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Готові списки джерел за темами / EFFICIENT CLASSIFICATION / Статті в журналах

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Статті в журналах з теми "EFFICIENT CLASSIFICATION"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

E., Niranjan. "Efficient Classification of Images in Wireless Endoscopy." Journal of Advanced Research in Dynamical and Control Systems 12, no. 04-Special Issue (March 31, 2020): 1650–55. http://dx.doi.org/10.5373/jardcs/v12sp4/20201646.

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2

SUN, H. W., K. Y. LAM, D. GOLLMANN, S. L. CHUNG, J. B. LI, and J. G. SUN. "Efficient Fingercode Classification." IEICE Transactions on Information and Systems E91-D, no. 5 (May 1, 2008): 1252–60. http://dx.doi.org/10.1093/ietisy/e91-d.5.1252.

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3

N., SHOBHA RANI. "An Efficient Deep Classification for Malayalam Handwritten Document." Journal of Research on the Lepidoptera 51, no. 2 (April 20, 2020): 01–12. http://dx.doi.org/10.36872/lepi/v51i2/301074.

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4

Naïve, Anna Fay E., and Jocelyn B. Barbosa. "Efficient Accreditation Document Classification Using Naïve Bayes Classifier." Indian Journal of Science and Technology 15, no. 1 (January 5, 2022): 9–18. http://dx.doi.org/10.17485/ijst/v15i1.1761.

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5

Ruggieri, S. "Efficient C4.5 [classification algorithm]." IEEE Transactions on Knowledge and Data Engineering 14, no. 2 (2002): 438–44. http://dx.doi.org/10.1109/69.991727.

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6

Bruno, Antonio, Giacomo Ignesti, Ovidio Salvetti, Davide Moroni, and Massimo Martinelli. "Efficient Lung Ultrasound Classification." Bioengineering 10, no. 5 (May 5, 2023): 555. http://dx.doi.org/10.3390/bioengineering10050555.

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Анотація:

A machine learning method for classifying lung ultrasound is proposed here to provide a point of care tool for supporting a safe, fast, and accurate diagnosis that can also be useful during a pandemic such as SARS-CoV-2. Given the advantages (e.g., safety, speed, portability, cost-effectiveness) provided by the ultrasound technology over other examinations (e.g., X-ray, computer tomography, magnetic resonance imaging), our method was validated on the largest public lung ultrasound dataset. Focusing on both accuracy and efficiency, our solution is based on an efficient adaptive ensembling of two EfficientNet-b0 models reaching 100% of accuracy, which, to our knowledge, outperforms the previous state-of-the-art models by at least 5%. The complexity is restrained by adopting specific design choices: ensembling with an adaptive combination layer, ensembling performed on the deep features, and minimal ensemble using two weak models only. In this way, the number of parameters has the same order of magnitude of a single EfficientNet-b0 and the computational cost (FLOPs) is reduced at least by 20%, doubled by parallelization. Moreover, a visual analysis of the saliency maps on sample images of all the classes of the dataset reveals where an inaccurate weak model focuses its attention versus an accurate one.

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7

Zhao, Puning, and Lifeng Lai. "Efficient Classification with Adaptive KNN." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 11007–14. http://dx.doi.org/10.1609/aaai.v35i12.17314.

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In this paper, we propose an adaptive kNN method for classification, in which different k are selected for different test samples. Our selection rule is easy to implement since it is completely adaptive and does not require any knowledge of the underlying distribution. The convergence rate of the risk of this classifier to the Bayes risk is shown to be minimax optimal for various settings. Moreover, under some special assumptions, the convergence rate is especially fast and does not decay with the increase of dimensionality.

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8

Kumar, Prabhat, SS Patil, Hemamalini HC, RH Chaudhari, and Rajeev Kumar. "Efficient classification of sugarcane genomes." Journal of Pharmacognosy and Phytochemistry 10, no. 1S (January 1, 2021): 227–32. http://dx.doi.org/10.22271/phyto.2021.v10.i1sd.13474.

