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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： N Roshan Ahmed S. Shridevi School of Computer Science and Engineering Vellore Institute of Technology Chennai India Center for Advanced Data Science Vellore Institute of Technology Chennai India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Text-based Statistical steganography is one of the most non-human detectable methods of embedding hidden messages in plain text format which is useful in concealing information. Steganalysis is its counter, the process of detecting if a text has any encrypted data in it. This paper applies quantum computing to create DeepKet Embedding, which optimizes the space requirements for word embeddings similar to Word2Vec. DeepKet is benchmarked against existing embedding layers and a significant size reduction is achieved while maintaining accuracy for steganalysis.

关键词： Steganography Quantum computing Accuracy Benchmark testing Internet of Things Artificial intelligence

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E-commerce Product Review Analysis based on Multi-class Support Vector Machine 1

E-commerce Product Review Analysis based on Multi-class Supp...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Alzubaidi, Laith H. Hatem Mohammed, Ibraheem Ranjith Kumar, Gotte Sarada, B. Radha, N. The Islamic University Najaf Iraq Al Rafidain University College Department of Computer Communications Engineering Bagdad Iraq School of Computer Science & Artificial Intelligence Sr University Warangal India Malla Reddy Engineering College for Women Department of CSE-Data Science Hyderabad India Department of Cse Annapoorana Engineering Salem India

ISBN: (纸本)9798350316469

Recently, an extensive number of consumers can choose their best options from automated retailers and compare products in online stores. Sentiment analysis is frequently employed in applications meant to demonstrate and provide client service as the voice of the user. On E-commerce websites, user reviews provide helpful information about the product. Sentiment analysis of the text reviews helps forecast product sales by assessing user sentiment regarding the product. The Word Embedding Attention approach assigns additional weight to words that have a strong association with a specific class. Sentiment analysis machine learning models provide a thorough depiction of capabilities and far surpass conventional feature-based methods. The obj ective of this research is to improve sentiment analysis performance by creating a weighted ensemble with a Multi-class Support Vector Machine (MSVM) model utilising novel word embedding techniques. Because of the weighted ensemble's higher generalisation skills, MSVM produces superior results. When comparing existing methods for sentiment analysis, the Weighted ensemble with MSVM has produced higher results in terms of accuracy, precision, f-score, and recall. The Weighted ensemble with an MSVM has achieved 99.87 % accuracy, 99.39% precision, 99.03% f-score, and 99.690/0 recall in sentiment analysis when comparing existing methods such as LSTM+FL, APSO-LSTM, ABCDM, SSentiA, and Hybrid CNN+LSTM. © 2023 IEEE.

关键词： E-commerece Multi-class Support Vector Machine Natural language processing Sentiment analysis Weighted ensemble Word embedding attention

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EvoGP: A GPU-accelerated Framework for Tree-based Genetic Programming

arXiv

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arXiv 2025年

作者： Wang, Lishuang Wu, Zhihong Sun, Kebin Li, Zhuozhao Cheng, Ran Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hong Kong

Tree-based Genetic Programming (TGP) is a key evolutionary algorithm widely used in symbolic regression, feature engineering, and scientific modeling. Its high computational demands make GPU acceleration essential for scalable and high-performance evolutionary computation. However, GPU acceleration of TGP faces three key challenges: inefficient tree encoding, highly heterogeneous genetic operations, and limited parallelism in fitness evaluation. To address these challenges, we introduce EvoGP, a comprehensive GPU-accelerated TGP framework. First, we design a tensorized encoding scheme to represent tree with different structures as tensors with the same shape, optimizing memory access and enabling efficient parallel execution. Second, we propose a unified parallel framework for genetic operations by leveraging shared computational primitives and implementing dedicated CUDA kernels for scalable performance. Third, we present a fully parallel fitness evaluation strategy for symbolic regression, exploiting both population-level and data-level parallelism to maximize GPU utilization. Moreover, we implement a comprehensive library to provide rich algorithm operators and benchmark problems. EvoGP is extensively tested on various tasks, including symbolic regression, classification, and robotics control, demonstrating its versatility and effectiveness across diverse application scenarios. Experimental results show that EvoGP achieves up to a 140.89× speedup over the state-of-the-art GPU-based TGP implementation, while maintaining or exceeding the accuracy of baseline methods. EvoGP is open-source and accessible at: https://***/EMI-Group/evogp. Copyright © 2025, The Authors. All rights reserved.

