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11th IEEE International Conference on Intelligent Computing and Information Systems, ICICIS 2023

作者： Ashraf, Mariam Tarek, Khadija Fathy, Naglaa Mahmoud, Mariam Emad, Rana Khaled, Khadija Amr, Noran Ain Shams University Faculty of Computer and Information Science Software Engineering Department Cairo Egypt Ain Shams University Faculty of Computer and Information Science Information Systems Department Cairo Egypt

ISBN: (纸本)9798350322101

In this paper we propose an improved recipe recommendation system that employs image recognition of food ingredients. The system is currently a mobile application that performs image recognition on uploaded or camera-captured images and recommends recipes containing the recognized ingredients. We used the ResNet-V2 architecture to build a convolutional neural network model for image recognition, which was able to identify 33 different food ingredients with an accuracy rate of 89%. The recommendation system uses the identified ingredient labels, as well as user preferences and restrictions, to display a list of recipes containing the identified ingredients. This feature allows users to discover new and exciting recipes based on the ingredients they currently have at home, without having to worry about dietary restrictions or other preferences. Overall, our system provides a convenient and personalized way for users to discover and prepare delicious meals based on their unique needs and preferences. © 2023 IEEE.

关键词： Image recognition

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Integration of Metaverse Recordings in Multimedia Information Retrieval 24

Integration of Metaverse Recordings in Multimedia Informatio...

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13th International Conference on software and computer Applications, ICSCA 2024

作者： Steinert, Patrick Wagenpfeil, Stefan Frommholz, Ingo Hemmje, Matthias L. University Of Hagen Faculty Of Mathematics And Computer Science Hagen Germany Pfh Private University Of Applied Sciences Faculty Of Business Computing And Software Engineering Göttingen Germany University Of Wolverhampton School Of Mathematics And Computer Science Wolverhampton United Kingdom

ISBN: (纸本)9798400708329

This paper addresses the challenges in integrating Metaverse Recordings in Multimedia Information Retrieval as a new type of multimedia. Specifically, we describe the characteristics of video content and explain the key differences of a metaverse to other videos. We present a specialized process to analyze and index Metaverse Recordings. We demonstrate that existing techniques can be used for metaverse-specific retrieval, but need content-specific adaptions. Furthermore, we evaluate a prototypical implementation based on the proposed methods with respect to the impact on the retrieval process. © 2024 Owner/Author.

关键词： Multimedia systems

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Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

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computers, Materials & Continua 2023年第10期77卷 681-697页

作者： Uzair Aslam Bhatti Sibghat Ullah Bazai Shumaila Hussain Shariqa Fakhar Chin Soon Ku Shah Marjan Por Lip Yee Liu Jing College of Information and Communication Engineering Hainan UniversityHaikou570228China Department of Computer Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Department of Computer Science Sardar Bahadur Khan Women’s UniversityQuettaPakistan Department of Computer Science Universiti Tunku Abdul RahmanKampar31900Malaysia Department of Software Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia

Crop diseases have a significant impact on plant growth and can lead to reduced *** methods of disease detection rely on the expertise of plant protection experts,which can be subjective and dependent on individual experience and *** address this,the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease *** this paper,we propose a novel approach that utilizes a convolutional neural network(CNN)model in conjunction with Inception v3 to identify plant leaf *** research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific *** models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct *** rigorous training and evaluation,the proposed system achieved an impressive accuracy rate of 99%.This mobile application serves as a convenient and valuable advisory tool,providing early detection and guidance in real agricultural *** significance of this research lies in its potential to revolutionize plant disease detection and management *** automating the identification process through deep learning algorithms,the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual *** integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.

关键词： Plant disease Inception v3 CNN crop diseases

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Trustworthiness Evaluation of Deep Learning Accelerators Using UVM-based Verification with Error Injection

Trustworthiness Evaluation of Deep Learning Accelerators Usi...

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2024 Design and Verification Conference and Exhibition Europe, DVCon Europe 2024

作者： Aboudeif, Randa Awaad, Tasneem A. AbdElsalam, Mohamed Ismail, Yehea Electronics and Communications Engineering Department The American University in Cairo Cairo Egypt Siemens Digital Industries Software Cairo Egypt Department of Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (纸本)9783800764396

Testing the reliability and trustworthiness of high-performance computing (HPC) applications has made Deep Learning Accelerators (DLAs) verification critically important. In this paper, we introduce a hardware verification framework with an error injection methodology based on the Universal Verification Methodology (UVM) for DLAs that is scalable, reusable, and efficient to test the robustness and resilience of Deep Neural Networks (DNNs) running on various DLA designs. Furthermore, the error injection methodology is applicable to simulation and hardware-assisted verification (HAV) platforms for emulation and FPGA prototyping. Our proposed error injection mechanism is evaluated using Nvidia Deep Learning Accelerator (NVDLA), an open-source DLA core. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Reusability

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Quantum software Models: Quantum Modules Tomography and Recovery Theorem 35

Quantum Software Models: Quantum Modules Tomography and Reco...

