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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Revathi, M. Kalaiarasi, P. Jeevitha, S. Akilandasowmya, G. St. Joseph's Institute of Technology Artificial Intelligence & Data Science Chennai India Kalasalingam University Department of Machine Learning Krishnankoil India Jerusalem College of Engineering Information Technology Chennai India Sri Sairam Institute of Technology Computer and Communication Engineering Chennai India

ISBN: (纸本)9798331518097

Brain is an important organ of the human body that helps in the proper functioning of the all the organs. Without brain, the functions of other organs will not happen since they cannot run without the commands of brain. Brain tumor refers to a situation where an abnormal group of cells are found in some part of the brain. There are many types of brain tumor that are identified. The tumors in brain is independent of age and it can grow at anytime. The symptoms that is exhibited by the brain tumor depends on the size of the tumor cell as well as its location. Machine learning algorithms helps in the earlier identification of the disease which can be used to give proper treatment. In this paper, a fuzzy c means based segmentation of image is done. Then classification of the segmented image is done by using Support Vector Machine. The results shows 98% percentage of accuracy. © 2024 IEEE.

关键词： Support vector machines

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Control, Confidentiality, and the Right to be Forgotten

arXiv

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arXiv 2022年

作者： Cohen, Aloni Smith, Adam Swanberg, Marika Vasudevan, Prashant Nalini Department of Computer and Data Science University of Chicago United States Department of Computer Science Boston University United States Department of Computer Science National University of Singapore Singapore

Recent digital rights frameworks give users the right to delete their data from systems that store and process their personal information (e.g., the "right to be forgotten" in the GDPR). How should deletion be formalized in complex systems that interact with many users and store derivative information? We argue that prior approaches fall short. Definitions of machine unlearning Cao and Yang [2015] are too narrowly scoped and do not apply to general interactive settings. The natural approach of deletion-as-confidentiality Garg et al. [2020] is too restrictive: by requiring secrecy of deleted data, it rules out social functionalities. We propose a new formalism: deletion-as-control. It allows users’ data to be freely used before deletion, while also imposing a meaningful requirement after deletion—thereby giving users more control. Deletion-as-control provides new ways of achieving deletion in diverse settings. We apply it to social functionalities, and give a new unified view of various machine unlearning definitions from the literature. This is done by way of a new adaptive generalization of history independence. Deletion-as-control also provides a new approach to the goal of machine unlearning, that is, to maintaining a model while honoring users’ deletion requests. We show that publishing a sequence of updated models that are differentially private under continual release satisfies deletion-as-control. The accuracy of such an algorithm does not depend on the number of deleted points, in contrast to the machine unlearning literature. © 2022, CC0.

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UniTE: A Survey and Unified Pipeline for Pre-training Spatiotemporal Trajectory Embeddings

arXiv

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arXiv 2024年

作者： Lin, Yan Zhou, Zeyu Liu, Yicheng Lv, Haochen Wen, Haomin Li, Tianyi Li, Yushuai Jensen, Christian S. Guo, Shengnan Lin, Youfang Wan, Huaiyu Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China Beijing101318 China Department of Computer Science Aalborg University Aalborg9220 Denmark

Spatiotemporal trajectories are sequences of timestamped locations, which enable a variety of analyses that in turn enable important real-world applications. It is common to map trajectories to vectors, called embeddings, before subsequent analyses. Thus, the qualities of embeddings are very important. Methods for pre-training embeddings, which leverage unlabeled trajectories for training universal embeddings, have shown promising applicability across different tasks, thus attracting considerable interest. However, research progress on this topic faces two key challenges: a lack of a comprehensive overview of existing methods, resulting in several related methods not being well-recognized, and the absence of a unified pipeline, complicating the development of new methods and the analysis of methods. We present UniTE, a survey and a unified pipeline for this domain. In doing so, we present a comprehensive list of existing methods for pre-training trajectory embeddings, which includes methods that either explicitly or implicitly employ pre-training techniques. Further, we present a unified and modular pipeline with publicly available underlying code, simplifying the process of constructing and evaluating methods for pre-training trajectory embeddings. Additionally, we contribute a selection of experimental results using the proposed pipeline on real-world datasets. Implementation of the pipeline is publicly available at https://***/Logan-Lin/UniTE. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

