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International Conference on Intelligent systems and Sustainable Computing, ICISSC 2022

作者： Ashwini, P. Suguna, N. Vadivelan, N. Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Associate Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Professor Department of Computer Science and Engineering Teegala Krishna Reddy Engineering College Hyderabad India

ISBN: (纸本)9789819947164

Breast cancer is today’s deadly health issue which causes high mortality in woman worldwide. The preliminary detection and classification may help for proper treatment of the same. Understanding the causes of this cancer using traditional machine learning techniques misread feature extraction are leading to a complication. Traditional machine learning models are best suited for feature-based raw data. The novel deep learning techniques have been introduced for diagnosis of breast abnormalities using various imaging modalities like mammogram, magnetic resonance imaging (MRI) and ultrasound with high accuracy. This survey focuses on challenges of classical machine learning models and supervene efficient prediction models using novel deep learning techniques. This review presents comparison analysis of traditional machine learning and deep learning models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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Dynamic L-Budget Clustering of Curves 19

Dynamic L-Budget Clustering of Curves

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19th Scandinavian Symposium on Algorithm Theory, SWAT 2024

作者： Buchin, Kevin Buchin, Maike Gudmundsson, Joachim Plätz, Lukas Thiel, Lea Wong, Sampson Faculty of Computer Science TU Dortmund University Germany Faculty of Computer Science Ruhr University Bochum Germany Faculty of Engineering The University of Sydney Australia Department of Computer Science University of Copenhagen Denmark

ISBN: (纸本)9783959773188

A key goal of clustering is data reduction. In center-based clustering of complex objects therefore not only the number of clusters but also the complexity of the centers plays a crucial role. We propose LBudget Clustering as unifying perspective on this task, optimizing the clustering under the constraint that the summed complexity of all centers is at most L. We present algorithms for clustering planar curves under the Fréchet distance, but note that our algorithms more generally apply to objects in metric spaces if a notion of simplification of objects is applicable. A scenario in which data reduction is of particular importance is when the space is limited. Our main result is an efficient (8 + Ε)-approximation algorithm with a (1 + Ε)-resource augmentation that maintains an L-budget clustering under insertion of curves using only O(LΕ−1) space and O∗(L3 log L + L2 log(r∗/r0)) time where O∗ hides factors of Ε−1 © Kevin Buchin, Maike Buchin, Joachim Gudmundsson, Lukas Plätz, Lea Thiel, and Sampson Wong;licensed under Creative Commons License CC-BY 4.0.

关键词： Approximation algorithms

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A Secure and Transparent Voting System Framework Using Finger Vein and Blockchain Technology 16

A Secure and Transparent Voting System Framework Using Finge...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Padal, Chittapuli Bhupathi Vatsavayi, Valli Kumari Andhra University College of Engineering Department of Computer Science and Systems Engieering Visakhapatnam530003 India

ISBN: (纸本)9798331505264

The traditional voting system faces widespread mistrust due to its vulnerability to exploitation, lack of transparency, and limited reliability, leading to concerns about the security of democratic rights. Attempts to digitize voting have also faced challenges, with current digital systems often failing to ensure the transparency and security necessary for fair elections. Both traditional and current digital voting systems are susceptible to exploitation, and their inherent weaknesses often lead to mistrust and vulnerability. This proposed solution is secure and transparent voting system framework that leverages finger vein recognition technique integrated with block chain technology to ensure the integrity and confidentiality of votes. Blockchain technology to create a more secure, transparent and reliable voting system. By utilizing blockchain, we aim to address the issues in both physical and digital voting systems, ensuring a fair election process that upholds voters' democratic rights. Blockchain technology offers a platform that maximizes transparency and reliability, fostering a trustworthy relationship between voters and election authorities. each Voter will be registered with Finger Vein as the password and this finger vein will be encrypted and secured by using Pepper-Salt algorithm. Our proposed framework allows voting activities to be conducted entirely online through blockchain, removing the need for physical polling stations. The system employs a scalable blockchain supported by flexible consensus algorithms, with a Chain Security Algorithm to enhance transaction security. © 2024 IEEE.

