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Efficient optimization based thresholding technique for analysis of alzheimer MRIs

INTERNATIONAL JOURNAL OF NEUROSCIENCE 2023年第2期133卷 201-214页

作者： Prabha, S. Sankaran, K. Sakthidasan Chitradevi, D. Hindustan Inst Technol & Sci Dept Elect & Commun Engn Chennai Tamil Nadu India Hindustan Inst Technol & Sci Dept Comp Sci & Engn Chennai Tamil Nadu India

Purpose study: Alzheimer is a type of dementia that usually affects older adults by creating memory loss due to damaged brain cells. The damaged brain cells lead to shrinkage in the size of the brain and it is very difficult to extract the grey matter (GM) and white matter (WM). The segmentation of GM and WM is a challenging task due to its homogeneous nature between the neighborhood tissues. In this proposed system, an attempt has been made to extract GM and WM tissues using optimization-based segmentation techniques. Materials and methods: The optimization method is considered for the classification of normal and alzheimer disease (ad) through magnetic resonance images (mri) using a modified cuckoo search algorithm. Gray Level Co-Occurrence Matrix (GLCM) features are calculated from the extracted GM and WM. Principal Component Analysis (PCA) is adopted for selecting the best features from the GLCM features. Support Vector machine (SVM) is a classifier which is used to classify the normal and abnormal images. Results: The proposed optimization algorithm provides most promising and efficient level of image segmentation compared to fuzzy c means (fcm), otsu, particle swarm optimization (pso) and cuckoo search (cs). The modified cuckoo yields high accuracy of 96%, sensitivity of 97% and specificity of 94% than other methods due to its powerful searching potential for the proper identification of gray and WM tissues. Conclusions: The results of the classification process proved the effectiveness of the proposed technique in identifying alzheimer affected patients due to its very strong optimization ability. The proposed pipeline helps to diagnose early detection of AD and better assessment of the neuroprotective effect of a therapy.

关键词： Alzheimer classification machine learning algorithm magnetic resonance imaging optimization segmentation

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A novel method for assessing cardiac function in patients with coronary heart disease based on wrist pulse analysis

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IRISH JOURNAL OF MEDICAL SCIENCE 2023年第6期192卷 2697-2706页

作者： Wu, Wen-jie Chen, Rui Guo, Rui Yan, Jian-jun Zhang, Chun-ke Wang, Yi-qin Yan, Hai-xia Zhang, Ye-qing Shanghai Univ Tradit Chinese Med Dept Basic Med Sci 1200 Cailun Rd Shanghai 201203 Peoples R China East China Univ Sci & Technol Inst Intelligent Percept & Diag Sch Mech & Power Engn 130 Meilong Rd Shanghai 200237 Peoples R China Shanghai Univ Tradit Chinese Med Shanghai Municipal Hosp Tradit Chinese Med Dept Chinese Internal Med Shanghai 200071 Peoples R China

BackgroundThe timely assessment of B-type natriuretic peptide (BNP) marking chronic heart failure risk in patients with coronary heart disease (CHD) helps to reduce patients' *** evaluate the potential of wrist pulse signals for use in the cardiac monitoring of patients with *** total of 419 patients with CHD were assigned to Group 1 (BNP < 95 pg/mL, n = 249), 2 (95 < BNP < 221 pg/mL, n = 85), and 3 (BNP > 221 pg/mL, n = 85) according to BNP levels. Wrist pulse signals were measured noninvasively. Both the time-domain method and multiscale entropy (MSE) method were used to extract pulse features. Decision tree (DT) and random forest (RF) algorithms were employed to construct models for classifying three groups, and the models' performance metrics were *** pulse features of the three groups differed significantly, suggesting different pathological states of the cardiovascular system in patients with CHD. Moreover, the RF models outperformed the DT models in performance metrics. Furthermore, the optimal RF model was that based on a dataset comprising both time-domain and MSE features, achieving accuracy, average precision, average recall, and average F1-score of 90.900%, 91.048%, 90.900%, and 90.897%, *** wrist pulse detection technology employed in this study is useful for assessing the cardiac function of patients with CHD.

关键词： B-type natriuretic peptide Chronic heart failure Coronary heart disease machine learning algorithm Multiscale entropy method Time-domain method Wrist pulse signal

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A Bias-Tunable Dual band Photodetector for Plastic Material classification

A Bias-Tunable Dual band Photodetector for Plastic Material ...

