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Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model

Computational Intelligence and Neuroscience 2022年 2022卷 4629178页

作者： Alharbe, Nawaf R. Munshi, Raafat M. Khayyat, Manal M. Khayyat, Mashael M. Abdalaha Hamza, Saadia Hassan Aljohani, Abeer A. Applied College Taibah University Medina Saudi Arabia Faculty of Applied Medical Sciences King Abdulaziz University Rabigh Saudi Arabia Department of Information Systems College of Computers and Information Systems Umm Al-Qura University Makkah Saudi Arabia Department of Information Systems and Technology Faculty of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Computer Science College of Science and Humanities in Al-Sulail Prince Sattam Bin Abdulaziz University Saudi Arabia

Esophageal cancer (EC) is a commonly occurring malignant tumor that significantly affects human health. Earlier recognition and classification of EC or premalignant lesions can result in highly effective targeted intervention. Accurate detection and classification of distinct stages of EC provide effective precision therapy planning and improve the 5-year survival rate. Automated recognition of EC can aid physicians in improving diagnostic performance and accuracy. However, the classification of EC is challenging due to identical endoscopic features, like mucosal erosion, hyperemia, and roughness. The recent developments of deep learning (DL) and computer-aided diagnosis (CAD) models have been useful for designing accurate EC classification models. In this aspect, this study develops an atom search optimization with a deep transfer learning-driven EC classification (ASODTL-ECC) model. The presented ASODTL-ECC model mainly examines the medical images for the existence of EC in a timely and accurate manner. To do so, the presented ASODTL-ECC model employs Gaussian filtering (GF) as a preprocessing stage to enhance image quality. In addition, the deep convolution neural network- (DCNN-) based residual network (ResNet) model is applied as a feature extraction approach. Besides, ASO with an extreme learning machine (ELM) model is utilized for identifying the presence of EC, showing the novelty of the work. The performance of the ASODTL-ECC model is assessed and compared with existing models under several medical images. The experimental results pointed out the improved performance of the ASODTL-ECC model over recent approaches. © 2022 Nawaf R. Alharbe et al.

关键词： Diseases

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GUI based Prototype Development for Discretized Zeta Converter 6

GUI based Prototype Development for Discretized Zeta Convert...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Porkodi, M. Raman, Ramakrishnan Shanmugam, A. Sanjay Krishna, G.R. Aruna Kumari, K. Sajitha, L.P. Azhagumurugan, R. Department of Electrical and Electronics Engineering Sona College of Technology Tamil Nadu Salem636005 India Maharashtra Pune412115 India Department of Mechatronics Engineering Kongu Engineering College Tamil Nadu Perundurai638060 India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Mathematics GSS Gitam University Andhra Pradesh Visakhapatnam530045 India Department of Computer Science and Business Systems RMK Engineering College Tamil Nadu Kavaraipettai601206 India Department of Electrical and Electronics Engineering Sri Sai Ram Engineering College Tamil Nadu Chennai600044 India

ISBN: (纸本)9798350398496

This research study has attempted to implement the zeta converter, also known as the inverse SEPIC converter, a closed loop periodic sequence based discrete controller. A discrete controller can be adjusted without the need of external passive modules and offers an upright, forceful reaction and acceptable voltage regulation. The Inverse SEPIC Converter is capable of producing output current that is not fluctuating and has a respectable degree of flexibility. In this study, the effects of both continuous and discrete controllers are taken into consideration. The Z-transform based controller gives a commendable transient response, according to the results. The controller is experimentally validated using Virtual Instrumentation and a Data Acquisition card (NI-6009). © 2023 IEEE.

关键词： Analog to digital conversion

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Auto capture on drug text detection in social media through NLP from the heterogeneous data

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Measurement: Sensors 2022年 24卷

作者： Lavanya, P.M. Sasikala, E. Department of Computer Science and Engineering SRM Institute of Science & Technology Kattankulathur India Department of Information Technology Easwari Engineering College Ramapuram India Department of Data Science & Business Systems SRM Institute of Science & Technology Kattankulathur India

Auto Capture Drug Text detects (ACDTD) that textual content has currently gained important activities in Pharmaceutical drugs research. The recent studies focused on the different resources of textual content-related messages including social networks. Where a huge quantity of consumers posted information about the usage of pharmaceutical drugs and it's combined and cleared neatly. Our textual content classification strategies depend on regenerating the features of a large set of designs;it represents the semantic rules from the short textual nuggets. Significantly use the detailed structured files and it is combined by instruction data from the various collections to regulate the classification approach. Social Media Online Natural Language Processing (SMONLP) with ACDTD class achieves the F-scores of 0.90, 0.738, and 0.878 for the three information files. Integrating the instruction messages from various compatible files enhances the ACDTD F-scores for the in-house information files to 0.797 and 0.854 respectively. The heterogeneous textual content training using the cases where information sets are maintained may reduce the cost and time in the future. © 2022 The Authors

