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Severity Recognition of Aloe vera Diseases Using Ai in Tensor Flow Domain

computers, Materials & Continua 2021年第2期66卷 2199-2216页

作者： Nazeer Muhammad Rubab Nargis Bibi Oh-Young Song Muhammad Attique Khan Sajid Ali Khan Department of IT&CS Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripur22620Pakistan COMSATS University Islamabad Wah CampusWah Cantt47040Pakistan Fatima Jinnah Women University Rawalpindi44000Pakistan Software Department Sejong UniversitySeoulSouth Korea Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Software Engineering Foundation University IslamabadIslamabadPakistan

Agriculture plays an important role in the economy of all ***,plant diseases may badly affect the quality of food,production,and ultimately the *** plant disease detection and management,agriculturalists spend a huge amount of ***,the manual detection method of plant diseases is complicated and ***,automated systems for plant disease detection using machine learning(ML)approaches are ***,most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input *** address the issue,this article proposes a fully automated method for plant disease detection and recognition using deep neural *** the proposed method,AlexNet and VGG19 CNNs are considered as pre-trained *** is capable to obtain the feature extraction of the given data with fine-tuning *** convolutional neural network feature extraction,it selects the best subset of features through the correlation coefficient and feeds them to the number of classifiers including K-Nearest Neighbor,Support Vector Machine,Probabilistic Neural Network,Fuzzy logic,and Artificial Neural *** validation of the proposed method is carried out on a self-collected dataset generated through the augmentation *** achieved average accuracy of our method is more than 96%and outperforms the recent techniques.

关键词： Plants diseases wavelet transform fast algorithm deep learning feature extraction classification

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ETHACA - A Container-Based Cyber Range for Research and Education

ETHACA - A Container-Based Cyber Range for Research and Educ...

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iEEE international Workshop on computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Nestoras Chouliaras ioanna Kantzavelou Grammati Pantziou Leandros Maglaras Panagiota Chouliara-Sidera Antonis Adamakos Panagiotis Chalatsakos Department of Informatics and Computer Engineering University of West Attica Athens Greece School of Computing Edinburgh Napier University Edinburgh UK Department of Computer Science and Biomedical Infromatics University of Thessaly Lamia Greece Senior Software Engineer at *** Athens Greece Faculty of Pure and Applied Sciences Open University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

The advent of industry 4.0 and the industrial internet of Things (iioT) has revolutionized manufacturing and production systems, enhancing efficiency and communication through the integration of advanced technologies such as ioT devices and Ai. However, this increased digitization has also led to heightened cybersecurity risks, with a notable rise in the frequency and complexity of cyber-attacks. Cyber Ranges emerges as a critical solution in addressing these challenges, offering a controlled environment for simulating cyber-attack scenarios, training cybersecurity teams, and developing robust defense strategies. The escalating demand for cybersecurity professionals has highlighted the crucial need for effective training solutions. This article presents an open-source Cyber Range constructed on container-based architecture, meticulously engineered for efficient operation with minimal resource consumption. in response to the scarcity of cybersecurity experts, this platform prioritizes extensive scalability and robust isolation between exercises, ensuring secure learning environments, and also integrates a complex developed exercise series aimed at strengthening cybersecurity learning experiences. These exercises encompass a variety of scenarios and challenges, catering to professionals at different skill levels. Each exercise replicates real-world cyber threats, offering practical, hands-on learning opportunities. Additionally, a comprehensive questionnaire accompanies these exercises, designed to gather feedback on participants' experiences.

关键词： Training Production systems Scalability Conferences Computational modeling computer architecture Fourth industrial Revolution Complexity theory Cyberattack industrial internet of Things

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Explaining the Applicability of the Stimuli-Organism-Response (SOR) Framework in UX Processes for Static and Dynamic Contexts

Explaining the Applicability of the Stimuli-Organism-Respons...

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interdisciplinary Approaches in Technology and Management for Social innovation (iATMSi), iEEE Conference on

作者： Emmanuel O.C. Mkpojiogu Obianuju E. Okeke-Uzodike Monday Jubrin Abdullahi Universiti Utara Malaysia Sintok Malaysia Department of Software Engineering Veritas University Abuja Nigeria Department of Applied Management Durban University of Technology Pietermaritzburg South Africa Department of Computer Science Airforce Institute of Technology Kaduna Kaduna State Nigeria West Africa

The implementation of the Stimuli-Organism-Response (SOR) framework introduced by Mehrabian and Russell in 1974 is described in this paper. The framework as an environmental psychology framework specifies that the organism of a person who interacts with a specified external object is influenced by specified external environmental factors. These factors make the individual to respond in some particular way by either accepting or avoiding the objects. With respect to user experience (UX), the external interactive objects are any interactive artifact which a user interacts with in certain specified context. The external environmental factors are design factors or quality attributes that impact or activate (arouse) the user’s organism. The organism is the user’s being that is influenced by external stimulating factors. The influenced organism responds by either approaching the object (interactive application) in delight; satisfaction and pleasure or avoiding the interactive product in disgust, frustration, and pain. Notwithstanding the benefit of this framework, the application is rarely employed in the context of UX. This study seeks to flag off awareness on how applicable the framework is to UX research.

