From Night to Light: A Bibliometric Analysis of the Global Research Trajectory of Sleep Disorders in Parkinson’s Disease by Shi L, Zhao X, Wu J, He C

“…Emerging keywords include machine learning, sleep quality, biomarkers, covid-19, and mouse model.Conclusion: This bibliometric analysis sheds light on the global landscape of PD-related sleep disorder research over the past two decades, highlighting key countries, institutions, authors, and journals driving advancements in the field. …”
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APAH: An autonomous IoT driven real-time monitoring system for Industrial wastewater by Nishant Chavhan, Resham Bhattad, Suyash Khot, Shubham Patil, Aditya Pawar, Tejasvi Pawar, Palomi Gawli

“…APAH utilizes advanced technologies including, the Internet of Things (IoT) and Machine learning (ML) to provide real-time monitoring and control of wastewater treatment processes. …”
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Temporal Convolutional Network Approach to Secure Open Charge Point Protocol (OCPP) in Electric Vehicle Charging by Ikram Benfarhat, Vik Tor Goh, Chun Lim Siow, Muhammad Sheraz, Teong Chee Chuah

“…The primary challenge within EVCS architecture lies in defending against various cyberattacks. Several machine learning models, including convolutional neural networks, recurrent neural networks, and long short-term memory, have been employed to enhance EVCS security. …”
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An optimized deep-forest algorithm using a modified differential evolution optimization algorithm: A case of host-pathogen protein-protein interaction prediction by Jerry Emmanuel, Itunuoluwa Isewon, Jelili Oyelade

“…The results were compared with standard optimization methods such as traditional Bayesian optimization, genetic algorithms, evolutionary strategies, and other machine learning models. The optimized model achieved an accuracy of 89.3 %, outperforming other models across all metrics, including a sensitivity of 85.4 % and a precision of 91.6 %. …”
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Automated skin melanoma diagnostics based on mathematical model of artificial convolutional neural network by D. A. Gavrilov, E. I. Zakirov, E. V. Gameeva, V. Yu. Semenov, O. Yu. Aleksandrova

“…This jerk is largely due to the emergence and development of the technology of deep convolu onal neural networks.Recent developments in the fi eld of image processing and machine learning open up the prospect of crea ng systems based on ar fi cial neural convolu onal networks, superior to humans in problems of image classifi ca on, in par cular, in solving problems of analysis of various medical images. …”
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Advances in colorectal cancer diagnosis using optimal deep feature fusion approach on biomedical images by Sultan Refa Alotaibi, Manal Abdullah Alohali, Mashael Maashi, Hamed Alqahtani, Moneerah Alotaibi, Ahmed Mahmud

“…Lately, computer-aided diagnosis (CAD) based on HI has progressed rapidly with the increase of machine learning (ML) and deep learning (DL) based models. …”
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Harnessing Internet Search Data as a Potential Tool for Medical Diagnosis: Literature Review by Gregory J Downing, Lucas M Tramontozzi, Jackson Garcia, Emma Villanueva

“…Leveraging advancements in machine learning, researchers have explored linking search data with health records to enhance screening and outcomes. …”
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Legal aspects of functional security standardisation of the Internet of Things by P. S. Klimushyn, V. Ye. Roh, T. P. Kolisnyk

“…To support the Internet of Things, technologies such as built-in devices, cloud and fog computing, big data processing, machine learning, and artificial intelligence are used to produce intelligent physical objects. …”
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Evaluation of the Impact of Morphological Differences on Scale Effects in Green Tide Area Estimation by Ke Wu, Tao Xie, Jian Li, Chao Wang, Xuehong Zhang, Hui Liu, Shuying Bai

“…Green tide patches were categorized into small, medium, and large sizes, and morphological features such as elongation, compactness, convexity, fractal dimension, and morphological complexity were designed and analyzed. Machine learning models, including Extra Trees, LightGBM, and Random Forest, among others, classified medium and large patches into striped and non-striped types, with Extra Trees achieving outstanding performance (accuracy: 0.9844, kappa: 0.9629, F1-score: 0.9844, MIoU: 0.9637). …”
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Automatic Classification of Difficulty of Texts From Eye Gaze and Physiological Measures of L2 English Speakers by Javier Melo, Leigh Fernandez, Shoya Ishimaru

“…In this study (<inline-formula> <tex-math notation="LaTeX">$N=30$ </tex-math></inline-formula>) we determined L2 speakers&#x2019; subjective difficulty while reading using language proficiency and objective text difficulty, combined with physiological data. We compared machine learning classifiers combining eye, skin and heart sensor data against models using each modality separately. …”
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Futility in TAVI: A scoping review of definitions, predictive criteria, and medical predictive models. by Charlie Ferry, Jade Fiery-Fraillon, Mario Togni, Stephane Cook