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9

Yoshinaga, Naoki, and Masaru Kitsuregawa. "Efficient Classification with Conjunctive Features." Journal of Information Processing 20, no. 1 (2012): 228–37. http://dx.doi.org/10.2197/ipsjjip.20.228.

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10

Lee, YoonSeok, and Sung-Eui Yoon. "Memory-Efficient NBNN Image Classification." Journal of Computing Science and Engineering 11, no. 1 (March 30, 2017): 1–8. http://dx.doi.org/10.5626/jcse.2017.11.1.1.

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11

Yang, Bing. "Efficient classification using salient regions." Optical Engineering 51, no. 7 (July 6, 2012): 077201. http://dx.doi.org/10.1117/1.oe.51.7.077201.

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12

Kyung-Tae Kim, Dong-Kyu Seo, and Hyo-Tae Kim. "Efficient classification of ISAR images." IEEE Transactions on Antennas and Propagation 53, no. 5 (May 2005): 1611–21. http://dx.doi.org/10.1109/tap.2005.846780.

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13

Gottlieb, Lee-Ad, Aryeh Kontorovich, and Robert Krauthgamer. "Efficient Classification for Metric Data." IEEE Transactions on Information Theory 60, no. 9 (September 2014): 5750–59. http://dx.doi.org/10.1109/tit.2014.2339840.

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14

SCHLEIF, F. M., THOMAS VILLMANN, BARBARA HAMMER, and PETRA SCHNEIDER. "EFFICIENT KERNELIZED PROTOTYPE BASED CLASSIFICATION." International Journal of Neural Systems 21, no. 06 (December 2011): 443–57. http://dx.doi.org/10.1142/s012906571100295x.

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Анотація:

Prototype based classifiers are effective algorithms in modeling classification problems and have been applied in multiple domains. While many supervised learning algorithms have been successfully extended to kernels to improve the discrimination power by means of the kernel concept, prototype based classifiers are typically still used with Euclidean distance measures. Kernelized variants of prototype based classifiers are currently too complex to be applied for larger data sets. Here we propose an extension of Kernelized Generalized Learning Vector Quantization (KGLVQ) employing a sparsity and approximation technique to reduce the learning complexity. We provide generalization error bounds and experimental results on real world data, showing that the extended approach is comparable to SVM on different public data.

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15

Wang, Xianlong, and Annie Qu. "Efficient classification for longitudinal data." Computational Statistics & Data Analysis 78 (October 2014): 119–34. http://dx.doi.org/10.1016/j.csda.2014.04.008.

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16

Pao, Derek, Yiu Keung Li, and Peng Zhou. "Efficient packet classification using TCAMs." Computer Networks 50, no. 18 (December 2006): 3523–35. http://dx.doi.org/10.1016/j.comnet.2006.01.009.

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17

Chen, Chia-Mei, and Shi-Hao Wang. "Advancing Malware Classification With an Evolving Clustering Method." International Journal of Applied Metaheuristic Computing 9, no. 3 (July 2018): 1–12. http://dx.doi.org/10.4018/ijamc.2018070101.

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This article describes how honeypots and intrusion detection systems serve as major mechanisms for security administrators to collect a variety of sample viruses and malware for further analysis, classification, and system protection. However, increased variety and complexity of malware makes the analysis and classification challenging, especially when efficiency and timely response are two contradictory yet equally significant criteria in malware classification. Besides, similarity-based classifications exhibit insufficiency because the mutation and fuzzification of malware exacerbate classification difficulties. In order to improve malware classification speed and attend to mutation, this research proposes the ameliorated progressive classification that integrates static analysis and improved k-means algorithm. This proposed classification aims at assisting network administrators to have a malware classification preprocess and make efficient malware classifications upon the capture of new malware, thus enhancing the defense against malware.

