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Articles de revues sur le sujet « Classification: Advanced Methods »

Auteur : Grafiati

Publié le 27 juillet 2024

Mis à jour le 31 juillet 2025

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A, Shruti. "Comparative Study of Advanced Classification Methods." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 3 (2015): 1216–20. http://dx.doi.org/10.17762/ijritcc2321-8169.150371.

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Gola, Jessica, Dominik Britz, Thorsten Staudt, et al. "Advanced microstructure classification by data mining methods." Computational Materials Science 148 (June 2018): 324–35. http://dx.doi.org/10.1016/j.commatsci.2018.03.004.

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Wei, Chien-Hung, Cheng-Chih Chang, and Sheng-Shih Wang. "Vehicle Classification Using Advanced Technologies." Transportation Research Record: Journal of the Transportation Research Board 1551, no. 1 (1996): 45–50. http://dx.doi.org/10.1177/0361198196155100106.

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Applying advanced technologies to existing problem domains is a highly desirable approach in many research areas. Among these techniques, image processing has been shown useful in transportation fields for such tasks as traffic pattern recognition, data collection, accident detection, and pavement evaluation. The integrated model with artificial neural networks (ANNs) has promising potential applications. The image processing and ANN model are combined to explore the feasibility of vehicle classification in real-world situations. Three methods were developed during the research process: ground

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Katona, Tamás, Gábor Tóth, Mátyás Petró, and Balázs Harangi. "Advanced Multi-Label Image Classification Techniques Using Ensemble Methods." Machine Learning and Knowledge Extraction 6, no. 2 (2024): 1281–97. http://dx.doi.org/10.3390/make6020060.

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Chest X-rays are vital in healthcare for diagnosing various conditions due to their low Radiation exposure, widespread availability, and rapid interpretation. However, their interpretation requires specialized expertise, which can limit scalability and delay diagnoses. This study addresses the multi-label classification challenge of chest X-ray images using the Chest X-ray14 dataset. We propose a novel online ensemble technique that differs from previous penalty-based methods by focusing on combining individual model losses with the overall ensemble loss. This approach enhances interaction and

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Jonáková, Lenka, and Ivan Nagy. "Power purchase strategy of retail customers utilizing advanced classification methods." Neural Network World 31, no. 2 (2021): 89–107. http://dx.doi.org/10.14311/nnw.2021.31.005.

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Powell, Jade, Daniele Trifirò, Elena Cuoco, Ik Siong Heng, and Marco Cavaglià. "Classification methods for noise transients in advanced gravitational-wave detectors." Classical and Quantum Gravity 32, no. 21 (2015): 215012. http://dx.doi.org/10.1088/0264-9381/32/21/215012.

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Guizani, Douraied, Erika Buday-Bódi, János Tamás, and Attila Nagy. "An advanced classification method for urban land cover classification." Acta Agraria Debreceniensis, no. 1 (June 3, 2024): 51–57. http://dx.doi.org/10.34101/actaagrar/1/13652.

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This manuscript presents a detailed comparative analysis of three advanced classification techniques that were used between 2018 and 2020 to classify land cover using Landsat8 imagery, namely Support Vector Machine (SVM), Maximum Likelihood Classification (MLSC), and Random Forests (RF). The study focuses on evaluating the accuracy of these methods by comparing the classified maps with a higher-resolution ground truth map, utilising 500 randomly selected points for assessment. The obtained results show that, compared to MLSC and RT, the Support Vector Machine (SVM) approach performs better. Th

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Taherian, Hessam, and Robert W. Peters. "Advanced Active and Passive Methods in Residential Energy Efficiency." Energies 16, no. 9 (2023): 3905. http://dx.doi.org/10.3390/en16093905.

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Energy efficiency in buildings is very important since it contributes significantly to fossil fuel consumption and consequently climate change. Several approaches have been taken by researchers and the industry to address the issue. These approaches are classified as either passive or active approaches. The purpose of this review article is to summarize a number of the technologies that have been investigated and/or developed. In this technical review paper, the more commonly used active and passive building energy conservation techniques are described and discussed. The pros and cons of both

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Chelak, Viktor, Oleksii Hornostal, Yehor Chelak, and Svitlana Gavrylenko. "ADVANCED METHODS FOR CLASSIFICATION QUALITY ASSESSMENT LEVERAGING ROC ANALYSIS AND MULTIDIMENSIONAL CONFUSION MATRIX." Advanced Information Systems 9, no. 1 (2025): 24–34. https://doi.org/10.20998/2522-9052.2025.1.03.