关键词： Voting machines

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A Nobel Approach to Identify the Rainfall Prediction Using Deep Convolutional Neural Networks Algorithm 6th

A Nobel Approach to Identify the Rainfall Prediction Using D...

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6th International Conference on Information and Communication Technology for Intelligent systems, ICTIS 2021

作者： Desai, Prachi Gandhi, Ankita Acharya, Mitali Department of Computer Science Engineering Faculty of Engineering and Technology Parul University Vadodara India

ISBN: (纸本)9789811935749

It is unlikely to have a big impact on a country's economic *** also helps to reduce the loss of life and property caused by natural *** study of rainfall prediction using machine learning techniques, with a special emphasis on *** India, about 70% of the population is dependent on agriculture and related *** forecasting has been an issue of major technical and economic importance in the agricultural *** rain prediction model is still mostly based on artificial neural networks, and it has only been used in India so *** the present study, a comparative analysis of the two rainfall forecasting techniques, and is more *** today’s technology, the ability to predict the rainfall is not very well-informed about the complexity of the *** methods used are the methods of statistical and mathematical methods that don’t work if there is a situation of a nonlinear *** existing installation may fail when the level of complexity of the information contained in it, increase it in the ***, this is the best way to get to the waiting;in the rain, it is a study of deep learning and neural networks, and genetic algorithms, is that it gives you more precise, it is used to predict the *** order to come up with a good rain is a joint, an appraisal is not *** simplest and easiest approach to get on a larger scale is to use weather *** the present study, it can be used in all types of weather stations, and the forecast will give access to some parts of the country.A technique for predicting month-to-month rain for a specific area in India using a deep CNN learning method as a replacement fortuning *** CNN was compared against an ANN. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Rain

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Integrated Web-Based Platform for AI-Enhanced Autism Assessment 2

Integrated Web-Based Platform for AI-Enhanced Autism Assessm...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Yasir Abdullah, R. Rk, Kamesh Kavin Prabu, S. Sri Subiksha, T. Venkatraman, P. Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798331534950

This research presents a novel web-based platform that leverages facial and vocal tracking to enhance the preliminary screening process for Autism Spectrum Disorder (ASD). By incorporating advanced machine learning algorithms to analyze multimodal data, the platform offers a non-invasive, accessible, and cost-effective solution for initial ASD evaluation. The system demonstrates high diagnostic accuracy, achieving 90% in facial pattern analysis, 85% in vocal pattern analysis, and 95% accuracy when both analyses are combined. Additionally, it includes communication features designed to foster a supportive environment for individuals with ASD. This paper provides an overview of the platform's development, functionality, and future potential, with an emphasis on its capacity to improve existing diagnostic methods and its accessibility for underserved communities. © 2024 IEEE.

关键词： Adversarial machine learning

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Predicting software vulnerability based on software metrics: a deep learning approach

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Iran Journal of computer science 2024年第4期7卷 801-812页

作者： Agbenyegah, Francis Kwadzo Asante, Micheal Chen, Jinfu Akpaku, Ernest Department of Computer Science Kwame Nkrumah University of Science and Technology Kumasi Ghana School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Department of Information Technology Faculty of Computing and Information Systems Ghana Communication Technology University Accra Ghana

The security of IT systems is the topmost priority of software developers. Software vulnerabilities undermine the security of computer systems. Lately, there have been a lot of reported issues of software vulnerabilities recorded in individuals and corporate systems. Software metrics-based techniques have been used and found to be effective due to low false positives and false negatives. However, there are software metrics that have not been investigated and could have an impact on the performance of the predictive model. In this study, we developed an ensemble deep learning algorithm made of LSTM, CNN, and MLP models with the non-investigated metrics as a feature to learn the latent representation of code metrics to predict anomalies in source code effectively. We trained the proposed model on the SySeVR datasets and compared the model's performance against five deep learning algorithms and four state-of-the-art SVPs. The proposed model performs better than the other deep learning with a classification accuracy of 96.17%, and a precision of 97.72%. The model's classification accuracy is 4.98% higher than that of the five deep learning models and 1.71% lower in precision than the state-of-the-art SVP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Deep learning Machine learning Software metrics Software security Software vulnerabilities

