Enhancing Agricultural Disease Detection: A Multi‐Model Deep Learning Novel Approach by Muhammad Khalid Hamid, Said Khalid Shah, Ghassan Husnain, Yazeed Yasin Ghadi, Shahab Ahmad Al Maaytah, Ayman Qahmash

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A control-driven transition strategy for enhanced multi-level threshold image segmentation optimization by Laith Abualigah, Mohammad H. Almomani, Saleh Ali Alomari, Raed Abu Zitar, Vaclav Snasel, Kashif Saleem, Aseel Smerat, Absalom E. Ezugwu

“…We also find that the proposed method got better results in solving large medical clustering applications.…”
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Early detection of Cercospora beticola and powdery mildew diseases in sugar beet using uncrewed aerial vehicle-based remote sensing and machine learning by Koç Mehmet Tuğrul, Rıza Kaya, Kemal Özkan, Merve Ceyhan, Uğur Gürel, Fatih Yavuz Fidantemiz

“…The study was accomplished by capturing images of uncrewed aerial vehicle (UAV) in field conditions. To effectively evaluate different detection methods in agricultural contexts, the study targeted two key areas: (1) monitoring Cercospora in fields without pesticide application, utilizing the Metos climate station early warning system alongside UAV-based image analysis, and (2) monitoring powdery mildew, which involved visual disease detection and targeted spraying based on UAV image processing. …”
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A method for detecting the rate of tobacco leaf loosening in tobacco leaf sorting scenarios by Yansong Wang, Yansong Wang, Chunjie Zhang, Chunjie Zhang, Mingjie Wu, Mingjie Wu, Ruilin Luo, Lin Lu, Zaiqing Chen, Zaiqing Chen, Lijun Yun, Lijun Yun

“…Experimental results indicate that the proposed tobacco leaf loosening rate detection method has significant practical application value, enabling effective monitoring and evaluation of tobacco leaf sorting quality, thereby further enhancing the quality of tobacco leaf sorting.…”
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The use of patient-reported outcome measures to improve patient-related outcomes – a systematic review by Joshua M. Bonsel, Ademola J. Itiola, Anouk S. Huberts, Gouke J. Bonsel, Hannah Penton

“…Organizational/policy-level application is in its infancy, and performance evaluation via in-depth analyses and PDCA-cycles may be useful. …”
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Approach to Enhancing Panoramic Segmentation in Indoor Construction Sites Based on a Perspective Image Segmentation Foundation Model by Juho Han, Sebeen Yoon, Mingyun Kang, Taehoon Kim

“…Panoramic images in indoor construction sites are gaining attention as valuable tools for process monitoring and quality assessment. However, despite the environmental complexity and the demand for high segmentation performance in indoor construction environments, the scarcity of specialized segmentation models and datasets has created a gap between technological advancements and practical application, thus hindering the effective utilization of panoramic images. …”
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Machine Learning-Driven Acoustic Feature Classification and Pronunciation Assessment for Mandarin Learners by Gulnur Arkin, Tangnur Abdukelim, Hankiz Yilahun, Askar Hamdulla

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Computer-aided diagnosis of Haematologic disorders detection based on spatial feature learning networks using blood cell images by Jamal Alsamri, Hamed Alqahtani, Ali M. Al-Sharafi, Abdulbasit A. Darem, Khalid Nazim, Abdul Sattar, Menwa Alshammeri, Ahmad A. Alzahrani, Marwa Obayya

“…Abstract Analyzing biomedical images is vital in permitting the highest-performing imaging and numerous medical applications. Determining the analysis of the disease is an essential stage in handling the patients. …”
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Geoinformation monitoring of the educational risk in educational institutions of higher education by Aleksandr N. Kolesenkov, Dmitry S. Zhuravlyov

