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Journal articles on the topic 'Test-Cost-Sensitive Learning'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Mahdi, Naghibi, Anvari Reza, Forghani Ali, and Minaei Behrouz. "Test-cost-sensitive Convolutional Neural Networks with Expert Branches." Signal & Image Processing: An International Journal (SIPIJ) 10, no. 5 (2019): 15–27. https://doi.org/10.5281/zenodo.3541564.

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It has been proven that deeper convolutional neural networks (CNN) can result in better accuracy in many problems, but this accuracy comes with a high computational cost. Also, input instances have not the same difficulty. As a solution for accuracy vs. computational cost dilemma, we introduce a new test-cost-sensitive method for convolutional neural networks. This method trains a CNN with a set of auxiliary outputs and expert branches in some middle layers of the network. The expert branches decide to use a shallower part of the network or going deeper to the end, based on the difficulty of i

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Mirhashemi, Mohammad, Reza Anvari, Morteza Barari, and Nasser Mozayani. "Test-Cost Sensitive Ensemble of Classifiers Using Reinforcement Learning." Revue d'Intelligence Artificielle 34, no. 2 (2020): 143–50. http://dx.doi.org/10.18280/ria.340204.

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Qiu, Chen, Liangxiao Jiang, and Chaoqun Li. "Randomly selected decision tree for test-cost sensitive learning." Applied Soft Computing 53 (April 2017): 27–33. http://dx.doi.org/10.1016/j.asoc.2016.12.047.

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Zhao, Hong, Fan Min, and William Zhu. "Test-Cost-Sensitive Attribute Reduction of Data with Normal Distribution Measurement Errors." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/946070.

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The measurement error with normal distribution is universal in applications. Generally, smaller measurement error requires better instrument and higher test cost. In decision making, we will select an attribute subset with appropriate measurement error to minimize the total test cost. Recently, error-range-based covering rough set with uniform distribution error was proposed to investigate this issue. However, the measurement errors satisfy normal distribution instead of uniform distribution which is rather simple for most applications. In this paper, we introduce normal distribution measureme

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Wang, Tao, Zhenxing Qin, Zhi Jin, and Shichao Zhang. "Handling over-fitting in test cost-sensitive decision tree learning by feature selection, smoothing and pruning." Journal of Systems and Software 83, no. 7 (2010): 1137–47. http://dx.doi.org/10.1016/j.jss.2010.01.002.

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Febriantono, M. Aldiki, Sholeh Hadi Pramono, and Rahmadwati Rahmadwati. "Perbandingan Metode Cost Sensitive pada Decision Tree dan NaÃ¯ve Bayes untuk Klasifikasi Data Multiclass." Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) 14, no. 1 (2020): 21–26. http://dx.doi.org/10.21776/jeeccis.v14i1.625.

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Abstrakâ€“ Knowledge discovery is the method of extracting information from data in making informed decisions. Seeing as classifiers do have a lot of learning patterns in the data, testing an imbalanced dataset becomes a major classification issue. The cost-sensitive approach on the decision tree C4.5 and nave Bayes is used to solve the rule of misclassification. The glass, lympografi, vehicle, thyroid, and wine datasets were collected from the UCI Repository and included in this analysis. Preprocessing attribute selection with particle swarm optimization was used to process the data collectio

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Ma, Jun, Jiande Wu, and Xiaodong Wang. "Fault Diagnosis Method of Check Valve Based on Multikernel Cost-Sensitive Extreme Learning Machine." Complexity 2017 (2017): 1–19. http://dx.doi.org/10.1155/2017/8395252.

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Check valve is one of the most important components and most easily damaged parts in high pressure diaphragm pump, which is a typical representative of reciprocating machinery. In order to ensure the normal operation of the pump, it is necessary to monitor its running state and diagnose fault. However, in the fault diagnosis of check valve, the classification models with single kernel function can not fully interpret the classification decision function, and meanwhile unreasonable assumption of diagnostic cost equalization has a significant impact on classification results. Therefore, the mult

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Li, Dongdong, Yingchun Yang, and Weihui Dai. "Cost-Sensitive Learning for Emotion Robust Speaker Recognition." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/628516.

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In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability

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Santiso, Sara, Arantza Casillas, and Alicia Pérez. "The class imbalance problem detecting adverse drug reactions in electronic health records." Health Informatics Journal 25, no. 4 (2018): 1768–78. http://dx.doi.org/10.1177/1460458218799470.

