Blockchain based solid waste classification with AI powered tracking and IoT integration by Aliaa M. Alabdali

“…Abstract Smart waste management is vital for reducing environmental impact and improving quality of life in smart cities. This study presents an AI-driven waste classification model that integrates IoT and Blockchain technologies. …”
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Implementing Remote Radiotherapy Planning to Increase Patient Flow at a Johannesburg Academic Hospital, South Africa: Protocol for a Prospective Feasibility Study by Duvern Ramiah, Sonwabile Ngcezu, Oluwatosin Ayeni, Okechinyere Achilonu, Mariam Adeleke, Theo Nair, Joseph Otten, Daniel Mmereki

“…Phase 1 (feasibility) encompasses system commissioning, including beam modeling, computed tomography (CT)-to-electron density calibration, multileaf collimator (MLC) optimization, and dose calculations using the anisotropic analytical algorithm. …”
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Eco-assessment of meat raw materials: A convolutional neural network approach to sustainable quality control by Verezubova Natalia, Sakovich Natalia, Yukovleva Olga, Chekulaev Artur, Verezubova Irina

“…Genetic algorithms are employed to optimize hyperparameters, which significantly improve model performance. …”
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A Machine Learning-Based Real-Time Remaining Useful Life Estimation and Fair Pricing Strategy for Electric Vehicle Battery Swapping Stations by Seyit Alperen Celtek, Seda Kul, A. Ozgur Polat, Hamed Zeinoddini-Meymand, Farhad Shahnia

“…The paper evaluates various machine learning algorithms for real-time RUL estimation regarding accuracy, computation time, and memory usage. …”
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Detection of Wormhole Attack in Vehicular Ad-hoc Network over Real Map using Machine Learning Approach with Preventive Scheme by Shahjahan Ali, Parma Nand, Shailesh Tiwari

“…The collected data is pre-processed and the k-NN & Random Forest algorithms are applied on this data, to make the model such type so that it can memorize the wormhole attack. …”
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Machine learning for asphaltene polarizability: Evaluating molecular descriptors by Arun K. Sharma, Owen McMillan, Selsela Arsala, Supreet Gandhok, Rylend Young

“…Accurate prediction of polarizability is essential for modeling intermolecular forces and improving force field design in petroleum and materials chemistry, issues that are central to industrial and chemical applications.…”
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Enhancing Healthcare With WBAN and Digital Twins: A Machine Learning Approach for Predictive Health Monitoring by Rishit Mahapatra, Deepak Sethi, Kaushik Mishra

“…Integrating digital twins with these models offers an innovative framework to improve healthcare efficiency, allowing for real-time simulation and more accurate forecasting of patient outcomes.…”
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Graph neural networks and transfer entropy enhance forecasting of mesozooplankton community dynamics by Minhyuk Jeung, Min-Chul Jang, Kyoungsoon Shin, Seung Won Jung, Sang-Soo Baek

“…Therefore, we demonstrate that incorporating the graph structure of ecosystem dynamics can improve the accuracy of mesozooplankton modeling by providing influential information about species of interest. …”
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Precision in practice: exploring the impact of ai and machine learning on ultrasound guided regional anaesthesia by Noor Ul Huda Bhatti, Syed Ghazi Ali Kirmani, Maryam Butt

“…Considering this, anaesthesiologists nationwide must integrate effective AI models to improve their clinical practice. This would minimize adverse outcomes in regional anaesthesia and ultimately enhance patient care and satisfaction. …”
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Enhancing river and lake wastewater reuse recommendation in industrial and agricultural using AquaMeld techniques by J. Priskilla Angel Rani, C. Yesubai Rubavathi

“…The main motivation is combining the two models into the MLP-RNN to improve prediction accuracy. …”
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Minimising surface-code failures using a color-code decoder by Asmae Benhemou, Kaavya Sahay, Lingling Lao, Benjamin J. Brown

