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Towards an intrusion detection system for detecting web attacks based on an ensemble of filter feature selection techniques

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Cyber-Physical Systems 2023年第3期9卷 244-259页

作者： Kshirsagar, Deepak Kumar, Sandeep Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India Department of Computer Engineering and IT College of Engineering Pune Pune India

The use of machine learning models in intrusion detection systems (IDSs) takes more time to build the model with many features and degrade the performance. The present paper proposes an ensemble of filter feature selection techniques (EFFST) to obtain a significant feature subset for web attack detection by selecting one-fourth split of the ranked features. The experimentation on the CICIDS 2017 dataset shows that the proposed EFFST method provides a detection rate of 99.9909%, with J48 using 24 features. The system’s performance is compared to the original features and traditional relevant feature selection methods employed in IDSs. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Intrusion detection

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Image-based Plant Diseases Detection using Deep Learning

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Materials Today: Proceedings 2023年 80卷 3500-3506页

作者： Panchal, Adesh V. Patel, Subhash Chandra Bagyalakshmi, K. Kumar, Pankaj Khan, Ihtiram Raza Soni, Mukesh Department of Computer Science & Engineering GEC Patan India School of Computer Science and Engineering VIT Bhopal University Sehore MP Bhopal India Electrical and Electronics Engineering SRI Ranganathar Institute of Engineering and Technology Coimbatore India Department of Computer Science & Engineering Institute of Engineering & Technology Uttar Pradesh Greater Noida India Jamia Hamdard Computer Science Department Delhi India Jagran Lakecity University Bhopal India

Agriculture plays a major role in developing countries like India, however the food security still remains a vital issue. Most of the crops get wasted due to lack of storage facility, transportation, and plant diseases. More than 15% of the crops get wasted in India due to diseases and hence it has become one of the major concern to be resolved. There is a need of automatic system that can identify these diseases and help farmers to take appropriate steps to get rid of crop loss. Farmers have followed the conventional method of identifying the plant disease with their naked eyes, and it not possible for all the farmers to identify these diseases the same way. With the advance in Artificial Intelligence, there is a need to incorporate the facilities of the computer vision in the field of agriculture. Deep Learning rich libraries and user as well as developer friendly environment to work with, all these qualities make Deep Learning as the favorable method to get started with this problem. In this paper we have used Deep Learning because of the advantages it offers to work with images especially in image classification to get improvised results. The methodology includes taking leaves of infected crops and label them as per the disease pattern. The images of infected leaves are processed pixel based operations are applied to improve the information from the image. As a next step feature extraction is done followed by image segmentation and at the last classification of crop diseases based on the patterns extracted from the diseased leaves. The CNN (Convolutional Neural Network) is used for the classification of diseases, for the demonstration purpose the public dataset is used consisting of around 87 K images (RGB type images) including healthy as well as diseased leaves. © 2021

关键词： Crops

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SK_DU Team: Cross-Encoder based Evidence Retrieval and Question Generation with Improved Prompt for the AVeriTeC Shared Task 7

SK_DU Team: Cross-Encoder based Evidence Retrieval and Quest...

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7th Fact Extraction and VERification Workshop, FEVER 2024

作者： Malviya, Shrikant Katsigiannis, Stamos Department of Computer Science Durham University United Kingdom

ISBN: (纸本)9798891761728

As part of the AVeriTeC shared task, we developed a pipelined system comprising robust and finely tuned models. Our system integrates advanced techniques for evidence retrieval and question generation, leveraging cross-encoders and large language models (LLMs) for optimal performance. With multi-stage processing, the pipeline demonstrates improvements over baseline models, particularly in handling complex claims that require nuanced reasoning, by improved evidence extraction, question generation and veracity prediction. Through detailed experiments and ablation studies, we provide insights into the strengths and weaknesses of our approach, highlighting the critical role of evidence sufficiency and context dependency in automated fact-checking systems. Our system secured a competitive rank, 7th on the development and 12th on the test data, in the shared task, underscoring the effectiveness of our methods in addressing the challenges of real-world claim verification. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Aspect-based Sentiment Evaluation of Chess Moves (ASSESS): an NLP-based Method for Evaluating Chess Strategies from Textbooks 10

Aspect-based Sentiment Evaluation of Chess Moves (ASSESS): a...

