Статті в журналах: "Classification: Advanced Methods"

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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, Marc Winter, Andreas Simon Schneider, Marc Ludovici, and Frank Mücklich. "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 (January 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 segmentation, background subtraction, and window segmentation. The first two methods were used to separate the objects of scene and nonscene from the actual traffic image. To reduce the complexity of neural networks, the image was divided into 16 windows and three characteristics (occupation rates of vehicles, of horizontal image lines, and of vertical image lines) of each window were extracted to generate 48 factors as the input units of the neural network. The backpropagation ANN model with one hidden layer is employed. The experiments show that the accurate recognition rates of heavy vehicles, small cars, and motorcycles are 98.5, 96.92, and 91.94 percent, respectively. The result implies the remarkable applicability of the proposed methods in transportation areas.

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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 (June 7, 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 feedback among models during training. Our method integrates multiple pre-trained CNNs using strategies like combining CNNs through an additional fully connected layer and employing a label-weighted average for outputs. This multi-layered approach leverages the strengths of each model component, improving classification accuracy and generalization. By focusing solely on image data, our ensemble model addresses the challenges posed by null vectors and diverse pathologies, advancing computer-aided radiology.

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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 (October 9, 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. The SVM model demonstrates enhanced precision in land cover classification, showcasing its effectiveness in discerning subtle differences in landscape features. Furthermore, using the precise classification results produced by the SVM method, this study examines the temporal variations in land cover between 2018 and 2020. The results provide insight into dynamic land cover changes and highlight the significance of applying reliable classification techniques for thorough temporal analysis with Landsat8 images.

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Taherian, Hessam, and Robert W. Peters. "Advanced Active and Passive Methods in Residential Energy Efficiency." Energies 16, no. 9 (May 5, 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 the active and passive energy techniques are described with appropriate reference citations provided. This review article provides a description to give an understanding of building conservation approaches. In the active classification, several methods have been reviewed that include earth-to-air heat exchangers, ground-source and hybrid heat pumps, and the use of new refrigerants, among other methods. In the passive classification, methods such as vegetated roofs, solar chimneys, natural ventilation, and more are discussed. Often, in a building, multiple passive and active methods can be employed simultaneously.

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Kabakchieva, Dorina. "Predicting Student Performance by Using Data Mining Methods for Classification." Cybernetics and Information Technologies 13, no. 1 (March 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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G. Syam Kumar. "Sports Videos Classification using Advanced Deep Neural Networks." International Transactions on Electrical Engineering and Computer Science 3, no. 2 (June 30, 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 histogram. Both of these methods analyze the contrast relationship among various parts of a video cum the colour matter that is present in a sector. In order to accomplish the goal of transfer learning, the maximum-mean-difference (MMD) procedure is utilized. Obtaining characteristics in sports video pictures is the foundation for the sports video image segregation approach that is dependent on deep-learning-coding model. This method is utilized in order that accomplish task of sports video segregation. As a consequence of the findings, it is clear that the overall segregation reaction of this procedure is significantly superior to that of other sports video classification methods that are currently in use. This results in a significant improvement in the classification effect of sports videos.

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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 (July 31, 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 should be noted that Random Forest Classifier also has more outliers compared to Gaussian Naïve Bayes Classifier when visualized using boxplots. Therefore, in selecting a classification model, a trade-off between higher performance and sensitivity to outliers needs to be considered. Further statistical testing and advanced evaluation are required to gain a deeper understanding of the impact and interpretation of the obtained results. This study provides valuable insights into understanding the comparison between these two classification models and their implications in different contexts.