“…Here we propose a decoder for the surface code that finds low-weight correction operators for errors produced by the depolarising noise model. The decoder is obtained by mapping the syndrome of the surface code onto that of the color code, thereby allowing us to adopt more sophisticated color-code decoding algorithms. …”
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A comprehensive review of data analytics and storage methods in geothermal energy operations by Ali Basem, Ahmed Kateb Jumaah Al-Nussairi, Dana Mohammad Khidhir, Narinderjit Singh Sawaran Singh, Mohammadreza Baghoolizadeh, Mohammad Ali Fazilati, Soheil Salahshour, S. Mohammad Sajadi, Ali Mohammadi Hasanabad

“…The development of reliable and affordable sensors, together with improvements in processing power, has made data-intensive algorithms and real-time operational decision-making applications in the field of geothermal energy. …”
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Advancements in Herpes Zoster Diagnosis, Treatment, and Management: Systematic Review of Artificial Intelligence Applications by Dasheng Wu, Na Liu, Rui Ma, Peilong Wu

“…Classification tasks (85.2%) dominated AI applications, with neural networks, particularly multilayer perceptron and convolutional neural networks being the most frequently used algorithms. AI applications were analyzed across three domains: (1) diagnosis, where mobile deep neural networks, convolutional neural network ensemble models, and mixed-scale attention-based models have improved diagnostic accuracy and efficiency; (2) treatment, where machine learning models, such as deep autoencoders combined with functional magnetic resonance imaging, electroencephalography, and clinical data, have enhanced treatment outcome predictions; and (3) management, where AI has facilitated case identification, epidemiological research, health care burden assessment, and risk factor exploration for postherpetic neuralgia and other complications. …”
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Machine Learning-Based Prediction of Feed Conversion Ratio: A Feasibility Study of Using Short-Term FCR Data for Long-Term Feed Conversion Ratio (FCR) Prediction by Xidi Yang, Liangyu Zhu, Wenyu Jiang, Yiting Yang, Mailin Gan, Linyuan Shen, Li Zhu

“…We employed nineteen machine learning algorithms, including Linear Regression, support vector machines (SVMs), and Gradient Boosting, using historical datasets to train and validate the models. …”
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Explainable Artificial Intelligence Driven Segmentation for Cervical Cancer Screening by Niruthikka Sritharan, Nishaanthini Gnanavel, Prathushan Inparaj, Dulani Meedeniya, Pratheepan Yogarajah

“…This novel segmentation framework employed LRP and GradCAM++ as XAI techniques to gain insight into the decision-making process of classification models, with GradCAM++ demonstrating greater effectiveness. …”
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Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation by B. Shamreen Ahamed, Meenakshi S. Arya, Auxilia Osvin V. Nancy

“…The technical improvements in healthcare sector today have given rise to many new inventions in the field of artificial intelligence. …”
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Quantitative Analysis of Sulfur Elements in Mars-like Rocks Based on Multimodal Data by Yuhang Dong, Zhengfeng Shi, Junsheng Yao, Li Zhang, Yongkang Chen, Junyan Jia

“…Furthermore, to optimize model performance, different feature selection algorithms were evaluated. …”
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Computer vision based automatic evaluation method of Y2O3 steel coating performance with SEM image by Jianhong Zhao, Huamin Yang, Yi Sui

“…By leveraging advanced computer vision algorithms, specifically a suitable model for long-term dendritic solidifications named Tang Rui Detect (TRD), the method achieves efficient and accurate detection and quantification of microstructure features. …”
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Smart IoT Energy Optimisation and Localisation Monitoring for E-Bike Sharing by Mawada Mohamed, Siti Fauziah Toha, Md Ataur Rahman, Moh. Khairudin

“…However, existing systems face challenges such as limited input parameters for modeling, leading to inefficiencies in energy optimization algorithms and power assist mechanisms. …”
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Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation by Zongqi Xia, Prerna Chikersal, Shruthi Venkatesh, Elizabeth Walker, Anind K Dey, Mayank Goel

“…Among the best-performing models with the least sensor data requirement, the ML algorithm predicted depressive symptoms with an accuracy of 80.6% (F1-score=0.76), high global MS symptom burden with an accuracy of 77.3% (F1-score=0.78), severe fatigue with an accuracy of 73.8% (F1-score=0.74), and poor sleep quality with an accuracy of 72.0% (F1-score=0.70). …”
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