关键词： Natural language processing systems

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Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-Trained Language Model

SSRN

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SSRN 2024年

作者： Geng, Yuxia Chen, Jiaoyan Zeng, Yuhang Chen, Zhuo Zhang, Wen Pan, Jeff Z. Wang, Yuxiang Xu, Xiaoliang School of Computer Science Hangzhou Dianzi University Hangzhou310018 China Department of Computer Science The University of Manchester ManchesterM13 9PL United Kingdom HDU-ITMO Joint Institute Hangzhou Dianzi University Hangzhou310018 China College of Computer Science and Technology Zhejiang University Hangzhou310028 China School of Software Technology Zhejiang University Ningbo315048 China School of Informatics The University of Edinburgh EdinburghEH8 9AB United Kingdom

Both graph structures and textual information play a critical role in Knowledge Graph Completion (KGC). With the success of Pre-trained Language Models (PLMs) such as BERT, they have been applied for text encoding for KGC. However, the current methods mostly prefer to fine-tune PLMs, leading to huge training costs and limited scalability to larger PLMs. In contrast, we propose to utilize prompts and perform KGC on a frozen PLM with only the prompts trained. Accordingly, we propose a new KGC method named PDKGC with two prompts - a hard task prompt which is to adapt the KGC task to the PLM pre-training task of token prediction, and a disentangled structure prompt which learns disentangled graph representation so as to enable the PLM to combine more relevant structure knowledge with the text information. With the two prompts, PDKGC builds a textual predictor and a structural predictor, respectively, and their combination leads to more comprehensive entity *** evaluation on two widely used KGC datasets has shown that PDKGC often outperforms the baselines including the state-of-the-art, and its components are all effective. Our codes and data are available at https://***/genggengcss/PDKGC. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Data Engineering and AI-Powered Skin Cancer Identification for Healthcare Applications

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Procedia computer science 2024年 246卷 179-188页

作者： Imran Ahmed Misbah Ahmad Abdellah Chehri Gwanggil Jeon School of Computing and Information Science Anglia Ruskin University Cambridge UK Animal and Agriculture Department Hartpury University Gloucester UK Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol UK Department of Mathematics and Computer Science at the Royal Military College of Canada (RMC) Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea

Skin cancer diagnosis, a critical task in the medical domain, can be revolutionized through the application of advanced deep-learning techniques. This work investigates the efficacy of Convolutional Neural Networks (CNNs) in the automated classification of skin cancer. The process begins with a comprehensive explanation of key CNN layers: Conv2D, MaxPool2D, Dropout, and Dense. The Conv2D layers employ learnable filters that transform localized image segments, while MaxPool2D contributes to downsampling, effectively reducing computational cost and overfitting risk. Integrating these layers enables the network to capture local and global characteristics, which is crucial for accurate classification. Adding Dropout layers enhances generalization and mitigates overfitting by introducing randomness during training. ReLU activation functions infuse non-linearity, and the Flatten layer facilitates the transition to fully connected layers. The proposed CNN architecture is meticulously designed considering filter counts, kernel sizes, and pooling dimensions. The trained model demonstrates promising performance by utilizing the HAM10000 dataset, encompassing diverse skin lesion images across seven classes. The CNN model’s parameters and architecture are systematically presented, offering insights into its design rationale. The model undergoes optimization with the Adam optimizer and annealing techniques to facilitate convergence. The model’s effectiveness is evaluated on validation and test datasets, demonstrating an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. Data augmentation strategies are introduced to enhance model generalization further. The results underscore CNN’s potential as a robust tool for automating skin cancer diagnosis, aligning with the broader trend of leveraging deep learning for medical image analysis

关键词： Image Processing Medical Imaging Deep learning Skin Cancer Data Analytics

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xIDS-EnsembleGuard: An Explainable Ensemble Learning-based Intrusion Detection System

xIDS-EnsembleGuard: An Explainable Ensemble Learning-based I...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Muhammad Adil Mian Ahmad Jan Safayat Bin Hakim Houbing Herbert Song Zhanpeng Jin Deptartment of Computer Science University at Buffalo Buffalo USA College of Computing and Informatics University of Sharjah Sharjah UAE School of Information Systems University of Maryland Baltimore County USA School of Future Technology South China University of Technology Guangdong China And University at Buffalo Buffalo USA

