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Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model

computers, Materials & Continua 2022年第6期71卷 5751-5764页

作者： Mahmoud Ragab Khalid Eljaaly Maha Farouk SSabir Ehab Bahaudien Ashary S.M.Abo-Dahab E.M.Khalil Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Faculty of Computers and InformationLuxor University85951Egypt Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia

The recent developments in biological and information technologies have resulted in the generation of massive quantities of data it speeds up the process of knowledge discovery from biological *** to the advancements of medical imaging in healthcare decision making,significant attention has been paid by the computer vision and deep learning(DL)*** the same time,the detection and classification of colorectal cancer(CC)become essential to reduce the severity of the disease at an earlier *** existing methods are commonly based on the combination of textual features to examine the classifier results or machine learning(ML)to recognize the existence of *** this aspect,this study focuses on the design of intelligent DL based CC detection and classification(IDL-CCDC)model for bioinformatics *** proposed IDL-CCDC technique aims to detect and classify different classes of *** addition,the IDLCCDC technique involves fuzzy filtering technique for noise removal ***,water wave optimization(WWO)based EfficientNet model is employed for feature extraction ***,chaotic glowworm swarm optimization(CGSO)based variational auto encoder(VAE)is applied for the classification of CC into benign or *** design of WWO and CGSO algorithms helps to increase the overall classification *** performance validation of the IDL-CCDC technique takes place using benchmark Warwick-QU dataset and the results portrayed the supremacy of the IDL-CCDC technique over the recent approaches with the maximum accuracy of 0.969.

关键词： Colorectal cancer deep learning medical imaging bioinformatics metaheuristics parameter tuning

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Relip: Reliable in-Band Parallel Communication for Magnetic MIMO Wireless Power Transfer System

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xinyu Zhou, Wangqiu Zhou, Hao Jiang, Shenyao Liu, Zhi Wang, Xiaoyan Ji, Yusheng Song, Qi City University of Hong Kong Department of Computer Science Hong Kong University of Science and Technology of China School of Computer Science and Technology Hefei230027 China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan Ibaraki University Graduate School of Science and Engineering Ibaraki310-8512 Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-0003 Japan

In magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems, receiver (RX) feedback communication is promising to enhance the capability and efficiency of the system. Although some studies have explored in-band implementations with low overhead costs, it has not been comprehensively investigated. In this paper, we propose Relip, a Reliable layer-level in-band parallel feedback communication mechanism for MIMO MRC-WPT systems, which addresses the impact of RX-RX couplings (i.e., non-negligible interference from strong couplings and positive effects of relay phenomenon), and provides a theoretical analysis of communication reliability. Technically, we first devise an On-Off based two-phase modulation mechanism to achieve RX identification and dependency detection under relay phenomenon. Then, we utilize observed channel decomposability to collect group-level power transfer channel conditions for eliminating the interference caused by strong RX-RX couplings. Furthermore, we perform RX selection to optimize the trade-off between communication reliability and time overhead. We design and implement the Relip prototype and conduct extensive experiments. The results validate the effectiveness of our mechanism, i.e., Relip can provide ≥99% average decoding accuracy for concurrent feedback communication of 14 devices, achieving an 18.31% improvement compared to the state-of-the-art solution. © 2002-2012 IEEE.

关键词： Inductive power transmission

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CNN-LSTM: Development of Offline Signature Authentication

CNN-LSTM: Development of Offline Signature Authentication

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Revathy Ganapathy Lingeshwaran D P. SuthanthiraDevi Harish Kumar M V Department of Computer Science and Engineering Vels Insitute of Science Technology and Advanced Studies Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai India

Offline Signature Authentication is a critical task in the field of document authentication, and its accuracy is essential for ensuring security while transactions. This research proposes two approaches: Initially Pre-trained CNN models are used to extract features from signature images, which are then combined with handcrafted features such as HOG and some other geometric features of signature. Such combined features are passed to bidirectional LSTM model in which drop out layer undergoes classification which differentiate real and forgery signature. The proposed system has potential applications in document authentication and security, subsequently combination of CNN models and additional features provides more comprehensive representation of signature images resulting in improved accuracy. Three signature datasets are utilized namely GDPS, CEDAR, and BHSig-Bengali each with varying signature styles and image quality. Our experimental outcomes reveal that Bidirectional Convolutional LSTM along with handcraft features attained maximum accuracy in offline signature verification system.