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This work focuses on adverse drug reaction extraction tackling the class imbalance problem. Adverse drug reactions are infrequent events in electronic health records, nevertheless, it is compulsory to get them documented. Text mining techniques can help to retrieve this kind of valuable information from text. The class imbalance was tackled using different sampling methods, cost-sensitive learning, ensemble learning and one-class classification and the Random Forest classifier was used. The adverse drug reaction extraction model was inferred from a dataset that comprises real electronic health

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Ji, Min, Lanfa Liu, and Manfred Buchroithner. "Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake." Remote Sensing 10, no. 11 (2018): 1689. http://dx.doi.org/10.3390/rs10111689.

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Earthquake is one of the most devastating natural disasters that threaten human life. It is vital to retrieve the building damage status for planning rescue and reconstruction after an earthquake. In cases when the number of completely collapsed buildings is far less than intact or less-affected buildings (e.g., the 2010 Haiti earthquake), it is difficult for the classifier to learn the minority class samples, due to the imbalance learning problem. In this study, the convolutional neural network (CNN) was utilized to identify collapsed buildings from post-event satellite imagery with the propo

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Ebiaredoh-Mienye, Sarah A., Theo G. Swart, Ebenezer Esenogho, and Ibomoiye Domor Mienye. "A Machine Learning Method with Filter-Based Feature Selection for Improved Prediction of Chronic Kidney Disease." Bioengineering 9, no. 8 (2022): 350. http://dx.doi.org/10.3390/bioengineering9080350.

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The high prevalence of chronic kidney disease (CKD) is a significant public health concern globally. The condition has a high mortality rate, especially in developing countries. CKD often go undetected since there are no obvious early-stage symptoms. Meanwhile, early detection and on-time clinical intervention are necessary to reduce the disease progression. Machine learning (ML) models can provide an efficient and cost-effective computer-aided diagnosis to assist clinicians in achieving early CKD detection. This research proposed an approach to effectively detect CKD by combining the informat

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Zhao, Hong, Fan Min, and William Zhu. "Cost-Sensitive Feature Selection of Numeric Data with Measurement Errors." Journal of Applied Mathematics 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/754698.

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Feature selection is an essential process in data mining applications since it reduces a model’s complexity. However, feature selection with various types of costs is still a new research topic. In this paper, we study the cost-sensitive feature selection problem of numeric data with measurement errors. The major contributions of this paper are fourfold. First, a new data model is built to address test costs and misclassification costs as well as error boundaries. It is distinguished from the existing models mainly on the error boundaries. Second, a covering-based rough set model with normal d

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Li, Der-Chiang, Szu-Chou Chen, Yao-San Lin, and Wen-Yen Hsu. "A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text Data." Symmetry 14, no. 3 (2022): 567. http://dx.doi.org/10.3390/sym14030567.

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The problem of imbalanced data has a heavy impact on the performance of learning models. In the case of an imbalanced text dataset, minority class data are often classified to the majority class, resulting in a loss of minority information and low accuracy. Thus, it is a serious challenge to determine how to tackle the high imbalance ratio distribution of datasets. Here, we propose a novel classification method for learning tasks with imbalanced test data. It aims to construct a method for data preprocessing that researchers can apply to their learning tasks with imbalanced text data and save

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LI, JINGKUAN, FAN MIN, and WILLIAM ZHU. "FAST RANDOMIZED ALGORITHM FOR MINIMAL TEST COST ATTRIBUTE REDUCTION." International Journal of Reliability, Quality and Safety Engineering 21, no. 06 (2014): 1450028. http://dx.doi.org/10.1142/s0218539314500284.

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Attribute reduction is a key data preprocessing technique, and has been widely studied in data mining, machine learning, and granular computing. Minimal test cost attribute reduction is one of important parts researched in cost-sensitive learning. The backtracking algorithm can obtain an optimal reduct, however on only small datasets due to the NP-hardness of the problem. Heuristic algorithms, such as the genetic one and the information gain based one, are employed to deal with this problem. In this paper, we propose the Fast Randomized Algorithm to obtain a satisfactory reduct more efficientl

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Markowitz, Jared, Ryan W. Gardner, Ashley Llorens, Raman Arora, and I.-Jeng Wang. "A Risk-Sensitive Approach to Policy Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 15019–27. http://dx.doi.org/10.1609/aaai.v37i12.26753.

