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12th international conference on Recent Trends in Computing, ICRTC 2024

12th International Conference on Recent Trends in Computing,...

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12th international conference on Recent Trends in Computing, ICRTC 2024

ISBN: (纸本)9789819788354

The proceedings contain 34 papers. The special focus in this conference is on Recent Trends in Computing. The topics include: Deep Learning-based Prediction of Human Ageing Related Proteins;the Insight of Clustering Algorithms for Emerging Networking Paradigms;Revolutionizing Cardiovascular Health: Exploring the Potential of AI-Driven Approaches in Diagnosing Arrhythmia Disorders;humor Detection in Telugu Social Media Text Using Cost-Sensitive Learning and Indian Language Embeddings;empirical Comparison of Sleep Disorder Prediction Using Machine Learning Techniques;securing Smart Grids: Decentralized Anomaly Detection Using Federated Learning and Recurrent Neural Networks;machine Learning-Powered Intrusion Detection for Network Security;secure Chat System: Harnessing the Power of Hybrid Encryption for Enhanced Security and Confidentiality;deep Neural Networks for Early Diabetes Mellitus Prediction;advancements in image Deblurring and Performance Metrics Using Deep Learning Technique;emotion-Levitating Music Recommendation System Through Real-Time Facial Recognition;smartVoice TrashMate: The Intelligent Voice-Controlled Trash Bin;an Innovative image Enhancement Algorithm for Color Filter Array images: A Novel Approach;Enhancing Secure and Efficient Communication in Free-Space Optical Networks Using Spectral Amplitude Coding - OCDMA technology;enhancing Citrus Fruit Classification and Quality Assessment Through Advanced Machine Learning Techniques;connecting Health for a Better Tomorrow Through internet of Medical Things;image Processing in Retail Marketing: Innovations for Customer Engagement, Sales, and Operational Efficiency;Leaf Checker: AI-based Plant Diagnostic Tool Using Alexnet and Mobilenet;ioT-Supported Indoor Navigation System Using Machine Learning Technique;a Customizable Virtual Assistant Enhanced with Vision and Speech Capabilities;An Unequal Cluster-based Routing Mechanism Using Lyrebird Optimization Algorithm to Achieve Network Longevity in WSNs.

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9th EAI international conference on IoT as a Service, IoTaaS 2023

9th EAI International Conference on IoT as a Service, IoTaaS...

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9th EAI international conference on IoT as a Service, IoTaaS 2023

ISBN: (纸本)9783031705069

The proceedings contain 33 papers. The special focus in this conference is on IoT as a Service. The topics include: Highway Obstacle Recognition based on Improved YOLOv7 and Defogging Algorithm;cloud Gaming Resource Management Platform based on Edge Intelligence;hardware-Efficient Polar Decoder for 5G internet of Things Communication;Design and Optimization of a Solar-Powered IRS and Relay Assisted MEC System;highway image Enhancement Method based on Jade;method and Practice of Borehole Group Detection in Borehole Drilling based on Six-Axis Sensing;Deep Reinforcement Learning-based Channel and Power Allocation in Multibeam LEO Satellite systems;CSI-based signal Reconstruction for WiFi Localization;an Indoor Navigation Algorithm Using Multi-dimensional Euclidean Distance and the Adaptive Particle Filter;Research on the PID Controllers Algorithm of the Quad Rotor;based on the Nonlinear State Equations of 6-DOF Robot with Application to Autonomous Flight;a Study on Collaborative Lane Change Decision Making of Multi-automated Vehicles based on Deep Graph Reinforcement Learning;a Short Term Traffic Prediction Model based on Deep Capture of Temporal Periodic Drift;multi-agent Reinforcement Learning for Cooperative On-Ramp Merging of Connected Automated Vehicles;SVM Multi-class Classification Method for Device Identification Using Eye Diagram Parameters;users Collaborative Computing for Hierarchical Federated Learning based on Incentive Mechanism;spatio-Temporal Traffic Prediction of Wireless Communication Network based on Multi-source Data;flying Ad-Hoc Network Routing Protocol based on Backward Algorithm;A Novel HPSO-IGWO Algorithm for Rapidly Searching Optimal Fire Rescue Paths based on IoT Architecture;A Hybrid WOA-MTCNN Algorithm for Accurate Face Detection based on IoT Architecture.

