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Deep learning methods for noisy sperm image classification from convolutional neural network to visual transformer:a comprehensive comparative study

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Intelligent Medicine 2024年第2期4卷 114-127页

作者： Ao Chen Chen Li md mamunur rahaman Yudong Yao Haoyuan Chen Hechen Yang Peng Zhao Weiming Hu Wanli Liu Shuojia Zou Ning Xu Marcin Grzegorzek Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information EngineeringNortheastern UniversityShenyangLiaoning 110169China Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA School of Arts and Design Liaoning Petrochemical UniversityFushunLiaoning 113001China Institute of Medical Informatics University of LübeckLüebeckGermany

Background With the gradual increase of infertility in the world,among which male sperm problems are the main factor for infertility,more and more couples are using computer-assisted sperm analysis(CASA)to assist in the analysis and treatment of ***,the rapid development of deep learning(DL)has led to strong results in image classification ***,the classification of sperm images has not been well studied in current deep learning methods,and the sperm images are often affected by noise in practical CASA *** purpose of this article is to investigate the anti-noise robustness of deep learning classification methods applied on sperm *** The SVIA dataset is a publicly available large-scale sperm dataset containing three *** this work,we used subset-C,which provides more than 125,000 independent images of sperms and impurities,including 121,401 sperm images and 4,479 impurity *** investigate the anti-noise robustness of deep learning classification methods applied on sperm images,we conducted a comprehensive comparative study of sperm images using many convolutional neural network(CNN)and visual transformer(VT)deep learning methods to find the deep learning model with the most stable anti-noise *** This study proved that VT had strong robustness for the classification of tiny object(sperm and impurity)image datasets under some types of conventional noise and some adversarial *** particular,under the influence of Poisson noise,accuracy changed from 91.45%to 91.08%,impurity precison changed from 92.7%to 91.3%,impurity recall changed from 88.8%to 89.5%,and impurity F1-score changed 90.7%to 90.4%.Meanwhile,sperm precision changed from 90.9%to 90.5%,sperm recall changed from 92.5%to 93.8%,and sperm F1-score changed from 92.1%to 90.4%.Conclusion Sperm image classification may be strongly affected by noise in current deep learning methods;the robustness with regard to noise of VT methods based

关键词： Computer-assisted sperm analysis Anti-noise Robustness Deep learning .Image classification Sperm image Conventional noise Adversarial attacks Convolutional neural network Visual transformer

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Effectiveness of hybrid ensemble machine learning models for landslide susceptibility analysis:Evidence from Shimla district of North-west Indian Himalayan region

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Journal of Mountain Science 2024年第7期21卷 2368-2393页

作者： SHARMA Aastha SAJJAD Haroon rahaman md Hibjur SAHA Tamal Kanti BHUYAN Nirsobha Department of Geography Faculty of SciencesJamia Millia IslamiaNew Delhi-110025India

The Indian Himalayan region is frequently experiencing climate change-induced ***,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide *** paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan *** examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)*** site-specific influencing factors were selected by employing a multicollinearity *** relationship between past landslide occurrences and influencing factors was established using the frequency ratio *** effectiveness of machine learning models was verified through performance *** landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and *** key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide *** results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility *** factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide *** texture,lineament density and elevation have been attributed to high and moderate ***,the study calls for devising suitable landslide mitig

关键词： Landslide susceptibility Site-specific factors Machine learning models Hybrid ensemble learning Geospatial techniques Himalayan region

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Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning

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Big Data Mining and Analytics 2024年第2期7卷 294-314页

作者： Shahriar Kaisar md mamunur Rashid Abdullahi Chowdhury Sakib Shahriar Shafin Joarder Kamruzzaman Abebe Diro Department of Information Systems and Business Analytics RMIT UniversityMelbourne 3000Australia School of Engineering and Technology Central Queensland UniversityRockhampton 4700Australia Faculty of Engineering Computer and Mathematical ScienceUniversity of AdelaideAdelaide 5005Australia Centre for Smart Analytics Federation University AustraliaBallarat 3350Australia School of Accounting Information Systems and Supply ChainRMIT UniversityMelbourne 3000Australia

Telemarketing is a well-established marketing approach to offering products and services to prospective *** effectiveness of such an approach,however,is highly dependent on the selection of the appropriate consumer base,as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested *** introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base,and the existing literature in this direction shows promising ***,the selection of influential features and the construction of effective learning models for improved performance remain a ***,from the modelling perspective,the class imbalance nature of the training data,where samples with unsuccessful outcomes highly outnumber successful ones,further compounds the problem by creating biased and inaccurate ***,customer preferences are likely to change over time due to various reasons,and/or a fresh group of customers may be targeted for a new product or service,necessitating model retraining which is not addressed at all in existing works.A major challenge in model retraining is maintaining a balance between stability(retaining older knowledge)and plasticity(being receptive to new information).To address the above issues,this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately.A novel online learning method is proposed for model retraining when new samples are available over *** newly introduced method equips the proposed approach to deal with dynamic data,leading to improved readiness of the proposed model for practical adoption,and is a highly useful addition to the *** experiments with real-world data show that the proposed approach achieves excellent results in all cases(e.g.,98.6

