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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Maryam Nikoupour Mohammad Reza Keyvanpour Seyed Vahab Shojaedini Department of Computer Engineering Faculty of Engineering Data Mining Laboratory Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

Seizures that take place repeatedly and without provocation are referred to as epilepsy. Epilepsy can be diagnosed with electroencephalography (EEG). One of the most influential challenges of the past few years has been the use of deep learning algorithms to replace manual inspection of medical signals by specialists, such as epilepsy signal classification. This paper presents a multi-label classification approach for epileptic seizures using deep learning. UCI machine learning repository’s epileptic seizure dataset has been used to classify epileptic seizure patients. 178 features are present in each of the 11500 samples in the dataset. Based on a variety of criteria, the proposed method may have a positive impact on epilepsy diagnosis, in most cases by approximately 6% compared with existing methods utilizing long short-term memory (LSTM) and autoencoder. It is possible thus to develop and apply gated recurrent unit-based methods with good potential for categorizing EEG signals for epilepsy diagnosis based on gated recurrent unit (GRU)-CNN based methods.

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Automatic Creation of Quality Images from Text using Multiple Generative Adversial Network

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Procedia computer science 2023年 230卷 955-963页

作者： M.S. Geetha Devasana V. Krishna Kumar R. Selvaraj T. Satheesh Assistant Professor Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore-641022 India Assistant Professor Department of Computer Science and Engineering Dr.N.G.P Institute of Technology Coimbatore-641048 India Assistant Professor Department of Artificial Intelligence and Data Science Dr.N.G.P Institute of Technology Coimbatore-641048 India

Automatic creation of realistic images is a tedious process even though the state-of-the-art AI/ML algorithms are employed. There is a lot of demand for such automatic image generators that could create high quality images. Many have this problem of visualizing things from the explanations they hear about. Thus, the text to image generation problem is necessary because it has significant applications in CAD, art generation and many more. This is a challenging task since the image should be realistic and consistent with the text. One of the most common uses of modern conditional generative models is the generation of visuals from natural language. Viewing an image makes us easily understand what that image is, rather than hearing someone describing the image. To bridge the semantic gap between text and image, Generative Adversarial Network (GAN) systems are used to achieve high accuracy. The proposed system helps in generation of superior quality images that are meaningfully consistent with the text.

关键词： Machine Learning Generative Adversarial Network (GAN) Text to Image and Natural Language Processing

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Video Object Forgery Detection using Image Processing Techniques

Video Object Forgery Detection using Image Processing Techni...

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

作者： P. Lavanya Kumari M. Siva N. Muthukumaran D. Dharani K. Kavitha Sahithi V. Ashwin Kumar Department of Computer Science and Engineering (Data Science) Vignan Institute of Technology and Science Hyderabad India Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

Video surveillance has been crucial for security in recent years, thus it’s critical to guarantee the accuracy of these recordings. Unfortunately, it is easy to fake surveillance films by erasing an object from a scene and leaving no visual evidence. In this study, an approach for video intra-frame forgery forensics based on the SSIM (Structural Similarity Index Measure) is proposed. This system can recognize forged video frames automatically. In order to extract the steganography features, the method first decompresses the input video into a sequence of frames. It next calculates each frame’s motion residual map. The ability to tell whether or not an object is deleted from a video is a key problem in video security.

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Enhancing Thematic Holy Quran Verse Retrieval through Vector Space Model and Query Expansion for Effective Query Answering 9

Enhancing Thematic Holy Quran Verse Retrieval through Vector...

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9th IEEE Information Technology International Seminar, ITIS 2023

作者： Al Haromainy, Muhammad Muharrom Sari, Anggraini Puspita Prasetya, Dwi Arman Subhan, Muh Lisdiyanto, Angga Septianto, Tri UPN Veteran Jawa Timur Department of Informatics Surabaya Indonesia UPN Veteran Jawa Timur Department of Data Science Surabaya Indonesia Politeknik Negeri Fakfak Department of Management Informatic Papua Barat Indonesia Nahdlatul Ulama University Sidoarjo Department of Computer Science Sidoarjo Indonesia Politeknik Negeri Madiun Department of Engineering Madiun Indonesia

ISBN: (纸本)9798350306835

The Quran provides valuable insights into various aspects of life, including information about the natural world, such as animals and plants. Developing an information retrieval system can greatly facilitate the search for specific content within the Quran. This system utilizes a cosine similarity approach and query expansion techniques to rank and expand the retrieved documents according to the user's needs. In this research, the Term Frequency-Inverse Document Frequency (TF-IDF) weighting method is employed, following preprocessing steps for both the documents and queries. The primary objectives of this study are to assess accuracy, precision, and recall values. Notably, the inclusion of query expansion has led to an improved recall value compared to previous research, resulting in a 99.32% accuracy rate, a 5.83% increase in precision (from 83.93%), and a system's ability to capture 8.75% of the relevant verse (compared to the previous 93.51%). This signifies that all verses can be invoked, displayed, or expanded, providing a more comprehensive query experience. © 2023 IEEE.