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18

R.S., Dr Sabeenian. "Efficient Gold Tree Child Items Classification System Using Deep Learning." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 1845–59. http://dx.doi.org/10.5373/jardcs/v12sp4/20201671.

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19

Sen, Bandita, and V. Vedanarayanan. "Efficient Classification of Breast Lesion based on Deep Learning Technique." Bonfring International Journal of Advances in Image Processing 6, no. 1 (February 29, 2016): 01–06. http://dx.doi.org/10.9756/bijaip.10446.

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20

Reddy, R. Vijaya Kumar, and U. Ravi Babu. "Efficient Handwritten Digit Classification using User-defined Classification Algorithm." International Journal on Advanced Science, Engineering and Information Technology 8, no. 3 (June 28, 2018): 970. http://dx.doi.org/10.18517/ijaseit.8.3.5397.

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21

Coelho, C. K., S. Das, and A. Chattopadhyay. "A hierarchical classification scheme for computationally efficient damage classification." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 223, no. 5 (May 1, 2009): 497–505. http://dx.doi.org/10.1243/09544100jaero428.

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This article presents a methodology for data mining of sensor signals in a structural health monitoring (SHM) framework for damage classification using a machine-learning-based approach called support vector machines (SVMs). A hierarchical decision tree structure is constructed for damage classification and experiments were conducted on metallic and composite test specimens with surface mounted piezoelectric transducers. Damage was induced in the specimens by fatigue, impact, and tensile loading; in addition, specimens with seeded delaminations were also considered. Data were collected from the surface mounted sensors at different severities of induced damage. A matching pursuit decomposition (MPD) algorithm was used as a feature extraction technique to preprocess the sensor data and extract the input vectors used in classification. Using this binary tree framework, the computational intensity of each successive classifier is reduced and the efficiency of the algorithm as a whole is increased. The results obtained using this classification show that this type of architecture works well for large data sets because a reduced number of comparisons are required. Due to the hierarchical set-up of the classifiers, performance of the classifier as a whole is heavily dependent on the performance of the classifier at higher levels in the classification tree.

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22

Janani, T., and A. Ramanan. "Feature Fusion for Efficient Object Classification Using Deep and Shallow Learning." International Journal of Machine Learning and Computing 7, no. 5 (October 2017): 123–27. http://dx.doi.org/10.18178/ijmlc.2017.7.5.633.

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23

Et. al., K. P. Moholkar ,. "Question Classification for Efficient QA System." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 1876–84. http://dx.doi.org/10.17762/turcomat.v12i2.1526.

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Анотація:

Natural Language Processing (NLP), a subfield of Artificial Intelligence (AI), supports the machine to understand and manipulate the human languages in different sectors. Subsequently, the Question and answering scheme using Machine learning is a challengeable task. For an efficient QA system, understanding the category of a question plays a pivot role in extracting suitable answer. Computers can answer questions requiring single, verifiable answers but fail to answer subjective question demanding deeper understanding of question. Subjective questions can take different forms entailing deeper, multidimensional understanding of context. Identifying the intent of the question helps to extract expected answer from a given passage. Pretrained language models (LMs) have demonstrated excellent results on many language tasks. The paper proposes model of deep learning architecture in hierarchical pattern to learn the semantic of question and extracting appropriate answer. The proposed method converts the given context to fine grained embedding to capture semantic and positional representation, identifies user intent and employs a encoder model to concentrate on answer span. The proposed methods show a remarkable improvement over existing system

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24

Ghosh, Bishwamittra, Dmitry Malioutov, and Kuldeep S. Meel. "Efficient Learning of Interpretable Classification Rules." Journal of Artificial Intelligence Research 74 (August 30, 2022): 1823–63. http://dx.doi.org/10.1613/jair.1.13482.