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2024 IEEE Sensors Conference

作者： Kumaran, A. Manakkakudy De Iacovo, A. Ballabio, A. Frigerio, J. Isella, G. Colace, L. Roma Tre Univ Dept Ind Elect & Mech Engn Via Vito Volterra 62 I-00146 Rome Italy Eye4NIR Srl Via Bartolomeo Colleoni 14 I-23801 Calolziocorte Italy Politecn Milan Dept Phys Via Anzani 42 I-22100 Como Italy

ISBN: (纸本)9798350363524;9798350363517

This work introduces a novel technique for efficient and accurate classification of plastic materials in recycling process. The approach is based on a voltage-tunable Germanium on Silicon photodetector that operates within the 400 nm - 1600 nm spectral range. Utilizing a lab bench setup with a broad-spectrum light source and standard components, material properties are analyzed through photocurrent measurements. By employing the K-Nearest Neighbors algorithm, the analysis achieved a high accuracy of 94.6% for classifying 6 different types of plastics. This work underscores the critical role of advanced photodetection techniques in enhancing the efficiency and accuracy of classification for plastic recycling, contributing to more effective waste management and environmental sustainability.

关键词： Dual band photodetector Plastic Material classification machine learning algorithm

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Artificial Neural Network-Based Hysteresis Model for Steel Braces in Concentrically Braced Frames

Artificial Neural Network-Based Hysteresis Model for Steel B...

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Annual Conference of the Canadian-Society-for-Civil-Engineering (CSCE)

作者： Pessiyan, Sepehr Mokhtari, Fardad Imanpour, Ali Univ Alberta Dept Civil & Environm Engn Edmonton AB Canada

ISBN: (纸本)9783031615290;9783031615276;9783031615269

This paper presents an artificial neural network-based metamodel for extracting the inelastic cyclic response of steel hollow structural section braces that are part of concentrically braced frames when subjected to earthquake ground motions. The proposed model constructed using the bidirectional long short-term memory (BiLSTM) algorithm is intended to predict the inelastic cyclic response of steel braces, namely the brace axial force, based on the input axial displacement and out-of-plane displacement signals. The architecture of the proposed metamodel is first described. The validity of the model is then tested when the brace is subjected to a set of displacement signals obtained from numerical nonlinear time-history analyses. The preliminary results confirm that the proposed model has the potential to predict the complex hysteresis response of steel braces, involving tensile yielding and compressive buckling.

关键词： Metamodels machine learning algorithm Artificial neural networks Seismic response

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The state of lead scoring models and their impact on sales performance

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INFORMATION TECHNOLOGY & MANAGEMENT 2024年第1期25卷 69-98页

作者： Wu, Migao Andreev, Pavel Benyoucef, Morad Univ Ottawa Fac Engn Ottawa ON K1N 6N5 Canada Univ Ottawa Telfer Sch Management Ottawa ON K1N 6N5 Canada

Although lead scoring is an essential component of lead management, there is a lack of a comprehensive literature review and a classification framework dedicated to it. Lead scoring is an effective and efficient way of measuring the quality of leads. In addition, as a critical Information Technology tool, a proper lead scoring model acts as an alleviator to weaken the conflicts between sales and marketing functions. Yet, little is known regarding lead scoring models and their impact on sales performance. Lead scoring models are commonly categorized into two classes: traditional and predictive. While the former primarily relies on the experience and knowledge of salespeople and marketers, the latter utilizes data mining models and machine learning algorithms to support the scoring process. This study aims to review and analyze the existing literature on lead scoring models and their impact on sales performance. A systematic literature review was conducted to examine lead scoring models. A total of 44 studies have met the criteria and were included for analysis. Fourteen metrics were identified to measure the impact of lead scoring models on sales performance. With the increased use of data mining and machine learning techniques in the fourth industrial revolution, predictive lead scoring models are expected to replace traditional lead scoring models as they positively impact sales performance. Despite the relative cost of implementing and maintaining predictive lead scoring models, it is still beneficial to supersede traditional lead scoring models, given the higher effectiveness and efficiency of predictive lead scoring models. This study reveals that classification is the most popular data mining model, while decision tree and logistic regression are the most applied algorithms among all the predictive lead scoring models. This study contributes by systematizing and recommending which machine learning method (i.e., supervised and/or unsupervised) shall be used to b

关键词： Lead scoring model Sales performance Data mining model machine learning algorithm Systematic literature review

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Micro Expression Recognition using ML based XGBoost Classifier 2

Micro Expression Recognition using ML based XGBoost Classifi...