关键词： machine learning online learning oversampling telemarketing imbalanced dataset ensemble model

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Vision Intelligence for Smart Sheep Farming: Applying Ensemble Learning to Detect Sheep Breeds

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ARTIFICIAL INTELLIGENCE IN AGRICULTURE 2024年第1期11卷 1-12页

作者： Himel, Galib Muhammad Shahriar Islam, md. Masudul rahaman, Mijanur Bangladesh Univ Business & Technol Dhaka Bangladesh

The ability to automatically recognize sheep breeds holds significant value for the sheep industry. Sheep farmers often require breed identification to assess the commercial worth of their flocks. However, many farmers specifically the novice one encounter difficulties in accurately identifying sheep breeds without experts in the field. Therefore, there is a need for autonomous approaches that can effectively and precisely replicate the breed identification skills of a sheep breed expert while functioning within a farm environment, thus providing considerable benefits the industry-specific to the novice farmers in the industry. To achieve this objective, we suggest utilizing a model based on convolutional neural networks (CNNs) which can rapidly and efficiently identify the type of sheep based on their facial features. This approach offers a cost-effective solution. To conduct our experiment, we utilized a dataset consisting of 1680 facial images which represented four distinct sheep breeds. This paper proposes an ensemble method that combines Xception, VGG16, InceptionV3, InceptionResNetV2, and DenseNet121 models. During the transfer learning using this pre-trained model, we applied several optimizers and loss functions and chose the best combinations out of them. This classification model has the potential to aid sheep farmers in precisely and efficiently distinguishing between various breeds, enabling more precise assessments of sector-specific classification for different businesses. (c) 2023 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Computer vision Sheep breed classification Image processing Transfer learning Ensemble learning Meta learner

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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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Ultra-high-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry-characterized extract of Aerides odorata Lour alleviates paracetamol-induced hepatotoxicity in animal model evidenced by biochemical,molecular,and computational studies

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Animal Models and Experimental Medicine 2024年第4期7卷 497-522页

作者： A.M.Abu Ahmed md.Atiar Rahman Farjana Sharmen A.S.M.Ali Reza md.Shahidul Islam md.mamunur Rashid md.Khalid Juhani Rafi Tanvir Ahmed Siddiqui md.Muzahid Ahmed Ezaj Srabonti Saha md.Nazim Uddin Walla Alelwani Department of Genetic Engineering and Biotechnology University of ChittagongChittagongBangladesh Department of Biochemistry and Molecular Biology University of ChittagongChittagongBangladesh Department of Pharmacy International Islami University ChittagongChittagongBangladesh Institute of Food Science and Technology Bangladesh Council of Scientific and Industrial ResearchDhakaBangladesh Department of Biochemistry College of ScienceUniversity of JeddahJeddahSaudi Arabia

Background:Many kinds of orchids have significant health benefits although ade-quate research on their biological functions is yet to be carried *** study inves-tigated the paracetamol-induced liver damage-protecting effect of epiphytic Aerides odorata methanol extract(AODE).Methods:The protective effects of AODE were studied by analyzing its effect on liver function parameters,messenger RNA(mRNA)expression,and tissue histopatho-logical *** results were confirmed by ligand-receptor interaction of molecular docking and multitarget interaction of network pharmacological ***:AODE significantly(p<0.05)minimized the dose-dependent increase in acid phosphatase,aspartate aminotransferase,alanine aminotransferase,alkaline phos-phatase,γ-glutamyl transferase,lactate dehydrogenase,and total bilirubin compared to the reference drug *** level decreased,and the antioxidant genes catalase(CAT),superoxide dismutase(SOD),β-actin,paraoxonase-1(PON1),and phosphofructokinase-1(PFK-1)were upregulated in AODE-treated paracetamol-intoxicated rats.A total of 376 compounds comprising phenols and flavonoids were identified using ultra-high-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry(UPLC-qTOF-MS).The online toxicity assessment using SwissADME and admetSAR exhibited drug-like,nontoxic,and potential pharmaco-logical ***,in silico analysis showed that isoacteoside,one of the identified compounds,exhibited the best docking score(−11.42)with the liver pro-tein human pituitary adenylate cyclase-1(Protein Data Bank ID:3N94).Furthermore,network pharmacology analysis identified the top 10 hub genes,namely AKT1(protein kinase B),CTNNB1(catenin beta-1),SRC(proto-oncogene c-Src),TNF(tumor necrosis factor),EGFR(epidermal growth factor receptor),HSP90AA1(heat shock protein 90α),MAPK3(mitogen-activated protein kinase 3),STAT3(signal transducer and activator of transcription 3),CASP3(caspase protein),

