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10th International Conference on communication and Signal Processing, ICCSP 2024

作者： Lakshmi, D. Jeyarani, J. Suguna, R. Muneeshwari, P. Valantina, G.Mary Jayaraman, Suganya Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Trichy India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Tamil Nadu Avadi India Rajalakshmi Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The paper in illustration will emphasize the necessity for an IoT big data and analytics on time fusion as a futuristic smart city management approach. The framework deals with the main problems observed in data assimilation, analytics, security and scalability. As a result, cities will enjoy the services and operations utilization of the urban data with the precision. Our approach is based upon the data collection with the use of the edge technologies, preprocessing, integration, and analysis of the data resulting in the creation of the tools of machine learning and the adoption of interoperability standards. The results have especially brought to the limelight the fantastic performance of the live analytics pipeline when considered as a whole, and the impressive throughput of 5,000 events per second alongside the very low latency of 50 milliseconds, indicating how the urban information flow is processed in the blink of an eye. With AI therein creating the precision rate of 88.7%-95.2%, which therefore means this level of an urban situation can pronounce a traffic congestion and air quality fluctuations. The situation where security systems that involve encryption, access controls, and privacy-preserving protocols, among other mechanisms, have been used for the management of urban data in a safe manner, away from any unauthorized people and hackers, is factual. For the sake of achieving scalability, the replacement of certain bottlenecks is applied in the system by means of horizontal scaling and optimal performance procedures assisting smart cities handle their increasing quantity of data. Usually the results are factorized to bring out the fact that the particular methodology is strong and thus can be used in real practice to transform and enhance urban resilience level. © 2024 IEEE.

关键词： Smart city

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Deep Learning Models for Understanding Audience Sentiments in Movie Tweets 5

Deep Learning Models for Understanding Audience Sentiments i...

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5th International Conference on electronics and Sustainable communication Systems, ICESC 2024

作者： Prasath, S. Ram Jeevitha, J.K. Margret, S. Krishnan, R. Santhana Raj, J. Relin Francis Nithila, Ezhil E Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Psna College of Engineering and Technology Department of Information Technology Dindigul India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Avadi India

ISBN: (纸本)9798350379945

This study explores the application of deep learning models for analyzing audience sentiments in movie tweets, aiming to provide a comprehensive understanding of audience reception. Leveraging Twitter data collected using Tweepy and supplemented with web-scraped content via BeautifulSoup, the study employs advanced Natural Language Processing (NLP) techniques. Text preprocessing using SpaCy ensures standardized input for subsequent analysis. Sentiment analysis utilizes RoBERTa, an advanced deep learning transformer model known for its robust performance in NLP tasks. RoBERTa effectively classifies tweets into positive, negative, or neutral sentiments, leveraging its contextual understanding to interpret nuanced language expressions prevalent in social media discourse. Simultaneously, RoBERTa is employed for emotion detection, identifying emotional states such as joy, anger, sadness, and more, crucial for gauging audience emotional responses to various aspects of movies. This dual-analysis framework provides detailed insights into audience reactions, facilitating a nuanced evaluation of movie reception across diverse demographic segments. Additionally, the study explores aspect-based sentiment analysis to delve deeper into specific movie attributes like plot, acting, and special effects. Visualizations using Plotly enhance the interpretability of sentiment and emotion distributions over time, supporting informed decision-making in the entertainment industry. © 2024 IEEE.

关键词： Tweets

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Primeval Identification and Diagnosis of Alzheimer's Disease Utilizing 3D CNN Models 10

Primeval Identification and Diagnosis of Alzheimer's Disease...

