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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...

引用

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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BTI Aging Influence and Mitigation in Neural Networks Oriented In-Memory Computing SRAMs 12

BTI Aging Influence and Mitigation in Neural Networks Orient...

引用

12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Dilopoulou, Christina Tsiatouhas, Yiorgos University of Ioannina Dept. of Computer Science and Engineering Ioannina Greece

ISBN: (纸本)9798350321074

Static random access memories (SRAMs) are commonly used in In-Memory Computing (IMC) architectures that perform neural network related computations directly in the memory unit, overcoming the data transfer constrains of traditional Von-Neumann architectures. However, Bias Temperature Instability (BTI) aging is a growing reliability concern for nanometer technology SRAMs. In this paper, we examine the BTI aging influence on SRAM based IMC architectures and propose an aging mitigation method to enhance their reliability and lifetime. According to our simulation results, BTI aging can affect the computation reliability of IMC cells. Aging mitigation is achieved by periodically alternating active with inactive transistors and vice-versa in these cells. © 2023 IEEE.

关键词： Reliability

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

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

引用

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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Lung Cancer Detection Using Machine Learning Methods

Lung Cancer Detection Using Machine Learning Methods

引用

2023 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2023

作者： Arka, Dipak Debnath Tafhim, Sad Md. Anan, Rawnak Muntaha Rahat, Nusaibah Ishan, Samiu Mostafa Tanvir, Sifat Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

Cancer-related death is now more common than ever with cancer of the lungs being the leading cause of it. In such regards, the survival percentage for individuals with lung cancer must be increased through early identification. In this paper, we propose a machine learning-based approach for lung cancer detection using data from patients. The proposed method has three stages: preprocessing, feature extraction, and classification. In preprocessing, we extracted the necessary data from the dataset we collected. In the feature extraction stage, we processed the raw data while keeping the original information unchanged, in order for the classifier to work on. Finally, in the classification stage, we use 6 different classifier models to classify the extracted features. We evaluated the proposed method on a publicly available dataset from Kaggle of lung cancer causes and age. The results of the trial confirm the feasibility of the suggested strategy to achieve high accuracy, sensitivity, specificity, and AUC score. In conclusion, using machine learning, The suggested approach offers a powerful and efficient way to detect lung cancer. The proposed method can potentially assist in the detection of lung cancer, thereby improving the survival rate of lung cancer patients. © 2023 IEEE.

关键词： Feature extraction

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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...

引用

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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Optimal IPL Playing 11 Team Selection 8

Optimal IPL Playing 11 Team Selection

引用

8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Ravichandran, Nilesh Nihal Chengappa, P.A. Rao Rebala, Pranav PES University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350334012

Since the inception of the Indian Premier League (IPL), the demand for quality data analysts has increased. The task to pick an ideal playing 11 from a huge squad is a real challenge and picking the best team can greatly improve a team's chances of winning. This project aims to predict the best possible playing 11 from a given set of players taking into consideration a number of factors like runs scored, runs conceded, strike rate, dot balls bowled etc. and this is essential as it can help pick an optimal eleven. Not only are the best batsmen and bowlers predicted but also the all-rounders which give a real balance to the team. Three models have been tested- XGBoost, Linear Regression and Random Forest. Linear Regression yields better results since it gives the least RMSE value. © 2023 IEEE.

关键词： Linear regression

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Multi-criteria Decision Theoritic Approach for Tour Package Selection using Fuzzy AHP and TOPSIS Methods: A Case Study on Cox's Bazar 8

Multi-criteria Decision Theoritic Approach for Tour Package ...

引用

8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Kamal, Marufa Rifat, Rakib Hossain Chakraborty Shruti, Abanti Rabiul Alam, Md. Golam BRAC Univeristy Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350334012

The global tourism business is expanding as more people travel and visit new locations both abroad and locally. Cox's Bazar is the longest natural unbroken sea beach in the world situated in Bangladesh accommodating millions of foreign and local tourists every year. This has been a very popular destination with various activities and beautiful natural attraction spots. There are numerous Cox's Bazar trip packages available for travelers to choose from. Compared to the large number of tour packages found online for this destination at present, this paper proposes a methodology to help the tourists to select a tour package based on their likings and different criteria. A decision-making selection process has been proposed using the Fuzzy Analytic Hierarchy Process(FAHP) and TOPSIS method to rank the packages based on 12 criteria that are closely knitted to the preference of users during the time of tour selection. A dataset of 100 alternative tour packages has been built with existing tour packages found from online travel agency websites and multiple decision makers are used to make pairwise criterion comparison. The tourists/users input fuzzy numbers, and then using the proposed methodology a ranking of all the alternatives is provided to aid them in choosing the optimal tour package. © 2023 IEEE.

关键词： Tourism

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

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

引用

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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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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