An adaptive neuro-fuzzy inference system for multinomial malware classification by Amos Orenyi Bajeh, Mary Olayinka Olaoye, Fatima Enehezei Usman-Hamza, Ikeola Suhurat Olatinwo, Peter ogirima Sadiku, Abdulkadir Bolakale Sakariyah

“…As the growth of technology increases, malware is getting more sophisticated and thereby more difficult to detect. Machine learning techniques have been extensively used for malware detection and classification. …”
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Automatic Segmentation of Ischemic Stroke Lesions in CT Perfusion Maps Using Deep Learning Networks by Lida Zare Lahijan, Saeed Meshgini, Reza Afrouzian

“…Accordingly, several machine learning-based techniques have recently been proposed to improve the segmentation accuracy of ischemic stroke lesions. …”
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The extension of the largest generalized-eigenvalue based distance metric ) in arbitrary feature spaces to classify composite data points by Mosaab Daoud

“…Analyzing patterns in data points embedded in linear and non-linear feature spaces is considered as one of the common research problems among different research areas, for example: data mining, machine learning, pattern recognition, and multivariate analysis. …”
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OBF-Psychiatric, a motor activity dataset of patients diagnosed with major depression, schizophrenia, and ADHD by Enrique Garcia-Ceja, Andrea Stautland, Michael A. Riegler, Pål Halvorsen, Salvador Hinojosa, Gilberto Ochoa-Ruiz, Jan O. Berle, Wenche Førland, Kristin Mjeldheim, Ketil Joachim Oedegaard, Petter Jakobsen

“…The dataset also contains data from a clinical sample diagnosed with various mood and anxiety disorders, as well as a healthy control group, making it suitable for building machine learning models and other analytical tools. It contains recordings from 162 individuals totalling 1565 days worth of motor activity data with a mean of 9.6 days per individual.…”
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Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worseni... by Jee Young Kim, Alifia Hasan, Katherine C Kellogg, William Ratliff, Sara G Murray, Harini Suresh, Alexandra Valladares, Keo Shaw, Danny Tobey, David E Vidal, Mark A Lifson, Manesh Patel, Inioluwa Deborah Raji, Michael Gao, William Knechtle, Linda Tang, Suresh Balu, Mark P Sendak

“…The use of data-driven technologies such as Artificial Intelligence (AI) and Machine Learning (ML) is growing in healthcare. However, the proliferation of healthcare AI tools has outpaced regulatory frameworks, accountability measures, and governance standards to ensure safe, effective, and equitable use. …”
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A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction by Tuong Le, Minh Thanh Vo, Bay Vo, Mi Young Lee, Sung Wook Baik

“…Many studies have been developed for bankruptcy prediction utilizing different machine learning approaches on various datasets around the world. …”
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Tuberculosis Classification using SVM and Modified CNN by SRINIVAS BABU N, SHASHIKIRAN S, M JAYANTHI, Rajani N, K M PALANISWAMY, M R KUSHALATHA

“…Recent technological improvements use a machine learning-based SVM and Modified CNN to identify specific diseases more accurately, as demonstrated in this research. …”
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Site-specific prediction of O-GlcNAc modification in proteins using evolutionary scale model. by Ayesha Khalid, Afshan Kaleem, Wajahat Qazi, Roheena Abdullah, Mehwish Iqtedar, Shagufta Naz

“…Computational approaches, including protein language models and machine learning algorithms, have emerged as valuable tools for predicting O-GlcNAc sites, reducing experimental costs, and enhancing efficiency. …”
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Geomagnetic Field Based Indoor Landmark Classification Using Deep Learning by Bimal Bhattarai, Rohan Kumar Yadav, Hui-Seon Gang, Jae-Young Pyun

“…The experimental results show that our proposed models outperform other traditional positioning approaches with machine learning methods, such as support vector machine and k-nearest neighbors.…”
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Metasurface-enabled multifunctional single-frequency sensors without external power by Masaya Tashiro, Kosuke Ide, Kosei Asano, Satoshi Ishii, Yuta Sugiura, Akira Uchiyama, Hiroki Wakatsuchi

