Adaptive algorithms for change point detection in financial time series by Alexander Musaev, Dmitry Grigoriev, Maxim Kolosov

“…Key findings reveal that while no universal solution exists for detecting change points in chaotic time series, integrating machine learning and multivariate approaches allows for more robust and adaptive forecasting models. …”
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Electricity theft detection in integrated energy systems considering multi-energy loads by Wenlong Liao, Dechang Yang, Leijiao Ge, Yixiong Jia, Zhe Yang

“…Simulation results demonstrate that the incorporation of heating and cooling loads can significantly enhance the performance of various machine learning models for electricity theft detection. …”
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Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization Algorithm by El Assri Nasima, Ennejjar Mohammed, Jallal Mohammed Ali, Chabaa Samira, Zeroual Abdelouhab

“…The primary focus is on evaluating the performance of two prominent and widely-used machine learning algorithms: Artificial Neural Networks (ANN) and Random Forest (RF). …”
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An Effective Methodology for Diabetes Prediction in the Case of Class Imbalance by Borislava Toleva, Ivan Atanasov, Ivan Ivanov, Vincent Hooper

“…Our methodology can outperform existing machine learning algorithms and complex deep learning models. …”
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Leveraging an Enhanced CodeBERT-Based Model for Multiclass Software Defect Prediction via Defect Classification by Rida Ghafoor Hussain, Kin-Choong Yow, Marco Gori

“…In this context, machine learning-driven methods, especially those leveraging natural language models, have gained significant traction for developing effective techniques. …”
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Automatic Detection and Classification of Eye Diseases from Retinal Images Using Deep Learning: A Comprehensive Research on the ODIR Dataset by Asadi Srinivasulu, Clement Varaprasad Karu, G Sreenivasulu, Gayathri R

“…Existing approaches include machine learning, filtering-based, and model-based methods. …”
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Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes by Runyu Jing, Yu Liang, Yi Ran, Shengzhong Feng, Yanjie Wei, Li He

“…Our findings indicated that the ensemble methodologies are more reliable for predicting cancer prognoses than single machine learning algorithms as well as for gene function evaluating. …”
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Performance Assessment of Multiple Classifiers Based on Ensemble Feature Selection Scheme for Sentiment Analysis by Monalisa Ghosh, Goutam Sanyal

“…This paper investigates the inability or incompetency of the widely used feature selection methods (IG, Chi-square, and Gini Index) with unigram and bigram feature set on four machine learning classification algorithms (MNB, SVM, KNN, and ME). …”
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Hybrid quantum neural networks show strongly reduced need for free parameters in entity matching by Lukas Bischof, Stefan Teodoropol, Rudolf M. Füchslin, Kurt Stockinger

“…In this paper, we evaluate quantum machine learning algorithms for entity matching on a hand-crafted data set and compare them to similar classical algorithms. …”
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A comprehensive analysis of deep learning and transfer learning techniques for skin cancer classification by Manishi Shakya, Ravindra Patel, Sunil Joshi

“…This research investigates three approaches for classifying skin cancer images. (1) Utilizing three fine-tuned pre-trained networks (VGG19, ResNet18, and MobileNet_V2) as classifiers. (2) Employing three pre-trained networks (ResNet-18, VGG19, and MobileNet v2) as feature extractors in conjunction with four machine learning classifiers (SVM, DT, Naïve Bayes, and KNN). (3) Utilizing a combination of the aforementioned pre-trained networks as feature extractors in conjunction with same machine learning classifiers. …”
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Utilization of geocoding for mapping infrastructure impacts and mobility due to floods in indonesia based on twitter analytics by Muhammad Imam Taufiq, Abba Suganda Girsang

“…The method used is a combination of Qualitative Analysis with Machine Learning and Geospatial Analysis to assess flooding impacts using Twitter data. …”
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Designing Channel Attention Fully Convolutional Networks with Neural Architecture Search for Customer Socio-Demographic Information Identification Using Smart Meter Data by Zhirui Luo, Qingqing Li, Ruobin Qi, Jun Zheng

“…In recent years, machine learning-based data-driven methods have gained popularity compared to traditional survey-based approaches, owing to their time and cost efficiency, as well as the availability of a large amount of high-frequency smart meter data. …”
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Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning by Mohamed Massaoudi, Maymouna Ez Eddin, Ali Ghrayeb, Haitham Abu-Rub, Shady S. Refaat

“…This article provides an updated review of the cutting-edge machine learning and data-driven techniques used for PGP in networked PSs. …”
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Automated 3D semantic segmentation of PCB X-ray CT images and netlist extraction by Adrian Phoulady, Yara Suleiman, Hongbin Choi, Nicholas May, Sina Shahbazmohamadi, Pouya Tavousi

“…This methodology includes high-resolution 3D imaging, watershed segmentation, machine learning-based semantic segmentation, and netlist extraction. …”
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Topology Unveiled: A New Horizon for Economic and Financial Modeling by Yicheng Wei, Junzo Watada, Zijin Wang

“…The most famous practical applications include the use of topological data analysis (TDA) and topological machine learning (TML) for different applied problems.…”
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Intelligent Classification of Stable and Unstable Slope Conditions Based on Landslide Movement by Long Tsang, Ali Ghorbani, Seyed Mohammad Hossein Khatami, Dmitrii Ulrikh

“…In this research, different machine learning-based approaches were used to analyze and manage this problem. …”
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Optimization of carbon footprint management model of electric power enterprises based on artificial intelligence. by Liangzheng Wu, Kaiman Li, Yan Huang, Zhengdong Wan, Jieren Tan

“…Firstly, a carbon footprint calculation model based on big data and AI is established, and then machine learning algorithm is used to deeply mine the carbon emission data of power enterprises to identify the main influencing factors and emission reduction opportunities. …”
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Machine and deep learning algorithms for sentiment analysis during COVID-19: A vision to create fake news resistant society. by Muhammad Tayyab Zamir, Fida Ullah, Rasikh Tariq, Waqas Haider Bangyal, Muhammad Arif, Alexander Gelbukh

“…This research employs machine learning classifiers, namely Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Decision Trees, and Random Forest, to analyze and compare the obtained results. …”
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Digital Transformation in the Concept of Internal Banking Risk Management by D. V. Pasinitsky

“…Based on the study, the author proposes to intensify the introduction of digital technologies in banking practice based on: artificial intelligence, machine learning, data mining.…”
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Implementasi Algoritma Random Forest Untuk Menentukan Penerima Bantuan Raskin by Ilham Kurniawan, Duwi Cahya Putri Buani, Abdussomad Abdussomad, Widya Apriliah, Rizal Amegia Saputra

“…Tujuan penelitian ini adalah menemukan akurasi tertinggi di antara algoritma klasifikasi prediktif yang diusulkan penerima bantuan raskin menggunakan tools python machine learning dan di implementasikan melalui suatu website. …”
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