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OIDS: A Smart Framework for Intrusion Detection in 5G WSN’s by using Ontology

OIDS: A Smart Framework for Intrusion Detection in 5G WSN’s...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Sover Singh Bisht Anuj Singh Rajput Sanjeev Kumar Mandal Department of Data Science Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Tula’s Institute Dehradun India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

The increasing depth and applications of 5G wireless sensor networks also raise the possibility of network intrusions. In this research, a network intrusion detection system based on the ontology notion is proposed. As it creates ontologies and maintains the relationships among sensor nodes in a network, it is referred upon as a smart system. This proposed system finding is able to secure data in 5G networks. The current detection technique’s parameters such as patrol nodes, live nodes, hop, sensor nodes, and energy (in joules) are used to gauge how well the suggested system performs. Attack estimation rate (AER) and precision are the types of metrics in use.

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Conformalized adaptive forecasting of heterogeneous trajectories 24

Conformalized adaptive forecasting of heterogeneous trajecto...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yanfei Zhou Lars Lindemann Matteo Sesia Department of Data Sciences and Operations University of Southern California Los Angeles CA Department of Computer Science University of Southern California Los Angeles CA

This paper presents a new conformal method for generating simultaneous forecasting bands guaranteed to cover the entire path of a new random trajectory with sufficiently high probability. Prompted by the need for dependable uncertainty estimates in motion planning applications where the behavior of diverse objects may be more or less unpredictable, we blend different techniques from online conformal prediction of single and multiple time series, as well as ideas for addressing heteroscedasticity in regression. This solution is both principled, providing precise finite-sample guarantees, and effective, often leading to more informative predictions than prior methods.

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Private Online Community Detection for Censored Block Models

arXiv

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arXiv 2024年

作者： Seif, Mohamed Xie, Liyan Goldsmith, Andrea J. Poor, H. Vincent Department of Electrical and Computer Engineering Princeton University United States School of Data Science The Chinese University of Hong Kong Shenzhen China

We study the private online change detection problem for dynamic communities, using a censored block model (CBM). Focusing on the notion of edge differential privacy (DP), we seek to understand the fundamental tradeoffs between the privacy budget, detection delay, and exact community recovery of community labels. We establish the theoretical lower bound on the delay in detecting changes privately and propose an algorithm capable of identifying changes in the community structure, while maintaining user privacy. Further, we provide theoretical guarantees for the effectiveness of our proposed method by showing necessary and sufficient conditions on change detection and exact recovery under edge DP. Simulation and real data examples are provided to validate the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Change detection

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DETR-SAM: Automated Few-Shot Segmentation With Detection Transformer and Keypoint Matching

DETR-SAM: Automated Few-Shot Segmentation With Detection Tra...

引用

Omni-layer Intelligent Systems (COINS), International Conference on

作者： Mohamadreza Khanmohamadi Bahar Farahani Department of Computer and Data Science Shahid Beheshti University Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

ISBN: (数字)9798350349597

ISBN: (纸本)9798350349603

Few-shot Segmentation (FSS) aims to segment objects in an image using only a few annotated examples. The Segment Anything Model (SAM) has recently gained attention in FSS due to its versatility and capability to handle various segmentation tasks with prompts. However, its potential to autonomously segment predefined visual categories (e.g., cars, faces) within a dataset without explicit human prompting remains underexplored. To address this gap, this work focuses on automating the process, eliminating the need for manual prompts to reduce ambiguity and improve contextual understanding. We propose a novel technique, DETR-SAM, which integrates the DEtection TRansformer (DETR) with a keypoint matching algorithm to generate automatic prompts for SAM to segment the objects within the image. In particular, DETR predicts the object position boundaries. To enhance segmentation accuracy, keypoint matching is employed to detect keypoints between support and query images. Evaluations on the FSS dataset show that our method achieves comparable performance to several state-of-the-art models. Furthermore, due to the utilization of the pretrain vision models, our method is robust to overfitting. DETRSAM stands out by automating prompt generation, showcasing its promising effectiveness in the FSS domain.

关键词： Image segmentation Visualization Accuracy Transformers Prediction algorithms Robustness Automobiles

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Urban Sound Classification Using DNN, CNN LSTM a Comparative Approach

Urban Sound Classification Using DNN, CNN LSTM a Comparative...

