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Multi-solutions with specific geometrical wave structures to a nonlinear evolution equation in the presence of the linear superposition principle

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Communications in Theoretical Physics 2023年第1期75卷 5-14页

作者： Hajar F Ismael Tukur Abdulkadir Sulaiman M S Osman Department of Mathematics Faculty of ScienceUniversity of ZakhoZakhoIraq Department of Computer Engineering Biruni UniversityIstanbulTurkey Department of Mathematics Faculty of ScienceCairo UniversityGiza 12613Egypt

Lump solutions are one of the most common solutions for nonlinear evolution *** study aspires to investigate the generalized Hietarintatype *** auspiciously provide multiple M-lump *** the other hand,collision phenomena to multiple M-lump waves with soliton wave solutions are also *** the collision,the amplitude of the lump will change significantly over the processes,whereas the amplitude of the soliton will just minimally *** it is of paramount importance,we use suitable values of parameter to put out the physical features of the reported results through three dimensional and contour *** results presented express physical features of lump and lump interaction phenomena of different kinds of nonlinear physical ***,this study serves to enrich nonlinear dynamics and provide insight into how nonlinear waves propagate.

关键词： the generalized Hietarintatype equation M-lump waves interaction phenomena hirota bilinear method

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An Adaptive Framework for Classification and Detection of Android Malware

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第21期18卷 59-73页

作者： Al Sharah, Ashraf Alrub, Yousef Abu Owida, Hamza Abu Elsoud, Esraa Abu Alshdaifat, Nawaf Khtatnaha, Hamzah Department of Electrical Engineering College of Engineering Technology Al-Balqa Applied University Amman Jordan Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Medical Engineering Faculty of Engineering Al-Ahliyya Amman University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Information Technology Applied Science Private University Amman Jordan

The hardware and software of a computer are controlled by its operating system (OS), which performs essential tasks such as input and output processing, file and memory management, and the management of peripheral devices such as disk drives and printers. Application software refers to programs designed for specific purposes, these applications, often freely available and open source, contribute to the rising number of downloads. In the third quarter of 2022, combined downloads from the Apple App Store and Google Play Reached an estimated 35.3 billion. However, the prevalence of insecurity in these applications and technologies heightens the potential for cybercrimes. Protection against unauthorized intruders is crucial in identifying malicious applications. Machine learning (ML) serves as a promising avenue for detecting malware attacks, offering potential solutions to bolster cybersecurity measures. We propose a novel approach utilizing ML to enhance malware detection accuracy by segmenting datasets into distinct groups. Our research employs supervised ML techniques on the CICMaldroid2020 dataset, which includes comprehensive information such as intent actions, permissions, and sensitive APIs. The dataset was partitioned into four groups, each containing 150 features, and analyzed across four experiments to distinguish between attack and benign classes. Our proposed model demonstrated exceptional performance, with the random forest algorithm achieving an accuracy of 98.6% and a precision of 98.75%. These results highlight the effectiveness of our segmentation approach and its significant contribution to advancing malware detection in Android applications, offering a promising direction for future cybersecurity solutions. © 2024 by the authors of this article.

关键词： Android malware

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Document Summarizer: A Machine Learning approach to PDF summarization 4

Document Summarizer: A Machine Learning approach to PDF summ...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Peddarapu, Rama Krishna Anogna, Sudagoni Harshini, M.N.S. Sri Sireesha, J. Rathna, K. Guna Department of Computer Science and Engineering Faculty of Computer Sciences and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India