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The object of the study is the process of classifying objects in scientific problems. The subject of the study is methods aimed at assessing the effectiveness of multiclass classification. The goal of the study is to study the classification process and develop a classifier evaluation module to increase the speed of such evaluation and reduce the time to build complex machine learning classifiers. Methods used: methods for evaluating machine learning classifiers, methods for constructing ROC curves, principles of parallel and distributed computing. Results obtained: an analytical review of the

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Lokman, Amar, Wan Zakiah Wan Ismail, and Nor Azlina Ab Aziz. "Fuzzy Frameworks: Advanced Models for Water Quality Classification." Semarak Proceedings of Applied Sciences and Engineering Technology 1, no. 1 (2025): 158–64. https://doi.org/10.37934/spaset.1.1.158164a.

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Monitoring water quality is essential for safeguarding the health and safety of aquatic ecosystems and public water sources. Conventional water quality classification techniques frequently encounter challenges due to the intrinsic uncertainty and imprecision present in environmental data. This study presents an innovative fuzzy logic framework, enhanced by a Genetic Algorithm (GA), to address these complexities. The GA optimizes membership functions within the fuzzy framework, refining the classification process and improving adaptability. The proposed method integrates fuzzy set theory with m

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G. Syam Kumar. "Sports Videos Classification using Advanced Deep Neural Networks." International Transactions on Electrical Engineering and Computer Science 3, no. 2 (2024): 92–100. http://dx.doi.org/10.62760/iteecs.3.2.2024.92.

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The field of digital content is experiencing a meteoric rise in popularity as a direct result of the rapid development of information technology. When it comes to the archiving of digital content on the assistant, the segregation in sports videos is of an extremely important part. Consequently, the utilization of deep-neural-network algorithm (DNN), convolutional-neural-network (CNN), and deliver learning allows for the correct segregation of sports video classification to be achieved. There are two methods that have been proposed: block-brightness-comparison-coding (BICC) cum block colour his

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Kabakchieva, Dorina. "Predicting Student Performance by Using Data Mining Methods for Classification." Cybernetics and Information Technologies 13, no. 1 (2013): 61–72. http://dx.doi.org/10.2478/cait-2013-0006.

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Abstract Data mining methods are often implemented at advanced universities today for analyzing available data and extracting information and knowledge to support decision-making. This paper presents the initial results from a data mining research project implemented at a Bulgarian university, aimed at revealing the high potential of data mining applications for university management.

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Nurul Rismayanti and Aulia Putri Utami. "Improving Multi-Class Classification on 5-Celebrity-Faces Dataset using Ensemble Classification Methods." Indonesian Journal of Data and Science 4, no. 2 (2023): 124–33. http://dx.doi.org/10.56705/ijodas.v4i2.78.

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This study aims to compare the performance between Random Forest Classifier and Gaussian Naïve Bayes Classifier in classification. Several evaluation metrics such as accuracy, precision, recall, and F1-score were used to analyze the performance of both models. The dataset used has specific characteristics that influence the evaluation results. The research findings indicate that Random Forest Classifier outperforms Gaussian Naïve Bayes Classifier in most of the evaluation metrics. Random Forest Classifier achieves higher accuracy and better precision, recall, and weighted F1-score. However, it

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Seto, Shinta, Takuji Kubota, Nobuhiro Takahashi, Toshio Iguchi, and Taikan Oki. "Advanced Rain/No-Rain Classification Methods for Microwave Radiometer Observations over Land." Journal of Applied Meteorology and Climatology 47, no. 11 (2008): 3016–29. http://dx.doi.org/10.1175/2008jamc1895.1.

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Abstract Seto et al. developed rain/no-rain classification (RNC) methods over land for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). In this study, the methods are modified for application to other microwave radiometers. The previous methods match TMI observations with TRMM precipitation radar (PR) observations, classify the TMI pixels into rain pixels and no-rain pixels, and then statistically summarize the observed brightness temperature at the no-rain pixels into a land surface brightness temperature database. In the modified methods, the probability distribution of

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Panaite, Doru-Răzvan, Vlad Barbu, Radu-Andrei Rosu, George Stoica, Şerban-Gabriel Doncean, and Mihaela Elena Breabăn. "Advanced methods for dealing with high data imbalance for Embryo Fertility Classification." Procedia Computer Science 246 (2024): 82–90. http://dx.doi.org/10.1016/j.procs.2024.09.230.

