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Personality Prediction System for Twitter Stream Using machine learning Models 1

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6th international conference on Soft computing and Signal Processing, ICSCSP 2023

作者： Prabakeran, S. Pattanaik, Pratik Chandrasekhar Abinaya, M. Faculty of Engineering and Technology School of Computing Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9789819986286

ISBN: (纸本)9789819986279

Users’ behaviour on social media platforms reflects many aspects of real life, including personality, and these platforms provide a rich amount of textual data. Individuals communicate their thoughts, feelings, and ideas regularly, resulting in a rich and informative collection of personal data that may be used to a wide range of situations. As the word subconscious suggests, this is commonly referred to as "subconscious crowdsourcing," which means that people are unintentionally and subconsciously sharing information with their network. These people are therefore ideal candidates for personality prediction due to the massive expansion of those social media sites over the past ten years. Tweets, for instance, are frequently quite brief and display an odd language usage, consisting of acronyms and unique forms compared to textual sources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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AI Content Detection 13th

AI Content Detection

引用

13th international Advanced computing conference, IACC 2023

作者： Sable, Rachna Baviskar, Vaishali Gupta, Sudhanshu Pagare, Devang Kasliwal, Eshan Bhosale, Devashri Jade, Pratik G H Raisoni College of Engineering and Management Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9783031566998

The rise of AI-generated data, mainly from models like ChatGPT, LLAMA2 poses serious difficulties to academic integrity and raises worries about plagiarism. The current research looks on the competences of various AI content recognition algorithms to distinguish between human and AI-authored material. This research looks at numerous research papers, publication years, datasets, machine learning approaches, and the benefits and drawbacks of detection methods in AI text detection. Various datasets and machine learning techniques are employed, with various types of classifier emerging as a top performer. This work creates an Extra tree classifier that can distinguish ChatGPT produced text from human authored content. "ChatGPT Paraphrase" dataset was used for model training and testing. The result shows that the proposed model resulted in 80.1% accuracy and outperformed the existing models namely Linear Regression (LR), Support Vector machine (SVM), Decision Tree, (DT), K-Nearest Neighbour (KNN), Ada Boost Classifier (ABC), Random Forest Classifier (RFC), Bagging Classifier (BG), Gradient Boosting Classifier (GBC). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision trees

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A Few-Shot learning for HAR Using 3D Siamese Network 15

A Few-Shot Learning for HAR Using 3D Siamese Network

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Guanghui, Hua Kumar, G. Hemantha Aradhya, V.N. Manjunath Punithavalli University of Mysore Dept. of studies in Computer Science Mysuru India Jss Science and Technology University Dept. of Computer Applications Mysuru India Bharathiar University Department of Computer Applications Coimbatore India

ISBN: (纸本)9798350370249

In artificial intelligence, deep learning techniques were applied in a wide field due to their ability to automatically learn complex patterns from raw data, such as image classification, object detection, image generation, and video analysis in computer vision. These applications are just the tip of a substantial iceberg. With the development of deep techniques to achieve state-of-art accuracy, learn precisely relevant features from raw data, and improve the robustness of models to make them versatile solutions. The traditional deep learning techniques require vast computational resources, including high-performance computer GPUs or TPUs, and can be expensive in terms of hardware. In Human Activity Recognition Systems (HARs). The video needs more computational resources than image processing. In this paper, we proposed a few-shot learning approach based on a 3D Siamese Network with depth video sequences to address the action video similarity or dissimilar problem. Convolutional Neural Networks (CNNs) are well-suited for images and spatial data processing. The 3DCNN is an extension of CNNs to incorporate an additional temporal dimension to capture the temporal pattern. From the 3D Siamese network, we extract more effective features with spatial-temporal action information from raw data. We employed the 3DCNNs Siamese Network (3DCNNs+SN) with shared weight to recognize or classify a new class of data without new sample data only based on learning training existing sample constructed model. In addition, we divided the benchmark dataset MSR Action 3D dataset into sub-dataset1 (SD-1), sub-dataset2 (SD-2) and sub-dataset3(SD-3) to train and validate our proposed method. For dissimilar sub-dataset 2 we got a 98.1% accuracy, 33% on high-similar sub-dataset 3 and 63.5% on merged sub-dataset SD-4 from sub-dataset2 (SD-2) and sub-dataset3(SD-3). © 2024 IEEE.

