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Adaptive Sparsification for Matroid Intersection 51

Adaptive Sparsification for Matroid Intersection

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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Quanrud, Kent Dept. of Computer Science Purdue University West LafayetteIN United States

ISBN: (纸本)9783959773225

We consider the matroid intersection problem in the independence oracle model. Given two matroids over n common elements such that the intersection has rank k, our main technique reduces approximate matroid intersection to logarithmically many primal-dual instances over subsets of size Õ(k). This technique is inspired by recent work by [2] and requires additional insight into structuring and efficiently approximating the dual LP. This combination of ideas leads to faster approximate maximum cardinality and maximum weight matroid intersection algorithms in the independence oracle model. We obtain the first nearly linear time/query approximation schemes for the regime where k ≤ n2/3 © Kent Quanrud.

关键词： Combinatorial mathematics

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Assessment Technology for Physiotherapy Practices using Deep Learning 2

Assessment Technology for Physiotherapy Practices using Deep...

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Vineeth, Nandhini Darshan, P.M. Deepu, K. Daragaj, Arihant Prabhu, Ananth G. Bms College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370188

The innovative technology for physiotherapy assessment employing deep learning aims to offer a precise and streamlined approach to evaluating physiotherapy needs. Conventional manual assessment techniques utilized in physiotherapy often consume time and are susceptible to human inaccuracies, resulting in erroneous diagnoses and treatment strategies. This technology tackles these challenges by employing sophisticated deep learning algorithms that identify angles and offer auditory guidance to patients based on their posture. The process commences with capturing the patient's video through a webcam and extracting frames utilizing OpenCV. Following this, the frames undergo processing via a media pipe library, a sophisticated tool designed for body key point detection. These identified key points are subsequently utilized to connect essential body segments for a particular exercise and compute the angular measurements between them. Using these measurements, the system assesses the correctness of the posture, offering audio feedback with repetition counts for accurate postures and tailored guidance for incorrect ones. The audio cues are exercise-specific, delivering precise instructions to aid the patient in refining their posture. The system also keeps track of patient's progress over time and provides a visual display of their improvement. This helps the patient monitor their progress and provides motivation for them to continue with their therapy. The use of learning algorithms and the media pipe library makes this solution an accurate, efficient, and cost-effective method for physiotherapy assessments. © 2024 IEEE.

关键词： Deep learning

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Species Density Analysis Using Deep Learning

Species Density Analysis Using Deep Learning

引用

2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Rekha, K.S. Shamlin, A Harini Saveetha Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309140

The primary objective of this project is to develop a machine learning model, specifically Convolutional Neural Network (CNN), to count the density of several particular species in an audio clip. The model is trained on a dataset containing calls of the specified species and other species. This innovation has significant implications for wildlife monitoring and ecological research, as it can help researchers track and understand the behavior of organisms in forest regions more efficiently. Manual effort in audio analysis can be reduced by machine learning, thereby saving time. By improving and broadening techniques, algorithms, and training methodologies, researchers can uncover more about animal behavior and contribute to our collective understanding of ecosystems. The application of this technology can be useful for analyzing the total number of species in a certain rainforest region, eventually reducing the burden on researchers and streamlining the entire process. Overall, this project has immense potential to pave the way for more efficient and accurate wildlife monitoring and ecological research that can ultimately benefit our planet's biodiversity. © 2024 IEEE.

关键词： Forest ecology

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A Likelihood Ratio Test of Genetic Relationship among Languages

A Likelihood Ratio Test of Genetic Relationship among Langua...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Akavarapu, V.S.D.S. Mahesh Bhattacharya, Arnab Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798891761148

Lexical resemblances among a group of languages indicate that the languages could be genetically related, i.e., they could have descended from a common ancestral language. However, such resemblances can arise by chance and, hence, need not always imply an underlying genetic relationship. Many tests of significance based on permutation of wordlists and word similarity measures appeared in the past to determine the statistical significance of such relationships. We demonstrate that although existing tests may work well for bilateral comparisons, i.e., on pairs of languages, they are either infeasible by design or are prone to yield false positives when applied to groups of languages or language families. To this end, inspired by molecular phylogenetics, we propose a likelihood ratio test to determine if given languages are related based on the proportion of invariant character sites in the aligned wordlists applied during tree inference. Further, we evaluate some language families and show that the proposed test solves the problem of false positives. Finally, we demonstrate that the test supports the existence of macro language families such as Nostratic and Macro-Mayan. © 2024 Association for Computational Linguistics.