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Machine learning has become omnipresent with applications in various safety-critical domains such as medical, law, and transportation. In these domains, high-stake decisions provided by machine learning necessitate researchers to design interpretable models, where the prediction is understandable to a human. In interpretable machine learning, rule-based classifiers are particularly effective in representing the decision boundary through a set of rules comprising input features. Examples of such classifiers include decision trees, decision lists, and decision sets. The interpretability of rule-based classifiers is in general related to the size of the rules, where smaller rules are considered more interpretable. To learn such a classifier, the brute-force direct approach is to consider an optimization problem that tries to learn the smallest classification rule that has close to maximum accuracy. This optimization problem is computationally intractable due to its combinatorial nature and thus, the problem is not scalable in large datasets. To this end, in this paper we study the triangular relationship among the accuracy, interpretability, and scalability of learning rule-based classifiers. The contribution of this paper is an interpretable learning framework IMLI, that is based on maximum satisfiability (MaxSAT) for synthesizing classification rules expressible in proposition logic. IMLI considers a joint objective function to optimize the accuracy and the interpretability of classification rules and learns an optimal rule by solving an appropriately designed MaxSAT query. Despite the progress of MaxSAT solving in the last decade, the straightforward MaxSAT-based solution cannot scale to practical classification datasets containing thousands to millions of samples. Therefore, we incorporate an efficient incremental learning technique inside the MaxSAT formulation by integrating mini-batch learning and iterative rule-learning. The resulting framework learns a classifier by iteratively covering the training data, wherein in each iteration, it solves a sequence of smaller MaxSAT queries corresponding to each mini-batch. In our experiments, IMLI achieves the best balance among prediction accuracy, interpretability, and scalability. For instance, IMLI attains a competitive prediction accuracy and interpretability w.r.t. existing interpretable classifiers and demonstrates impressive scalability on large datasets where both interpretable and non-interpretable classifiers fail. As an application, we deploy IMLI in learning popular interpretable classifiers such as decision lists and decision sets. The source code is available at https://github.com/meelgroup/mlic.

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25

Upadhyay, Bhargavi R., Alberto Ros, and Jalpa Shah. "Efficient classification of private memory blocks." Journal of Parallel and Distributed Computing 157 (November 2021): 256–68. http://dx.doi.org/10.1016/j.jpdc.2021.07.005.

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26

FARHADY, Hamid, and Akihiro NAKAO. "TagFlow: Efficient Flow Classification in SDN." IEICE Transactions on Communications E97.B, no. 11 (2014): 2302–10. http://dx.doi.org/10.1587/transcom.e97.b.2302.

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27

Lex, Elisabeth, Christin Seifert, Michael Granitzer, and Andreas Juffinger. "Efficient Cross-Domain Classification of Weblogs." International Journal of Intelligent Computing Research 1, no. 3 (September 1, 2010): 55–62. http://dx.doi.org/10.20533/ijicr.2042.4655.2010.0007.

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28

Thanajiranthorn, Chartwut, and Panida Songram. "Efficient Rule Generation for Associative Classification." Algorithms 13, no. 11 (November 17, 2020): 299. http://dx.doi.org/10.3390/a13110299.

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Анотація:

Associative classification (AC) is a mining technique that integrates classification and association rule mining to perform classification on unseen data instances. AC is one of the effective classification techniques that applies the generated rules to perform classification. In particular, the number of frequent ruleitems generated by AC is inherently designated by the degree of certain minimum supports. A low minimum support can potentially generate a large set of ruleitems. This can be one of the major drawbacks of AC when some of the ruleitems are not used in the classification stage, and thus (to reduce the rule-mapping time), they are required to be removed from the set. This pruning process can be a computational burden and massively consumes memory resources. In this paper, a new AC algorithm is proposed to directly discover a compact number of efficient rules for classification without the pruning process. A vertical data representation technique is implemented to avoid redundant rule generation and to reduce time used in the mining process. The experimental results show that the proposed algorithm archives in terms of accuracy a number of generated ruleitems, classifier building time, and memory consumption, especially when compared to the well-known algorithms, Classification-based Association (CBA), Classification based on Multiple Association Rules (CMAR), and Fast Associative Classification Algorithm (FACA).