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35th International Conference on software engineering and Knowledge engineering, SEKE 2023

作者： Exman, Iaakov Zvulunov, Ariel School of Computer Science Faculty of Sciences HIT – Holon Institute of Technology Holon Israel Software Engineering The Jerusalem College of Engineering Azrieli Jerusalem Israel

Quantum Tomography partially measures and then recovers the remaining density matrix quantum state, in order to verify that a certain device – processor or detector – indeed outputs the intended quantum state. However, single matrix element measurements rapidly increase in number with the density matrix dimension and are error-prone. This work proposes a novel quantum software viewpoint on quantum tomography. Instead of individual matrix elements, measurements and density matrix recovery are performed on higher-abstraction modules, i.e. semantically meaningful groups of matrix elements. Quantum Modules Tomography potentially reduce the necessary number of explicit measurements, while still allowing recovery of the whole density matrix. A Recovery Theorem is formulated and proved: density matrix diagonal measurements suffice to recover the whole system density matrix, in terms of modules. The recovery procedure is illustrated by a few case studies. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Recovery

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Induced Matching Below Guarantees: Average Paves the Way for Fixed-Parameter Tractability 40

Induced Matching Below Guarantees: Average Paves the Way for...

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40th International Symposium on Theoretical Aspects of computer Science, STACS 2023

作者： Koana, Tomohiro Faculty IV Institute of Software Engineering and Theoretical Computer Science Algorithmics and Computational Complexity Technische Universität Berlin Germany

ISBN: (纸本)9783959772662

In this work, we study the Induced Matching problem: Given an undirected graph G and an integer , is there an induced matching M of size at least ? An edge subset M is an induced matching in G if M is a matching such that there is no edge between two distinct edges of M. Our work looks into the parameterized complexity of Induced Matching with respect to "below guarantee" parameterizations. We consider the parameterization u − for an upper bound u on the size of any induced matching. For instance, any induced matching is of size at most n/2 where n is the number of vertices, which gives us a parameter n/2 − . In fact, there is a straightforward 9n/2− · nO(1)-time algorithm for Induced Matching [Moser and Thilikos, J. Discrete Algorithms]. Motivated by this, we ask: Is Induced Matching FPT for a parameter smaller than n/2 − ? In search for such parameters, we consider MM(G) − and IS(G) − , where MM(G) is the maximum matching size and IS(G) is the maximum independent set size of G. We find that Induced Matching is presumably not FPT when parameterized by MM(G) − or IS(G) − . In contrast to these intractability results, we find that taking the average of the two helps – our main result is a branching algorithm that solves Induced Matching in 49(MM(G)+IS(G))/2− · nO(1) time. Our algorithm makes use of the Gallai–Edmonds decomposition to find a structure to branch on. © Tomohiro Koana.

关键词： Parameterization

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An Acceptance Model of Using Mobile-Government Services(AMGS)

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computer Modeling in engineering & Sciences 2022年第5期131卷 865-880页

作者： Ahmad Althunibat Mohammad Abdallah Mohammed Amin Almaiah Nour Alabwaini Thamer Ahmad Alrawashdeh Department of Software Engineering Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmmanJordan Faculty of Computer Sciences and Information Technology King Faisal UniversityAl-AhsaSaudi Arabia

In recent years,the telecommunications sector is no longer limited to traditional communications,but has become the backbone for the use of data,content and digital applications by individuals,governments and companies to ensure the continuation of economic and social activity in light of social distancing and total closure inmost countries in the ***,electronic government(e-Government)andmobile government(m-Government)are the results of technological evolution and ***,it is important to investigate the factors that influence the intention to use m-Government services among Jordan’s *** paper proposed a new m-Government acceptance model in Jordan(AMGS);this model combines the Information System(IS)Success Factor Model and Hofstede Cultural Dimensions *** study was conducted by surveying different groups of the Jordanian *** questionnaire was used to collect data from203 *** regression analysis has been conducted to analyze the *** results indicate that the significant predictors of citizen intention to use m-Government services in Jordan are Information Quality,Service Quality,Uncertainty Avoidance,and Indulgence ***,the results also suggest that Power Distance is not a significant predictor of citizen intention to use m-Government services.

