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Extended Deep Learning algorithm for Improved Brain Tumor Diagnosis System

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Intelligent Automation & Soft Computing 2024年第1期39卷 33-55页

作者： M.Adimoolam K.Maithili N.M.Balamurugan R.Rajkumar S.Leelavathy Raju Kannadasan Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Arfat Ahmad Khan department of computer Engineering Government Polytechnic CollegeSevvapetTiruvallurTamil Nadu602025India department of computer science and Engineering KG Reddy College of Engineering and TechnologyHyderabadTelangana500075India department of computer science and Engineering Sri Venkateswara College of EngineeringSriperumbudurChennai602117India department of computer science and Engineering Annamalai UniversityChidambaramTamil Nadu608002India department of computer science and Engineering Aarupadai Veedu Institute of TechnologyVinayaka Mission Research FoundationPaiyanurTamil Nadu603104India department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117India department of computer science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical Engineering department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt department of computer science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system ***,this work proposed an Extended Deep Learning algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative *** addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were *** results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten *** in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved *** EDLA algorithm introduces novelty concerning its performance and particular activation *** proposed method will be utilized effectively in brain tumor detection in a precise and accurate *** algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses *** the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

关键词： Brain tumor extended deep learning algorithm convolution neural network tumor detection deep learning

Symmetries,optimal system,exact and soliton solutions of(3+1)-dimensional Gardner-KP equation

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Journal of Ocean Engineering and science 2024年第2期9卷 178-190页

作者： Amjad Hussain Ashreen Anjum M.Junaid-U-Rehman Ilyas Khan Mariam A.Sameh Amnah S.al-Johani department of Mathematics Quaid-I-Azam University 45320Islamabad 44000Pakistan department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-MajmaahP.O Box 66Majmaah 11952Saudi Arabia Electrical Engineering department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo 11845Egypt Mathematics department Faculty of ScienceUniversity of TabukTabukSaudi Arabia

In this research article,the(3+1)-dimensional nonlinear Gardner-Kadomtsov-Petviashvili(Gardner-KP)equation which depicts the nonlinear modulation of periodic waves,is analyzed through the Lie group-theoretic *** the Lie invariance condition,we find the symmetry *** pro-posed model yields eight-dimensional Lie ***,an optimal system of sub-algebras is com-puted,and similarity reductions are *** considered nonlinear partial differential equation is re-duced into ordinary differential equations(ODEs)by utilizing the similarity transformation method(STM),which has the benefit of yielding a large number of accurate traveling wave *** ODEs are further solved to get closed-form solutions of the Gardner-KP equation in some cases,while in other cases,we use the new auxiliary equation method to get its soliton *** evolution profiles of the obtained solutions are examined graphically under the appropriate selection of arbitrary parameters.

关键词： Gardner-KP equation Lie symmetry analysis Optimal system New auxiliary equation method Solitary wave solutions

Exploring the impact of tacrolimus on kidney transplant recipients: insights into immunological outcomes

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Journal of Chinese Pharmaceutical sciences 2024年第8期 737-750页

作者： Zainab A Abbood Ahmed Hamza al-Shammari Eman M.Jasim Amjad I.Oraibi department of Pharmacy Kut University College al-Manara College For Medical science/department of Pharmacy

To minimize graft rejection, it is crucial to reduce immunosuppression. In the present study, we investigated immunological markers in the blood of transplant patients who did not receive tacrolimus. We also assessed alloreactive CD4+CD25+Fox P3+ Tregs both before transplantation and after tacrolimus reduction. The comparative analysis included 50 individuals experiencing early rejection after tacrolimus discontinuation and 100 non-rejectors. Before reducing tacrolimus, rejectors exhibited elevated levels of memory CD8+ T cells compared to Tregs, while non-rejectors displayed a lower ratio of memory CD4+ T cells to Tregs. Following tacrolimus removal, rejectors showed an increase in na?ve T cells and a decrease in effector T cells. After transplantation, CD4+ Treg levels declined, but their anti-donor regulatory function remained consistent in both rejectors and non-rejectors. Immunological correlation revealed an association between acute rejection after tacrolimus discontinuation and the pre-reduction ratio of memory T cells to regulatory T cells. These indicators identified individuals whose immunosuppression could be safely reduced.

