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ANN: adversarial news net for robust fake news classification

SCIENTIFIC REPORTS 2024年第1期14卷 1-20页

作者： Maham, Shiza Tariq, Abdullah Khan, Muhammad Usman Ghani Alamri, Faten S. Rehman, Amjad Saba, Tanzila UET Khawarizmi Inst Comp Sci Natl Ctr Artificial Intelligence Lahore Pakistan Prince Sultan Univ CCIS Artificial Intelligence & Data Analyt Lab AIDA Riyadh 11586 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Dept Math Sci Coll Sci POB 84428 Riyadh 11671 Saudi Arabia

With easy access to social media platforms, spreading fake news has become a growing concern today. Classifying fake news is essential, as it can help prevent its negative impact on individuals and society. In this regard, an end-to-end framework for fake news detection is developed by utilizing the power of adversarial training to make the model more robust and resilient. The framework is named "ANN: Adversarial News Net," emoticons have been extracted from the datasets to understand their meanings concerning fake news. This information is then fed into the model, which helps to improve its performance in classifying fake news. The performance of the ANN framework is evaluated using four publicly available datasets, and it is found to outperform baseline methods and previous studies after adversarial training. Experiments show that Adversarial Training improved the performance by 2.1% over the Random Forest baseline and 2.4% over the BERT baseline method in terms of accuracy. The proposed framework can be used to detect fake news in real-time, thereby mitigating its harmful effects on society.

关键词： Fake news Adversarial training Figurative language Transformers FGSM BERT Longformer

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DeepFert: An Intelligent Fertility Rate Prediction Approach for Men Based on Deep Learning Neural Networks

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IEEE ACCESS 2023年 11卷 75006-75022页

作者： Naseem, Shahid Mahmood, Tariq Saba, Tanzila Alamri, Faten S. S. Bahaj, Saeed Ali Omer Ateeq, Hammad Farooq, Umer Univ Educ Dept Informat Sci Div Sci & Technol Lahore 54770 Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Univ Educ Fac Informat Sci Vehari Campus Vehari 61100 Pakistan Princess Nourah bint Abdulrahman Univ Coll Sci Dept Math Sci Riyadh 11671 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Business Adm MIS Dept Alkharj 11942 Saudi Arabia

Men's fertility depends on their bodies making normal sperm and delivering them. Semen analysis has been the test of choice for assessing the male partner in an infertile couple using a single threshold value to distinguish 'abnormal' and 'normal' parameters. In the semen analysis process, rA gime issues might affect the semen morphology, quality, and spermatozoa, and also reduce the risks of fertility due to food regimen including glycemic content and limited intake of nutrients. Determination of the connotation between adjustable rA gime and semen morphology is a complex task to determine fertility. The goal is this study is the prediction of men's fertility rate to analyze the connection between spermatozoa and the level of lifeblood. Impaired semen parameters alone cannot be used to predict fertility more accurately. Some factors might affect the spermatozoa, like impairing sperm function, morphology, and sustainability, and can reduce the men's fertility rate. In this article, deep learning on convolutional neural network (DLNN) is used to predict the men's fertility rate more quickly, accurately, and consistently from different age spam of men between 18-50 years old. The convolutional neural network performs the segmentation of sperm heads, while the deep learning algorithm allows us to calculate the movement speed of sperm heads. After the application of DLNN, we have achieved semen prediction 80.952% and sperm concentration 85.714% accuracy of sperm head detection on human spermatozoa sperm samples. The results of the experiments presented below will show the applicability of the proposed method to be used in automated artificial insemination workflow.

关键词： Fertility deep learning neural networks dwindled spermatozoa

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Steganography-assisted secure localization of smart devices in internet of multimedia things (IoMT)

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第11期80卷 17045-17065页

作者： Khan, Saira Abbas, Naveed Nasir, Mansoor Haseeb, Khalid Saba, Tanzila Rehman, Amjad Mehmood, Zahid Islamia Coll Peshawar Dept Comp Sci Peshawar 25120 Pakistan Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh 11586 Saudi Arabia Univ Engn & Technol Dept Comp Engn Taxila 47050 Pakistan

