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Video Harmony: Enhancing Emotional Well-being with Video Recommendations for Children with ASD 15

Video Harmony: Enhancing Emotional Well-being with Video Rec...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Raj, R. Imman pushpa Raj, A. Samson Arun Department of Computer Science Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331300579

Today's autistic children find it difficult to deal with people's emotions socially and tend to isolate themselves. VideoHarmony is an innovative approach designed to help children with Autism Spectrum Disorder (ASD) maintain their emotional health. Using convolutional neural networks and Haar Cascade to power advanced facial emotion recognition, the system can accurately understand facial expressions in real time. The video recommendation feature creates playlists that dynamically adjust based on the child's detected emotions through collaborative and content-based filtering algorithms. The user interface offers customizable options and an easy-to-use emotion panel for caregivers or parents. With the help of studies, user comments and observations, the positive effects of the system on the emotional states and activities of children are shown. The secret to VideoHarmony's success is its ability to provide personalized and responsive emotional support by combining cutting-edge technology with an intuitive user experience. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Lora-Based Data Transmission System for Smart Buildings 1

Lora-Based Data Transmission System for Smart Buildings

引用

1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Kumar, Sunny Verma, Karan National Institute of Technology Delhi Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350365016

The necessity of ease and convenience for users, a Smart Building with complex applications needs to be operated through a centrally automated system such that the data can be transmitted over a long range. Hence, a Long-Range technology is needed to extend the low-power communication standard of an effective range of 13-15 Km with coverage across the entire city using a single Long Range (LoRa) gateway. Presently, developing countries use cellular technologies such as 3G, LTE, and 5G, but these technologies have their limitations because of their less coverage and high power consumption. Therefore, this paper aims to compress LoRa data before transmission to the LoRa gateway using deep learning method. Also, Channel-Net is trained to represent the noise in the channel. A Deep Neural Network (DNN) architecture is required to compress and decompress the LoRa data. Moreover, in the spectrogram analysis of the chirps, the LoRa technology is used to differentiate different device signals. The results show an error in the compression-decompression Auto Encoder and the classification of devices. © 2024 IEEE.

关键词： Gateways (computer networks)

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Comparative Analysis of Deep Learning Architectures for Multilingual Digit Recognition 2

Comparative Analysis of Deep Learning Architectures for Mult...

引用

2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Mamatha Bai, B.G. Sarangi, Akruti Kashyap, Akanksha Yadav, Himanshi Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350373110

Digit recognition is foundational in pattern recog-nition and machine learning, with applications in document processing and optical character recognition. Current research often targets English digits, overlooking language-independent systems. This paper bridges this gap by analyzing deep-learning architectures and activation functions for Hindi, English, and Kannada digits. We evaluate CNN, LeNet, ResNet, MobileNet, and Inception (GoogleNet) models, and activation functions like sigmoid, tanh, linear, ELU, Leaky ReLU, and ReLU. The results indicate ResNet achieves 99.498% accuracy in English, 99.43% in Hindi, and 97.67% in Kannada datasets. This study enhances the design and optimization of robust digit recognition systems across diverse languages and scripts, promoting efficient models for practical applications. Despite advancements with CNNs, research predominantly focuses on English digits, highlighting the need for lanauage-independent recognition systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Opinion Dynamics & Synchronization in Higher-Order Networks: Using the Kuramoto Model and Simplicial Complexes

Opinion Dynamics & Synchronization in Higher-Order Networks:...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Sinha, Navneet Singh, Anurag National Institute of Technology Delhi Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350379716

Understanding opinion dynamics within complex networks is essential for comprehending collective decision-making processes. Traditional models often fail to account for intricate relationships inherent in complex networks, such as higher-order interactions. Numerous models have been developed historically to analyze the dynamics of opinions. The proposed work introduces a novel approach to study opinion dynamics using the Kuramoto model. Moreover, it investigates how network topology and the inter-node connections influence opinion synchronization by employing simplicial complexes to describe higher-order interactions and expand the Kuramoto model to accommodate these structures. The validity of the proposed framework is demonstrated through the study of real-world data extracted from the social network. In particular, this research enhances our understanding of how opinions propagate in complex networks, offering insights that can inform strategies for consensus-building, opinion manipulation, and influence maximization. © 2024 IEEE.

