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International Journal of Interactive Mobile Technologies 2022年第21期16卷 49-70页

作者： Islam, Gazi Zahirul Hossain, Md. Mobarak Faruk, Md. Nur, Fernaz Narin Hasan, Nayeem Khan, Khalid Mahbub Tumpa, Zerin Nasrin Department of Computer Science and Engineering Southeast University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka Bangladesh School of Computer Science Western Illinois University IL United States Department of Computer Science and Engineering University of Information Technology & Sciences Dhaka Bangladesh

Gas Leakage and its fatal effects are a great concern throughout the world, especially in developing countries like Bangladesh. Every year lots of people died and countless damages to assets occur due to the fire caused by the gas leakage. Not only that but gas leakage and explosion are also very harmful to the climate. Thus, a system to detect gas leakage and preventive measures is of utmost importance. In this project, we design and implement an intelligent IoT prototype to detect gas leakage, and the fire caught by gas leakage. Our goal is to minimize the effect of gas leakage by taking some protective measures. When the gas sensor, detects the gas leakage, the solenoid valve shuts off the gas line, and the exhaust fan starts to run. Again, when the flame sensor detects a fire, the sucker throws the fire extinguisher balls at the fire. The GSM SIM module notifies the user by sending a message to his smartphone. The buzzer sounds when a mishap occurs and the LCD monitor always shows the status of the system. In this way, we have efficiently designed and implemented a low-cost and intelligent gas leak detection and fire suppression system. © 2022, International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Internet of things

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An Improved Ant Lion Optimization Algorithm Based Convolutional Neural Network for Big data Classification

An Improved Ant Lion Optimization Algorithm Based Convolutio...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Umaeswari, P. Vellingiri, J. Kumar, Gotte Ranjith Devendhiran, A. Naik, Ashitha V R.M.K. Engineering College Department of Computer Science and Business Systems Kavaraipettai India Institute of Technology School of Computer Science Engineering and Information Systems Vellore India Sr University School of Computer Science & Artificial Intelligence Warangal India Vivekanandha College of Engineering for Women Department of Computer Science and Technology Namakkal India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350313413

The continuous advancement of remote sensor technology is contributing to a daily surge in data production, necessitating improvements in the accuracy of big data classification. This research proposes a unique feature selection method named as Improved Ant Lion Optimization (IALO) algorithm that integrates the behavior of animal migration with Ant Lion Optimization (ALO) algorithm. The proposed feature selection method enhances the performance of the ALO algorithm. The datasets utilized for the research are iris, wine, Cleveland, and Switzerland datasets. Next, the map reduces framework is utilized and then feature extraction is processed by Linear Discriminant Analysis (LDA). The feature selection is performed by the proposed IALO algorithm and the selected features are given to the Convolutional Neural Network (CNN) classifier for the big data classification. The performance of the proposed method is evaluated by parameters such as accuracy, computation time, specificity, and sensitivity. The proposed method attained a high accuracy of 98.05% with less computation time of 4.8 secs in the wine dataset which is comparatively higher than other existing methods such as Grey Wolf Optimization based CNN (GWO-CNN), Particle Swarm Optimization based CNN (PSO-CNN), Cuckoo Search Optimization based CNN (CSO-CNN), Ant Lion Optimization based CNN (ALO-CNN). © 2023 IEEE.

关键词： Ant Lion Optimization Big data Convolutional Neural Network MapReduce and Migration behavior

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Audio-visual keyword transformer for unconstrained sentence-level keyword spotting

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CAAI Transactions on Intelligence technology 2024年第1期9卷 142-152页

作者： Yidi Li Jiale Ren Yawei Wang Guoquan Wang Xia Li Hong Liu Key Laboratory of Machine Perception Peking UniversityShenzhen Graduate SchoolShenzhenChina College of Electronics and Information Engineering Sichuan UniversityChengduChina Department of Computer Science ETH ZurichZurichSwitzerland

