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DDT-Net:Deep Detail Tracking Network for Image Tampering Detection

computers, Materials & Continua 2025年第5期83卷 3451-3469页

作者： Jim Wong Zhaoxiang Zang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China College of Computer and Information Technology ChinaThree Gorges UniversityYichang443002China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650504China

In the field of image forensics,image tampering detection is a critical and challenging *** methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of *** deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational *** address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection *** contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise *** design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level ***,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region *** with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k *** addition,it has been extensively validated on other datasets,including CASIA and *** results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.

关键词： Image forensics image tampering detection image manipulation detection noise flow Bayar

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Developing Engagement in the Learning Management System Supported by Learning Analytics

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computer Systems science & Engineering 2022年第7期42卷 335-350页

作者： Suraya Hamid Shahrul Nizam Ismail Muzaffar Hamzah Asad W.Malik Department of Information System Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahSabahMalaysia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

Learning analytics is an emerging technique of analysing student par-ticipation and *** recent COVID-19 pandemic has signiﬁcantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to *** date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and ***,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notﬁrst identiﬁ***,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an *** required data was collected from an LMS that is currently utilised by a local Malaysian *** model was validated by a panel of experts as well as discussions with *** is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.

关键词： Engagement analysis learning analytics learning management system student engagement

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A Novel Pressure Sensor with Composite Sensitive Film for High Precision Measurement at Broad Range 38

A Novel Pressure Sensor with Composite Sensitive Film for Hi...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Chen, Xiaopeng Ling, Sijia Tong, Hanyang Xiao, Yujing Zhang, Jin Yu, Zhengyin Jin, Qinghui Ningbo University The Faculty of Electrical Engineering and Computer Science China Chinese Academy of Sciences Shanghai Institute of Microsystem and Information Technology China

ISBN: (纸本)9798331508890

Microelectromechanical system (MEMS) based pressure sensors have been utilized for decades;however, new trends in pressure sensors have recently emerged, such as increased sensitivity, a broader range and reduced chip size. This paper presents a tiny 0.6mm×0.8mm piezoresistive pressure sensor that offers exceptional sensitivity (0.27mV/kPa/3.3V) at a wide range of pressures (0-1MPa). The innovative composite pressure-sensitive film of the sensor is composed of a 10μm "*"-shaped single-crystalline silicon structure and a remarkably thin thickness of only 2.5μm poly-silicon film. It is developed utilizing the thin-film under bulk-silicon technique. And simultaneous improvement of detection sensitivity and linearity is achieved by etching out the nonlinear deformation region of the film. © 2025 IEEE.

关键词： Pressure sensors

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Copy-Move Forgery Detection Using Canny Edge Detector and SIFT-Based Blob Analysis 9th

Copy-Move Forgery Detection Using Canny Edge Detector and SI...

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9th International Conference on Computational science and technology, ICCST 2022

作者： Idris, Bashir Abdullah, Lili N. Selimun, Mohd Taufik Abdullah Halin, Alfian Abdul Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9789811984051

This work presents a copy-move forgery detection method based on blobs detection from canny edge-response images, followed by SIFT (Scale Invariant Feature Transform) key points analysis. The SIFT key points are extracted from grayscale images, which are then clustered according to their distance from within generated blobs of that image. Different key points in different blobs are ignored (unique non-duplicated key points) while the same key points in different blobs are matched. The assumption made here is that these duplicate regions are highly indicative of forgery. The proposed method is tested on the MICC-F220, MICC-8Multi, and CoMoFoD public benchmark datasets. The results demonstrate that copy-move forgery detection can be reliably performed on geometrically transformed images, i.e., rotated, scaled, combined and post-processed images such as JPEG compression, blurred, noise added, and contrast adjusted. Our method is also robust on multiple copy-move forgeries. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image compression

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An Optimized Neural Network with Bat Algorithm for DNA Sequence Classification

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computers, Materials & Continua 2022年第10期73卷 493-511页

作者： Muhammad Zubair Rehman Muhammad Aamir Nazri Mohd.Nawi Abdullah Khan Saima Anwar Lashari Siyab Khan Faculty of Computing and Information Technology Sohar UniversitySohar311Sultanate of Oman Soft Computing&Data Mining Centre(SMC) Faculty of Computer Science&Information TechnologyUniversiti Tun Hussein Onn Malaysia(UTHM)Batu Pahat86400Malaysia School of Electronics Computing and MathematicsUniversity of DerbyDerbyDE221GBUnited Kingdom Institute of Computer Sciences and Information Technology The University of Agriculture25120PeshawarPakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedBat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed BatLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as Bat levy flight Artificial NeuralNetwork (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) arecompared with the other state-of-the-art algorithms like Bat Artificial NeuralNetwork (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distributionback propagation (BatGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185,and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on *** on WL10 the proposed BatLFANN achieved 99.89899% accuracy withMSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853%accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.