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10th International Conference on Advanced Computing and communication Systems, ICACCS 2024

作者： Sujitha, K.L. Lynsha Helena Pratheeba, H.P. Rajeshwari, R. Ramaraj, Kottaimalai Murugan, Pallikonda Rajasekaran Thilagaraj, M. Mvj College of Engineering Department of Computer Science and Engineering Bengaluru India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Mvj College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350384369

An early-onset neurocognitive impairment, Alzheimer's disease (AD) affects individuals. The symptoms of this illness include diminished memory, strange conduct, and linguistic difficulties. Since AD is the source of 60-80% of instances of dementia, it is the single most prevalent cause of dementia. An AD that is neurodegenerative begins with moderate cognitive impairment (MCI) and progressively worsens. It damages brain tissue, impairs ability to think and remember, and makes it difficult to carry out basic duties. The development of more sophisticated therapies for AD depends on swift detection. For this reason, stopping and managing the progression of AD depends greatly on early detection. The primary goal is to create a comprehensive framework for medical image categorization according to different phases of Alzheimer's disease and early identification of the condition. This work employs a deep learning approach, more precisely convolutional neural networks (CNN). Simple CNN architectures that handle 2D and 3D structural brain magnetic resonance imaging (MRI) form the basis of the suggested methodology. The limited quantity of datasets presents the primary obstacles for medical images. Thus, in order to optimize the size of the dataset and avoid the issue of overfitting, methods to augment data are used. Techniques for resampling, such as downsampling and oversampling are employed to address imbalanced dataset collections. © 2024 IEEE.

关键词： Convolutional neural networks

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Combat with Class Overlapping in Software Defect Prediction Using Neighbourhood Metric

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SN computer Science 2023年第5期4卷 695页

作者： Gupta, Shivani Richa Kumar, Ranjeet Jain, Kusum Lata School of Computer Science Engineering Vellore Institute of Technology Chennai India Department of Computer science and Engineering Birla Institute of Technology Mesra Ranchi India School of Electronics Engineering Vellore Institute of Technology Chennai India School of Computer & Communication Engineering Manipal University Jaipur Jaipur India

The characteristics of data is a open problem which has been tended perceived in data analysis in machine learning research from last decades. The researcher defined some measures to identify the characteristics of the dataset by applying data complexity measures to find the fitness for purpose. The presence of class overlapping in data-sets, significantly affect performance of the classifiers. Data complexity measures provide quantitative insight in quality of the data set and overlapping existent in it. Machine learning techniques are also utilized by several researchers on healthcare datasets in software defect prediction. In this paper, our aim is to evaluates the effectiveness of new overlap measure: Near Enemy Ratio, and its effect on complexity measures and performance of the classifier. The new ration is based on nearest instances to the target instance. The experimental result offers insights in usefulness of the method and help us decide whether this solution should be applied on a particular data-set or not. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AUC Class imbalance Class overlap G-mean Recall Software defect prediction

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Highly sensitive optical biosensor based on electromagnetically induced transparency in a graphene-dielectric metasurface

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Optical and Quantum electronics 2025年第4期57卷 1-17页

作者： Bagheritabar, Mohsen Molaei Zarasvand, Mahdi Yazdani, Afsoon Molaei Zarasvand, Melika Salemizadeh Parizi, Mohammad Bahadori-Haghighi, Shahram Department of Electrical Engineering University of Cincinnati CincinnatiOH45221-0030 United States Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States Department of Electrical and Computer Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA30332 United States Department of Communication and Electronics Engineering Shiraz University Shiraz Iran

In this paper, a graphene-dielectric metasurface is proposed for effective optical biosensing. The structure is composed of a periodic array of double-slit split-ring resonators (SRRs) adjacent to silicon bars over a sheet of graphene and silicon dioxide substrate. Interactions between electric dipoles (bright modes) in silicon bars and magnetic dipoles (dark modes) excited in SRRs lead to a high quality factor resonance based on the phenomenon of electromagnetically induced transparency (EIT). High electric field confinement is achieved within the two gaps of SRRs at the resonance frequency, which result in strong light-analyte interaction. The structural parameters of the proposed metasurface are optimized to achieve the best performance. The biosensor is analyzed using the finite-element method (FEM) and the results are presented. Owing to the high Q-factor EIT resonance and the enhanced light-matter interaction inside the gaps, a high sensitivity of 496 nm/RIU and figure-of-merit (FOM) of as high as 741 RIU−1 are achieved that are higher than those reported in recently published works. The resolution and the linearity R2 value of the proposed biosensor are calculated to be 2.02e-4 and 0.999, respectively. The reported characteristics could be promising for sensing of various biomolecules such as hemoglobin. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Q factor measurement

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Development of Precision Medicine for Hepatic Cancer Evolving Molecular Profiling Techniques Through Artificial Intelligence 8

Development of Precision Medicine for Hepatic Cancer Evolvin...