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10th Workshop on Games and Natural Language Processing 2024

作者： Alrdahi, Haifa Batista-Navarro, Riza Department of Computer Science The University of Manchester United Kingdom

ISBN: (纸本)9782493814173

The chess domain is well-suited for creating an artificial intelligence (AI) system that mimics real-world challenges, including decision-making. Throughout the years, minimal attention has been paid to investigating insights derived from unstructured chess data sources. In this study, we examine the complicated relationships between multiple referenced moves in a chess-teaching textbook, and propose a novel method designed to encapsulate chess knowledge derived from move-action phrases. This study investigates the feasibility of using a modified sentiment analysis method as a means for evaluating chess moves based on text. Our proposed Aspect-Based Sentiment Analysis (ABSA) method represents an advancement in evaluating the sentiment associated with referenced chess moves. By extracting insights from move-action phrases, our approach aims to provide a more fine-grained and contextually aware ‘chess move’-based sentiment classification. Through empirical experiments and analysis, we evaluate the performance of our fine-tuned ABSA model, presenting results that confirm the efficiency of our approach in advancing aspect-based sentiment classification within the chess domain. This research contributes to the area of game-playing by machines and shows the practical applicability of leveraging NLP techniques to understand the context of strategic games. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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The Impact of Finite Size Effects on the Transmission of Electromagnetic Waves in a Defected, Perforated Waveguide

The Impact of Finite Size Effects on the Transmission of Ele...

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作者： Gruszka, K.M. Berski, S. Faculty of Computer Science and Artificial Intelligence Department of Computer Science Czestochowa University of Technology Darowskiego 73 Częstochowa42-201 Poland

This study investigates the impact of nite size effects on the transmission of electromagnetic waves through a defected, perforated two-dimensional waveguide. The research focuses on how transmission characteristics are affected by the number of perforations surrounding a point defect. As the number of perforations increases, the waveguide gradually acquires properties similar to those of an ideal photonic crystal. However, unlike the in finite photonic crystals often assumed in theoretical models, our waveguide has finite dimensions, leading to unique boundary effects. The paper outlines the thresholds at which the finite size effects become prominent, analyzing their in influence on wave propagation and transmission effeciency. Theoretical analysis is employed to identify critical points where the finite size alters expected outcomes, providing a deeper insight into the practical limitations and design potential of photonic waveguide-based devices. © 2025 Polska Akademia Nauk. All rights reserved.

关键词： Point defects

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Identifying Factors for Supporting Early Warning Flood using Clustering Approach And Geo-Spatial Analysis 8

Identifying Factors for Supporting Early Warning Flood using...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Duykers, James Lewi Ardana, Kidung Yulistiani, Risma Irwansyah, Edy Fitrianah, Devi Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Floods in Jakarta are very frequent and caused by many factors, from very high rainfall to flash floods. This natural disaster has greatlyimpact the society, from economy to health problems. In this research, we used ArcGIS Pro with unsupervised clustering method HDBSCAN to create a prototype that would make it easier for people to find out the area that is prone to floods. We received a lot of positive input from many respondents who had seen our prototype design and how it can assist people as the early warning to floods. We analyzed the factors lead the flood early warning system, those factors are high density population distance from river to the high-density population area, drainage system, rainfall intensity, and flood history. We will group flood point with the unsupervised clustering method. © 2023 The Authors. Published by Elsevier B.V.

关键词： clustering early warning system flood HDBSCAN Jakarta unsupervised learning

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Automatic emotion recognition using deep neural network

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Multimedia Tools and Applications 2025年 1-30页

作者： Sujatha, R. Chatterjee, Jyotir Moy Pathy, Baibhav Hu, Yu-Chen School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of CSE Graphic Era University Dehradun India School of Electrical Engineering Vellore Institute of Technology Vellore India Department of Computer Science Tunghai University Taichung City Taiwan

Emotions are a vital semantic part of human correspondence. Emotions are significant for human correspondence as well as basic for human–computer cooperation. Viable correspondence between people is possibly achieved when both the importance and the emotion of the correspondence are perceived by all groups included. Understanding the significance of language has generally been concentrated on in natural language processing (NLP) as a semantic examination. In NLP, the text can be handled appropriately for classification. Emotion detection from facial emotion is the subfield of social signal processing applied in a wide assortment of regions, explicitly for human and PC collaboration. Many researchers have proposed various approaches, generally utilizing machine learning concepts. Automatic emotion recognition (AER) is significant for working with consistent intuitiveness between a person and a smart device toward fully acknowledging an intelligent society. Many researchers examined cross-lingual and multilingual speech emotion as a stage toward language-free emotion acknowledgment in natural speech. In the present work, we are proposing a deep learning-based AER system using four openly accessible datasets, namely Basic Arabic Vocal Emotions Dataset (BAVED), Acted Emotional Speech Dynamic Database (AESDD), Urdu written in Latin/Roman Script (URDU), and Toronto Emotional Speech Set (TESS), by utilizing the Jupyter notebook and a Python library for music and audio synthesis named Librosa. The experimental results exhibited that the proposed approach achieves better than the existing approaches, i.e., the accuracy of the proposed system with the URDU dataset is 96.24%, the TESS dataset is 99.10%, the AESDD dataset is 65.97%, and the BAVED dataset is 73.12%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantics

