Konsep, prinsip, dan prosedur pengembangan modul sebagai bahan ajar by Abdul Gafur

“…First is instructional messagedesign (readiness and motivation, attention directing device, student’s activeparticipation, repetition, and feedback), and the second is the contextual teachingand learning (relating, experiencing, applying, cooperating, and transferring).Those principles should be used in all of the components of instructionalstrategies (pre-instructional activities, presenting instructional material, learningguidance, eliciting performance, feedback, testing, and follow up activities(enrichment and remedial).The steps in developing modular instruction begin with writing theobjectives, selecting instructional materials, determining instructional strategies,selecting media, developing instrument and evaluation procedures, and the last iswriting the reference. …”
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Towards a Conceptual Modeling of Trustworthiness in AI-Based Big Data Analysis by Sebastian Bruchhaus, Alexander Kreibich, Thoralf Reis, Marco X. Bornschlegl, Matthias L. Hemmje

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Developing expert gaze pattern in laparoscopic surgery requires more than behavioral training by Sicong Liu, Rachel Donaldson, Ashwin Subramaniam, Hannah Palmer, Cosette D. Champion, Morgan L. Cox, L. Gregory Appelbaum

“…Trained novices were shown to reach more than 98% (M = 98.62%, SD = 1.06%) of their behavioral learning plateaus, leading to equivalent behavioral performance to that of surgeons. …”
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The Role of Artificial Intelligence in Personalized Medicine: A Computer Science Perspective by Raul Mihai RADULESCU

“…The investigation defines an entire framework that outlines steps from gathering and preparing the data to the training, validation, and execution of the model, thus illustrating how predictive models can indeed be employed to suggest personalized therapies. …”
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Securing the internet of things: A comprehensive review of ransomware attacks, detection, countermeasures, and future prospects by Peizhi Yan, Tala Talaei Khoei

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Sourcing efficacy – The role of supportive intelligence by Aleksandar Erceg, Santhosh Joseph

“…Technological advances such as machine learning (ML) and artificial intelligence (AI) and their integration in FBN are significant transformative steps. …”
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Exploring Prospects of Artificial Intelligence-Based ChatGPT for Higher Education Context by Phumza Maureen Makgato-Khunou, Lloyd Daniel Nkoli Tlale

“…The findings indicated that it is necessary to take proactive steps to ensure that the future use of artificial intelligence-based ChatGPT in higher education is helpful and safe. …”
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Analysis and training of a traffic sign recognition neural network model by A. U. Mentsiev, T. G. Aigumov, E. M. Abdulmukminova

“…The research methodology included the following steps: collecting and preparing a variety of road sign data, creating and training a neural network model based on convolutional layers, applying data augmentation methods to improve model performance, and evaluating the model’s effectiveness on a test data set.Result. …”
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Parameter-efficient weakly supervised referring video object segmentation via chain-of-thought reasoning by Xing Wang, Zhe Xu, Yuanshi Zheng, Handing Wang

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Predicting Flood Inundation after a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network by Leon S. Besseling, Anouk Bomers, Suzanne J. M. H. Hulscher

“…We conclude that machine learning techniques are suitable for fast modelling of the complex dynamics of dike breach floods.…”
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PolyReg: Autoregressive Building Outline Regularization via Masked Attention Sequence Generation by Longfei Cui, Chao Li, Xin Chen, Xiao Wang, Haizhong Qian

“…Through a cleverly designed self-attention mask matrix, it achieves an autoregressive output of regularized building outline coordinates, eliminating the need for cumbersome post-processing steps. Experimental results show that on the Inria Aerial Image Labeling Dataset, compared with traditional methods and existing deep learning methods, the proposed method demonstrates significant advantages in metrics such as IoU, C-IoU, and Hausdorff distance. …”
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Direct Discrimination and Growth Estimation of Foodborne Bacteria in Raw Meat Using Electronic Nose by Wellington Belarmino Gonçalves, Wanderson Sirley Reis Teixeira, Aryele Nunes da Cruz Encide Sampaio, Otávio Augusto Martins, Evelyn Perez Cervantes, Mateus de Souza Ribeiro Mioni, Jonas Gruber, Juliano Gonçalves Pereira

