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Telecommunications Conference (ITC-Egypt), International

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

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

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.

关键词： Resistance Image coding Chaotic communication DNA Encryption Decoding Telecommunications

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Drone Gesture Control using OpenCV and Tello

Drone Gesture Control using OpenCV and Tello

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Conference on information Communication technology and Society (ICTAS)

作者： Nokuthula Khoza Pius Owolawi Vusumuzi Malele Department of Computer Systems Engineering Faculty of Information and Communication Technology Soshanguve Campus Tshwane University of Technology Pretoria South Africa Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus North-West University Vanderbijlpark South Africa

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography.

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A Sketch-Based Generation Model for Diverse Ceramic Tile Images Using Generative Adversarial Network

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Intelligent Automation & Soft Computing 2023年第9期37卷 2865-2882页

作者： Jianfeng Lu Xinyi Liu Mengtao Shi Chen Cui Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310000China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

Ceramic tiles are one of the most indispensable materials for interior *** ceramic patterns can’t match the design requirements in terms of diversity and interactivity due to their natural *** this paper,we propose a sketch-based generation method for generating diverse ceramic tile images based on a hand-drawn sketches using Generative Adversarial Network(GAN).The generated tile images can be tailored to meet the specific needs of the user for the tile *** proposed method consists of four ***,a dataset of ceramic tile images with diverse distributions is created and then pre-trained based on ***,for each ceramic tile image in the dataset,the corresponding sketch image is generated and then the mapping relationship between the images is trained based on a sketch extraction network using ResNet Block and jump connection to improve the quality of the generated ***,the sketch style is redefined according to the characteristics of the ceramic tile images and then double cross-domain adversarial loss functions are employed to guide the ceramic tile generation network for fitting in the direction of the sketch style and to improve the training ***,we apply hidden space perturbation and interpolation for further enriching the output textures style and satisfying the concept of“one style with multiple faces”.We conduct the training process of the proposed generation network on 2583 ceramic tile images *** measure the generative diversity and quality,we use Frechet Inception Distance(FID)and Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)*** experimental results prove that the proposed model greatly enhances the generation results of the ceramic tile images,with FID of 32.47 and BRISQUE of 28.44.

关键词： Ceramic tile pattern design cross-domain learning deep learning GAN generative adversarial networks ResNet Block

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Outage Performance of Two-Way Relaying Secure Multicast Networks Using Fountain Codes and Digital Network Coding 7

Outage Performance of Two-Way Relaying Secure Multicast Netw...

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7th International Conference on Green technology and Sustainable Development, GTSD 2024

作者： Hau, Nguyen Thi Nam, Pham Minh Tin, Phu Tran Duy, Tran Trung Hanh, Tan Industrial University of Ho Chi Minh City Ho Chi Minh city Viet Nam Sai Gon University Ho Chi Minh city Viet Nam Industrial University of Ho Chi Minh City Faculty of Electronics Technology Ho Chi Minh city Viet Nam Ton Duc Thang University Data Science Laboratory Faculty of Information Technology Ho Chi Minh city Viet Nam Posts and Telecommunications Institute of Technology Ho Chi Minh city Viet Nam

ISBN: (纸本)9798350386073

This paper studies secure multicast networks utilizing two-way relaying with Fountain codes. In this proposed framework, a node S and a group of nodes exchange Fountain packets through a common relay, in the presence of a passive eavesdropper. Our scheme employs three-phase digital network coding and randomize-and-forward techniques. Each node endeavors to collect sufficient Fountain packets to reconstruct the desired data. This paper assesses the performance of the proposed scheme using metrics such as outage probability, intercept probability, system outage probability, and system intercept probability, over Rayleigh fading channels. The findings reveal a trade-off between the outage performance and the intercept performance. Additionally, system parameters such as the relay's position, and the number of Fountain packet transmission significantly impact the system performance. © 2024 IEEE.

关键词： Fading channels

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Designing Robust Deep Learning Models for RealTime data Analysis in Machine Learning 3

Designing Robust Deep Learning Models for RealTime Data Anal...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Borade, Monali R. Gupta, Mukur Faiz, Ayaan Acharjya, Kalyan Rajesh, A. Nunciya, P.M. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Faculty of Engineering and Technology Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India

ISBN: (纸本)9798350319125

The developing call for real-time statistics analysis and selection-making has been a primary driver of improving deep studying models for the device getting to know. These models permit the evaluation of massive statistics sets, the generation of predictions from the statistics, and the speedy studying of new behaviors and patterns. Deep mastering fashions have been efficaciously used for various tasks, including natural language processing, computer imagination and prescient, and reinforcement studying. However, because of their complexity and a massive variety of parameters, they can be unreliable and volatile when uncovered to real-global records. This paper outlines strategies and techniques to lay out deep mastering fashions that can be robust to actual-world records. We also provide high-quality practices for deploying and retaining deep learning models in manufacturing packages. Those strategies include regularization techniques, model architectures, facts pre-processing techniques, and model validation and monitoring techniques. We additionally speak of the numerous strategies for assessing and improving the reliability of deep getting-to-know fashions. Subsequently, we provide a case study of a product that uses deep studying fashions to stumble on and classify objects in near-actual time in a manufacturing place. © 2023 IEEE.

