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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Xiusheng Duan Guohua Sun Jingya Cao Yuyang Zhang Jingchao Yang Duyu Han Department of Artificial Intelligence and Big Data Hebei Polytechnic Institute Shijiazhuang China Intelligent Application Technology Research and Development Center Hebei Provincial University Road Traffic Perception Shijiazhuang China School of Mechanical Engineering Shijiazhuang Tiedao University Shijiazhuang China Faculty of Robot Science and Engineering Northeastern University Shenyang China Hebei Jiaotong Vocational and Technical College Shijiazhuang China

In recent years, military science and technology has been developed rapidly, and new equipments has been equipped in the army. Augmented Reality (AR) technology provides the possibility to solve the problem of equipments operation training. But in the training process, One of the essential technologies is how to location and identify the operation keys. Obviously, the sample set of equipments operation keys is the basis of key recognition, and a certain scale of sample set is an indispensable element to ensure the performance of the recognition model. On the basis of manually establishing a small sample set, this paper puts forward a mechanism based on interactive automatic sample labeling and sample set expanding, the target recognition model was updated by incremental learning method at the same time.

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Mxenes as a versatile nanoplatform: Synthesis and emerging biomedical applications

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Journal of Industrial and engineering Chemistry 2025年 149卷 275-312页

作者： Amani, Ali Mohammad Vafa, Ehsan Mirzae, Maryam Abbasi, Milad Vaez, Ahmad Najdian, Atena Jahanbin, Alireza Kasaei, Seyed Reza Mosleh-Shirazi, Sareh Kamyab, Hesam Chelliapan, Shreeshivadasan Rajendran, Saravanan Guamán, Linda P. Department of Medical Nanotechnology School of Advanced Medical Sciences and Technologies Shiraz University of Medical Sciences Shiraz Iran Critical Care Nursing Nursing and Midwifery School Islamic Azad University Kazerun Branch Kazerun Iran Department of Tissue Engineering and Applied Cell Sciences School of Advanced Medical Sciences and Technologies Shiraz University of Medical Sciences Shiraz Iran The Persian Gulf Nuclear Medicine Research Center Bushehr Medical University Hospital School of Medicine Bushehr University of Medical Sciences Bushehr Iran Department of Materials Science and Engineering School of Engineering Shiraz University Zand Blvd. Shiraz*** Iran Shiraz Endocrinology and Metabolism Research Center Shiraz University of Medical Sciences Shiraz Iran Department of Materials Science and Engineering Shiraz University of Technology Shiraz Iran Department of Biomaterials Saveetha Dental College and Hospital Saveetha Institute of Medical and Technical Sciences Chennai600 077 India The KU-KIST Graduate School of Energy and Environment Korea University 145 Anam-ro Seongbuk-Gu Seoul02841 Korea Republic of Universidad UTE Quito170527 Ecuador Department of Smart Engineering and Advanced Technology Faculty of Artificial Intelligence Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia Instituto de Alta Investigacion Universidad de Tarapacá Arica1000000 Chile Quito170527 Ecuador

Recently, MXenes have garnered significant attention in academic research due to their remarkable structural, electrical, magnetic, optical, mechanical, and chemical properties. New advancements and emerging findings indicate that MXene, classified as a two-dimensional (2D) layered material, exhibits significantly more potential in the field of biomedicine and biotechnology compared to existing nanosystems. These applications include acting as antibacterial agents, biosensor systems, the delivery and loading of drugs, bioimaging, and therapeutic interventions. The unique characteristics of MXenes, such as their significant electrical conductivity, large surface area, low toxicity, magnetism, luminescence, and high biocompatibility, make them highly promising candidates for diagnostic, therapeutic, and theranostic applications. Surface modifications of MXenes exhibit biocompatibility and have multifunctional functions, including the ability to direct ligands towards specific spots for preferential aggregation, hence enabling their utilization in specialized applications. This paper provides an overview of the characteristics, modifications, and synthesis methods of MXene nanomaterials. The present article also delivers a comprehensive assessment of the practical uses of MXene-based nanomaterials in biomedicine, with a particular focus on biosensing, bioimaging, antibacterial effects, implants, and multifunctional therapeutic platforms. This paper also presents a discussion of the future prospects and challenges associated with the applications of MXenes in the biomedicine field. © 2025 The Korean Society of Industrial and engineering Chemistry

