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A Hybrid Regularization-Based Multi-Frame Super-Resolution Using Bayesian Framework

computer Systems science & Engineering 2023年第1期44卷 35-54页

作者： Mahmoud M.Khattab Akram M.Zeki Ali A.Alwan Belgacem Bouallegue Safaa S.Matter Abdelmoty M.Ahmed Faculty of Information and Communication Technology International Islamic University MalaysiaKuala LumpurMalaysia College of Computer Science King Khalid UniversityAbhaSaudi Arabia School of Theoretical&Applied Science Ramapo College of New JerseyRampao Valley RoadMahwahUSA Community College King Khalid UniversityAbhaSaudi Arabia

The prime purpose for the image reconstruction of a multi-frame super-resolution is to reconstruct a higher-resolution image through incorporating the knowledge obtained from a series of relevant low-resolution images,which is useful in ***,super-resolution image reconstruction methods are usually damaged by undesirable restorative artifacts,which include blurring distortion,noises,and stair-casing ***,it is always challenging to achieve balancing between image smoothness and preservation of the edges inside the *** this research work,we seek to increase the effectiveness of multi-frame super-resolution image reconstruction by increasing the visual information and improving the automated machine perception,which improves human analysis and interpretation ***,we propose a new approach to the image reconstruction of multi-frame super-resolution,so that it is created through the use of the regularization *** the proposed approach,the bilateral edge preserving and bilateral total variation regularizations are employed to approximate a high-resolution image generated from a sequence of corresponding images with low-resolution to protect significant features of an image,including sharp image edges and texture details while preventing *** experimental results of the synthesized image demonstrate that the new proposed approach has improved efficacy both visually and numerically more than other approaches.

关键词： Super-resolution regularized framework bilateral total variation bilateral edge preserving

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Script-Generated Picture Book technology Based on Large Language Models and AIGC 23

Script-Generated Picture Book Technology Based on Large Lang...

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7th International Conference on Digital technology in Education, ICDTE 2023

作者： Wang, Dejiang Zhai, Zhuoran Cheong, Ngai Peng, Li Intelligent Cloud Business Group Infrastructure Department Baidu China College of Art and Communication Tongji University China Faculty of Applied Sciences Macao Polytechnic University China Computer Science and Technology Hunan University of Science and Technology China

ISBN: (纸本)9798400708527

This paper mainly discusses how to use the large language models such as GPT and Ernie model combined with the AIGC tools represented by stable diffusion, which uses a random story script to generate images with fixed style, character characteristics, and continuous plots. The article provides a detailed introduction to how to build an assembly line, using a large language model and a story script to generate the prompt words required for stable diffusion. Subsequently, by comparing the characteristics of traditional picture book production and the image results of using language models word prompts, summarize the limitations of text to images. This leads to a supervised multi round iterative LoRA model scheme that utilizes the CLIP to achieve character IP fixation. Simultaneously using the ControlNet model and inpainting to preprocess and reprocess the image can achieve controllable character poses and fixed backgrounds in the picture book. Finally, we will evaluate and summarize the new scheme and analyze its strengths in picture book creation accordingly. © 2023 ACM.

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Advancing Data Security through Blockchain Integration in Encryption Algorithms

Advancing Data Security through Blockchain Integration in En...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Rajani, D. Raj, Vijilius Helena Dutt, Amit Singh, Jay Thandra, Nagarjuna Jabbar, Ahmed Sabah Abed AL-Zahra Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India New Horizon College Of Engineering Department Of Applied Sciences Bangalore India Lovely Professional University Phagwara India Gl Bajaj Institute Of Technology & Management U.P. Greater Noida India Mlr Institute Of Technology Department Of Computer Science And Engineering Telangana Hyderabad India The Islamic University Medical Laboratory Technology Department College Of Medical Technology Najaf Iraq