关键词： Information retrieval

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AI-Optimized Placement and Routing for PCB Design 9

AI-Optimized Placement and Routing for PCB Design

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Sathyaraj, P. Arulkumar, S. Ajina, A. Beshir, Kedir Umasankar, L. Arularasan, A.N. Rmk College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Puduvoyal601206 India Wachemo University Department of Electrical And Computer Engineering Ethiopia M S Ramaiah Institute of Technology Department of Artificial Intelligence And Machine Learning Bangalore560054 India S.A. Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Tiruvallur600077 India Panimalar Engineering College Department of Artificial Intelligence And Data Science Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

Rapid electronic device development requires more complicated and densely packed PCB designs. These systems need properly placed and connected electrical components for best performance and reliability. Complexity and time restrictions are rendering manual methods ineffective. Therefore, AI-based PCB insertion and routing automation methods have been developed. This research suggests using AI to optimize PCB component arrangement and routing. The system uses machine learning and optimization to improve design efficiency. The AI system creates layouts that match performance requirements, decrease signal interference, and meet manufacturing limits using previous design data and user preferences. A learning module that updates its knowledge base, an algorithm that chooses between design objectives, and real-time user input are examples. Signal integrity, thermal issues, and manufacturability are considered while modifying design requirements. Evaluation of the suggested methodology shows considerable gains in design efficiency, time-to-market, and electronic system performance. This research improves AI-automated PCB design, enabling more complex and effective electrical systems. AI in PCB design will revolutionize the industry by solving complicated design problems and speeding up the development of cutting-edge electronics. © 2024 IEEE.

关键词： Reinforcement learning

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On the Statistical Complexity for Offline and Low-Adaptive Reinforcement Learning with Structures

arXiv

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arXiv 2025年

作者： Yin, Ming Wang, Mengdi Wang, Yu-Xiang The Department of Electrical and Computer Engineering Princeton University United States The Halıcıoğlu Data Science Institute UC San Diego United States

This article reviews the recent advances on the statistical foundation of reinforcement learning (RL) in the offline and low-adaptive settings. We will start by arguing why offline RL is the appropriate model for almost any real-life ML problems, even if they have nothing to do with the recent AI breakthroughs that use RL. Then we will zoom into two fundamental problems of offline RL: offline policy evaluation (OPE) and offline policy learning (OPL). It may be surprising to people that tight bounds for these problems were not known even for tabular and linear cases until recently. We delineate the differences between worst-case minimax bounds and instance-dependent bounds. We also cover key algorithmic ideas and proof techniques behind near-optimal instance-dependent methods in OPE and OPL. Finally, we discuss the limitations of offline RL and review a burgeoning problem of low-adaptive exploration which addresses these limitations by providing a sweet middle ground between offline and online RL. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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An Actor-centric Causality Graph for Asynchronous Temporal Inference in Group Activity

An Actor-centric Causality Graph for Asynchronous Temporal I...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhao Xie Tian Gao Kewei Wu Jiao Chang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology

The causality relation modeling remains a challenging task for group activity recognition. The causality relations describe the influence on the centric actor (effect actor) from its correlative actors (cause actors). Most existing graph models focus on learning the actor relation with synchronous temporal features, which is insufficient to deal with the causality relation with asynchronous temporal features. In this paper, we propose an Actor-Centric Causality Graph Model, which learns the asynchronous temporal causality relation with three modules, i.e., an asynchronous temporal causality relation detection module, a causality feature fusion module, and a causality relation graph inference module. First, given a centric actor and its correlative actor, we analyze their influences to detect causality relation. We estimate the self influence of the centric actor with self regression. We estimate the correlative influence from the correlative actor to the centric actor with correlative regression, which uses asynchronous features at different timestamps. Second, we synchronize the two action features by estimating the temporal delay between the cause action and the effect action. The synchronized features are used to enhance the feature of the effect action with a channel-wise fusion. Third, we describe the nodes (actors) with causality features and learn the edges by fusing the causality relation with the appearance relation and distance relation. The causality relation graph inference provides crucial features of effect action, which are complementary to the base model using synchronous relation inference. Experiments show that our method achieves state-of-the-art performance on the Volleyball dataset and Collective Activity dataset.