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Adaptive KOA: Leaf disease classification using hyperspectral images for internet of Things (IoT)-based sustainable agriculture 5

Adaptive KOA: Leaf disease classification using hyperspectra...

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5th international conference on Emerging systems and Intelligent Computing, ESIC 2025

作者： Mallick, Pradeep Kumar Reddy, Annapareddy V. N. Mohanty, Manas Ranjan Sahoo, Biswajit Jagadev, Alok Kumar Du School of Computer Engineering Odisha Bhubaneswar India Lakireddy Bali Reddy College of Engg. Department of Information Technology Mylavaram Andhra Pradesh NTR District India

ISBN: (纸本)9798331522100

In recent times, the crops as well as the agriculture management is one of the important field to watch out, therefore the image processing business provide more benefit to the crops along with support precaution needs. In this research, an adaptive Kookaburra Optimization Algorithm (Adaptive KOA) is implemented for leaf disease classification using hyperspectral images for Sustainable Agriculture in *** first, the IoT modelis replicated and the routing is conductedto transmit the data from source to the Base Station (BS). Moreover, in BS the following steps are conducted. The input hyperspectral image (HSI)is subjected to a band selection module. The band selection is done using the implemented Adaptive Kookaburra Optimization Algorithm (Adaptive KOA). The developed Adaptive KOA is devised by incorporating Adaptive concept with Kookaburra Optimization Algorithm (KOA). The Selected bands are fed into the leaf segmentation module, where the Deep Embedded Clustering (DEC) technique is utilized for segmentation. Fusion of segmented image from different bands is carried out by the majority voting method. Finally, Leaf disease classification is carried out based on the 3D-Convolutional Neural Network (3DCNN) into normal and abnormal cases. The developed Adaptive KOA-3DCNN method has the maximum value for accuracy as 0.917%, highest value for True negative rate(TNR)as 0.927%, highest value for True positive rate (TPR) as 0.907%. © 2025 IEEE.

关键词： image segmentation

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Enhancing image Quality to Improve Medical image Classification: Application to Nuclear Medicine Planar images

Enhancing Image Quality to Improve Medical Image Classificat...

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18th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Boukhennoufa, Ouassim Zerhouni, Noureddine Comas, Laurent Nicod, Jean-Marc Boulahdour, Hatem SUPMICROTECH CNRS Institut FEMTO-ST BesançonF-25000 France CHUBesançn Médecine Nucléaire BesançonF-25000 France Université de Franche-Comté SINERGIES BesançonF-25000 France

Nuclear Medicine images are obtained by injecting small amounts of radio-tracers into the body to track specific organs. Particular cameras detect radiations emitted from the radio-tracers resulting in images that visualize the function of the organs rather than their structure. The association of the cameras and radio tracers causes low resolution and low signal-to-noise ratio, therefore, the images are often of poor quality. image Quality Enhancement (IQE) is one possible solution to this problem as it improves the clarity of the images by removing noise and correcting distortions. In this paper, we propose a methodology based on artificial intelligence (AI) with the integration of an IQE step for the detection of normal/abnormal parathyroid glands. Two different IQE techniques are employed, one based on a statistical filter and the other on AI. The enhanced images are processed with a Convolutional Neural Network (CNN), and Lasso regression for features extraction and selection. Finally, several AI models are used for binary image classification. The obtained results achieved an accuracy of 83% in distinguishing normal/abnormal parathyroid glands. IQE step significantly improves the accuracy of the AI model by 16.9% over the initial accuracy of 71%, demonstrating the importance of IQE in assessing image classification performance. © 2025 by SCITEPRESS– Science and technology Publications, Lda.

关键词： Nuclear medicine

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Anomaly Detection in internet of Things Using Weighted Botox Optimization Algorithm with Gated Recurrent Unit 3

Anomaly Detection in Internet of Things Using Weighted Botox...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Sujatha, J. Patil, Nagaraj B. Supriya Abas, Hayder Muhamed Jagadesh, Gona Sir M Visvesvaraya College of Engineering Department of Computer Science and Engineering Raichur India Visvesvaraya Technological University Belagavi India Government S.K.S.J Technological Institute Department of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of technology Department of Information Science and Engineering Bengaluru India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq School of Engineering Anurag University Hyderabad India