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Memory-enhanced text style transfer with dynamic style learning and calibration

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science China(Information sciences) 2024年第4期67卷 181-196页

作者： Fuqiang LIN Yiping SONG Zhiliang TIAN Wangqun CHEN Diwen DONG Bo LIU College of computer science and Technology National University of Defense Technology department of computer science and Engineering The Hong Kong University of Science and Technology Strategic Assessments and Consultation Institute Academy of Military Sciences

Text style transfer aims to rephrase a sentence to match the desired style while retaining the original content. As a controllable text generation task, mainstream approaches use content-independent style embedding as control variables to guide stylistic generation. Nonetheless, stylistic properties are contextsensitive even under the same style. For example, “delicious” and “helpful” convey positive sentiments,although they are more likely to describe food and people, respectively. Therefore, desired style signals must vary with the content. To this end, we propose a memory-enhanced transfer method, which learns fine-grained style representation concerning content to assist transfer. Rather than employing static style embedding or latent variables, our method abstracts linguistic characteristics from training corpora and memorizes subdivided content with the corresponding style representations. The style signal is dynamically retrieved from memory using the content as a query, providing a more expressive and flexible latent style space. To address the imbalance between quantity and quality in different content, we further introduce a calibration method to augment memory construction by modeling the relationship between candidate *** results obtained using three benchmark datasets confirm the superior performance of our model compared to competitive approaches. The evaluation metrics and case study also indicate that our model can generate diverse stylistic phrases matching context.

关键词： style transfer memory-enhanced method text generation deep learning text representation

DIR:Dynamic Request Interleaving for Improving the Read Performance of Aged Solid-State Drives

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Journal of computer science & Technology 2024年第1期39卷 82-98页

作者：聂世强张驰伍卫国 department of computer science and Technology Xi'an Jiaotong UniversityShaanci 710049China

Triple-level cell(TLC)NAND flash is increasingly adopted to build solid-state drives(SSDs)for modern computer *** TLC NAND flash effectively improves storage density,it faces severe reliability issues;in partic-ular,the pages exhibit different raw bit error rates(RBERs).Integrating strong low-density parity-check(LDPC)code helps to improve reliability but suffers from prolonged and proportional read latency due to multiple read retries for worse *** straightforward idea is that dispersing page-size data across several pages in different types can achieve a low-er average RBER and reduce the read ***,directly implementing this simple idea into flash translation lay-er(FTL)induces the read amplification issue as one logic page residing in more than one physical page brings several read *** this paper,we propose the Dynamic Request Interleaving(DIR)technology for improving the performance of TLC NAND flash-based SSDs,in particular,the aged ones with large *** exploits the observation that the la-tency of an I/O request is determined,without considering the queuing time,by the access of the slowest device page,i.e.,the page that has the highest *** grouping consecutive logical pages that have high locality and interleaving their encoded data in different types of device pages that have different RBERs,DIR effectively reduces the number of read re-tries for LDPC with limited read *** meet the requirement of allocating hybrid page types for interleaved data,we also design a page-interleaving friendly page allocation scheme,which splits all the planes into multi-plane re-gions for storing the interleaved data and single-plane regions for storing the normal *** pages in the multi-plane re-gion can be read/written in parallel by the proposed multi-plane command and avoid the read amplification *** on the DIR scheme and the proposed page allocation scheme,we build DIR-enable FTL,which integrates the pro

关键词： triple-layer cell solid-state drive(TLC SSD) performance interleaving data unbalanced bit error rate

Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

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computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan Jaffar Muhammad Rashid Mohammed S.alshuhri Sheeraz Akram Faculty of computer science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan department of computer science National University of TechnologyIslamabad45000Pakistan department of Radiological sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

ReChoreoNet: Repertoire-based Dance Re-choreography with Music-conditioned Temporal and Style Clues