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Integration of Swarm Intelligence with KNN for Optimal Nearest Neighbors Value Prediction 9

Integration of Swarm Intelligence with KNN for Optimal Neare...

引用

9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Anchalia, Aniketh Kakati, Anirudh Patil, Ambika Bhavana, B.N. Paudel, Ankit Chaudhari, Shilpa Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350334395

K-Nearest Neighbors (KNN), a simple and widely used algorithm, is extremely valuable in the field of machine learning models. Finding an optimal value of the nearest neighbor parameter in the KNN algorithm has been a problem that has existed for quite some time. In recent years, nature inspired algorithms have been widely emerging, with scope of being utilized in various engineering problems. This work proposes integrating a swarm intelligence optimization technique, the Sparrow Search Algorithm (SSA), for optimizing the process of finding K. The proposed hybrid model has been tested against four datasets of varying sizes and the results depict improvement in efficiency. The improvement is much larger as the size of the dataset increases, with around a 99% improvement in efficiency for the largest dataset tested on. KNN is also used to preprocess two of the datasets which have missing values, which result in improved accuracies of prediction. © 2023 IEEE.

关键词： Swarm intelligence

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Multiple language Discovery Abuse of Cyberspace with Advanced Transformer Architecture 2

Multiple language Discovery Abuse of Cyberspace with Advance...

引用

2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Singh, Arvind Tripathi, Abhinandan Buddha Institute of Technology Dept. of Computer Science & Engineering Gorakhpur India

ISBN: (纸本)9798350368109

The number of reported instances of cyber abuse has also increased in tandem with the growth in the quantity of people that are regularly using the internet. Users' online freedom and privacy are jeopardized by such occurrences. Traditionally, the removal of such content from the internet has been accomplished through manual moderation and reporting systems. This strategy has, however, certain drawbacks, such as a reliance on people, longer delays, and less privacy for data. Traditional recurrent sequence models and supervised machine learning have been used in previous attempts to automate this procedure, but they have a tendency to perform badly on non-English material. A versatile solution that can handle multilingual text is urgently needed, given the increasing diversity of consumers using the internet. Moreover, translations frequently lose important context and emotion from content written in vernacular languages. In this work, we offer a generative deep learning based method for the detection of cyber abuse throughout Hindi, English with mixed codes, and Hindi text. The method uses bidirectional transformer-based BERT *** the TRAC-1 standard aggressiveness identification task, the suggested architecture can get extremely positive outcomes on the task leaderboard for English and accomplish cutting-edge performance on the Hindi dataset with mixed codes. The outcomes show that the new strategy is superior to the current ones because they were obtained without the need of numerous models or ensemble-based methods. Effective models that utilize deep learning techniques to analyze multilingual text can process a wider variety of inputs, making them indispensable in combating these types of social ills. © 2024 IEEE.

关键词： Generative adversarial networks

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computer Modeling of Optimization in Seismoacoustic Tomographic Diagnostics of Velocity Inhomogeneities 3

Computer Modeling of Optimization in Seismoacoustic Tomograp...

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3rd IEEE International Conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024

作者： Gervas, Nikolai V. Gervas, Daria V. Zerkal, Sergey M. Novosibirsk State Technical University Dept. of Computer Science Novosibirsk Russia

ISBN: (纸本)9798331516321

This paper presents a computational technology for kinematic diagnostics of the velocity distribution of a probing signal in seismoacoustic media under conditions of linearization of the inverse kinematic problem. The problem formulation, solution construction, and a computational algorithm based on the computed tomography method are provided. The use of computed tomography is facilitated by organizing a tomographic observation system (arrangement of signal sources and receivers along a circle). Linearization is performed near the linear dept. function (Z variable in the X Y Z space), which is highlighted in the target velocity function and assumed to be known. Special attention is paid to organizing this component, as it significantly affects the accuracy and reliability of the solution obtained. The results of computer experiments that demonstrate the solution to the problem are presented. In addition, the prospects for further research aimed at advancing the proposed approach using artificial intelligence technologies, particularly those related to heuristic search, have been outlined. © 2024 IEEE.

