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Multi-Method Stacked Feature Selection Approach based IDS for IoT Networks 3

Multi-Method Stacked Feature Selection Approach based IDS fo...

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3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Kushwaha, Jai Prakash Bhadauria, Saumya Tapaswi, Shashikala Dept. of Computer Science and Engineering ABV-IIITM Gwalior India

In an era marked by the proliferation of interconnected devices, the Internet of Things (IoT) has emerged as a revolutionary technological paradigm. IoT networks encompass a vast array of devices, from smart appliances to industrial sensors, revolutionizing industries and everyday life. However, this ubiquitous connectivity has ushered in a new frontier of security challenges, necessitating the deployment of robust Intrusion Detection Systems (IDS). This paper presents a pioneering Multi-Method Stacked Feature Selection (M2SFS) approach-based IDS tailored explicitly for IoT networks. By orchestrating a diverse ensemble of feature selection techniques, the M2SFS framework adept.y curates the feature set derived from the CICIDS 2017 dataset. This comprehensive selection strategy optimally reduces dimensionality, mitigating the resource constraints inherent to IoT ecosystems. The proposed IDS, rooted in the M2SFS approach, demonstrates exceptional accuracy and efficiency in detecting anomalous network behaviors. By harnessing the collective strength of stacked feature selection, this IDS capitalizes on the synergistic potential of multiple feature selection methods. The result is an IDS uniquely adapted to the intricacies of IoT networks, effectively fortifying their security posture. Through rigorous experimentation using the CICIDS 2017 dataset, the proposed approach affirms the superiority of the M2SFS-based IDS over conventional methods. This study underscores the pressing need for IDS in IoT environments and highlights the promise of our innovative approach in safeguarding IoT networks against emerging threats. © 2023 Elsevier B.V.. All rights reserved.

关键词： Internet of things

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High Contrast and High Frame Rate Coherent Plane Wave Compounding by Means of 2D Spatio-Angular Interpolation Technique

High Contrast and High Frame Rate Coherent Plane Wave Compou...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Afrakhteh, Sajjad Iacca, Giovanni Demi, Libertario University of Trento Dept. of Information Engineering and Computer Science Italy

ISBN: (纸本)9798350346459

To address the low image quality issue in plane wave imaging (PWI), coherent plane-wave compounding (CPWC) was developed based on PWI along multiple directions followed by a coherent combination of the beamformed images. The main challenge with this approach is that there is a trade-off between image quality and frame rate which depends on how many angles are used in CPWC. Our primary goal here is to improve this trade-off by decreasing the number of angles required for image reconstruction. Here, we propose a sparse CPWC using a robust transmission coding pattern (TCP) followed by applying 2D spatio-angular interpolation. This idea allows us to improve the frame rate while preserving the image quality. We apply our proposed technique to the plane wave imaging challenge in the medical ultrasound (PICMUS) database. Results show that we can achieve a 3-fold frame rate improvement while preserving image quality. © 2023 IEEE.

关键词： Beamforming

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An IOTML Based Food Freshness Detection System 2

An IOTML Based Food Freshness Detection System

引用

2nd International Conference for Innovation in Technology, INOCON 2023

作者： Rashmi Navyashree, V.A. Sai Geetha, S. Pavan, B.K. Deepti, C. PES University Dept. of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350320923

Numerous food donation applications like ShareThe-Meal [1], Food Need [2], Waste No Food [3], etc., do not specify how food quality can be verified. The quality of food donated could be compromised. This report aims at verifying the quality of food donated to orphanages. The work described in this paper aims to provide authentic information regarding food quality. It also provides a platform to connect catering services and volunteers who carry out the process of food donation to the required beneficiaries. Sensors like MQ2 and MQ135 are used to detect freshness of food. Eclipse IDE, SQLYOG are used to develop the interactive platform. One of the major assumptions made is that the predicted food sensor values are based on samples collected from a few basic food items. It is highly unfeasible to ensure that the classification of food as spoilt or unspoilt is inclusive of all possible food items. This work allows us to assume the categorization of food quality based on only few basic food items. This work aims at a high rate of detection with regard to classification of food as SPOILT or FRESH. The methodology involves the detection of Carbon Monoxide(CO),Ammonia(NH3) and Benzene(C6H6) values in food. The mechanism involved in this work would definitely ensure the quality of food before it is donated to the orphanage. © 2023 IEEE.

