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Anatomize Machine Learning On Tweets, Facebook & Instagram To Assess Women’s Safety In Indian States

Anatomize Machine Learning On Tweets, Facebook & Instagram T...

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Signal Processing and Advance Research in computing (SPARC), International conference on

作者： Priya Patil Rajesh Bahuguna Law College Dehradun Uttaranchal University Dehradun India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Women are enduring a significant degree of abuse on the internet, encompassing online harassment, in multiple urban regions as well as rural too. This study will help in understanding the role of machine learning methods in improving their safety and security of women in India through applying different algorithms. The study centered on employing various machine learning algorithms, including NLKT, TextBolb, TF-IDF, Decision Tree, to extract genuine data from popular social media platforms like Twitter, Facebook, and Instagram. Women are experiencing a substantial amount of abuse on the internet, including online harassment, on internet. There has been a rise in the occurrence of violence against women, necessitating the implementation of more stringent legislation and measures to ensure the safety of women. This study aims to elucidate the impact of Machine Learning model on enhancing the safety and security of women in India by implementing various algorithms. This paper’s result examined several machine learning algorithms to gain a deeper grasp of the present state of trending hashtags, phrases, speech, letters, or symbols linked to women on the Internet in different parts of India. This showcases the efficacy of several AI algorithms in identifying and thwarting cybercrimes, while also offering prospects for future investigation.

关键词： Machine learning algorithms Social networking (online) Signal processing algorithms Legislation Machine learning Classification algorithms Security Web sites multimedia communication Computer crime

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Hybrid Image Encryption for WISN in Privacy Protection and Security of Public Big Data Using Chaotic Maps

Hybrid Image Encryption for WISN in Privacy Protection and S...

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Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), International conference on

作者： Nikhitha Geddada Aditi Sahu Suseela G Jayshnav S Suchi Arora Department of Networking and Communications SRM Institute of Science and Technology Chennai India Department of Computing Technologies SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Image big data has become pervasive in today's world. Every day huge volumes of data are being transferred across systems. Significant advancements have been made in networking and file transfer technologies. Due to the remarkable expansion of network infrastructures, multimedia transmission has become inevitable. Image data is being produced at a faster rate and the variety of data types is expanding rapidly. A number of big data-related surveillance applications use wireless image sensor networks (WISN). Important and sensitive data must be hidden from different hackers trying to break into the system. So, encryption is used to hide sensitive information from various hackers. Lorenz attractor along with the chaotic maps are used to withstand statistical attacks. Dual confusion and dual diffusion are implemented so as to ensure maximum chaos and makes the encryption system strong against attacks. The pixels of the image are scrambled using logistic maps so that only authorized users can decipher it. Through the integration of tent map and logistic map, this study approaches an efficient image encryption algorithm.

关键词： Wireless communication Data privacy Wireless sensor networks Computer hacking Surveillance Big Data Encryption

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Exploring Multimodal Sentiment Analysis Models: A Comprehensive Survey

Exploring Multimodal Sentiment Analysis Models: A Comprehens...

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International conference on multimedia Analysis and Pattern Recognition (MAPR)

作者： Phuong Q. Dao Thien B. Nguyen-Tat Mark Roantree Vuong M. Ngo HCMC Campus Foreign Trade University Vietnam University of Information Technology Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam Insight Centre for Data Analytics School of Computing Dublin City University Ireland Ho Chi Minh City Open University Ho Chi Minh City Vietnam

ISBN: (数字)9798350368437

ISBN: (纸本)9798350368444

The exponential growth of multimodal content across social media platforms, comprising text, images, audio, and video, has catalyzed substantial interest in artificial intelligence, particularly in multi-modal sentiment analysis (MSA). This study presents a comprehensive survey of 30 research papers published between 2020 and 2024 by eminent publishers such as Elsevier, ACM, IEEE, Springer, and others indexed in Google Scholar. Our analysis primarily focuses on exploring multimodal fusion techniques and features, with specific emphasis on the integration of text and image data. Additionally, the article offers an overview of the evolution, definition, and historical context of MSA. It delves into the current challenges and potential advantages of MSA, investigating recent datasets and sophisticated models. Furthermore, the study provides insights into prospective research directions. Notably, this review offers valuable recommendations for advancing research and developing more robust MSA models, thus serving as a valuable resource for both academic and industry researchers engaged in this burgeoning field.

关键词： Surveys Measurement Industries Sentiment analysis Analytical models Reviews Social networking (online)

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Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning

Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

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International conference on computing and networking Technology (ICCNT)

作者： Agus Pratondo Astri Novianty Budi Laksono Putro Siska Komala Sari Department of Multimedia Engineering School of Applied Sciences Telkom University Bandung Indonesia Department of Computer Engineering School of Electrical Engineering Telkom University Bandung Indonesia Department of Computer Science Education Indonesia University of Education Bandung Indonesia Diploma Program of Information System School of Applied System Telkom University Bandung Indonesia

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice.

