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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Tianyang Li Fan Zhang Xitian Fan Jianliang Shen Wei Guo Wei Cao Information Engineering University Zhengzhou China National Digital Switching System Engineering and Technological R&D Center Zhengzhou China Shanghai HONGZHEN Information Science & Technology Corporation Shanghai China Institute for Big Data Fudan University Shanghai China

Many models combining Transformers with convolutional neural networks (CNNs) for computer vision tasks have achieved state-of-the-art results. However, due to the different computation patterns between attention and convolution, using a dedicated Transformer or CNN accelerator will inevitably reduce the computing efficiency of the other. To overcome this problem, we propose a unified architecture for attention and convolution on FPGA. We reduce runtime overhead by offloading part of self-attention computations offline before inference. Furthermore, we present a unified mapping method according to the computing characteristics of attention-based and convolution-based models. This accelerator implements multi-head attention in Transformer, independent resNet-50 and hybrid blocks of attention and con-volution in BoTNet-50 at 200MHz on Xilinx Virtex Ultrascale+ XCVU37P. Experimental results show that the solution is nearly 3.62 times more energy-efficient than the NVIdIA V100 GPU, and the computational efficiency is 11.86% and 28.29% higher than the state-of-the-art Transformer and resNet-50 accelerators, respectively.

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dTrAN: A Special Use Case of rAN Optimization using digital Twin

arXiv

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arXiv 2024年

作者： Tunc, Caglar duran, Kubra Bilgin, Buse Kalem, Gökhan Canberk, Berk Next-Gen R&D/6GEN Lab Turkcell Istanbul Turkey School of Computing Engineering and The Built Environment Edinburgh Napier University United Kingdom Next-Gen R&D Turkcell Technology Istanbul Turkey BTS Group Istanbul Turkey Department of Artificial Intelligence and Data Engineering Istanbul Technical University Turkey

The emergence of beyond 5G (B5G) and 6G networks underscores the critical role of advanced computer-aided tools, such as network digital twins (dTs), in fostering autonomous networks and ubiquitous intelligence. Existing solutions in the dT domain primarily aim to model and automate specific tasks within the network lifecycle, which lack flexibility and adaptability for fully autonomous design and management. Unlike the existing dT approaches, we propose rAN optimization using the digital Twin (dTrAN) framework that follows a holistic approach from core to edge networks. The proposed dTrAN framework enables real-time data management and communication with the physical network, which provides a more accurate and detailed digital replica than the existing approaches. We outline the main building blocks of the dTrAN and describe the details of our specific use case, which is rAN configuration optimization, to demonstrate the applicability of the proposed framework for a real-world scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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Automated Injury detection and Alert Systems in Public Transportation Integrating IoT with Convolutional Neural Networks 2

Automated Injury Detection and Alert Systems in Public Trans...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Vimal, S.P. Peter, John Benito Jesudasan Kavitha, U. Bhuvaneswari, G. Manikandan, G. Thamizhamuthu, r. Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Capital One Services Llc RichmondVA United States Vel Tech RangarajanDr.Sagunthala R&d Institute of Science and Technology School of Commerce Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Rmk Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

Innovative methods to improve public transportation safety and response mechanisms have emerged due to Internet of Things (IoT) and artificial intelligence (AI) technologies. IoT devices and Convolutional Neural Networks (CNNs), a class of deep learning algorithms analyzing visual imagery, are used to create an innovative Automated Injury detection and Alert System (AIdAS) for public transportation. The proposed AIdAS architecture employs IoT sensors and cameras in buses and trains to monitor and evaluate passengers' physical health in real time. These sensors can detect unexpected impacts, odd motions, and distressing noises. A strong CNN model trained to detect passenger injury or bodily damage receives live images from the onboard cameras. An automatic alarm process is activated immediately upon injury detection. The closest emergency response teams and transportation system operators get complete event reports, including time, position (by GPS coordinates), and injury type. A quick reaction to accidents with this real-time alarm system might save lives and reduce public transportation injuries. AIdAS' use of IoT and CNN technology is innovative in public transportation safety. The technology significantly improved emergency response times and reduced unattended injuries in public transport networks. The system's excellent damage detection accuracy is due to CNNs' advanced image processing and pattern recognition skills and IoT devices' sensory inputs. It also addresses privacy issues and the necessity for strong data encryption and proposes solutions. Finally, it analyzes how AIdAS affects public transportation safety and future research and development in this critical field. © 2024 IEEE.