关键词： Ammonia

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Parkinson's Disease Prediction using Fisher Score based Recursive Feature Elimination

Parkinson's Disease Prediction using Fisher Score based Recu...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Aishwarya, Ravi Pavitra, K. Miranda, Primal Viola Keerthana, K. Kamatchi Priya, L. PES University Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350396485

Parkinson's disease is a neurological syndrome that manifests slowly and gradually, making it difficult to diagnose at an early stage. Voice alterations can be used as a detectable marker of early detection. The Synthetic Minority Oversampling Technique (SMOTE) is employed to address class imbalance issues in the datasets. For optimal feature selection, a novel approach called Fisher Score based Recursive Feature Elimination (FRFE) is proposed, and it is compared with state of art feature selection methods namely Correlation Coefficient, Mutual Information, Backward Feature Elimination, and Recursive Feature Elimination. The performance of models was evaluated across different classifiers using two voice datasets, with different features so as to confirm that FRFE works for any dataset irrespective of features. The FRFE performs better than the state of methods of comparison in terms of accuracy and variance. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Performance Analysis of Vision Transformer Based Architecture for Cursive Handwritten Text Recognition 26

Performance Analysis of Vision Transformer Based Architectur...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Chowdhury, Avishek Hossen, Md. Ahasan Hasan, Mohammad Rukunuddin Osmani, Sheikh Md Premier University Dept. of Computer Science & Engineering Chattogram Bangladesh

ISBN: (纸本)9798350359015

computers have been given vision by researchers across the world for many years. Now it is the era of digitization. Recognizing handwritten text is a must for a computer vision system. Due to the variation and complexity of the cursive writing style, the holistic approach is mostly used for the recognition of cursive scripts. Though Convolutional Neural Network (CNN) based models have been employed in literature for Holistic Handwritten Text Recognition (HTR) of different domains, recent breakthroughs in the image classification Vision Transformer (ViT) based models have not been utilized for HTR so far. In this research, we have designed a ViT-based model for the HTR of various cursive scripts. To validate the performance of the model, various handwritten datasets of cursive scripts have been used. The notable finding includes that the accuracy of the designed model has increased by up to 26% after applying the image data augmentation techniques. © 2023 IEEE.

关键词： Augmentation Cursive Handwritten Text Recognition (HTR) Vision Transformer

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Automatic Image Registration using SIFT and AKAZE: An application for Preservation of Cultural Heritage 21

Automatic Image Registration using SIFT and AKAZE: An applic...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Sawkmie, Lumlang Samal, Sonali Balabantaray, Bunil Kumar NIT Meghalaya Dept. of Computer Science and Engineering Shillong India

ISBN: (纸本)9798350358230

Automatic image registration, which aligns several images acquired from different viewpoints or timeframes, plays an important role in the analysis and preservation of cultural heritage. It serves as a crucial preprocessing step for performing comparative analysis, restoration, and reconstruction of cultural heritage images. In this paper, an automatic image registration approach is proposed that combines the strengths of both the Scale Invariant Feature Transform (SIFT) and the Accelerated KAZE (AKAZE) for accurate and robust registration of heritage images. While SIFT excels in matching images with varying scales and rotations, AKAZE is renowned for its robustness to non-linear transformations and challenging imaging conditions. This fusion of SIFT and AKAZE feature detection leads to a substantial increase in accurate correspondences, enhancing registration results. The effectiveness of this proposed approach has been assessed using the Affine Covariant Regions Dataset and the Indian Heritage Image Retrieval Dataset (IHIRD). The outcomes of the experiments show that the proposed method increases the number of correct correspondences when compared to those of SIFT or AKAZE alone. © 2023 IEEE.

关键词： Image registration

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QLEN: A Load Distribution Algorithm to Improve QoS Factors Among Fog Devices

QLEN: A Load Distribution Algorithm to Improve QoS Factors A...

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2023 International Conference on computer, Electronics and Electrical engineering and their applications, IC2E3 2023

作者： Hegde, Pranav R Karthik, Saileshwar Madan, Shashank Udyavar Raju Bhat, Sumukh Tsb, Sudarshan Pes University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350338003

This article entails the proposition of a heuristic multi-objective algorithm for load distribution among fog devices. The decision for routing is made based on a weighted heuristic. The proposed algorithm, QLEN aims to improve three quality of service factors, namely L (load balancing efficiency), E (energy consumption) and N (network usage);hence the name QLEN. The importance of these parameters can be varied according to the application. This is done by adjusting the weights to suit the needs of the incoming tasks. For the default use case, the weights are tuned for the optimization of all three of the parameters. These optimal weights are calculated using stochastic gradient descent. QLEN is compared against fundamental load balancing algorithms such as Round Robin, Throttled Round Robin and Priority Based Load Balancing algorithm. The environment simulates a health monitoring application, using the iFogSim simulator. The results show that QLEN consistently outperforms the compared algorithms with respect to the targeted parameters. The efficiency of load distribution achieved by QLEN on average performs 1.69% better than all the three algorithms;at the same time showing 54.86% lesser network usage, 3413.33 MJ lesser energy consumption and 10.39% reduced redirection to the cloud. The algorithm also scales efficiently, showing better performance as the number of fog devices increases. © 2023 IEEE.