“…Medical predictive models showed moderate sensitivity and specificity, except for machine learning, which shows promise for the future. However, few articles delve deeply into non-quantifiable parameters such as patient goals and objectives or ethical questions. …”
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Genetic analysis of scab disease resistance in common bean (Phaseolus vulgaris) varieties using GWAS and functional genomics approaches by Shadrack Odikara Oriama, Benard W. Kulohoma, Evans Nyaboga, Y. O. Masheti, Reuben Otsyula

“…Annotation of genes proteins with significant association values was conducted using a machine learning algorithm of support vector machine on prPred using python3 on Linux Ubuntu 18.04 computing platform with an accuracy of 0.935. …”
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CFD modelling and simulation of anaerobic digestion reactors for energy generation from organic wastes: A comprehensive review by Muhammad Usman Farid, Indiana A. Olbert, Andreas Bück, Abdul Ghafoor, Guangxue Wu

“…Research gaps and critical challenges are identified in different aspects such as reactor design, and configuration, mixing, multiphase flow, heat transfer, biokinetics as well as machine learning approaches.…”
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Missing Risk Factor Prediction in Cardiovascular Disease Using a Blended Dataset and Optimizing Classification With a Stacking Algorithm by Jannatul Mauya, Saad Sahriar, Sanjida Akther, Ruhul Amin, Sabba Ruhi, Md. Shamim Reza

“…ABSTRACT Machine learning is important in the treatment of heart disease because it is capable of analyzing large amounts of patient data, such as medical records, imaging tests, and genetic information, in order to identify patterns and predict the risk of developing heart disease. …”
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Decoding cyanide toxicity: Integrating Quantitative Structure-Toxicity Relationships (QSTR) with species sensitivity distributions and q-RASTR modeling by Kabiruddin Khan, Ramin Abdullayev, Gopala Krishna Jillella, Varun Gopalakrishnan Nair, Mahmoud Bousily, Supratik Kar, Agnieszka Gajewicz-Skretna

“…Key molecular descriptors, including topological, geometrical, and electronic properties, were computed using ALOGPS 2.1, ChemAxon, and Elemental-Descriptor 1.0. Three machine learning methods MLR, PLS, and kNN were employed to develop predictive models. …”
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Artificial Neural Networks as a Tool for High-Accuracy Prediction of In-Cylinder Pressure and Equivalent Flame Radius in Hydrogen-Fueled Internal Combustion Engines by Federico Ricci, Massimiliano Avana, Francesco Mariani

“…Furthermore, this methodology could subsequently be applied to conventional road engines exhibiting characteristics and performance similar to those of a specific optical engine used as the basis for the machine learning analysis, offering a practical advantage in real-time diagnostics.…”
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Distinguishing benign and malignant myxoid soft tissue tumors: Performance of radiomics vs. radiologists. by Joshua M Lawrenz, Can Cui, Samuel R Johnson, Katherine S Hajdu, Stephen W Chenard, Akhil Rekulapelli, Cullen P Moran, John J Block, Nicholson S Chadwick, Joanna L Shechtel, Brian Bingham, Leo Y Luo, Jennifer L Halpern, Herbert S Schwartz, Ginger E Holt, David S Smith, Benoit Dawant, Hakmook Kang

“…The purpose of this study is comparison of the diagnostic performance of a radiomic based machine learning (ML) model to musculoskeletal radiologists.…”
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Prediksi Rating Film IMDb Menggunakan Decision Tree by Rifqy Rosdiyah Ilmi, Fachrul Kurniawan, Sri Harini

“…Prediction of movie ratings values can be modeled through machine learning using the decision tree model. From this research, it can be concluded that the popularity of the film and the value of user votes on the IMDb page have an effect on the film rating value. …”
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Precision Adverse Drug Reactions Prediction with Heterogeneous Graph Neural Network by Yang Gao, Xiang Zhang, Zhongquan Sun, Payal Chandak, Jiajun Bu, Haishuai Wang

“…Abstract Accurate prediction of Adverse Drug Reactions (ADRs) at the patient level is essential for ensuring patient safety and optimizing healthcare outcomes. Traditional machine learning‐based methods primarily focus on predicting potential ADRs for drugs, but they often fall short of capturing the complexity of individual demographics and the variations in ADRs experienced by different people. …”
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How to Coordinate Urban Ecological Networks and Street Green Space Construction? Insights from a Multi-Scale Perspective by Shujun Hou, Ying Yu, Taeyeol Jung, Xin Han

“…This study examines the area within Chengdu’s Third Ring Road, employing the following methodologies: (1) constructing the regional ecological network using Morphological Spatial Pattern Analysis (MSPA), the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and circuit theory; (2) analyzing the street green view index (GVI) through machine learning semantic segmentation techniques; and (3) identifying key areas for the coordinated development of urban ecological networks and street green spaces using bivariate spatial correlation analysis. …”
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