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29

El-Sheikh, T. S., and M. M. Syiam. "An efficient technique for lithology classification." IEEE Transactions on Geoscience and Remote Sensing 27, no. 5 (September 1989): 629–32. http://dx.doi.org/10.1109/tgrs.1989.35946.

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30

Maji, Subhransu, Alexander C. Berg, and Jitendra Malik. "Efficient Classification for Additive Kernel SVMs." IEEE Transactions on Pattern Analysis and Machine Intelligence 35, no. 1 (January 2013): 66–77. http://dx.doi.org/10.1109/tpami.2012.62.

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31

Sharma, Piyush, and Kailash Chandra Ray. "Efficient methodology for electrocardiogram beat classification." IET Signal Processing 10, no. 7 (September 2016): 825–32. http://dx.doi.org/10.1049/iet-spr.2015.0274.

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32

Richardson, Eitan, and Michael Werman. "Efficient classification using the Euler characteristic." Pattern Recognition Letters 49 (November 2014): 99–106. http://dx.doi.org/10.1016/j.patrec.2014.07.001.

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33

Belkasim, S. O., M. Shridhar, and M. Ahmadi. "Pattern classification using an efficient KNNR." Pattern Recognition 25, no. 10 (October 1992): 1269–74. http://dx.doi.org/10.1016/0031-3203(92)90028-h.

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34

Dam, E. B., and M. Loog. "Efficient Segmentation by Sparse Pixel Classification." IEEE Transactions on Medical Imaging 27, no. 10 (October 2008): 1525–34. http://dx.doi.org/10.1109/tmi.2008.923961.

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35

Mills, Peter. "Efficient statistical classification of satellite measurements." International Journal of Remote Sensing 32, no. 21 (July 12, 2011): 6109–32. http://dx.doi.org/10.1080/01431161.2010.507795.

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36

Lin, Cong, Chi-Man Pun, Chi-Man Vong, and Don Adjeroh. "Efficient shape classification using region descriptors." Multimedia Tools and Applications 76, no. 1 (October 31, 2015): 83–102. http://dx.doi.org/10.1007/s11042-015-3021-7.

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37

Vlaeminck, Koert, Tim Stevens, Wim Van de Meerssche, Filip De Turck, Bart Dhoedt, and Piet Demeester. "Efficient packet classification on network processors." International Journal of Communication Systems 21, no. 1 (2007): 51–72. http://dx.doi.org/10.1002/dac.885.

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38

Akgun, O. C., and J. Mei. "An energy efficient time-mode digit classification neural network implementation." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2164 (December 23, 2019): 20190163. http://dx.doi.org/10.1098/rsta.2019.0163.

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This paper presents the design of an ultra-low energy neural network that uses time-mode signal processing). Handwritten digit classification using a single-layer artificial neural network (ANN) with a Softmin-based activation function is described as an implementation example. To realize time-mode operation, the presented design makes use of monostable multivibrator-based multiplying analogue-to-time converters, fixed-width pulse generators and basic digital gates. The time-mode digit classification ANN was designed in a standard CMOS 0.18 μm IC process and operates from a supply voltage of 0.6 V. The system operates on the MNIST database of handwritten digits with quantized neuron weights and has a classification accuracy of 88%, which is typical for single-layer ANNs, while dissipating 65.74 pJ per classification with a speed of 2.37 k classifications per second. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.

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39

Alhayali, Royida A. Ibrahem, Munef Abdullah Ahmed, Yasmin Makki Mohialden, and Ahmed H. Ali. "Efficient method for breast cancer classification based on ensemble hoffeding tree and naïve Bayes." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 2 (May 1, 2020): 1074. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp1074-1080.