关键词： improving maintaining production regularization techniques

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An Evaluation Metric for Single Image-to-3D Models Based on a Class Confidence Score of Object Detection Models

An Evaluation Metric for Single Image-to-3D Models Based on ...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Yuiko Uchida Ren Togo Keisuke Maeda Takahiro Ogawa Miki Haseyama School of Engineering Hokkaido University Japan Faculty of Information Science and Technology Hokkaido University Japan Data-Driven Interdisciplinary Research Emergence Department Hokkaido University Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

This paper proposes a novel evaluation metric for single image-to-3D models based on a class confidence score of an object detection model. Traditional metrics, such as Structural Similarity Index and CLIP-similarity, often fail to accurately evaluate the quality of generated 3D objects. This is particularly evident when the objects have similar luminance but different meanings, or when the object is blurred. Our proposed metric can overcome these limitations by evaluating both semantic and visual consistency. Experimental results demonstrate that the proposed metric exhibits a higher rank correlation coefficient with the order of the model publication year compared to conventional metrics.

关键词： Measurement Correlation coefficient Visualization Three-dimensional displays Accuracy Semantics Object detection Indexes Consumer electronics

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Enhance Efficient of Support Vector Machine with CNN and PSO for solving Handwritten Digits Recognition

Enhance Efficient of Support Vector Machine with CNN and PSO...

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International Computer science and Engineering Conference (ICSEC)

作者： Siwach Janprasit Narong Punkong Chiabwoot Ratanavilisagul Somkiat Kosolsombat Department of Computer and Information Science Faculty of Applied Science King Mongkut’s University of Technology North Bangkok (KMUTNB) Bangkok Thailand Digital Technology for Business Faculty of Management Science Kanchanaburi Rajabhat University (KRU) Kanchanaburi Thailand Department of Computer and Information Sciences Faculty of Applied Science King Mongkut's University of Technology North Bangkok (KMUTNB) Bangkok Thailand Data Science and Innovation College of Interdisciplinary Studies Thammasat University Thailand

ISBN: (数字)9798350366860

ISBN: (纸本)9798350366877

handwritten digit recognition is a crucial task in various fields such as postal mail sorting, bank check processing, and digitizing handwritten documents. This research aims to compare the effectiveness of using Convolutional Neural Network (CNN) for feature extraction combined with Support Vector Machine (SVM) for classification, against the traditional method of using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) for feature extraction. Additionally, Particle Swarm Optimization (PSO) is applied to optimize SVM parameters to further enhance model performance. The experiments are conducted on two datasets: the MNIST dataset and an individual handwriting dataset from 50 participants. The results indicate that the proposed method of using CNN for feature extraction and PSO-optimized SVM achieves superior accuracy compared to traditional DWT and DCT methods. Specifically, the proposed method achieved an accuracy of 99.05% on the MNIST dataset and 67.79% on the individual handwriting dataset, demonstrating its robustness and effectiveness in handwritten digit recognition.

关键词： Support vector machines Handwriting recognition Accuracy Feature extraction Vectors Discrete wavelet transforms Convolutional neural networks Discrete cosine transforms Particle swarm optimization Optimization

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Beyond Histogram Comparison: Distribution-Aware Simple-Path Graph Kernels

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Ye, Wei Tang, Shuhao Tian, Hao Chen, Qijun Shanghai Institute of Intelligent Science and Technology Tongji University College of Electronic and Information Engineering Shanghai201804 China Shanghai Innovation Institute Shanghai200231 China City University of Macau Faculty of Data Science Taipa China Tongji University College of Electronic and Information Engineering Shanghai201804 China

R-convolution graph kernels are conventional methods for graph classification. They decompose graphs into substructures and aggregate all the substructure similarity as graph similarity. However, the substructure similarity is based on graph isomorphism, which not only leads to binary similarity values but also cannot be aware of the probability distribution of substructures in each graph. Moreover, the simple sum aggregation is not aware of the probability distribution differences of substructures across graphs. These drawbacks cause inaccurate graph similarity. To resolve these problems, we propose a new method called the Distribution-Aware Simple-Path (DASP) graph kernel. The neural language models are employed to capture the probability distribution of substructures (specifically, simple paths) in each graph. A new metric called Probabilistic Minkowski Distance is developed to capture the probability distribution differences of simple paths across graphs. To further improve the performance, the label alphabet is expanded to enlarge the corpus of simple paths for the neural language models and DASP. Experiments demonstrate that DASP achieves the best classification accuracy on all the selected graph benchmark datasets. © 2020 IEEE.

关键词： Knowledge graph

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Content Based Image Retrieval Using Multi-deep Learning Models and K-Nearest Neighbor Approaches 2nd

Content Based Image Retrieval Using Multi-deep Learning Mode...

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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Hung, Bui Thanh Pramanik, Sabyasachi Kottursamy, Kottilingam Data Science Department Faculty of Information Technology Industrial University of Ho Chi Minh City Ho Chi Minh City Viet Nam Haldia Institute of Technology Haldia India SRM Institute of Science and Technology Kattankaluthur Chennai India

ISBN: (纸本)9783031689079

Content-based Image Retrieval (CBIR) is a pivotal process for querying images and retrieving relevant results. This research carries a significant impact across diverse applications in our daily lives. Despite the myriad of proposed approaches, this problem continues to pose a formidable challenge. In this study, we advance the quality of image search through the integration of K-Nearest Neighbor with multiple deep neural networks in CBIR. Initially, crucial features are extracted utilizing pre-trained CNN models. Subsequently, we gauge the similarity between feature vectors via the K-Nearest Neighbors algorithm. Our experimentation is conducted on the Oxford-IIIT Pet Image dataset, and the model's efficacy is assessed across diverse CBIR metrics. The results of our experiments substantiate the exceptional performance of our proposed system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Content based retrieval

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Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

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CAAI Transactions on Intelligence technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

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