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Enabling Delay-Sensitive IoT Application by Programmable Local 5G Edge 10

Enabling Delay-Sensitive IoT Application by Programmable Loc...

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10th IEEE International conference on Cloud networking (IEEE CloudNet)

作者： Amemiya, Koichiro Nakao, Akihiro Fujitsu Ltd Tokyo Japan Univ Tokyo Tokyo Japan

ISBN: (纸本)9781665435383

IoT services collect sensor and multimedia data from edge devices for capturing the status of the physical world. Delay-sensitive traffic, especially for monitoring and controlling the edge devices, should be transferred and processed in a priority manner even if congestion in the network occurs because of the system resource sharing with data-intensive and delay-tolerant traffic. A local 5G system is promising for achieving delay-sensitive IoT services because it enables the local 5G operator to control the programmable local 5G system and service level by themselves. But it isn't easy to install novel congestion control protocols to the non-programmable system components other than the local 5G system, such as proprietary IoT devices and wide-area networks operated by network carriers. Our contribution is three-fold: First, we propose a traffic control method for delay-sensitive IoT services installed only in 5G UPF or edge routers in DN without modifying IoT devices or controlling the wide-area network. It controls the latency of delay-sensitive traffic by classifying the delay-sensitive and delay-tolerant traffic, adding delays to, and modifying the receive window size in the packets of the delay-tolerant traffic. Second, we propose an implementation architecture for the programmable Whitebox switches utilizing BPF/XDP functionality. Finally, we evaluate our proposed method. The evaluation result shows that our proposed method keeps the latency of delay-sensitive traffic within the required latency for single and multiple Local 5G locations that share the obscure wide-area network.

关键词： edge computing IoT congestion control

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A Comparative Study of Majority and Weighted Voting Ensemble Techniques for Improving the Accuracy of False Information Detection

A Comparative Study of Majority and Weighted Voting Ensemble...

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Advances in computing, Communication Control and networking (ICAC3N), International conference on

作者： Mayur Dinesh Kumar Vishwakarma Department of Information Technology Delhi Technological University Delhi India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

A method of information dissemination that had never been seen before the digital era has emerged as a result of the use of social media platforms like Facebook, Twitter & Instagram and the development of the World Wide Web. Through these social media sites, a significant quantity of information may be fraudulent. Therefore, it is necessary to monitor this data. By feeding a new article or fact to the model, we may use a technique called machine learning-based fake news identification to determine its veracity. We will preprocess the data (in this example, text) before training the dataset. Preprocessing mostly entails eliminating redundant data. The dataset is then divided into two sections for testing and training. The data will next be vectorized using a variety of vectorization methods, including Countvectorizer, TF-IDF vectorizer, and n-grams. The various machine learning algorithms such as random forest, decision tree, logistic regression etc. are then trained using the vectorized data. The accuracy of cutting-edge false news detection is then increased by incorporating these results into ensemble models. The timeline of a dataset has an impact on how accurate the model is since newer information is not included in older datasets, making it impossible for the model to effectively forecast the veracity of newer information. After training, the model may be quickly tested using the testing dataset, and it will then be ready for usage.

关键词： Training Machine learning algorithms Social networking (online) Predictive models Web sites multimedia communication Fake news

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Social Media Influence Maximization Using Multi-Deep Neural Networks

Social Media Influence Maximization Using Multi-Deep Neural ...

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Emerging Research in Computational Science (ICERCS), International conference on

作者： UmaMaheswari P A. Kumar Kombaiya Department of Computer Science Chikkanna Government Arts College Tirupur India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

many people's daily lives revolve on online social networks. Millions of people have congregated digitally via social media. Studying social networks is difficult due to the huge number of individuals and data flows. On social media (SM), it is natural to see some people as more influential than others. Influence Maximization (IM) is the process of properly identifying influential users in order to maximize a particular piece of information throughout a network. In this paper proposed Multi-Deep Neural Networks to enhance the impacts. Multi-Deep Neural Network used Enhanced D-CNN (Deep Convolutional Neural Network) and DenseNet with BiCNN (Bi-directional Convolutional Neural Network) algorithms to investigate the largest user effect on Facebook, Instagram, and YouTube. Accuracy, precision, recall, and F-measure scores indicate the fastest increasing influencers on Facebook, Instagram, and YouTube. The result shows Instagram had the largest user growth between 2023 and 2024.

关键词： Video on demand Accuracy Social networking (online) Scientific computing Bidirectional control Feature extraction Web sites multimedia communication Convolutional neural networks

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Investigation of the separation zone for split-domain light field visualization

Investigation of the separation zone for split-domain light ...

