Crowd anomaly estimation and detection: A review by A. Hussein, M.W. Raed, A. Al-Shaikhi, M. Mohandes, B. Liu

“…We present a comprehensive literature review on crowd anomaly detection using disruptive technologies such as radio frequency identification, wireless sensor networks, Wi-Fi, and Bluetooth low energy, employing device-free noninvasive algorithms based on received signal strength indicator variations to detect the speed and direction of a moving crowd to predict the onset of a stampede. …”
Get full text

Deep-Learning and Dynamic Time Warping-Based Approaches for the Diagnosis of Reactor Systems by Hoejun Jeong, Jihyun Kim, Doyun Jung, Jangwoo Kwon

“…The experiment results demonstrate that the size and position of clamping force degradation can be accurately predicted. It is expected that this research will contribute to enhancing the precision and efficiency of internal structure monitoring in nuclear power plants.…”
Get full text

Early Detection of Parkinson’s Disease Using AI Techniques and Image Analysis by Marilena Ianculescu, Corina Petean, Virginia Sandulescu, Adriana Alexandru, Ana-Mihaela Vasilevschi

“…Methods: The best approach is selected to be integrated in a neurodegenerative disease management platform called NeuroPredict. The most innovative aspects of the presented approaches are related to the employed feature extraction techniques that convert hand-drawn spirals into a frequency spectra, so that frequency features may be extracted and utilized as inputs for various classification algorithms. …”
Get full text

Optimizing Energy Supply for Full Electric Vehicles in Smart Cities: A Comprehensive Mobility Network Model by Victor Fernandez, Virgilio Pérez, Rosa Roig

“…This study presents a comprehensive mobility network model designed to predict and optimize the energy supply for FEVs within smart cities. …”
Get full text

Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. by Hussein, Alkattan, Alhumaima, Ali Subhi, Oluwaseun, Adelaja A., Abotaleb, Mostafa, Mijwil, Maad M., Pradeep, Mishra, Sekiwu, Denis, Bamwerinde, Wilson, Turyasingura, Benson

“…The study deals with the use of data mining techniques to build a classification model to predict students' academic performance. The research indicates that the use of machine learning models and data mining methods can reveal hidden patterns and relationships in big data, making them indispensable tools in the field of education analysis. …”
Get full text

Hybrid PI and Fuzzy Logic Control for Energy Optimization in Train Operations by Hwan-Hee Cho, Jae-Won Kim, Min-Sup Song, Chi-Myeong Yun, Gyu-Jung Cho, Zhongbei Tian

“…Key features include predictive speed scanning and adaptive PI control, enabling comprehensive energy optimization. …”
Get full text

Modeling Chaotic Behavior of Chittagong Stock Indices by Shipra Banik, Mohammed Anwer, A. F. M. Khodadad Khan

“…Stock market prediction is an important area of financial forecasting, which attracts great interest to stock buyers and sellers, stock investors, policy makers, applied researchers, and many others who are involved in the capital market. …”
Get full text

Optimizing diesel engine heterogeneous combustion performance and NOx emissions: A next energy perspective with AI by Aditya Kolakoti

“…The combustion results are trained in a feed-forward artificial neural network (ANN) algorithm for the predictions, and an error histogram with 20 bins helps identify the accuracy of the trained model. …”
Get full text

Machine Learning and Experimental Validation Reveal MYH11 as a Novel Prognostic Biomarker and Therapeutic Target in Bladder Cancer by Tan Z, Chen X, Fu S, Huang Y, Li H, Gong C, Lv D, Yang C, Wang J, Ding M, Wang H

“…Finally, single-cell analysis identified key cells involved in BCa pathogenesis, and in vitro experiments validated the expression and function of key genes.Results: The risk model constructed by 8 prognostic genes identified using 101 algorithms effectively predicted the survival outcomes of BCa patients. …”
Get full text

Artificial intelligence-assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department: a pragmatic randomized controlled trial by Dung-Jang Tsai, Chin Lin, Wei-Ting Liu, Chiao-Chin Lee, Chiao-Hsiang Chang, Wen-Yu Lin, Yu-Lan Liu, Da-Wei Chang, Ping-Hsuan Hsieh, Chien-Sung Tsai, Yuan-Hao Chen, Yi-Jen Hung, Chin-Sheng Lin