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

In this paper, we focus on addressing the challenges of detecting malicious attacks in networks by designing an advanced Explainable Intrusion Detection System (xIDS). The existing machine learning and deep learning approaches have invisible limitations, such as potential biases in predictions, a lack of interpretability, and the risk of overfitting to training data. These issues can create doubt about their usefulness, and transparency, and decrease the trust of involved stakeholders. To overcome these challenges, we propose an ensemble learning technique called the "EnsembleGuard". This approach uses the predicted outputs of multiple models, including tree-based (LightGBM, GBM, Bagging, XGBoost, CatBoost) and deep learning models such as neural network (LSTM (long short-term memory networks) and GRU (gated recurrent unit), to maintain a balance and achieve trustworthy results. Our work is unique because it combines both tree-based and deep learning models to design an interpretable and explainable meta-model through model distillation. By considering the predictions of all individual models, our neta-model effectively addresses key challenges, and ensures both explainable and reliable results. We evaluate our model using well-known datasets, including UNSW-NB15, NSL-KDD, and CIC-IDS-2017, to assess its reliability against various types of attacks. During analysis, we found that our model outperforms both tree-based models and other comparative approaches when it comes to different kinds of attack scenarios.

关键词： Deep learning Analytical models Accuracy Intrusion detection Training data Predictive models NSL-KDD Reliability Ensemble learning Long short term memory

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Autonomous Number Plate Detection System for Vehicle Identification and Tracking

Autonomous Number Plate Detection System for Vehicle Identif...

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Recent Trends in Microelectronics, Automation, Computing and Communications systems (ICMACC), International Conference on

作者： Neha Sri Nareddy Abhinaya Polneni Rekha Baithi Pratyusha Kunchamwar Sunil Kumar Suvarna Durgabhavani Battu Naveena Budda Vikas Agarwal Premchander Munimanda AI IoT and Robotics Lab (AIR Lab) AGVs Group Autonomous Robotics Systems Limited Hyderabad India Department of Information Technology Malla Reddy Engineering College for Women Hyderabad India Ideabytes Software India Private Limited Hyderabad India School of Computer Science Georgia Tech Atlanta USA School of Computer Science and Engineering Malla Reddy Engineering College for Women Hyderabad India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

The autonomous number plate detection system plays a crucial role in modern transportation, law enforcement, and security infrastructure. Technical approaches using artificial intelligence, machine learning, and computer vision are actively researched to achieve ease of maintenance through real-time vehicle detection. The recent development has drastically improved the accuracy of object detection and recognition systems in deep neural networks. Using these techniques, vehicle number plates can easily be detected autonomously for vehicle identification and tracking. Here, we have used a custom-trained TensorFlow-based number plate detection method to autonomously detect vehicles using images captured by a smartphone, or surveillance camera. It showed an impressive mAP @ 0.5 of 95%, and demonstrated limitations in long-distance detection, leading us to designate it as the optimal vehicle detection model for accuracy. We designed and developed an autonomous number plate detection system for vehicle identification and tracking, which includes web-based, android-based, and embedded-based applications. In addition, we evaluated the proposed autonomous number plate detection system with real-time images and videos. The proposed system achieved real-time vehicle detection and tracking, boasting a commendable 90% detection accuracy.

关键词： Visualization Accuracy Law enforcement System performance Transportation Artificial neural networks Real-time systems Security License plate recognition Videos

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Detecting Characteristic Points for the Analysis of Bioimpedance Signal Through a Synergy of Fuzzy Rule-based models and Granular Neural Networks

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IEEE Transactions on Fuzzy systems 2024年

作者： Wang, Dan Richter, Monika Zhu, Xiubin Pedrycz, Witold Gacek, Adam Sobotnicki, Aleksander Li, Zhiwu Xi'an University of Science and Technology College of Computer Science and Technology Xi'an710054 China Center for Biomedical Engineering Lukasiewicz Research Network-Krakow Institute of Technology Zabrze41-800 Poland Xidian University School of Mechano-Electronic Engineering Xi'an710071 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey Macau University of Science and Technology Institute of Systems Engineering Taipa China

In this study, we propose a novel methodology for determining accurate positions of characteristic points encountered in the analysis of bioimpedance signals. The proposed approach fully utilizes two fundamental modeling pursuits based on fuzzy rule-based models and neural networks. We take advantages of the unique capabilities of fuzzy rule-based models to characterize the nonlinear relationship between the acquired bioimpedance signals and its temporal coordinate. The fuzzy modeling approach is used to approximate the process that generates the bioimpedance signals through a collection of rules (if-then statements). In the sequel, the parameters of the models are used as the inputs of neural network models to determine the position of characteristic points. We further augment the numeric neural network to its granular counterpart to accommodate the uncertainty in the available experimental evidence through allocating a certain level of information granularity across the parameter space. The resulting granular outputs (intervals) become reflective of the quality and a level of confidence associated with the prediction results. The quality of the prediction results is quantified in terms of the coverage and specificity criteria. The performance index is also enhanced to deal with the situation when the positions provided by experts are also information granules (intervals). The performance of the proposed approach is justified through a collection of experiments carried out on the collected real-world bioimpedance signals. Experimental results show that the proposed approach achieved higher accuracy in determining the position of characteristic points in comparison with other existing methods. © 1993-2012 IEEE.