To address the need for summarizing and extracting information efficiently, this paper highlights the growing challenge posed by the increasing number of PDF files. Reading lengthy documents is a tedious and time-consuming task. To save time and quickly comprehend the key points of a PDF, a PDF summarizer tool has been developed to tackle these issues. In today's professional environments, gathering and managing data from documents is critical. This article introduces an innovative solution called DocSum, which automates the process of summarizing extracted data. Powered by *** Core, the DocSum system allows users to upload PDF documents for processing and receive concise summaries in return. The system features a user-friendly interface that encourages interaction and engagement, utilizing AI and machine learning techniques to streamline document handling. Users can request specific summaries, enabling efficient document management workflows. By empowering users to seamlessly interact with vast amounts of information, DocSum enhances productivity and explores new ways to optimize document management. This solution is ideal for professionals seeking to stay informed and manage data effectively, while also keeping track of advancements in document handling technologies across multiple industries. It is not only helpful for professionals but also helpful for students who need quick revision or examinations, teachers to gain brief knowledge on the concepts. © 2025 The Authors. Published by Elsevier B.V.

关键词： Data handling

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Hybrid Color Texture Features Classification Through ANN for Melanoma

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Intelligent Automation & Soft Computing 2023年第2期35卷 2205-2218页

作者： Saleem Mustafa Arfan Jaffar Muhammad Waseem Iqbal Asma Abubakar Abdullah S.Alshahrani Ahmed Alghamdi Faculty of Computer Science&Information Technology Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia

Melanoma is of the lethal and rare types of skin *** is curable at an initial stage and the patient can survive *** is very difﬁcult to screen all skin lesion patients due to costly *** are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of *** challenges are required an automated system to classify the clinical features of melanoma and non-melanoma *** trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reﬂections,air bubbles,and oils on almost all *** contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular *** entropy and morphology-based automated mask selection is pro-posed for the active contour *** proposed method can improve the overall segmentation along with the boundary of melanoma *** this study,features have been extracted to perform the classiﬁcation on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been ***,hybrid fused texture features;such as local,color feature as global,shape features,and Artiﬁcial neural network(ANN)as classiﬁers have been proposed for the categorization of the malignant and *** had been carried out on datasets Dermis,DermQuest,and *** results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

关键词： Gray level co-occurrence matrix local binary pattern artiﬁcial neural networks support vector machines color skin cancer dermoscopic

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A new method for promoting the detection of breast cancer in thermograms: applying deep autoencoders for eliminating redundancies in parallel with preserving independent components

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Journal of Ambient Intelligence and Humanized Computing 2024年第12期15卷 4085-4099页

作者： Shojaedini, Seyed Vahab Bahramzadeh, Bahram Biomedical Engineering Department Iranian Research Organization for Science & ampTechnology Tehran Iran Department of Computer Engineering Faculty of Engineering Islamic Azad University E-Campus Tehran Iran

Although thermography has been proposed over the past decade as an effective method for breast cancer diagnosis, the complexity of thermograms presents a significant obstacle, making their interpretation challenging. Recently, deep learning has emerged as a promising framework for analyzing breast thermograms. However, the performance of deep neural networks heavily relies on the volume of training images, and the limited number of labeled thermograms necessitates the generation of synthetic images for training. This article introduces a novel method that eliminates redundant or destructive information in synthetic thermograms while preserving essential and independent features using deep autoencoder concepts. Consequently, the proposed strategy enhances the representation of synthetic images for training deep networks, leading to improved breast cancer detection in thermograms. Evaluating the performance of the proposed method on the DMR-IR dataset shows a significant improvement in detecting cancerous breasts in thermograms compared to benchmark methods. Integrating this innovation into the basic model achieved an average accuracy of 92.3%, sensitivity of 93%, and specificity of 91.4%, surpassing the basic model’s 89.1%, 86%, and 92.5%, respectively. The proposed method demonstrated superior performance in mitigating overfitting, as evidenced by a smaller gap between training and validation curves, resulting in a 7% improvement in accuracy and a 3.2% increase in sensitivity. Although there was a slight decrease of 1.1% in specificity, it was outweighed by other parameter improvements. Additionally, the method led to a significant reduction in the dispersion range of test results, approximately six times less than alternative approaches. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Lung cancer

