Article
Multidisciplinary Sciences
Wen-li Zhang, Zhao-yu Liu, Kun Liang, Yi Wang, Ke-fan Chen, Yao-wei Sun, Sheng Wang
Summary: This paper introduces a novel visual gas sensing technology based on optical absorption gas sensing technology and spatial heterodyne spectroscopy. This technology presents invisible gas information in the form of a two-dimensional visual fingerprint and successfully detects NO2 in a laboratory environment for the first time. Experimental results show that this technology has good response to different spectra and concentrations of NO2, laying a foundation for gas sensing applications.
SCIENTIFIC REPORTS
(2022)
Article
Horticulture
Jianlei Qiao, Guoqiang Su, Chang Liu, Yuanjun Zou, Zhiyong Chang, Hailing Yu, Lianjun Wang, Ruixue Guo
Summary: In this study, the electronic nose technique was used to detect and analyze naturally ripe and artificially ripe crab apples. Significant differences in chemical compositions were found between naturally ripe and artificially ripe fruits. The detection technology with an electronic nose proved to be an efficient and economical method for distinguishing artificially ripe fruits. Prediction models were also developed to accurately predict the quality indexes of fruits based on the electronic nose response data.
Article
Chemistry, Analytical
Juan C. Rodriguez Gamboa, Adenilton J. da Silva, Ismael C. S. Araujo, E. Eva Susana Albarracin, Cristhian M. A. Duran
Summary: Real-time gas classification is a crucial issue for various applications, and recent studies have shown that using SVM and DL models can effectively reduce response time while maintaining reliability. Experiments in this study demonstrated that the rapid detection approach has high potential, achieving reliable estimates with only a fraction of measurement data.
SENSORS AND ACTUATORS B-CHEMICAL
(2021)
Review
Chemistry, Analytical
Alishba T. John, Krishnan Murugappan, David R. Nisbet, Antonio Tricoli
Summary: Electonic noses, relying on an array of chemical gas sensors, are used for identifying various compounds, particularly in the food industry and environmental monitoring. Advances in nanofabrication, sensor design, neural networks, and system integration have improved their efficacy. Commercial and custom Enoses face challenges in wider adoption and use across different applications.
Review
Chemistry, Analytical
Rosalba Calvini, Laura Pigani
Summary: Devices such as electronic noses, electronic tongues, and electronic eyes have been developed for in situ study of real samples without much sample manipulation. These devices can evaluate sensory features and quantitatively detect analytes. Multivariate data analysis strategies can be used to analyze the output of these systems and relate specific signal patterns with the desired information. Data fusion techniques can combine the data from these devices to provide more accurate information about the sample than individual sensing devices.
Article
Biochemistry & Molecular Biology
Ali Khorramifar, Mansour Rasekh, Hamed Karami, James A. Covington, Sayed M. Derakhshani, Jose Ramos, Marek Gancarz
Summary: This study investigated five potato varieties using an electronic nose and chemometric methods, measuring various parameters and establishing regression models. The results indicated specific relationships among different potato varieties, sugar, and carbohydrates. The accuracy in predicting sugar and carbohydrates varied among different potato varieties.
Article
Chemistry, Analytical
Junyu Zhang, Yingying Xue, Qiyong Sun, Tao Zhang, Yuantao Chen, Weijie Yu, Yizhou Xiong, Xinwei Wei, Guitao Yu, Hao Wan, Ping Wang
Summary: The study developed a miniaturized electronic nose for semi-quantitative, simultaneous, and anti-interference detection of CO and CH4, using different models for evaluation, with BP-ANN model showing the best performance.
SENSORS AND ACTUATORS B-CHEMICAL
(2021)
Article
Chemistry, Analytical
Tharatorn Rungreungthanapol, Chishu Homma, Ken-ichi Akagi, Masayoshi Tanaka, Jun Kikuchi, Hideyuki Tomizawa, Yoshiaki Sugizaki, Atsunobu Isobayashi, Yuhei Hayamizu, Mina Okochi
Summary: Researchers designed an olfactory receptor mimetic peptide-modified graphene field-effect transistor (gFET) to address the low specificity challenge of graphene-based sensors for volatile organic compound (VOC) sensing. Peptides mimicking a fruit fly olfactory receptor were designed using a high-throughput analysis method and successfully achieved sensitive and selective detection of limonene. The peptide probe was bifunctionalized and facilitated facile sensor functionalization. This study demonstrates the advancement of a precise VOC detection system using a target-specific peptide selection and functionalization strategy for gFET sensors.
ANALYTICAL CHEMISTRY
(2023)
Article
Chemistry, Analytical
Cheng Qu, Chuanjun Liu, Yun Gu, Shuiqin Chai, Changhao Feng, Bin Chen
Summary: Electronic nose is widely used for detection and classification of gases. This study investigated the possibility of open-set learning models for gas recognition and classification based on a public electronic nose dataset. The results showed that for open-set detection, the CNN-based CAC method outperformed others, while for closed-set recognition, the CNN-based classification model achieved higher accuracy. Sensor drift had a significant negative impact on open-set gas recognition.
SENSORS AND ACTUATORS B-CHEMICAL
(2022)
Article
Chemistry, Analytical
Hongli Ma, Tao Wang, Bolong Li, Weiyang Cao, Min Zeng, Jianhua Yang, Yanjie Su, Nantao Hu, Zhihua Zhou, Zhi Yang
Summary: The novel quantification technique for electronic nose presented in this study, utilizing a double-step strategy combined with hierarchical classifier and partial least squares regression, demonstrates outstanding performance in identifying toxic gases and estimating concentrations. The approach is applicable for E-nose-based odor quantification.