关键词： DNA sequence classification bat algorithm levy flight back propagation neural network hybrid artificial neural networks(HANN)

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Algorithmic Loan Risk Prediction Method Based on PSO-GWO-Catboost 4

Algorithmic Loan Risk Prediction Method Based on PSO-GWO-Cat...

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4th International Conference on Machine Learning and computer Application, ICMLCA 2023

作者： Chen, Suihai How, Bong Chih Chiu, Po Chan Faculty of Computer Science and Information Technology's Student Unimas Borneo Sarawak94300 Malaysia

ISBN: (纸本)9798400709449

Loan risk analysis is a common challenge faced by global financial institutions. Under the background of big data, it is of practical significance to prevent loan risk by the machine learning algorithm. Aiming at the characteristics of unbalanced loan data and high noise, this paper proposes an improved Gray Wolf optimization strategy (PSO-GWO). PSO-GWO is used to optimize the parameters of the CatBoost model. In this method, the Gray Wolf algorithm (GWO) is further optimized by particle swarm optimization (PSO), and when combined with it, the convergence performance of the model is improved, the parameters of the model are reduced, and the model is simplified. To a certain extent, it avoids the inefficiency of the Gray Wolf algorithm, balances the ability of local search and global development, and improves the accuracy of the model. Compared with the traditional credit evaluation model, PSO-GWO-CatBoost has better accuracy and practical application value. © 2023 ACM.

关键词： Risk analysis

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Comparing LSTM Algorithm with Word Embedding: FastText and Word2Vec in Bahasa Batak-English Translation 12

Comparing LSTM Algorithm with Word Embedding: FastText and W...

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12th International Conference on information and Communication technology, ICoICT 2024

作者： Nasution, Nur Amalia Nababan, Erna Budhiarti Mawengkang, Herman Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Faculty of Mathematics and Natural Sciences Medan Indonesia

ISBN: (纸本)9798350363432

The Long-Short Term Memory (LSTM) algorithm is an advancement of the Recurrent Neural Network (RNN) method inspired by the thought patterns of humans. LSTMsand RNNs are often employed in machine translators to address sequential issues in text and provide accurate translations. In this problem, LSTM cannot stand alone. it requires word embeddings for low-dimensional vector representation of words, where similar or closely related words are placed closer to each other. With word embeddings, the semantic and contextual meanings of words can be better represented, allowing the model to understand and work with the text more effectively. In this paper, we propose using FastText and Word2Vec word embeddings to enhance the performance of the LSTM algorithm and compare the LSTM's performance with these word embeddings. Our results indicate that FastText embeddings improve the quality of Batak-English translations more effectively than Word2Vec. Our research suggests that FastText is more accurate and efficient in sentence translation. © 2024 IEEE.

关键词： Semantics

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Online Machine Learning Approach to Detect and Mitigate Low-Rate DDoS Attacks in SDN-Based Networks 5

Online Machine Learning Approach to Detect and Mitigate Low-...

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5th IEEE International Conference on Artificial Intelligence in Engineering and technology, IICAIET 2023

作者： Alashhab, Abdussalam Ahmed Mohd Zahid, Mohd Soperi Alashhab, Mohamed Alashhab, Shehabuldin Alasmarya Islamic University Faculty of Information Technology Department of Computer Science Zliten Libya Universiti Teknologi Petronas Department of Computer and Information Science Seri Iskandar Malaysia Universiti Sultan Azlan Shah Faculty of Management and Information Technology Kuala Kangsar Malaysia