“…Evaluation of the characteristics of training directions is carried out through the application of the classification method, the decision tree is used, which allows to order many specialties and training directions in a hierarchical, consistent structure according to the established rules. …”
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Implementation of Diagnostic Methods of Career Guidance in Computer Systems by Anna P. Sergushicheva

“…The comparison of personal characteristics and requirements in the created system is carried out by means of production rules and a genetic algorithm. Among the advantages of genetic algorithms are conceptual simplicity and wide applicability, resistance to dynamic changes in the environment and the ability to self-organization. …”
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Importance Sampling and Feature Fusion Paradigm-Boosted Multi-Modal Convolutional Neural Networks: Deployment in Composite Curing Process Monitored by Electro-Mechanical Impedance by Xin Zhao, Zeyuan Gao, Meng Li, Zhibin Han, Jianjian Zhu

“…This study develops the Importance Sampling Algorithm-optimized Multi-Modal CNNs (ISA-MM-CNNs) paradigm for EMI-based evaluation of composite curing processes. …”
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Review of surface measurement methods towards nondestructive internal surface assessment by Kwan Zhi Teh, Saikat Medya, Sugandhana Shanmuganathan, Swee Hock Yeo

“…Their current applications in literature are evaluated to understand the appropriateness for internal surface applications. …”
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Division of Multi-harmonic Responsibilities Based on DBSCAN Clustering and Interval Regression by Shilong CHEN, Tao WU, Cheng GUO, Zirui ZHANG, Jinghao SUN

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Improving machine learning models through explainable AI for predicting the level of dietary diversity among Ethiopian preschool children by Gizachew Mulu Setegn, Belayneh Endalamaw Dejene

“…The data were preprocessed to obtain quality data that are suitable for the ensemble ML algorithms to develop a model. …”
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An Enhanced ZigBee-Based Indoor Localization Method Using Multi-Stage RSSI Filtering and LQI-Aware MLE by Jianming Li, Shuyan Yu, Zhe Wei, Zhanpeng Zhou

“…These results suggest that the proposed method may offer a feasible solution for resource-constrained localization applications requiring robustness to signal degradation.…”
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From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep Learning by A. Burzyńska

“…The study utilized a substantial dataset with a total of 61,584 images, and the most effective model attained an impressive Root Mean Square Error (RMSE) of 0.81, underscoring the model's remarkable capacity to accurately detect and predict casting quality issues. The model's efficacy was evaluated through its application to both large and small, differently distributed data sets. …”
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Ethical and regulatory challenges in machine learning-based healthcare systems: A review of implementation barriers and future directions by Shehu Mohammed, Neha Malhotra

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A reliable framework for supply chain management using cluster based blockchain for intelligent industrial automation by Eesa Alsolami, Mahmoud Ahmad Al-khasawenh, Ala Abdulsalam Alarood, Ahmed Omar Alzahrani, Ahmed Aalameen Ibraheem, Mazdak Zamani

“…Software Development applications with 26 software metrics are used for the performance evaluation. …”
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Salp Navigation and Competitive based Parrot Optimizer (SNCPO) for efficient extreme learning machine training and global numerical optimization by Oluwatayomi Rereloluwa Adegboye, Afi Kekeli Feda, Ghanshyam G. Tejani, Aseel Smerat, Pankaj Kumar, Ephraim Bonah Agyekum

“…The results demonstrate that SNCPO consistently outperforms existing state-of-the-art algorithms, achieving superior convergence speed, solution quality, and robustness while effectively avoiding local optima. …”
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Random Forest-Based Prediction of the Optimal Solid Ink Density in Offset Lithography by Laihu Peng, Hao Fan, Yubao Qi, Jianqiang Li

“…Compared with the traditional method of determining the optimal solid ink density, the current printing equipment used to determine the optimal solid ink density will be faster at improving industrial production efficiency and product quality. To improve the efficiency of determining the optimal solid ink density, the Random Forest algorithm was applied for the first time to the prediction task of solid ink density in offset printing. …”
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