Cardiac Clarity: Harnessing Machine Learning for Accurate Heart-Disease Prediction by Sujith Santhosh, Krishnaraj Chadaga, R.Vijaya Arjunan, Sandra D'Souza

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An Autotuning Hybrid Method with Bayesian Optimization for Road Edge Extraction in Highway Systems from Point Clouds by Jingxu Chen, Qiru Cao, Mingzhuang Hua, Jinyang Liu, Jie Ma, Di Wang, Aoxiang Liu

“…The hyperparameters encompass high and low pixel gradient thresholds, neighborhood radius, and normal vector threshold. …”
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Reliable estimation via hybrid gradient boosting machine for mud loss volume in drilling operations by Xiaozhi Lu, Farag M. A. Altalbawy, Tarak Vora, R. Manjunatha, Debasish Shit, Shirin Shomurotova, Akshay Kumar, Atreyi Pramanik, Ajay Sharma, Raed H. C. Alfilh, Samim Sherzod, Mohammad Mahtab Alam

“…Initial data analysis included statistical evaluation, outlier detection using leverage diagnostics, and data normalization to ensure validity and consistency. …”
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Optimizing Heart Disease Prediction: A Comparative Analysis of Tree-Based Ensembles With Feature Expansion and Selection by K. Aswini, Kriti Arya

“…Cardiovascular disease (CVD) is the leading cause of death worldwide, emphasizing the importance of accurate early detection. This study examines the efficacy of tree-based ensemble machine learning models that have been improved using Feature Expansion and Selection (FES-EM). …”
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IT Diagnostics of Parkinson’s Disease Based on the Analysis of Voice Markers and Machine Learning by U. A. Vishniakou, Xia YiWei

“…This study was carried out in order to confirm experimentally the possibility of performing an assessment of these parameters for the detection of Parkinson’s disease in the early stages (IT diagnostics). …”
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Enhanced Fault Localization in Multi-Terminal HVDC Systems Using Improved Gaussian Process Regression by Abha Pragati, Manohar Mishra, Pritam Bhowmik, Josep M. Guerrero, Debadatta Amaresh Gadanayak

“…The IGPR model incorporates hyperparameter tuning of Gaussian process regression using Bayesian optimization. …”
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Distributed denial of service (DDoS) classification based on random forest model with backward elimination algorithm and grid search algorithm by Mohamed S. Sawah, Hela Elmannai, Alaa A. El-Bary, Kh. Lotfy, Osama E. Sheta

“…The DDoS-SDN dataset was used for training and evaluation, with feature selection via Backward Elimination (BE) and hyperparameter tuning using Grid Search with 5-fold Cross-Validation (CV = 5). …”
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NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meat by Kamrunnahar Khan Bristy, Dip Ghosh, Md. Abul Hashem

“…This study underscores the potential of integrating spectroscopic data with advanced machinelearning techniques for meat classification and fraud detection.…”
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An In-Depth Study of Personalized Anesthesia Management Models in Gastrointestinal Endoscopy Based on Multimodal Deep Learning by Hanqi Shi, Hongyu Wang, Xibing Ding, Zheng Dang

“…Based on these findings, a multimodal deep learning model was developed that integrates Long Short-Term Memory (LSTM) networks, hyperparameter geometric manifold optimization (GMO) methods and data-driven sparse matrix classifiers. …”
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Gradient Boosting Feature Selection for Integrated Fault Diagnosis in Series-Compensated Transmission Lines by Rab Nawaz, Abdul Wadood, Khawaja Khalid Mehmood, Syed Basit Ali Bukhari, Hani Albalawi, Aadel Mohammed Alatwi, Muhammad Sajid

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Demystifying multiple sclerosis diagnosis using interpretable and understandable artificial intelligence by Chadaga Krishnaraj, Khanna Varada Vivek, Prabhu Srikanth, Sampathila Niranjana, Chadaga Rajagopala, Palkar Anisha

“…Hence, supervised machine learning (ML) algorithms and several hyperparameter tuning techniques, including Bayesian optimization, have been utilized in this study to predict MS in patients. …”
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Enhancing pH prediction accuracy in Al2O3 gated ISFET using XGBoost regressor and stacking ensemble learning by Ashirbad Panda, Rishikesh Datar, Shreyas Deshpande, Gautam Bacher

“…An eXtreme Gradient Boosting (XGBoost) regression model was employed to predict pH levels using data obtained from IDS–VDS characteristics. Further, Hyperparameter optimization was performed to tune critical XGBoost-hyperparameters such as maximum depth, minimum child weight, estimators, learning rate, α, and λ. …”
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Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images by K. Lakshmi, Sibi Amaran, G. Subbulakshmi, S. Padmini, Gyanenedra Prasad Joshi, Woong Cho

“…Abstract Detecting brain tumours (BT) early improves treatment possibilities and increases patient survival rates. …”
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PatchDetect: Breast Cancer Detection combining Unet-ResNet-50 and Patch Embedding LSTM by Hadj Ahmed Bouarara, kadda benyahia

“…The model's performance was optimized using various hyperparameters, achieving an accuracy of 94%, recall of 93%, precision of 92%, and F-measure of 92% while maintaining a minimal error rate of 6%. …”
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An Efficient Optimal CapsNet Model-Based Computer-Aided Diagnosis for Gastrointestinal Cancer Classification by Fahdah A Almarshad, Prasanalakshmi Balaji, Liyakathunisa Syed, Eman Aljohani, Santhi Muttipoll Dharmarajlu, Thavavel Vaiyapuri, Nourah Ali AlAseem

“…This technique has great promise for transforming initial detection and involvement in the battle against dangerous diseases. …”
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Is Malware Detection Needed for Android TV? by Gokhan Ozogur, Zeynep Gurkas-Aydin, Mehmet Ali Erturk

“…We proposed a machine learning approach to detecting malware and evaluated our model. We compared the performance of nine classifiers and optimized the hyperparameters. …”
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CART-ANOVA-Based Transfer Learning Approach for Seven Distinct Tumor Classification Schemes with Generalization Capability by Shiraz Afzal, Muhammad Rauf, Shahzad Ashraf, Shahrin Bin Md Ayob, Zeeshan Ahmad Arfeen

“…<b>Background/Objectives:</b> Deep transfer learning, leveraging convolutional neural networks (CNNs), has become a pivotal tool for brain tumor detection. However, key challenges include optimizing hyperparameter selection and enhancing the generalization capabilities of models. …”
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Detection of network intelligence features with the decision tree model by N. P. Sharaev, S. N. Petrov

“…O b j e c t i v e s .  Early detection of network intelligence allows to reduce the risks of information security of organizations. …”
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Leveraging machine learning to proactively identify phishing campaigns before they strike by Kun Zhang, Haifeng Wang, Meiyi Chen, Xianglin Chen, Long Liu, Qiang Geng, Yu Zhou

“…This study introduces an advanced phishing detection framework using machine learning (ML) models. …”
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Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement by Sri Rossa Aisyah Puteri Baharie, Sugiyarto Surono, Aris Thobirin

“…These findings highlight the potential of HGD-GWO in enhancing predictive models, particularly for early diabetes detection.…”
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