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Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

computer systems Science & Engineering 2023年第4期45卷 753-767页

作者： Mesfer Al Duhayyim Areej A.Malibari Sami Dhahbi Mohamed K.Nour Isra Al-Turaiki Marwa Obayya Abdullah Mohamed Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh4545Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

Recently,computer aided diagnosis(CAD)model becomes an effective tool for decision making in healthcare *** advances in computer vision and artificial intelligence(AI)techniques have resulted in the effective design of CAD models,which enables to detection of the existence of diseases using various imaging *** cancer(OC)has commonly occurred in head and neck *** identification of OC enables to improve survival rate and reduce mortality ***,the design of CAD model for OC detection and classification becomes ***,this study introduces a novel computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification(CADOC-SFOFC)*** proposed CADOC-SFOFC model determines the existence of OC on the medical *** accomplish this,a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network(ResNet)***,feature vectors are fused and passed into the extreme learning machine(ELM)model for classification ***,SFO algorithm is utilized for effective parameter selection of the ELM model,consequently resulting in enhanced *** experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%.Therefore,the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency.

关键词： Oral cancer computer aided diagnosis deep learning fusion model seagull optimization classification

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Evaluating Usability of Learning Disability Assessment system 8

Evaluating Usability of Learning Disability Assessment Syste...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Fikriah Hamum Junaidi, Putri Sakinah Amira Imi, Nur Zakaria, Noor Azura Abdul Karim, Ruwinah Lubis, Muharman Rejab, Mazidah Mat International Islamic University Malaysia Kulliyyah of Information and Communication Technology Department of Computer Science Kuala Lumpur Malaysia Penawar Special Learning Centre Kuala Lumpur Malaysia Telkom University Information System Department Bandung Indonesia Universiti Tun Hussien Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia

ISBN: (纸本)9781665453899

The Learning Disability Assessment system (LeDAS) entails crucial decision making to produce a diagnosis referring to the information inserted by the therapists and parents. In this manner, accuracy of the information system is a pivot to generate an effective system. Research evaluation of LeDAS is necessary due to the fact that it is a recently developed system with over 3,000 patients currently using it thus it is imperative to evaluate the system to measure its usability. The purpose of this research is to evaluate the usability of the developed LeDAS web-based system in order to ensure the system can fulfill the user's satisfaction and be usable. The conducted survey targets therapists and parents to collect data. The data produced from this study will be used to provide insight into the need of furthering proficiency in generating a satisfactory system. This evaluation analyzed using the descriptive statistic, exploratory factor analysis (EFA), reliability testing and hypothesis testing. The result showed that 5 hypotheses were supported. © 2022 IEEE.

关键词： Factor analysis

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Smart Career Guidance system 4

Smart Career Guidance System

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4th IEEE International Conference on Computing and information Sciences, ICCIS 2021

作者： Kamal, Areej Naushad, Batul Rafiq, Hadia Tahzeeb, Shahab NED University of Engineering and Technology Department of Computer and Information System Engineering Karachi Pakistan

ISBN: (纸本)9781665494410

We have developed a career guidance system that helps those students who are about to begin their higher education. Most of the time, students are not aware of what career path to follow or which academic major is in accordance with their interests. The system analyzes students' skills, abilities, and interests and recommends the five fields which are most suitable for them. This project helps students identify a specific domain that fits their skills and interests. Smart Career Guidance system is a web-based application built on the Django framework. We have deployed various Machine Learning techniques and algorithms to mimic a one-on-one meeting with an experienced career counselor. The data was collected in the form of a questionnaire that was based on Holland Occupational Themes and the Theory of Multiple Intelligences. A total of 392 graduates completed this online survey. SMOTE oversampling is used to evaluate the machine learning classifiers since the data is highly imbalanced. We tested XGBoost and Random Forest classifiers for recommending the best-suited career options which furnish AUC-ROC performance scores of 0.9952 and 0.9963 respectively. A fine-tuned version of the Random Forest Classifier has successfully attained an AUC-ROC performance score of 0.9976 which indicates the minimal false-positive rate. Ms. Areej Kamal, Ms. Hadia Rafiq and Ms. Batul Naushad have collaboratively conducted all activities of the project including data collection and cleaning, literature review, testing of ML models and development of the final solution. Mr. Shahab Tahzeeb directed and supervised all phases of the project with his immense knowledge and expertise. © 2021 IEEE.