关键词： Synchronization

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Multiview Learning and Decision Fusion Based Ensemble to Predict Anti-Cancer Peptide 1

Multiview Learning and Decision Fusion Based Ensemble to Pre...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Kumar, Aditya Singh, Deepak National Institute of Technology-Raipur Computer Science and Engineering Department Raipur India

ISBN: (纸本)9798350365016

The spread of a life-threatening disease such as cancer presents a significant threat to the entire global population. The disease in question caused 10 million deaths worldwide in 2020, which has motivated us to explore innovative methods for treating cancer. Although numerous pharmaceutical solutions are accessible, they often have a hefty price tag and can adversely affect human cells. Anticancer peptides are a recently emerging field in cancer diagnosis, as they have a low likelihood of causing adverse reactions. The current study proposed using a multiview and decision fusion approach with stacking ensemble learning to predict anticancer peptides. Multiview learning and decision fusion is an emerging deep learning technique that enhances performance by integrating information from multiple views. Finally, the present work uses three 1-D conventional neural networks as the base classifier and a neural network as the meta-classifier to create a stacking ensemble for anticancer peptide prediction. The proposed ensemble scores a classification accuracy of 95.38%, an MCC value of 0.843, and an AUC score of 0.961 on a standard ENNAACT anticancer dataset, surpassing the performance of state-of-the-art approaches. © 2024 IEEE.

关键词： Anti-cancer Decision Fusion Ensemble Multi-view

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Advanced Construction Materials Based on Concrete to Protect the Living Space from Non-Ionizing Radiation 1

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32nd International Conference on Organization and technology of Maintenance, OTO 2023

作者： Mandrić, Vanja Rupčić, Slavko Vinko, Davor Dominković, Ivica Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9783031514944

ISBN: (纸本)9783031514937

This paper presents measurements of the reflection and transmission coefficient of electromagnetic waves through concrete and two concrete-based composites: concrete with steel fibers and concrete with carbon fibers with the aim of enhancing the attenuation of electromagnetic waves passing through concrete and concrete composites. The frequency range for which the measurements were carried out extends from 30 MHz to 18 GHz and includes most of the existing stationary (and mobile) radiation EM sources in the environment in which the general population moves (FM, DVBT2, 2G - 5G mobile radio systems, stationary radar border area control systems). The measurement results show that as the thickness of the concrete increases, the transmission through concrete and concrete composites decreases (parameter S12 decreases). Furthermore, composites with carbon fibers partially reduce, while those with steel fibers significantly reduce, the transmission of EM waves through such composites. The reduction of parameter S12 by composites with steel is up to 52 dB (at a frequency of 2.9 GHz) for a block thickness of 100 mm. It is important to emphasize that in the case of composites with steel fibers, the influence of the fibers on the transmission parameters is more significant than the thickness of the sample. The reason for this is the increase in electrical conductivity of composites with steel fibers due to the increased proportion of conductive components in the concrete. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Steel fibers

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Scams and Frauds in the Digital Age: ML-Based Detection and Prevention Strategies 25

Scams and Frauds in the Digital Age: ML-Based Detection and ...

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26th International Conference on Distributed Computing and Networking, ICDCN 2025

作者： Kolupuri, Sai Venkata Jaswant Paul, Ananya Bhowmick, Rajat Subhra Ganguli, Isha Computer Science and Engineering RV University Bengaluru India Indian Institute of Engineering Science and Technology Shibpur India University Bengaluru India

ISBN: (纸本)9798400710629

Over the past two decades, the commercial landscape has undergone a transformation, shifting from traditional marketing methods to digital and internet-based platforms. This shift, while boosting economic potential, has also made the digital world a prime target for cybercriminals. Online scams and fraud have become prevalent, affecting individuals and businesses globally. Financial losses from cyber fraud in India have surged significantly, rising from Rs 69.7 crore in FY 2023 to Rs 177.05 crore in FY 2024. This paper presents a detailed analysis of various forms of online scams and frauds, explaining their core methodologies and the severe impacts these schemes have on consumers. From phishing and identity theft to sophisticated financial fraud, the study dives deep into how these fraudulent activities evolve and adapt to target individuals and businesses alike. The paper also offers a thorough taxonomy of each type of scam to provide a structured understanding of the different fraud strategies. The paper highlights a range of Machine Learning (ML) and Deep Learning (DL) methods that are essential in identifying fraudulent patterns in real-time. In addition to these detection methods, the paper also outlines several ML/DL-based prevention strategies aimed at proactively stopping scams before they cause harm. Ultimately, by promoting advanced detection and prevention methods, this document aims to contribute to the development of a safer digital ecosystem. © 2025 Copyright held by the owner/author(s).

