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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

Optimization and implementation of management technology integrated with data analysis for college students' course evaluation and academic early warning by Xinxin Yang

“…The results show that the average accuracy rate of the prediction model is 89.12 %, which is better than other models, and the prediction accuracy rate of the potential early warning student group is >76.1 %. …”
Get full text

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. …”
Get full text

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. …”
Get full text

Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach by Sakhr Alshwayyat, Mahmoud Bashar Abu Al Hawa, Mustafa Alshwayyat, Tala Abdulsalam Alshwayyat, Siya sawan, Ghaith Heilat, Hanan M. Hammouri, Sara Mheid, Batool Al Shweiat, Hamdah Hanifa

“…To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five Machine Learning (ML) algorithms to predict the 5-year survival. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of ML models. …”
Get full text

Analysis of Brand Communication Influence of Professional Sports Clubs Based on Complex System Discrete Model by Hui Zhang

“…In recent years, because the complex system discrete model has brought good results in the application of many research directions such as human management analysis, control system prediction, and animal group prediction, how to combine the complex system discrete model with the prediction of enterprise brand communication effect has become a current research hotspot. …”
Get full text

Rapid and non-destructive monitoring of the drying process of glutinous rice using visible-near infrared hyperspectral imaging by Kabiru Ayobami Jimoh, Norhashila Hashim, Rosnah Shamsudin, Hasfalina Che Man, Mahirah Jahari

“…The best performance accuracy (RP2≥99.99░%)was obtained when the SG1D and Gaussian process regression (GPR) model were combined with iteratively retained informative variable algorithm (SG1D-IRIV-GPR), variable iterative space shrinkage (SG1D-VISSA-GPR) and variable combination population analysis (SG1D-VCPA-GPR) for the prediction of MC, GI, and ΔE, respectively. …”
Get full text

Visible, near-infrared, and shortwave-infrared spectra as an input variable for digital mapping of soil organic carbon by Vahid Khosravi, Asa Gholizadeh, Radka Kodešová, Prince Chapman Agyeman, Mohammadmehdi Saberioon, Luboš Borůvka

“…Thirty rasters were then created using interpolation of the selected spectra and served as the input variables – with and without EPCs – to test and compare the developed models and SOC predictive maps with each other and with those retrieved from the third approach: iii) kriging using OK of the measured and ML-predicted SOC. …”
Get full text

Investigation of aromatic compounds and olfactory profiles in cocoa pulp fermentation using yeast-based starters: A Volatilomics and machine learning approach by Haode Chang, Chunhe Gu, Quanmiao Zhang, Wenjing Zhang, Liru Ma, Fei Liu, Zhen Feng

“…The models showed high prediction accuracy, ranging from 0.85 for sourness by Gradient Boost Machine to 0.28 for sweetness by linear regression. …”
Get full text

Energy efficient and robust node localization in WSNs using LSTM optimized DV hop framework to mitigate multihop localization errors by Amjad Rehman, Tariq Mahmood, Tahani Jaser Alahmadi, Ahmed S. Almasoud, Tanzila Saba

“…The algorithm processes original data through filtering, analysis, and feature extraction to improve predicted node positions. …”
Get full text

Unveiling the pathogenic mechanisms of polyethylene terephthalate-microplastic-driven osteoarthritis and rheumatoid arthritis: PTGS2 signaling hub-oriented toxicity profiling by Jingkai Di, Shuang Wang, Lujia Liu, Keying Rong, Zijian Guo, Yingda Qin, Feida Wang, Chuan Xiang

“…Western blot (WB) and quantitative real-time polymerase chain reaction (qRT-PCR) experiments were conducted to verify the predicted results. The study identified 59 potential PET targets related to OA and 53 targets related to RA. …”
Get full text

Smart photonic wristband for pulse wave monitoring by Renfei Kuang, Zhuo Wang, Lin Ma, Heng Wang, Qingming Chen, Arnaldo Leal Junior, Santosh Kumar, Xiaoli Li, Carlos Marques, Rui Min

“…Several different speckle pattern processing algorithms and POFs with different core diameters were evaluated. …”
Get full text

Integrating Remote Sensing and AI for precision Monitoring of Soil and Vegetation Contamination by M. Spiralski, A. Miszczak, G. Siebielec, Ż. Piasecka, R. Trojnacki, P. Kwaśnik, J. Kotlarz, K. A. Kubiak-Siwinska, M. Kacprzak, S. Marciniak, K. A. Rotchimmel, K. P. Beben, J. Szymanski

“…The future of the study will be focused on the multi-temporal analyses, improving prediction accuracy and dataset and environmental risk mapping. …”
Get full text

PIC2O-Sim: A physics-inspired causality-aware dynamic convolutional neural operator for ultra-fast photonic device time-domain simulation by Pingchuan Ma, Haoyu Yang, Zhengqi Gao, Duane S. Boning, Jiaqi Gu

“…Directly applying off-the-shelf models to predict the optical field dynamics shows unsatisfying fidelity and efficiency since the model primitives are agnostic to the unique physical properties of Maxwell equations and lack algorithmic customization. …”
Get full text

Research on test strategy for randomness based on deep learning by Dongyu CHEN, Hua CHEN, Limin FAN, Yifang FU, Jian WANG

“…In order to achieve better test performance, researches on the randomness test strategies based on deep learning were conducted, including the batch average strategy proposed by EUROCRYPT 2021 and the selection strategy for data unit size.By introducing the randomness statistical test model based on deep learning methods, the statistical distribution and test power expression of two test strategies were theoretically derived, and it was pointed out that: (i) the batch average strategy could amplify the prediction accuracy of the model, but it was prone to an increase in the probability of the second type of error in statistics, instead reducing the statistical test power; (ii) the smaller data units of the deep model generally obtained the more powerful statistical tests.Based on the above understanding, a new bit-level deep learning model was proposed for randomness statistical tests, which gained the advantage of prediction with 80 times fewer parameters and 50% samples, compared with the previous work on linear congruent generator (LCG) algorithm, and achieved significant prediction advantages with 10～20 times fewer parameters by extending the model to apply to 5～7 rounds of Speck, compared with the model proposed by Gohr.…”
Get full text

A Method of Communication Delay Compensation for Urban Transit SystemBased on Long-term and Short-term Memory by HUANG Zihao, LI Hongbo, ZHANG Chao, XU Dongsheng

“…After measuring the communication parameters in a 4G communication test, the communication delay induced error is calculated and compared with the prediction method. The result shows that the prediction algorithm can reduce communication delay induced error by 21.8% and packet loss induced error by 25.8% ~ 26.9%, which can provide more accurate real-time train power information and make real-time improvement for energy flow more feasible.…”
Get full text