ISBN: (纸本)9798331508456

Anomaly Detection (AD) play a significant part in risk management and data security among various domains like medical image recognition, economic insurance security, internet of Things (IoT) management. However, several types of AD have been established in traditional research that concentrate on identifying anomaly with the help of classification issues. To overcome this problem, Weighted Botox Optimization Algorithm with Gated Recurrent Unit (WBOA-GRU) is proposed in this research for AD. The WBOA chooses optimal features and helps to reduce dimensionality through updating their locations based on its behavior. The GRU effectively handle sequential data when requiring less computational sources thereby enhancing the classification accuracy. The preprocessing uses two methods where hash encoding is applied to convert categorical data into numerical format and then normalization is applied to scale the data for ensuing reliability. The WBOA-GRU achieves accuracy of 99.25% for UNSW-NB15 dataset which is better than Rat Swarm Optimization with Improved Self-Attention based Gated Recurrent Unit (RSO-ISAGRU). © 2025 IEEE.

关键词： Risk management

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Medical image Noise Reduction Methods based on Cutting-Edge Hybrid Filtering Designs

Medical Image Noise Reduction Methods Based on Cutting-Edge ...

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international conference on Smart systems and Wireless Communication, SSWC 2024

作者： Saha, Tanusree Vishal, Kumar Magadh University Bodh Gaya India Department of Information Technology JIS College of Engineering Kalyani India Department of Mathematics Magadh University Bodh Gaya India

ISBN: (纸本)9789819613472

A high-resolution noise free medical image is an essential requirement for accurate disease detection. Medical image processing and analysis techniques have become utmost important in recent decades as a supplementary competence for medical professionals in the fields of diagnosis and prevention. Obtaining high-quality images without any interference is a crucial requirement for effectively managing therapeutic images. Medical images may contain many types of noises for involvement of simultaneous operation of multiple equipment involved in data capturing and transmission. Various filters have been suggested for eliminating different types of noises, each having its own merits and drawbacks depending on its capabilities and projected benefits. Our research depicts a technique for removing noises from medical images using a hybrid filtering approach based on ensemble adaptive mean filtering and Block-matching-3D filtering. The proposed methodologies specifically aim to reduce the speckle noise in brain MRI images obtained from ultrasonic therapy by employing hybrid filtering techniques. This proposed hybrid filtering algorithms integrate the most effective aspects of high-performance filters to achieve a de-noised image that retains fine details, edges, and textures. The statistical performance measurement metrics like Root Mean Square Error (RMSE), Peak signal-to-Noise Ratio (PSNR) are employed to evaluate the improved visual quality of the images. The results indicate that our proposed hybrid filtering technique functioning efficiently and provide better quality de-noised image compare with other existing de-noising techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image enhancement

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Enhancing Grayscale image Clarity Through Blind Deconvolution and PSF Estimation With Total Variation Regularization 3rd

Enhancing Grayscale Image Clarity Through Blind Deconvolutio...

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3rd international conference on Intelligent systems and Sustainable Computing, ICISSC 2023

作者： Narasimharao, Jonnadula Kumar, Voruganti Naresh Kirankumar, Adepu Pooja, Bejjanki Rambabu, D. Joshi, Ganpat Department of CSE CMR Technical Campus Telangana Hyderabad India Sreenidhi Institute of Science and Technology Telangana Hyderabad India Department of Computer Science and Engineering Madhav University Rajasthan India

ISBN: (纸本)9789819783540

Nowadays, the usage of digital cameras in daily life has increased, leading to a growing demand for enhancing the quality of captured images. One of the most challenging problems in image processing is the restoration of blurred or out-of-focus images. This paper presents Enhancing grayscale image clarity through blind deconvolution and PSF estimation with total variation regularization. This blind deconvolution approach is used to handle the problem of removing blur from acquired photos, making it easier to restore defocused grayscale photographs. Determining the ideal picture and the point spread function (PSF) may be challenging when using blind deconvolution, which aims to recreate a clear ideal image from a single fuzzy image. As a result, this work also introduces a unique PSF estimate technique that makes use of an adaptive filter and total variation regularization. Finally, the results obtained after implementation are demonstrated based on the blur type and performance metrics, which include the mean square error and the peak signal-to-noise ratio. The experimental outcomes indicate that our method is the best among various other methods in terms of deblurring performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adaptive filtering

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HySeg-Net: A Robust Interactive Hybrid Technique for image Segmentation and Classification in Hand Gesture Recognition 5th

HySeg-Net: A Robust Interactive Hybrid Technique for Image S...