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Application of Generative Graph Models in Biological Network Regeneration: A Selective Review and Qualitative Analysis

Application of Generative Graph Models in Biological Network...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Teji, Binon Roy, Swarup Guzzi, Pietro Hiram Bhandari, Dinabandhu Lab Department of Computer Applications Gangtok India University of Catanzaro School of Computer Science and Biomedical Engineering Italy Heritage Institute of Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350386226

Biological networks are essential for understanding the complex cellular mechanisms of living organisms. Simulating biological networks allows researchers to model and understand complex cellular processes without the need for extensive and costly experiments. The ability to generate synthetic graphs that closely resemble these real-world, complex biological processes is a vital research area. Graph generation in the context of biological networks is particularly significant because it can lead to insights into cellular functions, disease mechanisms, and therapeutic targets. Recent advances in deep learning, particularly in graph generative models, have opened new avenues for applications in the biological domain. These advancements have the potential to revolutionize our understanding of biological systems. However, despite the development of numerous effective graph generation models, there has been limited work assessing the qualitative aspects of the generated graphs within the context of biological networks. Addressing this gap is crucial for ensuring that synthetic graphs are not only structurally accurate but also biologically *** various graph generation models are available, their application to biological network recreation has been limited. In this paper, we focus on graph generation, specifically edge and node-independent models for biological networks. We assess four candidate models across four gene expression networks. Our systematic assessment examines the models' qualitative aspects, including graph structural properties, generation diversity, and computational efficiency. Our findings highlight the strengths and limitations of current models, offering insights to guide the development of more robust graph generation techniques that accurately replicate biological network characteristics. © 2024 IEEE.

关键词： Biotic

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Smart Basket: An E-Commerce Recommendation System 9

Smart Basket: An E-Commerce Recommendation System

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Pujari, Samket V. Tarun, R. Jain, Adeesh Neeraj, S. Prajwala, T.R. Pes University Computer Science Department Bangalore India

ISBN: (纸本)9798350394474

A novel platform addressing the challenges prevalent in grocery shopping, especially amidst the expanding market choices and pandemic-induced complexities. The platform offers a comprehensive solution by enabling the comparison of groceries from various online sources, accompanied by personalized recommendations, with a focus on improving accessibility, convenience, and cost-effectiveness for users. Key features include an Image Detection system for efficient item search, user-specific coupon manager, and a tailored recommendation engine. By amalgamating these functionalities, the platform aims to revolutionize the grocery shopping experience, providing users with a diverse range of options while simplifying the product discovery process. User profiles further enhance penalization by storing preferences, while a recommendation engine suggests products based on individual behavior. This platform presents a practical solution to streamline grocery shopping, catering to users' diverse needs through its array of user-friendly features. © 2024 IEEE.

关键词： Recommender systems

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Explainable Predictions for Brain Tumor Diagnosis Using InceptionV3 CNN Architecture 6th

Explainable Predictions for Brain Tumor Diagnosis Using Ince...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Bedi, Punam Ningshen, Ningyao Rani, Surbhi Gole, Pushkar Department of Computer Science University of Delhi Delhi India

ISBN: (纸本)9789819940707

Brain tumor is one of the deadliest diseases diagnosed in human beings. Doctors can identify the tumor with the Magnetic Resonance Imaging (MRI) technique which uses the magnetic field and computer-generated waves to create Brain images. With the advancement in the domain of computer Vision, various researchers have come up with different state-of-the-art frameworks that aid doctors in diagnosing Brain tumors quickly. Most of these research works exploited the effectiveness of Convolutional Neural Networks (CNNs) on the image data. However, the major drawback of these studies is that they do not provide human-interpretable explanations for the models’ predictions. In this research work, the InceptionV3 CNN architecture is used to detect Brain tumors from the MRI images. This paper also provides human-interpretable predictions for Brain tumor diagnosis through the Local Interpretable Model-agnostic Explanation (LIME) framework to enhance the trust of doctors in the predictions of InceptionV3 CNN architecture. The MRI images of the Brain tumor have been gathered from the publicly available dataset catalog website Kaggle. InceptionV3 architecture is chosen as it is found to be the best architecture among the various CNN architectures, attaining an accuracy of 99.81%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Lime

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