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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 (November 1, 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 brightness temperature under no-rain conditions is derived from unclassified TMI pixels without the use of PR. A test with the TMI shows that the modified (PR independent) methods are better than the RNC method developed for the Goddard profiling algorithm (GPROF; the standard algorithm for the TMI) while they are slightly poorer than corresponding previous (PR dependent) methods. M2d, one of the PR-independent methods, is applied to observations from the Advanced Microwave Scanning Radiometer for Earth Observing Satellite (AMSR-E), is evaluated for a matchup case with PR, and is evaluated for 1 yr with a rain gauge dataset in Japan. M2d is incorporated into a retrieval algorithm developed by the Global Satellite Mapping of Precipitation project to be applied for the AMSR-E. In latitudes above 30°N, the rain-rate retrieval is compared with a rain gauge dataset by the Global Precipitation Climatology Center. Without a snow mask, a large amount of false rainfall due to snow contamination occurs. Therefore, a simple snow mask using the 23.8-GHz channel is applied and the threshold of the mask is optimized. Between 30° and 60°N, the optimized snow mask forces the miss of an estimated 10% of the total rainfall.

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

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Powell, Jade, Alejandro Torres-Forné, Ryan Lynch, Daniele Trifirò, Elena Cuoco, Marco Cavaglià, Ik Siong Heng, and José A. Font. "Classification methods for noise transients in advanced gravitational-wave detectors II: performance tests on Advanced LIGO data." Classical and Quantum Gravity 34, no. 3 (January 13, 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 (April 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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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 agriculture. As data source, the farming enterprises database for 2014 has been used, from the FADN CZ system. The predictive model proposed exploits knowledge of actual size classes of the farms tested. Outcomes of the linear discriminatory analysis multifactor classification method have supported the chance of filing farming enterprises in the group of Small farms (98 % filed correctly), and the Large and Very Large enterprises (100 % filed correctly). The Medium Size farms have been correctly filed at 58.11 % only. Partial shortages of the process presented have been found when discriminating Medium and Small farms.

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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 (January 13, 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 different deep convolutional neural networks (CNN), i.e., Resnet101 and ShuffleNet, for deep feature extraction and classification. The results show that the best accuracy for detection of beats rhythm was using ResNet101 and scalogram of T-wave with an accuracy of 98.3%, while accuracy was 94.4% for detection using iris-spectrogram using also ResNet101 and QRS-Wave. Finally, based on these results we note that using deep features from time-frequency representation using one wave of ECG beat we can accurately detect basic rhythms such as normal, tachycardia, and bradycardia.

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Pralea, Ioana-Ecaterina, Radu-Cristian Moldovan, Alina-Maria Petrache, Maria Ilieș, Simona-Codruța Hegheș, Irina Ielciu, Raul Nicoară, et al. "From Extraction to Advanced Analytical Methods: The Challenges of Melanin Analysis." International Journal of Molecular Sciences 20, no. 16 (August 13, 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 applied in melanin analysis workflow, from extraction and purification to high-throughput methods, such as matrix-assisted laser desorption/ionization mass-spectrometry or pyrolysis gas chromatography. Our thorough comparative evaluation of analytical data published so far on melanin analysis has proven to be a difficult task in terms of finding equivalent results, even when the same matrix was used. Moreover, we emphasize the importance of prior knowledge of melanin types and properties in order to select a valid experimental design using analytical methods that are able to deliver reliable results and draw consistent conclusions.

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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 traditional competitors. In logistic regression we allow to select nonlinear effects by the fractional polynomial approach. Performance of the classifiers will be assessed by estimated misclassification rates and the Brier score. Results: We will show that boosting of simple base classifiers gives classification rules with improved predictive ability. However, the performance of boosting classifiers was not generally superior to the performance of logistic regression. In contrast to the computer-intensive methods the latter are based on classifiers which are much easier to interpret and to use. Conclusions: In medical applications, the logistic regression model remains a method of choice or, at least, a serious competitor of more sophisticated techniques. Refinement of boosting methods by using optimized number of boosting steps may lead to further improvement.