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Graphene oxide-coated microbottle resonators for relative humidity sensing

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Optoelectronics Letters 2024年第7期20卷 400-405页

作者： Huda Adnan Zain Malathy Batumalay Md Ashadi Md Johari Hazli Rafis Abdul Rahim Sulaiman Wadi Harun Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur 50603Malaysia Faculty of Data Science and Information Technology INTI International UniversityNilai 71800Malaysia1 Faculty of Electrical and Electronic Engineering Technology Universiti Teknikal Malaysia MelakaMelaka 76100Malaysia Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia MelakaMelaka 76100Malaysia

Graphene oxide(GO)is a 2D coating material used to improve fiber optics sensors’response to relative *** resonators(MBRs)have garnered more attention as sensing media *** MBR with a 190μm diameter was coated with ***,tapered fiber light coupling was used to investigate the relative humidity sensing performance in the range of 35—70%RH at 25℃.The MBR showed a higher Q factor before and after GO *** sensitivity of 0.115 dB/%RH was recorded with the 190μm GO-coated MBR sample compared to a sensitivity of 0.022 dB/%RH for the uncoated MBR *** results show that the MBR can be used in fiber optic sensing applications for environmental sensing.

关键词： humidity oxide bottle

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IRMIRS:Inception-ResNet-Based Network for MRI Image Super-Resolution

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computer Modeling in engineering & sciences 2023年第8期136卷 1121-1142页

作者： Wazir Muhammad Zuhaibuddin Bhutto Salman Masroor Murtaza Hussain Shaikh Jalal Shah Ayaz Hussain Department of Electrical Engineering Balochistan University of Engineering and TechnologyKhuzdar89100Pakistan Department of Computer Systems Engineering Balochistan University of Engineering and TechnologyKhuzdar89100Pakistan Department of Mechanical Engineering Balochistan University of Engineering and TechnologyKhuzdar89100Pakistan Department of Mechanical Engineering National Taiwan University of Science and Technology10607Taiwan Department of Information Systems Kyungsung UniversityBusan613010South Korea

Medical image super-resolution is a fundamental challenge due to absorption and scattering in *** challenges are increasing the interest in the quality of medical *** research has proven that the rapid progress in convolutional neural networks(CNNs)has achieved superior performance in the area of medical image ***,the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance(MR)images,adding extra noise in the models and more memory ***,conventional deep CNN approaches used layers in series-wise connection to create the deeper mode,because this later end layer cannot receive complete information and work as a dead *** this paper,we propose Inception-ResNet-based Network for MRI Image Super-Resolution known as *** our proposed approach,a bicubic interpolation is replaced with a deconvolution layer to learn the upsampling ***,a residual skip connection with the Inception block is used to reconstruct a high-resolution output image from a low-quality input *** and qualitative evaluations of the proposed method are supported through extensive experiments in reconstructing sharper and clean texture details as compared to the state-of-the-art methods.

关键词： Super-resolution magnetic resonance imaging ResNet block inception block convolutional neural network deconvolution layer

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***: A Japanese Token Level Pattern Matching and Processing Language with Dependency Analysis 23

***: A Japanese Token Level Pattern Matching and Processing ...

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12th International Conference on Software and computer Applications, ICSCA 2023

作者： Nakanishi, Tsuneo Ototake, Hokuto Tanabe, Toshifumi Yoshimura, Kenji Department of Electronics Engineering and Computer Science Faculty of Engineering Fukuoka University Fukuoka Japan

ISBN: (纸本)9781450398589

The quality of development documents written in natural language is basically ensured by developer’s review with quite a few man-hours. Natural language processing (NLP) tools can partially automate the time-consuming review. The authors have developed morpheme level pattern matching and processing tools morfgrep and morfawk for Japanese texts for this purpose;moreover, in this paper, extends them to perform pattern matching on dependency among phrases. Example applications of the tools, proofreading of development documents and domain-specific term extraction, are also presented. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Pattern matching

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