关键词： Phishing

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Answer Retrieval System using NLP for Documents: A Promising Solution for Research Queries 15

Answer Retrieval System using NLP for Documents: A Promising...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Niranjan, Prashant Y. Rajpurohit, Vijay S. Sannakki, Sanjeev S. Sambrekar, Kuldeep Medar, Ramesh Deshapande, Girish R. Sonawalkar, Prakash Department of Computer Science & Engineering KLS Gogte Institute of Technology Belagavi India

ISBN: (纸本)9798331300579

This paper presents a study of answer retrieval systems, which aim to automatically retrieve the most relevant answer(s) to a given question from a large collection of unstructured text data. Answer retrieval is a critical component in question-answering systems and has wide-ranging applications, from search engines to chatbots. We begin by providing an overview of the various techniques used in answer retrieval systems, including information retrieval models, neural network-based methods, and hybrid approaches. We then discuss the specific challenges faced by answer retrieval, such as the need to handle complex queries, the impact of noise and redundancy in the data, and the trade-offs between precision and recall. Next, we review the major datasets and evaluation metrics used in the field, highlighting their strengths and weaknesses. Finally, we discuss recent advances in answer retrieval systems, including the use of pre-trained language models, domain-specific knowledge, and multi-stage retrieval pipelines. Through our analysis, we identify key research directions for the continued development of answer retrieval systems, including the integration of conversational context, the use of multimodal data, and the development of more effective evaluation metrics. © Grenze Scientific Society, 2024.

关键词： Question answering

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Analysing Polycystic Ovary Syndrome Using Machine Learning Models 23th

Analysing Polycystic Ovary Syndrome Using Machine Learning M...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Gaur, Yana Chaudhary, Abhay Abraham, Ajith School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9783031647758

A lot of women of childbearing age have Polycystic Ovary Syndrome, or PCOS. It is a problem with the hormones that control reproduction. PCOS commonly affects women who are able to have children. In addition to irregular menstrual cycles, this condition is characterised by elevated testosterone levels and the formation of small ovarian cysts. People living with polycystic ovarian syndrome (PCOS) sometimes struggle with personality issues. Infertility, obesity, poor insulin resistance, and a high risk of diabetes and cardiovascular disease are all possible outcomes. PCOS, despite being common and having significant impacts on women's health, is still not widely acknowledged or well comprehended by the general population. Frequently, there is a deficiency in knowledge regarding the condition, leading to a delay in diagnosis, insufficient support, and the imposition of social stigma. Educating and raising knowledge about PCOS is important so that people who have it can get the right medical care and mental support right away, which will help them take better care of their general health and well-being. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cell proliferation

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Machine Learning-Based Fake News Detection System Using Blockchain 8th

Machine Learning-Based Fake News Detection System Using Bloc...

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8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Shwetha, A.N. Prabodh, C.P. Department of Computer Science Engineering Siddaganga Institute of Technology Karnataka Tumakuru India

ISBN: (纸本)9789819976218

Now a days, news on print and digital media has become very essential for everyone to stay aware with current happenings in the world. The increased amount of IT penetration and digitization has resulted in more number of people accessing various social media offerings like YouTube, Facebook, Twitter, Yahoo, etc. With the increased usage of social media, the amount of fake news also has increased disproportionately high. Fake news may cause economic and social problems. It is very difficult to find source of fake news and to make the concerned accountable for it. The accountability of authentic news is decreasing day by day. In this paper, we are proposing a Blockchain-based platform which acts as a media to share news. The source of news and each interaction will be recorded in the Blockchain and it is immutable. Users have read access to the news and they can also give rating to news based on its authenticity. Authors/journalists have write access, so that they can post their contents to media. Because all the interactions are available on Blockchain, it is easy to trace back the source of fake news if any. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Ethereum Fake news Logistic regression Machine learning

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