codes achieve better BER ***,two algorithms of channel estimation and equalization are considered and *** algorithms include the Least Squares(LS)and the Minimum Mean Square Error(MMSE).The simulation results show that the MMSE algorithm gives better BER performance than the LS algorithm.

关键词： Optical communication systems OWC IM/DD OFDM MMSE LS ADO-OFDM DCO-OFDM ACO-OFDM

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A Novel Image Encryption and Decoding Approach Based on DNA Coding and Arnold's Transformation for IoT Applications

A Novel Image Encryption and Decoding Approach Based on DNA ...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Shaltout, Nawal Abd El-Latif, Ahmed A. Al-Adrousy, Waleed Hammad, Mohamed Ateya, Abdelhamied A. Elmougy, Samir Mansoura University Faculty of Computers and Information Department of Computer Science Mansoura35516 Egypt Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science 32511 Egypt Menoufia University Faculty of Computers and Information Department of Information Technology Shibin El Kom32511 Egypt Zagazig University Department of Electronics and Communications Engineering Zagazig Egypt

ISBN: (纸本)9798350351408

Nowadays, the Internet of Things (IoT) is significantly utilized by an extensive range of applications that lead to the enhancement of different aspects of defense and military capabilities. In IoT, a wide range of smart devices that can communicate with each other are connected through it. Therefore, it is essential to protect multimedia data, especially images with an encryption approach that offers a sufficient degree of security in military applications. Hence, this paper introduces an effective scheme for image encryption and decoding relying on Arnold's transformation DNA coding. It proposes a new pseudo-random number generation PRNG using the Sine-Logistic map and Duffing map called SLDM. The encryption method involves three phases: 1- Key generation and PRNG. 2- confusion phase using the Arnold transformation method. 3- diffusion using DNA rules and operations. The results based on the USC-SIPI dataset, show that the suggested scheme is sufficiently resistant to every kind of attack. Furthermore, the suggested method yields NPCR scores of around 99.62% and UACI scores of approximately 33.48%. Due to its low complexity, it has a great deal of potential for safe and real-time picture applications. © 2024 IEEE.

关键词： Random number generation

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A Mamdani Fuzzy Inference System for Comprehensive Diabetes Risk Evaluation

A Mamdani Fuzzy Inference System for Comprehensive Diabetes ...

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

作者： Tanjina Ahmed Tuly Tanjid Ahammed Shafin Md. Zahid Hasan Md. Hasan Imam Bijoy Mohammad Shamsul Arefin Mohammad Shorif Uddin Department of Computer Science and Engineering Health Informatics Research Lab Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh Department of Computer Science and Engineering Jahangirnagar University Savar Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Diabetes mellitus is a chronic condition characterized by insufficient insulin production and the inability to effectively utilize the insulin hormone, leading to elevated blood glucose levels. As diabetes is incurable and persists throughout a patient's lifetime, effective management requires early identification of risk factors to ensure continuous and feasible treatment. This study proposes a unique decision support system based on the Mamdani Fuzzy Inference System (MFIS) to predict diabetes risk. This study utilizes a publicly available diabetes dataset containing 442 data that includes nine key health metrics such as BMI, blood glucose, cholesterol levels, and blood pressure to predict the risk of diabetes. The dataset aims to support early diagnosis and better diabetes management by providing insights into individual risk factors. This resource contributes to the development of more accurate decision-support systems in healthcare. We identify key variables and establish a membership function to assess individual risk. The Fuzzy Inference System (FIS) is well-suited for handling complex and uncertain conditions, making it an effective tool for this application. The advantages of the MFIS include its ability to incorporate expert knowledge through linguistic variables and fuzzy rules. This system demonstrates the practical benefits of MFIS in developing intelligent decision-making tools, particularly in the domain of diabetes risk prediction.