关键词： Students

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PDEGAN: A Panoramic Style Transfer Based on Generative Adversarial Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第4期9卷 2112-2121页

作者： Wang, Qinghua Long, Xinling Huang, Jingwei Chen, Yang Yang, Lirong Zhang, Fuquan College of Computer and Big Data Minjiang University Fuzhou350108 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China College of Computer and Big Data Fuzhou University Fuzhou China School of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou China College of foreign languages Dalian Jiaotong University Dalian116028 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China

Panoramic image plays an extremely important role in the application of 3D, but in some special scenarios, the depth information is also needed as an auxiliary, but to obtain the panoramic depth information needs a high cost, it is extremely difficult to obtain. Therefore, this paper proposes a panoramic depth estimation method called PDEGAN (Panoramic Depth Estimation Generative Adversarial Network). This method first enhances the style migration, transfers the input panoramic image to a style closer to the panoramic depth image domain, and then combines the conversion results with the original panoramic image as the input data for further depth estimation training. By using the style closer to the depth estimation, the texture, color and other characteristics of the image can be adjusted, so as to better highlight the depth information of the object in the image, so that the generator network is more focused on performing the image translation task, reduce the impact of style differences on the image translation task, and improve the generalization ability of the model. At the same time, the model will also use multi-scale feature fusion, combined with the attention mechanism, to better capture the depth information of different scales, viewing angles and channels. Not only that, but also by introducing depth gradient loss, surface normal loss to improve the accuracy and stability of depth estimation. To this end, we also made a set of related datasets for the related experiments of PDEGAN, and named it PDE Images. Finally, it is shown that the method proposed in this paper has more accurate estimation results than the traditional Pix2Pix image conversion model. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Generative adversarial networks

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Disk Failure Prediction Based on SW-Disk Feature Engineering 8

Disk Failure Prediction Based on SW-Disk Feature Engineering

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8th IEEE International Conference on Big Data Security on Cloud, 8th IEEE International Conference on High Performance and Smart Computing, and 8th IEEE International Conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2022

作者： Liang, Chenjun Deng, Li Zhu, Jincan Cao, Zhen Li, Chao College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Hubei University Department of Information Development and Management Wuhan China

ISBN: (纸本)9781665480697

With the rapid development of cloud storage, the number of disks has increased dramatically, and the absolute number of failed disks has also continued to increase. Since the impact of these failures on the quality of cloud services cannot be ignored, the demand for disk failure prediction is also increasing. In addition, the accuracy of the disk failure prediction algorithm largely depends on the input data set. Therefore, according to the large difference in the feature morphology in the disk tracking dataset, we analyze the change trend of features in different dimensions, and propose a new method. The SW-Disk feature engineering method for disk failure prediction constructs features from both horizontal and vertical dimensions. Including feature crossover between indicators and indicators to construct higher-order features. In the time dimension, the time series features under the multi-scale sliding window are constructed. We use the random forest prediction model to predict whether the disk will fail in the next 30 days, so as to reserve more time to take measures on the disk to achieve the purpose of improving the reliability of the cloud storage system. We validated our SW-Disk feature engineering method and prediction model on the Backblaze2019 disk dataset, and the experimental results demonstrate the effectiveness and superiority of our proposed method. © 2022 IEEE.

关键词： Decision trees

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Hyperparameter Tuning for Identifying Tree Seedling Mortality Using Logistic Regression on Environmental and Factors

Hyperparameter Tuning for Identifying Tree Seedling Mortalit...