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2nd IEEE World Conference on Communication and Computing (WCONF)

作者： Unnisa, Mahera Ganesan, V Bharath Inst Higher Educ & Res Elect & Commun Engn Chennai Tamil Nadu India

ISBN: (纸本)9798350395334;9798350395327

Micro-expressions, brief involuntary facial expressions lasting less than a second, are crucial for understanding human emotions and intentions. Accurate recognition of these expressions is essential in fields such as psychology, law enforcement, and human-computer interaction. Facial expression recognition is an emerging research area focusing on enhancing the accuracy of detection. This technology impacts various applications, including education, gaming, healthcare, and safety. To improve the performance of XGBoost, an algorithm leveraging genetic and hybrid optimization techniques, this work addresses the limitations of stochastic gradient descent (SGD). We evaluate our proposed method on benchmark micro-expression datasets like CASME, leveraging the power of XGBoost and demonstrating high accuracy and robustness. Implemented using Python, this approach provides an efficient solution for micro-expression recognition, with potential for further optimization in future work.

关键词： XGBoost Micro Expression machine learning algorithm OpenCV Python

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Prediction of Health of Corals Mussismilia hispida Based on the Microorganisms Present in their Microbiome 3rd

Prediction of Health of Corals Mussismilia hispida Based on ...

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3rd International Conference on Optimization, learning algorithms and Applications (OL2A)

作者： Barque, Barry Malick Soares Rodrigues, Pedro Joao de Paula Filho, Pedro Luiz Peixoto, Raquel Silva de Assis Leite, Deborah Catharine Fed Technol Univ Parana Curitiba Parana Brazil Polytechn Inst Braganca Braganca Portugal King Abdullah Univ Sci & Technol KAUST Thuwal Saudi Arabia Univ Fed Rio de Janeiro Inst Microbiol Prof Paulo De Goes Rio De Janeiro Brazil

ISBN: (纸本)9783031530241;9783031530258

One of the most diverse and productive marine ecosystems in the world are the corals, providing not only tourism but also an important economic contribution to the countries that have them on their coasts. Thanks to genome sequencing techniques, it is possible to identify the microorganisms that form the coral microbiome. The generation of large amounts of data, thanks to the low cost of sequencing since 2005, provides an opening for the use of artificial neural networks for the advancement of sciences such as biology and medicine. This work aims to predict the healthy microbiome present in samples of Mussismilia hispida coral, using machine learning algorithms, in which the algorithms SVM, Decision Tree, and Random Forest achieved a rate of 61%, 74%, and 72%, respectively. Additionally, it aims to identify possible microorganisms related to the disease in question in corals.

关键词： Coral reef Microbiome machine learning algorithm

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Characteristic genes and immune infiltration analysis of gastric cancer based on bioinformatics analysis and machine learning

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DISCOVER ONCOLOGY 2025年第1期16卷 1-15页

作者： Xia, Chengwei Liu, Yini Qing, Xin Chengdu Seventh Peoples Hosp Affiliated Canc Hosp Chengdu Med Coll Dept Thyroid & Breast Surg Chengdu Peoples R China Peoples Hosp Zhongjiang Dept Anesthesiol Deyang Peoples R China Chengdu Seventh Peoples Hosp Affiliated Canc Hosp Chengdu Med Coll Dept Hepatobiliary Vasc Surg Chengdu Peoples R China

