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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Sharanya G Deepanjali Chandrasekaran Muli Dipikha Sre Mithileysh Sathiyanarayanan Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India Research & Innovation MIT Square London City University of London London UK

Cyberbullying has been one of the adverse repercussions of social media these days. The intensity of cyberbullying is risen considerably as a due to the increased use of image sharing and textual comments. Cyberbullying can be defined as transmitting, publishing, or circulating unbearable, harmful, false, or cruel content about some other individual. To keep the site safe and secure, automated processes for detecting certain instances are now vital. Process mining seems to be a combination of methodologies that integrate data science with management system to aid in the evaluation of organizational functions using log information. Whenever images and language that appear to be benign are combined, they might send bullying texts. As a result, different methods for analysing text and photos may fail to detect all instances of cyberbullying. In this study, we attempted to detect various examples of cyberbullying by combining textual data. The proposed system discovers hidden connections between individuals and members of a group who have similar behaviours. We achieve this through a novel strategy that incorporates techniques including data mining, analyzation of business procedures, and much more. Our study summarizes complete methodological process involved in the proposed system from the computer-generated data source in which the data pertaining user behavior, actions, and processes in an OS, software, website, or other sources to provide the visual representation of the abnormalities. Moreover, phase of identifying user behavioral patterns is the one which deserves attention.

关键词： Visualization Publishing Soft sensors Cyberbullying Data science Prediction algorithms Behavioral sciences

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TieFake: Title-Text Similarity and Emotion-Aware Fake News Detection

TieFake: Title-Text Similarity and Emotion-Aware Fake News D...

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International Joint Conference on Neural Networks (IJCNN)

作者： Quanjiang Guo Zhao Kang Ling Tian Zhouguo Chen School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China 30th Research Institute of China Electronics Technology Group Corporation Chengdu China

Fake news detection aims to detect fake news widely spreading on social media platforms, which can negatively influence the public and the government. Many approaches have been developed to exploit relevant information from news images, text, or videos. However, these methods may suffer from the following limitations: (1) ignore the inherent emotional information of the news, which could be beneficial since it contains the subjective intentions of the authors; (2) pay little attention to the relation (similarity) between the title and textual information in news articles, which often use irrelevant title to attract reader' attention. To this end, we propose a novel Title-Text similarity and emotion-aware Fake news detection (TieFake) method by jointly modeling the multi-modal context information and the author sentiment in a unified framework. Specifically, we respectively employ BERT and ResNeSt to learn the representations for text and images, and utilize publisher emotion extractor to capture the author's subjective emotion in the news content. We also propose a scale-dot product attention mechanism to capture the similarity between title features and textual features. Experiments are conducted on two publicly available multi-modal datasets, and the results demonstrate that our proposed method can significantly improve the performance of fake news detection. Our code is available at https://***/UESTC-GQJ/TieFake.

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Single and shift-multiplexing recording properties in self-referential holographic data storage with designed additional pattern

Single and shift-multiplexing recording properties in self-r...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

作者： Chijiwa, Kazuki Takabayashi, Masanori Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Research Center for Neuromorphic AI Hardware Kyushu Institute of Technology 2-4 Hibikino Wakamatsu-ku Fukuoka Kitakyushu808-0196 Japan

The reconstruction quality and shift multiplexing properties of self-referential holographic data storage (SR-HDS) with additional patterns which are designed with a target intensity of nonuniform distributions such a...

ISBN: (纸本)9798350350012

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Penetration of Deep Learning in Human Health Care and Pharmaceutical Industries;the Opportunities and Challenges 3

Penetration of Deep Learning in Human Health Care and Pharma...

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3rd International Conference on Innovative Practices in technology and Management, ICIPTM 2023

作者： Raman, Ramakrishnan Radha, H.R. Inbamalar, T.M. Subhahan, D. Abdus Kumar, Ashok Bathrinath, S. Sarkar, Swagata Symbiosis Institute of Business Management Pune & Symbiosis International Maharashtra Pune India K. S. School of Engineering and Management Department of Chemistry Karnataka Bangalore India R.M.K. College of Engineering Department of Electronics and Communication Tamil Nadu Thiruvallur India B v Raju Institute of Technology Department of Computer Science Telangana Narsapur India Banasthali Vidyapith Department of Computer Science Rajasthan India Kalasalingam Academy of Research Department of Mechanical Engineering Tamil Nadu Krishnankoil India Sri Sai Ram Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350336238