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MiXiNG ON GENERALiZED ASSOCiAHEDRA

arXiv

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arXiv 2024年

作者： Chang, William Defant, Colin Frishberg, Daniel Department of Applied Mathematics University of California Los Angeles United States Department of Mathematics Harvard University CambridgeMA02138 United States Department of Computer Science and Software Engineering California Polytechnic State University San Luis ObispoCA United States

Eppstein and Frishberg recently proved that the mixing time for the simple random walk on the 1-skeleton of the associahedron is O(n3 log3 n). We obtain similar rapid mixing results for the simple random walks on the 1-skeleta of the type-B and type-D associahedra. We adapt Eppstein and Frishberg's technique to obtain the same bound of O(n3 log3 n) in type B and a bound of O(n13 log2 n) in type D;in the process, we establish an expansion bound that is tight up to logarithmic factors in type B. © 2024, CC BY.

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Metric spaces in which many triangles are degenerate

arXiv

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arXiv 2022年

作者： Chvátal, Vašek Kantor, ida Department of Computer Science and Software Engineering Concordia University Emeritus Montreal Canada Department of Applied Mathematics Charles University Visiting Prague Czech Republic Computer Science Institute Charles University Prague Czech Republic

Richmond and Richmond (American Mathematical Monthly 104 (1997), 713–719) proved the following theorem: if, in a metric space with at least five points, all triangles are degenerate, then the space is isometric to a subset of the real line. We prove that the hypothesis is unnecessarily strong: in a metric space on n points, (n3) − n + 5 arbitrarily placed or 3 (n−22) + 1 suitably placed degenerate triangles *** Codes 30L99, 51F99, 54E99 © 2022, CC BY.

关键词： Topology

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software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms

arXiv

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arXiv 2023年

作者： Akter, Mst Shapna Faruk, Md Jobair Hossain Anjum, Nafisa Masum, Mohammad Shahriar, Hossain Rahman, Akond Wu, Fan Cuzzocrea, Alfredo Department of Computer Science Kennesaw State University United States Department of Information Technology Kennesaw State University United States Department of Applied Data Science San Jose State University United States Department of Computer Science and Software Engineering Auburn University United States Department of Computer Science Tuskegee University United States iDEA Lab University of Calabria Rende Italy

The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. in general, SSC attacks execute during the software development processes lead to vulnerabilities in software products targeting downstream customers and even involved stakeholders. Machine Learning approaches are proven in detecting and preventing software security vulnerabilities. Besides, emerging quantum machine learning can be promising in addressing SSC attacks. Considering the distinction between traditional and quantum machine learning, performance could be varies based on the proportions of the experimenting dataset. in this paper, we conduct a comparative analysis between quantum neural networks (QNN) and conventional neural networks (NN) with a software supply chain attack dataset known as ClaMP. Our goal is to distinguish the performance between QNN and NN and to conduct the experiment, we develop two different models for QNN and NN by utilizing Pennylane for quantum and TensorFlow and Keras for traditional respectively. We evaluated the performance of both models with different proportions of the ClaMP dataset to identify the f1 score, recall, precision, and accuracy. We also measure the execution time to check the efficiency of both models. The demonstration result indicates that execution time for QNN is slower than NN with a higher percentage of datasets. Due to recent advancements in QNN, a large level of experiments shall be carried out to understand both models accurately in our future research. © 2023, CC BY.

关键词： Supply chains

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information Theoretic-based Feature Selection (iFS) for Machine Learning 1

Information Theoretic-based Feature Selection (IFS) for Mach...

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1st international Conference on Multidisciplinary engineering and applied science, iCMEAS 2021

作者： Suleiman, Muhammad A. Labadin, Jane Nile University of Nigeria Faculty of Natural and Applied Sciences Dept. of Software Engineering and Information Technology Abuja Nigeria University Malaysia Sarawak Faculty of Computer Science and Information Technology Kota Samarahan Malaysia

ISBN: (纸本)9781665434935

Mutual information (Mi) has been a popular choice for the selection criterion function of the feature selection techniques. However, only a handful of methods of formulating Mi estimator for regression tasks exist, despite the existence of numerous methods of Mi estimation for classification tasks in the literature. This work proposed a feature selection method based on mutual information for both classification and regression tasks. Unlike the existing Mi estimator for regression tasks which are widely non-parametric, the proposed method provides a suitable way to estimate Mi based on conventional kernel density estimation numerically. Two different experiments were conducted to evaluate the proposed iFS method. The first test is designed to assess the correctness of the formulated Mi estimator by comparing it with various existing benchmarks. The second experiment evaluates the proposed method using natural domain data. Overall results obtained from these experiments show that the proposed method was successfully able to select relevant features. © 2021 iEEE.

