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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： G. Arulkumaran Santhosh Jayagopalan Thanga Mariappan L P. Balamurugan School of Computing and Information Technology REVA University Bengaluru Department of Computing British Applied College Umm Al Quwain UAE Department of Smart Computing School of Information Technology and Engineering Vellore Institute of Technology Vellore Vellore Dist Tamil Nadu India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Dist Tamil Nadu India

One of the cornerstones of the future generation of computing is cloud computing, a form of internet-based computing. It allows for the instantaneous allocation of Internet-accessible resources whenever they are needed. Services such as software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) are made available to people and businesses through cloud computing. By giving customers a low-cost, scalable, and financially-independent place to store their data, cloud storage services have seen rapid revenue growth in recent years. An interoperable cloud computing system is built on open architectures and interfaces that support both private and public cloud services. Many businesses today are making the switch to the cloud because of the numerous advantages it offers in terms of data storage, retrieval, management, and availability. While cloud computing is receiving a lot of attention, it is imperative that security problems be addressed first. This study proposes a crypto-stegno mechanism in the cloud as a solution to cloud security problems. This study proposes a practical method for ensuring data privacy and security in cloud storage. In order to obtain their own public and private key, users must first register their information in the cloud and come up with a unique username and password. Cryptography, a means of concealing information and sending information in an unreadable format, is employed in the next level of security. Secure data transmission and storage on the web, electronic commerce, digital media privacy, and ATM transfer are all dependent on cryptography to some degree. Repudiation, integrity, authenticity, and confidentiality are all possible using today's cryptographic methods. Signcryption is a lightweight cryptographic system that combines encryption and signatures in a logical way to achieve higher security with less computing time.

关键词： Cloud computing Data privacy Technological innovation Software as a service Platform as a service Switches Signal processing

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Scalable Optimal Margin Distribution Machine

arXiv

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arXiv 2023年

作者： Wang, Yilin Cao, Nan Zhang, Teng Shi, Xuanhua Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

Optimal margin Distribution Machine (ODM) is a newly proposed statistical learning framework rooting in the latest margin theory, which demonstrates better generalization performance than the traditional large margin based counterparts. However, it suffers from the ubiquitous scalability problem regarding both computation time and memory storage as other kernel methods. This paper proposes a scalable ODM, which can achieve nearly ten times speedup compared to the original ODM training method. For nonlinear kernels, we put forward a novel distribution-aware partition method to make the local ODM trained on each partition be close and converge fast to the global one. When linear kernel is applied, we extend a communication efficient SVRG method to accelerate the training further. Extensive empirical studies validate that our proposed method is highly computational efficient and almost never worsen the generalization. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Improving Emergency Departments: Simulation-Based Optimization of Patients Waiting Time and the Number of Staff Present in A Hospital 2

Improving Emergency Departments: Simulation-Based Optimizati...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Attar, Hani Solyman, Ahmed Sayed, Rania A. Mohamed, Dana M. Hafez, Mohamed Deif, Mohanad A. Zarqa University Faculty of Engineering Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow United Kingdom University Department of Bioelectronics Cairo Egypt INTI-IU-University Faculty of Engineering Feqs Nilai Malaysia Department of Artificial Intelligence 12566 Egypt

ISBN: (纸本)9798350373363

The emergency department (ED), operating around the clock every day of the year, serves a diverse range of patients with varying medical conditions, making it the vital core of a hospital. Consequently, optimizing and simulating the ED's processes becomes essential to enhance the quality of care provided. This study offers a case analysis employing simulation to assess patient flows in a hospital's emergency department. Our objective is to evaluate the impacts of system enhancements within the ED. This model aims to measure patients' time from their ED entry, determine daily patient numbers, and calculate the overall patient movement time within the department. If the patient's condition is serious, he will be presented immediately to the doctor without waiting. A doctor will be added to the unit if the number of patients exceeds the standard limit. © 2023 IEEE.

关键词： Optimization

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Multi-Granular Multimodal Clue Fusion for Meme Understanding 39

Multi-Granular Multimodal Clue Fusion for Meme Understanding

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zheng, Li Fei, Hao Dai, Ting Peng, Zuquan Li, Fei Ma, Huisheng Teng, Chong Ji, Donghong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China National University of Singapore Singapore Singapore Laboratory for Advanced Computing and Intelligence Engineering Wuxi China North China Institute of Computing Technology beijing China