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Machine Intelligence Research 2024年第4期21卷 771-781页

作者： Ho Yin Au Jie Chen Junkun Jiang Yike Guo department of computer science Hong Kong Baptist UniversityHong Kong 999077China department of computer science and Engineering The Hong Kong University of Science and EngineeringHong Kong 999077China

To generate dance that temporally and aesthetically matches the music is a challenging problem in three ***,the generated motion should be beats-aligned to the local musical ***,the global aesthetic style should be matched between motion and *** third,the generated motion should be diverse and *** address these challenges,we propose ReChoreoNet,which re-choreographs high-quality dance motion for a given piece of music.A data-driven learning strategy is proposed to efficiently correlate the temporal connections between music and motion in a progressively learned cross-modality embedding *** beats-aligned content motion will be subsequently used as autoregressive context and control signal to control a normalizing-flow model,which transfers the style of a prototype motion to the final generated *** addition,we present an aesthetically labelled music-dance repertoire(MDR)for both efficient learning of the cross-modality embedding,and understanding of the aesthetic connections between music and *** demonstrate that our repertoire-based framework is robustly extensible in both content and *** quantitative and qualitative experiments have been carried out to validate the efficiency of our proposed model.

关键词： Generative model cross-modality learning normalizing flow tempo synchronization style transfer.

A privacy-preserving method for publishing data with multiple sensitive attributes

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 222-238页

作者： Tong Yi Minyong Shi Wenqian Shang Haibin Zhu School of computer science and Engineering Guangxi Normal UniversityGuilinChina School of computer science Communication University of ChinaBeijingChina department of computer science and Mathematics Nipissing UniversityNorth BayOntarioCanada

The overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes(SAs)have not considered the personalised privacy ***,sensitive information disclosure may also be caused by these personalised *** address the matter,this article develops a personalised data publishing method for multiple *** to the requirements of individuals,the new method partitions SAs values into two categories:private values and public values,and breaks the association between them for privacy *** the private values,this paper takes the process of anonymisation,while the public values are released without this *** algorithm is designed to achieve the privacy mode,where the selectivity is determined by the sensitive value frequency and undesirable *** experimental results show that the proposed method can provide more information utility when compared with previous *** theoretic analyses and experiments also indicate that the privacy can be guaranteed even though the public values are known to an *** overgeneralisation and privacy breach caused by the personalised requirement can be avoided by the new method.

关键词： data privacy data publishing

A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles

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Digital Communications and Networks 2024年第1期10卷 126-134页

作者： Naiyu Wang Wenti Yang Xiaodong Wang Longfei Wu Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and computer Engineering North China Electric Power UniversityChina department of Mathematics&computer science Fayetteville State UniversityFayettevilleNCUSA department of Electrical and computer Engineering Stevens Institute of TechnologyHobokenNJ07030USA department of computer science and Engineering Qatar UniversityDohaQatar

The application of artificial intelligence technology in Internet of Vehicles(lov)has attracted great research interests with the goal of enabling smart transportation and traffic ***,concerns have been raised over the security and privacy of the tons of traffic and vehicle *** this regard,Federated Learning(FL)with privacy protection features is considered a highly promising ***,in the FL process,the server side may take advantage of its dominant role in model aggregation to steal sensitive information of users,while the client side may also upload malicious data to compromise the training of the global *** existing privacy-preserving FL schemes in IoV fail to deal with threats from both of these two sides at the same *** this paper,we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL,which uses blockchain as the underlying distributed framework of *** improve the Multi-Krum technology and combine it with the homomorphic encryption to achieve ciphertext-level model aggregation and model filtering,which can enable the verifiability of the local models while achieving ***,we develop a reputation-based incentive mechanism to encourage users in IoV to actively participate in the federated learning and to practice *** security analysis and performance evaluations are conducted to show that the proposed scheme can meet the security requirements and improve the performance of the FL model.

关键词： Federated learning Blockchain Privacy-preservation Homomorphic encryption Internetof vehicles