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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. Optimization of heuristic classifiers is discussed. It is concluded that heuristic classifiers are more comprehensible than statistical ones; encounter less difficulties in dealing with combined categories; are flexible in the sense that new categories may readily be added or that existing ones may be refined stepwise. Statistical classifiers, on the other hand, are more easily adapted to another operating environment and require less involvement of cardiologists. Further research is needed to establish differences in performance between both methods. In relation to performance testing the issue is raised whether the ECG should be classified using as much prior information as possible, or whether it should be classified on itself, explicitly discarding information other than age and sex, while only afterwards other information will be used to reach a final diagnosis. Consequences of taking one of both positions are discussed.

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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 diagnosis. Nowadays, some research approaches to recognize human activity from videos and images have been proposed using machine learning (ML) and soft computational algorithms. However, advanced computer vision methods are still considered promising development directions for developing human activity classification approach from a sequence of video frames. This paper proposes an effective automated approach using feature fusion and ML methods. It consists of five steps, which are the preprocessing, feature extraction, feature selection, feature fusion, and classification steps. Two available public benchmark datasets are utilized to train, validate, and test ML classifiers of the developed approach. The experimental results of this research work show that the accuracies achieved are 99.5% and 99.9% on the first and second datasets, respectively. Compared with many existing related approaches, the proposed approach attained high performance results in terms of sensitivity, accuracy, precision, and specificity evaluation metric.

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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 (March 14, 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 improve the classification performance under such challenging situations, respectively based on data augmentation and the combination of local and global prediction results. Compared with existing methods, our methods are conceptually simple and computationally efficient, while achieving satisfying classification accuracy. Experimental results on datasets comprising computer-graphics images generated by four popular and advanced graphics rendering engines demonstrate the effectiveness of the proposed methods.

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Červená, Lenka, Pavel Kříž, Jan Kohout, Martin Vejvar, Ludmila Verešpejová, Karel Štícha, Jan Crha, Kateřina Trnková, Martin Chovanec, and Jan Mareš. "Advanced Statistical Analysis of 3D Kinect Data: A Comparison of the Classification Methods." Applied Sciences 11, no. 10 (May 17, 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 the clinician’s subjective opinion. This paper compares different methods of supervised learning classification that should be independent of the clinician’s opinion. We compare a parametric model (based on logistic regression), non-parametric model (based on random forests), and neural networks. The classification problem that we have studied combines a limited dataset (it contains only 122 measurements of 93 patients) of complex observations (each measurement consists of a collection of time curves) with an ordinal response variable. To balance the frequencies of the considered classes in our data set, we reclassified the samples from HB4 to HB3 and HB5 to HB6—it means that only four HB grades are used for classification algorithm. The parametric statistical model was found to be the most suitable thanks to its stability, tractability, and reasonable performance in terms of both accuracy and precision.

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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 (April 8, 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, SURF, BRISK, and those leveraging deep learning. The paper summarizes various detection and classification methods, along with the datasets utilized, for quick reference. Additionally, it provides suggestions for future research directions and recommendations to enhance traffic sign recognition performance..

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Rosello, Olivier, Federico Solla, Ioana Oborocianu, Edouard Chau, Tony ElHayek, Jean-Luc Clement, and Virginie Rampal. "Advanced containment methods for Legg-Calvé-Perthes disease: triple pelvic osteotomy versus Chiari osteotomy." HIP International 28, no. 3 (November 10, 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 TPO, and 10, Chiari osteotomy. Two independent observers assessed sequential radiographs and medical data. Each hip was preoperatively classed by clinical data, Catteral, Herring and Salter-Thompson classification, centre-edge angle (CE), and acetabular index (AI). The 2 groups were first tested for their comparability. After surgery the hips were classified by Stulberg classification, CE, AI, Harris Hip Score (HHS) and performance of further surgery. Chiari osteotomy and TPO have been secondary compared on these data by Wilcoxon test. Results: Average follow-up was 4.2 years. The 2 groups were comparable before surgery. At first and last follow-up examination, statistically significantly superior results in patients with TPO regarding Stulberg classification (p = 0.01), AI (p = 0.002), pain (p = 0.02) and function (p = 0.01) in the HHS score were found. No differences were noticed concerning CE angle. Conclusions: In our series, TPO provided better radiologic and clinical outcomes compared to Chiari osteotomy, specially concerning the final Stulberg classification. We concluded that TPO should be preferred when indicated.