关键词： Tensors

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RVISA: Reasoning and Verification for Implicit Sentiment Analysis

arXiv

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arXiv 2024年

作者： Lai, Wenna Xie, Haoran Xu, Guandong Li, Qing Department of Computing Hong Kong Polytechnic University Hong Kong School of Computer Science The Data Science Institute University of Technology Sydney Australia School of Data Science Lingnan University Hong Kong School of Computer Science The Data Science Institute University of Technology Sydney SydneyNSW2007 Australia Education University of Hong Kong Hong Kong Department of Computing Hong Kong Polytechnic University Hong Kong

With an increasing social demand for fine-grained sentiment analysis (SA), implicit sentiment analysis (ISA) poses a significant challenge with the absence of salient cue words in expressions. It necessitates reliable reasoning to understand how the sentiment is aroused and thus determine implicit sentiments. In the era of Large Language Models (LLMs), Encoder-Decoder (ED) LLMs have gained popularity to serve as backbone models for SA applications, considering impressive text comprehension and reasoning ability among diverse tasks. On the other hand, Decoder-only (DO) LLMs exhibit superior natural language generation and in-context learning capabilities. However, their responses may contain misleading or inaccurate information. To identify implicit sentiment with reliable reasoning, this study proposes RVISA, a two-stage reasoning framework that harnesses the generation ability of DO LLMs and the reasoning ability of ED LLMs to train an enhanced reasoner. Specifically, we adopt three-hop reasoning prompting to explicitly furnish sentiment elements as cues. The generated rationales are utilized to fine-tune an ED LLM into a skilled reasoner. Additionally, we develop a straightforward yet effective verification mechanism to ensure the reliability of the reasoning learning. We evaluated the proposed method on two benchmark datasets and achieved state-of-the-art results in ISA performance. © 2024, CC BY-NC-SA.

关键词： Sentiment analysis

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Design and Implementation of an IoT-based AI Surveillance Car with Obstacle Detection, Line Following, and Live Streaming

Design and Implementation of an IoT-based AI Surveillance Ca...

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International Conference on Secure Cyber Computing and Communication (ICSCCC)

作者： Arbab Ali Kuldeep Kaur Noor Ul Ain Abhishek Kumar Shukla Computer Science (Data Science) Gulzar Group of Institutions Ludhiana India Computer Science Gulzar Group of Institutions Ludhiana India Computer Science (Artificial Intelligence & Machine Learning) Gulzar Group of Institutions Ludhiana India

The research paper presents the design and implementation of an AI Surveillance car that can perform object detection, line following, and live streaming. The proposed system is based on Internet of Things (IoT) technology, which allows the car to be remotely controlled and monitored. The car's capabilities include real-time object detection, which enables it to detect and track objects in its surroundings, line following, which allows it to follow a predefined path, and live streaming, which provides remote access to the car's camera feed. The results show that the proposed system can accurately detect obstacles and follow lines, while also providing high-quality live-streaming capabilities. Overall, the research provides a comprehensive and practical approach to designing an AI Surveillance car using IoT technology.

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Object Detection for Autonomous Vehicles in Urban Areas Using Deep Learning 9th

Object Detection for Autonomous Vehicles in Urban Areas Us...

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9th Future Technologies Conference, FTC 2024

作者： Arslan, Muhammad Mubeen, Muhammad Usman, Syed Muhammad Data Lab - Doyle 206 Loyola University Chicago Chicago United States University of the People Pasadena United States Department of Computer Science Bahria School of Engineering and Applied Sciences Bahria University Islamabad44000 Pakistan

ISBN: (纸本)9783031731242

This study details the development of a reliable automated system for detecting urban traffic using a custom dataset, designed specifically for this research. We developed a novel architecture based on the YOLOv8 framework, featuring a vehicle-mounted camera on a rotatable platform to continuously stream video data via a 4G network to a control center. The system processes this data using advanced deep learning techniques, achieving an impressive 85.5% accuracy rate in real-time identification of pedestrians, vehicles, and bicycles. This accuracy surpasses current state-of-the-art methods and demonstrates exceptional speed and reliability, crucial for the safety and efficiency of autonomous vehicles. By significantly reducing misclassification, our method not only enhances the safety of urban navigation but also sets a new benchmark in the application of deep learning technologies. This work bridges the technological divide between developed and developing nations, marking a substantial advancement in the field of autonomous vehicle technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Overview of the NLPCC 2023 Shared Task 10: Learn to Watch TV: Multimodal Dialogue Understanding and Response Generation 12th

Overview of the NLPCC 2023 Shared Task 10: Learn to Watch...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Wang, Yueqian Wang, Yuxuan Zhao, Dongyan Wangxuan Institute of Computer Technology Peking University Beijing China Center for Data Science AAIS Peking University Beijing China Artificial Intelligence Institute of Peking University Beijing China

ISBN: (纸本)9783031446986

In this paper, we present an overview of NLPCC 2023 Shared Task 10, Multimodal Dialogue Understanding and Response Generation, which includes four sub-tasks: dialogue scene identification, dialogue session identification, dialogue response retrieval, and dialogue response generation. A bilingual multi-modal dialogue dataset consisting of 100M utterances was made public for the shared task. This dataset contains 119K dialogue scene boundaries and 62K dialogue session boundaries annotated manually. This paper presents details of this shared task, dataset, evaluation metric and evaluation results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Natural language processing systems

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Toward Improved Knowledge Retention: A Template for Describing data science Projects

Toward Improved Knowledge Retention: A Template for Describi...

