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ARITM: A Perceptual Quality Magnification of Noise Suppressed Digital Images using Adaptive Recursion and Image Transformation Model

ARITM: A Perceptual Quality Magnification of Noise Suppresse...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International conference on

作者： K. Kannan G. Divya L. Chithra V. Aruna V. Mohanavel Department of ECE R.M.K. College of Engineering and Technology Puduvoyal Chennai Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of ECE S.A.Engineering College Chennai Department of ECE Panimalar Engineering College Chennai Centre for Materials Engineering and Regenerative Medicine Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Mechanical Engineering Chandigarh University Mohali Punjab India

In the realm of digital image processing and enhancement, the relentless pursuit of achieving higher perceptual quality in noise-suppressed images has remained a compelling challenge. This abstract presents a novel approach that leverages the synergy between adaptive recursion and a sophisticated image transformation model to magnify the perceptual quality of noise-suppressed digital images. The proliferation of digital imagery in various applications, from medical diagnostics to multimedia content, necessitates the development of advanced techniques to preserve and enhance image fidelity. While noise suppression techniques have made significant strides in reducing unwanted artifacts, they often introduce trade-offs between noise reduction and preservation of image details. Our proposed method begins by employing adaptive recursion, where the image is divided into local regions, each analyzed independently for noise characteristics and visual features. Adaptive recursion ensures that noise reduction is applied selectively, preserving important image details, even in challenging scenarios. The synergy between adaptive recursion and the image transformation model is a key highlight of our approach. By iteratively applying the transformation model within each local region, we adaptively enhance image features that contribute to perceptual quality, such as sharpness, contrast, and color fidelity. This adaptive enhancement process ensures that image details are magnified while suppressing noise artifacts, ultimately resulting in images of superior perceptual quality. Our experimental results, conducted on diverse datasets with various levels of noise, demonstrate significant improvements in perceptual quality compared to state-of-the-art methods. Additionally, we evaluate the computational efficiency of our approach, highlighting its feasibility for real-time and high-throughput image enhancement applications.

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Arabic hate speech detection system based on AraBERT

Arabic hate speech detection system based on AraBERT

引用

IEEE International conference on Cognitive Informatics

作者： Palé Ollo Salomon Zied Kechaou Ali Wali Higher Institute of Computer Science and Multimedia of Sfax sfax tunisia REGIM-lab: Research Groups on Intelligent Machines universty of sfax national engineering school of sfax (ENIS) sfax tunisia Higher School of commerce of Sfax sfax tunisia

Tunisia has entered a phase freedom of speech with access to social media since the Jasmine Revolution in 2011. Toxic contents such as abusive and hateful speeches have become omnipresent on Tunisian social media. Considering the side effects of these toxic contents on the psychology of users, it is necessary to detect them automatically. The dialect of Tunisian is underrepresented. As a consequence, there is not enough data set. In this paper, we present the data collection process with the aim of having a Tunisian reference dataset, to evaluate different models of hate speech and abuse detection. We also present our neural network model based on AraBERT. Our experimental results on our dataset shows that the AraBERT model performs better with an F1 score of 0.99.

关键词： Social networking (online) Computational modeling Hate speech Neural networks Psychology Data collection Data models

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Abnormal High-Density Crowd Dataset 4

Abnormal High-Density Crowd Dataset

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4th International conference on multimedia computing, networking and Applications, MCNA 2020

作者： Mahmoud, Samar Arafaf, Yasmine Sciences University of Greenwich School of Computing and Mathematical London United Kingdom

ISBN: (纸本)9781728183732

Anomaly detection within crowded environments is a key challenge in the computer vision and crowd behaviour understanding fields. Furthermore, anomaly detection within high-density crowds remains an insufficiently explored area. In this paper, we propose a novel abnormal high-density crowd dataset. The proposed dataset adheres to the same constraints as some of the benchmark datasets such as UCSD, UMN and Avenue dataset. These constraints include occurrences of both normal and abnormal behaviour and validated abnormal behaviour annotations. Additionally, this dataset includes footage of only high-density crowds, whereas benchmark datasets include low to medium density crowds. We have taken into consideration privacy issues, the veracity of annotations and pre-processing of the dataset. We evaluate the dataset against state-of-The-Art crowd anomaly detection methods. The generated results indicate that training/testing these methods on high-density crowds decreases their detection performance. © 2020 IEEE.

关键词： Anomaly detection

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TailCmp - A Tail Latency Evaluation Solution of Public Cloud and Labeled von Neumann Architecture based Cloud Prototype

TailCmp - A Tail Latency Evaluation Solution of Public Cloud...