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Standard deep reinforcement learning (DRL) aims to maximize expected reward, considering collected experiences equally in formulating a policy. This differs from human decision-making, where gains and losses are valued differently and outlying outcomes are given increased consideration. It also fails to capitalize on opportunities to improve safety and/or performance through the incorporation of distributional context. Several approaches to distributional DRL have been investigated, with one popular strategy being to evaluate the projected distribution of returns for possible actions. We propo

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Lv, Dongdong, Shuhan Yuan, Meizi Li, and Yang Xiang. "An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy." Mathematical Problems in Engineering 2019 (April 14, 2019): 1–30. http://dx.doi.org/10.1155/2019/7816154.

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According to the forecast of stock price trends, investors trade stocks. In recent years, many researchers focus on adopting machine learning (ML) algorithms to predict stock price trends. However, their studies were carried out on small stock datasets with limited features, short backtesting period, and no consideration of transaction cost. And their experimental results lack statistical significance test. In this paper, on large-scale stock datasets, we synthetically evaluate various ML algorithms and observe the daily trading performance of stocks under transaction cost and no transaction c

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Siebert, Markus, Michael Fister, Christian Spieker, and Daniel Stengler. "Different Approaches to Artificial Intelligence–Based Predictive Maintenance on an Axle Test Bench with Highly Varying Tests." Applied Sciences 15, no. 10 (2025): 5239. https://doi.org/10.3390/app15105239.

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Maintenance measures are widespread in the industrial environment, and various approaches to maintenance using artificial intelligence are increasingly gaining ground. Predictive assessments of system conditions ensure greater reliability and cost reductions through longer service life. The implementation of a machine learning and a deep learning algorithm for predictive maintenance through early damage detection on an electric rear axle test bench is presented in this paper. The algorithms were selected based on extensive literature research. This paper deals with the question of whether the

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Bian, Jing, Xin-guang Peng, Ying Wang, and Hai Zhang. "An Efficient Cost-Sensitive Feature Selection Using Chaos Genetic Algorithm for Class Imbalance Problem." Mathematical Problems in Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/8752181.

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In the era of big data, feature selection is an essential process in machine learning. Although the class imbalance problem has recently attracted a great deal of attention, little effort has been undertaken to develop feature selection techniques. In addition, most applications involving feature selection focus on classification accuracy but not cost, although costs are important. To cope with imbalance problems, we developed a cost-sensitive feature selection algorithm that adds the cost-based evaluation function of a filter feature selection using a chaos genetic algorithm, referred to as C

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Liu, Zhenbing, Chunyang Gao, Huihua Yang, and Qijia He. "A Cost-Sensitive Sparse Representation Based Classification for Class-Imbalance Problem." Scientific Programming 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/8035089.

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Sparse representation has been successfully used in pattern recognition and machine learning. However, most existing sparse representation based classification (SRC) methods are to achieve the highest classification accuracy, assuming the same losses for different misclassifications. This assumption, however, may not hold in many practical applications as different types of misclassification could lead to different losses. In real-world application, much data sets are imbalanced of the class distribution. To address these problems, we propose a cost-sensitive sparse representation based classi

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Baptista, Telmo, Carlos Soares, Tiago Oliveira, and Filipe Soares. "Federated Learning for Computer-Aided Diagnosis of Glaucoma Using Retinal Fundus Images." Applied Sciences 13, no. 21 (2023): 11620. http://dx.doi.org/10.3390/app132111620.

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Deep learning approaches require a large amount of data to be transferred to centralized entities. However, this is often not a feasible option in healthcare, as it raises privacy concerns over sharing sensitive information. Federated Learning (FL) aims to address this issue by allowing machine learning without transferring the data to a centralized entity. FL has shown great potential to ensure privacy in digital healthcare while maintaining performance. Despite this, there is a lack of research on the impact of different types of data heterogeneity on the results. In this study, we research

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Wang, Ke, Qingwen Xue, Yingying Xing, and Chongyi Li. "Improve Aggressive Driver Recognition Using Collision Surrogate Measurement and Imbalanced Class Boosting." International Journal of Environmental Research and Public Health 17, no. 7 (2020): 2375. http://dx.doi.org/10.3390/ijerph17072375.

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Real-time recognition of risky driving behavior and aggressive drivers is a promising research domain, thanks to powerful machine learning algorithms and the big data provided by in-vehicle and roadside sensors. However, since the occurrence of aggressive drivers in real traffic is infrequent, most machine learning algorithms treat each sample equally and prone to better predict normal drivers rather than aggressive drivers, which is our real interest. This paper aims to test the advantage of imbalanced class boosting algorithms in aggressive driver recognition using vehicle trajectory data. F

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22

Xu, Zilong, Hong Zhao, Fan Min, and William Zhu. "Ant Colony Optimization with Three Stages for Independent Test Cost Attribute Reduction." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/510167.

