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Enhancing cyber security: a comprehensive approach to the classification and prediction of significant cyber incidents (SCI) through data mining and variational neural network with fox optimization algorithm

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International Journal of computers and Applications 2024年第8期46卷 621-627页

作者： Mallick, Bhawna Deportment of Computer Science and Engineering Sharda University Greater Noida India

The rapid advancement of technology and the proliferation of IoT devices have rendered cyberspace vulnerable, resulting in Significant Cyber Incidents (SCIs). This paper proposes an Enhancing Cyber security: a Comprehensive Approach to the Classification with Prediction of Significant Cyber Incidents through Data Mining with Variation Neural Network with Fox Optimization Algorithm (ECS-SCI-DM-VNN-FOA). The dataset is split into pre-pandemic and post-pandemic SCI subsets, according to the report from the Center for Strategic and International Studies (CSIS). Adaptive Variation Bayesian Filter (AVBF) is utilized to remove the noise from the input data. Then the preprocessed input data is supplied to the Improved Window Adaptive Gray Level Co-Occurrence Matrix (IWAGM) for feature extraction. The proposed approach is implemented in Python and its efficacy is evaluated under some metrics, like accuracy, precision, recall, FI-score, sensitivity, computational time, recall, and RoC. The proposed ECS-SCI-DM-VNN-FOA gives 24.91%, 23.76% and 25.98% high accuracy and 30.45%, 23.67% and 29.32% high precision compared with the existing methods, like classification with prediction of cyber events utilizing data mining with machine learning (CRP-SML), Cyber risk prediction via social media big data analytics along statistical machine learning (PECI-MUIP), Mining user interaction patterns in the dark web to forecast enterprise cyber occurrences (CTPA-CSCS) respectively. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Data mining

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Automation of Short Answer Grading Techniques: Comparative Study using Deep Learning Techniques 5

Automation of Short Answer Grading Techniques: Comparative S...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Divya, Arunima Haridas, Vivek Narayanan, Jayasree Amrita School of Computing Deportment of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9781665493604

Automatic short answer grading (ASAG) techniques have been shown to cut down on the time and work needed to grade exams, and it is a method that is becoming more and more common, especially with the rise in popularity of online courses. This study compares the results of 7 pre-trained embedding models using just one feature to automatically grade brief responses: the similarity between the model answer's and the student answer's embeddings. Regression models are developed and evaluated to predict a short answer's score based on the similarities between all pairs of answers in the Mohler dataset. The predictions are evaluated by comparing the Root Mean Squared Error (RMSE) and Pearson correlation scores of each model. © 2023 IEEE.

关键词： Grading

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Fuzzy Computing Testing and Study Hydroponic Dosing Systems for Rural Indian Subsistence Farmers Using AI and IoT 4

Fuzzy Computing Testing and Study Hydroponic Dosing Systems ...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Pai, H. Aditya Jyothi, N.T. Lal Jeyan, J.V. Muruga Chandrappa Jain, Prithviraj Arvindhan, M. Lips Research and Dlcard India Lips Research Foundation India Dept. of Computer Science and Engineering Bengaluru India Deportment of Computer Science and Engineering India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350387490

In this study, we look at how well smart hydroponics systems supported by the Internet of Things (IoT) work for small-scale farmers in India's rural areas. The framework undergoes training using Fuzzy Logic artificial intelligence. A fuzzy logic model was created as part of the research carried out in this study. The monitor is connected to the Internet of Things (IoT) collects the data., this framework accurately finds and modifies the output. These instruments are useful in observing the environment variables of the hydroponic setup as well as keeping the fertilizer solution in check. An initial test carried out shows that the real time data generated and collected for testing made up the model evaluation. These are the phases that are undertaken to ensure that the accurate determination of the values are noted for the purpose of changing the environment variables of the hydroponic setup. The study findings describe how the system successfully determines the correct values that are required for the microcontroller., thus confirming that efficacy of Fuzzy Computing and AI used in the hydroponics setup. Here., the emphasis is given to provide accuracy towards the Fuzzy Computing and AI for rural subsistence farmers., especially in the lower to middle income nations. The research also mentions about importance of integrating solar power-enabled system with nearby available Wi-Fi networks., in contrast with the cloud-based IoT platforms so as to optimize the use of Fuzzy Computing - AI in the hydroponics system within the particular environments. © 2024 IEEE.

