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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Rath, Subashree Gurulakshmi, A.B. Aditya, S. Naik, Vaibhav R Pawar, Amith Karoor, Vikram Computer Science and Engineering New Horizon College of Engineering Bengaluru India Electronics and Communication Engineering New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798331518097

Predicting earthquakes is one of the most challenging scientific endeavors due to the complex and dynamic nature of Earth's crust and the multitude of factors influencing seismic activity. Earthquake prediction research remains an essential scientific frontier, offering hope for better mitigation strategies and reduced impact in the future. This study examines the prediction of earthquake magnitudes using machine learning models, namely Random Forest Regression, Decision Tree Regression, and Support Vector Regression. Data such as latitude, longitude and depth were used to test and train the models to forecast magnitude. The performance of each model was evaluated using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). According to the results, the Support Vector Regression model outperforms the others. © 2024 IEEE.

关键词： Prediction models

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Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

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Intelligent Automation & Soft Computing 2023年第4期36卷 353-368页

作者： R.Anandha Murugan B.Sathyabama Department of Computer Science and Engineering K.L.N.College of Engineering630612TamilnaduIndia Department of Electronics and Communication Engineering Thiagarajar College of EngineeringMadurai625015TamilnaduIndia

Improved picture quality is critical to the effectiveness of object recog-nition and *** consistency of those photos is impacted by night-video systems because the contrast between high-proﬁle items and different atmospheric conditions,such as mist,fog,dust *** pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient *** recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance ***,the iden-tiﬁcation of objects in the night world also poses further problems because of the distorted backdrop and dim *** Correlation aware LSTM based YOLO(You Look Only Once)classiﬁer method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this *** order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerﬁlter for removing the noise in the *** process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set ***,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalﬁndings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mec

关键词： Object monitoring night vision image SSAN dataset adaptive internal linear embedding uplift linear discriminant analysis recurrent-phase level set segmentation correlation aware LSTM based yolo classiﬁer algorithm

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Ecological Surveillance and Crisis Management: Early Forest Fire Prediction and Endangered Species Identification 6

Ecological Surveillance and Crisis Management: Early Forest ...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ashok, K. Chatterjee, Pratik Jha, Aman Kumar Herle, Akshay Gupta, Shaksham Das, Saumyadeep New Horizon College of Engineering Computer Science and Engineering Bangalore India New Horizon College of Engineering Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798350306613

In the realm of ecological surveillance and crisis management, a pioneering approach combining some of the advanced machine learning techniques with bioacoustic analysis is to be delved. The proposed system integrates the predictive capabilities of Mel-frequency cepstral coefficients (MFCC) and other sophisticated models of Deep Learning like CRNN which is a hybrid and composite neural network architecture that combines elements of both the convolutional layers and recurrent layers, for the dual objectives of early forest fire prediction and endangered species identification. The soundscapes of avian habitats are leveraged for early forest fire prediction, wherein a classification system discerns sounds produced by birds into two categories: Panic and normal. This real-Time analysis enables timely intervention strategies in the event of potential fire outbreaks. Simultaneously, the study addresses the critical issue of endangered species identification. By employing MFCC and other sound-based models, the system recognizes and classifies species-specific vocalizations during predefined observation periods. The quantification of hits, where a detected sound precisely matches a trained species' vocal signature, serves as a decisive metric in determining the endangered status of a species. © 2024 IEEE.

关键词： Convolutional Recurrent Neural Networks Mel frequency cepstral coefficients

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An ensemble framework of decision trees for class imbalance using partitioning

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International Journal of Innovative Computing and Applications 2024年第1期15卷 1-13页

作者： Kadappa, Vijayakumar Guggari, Shankru Veerabhaskar, Rajeshwari Devi Doddapoojari Department of Computer Applications B.M.S. College of Engineering Bengaluru560019 India School of Computer Science and Engineering KLE Technological University Hubballi580031 India Department of Electronics and Communication Engineering Global Academy of Technology Bengaluru560098 India

Decision tree classifiers are widely used in machine learning and data mining due to their intuitiveness. However, they do not perform well for class-imbalanced data due to bias creation towards the majority class. Therefore, handling class-imbalanced data is an active research area in many applications of machine learning. Feature set partitioning paradigm is proven to be effective for classification by many researchers. In this paper, we extend the ideas of partitioning to propose a decision tree ensemble to overcome class-imbalance issues. The framework consists of balancing the data, partitioning the feature set, building local decision trees, then combining decisions. Some instances of the proposed framework (e.g., Ferrer diagram-based approach) are used to validate the performance of the framework. An empirical study is carried out using University of California Irvine (UCI), Knowledge Extraction Evolutionary Learning (KEEL), and other datasets. The performance of the instances of the proposed framework demonstrates improved performance compared to other benchmark machine learning approaches. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Adversarial machine learning