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Mellinger, David K., Yang Lu, Curtis Lending, et al. "Advanced methods for passive acoustic detection, classification, and localization of marine mammals." Journal of the Acoustical Society of America 141, no. 5 (2017): 3604. http://dx.doi.org/10.1121/1.4987711.

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Yaqoob, Abrar, Mushtaq Ahmad Mir, G. V. V. Jagannadha Rao, and Ghanshyam G. Tejani. "Transforming Cancer Classification: The Role of Advanced Gene Selection." Diagnostics 14, no. 23 (2024): 2632. http://dx.doi.org/10.3390/diagnostics14232632.

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Background/Objectives: Accurate classification in cancer research is vital for devising effective treatment strategies. Precise cancer classification depends significantly on selecting the most informative genes from high-dimensional datasets, a task made complex by the extensive data involved. This study introduces the Two-stage MI-PSA Gene Selection algorithm, a novel approach designed to enhance cancer classification accuracy through robust gene selection methods. Methods: The proposed method integrates Mutual Information (MI) and Particle Swarm Optimization (PSO) for gene selection. In the

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Powell, Jade, Alejandro Torres-Forné, Ryan Lynch, et al. "Classification methods for noise transients in advanced gravitational-wave detectors II: performance tests on Advanced LIGO data." Classical and Quantum Gravity 34, no. 3 (2017): 034002. http://dx.doi.org/10.1088/1361-6382/34/3/034002.

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Lowery, Robert B. W., and Jason H. Calhoun. "Fractures of the Calcaneus Part I: Anatomy, Injury Mechanism, and Classification." Foot & Ankle International 17, no. 4 (1996): 230–35. http://dx.doi.org/10.1177/107110079601700409.

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Calcaneal fractures have been treated by closed methods since the time of Hippocrates. The understanding of the anatomy, injury mechanism, and classification of these fractures has advanced since surgical treatment was introduced in 1850. Despite 145 years of different treatment techniques, no consensus has been reached. Investigation into the injury patterns, anatomy, and outcomes has lead to the advances reviewed in this article.

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Saleki, Meisam, Kamran Mostafaei, Zakaria Bin Endut, and Mohammad Nabi Kianpour. "ADVANCED CLUSTERING TECHNIQUES FOR TIN DEPOSIT CLASSIFICATION IN MALAYSIA: A MACHINE LEARNING APPROACH." Rudarsko-geološko-naftni zbornik 40, no. 3 (2025): 131–45. https://doi.org/10.17794/rgn.2025.3.10.

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This study explores the application of advanced clustering techniques—Spectral Clustering, Gaussian Mixture Models (GMM), and a hybrid approach combining Autoencoders with K-Means—to classify tin deposits in Malaysia. Geochemical data from 28 tin samples across regions such as Pengkalan Hulu North, Menglembu, Klian Intan, and Sungai Lembing were analysed to identify distinct mineralization patterns. The results revealed that the integration of Autoencoders with K-Means yielded the highest clustering quality, with a Silhouette Score above 0.4 and a Calinski-Harabasz Index of 90 at four clusters

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Faizrakhmanov, R. R., K. I. Konovalova, and A. I. Egorova. "Macular hole: classification approaches." POINT OF VIEW. EAST – WEST 12, no. 2 (2025): 54–57. https://doi.org/10.25276/2410-1257-2025-2-54-57.

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Abstract Objective. To analyze existing classifications of macular holes, identify their advantages and limitations, and justify the need for developing a universal system to optimize diagnosis, prognosis, and treatment. Material and methods. A systematic analysis of scientific publications by domestic and international authors was conducted using the PubMed, Medline, and eLIBRARY.RU databases from 1990 to 2024, focusing on macular hole classifications. Results. An analysis of existing classifications of macular holes, including their advantages and limitations, is presented. The main classifi

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Pralea, Ioana-Ecaterina, Radu-Cristian Moldovan, Alina-Maria Petrache, et al. "From Extraction to Advanced Analytical Methods: The Challenges of Melanin Analysis." International Journal of Molecular Sciences 20, no. 16 (2019): 3943. http://dx.doi.org/10.3390/ijms20163943.

