Gotowe bibliografie tematyczne / CNN ALGORITHM

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Gotowa bibliografia na temat „CNN ALGORITHM”

Autor: Grafiati

Data publikacji: 11 września 2023

Data aktualizacji: 25 lipca 2025

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Artykuły w czasopismach na temat "CNN ALGORITHM"

Xu, Siyi, Wenwen Liu, Chengpei Wu, and Junli Li. "CNN-HT: A Two-Stage Algorithm Selection Framework." Entropy 26, no. 3 (2024): 262. http://dx.doi.org/10.3390/e26030262.

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The No Free Lunch Theorem tells us that no algorithm can beat other algorithms on all types of problems. The algorithm selection structure is proposed to select the most suitable algorithm from a set of algorithms for an unknown optimization problem. This paper introduces an innovative algorithm selection approach called the CNN-HT, which is a two-stage algorithm selection framework. In the first stage, a Convolutional Neural Network (CNN) is employed to classify problems. In the second stage, the Hypothesis Testing (HT) technique is used to suggest the best-performing algorithm based on the s

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Reddy, Y. Venkat Sai, G. Chandana, G. Chetan Redddy, Ayush Kumar, Suvarna Kumar, and Dr Syed Siraj Ahmed. "Lung Cancer Detection using YOLO CNN Algorithm." International Journal of Research Publication and Reviews 4, no. 5 (2023): 5297–300. http://dx.doi.org/10.55248/gengpi.4.523.43476.

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Tiancheng, Li, Ren Qing-dao-er-ji, and Qiu Ying. "Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia." Advances in Meteorology 2019 (December 6, 2019): 1–13. http://dx.doi.org/10.1155/2019/5176576.

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Hazards of sandstorm are increasingly recognized and valued by the general public, scientific researchers, and even government decision-making bodies. This paper proposed an efficient sandstorm prediction method that considered both the effect of atmospheric movement and ground factors on sandstorm occurrence, called improved naive Bayesian-CNN classification algorithm (INB-CNN classification algorithm). Firstly, we established a sandstorm prediction model based on the convolutional neural network algorithm, which considered atmospheric movement factors. Convolutional neural network (CNN) is a

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Diqi, Mohammad. "Waste Classification using CNN Algorithm." International Conference on Information Science and Technology Innovation (ICoSTEC) 1, no. 1 (2022): 130–35. http://dx.doi.org/10.35842/icostec.v1i1.17.

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One of the cornerstones to efficient waste management is proper and accurate waste classification. However, people find it challenging to categorize such a big and diverse amount of waste. As a result, we employ deep learning to classify waste efficiently. This paper uses the CNN algorithm to provide a problem-solving strategy to waste classification. The model achieves an accuracy of 0.9969 and a loss of 0.0205. As a result, we argue that employing CNN algorithms to categorize waste yields better results and reduces losses efficiently.

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Bahaa, Ahmed, Abdalla Sayed, Laila Elfangary, and Hanan Fahmy. "A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach." PLOS ONE 17, no. 12 (2022): e0278493. http://dx.doi.org/10.1371/journal.pone.0278493.

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Due to the huge number of connected Internet of Things (IoT) devices within a network, denial of service and flooding attacks on networks are on the rise. IoT devices are disrupted and denied service because of these attacks. In this study, we proposed a novel hybrid meta-heuristic adaptive particle swarm optimization–whale optimizer algorithm (APSO-WOA) for optimization of the hyperparameters of a convolutional neural network (APSO-WOA-CNN). The APSO–WOA optimization algorithm’s fitness value is defined as the validation set’s cross-entropy loss function during CNN model training. In this stu

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Roopa, Sri Paladugu, Immadisetty Anusha, and Ramesh M. "Skin Cancer Detection using CNN Algorithm." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 45–49. https://doi.org/10.35940/ijeat.E1079.089620.