关键词： Acceptance test e-Government m-Government acceptance model IS success factor model Hofstede cultural dimensions theory

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Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography

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computers, Materials & Continua 2024年第3期78卷 3303-3321页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy Muhammad Adnan Khan Muhammad Hasnain Azam Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi00000United Arab Emirates Department of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Computer Science Taibah UniversityMedina42353Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Engineering and Development Dubizzle LabsLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam-si13120Korea

Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or *** cardiotocography measures infer wrongly and give wrong predictions because of human *** traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as *** condition is very important to measure at numerous stages and give proper medications to the fetus for its *** the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional *** techniques play a pivotal role in detecting fetal disease in its early *** research article uses Federated machine learning(FML)and ML techniques to classify the condition of the *** study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the ***,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,***,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.

关键词： Cardiotocography ML FML fetal disease bio-signal

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A Fractional Fourier Based Medical Image Authentication Approach

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computers, Materials & Continua 2022年第2期70卷 3133-3150页

作者： Fayez Alqahtani Mohammed Amoon Walid El-Shafai Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Patient medical information in all forms is crucial to keep private and secure,particularly when medical data communication occurs through insecure ***,there is a bad need for protecting and securing the color medical images against impostors and *** this paper,an optical medical image security approach is *** is based on the optical bit-plane Jigsaw Transform(JT)and Fractional Fourier Transform(FFT).Different kernels with a lone lens and a single arbitrary phase code are exploited in this security approach.A preceding bit-plane scrambling process is conducted on the input color medical images prior to the JT and FFT processes to accomplish a tremendous level of robustness and *** confirm the efficiency of the suggested security approach for secure color medical image communication,various assessments on different color medical images are examined based on different statistical security ***,a comparative analysis is introduced between the suggested security approach and other conventional cryptography *** simulation outcomes acquired for performance assessment demonstrate that the suggested security approach is highly *** has excellent encryption/decryption performance and superior security results compared to conventional cryptography approaches with achieving recommended values of average entropy and correlation coefficient of 7.63 and 0.0103 for encrypted images.

关键词： Medical image security encryption JT FFT cryptosystem

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Enhancing Communication Accessibility:UrSL-CNN Approach to Urdu Sign Language Translation for Hearing-Impaired Individuals

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computer Modeling in engineering & Sciences 2024年第10期141卷 689-711页

作者： Khushal Das Fazeel Abid Jawad Rasheed Kamlish Tunc Asuroglu Shtwai Alsubai Safeeullah Soomro Department of Computer Engineering Modeling Electronics and Systems EngineeringUniversity of CalabriaRende Cosenza87036Italy Department of Information Systems University of Management and TechnologyLahore54770Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim UniversityIstanbul34303Turkey Department of Software Engineering Istanbul Nisantasi UniversityIstanbul34398Turkey Department of Computer Science COMSATS University IslamabadLahore CampusLahore54700Pakistan Faculty of Medicine and Health Technology Tampere UniversityTampere33720Finland Department of Computer Science College of Computer Engineering and Sciences in Al-KharjPrince Sattam Bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Second Department of Computer Science College of Engineering and ComputingGeorge Mason UniversityFairfaxVA 4418USA

Deaf people or people facing hearing issues can communicate using sign language(SL),a visual *** works based on rich source language have been proposed;however,the work using poor resource language is still *** other SLs,the visuals of the Urdu Language are *** study presents a novel approach to translating Urdu sign language(UrSL)using the UrSL-CNN model,a convolutional neural network(CNN)architecture specifically designed for this *** existingworks that primarily focus on languageswith rich resources,this study addresses the challenge of translating a sign language with limited *** conducted experiments using two datasets containing 1500 and 78,000 images,employing a methodology comprising four modules:data collection,pre-processing,categorization,and *** enhance prediction accuracy,each sign image was transformed into a greyscale image and underwent noise *** analysis with machine learning baseline methods(support vectormachine,GaussianNaive Bayes,randomforest,and k-nearest neighbors’algorithm)on the UrSL alphabets dataset demonstrated the superiority of UrSL-CNN,achieving an accuracy of ***,our model exhibited superior performance in Precision,Recall,and F1-score *** work not only contributes to advancing sign language translation but also holds promise for improving communication accessibility for individuals with hearing impairments.

关键词： Convolutional neural networks Pakistan sign language visual language

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