As one of the most effective methods to improve the accuracy and robustness of speech tasks,the audio-visual fusion approach has recently been introduced into the field of Keyword Spotting(KWS).However,existing audio-visual keyword spotting models are limited to detecting isolated words,while keyword spotting for unconstrained speech is still a challenging *** this end,an Audio-Visual Keyword Transformer(AVKT)network is proposed to spot keywords in unconstrained video *** authors present a transformer classifier with learnable CLS tokens to extract distinctive keyword features from the variable-length audio and visual *** outputs of audio and visual branches are combined in a decision fusion *** humans can easily notice whether a keyword appears in a sentence or not,our AVKT network can detect whether a video clip with a spoken sentence contains a pre-specified ***,the position of the keyword is localised in the attention map without additional position ***-imental results on the LRS2-KWS dataset and our newly collected PKU-KWS dataset show that the accuracy of AVKT exceeded 99%in clean scenes and 85%in extremely noisy *** code is available at https://***/jialeren/AVKT.

关键词： artificial intelligence multimodal approaches natural language processing neural network speech processing

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A Study on the Application of Using Hypernetwork and Low Rank Adaptation for Text-to-Image Generation Based on Diffusion Models 6

A Study on the Application of Using Hypernetwork and Low Ran...

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6th International Youth Conference on Radio Electronics, Electrical and Power Engineering, REEPE 2024

作者： Levin, Artyom O. Belov, Yuri S. Bauman Moscow State Technical University Kaluga Branch Computer Science Information Technology Department Kaluga Russia

ISBN: (纸本)9798350382891

Recent advances in the field of image generation have attracted attention due to the growing number of diverse data sources and test samples. A primary driver of this evolution is the application of neural networks, particularly for generating high-quality images from textual prompts. Despite the potential of diffusion models in this sector, they typically face computational challenges associated with vast datasets. This paper describes the research on two existing solutions: Hypernetworks and Low-Rank Adaptation (LoRA), both aiming to streamline and optimize the image generation process. While hypernetworks dynamically adjust model parameters based on the input text, increasing flexibility and performance, LoRA efficiently adapts the primary model style without requiring it to be trained from scratch. Using the Stable Diffusion 1.5 model as a benchmark, this research evaluates the influence of hypernetwork and LoRA modifications. The results indicate that both approaches provide efficient and highly accurate image generation, confirming their efficacy in contemporary image generation tasks. © 2024 IEEE.

关键词： Diffusion

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Fine-Grained Soft Ear Biometrics for Augmenting Human Recognition

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computer Systems science & Engineering 2023年第11期47卷 1571-1591页

作者： Ghoroub Talal Bostaji Emad Sami Jaha Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia

Human recognition technology based on biometrics has become a fundamental requirement in all aspects of life due to increased concerns about security and privacy ***,biometric systems have emerged as a technology with the capability to identify or authenticate individuals based on their physiological and behavioral *** different viable biometric modalities,the human ear structure can offer unique and valuable discriminative characteristics for human recognition *** recent years,most existing traditional ear recognition systems have been designed based on computer vision models and have achieved successful ***,such traditional models can be sensitive to several unconstrained environmental *** such,some traits may be difficult to extract automatically but can still be semantically perceived as soft *** research proposes a new group of semantic features to be used as soft ear biometrics,mainly inspired by conventional descriptive traits used naturally by humans when identifying or describing each ***,the research study is focused on the fusion of the soft ear biometric traits with traditional(hard)ear biometric features to investigate their validity and efficacy in augmenting human identification *** proposed framework has two subsystems:first,a computer vision-based subsystem,extracting traditional(hard)ear biometric traits using principal component analysis(PCA)and local binary patterns(LBP),and second,a crowdsourcing-based subsystem,deriving semantic(soft)ear biometric *** feature-level fusion experiments were conducted using the AMI database to evaluate the proposed algorithm’s *** obtained results for both identification and verification showed that the proposed soft ear biometric information significantly improved the recognition performance of traditional ear biometrics,reaching up to 12%for LBP and 5%for PCA descriptors;when fusing all three capa

关键词： Ear biometrics soft biometrics human ear recognition semantic features feature-level fusion computer vision machine learning

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Leveraging Context-Aware Prompting for Commit Message Generation

