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Explainable Artificial Intelligence(XAI)Model for Cancer Image Classification

Computer Modeling in Engineering & Sciences 2024年第10期141卷 401-441页

作者： Amit singhal Krishna Kant Agrawal Angeles Quezada Adrian Rodriguez Aguiñaga Samantha Jiménez Satya Prakash Yadav Department of Computer Science&Engineering Raj Kumar Goel Institute of TechnologyGhaziabad201017India Department of School of Computing Science and Engineering Galgotias UniversityGreater Noida203201India Tecnológico Nacional de México Campus Tijuana Baja California22414México Universidad Autónoma de Baja California TijuanaBaja California22414México Department of Computer Science and Engineering G.LBajaj Institute of Technology and Management(GLBITM)Affiliated to Dr.A.P.J.Abdul Kalam Technical UniversityLucknow201306India School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India

The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare *** is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these *** models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial ***,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer *** research presents an advanced investigation of XAI models to classify cancer *** describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare *** addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging *** proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive *** proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%*** will discuss the potential applications of the proposed XAI models in the smart healthcare *** will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.

关键词： Explainable artificial intelligence artificial intelligence XAI healthcare cancer image classification

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Comprehensive Analysis of Gender Classification Accuracy across Varied Geographic Regions through the Application of Deep Learning Algorithms to Speech Signals

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Computer Systems Science & Engineering 2024年第3期48卷 609-625页

作者： Abhishek singhal Devendra Kumar Sharma Department of Electronics and Communication Engineering Faculty of Engineering and TechnologySRM Institute of Science and TechnologyDelhi–NCR CampusGhaziabad201204India

This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions,employing a deep learning classification algorithm for speech signal *** this study,speech samples are categorized for both training and testing purposes based on their geographical *** 1 comprises speech samples from speakers outside of India,whereas Category 2 comprises live-recorded speech samples from Indian *** speech samples are likewise classified into four distinct sets,taking into consideration both geographical origin and the language spoken by the ***,the results indicate a noticeable difference in gender identification accuracy among speakers from different geographical *** speakers,utilizing 52 Hindi and 26 English phonemes in their speech,demonstrate a notably higher gender identification accuracy of 85.75%compared to those speakers who predominantly use 26 English phonemes in their conversations when the system is trained using speech samples from Indian *** gender identification accuracy of the proposed model reaches 83.20%when the system is trained using speech samples from speakers outside of *** the analysis of speech signals,Mel Frequency Cepstral Coefficients(MFCCs)serve as relevant features for the speech *** deep learning classification algorithm utilized in this research is based on a Bidirectional Long Short-Term Memory(BiLSTM)architecture within a Recurrent Neural Network(RNN)model.

关键词： Deep learning recurrent neural network voice signal mel frequency cepstral coefficients geographical area gender

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Incidence of exclusive extrapelvic skeletal metastasis in prostate carcinoma on bone scintigraphy

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World Journal of Radiology 2024年第7期16卷 265-273页

作者： Parneet Singh Kanhaiyalal Agrawal Ashique Rahman Tejasvini singhal Girish Kumar Parida Gopinath Gnanasegaran Department of Nuclear Medicine All India Institute of Medical SciencesBhubaneshwar 751019OdishaIndia Department of Nuclear Medicine Royal Free HospitalLondon NW32QGUnited Kingdom

