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4th International Conference for Emerging technology, INCET 2023

作者： Hariharan, S. Dhanush Sridharan, Deveshwaran Kalaiyarasi, R. Mohanaprakash, T.A. Tamilselvi, C. Sam, Dahlia Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India Rajalakshmi Institue of Technology Department of Artificial Intelligence and Data Science Chennai India Panimalar Engineeering College Department of CSE Chennai India Dr. M.G.R Educational and Research Institute Department of IT Chennai India

ISBN: (纸本)9798350335750

Diabetes is an ongoing infection that influences a large number of individuals overall and can prompt serious unexpected issues whenever left untreated. Early identification of diabetes can altogether diminish the risk of intricacies and work on significant results. Lately, the utilization of wearable technology has arisen as a promising device for illness identification and checking. Smartwatches furnished with bioactive sensors can give ceaseless, painless observing of body vitals, making them ideal for diabetes screening. This study proposes a framework that uses patient information for preparing a hybrid AI model to distinguish the presence of diabetes. The framework consolidates body vitals estimated utilizing a smartwatch with a bioactive sensor to get exact and nonstop information on the wearer's wellbeing status. The mixture model coordinates both profound learning and conventional AI calculations to accomplish predominant precision in identifying diabetes. The framework gathers information on different body vitals, for example, pulse, circulatory strain, and skin conductance, which are known to be firmly connected with diabetes. The gathered information is pre-handled and afterward used to prepare the hybrid model. The profound learning calculation is utilized to remove significant level highlights from the crude information, while the conventional AI calculation is utilized to arrange the information into diabetic or non-diabetic classifications. The cross breed model is intended to work on the accuracy of diabetes location by integrating the qualities of both profound learning and conventional AI. © 2023 IEEE.

关键词： COVID-19

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Clause-level Relationship-aware Math Word Problems Solver

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Machine intelligence Research 2022年第5期19卷 425-438页

作者： Chang-Yang Wu Xin Lin Zhen-Ya Huang Yu Yin Jia-Yu Liu Qi Liu Gang Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Data ScienceUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou 450001China

Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence *** math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level *** this end,inspired by the theory of two levels of process in comprehension,we propose a novel clause-level relationship-aware math solver(CLRSolver)to mimic the process of human comprehension from lower level to higher ***,in the lower-level processes,we split problems into clauses according to their natural division and learn their *** the higher-level processes,following human′s multi-view understanding of clause-level relationships,we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local ***,we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global ***,we enhance the representation of clauses based on the learned clause-level dependency *** expression generation,we develop a tree-based decoder to generate the mathematical *** conduct extensive experiments on two datasets,where the results demonstrate the superiority of our framework.

关键词： artificial intelligence(AI) artificial neural network(ANN) computational mathematics machine intelligence machine learning

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Quantum Computing's Double-Edged Sword Unravelling the Vulnerabilities in Quantum Key Distribution for Enhanced Network Security

Quantum Computing's Double-Edged Sword Unravelling the Vulne...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Anantraj, I. Umarani, B. Karpagavalli, C. Usharani, C. Lakshmi, S. Jothi Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Namakkal India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Rajapalayam India

ISBN: (纸本)9798350319088

Quantum computing has emerged as both a boon and a bane in the realm of network security. This paper delves into the intriguing interplay between quantum computing and the vulnerabilities of Quantum Key Distribution (QKD) protocols, a cornerstone of secure quantum communication. While QKD offers unmatched security through the principles of quantum mechanics, it is not impervious to attacks in the age of rapidly advancing quantum computing. We examine these vulnerabilities, highlighting the challenges of ensuring the long-term security of quantum networks in a post-quantum world. Through a comprehensive exploration of threat models and the practicality of QKD, we uncover potential weaknesses in quantum network security. In particular, we investigate QKD's susceptibility to evolving quantum algorithms, hardware vulnerabilities, and post-processing security concerns. This work aims to provide a roadmap for understanding and addressing these vulnerabilities, ensuring the continued robustness of quantum communication networks, and fostering the development of post-quantum cryptographic solutions. As quantum computing advances, its implications for quantum network security are far-reaching. This study underlines the critical need for research and innovation in securing quantum communication. By unravelling the vulnerabilities within Quantum Key Distribution and exploring post-quantum cryptographic alternatives, this research contributes to the development of more resilient and secure quantum networks, safeguarding sensitive information and communications in an increasingly quantum-enabled world. © 2023 IEEE.