Leveraging Context-Aware Prompting for Commit Message Genera...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Jiang, Zhihua Chen, Jianwei Rao, Dongning Ye, Guanghui Department of Computer Science Jinan University Guangzhou510632 China School of Computer Guangdong University of Technology Guangzhou510006 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (纸本)9798891761643

Writing comprehensive commit messages is tedious yet important, because these messages describe changes of code, such as fixing bugs or adding new features. However, most existing methods focus on either only the changed lines or nearest context lines, without considering the effectiveness of selecting useful contexts. On the other hand, it is possible that introducing excessive contexts can lead to noise. To this end, we propose a code model COMMIT (Context-aware prOMpting based comMIt-message generaTion) in conjunction with a code dataset CODEC (COntext and metaData Enhanced Code dataset). Leveraging program slicing, CODEC consolidates code changes along with related contexts via property graph analysis. Further, utilizing CodeT5+ as the backbone model, we train COMMIT via context-aware prompt on CODEC. Experiments show that COMMIT can surpass all compared models including pre-trained language models for code (code-PLMs) such as CommitBART and large language models for code (code-LLMs) such as Code-LlaMa. Besides, we investigate several research questions (RQs), further verifying the effectiveness of our approach. We release the data and code at: https://***/Jnunlplab/***. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Early Recognition and Ranking of Knee Osteoarthritis by the Assistance of Enhanced Deep Learning on Knee MR Image Data 8th

Early Recognition and Ranking of Knee Osteoarthritis by the ...

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8th International Conference on computers, Management and Mathematical sciences, ICCM 2022

作者： Murali, Molleti Bala Purnima, Varigala Sai Laxmi, Kodamanchili Venkata Sampath, Rajana Sai Mahanty, Mohan Department of Computer Science and Engineering Vignan’s Institute of Information Technology-Duvvada Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9783031511660

Osteoarthritis (OA) of the knee is an inflammation that impacts the knee bone due to the significant weight-bearing of the body. The disease results in degeneration and rupture of the cartilage elements in the knee joint, causing severe pain. Unfortunately, the prevalence of OA has been increasing globally, with a 113.25% increase in cases from 1990 to 2019. Currently, more than 350 million people globally suffer from arthritis, and it is estimated that by 2040, about 78 million US adults will have this condition. The diagnosis of OA is primarily carried out by evaluating symptoms and comparing plain radiographs, which can be subjective. However, Convolution Neural Networks (CNNs), one of the best deep learning technological advances, are currently attracting interest as a potential remedy for healthcare problems. Consequently, this study’s objective aims to design and implement a categorization scheme that can aid doctors in reducing their workload and assist rheumatologists in assessing the severity of the pain accurately. Furthermore, this will enable them to make the best diagnosis and recommend the most appropriate treatment. By using our proposed model, i.e., Enhanced MobileNet-V2, rheumatologists can make informed decisions on the severity of the condition, which can lead to better treatment outcomes for the patients. The proposed model Enhanced MobileNet-V2 was trained on the digital knee X-ray image dataset and achieved 91% accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diagnosis

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Robust Sparse Regression with Non-Isotropic Designs 38

Robust Sparse Regression with Non-Isotropic Designs

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Liu, Chih-Hung Novikov, Gleb Department of Electrical Engineering National Taiwan University Taiwan Lucerne School of Computer Science and Information Technology Switzerland