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The generic term “melanin“ describes a black pigment of biological origin, although some melanins can be brown or even yellow. The pigment is characterized as a heterogenic polymer of phenolic or indolic nature, and the classification of eu-, pheo- and allo- melanin is broadly accepted. This classification is based on the chemical composition of the monomer subunit structure of the pigment. Due to the high heterogeneity of melanins, their analytical characterization can be a challenging task. In the present work, we synthesized the current information about the analytical methods which can be

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Hloušková, Zuzana, and Marie Prášilová. "Classification of Specialized Farms Applying Multivariate Statistical Methods." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 65, no. 3 (2017): 1007–14. http://dx.doi.org/10.11118/actaun201765031007.

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Classification of specialized farms applying multivariate statistical methods The paper is aimed at application of advanced multivariate statistical methods when classifying cattle breeding farming enterprises by their economic size. Advantage of the model is its ability to use a few selected indicators compared to the complex methodology of current classification model that requires knowledge of detailed structure of the herd turnover and structure of cultivated crops. Output of the paper is intended to be applied within farm structure research focused on future development of Czech agricultu

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Zyout, Ala’a, Hiam Alquran, Wan Azani Mustafa, and Ali Mohammad Alqudah. "Advanced Time-Frequency Methods for ECG Waves Recognition." Diagnostics 13, no. 2 (2023): 308. http://dx.doi.org/10.3390/diagnostics13020308.

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ECG wave recognition is one of the new topics where only one of the ECG beat waves (P-QRS-T) was used to detect heart diseases. Normal, tachycardia, and bradycardia heart rhythm are hard to detect using either time-domain or frequency-domain features solely, and a time-frequency analysis is required to extract representative features. This paper studies the performance of two different spectrum representations, iris-spectrogram and scalogram, for different ECG beat waves in terms of recognition of normal, tachycardia, and bradycardia classes. These two different spectra are then sent to two di

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Stollhoff, R., W. Sauerbrei, and M. Schumacher. "An Experimental Evaluation of Boosting Methods for Classification." Methods of Information in Medicine 49, no. 03 (2010): 219–29. http://dx.doi.org/10.3414/me0543.

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Summary Objectives: In clinical medicine, the accuracy achieved by classification rules is often not sufficient to justify their use in daily practice. In order to improve classifiers it has become popular to combine single classification rules into a classification ensemble. Two popular boosting methods will be compared with classical statistical approaches. Methods: Using data from a clinical study on the diagnosis of breast tumors and by simulation we will compare AdaBoost with gradient boosting ensembles of regression trees. We will also consider a tree approach and logistic regression as

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Kors, J. A., and J. H. van Bemmel. "Classification Methods for Computerized Interpretation of the Electrocardiogram." Methods of Information in Medicine 29, no. 04 (1990): 330–36. http://dx.doi.org/10.1055/s-0038-1634792.

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AbstractTwo methods for diagnostic classification of the electrocardiogram are described: a heuristic one and a statistical one. In the heuristic approach, the cardiologist provides the knowledge to construct a classifier, usually a decision tree. In the statistical approach, probability densities of diagnostic features are estimated from a learning set of ECGs and multivariate techniques are used to attain diagnostic classification. The relative merits of both approaches with respect to criteria selection, comprehensibility, flexibility, combined diseases, and performance are described. Optim

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Ibrahim, Muhammad Junaid, Jaweria Kainat, Hussain AlSalman, Syed Sajid Ullah, Suheer Al-Hadhrami, and Saddam Hussain. "An Effective Approach for Human Activity Classification Using Feature Fusion and Machine Learning Methods." Applied Bionics and Biomechanics 2022 (February 2, 2022): 1–14. http://dx.doi.org/10.1155/2022/7931729.

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Recent advances in image processing and machine learning methods have greatly enhanced the ability of object classification from images and videos in different applications. Classification of human activities is one of the emerging research areas in the field of computer vision. It can be used in several applications including medical informatics, surveillance, human computer interaction, and task monitoring. In the medical and healthcare field, the classification of patients’ activities is important for providing the required information to doctors and physicians for medication reactions and

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Bouhamidi, Yacine, and Kai Wang. "Simple Methods for Improving the Forensic Classification between Computer-Graphics Images and Natural Images." Forensic Sciences 4, no. 1 (2024): 164–83. http://dx.doi.org/10.3390/forensicsci4010010.

