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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Thangam, S. Jahnavi, M. Chandana, K. Dheeraj, K. Janet Kumari, J Jesy Saleem Raja, A. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India University of Technology and Applied Sciences-Shinas College of Computing and Information Sciences It Department Oman

ISBN: (纸本)9798350309140

Green Vehicular Ad-hoc Networks (VANETs) are gaining significance in smart mobility because of growing concerns about the environment. The research introduces a novel method for managing traffic in such networks by combining ant-inspired routing methods with local search optimization approaches. The solution, which draws inspiration from the efficient path-finding abilities of ants, facilitates decentralized communication and sharing of traffic data among cars. It allows rapid adaptation to changing traffic circumstances and environmental elements. Local search optimization enhances routing decisions by considering the near vicinity of each vehicle. Extensive simulations demonstrate notable enhancements in traffic efficiency, decreased congestion, and improved energy conservation as compared to conventional routing methods. The suggested solutions also enhance the development of environmentally friendly and enduring transportation networks. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Detection of Cancer Cells Using Convolutional Neural Networks (CNNs) 2

Detection of Cancer Cells Using Convolutional Neural Network...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Mohiuddin, Md. Ghouse Reddy, D. Laxmi Sreenivasa Ahmed, Aijaz University Pg College Palamamuru University Dept. of Computer Science Telangana State Mahabubnagar India Chaitanya Bharthi Institute of Technology - Cbit Dept. of It Telangana State Hyderabad India University of Technology & Applied Science Dept. of It Shinas Oman

ISBN: (纸本)9798331542573

The fastest spreading dieses among the cancer dieses throughout the world is the Breast cancer. Identifying it at the prior stage will be helpful for effective treatment. Convolutional Neural Networks (CNNs) is an efficient technique which has demonstrated significant ability in classifying and recognizing patterns in medical images. In this research paper we have explored the application of deep learning models for detecting cancer cells from histopathological images, and focused on various architectures, training methods, and assessment measures. Here we specifically evaluated the functioning of four well-known models of CNN comprising AlexNet, VGG16, InceptionV3 and, ResNet50 by employing the dataset from BreakHis for binary classification of the histopathological images into Malignant and Benign classes. Each model is fine-tuned and trained through transfer learning. Here we used cross-entropy loss with Adam optimizer for binary classification, through which the learning rate is adaptively adjusted, and facilitating efficient handling of large datasets. In this research work for training our models we applied Stratified k-fold cross-validation approach for ensuring stability and generalizability. The effectiveness and performance of the models are assessed with the help of Accuracy, Precision, & Recall, and F1-Score. Comparative analysis indicates that ResNet50 outperforms the other models in detecting cancer cells. Finally, we discussed the potencies and weakness of the applied models for offering valuable perceptions into their efficacy for medical image classification tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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ASL Recognition using Deep Learning Algorithms

ASL Recognition using Deep Learning Algorithms

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Khan, Ayesha Ali, Raja Hashim Akmal, Urooj Ramazan, Alishba Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany

ISBN: (纸本)9798331504465

There is a considerably vast communication gap between deaf and hearing individuals, and one potential solution is the development of American Sign Language (ASL) recognition technology. While methods utilizing deep learning and convolutional neural networks (CNNs) have shown promising results in general ASL recognition tasks, the potential of optimized CNN architectures for ASL recognition remains *** research identifies the ideal CNN structure to achieve best ASL recognition in static images. The VGG16 model has come out to be very effective;however, analyzing multiple CNN implementations may provide interesting perspectives which could in turn expose features that could potentially be helpful in ASL recognition. This experiment compares the performance of a fine-tuned ResNet50 CNN model with a baseline VGG16 model in ASL recognition. Through analysis of each model’s respective strengths and weaknesses, the study aims to identify areas within the VGG16 architecture that may be further optimized to produce precise *** research project provides guidance for picking suitable CNN architectures and offers valuable insights for improving VGG16 model to achieve even better accuracy and robust results. © 2024 IEEE.

关键词： Convolutional neural networks

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A UWB Based Tracking and Management System for Streamlining Steel Ladle Operations 10

A UWB Based Tracking and Management System for Streamlining ...

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

作者： Srinivasan, V. Vaishnavi, S. Aravindh, V. Ashwin, V. Nishok, M. Sri Ramakrishna Engineering College Department of Mechanical Engineering Tamil Nadu Coimbatore India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384369

Ladle monitoring systems are crucial for optimizing steel melting operations, enhancing material and equipment flow, reducing wait times, boosting production, facilitating rapid problem-solving, and ensuring steel quality. While GPS and RFID are common tracking methods, GPS's accuracy is indoor limited, and RFID's precision is constrained by range and proximity. However, a proposed Ultra-Wideband (UWB) system, offering precise tracking by utilizing radio waves. UWB translates pulses into data, operating at a high frequency. UWB anchors transmit signals to UWB tags, calculating roundtrip time for location determination (Time of Flight). These values are converted into location coordinates using a plane position algorithm and stored in a database. A web app in the centralized control system retrieves and displays tracked data, including the ladle's current position, ensuring efficient material flow and real-time monitoring of steel quality. © 2024 IEEE.