关键词： Spatio-temporal data

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Arrhythmia Detection and Classification with Hybrid Deep learning with Pity Beetle Optimization Model 2

Arrhythmia Detection and Classification with Hybrid Deep Lea...

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2nd IEEE international conference on Advances in Information Technology, ICAIT 2024

作者： Vinod, D. Karthikayan, P.N. Ojashwini, R.N. Srimathi, S. Saranya, R. Vigneshwaran, P. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of Information Technology St Joseph' s Institute of Technology OMR Chennai600119 India Department of CSE RajaRajeswari College of Engineering Ramohalli Cross Kumbalgodu Mysore Road Karnataka Bengaluru560 074 India Department of Bio Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of CSE RMK College of Engineering and Technology Chennai India Department of EEE K.Ramakrishnan College of Engineering Trichy India

ISBN: (纸本)9798350383867

Arrhythmia classification is a crucial field within the domain of medical diagnostics, utilizing advanced machine learning (ML) and Deep learning (DL) methods to accurately recognize and categorize different cardiac arrhythmias, and abnormalities in the heart' s rhythm. This technology performs a significant part in cardiac healthcare, as arrhythmias may result in serious health problems, comprising stroke and heart attacks. By analyzing electrocardiogram (ECG) data, arrhythmia classification allows accurate diagnosis and earlier detection of irregular heart rhythms, facilitating timely involvement and tailored treatment approaches. It permits medical specialists to make informed decisions, leading to enhanced patient outcomes and improved cardiac medical management. In this study, we propose an Arrhythmia Detection and Classification with Hybrid Deep learning with the Pity Beetle Optimization Algorithm (ADC-HDLPBO) method. The ADC-HDLPBO technique is targeted at advancing the domain of arrhythmia analysis in cardiac healthcare. The proposed ADC-HDLPBO system encompasses classification through a combination of Convolutional Long Short-Term Memory networks (CLSTM) and Autoencoders (AE), developed to take temporal dependencies and recreate important features. For precision and performance optimization, the model incorporates PBA for hyperparameter tuning, adapting to the unique dataset features. The ADC-HDLPBO algorithm marks a major leap forward in arrhythmia detection and classification, providing a robust and accurate solution for the earliest diagnosis of cardiac abnormalities. © 2024 IEEE.

关键词： Diseases

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Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates 41

Nonsmooth Implicit Differentiation: Deterministic and Stocha...

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41st international conference on machine learning, ICML 2024

作者： Grazzi, Riccardo Pontil, Massimiliano Salzo, Saverio CSML Istituto Italiano di Tecnologia Genoa Italy Department of Computer Science University College London United Kingdom Dipartimento di Ingegneria Informatica Automatica e Gestionale Universitá La Sapienza Rome Italy

We study the problem of efficiently computing the derivative of the fixed-point of a parametric nondifferentiable contraction map. This problem has wide applications in machine learning, including hyperparameter optimization, meta-learning and data poisoning attacks. We analyze two popular approaches: iterative differentiation (ITD) and approximate implicit differentiation (AID). A key challenge behind the nonsmooth setting is that the chain rule does not hold anymore. We build upon the work by Bolte et al. (2022), who prove linear convergence of nonsmooth ITD under a piecewise Lipschitz smooth assumption. In the deterministic case, we provide a linear rate for AID and an improved linear rate for ITD which closely match the ones for the smooth setting. We further introduce NSID, a new stochastic method to compute the implicit derivative when the contraction map is defined as the composition of an outer map and an inner map which is accessible only through a stochastic unbiased estimator. We establish rates for the convergence of NSID, encompassing the best available rates in the smooth setting. We also present illustrative experiments confirming our analysis. Copyright 2024 by the author(s)

关键词： Stochastic systems

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Smart Surveillance: Real-Time Object Detection and Recognition with Interactive Polygon Region Using Deep learning

Smart Surveillance: Real-Time Object Detection and Recogniti...