ISBN: (纸本)9798350366846

This piece describes a complicated blockchain-based system for keeping data safe that is not controlled by a single entity. The suggested design is made up of DKM, SCAC, ILE, QRC, and DAE. These stand for Decentralized Key Management and Immutable Ledger Encryption, Smart Contract Access Control, and Quantum-Resistant Cryptography. The goal of every program is to make data safer. The ILE algorithm uses the DKM algorithm's randomized key sharing to store data that can't be changed. As a part of a full security system, the SCAC Algorithm uses smart contracts to flexibly limit user access. To protect against possible quantum threats, QRC uses lattice-based encryption. Dynamic encryption based on agreement is made possible by the DAE algorithm. The study on ablation shows that these ways make the design safer. The suggested system works better than the most recent ones in terms of speed, storage efficiency, decentralization, resistance to quantum attacks, access control freedom, and the usefulness of an audit record. Finally, the suggested design is useful in many situations because it meets both current and future protection needs. As blockchain technology grows, our proactive method guard's data security and makes it more resistant to new cryptographic flaws. This makes decentralized safe data management possible. © 2024 IEEE.

关键词： Smart contract

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A system for detecting scratches on the surface of small-sized metal shafts after the grinding process using optical techniques combined with computer vision.

A system for detecting scratches on the surface of small-siz...

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International Symposium on applied science 2024, ISAS 2024

作者： Dau, Sy Hieu An, Khang Le Nguyen Thi, Phuc Dang Department of Applied Physics Applied Sciences Faculty Ho Chi Minh city University of Technology Vietnam National University HCMC Ho Chi Minh City Viet Nam Department of Computer Science Faculty of Information Technology Industrial University of Ho Chi Minh City Ho Chi Minh City Viet Nam

During the grinding process of metal machine parts requiring a high degree of smoothness, the use of diamond-surfaced grinding tools, specialized grinding stones, or high-hardness metals are common choices. However, during grinding, surface tool damage or misalignment between grinding surfaces can cause defects such as cracks, scratches, or complete surface destruction. Detecting small scratches is one of the biggest challenges due to their size and distribution, making it difficult to quickly identify them using the human eye. Automated systems, on the other hand, struggle to highlight scratches because automatic optical lighting systems face strong reflections from the metal surface, especially cylindrical surfaces after being polished. As a result, the images obtained lack controlled reflection positions and contrast between normal and scratched areas, leading to inefficiency in detecting these scratches. In this study, we propose an optical system combined with lighting techniques to highlight scratches on the surface of a cylindrical machine part after polishing, thereby detecting and classifying these defects to make pass-fail decisions in practical production line applications. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Grinding (machining)

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Underwater Target Detection Based on Reinforcement Learning and Ant Colony Optimization

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Journal of Ocean University of China 2022年第2期21卷 323-330页

作者： WANG Xinhua ZHU Yungang LI Dayu ZHANG Guang School of Computer Science Northeast Electric Power UniversityJilin 132012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and TechnologyJilin UniversityChangchun 130012China State Key Laboratory of Applied Optics Changchun Institute of OpticsFine Mechanics and PhysicsChinese Academy of SciencesChangchun 130033China

Underwater optical imaging produces images with high resolution and abundant information and hence has outstanding advantages in short-distance underwater target ***,low-light and high-noise scenarios pose great challenges in un-derwater image and video *** improve the accuracy and anti-noise performance of underwater target image edge detection,an underwater target edge detection method based on ant colony optimization and reinforcement learning is proposed in this ***,the reinforcement learning concept is integrated into artificial ants’movements,and a variable radius sensing strategy is pro-posed to calculate the transition probability of each *** methods aim to avoid undetection and misdetection of some pixels in image ***,a double-population ant colony strategy is proposed,where the search process takes into account global search and local search *** results show that the algorithm can effectively extract the contour information of underwater targets and keep the image texture well and also has ideal anti-interference performance.

关键词： ant colony optimization reinforcement learning underwater target edge detection

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DL-CMFD: Deep Learning-Based Copy-Move Forgery Detection Using Parallel Feature-Extractor

DL-CMFD: Deep Learning-Based Copy-Move Forgery Detection Usi...