ISBN: (纸本)157735897X

With the continuous emergence of various social media platforms frequently used in daily life, the multimodal meme understanding (MMU) task has been garnering increasing attention. MMU aims to explore and comprehend the meanings of memes from various perspectives by performing tasks such as metaphor recognition, sentiment analysis, intention detection, and offensiveness detection. Despite making progress, limitations persist due to the loss of fine-grained metaphorical visual clue and the neglect of multimodal text-image weak correlation. To overcome these limitations, we propose a multi-granular multimodal clue fusion model (MGMCF) to advance MMU. Firstly, we design an object-level semantic mining module to extract object-level image feature clues, achieving fine-grained feature clue extraction and enhancing the model’s ability to capture metaphorical details and semantics. Secondly, we propose a brand-new global-local cross-modal interaction model to address the weak correlation between text and images. This model facilitates effective interaction between global multimodal contextual clues and local unimodal feature clues, strengthening their representations through a bidirectional cross-modal attention mechanism. Finally, we devise a dual-semantic guided training strategy to enhance the model’s understanding and alignment of multimodal representations in the semantic space. Experiments conducted on the widely-used MET-MEME bilingual dataset demonstrate significant improvements over state-of-the-art baselines. Specifically, there is an 8.14% increase in precision for offensiveness detection task, and respective accuracy enhancements of 3.53%, 3.89%, and 3.52% for metaphor recognition, sentiment analysis, and intention detection tasks. These results, underpinned by in-depth analyses, underscore the effectiveness and potential of our approach for advancing MMU. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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Joint Automatic Speech Recognition And Structure Learning For Better Speech Understanding

Joint Automatic Speech Recognition And Structure Learning Fo...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hu, Jiliang Li, Zuchao Shen, Mengjia Ai, Haojun Li, Sheng Zhang, Jun Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China National Institute of Information and Communications Technology Japan

ISBN: (纸本)9798350368741

Spoken language understanding (SLU) is a structure prediction task in the field of speech. Recently, many works on SLU that treat it as a sequence-to-sequence task have achieved great success. However, This method is not suitable for simultaneous speech recognition and understanding. In this paper, we propose a joint speech recognition and structure learning framework (JSRSL), an end-to-end SLU model based on span, which can accurately transcribe speech and extract structured content simultaneously. We conduct experiments on name entity recognition and intent classification using the Chinese dataset AISHELL-NER and the English dataset SLURP. The results show that our proposed method not only outperforms the traditional sequence-to-sequence method in both transcription and extraction capabilities but also achieves state-of-the-art performance on the two datasets. © 2025 IEEE.

关键词： Information Extraction Speech Recognition Spoken Language Understanding

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High accurate gold prize rate prediction using random forest regression algorithm

High accurate gold prize rate prediction using random forest...

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7th IET Smart Cities Symposium (SCS 2023)

作者： A. Sivasangari R. Deepa K. Geetha Rani R. Surendran T. Tamilvizhi School of Computing Sathyabama Institute of Science and Technology Chennai India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering Jain (Deemed-to-be University) Bangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai 600123 India

In the past, one of the precious resources utilized to fund trading purchases was gold. The nations that have gold resources are regarded as rich countries. The nation's gold reserves show how strong its economy is. Not only have government departments participated in gold reserves, but also a number of private individuals and businesses. The routine of the world's top financial prudence affects gold rates. An increase in investors has been drawn to gold due to the recent fluctuations in price. Transactions involving gold are risky, and price fluctuations in gold are unpredictable due to the unpredictability of the gold market. The suggested uses machine learning to forecast the price of gold. Generally, machine learning is used to predict economic variables. To train a prediction model, we use supervised learning algorithms such as multiple linear regression and random forest regression.

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K∞ Function-Based Consensus Tracking Control for Multi-Agent Systems With Unknown Disturbances

K∞ Function-Based Consensus Tracking Control for Multi-Agen...

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Annual Conference of Industrial Electronics Society

作者： Boda Ning Qing-Long Han Centre for Sustainable Infrastructure and Digital Construction Swinburne University of Technology Melbourne Australia School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

In this paper, consensus tracking is investigated for multi-agent systems (MASs) with unknown disturbances. A K ∞ function-based control strategy is proposed to guarantee that the tracking errors between each follower and the leader are converged to a small neighborhood of zero in finite-time. To be more specific, by introducing an error auxiliary considering local interactions, a K ∞ function-based adaptive controller is designed such that the error auxiliary is constrained to an adjustable region. Then, through a matrix-based rigorous proof, practical consensus tracking is realized in finite-time for MASs. A distinguished feature of the K ∞ function-based control is its independence on the use of the bound information of unknown disturbances. Another advantage of the proposed controller is its capability of alleviating the input chattering issue. Finally, numerical examples are presented to validate the effectiveness of the K ∞ function-based controller.

关键词： Industrial electronics Control systems Multi-agent systems

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Machine Learning Heart Disease Prediction Using KNN and RTC Algorithm 3

Machine Learning Heart Disease Prediction Using KNN and RTC ...