ISBN: (纸本)9798350304152

Software Defined Networking (SDN) technology provides rapid configuration, scalability, and management through its dynamic, programmable architecture that outperforms traditional network architecture with limitations on scalability and management. However, the threat of Distributed Denial of Service (DDoS) attacks remains difficult to detect and threatens traditional and SDN-based networks. Fortunately, Machine Learning (ML) and Deep Learning (DL) technologies along with SDN have proven to have a superior potential to deal with these threats effectively. However, most of the previous studies focused on resolving high-rate DDoS attacks, and only a few dealt with the detection of Low-rate DDoS attacks that are difficult to detect due to their similarity to legitimate traffic. In addition, these studies do not utilize up to date data sets that contain the new features. To address this issue, we propose an online machine learning model that utilizes Stochastic Gradient Descent (SGD) optimizer and Explainable Boosting Machine (EBM) classifier to detect LDDoS attacks in SDN-based networks. Our model is designed to process large amounts of network traffic data in real-time and updates the model parameters incrementally to continually train the model on expected DDoS attacks, as the attack may change and appear differently. We evaluated the proposed approach in an SDN-simulated environment using Mininet and the Ryu controller. Our experimental results show that the proposed EBM model achieves high accuracy and outperforms existing methods, with 99% accuracy on the training data. The proposed system effectively counters LDDoS attacks and adapts to future mutations and zero-day DDoS attacks. © 2023 IEEE.

关键词： Stochastic systems

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Improving Route-Finding Performance of Dijkstra Algorithm and Maintaining Path Visual Cue Using Douglas-Peucker Algorithm 23

Improving Route-Finding Performance of Dijkstra Algorithm an...

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8th International Conference on Sustainable information Engineering and technology, SIET 2023

作者： Pinandito, Aryo Kharisma, Agi Putra Akbar, Muhammad Aminul Information System Department Faculty of Computer Science Universitas Brawijaya Malang Indonesia Media Game and Mobile Technology Laboratory Information System Department Faculty of Computer Science Universitas Brawijaya Malang Indonesia

ISBN: (纸本)9798400708503

Smartphones and their mobile applications have become an inseparable part of modern daily life. One of them is a public transportation app that helps people commute with public transportation. Many public transportation service providers have mobile applications as a companion for their customers to improve their quality of service. The application provides useful information and/or important features regarding their service, such as timetables or route finding for public transit. Such features run a slow algorithm to provide real-time or time-based information to the users. Dijkstra is one example of an algorithm that allows users to find the shortest path from one location to another in a public transit network. However, Dijkstra is categorized as a greedy algorithm because it looks at all possible solutions. It takes longer for Dijkstra to find the optimum solution when the number of points in a network graph is high. This study attempted to improve the overall route-finding processing time and reduce the memory footprint while keeping the visual cue of a route path reasonably consistent by utilizing Douglas-Peucker line simplification algorithm to simplify the path and reduce the number of points that Dijkstra algorithm processes. The result suggested that Douglas-Peucker simplification process took 0.5% additional memory footprint and put 5.13% additional processing time into the overall route-finding processing time. However, it reduces the overall memory footprint and processing time by 72.56% and 80.83%, respectively. The result conveyed a significant improvement in the practical implementation of Douglas-Peucker algorithm to reduce the input for the Dijkstra algorithm in finding the optimal route, thereby improving the overall route-finding performance. © 2023 ACM.

关键词： Transportation routes

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Automated Autism Spectral Disorder Classification Using Optimal Machine Learning Model

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computers, Materials & Continua 2023年第3期74卷 5251-5265页

作者： Hanan Abdullah Mengash Hamed Alqahtani Mohammed Maray Mohamed K.Nour Radwa Marzouk Mohammed Abdullah Al-Hagery Heba Mohsen Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial Intelligence and Unit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Autism Spectrum Disorder (ASD) refers to a neuro-disorder wherean individual has long-lasting effects on communication and interaction *** information technologywhich employs artificial intelligence(AI) model has assisted in early identify ASD by using pattern *** advances of AI models assist in the automated identification andclassification of ASD, which helps to reduce the severity of the *** study introduces an automated ASD classification using owl searchalgorithm with machine learning (ASDC-OSAML) model. The proposedASDC-OSAML model majorly focuses on the identification and classificationof ASD. To attain this, the presentedASDC-OSAML model follows minmaxnormalization approach as a pre-processing stage. Next, the owl searchalgorithm (OSA)-based feature selection (OSA-FS) model is used to derivefeature subsets. Then, beetle swarm antenna search (BSAS) algorithm withIterative Dichotomiser 3 (ID3) classification method was implied for ASDdetection and classification. The design of BSAS algorithm helps to determinethe parameter values of the ID3 classifier. The performance analysis of theASDC-OSAML model is performed using benchmark dataset. An extensivecomparison study highlighted the supremacy of the ASDC-OSAML modelover recent state of art approaches.

关键词： Autism spectral disorder machine learning owl search algorithm feature selection id3 classifier

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