The proceedings contain 35 papers. The special focus in this conference is on Advances in Distributed Computing and Machine Learning. The topics include: Computer-Aided Bundle Branch Block Detection Using Symbolic Fea...
ISBN:
(纸本)9789819718405
The proceedings contain 35 papers. The special focus in this conference is on Advances in Distributed Computing and Machine Learning. The topics include: Computer-Aided Bundle Branch Block Detection Using Symbolic Features of ECG Signal;block-Chain and Cloud-Based Tender Allocation System;reliability Assessment of IoT-Enabled systems Using Fault Trees and Bayesian Networks;resnet-50 Integrated with Attention Mechanism for Remote Sensing Classification;revolutionizing Data Annotation with Convergence of Deep Learning and Active Learning to Enhance Credibility on Twitter Datasets;statistical and Deep-Learning Approaches for Individual Carbon Footprint Calculation in India;intelligent Healthcare System Using Emerging Technologies: A Comprehensive Survey;Efficient Energy Management by Using SJF Scheduling in Wireless Sensor Network;cost Effective and Energy Efficient Drip Irrigation System for IoT Enabled Smart Agriculture;facial Detection and Recognition in Drone imagery Using FaceNet;valluvan: processing Name Board images to Enhance Communication for Native Tamil Speakers;Optimized VM Migration for Energy and Cost Reduction Using TSO Algorithm in Cloud Computing;A Study on the Mental Health Among Indian Population in the Post COVID-19 Pandemic Using Computational Intelligence;Cloud-Based Anomaly Detection for Broken Rail Track Using LSTM Autoencoders and Cross-modal Audio Analysis;Face Recognition Using CNN for Monitoring and Surveillance of Neurological Disorder Patients;a Novel Approach to Breast Cancer Histopathological image Classification Using Cross-colour Space Feature Fusion and Quantum–Classical Stack Ensemble Method;comparative Analysis of Deep Learning-Based Hybrid algorithms for Liver Disease Prediction;preface;performance Improvements of Covert Timing Channel Detection in the Era of Artificial Intelligence;sign Detection Using an N-Gram Language Model and MobileNet.
Cancer detection from histopathology images is based on extraction of spatial information from whole slide images(WSI) using imageprocessing tools. Machine learning algorithms and state-of-the art convolution neural ...
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In order to increase the chaotic parameter range, improve the initial sensitivity of the chaotic system and the security of the image encryption algorithm, This paper proposed a new image encryption based on combinati...
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Especially in static situations, image stitching has been routinely used to seamlessly merge many photographs into a panorama. Using it in dynamic and real-time environments, however, presents a number of difficulties...
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ISBN:
(数字)9798331509293
ISBN:
(纸本)9798331509309
Especially in static situations, image stitching has been routinely used to seamlessly merge many photographs into a panorama. Using it in dynamic and real-time environments, however, presents a number of difficulties, such as managing shifting objects, changing surroundings, and processing limitations. An image stitching look at that works well in dynamic real-time situations is presented in this paper. The method makes use of GNN-based feature matching, dynamic object removal, and real-time synthesis techniques. Comparative studies suggest that, in comparison to traditional methods like SIFT and ORB, the suggested approach meets real-time processing requirements while achieving greater registration precision, lower homography RMSE, and consistent panoramic outputs. The findings imply that the algorithm might be used in fields where precision and efficiency are required in dynamic circumstances, like parking management systems, sports broadcasting, and autonomous driving.
Sign language is the primary means of communication for the deaf and mute community. The regional and national differences in sign language have led to barriers between different sign language systems, making communic...
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ISBN:
(数字)9798350356656
ISBN:
(纸本)9798350356663
Sign language is the primary means of communication for the deaf and mute community. The regional and national differences in sign language have led to barriers between different sign language systems, making communication between deaf and mute individuals from different regions challenging. This paper proposes a vision-tactile fusion approach to recognize sign language. The scheme applies a flexible sensor data glove to monitor the angle changes of finger joints, as well as visual imageprocessing technology, to integrate tactile and visual data for sign language recognition. The integration is performed through three methods: weighted average, weighted multiplication, and attention weights, with accuracy rates of 95.55%, 97.87%, and 98.83%, respectively.
In order to meet the real-time detection and processing requirements of on-board targets in the field of remote sensing imageprocessing, this paper carries out relevant research from the perspective of software optim...