Steganography is a secure way of communicating secret visual data/information from one place to another via multimedia carriers. Digital steganography, spatial and frequency domain has taken several methods over the past two decades. In this work, we present steganography-assisted secure localization of smart devices for Visual Sensor Network (VSN). The proposed system is based on achromatic-component (Y-Plane) of the YCbCr color space and maximum likelihood estimation algorithm (MLEA) in the spatial domain. data/information security via multimedia carriers is the main concern in this technological era, if secret information is captured by a Visual Sensor network, (VSN) then the capture information in the case to send from one place to another needs fundamental security to keep the information confidential. In the proposed steganography-assisted Visual Sensor Network (VSN) the camera nodes are intelligent to process the image data and to extract the important information and the rich-description are provided to the system's user for further action. In the proposed technique first, the input image is rotated on 90 degrees and transformed into YCbCr color space. Furthermore, the secret data is separated into different blocks of the same size based on key giving sub-blocks in Y-Plane, secret information is then encrypted using an MLE algorithm (MLEA) and the corresponding blocks of secret data is embedded in the sub-image of Y-Plane. The experimental results authenticate that the proposed technique does not only boost the visual quality of stegno-images but also delivers good imperceptibility, robustness, and security as compared to state-of-the-art approaches.

关键词： Image steganography Information security Image quality LSB Visual sensor network (VSN) Secret key Embedding

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Enhancing Prognosis Accuracy for Ischemic Cardiovascular Disease Using K Nearest Neighbor Algorithm: A Robust Approach

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IEEE ACCESS 2023年 11卷 97879-97895页

作者： Muhammad, Ghulam Naveed, Saad Nadeem, Lubna Mahmood, Tariq Khan, Amjad R. Amin, Yasar Bahaj, Saeed Ali Omer Univ Engn & Technol Dept Telecommun Engn Taxila 47050 Pakistan Prince Sultan Univ Coll Comp & Informat Sci CCIS Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Univ Educ Fac Informat Sci Vehari Campus Vehari 61100 Pakistan Prince Sattam bin Abdulaziz Univ Coll Business Adm MIS Dept Al Kharj 11942 Saudi Arabia Hadhramout Univ Coll Engn & Petr Dept Comp Engn Mukalla Hadhramout Yemen

Ischemic Cardiovascular diseases are one of the deadliest diseases in the world. However, the mortality rate can be significantly reduced if we can detect the disease precisely and effectively. Machine Learning (ML) models offer substantial assistance to individuals requiring early treatment and disease detection in the realm of cardiovascular health. In response to this critical need, this study developed a robust system to predict ischemic disease accurately using ML-based algorithms. The dataset obtained from Kaggle encompasses a comprehensive collection of over 918 observations, encompassing 12 essential features crucial for predicting ischemic disease. In contrast, much-existing research relies primarily on datasets comprising only 303 instances from the UCI repository. Six ML-based algorithms, including K Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Decision Trees (DT), are trained on the ischemic heart data. The effectiveness of the proposed methodologies is meticulously evaluated and benchmarked against cutting-edge techniques, employing a range of performance criteria. The empirical findings manifest that the KNN classifier produced optimized results with 91.8% accuracy, 91.4% recall, 91.9% F1 score, 92.5% precision, and AUC of 90.27%.

关键词： Diseases Classification tree analysis Predictive models Support vector machines Prediction algorithms Heart Radio frequency Cardiovascular diseases Risk management Ischemic cardiovascular diseases healthcare and health risks SVM RF GNB KNN

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A robust technique for copy-move forgery detection from small and extremely smooth tampered regions based on the DHE-SURF features and mDBSCAN clustering

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AUSTRALIAN JOURNAL OF FORENSIC SCIENCES 2021年第4期53卷 459-482页

作者： Bilal, Muhammad Habib, Hafiz Adnan Mehmood, Zahid Yousaf, Rehan Mehmood Saba, Tanzila Rehman, Amjad Univ Engn & Technol Dept Software Engn Taxila Pakistan Univ Engn & Technol Dept Comp Engn Taxila Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh Saudi Arabia

The keypoint-based copy-move forgery (CMF) detection is one of the most widely used CMF detection methods;however the keypoint-based CMF detection method cannot effectively and efficiently detect the small and extremely smooth tampered regions in the input image. A CMF detection method is proposed to tackle the above mention problem. In the proposed CMF detection method, the contrast of the input image is adjusted using the dynamic histogram equalization (DHE) method. A speeded-up robust feature (SURF) descriptor is used to extract features from the tampered image and matched using Euclidean distance. The novel modified density-based spatial clustering of application with noise (mDBSCAN) clustering technique is applied to the matched features to generate the binary mask followed by the detection of CMF regions. Three standard datasets, MICC-F220, MICC-F2000, and CoMoFoD, are used to evaluate the proposed CMF detection method performance. The experimental results indicate that the proposed CMF detection method outshines the state-of-the-art CMF detection method in terms of precision (P) and recall (R).