SENSORS AND ACTUATORS B-CHEMICAL
(2022)
Article
Engineering, Electrical & Electronic
Wentian Zhang, Taoping Liu, Amber Brown, Maiken Ueland, Shari L. Forbes, Steven Weidong Su
Summary: The whisky market is prone to fraudulent activities, and it is challenging for most consumers to distinguish fraudulent beverages. A new electronic nose prototype called NOS.E has been developed to identify the differences between whiskies based on their odour. This study demonstrates the high accuracy of the proposed e-nose solution in brand name, region, and style classification, which was further validated using GC x GC-TOFMS.
IEEE SENSORS JOURNAL
(2022)
Article
Green & Sustainable Science & Technology
Korosh Mahmodi, Mostafa Mostafaei, Esmaeil Mirzaee-Ghaleh
Summary: This study used an electronic nose, artificial neural network, and response surface method to analyze various biodiesel and petroleum diesel blended fuels. The results showed that the artificial neural network method achieved a 100% accuracy in classifying and discriminating pure biodiesel fuels, while the response surface method had an accuracy of 92.4%. The artificial neural network method also demonstrated high accuracy in identifying and classifying different blended fuels, with accuracies ranging from 96.5% to 100%.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Article
Chemistry, Multidisciplinary
Garam Bae, Minji Kim, Wooseok Song, Sung Myung, Sun Sook Lee, Ki-Seok An
Summary: This study systematically investigates the impact of selectivity for a target gas on the prediction accuracy of gas concentration using machine learning. The results show a proportional relationship between selectivity factor and prediction accuracy, suggesting that combining sensors with different selectivity factors can enhance the prediction accuracy.
Article
Computer Science, Hardware & Architecture
Dongmei Wang, Yiwen Liang, Hongbin Dong, Chengyu Tan, Zhenhua Xiao, Sai Liu
Summary: The study focuses on the limitations of innate immune algorithms and parameters, introducing the concept of Innate Immune Memory (IIM) for better adaptation. By modifying the Dendritic Cell Algorithm (DCA) based on IIM, the algorithm's accuracy and adaptability have been improved significantly.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Automation & Control Systems
Youbin Yao, Bin Chen, Chuanjun Liu, Cheng Qu
Summary: This study proposes a combined model for gas identification and detection using electronic nose, addressing the issues of sensor drift and intrusion of unknown gases. The model learns a discriminative embedding subspace and utilizes class anchoring to achieve drift compensation and open-set recognition.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Saban Gulcu, Mostafa Mahi, Omer Kaan Baykan, Halife Kodaz
Article
Computer Science, Interdisciplinary Applications
Yilmaz Atay, Murat Aslan, Halife Kodaz
JOURNAL OF COMPUTATIONAL SCIENCE
(2018)
Article
Computer Science, Artificial Intelligence
Mostafa Mahi, Omer Kaan Baykan, Halife Kodaz
APPLIED SOFT COMPUTING
(2018)
Article
Computer Science, Information Systems
Semih Yumusak, Erdogan Dogdu, Halife Kodaz
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
(2018)
Article
Computer Science, Artificial Intelligence
Yunus Emre Goktepe, Halife Kodaz
Article
Green & Sustainable Science & Technology
Mehmet Beskirli, Ismail Koc, Huseyin Hakli, Halife Kodaz
Article
Medicine, Research & Experimental
Selda Kayaalti, Omer Kayaalti, Bekir Hakan Aksebzeci
JOURNAL OF APPLIED BIOMEDICINE
(2020)
Article
Computer Science, Artificial Intelligence
Mostafa Mahi, Omer Kaan Baykan, Halife Kodaz
Summary: This paper presents a simple greedy algorithm to optimize the transmission cost of fragments in distributed databases. Experimental results demonstrate that the proposed method outperforms in terms of execution time and total cost.
Article
Computer Science, Artificial Intelligence
Murat Karakoyun, Ahmet Ozkis, Halife Kodaz
APPLIED SOFT COMPUTING
(2020)
Article
Multidisciplinary Sciences
Mehmet Demirtas, Mehmet Akif Erismis, Salih Gunes
SN APPLIED SCIENCES
(2020)
Article
Computer Science, Artificial Intelligence
Mehmet Dursun, Seral Ozsen, Salih Gunes, Bayram Akdemir, Sebnem Yosunkaya
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
(2019)
Article
Engineering, Multidisciplinary
Mehmet Beskirli, Ismail Koc, Halife Kodaz
TEHNICKI VJESNIK-TECHNICAL GAZETTE
(2019)
Proceedings Paper
Business
Suhair Saud, Halife Kodaz, Ismail Babaoglu
9TH INTERNATIONAL CONFERENCE ON ADVANCES IN INFORMATION TECHNOLOGY (IAIT-2017)
(2018)
Article
Engineering, Electrical & Electronic
Mehmet Demirtas, Mehmet Akif Erismis, Salih Gunes
ELEKTRONIKA IR ELEKTROTECHNIKA
(2020)
Article
Critical Care Medicine
Selda Kayaalti, Omer Kayaalti, Bekir Hakan Aksebzeci
TURKISH JOURNAL OF INTENSIVE CARE-TURK YOGUN BAKIM DERGISI
(2018)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