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From the information forensics point of view, it is important to correctly classify between natural images (outputs of digital cameras) and computer-graphics images (outputs of advanced graphics rendering engines), so as to know the source of the images and the authenticity of the scenes described in the images. It is challenging to achieve good classification performance when the forensic classifier is tested on computer-graphics images generated by unknown rendering engines and when we have a limited number of training samples. In this paper, we propose two simple yet effective methods to im

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Vijaya Kumar C. N.. Suresh Kumar H. S., Rakshitha K. C., Ningappa Uppa,. "ADM- Road Eye: Advanced Traffic Sign Detection." Journal of Electrical Systems 20, no. 5s (2024): 355–65. http://dx.doi.org/10.52783/jes.1976.

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In recent years, a plethora of systems have emerged for recognizing traffic signs. This paper offers a comprehensive overview of the latest and most effective approaches in detecting and categorizing traffic signs. The primary goal of detection techniques is to pinpoint the precise areas containing traffic signs, which are classified into three main categories: color-based, shape-based, and learning-based methods of Alex net, Desnse net, and Mobil net (ADM) models. Moreover, methods of classification are divided into two groups; those relying on manually crafted features such as HOG, LBP, SIFT

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Červená, Lenka, Pavel Kříž, Jan Kohout, et al. "Advanced Statistical Analysis of 3D Kinect Data: A Comparison of the Classification Methods." Applied Sciences 11, no. 10 (2021): 4572. http://dx.doi.org/10.3390/app11104572.

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This paper focuses on the statistical analysis of mimetic muscle rehabilitation after head and neck surgery causing facial paresis in patients after head and neck surgery. Our work deals with an evaluation problem of mimetic muscle rehabilitation that is observed by a Kinect stereo-vision camera. After a specific brain surgery, patients are often affected by face palsy, and rehabilitation to renew mimetic muscle innervation takes several months. It is important to be able to observe the rehabilitation process in an objective way. The most commonly used House–Brackmann (HB) scale is based on th

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Rosello, Olivier, Federico Solla, Ioana Oborocianu, et al. "Advanced containment methods for Legg-Calvé-Perthes disease: triple pelvic osteotomy versus Chiari osteotomy." HIP International 28, no. 3 (2017): 297–301. http://dx.doi.org/10.5301/hipint.5000569.

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Introduction: The goal of intervention in Legg-Calvé-Perthes disease (LCPD) is to prevent femoral head deformation by containing the head within the acetabulum. Currently, surgical containment methods are the mainstay of treatment, and pelvic osteotomies have been shown to be successful. They include triple pelvic osteotomy (TPO), Salter osteotomy, Chiari osteotomy and shelf procedure. The purpose of this study was to compare clinical and radiologic results for Chiari osteotomy and TPO in LCPD. Methods: 29 children treated between 1980 and 2010 for LCPD in 2 centres were reviewed. 19 underwent

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Sarrionandia, Xabier, Javier Nieves, Beñat Bravo, Iker Pastor-López, and Pablo G. Bringas. "An Objective Metallographic Analysis Approach Based on Advanced Image Processing Techniques." Journal of Manufacturing and Materials Processing 7, no. 1 (2023): 17. http://dx.doi.org/10.3390/jmmp7010017.

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Metallographic analyses of nodular iron casting methods are based on visual comparisons according to measuring standards. Specifically, the microstructure is analyzed in a subjective manner by comparing the extracted image from the microscope to pre-defined image templates. The achieved classifications can be confused, due to the fact that the features extracted by a human being could be interpreted differently depending on many variables, such as the conditions of the observer. In particular, this kind of problem represents an uncertainty when classifying metallic properties, which can influe

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Tsabithah, Kayla Azka Dhiya, Grahani Switamy Br Manik, Muhammad 'Anil 'Aziz, and Robert Kurniawan. "Perbandingan Classification Advanced Methods Dalam Klasifikasi Indeks Kekeringan di Provinsi Nusa Tenggara Timur." PROSIDING SEMINAR NASIONAL SAINS DATA 4, no. 1 (2024): 249–57. https://doi.org/10.33005/senada.v4i1.186.