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Minimal test cost attribute reduction is an important problem in cost-sensitive learning. Recently, heuristic algorithms including the information gain-based algorithm and the genetic algorithm have been designed for this problem. However, in many cases these algorithms cannot find the optimal solution. In this paper, we develop an ant colony optimization algorithm to tackle this problem. The attribute set is represented as a graph with each vertex corresponding to an attribute and weight of each edge to pheromone. Our algorithm contains three stages, namely, the addition stage, the deletion s

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XU, XIN, and WEI WANG. "AN INCREMENTAL GRAY RELATIONAL ANALYSIS ALGORITHM FOR MULTI-CLASS CLASSIFICATION AND OUTLIER DETECTION." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 06 (2012): 1250011. http://dx.doi.org/10.1142/s0218001412500115.

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The incremental classifier is superior in saving significant computational cost by incremental learning on continuously increasing training data. However, existing classification algorithms are problematic when applied for incremental learning for multi-class classification. First, some algorithms, such as neural network and SVM, are not inexpensive for incremental learning due to their complex architectures. When applied for multi-class classification, the computational cost would rise dramatically when the class number increases. Second, existing incremental classification algorithms are usu

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Chen, Yizhou, and Heng Dai. "Improving Cross-Project Defect Prediction with Weighted Software Modules via Transfer Learning." Journal of Physics: Conference Series 2025, no. 1 (2021): 012100. http://dx.doi.org/10.1088/1742-6596/2025/1/012100.

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Abstract Cross-project defect prediction is investigated to resolve the trouble that software program defect prediction besides historic data. However, there are differences in distribution of software metrics of different software projects, which decreases the overall performance of cross-project defect prediction. This research presents a transfer learning-based cross-project fault prediction approach. The weights of the training software modules are determined by analogy with gravity and compared to those in the test set. The costs associated with different prediction errors are considered.

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Zhou, Zhaohui, Shijie Shi, Fasong Wang, Yanbin Zhang, and Yitong Li. "Joint Client Selection and CPU Frequency Control in Wireless Federated Learning Networks with Power Constraints." Entropy 25, no. 8 (2023): 1183. http://dx.doi.org/10.3390/e25081183.

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Federated learning (FL) represents a distributed machine learning approach that eliminates the necessity of transmitting privacy-sensitive local training samples. However, within wireless FL networks, resource heterogeneity introduces straggler clients, thereby decelerating the learning process. Additionally, the learning process is further slowed due to the non-independent and identically distributed (non-IID) nature of local training samples. Coupled with resource constraints during the learning process, there arises an imperative need for optimizing client selection and resource allocation

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Horta, Euler Guimarães, Cristiano Leite de Castro, and Antônio Pádua Braga. "Stream-Based Extreme Learning Machine Approach for Big Data Problems." Mathematical Problems in Engineering 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/126452.

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Big Data problems demand data models with abilities to handle time-varying, massive, and high dimensional data. In this context, Active Learning emerges as an attractive technique for the development of high performance models using few data. The importance of Active Learning for Big Data becomes more evident when labeling cost is high and data is presented to the learner via data streams. This paper presents a novel Active Learning method based on Extreme Learning Machines (ELMs) and Hebbian Learning. Linearization of input data by a large size ELM hidden layer turns our method little sensiti

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Wang, Lei, Lei Zhao, Guan Gui, Baoyu Zheng, and Ruochen Huang. "Adaptive Ensemble Method Based on Spatial Characteristics for Classifying Imbalanced Data." Scientific Programming 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/3704525.

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The class imbalance problems often reduce the classification performance of the majority of standard classifiers. Many methods have been developed to solve these problems, such as cost-sensitive learning methods, synthetic minority oversampling technique (SMOTE), and random oversampling (ROS). However, the existing methods still have some problems due to the possible performance loss of useful information and overfitting. To solve the problems, we propose an adaptive ensemble method by using the most advanced feature of self-adaption by considering an average Euclidean distance between test da

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Dalal, Virupaxi Balachandra, and Satish S. Bhairannawar. "Efficient electro encephelogram classification system using support vector machine classifier and adaptive learning technique." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 1 (2022): 291–97. https://doi.org/10.11591/ijeecs.v25.i1.pp291-297.