关键词： Biocompatibility

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FL-Tuning: Layer Tuning for Feed-Forward Network in Transformer

arXiv

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arXiv 2022年

作者： Liu, Jingping Song, Yuqiu Xue, Kui Sun, Hongli Wang, Chao Chen, Lihan Jiang, Haiyun Liang, Jiaqing Ruan, Tong School of Information Science and Engineering East China University of Science and Technology China Shanghai Artificial Intelligence Laboratory Shanghai China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Tencent AI Lab Shenzhen China

Prompt tuning is an emerging way of adapting pre-trained language models to downstream tasks. However, the existing studies are mainly to add prompts to the input sequence. This way would not work as expected due to the intermediate multi-head self-attention and feed-forward network computation, making model optimization not very smooth. Hence, we propose a novel tuning way called layer tuning, aiming to add learnable parameters in Transformer layers. Specifically, we focus on layer tuning for feed-forward network in the Transformer, namely FL-tuning. It introduces additional units into the hidden layer of each feed-forward network. We conduct extensive experiments on the public CLUE benchmark. The results show that: 1) Our FL-tuning outperforms prompt tuning methods under both full-data and few-shot settings in almost all cases. In particular, it improves accuracy by 17.93% (full-data setting) on WSC 1.0 and F1 by 16.142% (few-shot setting) on CLUENER over P-tuning v2. 2) Our FL-tuning is more stable and converges about 1.17 times faster than P-tuning v2. 3) With only about 3% of Transformer's parameters to be trained, FL-tuning is comparable with fine-tuning on most datasets, and significantly outperforms fine-tuning (e.g., accuracy improved by 12.9% on WSC 1.1) on several datasets. The source codes are available at https://***/genggui001/FL-Tuning. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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The Performance Analysis of Digital Signature Protocols for Enhancing Network Security

The Performance Analysis of Digital Signature Protocols for ...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： N.T. Velusudha Aparna A. Junnarkar Niyati Sharma Nitin N. Sakhare Dipannita Mondal N. V. Balaji Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune India Department of Management Mangalayatan University Aligarh India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Artificial Intelligence And Data Science Department Dr. D.Y. Patil College Of Engineering And Innovation Talegaon Department of Computer Science Karpagam Academy of Higher Education Coimbatore

Comprehensive investigation the observation of virtual signature protocols and their position in making sure of community safety. This paper analyzes the performance of diverse digital signature protocols, including ECDSA, RSA, and DSA, and examines how they can be applied to decorate network safety. The paper discusses the blessings of virtual signatures over traditional techniques, including hash functions and symmetric ciphers. The paper also explores the privacy issues arising during virtual signing and proposes ways to mitigate their outcomes. The authors also gift algorithms for verifying virtual signatures. Furthermore, the paper examines numerous simulated environments to analyze the performance of virtual signature protocols. The outcomes display that digital signature protocols can be used to enhance the safety of networks and applications. Via leveraging virtual signature protocols, its miles possible to acquire a more stage of network safety in comparison to traditional methods

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Intelligent Personalized Nutrition Guidance System Using IoT and Machine Learning Algorithm

Intelligent Personalized Nutrition Guidance System Using IoT...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： P. Santhuja Elangovan Guruva Reddy Subramani Roy Choudri M. Muthulekshmi S. Balaji Department of Computer Science Engineering CMR Technical Campus Hyderabad Telangana India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad Telangana India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Information Technology Sri Shanmugha College of Engineering and Technology Salem Tamil Nadu India