“…Secondary outcomes included echocardiogram utilization rates, positive predictive value for low EF detection, and cardiology consultation rates. …”
Get full text

Pustular reaction in adult-onset immunodeficiency due to anti-interferon-gamma autoantibodies by Narachai Julanon, Pojsakorn Danpanichkul, Charoen Choonhakarn, Suteeraporn Chaowattanapanit, Siriluck Anunnatsiri, Ploenchan Chetchotisakd, Salin Kiratikanon, Rujira Rujiwetpongstorn, Napatra Tovanabutra, Siri Chiewchanvit, Romanee Chaiwarith, Phichayut Phinyo, Phichayut Phinyo, Mati Chuamanochan, Mati Chuamanochan

“…In cases where these hallmark features were absent, a predictive algorithm incorporating the presence of a concomitant infections, serum globulin concentration, and alkaline phosphatase level demonstrated robust utility in estimating the likelihood of AOID-associated pustular reaction.…”
Get full text

An MCV Nonhydrostatic Atmospheric Model with Height-Based Terrain following Coordinate: Tests of Waves over Steep Mountains by Xingliang Li, Xueshun Shen, Feng Xiao, Chungang Chen

Get full text

Machine learning unveils multiple Pauli blockades in the transport spectroscopy of bilayer graphene double-quantum dots by Ankan Mukherjee, Anuranan Das, Adil Anwar Khan, Bhaskaran Muralidharan

“…Through numerical predictions and validations against test data, we identify where and how many Pauli blockades are likely to occur. …”
Get full text

Leveraging Graph Neural Networks for IoT Attack Detection by Mevlüt Uysal, Erdal Özdoğan, Onur Ceran

“…It leverages GNNs to model spatial dependencies and interactions within IoT networks and utilizes XGBoost to distill complex features for predictive analysis. The late fusion technique combines predictions from both models to enhance overall performance. …”
Get full text

Graph neural network-based water contamination detection from community housing information by Raphael Anaadumba, Yigit Bozkurt, Connor Sullivan, Madhavi Pagare, Pradeep Kurup, Benyuan Liu, Mohammad Arif Ul Alam

“…Specifically, the GAT achieved an accuracy of 0.80, precision of 0.71, and recall of 0.93, outperforming XGBoost, a classical machine learning algorithm, which had an accuracy of 0.70, precision of 0.66, and recall of 0.67.Discussion: In addition to its predictive capabilities, the GAT model identifies key factors contributing to lead contamination, enabling more precise targeting of at-risk areas. …”
Get full text

MAD-RAPPEL: Mobility Aware Data Replacement And Prefetching Policy Enrooted LBS by Ajay K. Gupta, Udai Shanker

“…The cache replacement and invalidation module uses the next location prediction algorithm by the application of the mobility Markov model on mobile user’s trajectories. …”
Get full text

Radar Working State Recognition Based on Improved HPSO-BP by Huiqin Li, Yanling Li, Xuemei Wang, Zhe Xu, Xinli Yin

“…First, the model improves the HPSO algorithm through the nonlinear decreasing inertia weight by adding the deceleration factor and asynchronous learning factor. …”
Get full text

Field calibration of fine particulate matter low-cost sensors in a highly industrialized semi-arid conurbation by Mariana Villarreal-Marines, Michael Pérez-Rodríguez, Yasmany Mancilla, Gabriela Ortiz, Alberto Mendoza

“…When using rank-level confusion matrices, True Positive air quality classification of predicted PM2.5 levels by XGBoost rated between 71% and 88%.…”
Get full text

An Innovative Proposal for Developing a Dynamic Urban Growth Model Through Adaptive Vector Cellular Automata by Ahmet Emir Yakup, Ismail Ercument Ayazli

“…For the simulation phase, an adaptive VCA-based urban growth model was developed to predict LULC changes through to 2040. The results demonstrate that the proposed algorithm can achieve a satisfactory level of accuracy in modeling urban growth.…”
Get full text

From word models to executable models of signaling networks using automated assembly by Benjamin M Gyori, John A Bachman, Kartik Subramanian, Jeremy L Muhlich, Lucian Galescu, Peter K Sorger

“…Abstract Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. …”
Get full text