Harnessing artificial intelligence for predictive modelling in oral oncology: Opportunities, challenges, and clinical Perspectives by Vishnu Priya Veeraraghavan, Shikhar Daniel, Arun Kumar Dasari, Kaladhar Reddy Aileni, Chaitra patil, Santosh R. Patil

“…Various AI algorithms, including supervised learning, unsupervised learning, and deep learning approaches, have been discussed in the context of oral cancer prediction. …”
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MobNas ensembled model for breast cancer prediction by Tariq Shahzad, Sheikh Muhammad Saqib, Tehseen Mazhar, Muhammad Iqbal, Ahmad Almogren, Yazeed Yasin Ghadi, Mamoon M. Saeed, Habib Hamam

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Application of deep learning algorithm for judicious use of anti-VEGF in diabetic macular edema by Anwesa Mondal, Apurba Nandi, Subhasish Pramanik, Lakshmi Kanta Mondal

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Pavement performance model for road maintenance and repair planning: a review of predictive techniques by Krishna Singh Basnet, Jagat Kumar Shrestha, Rabindra Nath Shrestha

“…This paper provides a review of predictive analytics for roads, identifying gaps and limitations in current methodologies. …”
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Corrosion Risk Assessment in Coastal Environments Using Machine Learning-Based Predictive Models by Marta Terrados-Cristos, Marina Diaz-Piloneta, Francisco Ortega-Fernández, Gemma Marta Martinez-Huerta, José Valeriano Alvarez-Cabal

“…Among the models tested, tree-based algorithms, particularly gradient boosting, provided the highest prediction accuracy (F1 score: 0.8673). …”
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Calibrating Microscopic Traffic Simulation Model Using Connected Vehicle Data and Genetic Algorithm by Abolfazl Afshari, Joyoung Lee, Dejan Besenski, Branislav Dimitrijevic, Lazar Spasovic

“…Results showed significant improvements, including a 14.19% reduction in mean error, an 18.27% reduction in median error, and a 22.57% reduction in the 75th percentile error during calibration. …”
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Prediction of induction motor faults using machine learning by Ademola Abdulkareem, Tochukwu Anyim, Olawale Popoola, John Abubakar, Agbetuyi Ayoade

“…The study involved the acquisition of a dataset comprising healthy and faulty conditions of four 3-phase induction motors, along with relevant features for fault prediction. Multiple machine learning algorithms were trained using this dataset, exhibiting promising performance. …”
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Application of statistical methods for predicting udp-flood attacks by M. V. Tumbinskaya, V. V. Volkov, B. G. Zagidullin

“…Our empirical study was based on the following factors: the lack of effective means of protection against DDoS attacks, the specificity of UDP-flood attacks, and the lack of prediction models that adequately describe the process under study. …”
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Predictive modeling of punchouts in continuously reinforced concrete pavement: a machine learning approach by Ghazi Al-Khateeb, Ali Alnaqbi, Waleed Zeiada

“…In this study, a comprehensive approach that integrates descriptive statistics, correlation analysis, and machine learning algorithms is employed to develop models and predict punchouts in CRCP. …”
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A predictive healthcare model using machine learning and psychological factors for medication adherence by Junwu Dong, Minyi Chu, Yirou Xu

“…This study aims to develop a predictive model for medication adherence behaviour (MAB) employing machine learning techniques, addressing the limitations of traditional correlation-based approaches. …”
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Detecting Student Engagement in an Online Learning Environment Using a Machine Learning Algorithm by Youssra Bellarhmouch, Hajar Majjate, Adil Jeghal, Hamid Tairi, Nadia Benjelloun

“…This study demonstrates that engagement in quizzes and exams predicts engagement in lessons, promoting personalized learning. …”
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Double Filter and Double Wrapper Feature Selection Algorithm for High-Dimensional Data Analysis by Hong Chen, Yuefeng Zheng