BACKGROUND Bone is one of the common sites of metastasis from prostate *** scintigraphy(BS)is one of the most sensitive imaging modalities currently used for bone metastatic *** metastasis in prostate carcinoma commonly involves pelvic bones but rarely involves extrapelvic-extraspinal *** To retrospectively analyze the BS data to determine the pattern of skeletal metastases in the prostate *** This retrospective observational study involves patients with biopsy-proven prostate carcinoma referred for BS for staging *** with abnormal BS were evaluated for the pattern of skeletal involvement and data were pre-sented in descriptive format in the form of *** A total of 150 patients with biopsy-proven prostate cancer who were referred for staging were included in the *** of 150 patients(8.67%)had no abnormal uptake on planar images,ruling out metastatic ***-four patients(16%)had heterogeneous uptake in the spine with distribution charac-teristic of degenerative disease and no scan pattern of metastatic *** patients(20%)had multifocal uptake involving both pelvic and extra pelvic bones on planar images typical for skeletal metastasis and were considered ***-three out of 150 patients(55.3%)had increased tracer uptake,which was indeterminate,thus,single photon emission computed tomography-computed tomography(SPECT-CT)was acquired,which showed 51 with metastatic disease,31 benign lesions,and one indeterminate *** of 150 patients had exclusive pelvic bone uptake,which was found to be metastatic in 4/7 patients in *** six out of 150 patients showed exclusive extrapelvic tracer uptake,of which only 3 had vertebral metastatic *** of the patients with increased uptake exclusively in the extrapelvic-extraspinal location was *** The incidence of exclusive extrapelvic skeletal metastatic disease in prostate carcinom

关键词： Pelvic Prostate cancer Bone scan Single photon emission computed tomography-computed tomography Skeletal metastasis

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以掺氢天然气为燃料直接内重整固体氧化物电池堆的稳定性

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电化学（中英文） 2024年第1期30卷 46-54页

作者：汤亚飞武安祺韩贝贝刘华包善军林王林陈铭官万兵 Subhash C.singhal 中国科学院宁波材料技术与工程研究所浙江宁波315201 浙江启明电力集团有限公司浙江舟山316099 浙江大学舟山海洋研究中心浙江舟山316021 丹麦技术大学能源转换与储存系川麦哥本哈根

本文研究了掺氢天然气直接内重整平管型固体氧化物电池短堆的长期稳定性和衰减机理。通过约3000小时的实测实验,结果显示,电堆的总体衰减率为2.3%·kh^(-1),电堆中三个金属连接板的面积比电阻分别增加了0.276Ω·cm^(2)、0.254Ω·cm^(2... 详细信息

本文研究了掺氢天然气直接内重整平管型固体氧化物电池短堆的长期稳定性和衰减机理。通过约3000小时的实测实验,结果显示,电堆的总体衰减率为2.3%·kh^(-1),电堆中三个金属连接板的面积比电阻分别增加了0.276Ω·cm^(2)、0.254Ω·cm^(2)和0.249Ω·cm^(2),但电堆中两个电池的电压反而分别增加了3.38 m V·kh^(-1)和3.78 m V·kh^(-1)。电堆衰减主要由金属连接件表层氧化及其与阴极集流层材料反应生成Sr CrO_(4)物质,两者共同作用增大了电池与金属连接体间的界面电阻所致。结果表明,以掺氢天然气为燃料直接内重整平管型固体氧化物燃料电池电堆具有良好的稳定性。本文工作为掺氢天然气在固体氧化物燃料电池堆中的直接内重整应用提供了理论参考与实验依据。

关键词：掺氢天然气内重整稳定性固体氧化物电堆

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以掺氢天然气为燃料直接内重整固体氧化物电池堆的稳定性（英文）

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电化学(中英文) 2024年第1期 49-57页

作者：汤亚飞武安祺韩贝贝刘华包善军林王林陈铭官万兵 Subhash C.singhal 中国科学院宁波材料技术与工程研究所浙江启明电力集团有限公司浙江大学舟山海洋研究中心丹麦技术大学能源转换与储存系

本文研究了掺氢天然气直接内重整平管型固体氧化物电池短堆的长期稳定性和衰减机理。通过约3000小时的实测实验，结果显示，电堆的总体衰减率为2.3%kh–1，电堆中三个金属连接板的面积比电阻分别增加了0.276Ω·cm2、0.254Ω·cm2和0.24... 详细信息

本文研究了掺氢天然气直接内重整平管型固体氧化物电池短堆的长期稳定性和衰减机理。通过约3000小时的实测实验，结果显示，电堆的总体衰减率为2.3%kh–1，电堆中三个金属连接板的面积比电阻分别增加了0.276Ω·cm2、0.254Ω·cm2和0.249Ω·cm2，但电堆中两个电池的电压反而分别增加了3.38 m V·kh–1和3.78 m V·kh–1。电堆衰减主要由金属连接件表层氧化及其与阴极集流层材料反应生成Sr Cr O4物质，两者共同作用增大了电池与金属连接体间的界面电阻所致。结果表明，以掺氢天然气为燃料直接内重整平管型固体氧化物燃料电池电堆具有良好的稳定性。本文工作为掺氢天然气在固体氧化物燃料电池堆中的直接内重整应用提供了理论参考与实验依据。

