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GliomaCNN: An Effective Lightweight CNN Model in Assessment of Classifying Brain Tumor from Magnetic Resonance Images Using Explainable AI

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2425-2448页

作者： Md.Atiqur rahman Mustavi Ibne Masum Khan Md Hasib M.F.Mridha Sultan Alfarhood Mejdl Safran Dunren Che Department of Computer Science and Engineering Ahsanullah University of Science and TechnologyDhaka1208Bangladesh Department of Computer Science and Software Engineering The University of Western AustraliaPerthWA 6009Australia Department of Computer Science American International University-BangladeshDhaka1229Bangladesh Department of Computer Science College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia School of Computing Southern Illinois UniversityCarbondale62901USA

Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global *** study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging(MRI).It focuses on distinguishing between Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG).LGGs are benign and typically manageable with surgical resection,while HGGs are malignant and more *** research introduces an innovative custom convolutional neural network(CNN)model,*** stands out as a lightweight CNN model compared to its *** research utilized the BraTS 2020 dataset for its *** with the gradient-boosting algorithm,GliomaCNN has achieved an impressive accuracy of 99.1569%.The model’s interpretability is ensured through SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM++).They provide insights into critical decision-making regions for classification *** challenges in identifying tumors in images without visible signs,the model demonstrates remarkable performance in this critical medical application,offering a promising tool for accurate brain tumor diagnosis which paves the way for enhanced early detection and treatment of brain tumors.

关键词： Deep learning magnetic resonance imaging convolutional neural networks explainable AI boosting algorithm ablation

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Machine learning-based multi-objective optimization and thermal assessment of supercritical CO_(2) Rankine cycles for gas turbine waste heat recovery

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Energy and AI 2024年第2期16卷 363-384页

作者： Asif Iqbal Turja Ishtiak Ahmed Khan Sabbir rahman Ashraf Mustakim Mohammad Ishraq Hossain M Monjurul Ehsan Yasin Khan Department of Mechanical and Production Engineering(MPE) Islamic University of Technology(IUT)Board BazarGazipur1704Bangladesh

Technologies for utilizing waste heat for power generation have attracted significant attention in recent years due to their potential to enhance energy efficiency and reduce greenhouse gas *** research focuses on the comparative and optimization analysis of three supercritical carbon dioxide(sCO_(2))Rankine cycles(simple,cascade,and split)for gas turbine waste heat *** study begins with parametric analysis,investigating the significant effects of key variables,including turbine inlet temperature,condenser inlet temperature,and pinch point temperature,on the thermal performance of advanced sCO_(2) power *** identify the most efficient cycle configuration,a multi-objective optimization approach is *** approach combines a Genetic Algorithm with machine learning regression models(Random Forest,XGBoost,Artificial Neural Network,Ridge Regression,and K-Nearest Neighbors)to predict cycle performance using a dataset extracted from cycle *** decision-making process for determining the optimal cycle configuration is facilitated by the TOPSIS(technique for order of preference by similarity to the ideal solution)*** study's major findings reveal that the split cycle outperforms the simple and cascade configurations in terms of power generation across various operating *** optimized split cycle not only demonstrates superior power output but also exhibits enhanced net power output,heat recovery,system and exergy efficiency of 7.99 MW,76.17%,26.86%and 57.96%,respectively,making it a promising choice for waste heat recovery *** research has the potential to contribute to the advancement and widespread adoption of waste heat recovery in energy technologies boosting system efficiency and economic *** provides a new perspective for future research,contributing to the improvement of energy generation infrastructure.

关键词： Supercritical co2 Rankine cycle Waste heat recovery Gas turbine Thermodynamic analysis Machine Learning Optimization

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Computer vision-based six layered ConvNeural network to recognize sign language for both numeral and alphabet signs

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Biomimetic Intelligence & Robotics 2024年第1期4卷 45-58页

作者： Muhammad Aminur Rahaman Kabiratun Ummi Oyshe Prothoma Khan Chowdhury Tanoy Debnath Anichur rahman Md.Saikat Islam Khan Department of Computer Science and Engineering Green University of BangladeshDhakaBangladesh Department of Computer Science and Engineering National Institute of Textile Engineering and Research(NITER)Constituent Institute of Dhaka UniversityDhakaBangladesh Department of Computer Science and Engineering Mawlana Bhashani Science and Technology UniversityTangailBangladesh

