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An Extensive Survey on Audio-to-Text and Text Summarization for Video Content 3

An Extensive Survey on Audio-to-Text and Text Summarization ...

引用

3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Raut, Nitin B. Pranesh, A.S. Nagulan, B. Pranesh, S. Vasantharajan, R. KPR Institute of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore India Grids and Guides Chennai India

ISBN: (纸本)9798350343632

This literature review examines evolving trends in audio-to-text and text summarization techniques applied to YouTube video content. With the rapid expansion of online video platforms, the demand for efficient transcription and summarization of multimedia data has grown significantly. This study offers a comprehensive analysis of current methodologies, algorithms, and tools for converting audio content to text, highlighting the intricacies of speech recognition technologies and their applicability in various contexts. Additionally, the survey explores text summarization techniques specifically designed for YouTube videos, including both extractive and abstractive methods. It assesses their effectiveness in condensing lengthy video transcripts into concise and coherent textual representations. Special emphasis is given to NLP (Natural Language Processing) algorithms and ML (Machine Learning) models that aid in extracting essential information while maintaining contextual relevance. © 2023 IEEE.

关键词： Overfitting Reinforcement Learning Supervised Learning Testing Data Training Data Unsupervised Learning

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Image based and Point Cloud based Methods for 3D View Reconstruction in Real-time Environment 14

Image based and Point Cloud based Methods for 3D View Recons...

引用

2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vasikarla, Shantaram Vellore Institute of Technology School of Computer Science and Engineering Vellore India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati India Indian Institute of Technology Dept. of Electrical Engineering Kanpur India California State University Dept. of Computer Science NorthridgeCA United States

ISBN: (纸本)9798350360134

3D view reconstruction in a real-time unknown environment poses several challenges to vision-based applications. Herein, this paper outlines real-time experimental results and analysis of 3D view reconstruction methods. Since real-time experimentation requires inputs from different sensors, this paper outlines the results of methods from the following categories: image based methods and point cloud based methods. This paper explains the detailed steps involved in 3D view reconstruction followed by an explanation of the methods. It provides detailed discussions, advantages, limitations, and empirical results, along with presenting differences between algorithms. For analysis of experimental results, two benchmarked datasets i.e., KITTI 3D benchmark suite and ShapeNet are considered. Furthermore, the paper explores potential future scopes, including the utilization of generative models, unsupervised learning, advanced sensors, and many more. © 2024 IEEE.

关键词： Network coding

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Design and Implementation of a Distributed Network Management System for a Multi-Branch University using GNS3 14

Design and Implementation of a Distributed Network Managemen...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Hosen, Md Apu Hoshen Moz, Shahadat Akber, Md. Ali Northern University of Business and Technology Khulna Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350335095

The rapid digitalization of communication necessitates continuous skill updates to keep pace with evolving technology. Network management involves multiple functionalities that play a crucial role in optimizing network performance and ensuring continuous availability. It is very important to ensure a proper network management system for a university that has multiple branches to effectively manage and monitor their network infrastructure. A proper network management system provides centralized control and visibility over the entire network infrastructure, allowing administrators to efficiently manage and monitor network devices, configurations, and performance from a single interface. this research has developed a network management system that facilitates real-time network monitoring and troubleshooting, enabling proactive identification and resolution of issues to ensure smooth operations across all branches. It simplifies configuration management, ensuring consistency and compliance in network settings and updates. Resource optimization is achieved through insights into network utilization and performance metrics, allowing administrators to allocate resources efficiently and plan for future expansion. Security and compliance measures can be enforced, protecting sensitive data and ensuring adherence to regulatory requirements. This study utilized GNS3 to create a network layout for a university with multiple branches, along with essential security configurations for the network design. A diverse range of protocols was implemented to safeguard and cater to the users of the secure university network. Furthermore, Switches, Data Routers, firewalls, and BGP Routers were incorporated into the network to enhance communication efficiency and streamline operations. © 2023 IEEE.