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8th IET Smart Cities Symposium, SCS 2024

作者： Balamurugan, K.S. Navaneethakrishnan, Sundara Rajulu Tamilvizhi, T. Kumutha, D. Surendran, R. Kalyani, G. Department of Electronics and Communication Eng Karpaga Vinayaga College of Engineering and Technology Chengalpattu India Department of Computer Science and Engineering Dhanalakshmi Srinivasan University Trichy Samayapuram Campus Tiruchirappalli India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Electronics and Communication Eng Jeppiaar Institute of Technology Sriperumudur India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Ruralcare Innovators LLP Tamil Nadu Madurai India

ISBN: (纸本)9781837242672

Hepatic cancer particularly hepatocellular carcinoma leads to larger global health concerns with the highest mortality and morbidity rates. Precision medicine provides an exact solution for individual patients with their disease. The mechanism of hepatic cancer progression and identification of prognostic indications for personalized therapeutic interpolations are obtained through tissue microenvironment analysis and molecular profiling. The proposed system involves the extraction and identification of critical prognostic factors from molecular datasets using deep learning with hybrid optimization techniques such as ant colony optimization and genetic algorithm. The important objective of the innovation is to identify the intricate interplay between genetic, epigenetic and microenvironment factors within the cancer tissues. This is initiated through a multi-omics approach that involves genomics, epigenomics, transcriptomics and proteomics data. These obtained datasets are preprocessed to obtain consistency in the system. Various external noise factors and dimensions are reduced using feature extraction techniques to extract the necessary information attributes. The hybrid optimization techniques are initiated to provide optimum features for accurate prognostic predictions. The integration of GA and ACO helps in enhancing the exploration and exploitation of the feature space. The validation process is performed on a large dataset that contains the exact information of hepatic cancer patients. The observed prognostic features are calculated for consistency and robustness with clinical relevance. The patient outcomes are trained and tested using selected features for identifying the prognostic values of tissue microenvironment analysis. The uniqueness of hybrid optimization techniques is assessed through a comparative analysis of the proposed system with existing models. Thus, precision medicine is developed through the personalization of a patient's condition in hepat

关键词： Diseases

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Health Care Data Mining using Intelligent Classification Techniques 5

Health Care Data Mining using Intelligent Classification Tec...

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5th IEEE International Conference on Advances in Computing, communication Control and Networking, ICAC3N 2023

作者： Antil, Jyoti Rishi, Rahul Sharma, Sonia Yadav, Rajkumar University Institute Of Engineering And Technology Mdu Dept. Of Computer Science Engineering Hrayana Rohtak India University Institute Of Engineering And Technology Mdu Dept. Of Electronic And Communication Engineering Hrayana Rohtak India

ISBN: (纸本)9798350330861

Classification models can be learned from complicated multivariate temporal data and this is a topic we investigate in electronic health record systems. The difficulty is to come up with a collection of attributes that accurately reflect the data's temporal nature. Using temporal abstractions and pattern mining, we extract the classification features from the data. There are frequently a lot of temporal patterns that aren't related to the classification job that are returned by temporal pattern mining. © 2023 IEEE.

关键词： Classification (of information)

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Nano-scale Quantum Modified SnTe/Pb0.98Sn0.02Te Supper lattice Structure Based Highly Sensitive Exotic p+-i-n+ IR Photo-detector 14

Nano-scale Quantum Modified SnTe/Pb0.98Sn0.02Te Supper latti...