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IEEE International conference on Big Data and Cloud computing (BdCloud)

作者： Xiangyu Kong Xuesong Gao Shibao Pan Yizhi Zhou Yanan Yang Laiping Zhao Heng Qi School of Computer Science and Technology Dalian University of Technology Dalian China College of Intelligence and Computing Tianjin University Tianjin China State-Key Laboratory of Digital Multimedia Technology Qingdao China

Tail latency is the key performance metric for cloud computing systems (CCSs). When latency-critical (LC) applications and best-effort batch (BE) jobs are co-located on CCSs, the unmanaged contention for computing resources usually leads to significant fluctuations in tail latency, which have a severe negative impact to the end user experience. Therefore, some promising computing systems and effective scheduling solutions are proposed to guarantee better quality of service (QoS), e.g., La-beled von Neumann Architecture (LvNA) based cloud prototype system. However, relatively few work focuses on the tail latency observation and evaluation of public CCSs and newest prototype systems. Therefore, we introduce an evaluation framework named TailCmp which includes nine representative workloads ranging over different tail latency requirements and application domains. With the TailCmp, we can collect and analyze four evaluation metrics including tail latency entropy. In this paper, we conduct a large number of experiments with TailCmp on three CCSs (two public CCSs and one LvNA-based prototype system). The results show that the co-location on existing public CCSs can seriously affect the QoS of LC workloads, while the labeling mechanism in Lv Na - based prototype can improve the performance of LC workloads in co-location.

关键词： Cloud computing Limiting Processor scheduling Prototypes Tail Quality of service Computer architecture

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Performance Comparison of Word2Vec Models for Detecting Arabic Hate Speech on Social Networks

Performance Comparison of Word2Vec Models for Detecting Arab...

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Emerging Trends in computing and Engineering Applications (ETCEA), International conference on

作者： Samar Al-Saqqa Arafat Awajan Bassam Hammo Department of Computer Science Princess Sumaya University for Technology Amman Jordan Department of Information Technology King Abdullah II School for IT The University of Jordan Amman Jordan Department of Computer Science Mutha University Amman Jordan Department of Software Engineering Princess Sumaya University for Technology Amman Jordan Department of CIS King Abdullah II School for IT The University of Jordan Amman Jordan

ISBN: (纸本)9781665477109

Detecting hate speech has become increasingly important for online communities. Despite emerging research to address the problem, more efforts are still needed to improve the performance of detection methods for the Arabic language. In this paper, we compare the performance of two Word2Vec models; the continuous bag of words (CBoW) and skip-gram using a dataset collected from multiple social media platforms; Facebook, Twitter, YouTube, and Instagram. We compared seven machine learning (ML) algorithms and measured their performance using different evaluation metrics. The CBoW model outperformed the skip-gram model in terms of accuracy. In addition, using a higher embedding dimensional value resulted in a better performance.

关键词： Measurement Video on demand Machine learning algorithms Social networking (online) Computational modeling Hate speech multimedia Web sites

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Immersive Technologies: A Literature Review on Brand Engagement and Consumer Behaviour

Immersive Technologies: A Literature Review on Brand Engagem...

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Technological Engineering and its Applications in Sustainable Development (ICTEASD), International conference for

作者： Nor Hazlen Kamaruddin Mohd Ekram Alhafis Bin Hashim Wan Azani Wan Mustafa Suraya Md Nasir Mohd Fauzi Harun Laith H. Jasim Multimedia Creative Departmet Faculty of Art Sustainability & Creative Industry Sultan Idris Educational University Perak Malaysia Advanced Computing (AdvCOMP) Centre of Excellence Universiti Perlis Malaysia Faculty of Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350336474

ISBN: (纸本)9798350336481

The evolution of marketing through digital means has been greatly influenced by the emergence of virtual influencers on social media and the incorporation of immersive technologies such as AR, VR, and XR. This study examines the development of virtual influencers as an emerging frontier in consumer interaction, highlighting their unique benefits compared to traditional influencers and their potential for tailored and managed brand promotion. Digital avatars provide a unique method to engage audiences, but they also present ethical dilemmas and require scientific evidence to confirm their efficacy. The methodology includes a comprehensive literature study on many aspects of digital marketing, such as the influence of immersive technologies on brand storytelling and engagement, and the significance of gamification and video-based learning in improving user interaction from 2013 to 2024. The analysis highlights a significant deficiency in existing research about the enduring impacts of these technologies on brand loyalty and consumer behaviour. The digital age requires creative marketing methods that are ethical and align with consumers' desire for authenticity and engagement. Future studies should further investigate these aspects to ensure that virtual influencers and immersive technology are used properly to cultivate significant brand connections.