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Complex modern signal processing is used to automate the analysis of electro encephelogram (EEG) signals. For the diagnosis of seizures, approaches that are simple and precise may be preferable rather than difficult and time-consuming. In this paper, efficient EEG classification system using support vector machine (SVM) and Adaptive learning technique is proposed. The database EEG signals are subjected to temporal and spatial filtering to remove unwanted noise and to increase the detection accuracy of the classifier by selecting the specific bands in which most of the EEG data are present. The

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Liu, Jianguo, Yingzhi Chen, Fuwu Yan, et al. "Vision-based feet detection power liftgate with deep learning on embedded device." Journal of Physics: Conference Series 2302, no. 1 (2022): 012010. http://dx.doi.org/10.1088/1742-6596/2302/1/012010.

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Abstract Kick-Open power liftgates are the standard configuration for most high-end SUVs and sedans in recent years, but the traditional sensor-based trunk opening systems mainly rely on detecting the distance change between foot and vehicle to monitor the users’ operation intention, which is more sensitive to interference like weather change or unintentional body movement. We proposed a novel vision-based feet position detection power liftgate system, which detects the feet’ position through a deep learning model. Its framework is based on Nanodet, and the model is quantified and then convert

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30

Taheri, Seyed Iman, Mohammadreza Davoodi, and Mohd Hasan Ali. "A Simulated-Annealing-Quasi-Oppositional-Teaching-Learning-Based Optimization Algorithm for Distributed Generation Allocation." Computation 11, no. 11 (2023): 214. http://dx.doi.org/10.3390/computation11110214.

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Conventional evolutionary optimization techniques often struggle with finding global optima, getting stuck in local optima instead, and can be sensitive to initial conditions and parameter settings. Efficient Distributed Generation (DG) allocation in distribution systems hinges on streamlined optimization algorithms that handle complex energy operations, support real-time decisions, adapt to dynamics, and improve system performance, considering cost and power quality. This paper proposes the Simulated-Annealing-Quasi-Oppositional-Teaching-Learning-Based Optimization Algorithm to efficiently al

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Goodarzi, Payman, Julian Schauer, and Andreas Schütze. "Robust Distribution-Aware Ensemble Learning for Multi-Sensor Systems." Sensors 25, no. 3 (2025): 831. https://doi.org/10.3390/s25030831.

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Detecting distribution and domain shifts is critical in decision-sensitive applications, such as industrial monitoring systems. This paper introduces a novel, robust multi-sensor ensemble framework that integrates principles of automated machine learning (AutoML) to address the challenges of domain shifts and variability in sensor data. By leveraging diverse model architectures, hyperparameters (HPs), and decision aggregation strategies, the proposed framework enhances adaptability to unnoticed distribution shifts. The method effectively handles tasks with various data properties, such as the

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Dalal, Virupaxi Balachandra, and Satish S. Bhairannawar. "Efficient electro encephelogram classification system using support vector machine classifier and adaptive learning technique." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 1 (2022): 291. http://dx.doi.org/10.11591/ijeecs.v25.i1.pp291-297.

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Complex <span>modern signal processing is used to automate the analysis of electro encephelogram (EEG) signals. For the diagnosis of seizures, approaches that are simple and precise may be preferable rather than difficult and time-consuming. In this paper, efficient EEG classification system using support vector machine (SVM) and Adaptive learning technique is proposed. The database EEG signals are subjected to temporal and spatial filtering to remove unwanted noise and to increase the detection accuracy of the classifier by selecting the specific bands in which most of the EEG data are

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Bdair, Tariq, Nassir Navab, and Shadi Albarqouni. "Semi-Supervised Federated Peer Learning for Skin Lesion Classification." Machine Learning for Biomedical Imaging 1, April 2022 (2022): 1–37. http://dx.doi.org/10.59275/j.melba.2022-8g82.

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Globally, Skin carcinoma is among the most lethal diseases. Millions of people are diagnosed with this cancer every year. Sill, early detection can decrease the medication cost and mortality rate substantially. The recent improvement in automated cancer classification using deep learning methods has reached a human-level performance requiring a large amount of annotated data assembled in one location, yet, finding such conditions usually is not feasible. Recently, federated learning (FL) has been proposed to train decentralized models in a privacy-preserved fashion depending on labeled data at

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Guo, Jiaxu, Juepeng Zheng, Yidan Xu, et al. "LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)." Geoscientific Model Development 17, no. 9 (2024): 3975–92. http://dx.doi.org/10.5194/gmd-17-3975-2024.