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2024 international conference on Advances in data Engineering and Intelligent computing Systems, ADICS 2024

作者： Krishnan, Unni Kalaimani, Thyagarajan Sethuraman, R. Sathyabama Institute Of Science And Technology Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The Traditional Surveillance system lacks the capability to focus on particular region at a time during the surveillance. This cause important detail in that region to be missed out and go unnoticed. This is a major issue as it causes security concern. Detection of any objects in that region should be done in real-time. The proposed system allows the user to create a polygon region in area of interest in the video and by using the capability of YOLOv8, which stands for You Only Look Once Version 8 this system can detect the object and classify them and then converting to csv file. By allowing the users to create the polygon region it gives the surveillance camera capability of highly precise monitoring. This system helps to monitor the regions that changes dynamically. Changing Traditional surveillance system as a whole can be expensive and time consuming. Our system helps to bring a cost-effective solution without the need of whole changing whole system. It provides extra security to current infrastructure without the need for a complete overhaul, making it a good choice for various security setups. © 2024 IEEE.

关键词： Object recognition

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Workshop on Research and Applications of Foundation Models for data Mining and Affective computing, RAFDA 2024 and international Workshop on Temporal Analytics, IWTA 2024 held in conjunction with the 27th Pacific-Asia conference on Knowledge Discovery and data Mining, PAKDD 2023

Workshop on Research and Applications of Foundation Models f...

引用

Workshop on Research and Applications of Foundation Models for data Mining and Affective computing, RAFDA 2024 and international Workshop on Temporal Analytics, IWTA 2024 held in conjunction with the 27th Pacific-Asia conference on Knowledge Discovery and data Mining, PAKDD 2023

ISBN: (纸本)9789819726493

The proceedings contain 13 papers. The special focus in this conference is on Research and Applications of Foundation Models for data Mining and Affective computing and Temporal Analytics. The topics include: InteraRec: Interactive Recommendations Using Multimodal Large Language Models;research on Dynamic Community Detection Method Based on Multi-dimensional Feature Information of Community Network;From Tweets to Token Sales: Assessing ICO Success Through Social Media Sentiments;enhanced Graph Neural Network for Session-Based Recommendation with Static and Dynamic Information;construction of Academic Innovation Chain Based on Multi-level Clustering of Field Literature;DLVS4Audio2Sheet: Deep learning-Based Vocal Separation for Audio into Music Sheet Conversion;Explainable AI for Stress and Depression Detection in the Cyberspace and Beyond;finding Foundation Models for Time Series Classification with a PreText Task;next Item and Interval Prediction of New Users Using Meta-learning on Dynamic Network;adaptive Knowledge Sharing in Multi-Task learning: Insights from Electricity data Analysis;handling Concept Drift in Non-stationary Bandit Through Predicting Future Rewards.

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Exploration of Railway Signal Unloading Task Based on Deep Reinforcement learning Method 20th

Exploration of Railway Signal Unloading Task Based on Deep R...

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20th international conference on Intelligent computing (ICIC)

作者： Ke, Ting Zheng, Yaozong Liu, Zhanshuo Shen, Jianan Tianjin Univ Sci & Technol Coll Artificial Intelligence Tianjin 30057 Peoples R China

ISBN: (纸本)9789819755806;9789819755813

The conventional railway signal equipment maintenance platform typically gathers diverse monitoring data within cloud computing data centers, where the data is subsequently integrated, analyzed, and utilized. However, extant task offloading research primarily relies on a centralized architecture, which is susceptible to single points of failure and afflicted by issues of high energy consumption and time delays. Conversely, Deep Q-learning (DQN), as a pivotal deep reinforcement learning methodology, has demonstrated efficacy in addressing challenges within continuous state spaces. Drawing inspiration from this, this study introduces a cloud-edge collaborative offloading strategy grounded in deep reinforcement learning, denoted as Multi-Dimensional Deep Q-Network (MDQN). MDQN integrates Temporal Difference (TD) updates and experience replay mechanisms into reinforcement learning techniques, dynamically allocating tasks from users to multiple Mobile Edge computing (MEC) servers, thereby optimizing offloading decision latency, reducing overall service latency, and minimizing user-perceived overall latency and device energy consumption. To validate its efficacy, experiments are conducted leveraging self-generated datasets. Experimental findings indicate that, compared to conventional greedy and round-robin offloading algorithms, MDQN is adept at substantially diminishing task execution overhead, yielding savings of up to 80% or more in resources encompassing energy and time consumption across varying server node densities, and attaining superior offloading strategies. The contributions of this study are significant for elevating the sophistication of intelligent railway signal maintenance, curtailing system maintenance expenditures, and enhancing system utilization efficiency.