“…With the advent of the big data era, we often deal with datasets containing a large number of redundant features, and in this context, dimensionality reduction of data becomes crucial. To address this issue, this study proposes a double filter and double wrapper (DFDW) feature selection algorithm for high-dimensional data. …”
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A robust and statistical analyzed predictive model for drug toxicity using machine learning by Deepak Rawat, Rohit Bajaj, Rachit Manchanda, Ankush Mehta, Prabhu Paramasivam, Suraj Kumar Bhagat, Abinet Gosaye Ayanie

“…Abstract Over the years, toxicity prediction has been a challenging task. Artificial intelligence and machine learning provide a platform to study toxicity prediction more accurately with a reduced time span. …”
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Accounting and Financial Management Cost Accounting Integrating Rough Set Knowledge Recognition Algorithm by Xiaoli Liu

“…According to the discovery model of classification knowledge, the attribute reduction of decision table, classification rule reduction, and classification algorithm under the condition of missing attribute are discussed. …”
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Reducing the Environmental Impact of Sewer Network Overflows Using Model Predictive Control Strategy by I. Vasiliev, L. Luca, M. Barbu, R. Vilanova, S. Caraman

“…The main objective of the paper is to minimize the wastewater quantity and the pollutant loads that overflow from the SN. The proposed algorithm to achieve this goal is Model Predictive Control using Particle Swarm Optimization as optimization method. …”
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Research on optimization of indoor positioning batch ranging system based on DS-TWR algorithm by Hongwei SUN, Xuehong CAO, Liangbao JIAO, Lin MENG, Ziheng LIU, Feng YUAN

“…Aiming at the current problems of the ultra wide band (UWB) indoor positioning system based on the double sided two-way ranging (DS-TWR) algorithm, there are more communication times and a higher conflict rate in a multi-tag environment, an improved algorithm was proposed.The Hash algorithm to hash and distribute the communication content between the tag and the anchor was used, so that the anchor could reply to multiple tags in a regular and unified manner during each ranging process, which greatly reduced the number of times the anchor sends RES (responds) data.Experimental results show that the improved algorithms’ communication times between a tag and the anchor were reduced by 15% compared with the traditional DS-TWR algorithm, which increases the time proportion of the anchor receiving state in ranging, thereby reducing the anchor receiving RNG (range) data.The collision rate of packets, the success rate of ranging has increased by 43.6%.Due to the reduction in the number of communications required in each positioning cycle and the reduction in the collision rate between data packets, a smaller channel capacity would be required, thereby increasing the label capacity of the positioning system, which has strong engineering significance.…”
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Predictive modeling of gestational weight gain: a machine learning multiclass classification study by Audêncio Victor, Hellen Geremias dos Santos, Gabriel Ferreira Santos Silva, Fabiano Barcellos Filho, Alexandre de Fátima Cobre, Liania A. Luzia, Patrícia H.C. Rondó, Alexandre Dias Porto Chiavegatto Filho

“…This study aims to develop and evaluate machine learning models to predict GWG categories: below, within, or above recommended guidelines. …”
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Analyzing Random Forest’s Predictive Capability for Type 1 Diabetes Progression by Niels F. Cleymans, Mark Van De Casteele, Julie Vandewalle, Aster K. Desouter, Frans K. Gorus, Kurt Barbe

“…The second model predicts the transition from multiple AAb+ to the onset of T1D. …”
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A Correlation-Based Feature Selection Algorithm for Operating Data of Nuclear Power Plants by Yuxuan He, Hongxing Yu, Ren Yu, Jian Song, Haibo Lian, Jiangyang He, Jiangtao Yuan

“…The experiments and comparison results show that the proposed algorithm is effective in realizing the dimensionality reduction of nuclear power plant operating data.…”
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Machine learning based risk analysis and predictive modeling of structure fire related casualties by Andres Schmidt, Eric Gemmil, Russ Hoskins

“…The network model achieves a prediction accuracy of 92.5 % for the classification of structural fire-related casualty severities. …”
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