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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 (January 4, 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 influence the integrity of castings that play critical roles in safety devices or structures. Although there are existing solutions working with extracted images and applying some computer vision techniques to manage the measurements of the microstructure, those results are not too accurate. In fact, they are not able to characterize all specific features of the image and, they cannot be adapted to several characterization methods depending on the specific regulation or customer. Hence, in order to solve this problem, we propose a framework to improve and automatize the evaluations by combining classical machine vision techniques for feature extraction and deep learning technologies, to objectively make classifications. To adapt to the real analysis environments, all included inputs in our models were gathered directly from the historical repository of metallurgy from the Azterlan Research Centre (labeled using expert knowledge from engineers). The proposed approach concludes that these techniques (a classification under a pipeline of deep neural networks and the quality classification using an ANN classifier) are viable to carry out the extraction and classification of metallographic features with great accuracy and time, and it is possible to deploy software with the models to work on real-time situations. Moreover, this method provides a direct way to classify the metallurgical quality of the molten metal, allowing us to determine the possible behaviors of the final produced parts.

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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 technologies such as deep learning, complex network, swarm intelligence, moving target defense (MTD), and software-defined network (SDN) for botnet detection. From the four dimensions of service, intelligence, collaboration, and assistant, a common bot detection evaluation system (CBDES) is proposed, which defines a new global capability measurement standard. Combing with expert scores and objective weights, this survey proposes quantitative evaluation and gives a visual representation for typical detection methods. Finally, the challenges and future trends in the field of botnet detection are summarized.

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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 (July 14, 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 of the input. To alleviate this cognitive burden, an alternative model-agnostic framework is proposed here. This framework is instantiated to address explanation problems in the context of ML image classification systems, without relying on pixel relevance maps and other low-level features of the input. More specifically, one obtains sets of middle-level properties of classification inputs that are perceptually salient by applying sparse dictionary learning techniques. These middle-level properties are used as building blocks for explanations of image classifications. The achieved explanations are parsimonious, for their reliance on a limited set of middle-level image properties. And they can be contrastive, because the set of middle-level image properties can be used to explain why the system advanced the proposed classification over other antagonist classifications. In view of its model-agnostic character, the proposed framework is adaptable to a variety of other ML systems and explanation problems.

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Anis J, Kazi, Dr Sameer Shafi, Waghmare Harsh, Shaikh Sameena, Ritthe Punam, Rudrurkar Muktai, Patil Sneha, and Shaikh Imran. "A Comprehensive Review on Nanoparticle Classification and Synthesis Methods." Asian Journal of Pharmaceutical Research and Development 11, no. 6 (December 15, 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 are portable, cheaper, safer, and easier to administer. They were synthesized by various methods for research and commercial uses which are classified into three types-chemical, physical and mechanical processes which had sawn a vast improvement. It plays a major role in the development of innovative methods to produce new products to suitable existing production equipment and to reformulate new material and chemicals with improved performance resulting in less consumption of energy and material and reduce harm to the environment as well as environmental remediation.

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Ramanaiah, Preethi. "Proteomics Data Classification Using Advanced Machine Learning Algorithm." American Journal of Artificial Intelligence 8, no. 1 (May 17, 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 data, revealing crucial insights, identifying proteins, and predicting their functions. Third, implement blockchain technology to safeguard the authenticity and integrity of the proteomics data, providing an auditable and tamper-proof record. Implemented a user-friendly web interface, facilitating collaboration among researchers and scientists by granting access to shared data and results. This study included various classification methods for the investigation of protein classification, namely, random forests, logistic regression, neural networks, support vector machines, and decision trees. In conclusion, the proposed work is poised to revolutionize proteomics research by enhancing data analytics capabilities and securing data integrity, thereby enabling scientists to make more informed and confident discoveries in this critical field.

