Electricity Demand Forecasting Using Deep Polynomial Neural Networks and Gene Expression Programming During COVID-19 Pandemic by Cagatay Cebeci, Kasım Zor

“…The power-generation mix of future grids will be quite diversified with the ever-increasing share of renewable energy technologies. Therefore, the prediction of electricity demand will become crucial for resource optimization and grid stability. …”
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Imbalanced Power Spectral Generation for Respiratory Rate and Uncertainty Estimations Based on Photoplethysmography Signal by Soojeong Lee, Mugahed A. Al-antari, Gyanendra Prasad Joshi, Yeong Hyeon Gu

“…Therefore, we confirm that IPSG is efficiently trained to predict the complex nonlinear relationship between the feature vectors obtained from the photoplethysmography signal and the reference RR. …”
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Drought Detection in Satellite Imagery: A Layered Ensemble Machine Learning Approach by Muhammad Owais Raza, Naeem Ahmed Mahoto, Mana Saleh Al Reshan, Ali Alqazzaz, Adel Rajab, Asadullah Shaikh

“…Abstract Drought has been a major calamity due to climate change in recent years. Predicting drought has grabbed the attention of meteorologists and climate scientists, who study and look for modern techniques. …”
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Assessment of Melon Fruit Nutritional Composition Using VIS/NIR/SWIR Spectroscopy Coupled with Chemometrics by Dimitrios S. Kasampalis, Pavlos Tsouvaltzis, Anastasios S. Siomos

“…The objective of this study was to evaluate the feasibility of using visible, near-infrared, and short-wave infrared (VIS/NIR/SWIR) spectroscopy coupled with chemometrics for non-destructive prediction of nutritional components in Galia-type melon fruit. …”
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A novel ensemble of hybrid starling murmuration optimized stacked gated Ghostnets for robust classification of DDoS attacks by P. Mamatha, S. Balaji, S. Sai Anuraghav

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Global infectious disease early warning models: An updated review and lessons from the COVID-19 pandemic by Wei-Hua Hu, Hui-Min Sun, Yong-Yue Wei, Yuan-Tao Hao

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Emerging technologies and applications in multimodality imaging for ischemic heart disease: current state and future of artificial intelligence by Praveen Kumar Chandra Sekar, Ramakrishnan Veerabathiran

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Structure and Antibiotic Resistance of the Main Causative agents of Infections Associated with the Provision of Medical care by N. M. Voropaeva, U. M. Nemchenko, E. V. Grigorova, N. L. Bel'kova, N. N. Chemezova, E. D. Savilov

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SPARQ: Efficient Entanglement Distribution and Routing in Space–Air–Ground Quantum Networks by Mohamed Shaban, Muhammad Ismail, Walid Saad

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Different oscillatory mechanisms of dementia-related diseases with cognitive impairment in closed-eye state by Talifu Zikereya, Yuchen Lin, Zhizhen Zhang, Ignacio Taguas, Kaixuan Shi, Chuanliang Han

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Large language models in the management of chronic ocular diseases: a scoping review by Jiatong Zhang, Xiaoxi Song, Bocheng Tian, Mingke Tian, Zhichang Zhang, Jing Wang, Ting Fan

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Google Earth Engine-based Mangrove Mapping and Change Detections for Sustainable Development in Tien Yen District, Quang Ninh Province, Vietnam by M. H. Nguyen, N. T. Nguyen, G. Y. I. Ryadi, M. V. Nguyen, T. L. Duong, C.-H. Lin, T. B. Nguyen

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An analytic research and review of the literature on practice of artificial intelligence in healthcare by Salma Mizna, Suraj Arora, Priyanka Saluja, Gotam Das, Waled Abdulmalek Alanesi

“…AI applications in AR/VR can transform medical education by allowing healthcare professionals to practice intricate procedures in a safe environment. …”
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Using artificial intelligence and promoter-level transcriptome analysis to identify a biomarker as a possible prognostic predictor of cardiac complications in male patients with Fa... by Hiroshi Kobayashi, Norio Nakata, Sayoko Izuka, Kenichi Hongo, Masako Nishikawa