关键词： Railway Signal Task Offloading DQN Edge-Cloud Collaboration Resource Allocation

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Generative Models and Diffusion Models for Skin Sore Detection and Treatment 4

Generative Models and Diffusion Models for Skin Sore Detecti...

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4th international conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Kalaivani, A. Devi, A. Sangeetha Shanmugapriya, A. Nallamuthu Gounder Mahalingam College Department of Computer Science Pollachi Tamil Nadu Coimbatore India Nehru Institute of Engineering and Technology Tamil Nadu Coimbatore India Sri Eshwar College of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331529635

Generative models, particularly Generative Adversarial Networks (GANs) and Diffusion Models, have emerged as powerful tools in healthcare, showing promise in enhancing skin sore detection and treatment. This article focuses on a key problem in dermatology: the challenge of accurately diagnosing skin sores using traditional image analysis techniques, which often struggle with variations in lighting, angles, and lesion appearances. By leveraging large and diverse datasets like the international Skin Imaging Collaboration (ISIC) archive, GANs and Diffusion Models can overcome these limitations, generating high-quality, augmented data that improve diagnostic precision. This study compares the performance of GANs and Diffusion Models with traditional machine learning methods using the ISIC dataset, known for its comprehensive collection of labeled skin sore and lesion images. Results indicate that both models outperform traditional approaches, with Diffusion Models in particular demonstrating superior image generation capabilities for rare or complex skin sores. Additionally, these generative models can simulate disease progression, aiding in early detection and personalized treatment planning. This article emphasizes how generative models, when trained on robust datasets like ISIC, offer more accurate, efficient, and scalable solutions for skin sore detection compared to conventional methods, facilitating future developments for AI-driven advancements in dermatological care. © 2024 IEEE.

关键词： Generative adversarial networks

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Research on SSVEP Recognition Method Based on Residual Network 2

Research on SSVEP Recognition Method Based on Residual Netwo...

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2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Bai, Xue Jiang, Yueqiu Wu, Xiaolin School of Information Science and Engineering Shenyang Ligong University Shenyang China School of Mathematics and Statistics Liaoning University Shenyang China

ISBN: (纸本)9798350368888

Currently, Brain-Computer Interface (BCI) is a research hotspot, playing a crucial role in various fields. The difficulty of research is to identify the obtained Electroencephalography (EEG) signals. There are now a large number of recognition methods, but they all have certain shortcomings. Canonical Correlation Analysis (CCA), which is highly sensitive to noise and has poor performance for frequency or phase dense stimuli. Some researchers also use machine learning for signal recognition, but it requires relatively complex feature extraction. Research results indicate that its accuracy is relatively low. In order to tackle the aforementioned challenges such as low recognition accuracy and complex feature extraction, the paper proposes a deep learning-based method for recognizing steady-state visually evoked potentials (SSVEPs). The neural network employs a residual network (ResNet)for implementation. This method utilizes residual networks to extract features from signal data and complete model training through fully connected operations. Input samples into the model to achieve adaptive feature extraction and end-to-end classification of EEG signals. The recognition method proposed in this article can fully extract signal features, achieve effective recognition of SSVEP signals under short-term visual stimuli, and have high target recognition efficiency. The experimental results indicate that compared to the aforementioned recognition methods, the method proposed in this paper exhibits improvements in both recognition accuracy and efficiency. © 2024 IEEE.

关键词： Electroencephalography

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