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Abstract. The single-column model, with its advantages of low computational cost and fast execution speed, can assist users in gaining a more intuitive understanding of the impact of parameters on the simulated results of climate models. It plays a crucial role in the study of parameterization schemes, allowing for a more direct exploration of the influence of parameters on climate model simulations. In this paper, we employed various methods to conduct sensitivity analyses on the 11 parameters of the Single Column Atmospheric Model (SCAM). We explored their impact on output variables such as

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Li, Yang, Jiayue Chang, and Ying Tian. "Improved cost-sensitive multikernel learning support vector machine algorithm based on particle swarm optimization in pulmonary nodule recognition." Soft Computing 26, no. 7 (2022): 3369–83. http://dx.doi.org/10.1007/s00500-021-06718-w.

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AbstractIn the lung computer-aided detection (Lung CAD) system, the region of interest (ROI) of lung nodules has more false positives, making the imbalance between positive and negative (true positive and false positive) samples more likely to lead to misclassification of true positive nodules, a cost-sensitive multikernel learning support vector machine (CS-MKL-SVM) algorithm is proposed. Different penalty coefficients are assigned to positive and negative samples, so that the model can better learn the features of true positive nodules and improve the classification effect. To further improv

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Zhao, Mingming, Zhiheng You, Huayun Chen, Xiao Wang, Yibin Ying, and Yixian Wang. "Integrated Fruit Ripeness Assessment System Based on an Artificial Olfactory Sensor and Deep Learning." Foods 13, no. 5 (2024): 793. http://dx.doi.org/10.3390/foods13050793.

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Artificial scent screening systems, inspired by the mammalian olfactory system, hold promise for fruit ripeness detection, but their commercialization is limited by low sensitivity or pattern recognition inaccuracy. This study presents a portable fruit ripeness prediction system based on colorimetric sensing combinatorics and deep convolutional neural networks (DCNN) to accurately identify fruit ripeness. Using the gas chromatography–mass spectrometry (GC-MS) method, the study discerned the distinctive gases emitted by mango, peach, and banana across various ripening stages. The colorimetric s

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Wang, Weilun, Goutam Chakraborty, and Basabi Chakraborty. "Predicting the Risk of Chronic Kidney Disease (CKD) Using Machine Learning Algorithm." Applied Sciences 11, no. 1 (2020): 202. http://dx.doi.org/10.3390/app11010202.

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Background: Creatinine is a type of metabolite of blood that is strongly correlated to glomerular filtration rate (GFR). As measuring GFR is difficult, creatinine value is used for indirectly determining GFR and then the stage of chronic kidney disease (CKD). Adding a creatinine test into routine health examination could detect CKD. As more items for comprehensive examination means higher cost, creatinine testing is not included in the routine health examination in many countries. An algorithm based on common test results, without creatinine test, to evaluate the risk of CKD will increase the

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Skemp, Eleanor, Hyun Jin Cho, and Brian Carpenter. "RACIAL COMPARISONS OF RECEPTIVITY TO A BLOOD-BASED BIOMARKER TEST FOR ALZHEIMER RISK." Innovation in Aging 7, Supplement_1 (2023): 1104–5. http://dx.doi.org/10.1093/geroni/igad104.3547.

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Abstract Blood-based biomarker (BBB) tests to assess risk for developing Alzheimer disease (AD) have recently been approved for clinical use. Little is known about people’s interest and concerns about these new tests, although the relative simplicity of the tests may help reduce disparities in early diagnosis for AD in historically marginalized groups, such as African Americans. The current report uses data from a national survey of adults in the United States (N = 1,274) conducted in 2022, oversampling African Americans. In addition to demographic questions, respondents rated the importance o

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Zhu, Yizheng, Yuncheng Wu, Zhaojing Luo, Beng Chin Ooi, and Xiaokui Xiao. "Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs." Proceedings of the VLDB Endowment 17, no. 9 (2024): 2321–34. http://dx.doi.org/10.14778/3665844.3665860.

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Federated Learning (FL) emerges as a viable solution to facilitate data collaboration, enabling multiple clients to collaboratively train a machine learning (ML) model under the supervision of a central server while ensuring the confidentiality of their raw data. However, existing studies have unveiled two main risks: (i) the potential for the server to infer sensitive information from the client's uploaded updates (i.e., model gradients), compromising client input privacy, and (ii) the risk of malicious clients uploading malformed updates to poison the FL model, compromising input integrity.

