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CP_DeepNet: a novel automated system for COVID-19 and pneumonia detection through lung X-rays

Multimedia Tools and Applications 2024年第41期83卷 88681-88698页

作者： Mehmood, Muhammad Hamza Hassan, Farman Rahman, Auliya Ur Khan, Wasiat Mostafa, Samih M. Ghadi, Yazeed Yasin Algarni, Abdulmohsen Ali, Mudasser School of Computer Science and Technology University of Science and Technology of China Hefei China University of Bologna Bologna Italy UET Peshawar KPK Peshawar Pakistan UST Bannu KPK Bannu Pakistan Computer Science Department Faculty of Computers and Information South Valley University Qena83523 Egypt Department of Computer Science Al Ain University Al Ain United Arab Emirates Department of Computer Science King Khalid University Abha61421 Saudi Arabia Arid University Punjab Pakistan

In recent years, the COVID-19 outbreak has affected humanity across the globe. The frequent symptoms of COVID-19 are identical to the normal flu, such as fever and cough. COVID-19 disseminates rapidly, and it has become a prominent cause of mortality. Nowadays, the new wave of COVID-19 has created significant impacts in China. This virus can have detrimental effects on people of all ages, particularly the elderly, due to their weak immune systems. The real-time polymerase chain reaction (RT-PCR) examination is typically performed for the identification of coronavirus. RT-PCR is an expensive and time requiring method, accompanied by a significant rate of false negative detections. Therefore, it is mandatory to develop an inexpensive, fast, and reliable method to detect COVID-19. X-ray images are generally utilized to detect diverse respiratory conditions like pulmonary infections, breathlessness syndrome, lung cancer, air collection in spaces of the lungs, etc. This study has also utilized a chest X-ray dataset to identify COVID-19 and pneumonia. In this research work, we proposed a novel deep learning model CP_DeepNet, which is based on a pre-trained deep learning model such as SqueezeNet, and further added three blocks of convolutional layers to it for assessing the classification efficacy. Furthermore, we employed a data augmentation method for generating more images to overcome the problem of model overfitting. We utilized COVID-19 radiograph dataset for evaluating the performance of the proposed model. To elaborate further, we obtained significant results with accuracy of 99.32%, a precision of 100%, a recall of 99%, a specificity of 99.2%, an area under the curve of 99.78%, and an F1-score of 99.49% on CP_DeepNet for the binary classification of COVID-19 and normal class. We also employed CP_DeepNet for the multiclass classification of COVID-19, pneumonia, and normal person, in which CP_DeepNet achieved accuracy, precision, recall, specificity, area under curve

关键词： COVID-19

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Enhancing Phishing URL Detection: A Comparative Study of Machine Learning Algorithms 23

Enhancing Phishing URL Detection: A Comparative Study of Mac...

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2023 Asia Conference on Artificial Intelligence, Machine Learning and Robotics, AIMLR 2023

作者： Alsarhan, Ayoub Igried, Bashar Bani Saleem, Raad Mohammad Alauthman, Mohammad Aljaidi, Mohammad Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Computer Science and Applications Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology TheHashemite University Zarqa Jordan Department of Information Security University of Petra Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (纸本)9798400708312

Phishing constitutes a significant threat in the digital world, often exploiting human vulnerabilities to illicitly obtain sensitive data such as personal credentials, financial details, and private information. Misusing this information results in substantial financial loss and personal harm to victims. This study introduces an innovative approach to mitigate the risk of phishing attacks by employing machine learning algorithms to detect phishing URLs. The proposed method applies a suite of algorithms, including j48, Naïve Bayes, JRip, and Decision Table, to a robust dataset of 11,430 URLs, each with 87 extracted features. The results reveal the considerable potential of machine learning in identifying phishing threats. Furthermore, the study explores a novel server-side analysis concept where the server scrutinizes links transmitted via emails or social communication platforms such as WhatsApp, Messenger, and Instagram. The application of phishing detection algorithms filters and prevents the delivery of phishing links, thus reducing the potential harm to users. This research is poised to significantly contribute to cybersecurity by enhancing phishing detection mechanisms' accuracy and efficiency. © 2023 ACM.