Nowadays, dietary issues are increasing around the world. Numerous problems, such as weight gain, obesity, diabetes, etc., can arise from an unbalanced diet. By integrating image processing, the system can assess food images in new ways to provide personalized solutions for better eating habits. The system uses machine learning, IoT, and image processing techniques to evaluate food image data and extract useful information. IoT devices like smartphones or specialized cameras take images of food and send to the cloud for analysis. The Support Vector Machine (SVM) is a machine learning technique that is integrated with Internet of Things (IoT) technology in this study to propose a novel method for creating a system that uses personalized nutritional suggestions. The SVM algorithm is then used to analyze and analyze this data to find patterns, correlations, and individual dietary requirements. A cloud database is used to process and store all the data related to food consumption. The system analyzes food images and determines the nutrition and caloric content of the food using image processing and segmentation. The system takes up new abilities using an extensive collection of dietary records that have been labeled and include details about the sorts of foods, portion sizes, and nutritional value. The system can predict and identify users' nutritional needs, deficits, and personalized dietary objectives by using this trained SVM model to their nutritional data.

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A Novel Design of Deep Learning Assisted Drug Recommendation Model using Sentimental Inspection

A Novel Design of Deep Learning Assisted Drug Recommendation...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： ShakthiMurugan K H C. Gnanaprakasam M. Swarna R. Geetha N V Krishnamoorthy Department of Electronics and Communication Engineering Narayana Engineering College Gudur AndhraPradesh India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai Tamilnadu India Department of Electronics and Communication Engineering Dr. M.G.R. Educational And Research Institute University Chennai Tamilnadu India Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu Chennai Department of Mechanical Engineering Sri Krishna College of Engineering and Technology Kuniyamuthur Coimbatore Tamilnadu India

The field of Natural Language Processing includes Sentiment Analysis as one of its core algorithms for identifying emotional tone in written content. As a result of the corona virus, many people have died, and the medical community as a whole is in shock. As a result of the shortage, many began self-medicating without first consulting with a professional, worsening their health. In recent years, machine learning has proven useful in a wide range of contexts, and novel efforts have been made to automate formerly laborious tasks. Our group decided to focus on studying medicine evaluations written by others, with each review including a star rating from 1 to 10. Since there are several reviews for pharmaceuticals that treat the same medical problem, we thought it would be interesting to see if the reviews for these ailments use the same terms or if the conditions' specific vocabulary choices affect the products' overall scores. To that end, we set out to construct supervised ML categorization techniques that can deduce the rating's category from the review's content. Our primary focus had been on implementing various embedding’s, including Term Frequency Inverse Document Frequency (TFIDF). Accuracy, recall, f1score, reliability, and AUC were used to assess the anticipated emotions.

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Impact of Language Guidance: A Reproducibility Study

arXiv

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arXiv 2025年

作者： Puniani, Cherish Sinha, Advika Singhi, Shree Yadav, Aayan Department of Mechanical Engineering Indian Institute of Technology Roorkee India Department of Data Science & Artificial Intelligence Indian Institute of Technology Roorkee India

Modern deep-learning architectures need large amounts of data to produce state-of-the-art results. Annotating such huge datasets is time-consuming, expensive, and prone to human error. Recent advances in self-supervised learning allow us to train huge models without explicit annotation. Contrastive learning is a popular paradigm in self-supervised learning. Recent works like SimCLR and CLIP rely on image augmentations or directly minimizing cross-modal loss between image and text. Banani et al. (2023) propose to use language guidance to sample view pairs. They claim that language enables better conceptual similarity, eliminating the effects of visual variability. We reproduce their experiments to verify their claims and find that their dataset, RedCaps, contains low-quality captions. We use an off-the-shelf image captioning model, BLIP-2, to replace the captions and improve performance, and we also devise a new metric to evaluate the semantic capabilities of self-supervised models based on interpretability methods. © 2025, CC BY.

关键词： Supervised learning

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Blister Package Classification Using ResNet-101 for Identification of Medication 25

Blister Package Classification Using ResNet-101 for Identifi...

