Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities by Izabela Rojek, Dariusz Mikołajewski, Krzysztof Galas, Adrianna Piszcz

“…Advanced deep learning algorithms play a key role in optimizing energy usage in smart cities, leveraging massive datasets to increase efficiency and sustainability. …”
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MLPNN and Ensemble Learning Algorithm for Transmission Line Fault Classification by Tanbir Rahman, Talab Hasan, Arif Ahammad, Imtiaz Ahmed, Nainaiu Rakhaine

“…The power transmission system is modeled using Simulink and the machine learning algorithms. In the IEEE 3-bus system, all of the learning types achieve approximately 99% accuracy in imbalanced and noisy data states, respectively, except CatBoost and decision tree, in the classification of line to line, line to line to line, line to line to ground, line to ground types of faults, and no fault. …”
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Efficacy of swarm-based neural networks in automated depression detection by Alwan Atta, Dina ElSayad, Doaa Ezzat, Safaa Amin, Mahmoud ElGamal

“…Therefore, the model achieved 0.92 F1 on the CMDC dataset and 0.82 on the MODMA dataset, establishing strong performance across different distributions of data. The results highlight how the integration of deep learning techniques with metaheuristic optimization algorithms can provide optimal and reliable depression diagnosis and thus create promising directions for further research in this area.…”
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Applying Machine Learning Sampling Techniques to Address Data Imbalance in a Chilean COVID-19 Symptoms and Comorbidities Dataset by Pablo Ormeño-Arriagada, Gastón Márquez, David Araya, Carla Rimassa, Carla Taramasco

“…However, imbalanced data in medical datasets pose significant challenges for machine learning models, leading to bias and poor generalization. …”
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Development and evaluation of statistical and artificial intelligence approaches with microbial shotgun metagenomics data as an untargeted screening tool for use in food production by Kristen L. Beck, Niina Haiminen, Akshay Agarwal, Anna Paola Carrieri, Matthew Madgwick, Jennifer Kelly, Victor Pylro, Ban Kawas, Martin Wiedmann, Erika Ganda

“…ABSTRACT The increasing knowledge of microbial ecology in food products relating to quality and safety and the established usefulness of machine learning algorithms for anomaly detection in multiple scenarios suggests that the application of microbiome data in food production systems for anomaly detection could be a valuable approach to be used in food systems. …”
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Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm by Hongyan Wang

“…This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. …”
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Theoretical approaches to detecting anomalies in meter readings in scientific literature by D.V. Furikhata, T.A. Vakalyuk

“…Particular attention is paid to analysing the effectiveness of various machine learning algorithms for anomaly detection. Statistical methods based on probabilistic models are considered, which allow relatively accurate determination of normal limits provided sufficient historical data is available. …”
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Enhancing anomaly detection in IoT-driven factories using Logistic Boosting, Random Forest, and SVM: A comparative machine learning approach by Mohammed Aly, Mohamed H. Behiry

“…Abstract Three machine learning algorithms—Logistic Boosting, Random Forest, and Support Vector Machines (SVM)—were evaluated for anomaly detection in IoT-driven industrial environments. …”
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YOLO-Based Real-Time Detection of Power Line Poles from Unmanned Aerial Vehicle Inspection Vision by Jingdong GUO, Bin CHEN, Renshu WANG, Jiayu WANG, Linlin ZHONG

“…In this paper a real-time detection model based on YOLO deep learning algorithm is presented to detect the status of power line poles automatically from the UAV vision data after disaster. …”
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Application of machine learning for seam profile identification in robotic welding by Fatemeh Habibkhah, Mehrdad Moallem

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RETRACTED: Computerized Detection of Calcium Oxalate Crystal Progression by Hanan A. Hosni Mahmoud

“…We employed deep learning for feature extraction. The deep learning technique uses transfer learning, which allows the proposed detection model to be trained on only a small amount of data regarding calcium oxalate crystals for the determination of the presence of calcium oxalate crystals and the severity of the cases. …”
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Comparative analysis of machine learning models for malaria detection using validated synthetic data: a cost-sensitive approach with clinical domain knowledge integration by Gudi V. Chandra Sekhar, Chekol Alemu

“…Abstract Malaria remains a critical global health challenge requiring innovative diagnostic approaches. Machine learning offers promising solutions for automated detection, but systematic algorithm comparison using clinically validated data remains limited. …”
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The application of deep learning-based technique detection model in table tennis teaching and learning by Shunshui He

“…This proves that the proposed technology detection model has good algorithm performance and data analysis ability, and can provide data support for table tennis training and teaching work.…”
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Leveraging explainable artificial intelligence with ensemble of deep learning model for dementia prediction to enhance clinical decision support systems by Mohamed Medani, Ghada Moh. Samir Elhessewi, Mohammed Alqahtani, Somia A. Asklany, Sulaiman Alamro, Da’ad Albalawneh, Menwa Alshammeri, Mohammed Assiri

“…Accepting a structure uniting explainability in artificial intelligence (XAI) with intricate systems will enable us to classify analysts of dementia incidence and then verify their occurrence in the survey as recognized or suspected risk factors. Deep learning (DL) and machine learning (ML) are current techniques for detecting and classifying dementia and making decisions without human participation. …”
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Multimodal deep learning for art behavior analysis and personalized teaching path generation by Yikun Li, Jie Shi

“…This study focuses on art education, aiming to address the lack of personalization in traditional teaching methods by proposing innovative solutions using multimodal deep learning technology. The research constructs a Multimodal Feature Fusion Network (MFFN) and an Evolutionary Path Generation Algorithm (EPG), utilizing Kinect sensors, digital tablets, and other devices to collect multi-modal data including visual, tactile, behavioral, and environmental information, establishing a large-scale art learning dataset. …”
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Towards a minimum universal features set for IoT DDoS attack detection by Osama Ebrahem, Salah Dowaji, Suhel Alhammoud

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Automated detection of bicycle helmets using deep learning by Felix Wilhelm Siebert, Christoffer Riis, Kira Hyldekær Janstrup, Hanhe Lin, Jakob Kristensen, Oguzhan Gül, Frederik Boe Hüttel

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Detection of GenAI-produced and student-written C# code: A comparative study of classifier algorithms and code stylometry features by Adewuyi Adetayo Adegbite, Eduan Kotzé

“…This study tested the ability of six classifier algorithms to detect GenAI C# code and to distinguish it from C# code written by students at a South African university. …”
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Segmentation versus detection: Development and evaluation of deep learning models for prostate imaging reporting and data system lesions localisation on Bi‐parametric prostate magn... by Zhe Min, Fernando J. Bianco, Qianye Yang, Wen Yan, Ziyi Shen, David Cohen, Rachael Rodell, Dean C. Barratt, Yipeng Hu

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Improvement of signal detection based on using machine learning by Bassam Abd

“…The SVM classifier is a supervised learning algorithm that uses the closest data points as "support vectors" to build a hyperplane that divides classes. …”
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