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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： M.K. Srinath N. Lakshmi Deepthi Ginni Nijhawan Nadimpalli Venkata Ganapathi Raju Taqi Mohammed Khattab Al-Rubaye Neeraj Chahuan Department of Mechanical Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India Department of Medical Laboratory Technology College of Medical Technologies The Islamic University Najaf Iraq Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

An online digital twin for controlling a clamping system used in composite component machining is introduced in this study. It is essential to support significant and freeform composite pieces so they do not flex while drilled. If the component is bent, it could tear the margins of the drilled holes and cause delamination. To prevent bending during drilling, the latest active clamping technology enables the realization of a stable fixture specific to the region. The quality of the holes that are drilled is greatly enhanced by this. An Internet of Things (IoT) edging device, capable of bidirectional connection with the cloud, is included inside the clamping device. The digital twin in the cloud uses computer vision to assess the drilled hole quality, track drill wear, and flag improper active clamping device functioning. To learn more about how the drill with the active clamping device works, all the data is saved in the cloud. The whole rollout takes place in the cloud on Microsoft Azure. This makes the regular machine compatible with Industry 4.0 standards.

关键词： Drilling Cloud computing computer vision Force computer architecture Digital twins Fourth Industrial Revolution Clamps Internet of Things Monitoring

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LLMRec: Benchmarking Large Language Models on Recommendation Task

arXiv

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arXiv 2023年

作者： Liu, Junling Liu, Chao Zhou, Peilin Ye, Qichen Chong, Dading Zhou, Kang Xie, Yueqi Cao, Yuwei Wang, Shoujin You, Chenyu Yu, Philip S. Alibaba Group China Ant Group China The Hong Kong University of Science and Technology Guangzhou China School of Electronic and Computer Engineering Peking University China The Hong Kong University of Science and Technology Hong Kong Data Science Institute University of Technology Sydney Australia Department of Electrical Engineering Yale University United States Department of Computer Science University of Illinois Chicago United States

Recently, the fast development of Large Language Models (LLMs) such as ChatGPT has significantly advanced NLP tasks by enhancing the capabilities of conversational models. However, the application of LLMs in the recommendation domain has not been thoroughly investigated. To bridge this gap, we propose LLMRec, a LLM-based recommender system designed for benchmarking LLMs on various recommendation tasks. Specifically, we benchmark several popular off-the-shelf LLMs, such as ChatGPT, LLaMA, ChatGLM, on five recommendation tasks, including rating prediction, sequential recommendation, direct recommendation, explanation generation, and review summarization. Furthermore, we investigate the effectiveness of supervised finetuning to improve LLMs' instruction compliance ability. The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation. However, they demonstrated comparable performance to state-of-the-art methods in explainability-based tasks. We also conduct qualitative evaluations to further evaluate the quality of contents generated by different models, and the results show that LLMs can truly understand the provided information and generate clearer and more reasonable results. We aspire that this benchmark will serve as an inspiration for researchers to delve deeper into the potential of LLMs in enhancing recommendation performance. Our codes, processed data and benchmark results are available at https://***/williamliujl/LLMRec. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Feed Two Birds with One Scone: Exploiting Wild data for Both Out-of-Distribution Generalization and Detection

arXiv

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arXiv 2023年

作者： Bai, Haoyue Canal, Gregory Du, Xuefeng Kwon, Jeongyeol Nowak, Robert Li, Yixuan Department of Computer Sciences University of Wisconsin Madison United States Institute for Foundations of Data Science University of Wisconsin Madison United States Department of Electrical and Computer Engineering University of Wisconsin Madison United States

Modern machine learning models deployed in the wild can encounter both covariate and semantic shifts, giving rise to the problems of out-of-distribution (OOD) generalization and OOD detection respectively. While both problems have received significant research attention lately, they have been pursued independently. This may not be surprising, since the two tasks have seemingly conflicting goals. This paper provides a new unified approach that is capable of simultaneously generalizing to covariate shifts while robustly detecting semantic shifts. We propose a margin-based learning framework that exploits freely available unlabeled data in the wild that captures the environmental test-time OOD distributions under both covariate and semantic shifts. We show both empirically and theoretically that the proposed margin constraint is the key to achieving both OOD generalization and detection. Extensive experiments show the superiority of our framework, outperforming competitive baselines that specialize in either OOD generalization or OOD detection. Code is publicly available at https://***/deeplearning-wisc/scone. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Advanced Dual Module Weapon Detection for Public Safety and Surveillance System

Advanced Dual Module Weapon Detection for Public Safety and ...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Manikanta Sirigineedi M. Sowmiya HemavathI. U V.C. Bharathi Sayali S Karmode R. Sathya Department of Information Technology Vishnu Institute of Technology Vishnupur Bhimavaram Andhra Pradesh India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology AVADI School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India Information Technology Mahatma Gandhi Mission's College of Engineering & Technology Kamothe Navi Mumbai Maharashtra India Department of IT Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This research presents a novel weapon detection system that combines state-of-the-art technologies for enhanced security. The system integrates a Convolutional Neural Network (CNN) for precise image-based weapon classification and the YOLO algorithm for real-time detection in live camera feeds. The proposed system generates email alerts to promptly inform users of detected weapons, enabling timely action to mitigate risks. By combining these advanced modules, the proposed system offers an effective solution for addressing weapon-related violence and improving security measures in various environments.