关键词： Ethics Technological innovation Solid modeling Consumer behavior Uncertainty Social networking (online) Extended reality

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An Organized Study of Opinion Methods

An Organized Study of Opinion Methods

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International conference on Contemporary computing and Informatics (IC3I)

作者： Ms. Komal Sandhu Durgesh Nandan Department of Master of Computer Application Chandigarh School of Business Jhanjeri India Department of Agriculture Sanskriti University Mathura Uttar Pradesh India

Emotion recognition serves as one of the key research areas as Facebook use on web. On instagram, Google, Wikipedia, as well as others, individuals share their views, ideas, emotions, feelings, and views, bringing a considerable measure with fire to modern life. Ai, sometimes referred to as recommender systems, focuses on categorizing and predicting men’s sentiments regarding that same issue. It is sometimes referred to as “emotional ore” or “mood coal.” It involves classifying written texts into pro or con groups depending on the stated viewpoint on a particular problem. Despite the simple fact that recommendation system may seem to be similar to text categorization, it faces a variety of issues which has inspired much study in this field. To improve the tone study, many automation (ML) and also dictionary strategies were created in the story. In this work, we just provide results of an university level that aims to assess the state of the science. In order for new called electronic to be developed by researchers in the future who have more information and can address in all issues and get the best outcomes.

关键词： Support vector machines Dictionaries Social networking (online) Text categorization multimedia Web sites Ontologies Multilayer perceptrons

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Fraud Account Detection on Social Network using Machine Learning Techniques

Fraud Account Detection on Social Network using Machine Lear...

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Advancements in Smart, Secure and Intelligent computing (ASSIC), International conference on

作者： E. Anupriya N. Kumaresan V. Suresh S. Dhanasekaran K. Ramprathap P. Chinnasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Dr. N. G. P Institute of Technology Coimbatore India Department of Information Technology Kalasalingam Academy of Research and Education Srivilliputtur India Department of Management Studies M. Kumarasamy College of Engineering Karur India

Nowadays, a person's impact is frequently determined by the number of followers he or she has on social media. To this aim, the prevalence of false accounts is one of the most pressing issues, with the potential to disrupt a wide range of real-world and economic activity. Bot followers are dangerous to social media as these could alter perceptions of popularity and influence, which can have a ample amount of impact on every sector. As a result, new approaches must be developed to enable the detection and classification of bogus accounts. This study gives novel method for distinguishing original profiles. The method uses information gathered automatically from huge data to characterize typical patterns of fake account.

关键词： Support vector machines Economics Social networking (online) multimedia Web sites Pressing Machine learning Chatbots

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Scientific and Technological News Recommendation Based on Knowledge Graph with User Perception

Scientific and Technological News Recommendation Based on Kn...

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IEEE International conference on Cloud computing and Intelligence Systems (CCIS)

作者： Yuyao Zeng Junping Du Zhe Xue Ang Li School of Computer Science Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665456579

Existing research usually utilizes side information such as social network or item attributes to improve the performance of collaborative filtering-based recommender systems. In this paper, the knowledge graph with user perception is used to acquire the source of side information. We proposed KGUPN to address the limitations of existing embedding-based and path-based knowledge graph-aware recommendation methods, an end-to-end framework that integrates knowledge graph and user awareness into scientific and technological news recommendation systems. KGUPN contains three main layers, which are the propagation representation layer, the contextual information layer and collaborative relation layer. The propagation representation layer improves the representation of an entity by recursively propagating embeddings from its neighbors (which can be users, news, or relationships) in the knowledge graph. The contextual information layer improves the representation of entities by encoding the behavioral information of entities appearing in the news. The collaborative relation layer complements the relationship between entities in the news knowledge graph. Experimental results on real-world datasets show that KGUPN significantly outperforms state-of-the-art baselines in scientific and technological news recommendation.

关键词： Cloud computing Social networking (online) Collaboration Information filters Encoding Behavioral sciences Intelligent systems

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Sentiment Analysis on Cryptocurrency Based on Tweets and Retweets Using Support Vector Machines and Chi-Square

Sentiment Analysis on Cryptocurrency Based on Tweets and Ret...

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International conference on Informatics and computing (ICIC)

作者： Isabella Donita Hasan Raymond Sunardi Oetama Aldo Lionel Saonard Information System Dept Universitas Multimedia Nusantara Tangerang Indonesia

ISBN: (纸本)9798350345728

The high public interest in cryptocurrencies is increasing over time. Cryptocurrencies are gaining worldwide attention because of their unique and unpredictable nature. Nowadays, cryptocurrencies can be used for various things, one of which is for investment. In investing in cryptocurrencies, various analyzes are needed to consider trade flows. One way to analyze it is by analyzing public sentiment which consists of positive and negative sentiments. However, there has been no previous research on the use of machine learning approaches and feature selection in sentiment analysis of Twitter tweets and retweets. The research was conducted by analyzing public sentiment using a machine learning algorithm, namely the Support Vector Machine. In addition to using the Support Vector Machine, Chi-square is also used in feature selection to help reduce noise in a sentence. Retweets will considerably improve sentiment label classification. The performance of sentiment analysis can be improved across the board by at least 91%. The sentiment analysis findings from this study are positive. This implies that a trader can take cryptocurrencies like Bitcoin, Ethereum, and Ripple into account while deciding on the type of investment.

关键词： Sentiment analysis Machine learning algorithms Social networking (online) Support vector machine classification Machine learning Feature extraction Cryptocurrency

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