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Informatica (Slovenia) 2024年第2期48卷 147-158页

作者： Srinivas, Kalyanapu Ch, Vijayalakshmi Borra, Subba Reddy Raju, K. Srujan Rao, Ganga Rama Koteswara Satyanarayana, K.N.V. Kumar, Pala Mahesh Department of Artificial Intelligence and Data Science Seshadri Rao Gudlavalleru Engineering College Andhra Pradesh Gudlavalleru India Department of Electronics and Communication Engineering St. Peter’s Engineering College Maisammaguda Telangana Hyderabad India Maisammaguda Telangana Hyderabad India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Medchal Road Telangana Hyderabad India Department of Computer Science and Information Technology Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram India Department of Electronics and Communication Engineering Sagi Rama Krishnam Raju Engineering College Andhra Pradesh Bhimavaram India SAK Informatics Telangana Hyderabad India

The irregularity in the heartbeats caused cardiac arrhythmia, which resulted in serious health problems. This cardiac arrhythmia is monitored by electrocardiogram (ECG) signals. As a result, an accurate and timely analysis of ECG data can prevent serious health problems. However, the conventional manual prediction systems and artificial intelligence (AI) methods failed to detect cardiac arrhythmia because they failed to extract the deep salient features from the ECG dataset. So, this research work implements a model named CardiacNet, which is used to identify and classify cardiac arrhythmias from an MIT-BIH-based dataset. Initially, the pre-processing operation is performed to remove the non-linearities from the dataset. Then, unsupervised machine learning algorithm-based principal component analysis (UML-PCA) is used to extract the features of the pre-processed dataset. Further, the optimal feature selection operation is carried out using improved Harris Hawk's optimization (IHHO), which is a naturally inspired model. Moreover, a customized convolutional neural network (CCNN) model performs the classification of various cardiac arrhythmia diseases using IHHO features. The simulation results show that the proposed CardiacNet resulted in an accuracy of 97.57%, sensitivity of 98.29%, specificity of 97.97%, F-measure of 97.40%, precision of 98.66%, Matthew's correlation coefficient (MCC) of 98.17%, dice of 98.96%, and Jaccard of 97.12%. The performance comparisons show that the proposed CardiacNet resulted in improved metrics over all existing methods. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Image Enhancement Using Adaptive Fractional Order Filter

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computer Systems Science & engineering 2023年第5期45卷 1409-1422页

作者： Ayesha Heena Nagashettappa Biradar Najmuddin M.Maroof Surbhi Bhatia Arwa Mashat Shakila Basheer Department of Electronics and Communication Engineering Bheemanna Khandre Institute of TechnologyBhalkiVisveswaraya Technological UniversityBelagaviIndia Department of Electronics and Communication Engineering Khaja Banda Nawaz College of EngineeringVisveswaraya Technological UniversityBelagaviIndia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl-HofufAl-AhsaSaudi Arabia Faculty of Computing&Information Technology King Abdulaziz UniversityP.O.Box 344Rabigh21911Saudi Arabia Department of Information Systems College of Computer and Information Science Princess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia

Image enhancement is an important preprocessing task as the contrast is low in most of the medical images,Therefore,enhancement becomes the mandatory process before actual image processing should *** research article proposes an enhancement of the model-based differential operator for the images in general and Echocardiographic images,the proposed operators are based on Grunwald-Letnikov(G-L),Riemann-Liouville(R-L)and Caputo(Li&Xie),which are the definitions of fractional order *** this fractional-order,differentiation is well focused on the enhancement of echocardiographic *** provoked for developing a non-linear filter mask for image *** designed filter is simple and effective in terms of improving the contrast of the input low contrast images and preserving the textural features,particularly in smooth *** novelty of the proposed method involves a procedure of partitioning the image into homogenous regions,details,and ***,a fractional differential mask is appropriately chosen adaptively for enhancing the partitioned pixels present in the *** is also incorporated into the Hessian matrix with is a second-order derivative for every pixel and the parameters such as average gradient and entropy are used for qualitative *** wide range of existing state-of-the-art techniques such as fixed order fractional differential filter for enhancement,histogram equalization,integer-order differential methods have been *** proposed algorithm resulted in the enhancement of the input images with an increased value of average gradient as well as entropy in comparison to the previous *** values obtained are very close(almost equal to 99.9%)to the original values of the average gradient and entropy of the *** results of the simulation validate the effectiveness of the proposed algorithm.