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3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Senthil, G.A. Prabha, R. Razmah, M. Veeramakali, T. Sridevi, S. Yashini, R. Agni College of Technology Dept of Information Technology Chennai India Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Chennai India School of Computing SRM Institute of Science and Technology Dept of Data Science and Business Systems Chennai India Sri Ramachandra Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665462754

Heart Disease or cardiovascular disease refers to the range of heart conditions like cardiac arrest, coronary artery disease. Heart disease can be very well hindered through certain lifestyle changes. There is a significant increase in the mortality rate recently due to the distinctive heart diseases. Machine learning uses mathematical models to work efficiently with the enormous amount of data. It plays a crucial role in medical science in the prediction of distinctive diseases. Cardiologists inspects the heart functionality using electrocardiography, computed tomography. These tests are quite expensive for a common man. Recent times, the life span of a human is guaranteed only with the support of medications. As prevention is better than the cure, machine learning helps to predict the vulnerability of a heart disease with few elemental symptoms and health factors. It is been fed by the basic data of the patients like age, sex. Machine learning helps to predict the vulnerability in advance which provides the cardiologists with great acumen for the adaption of the treatment. Machine learning algorithms have proven to produce reliable and accurate output with the help of the inputs. The algorithms used in the article include KNN and decision tree classifier which is compared to yield the desired and efficient output. © 2022 IEEE.

关键词： Heart

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Multi-Module Deep Learning and IoT-Based Pest Detection System Using Sound Analytics in Large Agricultural Field

Multi-Module Deep Learning and IoT-Based Pest Detection Syst...

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2023 IEEE International Conference on Electrical, Electronics, Communication and Computers, ELEXCOM 2023

作者： Ali, Md. Akkas Dhanaraj, Rajesh Kumar Sharma, Anupam Kumar Balusamy, Balamurugan Geetha, V. Grover, Veena School of Computing Science and Engineering Galgotias University U.P Greater Noida India Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj Bangladesh Pune India Shiv Nadar University India School of Applied Sciences Reva University Dept. of Computer Science Bangalore India Noida Institute of Engineering and Technology U.P Greater Noida India

ISBN: (纸本)9798350305111

Agriculture has progressed to an advanced stage worldwide and into an economical source of sophisticated agricultural practices. It is a significant milestone in the history of agriculture. The broad embrace of information and today's advanced technology has paved the way for this evolution. But pests cause substantial harm to farming, which has serious financial, ecological, and societal repercussions. That emphasizes the significance of computerized systems for early pest detection. Agricultural issues have recently emerged as a significant focus for machine learning-based research. This study offers an efficient Internet of Things (IoT)-enabled pest detection system that uses pest sound analytics in large agricultural fields. The system is built on a model called a Graph-based Pyramid Attention-multi module deep convolutional neural network (GPA-Net). The GPA-Net platform comprises a variety of essential subcomponents, a couple of which are the CSPNet, CTA, and SPA. The GPA-Net model has been trained using the sounds of the pests. The outcomes of the demonstrations revealed that the recommended GPA-Net model successfully identified the pest and outscored the state-of-the-art models YOLOv5, Faster RCNN, and ResNet-50 with 99.75% accuracy and an F1 score of 98.99%. © 2023 IEEE.

关键词： Internet of things

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Semi-Supervised Framework for Dual Encoder Attention Network: Classification of Retinopathy in Optical Coherence Tomography Images

Semi-Supervised Framework for Dual Encoder Attention Network...

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International Conference on Computer and Automation engineering, ICCAE

作者： Huaqiao Qiu Jin Zheng School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350370058

ISBN: (纸本)9798350370164

Due to the high inter-class similarity and intra-class differences in retinal optical coherence tomography (OCT) images, subtle pathological attributes can serve as important dis-criminative clues, and semi-supervised medical image tasks still face challenges. To address this challenge, a new semi-supervised framework for medical image classification is proposed. This method introduces a deep feature fusion classification method based on dual encoders. The different branches of the encoder can extract different feature information, and their features are fused to achieve information complementarity, so that the model can learn subtle pathological attributes in medical images. To improve the model's discrimination and classification performance, an adaptive attention (AA) mechanism is introduced to capture key regions. The proposed method is evaluated on a public OCT dataset. Only 10% of labeled data is used for training, achieving an accuracy of 96.02 %, which is equivalent to the performance of fully supervised models trained with 100% labeled data. Experimental results show that the performance of this method is better than that of supervised benchmark methods and other semi-supervised methods.

关键词： Adaptation models Pathology Optical coherence tomography Semisupervised learning Feature extraction Retina Data models

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