Computational medicine has emerged as a result of the advancement of medical technology, which has led to the emergence of the big data era in the biomedical area, which is supported by artificial intelligence technology. To advance the development of precision medicine, people must be able to extract the valuable information from this vast biomedical data. In the past, professionals in the field of feature engineering and domain knowledge were typically utilised to extract the features from the biological data using machine learning techniques, which took a lot of time and resources. Modern machine learning techniques like deep learning (DL) have an advantage over them in that they can automatically find strong, complex features from fresh data without the necessity for succeeding engineering. The study of DL's applications in the fields of genomics, drug development, electronic health records, and medical imaging suggests that deep learning has clear advantages in maximising the use of biomedical data. Deep learning is becoming increasingly important in the field of medicine and health due to its large range of potential applications. The lack of data, interpretability, data privacy, and heterogeneity are some of the limitations of deep learning in computational medical health. A resource for improving the use of deep learning in medical health is provided by the analysis and discussion of these difficulties. © 2023 IEEE.

关键词： Health care

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A Multi-Stage Adaptive Feature Fusion Neural Network for Multimodal Gait Recognition

arXiv

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arXiv 2023年

作者： Zou, Shinan Xiong, Jianbo Fan, Chao Yu, Shiqi Tang, Jin School of Automation Central South University China Department of Computer Science and Engineering Southern University of Science and Technology China Research Institute of Trustworthy Autonomous System Southern University of Science and Technology China

Gait recognition is a biometric technology that has received extensive attention. Most existing gait recognition algorithms are unimodal, and a few multimodal gait recognition algorithms perform multimodal fusion only once. None of these algorithms may fully exploit the complementary advantages of the multiple modalities. In this paper, by considering the temporal and spatial characteristics of gait data, we propose a multi-stage feature fusion strategy (MSFFS), which performs multimodal fusions at different stages in the feature extraction process. Also, we propose an adaptive feature fusion module (AFFM) that considers the semantic association between silhouettes and skeletons. The fusion process fuses different silhouette areas with their more related skeleton joints. Since visual appearance changes and time passage co-occur in a gait period, we propose a multiscale spatial-temporal feature extractor (MSSTFE) to learn the spatial-temporal linkage features thoroughly. Specifically, MSSTFE extracts and aggregates spatial-temporal linkages information at different spatial scales. Combining the strategy and modules mentioned above, we propose a multi-stage adaptive feature fusion (MSAFF) neural network, which shows state-of-the-art performance in many experiments on three datasets. Besides, MSAFF is equipped with feature dimensional pooling (FD Pooling), which can significantly reduce the dimension of the gait representations without hindering the accuracy. https://***/ShinanZou/MSAFF Copyright © 2023, The Authors. All rights reserved.

关键词： Gait analysis

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Data Heterogeneity Challenge in Consumer Electronics: A Federated Learning Solution

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IEEE Transactions on Consumer Electronics 2025年

作者： Liu, Zhenyu Wu, Yunting Ge, Huanhuan Wang, Jinlong Lyu, Zhihan Qingdao University of Technology School of Information and Control Engineering Shandong Qingdao China School of Information and Control Engineering Innovation Institute for Sustainable Maritime Architecture Research and Technology Shandong Qingdao China School of Computer Science and Technology Xidian University Xi’an710126 China

Federated learning(FL) enables collaborative model training across consumer electronic devices while preserving data privacy. However, the non-independent and identically distributed nature of data in real-world scenarios significantly impacts personalization performance. To address this challenge, we propose pFedPKD, in this framework, we first employ model pruning to simplify the global model, thereby enhancing its adaptability and improving the efficiency of knowledge transfer between different electronics devices. Moreover, we introduce an attention-based knowledge transfer mechanism, which leverages knowledge distillation to transfer the attention map and predictions of the pruned global model to the local models, thus improving their generalization capability and performance. Extensive experiments on three datasets demonstrate that pFedPKD consistently outperforms state-of-the-art methods, achieving higher personalization accuracy and robustness under heterogeneous data distributions. This work provides an efficient solution for applying FL in consumer electronics, offering improved adaptability and resource efficiency. © 1975-2011 IEEE.