关键词： Adaptive filter differential filter enhancement mask fractional differential mask fractional-order calculus hessian matrix

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Age-Based Automatic Voice Conversion Using Blood Relation for Voice Impaired

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computers, Materials & Continua 2022年第2期70卷 4027-4051页

作者： Palli Padmini C.Paramasivam G.Jyothish Lal Sadeen Alharbi Kaustav Bhowmick Department of Electronics&Communication Engineering Amrita School of EngineeringBengaluruAmrita Vishwa VidyapeethamIndia Center for Computational Engineering and Networking(CEN) Amrita School of EngineeringCoimbatoreAmrita Vishwa VidyapeethamIndia Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Electronics and Communication Engineering PES UniversityBengaluruIndia

The present work presents a statistical method to translate human voices across age groups,based on commonalities in voices of blood *** age-translated voices have been naturalized extracting the blood relation features e.g.,pitch,duration,energy,using Mel Frequency Cepstrum Coefficients(MFCC),for social compatibility of the *** system has been demonstrated using standard English and an Indian *** voice samples for resynthesis were derived from 12 families,with member ages ranging from 8–80 *** voice-age translation,performed using the Pitch synchronous overlap and add(PSOLA)approach,by modulation of extracted voice features,was validated by perception *** translated and resynthesized voices were correlated using Linde,Buzo,Gray(LBG),and Kekre’s Fast Codebook generation(KFCG)*** translated voice targets,a strong(θ>∼93%andθ>∼96%)correlation was found with blood relatives,whereas,a weak(θ<∼78%andθ<∼80%)correlation range was found between different families and different gender from same *** study further subcategorized the sampling and synthesis of the voices into similar or dissimilar gender groups,using a support vector machine(SVM)choosing between available voice ***,∼96%,∼93%,and∼94%accuracies were obtained in the identification of the gender of the voice sample,the age group samples,and the correlation between the original and converted voice samples,*** results obtained were close to the natural voice sample features and are envisaged to facilitate a near-natural voice for speech-impaired easily.

关键词： Blood relations KFCG LBG MFCC vector quantization correlation speech samples same-gender dissimilar gender voice conversion PSOLA SVM

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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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An Artificial Intelligence strategy for scrutiny of ChatGPT Tweets 1

An Artificial Intelligence strategy for scrutiny of ChatGPT ...

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1st IEEE International Conference on Advances in Computing, communication and Networking, ICAC2N 2024

作者： Tuteja, Disha Singh, Neetu Kaur, Surinder Gupta, Ankit Raj Bansal Ankit Gupta, Monica Guru Tegh Bahadur Institute of Technology Department of Electronics and Communication Engineering New Delhi India Bharati Vidyapeeth's College of Engineering Department of Information Technology New Delhi India Bennett University Department of Computer Science Engineering UP Greater Noida India Bharati Vidyapeeth's College of Engineering Department of Electronics and Communication Engineering New Delhi India

ISBN: (纸本)9798350356816

In this new age of social media, understanding public sentiment is a crucial endeavor with complex implications. This work presents a Twitter sentiment analysis model covering tweets related to ChatGPT and the recently launched GPT-4, a revolutionary language model. Hence, these tweets open up new realm opportunities for AI development. Our model performs competently with a combination of Natural Language Processing techniques and machine learning models. Extensive tests are performed on three machine learning models - Logistic Regression, Naive Bayes and Support Vector Machine - using parameters as accuracy, precision, recall, F1-Score, and support. Notably, with an accuracy of 83.41% and a balanced F1-Score of 0.7666, our results suggest Logistic Regression as the optimal model, demonstrating its efficiency in classifying sentiments of ChatGPT and GPT-4 tweets. This study is a pioneering step in the field of sentiment analysis, exploring the largely uncharted areas by analyzing the sentiment of GPT-4 tweets, thus providing vital insights into understanding, and utilizing social media within this specific context and encouraging further process. © 2024 IEEE.

