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arXiv 2023年

作者： Liu, Wenjie Li, Zixian Yuan, Gonglin Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China School of Mathematics and Information Science Center for Applied Mathematics of Guangxi Guangxi University Nanning530004 China

in this paper, we present a polynomial-complexity algorithm to construct a special orthogonal matrix for the deterministic remote state preparation (DRSP) of an arbitrary n-qubit state, and prove that if n > 3, such matrices do not exist. Firstly, the construction problem is split into two sub-problems, i.e., finding a solution of a semi-orthogonal matrix and generating all semi-orthogonal matrices. Through giving the definitions and properties of the matching operators, it is proved that the orthogonality of a special matrix is equivalent to the cooperation of multiple matching operators, and then the construction problem is reduced to the problem of solving an XOR linear equation system, which reduces the construction complexity from exponential to polynomial level. Having proved that each semi-orthogonal matrix can be simplified into a unique form, we use the proposed algorithm to confirm that the unique form does not have any solution when n > 3, which means it is infeasible to construct such a special orthogonal matrix for the DRSP of an arbitrary n-qubit state. © 2023, CC BY.

关键词： Matrix algebra

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CNNs in Crop Care: A Comparative Analysis of Tomato Disease Detection Models 2

CNNs in Crop Care: A Comparative Analysis of Tomato Disease ...

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2nd international Conference on Cyber Resilience, iCCR 2024

作者： Ali, Ali M Nashwan, Shadi Al-Qerem, Ahmad Aldweesh, Amjad Alauthman, Mohammad Elgamal, Zenab Almomani, Ammar AlAhliyya Amman University Faculty Of Engineering Communications And Computer Engineering Department Amman19328 Jordan Faculty Of Information Technology Cybersecurity Department Amman11831 Jordan Zarqa University Faculty Of Information Technology Computer Science Department Zarqa13110 Jordan College Of Computing And It Shaqra University Shaqra Saudi Arabia University Of Petra Faculty Of Information Technology Department Of Information Security Amman Jordan School Of Computing Skyline University College University City Of Sharjah Sharjah United Arab Emirates School Of Computing Skyline University College Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I.T. Department Irbid Jordan

ISBN: (纸本)9798350394962

Timely and accurate detection of plant diseases is critical for sustainable crop production. This paper presents a comparative analysis of convolutional neural network (CNN) models for automated tomato leaf disease detection, including VGG16, VGG19, AlexNet and Google Net. A dataset of tomato leaf images with multiple disease types was utilized to train and test the performance of each CNN architecture. Our results demonstrate that the VGG16 model achieved the highest accuracy of 99.38% in classifying tomato leaf diseases. Compared to AlexNet and VGG19 models which obtained 98.67% and 99.29% accuracy respectively, the superior performance of VGG16 highlights its suitability for deploying robust tomato disease detection systems to enable prompt disease control interventions. Our work provides valuable insights on harnessing deep learning for agricultural disease surveillance, allowing farmers and agronomists to identify crop infections early and implement timely management practices to reduce yield losses. © 2024 iEEE.

关键词： Convolutional neural networks

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Transformative Applications and Key Challenges of Generative Ai

Transformative Applications and Key Challenges of Generative...

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international iEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Attila Kovari Jozsef Katona Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Department of Institute of Engineering University of Dunaújváros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The paper discusses the integration and use of generative artificial intelligence technologies in education. Generative Ai, such as OpenAi, in particular the GPT model, has fundamentally changed the way humans interact with machines. Advanced technologies in the field of generative Ai in education are mostly used to personalize learning experiences by dynamically customizing learning materials according to the needs of learners, thus enabling the improvement of outcomes. This article explores the use of generative Ai tools with the aim of assessing how useful students perceive these tools to be. The findings show mixed receptivity from users, highlighting the critical balance in using innovative Ai capabilities through Ai.

关键词： Power engineering Accuracy Generative Ai Education Chatbots Market research Problem-solving Reliability

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Framework of software design patterns for energy-aware embedded systems 15

Framework of software design patterns for energy-aware embed...

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15th international Conference on Evaluation of Novel Approaches to software engineering, ENASE 2020

作者： Schaarschmidt, Marco Uelschen, Michael Pulvermuller, Elke Westerkamp, Clemens Faculty of Engineering and Computer Science University of Applied Sciences Osnabrück Germany Software Engineering Research Group University of Osnabrück Germany

ISBN: (纸本)9789897584213

With the increasing size and complexity of embedded systems, the impact of software on energy consumption is becoming more important. Previous research focused mainly on energy optimization at the hardware level. However, little research has been carried out regarding energy optimization at the software design level. This paper focuses on the software design level and addresses the gap between software and hardware design for embedded systems. This is achieved by proposing a framework for software design patterns, which takes aspects of power consumption and time behavior of the hardware level into account. We evaluate the expressiveness of the framework by applying it to well-known and novel design patterns. Furthermore, we introduce a dimensionless numerical efficiency factor to make possible energy savings quantifiable. © Copyright 2020 by SCiTEPRESS - science and Technology Publications, Lda. All rights reserved.