关键词： Fuzzy logic Decision support systems Accuracy Uncertainty Predictive models Blood pressure Diabetes Glucose Insulin Blood

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Towards Safe Synthetic Image Generation On the Web: A Multimodal Robust NSFW Defense and Million Scale Dataset 25

Towards Safe Synthetic Image Generation On the Web: A Multim...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Muhammad Shahid Muneer Simon S. Woo DASH Lab Computer Science and Engineering Department Sungkyunkwan University Suwon-Si Republic of Korea DASH Lab Computer Science and Engineering Department Sungkyunkwan University Suwon Republic of Korea

ISBN: (纸本)9798400713316

In the past years, we have witnessed the remarkable success of Text-to-Image (T2I) models and their widespread use on the web. Extensive research in making T2I models produce hyper-realistic images has led to new concerns, such as generating Not-Safe-For-Work (NSFW) web content and polluting the web society. To help prevent misuse of T2I models and create a safer web environment for users features like NSFW filters and post-hoc security checks are used in these models. However, recent work unveiled how these methods can easily fail to prevent misuse. In particular, adversarial attacks on text and image modalities can easily outplay defensive measures. %Exploiting such leads to the growing concern of preventing adversarial attacks on text and image modalities. Moreover, there is currently no robust multimodal NSFW dataset that includes both prompt and image pairs and adversarial examples. This work proposes a million-scale prompt and image dataset generated using open-source diffusion models. Second, we develop a multimodal defense to distinguish safe and NSFW text and images, which is robust against adversarial attacks and directly alleviates current challenges. Our extensive experiments show that our model performs well against existing SOTA NSFW detection methods in terms of accuracy and recall, drastically reducing the Attack Success Rate (ASR) in multimodal adversarial attack scenarios. Code: https://***/shahidmuneer/multimodal-nsfw-defense.

关键词： content moderation

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A Comprehensive Study on Disaster Tweet Classification on Social Media Information 6th

A Comprehensive Study on Disaster Tweet Classification on So...

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Dasari, Siva Krishna Srinivas, Gorla Prasad Reddy, P.V.G.D. AI Research Lab Department of Computer Science and Engineering GMR Institute of Technology Rajam Vizianagaram India GITAM Deemed To Be University Visakhapatnam India Andhra University Visakhapatnam India

ISBN: (纸本)9789819986279

Twitter is a popular social media platform where people share their opinions. The impact of these opinions plays a critical role when a sudden unexpected situation or any natural disaster may happen. In such a large volume of information, identifying or extracting informative tweets has become challenging in the present case. Many researchers focus on this area in the classification of informative tweets. In this work, we are presenting a comprehensive review based on different approaches. This work mainly focuses on other methods, namely machine learning, deep learning, and transfer learning, for identifying informative tweets on natural disasters. We also concentrate on separate datasets and their performance with other state-of-the-art models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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An Effective Tensor Regression with Latent Sparse Regularization

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Journal of Data science 2022年第2期20卷 228-252页

作者： Chen, Ko-Shin Xu, Tingyang Liang, Guannan Tong, Qianqian Song, Minghu Bi, Jinbo Department of Computer Science and Engineering University of Connecticut United States Tencent AI Lab China Department of Biomedical Engineering University of Connecticut United States

As data acquisition technologies advance, longitudinal analysis is facing challenges of exploring complex feature patterns from high-dimensional data and modeling potential temporally lagged effects of features on a response. We propose a tensor-based model to analyze multidimensional data. It simultaneously discovers patterns in features and reveals whether features observed at past time points have impact on current outcomes. The model coefficient, a k-mode tensor, is decomposed into a summation of k tensors of the same dimension. We introduce a so-called latent F-1 norm that can be applied to the coefficient tensor to performed structured selection of features. Specifically, features will be selected along each mode of the tensor. The proposed model takes into account within-subject correlations by employing a tensor-based quadratic inference function. An asymptotic analysis shows that our model can identify true support when the sample size approaches to infinity. To solve the corresponding optimization problem, we develop a linearized block coordinate descent algorithm and prove its convergence for a fixed sample size. Computational results on synthetic datasets and real-life fMRI and EEG datasets demonstrate the superior performance of the proposed approach over existing techniques. © 2022 The Author(s).

关键词： longitudinal data quadratic inference function tensors

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Predicting Heat Stroke Risk: A Clinical Decision Support System using Fuzzy Association Mining Approach

Predicting Heat Stroke Risk: A Clinical Decision Support Sys...