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Energy-Aware Task Allocation for Teams of Multi-mode Robots

arXiv

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arXiv 2025年

作者： Ito, Takumi Funada, Riku Sampei, Mitsuji Notomista, Gennaro Department of Systems and Control Engineering Institute of Science Tokyo Tokyo152-8550 Japan Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

This work proposes a novel multi-robot task allocation framework for robots that can switch between multiple modes, e.g., flying, driving, or walking. We first provide a method to encode the multi-mode property of robots as a graph, where the mode of each robot is represented by a node. Next, we formulate a constrained optimization problem to decide both the task to be allocated to each robot as well as the mode in which the latter should execute the task. The robot modes are optimized based on the state of the robot and the environment, as well as the energy required to execute the allocated task. Moreover, the proposed framework is able to encompass kinematic and dynamic models of robots alike. Furthermore, we provide sufficient conditions for the convergence of task execution and allocation for both robot models. Copyright © 2025, The Authors. All rights reserved.

关键词： Constrained optimization

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Deep Learning-based Cluster Analysis for Healthcare Data Classification

Deep Learning-based Cluster Analysis for Healthcare Data Cla...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： M. Gayathri V. Kavitha Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India

The use of cutting-edge technology in the medical field results in the production of massive volumes of data on a daily basis. Various categories of information are applied in the domain of healthcare, including clinic information, medical record data, and genetic information. In addition, real-time monitoring in the medical industry produces enormous volumes of data, and properly interpreting such enormous amounts of data is a significant problem. The evaluation of medical information becomes more required so that suitable drugs may be supplied and issues can be avoided by taking appropriate measures depending on the history of the patient. Automation makes data analysis more effective, but performance might deteriorate when there are problems with data integrity, diversity, or consistency. Automation makes data analysis more effective. In order to handle the management of massive amounts of data, many models that are powered by neural networks have been developed; still researchers are currently attempting to develop a superior model that is more accurate. Hence, fuzzy c denotes the process of clustering, whereas generative adversarial networks are used in this study to clusters and classify medical data, aiming to achieve the maximum degree of efficiency in classification. The experiment will use both the benchmark lung cancer sample and the arrhythmias sample. The proposed model achieves a maximum accuracy of 97.3% for dataset 1 and 98.2% for dataset 2, outperforming other methods such as support vector machine, decision tree, and random forest algorithms.

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Optimally Scheduling Single-Arm Multicluster Tools for Manufacturing Hybrid-Type Wafers

Optimally Scheduling Single-Arm Multicluster Tools for Manuf...

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IEEE International Conference on systems, Man and Cybernetics

作者： GengHong Wang QingHua Zhu Yan Hou Yan Qiao NaiQi Wu MengChu Zhou School of Computer Science and Technology Guangdong University of Technology Guangzhou China Institute of Systems Engineering Macau University of Science and Technology Macau China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

In semiconductor manufacturing, multicluster tools are widely employed for many wafer fabrication processes. With the demand for high-mix integrated circuit chips and shrinkage of circuit width, a scheme in which multiple wafer types are fabricated inside multicluster tools is adopted by wafer foundries to make more profits. Multiple wafer types, multiple robots, and wafer residency time constraints make the resulting scheduling problems challenging. This work focuses on scheduling a single-arm multicluster tool to process two wafer types concurrently subject to wafer residency time constraints in which a conventional one-wafer cycle and backward strategy are not efficient. With such properties, several necessary and sufficient conditions are presented to check the feasibility of a periodic schedule. Polynomial-time-complex algorithms are proposed to examine a tool's schedulability and coordinate multiple robots to handle wafers for schedulable scenarios. The cycle time of an obtained schedule can reach the lower bound. A practical example is used to show the effectiveness of the proposed algorithm.

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Predicting Types of Urinary Crystals and Diameter Using Image Processing Techniques and Deep Learning Models 24

Predicting Types of Urinary Crystals and Diameter Using Imag...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Huang, Yu-Ju Tseng, Yi-Shiou Chen, Tzu-Jung Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Department of Surgery Far Eastern Memorial Hospital Graduate Institute of Medicine Yuan Ze University Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Kidney stones are primarily crystals formed from ion oversaturation in urine. Currently, the diagnosis of kidney stones involves experienced professionals manually interpreting images of urinary crystals under a microscope. In recent years, with the rapid development of artificial intelligence (AI), many researchers have proposed various methods to assist in diagnosis, hoping to improve medical quality. This study explored the application of AI in the medical field, aiming to automate the interpretation of urinary crystal images, such as red blood corpuscles (RBCs), calcium oxalate monohydrate (COM), calcium oxalate dihydrate octahedron (COD-OCT), calcium oxalate dihydrate dodecahedron (COD-DOD), debris, and others, and to use image processing techniques to classify urinary crystal types. This research is expected to assist medical personnel in quickly determining the categories of urinary crystals. Ultimately, our model achieved an F1-score of 0.93, indicating that this architecture has significant potential. © 2024 Copyright ACM.