关键词： Aerides odorata animal model mRNA expression PCM-induced hepatotoxicity UPLC-qTOF-MS

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Insights into Manipulation: Unveiling Tampered Images Using Modified ELA, Deep Learning, and Explainable AI

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Journal of Computer and Communications 2024年第6期12卷 135-151页

作者： md. Mehedi Hasan md. Masud Rana Abu Sayed md. Mostafizur rahaman Department of Information and Communication Technology Bangladesh University of Professionals Dhaka Bangladesh Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh

Digital image forgery (DIF) is a prevalent issue in the modern age, where malicious actors manipulate images for various purposes, including deception and misinformation. Detecting such forgeries is a critical task for maintaining the integrity of digital content. This thesis explores the use of Modified Error Level Analysis (ELA) in combination with a Convolutional Neural Network (CNN), as well as, Feedforward Neural Network (FNN) model to detect digital image forgeries. Additionally, incorporation of Explainable Artificial Intelligence (XAI) to this research provided insights into the process of decision-making by the models. The study trains and tests the models on the CASIA2 dataset, emphasizing both authentic and forged images. The CNN model is trained and evaluated, and Explainable AI (SHapley Additive exPlanation— SHAP) is incorporated to explain the model’s predictions. Similarly, the FNN model is trained and evaluated, and XAI (SHAP) is incorporated to explain the model’s predictions. The results obtained from the analysis reveals that the proposed approach using CNN model is most effective in detecting image forgeries and provides valuable explanations for decision interpretability.

关键词： IFD DIF ELA CNN FNN XAI SHAP CASIA2.0

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Increasing the accuracy and reproducibility of positron emission tomography radiomics for predicting pelvic lymph node metastasis in patients with cervical cancer using 3D local binary pattern-based texture features

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智慧医学（英文） 2024年第3期04卷 153-160页

作者： Yu Yang Li Xiaoran Du Tianming rahaman md Grzegorzek Marcin Jerzy Li Chen Sun Hongzan Department of Radiology Shengjing Hospital of China Medical University Shenyang Liaoning 110001 China Liaoning Provincial Key Laboratory of Medical Imaging School of Computer Science and Engineering Northeastern University Shenyang Liaoning 110169 China Department of Radiology Xuanwu Hospital Capital medical University Beijing 100053 China Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics Peking University Health Science Center Beijing 100191 China College of Medicine and Biological Information Engineering Northeastern University Shenyang Liaoning 110169 China The University of New South Wales Australia University of Lübeck Germany

Background:The reproducibility of positron emission tomography (PET) radiomics features is affected by several factors, such as scanning equipment, drug metabolism time and reconstruction algorithm. We aimed to explore the role of 3D local binary pattern (LBP)-based texture in increasing the accuracy and reproducibility of PET radiomics for predicting pelvic lymph node metastasis (PLNM) in patients with cervical ***:We retrospectively analysed data from 177 patients with cervical squamous cell carcinoma. They underwent 18F-fluorodeoxyglucose ( 18F-FDG)whole-body PET/computed tomography (PET/CT), followed by pelvic 18F-FDG PET/magnetic resonance imaging (PET/MR). We selected reproducible and informative PET radiomics features using Lin’s concordance correlation coefficient, least absolute shrinkage and selection operator algorithm, and established 4 models, PET/CT, PET/CT-fusion, PET/MR and PET/MR-fusion, using the logistic regression algorithm. We performed receiver operating characteristic (ROC) curve analysis to evaluate the models in the training data set (65 patients who underwent radical hysterectomy and pelvic lymph node dissection) and test data set (112 patients who received concurrent chemoradiotherapy or no treatment). The DeLong test was used for pairwise comparison of the ROC curves among the models. Results:The distribution of age, squamous cell carcinoma (SCC), International Federation of Gynaecology and Obstetrics stage and PLNM between the training and test data sets were different ( P PET/MR = PET/MR-fusion (0.954). There was no statistical difference between the ROC curve of PET/CT and PET/MR models in both

关键词： Positron emission tomography Radiomics Cervical cancer Reproducibility Pelvic lymph node metastasis

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The link between intracellular calcium signaling and exosomal PD-L1 in cancer progression and immunotherapy

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Genes & Diseases 2024年第1期11卷 321-334页