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Convolution Neural Network with Unsupervised Machine Learning Approach for Feature Extraction and Brain Tumor Detection in Human beings

Convolution Neural Network with Unsupervised Machine Learnin...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： G. Sathish Kumar M. Nivaashini G. Uma Maheshwari D. Rasi B. Magesh Kumar R. Arun Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computer Science and Engineering Dr. Mahalingam College of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore Tamil Nadu India

Brain tumors are atypical progress of cells in the brain or the contiguous tissues. Tumors can be either non-cancerous or cancerous. The prognosis for a person with a brain tumor varies significantly. Benign tumors are generally less aggressive and may be curable with surgery. Malignant tumors can be more challenging to treat and may have a poorer prognosis. The outcome also depends on the stage at which the tumor is diagnosed. Detecting brain tumors using magnetic resonance imaging (MRI) images with convolutional neural networks (CNNs) is a common and effective approach in medical image analysis. In this research, the unsupervised machine learning approach called self-organizing map (SOM) is implemented for effective feature extraction and CNN with ResNet architecture is employed for brain tumor exposure efficiently. The proposed SOMResNet algorithm takes the MRI images as the input and perform feature extraction. Again, the mined essential features are given as the input for SOMResNet to identify the tumor in the human brain. The accuracy of the proposed SOMResNet is compared with softmax, ReLu, Tanh, decision tree classifiers. The accuracy of SOMResNet is 97.5%, Sensitivity is 98.0%, specificity rate is 97.1%, precision rate is 97.3% with 97.6% of F-Score value. The result shows that the SOMResNet algorithm outperforms than the traditional algorithms in brain tumor detection.

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Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning 29

Parsing Natural Language into Propositional and First-Order ...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Lu, Xuantao Liu, Jingping Gu, Zhouhong Tong, Hanwen Xie, Chenhao Huang, Junyang Xiao, Yanghua Wang, Wenguang Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China SenseDeal Intelligent Technology Co. Ltd. Beijing China School of Information Science and Engineering East China University of Science and Technology China Fudan-Aishu Cognitive Intelligence Joint Research Center China DataGrand Inc. Shanghai China

Semantic parsing converts natural language utterances into structured logical expressions. We consider two such formal representations: Propositional Logic (PL) and First-order Logic (FOL). The paucity of labeled data is a major challenge in this field. In previous works, dual reinforcement learning has been proposed as an approach to reduce dependence on labeled data. However, this method has the following limitations: 1) The reward needs to be set manually and is not applicable to all kinds of logical expressions. 2) The training process easily collapses when models are trained with only the reward from dual reinforcement learning. In this paper, we propose a scoring model to automatically learn a model-based reward, and an effective training strategy based on curriculum learning is further proposed to stabilize the training process. In addition to the technical contribution, a Chinese-PL/FOL dataset is constructed to compensate for the paucity of labeled data in this field. Experimental results show that the proposed method outperforms competitors on several datasets. Furthermore, by introducing PL/FOL generated by our model, the performance of existing Natural Language Inference (NLI) models is further enhanced. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Reinforcement learning

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Liver Disease Prediction using Machine learning Classification Techniques 11

Liver Disease Prediction using Machine learning Classificati...

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11th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2022

作者： Gupta, Ketan Jiwani, Nasmin Afreen, Neda Divyarani, D. University of the Cumberlands United States Jamia Millia Islamia Department of Computer Engineering New Delhi India Pesit University Electronic City Data Science and Machine Learning Bangaluru India

ISBN: (纸本)9781665480383

Machine Learning is a process which is used to discover patterns in huge data/ large data set to enable decision, thereby allowing machines to go through a learning process (i.e. supervised, unsupervised and semi-supervised or reinforced). The data set used in this paper is Liver Patient taken from UCI Repository (i.e. Supervised Learning). There is a plenty of data on patients undergoing medical examination at hospitals and these data has been extracted on liver patients whose information can be further used for future improvement of their conditions. In other words, historical and classified input of patients and output data is fed into various algorithms or classifiers for predicting the future data of patients. The algorithms used here for predicting liver patients are Logistic regression, Decision Tree, Random Forest, KNNeighbor, Gradient Boosting, Extreme Gradient Boosting, LightGB. Based on the analysis and result calculations, it was found that these algorithm has obtained good accuracy after feature selection. © 2022 IEEE.

关键词： Decision trees

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