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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 (July 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 extraction using wavelet packet analysis is 92%. Then this paper analyzes the EEG data collected by Emotiv epoc+ system. The classification accuracy of wavelet packet extracted features can still be maintained at more than 80%, while the classification accuracy of CSP extracted feature is decreased obviously. Experimental results show that the method of wavelet packet analysis towards competition data and Emotiv epoc+ system data can both get a desirable outcome.

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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 (January 28, 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 both linear and nonlinear measures, is performed either directly from the EEG signals, or from the derived sub-bands of tunable-Q wavelet transform (TQWT), or even from the intrinsic mode functions (IMFs) of multivariate empirical mode decomposition (MEMD). The classification procedure is executed using a support vector machine (SVM). The performance of the proposed method is evaluated through a publicly available database from which six binary classification cases are formulated to discriminate between healthy, seizure and non-seizure EEG signals. Our results show high performance in terms of accuracy (ACC), sensitivity (SEN) and specificity (SPE) compared to the state-of-the-art approaches. Thus, the proposed approach for automatic seizure detection can be considered as a valuable alternative to existing methods, able to alleviate the overload of visual analysis and accelerate the seizure detection.

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Chen, Hongwei. "Studies Advanced in Image Classification based on Deep Learning." Applied and Computational Engineering 8, no. 1 (August 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 compared the recognition results of different methods on classic image classification datasets. Lastly, we review some current issues with picture classification and talk about the direction of deep neural network development in the use of image classification technology.

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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 (July 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 the knowledge base and the case data than on the inference methods of the expert systems. Methods relying exclusively on empirical knowledge (Bayes, case-based classification) tend to have slightly higher overall performance scores due to a diagnostic bias toward ordinary and common diseases. By contrast, methods operating with expert knowledge (e. g., heuristic classification) perform slightly worse overall, but are more sensitive toward uncommon (serious) diseases.

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35

Krivosheina, E. I., M. Yu Kartashov, and E. V. Naidenova. "Advanced Laboratory Methods for Detecting Yellow Fever Pathogen." Problems of Particularly Dangerous Infections, no. 2 (July 21, 2021): 24–32. http://dx.doi.org/10.21055/0370-1069-2021-2-24-32.

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Yellow fever is an acute infectious disease of viral nature, the causative agent of which is vector-borne –is transmitted through the bites of infected mosquitoes. Massive epidemics caused by the yellow fever virus are observed in the countries of Africa, South and Central America annually. Imported cases are also registered in non-endemic territories. The review presents the currently available data on the distribution, structure and classification of the yellow fever virus, the identification of its genetic variants depending on the geographical distribution, as well as modern methods of detection and identification of the pathogen in samples taken from sick and dead people. It considers the possibility of using virological, immunoserological and molecular-genetic methods for the diagnosis of yellow fever in different periods from the onset of the disease and in retrospective studies. The lists of diagnostic drugs of domestic and foreign production for the detection of agent markers (antigen, RNA), as well as specific antibodies of IgM and IgG classes, approved for use on the territory of the Russian Federation, are provided. The relevance of further development, improvement and introduction into laboratory practice of reagent kits that allow to detect the yellow fever virus in samples from sick people in a short time, with high efficiency and specificity is demonstrated. This will help to establish a diagnosis promptly and conduct timely anti-epidemic measures, as well as to determine the level of the population stratum immune to the pathogen in endemic regions and evaluate the effectiveness of immunization for the vaccinated contingent.

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Hammachukiattikul, P., E. Sekar, A. Tamilselvan, R. Vadivel, N. Gunasekaran, and Praveen Agarwal. "Comparative Study on Numerical Methods for Singularly Perturbed Advanced-Delay Differential Equations." Journal of Mathematics 2021 (June 4, 2021): 1–15. http://dx.doi.org/10.1155/2021/6636607.