关键词： Sensitive data

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Breast Cancer Detection Using Breastnet-18 Augmentation with Fine Tuned Vgg-16

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Intelligent Automation & Soft Computing 2023年第5期36卷 2363-2378页

作者： S.J.K.Jagadeesh Kumar P.Parthasarathi Mofreh A.Hogo Mehedi Masud Jehad F.Al-Amri Mohamed Abouhawwash Department of Computer Science and Engineering Kathir College of EngineeringCoimbatore641062India Department of Computer Science and Engineering Bannari Amman Institute of TechnologySathyamangalam638401TamilnaduIndia Electrical Engineering Department Faculty of EngineeringBenha UniversityBenha13518Egypt Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Women from middle age to old age are mostly screened positive for Breast cancer which leads to *** over the past decades,the overall sur-vival rate in breast cancer has improved due to advancements in early-stage diag-nosis and tailored *** all hospital brings high awareness and early detection technologies for breast *** increases the survival rate of *** traditional breast cancer treatment takes so long,early cancer techniques require an automation *** research provides a new methodol-ogy for classifying breast cancer using ultrasound pictures that use deep learning and the combination of the best ***,after successful learning of Convolutional Neural Network(CNN)algorithms,data augmentation is used to enhance the representation of the feature *** it uses BreastNet18 withﬁne-tuned VGG-16 model for pre-training the augmented *** feature classiﬁcation,Entropy controlled Whale Optimization Algorithm(EWOA)is *** features that have been optimized using the EWOA were utilized to fuse and optimize the *** identify the breast cancer pictures,training classiﬁers are *** using the novel probability-based serial technique,the best-chosen characteristics are fused and categorized by machine learning *** main objective behind the research is to increase tumor prediction accuracy for saving human *** testing was performed using a dataset of enhanced Breast Ultrasound Images(BUSI).The proposed method improves the accuracy com-pared with the existing methods.

关键词： Deep learning classiﬁcation data augmentation feature extraction the fusion of features breast cancer optimization classiﬁcation

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Development of Mobile App to Support the Mobility of Visually Impaired People

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computers, Materials & Continua 2022年第11期73卷 3473-3495页

作者： R.Meenakshi R.Ponnusamy Saleh Alghamdi Osama Ibrahim Khalaf Youseef Alotaibi Center for Artificial Intelligence&Research Chennai Institute of TechnologyKundrathur600069India Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Al-Nahrain Nano-Renewable Energy Research Center Al-Nahrain UniversityBaghdadIraq Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia

In 2017,it was estimated that the number of persons of all ages visually affected would be two hundred and eighty-five million,of which thirty-nine million are *** are several innovative technical solutions available to facilitate the movement of these *** next big challenge for technical people is to give cost-effective *** of the challenges for people with visual impairments is navigating safely,recognizing obstacles,and moving freely between locations in unfamiliar environments.A new mobile application solution is developed,and the application can be installed in android *** application will visualize the environment with portable cameras and persons with visual impairment directly to the *** designed system mainly uses the YOLO3 program to identify and locate the distance between the objects and the ***,it determines the direction of the ***,the system will give the voice command to teach/inform the visually impaired people to navigate the *** is a novel work at the global *** proposed approach is cost-effective and affordable to all strata of society.

关键词： Visual Impairment cost-effective YOLO(“You Only Look Once”) computer vision object recognition CNN(Convolution Neural Networks) virtual mapping navigate voice command

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Kernel Dimensionality Reduction evaluation on various dimensions of effective subspaces for cancer patient survival analysis

Kernel Dimensionality Reduction evaluation on various dimens...

