Analysis to Predict the Number of New Students At UNU Pasuruan using Arima Method by Fachri Ayudi Fitrony, Laksmita Dewi Supraba, Tessa Rantung, I Made Artha Agastya, Kusrini Kusrini

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Issues and solutions in integrated radionuclide diagnosis and treatment by HONG Yena, ZHANG Yü, SHI Kuangyu, LI Biao, GUO Rui

“…Artificial intelligence is employed to reconstruct full-dose images or non-CT-attenuation-corrected images, thereby reducing imaging radiation dose. Machine learning models are utilized to optimize personalized therapeutic dose prediction. …”
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Developing a Nomogram for Predicting the Probability of Shoulder Redislocation after the First Episode in Young Patients by Chih-Kai Hong, Chin Kuo, Chia-Li Chang, Wei-Ren Su, Ta-Wei Tai

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Research and application of adaptive algorithm for 5G voice quality evaluation by Yuxiang ZHAO, Yaxin JI, Li YU, Tianyi ZHOU, Hang ZHOU

“…MOS (mean opinion score) is usually used to evaluate voice quality in the industry.It can objectively and fairly reflect the user’s voice service perception.It is difficult and costly to obtain data by road test, so a trained supervised learning model is usually used to predict the MOS score.However, the operator voice data has the characteristics of low percentage of MOS low score data and time sequence change, which affects the accuracy and generalization of the model prediction.Based on the study of existing data acquisition systems and machine learning algorithms of operators, an adaptive algorithm for MOS evaluation of 5G speech quality was proposed.Firstly, POLQA algorithm test equipment based on full parameter evaluation obtained training data to ensure the accuracy of training samples.Secondly, by means of data enhancement, the difficulty of acquiring poor quality samples was solved.Finally, based on the adaptive algorithm selection, the optimal MOS prediction model could be selected periodically and dynamically according to the timing changes of data features, so as to achieve large-scale and intelligent evaluation of 5G voice quality.…”
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FPGA-accelerated SpeckleNN with SNL for real-time X-ray single-particle imaging by Abhilasha Dave, Cong Wang, James Russell, Ryan Herbst, Jana Thayer

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A Convolutional Neural Network Tool for Early Diagnosis and Precision Surgery in Endometriosis-Associated Ovarian Cancer by Christian Macis, Miriam Santoro, Vladislav Zybin, Stella Di Costanzo, Camelia Alexandra Coada, Giulia Dondi, Pierandrea De Iaco, Anna Myriam Perrone, Lidia Strigari

“…Furthermore, the performance of each hybrid model and the majority voting ensemble of the three competing ML models were evaluated using trained and refined hybrid CNN models combined with Support Vector Machine (SVM) algorithms, with the best-performing model selected as the benchmark. …”
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Traffic Classification in Software-Defined Networking Using Genetic Programming Tools by Spiridoula V. Margariti, Ioannis G. Tsoulos, Evangelia Kiousi, Eleftherios Stergiou

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Container scheduling techniques: A Survey and assessment by Imtiaz Ahmad, Mohammad Gh. AlFailakawi, Asayel AlMutawa, Latifa Alsalman

“…The survey is structured around classifying the scheduling techniques into four categories based on the type of optimization algorithm employed to generate the schedule namely mathematical modeling, heuristics, meta-heuristics and machine learning. …”
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SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study by Abrar Yaqoob, Navneet Kumar Verma, Mushtaq Ahmad Mir, Ghanshyam G. Tejani, Nashwa Hassan Babiker Eisa, Hind Mamoun Hussien Osman, Mohd Asif Shah

“…Future work will explore the integration of other nature-inspired algorithms and deep learning models to further enhance performance and clinical applicability.…”
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Balancing Privacy and Performance: A Differential Privacy Approach in Federated Learning by Huda Kadhim Tayyeh, Ahmed Sabah Ahmed AL-Jumaili

“…Federated learning (FL), a decentralized approach to machine learning, facilitates model training across multiple devices, ensuring data privacy. …”
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Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDAS by Ranjit Lall, Thomas Robinson

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Prediksi Gagal Jantung Menggunakan Artificial Neural Network by Simeon Yuda Prasetyo

“…In previous studies, there have been many studies related to the application of machine learning to predict heart failure and obtained quite good results, ranging from 85 percent to 90 percent, with sophisticated models optimized using neural networks. …”
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Breast cancer survival prediction using an automated mitosis detection pipeline by Nikolas Stathonikos, Marc Aubreville, Sjoerd deVries, Frauke Wilm, Christof A Bertram, Mitko Veta, Paul J vanDiest

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Real-Time Heterogeneous Collaborative Perception in Edge-Enabled Vehicular Environments by Samuel Thornton, Nithin Santhanam, Rajeev Chhajer, Sujit Dey

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Leveraging ultrasonic-derived phenotypes and estimated breeding value to improve abdominal fat weight prediction in chickens throughout the egg laying period by Penghao Li, Zhengda Li, Fan Ying, Dan Zhu, Dawei Liu, Xianyi Song, Jie Wen, Guiping Zhao, Bingxing An

“…This study estimated hens’ AF weight among whole laying period through fitting vivo phenotypes by ten machine learning techniques (including generalized linear model, GLM; multiple linear regression, MLR; ridge regression, RR; LASSO; elastic net, EN; k nearest neighbours, KNN; SVM biased linear and Gaussian kernel; Random forests, RF and XGBoost). …”
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Multicriteria decision-making framework for robust energy management AI solutions by Salem Garfan, A.H. Alamoodi, Suliana Sulaiman, O.S. Albahri, A.S. Albahri, Iman Mohamad Sharaf

“…The emergence of artificial intelligence (AI) has catalyzed advancements in energy conservation and management, leading to the development of numerous smart energy management systems leveraging the internet of things and AI methodologies. Various machine learning (ML) models have been utilized for energy-saving and consumption prediction solutions, posing challenges in selecting the most effective model. …”
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Modeling and Monitoring of the Tool Temperature During Continuous and Interrupted Turning with Cutting Fluid by Hui Liu, Markus Meurer, Thomas Bergs

“…These findings highlight the potential of analytical models for optimizing thermal management in metal turning processes.…”
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Structural design, modeling and simulation analysis of a cage broiler inspection robot by Yongmin Guo, Xinwei Yu, Wanchao Zhang, Changxi Chen, Liji Yu

“…The mathematical description of the robot is based on a static kinematic model to ensure efficient navigation within the enclosed environment. …”
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Predicting heat transfer Performance in transient flow of CNT nanomaterials with thermal radiation past a heated spinning sphere using an artificial neural network: A machine learn... by S.R. Mishra, P.K. Pattnaik, Rupa Baithalu, P.K. Ratha, Subhajit Panda

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Predicting Winter Ammonia and Methane Emissions from a Naturally Ventilated Dairy Barn in a Cold Region Using an Adaptive Neural Fuzzy Inference System by Hualong Liu, Xin Wang, Tana, Tiezhu Xie, Hurichabilige, Qi Zhen, Wensheng Li

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