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Provinsi Nusa Tenggara Timur tergolong sebagai wilayah kering dengan hanya empat bulan basah dan delapan bulan kering. Penelitian ini bertujuan untuk mengetahui gambaran tingkat kekeringan pada level kecamatan melalui indikator Normalized Difference Drought Index (NDDI), serta membandingkan ketiga Classification Advanced Methods, yakni K-Nearest Neighbor (KNN), Support Vector Machine (SVM), dan Random Forest (RF), untuk menentukan metode klasifikasi terbaik dalam mengidentifikasi setiap tingkat kekeringan dengan menggunakan berbagai indikator kekeringan berbasis satelit. Hasil penelitian ini m

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Babu, Katta Rajesh, K. Charan Subhash, S. Sumanth, Ainala Karthik, G. Megana Ram, and D. Rajendra Prasad. "Performance Analysis of Face Forgery Recognition and Classification Using Advanced Deep Learning Methods." Metallurgical and Materials Engineering 31, no. 4 (2025): 355–67. https://doi.org/10.63278/1444.

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The adoption of web technology has come to be accompanied by a number of worrying security issues, one of which is deep fakes that are now counted among the top visual deceits in the field. The need for identifying such manipulations which is on the rise is the need for stronger methods that can be used to identify such manipulations. This article deals with the usage of fully connected neural networks (FCNN), convolutional neural networks (CNN), and deep convolutional neural networks (DCNN) to determine if a presented facial image is original or fake. In this case, the methods apply the use o

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Subedi, Sachin, Ramesh Bahadur Bist, Xiao Yang, Guoming Li, and Lilong Chai. "Advanced Deep Learning Methods for Multiple Behavior Classification of Cage-Free Laying Hens." AgriEngineering 7, no. 2 (2025): 24. https://doi.org/10.3390/agriengineering7020024.

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The welfare of hens in cage-free systems is closely linked to their behaviors, such as feeding, drinking, pecking, perching, bathing, preening, and foraging. To monitor these behaviors, we developed and evaluated deep learning models based on YOLO (You Only Look Once), an advanced object detection technology known for its high accuracy, speed, and compact size. Three YOLO-based models—YOLOv5s_BH, YOLOv5x_BH, and YOLOv7_BH—were created to track and classify the behaviors of laying hens in cage-free environments. A dataset comprising 1500 training images, 500 validation images, and 50 test image

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Apicella, A., F. Isgrò, R. Prevete, and G. Tamburrini. "Middle-Level Features for the Explanation of Classification Systems by Sparse Dictionary Methods." International Journal of Neural Systems 30, no. 08 (2020): 2050040. http://dx.doi.org/10.1142/s0129065720500409.

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Machine learning (ML) systems are affected by a pervasive lack of transparency. The eXplainable Artificial Intelligence (XAI) research area addresses this problem and the related issue of explaining the behavior of ML systems in terms that are understandable to human beings. In many explanation of XAI approaches, the output of ML systems are explained in terms of low-level features of their inputs. However, these approaches leave a substantive explanatory burden with human users, insofar as the latter are required to map low-level properties into more salient and readily understandable parts o

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Xing, Ying, Hui Shu, Hao Zhao, Dannong Li, and Li Guo. "Survey on Botnet Detection Techniques: Classification, Methods, and Evaluation." Mathematical Problems in Engineering 2021 (April 14, 2021): 1–24. http://dx.doi.org/10.1155/2021/6640499.

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With the continuous evolution of the Internet, as well as the development of the Internet of Things, smart terminals, cloud platforms, and social platforms, botnets showing the characteristics of platform diversification, communication concealment, and control intelligence. This survey analyzes and compares the most important efforts in the botnet detection area in recent years. It studies the mechanism characteristics of botnet architecture, life cycle, and command and control channel and provides a classification of botnet detection techniques. It focuses on the application of advanced techn

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Prof. Dipali Ghatge, Prathmesh Sankpal, Vedantika Gharge, Rutuja Patil, and Shivam Zanjurne. "Exploratory Analysis of Skin Cancer Dermatoscopic Image Datasets and Classification Methods." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 12 (2024): 2781–95. https://doi.org/10.47392/irjaeh.2024.0385.