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Novel Framework of Segmentation 3D MRI of Brain Tumors

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computers, Materials & Continua 2023年第2期74卷 3489-3502页

作者： Ibrahim Mahmoud El-Henawy Mostafa Elbaz Zainab H.Ali Noha Sakr Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt Department of Computer Science Faculty of Computers and InformaticsKafrelsheikh UniversityKafrelsheikhEgypt Department of Embedded Network Sys.and Tec. Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Medical image segmentation is a crucial process for computer-aided diagnosis and *** image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis and *** and speckle noise are different types of noise in magnetic resonance imaging(MRI)that affect the accuracy of the segmentation process ***,image enhancement has a significant role in MRI *** paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free *** proposed techniques consider the values of Peak Signal to Noise Ratio(PSNR)and the level of noise as inputs to the attention-U-Net model for segmentation of the *** framework has been divided into three stages:removing speckle and Rician noise,the segmentation stage,and the feature extraction *** framework presents solutions for each problem at a different stage of the *** the first stage,the framework uses Vibrational Mode Decomposition(VMD)along with Block-matching and 3D filtering(Bm3D)algorithms to remove the ***,the most significant Rician noise-free images are passed to the three different methods:Deep Residual Network(DeRNet),Dilated Convolution Auto-encoder Denoising Network(Di-Conv-AE-Net),andDenoising Generative Adversarial Network(DGAN-Net)for removing the speckle *** Bm3D have achieved PSNR values for levels of noise(0,0.25,0.5,0.75)for reducing the Rician noise by(35.243,32.135,28.214,24.124)and(36.11,31.212,26.215,24.123)*** framework also achieved PSNR values for removing the speckle noise process for each level as follows:(34.146,30.313,28.125,24.001),(33.112,29.103,27.110,24.194),and(32.113,28.017,26.193,23.121)forDeRNet,Di-Conv-AE-Net,and DGAN-Net,*** experiments that have been conducted have proved the efficiency of the proposed framework a

关键词： MRI Rician noise speckle noise segmentation deep learning

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Smart Agriculture: Enhancing Crop Management through IoT-Based Real-Time Monitoring and Automation 9

Smart Agriculture: Enhancing Crop Management through IoT-Bas...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Paliyanny, Hushalictmy Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Nalluri, Madhusudana Rao Cholla, Ravindra Raman Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India Icfai Foundation for Higher Education Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331510732

Internet of Things plays an important role in agriculture in order to provide an innovative and smart solution to traditional farming. IOT is all about connecting physical devices to the internet and can access from a remote place. So that crop monitoring becomes easy even if the farmer is in a remote place. Due to automation, human efforts are required less. Also the IoT data helps the farmer make decisions about planting, harvesting or fertilizing. This paper explains the system we have implemented for monitoring the agriculture with the help of IoT. The system is developed in order to provide real time information on crop conditions to the farmer. For that, it uses various types of sensors connected to the microcontroller in order to measure parameters like temperature, soil moisture, and water level, and accordingly, it activates actuators like a fan, motor or pump to manage ventilation and irrigation. We have used the GSM module to send the alert to the farmer and the WiFi module is used to send the agriculture data to the cloud platform. So that farmer can monitor the farm or crops remotely. The system is continuously collecting farm data from the sensors. If the crop soil is very dry them system will turn on the water pump. Also, for high temperature, the fan will get activated in order to cool down that area. This feature is mostly used in indoor farming. The farmer can monitor the farm condition in real time with the help of cloud platform. This system is helpful for improving productivity, crop management, and save resources i.e. efficient use of water which prevents water wastage. © 2024 IEEE.