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Enhanced perceptual wavelet packet features for spontaneous Kannada sentence recognition under uncontrolled conditions

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International Journal of Speech Technology 2025年第1期28卷 153-174页

作者： Shanthamallappa, Mahadevaswamy Pradeep Kumar, B.P. Department of Electronics and Communication Engineering Vidyavardhaka College of Engineering Mysuru Visvesvaraya Technological University Karnataka India Department of Computer Science and Design Atria Institute Of Technology Bengaluru Visvesvaraya Technological University Karnataka India

A Robust Automatic Speech Recognition (ASR) system is proposed through a hybrid combination of Perceptual Wavelet Packet features, Deep Neural Network-Hidden Markov Model (DNN-HMM) acoustic models, and n-gram language models for recognizing Spontaneously spoken Kannada sentences. Two Experiments are performed on own Kannada speech corpus and the widely recognized TIMIT corpus. First one is the experiment on the clean speech datasets and the second one is on the speech degraded by noise data chosen from Noise-US dataset. The proposed system performance is evaluated through the metric Word Error Rate (WER). A comparative study on the performance analysis is presented. The results of the experimental investigations achieved an average reduction in Word Error Rate (WER) of 1.8% over Mel Frequency Cepstral Coefficients (MFCCs) and 2.1% over Perceptual Linear Prediction (PLP) features. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Hidden Markov models

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Combining Fuzzy Logic and Reinforcement Learning for Resource Management in Edge Computing 5

Combining Fuzzy Logic and Reinforcement Learning for Resourc...

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

作者： Prasath, S. Ram Selvi, M. Krishnan, R. Santhana Nithila, Ezhil E. Malar, P. Stella Rose Sankar, G. Ram Scad College of Engineering and Technology Computer Science and Engineering Cheranmahadevi India Rajarajeswari College of Engineering Computer Science and Engineering Bangalore India Scad College of Engineering and Technology Electronics and Communication Engineering Chranmahadevi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Electronics and Communication Engineering Chennai Avadi India J.P. College of Engineering Electronics and Communication Engineering Thenkasi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350379945

With the wide spread of Internet of Things (loT) devices, edge computing plays a decisive part in meeting the computational demands of loT applications. Efficient resource allocation in such networks is essential to ensure optimal utilization of resources and meet the quality of service (QoS) necessities of loT applications. This research addresses the challenge of dynamic resource allocation in edge computing networks for loT applications through a novel hybrid AI approach, 'FledegeRL.' By integrating fuzzy logic and reinforcement learning, FledegeRL aims to optimize resource utilization and enhance system performance. The proposed framework is evaluated through simulation analysis and compared with existing algorithms, including Q-Iearning, Fuzzy Logic Control, and Ant Colony Optimization, using metrics such as resource allocation accuracy, average throughput, and network overhead. The results demonstrate that FledegeRL consistently outperforms these algorithms, achieving higher accuracy, greater throughput, and lower network overhead. This highlights the potential of FledegeRL as an effective solution for dynamic resource allocation in edge computing environments. © 2024 IEEE.

关键词： Quality of service

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Identifying Hoaxes in Fake Spotter using XG Boost Machine Learning based Classification Method 4

Identifying Hoaxes in Fake Spotter using XG Boost Machine Le...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Chitti, Sridevi Rinku, Dhruva R. Juluru, Tarun Kumar Rao, P. Ramchandar Gouthami, Shiramshetty Kakatiya Institute of Technology and Science Department of Electronics and Communication Engineering Warangal India Cvr College of Engineering Department of Electronics and Communication Engineering Hyderabad India Jayamukhi Institute of Technological Sciences Department of Computer Science Engineering Warangal India

ISBN: (纸本)9798331529635

Social media has greatly streamlined communication in recent years which uses network, shares information, and keep up with current events. Many posts on social media are questionable and meant to deceive. There is a lot of fake news everywhere and online fake news could cause serious societal issues. It is crucial for us to identify false and misleading internet content. Technically, it is hard for us to differentiate between real and fake information. The development and sharing of content have been made easier by social media platforms, leading to a significant amount that must be examined. Information on the internet is diverse making this process difficult. In order to assess the reliability and purpose of statements, humans and machines must work together. Manually identifying fake news is challenging, and it can only be done when the individual doing the identification has extensive understanding of news. Therefore, there is a need for machine learning classifiers that can identify the false news automatically. The main goal is to measure the accuracy of deceptive detection utilizing various machine learning algorithms. Several machine learning (ML) techniques, such as Decision Trees (DT), Random Forest (RF), Logistic Regression (LR), Passive Aggressive Classifier (PAC), Extra Trees Classifier (ETC) and boosting algorithms were employed on datasets to determine if the content is authentic or untrue in order to identify hoaxes on different platforms for online networking sites. © 2024 IEEE.