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International Scientific-Professional conference on Information Technology (IT)

作者： Kamran Javidi Maria G. Martini Peter A. Kara Department of Computer Science Wireless Multimedia and Networking Research Group School of Computer Science and Mathematics Faculty of Engineering Computing and the Environment Kingston University Penrhyn Road Campus Kingston upon Thames London UK Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

Light field visualization commonly provides a single content over the entire field of view. However, the angularly-selective nature of the technology enables the simultaneous visualization of different contents at different viewing angles. Yet segmenting the valid viewing area comes with content interference as well in forms of separation zones, resulting invalid viewing domains. In this paper, we introduce a study on the separation zone of split-domain light field visualization. We created a static-content-based scenario in which we split the valid viewing area into two distinct domains, with a separation zone in between them. This was achieved by merging two light field contents in the middle with an instantaneous switch. The resulting visualization on a light field display was captured by a DSLR camera from two viewing distances, and it was compared to the crosstalk effect caused by insufficient angular resolution.

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AMOSI:Attention-Based Multimodal Sentiment Intensity Analysis with LSTM-CNN Architecture for Online Opinion Videos

AMOSI:Attention-Based Multimodal Sentiment Intensity Analysi...

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International conference on computing and networking Technology (ICCNT)

作者： V V V Bhagya Sree G Bharathi Mohan R Prasanna Kumar M Rithani Gundala Pallavi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Through online video sharing websites, people share their thoughts, experiences, and reviews on a daily basis. Researching the subjectivity and sentiment in these opinion videos is becoming more and more popular among academics and industry professionals. Although sentiment analysis works well for text, there isn’t much research done on it when it comes to videos and other multimedia. The largest obstacles to research in this area are the absence of appropriate methodology, baselines, datasets,and statistical analysis of the relationships between data from various modality sources. The Multimodal Opinionlevel Sentiment Intensity dataset (MOSI), the first opinion-level annotated corpus of sentiment and subjectivity analysis in online videos, is presented to the scientific community in this paper. With labels for subjectivity, sentiment intensity, per-frame and per-opinion annotated visual features, the dataset is meticulously annotated. User-generated opinion videos are increasingly prevalent, necessitating advanced sentiment analysis techniques. We present an attention-based LSTM-2D CNN model for multimodal sentiment intensity prediction. Our contributions are three-fold: 1) We collect new annotations of visual gestures and acoustic effects in online videos to augment the MOSI dataset. 2) We design an end-to-end neural architecture to model textual, visual and acoustic modalities. Bi-directional LSTMs capture temporal dependencies in text and audio. 2D CNNs extract spatial visual features. A hierarchical attention mechanism identifies important utterances and frames. 3) Extensive experiments demonstrate 74.7% accuracy, outperforming prior BC-LSTM models by 1.7%. Ablation studies validate the impact of multimodal fusion and attention. Our work provides an improved neural baseline on MOSI, with detailed analysis of multi-view sequential modelling for opinion videos. The additional annotations and strong results will aid future research in this emerging domain.

关键词： Visualization Sentiment analysis Attention mechanisms Annotations Computer architecture Predictive models Streaming media Feature extraction Acoustics Data models

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CDN and SDN Support and Player Interaction for HTTP Adaptive Video Streaming 21

CDN and SDN Support and Player Interaction for HTTP Adaptive...

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12th ACM multimedia Systems conference (MMSys)

作者： Farahani, Reza Alpen Adria Univ Klagenfurt Inst Informat Technol ITEC Christian Doppler Lab ATHENA Klagenfurt Austria

ISBN: (纸本)9781450384346

Video streaming has become one of the most prevailing, bandwidth-hungry, and latency-sensitive Internet applications. HTTP Adaptive Streaming (HAS) has become the dominant video delivery mechanism over the Internet. Lack of coordination among the clients and lack of awareness of the network in pure client-based adaptive video bitrate approaches have caused problems, such as sub-optimal data throughput from Content Delivery Network (CDN) or origin servers, high CDN costs, and non-satisfactory users' experience. Recent studies have shown that network-assisted HAS techniques by utilizing modern networking paradigms, e.g., Software-Defined networking (SDN), Network Function Virtualization (NFV), and edge computing can significantly improve HAS system performance. In this doctoral study, we leverage the aforementioned modern networking paradigms and design network-assistance for/by HAS clients to improve HAS systems performance and CDN/network utilization. We present four fundamental research questions to target different challenges in devising a network-assisted HAS system.

关键词： Video streaming Network-Assisted Video Streaming HAS DASH QoE Edge computing SDN NFV CDN P2P Video Transcoding

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