关键词： Knowledge transfer

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Adoption of Precision Agriculture Process with Wireless Environment Plant Disease Monitoring System using Smartphone

Adoption of Precision Agriculture Process with Wireless Envi...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： S. R. Rajabalayanan S. R. Barkunan S. R. Nirmaliswarran Departmentt of Mechanical Engineering Hindusthan Institute of Technology Coimbatore India Department of ECE K.Ramakrishnan College of Technology Tiruchirappalli India Department of Computer Science & Engineering PSG Institute of Technology and Applied Research Coimbatore India

Agriculture is a backbone of India. Precision agriculture focuses on delivering tools for harvest data, task coordination, and growth updates, thereby enhancing agricultural productivity through the incorporation of sensor network technologies to monitor various farming activities. This studyr introduces a solar-powered wireless system for detecting plant diseases in the environment. A comprehensive wireless sensing system is showcased, comprising sensors for temperature, humidity, and soil moisture, along with a microcontroller, IR transmitter and receiver, camera, and GSM module, enabling extended periods of environmental monitoring ARM processor used for ADC and sends the collected data to mobile via GSM. A camera is periodically enabled for capturing the plant picture for diseases monitoring and IR unit used for the purpose of plant growth monitoring. It use soil energy to power the temperature sensor to develop plant growth monitoring system and plant disease monitoring, also develop android application to monitor such conditions by using this technology to achieve a green electronics system.

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Exploring the Use of Machine Learning Techniques for Enhancing Real-Time Data Access

Exploring the Use of Machine Learning Techniques for Enhanci...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Sourav Rampal Shreenidhi H S Manish Shrivastava Akhilendra Pratap Singh Reena R Santosh D. Kumar Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA

Actual-time statistics to date has updated, increasingly vital as the quantity of virtual information maintains updated growth. Effective choice-making depends on the capability to update access and analyze records. Device up-to-date techniques provide the capacity up-to-date enhance actual-time statistics up-to-date by using offering insights from disparate pieces of information. This paper addresses the potential benefits of using a system with date knowledge and updated, up-to-date strategies to broaden extra clever statistics up to date updated systems. It summarizes the contemporary nation of the art and highlights studies in machine learning and up-to-date imaginative and prescient real-time information up-to-date. It additionally discusses present-day challenges and shows viable instructions for future studies. The studies network up-to-date recall how gadgets are up to date know that updated strategies may be used to facilitate real-time get entry updated the maximum precious pieces of facts.

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Mgfnet: Cross-Scene Crowd Counting Via Multistage Gated Fusion Network

SSRN

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SSRN 2023年

作者： Liu, Yanbo Cao, Guo Hu, Yingxiang Shi, Hao Shang, Yanfeng School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Third Research Institute of the Ministry of Public Security Shanghai200000 China

Existing crowd counting methods are mainly trained and tested in similar scenarios. When the testing and training scenarios of the model are different, the performance of the crowd counting methods sharply decreases. In practical applications, the lack of cross-scene capability severely limits the application of existing methods. To address this problem, we propose a multistage gated fusion network (MGFNet). MGFNet is primarily composed of dynamic gated convolution units (DGCU) and multilevel scale attention blocks (MSAB) modules. Specifically, DGCU uses a dynamic gating path to supplement detailed information to reduce the loss of crowd information and overestimation of background in different scenarios. MSAB calibrates crowd information at different scales and perspectives in different scenes by generating attention maps with discriminative information. In addition, we used a new global local consistency loss to optimize the model to adapt to significant changes in crowd density and distribution. Extensive experiments on four different types of scene counting benchmarks show that the proposed MGFNet achieves superior cross-scene counting performance. © 2023, The Authors. All rights reserved.

关键词： Convolution

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Cross Contrasting Feature Perturbation for Domain Generalization

arXiv

引用

arXiv 2023年

作者： Li, Chenming Zhang, Daoan Huang, Wenjian Zhang, Jianguo Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Peng Cheng Laboratory Shenzhen China

Domain generalization (DG) aims to learn a robust model from source domains that generalize well on unseen target domains. Recent studies focus on generating novel domain samples or features to diversify distributions complementary to source domains. Yet, these approaches can hardly deal with the restriction that the samples synthesized from various domains can cause semantic distortion. In this paper, we propose an online one-stage Cross Contrasting Feature Perturbation (CCFP) framework to simulate domain shift by generating perturbed features in the latent space while regularizing the model prediction against domain shift. Different from the previous fixed synthesizing strategy, we design modules with learnable feature perturbations and semantic consistency constraints. In contrast to prior work, our method does not use any generative-based models or domain labels. We conduct extensive experiments on a standard DomainBed benchmark with a strict evaluation protocol for a fair comparison. Comprehensive experiments show that our method outperforms the previous state-of-the-art, and quantitative analyses illustrate that our approach can alleviate the domain shift problem in out-of-distribution (OOD) scenarios. https://***/hackmebroo/CCFP Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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