关键词： Embedded systems

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Modeling the One Class of Radionuclide Migration Processes in Soils under Non-isothermal Conditions Using Perturbation Theory Methods

Modeling the One Class of Radionuclide Migration Processes i...

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international Conference on Advanced computer information Technologies (ACiT)

作者： Andrii Bomba Oksana Ostapchuk Roman Vovk Miroslav Kopnický Department of Computer Science and Applied Mathematics National University of Water and Environmental Engineering Rivne Ukraine Department of Software Engineering Ivano-Frankivsk National Technical University of Oil and Gas Ivano-Frankivsk Ukraine Department of Catholic Theology Catholic University in Ružomberok Spišská Kapitula Slovakia

Based on the synthesis of complex analysis methods, perturbation theory, and characteristics, a new approach has been developed for accounting for osmosis and temperature in predicting the migration processes of radionuclides under non-isothermal conditions in quasi-ideal porous media (in curved regions) bounded by streamlines and equipotential lines. The solution to the corresponding degenerate problem was obtained based on the developed method of nonlinear inversion of boundary value problem solutions into quasi-conformal mappings. An algorithm for calculating a uniformly dynamic grid, streamlines, and speed of ideal filtration field was constructed. With the developed method of characteristics, formulas for the approximate solution of diffusion-convection mass transfer problems in perturbed osmosis and heat filtration fields were obtained.

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Research on Deep Learning of Convolutional Neural Network for Action Recognition of intelligent Terminals in the Big Data Environment and its intelligent software Application

Research on Deep Learning of Convolutional Neural Network fo...

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iEEE international Conference on Automation, Electronics and Electrical engineering (AUTEEE)

作者： Jiahan Sun Shengyuan Zhang Jieting Lian Lei Fu Zijie Zhou Yuxin Fan Kai Xu Shenyang Jianzhu University Shenyang China Master of Professional Science in Applied Statistics- Data Science College of Computing and Information Science Cornell University New York America Master of Science in Database Technology School of Professional Studies New York University New York America Master of Science in Computer Software Engineering College of Engineering Northeastern University Boston Massachusetts USA University of Pennsylvania Philadelphia Pennsylvania USA PipeChina Zhejiang Pipeline Network Co. LTD. Hangzhou China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

To enhance the accuracy and reliability of big data network traffic intrusion detection, an innovative detection model based on the two-branch convolution and gated recurrent unit was constructed, and its application effectiveness in the field of software engineering was thoroughly explored. This model first performs preprocessing operations on the original network traffic data, and then imports it as input information into the core architecture system jointly composed of the two-branch convolution and gated recurrent unit. Among them, the two-branch convolution focuses on quickly and accurately extracting the multi-dimensional features of the network traffic data, while the gated recurrent unit emphasizes on modeling and analyzing the long-term correlation patterns in the feature sequence. Through the organic combination of the two, the potential features of the data are deeply mined. Subsequently, the obtained feature output is directly transmitted to the classifier, so as to achieve accurate discrimination of various types of network traffic such as normal traffic, DoS attack traffic, DDoS attack traffic, port scanning traffic, and SQL injection attack traffic. The comparison experiment data shows that for network traffic data from different sources and with diverse patterns, the detection accuracy rate of this model is as high as 84.3%. This result strongly verifies its high efficiency and practicality in the field of big data network traffic intrusion detection, provides a solid technical guarantee foundation for the network security protection link in software engineering, has important theoretical and practical values, and is expected to promote the further development and application expansion of network security technologies.

关键词： Accuracy Convolution Telecommunication traffic Big Data Logic gates Feature extraction Data models software Convolutional neural networks software engineering

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Comparative Analysis of Various image Splicing Algorithms 8th

Comparative Analysis of Various Image Splicing Algorithms

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8th international Workshop on Soft Computing Applications, SOFA 2018

作者： ur Rhhman, Hafiz Arif, Muhammad Ullah, Anwar Al-Azani, Sadam Balas, Valentina Emilia Geman, Oana Khan, Muhammad Jalal islam, Umar School of Computer Science and Educational Software Guangzhou University Guangzhou510006 China Wollongong Joint Institute Central China Normal University WuhanHubei China Department of Information and Computer Science King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Automatics and Applied Informatics Aurel Vlaicu University of Arad Arad Romania Faculty of Electrical Engineering and Computer Science University Suceava Suceava Romania Department of Computer Science COMSATS University Abbottabad Abbottabad Pakistan

ISBN: (纸本)9783030521899

Daily millions of images are uploaded and download to the web, as a result the data is available in the paperless form in the computer system for organization. Nowadays, with the help of powerful computer software such as Photoshop and Corel Draw, it is very easy to alter the contents of the authenticated image without leaving any clues. This led to a big problem due to the negative impact of image splicing. it is highly recommended to develop image tampering detection technique to recognize the authentic and temper images. in this paper, we propose an enhanced technique for blind images splicing by combing Discrete Cosine Transform Domain (DTC) and Markov feature in the spatial domain. Moreover, Principal Component Analysis (PCA) is used to select the most significant features. Finally, Support Vector Machine (SVM) is applied to classify the image as being tempered or genuine on the publicly available dataset using ten-fold cross-validation. By applying different statistical techniques, the results showed that the proposed technique performs better than other available detection techniques in the literature. © 2021, Springer Nature Switzerland AG.