关键词： Hydroponics

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Evolution of Mobile Computing: From Text-Based to Visual-Based Interactions

Evolution of Mobile Computing: From Text-Based to Visual-Bas...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Singh, Akhilendra Pratap Sharma, Deeplata Soumya, K. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Deportment of Chemistry Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Cell computing has seen a marked evolution over the past many years, beginning with text-primarily based interactions among carrier and device customers and progressing to visually pushed reports primarily based on large screens and touch inputs. Early mobile phones featured bodily keypads that enabled users to interact with the tool via written instructions, such as coming in with cellphone numbers and sending text messages. This approach changed into nicely appropriate for brief data retrieval and typing in brief bursts. As the generation matured, touchscreens became a more popular entry method. The creation of gestures, such as swiping and multi-contact, revolutionized the manner customers interacted with their gadgets, allowing them to freely discover and access content in a green and intuitive way. Moreover, larger bodily sizes coupled with excessive-decision presentations allowed customers to control content and better appreciate the visuals easily. The evolution of cell computing has additionally created a platform for bringing collectively disparate technologies. Cellular apps, for example, are able to combine text, photographs, sound, and video to offer a multi-modal reveal for users. It has made using cell devices more engaging and enabled customers to interaction with complex structures in an optimized form thing. © 2024 IEEE.

关键词： Combine Computing Customers Interaction Photographs Revolutionized Touchscreens

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Prediction of Web Page Relevance in Focused Crawling Using Artificial Neural Networks 3

Prediction of Web Page Relevance in Focused Crawling Using A...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Dhabliya, Dharmesh Alawadi, Ahmed Hussein Varkale, Akshay Aljanabi, Mohammad Vishwakarma Institute of Information Technology Department of Information Technology Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Deportment of Computer Science & Engineering Bhopal India Alirqia University Department of Computer Baghdad Iraq

ISBN: (纸本)9798350342338

In the rapidly expanding digital ecosystem, efficient web crawling is paramount to extract relevant information from the vast expanse of the internet. This research addresses the challenge of predicting web page relevance in focused crawling using Artificial Neural Networks (ANNs). The study leverages a curated dataset of web pages, annotated for their relevance to specific domains. A multi-layer perceptron architecture was employed for the ANN, with features extracted from the web pages serving as inputs. The model was trained and validated using a split of 70% training and 30% testing data. Experimental results indicate that the ANN achieved an accuracy of 93.20%, outperforming traditional methods by a margin of 8%. This demonstrates the potential of ANNs in enhancing the efficiency of focused web crawlers, ensuring more accurate data extraction in less time. The findings pave the way for integrating deep learning techniques into web crawling systems, optimizing them for the evolving digital landscape. © 2023 IEEE.

关键词： Artificial Neural Networks Data Extraction Deep Learning Techniques Focused Crawling Web Page Relevance

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Automation of Short Answer Grading Techniques: Comparative Study using Deep Learning Techniques

Automation of Short Answer Grading Techniques: Comparative S...

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International Conference on Electronics, Communication and Computing Technologies (ICECCT)

作者： Arunima Divya Vivek Haridas Jayasree Narayanan Deportment of Computer Science and Engineering Amrita School of Computing Amritapuri India

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Potato Leaf Disease Detection and Classification Using Deep Learning Technique 4

Potato Leaf Disease Detection and Classification Using Deep ...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Sharma, Aditi Singh, Vineeta Sharma, Chandani Ansari, Ghazala Kumar, Santosh Joshi, Kapil Astana It University Department of Computer Science and Engg. Astana Kazakhstan Chhatrapati Shahu Ji Maharaj University Department of Computer Science and Engineering Kalyanpur Uttar Pradesh U.P. Kanpur208024 India Department of Cse Ambale Haryana Mullana India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Ghaziabad India Sharda School of Basic Sciences and Research Sharda University Department of Mathematics UP Greater Noida201310 India Uttaranchal University Deportment of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350387490

With the advancement of agriculture technology and the use of machine learning in diagnosis plant disease, it becomes more effective to research in plant health agriculture development. This paper talks about how deep learning and convolutional neural networks can help identify potato diseases. It looks at some research that used these methods to accurately recognize and group potato diseases. It also covers topics like weakly supervised learning, spectral imaging, and combining CNN and RNN for potato disease detection. It shows how transfer learning and hyperspectral imaging can improve how well deep learning models diagnose potato diseases. It compares different methods like k-means, multiclass SVM, deep belief networks, and convolutional ne-ural networks to see how good deep learning models are. It suggests that deep learning can find and group different types of potato diseases and proposes a deep learning model that can automatically diagnose crop diseases and pests on potatoes. It ends by saying deep learning can provide reliable and early warning systems for farmers and improve farming practices. © 2024 IEEE.

