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Accurate Detection of Aquatic Debris in Ocean Surfaces using AlexNet Algorithm over ResNet-50 Algorithm 3

Accurate Detection of Aquatic Debris in Ocean Surfaces using...

引用

3rd International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2023

作者： Kamal, T.M. Bharatha Devi, N. Saveetha School of Engineering Dept. of Computer Science Chennai India

ISBN: (纸本)9798350306989

This research study intends to detect the aquatic debris accurately on Ocean surfaces using Novel AlexNet algorithm in comparison with ResNet-50 algorithm. Novel AlexNet and ResNet-50 are the two groups considered for this research with 20 iterations done for each group whose G-power is 80% and confidence level is 95%. The dataset consists of 724 JPG file format images depicting clear ocean and 324 JPG file format images depicting aquatic debris, including images with noise. This work demonstrates that the Novel AlexNet algorithm achieves an accuracy of 93.77% which identifies objects accurately over the ResNet50 algorithm with an accuracy of 91.47% and there is no statistical significance difference between the Novel Alexnet Algorithm and ResNet-50 Algorithm for the value of p=0.076 (Independent Sample T-test p © 2023 IEEE.

关键词： Debris

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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...

引用

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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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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Decentralized User Identity Management using Blockchain 2

Decentralized User Identity Management using Blockchain

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Srinivas, V. Jha, Aniruddha Krishna Ganesh, G. Nitish, V. Jadon, Shruti PES University Dept. of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350347982

When it comes to identity, there have been issues with security, privacy and usability for a long time. With the growth of the internet and tremendous amounts of data, the need to shift to a more apt system becomes apparent. These concerns lead to the question of our identity online and this is where Identity Management Systems(IdMs) come in. A lot of studies focus on the benefits of IdMs on the blockchain, these include but are not limited to traceability, transparency, security, and scalability. Identity management is a long and tedious process as of now, in this study different existing solutions have been reviewed, and solutions dealing with decentralized IdMs and provide an implementation of a stable, secure, and scalable Decentralized Identity Management system. © 2023 IEEE.

关键词： Blockchain

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Analysis of Fictional Character Backstories Using Natural Language Processing and Deep Learning 26

Analysis of Fictional Character Backstories Using Natural La...

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

作者： Tanvir, Sifat Khan, Md. Rafid Jahan Oyshi, Ishmam Ishan Tonmoy, Md. Sahil Zaman Rafi, Mashrafi Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

With the overwhelming successes of industries like Marvel and DC over the years, fictional characters have become more and more popular among the general mass. However, generation of new fictional characters do not seem to be as appealing as before. This paper revolves around the idea of generating fictional character backstories using GPT-3.5. A dataset consisting of AI generated pre-existing and newly created fictional characters has been prepared. A sequential neural network model was designed by the authors and pre-trained using the popular Superheroes NLP dataset from Kaggle. The proposed model and some state-of-the-art pre-trained text classification BERT models have been compared side by side. The AI generated dataset has been used as the test data to see how the models perform in identifying the moral alignment of the characters. A comparative analysis is also done between the two datasets. © 2023 IEEE.

关键词： BERT Neural Network NLP RoBERTa Sequential Model

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Balancing Safety and Security in Autonomous Driving Systems: A Machine Learning Approach with Safety-First Prioritization 40

Balancing Safety and Security in Autonomous Driving Systems:...

引用

40th Annual computer Security Applications Conference Workshops, ACSACW 2024

作者： Hasani, Afshin Nia, Mehran Alidoost Atani, Reza Ebrahimi University of Guilan Dept. of Computer Engineering Rasht Iran Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (纸本)9798331532819

In the era of autonomous vehicles (AVs), ensuring the safety and security of the system is an ongoing challenge, particularly when faced with increasingly cyber-Attacks such as GPS spoofing and man-in-The-middle. This paper presents a novel reinforcement learning (RL)-based framework that prioritizes safety over security concerns in AVs, ensuring that life-critical tasks such as collision avoidance and speed control are always maintained, even in the presence of security threats. Our system leverages the MAPE-K loop to dynamically adapt to changing conditions, using RL in the Analyzing phase to make real-Time decisions that maximize cumulative safety rewards while addressing security risks that directly affect operational integrity. We demonstrate how our RL-based self-Adaptive system mitigates security threats while maintaining safe driving behaviors through various simulations and experiments. The proposed system shows significant improvements in collision avoidance and threat detection, ultimately ensuring that the AV's safety is preserved in even the most challenging scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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A Multi-Model Rouge Nodes Detection System for Fog Computing

A Multi-Model Rouge Nodes Detection System for Fog Computing

引用

2023 Congress in computer science, computer engineering, and Applied Computing, CSCE 2023

作者： Bui, Thanh Duc Panda, Brajendra University of Arkansas Dept. of Computer Science and Computer Engineering Fayetteville United States

ISBN: (纸本)9798350327595

With the increasing speed of advance of both Internet of Things (IoT) and the demands of utilizing IoT devices in different locations with high compatibility, fog computing has been foreseen as a sustainable solution. However, new challenges related to fog computing's cyber security also emerge. Among various cyber-attacks on the fog computing system, one standard method is to implement corrupted internal nodes that allow malicious/compromised access from attackers, threatening the confidentiality of users' information and the system's performance. In this paper, we propose a method to quantify the distribution of each attribute value from historical access logs from each node within a fog computing system. We also utilize an unsupervised machine learning method to separate nodes into smaller clusters with respect to different models to improve the forecasting ability of the system. Finally, our models are evaluated over simulated data using real access logs. Our experimental results illustrate that our proposed method achieves some advanced performance over applying an overall classification, even using the same classification machine learning methods and dataset. © 2023 IEEE.