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

作者： Abdullah Al Noman Rinvi Jaman Riti Md. Zahid Hasan Md. Hasan Imam Bijoy Mohammad Shorif Uddin Mohammad Shamsul Arefin Department of Computer Science and Engineering Health Informatics Research Lab Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Jahangirnagar University Savar Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Heat stroke is a serious medical condition that requires immediate treatment and is exacerbated by intense heat and climate change. This paper introduces a Clinical Decision Support System (CDSS) for heat stroke risk assessment based on Fuzzy Association Rule Mining. The system evaluates key attributes extracted from the Stanford Bioengineering Senior Capstone Project dataset, including daily water intake, cardiovascular history, heat index, environmental and rectal temperatures, blood pressure, pulse rate, humidity, age, and sex. These attributes help identify detailed patterns associated with heat stroke risk. Using fuzzy logic, the CDSS addresses the inherent vagueness of medical information through a set of "if- then" rules designed for healthcare practitioners. The system is tested with historical data, demonstrating its effectiveness in recognizing critical parameters to provide personalized, timely attention to potential heat stroke cases. It aims to reduce heat- related illnesses and fatalities by enabling rapid data-driven decision-making in healthcare. Despite certain limitations, this study highlights the necessity of intelligent systems for proactive health management.

关键词： Decision support systems Climate change Fuzzy systems Risk management Predictive models Medical services Medical conditions

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Explainable data transformation recommendation for automatic visualization

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Frontiers of Information Technology & Electronic engineering 2023年第7期24卷 1007-1027页

作者： Ziliang WU Wei CHEN Yuxin MA Tong XU Fan YAN Lei LV Zhonghao QIAN Jiazhi XIA State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China School of Computer Science and Engineering Central South UniversityChangsha 410083China

Automatic visualization generates meaningful visualizations to support data analysis and pattern finding for novice or casual users who are not familiar with visualization *** automatic visualization approaches adopt mainly aggregation and filtering to extract patterns from the original ***,these limited data transformations fail to capture complex patterns such as clusters and *** recent advances in feature engineering provide the potential for more kinds of automatic data transformations,the auto-generated transformations lack explainability concerning how patterns are connected with the original *** tackle these challenges,we propose a novel explainable recommendation approach for extended kinds of data transformations in automatic *** summarize the space of feasible data transformations and measures on explainability of transformation operations with a literature review and a pilot study,respectively.A recommendation algorithm is designed to compute optimal transformations,which can reveal specified types of patterns and maintain *** demonstrate the effectiveness of our approach through two cases and a user study.

关键词： Data transformation Data transformation recommendation Automatic visualization Explainability

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Enhancing Cancer Gene Prediction through Aligned Fusion of Multiple PPI Networks Using Graph Transformer Models

Enhancing Cancer Gene Prediction through Aligned Fusion of M...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Han, Zebei Yu, Gufeng Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China

ISBN: (纸本)9798350337488

Despite the considerable progress that has been made in cancer research, identifying cancer genes remains a significant challenge due to the intricate nature of the disease. Given the importance of incorporating gene interaction relationships in identifying potential cancer genes, Graph Neural Networks (GNNs) have garnered increasing attention for their ability to model gene associations effectively. Particularly, recent studies have demonstrated the superiority of GNNs in deciphering the knowledge embedded in Protein-Protein Interaction (PPI) networks for cancer gene prediction. However, these studies primarily focused on a single PPI network, overlooking the valuable insights encapsulated within other PPI networks. Additionally, previous endeavors often centered around pan-cancer datasets, neglecting the importance of predicting specific cancer genes. To address these limitations, we present a novel method called MPIT, which employs Graph Transformer Networks (GTNs) to identify specific cancer driver genes. MPIT effectively integrates data from diverse PPI and multi-omics data via the alignment and fusion of gene representations learned from different PPI networks. We collect three distinct cancer cell line datasets to assess the model performance. Our experimental findings demonstrate the superiority of MPIT over the existing methods, achieving the state-of-the-art performance across all three datasets. © 2023 IEEE.

关键词： Cancer genes feature alignment feature fusion Graph Transformer Network specific cancer genes

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