关键词： Principal component analysis

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A Novel Explainable Artificial intelligence Model in image Classification problem

arXiv

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arXiv 2023年

作者： Cao, Hung Quoc Nguyen, Hung Truong Thanh Nguyen, Khang Vo Thanh Nguyen, Phong Xuan FSO.QNH.QAI.AIC FPT Software Binh Dinh Viet Nam FSO.QNH.QAI.AIC FPT Software Viet Nam Department of Computer Science Frankfurt University of Applied Sciences Frankfurt am Main Germany Graduate School of Engineering Advanced Interdisciplinary Studies The University of Tokyo Japan

in recent years, artificial intelligence is increasingly being applied widely in many different fields and has a profound and direct impact on human life. Following this is the need to understand the principles of the model making predictions. Since most of the current high-precision models are black boxes, neither the Ai scientist nor the end-user deeply understands what’s going on inside these models. Therefore, many algorithms are studied for the purpose of explaining Ai models, especially those in the problem of image classification in the field of computer vision such as LiME, CAM, GradCAM. However, these algorithms still have limitations such as LiME’s long execution time and CAM’s confusing interpretation of concreteness and clarity. Therefore, in this paper, we propose a new method called Segmentation - Class Activation Mapping (SeCAM) that combines the advantages of these algorithms above, while at the same time overcoming their disadvantages. We tested this algorithm with various models, including ResNet50, inception-v3, VGG16 from imageNet Large Scale Visual Recognition Challenge (iLSVRC) data set. Outstanding results when the algorithm has met all the requirements for a specific explanation in a remarkably concise time. © 2023, CC BY.

关键词： Machine learning

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Exploring Gene Regulatory interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma

arXiv

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arXiv 2024年

作者： Bhattacharjya, Abanti islam, Md Manowarul Uddin, Md Ashraf Talukder, Md. Alamin Azad, A.K.M. Aryal, Sunil Paul, Bikash Kumar Tasnim, Wahia Abdulllah Almoyad, Muhammad Ali Moni, Mohammad Ali School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Department of Software Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Riyadh13318 Saudi Arabia Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Bangladesh Department of Basic Medical Sciences College of Applied Medical Sciences King Khalid University Saudi Arabia Artificial Intelligence & Data Science Faculty of Health and Behavioural Sciences The University of Queensland Australia

With the advent of information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various Bioinformatics toolkits has facilitated the rapid processing and analysis of vast quantities of biological data for human perception. Most studies focus on locating two connected diseases and making some observations to construct diverse gene regulatory interaction networks, a forerunner to general drug design for curing illness. For instance, Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. in this study, we select EGFR-mutated lung adenocarcinoma and Hypopharyngeal cancer by finding the Lung metastases in hypopharyngeal cancer. To conduct this study, we collect Mircorarray datasets from GEO (Gene Expression Omnibus), an online database controlled by NCBi. Differentially expressed genes, common genes, and hub genes between the selected two diseases are detected for the succeeding move. Our research findings have suggested common therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). Our suggested therapeutic molecules will be fruitful for patients with those two diseases simultaneously. © 2024, CC BY.

关键词： Topology

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Diabetes Prediction Using Machine Learning

Diabetes Prediction Using Machine Learning

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Communication & Computing (WCONF), World Conference on

作者： Sandeep Bhatia Amit Kumar Goel Bharat Bhushan Naib Khushwant Singh Mohit Yadav Ashish Saini Department of CSE Galgotias University Greater Noida Uttar Pradesh India Department of Computer Science & Engineering U.I.E.T Maharshi Dayanand University Rohtak India Department of Applied Sciences Mathematics U.I.E.T Maharshi Dayanand University Rohtak India Department of Computer Science & Applications U.I.E.T Maharshi Dayanand University Rohtak India

Patient’s infirmity Assumption system is a psychological gauge structure which predicts an infection in light of the information or signs went into the system and gives the specific happens. We suggest a creative replacement for the traditional methodology that resolves the drawn-out issues with expert course of action arranging. This approach is the remedy if one isn’t too certifiable and simply has to retain what kind of illness they are dealing with. it very well may be a structure that gives the client urging on the most proficient method to keep their prosperity system with everything looking good and offers a strategy to recognize disease using this expectation. The prosperity section is indispensable to the treatment of patients’ diseases, so it is furthermore a casing of help to the medical services portion that the client will be taught and helped inside the event that the individual decides not to visit a center or other facility, allowing the client to remember around their condition by entering the incidental effects and some other fitting information.

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