“…Moreover, we provide a method for predicting physical parameters via the machine learning-based approach of random forest regression. …”
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Automated Classification of Circulating Tumor Cells and the Impact of Interobsever Variability on Classifier Training and Performance by Carl-Magnus Svensson, Ron Hübler, Marc Thilo Figge

“…The automated analysis of medical data is a growing field where different machine learning techniques are used to minimize the time-consuming task of manual analysis. …”
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Market Trends in 2024 in the IT Project Management Industry by Nicoleta-Madalina Stan, Adrian Bogorin Predescu, Aurel Mihail Titu

“…The emergence of artificial intelligence and machine learning is transforming project management tools, offering predictive analytics and improved decision-making abilities. …”
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BharatSim: An agent-based modelling framework for India. by Philip Cherian, Jayanta Kshirsagar, Bhavesh Neekhra, Gaurav Deshkar, Harshal Hayatnagarkar, Kshitij Kapoor, Chandrakant Kaski, Ganesh Kathar, Swapnil Khandekar, Saurabh Mookherjee, Praveen Ninawe, Riz Fernando Noronha, Pranjal Ranka, Vaibhhav Sinha, Tina Vinod, Chhaya Yadav, Debayan Gupta, Gautam I Menon

“…BharatSim uses a synthetic population created by applying statistical methods and machine learning algorithms to survey data from multiple sources, including the Census of India, the India Human Development Survey, the National Sample Survey, and the Gridded Population of the World. …”
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Efficient Preference Clustering via Random Fourier Features by Jingshu Liu, Li Wang, Jinglei Liu

“…Approximations based on random Fourier features have recently emerged as an efficient and elegant method for designing large-scale machine learning tasks. Unlike approaches using the Nyström method, which randomly samples the training examples, we make use of random Fourier features, whose basis functions (i.e., cosine and sine ) are sampled from a distribution independent from the training sample set, to cluster preference data which appears extensively in recommender systems. …”
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Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network by Hao Peng, Han Wu, Junwu Wang

“…Three conventional error analysis tools in machine learning (the coefficient of determination, the root mean squared error, and the mean absolute error) also highlight the feasibility and advancement of the proposed method.…”
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Cellular senescence-associated genes in rheumatoid arthritis: Identification and functional analysis. by You Ao, Qing Lan, Tianhua Yu, Zhichao Wang, Jing Zhang

“…We then pinpointed three key genes (DHX9, CYR61, and ITGB) using random forest and LASSO Cox regression machine learning techniques. An integrated diagnostic model was created using these genes. …”
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A Classifier Based on K-Nearest Neighbors Using Weighted Summation of Reconstruction Errors by Rassoul Hajizadeh, Mohammad Ali Hosseinzadeh

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Efficient diagnostic model for iron deficiency anaemia detection: a comparison of CNN and object detection algorithms in peripheral blood smear images by Navya K. T, Seemitr Verma, Keerthana Prasad, Brij Mohan Kumar Singh

“…This study uses machine learning and computer vision techniques for the automatic identification of hypochromic microcytes from Peripheral Blood Smear (PBS) images to improve IDA diagnosis. …”
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Survey of data factor market： value， pricing， and trading by YANG Ming, FENG Honglin, WANG Xin, HUO Jidong, JIAO Xuguo, ZHANG Heng

“…For data asset value assessment, traditional methods such as the cost method, income method, and market method were discussed, as well as emerging methods based on machine learning. In terms of data pricing models, existing methods were categorized into two main types: pricing for dataset products and pricing for data service products. …”
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A Comparative Study on Application of Computer Vision and Fluorescence Imaging Spectroscopy for Detection of Huanglongbing Citrus Disease in the USA and Brazil by Caio B. Wetterich, Ratnesh Kumar, Sindhuja Sankaran, José Belasque Junior, Reza Ehsani, Luis G. Marcassa

“…The overall objective of this work was to develop and evaluate computer vision and machine learning technique for classification of Huanglongbing-(HLB)-infected and healthy leaves using fluorescence imaging spectroscopy. …”
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