关键词： Quantum cryptography

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Audio-Based Emotion Analysis Using Deep Learning Techniques

Audio-Based Emotion Analysis Using Deep Learning Techniques

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Trends in engineering Systems and Technologies (ICTEST), International Conference on

作者： Siji Jose Pulluparambil Akshay S Don Sanson Fernando J Thiruthanathil Gokul Kiran R Department of Artificial Intelligence and Data Science Adi Shankara Institute of Engineering and Technology Kalady India

ISBN: (数字)9798331505370

ISBN: (纸本)9798331505387

This paper uses audio parameters such as MFCCs, zero-crossing rate, chroma features, RMS values, and Mel spectrograms to provide a novel approach to machine learning for speech emotion recognition. Heterogeneous audio recordings from datasets RAVDESS, CREMA-D, TESS, and SAVEE are used for the experiment. data augmentation of the dataset was done by performing pitch modification, stretching, shifting, and addition of noise in order to improve model robustness. CNN, being competitive with regard to emotion categorization and sequential data analysis, is being explored for possible application to sentiment analysis, mental health monitoring, or even human-computer interaction; the work, thus, makes it possible for advancing a scalable, intuitive, yet accurate system in speech emotion recognition.

关键词： Emotion recognition Sentiment analysis Computational modeling Noise Speech recognition data augmentation Feature extraction Transformers data models Spectrogram

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Vision-Based PM2.5 Concentration Estimation with Natural Scene Statistical Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第6期5卷 2805-2815页

作者： Wang, Guangcheng Shi, Quan Wang, Han Gu, Ke Wei, Mengting Wong, Lai Kuan Wang, Mingxing Nantong University School of Transportation and Civil Engineering Nantong226019 China Beijing University of Technology Faculty of Information Technology Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Artificial Intelligence Institute Beijing100124 China Chinese Academy of Sciences Institute of Software Beijing100045 China Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia Environmental Science and Technology Company Ltd. Beijing102100 China

As the primary pollutant in China's urban atmosphere, particulate matter (PM)2.5 poses a great threat to the health of residents and ecological stability. An efficient and effective PM2.5 concentration monitoring is essential. Nonetheless, the popular devices for PM2.5 monitoring are developed based on two standards: the microoscillation balance method and the β-ray method, which have high purchase and maintenance costs and slow calculation rates. To this end, we put forward a real-time and reliable vision-based estimation algorithm of PM2.5 concentration. To be specific, the proposed method first develops two natural scene statistical analysis-based visual priors to measure saturation and structural information losses caused by the 'haze' formed by PM2.5. Moreover, we develop a lightweight deep belief network-deep neural network-based PM2.5 concentration estimation model, which learns the mapping from the designed visual priors to PM2.5 concentrations. Experiments confirm the superiority of our vision-based PM2.5 concentration estimation method by comparison with state-of-the-art photo-based PM2.5 monitoring methods. © 2020 IEEE.

关键词： Entropy

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Early Detection of Black Fungus Using Deep Learning Models for Efficient Medical Diagnosis 4

Early Detection of Black Fungus Using Deep Learning Models f...