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In this paper, we consider a class of singularly perturbed advanced-delay differential equations of convection-diffusion type. We use finite and hybrid difference schemes to solve the problem on piecewise Shishkin mesh. We have established almost first- and second-order convergence with respect to finite difference and hybrid difference methods. An error estimate is derived with the discrete norm. In the end, numerical examples are given to show the advantages of the proposed results (Mathematics Subject Classification: 65L11, 65L12, and 65L20).

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37

Marapareddy, Ramakalavathi, James Aanstoos, and Nicolas Younan. "Advanced Unsupervised Classification Methods to Detect Anomalies on Earthen Levees Using Polarimetric SAR Imagery." Sensors 16, no. 6 (June 16, 2016): 898. http://dx.doi.org/10.3390/s16060898.

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38

Pibernik, Richard. "Advanced available-to-promise: Classification, selected methods and requirements for operations and inventory management." International Journal of Production Economics 93-94 (January 2005): 239–52. http://dx.doi.org/10.1016/j.ijpe.2004.06.023.

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39

Ya’acob, Norsuzila, Nik Nur Shaadah Nik Dzulkefli, Mohd Azri Abdul Aziz, Azita Laily Yusof, and Roslan Umar. "A review on features and methods of potential fishing zone." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (June 1, 2024): 2508. http://dx.doi.org/10.11591/ijece.v14i3.pp2508-2521.

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This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.

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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 (December 20, 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 error rate. They provide various perspectives on the assessment and interpretation of mucosa changes. This article provides a comprehensive and critical view of the available classification systems as well as their advantages and disadvantages.

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Zhabchenko, I. A., and S. N. Zanko. "CERVICAL INSUFFICIENCY: CLASSIFICATION, ETIOPATHOGENESIS, DIAGNOSIS, METHODS OF PROPHYLAXIS AND CORRECTIONS (CLINICAL LECTURE)." Reproductive Medicine, no. 1(42) (March 20, 2020): 35–47. http://dx.doi.org/10.37800/rm2020-1-4.

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Тhe clinical lecture presents modern views on the problem of cervical insufficiency and its role in the miscarriage. Etiological agents of the cervical insufficiency development, its classification, diagnostic and differential diagnosis with short cervix have been reviewed. Particular attention has been drawn to the role of progesterone, microelements and connective tissue for development and progress of the mentioned pathology. The historical aspect was applied both for methods of possible treatments and prophylaxis of the cervical insufficiency and for advanced surgical and conservative correction. The algorithm of the obstetric actions for the cervical insufficiency is proposed.

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42

Sadashiv, Wagh Kishor, Kamthe Siddhesh Sanjay, Sawant Vikrant Pradeep, Chavan Akash Ani, and Kakde Anirudha Janardhan. "Detection and Classification of Leukemia Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 3235–37. http://dx.doi.org/10.22214/ijraset.2022.41848.

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Abstract: The practice of medicine is getting modernized every year and continuously moving towards more automated systems that help and improves the healthcare practice to be more productive with treatments and accurate in their assessments. Leukemia is a form of cancer that can be a fatal disease, and to rehabilitate and treat it requires a correct and early diagnosis. Standard methods have transformed into automated computer tools for analyzing, diagnosing, and predicting symptoms. Advanced methods can be used to help patients detect terminal disorders such as leukemia, which is a fatal disorder and common cancer type amongst children. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Because advanced CNN models, such as ResNet and DenseNet, are deeper and more complex having the ability to learn better. Keywords: Leukemia, Deep Learning, Classification, DenseNet, ResNet, CNN

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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 (January 18, 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 vision models applied in the field of fish classification, followed by a detailed discussion of specific applications of various classification methods. Furthermore, the challenges and future research directions in the field of fish classification are discussed. This paper would help researchers and practitioners to understand the applicability of machine vision in fish classification and encourage them to develop advanced algorithms and models to address the complex problems that exist in fish classification practice.