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Advanced information Scientific Development (ICAISD), International Conference on

作者： Siti Khotimatul Wildah Abdul Latif Computer Technology Universitas Bina Sarana Informatika Jakarta Indonesia Information System Universitas Bina Sarana Informatika Jakarta Indonesia

ISBN: (数字)9798350392036

ISBN: (纸本)9798350392043

Tree-seedling mortality is a significant challenge in forest management and agricultural practices, particularly in areas affected by climate change. The primary causes of seedling mortality are related to environmental factors and soil conditions. The early identification of these factors is crucial for improving seedling survival and land productivity. While existing studies have applied machine learning techniques to predict seedling mortality, they have often overlooked the importance of hyperparameter tuning in enhancing model performance. This study addresses this gap by focusing on the impact of hyperparameter tuning on the Logistic Regression models used to predict tree seedling mortality. Using a tree survival prediction dataset containing diverse environmental and soil factors, the model was optimized to achieve significant improvements. The tuned Logistic Regression model outperformed the standard approaches, achieving an accuracy of 93.71% and AUC of 97.81%. These results underscore the importance of hyperparameter optimization in boosting predictive accuracy, and demonstrate its potential to support improved forest conservation and management efforts more effectively than traditional models.

关键词： Productivity Logistic regression Accuracy Mortality Forestry Predictive models Soil Hyperparameter optimization Tuning Standards

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On the information Freshness of A Two-Sensor Status Update system

On the Information Freshness of A Two-Sensor Status Update S...

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IEEE Conference on Vehicular technology (VTC)

作者： Tianqing Yang Zhengchuan Chen Howard H. Yang Nikolaos Pappas Min Wang Yunjian Jia Tony Q. S. Quek School of Microelectronics and Communication Engineering Chongqing University Chongqing China Zhejiang University/University of Illinois at Urbana-Champaign Institute Zhejiang University Zhejiang China Department of Computer and Information Science Linköping University Linköping Sweden School of Optoelectronics Engineering Chongqing University of Posts and Telecommunications Chongqing China Information System Technology and Design Pillar Singapore University of Technology and Design Singapore

This work studies the average Age of information (AoI) of a remote monitoring system in which two sensors observe the same physical process and update the status to a common monitor using orthogonal channels. While using redundant devices to update the status of a process can improve the information timeliness at the monitor, the out-of-order arrivals of updates impose a challenge to the AoI analysis. We first model the system as two parallel M/M/1/1 queues. By leveraging tools from stochastic hybrid systems, we obtain analytically the average AoI of the system. In particular, when the arrival or service rates are the same for the two sensors, the average AoI is given in closed form. Our analysis reveals that the average AoI of the considered system is reduced by 16.44% compared to the single-sensor system when the arrival and service rates are equal to 1. Numerical results show that the considered system outperforms the M/M/2 system in average AoI at high arrival rates.

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AISLE: Self-supervised Representation Learning for the Investigation of Advanced Persistent Threat 7

AISLE: Self-supervised Representation Learning for the Inves...

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7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Jia, Zian Wang, Xiaosu Xiong, Yun Zhang, Yao Zhao, JinJing Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China National Key Laboratory of Science and Technology on Information System Security China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

Advanced Persistent Threats (APT) are difficult to detect and defend due to their high variability and concealment. Current APT detection and investigation approaches suffer from two major problems. First, most recent models heavily rely on heuristic rules derived from a large amount of expensive prior knowledge, which in turn prevents the models from generalizing to new types of APT attacks. Second, the development of existing fully supervised models is limited by the scarcity of APT attack data. In this paper, we first construct the provenance graph by introducing logical entities to reduce the dependency on prior knowledge. Then we propose a novel self-supervised representation-learning based model, AISLE, for the investigation of the APT attack, which consists of a graph structure encoder (GSE) and an entity interaction pattern encoder (IPE). The GSE employs a graph attention auto-encoder to effectively compress the structural information of the graph;and the IPE employs a LSTM auto-encoder to extract the interaction patterns of entities from their interaction history. The two entity embeddings calculated respectively by the two auto-encoders are concatenated to form the final representation of the entity, which is evaluated by the attack investigation module to identify attack entities. Our model is evaluated on ten real-world APT attacks in a realistic virtual environment. The results show that our model can achieve superior generalization ability and consistently outperform the latest state-of-the-art APT attack investigation approaches. © 2022 IEEE.