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<p><span>The most dangerous type of cancer suffered by women above 35 years of age is breast cancer. Breast Cancer datasets are normally characterized by missing data, high dimensionality, non-normal distribution, class imbalance, noisy, and inconsistency. Classification is a machine learning (ML) process which has a significant role in the prediction of outcomes, and one of the outstanding supervised classification methods in data mining is Naives Bayess Classification (NBC). Naïve Bayes Classifications is good at predicting outcomes and often outperforms other classifications techniques. Ones of the reasons behind this strong performance of NBC is the assumptions of conditional Independences among the initial parameters and the predictors. However, this assumption is not always true and can cause loss of accuracy. Hoeffding trees assume the suitability of using a small sample to select the optimal splitting attribute. This study proposes a new method for improving accuracy of classification of breast cancer datasets. The method proposes the use of Hoeffding trees for normal classification and naïve Bayes for reducing data dimensionality.</span></p>

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40

Ershadi, Mohammad Mahdi, and Abbas Seifi. "An efficient Bayesian network for differential diagnosis using experts' knowledge." International Journal of Intelligent Computing and Cybernetics 13, no. 1 (March 9, 2020): 103–26. http://dx.doi.org/10.1108/ijicc-10-2019-0112.

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Анотація:

PurposeThis study aims to differential diagnosis of some diseases using classification methods to support effective medical treatment. For this purpose, different classification methods based on data, experts’ knowledge and both are considered in some cases. Besides, feature reduction and some clustering methods are used to improve their performance.Design/methodology/approachFirst, the performances of classification methods are evaluated for differential diagnosis of different diseases. Then, experts' knowledge is utilized to modify the Bayesian networks' structures. Analyses of the results show that using experts' knowledge is more effective than other algorithms for increasing the accuracy of Bayesian network classification. A total of ten different diseases are used for testing, taken from the Machine Learning Repository datasets of the University of California at Irvine (UCI).FindingsThe proposed method improves both the computation time and accuracy of the classification methods used in this paper. Bayesian networks based on experts' knowledge achieve a maximum average accuracy of 87 percent, with a minimum standard deviation average of 0.04 over the sample datasets among all classification methods.Practical implicationsThe proposed methodology can be applied to perform disease differential diagnosis analysis.Originality/valueThis study presents the usefulness of experts' knowledge in the diagnosis while proposing an adopted improvement method for classifications. Besides, the Bayesian network based on experts' knowledge is useful for different diseases neglected by previous papers.

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41

Hsu, S. H., T. C. Hsia, and M. C. Wu. "An efficient method for creating benchmark classifications for automatic workpiece classification systems." International Journal of Advanced Manufacturing Technology 14, no. 7 (July 1998): 481–94. http://dx.doi.org/10.1007/bf01351394.

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42

Zhong, Jincheng, and Shuhui Chen. "Efficient multi-category packet classification using TCAM." Computer Communications 169 (March 2021): 1–10. http://dx.doi.org/10.1016/j.comcom.2020.12.027.

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43

Assam, Muhammad, Hira Kanwal, Umar Farooq, Said Khalid Shah, Arif Mehmood, and Gyu Sang Choi. "An Efficient Classification of MRI Brain Images." IEEE Access 9 (2021): 33313–22. http://dx.doi.org/10.1109/access.2021.3061487.

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44

WANG, Pi-Chung. "Efficient Packet Classification with a Hybrid Algorithm." IEICE Transactions on Information and Systems E92-D, no. 10 (2009): 1915–22. http://dx.doi.org/10.1587/transinf.e92.d.1915.

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45

Zhu, Linchao, Du Tran, Laura Sevilla-Lara, Yi Yang, Matt Feiszli, and Heng Wang. "FASTER Recurrent Networks for Efficient Video Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 13098–105. http://dx.doi.org/10.1609/aaai.v34i07.7012.