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Vladimirov, Lyubomir, та Кrassimira Hristova. "IMPROVE DIALOG CATEGORIZATION OF THE METHODS FOR RISK ASSESSMENT OF ЕNVIRONMENTAL DANGER ECONOMICAL ACTIVITIES". Journal scientific and applied research 4, № 1 (10 жовтня 2013): 146–53. http://dx.doi.org/10.46687/jsar.v4i1.93.

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The purpose of this work is to propose an advanced categorization of methods for risk assessment of environmentally hazardous sites that will help the consumers. To achieve it three tasks are solved: 1. Defining features of categorization methods; 2. Classification of methods for risk assessment; 3. Compilation of a catalog, which allows easy and proper choice of methods for analysis of environmental risk and application in solving practical problems.

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Abah, Ibochi Andrew, and Richard jeremiah Uriah. "Assessing the Accuracy of Different Supervised Classification Methods of Satellite Image." Engineering & Technology Review 1, no. 1 (August 9, 2020): 1–10. http://dx.doi.org/10.47285/etr.v1i1.34.

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Assessing the accuracy of the classification map is an essential area in remote sensing digital image process. This is because a poorly classified map will result in inestimable errors of spatial analysis and modeling arising from the use of such data. This study was designed to evaluate different supervised classification algorithms in terms of accuracy assessment with a view of recommending an appropriate algorithm for image processing. The analysis was carried out using Andoni L.G.A. Rivers State, Nigeria as the study area. Supervised classification of ETM+ 2014 Landsat image of the study area was carried out using ENVI 5.0 software. Seven land use/land cover categories were identified on the image data and appropriate information classes were also assigned using region of interest. The classifiers adopted for the study include SAM, SVM, and MDC and each classifier was set using appropriate thresholds and parameters. The output error matrix of the classified map produced overall accuracy and kappa coefficient for MDC as 94.00% and 0.91, SAM as 64.45% and 0.53, and SVM as 98.92% and 0.98 respectively. The overall accuracy obtained from SVM indicates that a perfect classification map will be produced from the algorithm. The advanced supervised classification should be utilized for classification of land use/ land cover for both high and medium resolution images for improved classification accuracy.

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46

Hussain, Mubashir, Jun Zou, He Zhang, Ru Zhang, Zhu Chen, and Yongjun Tang. "Recent Progress in Spectroscopic Methods for the Detection of Foodborne Pathogenic Bacteria." Biosensors 12, no. 10 (October 13, 2022): 869. http://dx.doi.org/10.3390/bios12100869.

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Detection of foodborne pathogens at an early stage is very important to control food quality and improve medical response. Rapid detection of foodborne pathogens with high sensitivity and specificity is becoming an urgent requirement in health safety, medical diagnostics, environmental safety, and controlling food quality. Despite the existing bacterial detection methods being reliable and widely used, these methods are time-consuming, expensive, and cumbersome. Therefore, researchers are trying to find new methods by integrating spectroscopy techniques with artificial intelligence and advanced materials. Within this progress report, advances in the detection of foodborne pathogens using spectroscopy techniques are discussed. This paper presents an overview of the progress and application of spectroscopy techniques for the detection of foodborne pathogens, particularly new trends in the past few years, including surface-enhanced Raman spectroscopy, surface plasmon resonance, fluorescence spectroscopy, multiangle laser light scattering, and imaging analysis. In addition, the applications of artificial intelligence, microfluidics, smartphone-based techniques, and advanced materials related to spectroscopy for the detection of bacterial pathogens are discussed. Finally, we conclude and discuss possible research prospects in aspects of spectroscopy techniques for the identification and classification of pathogens.

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PHAM, TUAN D., DAT T. TRAN, XIAOBO ZHOU, and STEPHEN T. C. WONG. "INTEGRATED ALGORITHMS FOR IMAGE ANALYSIS AND CLASSIFICATION OF NUCLEAR DIVISION FOR HIGH-CONTENT CELL-CYCLE SCREENING." International Journal of Computational Intelligence and Applications 06, no. 01 (March 2006): 21–43. http://dx.doi.org/10.1142/s1469026806001769.