关键词： CMF detection method SURF features DBSCAN clustering

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An Improved Automatic Image Annotation Approach Using Convolutional Neural Network-Slantlet Transform

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IEEE ACCESS 2022年 10卷 7520-7532页

作者： Adnan, Myasar Mundher Rahim, Mohd Shafry Mohd Khan, Amjad Rehman Saba, Tanzila Fati, Suliman Mohamed Bahaj, Saeed Ali Univ Technol Malaysia Fac Engn Sch Comp Johor Baharu 81310 Malaysia Dept Islamic Univ Najaf 54001 Iraq Prince Sultan Univ Coll Comp & Informat Sci CCIS Artificial Intelligence & Data Analyt Lab Riyadh 11586 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Business Adm MIS Dept Al Kharj 11942 Saudi Arabia

Every day, websites and personal archives generate an increasing number of photographs. The extent of these archives is unfathomable. The ease of usage of these enormous digital image collections contributes to their popularity. However, not all of these databases provide appropriate indexing data. As a result, it's tough to find information that the user is interested in. Thus, in order to find information about an image, it is necessary to classify its content in a meaningful way. Image annotation is one of the most difficult issues in computer vision and multimedia research. The objective is to convert an image into a single or numerous labels. This necessitates a grasp of the visual content of an image. The necessity for unambiguous information to build semantic-level concepts from raw image pixels is one of the challenges of image annotation. Unlike text annotation, where a dictionary links words to their meaning, raw picture pixels are insufficient to construct semantic-level notions directly. A simple syntax, on the other hand, is well specified for combining letters to form words and words to form sentences. The automatic feature extraction for automatic annotation was the emphasis of this paper. And they employed a deep learning convolutional neural network to build and improve image coding and annotation capabilities. Performance of the suggested technique on the Corel-5K, ESP-Game, and IAPRTC-12 datasets. Finally, experimental findings on three data sets were used to demonstrate the usefulness of this model for image annotation.

关键词： Convolutional neural networks Feature extraction Image annotation Annotations Deep learning Computer architecture Visualization Automatic image annotation deep learning features extraction slantlet transform technological development

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Leveraging CNN-Transformer Hybrid Architecture for Accurate Breast Tumor Segmentation in Mammograms

Leveraging CNN-Transformer Hybrid Architecture for Accurate ...

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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Mudassar Ali Muhammad Zeeshan Tahir Iqra Mariam Khadija Nawaz Tariq Mahmood Samia Allaoua Chelloug College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Computer Science and Technology Zhejiang University Hangzhou China School of Electronics and Information Engineering Liaoning University of Technology Jinzhou China Faculty of Information Sciences University of Education Vehari Pakistan Artificial Intelligence and Data Analytics Lab CCIS PSU Riyadh SA Department of Information Technology CCIS PNU Riyadh SA

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

Breast cancer detection through mammograms is very crucial for early diagnosis, and tumor segmentation is an important step in treatment planning. This paper proposes a novel approach by combining CNNs with transformers, which we call the Hybrid CNN + Transformer model, for the task of breast tumor segmentation. It leverages the strengths of CNN s in local feature extraction to capture fine details, such as tumor boundaries, and combines them with transformers that model long-range dependencies and contextual relationships across images for the exact delineation of tumor regions in complex tissue structures. Our methodology will be tested on the high-quality mammog-raphy dataset annotated with both benign and malignant lesions, the so-called INbreast dataset. Herein, the CNN backbone-first for instance, ResNet or EfficientNet-extracts the input mammo-gram hierarchically; features are fed into a transformer layer in order to capture global dependencies, after which the final segmentation mask is generated by using a U-Net style decoder for a pixel-wise tumor classification. The segmentation accuracies of our models demonstrated the significant improvement achieved in tumor segmentation, using Hybrid CNN + Transformer when compared with tra-ditional CNN-based models on challenging tumor boundaries and complex tissue patterns. An accurate, robust, completely automatic method for tumor segmentation will thus enable improved diagnostic feasibility on clinical grounds. Furthermore, the combination of learning at both local and global features improved generalizability to previously unseen mammogram images and therefore strengthened clinical applicability.