关键词：掺氢天然气内重整稳定性固体氧化物电堆

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Tackling imbalanced data in cybersecurity with transfer learning: a case with ROP payload detection

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Cybersecurity 2023年第2期6卷 29-43页

作者： Haizhou Wang Anoop singhal Peng Liu College of Information Sciences and Technology The Pennsylvania State UniversityState CollegeUSA The National Institute of Standards and Technology GaithersburgUSA

In recent years,deep learning gained proliferating popularity in the cybersecurity application domain,since when being compared to traditional machine learning methods,it usually involves less human efforts,produces better results,and provides better ***,the imbalanced data issue is very common in cybersecurity,which can substantially deteriorate the performance of the deep learning *** paper introduces a transfer learning based method to tackle the imbalanced data issue in cybersecurity using return-oriented programming payload detection as a case *** achieved 0.0290 average false positive rate,0.9705 average F1 score and 0.9521 average detection rate on 3 different target domain programs using 2 different source domain programs,with 0 benign training data sample in the target *** performance improvement compared to the baseline is a trade-off between false positive rate and detection *** our approach,the total number of false positives is reduced by 23.16%,and as a trade-off,the number of detected malicious samples decreases by 0.68%.

关键词： Domain adaptation Return-oriented programming Imbalanced dataset

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Next-Generation Intelligent MXene-Based Electrochemical Aptasensors for Point-of-Care Cancer Diagnostics

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Nano-Micro Letters 2022年第6期14卷 248-281页

作者： Arpana Parihar Ayushi singhal Neeraj Kumar Raju Khan MohdAkram Khan Avanish KSrivastava Industrial Waste Utilization Nano and BiomaterialsCSIR-Advanced Materials and Processes Research Institute(AMPRI)Hoshangabad RoadBhopal 462026MPIndia Academy of Scientific and Innovative Research(AcSIR) Ghaziabad 201002India

Delayed diagnosis of cancer using conventional diagnostic modalities needs to be addressed to reduce the mortality rate of ***,2 D nanomaterial-enabled advanced biosensors have shown potential towards the early diagnosis of *** high surface area,surface functional groups availability,and excellent electrical conductivity of MXene make it the 2 D material of choice for the fabrication of advanced electrochemical biosensors for disease ***-enabled electrochemical aptasensors have shown great promise for the detection of cancer biomarkers with a femtomolar limit of ***,the stability,ease of synthesis,good reproducibility,and high specificity offered by MXene-enabled aptasensors hold promise to be the mainstream diagnostic *** this review,the design and fabrication of MXene-based electrochemical aptasensors for the detection of cancer biomarkers have been ***,various synthetic processes and useful properties of MXenes which can be tuned and optimized easily and efficiently to fabricate sensitive biosensors have been ***,futuristic sensing applications along with challenges will be deliberated herein.

关键词： MXene Electrochemical devices POCT Aptamer Cancer diagnostics

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Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

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Computers, Materials & Continua 2023年第3期74卷 6755-6774页

作者： Shweta singhal Nishtha Jatana Ahmad F Subahi Charu Gupta Osamah Ibrahim Khalaf Youseef Alotaibi Indira Gandhi Delhi Technical University for Women Delhi110006India Maharaja Surajmal Institute of Technology New Delhi110058India Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia Bhagwan Parshuram Institute of Technology RohiniNew Delhi110085India Al-Nahrain University Al-Nahrain Nanorenewable Energy Research CenterBaghdad64074Iraq Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia

Software needs modifications and requires revisions *** to these revisions,retesting software becomes essential to ensure that the enhancements made,have not affected its bug-free *** time and cost incurred in this process,need to be reduced by the method of test case selection and *** is observed that many nature-inspired techniques are applied in this *** Buffalo Optimization is one such approach,applied to regression test selection and *** this paper,the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and *** proposed algorithm converges in polynomial time(O(n^(2))).In this paper,the empirical evaluation of applying African Buffalo Optimization for test case prioritization is done on sample data set with multiple *** astounding 62.5%drop in size and a 48.57%drop in the runtime of the original test suite were *** obtained results are compared with Ant Colony *** comparative analysis indicates that African Buffalo Optimization and Ant Colony Optimization exhibit similar fault detection capabilities(80%),and a reduction in the overall execution time and size of the resultant test *** results and analysis,hence,advocate and encourages the use of African Buffalo Optimization in the area of test case selection and prioritization.