关键词： Sensitive data

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Personality Prediction Using ML and NLP 7

Personality Prediction Using ML and NLP

引用

2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Mahandule, Adinath Fiske, Gaurav Malusare, Vedang Bhambure, Omkar Shedge, Rajashree Ramrao Adik Institute of Technology Dept. of Computer Engineering Navi Mumbai400706 India

ISBN: (纸本)9798350304268

Estimating a person's personality is essential in understanding how they manage stress, lead, communicate, collaborate, and influence others. One of the critical factors that affect how people interact with their environment is their personality. This project aims to utilize machine learning and also natural language processing techniques to predict the personality of students based on a questionnaire. The system analyzes the user's personality in the database, which already records personality traits. The system then identifies the user's personality based on the MBTI model, a widely used personality model. This project's significance lies in the capability of the system to aid students, teachers, and businesses in identifying students' personalities to plan their actions better. The ML and NLP techniques implemented in this system can categorize or anticipate students' personalities, thus assisting businesses in their hiring process. © 2023 IEEE.

关键词： Catboost Gradient Boosting Machine Learning Myers-Briggs Type Indicator (MBTI) Natural Language Processing TF-IDF(Term Frequency-Inverse Document Frequency)

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Youtube Spam Detection Scheme Using Stacked Ensemble Machine Learning Model

Youtube Spam Detection Scheme Using Stacked Ensemble Machine...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Shabadi, Laxmi Chaitra, Y.L. Srikanth, P. Vijay Kumar, L. Kashyap, Udisha Sjb Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350300826

This work introduced a method to identify English spam comments on YouTube, a platform that has experienced a significant increase in spam activity. Despite YouTube's efforts to implement its own spam filtering system, it still struggles to effectively block spam comments. To address such an issue, we analyzed existing research on YouTube spam comment detection and performed classification experiments using an ensemble model that combines various techniques. A stacked ensemble model combines the predictions of multiple individual models to improve the accuracy and robustness of spam detection in social media applications. It leverages the strengths of different models and captures a broader range of features, leading to a more accurate classification of spam and non-spam content. It has then implemented a web extension to hide the spam comments from the page by modifying the CSS (Cascading Style Sheets). The comment data utilized in experiments was sourced from popular music videos by artists such as Psy, Katy Perry, LMFAO, Eminem, and Shakira. © 2023 IEEE.

关键词： Classification (of information)

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A Transpose-SELDNet for Polyphonic Sound Event Localization and Detection 8

A Transpose-SELDNet for Polyphonic Sound Event Localization ...

引用

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

作者： Spoorthy, V. Koolagudi, Shashidhar G. National Institute of Technology Karnataka Surathkal Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798350334012

Human beings have the ability to identify a particular event occurring in a surrounding based on sound cues even when no visual scenes are presented. Sound events are the auditory cues that are present in a surrounding. Sound event detection (SED) is the process of determining the beginning and end of sound events as well as a textual label for the event. The term sound source localization (SSL) refers to the process of identifying the spatial location of a sound occurrence in addition to the SED. The integrated task of SED and SSL is known as Sound Event Localization and Detection (SELD). In this proposed work, three different deep learning architectures are explored to perform SELD. The three deep learning architectures are SELDNet, D-SELDNet (depthwise Convolution), and T-SELDNet (Transpose Convolution). Two sets of features are used to perform SED and Direction-of-Arrival (DOA) estimation tasks in this work. D-SELDNet uses a depthwise convolution layer which helps reduce the model's complexity in terms of computation time. T-SELDNet uses Transpose Convolution, which helps in learning better discriminative features by retaining the input size and not losing necessary information from the input. The proposed method is evaluated on the First-order Ambisonic (FOA) array format of the TAU-NIGENS Spatial Sound Events 2020 dataset. An improvement has been observed as compared to the existing SELD systems with the proposed T-SELDNet. © 2023 IEEE.

关键词： Convolution

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Harnessing Machine Learning for Uterine Fibroid Detection Using Advanced Classification Techniques

Harnessing Machine Learning for Uterine Fibroid Detection Us...

引用

2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Deepak Kumar, A. Suhaasini, S. Swetha Shri, B. Muthukumar, S. Sudhahar, T.N. Menaga, D. St. Joseph's Institute of Technology Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350319200

In the 40 to 50 age range, uterine fibroids are prevalent in over 70% of women. The proposed system employs a dataset comprising 1985 ultrasound images of uterus for fibroid detection. Utilizing advanced classification techniques like ResNet-152 and MobileNet V2, and an ensemble of Logistic Regression and MobileNet V2, the proposed system attained remarkable precision at 87.23% and an impressive accuracy of 91.67%. This outperformed the previous HIFU-Net Segmentation method's precision of 84.48% on MRI scans, highlighting the potential of advanced techniques in medical diagnostics. © 2023 IEEE.