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Skin cancer is a critical global health issue, where early detection significantly improves treatment outcomes. In this review paper, titled Exploratory Analysis of Skin Cancer Dermatoscopic Image Datasets and Classification Methods, we systematically explore the intersection of artificial intelligence (AI) and skin cancer diagnosis. Our approach began with a detailed literature survey of 40 research studies, providing insights into advancements and challenges in AI-based classification methods. This survey emphasizes the role of machine learning algorithms, particularly convolutional neural n

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Kavalcı Yılmaz, Esra, and Rezan Bakır. "Advanced Android Malware Detection: Merging Deep Learning and XGBoost Techniques." Bilişim Teknolojileri Dergisi 18, no. 1 (2025): 45–61. https://doi.org/10.17671/gazibtd.1553548.

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The increasing importance of Android devices in our lives brings with it the need to secure personal information stored on these devices, such as contact details, documents, location data, and browser data. These devices are often targeted by attacks and malware designed to steal this data. In response, this work takes a novel approach to Android malware detection by integrating deep learning with traditional machine learning algorithms. An extensive experimental study was conducted using the DroidCollector network traffic analysis dataset. Eight different deep learning methods are analysed fo

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Tao, Wang, Wu Linyan, Li Yanping, Gao Nuo, and Zhang Weiran. "Learning Advanced Brain Computer Interface Technology." International Journal of Technology and Human Interaction 15, no. 3 (2019): 14–27. http://dx.doi.org/10.4018/ijthi.2019070102.

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Feature extraction is an important step in electroencephalogram (EEG) processing of motor imagery, and the feature extraction of EEG directly affects the final classification results. Through the analysis of various feature extraction methods, this article finally selects Common Spatial Patterns (CSP) and wavelet packet analysis (WPA) to extract the feature and uses Support Vector Machine (SVM) to classify and compare these extracted features. For the EEG data provided by GRAZ University, the accuracy rate of feature extraction using CSP algorithm is 85.5%, and the accuracy rate of feature ext

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Liu, Tianyu, Hongbing Chen, Junfeng Ren, et al. "Urban Functional Zone Classification via Advanced Multi-Modal Data Fusion." Sustainability 16, no. 24 (2024): 11145. https://doi.org/10.3390/su162411145.

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The classification of urban functional zones is crucial for improving land use efficiency and promoting balanced development across urban areas. Existing methods for classifying urban functional zones using mobile signaling data face challenges primarily due to the limitations of single data sources, insufficient utilization of multidimensional data, and inherent inaccuracies in mobile signaling data. To address these issues, this study proposes an innovative classification method that employs advanced multimodal data fusion techniques to enhance the accuracy and reliability of functional zone

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Anis J, Kazi, Dr Sameer Shafi, Waghmare Harsh, et al. "A Comprehensive Review on Nanoparticle Classification and Synthesis Methods." Asian Journal of Pharmaceutical Research and Development 11, no. 6 (2023): 36–43. http://dx.doi.org/10.22270/ajprd.v11i6.1336.

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Nanoparticles synthesis and the study of their size and properties are important in medicine as well as biological fields nanotechnology using nanoparticles such as metals, semiconductors and metal oxides are of great interest for a wide of variety of applications in the field of data, energy, environmental and medical tools due to their unique or improved properties. Current review focus on nanoparticle, types, characterization and most advanced application related to nanotechnology.It has the potential to revolutionize a series of medical and biotechnology tools and procedures so that they a

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Alzahrani, Salwa, Haneen Banjar, and Rsha Mirza. "Systematic Review of EEG-Based Imagined Speech Classification Methods." Sensors 24, no. 24 (2024): 8168. https://doi.org/10.3390/s24248168.

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This systematic review examines EEG-based imagined speech classification, emphasizing directional words essential for development in the brain–computer interface (BCI). This study employed a structured methodology to analyze approaches using public datasets, ensuring systematic evaluation and validation of results. This review highlights the feature extraction techniques that are pivotal to classification performance. These include deep learning, adaptive optimization, and frequency-specific decomposition, which enhance accuracy and robustness. Classification methods were explored by comparing

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Chen, Hongwei. "Studies Advanced in Image Classification based on Deep Learning." Applied and Computational Engineering 8, no. 1 (2023): 641–46. http://dx.doi.org/10.54254/2755-2721/8/20230287.

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Accurate and efficient image classification is one of the important research topics in image analysis, and it has also been a research hotspot in the computer vision community.Deep neural networks are increasingly being used for picture categorization and processing in recent years as a result of advancements in machine learning technology.In this article, we introduce the research progress of image recognition technology based on depth learning, including the design ideas, principles, structures, advantages and disadvantages of several depth neural networks. In addition, we quantitatively com

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Ramanaiah, Preethi. "Proteomics Data Classification Using Advanced Machine Learning Algorithm." American Journal of Artificial Intelligence 8, no. 1 (2024): 13–21. http://dx.doi.org/10.11648/j.ajai.20240801.13.

