Linking Animal Feed Formulation to Milk Quantity, Quality, and Animal Health Through Data-Driven Decision-Making by Oreofeoluwa A. Akintan, Kifle G. Gebremedhin, Daniel Dooyum Uyeh

“…Leveraging advanced analytical techniques, such as machine learning and optimization algorithms, have created highly accurate feed formulations tailored to specific livestock needs. …”
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A novel classification method for balance differences in elite versus expert athletes based on composite multiscale complexity index and ranking forests. by Yuqi Cheng, Dawei Wu, Ying Wu, Youcai Guo, Xinze Cui, Pengquan Zhang, Jie Gao, Yanming Fu, Xin Wang

“…We calculated the CMCI and used four machine learning algorithms-Logistic Regression, Support Vector Machine(SVM), Naive Bayes, and Ranking Forest-to combine these features and assess each participant's balance ability. …”
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Clinicopathological study of adrenal pheochromocytoma and extra-adrenal paragangliomas with reference to GAPP and PASS scoring systems by Akash Mondal, Moumita Sengupta, Soumya Dey, Anish Kar, Krishnendu Maiti, Debansu Sarkar, Uttara Chatterjee

“…Pheochtomocytoma of adrenal gland scaled score (PASS) and the grading of adrenal pheochromocytoma and paraganglioma (GAPP) score are two histological algorithms used to predict metastatic potential, but neither has been regarded as ‘gold-standard’. …”
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Human-Centered AI for Migrant Integration Through LLM and RAG Optimization by Dagoberto Castellanos-Nieves, Luis García-Forte

“…Traditionally, the focus on algorithms alone has shifted toward a more comprehensive understanding of AI’s potential to shape technology in ways which better serve human needs, particularly for disadvantaged groups. …”
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A distributed scheme for energy-efficient event-based target recognition using Internet of Multimedia Things by Manal Alsabhan, Adel Soudani, Manan Almusallam

“…Some solutions presented in the literature, such as image compression, do not efficiently solve this problem because of the algorithms’ computational complexities. Thus, detecting the event of interest locally before the communication using shape-based descriptors would avoid useless data transmission and would extend the network lifetime. …”
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Fuzzy-Swarm Intelligence-Based Short-Term Load Forecasting Model as a Solution to Power Quality Issues Existing in Microgrid System by Demsew Mitiku Teferra, Livingstone M. H. Ngoo, George N. Nyakoe

“…The swarm intelligence load forecast model based on particle swarm optimization algorithms can improve the limitations of the fuzzy system and increase its forecasting performance. …”
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Machine learning models for predicting the bearing capacity of shallow foundations: A Comparative study and sensitivity analysis by Hamid Mohammadnezhad, Seyedmohammad Eslami

“…With the development of new methods such as Machine Learning (ML) algorithms in recent decades, a resolution to these challenges has been identified. …”
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Simulation-based Analysis of 20 kW PV System by Atta Ur Rehman, Imtiaz Ahmad

“…To get the most power out of the solar panel under the circumstances, algorithms like MPPT are applied. MPPT also regulates the current supplied to the battery. …”
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Enhanced CLIP-GPT Framework for Cross-Lingual Remote Sensing Image Captioning by Rui Song, Beigeng Zhao, Lizhi Yu

“…Remote Sensing Image Captioning (RSIC) aims to generate precise and informative descriptive text for remote sensing images using computational algorithms. Traditional “encoder-decoder” approaches face limitations due to their high training costs and heavy reliance on large-scale annotated datasets, hindering their practical applications. …”
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Echoing Mechanism of Juvenile Delinquency Prevention and Occupational Therapy Education Guidance Based on Artificial Intelligence by Fang Hou

“…Two crime type prediction algorithms based on time-crime type count vectorization and dense neural network and crime type prediction based on the fusion of dense neural network and long- and short-term memory neural network are proposed. …”
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The Scope and Direction of Changes in the Revenues of Local Government Units in Poland in the New Act of October 1, 2024 by Dagmara Hajdys

“…These include the "determinants" adopted for the various algorithms used to determine the expenditure needs of each LGU. …”
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An Automated Clinical Laboratory Decision Support System for Test Utilization, Medical Necessity Verification, and Payment Processing by Safedin Beqaj, Rojeet Shrestha, Tim Hamill