关键词： ESP 32 Indoor Positioning System Plane position algorithm Real Time monitoring RFID and GPS Time of Flight Turtle Graphics UWB

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Updating Top-k Dominate Individuals with Incomplete Data Addition

Updating Top-k Dominate Individuals with Incomplete Data Add...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Wu, Jimmy Ming-Tai Wang, Ke Yan, Huizhen Chen, Chao-Chun Chen, Pei-Wei Lin, Jerry Chun-Wei Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China National Cheng Kung University Institute of Manufacturing Information and Systems Tainan70101 Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9781665475549

Top-k dominance (TKD) query is an extended query method of skyline query and top-k query, which reveals the top-k dominant individuals in an incomplete dataset by analyzing the dominance relationships between individuals and is a common decision tool in intelligent recommendation applications. This research proposes two parallel query algorithms based on Spark computing engine to address the shortcomings of the parallel top-k-dominated query algorithms for dynamic incomplete datasets. The designed model achieves good performance in terms of runtime performance compared to previous studies. © 2023 IEEE.

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Heterologous Image Matching Based on Salience Region Using Q-test and Kernel Density Estimation 4

Heterologous Image Matching Based on Salience Region Using Q...

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4th International Conference on computer Vision and Pattern Analysis, ICCPA 2024

作者： Zhai, Jinlong Yan, Xiaohu Xu, Yang School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-MacaoGreater Bay Area Shenzhen Polytechnic University Shenzhen518055 China

ISBN: (纸本)9781510682528

To address the matching problem caused by the salience differences in spatial features, spectrum and contrast between heterologous images, a heterologous image matching method based on salience region using Q-test and kernel density estimation is proposed in this paper. Firstly, the center pixel point of each sub-region in the image is detected for salience difference with other points in its neighborhood by using the Q-test method. The detected points are defined as Q-points. Secondly, the point with the largest kernel density in all Q-points is calculated by using kernel density estimation function and defined as the M-point. Then, the region size is defined according to the coordinates of M-point, and the salience region is detected. Finally, to comprehensively evaluate the performance of the proposed method, five common types of heterologous images are selected as data sources, including point cloud depth maps, infrared images, electronic navigational maps, synthetic aperture radar images, and night-time light images. Based on the salience regions, comparative experiments are carried out using histogram of absolute phase consistency gradients (HAPCG) and histogram of the orientation of the weighted phase descriptor (HOWP). The results show that the matching performance of the proposed method is better than that of the original algorithm without salience region, especially on the type of electronic navigational map. © 2024 SPIE.

关键词： Radar imaging

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Microscale Electromagnetic Tracking Using a Second-Generation Quantum Sensor 17

Microscale Electromagnetic Tracking Using a Second-Generatio...

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17th International Workshop on Impedance Spectroscopy, IWIS 2024

作者： Thuilot, Thomas Simoneit, Marc Brodmann, Jonas Schafer, Michael Hennig, Andreas Kanoun, Olfa Institute for Measurement Engineering and Sensor Technology Ruhr West University of Applied Sciences Mülheim a. d. Ruhr Germany Institute of Computer Science Ruhr West University of Applied Sciences Bottrop Germany Measurement and SensorTechnology Chemnitz University of Technology Chemnitz Germany

ISBN: (纸本)9798331507626

This study demonstrates the application of second-generation quantum sensors in one-dimensional tracking. Due to their compact size, these magnetic field-sensitive sensors are particularly advantageous for medical and industrial settings where conventional sensors may be too large or vulnerable to electromagnetic interference. The proposed sensor setup achieves an average resolution of 1.22 mV/mm for tracking within a 100 mm range. The study also explores the nature and impact of various noise sources and discusses potential future techniques for mitigating these interferences. © 2024 IEEE.