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The project “Disease Prediction Model” focuses on predicting the type of skin cancer. It deals with constructing a Convolutional Neural Network(CNN) sequential model in order to find the type of a skin cancer which takes a huge troll on mankind well-being. Since development of programmed methods increases the accuracy at high scale for identifying the type of skin cancer, we use Convolutional Neural Network, CNN algorithm in order to build our model . For this we make use of a sequential model. The data set that we have considered for this project is collected from NCBI, which is w

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Ramekar, Aditya Dhanraj, Pooja Rajendra Sanas, Akshay Rajendra Ghodekar, Shailesh Ramesh, and Prof S. S. Bhagat. "Crop Prediction Using CNN Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 2714–19. http://dx.doi.org/10.22214/ijraset.2022.41873.

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Abstract: In general, agriculture is the backbone of India and also plays an important role in Indian economy by providing a certain percentage of domestic product to ensure the food security. But now-a-days, food production and prediction is getting depleted due to unnatural climatic changes, which will adversely affect the economy of farmers by getting a poor yield and also help the farmers to remain less familiar in forecasting the future crops. This research work helps the beginner farmer in such a way to guide them for sowing the reasonable crops by deploying machine learning, one of the

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Jagtap, Prof Priyanka T. "Disease Prediction Using CNN Algorithm." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35207.

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In this project, I have endeavored to revolutionize disease detection by harnessing the power of artificial intelligence, allowing users to effortlessly and accurately identify three specific diseases from the comfort of their own homes with just a few clicks. This innovative approach eliminates the need to endure days of anticipation for traditional diagnostic reports, ensuring that treatment can commence promptly. The cornerstone of this project lies in the application of Convolutional Neural Networks (CNNs), a cutting-edge deep learning technology. These networks are designed to take input

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Jin, Huang J., and Cathern Hibbins. "Effective Ransomware Attacks Detection Using CNN Algorithm." International Journal of Informatics and Computation 5, no. 2 (2023): 46. http://dx.doi.org/10.35842/ijicom.v5i2.62.

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This study identified ransomware threats in social media platforms by evaluating the performance of Assessing different machine-learning algorithms in various aspects of detecting and classifying ransomware content. The primary problem revolves around the need to enhance cybersecurity within the dynamic landscape of social media, where users are increasingly susceptible to malicious attacks. The research objectives involve assessing the effectiveness of different algorithms, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN),

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Bhalake, Prof. S. B., Aditya Borgave, Nitin Abhang, Nitin Shinde, and Navanath Thorat. "Predictive Maintenance Failure Analysis of Hacksaw Blade Using ML Algorithm." International Journal of Ingenious Research, Invention and Development 1, no. 4 (2023): 18–23. https://doi.org/10.5281/zenodo.8050907.

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<em>The In this Research Article we are going to study Predictive Maintenance Failure Analysis of Hacksaw Blade Using ML Algorithm, with the help of previous paper related to algorithm such as ANN, CNN, SVM, KNN, PCA, etc. Different Parameters are studied related Hacksaw operation and Collected Data of same for analyze collected data we are going to used ANN algorithms with the help of ANN algorithm we will find the fault in Hacksaw operation. By using this we will obtain efficiency of hacksaw operation.</em>

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Rozprawy doktorskie na temat "CNN ALGORITHM"

Shaif, Ayad. "Predictive Maintenance in Smart Agriculture Using Machine Learning : A Novel Algorithm for Drift Fault Detection in Hydroponic Sensors." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42270.

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The success of Internet of Things solutions allowed the establishment of new applications such as smart hydroponic agriculture. One typical problem in such an application is the rapid degradation of the deployed sensors. Traditionally, this problem is resolved by frequent manual maintenance, which is considered to be ineffective and may harm the crops in the long run. The main purpose of this thesis was to propose a machine learning approach for automating the detection of sensor fault drifts. In addition, the solution’s operability was investigated in a cloud computing environment in terms of

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Reiling, Anthony J. "Convolutional Neural Network Optimization Using Genetic Algorithms." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1512662981172387.