“…Often, providers do not have access to an appropriate tool that uses evidence-based guidelines or algorithms to ensure that tests are not duplicated, overused, or underused. …”
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Bridging the Gap in the Adoption of Trustworthy AI in Indian Healthcare: Challenges and Opportunities by Sarat Kumar Chettri, Rup Kumar Deka, Manob Jyoti Saikia

“…It finds that the existing studies mostly used conventional machine learning (ML) algorithms and artificial neural networks (ANNs) for a variety of tasks, such as drug discovery, disease surveillance systems, early disease detection and diagnostic accuracy, and management of healthcare resources in India. …”
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Classification and Regression Trees analysis identifies patients at high risk for kidney function decline following hospitalization. by Weihao Wang, Wei Zhu, Janos Hajagos, Laura Fochtmann, Farrukh M Koraishy

“…Estimated glomerular filtration rate (eGFR) decline is associated with negative health outcomes, but the use of decision tree algorithms to predict eGFR decline is underreported. …”
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Advanced Shuttle Strategies for Parallel QCCD Architectures by Weining Dai, Kevin A. Brown, Thomas G. Robertazzi

“…Through simulations, we demonstrate that our strategy not only substantially outstrips the linear model but also exhibits better performance over other parallel strategies that employ greedy algorithms. This is achieved through our nuanced resolution of complexities, such as traffic blocks and trap capacity limitations. …”
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Performance of artificial neural networks and traditional methods in determining selected growth parameters of Alburnus sellal Heckel, 1843 by Ozcan Ebru Ifakat

“…In this study, predictions were made on the growth performance of Alburnus sellal Heckel, 1843 from the Munzur River using back propagation artificial neural networks and ANN algorithms. Statistical growth models used in fish biology and results obtained from artificial neural networks were compared. …”
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The Effect of Emotional Intelligence on Higher Education: A Pilot Study on the interplay Between Artificial Intelligence, Emotional Intelligence, and E-Learning by Abdullah Alenezi

“…Moreover, by exploring the reciprocal influence between Emotional Intelligence and AI algorithms, this research endeavors to contribute to the refinement of AI technologies, fostering greater personalization and adaptability in educational settings. …”
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The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection by Tarek Berghout

“…Some models integrate with Internet of Things (IoT) frameworks or federated learning for real-time diagnostics and privacy, often paired with optimization algorithms. However, the adoption of eXplainable AI (XAI) remains limited, despite its importance in building trust in medical diagnostics. …”
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Monitoring Population Phenology of Asian Citrus Psyllid Using Deep Learning by Maria Bibi, Muhammad Kashif Hanif, Muhammad Umer Sarwar, Muhammad Irfan Khan, Shouket Zaman Khan, Casper Shikali Shivachi, Asad Anees

“…In the current study, several prediction models were developed based on regression algorithms of machine learning to monitor different phenological stages of Asian citrus psyllid to predict its population about different abiotic variables (average maximum temperature, average minimum temperature, average weekly temperature, average weekly relative humidity, and average weekly rainfall) and biotic variable (host plant phenological patterns) in citrus-growing regions of Pakistan. …”
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Large-scale S-box design and analysis of SPS structure by Lan ZHANG, Liangsheng HE, Bin YU

“…A class of optimal linear transformation P over a finite field<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow> <msup> <mrow> <mrow><mo>(</mo> <mrow> <msubsup> <mi>F</mi> <mn>2</mn> <mi>m</mi> </msubsup> </mrow> <mo>)</mo></mrow></mrow> <mn>4</mn> </msup> </mrow></math></inline-formula> was constructed based on cyclic shift and XOR operation.Using the idea of inverse proof of input-output relation of linear transformation for reference, a proof method was put forward that transformed the objective problem of optimal linear transformation into several theorems of progressive relation, which not only solved the proof of that kind of optimal linear transformation, but also was suitable for the proof of any linear transformation.By means of small-scale S-box and optimal cyclic shift-XOR linear transformation P, a large-scale S-box model with 2-round SPS structure was established, and a series of lightweight large-scale S-boxes with good cryptographic properties were designed.Only three kind of basic operations such as look-up table, cyclic shift and XOR were used in the proposed design scheme, which improved the linearity and difference uniformity of large-scale S-boxes.Theoretical proof and case analysis show that, compared with the existing large-scale S-box construction methods, the proposed large-scale S-box design scheme has lower computational cost and better cryptographic properties such as difference and linearity, which is suitable for the design of nonlinear permutation coding of lightweight cryptographic algorithms.…”
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