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25th International Computer science and engineering Conference, ICSEC 2021

作者： Hnoohom, Narit Maitrichit, Nagorn Chotivatunyu, Pitchaya Sornlertlamvanich, Virach Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakorn Pathom Thailand Musashino University Faculty of Data Science Tokyo Japan University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665411974

This research aimed to fine-Tune image classification with deep learning techniques to verify the dispensing of prescriptions in hospitals. The proposed approach will be able to help pharmacies reduce the errors that lead to patients receiving the wrong medications. The image classification model uses a double-side transformed image dataset with download from Highlighted Deep Learning (HDL) paper. The dataset collected two-hundred seventy-Two images for types of medicine blister packs, including 72 images of front-side and backside merged with a horizontal cropped background, which were used for training the model. The blister package image dataset uses a deep learning model with a ResNet-101 pre-Trained model from the TensorFlow framework. The experimental results indicated that the TensorFlow framework achieved higher precision, recall, and F1-score than the Caffe framework. A ResNet-101 model with histogram equalization in the front and backside has the highest accuracy at 100 percent. © 2021 IEEE.

关键词： Image classification

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A Robust Blockchain Assisted Electronic Voting Mechanism with Enhanced Cyber Norms and Precautions

A Robust Blockchain Assisted Electronic Voting Mechanism wit...

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International Conference on science technology engineering and Management (ICONSTEM)

作者： N V Krishnamoorthy Shakthi Murugan K H C. Gnanaprakasam M. Swarna R. Geetha Department of Mechanical Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamilnadu India Department of Electronics and Communication Engineering Narayana Engineering College Gudur AndhraPradesh India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai Tamilnadu India Department of Electronics and Communication Engineering Dr. M.G.R. Educational And Research Institute University Chennai Tamilnadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai India

Voting is the primary right to every citizen and that should be loyal enough to select a correct leader to the respective country. Every person has the duty to choose the proper leader for their nation by exercising their fundamental right to vote. When voting at a polling place that uses paper ballots, citizens must be present in order to deposit their vote. The vast majority of people throughout the world has neglected this need and has avoided doing their civic responsibility. In these cases, electronic voting systems are generally viewed as a viable option. The challenge here is protecting voters' privacy and ensuring their votes get to the right people. This study presented Blockchain Assisted Enhanced Cyber Norms (BAECN), a revolutionary electronic voting system that ensures voters' ballots are cast accurately and without delay. One possible answer is Blockchain technology, which uses a distributed ledger in which many people share ownership of the data. The Bitcoin protocol, also referred as the decentralized Banking network, has incorporated Blockchain technology. To perform a transaction or carry out a transaction over the Blockchain network, gas is the charge that must be paid. One kind of voter fraud in electronic voting systems can be mitigated by the use of Blockchain: the redistribution of voter registration data. The proposed methodology, on the other hand, offers a revolutionary online voting mechanism built on Blockchain technology, which fixes all the issues that were found. This research describes the findings of a real-world use of Blockchain technology implementation of a smart-contracts application that increases election security while also lowering associated costs.

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Low-Light Enhancement Effect on Classification and Detection: An Empirical Study

arXiv

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arXiv 2024年

作者： Wu, Xu Lai, Zhihui Jie, Zhou Gao, Can Hou, Xianxu Zhang, Ya-Nan Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

Low-light images are commonly encountered in real-world scenarios, and numerous low-light image enhancement (LLIE) methods have been proposed to improve the visibility of these images. The primary goal of LLIE is to generate clearer images that are more visually pleasing to humans. However, the impact of LLIE methods in high-level vision tasks, such as image classification and object detection, which rely on high-quality image datasets, is not well explored. To explore the impact, we comprehensively evaluate LLIE methods on these high-level vision tasks by utilizing an empirical investigation comprising image classification and object detection experiments. The evaluation reveals a dichotomy: While Low-Light Image Enhancement (LLIE) methods enhance human visual interpretation, their effect on computer vision tasks is inconsistent and can sometimes be harmful. Our findings suggest a disconnect between image enhancement for human visual perception and for machine analysis, indicating a need for LLIE methods tailored to support high-level vision tasks effectively. This insight is crucial for the development of LLIE techniques that align with the needs of both human and machine vision. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine vision

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