关键词： Cloud Computing Cyber Security data Mining Machine Learning

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End-to-End Person Re-Identification for Security and Analysis 4

End-to-End Person Re-Identification for Security and Analysi...

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4th International Conference for Emerging Technology, INCET 2023

作者： Vishwas, R. Sathvik Rao, M.P. Siddhanth, M. Uthpal, P. Kanwal, Preet PES University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350335750

There are millions of CCTVs deployed in public and private places which gather tremendous amounts of video data. It requires a huge amount of manpower to monitor this video data and get the required information extracted in a timely manner. To handle this problem the paper proposes a novel architecture to automate the Video surveillance system, capable of tracking each individual across a multi camera network in real time. Numerous important technologies are incorporated into the Video surveillance system, such as person detection, person re-identification and person *** effectiveness of person re-identification has been greatly improved by researchers, but majority of these methodologies use manually annotated bounding boxes for person detection and only concentrate on matching between query image and cropped gallery images with no regard to person tracking. The paper proposes the following pipeline of processes to build an end-to-end video surveillance system.•Collecting CCTV footage from a network of cameras•Person Detection using YOLOv4•Person Re-Identification using Locally Aware Transformer model for feature extraction•Person Tracking using Deep - SORT algorithm•Logs generation for further analysis and insightsThe Person Re-ID model trained using the aforementioned algorithms achieved an average Rank 1 accuracy of 81% when tested using the custom generated datasets. The resulting product is an End-to-End Person Re-ID Framework deployed as a Web portal which tracks persons across cameras and generates human readable logs for analysis. © 2023 IEEE.

关键词： Feature extraction

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Proposed Model for Prediction of Stock Market Price of Netflix

Proposed Model for Prediction of Stock Market Price of Netfl...

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2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023

作者： Patwal, Pratap Singh Srivastava, Anoop Kumar Laxmi Devi Institute of Engineering & Technology Dept. of Computer Science & Engineering Rajasthan Alwar India Aced Alliance University Dept. of Computer Science & Engineering Karnataka Bengaluru India

ISBN: (纸本)9781665456272

Accurate prediction of stock market price is highly challenging. This paper presents a proposed model for prediction of stock market price of Netflix. We have considered a five-year data set (April, 2017 - April, 2022) of Netflix. An Exploratory data Analysis (EDA) of Netflix's stock price data for predicting its stock market prices using time series is done. The implementation of the model is done using Python language. We imported five-years data and applied several techniques: importing libraries, calculating stock return, line plot, plot all, plot return year wise, plot histogram, plot kernel density, plot box plot, differencing method, resample daily to monthly data etc. EDA proved that using time series technique achieved better results in prediction of stock price and visualizing. © 2023 IEEE.

关键词： Forecasting

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Exploring Deep Learning Techniques for Automated Cataract Detection in Digital Camera Imagery

Exploring Deep Learning Techniques for Automated Cataract De...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Kamepalli, Sujatha Veeranjaneyulu, N. Bodapati, Jyostna Devi Vignan's Foundation For Science Technology And Research Dept. Department Of Information Technology And Computer Application Guntur India Vignan's Foundation For Science Technology And Research Dept. Department Of Advnced Computer Science And Engineering Guntur India

ISBN: (纸本)9798350371567

Cataracts, a prevalent eye condition impacting vision, demand accurate and prompt identification for effective risk reduction and blindness prevention. Traditional methods of cataract diagnosis rely heavily on manual examination by ophthalmologists, which can be time-consuming and subject to human error. With the advent of digital imaging technology, there is an opportunity to enhance the efficiency of cataract detection. The deep neural network models VGG19 and ResNet-50 are recommended in this paper for automatic cataract identification on digital camera images. To optimize network performance, adjustments to loss and activation functions were made, incorporating minimal kernels, reduced training parameters, and layers. This resulted in a ResNet-50 model with significantly decreased computational cost and average operating time compared to the cutting-edge models. The proposed ResNet 50 model achieved an average accuracy of 93.41%, outperforming the VGG 19 model, which had an accuracy of 91.13%. This research contributes to the evolution of cataract detection methodologies, emphasizing the potential of deep learning models, particularly ResNet-50, in improving diagnostic accuracy and operational efficiency for enhanced eye health outcomes. © 2024 IEEE.

关键词： Digital cameras

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