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IEEE International Conference on Big data

作者： Christian Haertel Daniel Staegemann Matthias Pohl Christian Daase Klaus Turowski Faculty of Computer Science Otto-von-Guericke-University Magdeburg Germany Institute of Data Science German Aerospace Center (DLR) Jena Germany

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

data science (DS) aims to extract knowledge from large amounts of data. Organizations can use the retrieved insights to achieve various performance improvements. However, DS projects often fail to fulfill their objectives due to the explorative nature of this discipline and technical as well as managerial challenges. Consequently, new approaches to support DS project execution are sought after. A viable contribution in this regard is improving knowledge retention in DS to predict socio-technical obstacles of an undertaking and derive best practices. Therefore, in this work, a template for describing the central characteristics of DS projects is proposed using a Design science Research approach. The artifact is structured based on the common DS project stages and features 32 fields, enabling comparability and transparency in DS. The applicability of the template is demonstrated based on three DS use cases from the literature. While further steps for evaluation are pending, the template can serve as a foundation for developing a categorization model for DS projects in the future.

关键词： Organizations data science Big data data mining Best practices

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Deep Learning Strategies for Multiclass Skin Disease Classification 5th

Deep Learning Strategies for Multiclass Skin Disease Classi...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Venkat, Nakka Lokeswara Satya Tejasri, Masina Naga Vijaya Uppalapati, Padma Jyothi Rao, V. V. R. Maheswara Sripada, V. S. S. Lakshmi Murty, P. Sita Rama Department of Artificial Intelligence and Data Science Vishnu Institute of Technology Andhra Pradesh Bhimavaram534201 India Department of Computer Science and Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534201 India Department of CSE Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram534201 India Andhra Pradesh Bhimavaram534201 India

ISBN: (纸本)9783031770746

Skin diseases represent a significant global health challenge, necessitating accurate and timely diagnosis for effective treatment. In this paper, we delve into the intricate realm of skin disease classification, leveraging advanced deep learning techniques. Skin diseases, ranging from common conditions like eczema and atopic dermatitis to potentially life-threatening melanoma, pose complex diagnostic challenges due to their varied visual characteristics. Accurate classification demands the ability to discern subtle patterns, textures, and features within medical images. Our paper introduces a novel methodology that includes the integration of our proposed model into the existing deep learning frameworks. Unlike conventional approaches involving the replacement of output layers, our model maintains the integrity of pre-trained structures. It achieves this by utilizing a blend of VGG16 and EfficientNet B2, incorporating dense activations for improved regularization. This novel approach seeks to improve the model’s flexibility to various skin disease patterns while maintaining accurate classification without changing the pre-trained models’ fundamental design. The model’s performance is evaluated in-depth using a variety of criteria, including accuracy, precision, recall, and F1 score. The outcomes of this analysis unveil distinct F1 scores for each disease class across different models, thereby illuminating the models’ strengths and limitations in the context of disease classification. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Dermatitis

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Sal-Guide Diffusion: Saliency Maps Guide Emotional Image Generation through Adapter

Sal-Guide Diffusion: Saliency Maps Guide Emotional Image Gen...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiangru Lin Shenghua Zhong Yan Liu Gong Chen College of Computer Science Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science Shenzhen University Shenzhen China Department of Computer Science The Hong Kong Polytechnic University HK China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

The existing text-to-image generation methods based on stable diffusion yield better results in low-semantic prompt but often neglect the generation quality of high-semantic prompt such as emotional vocabulary, resulting in poor emotional image generation. In order to address this issue, we propose a novel approach called Sal-Guide Diffusion, which leverages saliency maps to guide emotional image generation with the goal of producing superior emotionally expressive images. In order to let the saliency maps guide the diffusion process, we introduce a lightweight adapter to extract emotional information from saliency maps and incorporate it into the diffusion process. Experimental results demonstrate that our proposed method generates higher-quality images across eight emotional dimensions, excelling in both generalization, emotional congruence, and subjective preference compared to stable diffusion or similar methods.

关键词： Vocabulary Image synthesis Buildings Diffusion processes Text to image data mining

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