People who have trouble communicating verbally are often dependent on sign language,which can be difficult for most people to understand,making interaction with them a difficult *** Sign Language Recognition(SLR)system takes an input expression from a hearing or speaking-impaired person and outputs it in the form of text or voice to a normal *** existing study related to the Sign Language Recognition system has some drawbacks,such as a lack of large datasets and datasets with a range of backgrounds,skin tones,and *** research efficiently focuses on Sign Language Recognition to overcome previous *** importantly,we use our proposed Convolutional Neural Network(CNN)model,“ConvNeural”,in order to train our ***,we develop our own datasets,“BdSL_OPSA22_STATIC1”and“BdSL_OPSA22_STATIC2”,both of which have ambiguous backgrounds.“BdSL_OPSA22_STATIC1”and“BdSL_OPSA22_STATIC2”both include images of Bangla characters and numerals,a total of 24,615 and 8437 images,***“ConvNeural”model outperforms the pre-trained models with accuracy of 98.38%for“BdSL_OPSA22_STATIC1”and 92.78%for“BdSL_OPSA22_STATIC2”.For“BdSL_OPSA22_STATIC1”dataset,we get precision,recall,F1-score,sensitivity and specificity of 96%,95%,95%,99.31%,and 95.78%***,in case of“BdSL_OPSA22_STATIC2”dataset,we achieve precision,recall,F1-score,sensitivity and specificity of 90%,88%,88%,100%,and 100%respectively.

关键词： Conv NeuralSign language CNN Static Feature extraction Convolution2D Fully connected layer Dropout

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A Review and Bibliometric Analysis of the Current Studies for the 6G Networks

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2165-2206页

作者： Qusay M.Salih Md.Arafatur rahman Ahmad Firdaus Mohammed Rajih Jassim Hasan Kahtan Jasni Mohamad Zain Ahmed Hussein Ali Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)PekanPahang26600Malaysia Department of Medical Equipment Technology Engineering Al Salam University CollegeBaghdadIraq School of Mathematics and Computer Science University of WolverhamptonWolverhampton01902UK Department of Production Engineering and Metallurgy University of TechnologyBaghdadIraq Cardiff School of Technologies Cardiff Metropolitan UniversityCardiffCF52YBUK Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARAShah AlamSelangor40450Malaysia Department Computer Science Al-Salam University CollegeBaghdadIraq

The race to develop the next generation of wireless networks,known as Sixth Generation(6G)wireless,which will be operational in 2030,has already *** realize its full potential over the next decade,6G will undoubtedly necessitate additional improvements that integrate existing solutions with cutting-edge ***,the studies about 6G are mainly limited and scattered,whereas no bibliometric study covers the 6G ***,this study aims to review,examine,and summarize existing studies and research activities in *** study has examined the Scopus database through a bibliometric analysis of more than 1,000 papers published between 2017 and ***,we applied the bibliometric analysis methods by including(1)document type,(2)subject area,(3)author,and(4)country of *** study’s results reflect the research 6G community’s trends,highlight important research challenges,and elucidate potential directions for future research in this interesting area.

关键词： 6G bibliometric wireless networks review

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Realizing High Thermoelectric Performance in n-Type Se-Free Bi2Te3 Materials by Spontaneous Incorporation of FeTe2 Nanoinclusions

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Energy & Environmental Materials 2024年第4期7卷 344-354页

作者： Jamil Ur rahman Woo Hyun Nam Yong-Jae Jung Jong Ho Won Jong-Min Oh Nguyen Van Du Gul rahman Víctor M.García-Suárez Ran He Kornelius Nielsch Jung Young Cho Won-Seon Seo Jong Wook Roh Sang-il Kim Soonil Lee Kyu Hyoung Lee Hyun Sik Kim Weon Ho Shin Leibniz Institute for Solid State and Materials Research Advanced Materials Convergence R&D Division Korea Institute of Ceramic Engineering & Technology Department of Electronic Materials Engineering Kwangwoon University Department of Energy Engineering Dankook University Faculty of Fundamental Science Phenikaa University Department of Physics Quaid-i-Azam University Departamento de Física Universidad de Oviedo Nanomaterials and Nanotechnology Research Center-CINN Department of Materials Science and Engineering Yonsei University School of Nano & Materials Science and Engineering Kyungpook National University Department of Materials Science and Engineering University of Seoul School of Materials Science and Engineering Changwon National University

Bi2Te3-based materials have drawn much attention from the thermoelectric community due to their excellent thermoelectric performance near room ***,the stability of existing n-type Bi2（Te,Se）3materials is still low due to the evaporation energy of Se (37.70 kJ mol-1) being much lower than that of Te (52.55 kJ mol-1).The evaporated Se from the material causes problems in interconnects of the module while degrading the efficiency.

