Enhanced Brain Tumor Segmentation Using CBAM-Integrated Deep Learning and Area Quantification by Rafiqul Islam, Sazzad Hossain

“…The qualitative metrics typically analyze the overlap between predicted tumor masks and ground truth annotations, providing information on the segmentation algorithms’ accuracy and dependability. …”
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Metro Timetabling for Time-Varying Passenger Demand and Congestion at Stations by Keping Li, Hangfei Huang, Paul Schonfeld

“…Branch-and-bound and frequency determining algorithms are designed to solve OST. A novel rolling optimization algorithm is designed to solve OH. …”
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A Multi-Model Machine Learning Framework for Identifying Raloxifene as a Novel RNA Polymerase Inhibitor from FDA-Approved Drugs by Nhung Thi Hong Van, Minh Tuan Nguyen

“…We developed a multi-model machine learning framework combining five traditional algorithms (ExtraTreesClassifier, RandomForestClassifier, LGBMClassifier, BernoulliNB, and BaggingClassifier) with a CNN deep learning model to identify potential RdRP inhibitors among FDA-approved drugs. …”
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TSCAPE: time series clustering with curve analysis and projection on an Euclidean space by Jeremy Renaud, Raphaël Couturier, Christophe Guyeux, Benoit Courjal

“…The results demonstrate the method's superiority over “only distance comparison clustering techniques”, like dynamic time warping and K-Means, with future prospects aimed at predictive applications and refining the clustering process by exploring alternative, more powerful clustering algorithms.…”
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Impact of Advanced Agriculture Technologies and Energy Consumption on Crop Yields in Modern Agriculture Using Deep Learning Techniques by Khan Baz, Zhu Zhen, Hashmat Ali

“…First, advanced machine learning algorithms are employed to investigate the combined impact of the farming Product Complexity Index on agricultural yields. …”
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The Future of Minimally Invasive GI and Capsule Diagnostics (REFLECT), October 2024 by Lea Østergaard Hansen, Alexandra Agache, Anastasios Koulaouzidis

“…The symposium also highlighted the significance of predictive models for patient selection and developments in panenteric CE. …”
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Sentiment Analysis of User Reviews of the AdaKami Online Loan App from the App Store Using SVM and Naive Bayes by Wava Lativa Azzahra, Jamaludin Indra, Rahmat Rahmat, Sutan Faisal

“…The classification model was built using two machine learning algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB). …”
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H-DSAE: a hybrid technique to recognize heart disease by K. Uma Maheswari, A. Valarmathi

“…The next phase of the project will focus on developing more advanced classification and features algorithms. This will help improve the efficiency of the system.…”
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Generation of realistic input parameters for simulating atmospheric point-spread functions at astronomical observatories by Claire-Alice Hébert, Joshua E. Meyers, My H. Do, Patricia R. Burchat, LSST Dark Energy Science Collaboration

“…We make quantitative predictions for two-point correlation functions (2PCF) that are used in analyses of cosmic shear. …”
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A deep hybrid learning pipeline for accurate diagnosis of ovarian cancer based on nuclear morphology. by Duhita Sengupta, Sk Nishan Ali, Aditya Bhattacharya, Joy Mustafi, Asima Mukhopadhyay, Kaushik Sengupta

“…This novel Deep Hybrid Learning model, though derived from classical machine learning algorithms and standard CNN, showed a training and validation AUC score of 0.99 whereas the test AUC score turned out to be 1.00. …”
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Innovative data techniques for centrifugal pump optimization with machine learning and AI model. by Gaurav Sandeep Dave, Amar Pradeep Pandhare, Atul Prabhakar Kulkarni, Dhananjay Vasant Khankal

“…Additionally, it decreases model training time by 180 seconds, facilitating predictive maintenance of critical performance metrics and minimizing downtime. …”
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Artificial intelligence-augmented smart grid architecture for cyber intrusion detection and mitigation in electric vehicle charging infrastructure by Ankita Sharma, Shalli Rani, Mohammad Shabaz

“…It incorporates AI-driven security models and anomaly detection algorithms to enhance grid resilience and optimize energy utilization. …”
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Application of “high-fidelity and high-resolution” processing technology in thin reservoir identification: A case study of the deep Permian Maokou Formation in Penglai area, centra... by PENG Haotian, HE Qinglin, CHEN Kang, ZENG Ming, HE Zongqiang, TU Zhihui, KONG Lingxia, QU Bozhang

“…The method used here employs a robust compressed sensing spectral inversion algorithm. It determines initial reflection coefficients using a thin-layer matching pursuit algorithm and then performs post-stack sparse inversion based on the L0-norm compressed sensing theory. …”
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Issues of diagnostics and treatment of abdominal pain at irritable bowel syndrome at outpatient stage of the healthcare by Yu. A. Kucheryavy, T. L. Lapina, M. A. Livzan, M. F. Osipenko

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A Hybrid Automata-Driven Machine Learning Framework for Real-Time Energy Optimization in Smart Buildings by Rikame Rajashri, Ranjan Mritunjay Kr., Jadhav Monali, Wankhede Shrushti, Bankar Sakshi, Bachhav Pranjal

“…These inputs train the predictive models like Random Forest, XGBoost, LightGBM, and Neural Networks to predict energy requirements. …”
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Research on Iterative Learning Semi-active Control for Curve Passing of High-speed Train by Yongjun AI, Chunjun CHEN, Xin LI, Jianrong ZHOU

“…According to the repeatability of train running and the big data, the PD iterative learning control algorithm was designed, and the semi-active control simulation which combined control algorithm with dynamic software was carried out to compare with the results of the passive working conditions. …”
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Adaptive pilot design for OFDM based on deep reinforcement learning by Qiaoshou LIU, Xiong ZHOU, Shuang LIU, Yifeng DENG

“…For orthogonal frequency division multiplexing (OFDM) systems, an adaptive pilot design algorithm based on deep reinforcement learning was proposed.The pilot design problem was formulated as a Markov decision process, where the index of pilot positions was defined as actions.A reward function based on mean squared error (MSE) reduction strategy was formulated, and deep reinforcement learning was employed to update the pilot positions.The pilot was adaptively and dynamically allocated based on channel conditions, thereby utilizing channel characteristics to combat channel fading.The simulation results show that the proposed algorithm has significantly improved channel estimation performance compared with the traditional pilot uniform allocation scheme under three typical multipath channels of 3GPP.…”
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Coordinated beamforming for cognitive multi-cell networks with wiretap channels by Dong-hua CHEN, Xiu-xiu ZHANG, Wei-bo XIE

“…Owing to the coupling among the vectors to be optimized，the problem becomes non-convex and non-linear.Accordingly，a centralized coordinated beamforming design method was proposed by constraining the leakage interference and using the semi-definite relaxation，and on the base of it，a decentralized algorithm was also proposed by decomposing the centralized design problem into a group of sub-problems on each cell’s base by using dual decomposition.Simulation results show that the proposed schemes make effective reduction in power consumption while guarantee the legal users’ secrecy rate requirement and the cognitive interference constraint.Moreover，the proposed decentralized method not only reduces the computational complexity，but also has the same performance as the centralized algorithm.…”
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Privacy-preserved data hiding using compressive sensing and fuzzy C-means clustering by Ming Li, Lanlan Wang, Haiju Fan

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Cultivation Method Analysis for Teachers’ Teaching Ability Driven by Artificial Intelligence Technology by Yanfang Chen, Shasha Xu

“…Aiming at the issues in BP network, this work constructs IPSO by improving the weight decay strategy and learning factor of PSO algorithm. Then, it uses IPSO to optimize the BP to construct IPSO-BP. …”
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