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International Conference on information science, Signal Processing and Their Applications

作者： Chin, Y.S. Wasito, Ito Mohd Hashim, S.Z. Department of Software Engineering Faculty of Computer Science and Information Systems University Technology Malaysia Skudai Johor Bahru Malaysia Faculty of Computer Science University of Indonesia Jakarta Indonesia

ISBN: (纸本)9781424471676

In this research, we have extended the use of Kernel Dimensionality Reduction (KDR) in the context of semi supervised learning in particular for micro-array DNA clustering application. We have proposed a new model call K-means-KDR for survival analysis which we aimed to improve the genes classification performance and study the dimension of effective subspaces in cancer patient survival analysis. KDR method was extended and combined with the K-means clustering technique, Cox's proportional hazards regression model and log rank test where KDR contributes in gene classification to determine subgroups from the patient's group. Results from the experiments have indicated that our model has outperformed Support Vector Machines (SVM) in gene classification. We also observed that the best value for dimension of effective subspaces (K) for microarray DNA data is between 10%-20% of the total patients. © 2010 IEEE.

关键词： Support vector machines

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Combining Multiple Individual Clusterings of Chemical Structures Using Cluster-Based Similarity Partitioning Algorithm

Combining Multiple Individual Clusterings of Chemical Struct...

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作者： Saeed, Faisal Salim, Naomie Abdo, Ammar Hentabli, Hamza Faculty of Computer Science and Information Systems Universiti Teknologi Malaysia Malaysia Information Technology Department Sanhan Community College Sana'a Yemen Computer Science Department Hodeidah University Hodeidah Yemen

ISBN: (纸本)9783642353253

Many types of clustering techniques for chemical file structures have been used in the literature, but it is known that any single method will not always give the best results for all types of applications. Recent work on consensus clustering methods is motivated because of the successes of combining multiple classifiers in many areas and the ability of consensus clustering to improve the robustness, novelty, consistency and stability of clustering. In this paper, Cluster-based Similarity Partitioning Algorithm (CSPA) was examined for improving the quality of chemical structures clustering. The effectiveness of clustering was evaluated based on the ability to separate active from inactive molecules in each cluster and compared with the Ward's clustering method. The chemical dataset MDL Drug Data Report (MDDR) database was used for experiments. The results were obtained by combining multiple individual clusterings with different distance measures. Experiments suggest that the effectiveness of consensus partition depends on the consensus generation step so that the effective individual clusterings with different distance measures can obtain more robust and stable consensus clustering. © Springer-Verlag Berlin Heidelberg 2012.

关键词： Cluster analysis

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Web Service Ranking based on QoS and Use Preference 13

Web Service Ranking based on QoS and Use Preference

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13th International Conference on information and Knowledge technology, IKT 2022

作者： Siadat, Seyed Hossein Ramezani, Danial Ahani, Fatemeh Kharazmi University Faculty of Management Department of Operation Management and Information Technology Tehran Iran Islamic Azad University Faculty of Electrical Engineering Computer and Information Technology Qazvin Iran Islamic Azad University Department of Computer Science Iran

ISBN: (纸本)9798350320626

One of the most important issues of ranking is thousands of web service available. Finding and implementing algorithms that do ranking well, is very essential. The important issue is that after ranking and choosing their best by different algorithms and suggesting them to user he is satisfied. Different algorithms presented so far for this purpose have focused on processing the content of user application and they have tried to do this selection based on word key or meaning or quality features, yet psychological features of users has impact on their satisfaction of suggested services of algorithms which hasn't been noted to this dependency. The purpose of this research is ranking web services and suggesting their best considering psychological features of persons. We review 2 ways of ranking. At first using qualitative model we consider qualitative features and then psychological features in qualitative and psychological combined model besides qualitative features. In qualitative and psychological combined model psychological valid test NEO-FFI has been used to get people psychological features and put them in related clusters given persons psychological features, on one hand, we obtain the amount of people importance to qualitative features using 'prioritizing to web services features' form and then the relationships between persons psychological features and qualitative features of services are computed and Correlation coefficients indicating the importance of the qualitative feature in terms of each cluster persons are obtained and we can obtain the amount of general quality of each web service by its impact on qualitative features of data base of real life of QWS consisting 2500 web services and rank them and suggest the best to user. Above forms (given kokran formula) were distributed among 66 IT experts from 5 universities. At last 10 services which were suggested by qualitative and qualitative and psychological combined models, gave to 20 users for 14 days an