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4th IEEE International Conference on Emerging Techniques in Computational intelligence, ICETCI 2024

作者： Hassan, Esraa Saber, Abeer El-Kenawy, El-Sayed M. Bhatnagar, Roheet Shams, Mahmoud Y. Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning Kafrelsheikh33516 Egypt Damietta University Faculty of Computers and Artificial intelligence Information technology Dept. Damietta34517 Egypt Delta Higher Institute of Engineering and Technology Dept. of Communications and Electronics Mansoura35111 Egypt Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350387063

Mucormycosis, also known as black fungus, is a rare infection caused by mould that can affect the lungs, brain, skin, and sinuses. People with weakened immune systems due to underlying health conditions (e.g., organ transplant, uncontrolled diabetes, cancer, neutropenia, AIDS, and COVID-19) or drug use are more likely to contract the infection. Recent reports have indicated that a small number of COVID-19 survivors with co-morbidities are particularly vulnerable to black fungus. Therefore, recovered COVID-19 patients should seek medical attention immediately if they experience any mucormycosis symptoms. Patients with COVID-19 have numerous concerns regarding this life-threatening infection, particularly those with immunocompromised states such as solid organ transplant (SOT) and hematopoietic stem cell transplant (HSCT) or tumors, who are more likely to be infected. Early diagnosis and treatment are essential to prevent patient mortality. In this paper, we present the Multi-Class Black Fungus (MCBF) dataset, which consists of three compressed folders containing images. The first folder includes 66 images of eye black fungus after data preprocessing, while the second folder contains 63 images of mouth black fungus after data preprocessing. The third contains 62 images of the mouth after applying data preprocessing, and the last one contains 79 images of black skin fungus after applying data preprocessing. The MCBF dataset contains images that aid in training and validation when using deep learning algorithms to recognise and classify black fungus diseases, then we apply a resnet(50) pretrained model by using MCBF accuracy reaches 96.12%. © 2024 IEEE.

关键词： COVID-19

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CAPTCHA in the Age of Automation: Enhancing Security and Ensuring User Convenience

CAPTCHA in the Age of Automation: Enhancing Security and Ens...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： M. Sakthivel E. Naveenkumar S. Mukilarasan A. Boopathi V. Akila A.B. Rithanya Department of Artificial Intelligence and Data Science Sri Shanmugha College of Engineering and Technology Salem India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a security feature commonly used to distinguish human users from bots. This paper provides an in-depth review of different CAPTCHA methods, such as text, image, audio, and video-based systems. It explores the progression of CAPTCHA technologies, focusing on their role in preventing automated attacks while also addressing issues like accessibility for users with disabilities. Furthermore, recent innovations, including machine learning-powered reCAPTCHA systems, are evaluated for their potential to improve security and the challenges they pose for user convenience. The analysis highlights the need for more inclusive and flexible CAPTCHA solutions to keep pace with the ever-evolving capabilities of automated bots.

关键词： Technological innovation Social networking (online) Reviews Prevention and mitigation Machine learning Standardization Robustness Regulation Usability CAPTCHAs

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Total losses and dispersion effects management and upgrading fiber reach in ultra-high optical transmission system based on hybrid amplification system

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Journal of Optical Communications 2025年第2期46卷 289-298页

作者： Ramkumar, Govindaraj Shahila, Ferlin Deva Lingaraj, Vanitha Chandran, Prabhu Chidambaram, Vivek Arumugam, Parimala Ramadan, Hegazy Mahmoud Saveetha University Tamil Nadu Chennai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of ECE S.A. Engineering College Tamilnadu Chennai India Department of ECE Prathyusha Engineering College Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamilnadu Chennai India Department of ECE Sree Sastha Institute of Engineering and Technology Tamilnadu Chennai India Sina Institute of Technology Sina Egypt