关键词： Feature Selection

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Rain Predictive Model using Machine learning Techniques

Rain Predictive Model using Machine learning Techniques

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Multi-disciplinary Conference in Emerging Research Trends (iMCERT), international

作者： Muhammad Shahbaz Muneer Syed Muhammad Nabeel Mustafa Syeda Sundus Zehra Haniya Maqsood Dpertment of Computational Finance NED University of Engineering and Technology Karachi Pakistan Dept. of Computer Science & I.T NED University of Engineering and Technology Karachi Pakistan Dept. of Software Engineering Sir Syed Uuniversity of Engineering and Technology Karachi Pakistan Department of Computer and Information Systems Engineering NED University of Engineering and Technology Karachi Pakistan

Climate is rapidly changing around the world. Over time, there have been significant changes in the weather. Rainfall is now erratic due to climate change. The frequency of extreme weather events like droughts and floods has increased due to climate change, necessitating the need for more precise and timely rainfall forecasts. For strategic reasons including agriculture, water resource management, and architectural design, rain forecasting is crucial. The naturally occurring non-stationary component in the rainfall time series impairs model performance for practical hydrologists and drought risk assessors. We present a rain predicting model based on machine learning to address the forecasting issue. in our work, we predict the possibility of rain the next day on the basis of last 10 years' data. The variables that were calculated during the experiments were humidity, pressure, evaporation, sunshine, rainfall, and so on. Random Forest gave the 90% accuracy with 0.904 Area under Curve, highest out of all the algorithms. The model's performance will significantly aid in the rain forecast.

关键词： Climate change Rain Machine learning algorithms Droughts Weather forecasting Predictive models Prediction algorithms

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Proceedings of the 14th international Conference on Education Technology and computers

作者： Veronica Segarra-Faggioni Sylvie Ratté Frank De Jong Dept. Software and IT Engineering École de technologie supérieure Canada and Dept. Computer Science and Electronics Universidad Tecnica Particular de Loja Ecuador Dept. Software and IT Engineering École de technologie supérieure Canada Dept. Learning and Innovation Aeres Wageningen Applied University Netherlands

ISBN: (纸本)9781450397766

Knowledge Building as a pedagogy supports collaborative work between students to improve ideas. The result of knowledge-building discourse in student communities is the development of academic artifacts. Student reports are academic artifacts that can result from a knowledge-building process during their learning. However, the automatic analysis of student reports is quite challenging due to the documents’ length and written language. Given this context, our goal is to analyze the semantic similarity between student reports and curriculum literature using the information coverage measure based on the Skip-gram word2vec model. Word2vec is a well-known set of techniques that provides a way to create a representation of words to predict the nearby words between every word and its context to capture the internal semantic and syntactic information. in this work, through the experimental comparison between the vocabulary list, the best similarity value is 0.90 using word2vec. The results show that each student report was aligned with each document in the curriculum literature using the information coverage measure based on word2vec.

关键词： Machine learning natural language processing word2vec

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Frameworks, Applications and Challenges in Streaming Big Data Analytics: A Review

Frameworks, Applications and Challenges in Streaming Big Dat...

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innovations in computer science & software engineering (iCONiCS), international Conference on

作者： Syed Muhammd Nabeel Mustafa Muhammad Umer Farooque Muhammad Tahir Shariq Mahmood Khan Rohail Qamar Dept. of Computer Science & I.T NED University of Engineering and Technology Karachi Pakistan Dept. of Software Engineering Sir Syed University of Engineering and Karachi Pakistan

Data output has increased dramatically in the twenty-first century. The speed at which data is introduced into the stream has increased as a result of the digitization of practically all industries. Big data refers to this enormous amount of data. A complex network of data with volume, velocity, diversity, authenticity, and value makes up this information. To derive useful insights from the data, it became necessary to examine the data. The term "streaming Big data" refers to data that changes very quickly. Big data analytics that are live or streaming have significantly improved analytics. instantaneous analytics of the real-time data are provided by streaming big data analytics, assisting the decision-makers. Big data analytics in real-time has taken center stage in business. We thoroughly examine the phenomenon of streaming Big data analytics in this review study. We also look at the various streaming analytics frameworks in use. Additionally, we look into the fields in which streaming Big Data analytics are applied as well as the difficulties encountered.

关键词： Transportation Medical services Big Data Real-time systems Smart grids internet of Things Sparks

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