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2023 IEEE International Conference on computer, Information, and Telecommunication Systems, CITS 2023

作者： Faheem, Layeba Mukherjee, Soumya Obaidat, Mohammad S. Pal, Arup Kumar Islam, Sk Hafizul Sadoun, Balqies Dhanbad Department of Computer Science and Engineering Jharkhand Dhanbad826004 India University of Science and Technology Beijing100083 China Amity University UP Noida201313 India Indian Institute of Information Technology Department of Computer Science and Engineering West Bengal Kalyani741235 India College of Engineering Al-Balqa' Applied University Al-Salt19117 Jordan

ISBN: (纸本)9798350336092

Due to readily available software, anyone can easily manipulate the image and create a piece of false information. Among different types of image forgery, copy-move image forgery is widely practiced due to its sophistication and difficulty distinguishing it from the original image. We proposed a new deep learning-based copy-move forgery detection (DL-CMFD) technique to identify the forged image by successfully localizing the forged area to address this challenge. To attain this, the scale-invariant features are extracted from the forged image using the parallel feature extraction technique utilizing a batch normalization-based convolution neural network with the aid of the ReLU activation function. In the next step, to identify the identical features, self-correlation has been calculated, and further, percentile pooling is used to find the top k-percent features. Lastly, a masked image is produced as the output. The proposed scheme has been deployed on the different available datasets like CoMoFD and COCO to testify to the efficiency and accuracy. The proposed scheme is found to be robust because it can detect the forged area accurately when deployed on post-processed forged images. We made various statistical parametric evaluations to evaluate the efficacy and conformity of the proposed copy-move forgery detection scheme, and a comparison analysis is also made. © 2023 IEEE.

关键词： Convolutional neural networks

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VigilantAI: Real-time detection of anomalous activity from a video stream using deep learning

VigilantAI: Real-time detection of anomalous activity from a...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Javed, Danish Arshad, Usama Peerzada, Shuhrabeel Saud, Muhammad Ramiz Ali, Nisar Ali, Raja Hashim Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

In an era where artificial intelligence (AI) solutions are increasingly integrated into various sectors, we have utilized Artificial Intelligence for enhancing public safety through realtime detection of illegal activities such as robberies and threats at gunpoint using CCTV footage. With the proliferation of deep learning in object detection, the study focuses on deploying the YoloV5 model, trained on a custom dataset compiled from diverse CCTV sources and movies, to identify specific criminal actions. One of the major problems faced in this field is the availability of a large robust labeled dataset on which a deep learning model can be trained. For this purpose, we have created our own dataset by converting various CCTV footage and movies into images, and then labeling them with the correct class. In addition, we also augmented data by using various data augmentation techniques for the chosen images. This dataset, enriched through augmentation techniques and annotated with bounding boxes, allows for the precise detection of threats, achieving an accuracy rate of 85%. Our system stands out by not only spotting these activities but also by instantly alerting security personnel, facilitating a rapid response to potentially dangerous situations. This capability is important for law enforcement agencies worldwide, offering them an advanced tool to act swiftly and prevent crimes, thereby enhancing public security. The essence of our work demonstrates the practical application and significant impact of AI in bolstering security measures, providing a solid foundation for future enhancements in the field. Through this initiative, we aim to foster a safer environment in public spaces, reducing crime rates and increasing the general public’s sense of safety. © 2024 IEEE.

关键词： Crime

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Rapid and Efficient Facial Landmark Identification by Light and High Resolution Network using Artificial Intelligence

Rapid and Efficient Facial Landmark Identification by Light ...