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Advances in fluorescent probing and microscopic imaging technology provide important tools for biomedical research in studying the structures and functions of cells and molecules. Such studies require the processing and analysis of huge amounts of image data, and manual image analysis is very time consuming, thus costly, and also potentially inaccurate and poor reproducibility. In this paper, we present and combine several advanced computational, probabilistic, and fuzzy-set methods for the computerized classification of cell nuclei in different mitotic phases. We tested our proposed methods with real image sequences recorded over a period of twenty-four hours at every fifteen minutes with a time-lapse fluorescence microscopy. The experimental results have shown that the proposed methods are effective for the task of classification.

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48

Jashi, B. G., D. V. Orlov, T. N. Zhdanova, and V. S. Kulikov. "ADVANCED TECHNOLOGIES: PATIENT CHOICE." Journal of Volgograd State Medical University 80, no. 4 (December 30, 2021): 65–69. http://dx.doi.org/10.19163/1994-9480-2021-4(80)-65-69.

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Cataract surgery is one of the most frequently performed ophthalmic surgical interventions. Cataract extraction technologies are being improved every year. The methods differ in technical features and implantable intraocular lenses. The choosing of the treatment method also depends on many factors. This paper analyzes the patient's choice of treatment method from the point of view of the "Theory of Generations", developed in 1991 by economist N. Howe and historian V. Strauss, based on retrospective material. The study showed that according to this classification, representatives of Generation X and "baby boomers" choose high-tech interventions more often than others

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Sukhatskiy, Yuriy, Zenovii Znak, Martyn Sozanskyi, Mariana Shepida, Parag R. Gogate, and Volodymyr Tsymbaliuk. "Activated Periodates and Sodium Percarbonate in Advanced Oxidation Processes of Organic Pollutants in Aqueous Media: A Review." Chemistry & Chemical Technology 18, no. 2 (June 14, 2024): 119–30. http://dx.doi.org/10.23939/chcht18.02.119.

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The methods of periodates and sodium percarbonate activation are considered for planning strategic approaches to increasing the efficiency and intensity of oxidative degradation of organic pollutants in aquatic environments. A classification of periodate activation methods is proposed, including activation methods by external energy effects, catalytic activation methods, and other activation methods (e.g., by hydrogen peroxide, by hydroxylamine, activation in alkaline medium). Activation methods for sodium percarbonate were divided into homogeneous and heterogeneous activation methods.

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Das, Ritwika, Anil Rai, and Dwijesh Chandra Mishra. "CNN_FunBar: Advanced Learning Technique for Fungi ITS Region Classification." Genes 14, no. 3 (March 3, 2023): 634. http://dx.doi.org/10.3390/genes14030634.

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Fungal species identification from metagenomic data is a highly challenging task. Internal Transcribed Spacer (ITS) region is a potential DNA marker for fungi taxonomy prediction. Computational approaches, especially deep learning algorithms, are highly efficient for better pattern recognition and classification of large datasets compared to in silico techniques such as BLAST and machine learning methods. Here in this study, we present CNN_FunBar, a convolutional neural network-based approach for the classification of fungi ITS sequences from UNITE+INSDC reference datasets. Effects of convolution kernel size, filter numbers, k-mer size, degree of diversity and category-wise frequency of ITS sequences on classification performances of CNN models have been assessed at all taxonomic levels (species, genus, family, order, class and phylum). It is observed that CNN models can produce >93% average accuracy for classifying ITS sequences from balanced datasets with 500 sequences per category and 6-mer frequency features at all levels. The comparative study has revealed that CNN_FunBar can outperform machine learning-based algorithms (SVM, KNN, Naïve-Bayes and Random Forest) as well as existing fungal taxonomy prediction software (funbarRF, Mothur, RDP Classifier and SINTAX). The present study will be helpful for fungal taxonomy classification using large metagenomic datasets.

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