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5th international conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Narayan, Satya Gajrani, Jyoti Jain, Vinesh Kumar Jat, Dharm Singh Engineering College Ajmer Ajmer India NUST Namibia South Africa

ISBN: (纸本)9789819761050

image segmentation is helpful for a variety of tasks in many fields, including image analysis, object recognition and characterization, content-based image recovery, and foreground detection. Several models have been developed by considering spatial analysis and adjustment of skin models to continuously enhance the segmentation process. However, the field of hand image segmentation using skin identification is still controversial due to high pixel variation and particularly low human skin shading and thus, limits the performance of models. This research designs a HySeg-Net framework, by proposing marker-based Automated Maximal Similarity-based Region Merging (AMSRM) that enhances the segmentation for recognition of foreground objects. The approach also leverages stoppage limit, region merging, and convolutional neural networks (CNN) techniques to make the framework robust for skin data belonging to different geographical regions. The proposed technique is validated on two standard datasets, namely American Sign Language and Polish Sign Language with varying backgrounds. The framework achieves significant improvements in various parameters including Probability Random Index (PRI), Global Consistent Error (GCE), Variation of Information (VOI), Mean Square Error (MSE), and subsequently peak signal-to-noise ratio (PSNR). Ablation study demonstrates that the results of HySeg-Net are better as compared to the state-of-the-art techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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A Cooperative Spectrum Sensing Method based on Feature Extraction and Fusion Clustering 14th

A Cooperative Spectrum Sensing Method Based on Feature Extr...

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14th EAI international conference on Wireless and Satellite systems, WiSATS 2024

作者： Li, Jian Li, Yue Chen, Xiaoxu Heilongjiang University Harbin150080 China

ISBN: (纸本)9783031861956

To improve the performance of spectrum sensing at low SNR, a collaborative spectrum sensing method based on feature extraction and fusion clustering is proposed (FEFC). First, the sampling matrix of the received signal is vectorially decomposed to obtain the I and Q component signals. Second, Cholesky decomposition is applied to the covariance matrices of the I and Q signals to fully extract their features and construct the two dimensional feature vectors. The K-Means clustering algorithm is used to optimize the initial parameters of the Gaussian Mixture Model (GMM), effectively preventing it from falling into local minima under low SNR. Finally, the feature vectors of the signals are classified using GMM clustering optimized by the K-Means algorithm to obtain the final spectrum sensing results. Simulation results show that this method reduces the convergence time of GMM and improves the accuracy of model classification. It effectively enhances the performance of spectrum sensing compared to other mainstream methods. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： signal to noise ratio

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An Important and Successful Multi-image Stereography-based Secret Communication System for Military Applications 12th

An Important and Successful Multi-image Stereography-Based S...

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12th international conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Ravi, C.N. Suhasini, T.S. Narasimharao, Jonnadula Mahendar, A. Amruthraj, B. Venkateswara Reddy, R. Department of Computer Science and Engineering CMR Engineering College Telangana Hyderabad India CMR Engineering College Telangana Hyderabad India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad India CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819601387

A significant factor in data transfer is computer science. Security considerations make it difficult to transmit sensitive data or have conversations via the internet. Typically, cryptography is used to transmit text-based underground messages and conceal in sequence. These days, there are numerous methods for concealing information on any media. A technique like this is steganography. The creation of a covert communication system that uses multi-image steganography will enable safe communication among the dispatcher and the recipient free from hacker intervention. image steganography is the main technique for information hiding. It involves integrating the cipher text within a picture called the cover picture that is very difficult for strangers to view through an unassisted eye. Within a cover image, the concealed data may take the shape of any kind of text, picture, music, or even video. The secret code is split up into several pieces and etched into several cover images, according to the conceptual notion of multi-image steganography. Thus, in order to make it extremely difficult for the hackers to hide the data, we suggested two image steganography concepts. In order to create a safe and protected system, this learning suggest by means of the Advanced Encryption Standard (AES) approach of cryptography with the Least Significant Bit (LSB) method of steganography. Here, a symmetric key—which is used by both the sender and the recipient to encrypt and decode data—is used by both parties. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stereo image processing

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