This work demonstrated the total losses and dispersion effects management and upgrading the fiber reach in ultra-high optical transmission system based on hybrid amplification system. Based on the requirements of the system, a number of trade-offs must be made between the numerous interconnected performance characteristics of each component in the design of a high quality transmission link. The transmission distance is the greatest distance that an optical signal has to travel. It affects the choice of optical component, fiber type, and signal amplification techniques. Fiber amplifiers or signal regeneration may be required to compensate for signal loss over greater distances. Reviewing these crucial aspects helps when selecting the best optical components and system layout. This ensures the optical link performs as expected. Hybrid amplification system unit (EDFA/Raman/EDFA), (EDFA/Raman), (BFA/PDFA) and (Raman/EDFA) is employed in the transmission system to strength the signal and upgrade the system performance operation efficiency. Space division multiplexing scheme is also employed with the propagation scheme in order to upgrade and control of the total losses and dispersion effects. The optimum total losses/dispersion/repeater spacing are studied with the variations of the total number of links per fiber core channel. © 2024 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Amplification

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Surveying Flash Floods in Urban Indian Environment: A Review of Machine Learning Applications

Surveying Flash Floods in Urban Indian Environment: A Review...

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International Conference on Water Resources, Ocean, and Environmental engineering, ICWROEE 2024

作者： Blessy, Sardar Rechel Supraja, Balerao Rathi, Kushal Devi, Kamalini Vasanth, K. Srilatha, Pulipati Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India Department of Civil Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India Department of Electronics and Communications Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India

ISBN: (纸本)9789819776986

This survey paper conducts a thorough examination of the global challenge posed by urban flash floods, analyzing the intricate factors influencing the occurrence and severity of these events. Drawing on a diverse array of literature and case studies, the study delves into the complex dynamics of urban flash floods and their far-reaching impacts on human populations, infrastructure, and the natural environment. Highlighting the imperative for proactive flood risk management and resilient urban planning, the research puts forth actionable recommendations to bolster urban resilience in the face of mounting flood risks. In addition to evaluating machine learning algorithms like Iso clustering and ensemble classifiers for rapid flood mapping and precise flood susceptibility assessment, the study also explores the utilization of satellite imagery for enhanced flood monitoring and analysis. By integrating insights from both machine learning techniques and satellite imaging, the research aims to provide a comprehensive understanding of urban flash floods and offer practical strategies to mitigate their adverse effects. The study underscores the critical importance of implementing robust flood management measures, advanced early warning systems, and sustainable urban development practices to address the escalating threat of flash floods in urban areas and safeguard vulnerable communities, critical infrastructure, and ecological systems from the devastating consequences of these events. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Risk management

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Development of an Intelligent System for Predictive Analysis of Student Academic Performance using Machine Learning Techniques 8

Development of an Intelligent System for Predictive Analysis...

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8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Tripathi, Anand Reddy, K.T.V. Dept. of Artificial Intelligence and Data Science Datta Meghe Institute of Higher Education and Research India Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Wardha India

ISBN: (纸本)9798331512248

This research proposes an intelligent system utilizing machine learning techniques to predict student academic performance. A Random Forest classification model was developed based on a dataset consisting of key attributes such as attendance, previous grades, hours spent weekly on studying, medical emergencies, and assignment scores. The model classifies students into three categories: High-Performing, Average, and At-Risk, achieving an impressive accuracy of 99%. Attendance and previous grades emerged as the key determinants of academic success. To facilitate real-world application, this project also includes an interactive Streamlit-based web application, enabling educators to make instant data-driven predictions by inputting students' academic details. This tool empowers educators with actionable insights, allowing them to implement timely interventions, optimize resource allocation, and adapt learning experiences for improved student outcomes. The study underscores the rapid advancements achieved through predictive analytics in education. By integrating artificial intelligence into pedagogic practice, institutions can proactively address learning difficulties, enhance student engagement, and foster academic excellence. © 2025 IEEE.

关键词： Academic Achievement Academic Success artificial intelligence in Education At-Risk Students data Visualization data-Driven Insights Early Warning System Education technology Educational data Mining Learning Analytics Machine Learning Performance Prediction Personalized Learning Predictive Analytics Proactive Intervention Random Forest Classifier Real-Time Predictions Streamlit Application Student Outcomes Student Performance

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