关键词： Tweets

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A Real-Time Oral Cavity Gesture Based Words Synthesizer Using Sensors

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computers, Materials & Continua 2022年第6期71卷 4523-4554页

The present system experimentally demonstrates a synthesis of syllables and words from tongue manoeuvers in multiple languages,captured by four oral sensors *** an experimental demonstration of the system used in the oral cavity,a prototype tooth model was *** on the principle developed in a previous publication by the author(s),the proposed system has been implemented using the oral cavity(tongue,teeth,and lips)features alone,without the glottis and the *** positions of the sensors in the proposed system were optimized based on articulatory(oral cavity)gestures estimated by simulating the mechanism of human *** system has been tested for all English alphabets and several words with sensor-based input along with an experimental demonstration of the developed algorithm,with limit switches,potentiometer,and flex sensors emulating the tongue in an artificial oral *** system produces the sounds of vowels,consonants,and words in English,along with the pronunciation of meanings of their translations in four major Indian languages,all from oral cavity *** experimental setup also caters to gender mapping of *** sound produced from the hardware has been validated by a perceptual test to verify the gender and word of the speech sample by listeners,with∼98%and∼95%accuracy,*** a model may be useful to interpret speech for those who are speech-disabled because of accidents,neuron disorder,spinal cord injury,or larynx disorder.

关键词： English vowels and consonants oral cavity proposed system sensors speech-disabled speech production vocal tract model

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AQROM:A quality of service aware routing optimization mechanism based on asynchronous advantage actor-critic in software-defined networks

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Digital communications and Networks 2024年第5期10卷 1405-1414页

作者： Wei Zhou Xing Jiang Qingsong Luo Bingli Guo Xiang Sun Fengyuan Sun Lingyu Meng School of Information and Communication Guilin University of Electronic TechnologyGuilin541004China The 34th Research Institute of China Electronics Technology Group Corporation Guilin541004China Guangxi Key Laboratory of Optical Network and Optical Information Security Guilin541004China State Key Laboratory of Information Photonics and Optical Communications BeijingUniversity of Posts and TelecommunicationsBeijing100876China Department of Electrical and Computer Engineering University of New MexicoAlbuquerqueNM87131USA

In Software-Defined Networks(SDNs),determining how to efficiently achieve Quality of Service(QoS)-aware routing is challenging but critical for significantly improving the performance of a network,where the metrics of QoS can be defined as,for example,average latency,packet loss ratio,and *** SDN controller can use network statistics and a Deep Reinforcement Learning(DRL)method to resolve this *** this paper,we formulate dynamic routing in an SDN as a Markov decision process and propose a DRL algorithm called the Asynchronous Advantage Actor-Critic QoS-aware Routing Optimization Mechanism(AQROM)to determine routing strategies that balance the traffic loads in the *** can improve the QoS of the network and reduce the training time via dynamic routing strategy updates;that is,the reward function can be dynamically and promptly altered based on the optimization objective regardless of the network topology and traffic *** can be considered as one-step optimization and a black-box routing mechanism in high-dimensional input and output sets for both discrete and continuous states,and actions with respect to the operations in the *** simulations were conducted using OMNeT++and the results demonstrated that AQROM 1)achieved much faster and stable convergence than the Deep Deterministic Policy Gradient(DDPG)and Advantage Actor-Critic(A2C),2)incurred a lower packet loss ratio and latency than Open Shortest Path First(OSPF),DDPG,and A2C,and 3)resulted in higher and more stable throughput than OSPF,DDPG,and A2C.

关键词： Software-defined networks Asynchronous advantage actor-critic QoS-aware routing optimization mechanism

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Features Responsible for Spread of Zoonotic Disease Brucella: A Study on Central India Population

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SN computer Science 2025年第1期6卷 1-19页

作者： Namdev, Nishant Makhijani, Richa Dorshetwar, Kirti Parate, Mayur Shukla, Jayshree L. Husain, Aliabbas A. Kashyap, Rajpal S. Department of Basic Sciences Indian Institute of Information Technology Maharashtra Nagpur India Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur India Department of Electronics and Communication Engineering Indian Institute of Information Technology Maharashtra Nagpur India Research Centre Central India Institute of Medical Sciences Maharashtra Nagpur India