关键词： Image segmentation Adaptation models Accuracy Breast tumors Computational modeling Transformers Mammography Breast cancer Convolutional neural networks Tumors

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NC-ALG: Graph-Based Active Learning under Noisy Crowd 40

NC-ALG: Graph-Based Active Learning under Noisy Crowd

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zhang, Wentao Wang, Yexin You, Zhenbang Li, Yang Cao, Gang Yang, Zhi Cui, Bin Center for Machine Learning Research Peking University China Key Lab of High Confidence Software Technologies Peking University China Institute of Advanced Algorithms Research Shanghai China Institute of Computational Social Science Peking University Qingdao China National Engineering Labratory for Big Data Analytics and Applications China TEG Tencent Inc. Department of Data Platform China Beijing Academy of Artificial Intelligence China

ISBN: (纸本)9798350317152

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active Learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed. © 2024 IEEE.

关键词： Crowdsourcing

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Noise filtering approach to improve handwritten digit recognition using customized CNN for Cerebral Palsy individuals

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The European Physical Journal Special Topics 2025年 1-19页

作者： Muthureka, K. Srinivasulu Reddy, U. Janet, B. Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology-Tiruchirappalli Tiruchirappalli India Centre of Excellence (CoE) in Artificial Intelligence National Institute of Technology-Tiruchirappalli Tiruchirappalli India Information Processing Lab Department of Computer Applications Tiruchirappalli India

Automatic recognition of handwritten digits in people with Cerebral Palsy (CP) is a serious challenge that requires advancements in data preparation for improved predictive accuracy. The disorganized digit pixel patterns caused by CP people’ innate motor coordination problems result in the classification errors due to the noise and outlier. Despite the fact that many handwritten digit databases for normal people are available, none expressly cater to those with shaky hands/CP dataset. This highlights the importance of an efficient pre-processing method for mitigating variances in CP individuals’ digit pixel patterns. A total of 43,000 images from 43 CP people were collected and organized into 10 different folders for data labeling. The authors used the K-Means Based Noise Removal (KMNR) approach to remove noise and outliers from a CP and tested it on a customized CNN. The KMNR model achieved an accuracy of 98.86%, compared to 93.54% without it. The F1-score values for state-of-the-art models without KMNR ranged from 85.00% to 97.00%, but after integrating KMNR, they increased to 92.00% to 98.00%. The precision values ranged from 85.00% to 97.00% in the absence of KMNR, but increased to 91.00% to 98.00% in the presence of KMNR. The recall values ranged from 85.00% to 97.00% without KMNR, but increased to 87.00% to 98.00% with KMNR.

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Seamless Crime Scene Reconstruction in Mixed Reality for Investigation Training: A Design and Evaluation Study

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IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 2024年 17卷 856-873页

作者： Albeedan, Meshal Kolivand, Hoshang Hammady, Ramy Saba, Tanzila Liverpool John Moores Univ LJMU Fac Engn & Technol Sch Comp Sci & Math Liverpool L3 3AF England Staffordshire Univ Sch Comp & Digital Technol Stoke On Trent ST4 2DE England Univ Essex Dept Comp Sci & Elect Engn Colchester CO4 3SQ England Helwan Univ Fac Appl Arts Cairo Egypt Prince Sultan Univ Coll Comp & Informat Sci CCIS Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia

Investigation training at the real crime scene is a critical component of forensic science education. However, bringing young investigators to real crime scenes is costly and faces significant challenges. Mixed reality (MR) is one of the most evolving technologies that provides unlimited possibilities for practical activities in the education sector. This article aims to propose and evaluate a novel design of an MR system using Microsoft HoloLens 2.0 that is tailored to work in a spatial 3-D-scanned and reconstructed crime scene. The system was designed to be a cost-effective experience that helps young Kuwaiti police officers enhance their investigation skills. The proposed system has been evaluated through system usability, user interaction, and performance metrics quantitatively via 44 young police officers and qualitatively using the think-aloud protocols via a group of experts. Both groups showed positive levels of usability, user interaction, and overall satisfaction, with minimal negative feedback. Based on the positive feedback, the system will be taken into the commercialization stage in the future. Despite the high cost of the MR device, experts stated that the system is needed as an essential tool for crime scene education and investigation practices.

关键词： Training Three-dimensional displays Mixed reality Forensics Virtual reality Usability Law enforcement 3-D reconstruction 3-D scanning crime investigation crime scene investigation training mixed reality (MR) photogrammetry usability

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