关键词： Deep learning

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Implementation Graph Database Framework for Credit Card Fraud Detection

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Procedia computer science 2023年 227卷 326-335页

作者： Nurrahmi Mauliddiah Suharjito Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480

Credit card use is becoming more and more commonplace every day. Financial organizations and credit card customers lose a lot of money because of complicated illegal transactions. Fraudsters constantly stay on top of new technology to quickly perpetrate fraud against customer transaction patterns. We analyze credit card transaction networks and identify suspicious patterns, such as transactions connected to multiple accounts or unusual transaction patterns, transactions made at unusual times, and to monitor credit card transactions in real-time and quickly identify suspicious transactions. TigerGraph is used to analyze data, display results on a dashboard, and send notifications via email. One meth’\ Vc 1``13-od commonly used in anomaly detection is to compare data values against the standard deviation. In this research, we explain the use of TigerGraph as a platform for anomaly detection above the standard deviation, as well as the use of the Louvain algorithm in finding merchant communities used by fraudsters. The data used in this study comes from Sparkov simulation data obtained from Kaggle. Our results show that by using TigerGraph, we managed to achieve a very high accuracy rate of 99.77%, precision 82.84%, recall 72.38%, and f1-score 77,26% in predicting transaction fraud on Sparkov simulation data. This is much better than the results reported in a paper that uses the supervised machine learning method with the AdaBoost algorithm which achieves the highest accuracy of 77%.

关键词： Credit card fraud detection TigerGraph Dashboard Alert

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Multimodal Sentimental Classification using Long-Short Term Memory

Multimodal Sentimental Classification using Long-Short Term ...

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Integrated Intelligence and Communication systems (ICIICS), International Conference on

作者： Busani Sravani Pushpa Mohan A. H. A. Hussein Gotte Ranjith Kumar P. Umaeswari CSE Malla Reddy Engineering College for Women Hyderabad India Department of computer science and engineering HKBK College of Engineering Bangalore India College of Pharmacy Ahl Al Bayt University Karbala Iraq School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computer Science and Business Systems R.M.K. Engineering College Kavaraipettai India

The increasing popularity of mobile devices and social networks has leads people to share images and text to express their emotions and opinions. This paper proposed a deep learning-based technique for multimodal sentimental analysis. For this work, the CMU-MOSI and CMU-MOSEI dataset is utilized which contains video, audio and text clips. The inputs are in the form of video, audio and text, so individual preprocessing and feature extraction techniques are performed with separate techniques. Then, the extracted features from separate techniques are concatenated and given as input for the classification of sentimental analysis. The Long-Short Term Memory (LSTM) is utilized in this research for classification. The performance metrics like 7-class accuracy (Acc-7), binary accuracy (Acc-2), F1-score, Mean Absolute Error (MAE) and Correlation Coefficient (CORR) are utilized to evaluate the LSTM model. The attained result shows that the LSTM model obtains better Acc-2 of 91.57% on CMU-MOSI dataset and 91.28% of CMU-MOSEI dataset when compared to existing techniques like Multi-Tensor Fusion Network with Cross-Modal Modeling (MTFN-CMM), Sparse and Cross-Attention Network (SCANET) and Sparse and Cross-Attention Network (SCANET).

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CoLRIO: LiDAR-Ranging-Inertial Centralized State Estimation for Robotic Swarms

arXiv

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arXiv 2024年

作者： Zhong, Shipeng Chen, Hongbo Qi, Yuhua Feng, Dapeng Chen, Zhiqiang Wu, Jin Wen, Weisong Liu, Ming The School of Systems Science and Engineering Sun Yatsen University Guangzhou510006 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Aeronautical and Aviation Engineering Hong Kong Polytechnic University Hong Kong

Collaborative state estimation using different heterogeneous sensors is a fundamental prerequisite for robotic swarms operating in GPS-denied environments, posing a significant research challenge. In this paper, we introduce a centralized system to facilitate collaborative LiDAR-ranging-inertial state estimation, enabling robotic swarms to operate without the need for anchor deployment. The system efficiently distributes computationally intensive tasks to a central server, thereby reducing the computational burden on individual robots for local odometry calculations. The server back-end establishes a global reference by leveraging shared data and refining joint pose graph optimization through place recognition, global optimization techniques, and removal of outlier data to ensure precise and robust collaborative state estimation. Extensive evaluations of our system, utilizing both publicly available datasets and our custom datasets, demonstrate significant enhancements in the accuracy of collaborative SLAM estimates. Moreover, our system exhibits remarkable proficiency in large-scale missions, seamlessly enabling ten robots to collaborate effectively in performing SLAM tasks. In order to contribute to the research community, we will make our code open-source and accessible at https://***/PengYu-team/Co-LRIO. Copyright © 2024, The Authors. All rights reserved.

关键词： State estimation

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