关键词： Magnetometry

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Breakthroughs in Resource Management for Mobile, Wireless, and Ad-Hoc Network Architectures

Breakthroughs in Resource Management for Mobile, Wireless, a...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Devi, V.S. Anusuya Patro, E. Krishna Rao Nagpal, Amandeep Singh, Dev Vikram Lakshmi, B. Jabbar, Ahmed Sabah Abed AL-Zahra New Horizon College Of Engineering Departmento Of Applied Sciences Bangalore India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Dxc Technologies India Mlr Institute Of Technology Department Of Computer Science And Engineering Telangana Hyderabad India College Of Medical Technology The Islamic University Medical Laboratory Technology Department Najaf Iraq

ISBN: (纸本)9798350366846

This research introduces a comprehensive suite of resource management algorithms tailored for mobile, wireless, and ad-hoc networks. The suite comprises the Dynamic Resource Allocation (DRA), Intelligent Energy-Aware Routing (IEAR), Predictive Spectrum Access (PSA), Secure Resource Allocation (SRA), and Quality of Service Enhancement (QoSE) algorithms, each addressing specific aspects crucial to network optimization. The DRA algorithm dynamically allocates resources by considering real-time demand, spectrum availability, and interference. Through an ablation study, it was revealed that each component, from real-time measurements to dynamic adjustments and iterative learning, significantly contributes to the algorithm's robustness and efficiency. The DRA algorithm excels in spectrum utilization, making it instrumental in enhancing the performance of wireless networks. A comparative evaluation against existing methods, although illustrated with dummy values for illustrative purposes, highlighted the superiority of the proposed suite. The proposed method consistently outperformed existing techniques across critical parameters such as throughput, energy efficiency, latency, scalability, QoS satisfaction, and various system attributes. Graphical representations further reinforced the promising nature of the proposed algorithms. Figures depicting throughput, latency, QoS satisfaction, and system attribute trends visually demonstrated the dominant performance of the proposed method in diverse network scenarios. In conclusion, this research presents a promising suite of resource management algorithms, offering advancements in spectrum utilization, energy efficiency, low latency, and overall network reliability. © 2024 IEEE.

关键词： Quality of service

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Machine Learning Models for Heterogenous Network Security Anomaly Detection

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Journal of computer and Communications 2024年第6期12卷 38-58页

作者： Mercy Diligence Ogah Joe Essien Martin Ogharandukun Monday Abdullahi Department of Computer Science Veritas University Abuja Nigeria Department of Pure and Applied Physics Veritas University Abuja Nigeria Department of Computer Science Air Force Institute of Technology Kaduna Nigeria

The increasing amount and intricacy of network traffic in the modern digital era have worsened the difficulty of identifying abnormal behaviours that may indicate potential security breaches or operational interruptions. Conventional detection approaches face challenges in keeping up with the ever-changing strategies of cyber-attacks, resulting in heightened susceptibility and significant harm to network infrastructures. In order to tackle this urgent issue, this project focused on developing an effective anomaly detection system that utilizes Machine Learning technology. The suggested model utilizes contemporary machine learning algorithms and frameworks to autonomously detect deviations from typical network behaviour. It promptly identifies anomalous activities that may indicate security breaches or performance difficulties. The solution entails a multi-faceted approach encompassing data collection, preprocessing, feature engineering, model training, and evaluation. By utilizing machine learning methods, the model is trained on a wide range of datasets that include both regular and abnormal network traffic patterns. This training ensures that the model can adapt to numerous scenarios. The main priority is to ensure that the system is functional and efficient, with a particular emphasis on reducing false positives to avoid unwanted alerts. Additionally, efforts are directed on improving anomaly detection accuracy so that the model can consistently distinguish between potentially harmful and benign activity. This project aims to greatly strengthen network security by addressing emerging cyber threats and improving their resilience and reliability.

关键词： Cyber-Security Network Anomaly Detection Machine Learning Random Forest Decision Tree Gaussian Naive Bayes

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Deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification 2nd

Deep learning-based cognitive digital twin system for wrist ...

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2nd International Conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Chaudhary, Jitendra Kumar Parimalam, T. Alghayadh, Faisal Yousef Keshta, Ismail Soni, Mukesh Degadwala, Sheshang School of Computing Graphic Era Hill University Bhimtal Campus Uttrakhand India Department of Computer Science Nandha Arts and Science College Tamilnadu Erode India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9781032738659

In analyzing and recognizing wrist pulse signals, it isn’t easy to mine the nonlinear information of wrist pulse signals using analysis methods such as time and frequency. Traditional machine learning methods require the manual definition of features and cannot perform self-learning of features. A cognitive digital twin technique for pulse analysis and recognition based on threshold-less recursive graph and CNN is proposed. The wrist pulse signal is converted into a threshold-free recursive graph based on the nonlinear dynamics’ theory. The VGG-16 CNN automatically extracts the nonlinear features of the recursive graph, and a pulse condition classification model is established. Experimental finding several that the classification of the proposed method accuracy can reach 98.14%, as compared with the existing pulse classification methods. This study offers a novel concept and strategy for classifying pulse signals, and it has application to the objectification of pulse diagnosis. © 2025 The Author(s).

关键词： Graphic methods

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