作者： md Rakibul Alam md Mizanur Rahman Zhiguo Li Department of Toxicology and Cancer Biology College of MedicineUniversity of KentuckyLexingtonKY 40506USA Department of Medicine(Nephrology) Faculty of Medicine and DentistryUniversity of AlbertaEdmontonAlberta T6E2H7Canada

Exosomes are small membrane vesicles containing microRNA,RNA,DNA fragments,and proteins that are transferred from donor cells to recipient *** cells release exo-somes to reprogram the factors associated with the tumor microenvironment(TME)causing tu-mor metastasis and immune *** evidence revealed that cancer cell-derived exosomes carry immune inhibitory molecule program death ligand 1(PD-L1)that binds with re-ceptor program death protein 1(PD-1)and promote tumor progression by escaping immune ***,some FDA-approved monoclonal antibodies are clinicallyused for cancer treatment by blocking PD-1/PD-L1 *** notable treatment outcomes,some pa-tients show poor drug *** PD-L1 plays a vital role in lowering the treatment response,showing resistance to PD-1/PD-L1 blockage therapy through recapitulating the ef-fect of cell surface *** enhance therapeutic response,inhibition of exosomal PD-L1 is *** signaling is the central regulator of tumorigenesis and can regulate exosome biogenesis and secretion by modulating Rab GTPase family and membrane fusion ***-mune checkpoints are also connected with calcium signaling and calcium channel blockers like amlodipine,nifedipine,lercanidipine,diltiazem,and verapamil were also reported to suppress cellular PD-L1 ***,to enhance the PD-1/PD-L1 blockage therapy response,the reduction of exosomal PD-L1 secretion from cancer cells is in our therapeutic *** this review,we proposed a therapeutic strategy by targeting calcium signaling to inhibit the expression of PD-L1-containing exosome levels that could reduce the anti-PD-1/PD-L1 therapy resistance and increase the patient's drug response rate.

关键词： Calcium signaling CD8^(+)T cells Exosomal PD-L1 Exosomes biogenesis Immunosuppression Immunotherapy

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Synergistic enhancement of photocatalytic and antimicrobial efficacy of nitrogen and erbium co-doped ZnO nanoparticles

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Journal of Rare Earths 2024年第5期42卷 859-868,I0002页

作者： Sudipta Mondal Moniruzzaman Jamal Sikder Ashikuzzaman Ayon md Jannatul Ferdous Anik md Muktadir Billah Department of Materials and Metallurgical Engineering Bangladesh University of Engineering and TechnologyDhaka1000Bangladesh School of Materials Engineering Purdue UniversityWest LafayetteIN47907USA Department of Materials Science and Engineering University of CaliforniaBerkeleyCA94720USA Department of Materials Science and Engineering Northwestern UniversityEvanstonI28USA

Using the chemical co-precipitation approach,a series of nitrogen(N) and erbium(Er) co-doped ZnO nanoparticles(NPs) was effectively synthesized to enhance the photocatalytic and antibacterial *** characterization techniques,including X-ray diffraction(XRD),X-ray photoelectron spectro scopy(XPS),scanning electron microscopy(SEM),UV-vis,and photoluminescence(PL) spectroscopy,were carried out to validate the evaluated photocatalytic and antibacterial *** analysis confirms the pure wurtzite ZnO phase without the presence of any secondary *** analysis confirms the succe ssful incorporation of nitrogen and erbium into the ZnO *** optical bandgap of ZnO calculated from UV-vis spectroscopy shows a redshift after Er-N co-doping,with the lowest bandgap of 3.215 eV calculated for Zn_(0.97)Er_(0.03)N_(0.01)O_(0.99) *** images demonstrate the formation of nanorods after N-Er co-doping,followed by gradually increase d rod diameter and length after N-Er ***,the photocatalytic activities of ZnO samples we re measured by their ability to facilitate the photodegradation of Rhodamine B under UV *** with 1 mol% N doping exhibits 88% photodegradation of RhB under UV light within 360 min,and the photodegradation and antibacterial activity are greatly improved with Er *** fact,3 mol% Er-1 mol% N doped ZnO NPs demonstrate the highest photocatalytic activity,with approximately 96% degradation after 360 min,as well as superior antibacterial activity against Staphylococcus aureus(Gram-positive bacteria) and Pseudomonas aeruginosa(Gram-negative bacteria) with the highest zone of inhibition(ZOI) of 16 nm,due to nanorod formation,increased reactive oxygen species(ROS),and decreased electron-hole recombination,as validated by SEM,XPS,and PL spectroscopy.

关键词： Chemical co-precipitation Co-doping Bandgap Photocatalysis Rare earths Antimicrobial

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