关键词： Fuzzy models

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Digital Twin-Enabled Real-Time Optimization System for Traffic and Power Grid Management in 6G-Driven Smart Cities

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IEEE Internet of Things Journal 2025年

作者： Prathiba, Sahaya Beni Krishnamoorthy, Sri Ram Kannan, Karuna Soundari Selvaraj, Arikumar K. Ranganayakulu, Dhanalakshmi Fang, Kai Gadekallu, Thippa Reddy Vellore Institute of Technology Centre for Cyber Physical Systems School of Computer Science and Engineering Chennai600127 India Vellore Institute of Technology School of Electronics Engineering Chennai600127 India College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur603 203 India Zhejiang A and F University College of Mathematics and Computer Science Hangzhou311300 China Zhejiang A and F University Division of Research and Development Lovely Professional University College of Mathematics and Computer Science Hangzhou Phagwara311300 India Chitkara University Center of Research Impact and Outcome Punjab Rajpura140401 India

The advent of 6G-enabled Internet of Everything(IoE) technologies is set to revolutionize urban infrastructures by providing fast, consistent, and low-delay capabilities for communication. 6G connectivity will integrate traffic and power grids for adaptive urban management. However, current traffic networks and power grids face critical challenges such as fragmented data processing, delayed responses, and outdated resource management leading to inefficiencies like traffic congestion and power outages. In 6G-enabled smart grid cities, system complexity and interdependence demand dynamic, real-time solutions, further exacerbating inefficiencies. To address these issues, this study introduces Digital Twin-enabled Real-time Optimization System (DT-ROS), a dynamic framework designed to optimize urban traffic and power grid systems. DT-ROS integrates a dual-tier Digital Twin (DT) and an advanced scheduling framework based on Priority Age of Information Deep Q Scheduler (PAoI-QS). The dual-tier framework builds an elementary and Integrated Digital Twin (IDT) with Auto-Regressive Integrated Moving Average (ARIMA)-based forecasting for accurate real-time traffic and energy demand predictions. The advanced scheduling framework minimizes the Age of Information (AoI), ensuring decision-making relies on the most current and relevant data. By continuously monitoring and processing real-time data, DT-ROS creates virtual models to simulate system behavior and dynamically allocate resources. Simulation results demonstrate the effectiveness of DT-ROS, achieving a 30% reduction in traffic congestion and a 25% improvement in power grid stability compared to existing methods. To create effective, robust, and sustainable urban systems for future smart cities, DT-ROS addresses traffic and electricity problems. © 2014 IEEE.

关键词： Smart power grids

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A UNIFIED SUBSTRATE FOR BODY-BRAIN CO-EVOLUTION

arXiv

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arXiv 2022年

作者： Pontes-Filho, Sidney Walker, Kathryn Najarro, Elias Nichele, Stefano Risi, Sebastian Department of Computer Science Oslo Metropolitan University Oslo Norway Department of Computer Science Norwegian University of Science and Technology Trondheim Norway Digital Design Department IT University of Copenhagen Copenhagen Denmark Department of Holistic Systems Simula Metropolitan Centre for Digital Engineering Oslo Norway Department of Computer Science and Communication Østfold University College Halden Norway

The discovery of complex multicellular organism development took millions of years of evolution. The genome of such a multicellular organism guides the development of its body from a single cell, including its control system. Our goal is to imitate this natural process using a single neural cellular automaton (NCA) as a genome for modular robotic agents. In the introduced approach, called Neural Cellular Robot Substrate (NCRS), a single NCA guides the growth of a robot and the cellular activity which controls the robot during deployment. We also introduce three benchmark environments, which test the ability of the approach to grow different robot morphologies. In this paper, NCRSs are trained with covariance matrix adaptation evolution strategy (CMA-ES), and covariance matrix adaptation MAP-Elites (CMA-ME) for quality diversity, which we show leads to more diverse robot morphologies with higher fitness scores. While the NCRS can solve the easier tasks from our benchmark environments, the success rate reduces when the difficulty of the task increases. We discuss directions for future work that may facilitate the use of the NCRS approach for more complex *** Codes 68T40 © 2022, CC BY.

关键词： Covariance matrix

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