“…Cardiac complications, such as cardiomyopathy, cardiac muscle fibrosis, and severe arrhythmia, are the most common mortality causes in patients with Fabry disease. To predict cardiac complications of Fabry disease, we extracted RNA from the venous blood of patients for cap analysis of gene expression (CAGE), performed likelihood ratio tests for each RNA expression dataset obtained from individuals with and without cardiac complications, and analyzed the correlation between cardiac functional factors observed using magnetic resonance imaging data extracted using artificial intelligence algorithms and RNA expression. …”
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Circadian phase resetting via single and multiple control targets. by Neda Bagheri, Jörg Stelling, Francis J Doyle

“…Through sensitivity analysis, we identify additional control targets whose individual and simultaneous manipulation (via a model predictive control algorithm) out-perform the open-loop light-based phase recovery dynamics by nearly 3-fold. …”
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A health management system for large vertical mill by Sugai Han, Ansheng Li, Hongchao Wang, Xiaoyun Gong, Liangwen Wang, Yixiang Huang, Yanming Li, Wenliao Du

“…Especially, a hybrid condition prognosis method based on backtracking search optimization algorithm and neural network is developed, and in comparison with traditional back propagation neural network and ant colony neural network, the developed backtracking search optimization algorithm and neural network gets superior hybrid prediction performance in prediction accuracy and training efficiency. …”
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Hyperspectral imaging for detection of macronutrients retained in glutinous rice under different drying conditions by Kabiru Ayobami Jimoh, Norhashila Hashim, Rosnah Shamsudin, Hasfalina Che Man, Mahirah Jahari, Puteri Nurain Megat Ahmad Azman, Daniel I. Onwude

“…The result shows the raw spectra-based model had a prediction accuracy (Rp2) of 0.6493, 0.9521, 0.4594, and 0.9773 for PC, MC, FC, and AC, respectively. …”
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Promoter Region and Regulatory Elements of IGF and VIP Genes Associated With Reproductive Traits in Chicken by Bosenu Abera, Hunduma Dinka, Hailu Dadi, Habtamu Abera

“…Several in silico tools, such as Neural Network Promoter Prediction (NNPP), Multiple Expectation maximizations for Motif Elicitation (MEME-Suite), GC-Profiles, microsatellite prediction (MISA-web), CLC Genomics, Multiple Association Network Integration Algorithm (GeneMANIA), and Gene Ontology for Motifs (GOMO), were used to characterize the promoter regions and regulatory elements of IGF and VIP genes. …”
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Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks by KANG Feng, TAN Huochao, SU Liwei, JIAN Donglin, WANG Shuai, QIN Hao, ZHANG Yongjun

“…Therefore, this paper proposes a user energy service demand prediction model based on feature selection. The methodology includes introducing a sampling algorithm to solve the class imbalance problem in the data on the basis of analysing the user energy service data, reducing the dimensionality of the data based on an autoencoder to ensure efficient clustering of the K-mean algorithm, constructing a feature selection algorithm based on a lightweight gradient lifting machine to filter the effective features and improve the training efficiency of the prediction model, and establishing a bidirectional long- and short-term memory neural network multi-label predicting model based on an attentional mechanism to refine the user’s energy service demand. …”
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Cerebral gray matter volume identifies healthy older drivers with a critical decline in driving safety performance using actual vehicles on a closed-circuit course by Handityo Aulia Putra, Kaechang Park, Kaechang Park, Fumio Yamashita

“…Feature selection and classification were performed using the Random Forest machine learning algorithm, optimized to identify the most predictive GM regions.ResultsOut of 114 GM regions, eleven were selected as optimal predictors: left angular gyrus, frontal operculum, occipital fusiform gyrus, parietal operculum, postcentral gyrus, planum polare, superior temporal gyrus, and right hippocampus, orbital part of the inferior frontal gyrus, posterior cingulate gyrus, and posterior orbital gyrus. …”
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