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2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021

作者： Bubashait, Mohamed Hewahi, Nabil Big Data Science and Analytics Master Program College of Science University of Bahrain Sakhier Bahrain University of Bahrain Department of Computer Science College of IT Sakhier Bahrain

ISBN: (纸本)9781665440325

Like air pollution, sound pollution has grown to be a major concern for city residents, designers, and developers. Detecting and recognizing sound types and sources in cities and suburban areas or any environment have become a necessity for the quality of life as well as security. In recent years, researchers have explored many models using Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) Neural Network, and different combinations of these techniques, which produced promising results when combined with spectrogram images, or its different variations, to classify urban sounds. This research uses the DNN model performance as a baseline to compare the CNN and LSTM models' performance for classifying urban sound using Mel scale cepstral analysis (MEL) spectrum images using an open-source library called Librosa for sound processing. Models' performance was evaluated using the UrbanSound8k dataset. The CNN model underperformed with an accuracy rate of 87.15% and f1 score of 85.63% compared to both the DNN base model and the LSTM model. In contrast, comparing the LSTM model with CNN, LSTM shows better accuracy performance on test data with 90.15%, and f1 score of 90.15%. © 2021 IEEE.

关键词： Long short-term memory

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Augmented Reality and Artificial Intelligence in Sign Language Expression

Augmented Reality and Artificial Intelligence in Sign Langua...

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IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： Kaviyaraj R P. Sathya P. Nandhini Vinoth Kumar P Sureshkumar Chelliah Ezhilmathi S Department of computational intelligence SRM institute of science and technology Kattankulathur India Department of Computer Science and Engineering Sri Shanmuga Collage of Engineering and Technology Sankari India Department of Artificial Intelligence and Data Science St. Joseph's Institute of Technology OMR Chennai

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Sign language, the vibrant tapestry of hand gestures and facial expressions, is the lifeblood of Deaf and hardof-hearing communities. For millions of signers, American Sign Language (ASL) runs deeper than communication, fundamental to identity, expression, and belonging. And yet an unshakeable communication gap leaves users of ASL frequently marooned away from the hearing world, kept from education, healthcare, or employment, or from basic, everyday transactions. By posing this new unsolved challenge to the power and promise of Artificial Intelligence (AI), this work leads the way towards closing that chasm by real-time recognition and translation of full ASL. Our approach employs a novel variant of Random Forest and utilizes cutting-edge video processing techniques to identify and understand the nuanced, often exquisitely delicate detail of ASL signing, at unprecedented levels of accuracy, and at speed. Another layer of innovation that characterizes our work is our integration of augmented reality (AR). By embedding AR along with our translator of artificial intelligence tech, we intend to completely change the way American Sign Language (ASL) is conveyed by directly engraining our already robust Random Forest model and advanced video processing techniques to project the ASL translation directly into your visual field in real time. The goal to demystify this complex and vivid language and, in doing so, to remove the communication barriers that persist between the Deaf community and the rest of the world, thus, fostering inclusion.

关键词： Sign language Computational modeling Signal processing algorithms Assistive technologies Predictive models Streaming media Feature extraction

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Searching with Extended Guard and Pivot Loop

Searching with Extended Guard and Pivot Loop

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2021 Prague Stringology Conference, PSC 2021

作者： Pakalén, Waltteri Tarhio, Jorma Watson, Bruce W. Department of Computer Science Aalto University Finland Information Science Centre for AI Research School for Data-Science & Computational Thinking Stellenbosch University South Africa

ISBN: (纸本)9788001068694

We explore practical optimizations on comparison-based exact string matching algorithms. We present a guard test that compares q-grams between the pattern and the text before entering the match loop, and evaluate experimentally the benefit of optimization of this kind. As a result, the Brute Force algorithm gained most from the guard test, and it became faster than many other algorithms for short patterns. In addition, we present variations of a recent algorithm that uses a special skip loop where a pivot, a selected position of the pattern, is tested at each alignment of the pattern and in case of failure;the pattern is shifted based on the last character of the alignment. The variations include alternatives for the pivot and the shift function. We show the competitiveness of the new algorithm variations by practical experiments. © Czech Technical University in Prague, Czech Republic.

关键词： String searching algorithms

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