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Martínez-Florez, Juan F., Juan D. Osorio, Judith C. Cediel, et al. "Short-Term Memory Binding Distinguishing Amnestic Mild Cognitive Impairment from Healthy Aging: A Machine Learning Study." Journal of Alzheimer's Disease 81, no. 2 (2021): 729–42. http://dx.doi.org/10.3233/jad-201447.

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Background: Amnestic mild cognitive impairment (aMCI) is the most common preclinical stage of Alzheimer’s disease (AD). A strategy to reduce the impact of AD is the early aMCI diagnosis and clinical intervention. Neuroimaging, neurobiological, and genetic markers have proved to be sensitive and specific for the early diagnosis of AD. However, the high cost of these procedures is prohibitive in low-income and middle-income countries (LIMCs). The neuropsychological assessments currently aim to identify cognitive markers that could contribute to the early diagnosis of dementia. Objective: Compare

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Wei, Peng. "Alzheimer's disease intelligent detection combining XGBOOST and NARX." Applied and Computational Engineering 49, no. 1 (2024): 1–10. http://dx.doi.org/10.54254/2755-2721/49/20241045.

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Due to the current situation of mental health illness, which causing a huge impact on the society. In this paper, an attempt has been made to analyses and predict the data from ANDI using single and composite algorithms. This paper used chi-square test, Spearmans correlation coefficient and maximum mutual information number, cost-sensitive learning, SMOTE, ADASYN, SMOTE+ENN, SMOTE+TOMEK to investigations. Specifically, this paper adopted the random forest to fill the data, and besides, given the fact that the data shows the characteristics of imbalance, this paper identifies the method of SMOT

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Pavel, Mahir Afser, Rafiul Islam, Shoyeb Bin Babor, Riaz Mehadi, and Riasat Khan. "Non-small cell lung cancer detection through knowledge distillation approach with teaching assistant." PLOS ONE 19, no. 11 (2024): e0306441. http://dx.doi.org/10.1371/journal.pone.0306441.

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Non-small cell lung cancer (NSCLC) exhibits a comparatively slower rate of metastasis in contrast to small cell lung cancer, contributing to approximately 85% of the global patient population. In this work, leveraging CT scan images, we deploy a knowledge distillation technique within teaching assistant (TA) and student frameworks for NSCLC classification. We employed various deep learning models, CNN, VGG19, ResNet152v2, Swin, CCT, and ViT, and assigned roles as teacher, teaching assistant and student. Evaluation underscores exceptional model performance in performance metrics achieved via co

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Yang, Xin, Shichen Gao, Qian Sun, et al. "Classification of Maize Lodging Extents Using Deep Learning Algorithms by UAV-Based RGB and Multispectral Images." Agriculture 12, no. 7 (2022): 970. http://dx.doi.org/10.3390/agriculture12070970.

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Lodging depresses the grain yield and quality of maize crop. Previous machine learning methods are used to classify crop lodging extents through visual interpretation and sensitive features extraction manually, which are cost-intensive, subjective and inefficient. The analysis on the accuracy of subdivision categories is insufficient for multi-grade crop lodging. In this study, a classification method of maize lodging extents was proposed based on deep learning algorithms and unmanned aerial vehicle (UAV) RGB and multispectral images. The characteristic variation of three lodging extents in RG

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Elena-Adriana, Mînăstireanu, and Meșniță Gabriela. "Methods of Handling Unbalanced Datasets in Credit Card Fraud Detection." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 11, no. 1 (2020): 131–43. https://doi.org/10.18662/brain/11.1/19.

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Nowadays fraudulent transactions of every type represent a major concern in the financial industry due to the total amount of money that are lost every year. Manually analyzing fraudulent transactions is unfeasible if we think at the huge amount of data and the complexity of bank fraud in the digitization era. In this context, the problem to detect the fraud can be achieved by machine-learning algorithms due to their ability of detecting small anomalies in very large datasets. The problem that arise here is that the datasets are highly unbalanced meaning that the non-fraudulent cases heavily d

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Mahmoud, Nesma Talaat Abbas, Indrek Virro, A. G. M. Zaman, et al. "Robust Object Detection Under Smooth Perturbations in Precision Agriculture." AgriEngineering 6, no. 4 (2024): 4570–84. https://doi.org/10.3390/agriengineering6040261.