关键词： Soil moisture

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A Review on Implementation of Wearable Devices in Healthcare 7

A Review on Implementation of Wearable Devices in Healthcare

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Suman, Saurabh Choudhary, Vishal Kumar Pacharaney, Utkarsha Kumar, Abhishek Kumar, Dhiraj Raushan, Rahul Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India Faculty of Engineering And Technology Department of Artificial Intelligence And Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798350359299

The capacity to get additional patient data, including clinical, behavioral, and self-monitored data, has enhanced by the expanding usage of wearable technology. Large volumes of previously unobtainable data are now available, providing a chance to create new instruments with sophisticated analysis and decision support systems for application in clinical settings. Additionally, it gives patients additional options by arming them with personalized decision-support resources and information, enabling them to take charge of their own health care while maintaining their sense of autonomy. Especially well-suited for miniaturization and integration into microfluidic devices are biosensors with electrochemical sensors. Applications cover activities in the domains of diagnostics, food and environmental control, and the identification of complete cells, the parts of cells, proteins, and tiny compounds. Microfluidic technologies can be as basic as sample transport tubes or as sophisticated as platforms with extra components for sample preparation and sensing electrode integration. The kind of detection, application, and sample material all influence the microfluidics design. The next issue for the healthcare industry will be how to use newly acquired information in a way that benefits patients, healthcare organizations, and professionals all while reducing costs and workloads. Given the current limitations, the challenge for wearable technology designers is to provide solutions that healthcare practitioners can readily integrate and use. © 2024 IEEE.

关键词： Biosensors

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Water level prediction using deep learning models:A case study of the Kien Giang River,Quang Binh Province

River

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River 2023年第4期2卷 468-479页

作者： Trieu T.Hieu Ta Q.Chieu Dinh N.Quang Nguyen D.Hieu Departments of Computer Science and Statistical and Actuarial Sciences Western UniversityLondonOntarioCanada VCREME Center of Research HanoiVietnam Faculty of Computer Science and Engineering Thuyloi UniversityHanoiVietnam Faculty of Civil Engineering Thuyloi UniversityHanoiVietnam

Time-series water level prediction during natural disasters,for example,typhoons and storms,is crucial for both flood control and *** data-driven models that harness deep learning(DL)techniques has emerged as an attractive and effective approach to water level *** paper proposed an innovative data-driven methodology using DL network architectures of Gated Recurrent Unit(GRU),Long Short-Term Memory(LSTM),and Bidirectional Long-Short Term Memory(Bi-LSTM)to predict the water level at the Le Thuy station in the Kien Giang *** models were implemented and validated based on hourly rainfall and water level observations at meteo-hydrological *** combinations of input variables with different time leads and time lags were established to evaluate the forecast capability of three proposed models by using five metrics,that is,R2,MAE,RMSE,Max Error Value,and Max Error *** results revealed that the LSTM model outperformed the Bi-LSTM and GRU models,when water level and rainfall observations for one-time lag at three stations were used to predict the water level at the Le Thuy station with 1-h time lead,with the five metrics registering at 0.999;3.6 cm;2.6 cm;12.9 cm;and−1 h,respectively.

关键词： Bi-LSTM deep learning GRU Kien Giang River LSTM water level prediction

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Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

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computers, Materials & Continua 2023年第1期74卷 1819-1835页

作者： Zakarya Farea Shaaf Muhammad Mahadi Abdul Jamil Radzi Ambar Ahmed Abdu Alattab Anwar Ali Yahya Yousef Asiri Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn MalaysiaParit RajaBatu Pahat86400JohorMalaysia Department of Computer Science College of Science and ArtsSharurahNajran UniversityNajran61441Saudi Arabia Department of Computer Science Faculty of Computer Science and Info.SystemsThamar UniversityDhamar87246Yemen Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(ROI).The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or ***,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different ***,this study proposed a region-based convolutional network(Faster R-CNN)for the LV localization from short-axis cardiac MRI images using a region proposal network(RPN)integrated with deep feature classification and *** was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable *** experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,*** model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification ***,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.

关键词： Cardiac short-axis MRI images automatic left ventricle localization deep learning models faster R-CNN

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