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Typical video classification methods often divide a video into short clips, do inference on each clip independently, then aggregate the clip-level predictions to generate the video-level results. However, processing visually similar clips independently ignores the temporal structure of the video sequence, and increases the computational cost at inference time. In this paper, we propose a novel framework named FASTER, i.e., Feature Aggregation for Spatio-TEmporal Redundancy. FASTER aims to leverage the redundancy between neighboring clips and reduce the computational cost by learning to aggregate the predictions from models of different complexities. The FASTER framework can integrate high quality representations from expensive models to capture subtle motion information and lightweight representations from cheap models to cover scene changes in the video. A new recurrent network (i.e., FAST-GRU) is designed to aggregate the mixture of different representations. Compared with existing approaches, FASTER can reduce the FLOPs by over 10× while maintaining the state-of-the-art accuracy across popular datasets, such as Kinetics, UCF-101 and HMDB-51.

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Shim, Hyun-Bo, and Young-Bae Park. "An Efficient Fingerprint Classification using Gabor Filter." KIPS Transactions:PartB 9B, no. 1 (February 1, 2002): 29–34. http://dx.doi.org/10.3745/kipstb.2002.9b.1.029.

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47

ABIDIN, ADNAN ADNAN, Hamzah Hamzah, and Marselina Endah. "Efficient Fruits Classification Using Convolutional Neural Network." International Journal of Informatics and Computation 3, no. 1 (October 29, 2021): 1. http://dx.doi.org/10.35842/ijicom.v3i1.31.

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Classification of fruits is a growing research topic in image processing. Various papers propose various techniques to deal with the classification of apples. However, some traditional classification methods remain drawbacks to producing an effective result with the big dataset. Inspired by deep learning in computer vision, we propose a novel learning method to construct a classification model, which can classify types of apples quickly and accurately. To conduct our experiment, we collect datasets, do preprocessing, train our model, tune parameter settings to get the highest accuracy results, then test the model using new data. Based on the experimental results, the classification model of green apples and red apples can obtain good accuracy with little loss. Therefore, the proposed model can be a promising solution to deal with apple classification.

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O’Shaughnessy, Stephen, and Frank Breitinger. "Malware family classification via efficient Huffman features." Forensic Science International: Digital Investigation 37 (July 2021): 301192. http://dx.doi.org/10.1016/j.fsidi.2021.301192.

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Nayak, Dillip Ranjan, Neelamadhab Padhy, Pradeep Kumar Mallick, Mikhail Zymbler, and Sachin Kumar. "Brain Tumor Classification Using Dense Efficient-Net." Axioms 11, no. 1 (January 17, 2022): 34. http://dx.doi.org/10.3390/axioms11010034.

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Brain tumors are most common in children and the elderly. It is a serious form of cancer caused by uncontrollable brain cell growth inside the skull. Tumor cells are notoriously difficult to classify due to their heterogeneity. Convolutional neural networks (CNNs) are the most widely used machine learning algorithm for visual learning and brain tumor recognition. This study proposed a CNN-based dense EfficientNet using min-max normalization to classify 3260 T1-weighted contrast-enhanced brain magnetic resonance images into four categories (glioma, meningioma, pituitary, and no tumor). The developed network is a variant of EfficientNet with dense and drop-out layers added. Similarly, the authors combined data augmentation with min-max normalization to increase the contrast of tumor cells. The benefit of the dense CNN model is that it can accurately categorize a limited database of pictures. As a result, the proposed approach provides exceptional overall performance. The experimental results indicate that the proposed model was 99.97% accurate during training and 98.78% accurate during testing. With high accuracy and a favorable F1 score, the newly designed EfficientNet CNN architecture can be a useful decision-making tool in the study of brain tumor diagnostic tests.

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Qi, Qi, Yanlong Li, Jitian Wang, Han Zheng, Yue Huang, Xinghao Ding, and Gustavo Kunde Rohde. "Label-Efficient Breast Cancer Histopathological Image Classification." IEEE Journal of Biomedical and Health Informatics 23, no. 5 (September 2019): 2108–16. http://dx.doi.org/10.1109/jbhi.2018.2885134.

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