关键词： Adversarial machine learning

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A Hybrid Optimization Approach for Secured Data Transmission in Wireless Sensor Networks 10

A Hybrid Optimization Approach for Secured Data Transmission...

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

作者： Ramya, R.S. Jayanthy, S. Sri Ramakrishna Engineering College Computer Science and Engineering Coimbatore India Sri Ramakrishna Engineering College Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350384369

In this paper, we present a hybrid pareto optimality based lion swarm optimization and enhanced encryption standard technique for safe data transmission in WSN. Optimal, improved, and energy-efficient data transmission will be the emphasis of this endeavour to ensure proper tomato crop choices are made. In order to facilitate rapid and secure data transfer, this activity implemented multipath routing. In this study, we use a hybrid Pareto optimization technique based on the Lion Swarm Optimization (HPLSO) algorithm to quickly route data over many paths while satisfying a set of performance and latency requirements. Then, based on factors like propagation speed, distance, residual energy, and transmission range, the nodes grab the data packets for a time to prevent collisions and redundant packet transfers. The optimal node has a short retention period and is very likely to transmit data packets. If a node detects that data packets have been sent, it will simply delete them from the buffer. If the wait period hasn't expired, the packet shouldn't be sent on. The most efficient node in terms of power usage is chosen from among the nodes with the fewest packet losses using frontal Pareto optimization. In this paper, a new AES method using two keys is introduced, reducing the hacking difficulty while increasing the security of data transmission. Before being included to the forwarder's node list, each node in your region must register with the verification node, where its authentication will be validated. In terms of throughput, network lifetime, power consumption, packet loss rate, and latency, the results demonstrate that the proposed HPLSOAES architecture provides superior performance. © 2024 IEEE.

关键词： Packet loss

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Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

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computer Systems science & engineering 2023年第1期44卷 579-593页

作者： V.R.Kavitha K.Nimala A.Beno K.C.Ramya Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Computer Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Networking and Communications SRM Institute of Science and TechnologyChennaiIndia Department of Electronics and Communication Engineering Dr.Sivanthi Aditanar College of EngineeringTiruchendur628215India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of computer Vision(CV)and Natural Language Processing(NLP)for generating the image *** use in several application areas namely recommendation in editing applications,utilization in virtual assistance,*** development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual *** this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)*** OHHO-DLIC technique involves the design of distinct levels of ***,the feature extraction of the images is carried out by the use of EfficientNet ***,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as *** last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM *** experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

关键词： Image captioning natural language processing artificial intelligence machine learning deep learning

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DESIGN OF RF FEEDING NETWORK [1:8] USING UNEQUAL AMPLITUDE HYBRID COUPLER FOR LOW SLL X-BAND RADAR APPLICATIONS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 1-19页

作者： Kothapudi, Venkata Kishore Sunayana, Lella Divya, Shaik Kumar, Vijay Pappula, Lakshman Gottapu, Srinivasa Kiran Guntur District Andhra Pradesh Vadlamudi522213 India Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Electrical Electronics and Communication Engineering GITAM School of Technology GITAM University Andhra Pradesh Visakhapatnam530045 India School of Science and Engineering Engineering and Computer Science Department Benedict College 1600 Harden St. ColumbiaSC29204 United States

The design simulation and manufacturing of an x-band frequency uneven amplitude 90° hybrid coupler are described in this paper. This hybrid coupler is used to create a feeder network with eight output ports operating at 9.3 GHz. The overall goal is to design and analyze an uneven power feeding network (1:8) for low SLL x-band radar applications employing a hybrid coupler. The specific goal is to create a hybrid coupler with equal amplitude, a hybrid coupler with uneven amplitude [Taylor’s amplitude = –17 dB], and a feeding network using hybrid couplers. The goal of this research is to improve the ability of a single transmitter to provide power to more than one antenna, all at a time either equally or unequally, and this can be achieved by designing a desired number of hybrid couplers and connecting them to the specific number of antennas at the output end and to the single transmitter at the input end. © 2025 by Begell House, Inc.

关键词： Transmitters

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