关键词： Inverse problems

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Is Gamification of Neurofeedback Training effective for ADHD treatment? A Systematic Review 13

Is Gamification of Neurofeedback Training effective for ADHD...

引用

13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Kohnen, Henriette Mavromoustakos-Blom, Paris Alimardani, Maryam Dept. of CSAI Tilburg University Tilburg Netherlands Dept. of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands

ISBN: (纸本)9798331521929

This systematic literature review examines the efficacy of gamification in neurofeedback (NF) therapy approaches for the treatment of Attention-Deficit/Hyperactivity Disorder (ADHD). In recent research, gamification of brain-computer interfaces (BCI) has been shown to enhance user engagement and motivation when performing otherwise dull and tiring NF tasks. However, given the attentional deficits and hyperactivity symptoms in people with ADHD, it is possible that introducing game elements yields adverse effects such as distraction for this population. Therefore, following the PRISMA protocol, the present review synthesized findings from 13 empirical studies to determine 1) whether gamification of NF training is effective in ADHD treatments and 2) in what way gamification of the BCI system was applied in past NF research for ADHD. Results suggest that while many NF games have been introduced as potentially useful applications for ADHD, there is a systematic lack of controlled studies with sufficient sample size to evaluate their effectiveness. Additionally, an overall heterogeneity of the games and evaluation measures was observed which did not allow for robust conclusions on specific design recommendations for gamification. Based on this review, we present open questions and potential research directions for future researchers. © 2025 IEEE.

关键词： computer games

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An Effective Pre-Owned Car Price Prediction Based on Multi Linear Regression Technique 1

An Effective Pre-Owned Car Price Prediction Based on Multi L...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Kumar, Aman Singh, Dheeraj Chauhan, Keshav Guru Prasad, M.S. Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350341737

The market for the purchase and sale of second- hand automobiles has seen a trend towards expansion in recent years. Used vehicle buyers and sellers are able to conduct business at rates that are acceptable and fair thanks to scientific examination of automobile prices. This contributes to a reduction in the risks associated with conducting transactions in the used car market and encourages the market to continue to grow in a sustainable manner. Even though a lot of effort has been done on predicting the price of used cars, there are still a lot of obstacles standing in the way of an accurate and reliable estimate. Predictions of the prices of used automobiles that are accurate can assist individuals in making wise choices and avoiding the inflated costs of used vehicles on the market to the greatest extent possible. In this work multi linear regression technique is used, which is a type of supervised machine learning algorithm to build a model for predicting the price of pre-owned cars effectively. The dataset used for the prediction was collected from Kaggle webpage. After training and testing the model, the proposed model deployed using flask framework. The accuracy of the proposed model is 89.23% based on testing data. © 2023 IEEE.

关键词： Automobiles

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Navigating Breakthroughs in Pediatric Brain Stroke Detection: A Comprehensive Survey

Navigating Breakthroughs in Pediatric Brain Stroke Detection...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Deepak, N.R. Paital, Santosh Kumar Verma, Saritha Choeyang, Tenzin Singh, Udit Kumar Bhavya Hkbk College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Brain strokes, characterized by sudden interruptions in cerebral blood flow, pose a significant health concern, especially in children, where detection is intrinsically challenging due to limitations in existing methodologies. Recent attempts leveraging Machine Learning with Deep Learning techniques have grappled with accuracy and efficiency shortcomings in addressing this critical issue. In response, our innovative model integrates the Extreme Learning Machine (ELM), Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs) algorithms. Rigorously evaluated using a comprehensive dataset, our ELM + CNN + GAN model demonstrates unparalleled accuracy, precision, and recall rates, surpassing benchmarks set by conventional approaches like the Random Forest algorithm and the AlexNet + SVM hybrid model. Notably, our model excels in diagnostic speed, offering a promising avenue for swift and efficient early detection of pediatric brain strokes. The strategic incorporation of ELM, recognized for its efficiency in single layer neural networks, along with the analytical power of CNN for image analysis and GAN for synthetic data generation, establishes a robust and comprehensive model. By outperforming existing methods, our approach signifies a substantial leap forward in addressing this critical health concern, revolutionizing stroke detection with enhanced accuracy and speed. © 2024 IEEE.

关键词： Generative adversarial networks

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