关键词： Energy utilization

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Blockchain-Enabled IPFS Cloud Solution for Storing and Sharing Patient Data Securely and Efficiently 6

Blockchain-Enabled IPFS Cloud Solution for Storing and Shari...

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6th International Conference on Electrical Information and Communication Technology, EICT 2023

作者： Mainuddin, Md. Rahman, Md. Mijanur Bin Sajid, Zaid Anik, M. Atiqur Rahman Southeast University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350370836

Modern healthcare generates a staggering volume of data daily, posing a challenge not only in managing and sharing it but also in ensuring security and privacy. Traditional centralized cloud computing solutions, while convenient, raise concerns about centralized authority and vulnerability to breaches. With cloud technology, blockchain technology emerges as a potent solution to address these issues due to its inherent characteristics of security, immutability, and decentralization. This research introduces a patient-centric framework called "MedVault Nexus,"which facilitates secure and efficient sharing of patient health records with medical practitioners and healthcare institutions. The framework employs blockchain technology and leverages the distributed cloud storage system known as the Interplanetary File System (IPFS). The system securely stores patient data on IPFS using robust encryption algorithms such as the Advanced Encryption Standard (AES) and the Rivest-Shamir-Adleman (RSA) algorithm. The MedVault Nexus's security was evaluated using the blockchain security testing tool known as MythX, which revealed the absence of vulnerabilities. The system's performance was assessed by analyzing encryption-decryption times and smart contract costs. The results demonstrated favorable comparisons, signifying the MedVault Nexus's efficiency and cost-effectiveness. Finally, the framework stands as a secure and efficient solution for addressing real-world challenges in healthcare data management. © 2023 IEEE.

关键词： Smart contract

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Detection and Comparison of Abusive and Hate Speech in English and Hinglish with Emojis using Deep Learning and Non-Deep Learning Techniques 4

Detection and Comparison of Abusive and Hate Speech in Engli...

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4th International Conference for Emerging Technology, INCET 2023

作者： Bhaskara, Sneha Sneha, P. Seth, Srishti Mohanty, Stuti Kanwal, Preet PES University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350335750

Abusive language and hate speech in tweets and comments on various social media platforms have become ubiquitous. This paper focuses on classifying an input text in English, Hindi and Hinglish (Hindi written in English) along with emojis and emoticons into three categories - Abusive, Hate Speech or Neither based on the confidence scores of each class. Abusive language on social media is textual content that contains disparaging words. Hate speech can be threatening and can hurt the sentiments of a person or a community. To gain better insights, results are captured with and without lemmatization and stemming. Accuracy is used as the metric to evaluate the classification models. Deep learning models BERT and RoBERTa are trained separately on plain English data (PE), Hinglish data along with emojis (HEmo) and the combined full dataset (CF). The same process is repeated for five non-deep learning models - Decision trees, k-Nearest Neighbours (kNN), Support Vector Machines (SVM), Random Forest and Naïve Bayes. The deep learning model RoBERTa results in the highest accuracy of 0.9938 on the PE dataset, 0.9896 on the EHEmo dataset and 0.9899 on the CF dataset. SVM model performed best among traditional classifiers with an accuracy of 0.7996 on the PE dataset, 0.7899 on the EHEmo dataset and 0.7913 on the CF dataset. © 2023 IEEE.

关键词： Deep learning

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Investigating ChatGPT's Potential to Assist in Requirements Elicitation Processes 49

Investigating ChatGPT's Potential to Assist in Requirements ...

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49th Euromicro Conference on Software engineering and Advanced applications, SEAA 2023

作者： Ronanki, Krishna Berger, Christian Horkoff, Jennifer University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden

ISBN: (纸本)9798350342352

Natural Language Processing (NLP) for Requirements engineering (RE) (NLP4RE) seeks to apply NLP tools, techniques, and resources to the RE process to increase the quality of the requirements. There is little research involving the utilization of Generative AI-based NLP tools and techniques for requirements elicitation. In recent times, Large Language Models (LLM) like ChatGPT have gained significant recognition due to their notably improved performance in NLP tasks. To explore the potential of ChatGPT to assist in requirements elicitation processes, we formulated six questions to elicit requirements using ChatGPT. Using the same six questions, we conducted interview-based surveys with five RE experts from academia and industry and collected 30 responses containing requirements. The quality of these 36 responses (human-formulated + ChatGPT-generated) was evaluated over seven different requirements quality attributes by another five RE experts through a second round of interview-based surveys. In comparing the quality of requirements generated by ChatGPT with those formulated by human experts, we found that ChatGPT-generated requirements are highly Abstract, Atomic, Consistent, Correct, and Understandable. Based on these results, we present the most pressing issues related to LLMs and what future research should focus on to leverage the emergent behaviour of LLMs more effectively in natural language-based RE activities. © 2023 IEEE.

关键词： Requirements engineering

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