“…Concerning the machine learning algorithms, the accuracy varied from 93.8 to 100% for beef and chicken, while for pork, it varied from 75% to 100%. …”
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Radiomics in pediatric brain tumors: from images to insights by Pranjal Rai, Sabha Ahmed, Abhishek Mahajan

“…Recent studies combining radiomics with machine learning algorithms — including support vector machines, random forests, and deep learning CNNs — have demonstrated promising performance, with AUCs ranging from 0.75 to 0.98 for tumor classification and 0.77 to 0.88 for molecular subgroup prediction, across cohorts from 50 to over 450 patients, with internal cross-validation and external validation in some cases. …”
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A Novel Policy Distillation With WPA-Based Knowledge Filtering Algorithm for Efficient Industrial Robot Control by Gilljong Shin, Seongjin Yun, Won-Tae Kim

“…We perform the well-designed experiments, which show 11% compression enhancement and 5% reduction in execution time and steps required for the tasks, using a robot arm and a UGV as test environments.…”
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Towards universal early screening for cerebral palsy: a roadmap for automated General Movements Assessment by Alicia J. Spittle, Peter B. Marschik, Lars Adde, Nadia Badawi, Rachel Byrne, Arend F. Bos, Alain Chatelin, John Coughlan, Francesca Fedeli, Andrea Guzzetta, Edmond S.L. Ho, Michelle J. Johnson, Amanda Kwong, Alistair McEwan, Catherine Morgan, Anderson Mughogho, Deirdre M. Murray, Silvia Orlandi, Colleen Peyton, Laura A. Prosser, Anina Ritterband-Rosenbaum, Truyen Tran, Dajie Zhang, Elyse Passmore

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Elevating Accuracy: Enhanced Feature Selection Methods for Type 2 Diabetes Prediction by Ghazaleh Kakavand Teimoory, MohammadReza Keyvanpour

“…In the second section, we elucidated all preprocessing steps applied to this dataset, and in the third section, we evaluated the model using the selected algorithm under investigation. …”
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Feature Selection for Hypertension Risk Prediction Using XGBoost on Single Nucleotide Polymorphism Data by Lailil Muflikhah, Tirana Noor Fatyanosa, Nashi Widodo, Rizal Setya Perdana, Solimun, Hana Ratnawati

“…Extreme gradient boosting (XGBoost) is an ensemble machine learning method employed here for feature selection, which incrementally adjusts weights in a series of steps. …”
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Extracting Meso- and Microscale Patterns of Urban Morphology Evolution: Evidence from Binhai New Area of Tianjin, China by Xiaojin Huang, Ran Cheng, Jun Wu, Wenjian Yang, Longhao Zhang, Pengbo Li, Wenzhe Zhu

“…The framework includes three steps: constructing specific urban morphology datasets, semantic segmentation to extract urban form, and mapping urban form evolution using the Tile-based Urban Change (TUC) classification system. …”
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MmNet: Identifying Mikania micrantha Kunth in the wild via a deep Convolutional Neural Network by Xi QIAO, Yan-zhou LI, Guang-yuan SU, Hong-kun TIAN, Shuo ZHANG, Zhong-yu SUN, Long YANG, Fang-hao WAN, Wan-qiang QIAN

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Data Compactness Versus Prediction Performance: Achieving Both by Pruning Redundant Samples With Dominant Patterns and Hamming Distance Based Sampling Scheme by Abdul Majeed, Seong Oun Hwang

“…Machine learning (ML) practitioners are always in pursuit of refined data to develop robust and generalizable ML models to solve real-world problems. …”
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