关键词： Bi2Te3 energy harvesting FeTe2 nanoinclusion n-type materials thermoelectric

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Frame Length Dependency for Fundamental Frequency Extraction in Noisy Speech

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Journal of Signal and Information Processing 2024年第1期15卷 1-17页

作者： Md. Saifur rahman Any Chowdury Nargis Parvin Arpita Saha Moinur rahman Department of Information and Communication Technology Comilla University Cumilla Bangladesh Department of Computer Science and Engineering Bangladesh Army International University of Science and Technology (BAIUST) Cumilla Bangladesh Department of Information Technology University of Information Technology and Sciences (UITS) Dhaka Bangladesh

The fundamental frequency plays a significant part in understanding and perceiving the pitch of a sound. The pitch is a fundamental attribute employed in numerous speech-related works. For fundamental frequency extraction, several algorithms have been developed which one to use relies on the signal’s characteristics and the surrounding noise. Thus, the algorithm’s noise resistance becomes more critical than ever for precise fundamental frequency estimation. Nonetheless, numerous state-of-the-art algorithms face struggles in achieving satisfying outcomes when confronted with speech recordings that are noisy with low signal-to-noise ratio (SNR) values. Also, most of the recent techniques utilize different frame lengths for pitch extraction. From this point of view, This research considers different frame lengths on male and female speech signals for fundamental frequency extraction. Also, analyze the frame length dependency on the speech signal analytically to understand which frame length is more suitable and effective for male and female speech signals specifically. For the validation of our idea, we have utilized the conventional autocorrelation function (ACF), and state-of-the-art method BaNa. This study puts out a potent idea that will work better for speech processing applications in noisy speech. From experimental results, the proposed idea represents which frame length is more appropriate for male and female speech signals in noisy environments.

关键词： Pitch Estimation Fundamental Frequency BaNa ACF Frame Length

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Diabetic Retinopathy Detection: A Hybrid Intelligent Approach

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Computers, Materials & Continua 2024年第9期80卷 4561-4576页

作者： Atta rahman Mustafa Youldash Ghaida Alshammari Abrar Sebiany Joury Alzayat Manar Alsayed Mona Alqahtani Noor Aljishi Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia

Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy(DR).Early detection and treatment are crucial to prevent complete blindness or partial vision *** detection methods,which involve ophthalmologists examining retinal fundus images,are subjective,expensive,and ***,this study employs artificial intelligence(AI)technology to perform faster and more accurate binary classifications and determine the presence of *** this regard,we employed three promising machine learning models namely,support vector machine(SVM),k-nearest neighbors(KNN),and Histogram Gradient Boosting(HGB),after carefully selecting features using transfer learning on the fundus images of the Asia Pacific Tele-Ophthalmology Society(APTOS)(a standard dataset),which includes 3662 images and originally categorized DR into five levels,now simplified to a binary format:No DR and DR(Classes 1-4).The results demonstrate that the SVM model outperformed the other approaches in the literature with the same dataset,achieving an excellent accuracy of 96.9%,compared to 95.6%for both the KNN and HGB *** approach is evaluated by medical health professionals and offers a valuable pathway for the early detection of DR and can be successfully employed as a clinical decision support system.