关键词： Test case prioritization regression testing test case selection African buffalo optimization nature-inspired meta-heuristic

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Autism Spectrum Disorder Prediction by an Explainable Deep Learning Approach

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Computers, Materials & Continua 2022年第4期71卷 1459-1471页

作者： Anupam Garg Anshu Parashar Dipto Barman Sahil Jain Divya singhal Mehedi Masud Mohamed Abouhawwash Computer Science and Engineering Department Thapar Institute of Engineering and TechnologyPatialaIndia School of Computer Science and Statistics Trinity College DublinDublinIreland University Institute of Biotechnology Chandigarh UniversityMohaliIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Autism Spectrum Disorder (ASD) is a developmental disorderwhose symptoms become noticeable in early years of the age though it canbe present in any age group. ASD is a mental disorder which affects the communicational, social and non-verbal behaviors. It cannot be cured completelybut can be reduced if detected early. An early diagnosis is hampered by thevariation and severity of ASD symptoms as well as having symptoms commonly seen in other mental disorders as well. Nowadays, with the emergenceof deep learning approaches in various fields, medical experts can be assistedin early diagnosis of ASD. It is very difficult for a practitioner to identifyand concentrate on the major feature’s leading to the accurate prediction ofthe ASD and this arises the need for having an automated approach. Also,presence of different symptoms of ASD traits amongst toddlers directs tothe creation of a large feature dataset. In this study, we propose a hybridapproach comprising of both, deep learning and Explainable Artificial Intelligence (XAI) to find the most contributing features for the early and preciseprediction of ASD. The proposed framework gives more accurate predictionalong with the recommendations of predicted results which will be a vital aidclinically for better and early prediction of ASD traits amongst toddlers.

关键词： Deep learning explainable artificial intelligence autism spectrum disorder machine learning

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Intelligent System Application to Monitor the Smart City Building Lighting

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Computers, Materials & Continua 2023年第5期75卷 3159-3169页

作者： Tzu-Chia Chen Ngakan Ketut Acwin Dwijendra Saurabh singhal R.Sivaraman Amr Mamdouh Department of Industrial Engineering and Management Ming Chi University of TechnologyNew Taipei City24301Taiwan Department of Architecture Faculty of EngineeringUdayana UniversityBali80361Indonesia Department of Computer Engineering and Applications GLA UniversityMathuraUttar PradeshIndia Department of Mathematics Dwaraka Doss Goverdhan Doss Vaishnav CollegeArumbakkamUniversity of MadrasChennaiIndia Architectural Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egyp

A smart city incorporates infrastructure methods that are environmentally responsible,such as smart communications,smart grids,smart energy,and smart *** city administration has prioritized the use of cutting-edge technology and informatics as the primary strategy for enhancing service quality,with energy resources taking *** achieve optimal energy management in themultidimensional system of a city tribe,it is necessary not only to identify and study the vast majority of energy elements,but also to define their implicit *** is because optimal energy management is required to reach this *** lighting index is an essential consideration when evaluating the comfort *** order to realize the concept of a smart city,the primary objective of this research is to create a system for managing and monitoring the lighting *** is possible to identify two distinct phaseswithin the intelligent *** data collection concludes,the monitoring system will be *** the second step,the operation of the control system is analyzed and its effect on the performance of the numerical model is *** evaluation is based on the proposed *** optimized resultswere deemed satisfactory because they maintained the brightness index value(79%)while consuming less *** intelligent implementation system generated satisfactory outcomes,which were observed 1.75 times on average.

关键词： Smart city lighting index residential building energy consumption

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