Zoonotic diseases pose a growing health risk in developing nations because of the ongoing animal-human interaction. India has experienced significant urbanization in recent years, with a large population migration from rural to urban areas. One of the most common and feared risks to which humans are exposed is zoonotic diseases. Numerous studies conducted in India have identified specific zoonoses, such as Listeriosis, Brucellosis, and Bovine tuberculosis, as extremely important to human health. The rough set method is appropriate for several real-world applications and is a component of the computing strategy. Rough sets offer several noteworthy benefits. The rough sets encounter small data sets and provide helpful techniques for revealing hidden patterns in data. In this work, a cross-sectional study was conducted to investigate the prevalence of zoonosis in the Central India Population. Three states were studied for zoonotic infection prevalence, including Maharashtra, Madhya Pradesh, and Chhattisgarh. A total of 7026 samples were recruited by inclusion and exclusion criteria for the study. The lifestyle and animal exposure data of this population was collected and tested for Brucella. On this data, the rough set approach yielded an accuracy of 87.5% for the Brucella-infected case. Our approach has resulted in selecting features responsible for diagnosing the lifestyle parameters which could result in the spread of Brucella. This research also follows the UNSDG goal of good health, essential to sustainable development. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Brucella Feature selection Pattern recognition Rough sets Zoonotic disease

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IoT-Enabled Advanced Foam Firefighting E-Vehicle 1

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1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Ravi, R. Kannadhasan, S. Mangaleswaran, M. Bharathi, R. Kabilan, R. Mallika Pandeeswari, R. Department of Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India Department of Electronics and Communication Engineering Study World College of Engineering Coimbatore India Department of AI & DS S.A. Engineering College Chennai India Department of Information Technology M. Kumarasamy College of Engineering Karur India Department of Electronics and Communication Engineering Francis Xavier Engineering College Tirunelveli India

ISBN: (数字)9789819981359

ISBN: (纸本)9789819981342

A natural or man-made calamity may nevertheless happen periodically, despite the many fire safety measures in place. In the case of a fire, we must deploy hazardous human resources to put out flames and save lives. Robots may very well replace people in the firefighting process due to advancements in technology, notably in robotics. Because they would not have to take the possibility of endangering people's lives, firemen would be more effective as a result. The foam and water systems will automatically detect fires thanks to an Arduino-built firefighting robot. In this proposed method, the Arduino is utilized to build a basic robot that can move in response to instructions. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Global system for mobile communications

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An Effective Biomedical Named Entity Recognition by Handling Imbalanced Data Sets Using Deep Learning and Rule-Based Methods

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SN computer Science 2023年第5期4卷 650页

作者： Archana, S.M. Prakash, Jay Singh, Pramod Kumar Ahmed, Waquar Computer Science and Engineering National Institute of Technology Kerala Calicut India Computer Science and Engineering ABV-Indian Institute of Information Technology and Management Madhya Pradesh Gwalior India Electronics and Communication Engineering National Institute of Technology Kerala Calicut India

Biomedical named entity recognition (Bio-NER) is an important and fundamental task for biomedical text mining. Bio-NER has various applications, especially in Natural Language Processing, and its performance heavily impacts dependent tasks. However, the class imbalance problem is the major issue for an effective Bio-NER. Over the past few years, rule-based methods and deep learning-based methods have been widely used. However, they are not able to effectively handle class imbalance issues. Therefore, we proposed a hybrid method by taking advantage of both methods for Bio-NER, which is named Hybrid Bio-NER (HBio-NER). Furthermore, we propose a training data format called ‘single_entity-O’, which reduces the class imbalance problem. Data preprocessing and ‘single_entity-O’ data representation format improve the class balance, further improving the HBio-NER system performance. HBio-NER identifies the named entities using the Bidirectional Long Short-Term Memory (Bi-LSTM) model and named entity boundaries (begin, inside of entity mentions) using rules. The effectiveness of the proposed method is tested on two data sets (NCBI disease and CHEMDNER chemical data sets) with different word representation models (GloVe, Word2Vec and FastText). The comparative analysis indicates that significant performance gain is achieved by the proposed method especially for disease named entity recognition. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Biomedical named entity recognition Biomedical text mining Deep learning Imbalanced data handling Natural language processing

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