关键词： Graph neural networks

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Modelling and Simulation of COVID-19 Outbreak Prediction Using Supervised Machine Learning

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computers, Materials & Continua 2021年第3期66卷 2397-2407页

作者： Rachid Zagrouba Muhammad Adnan Khan Atta-ur-Rahman Muhammad Aamer Saleem Muhammad Faheem Mushtaq Abdur Rehman Muhammad Farhan Khan Department of Computer Information System College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science Riphah International UniversityLahore CampusLahore54000Pakistan Hamdard Institute of Engineering and Technology Hamdard UniversityIslamabad44000Pakistan Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan School of Computer Science NCBA&ELahore54000Pakistan Department of Forensic Sciences University of Health SciencesLahore54000Pakistan

Novel Coronavirus-19(COVID-19)is a newer type of coronavirus that has not been formally detected in *** is established that this disease often affects people of different age groups,particularly those with body disorders,blood pressure,diabetes,heart problems,or weakened immune *** epidemic of this infection has recently had a huge impact on people around the globe with rising mortality *** levels of mortality are attributed to their transmitting behavior through physical contact between *** is extremely necessary to monitor the transmission of the infection and also to anticipate the early stages of the disease in such a way that the appropriate timing of effective precautionary measures can be *** latest global coronavirus epidemic(COVID-19)has brought new challenges to the scientific *** Intelligence(AI)-motivated methodologies may be useful in predicting the conditions,consequences,and implications of such an *** forecasts may help to monitor and prevent the spread of these *** article proposes a predictive framework incorporating Support Vector Machines(SVM)in the forecasting of a potential outbreak of *** findings indicate that the suggested system outperforms cutting-edge *** method could be used to predict the long-term spread of such an outbreak so that we can implement proactive measures in *** findings of the analyses indicate that the SVM forecasting framework outperformed the Neural Network methods in terms of accuracy and computational *** proposed SVM system model exhibits 98.88%and 96.79%result in terms of accuracy during training and validation respectively.

关键词： Coronavirus outbreak machine learning artificial intelligence

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Deep Learning Approach for High Recall Pneumonia Classification with Swin Transformer and L2 Regularization 23

Deep Learning Approach for High Recall Pneumonia Classificat...

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8th International Conference on Sustainable information Engineering and technology, SIET 2023

作者： Pinasthika, Krisna Sofyanda, Erika Yussi Ulumiyah, Silfiatul Muflikhah, Lailil Computer Vision Research Group Brawijaya University Indonesia Technology Education Learning Research Group Brawijaya University Indonesia Information System Research Group Brawijaya University Indonesia Department of Informatics Engineering Brawijaya University Indonesia

ISBN: (纸本)9798400708503

Pneumonia is one of the lung infections that can be lethal to people. Pneumonia can be diagnosed by conducting an X-ray scan of the human chest. Even though X-ray scanning is straightforward, the process of identifying pneumonia in X-ray data is still difficult to do even by radiologists. Therefore, a computational algorithm is needed that can be used to analyze X-ray image data. The use of Deep Learning (DL) methods for analyzing images has been proven to work well in various cases, especially X-ray data. Based on previous research, the transformer architecture has the advantage of analyzing with a larger receptive area if compared to Convolutional Neural Networks (CNN) which have limited kernel size. In this study, we used the Swin transformer in classifying pneumonia diseases through X-ray images. Through the study conducted, the Swin Transformer Architecture is able to provide a fairly good evaluation metric when compared to the use of convolutional neural networks. The Tiny variant of the Swin Transformer architecture is able to provide evaluation metrics with a precision value of 0.870, recall of 0.995, and F1-Score of 0.928. On the other hand, the use of L2 regularization in stage 3 and stage 4 of the Swin Tiny architecture is able to provide a high recall value of 0.997, but the use of L2 regularization does not provide an increase in precision and F1-Score. In biomedical cases that are only concerned with recall values, the use of L2 regularization on Swin Tiny can provide advantages. © 2023 ACM.

关键词： computer vision

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