关键词： Convolutional neural networks

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Modeling PrOTAC degradation Activity with Machine Learning

arXiv

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arXiv 2024年

作者： ribes, Stefano Nittinger, Eva Tyrchan, Christian Mercado, rocío Department of Computer Science and Engineering Section for Data Science and AI Chalmers University of Technology Chalmersplatsen 4 Gothenburg412 96 Sweden BioPharmaceuticals R&D AstraZeneca Pepparedsleden 1 Mölndal431 83 Sweden

PrOTACs are a promising therapeutic modality that harnesses the cell’s built-in degradation machinery to degrade specific proteins. despite their potential, developing new PrOTACs is challenging and requires significant domain expertise, time, and cost. Meanwhile, machine learning has transformed drug design and development. In this work, we present a strategy for curating open-source PrOTAC data and an open-source deep learning tool for predicting the degradation activity of novel PrOTAC molecules. The curated dataset incorporates important information such as pdC50 , dmax , E3 ligase type, POI amino acid sequence, and experimental cell type. Our model architecture leverages learned embeddings from pretrained machine learning models, in particular for encoding protein sequences and cell type information. We assessed the quality of the curated data and the generalization ability of our model architecture against new PrOTACs and targets via three tailored studies, which we recommend other researchers to use in evaluating their degradation activity models. In each study, three models predict protein degradation in a majority vote setting, reaching a top test accuracy of 80.8% and 0.865 rOC AUC, and a test accuracy of 62.3% and 0.604 rOC AUC when generalizing to novel protein targets. Our results are not only comparable to state-of-the-art models for protein degradation prediction, but also part of an open-source implementation which is easily reproducible and less computationally complex than existing approaches. © 2024, CC BY.

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Hybrid Visual Verification

Hybrid Visual Verification

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International Conference on Confluence The Next Generation Information technology Summit (Confluence)

作者： S.Nikkath Bushra A. Syed Ismail P. Sasigresa Edwin Singh C Department of Computing Technologies SRM Institute of Scienece and Technology KattankulathurChengalpattu India Department of Data Science and Business Systems SRM Institute of Scienece and Technology KattankulathurChengalpattu India Department: Computer Science and Engineering Veltech University Department: Computer Science and Engineering Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

data security is becoming increasingly important as cloud computing advances. data security is the fundamental problem of all distributed computing systems. Cloud computing enables access to distributed applications and services belonging to various organizations spread across different locations. Cloud computing raises security concerns, because it shares data with distributed users via network in an open terrain. In this system, some of the most important security services like image-based security are handled by the cloud computing system. The proposed scheme takes care of several attacks like botnets, malware and guessing attacks. This scheme is contrasted withseveral established schemesand a new form of authentication is used for the purpose of storing files in a secure manner.

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Global visibility of publications through digital Object Identifiers

arXiv

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arXiv 2023年

作者： Turki, Houcemeddine Fraumann, Grischa Taieb, Mohamed Ali Hadj Aouicha, Mohamed Ben Data Engineering and Semantics Research Unit Faculty of Sciences of Sfax University of Sfax Sfax Tunisia R&D Department TIB – Leibniz Information Centre for Science and Technology Hannover Germany

This brief research report analyzes the availability of digital Object Identifiers (dOIs) worldwide, highlighting the dominance of large publishing houses and the need for unique persistent identifiers to increase the visibility of publications from developing countries. The study reveals that a considerable amount of publications from developing countries are excluded from the global flow of scientific information due to the absence of dOIs, emphasizing the need for alternative publishing models. The authors suggest that the availability of dOIs should receive more attention in scholarly communication and scientometrics, contributing to a necessary debate on dOIs relevant for librarians, publishers, and scientometricians. © 2023, CC BY.

关键词： developing countries

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Vehicle Prediction for BUS Identification Output from our route Testing real Time Algorithm

Vehicle Prediction for BUS Identification Output from our Ro...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： M. Misba r. ramya Joel dickson L. Sharmila J. Kavitha K. Udayakumar Department of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Artificial Intelligence and Data Science St. Joseph's College of Engineering Chennai Bethlehem Institute of Engineering Karungal Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Traffic generation in Indian cities is of a mixed nature, comprising over a dozen types of vehicles that can be broadly categorized into slow-moving and fast-moving vehicles. This study highlights the challenges associated with the inefficient operation of the existing bus system. GPS and mobile networks form integral components of the current system, enabling data storage and management using PostgreSQL. The system provides robust and reliable data to fulfill requests from other software applications. Operating on cloud platforms, it is compatible with all major web browsers. The proposed algorithm continuously tracks the real-time location of buses and their corresponding WMS (Web Map Service) location, alerting users through marker notifications. This paper also explores measures to evaluate the efficiency and effectiveness of bus services. recognizing that an inadequate public transport system impedes economic growth and development, this study emphasizes the need to enhance the overall quality and accessibility of public transportation. The findings aim to improve the operational efficiency of bus services, contributing to a more effective and sustainable transport system.