关键词： Classification Ensemble HIFU-Net MobileNet V2 ResNet-152 Uterine fibroids

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Federated Learning in Malware Detection 28

Federated Learning in Malware Detection

引用

28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Serpanos, Dimitrios Xenos, Georgios University of Patras Computer Technology Institute and Press DIOPHANTUS Dept. of Electrical and Computer Engineering Patras Greece University of Patras Campus Computer Technology Institute and Press DIOPHANTUS Patras Greece

ISBN: (纸本)9798350339918

Malware detection constitutes a fundamental step in safe and secure computational systems, including industrial systems and the Internet of Things (IoT). Modern malware detection is based on machine learning methods that classify software samples as malware or benign, based on features that are extracted from the samples through static and/or dynamic analysis. State-of-the-art malware detection systems employ Deep Neural Networks (DNNs) whose accuracy increases as more data are analyzed and exploited. However, organizations also have significant privacy constraints and concerns which limit the data that they share with centralized security providers or other organizations, despite the malware detection accuracy improvements that can be achieved with the aggregated data. In this paper we investigate the effectiveness of federated learning (FL) methods for developing and distributing aggregated DNNs among autonomous interconnected organizations. We analyze a solution where multiple organizations use independent malware analysis platforms as part of their Security Operations Centers (SOCs) and train their own local DNN model on their own private data. Exploiting cross-silo FL, we combine these DNNs into a global one which is then distributed to all organizations, achieving the distribution of combined malware detection models using data from multiple sources without sample or feature sharing. We evaluate the approach using the EMBER benchmark dataset and demonstrate that our approach effectively reaches the same accuracy as the non-federated centralized DNN model, which is above 93%. © 2023 IEEE.

关键词： Deep neural networks

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Impact of Machine Learning in Music Genre Classification using CNN 14

Impact of Machine Learning in Music Genre Classification usi...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Venkatesh, Jorige Kannan, Karthik Ayyadurai, M. Sathish, M.G. SRM Institute of Science and Technology Dept. of Computer Science and Engineering Chennai Ramapuram India

ISBN: (纸本)9798350335095

The rapid expansion of available music tracks has made the classification of music genres increasingly crucial. To achieve accurate genre predictions, machine learning techniques have been widely employed and proven highly efficient. In this study, we focused on enhancing the accuracy of music genre classification systems by training a Convolutional Neural Network (CNN) model using Keras. Music genre classification serves as a valuable tool for music learning, offering a reliable method of categorization. Given the immense amount of data and the inherent complexity in identifying music genres, we leveraged the power of deep neural networks to classify ten distinct genres. To evaluate the effectiveness of our approach, we compared the performance of the CNN model to other existing models using the GITZAN dataset. Our results demonstrated the success of the CNN model in accurately classifying music genres, surpassing previous studies while requiring less data. This research contributes to the field of music genre classification by providing an advanced and efficient solution using deep learning techniques. © 2023 IEEE.

关键词： Music

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Campus Network Configuration, Monitoring and Data Flow Simulation using Cisco Packet Tracer 6

Campus Network Configuration, Monitoring and Data Flow Simul...

引用

6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Moz, Shahadat Hoshen Hosen, Md Apu Tanny, Nice Fatema Islam Dept. of Computer Science and Engineering Northern University of Business and Technology Khulna Khulna Bangladesh

ISBN: (纸本)9798350398496

The key to communication is being digitalized every day, in order to keep up with technology everyone needs to update their skillset with it. A campus network is connected to us more because all are often a part of it. Everyone uses it most of the time for their daily activities on campus and also for educational purposes, depending on their needs. To ensure that data cannot be corrupted, it is crucial to make specific alterations to it. Integrity and reliability are key issues in all information switch difficulties in order to ensure safe and simple transfers among customers. As a result, this research has developed a secure campus network and a stable campus community for sending and getting data among end users with high levels of security. Using a Cisco packet tracer, this study proposed a topology for a campus of multiple networks and virtual local area networks (VLANs), as well as the most crucial security configurations for the networking used in this design. There used a massive variety of protocols to defend and accommodate the customers of the secure campus network scheme and also added switches, routers, firewalls, Wi-Fi routers, IProuters and printers to this network in order to make communication smoother and more efficient. Digital campus is the place where the next generation goes to gain education and knowledge about the world makes the campus network more secure and tech-focused. The students and other personnel can learn and even get familiar with them all and also helpeveryone. © 2023 IEEE.

关键词： Network security

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