关键词： Agriculture CNN Deep learning Potato leaf RNN

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Analysis for Text Detection on Scenic Images Using Deep Learning 3

Analysis for Text Detection on Scenic Images Using Deep Lear...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Vardhan, Harsh Joshi, Kamal Chhabra, Prachi Shrivastava, Aparna Kumar, Sunil Chhabra, Gunjan Jss Academy of Technical Education Department of Information Technology UP Noida India Csjm University Deportment of Information Technology UP Kanpur India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350342338

Text detection in scenic images is the process of precisely localizing instances of text in them. This detection is required for a numerous computer vision applications including text recognition, autonomous vehicles, script narrators, translation apps etc. Text detection and localization refer to the process of identifying and locating text within an image or document. Scene text detection was mostly limited to putting together handcrafted low-level attributes of the texts contained in images until the emergence of deep learning in this area. A combination of methods or exploring domainspecific approaches help in enhancing the efficacy of detecting text in scenic images. In this paper, we have implemented the performance of three text detection models EAST, CRAFT and Text Fuse Net on two benchmark public datasets used for detection and localization. Experiments on two benchmark datasets show that the Text Fuse Net achieves state-of-the-art performance. Specifically, we have achieved an F measure of 94.6% and 92.2% on IC-2013 and IC-2015. respectively, followed by CRAFT [89.8%, 84.8%] and EAST [85.3%, 80.7%1. © 2023 IEEE.

关键词： CRAFT EAST IC-13 IC-15 Text Fuse Net

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Fuzzy Computing Testing and Study Hydroponic Dosing Systems for Rural Indian Subsistence Farmers Using AI and IoT

Fuzzy Computing Testing and Study Hydroponic Dosing Systems ...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： H. Aditya Pai N.T. Jyothi J.V. Muruga Lal Jeyan Chandrappa Prithviraj Jain M. Arvindhan LIPS Research and DLCARD India LIPS Research Foundation India Dept. of Computer Science and Engineering JAIN (Deemed-to-be University) Bengaluru India Deportment of Computer Science and Engineering NMAMIT Nitte (Deemed to be University) India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

关键词： Fuzzy logic Cloud computing Accuracy Microcontrollers Hydroponics Internet of Things Artificial intelligence Wireless fidelity Testing Fertilizers

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AI-Driven Control Strategies for Power Electronics Converters 9

AI-Driven Control Strategies for Power Electronics Converter...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Lalitha, S.D. Danamaraju, Chaitanya Raj, M. Prabhu Kumar, Dinesh Sethuraman, R. Mohanaprlya, V. R.M.K. Engineering College Department of Computer Science And Engineering Tamil Nadu Kavaraipettai601206 India Rns Institute of Technology Department of Electrical And Electronics Engineering Tamil Nadu Bengaluru560098 India Dr. Mahalingam College of Engineering And Technology Department of Electrical And Electronics Engineering Tamil Nadu Pollachi642003 India Iet Dr. RammanoharLohia Avadh University Deportment of Electrical Engineering Uttar Pradesh Ayodhya224001 India Karpagam Academy of Higher Education Department of Electrical And Electronics Engineering Tamil Nadu Coimbatore641021 India Bannari Amman Institute of Technology Department of Electrical And Electronics Engineering Tamil Nadu Erode638401 India

ISBN: (纸本)9798350365092

Artificial intelligence (AI) in converter control techniques has transformed power electronics. The integration improved power electronic system adaptability, performance, and efficiency. This research examines power electronics converter AI-driven control techniques in detail. When dealing with complex and nonlinear systems, conventional power converter control approaches like PID controllers are limited. Neural networks, fuzzy logic, and machine learning algorithms offer promising solutions to these problems. AI-driven control approaches are used to inverters, rectifiers, and DC-DC converters in the research. The benefits of AI include greater robustness to parameter fluctuations, harmonic distortion reduction, and transient response. Artificial intelligence allows adaptive and predictive control, enabling intelligent and self-governing power electronic systems. These include large datasets, complex training, and real-time computations. Overall, power electronics converters with AI-powered control techniques affect their design and operation. Power electronics and artificial intelligence offer new opportunity to construct smart, adaptive, and high-performing energy conversion systems. This research is essential for researchers, engineers, and practitioners interested in the latest advances in artificial intelligence and power electronics. © 2024 IEEE.

关键词： Adaptive control systems

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