关键词： Urban areas Software algorithms Memory Prediction algorithms real-time systems Software Maintenance Software reliability Public transportation Testing

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Next-Gen Home Automation: Sensor-Based Connectivity and Appliance Control 1st

Next-Gen Home Automation: Sensor-Based Connectivity and Appl...

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1st International research Conference on Computing Technologies for Sustainable development, IrCCTSd 2024

作者： Nithiyaraj, E. Emerson Srinivass, S. Mukilan, K. Muthukumar, M. Nanmaran, r. Srimathi, S. Department of Artificial Intelligence and Data Science Mepco Schlenk Engineering College Sivakasi India Department of ECE Ramco Institute of Technology Rajapalayam India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Biotechnology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9783031823824

Internet of Things (IoT) integrated smart home automation systems use sensor networks and communication modules to extend home energy efficiency, security, and convenience. IoT transforms houses into intelligent environment that are remotely monitored and controlled from anywhere using communication technologies. In this paper, a NodeMCU microcontroller and a variety of sensors, such as a PIr for motion detection, a gas sensor for monitoring hazardous gases, a flame sensor for fire detection, a dHT11 for temperature and humidity sensing, as well as smart locks, lights and fans for environmental control, are used to develop and implement a smart home system. With a user-friendly smartphone interface, the suggested system enables residents to monitor and operate their home appliances remotely in real- time, improving security, comfort, and energy efficiency. This article intends to further the development of smart home technology by providing a thorough overview of system design, sensor integration, and functionality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart homes

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Post-Quantum Encryption for Mathematical Model Using Quantum Machine Learning Approach

Post-Quantum Encryption for Mathematical Model Using Quantum...

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Women in Computing (InCoWoCo), International Conference for

作者： J. Kavitha V. Gayatri r. rajani L. Sharmila V.S Harilakshmi M. Misba School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Geethanjali Institute of Science and Technology Andhra Pradesh India Department of Artifical Intelligence and Data Science Holy Cross College (Autonomous) Nagercoil India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Because of the increasing emphasis put on the aviation industry in as a main industry of the global transportation industry, a number of organizations depend on different flight companies to get in touch with parts of the world which may be an effective measure to control the disease from spreading to other areas of the world. However, severe conditions can actually influence airline services in a given area of operation most significantly, flight delays. In this case, service encounters took place between the airline as a large organizational sector and the individual traveling as customers. The working for this issue is to accurately expect such flight delays though enables enhanced performers while in travel in order to be fit for destinations. disability which will be occasioned by the existence of the aforementioned impediment. Quantum machine learning is used to examine the measures that were used in building models to analyze probability of flights. Problems associated with late and early arrival and; delays due to unfavorable weather conditions. In the initial the process of project management, in which at the planned stage of the work, we study the feasibility of integration of Support Vector Machine with Python-based linear regression.

关键词： Support vector machines Accuracy Atmospheric modeling Transportation industry Project management Machine learning Mathematical models delays reliability Airline industry

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Classification of EEG signals using Machine learning algorithms 2

Classification of EEG signals using Machine learning algorit...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Suganyadevi, S. Shanmuga Priya, S. Kiruba, B. Gomathi, M. Kalshetty, Jagadevi. N. Kpr Institute of Engineering and Technology Department of ECE Coimbatore India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

An alternative to human expert-performed manual identification is automatic detection of epilepsy using electroencephalogram (EEG) data. Automatic epilepsy detection from EEG data need high classification performance in order to eliminate false positives. A classification strategy for automated epilepsy identification using EEG data is being proposed in this work. The signals generated form the EEG device were transformed using the dWT before feature extraction was carried out. Based on various statistical parameters and crossing frequency features, an algorithm dubbed GBMs fusion was developed to identify EEG data. As an added bonus, the significant traits were first selected using a genetic algorithm. EEG data from the University of Bonn has been used to test the suggested method's ability to distinguish between normal and ictal EEG patterns. Experimentation has shown that the suggested GBMs fusion may increase the EEG classification performance. It is also possible to identify epilepsy from EEG data with a 100% accuracy using the suggested GBMs fusion. © 2022 IEEE.

关键词： Electroencephalography

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