关键词： Diabetic retinopathy transfer learning machine learning fundus images binary classification APTOS

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Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

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Computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saad Arif Sara Rehman Muhammad Farrukh Qureshi Nagwan abdel Samee Maali Alabdulhafith Yeong Hyeon Gu Mohammed A.Al-masni Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid *** and timely diagnosis of these disorders is crucial for effective treatment and patient *** research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection *** forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this *** ensemble learning,random forest,adaptive boosting,and bagging classifiers are *** effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature *** experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,*** enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical *** significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed *** research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

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Identification of an Aux/IAA regulator for flesh firmness using combined GWAS and bulked segregant RNA-Seq analysis in watermelon

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Horticultural Plant Journal 2024年第5期10卷 1198-1213页

作者： Muhammad Anees Hongju Zhu Muhammad Jawad Umer Chengsheng Gong Pingli Yuan Xuqiang Lu Nan He Mohamed omar Kaseb Dongdong Yang Yong Zhao Wenge Liu Henan Joint International Research Laboratory of South Asian Fruits and Cucurbits Zhengzhou Fruit Research InstituteChinese Academy of Agricultural SciencesZhengzhouHenan 450009China Henan Key Laboratory of Fruit and Cucurbit Biology Zhengzhou Fruit Research InstituteChinese Academy of Agricultural SciencesZhengzhouHenan 450009China State Key Laboratory of Cotton Biology Institute of Cotton ResearchChinese Academy of Agricultural Science(ICRCAAS)AnyangHenan 455000China National Key Laboratory for Germplasm Innovation&Utilization of Horticultural Crops Zhengzhou Fruit Research InstituteChinese Academy of Agricultural SciencesZhengzhouHenan 450009China

Watermelon is a highly cultivated fruit crop renowned for its quality properties of fruit *** various quality factors,fruit flesh firmness is a crucial quality parameter influencing the fruit texture,shelf life and its commercial *** auxin/indole-3-acetic acid(Aux/IAA)plays a significant role in fruit development and ripening of non-climacteric ***,the regulatory mechanism of Aux/IAA in controlling fruit flesh firmness and ripening in watermelon remains *** this study,we employed an integrative approach combining genome-wide association study(GWAS)and bulked segregant RNA-Seq analysis(BSR-Seq)to identify an overlapping candidate region between 12776310 and 12968331 bp on chromosome 6,underlying an auxin-responsive gene(Aux/IAA)associated with flesh firmness in *** analysis,followed by real-time quantitative reverse transcription PCR(qRT-PCR),confirmed that the expression of Aux/IAA was consistently higher in fruits with high flesh *** sequence alignment revealed a single base mutation in the coding region of Aux/***,the concomitant Kompetitive/Competitive allele-specific PCR(KASP)genotyping data sets for F2 population and germplasm accessions identified Aux/IAA as a strong candidate gene associated with flesh ***/IAA was enriched in the plant hormone signal transduction pathway,involving cell enlargement and leading to low flesh *** determined the higher accumulation of abscisic acid(ABA)in fruits with low flesh firmness than hard ***,overexpression of Aux/IAA induced higher flesh firmness with an increased number of fruit flesh cells while reducing ABA content and flesh cell ***,the allelic variation in Aux/IAA for soft flesh firmness was found to exist in Citrullus mucosospermus and gradually fixed into Citrullus lanatus during domestication,indicating that soft flesh firmness was a domesticated *** findings significantly enhanced our unders

关键词： Watermelon Flesh firmness GWAS BSR-Seq KASP marker analysis Transient overexpression

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A review of concrete bridge surface defect detection based on deep learning

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Optoelectronics Letters 2024年

作者： LIAO Yanna HUANG Chaoyang abdel-Hamid Soliman School of Electronic Engineering， Xi'an University of Posts and Telecommunications School of Digital， Technology， Innovation and Business， Staffordshire University

The detection of surface defects in concrete bridges using deep learning is of significant importance for reducing operational risks， saving maintenance costs， and driving the intelligent transformation of bridge defect detection. In contrast to the subjective and inefficient manual visual inspection， deep learning-based algorithms for concrete defect detection exhibit remarkable advantages， emerging as a focal point in recent research. This paper comprehensively analyzes the research progress of deep learning algorithms in the field of surface defect detection in concrete bridges in recent years. It introduces the early detection methods for surface defects in concrete bridges and the development of deep learning. Subsequently， it provides an overview of deep learning-based concrete bridge surface defect detection research from three aspects: image classification， object detection， and semantic segmentation. The paper summarizes the strengths and weaknesses of existing methods and the challenges they face. Additionally， it analyzes and prospects the development trends of surface defect detection in concrete bridges.

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