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Machine learning algorithms are increasingly used to enhance agricultural productivity cost-effectively. A critical task in precision agriculture is locating a plant’s root collar. This is required for the site-specific fertilization of the plants. Though state-of-the-art machine learning models achieve stellar performance in object detection, they are often sensitive to noisy inputs and variation in environment settings. In this paper, we propose an innovative technique of smooth perturbations to improve the robustness of root collar detection tasks using the YOLOv5 neural network model. We t

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Kruse, Jakob Adrian, Leon Ciechanowski, Ambre Dupuis, Ignacio Vazquez, and Peter A. Gloor. "Leveraging the Sensitivity of Plants with Deep Learning to Recognize Human Emotions." Sensors 24, no. 6 (2024): 1917. http://dx.doi.org/10.3390/s24061917.

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Recent advances in artificial intelligence combined with behavioral sciences have led to the development of cutting-edge tools for recognizing human emotions based on text, video, audio, and physiological data. However, these data sources are expensive, intrusive, and regulated, unlike plants, which have been shown to be sensitive to human steps and sounds. A methodology to use plants as human emotion detectors is proposed. Electrical signals from plants were tracked and labeled based on video data. The labeled data were then used for classification., and the MLP, biLSTM, MFCC-CNN, MFCC-ResNet

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Baker, Matthew, James Munro Cameron, Alexandra Sala, et al. "Multicancer early detection with a spectroscopic liquid biopsy platform." Journal of Clinical Oncology 40, no. 16_suppl (2022): 3034. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.3034.

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3034 Background: A rapid, low-cost, sensitive, multi-cancer early detection (MCED) test would be transformational in the diagnostics field. Earlier cancer detection and instigation of treatment can increase survival rates. An effective test must accurately identify the small proportion of patients with typically non-specific symptoms who actually have cancer. Such symptoms don’t easily segregate by organ system, necessitating a multi-cancer approach. Methods: In this large-scale study ( n = 2094 patients) we applied the Dxcover Cancer Liquid Biopsy to differentiate cancer against non-cancer, a

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Baker, Matthew, James Munro Cameron, Alexandra Sala, et al. "Multicancer early detection with a spectroscopic liquid biopsy platform." Journal of Clinical Oncology 40, no. 16_suppl (2022): 3034. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.3034.

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3034 Background: A rapid, low-cost, sensitive, multi-cancer early detection (MCED) test would be transformational in the diagnostics field. Earlier cancer detection and instigation of treatment can increase survival rates. An effective test must accurately identify the small proportion of patients with typically non-specific symptoms who actually have cancer. Such symptoms don’t easily segregate by organ system, necessitating a multi-cancer approach. Methods: In this large-scale study ( n = 2094 patients) we applied the Dxcover Cancer Liquid Biopsy to differentiate cancer against non-cancer, a

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Prasad Battula, Krishna, and B. Sai Chandana. "Multi-class Cervical Cancer Classification using Transfer Learning-based Optimized SE-ResNet152 model in Pap Smear Whole Slide Images." International journal of electrical and computer engineering systems 14, no. 6 (2023): 623. http://dx.doi.org/10.32985/ijeces.14.6.1.

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Among the main factors contributing to death globally is cervical cancer, regardless of whether it can be avoided and treated if the afflicted tissues are removed early. Cervical screening programs must be made accessible to everyone and effectively, which is a difficult task that necessitates, among other things, identifying the population's most vulnerable members. Therefore, we present an effective deep-learning method for classifying the multi-class cervical cancer disease using Pap smear images in this research. The transfer learning-based optimized SE-ResNet152 model is used for effectiv

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Mousavi, Ali, Raziyeh Pourdarbani, Sajad Sabzi, et al. "Classification of Healthy and Frozen Pomegranates Using Hyperspectral Imaging and Deep Learning." Horticulturae 10, no. 1 (2024): 43. http://dx.doi.org/10.3390/horticulturae10010043.

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Pomegranate is a temperature-sensitive fruit during postharvest storage. If exposed to cold temperatures above its freezing point for a long time, it will suffer from cold stress. Failure to pay attention to the symptoms that may occur during storage will result in significant damage. Identifying pomegranates susceptible to cold damage in a timely manner requires considerable skill, time and cost. Therefore, non-destructive and real-time methods offer great benefits for commercial producers. To this end, the purpose of this study is the non-destructive identification of healthy frozen pomegran

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