BackgroundGastric cancer (GC), a common and deadly malignancy worldwide, is a serious burden on society and individuals. However, available diagnostic biomarkers for GC are very limited. The current study aimed to identify potential diagnostic biomarkers for GC and analyze the activity of infiltrating immune cells in this *** data for GC were acquired from the Gene Expression Omnibus (GEO) database. The limma package was utilized to normalize these data, thus identifying differentially expressed genes (DEGs). For normalized data of samples, we established a weighted gene co-expression network (WGCNA) to reveal key genes in the significant module. Afterward, we obtained overlapping genes by intersecting the DEGs and the key genes from the WGCNA module. Next, after applying the three algorithms (LASSO, RandomForest, and SVM-RFE) to analyze these overlapping genes and take the intersection, we established a GC diagnosis. The diagnostic significances of these identified genes were evaluated with receiver operating characteristic (ROC) curves and validated in the external dataset. Furthermore, ssGSEA and CIBERSORT were employed for evaluating the infiltrating immune cells and the association of the immune cells and diagnostic ***, we identified 49 overlapping genes, and the results of enrichment analysis demonstrated that these genes may be involved in the signaling transduction-related process. Finally, BANF1, DUSP14, and VMP1 were regarded as key biomarkers in GC patients based on the overlapping genes that we found, and these three biomarkers demonstrated great diagnostic significance. Additionally, the hub biomarkers had different levels of association with macrophages, neutrophils, memory B cells, and plasma ***1, DUSP14, and VMP1 are promising diagnostic biomarkers for GC, and infiltrating immune cells may dramatically affect gastric carcinogenesis and progression.

关键词： Gastric cancer Biomarker WGCNA machine learning algorithm Immune cell infiltration

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Comparative analysis of nonlinear behavioral models for GaN HEMTs based on machine learning techniques

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2024年第2期37卷

作者： Liu, Bo Cai, Jialin Hangzhou Dianzi Univ Minist Educ Key Lab RF Circuit & Syst Hangzhou Peoples R China

A variety of novel behavioral modeling techniques have been reported to accurately capture the nonlinear characteristics of GaN devices. For the purpose of describing GaN HEMT large-signal behavior, machine learning-based approaches have been proposed. There is, however, a lack of comparison and analysis of their performance with different machine learning techniques in these studies. An examination of machine learning-based large-signal modeling techniques is presented in this article. To develop large-signal models of GaN HEMT, a range of commonly used modeling techniques such as artificial neural networks (ANN), random sample consistency (RANSAC), support vector regression (SVR), Gaussian process regression (GPR), decision trees (DTs), and genetic algorithm-assisted ANN (GA-ANN) are described and employed. Afterwards, integrated modeling techniques such as Bootstrap aggregation (BA), random forests (RF), AdaBoost, and gradient tree boosting (GTB) are reviewed and tested for their capabilities in developing GaN HEMT LSM. The hyperparameters of each built model are modified using the random search optimization (RSO) method, and five-fold cross-validation is used during validation. In order to identify optimal algorithms for nonlinear behavioral modeling of GaN devices, the model prediction results are comprehensively analyzed.

关键词： computational modeling ensemble methods GaN HEMT large-signal modeling machine learning algorithm

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A study of the interaction space of two lactate dehydrogenase isoforms (LDHA and LDHB) and some of their inhibitors using proteochemometrics modeling

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BMC CHEMISTRY 2023年第1期17卷 1-13页

作者： Damavandi, Sedigheh Shiri, Fereshteh Emamjomeh, Abbasali Pirhadi, Somayeh Beyzaei, Hamid Univ Zabol Dept Bioinformat Lab Computat Biotechnol & Bioinformat CBB Lab Zabol Iran Univ Zabol Fac Sci Dept Chem Zabol Iran Univ Zabol Fac Agr Dept Plant Breeding & Biotechnol PBB Zabol Iran Shiraz Univ Med Sci Med & Nat Prod Chem Res Ctr Shiraz Iran

Lactate dehydrogenase (LDH) is a tetramer enzyme that converts pyruvate to lactate reversibly. This enzyme becomes important because it is associated with diseases such as cancers, heart disease, liver problems, and most importantly, corona disease. As a system-based method, proteochemometrics does not require knowledge of the protein's three-dimensional structure, but rather depends on the amino acid sequence and protein descriptors. Here, we applied this methodology to model a set of LDHA and LDHB isoenzyme inhibitors. To implement the proteochemetrics method, the camb package in the R Studio Server programming environment was used. The activity of 312 compounds of LDHA and LDHB isoenzyme inhibitors from the valid Binding DB database was retrieved. The proteochemometrics method was applied to three machine learning algorithms gradient amplification model, random forest, and support vector machine as regression methods to find the best model. Through the combination of different models into an ensemble (greedy and stacking optimization), we explored the possibility of improving the performance of models. For the RF best ensemble model of inhibitors of LDHA and LDHB isoenzymes, and were 0.66 and 0.62, respectively. LDH inhibitory activation is influenced by Morgan fingerprints and topological structure descriptors.

关键词： Proteochemometrics machine learning algorithm Isoenzyme Camb package Morgan fingerprints

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