关键词： Web services

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A survey on VV&A of large-scale simulations

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International Journal of Crowd science 2019年第1期3卷 63-86页

作者： Wang, Yanan Li, Jianqiang Hongbo, Sun Li, Yuan Akhtar, Faheem Imran, Azhar Beijing University of Technology Beijing China Yantai University Yantai China Faculty of Information Technology Beijing University of Technology Beijing China and Department of Computer Science Sukkur IBA University Sukkur Pakistan School of Software Engineering Beijing University of Technology Beijing China

Purpose: Simulation is a well-known technique for using computers to imitate or simulate the operations of various kinds of real-world facilities or processes. The facility or process of interest is usually called a system, and to study it scientifically, we often have to make a set of assumptions about how it works. These assumptions, which usually take the form of mathematical or logical relationships, constitute a model that is used to gain some understanding of how the corresponding system behaves, and the quality of these understandings essentially depends on the credibility of given assumptions or models, known as VV&A (verification, validation and accreditation). The main purpose of this paper is to present an in-depth theoretical review and analysis for the application of VV&A in large-scale simulations. Design/methodology/approach: After summarizing the VV&A of related research studies, the standards, frameworks, techniques, methods and tools have been discussed according to the characteristics of large-scale simulations (such as crowd network simulations). Findings: The contributions of this paper will be useful for both academics and practitioners for formulating VV&A in large-scale simulations (such as crowd network simulations). Originality/value: This paper will help researchers to provide support of a recommendation for formulating VV&A in large-scale simulations (such as crowd network simulations). © 2019, Yanan Wang, Jianqiang Li, Sun Hongbo, Yuan Li, Faheem Akhtar and Azhar Imran.

关键词： Software engineering

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Research on semantic++ computing based on big data environment 12

Research on semantic++ computing based on big data environme...

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12th Web information system and Application Conference, WISA 2015

作者： Liang, Ye Zhang, Guigang Xing, Chunxiao Zhang, Yong Li, Chao Research Institute of Information Technology Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9781467393720

With the development of cloud computing and IoT, more and more data-intensive applications have come into being. It is very import to process the big data for these data-intensive applications. In this paper, we do some analysis about the basic concepts of big data and semantic++ computing. It includes the definition of big data, semantic computing and semantic++ computing. In this paper, we make a detail analysis about the semantic++ understanding based on big data. It mainly includes semantic++ storage of big data resources, semantic++ information acquirement of big data resources, semantic++ resources management, semantic++ processing of big data, semantic++ service of big data, semantic++ security and privacy of big data, semantic++ interface and applications based on big data. Two kinds of semantic++ applications are proposed. In this paper, a semantic++ search and recommendation system framework is analyzed. Finally, the simulation experiment shows that the semantic++ search and recommendation method is more efficient than the traditional method. © 2015 IEEE.

关键词： Cloud computing

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A Survey on Energy-Aware Task Scheduling Approaches for Cyber-Physical systems using Machine Learning

A Survey on Energy-Aware Task Scheduling Approaches for Cybe...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Shobak, Mohamad Walid Ishak, Suhaimi Abd Ahmad Kamal, Hariz Syafiq Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Malaysia

ISBN: (纸本)9798350302684

This paper presents a survey of energy-aware task scheduling in Cyber-Physical systems (CPSs) using Machine Learning approaches. It discusses the challenges of energy-aware scheduling in CPSs, outlining the important considerations when designing scheduling algorithms. It reviews the existing literature on energy-aware CPS scheduling and provides an overview of the proposed machine learning-based approaches. Additionally, it identifies promising research directions and provides insights into the use of machine learning for energy-aware CPS scheduling. © 2023 IEEE.

关键词： Cyber Physical system

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