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2023 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications, ICRAMET 2023

作者： Rahman, Saad Mamoun Abdel Armi, Nasrullah Abdelhag, Mohammed Eltahir Amin, Sherif Tawfik Eishah, Hassan Abu King Faisal University Department of Applied College Computer Science Saudi Arabia Research Center for Telecommunication National Research & Innovation Agency Bandung Indonesia Jazan University Department of Information Technology & Security Jazan Saudi Arabia Jazan University Department of Computer Science Jazan Saudi Arabia

ISBN: (纸本)9798350343892

The ability to detect facial landmarks is in high demand because of its usefulness in monitoring driver health in real time. Heat map-based approaches are well-known for their exact accuracy in landmark position prediction, tiny, and excellent-resolution networks may accomplish quick estimations. While tiny high-resolution networks (THRN) have been successful in connecting feature maps of varying resolutions, the fusion block's costly computation remains an issue. Furthermore, THRN does not make use of the powerful output module seen in high-resolution networks. We propose an entirely novel design called THRN to address these issues. Using multi-resolution feature maps and an innovative fusion block, THRN Plus improves in two ways. First, it reduces the processing timing of its output module. On two different face landmark datasets, we ran extensive trials and showed that THRN greatly improved baseline techniques while keeping the latest in exactness at a cost to computation of roughly ten million FLOPS. © 2023 IEEE.

关键词： Artificial intelligence

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IoT Devices Authentication Using Artificial Neural Network

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computers, Materials & Continua 2022年第2期70卷 3701-3716页

作者： Syed Shabih Ul Hasan Anwar Ghani Ikram Ud Din Ahmad Almogren Ayman Altameem Department of Computer Science and Software Engineering International Islamic University Islamabad45000Pakistan Department of Information Technology The University of Haripur22620HaripurPakistan Chair of Cyber Security Department of Computer ScienceCollege of Computer and Information SciencesKing Saud UniversityRiyadh11633Saudi Arabia Chair of Cyber Security Department of Natural and Engineering SciencesCollege of Applied Studies and Community ServicesKing Saud UniversityRiyadh11543Saudi Arabia

User authentication is one of the critical concerns of information *** tend to use strong textual passwords,but remembering complex passwords is hard as they often write it on a piece of paper or save it in their mobile *** passwords are slightly unprotected and are easily *** attacks include dictionary,shoulder surfing,and brute *** passwords overcome the shortcomings of textual passwords and are designed to aid memorability and ease of *** paper proposes a Process-based Pattern Authentication(PPA)system for Internet of Things(IoT)devices that does not require a server to maintain a static password of the login *** server stores user’s information,which they provide at the time of registration,i.e.,the R-code and the symbol,but the P-code,i.e.,the actual password,will change with every login attempt of *** this scheme,users may draw a pattern on the basis of calculation from the P-code and Rcode in the PPA pattern,and can authenticate themselves using their touch dynamic behaviors through Artificial Neural Network(ANN).The ANN is trained on touch behaviors of legitimate users reporting superior performance over the existing *** experimental purposes,PPA is implemented as a prototype on a computer system to carry out experiments for the evaluation in terms of memorability and *** experiments show that the system has an effect of 5.03%of the False Rejection Rate(FRR)and 4.36%of the False Acceptance Rate(FAR),respectively.

关键词： Implicit authentication behavioral authentication artificial neural network processed pattern authentication

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Metaheuristic Based Clustering with Deep Learning Model for Big Data Classification

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computer Systems science & Engineering 2023年第1期44卷 391-406页

作者： R.Krishnaswamy Kamalraj Subramaniam V.Nandini K.Vijayalakshmi Seifedine Kadry Yunyoung Nam Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India Department of Biomedical Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatore641021India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600077India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two *** the same time,clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in ***-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial *** data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce *** this motivation,this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics(MR-HDBCC).The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big *** addition,the MR-HDBCC technique involves three distinct processes namely pre-processing,clustering,and *** proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)techni-que which is capable of detecting random shapes and diverse clusters with noisy *** improving the performance of the DBSCAN technique,a hybrid model using cockroach swarm optimization(CSO)algorithm is developed for the exploration of the search space and determine the optimal parameters for density based ***,bidirectional gated recurrent neural network(BGRNN)is employed for the classification of big *** experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.

关键词： Big data data classification clustering mapreduce dbscan algorithm

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