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Proteomics, the study of proteins and their functions within biological systems, has become increasingly data-intensive, presenting both opportunities and challenges. This project addresses the need for advanced data analytics and data integrity in proteomics research. Leveraging the power of machine learning (ML) and blockchain technology, this attempt aims to transform proteomics research. This work encompasses three key objectives. First, collect, clean, and integrate proteomics data from diverse sources, ensuring data quality and consistency. Second, employ ML algorithms to analyze this da

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Rifiana, Arief, Widodo Suryarini, Bima Kurniawan Ary, Hustinawaty, and Arkan Faisal. "Advanced content-based retrieval for digital correspondence documents with ontology classification." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1665~1677. https://doi.org/10.11591/eei.v11i3.3376.

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The growth of digital correspondence documents with various types, different naming rules, and no sufficient search system complicates the search process with certain content, especially if there are unclassified documents, the search becomes inaccurate and takes a long time. This research proposed archiving method with automatic hierarchical classification and the content-based search method which displays ontology classification information as the solution to the content-based search problems. The method consists of preprocessing (creation of automatic hierarchical classification model using

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Mahjoub, Chahira, Régine Le Bouquin Jeannès, Tarek Lajnef, and Abdennaceur Kachouri. "Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods." Biomedical Engineering / Biomedizinische Technik 65, no. 1 (2020): 33–50. http://dx.doi.org/10.1515/bmt-2019-0001.

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AbstractElectroencephalography (EEG) is a common tool used for the detection of epileptic seizures. However, the visual analysis of long-term EEG recordings is characterized by its subjectivity, time-consuming procedure and its erroneous detection. Various epileptic seizure detection algorithms have been proposed to deal with such issues. In this study, a novel automatic seizure-detection approach is proposed. Three different strategies are suggested to the user whereby he/she could choose the appropriate one for a given classification problem. Indeed, the feature extraction step, including bo

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Ohmann, C., K. Goos, F. Puppe, O. Mootz, and B. Puppe. "Evaluating Four Diagnostic Methods with Acute Abdominal Pain Cases." Methods of Information in Medicine 34, no. 04 (1995): 361–68. http://dx.doi.org/10.1055/s-0038-1634613.

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Abstract:Contemporary work in medical decision support is characterized by a multitude of methods. To investigate their relative strengths and weaknesses, we built four diagnostic expert systems based on different methods (Bayes, case-based classification, heuristic classification) for analysis of the same set of 1254 cases of acute abdominal pain previously documented in a prospective multicenter study. The results of the comparative evaluation indicate that differences in overall performance are relatively small (statistically not significant). The performance depends more on the quality of

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Kántor, Peter, Lucia Staníková, Anna Švejdová, Karol Zeleník, and Pavel Komínek. "Narrative Review of Classification Systems Describing Laryngeal Vascularity Using Advanced Endoscopic Imaging." Journal of Clinical Medicine 12, no. 1 (2022): 10. http://dx.doi.org/10.3390/jcm12010010.

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Endoscopic methods are critical in the early diagnosis of mucosal lesions of the head and neck. In recent years, new examination methods and classification systems have been developed and introduced into clinical practice. All of these new techniques target the notion of optical biopsy, which tries to assess the nature of the lesion before histology examination. Many methods suffer from interpretation issues due to subjective interpretation of the findings. Therefore, multiple classification systems have been developed to assist the proper interpretation of mucosal findings and reduce the erro

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Li, Daoliang, Qi Wang, Xin Li, Meilin Niu, He Wang, and Chunhong Liu. "Recent advances of machine vision technology in fish classification." ICES Journal of Marine Science 79, no. 2 (2022): 263–84. http://dx.doi.org/10.1093/icesjms/fsab264.

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Abstract Automatic classification of different species of fish is important for the comprehension of marine ecology, fish behaviour analysis, aquaculture management, and fish health monitoring. In recent years, many automatic classification methods have been developed, among which machine vision-based classification methods are widely used with the advantages of being fast and non-destructive. In addition, the successful application of rapidly emerging deep learning techniques in machine vision has brought new opportunities for fish classification. This paper provides an overview of machine vi

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