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14th International Conference on Computing communication and Networking Technologies, ICCCNT 2023

作者： Bhadra, Bias Chinara, Suchismita Kundu, Abhijit National Institute of Technology Rourkela Department of Computer Science Engineering Odissa India Chaibasa Engineering College Department of Electronics & Communication Engineering Jharkhand India

ISBN: (纸本)9798350335095

In this paper, asymmetrical super-lattice SnTe/Pb0.98Sn0.02Te exotic p+-i-n+ nano-scale photo-sensor has been reported with significant improvement of photo-responsivity and quantum efficiency in the near infrared wavelength region. The photo-electric characteristics of the proposed Device-Under-Test (DUT) are studied by developing self consistent Non-linear Quantum Modified Drift-Diffusion (NQMDD) Model with appropriate boundary conditions. The proposed NQMDD model is validated by comparing the simulated results with the experimental results under a-like operating-conditions. An excellent agreement, in between the simulated and experimental results, under a-like operating-conditions, reveals the validity of the proposed NQMDD simulator. The performance of the DUT is enhanced by incorporation of alternate thick - SnTe and thin- Pb0.98 Sn0.02Te layers into the active region of the device. The extensive study proves that the photo-electric characteristics of the DUT have improved significantly compared to its Si counterpart. To the best of authors' knowledge this is the first report on asymmetrical super-lattice SnTe/Pb0.98Sn0.02Te exotic p+-i-n+ nano-scale IR photo-sensor. The proposed DUT will play an important role for device-engineering for the development of highly sensitive circuits in defence and bio-medical industries. © 2023 IEEE.

关键词： Quantum efficiency

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Enhanced Physical Layer Security for Full-Duplex Symbiotic Radio with AN Generation and Forward Noise Suppression

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IEEE Transactions on communications 2024年第7期72卷 3905-3918页

作者： Jin, Chi Chang, Zheng Hu, Fengye Chen, Hsiao-Hwa Hamalainen, Timo University of Jyväskylä Faculty of Information Technology Jyväskylä40014 Finland University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Jilin University College of Communication Engineering Changchun130012 China National Cheng Kung University Department of Engineering Science Tainan70101 Taiwan

Due to the constraints on power supply and limited encryption capability, data security based on physical layer security (PLS) techniques in backscatter communications has attracted a lot of attention. In this work, we propose to enhance PLS in a full-duplex symbiotic radio (FDSR) system with a proactive eavesdropper, which may overhear the information and interfere legitimate communications simultaneously by emitting attack signals. To deal with the eavesdroppers, we propose a security strategy based on pseudo-decoding and artificial noise (AN) injection to ensure the performance of legitimate communications through forward noise suppression. A novel AN signal generation scheme is proposed using a pseudo-decoding method, where AN signal is superimposed on data signal to safeguard the legitimate channel. The phase control in the forward noise suppression scheme and the power allocation between AN and data signals are optimized to maximize security throughput. The formulated problem can be solved via problem decomposition and alternate optimization algorithms. Simulation results demonstrate the superiority of the proposed scheme in terms of security throughput and attack mitigation performance. 0090-6778 © 2024 IEEE.

关键词： Backscattering

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Distributed adaptive differential evolution algorithm for mathematical functions optimization and development of optical band pass filter

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Optical and Quantum electronics 2025年第1期57卷 1-25页

作者： Ghosh, Arup Kumar Garai, Gautam Bhattacharjee, Subhankar Department of Electronics and Communication Engineering Techno International New Town Kolkata India Computer Section Saha Institute of Nuclear Physics 1/AF Bidhannagar Kolkata700064 India

In this paper, we have discussed an innovative method for crafting a one-dimensional photonic crystal (1D PhC) based Optical Band Pass Filter (OBPF) using a Differential Evolution (DE) algorithm. 1D PhC-based OBPF plays a vital role in optical communication systems due to its ability to control light propagation. The proposed technique optimizes the photonic crystal structure to achieve the desired optical bandpass characteristics efficiently. The DE algorithm streamlines the design process and enables fast exploration of the design space. The effectiveness of this approach is validated through simulations and comparisons with the existing well-known methods. The OBPF comprises multiple dielectric layers with varying thicknesses and refractive indices to enable high transmission at specific wavelengths and low transmission elsewhere. The Optimization techniques are employed to adjust the parameters like thickness, refractive index, and number of layers. The DE method, thus, enhances the performance of OBPF. MATLAB implementation showcases significant outcomes with high transmittance (99.9727%), low reflectance (0.0273%), and a narrow Full Width at Half Maximum of 0.5 nm that affirms its efficacy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crystal filters

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