We develop a technique to design efficiently computable estimators for sparse linear regression in the simultaneous presence of two adversaries: oblivious and adaptive. Consider the model y∗ = X∗β∗ + η where X∗ is an n × d random design matrix, β∗ ∈ Rd is a k-sparse vector, and the noise η is independent of X∗ and chosen by the oblivious adversary. Apart from the independence of X∗, we only require a small fraction entries of η to have magnitude at most 1. The adaptive adversary is allowed to arbitrarily corrupt an Ε-fraction of the samples (X1∗, y1∗), ..., (Xn∗, yn∗ ). Given the Ε-corrupted samples (X1, y1), ..., (Xn, yn), the goal is to estimate β∗. We assume that the rows of X∗ are iid samples from some d-dimensional distribution D with zero mean and (unknown) covariance matrix Σ with bounded condition number. We design several robust algorithms that outperform the state of the art even in the special case of Gaussian noise η ∼ N(0, 1)n. In particular, we provide a polynomial-time algorithm that with high probability recovers β∗ up to error O(√Ε) as long as n ≥ O∼ (k2/Ε), only assuming some bounds on the third and the fourth moments of D. In addition, prior to this work, even in the special case of Gaussian design D = N(0, Σ) and noise η ∼ N(0, 1), no polynomial time algorithm was known to achieve error o(√Ε) in the sparse setting n 2. We show that under some assumptions on the fourth and the eighth moments of D, there is a polynomial-time algorithm that achieves error o(√Ε) as long as n ≥ O∼ (k4/Ε3). For Gaussian distribution D = N(0, Σ), this algorithm achieves error O(Ε3/4). Moreover, our algorithm achieves error o(√Ε) for all log-concave distributions if Ε ≤ 1/polylog(d). Our algorithms are based on the filtering of the covariates that uses sum-of-squares relaxations, and weighted Huber loss minimization with 1 regularizer. We provide a novel analysis of weighted penalized Huber loss that is suitable for heavy-tailed designs in the presence of two adversaries

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Integrating AI and computer Vision for Effective Foreign Object Detection on Airport Runways 3

Integrating AI and Computer Vision for Effective Foreign Obj...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Annie Silviya, S.H. Kingsley, S. Agitha, W. Sriman, B. Priyadarshan, V.J. Mohana, V. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Easwari Engineering College Department of Computer Science and Engineering Chennai India P.B. College of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350322842

The FOD-R Dataset is a collection of images that depict common types of foreign object debris (FOD) that can be found on runways or taxiways. The dataset has primarily been annotated using bounding boxes to facilitate object detection. However, the FOD-xR Dataset includes an additional feature - color codes - that provide information on the ease of removing the debris. Lightweight objects are marked in green, while heavy objects are marked in red. This centralized system simplifies coordination efforts, making it easier for organizations to manage FOD removal effectively. This, in turn, can have a positive impact on an organization's bottom line by minimizing delays and damage caused by FOD incidents. Overall, the FOD-R and FOD-xR Datasets are valuable resources for organizations looking to improve their FOD detection and removal processes. © 2023 IEEE.

关键词： Object detection

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Analyzing Risk Elements in Cardiovascular Diseases Prediction using Neural Networks Algorithm 10

Analyzing Risk Elements in Cardiovascular Diseases Predictio...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Yadav, Dhyan Chandra Pal, Saurabh Yadav, Rakesh Kumar Pant, Hemlata J S University Department of Computer Science Shikohabad India Vbsp University Department of Computer Applications Jaunpur India Maharishi University of Information Technology Department of Computer Science and Engineering Lucknow India Ambalika Institute of Management and Technology Department of Information Technology Lucknow India

ISBN: (纸本)9789380544472

Globally, cardiovascular disease is the leading cause of death. In CVDs, the heart is a vital organ for supplying blood to other body organs. Complex datasets with heart problems are predicted by machine learning methods. Analysts and experts in diagnosis can easily locate reports of data on CVDs at the primary level. In the study, we investigate the performance of neural networks when used on datasets for CVD prediction. The performance of the method is assessed based on the characteristics of the dataset using Random Forest and Extra Tree feature selection strategies with the following basic analyses: accuracy score, precision, recall, f1-score, and roc. Our study highlights the critical properties of CVD datasets in order to determine which accuracy measures work best for predicting and diagnosing CVD. Our main findings were predictive Neural Network performance with an accuracy metric based on Random Forest and Extra Tree, highlighting key features for CVD diagnosis and prediction. Finally, a neural network using the Extra Tree features selection strategy achieved 97% accuracy, while a neural network using Random Forest achieved 98% accuracy. These can be effectively used to improve current CVD diagnosis efforts and help patients all around the world. Especially in places where there is a scarcity of medical professionals. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Forecasting

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