Paradigm of tunable clustering using Binarization of Consensus Partition Matrices (Bi-CoPaM) for gene discovery. by Basel Abu-Jamous, Rui Fa, David J Roberts, Asoke K Nandi

“…Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. …”
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Analysis of Multivariate Indoor Building Data: A Comparative Study of Time-Series Clustering Methods by Quoc-Dung Ngo, Ly-Huynh Phan

“…Moreover, incorporating derivative-based preprocessing enhances the ability of clustering algorithms to distinguish subtle temporal dynamics, making this approach highly relevant for applications in smart building monitoring and environmental control.…”
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A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves by James M. Trauer, Angus E. Hughes, David S. Shipman, Michael T. Meehan, Alec S. Henderson, Emma S. McBryde, Romain Ragonnet

“…We aimed to identify the key processes relevant to COVID-19 transmission dynamics and thereby develop a model that could quantify relevant epidemiological parameters.The pipeline's advantages include markedly increased speed, an expressive application programming interface, the transparency of open-source development, easy access to a broad range of calibration and optimisation tools and consideration of the full workflow from input manipulation through to algorithmic generation of the publication materials. …”
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Multi-jet production in deep inelastic scattering with Pythia by Ilkka Helenius, Joni O. Laulainen, Christian T. Preuss

“…We consider up to five outgoing partons using two different merging algorithms. We vary the relevant scale choices and compare to experimental data by the H1 collaboration at the HERA collider. …”
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Analytical Solution of Fractional Oldroyd-B Fluid via Fluctuating Duct by Ying Qing Song, Aamir Farooq, Muhammad Kamran, Sadique Rehman, Muhammad Tamoor, Rewayat Khan, Asfand Fahad, Muhammad Imran Qureshi

“…The flow field’s graphs with the influences of relevant parameters are also mentioned.…”
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ULTRASOUND VIDEO ANALYSIS FOR UNDERSTANDING INFANT BREASTFEEDING by G. Monaci, M. Woolridge

“…The proposed algorithms provide, for the firrst time, quantitative indications of the type of activities carried out during breastfeeding by the baby, promising unprecedented advancements in the field.…”
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Helicopter reconnaissance indicators sensitivity analysis based on machine learning by JIANG Xinyi, LOU Benchao, ZENG Weiping

“…Secondly, sensitivity analysis of helicopter reconnaissance mission effectiveness is achieved through task planning, constructing indicator systems, collecting relevant data, evaluating effectiveness, and using machine learning algorithms for modeling and training. …”
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Developing Contextual Ontology for Chronic Diseases: AI-Enhanced Extension and Prediction in an Asthma Case Study by Batoul Msheik, Mehdi Adda, Hamid Mcheick, Youmna Nasser, Mohamed Dbouk

“…We designed a general contextual ontology framework by integrating the extended ontology with artificial intelligence algorithms, identifying relevant parameters, and extracting rules to enhance knowledge representation and support clinical decision-making. …”
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Development of a composite index for the assessment of food systems in the Philippines by Maria Julia Golloso-Gubat, Angelina Felix, Nancy A. Tandang, Cecilia Cristina S. Acuin, Prudenciano U. Gordoncillo

“…Initially, a set of indicators were generated by Delphi approach, and relevant local data were used to develop algorithms to quantitatively operationalize the indicators which were subsequently grouped into domains by Principal Component Analysis (PCA). …”
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Empowering Fuel Cell Electric Vehicles Towards Sustainable Transportation: An Analytical Assessment, Emerging Energy Management, Key Issues, and Future Research Opportunities by Tuhibur Rahman, Md. Sazal Miah, Tahia F. Karim, Molla Shahadat Hossain Lipu, Abu M. Fuad, Zia Ul Islam, M. M. Naushad Ali, Mohammed Nazmus Shakib, Shafrida Sahrani, Mahidur R. Sarker

“…Furthermore, the state-of-the-art research domains of FCEVs are thoroughly discussed in order to explore various research domains, relevant outcomes, and existing challenges. Additionally, this paper addresses open issues and challenges and offers valuable future research opportunities for advancing FCEVs, emphasizing the importance of suitable algorithms, controllers, and optimization techniques to enhance their performance. …”
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Reliability and Quality of Complex Systems by I.V. Peshkov, V.A. Zhigulin, N.A. Fortunova