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ISBN:
(数字)9798350363951
ISBN:
(纸本)9798350363968
In order to meet the real-time detection and processing requirements of on-board targets in the field of remote sensing imageprocessing, this paper carries out relevant research from the perspective of software optimization and proposes an onboard adaptive image windowing processing method based on differential evolution algorithm to solve the problem of shortage of on-board hardware computing resources. Based on the adaptive differential evolution algorithm, combined with the engineering application scenario, the algorithm innovatively designs an individual objective function to evaluate the “advantages and disadvantages“ of the algorithm evolution results. By coordinating all the target positions to be tracked in the image plane, the algorithm optimizes the number of windows, and considers the application boundary of the algorithm, which can effectively reduce the amount of data to be processed and greatly improve the timeliness of real-time detection on the board without losing the calculation accuracy.
The growing incidence of blindness, specifically linked to diabetic retinopathy (DR), highlights the need for expandable retina screening programs. The purpose of this work is to determine the distinction between norm...
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Crowdsourcing is becoming increasingly important for contemporary applications in machine learning and artificial intelligence. However, crowdsourcing systems may be susceptible to adversarial attacks where a subset o...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Crowdsourcing is becoming increasingly important for contemporary applications in machine learning and artificial intelligence. However, crowdsourcing systems may be susceptible to adversarial attacks where a subset of annotators deliberately provide erroneous responses. This paper introduces a novel algorithm for identifying adversarial attacks in crowdsourcing systems by recasting the problem as dense subgraph detection in bipartite graphs. In particular, we represent crowdsourced data as a weighted bipartite graph between workers and data points which are connected with edges whose weights are computed from annotators’ responses. The constructed bipartite graph is then analyzed with a sequential peeling algorithm to detect the dense subgraph that includes adversarial attacks. Compared to previous methods where only adversaries are detected, our proposed method can simultaneously identify adversarial annotators as well as affected data points. Preliminary results on real datasets showcase the potential of this novel approach.
Mobile devices running the Android operating system have become essential in contemporary life, resulting in a substantial increase in malicious software designed to target the platform. This paper introduces a new me...
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ISBN:
(数字)9798331538934
ISBN:
(纸本)9798331538941
Mobile devices running the Android operating system have become essential in contemporary life, resulting in a substantial increase in malicious software designed to target the platform. This paper introduces a new method that combines computer vision methods with conventional cybersecurity techniques to improve the identification and prevention of Android malware. We propose a novel algorithm called VisionMalNet, which utilizes image-based features extracted from application behaviors, permissions, and code structures. Unlike signature-based or heuristic approaches, VisionMalNet translates malware attributes into visual formats, thereby facilitating sophisticated pattern detection via convolutional neural networks (CNNs). Experimental findings indicate a substantial increase in detection precision, resulting in a success rate of 97.6%, surpassing the performance of current pioneering methods. This study offers a thorough and practical approach to counteracting increasingly sophisticated malware threats, closing the divide between cybersecurity and computer vision.
With the increasing complexity of traffic management systems, there is a critical need for efficient, high-performance solutions capable of processing visual data in real time to improve traffic control and safety. Tr...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
With the increasing complexity of traffic management systems, there is a critical need for efficient, high-performance solutions capable of processing visual data in real time to improve traffic control and safety. Traditional processors often face computational bottlenecks and latency issues when deploying neural networks for image recognition tasks. To address these limitations, this work proposes a dedicated neural processor implemented on the Cyclone iv FPGA, optimized for traffic signal imageprocessing. The design leverages the parallelism and reconfigurability of FPGAs, enabling significant acceleration of computations while maintaining low power consumption. At its core, the processor integrates a neural network architecture with 4 layers tailored for detecting and classifying traffic signal colors, such as red, yellow, and green. This neural network is implemented using VHDL, ensuring efficient use of FPGA resources while achieving high computational performance. Key features of the design include real-time data processing, a focus on speed and accuracy, and a compact implementation that balances resource utilization. The effectiveness of the FPGA-based neural processor is evaluated using real-world traffic signal image datasets, demonstrating robust performance with 92% classification accuracy. Comprehensive evaluations of processing speed, classification accuracy, and resource utilization affirm the efficacy of the proposed neural network-based FPGA solution in addressing the complexities of real-time traffic signal processing and modern traffic management challenges.
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