关键词： YOLO Weapons Surveillance Real-time systems Public security Classification algorithms Security Convolutional neural networks Feeds Expert systems

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Joint Beam Scheduling and Power Allocation for SWIPT in Mixed Near- and Far-Field Channels

arXiv

引用

arXiv 2023年

作者： Zhang, Yunpu You, Changsheng Yuan, Weijie Liu, Fan Zhang, Rui Department of Electronic and Electrical Engineering Southern University of Science and Technologyy Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore

Extremely large-scale array (XL-array) has emerged as a promising technology to enhance the spectrum efficiency and spatial resolution in future wireless networks, leading to a fundamental paradigm shift from conventional far-field communications towards the new near-field communications. Different from the existing works that mostly considered simultaneous wireless information and power transfer (SWIPT) in the far field, we consider in this paper a new and practical scenario, called mixed near- and far-field SWIPT, in which energy harvesting (EH) and information decoding (ID) receivers are located in the near- and far-field regions of the XL-array base station (BS), respectively. Specifically, we formulate an optimization problem to maximize the weighted sum-power harvested at all EH receivers by jointly designing the BS beam scheduling and power allocation, under the constraints on the ID sum-rate and BS transmit power. To solve this non-convex optimization problem, an efficient algorithm is proposed to obtain a suboptimal solution by leveraging the binary variable elimination and successive convex approximation methods. Numerical results demonstrate that our proposed joint design achieves substantial Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

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Optimizing Crop Spraying with Smart Drones using Deep Neural Networks and Image Processing

Optimizing Crop Spraying with Smart Drones using Deep Neural...

引用

Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Shanmuga Priya S Piyush Charan V. M. Revathi Swapna Choudhary T. Prabakaran P K Sudhakar Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai India Department of Electronics and Communication Engineering Manav Rachna University Faridabad Haryana India Department of Mathematics Bannari Amman Institute of Technology Tamilnadu India Department of Electronics & Telecommunication Engineering G H Raisoni College of Engineering Nagpur Maharashtra India Department of Computer Science and Engineering Joginpally B.R. Engineering College Hyderabad Telangana India Department of Mathematics Rajalakshmi Institute of Technology Tamilnadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Due to climate change, environmental issues, and the necessity to nourish a growing human population, intelligent drones for agricultural spraying are gaining popularity. The utilisation of IoT smart drones in agriculture enhances food security by facilitating more precise operations, optimising resource utilisation, and reducing environmental impact. The proposed process consists of three steps: data preparation, feature extraction, and model training. Agricultural images are subjected to scaling, denoising, augmentation, and background elimination in the preprocessing phase. Significant attributes such as hue, shape, density edge, and HOC are identified by the feature extraction module. The study employs an ABiLSTM model that utilises historical data to determine optimal timings for crop spraying, hence enhancing prediction accuracy. The results indicate that the ABiLSTM model effectively addresses challenges in complex feature classification and prediction, achieving an impressive 95.62% accuracy rate. This study illustrates how AI-powered smart drones may transform precision agriculture, offering a sustainable and economical solution to the issues encountered by modern farmers. This research establishes a foundation for future AI-driven agricultural techniques that can enhance crop management through deep learning and the Internet of Things. This will assist farmers in fulfilling future food supply demands.

关键词： Training Accuracy Noise reduction Crops Spraying Predictive models Feature extraction data models Drones Climate change Artificial neural networks

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Using Meta-Heuristic Methods to Detect Botnet Attacks in Internet of Things

SSRN

引用

SSRN 2023年

作者： Mardani, Shayan Boroujeni, Farsad Zamani Akbari, Mehdi Department of Engineering Sepahan Institute of Higher Education Science and Technology Iran Department of Computer Engineering Islamic Azad University Science and Research Branch Tehran Iran Faculty of Computer Engineering Islamic Azad University Najafabad Branch Najafabad Iran Big Data Research Center Islamic Azad University Najafabad Branch Najafabad Iran