“…One of the leading directions in the development of direction-of-arrival estimation systems is the use of digital antenna arrays and super-resolution algorithms. The task of implementing a system for calibrating the effect of mutual coupling on FPGAs and integrating it into a common computing unit is poorly covered in the literature and is relevant. …”

Use of Aposteriori Information in the Implementation of Radar Recognition Systems Using Neural Network Technologies by Dmitrii F. Beskostyi, Sergei G. Borovikov, Yurii V. Yastrebov, Ilya A. Sozontov

“…The development of modern MRRS has created objective prerequisites for the use of progressive and new methods and algorithms for the processing of signals using neural networks. …”
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Survival Prediction in Brain Metastasis Patients Treated with Stereotactic Radiosurgery: A Hybrid Machine Learning Approach by Tuğçe Öznacar, İpek Pınar Aral, Hatice Yağmur Zengin, Yılmaz Tezcan

“…The goal was to compare the performance of multiple algorithms and identify the most effective model for clinical use. …”
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Crop yield prediction using machine learning: An extensive and systematic literature review by Sarowar Morshed Shawon, Falguny Barua Ema, Asura Khanom Mahi, Fahima Lokman Niha, H.T. Zubair

“…Also, the most applied machine learning algorithms are Linear Regression (LR), Random Forest (RF), and Gradient Boosting Trees (GBT) whereas the most applied deep learning algorithms are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM). …”
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Artificial Intelligence Systems for Solving Problems of Agro­Industrial Complex Digitalization and Robotization by A. L. Ronzhin, A. I. Savel'ev

“…The digital transformation of agricultural technological processes is substantiated to be relevant in the context of the exacerbating global problems of food security, the agricultural business stagnation in the regions of the northern Non-Black Earth Region, as well as the ESG transformation of the economy.Research purpose To develop control algorithms for unmanned aerial vehicles (UAVs) based on numerical methods of machine learning to ensure the monitoring of the crops state and the improving of the production process planning and operational management.Materials and methods The following methodology was used: the original methods of machine learning, knowledge engineering and computer modeling for organizational and technological processes of technical objects’ life cycle in industry and products’ life cycle in the national economy,  as well as mathematical and algorithmic models, methods and prototypes of proactive automation tools for information, physical and energy interaction of heterogeneous robotic and cyber-physical complexes.Results and discussion Artificial intelligence systems were created for the photogrammetric processing of visible spectrum images and those taken with multispectral video cameras with the construction of orthophotomaps, digital elevation models. …”
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Making a Real-Time IoT Network Intrusion-Detection System (INIDS) Using a Realistic BoT–IoT Dataset with Multiple Machine-Learning Classifiers by Jawad Ashraf, Ghulam Musa Raza, Byung-Seo Kim, Abdul Wahid, Hye-Young Kim

“…Using the Pearson Correlation Coefficient, we extracted the most relevant features from the BoT–IoT dataset. After rigorous preprocessing, we used these data to train our algorithms. …”
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Enhancing clinical decision-making in closed pelvic fractures with machine learning models by Dian Wang, Yongxin Li, Li Wang

“…This study aimed to utilize various machine learning (ML) algorithms to predict HI and death in patients with closed pelvic fractures and identify relevant risk factors. …”
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Editor’s Pick: Regulatory and Ethical Issues in the New Era of Radiomics and Radiogenomics by Filippo Pesapane

“… Radiomics is a science that investigates a large number of features from medical images using data-characterisation algorithms, with the aim to analyse disease characteristics that are indistinguishable to the naked eye. …”
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Ensemble-RNN: A Robust Framework for DDoS Detection in Cloud Environment by Asha Varma Songa, Ganesh Redy Karri

“…It combines an Ensemble of multiple Machine Learning (ML) algorithms with a Recurrent Neural Network (RNN).  The framework aims to address the issue by selecting the most relevant features using the ensemble of six ML algorithms. …”
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Improving the Performance of CPA Attacks for Ciphers Using Parallel Implementation of S-Boxes by Fu Yao, Yongzhuang Wei, Hua Chen, Enes Pasalic

“…Since their introduction in early 2000, CPA (correlation power analysis), as a cryptographic tool, has been widely used in the cryptanalysis of cryptographic algorithms (being applicable to both symmetric key ciphers as well as to public key encryption schemes). …”
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