The need for timely detection of botnet attacks due to the increase in the number of Internet of Things devices is very necessary to minimize security risks and ensure network security. Recently, botnet attack detection methods are based on artificial intelligence techniques;And in order to deal with the growing dimensions of Internet of Things data, they have used various filtering and wrapping feature selection methods. In this paper, for the challenge of IoT data dimensions in detecting botnet attacks, a comparison has been made between 7 different old and new meta-heuristic methods including PSO, Giza, RSO, Firefly, PEC, BAT and GWO. In this model, decision tree algorithm is used to detect attacks. The experimental results of the methods on two data sets IOT-BotNet and N-BaIoT show that all meta-heuristic methods in binary classification have similar performance and have been able to achieve accuracy above 99%. In detecting the type of attacks (multi-class classification), the results show that the best performance belongs to RSO, Giza, which reached an accuracy of over 99% and are among the latest meta-heuristic methods. The results of the methods show that meta-heuristic feature selection algorithms can effectively reduce data dimensions;So that the accuracy of the proposed attack detection model has increased compared to previous methods. © 2023, The Authors. All rights reserved.

关键词： Botnet

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GPR-SCSANet: Unequal-Length Time Series Normalization with Split-Channel Residual Convolution and Self-Attention for Brain Age Prediction

GPR-SCSANet: Unequal-Length Time Series Normalization with S...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Fangling Sun Chuang Liang Tülay Adali Daoqiang Zhang Rongtao Jiang Vince D. Calhoun Shile Qi Department of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA Department of Radiology and Biomedical Imaging Yale School of Medicine New Haven USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Functional magnetic resonance imaging (fMRI), as a non-invasive method to reveal brain function alterations, frequently yields time series with unequal lengths in real-world scenarios, which may arise from factors such as motion artifacts, participant state, and differing scan protocols. This variability conflicts with the traditional methods relying on isometric inputs, which poses a significant challenge for the downstream applications such as brain age prediction. To address this challenge, we introduced Gaussian Process Regression (GPR) to normalize the length of time series and proposed split-channel residual convolution (SC) and self-attention mechanisms (SA) to perform brain age estimation, called GPR-SCSANet. Results showed that the proposed framework, GPR-SCSANet, is able to fully utilize the inherent information and learn richer feature representations from unequal-length fMRI time courses, which significantly improved the prediction accuracy across 3 brain atlases and 5 prediction models. The results demonstrated the effectiveness and robustness of the proposed GPR-SCSANet, showcasing the potential for broader applications in brain age prediction task.

关键词： Accuracy Protocols Convolution Time series analysis Predictive models Functional magnetic resonance imaging Brain modeling Robustness data models Stability analysis

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A Machine Learning Model for Augmenting the Media Accessibility for the Disabled People

A Machine Learning Model for Augmenting the Media Accessibil...

引用

Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Hari Krishnan Andi S. Senbagam Dilip Kumar Sharma Mayura Shelke N K Senthil Kumar Ramesh Chandra Poonia Centre for Postgraduate Studies Asia Metropolitan University Malaysia Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram Tamil Nandu India Department of Mathematics Jaypee University of Engineering and Technology Guna Madhya Pradesh India Department of Artificial Intelligence and Data Science AISSMS IOIT Pune India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University) Chennai Tamil Nadu India Department of Computer Science CHRIST (Deemed to be University) Delhi-NCR Ghaziabad Uttar Pradesh India

In an era characterized by the proliferation of digital media, the need to efficiently use multimedia content has become paramount. This article discusses an innovative technique called “Fast Captioning (FC)” to improve media accessibility, especially for people with disabilities and others with time restrictions. Modern Machine Learning (ML) algorithms are incorporated into the framework, which speeds up video consumption while maintaining content coherence. The procedure includes extracting complex features like Word2Vec embeddings, part-of-speech tags, named entities, and syntactic relationships. Using annotated data, a ML model is trained to forecast semantic similarity scores between words and frames. The predicted scores seamlessly integrate into equations that calculate similarity, thus enhancing content comprehension. Through this all-encompassing approach, the article offers a comprehensive solution that balances the requirements of contemporary media with the accessibility requirements of people with disabilities, producing a more inclusive digital environment. Machine Learning-based Media Augmentation (ML-MA) has achieved the highest accuracy of 96%, and the captioning is accurate.

关键词：

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Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning

引用

Procedia computer science 2023年 216卷 57-66页

作者： Mira Hayati Kahlil Muchtar Roslidar Novi Maulina Irfan Syamsuddin Gregorius Natanael Elwirehardja Bens Pardamean Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh 23111 Indonesia Telematics Research Center (TRC) Universitas Syiah Kuala Banda Aceh 23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar 90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-aided diagnosis of diabetic retinopathy.

关键词： Diabetic retinopathy Deep Learning CNN Image Enhancement CLAHE

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