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Brandt, Carl-Simon, Jonathan Kleivard, and Andreas Turesson. "Convolutional, adversarial and random forest-based DGA detection : Comparative study for DGA detection with different machine learning algorithms." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20103.

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Malware is becoming more intelligent as static methods for blocking communication with Command and Control (C&C) server are becoming obsolete. Domain Generation Algorithms (DGAs) are a common evasion technique that generates pseudo-random domain names to communicate with C&C servers in a difficult way to detect using handcrafted methods. Trying to detect DGAs by looking at the domain name is a broad and efficient approach to detect malware-infected hosts. This gives us the possibility of detecting a wider assortment of malware compared to other techniques, even without knowledge of the

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El-Shafei, Ahmed. "Time multiplexing of cellular neural networks." Thesis, University of Kent, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365221.

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MOREIRA, André Luis Cavalcanti. "An adaptable storage slicing algorithm for content delivery networks." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/17331.

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Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-07-12T12:20:38Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Thesis - André Luis Cavalcanti Moreira.pdf: 3666881 bytes, checksum: 956e0e6be2bd9f076c0d30eea9d3ea25 (MD5)<br>Made available in DSpace on 2016-07-12T12:20:38Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Thesis - André Luis Cavalcanti Moreira.pdf: 3666881 bytes, checksum: 956e0e6be2bd9f076c0d30eea9d3ea25 (MD5) Previous issue date: 2015-08-28<br

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Yavaş, Gökhan. "Algorithms for Characterizing Structural Variation in Human Genome." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1279345476.

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Tamaki, Suguru. "Improved Algorithms for CNF Satisfiability Problems." 京都大学 (Kyoto University), 2006. http://hdl.handle.net/2433/68895.

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Pallotti, Davide. "Integrazione di dati di disparità sparsi in algoritmi per la visione stereo basati su deep-learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16633/.

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La visione stereo consiste nell’estrarre informazioni di profondità da una scena a partire da una vista sinistra e una vista destra. Il problema si riduce a determinare punti corrispondenti nelle due immagini, che nel caso di immagini rettificate risultano traslati solo orizzontalmente, di una distanza detta disparità. Tra gli algoritmi stereo tradizionali spiccano SGM e la sua implementazione rSGM. SGM minimizza una funzione di costo definita su un volume dei costi, che misura la somiglianza degli intorni di potenziali punti omologhi per numerosi valori di disparità. L’abilità delle reti ne

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Wathugala, Wathugala Gamage Dulan Manujinda. "Formal Modeling Can Improve Smart Transportation Algorithm Development." Thesis, University of Oregon, 2017. http://hdl.handle.net/1794/22608.

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201 pages<br>Ensuring algorithms work accurately is crucial, especially when they drive safety critical systems like self-driving cars. We formally model a published distributed algorithm for autonomous vehicles to collaborate and pass thorough an intersection. Models are built and validated using the “Labelled Transition System Analyser” (LTSA). Our models reveal situations leading to deadlocks and crashes in the algorithm. We demonstrate two approaches to gain insight about a large and complex system without modeling the entire system: Modeling a sub system - If the sub system has is

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Essink, Wesley. "CNC milling toolpath generation using genetic algorithms." Thesis, University of Bath, 2017. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.715245.

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The prevalence of digital manufacturing in creating increasingly complex products with small batch sizes, requires effective methods for production process planning. Toolpath generation is one of the challenges for manufacturing technologies that function based on the controlled movement of an end effector against a workpiece. The current approaches for determining suitable tool paths are highly dependent on machine structure, manufacturing technology and product geometry. This dependence can be very expensive in a volatile production environment where the products and the resources change qui

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Książki na temat "CNN ALGORITHM"

1944-, Webb William, ed. Cake-cutting algorithms: Be fair if you can. A.K. Peters, 1998.

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Quadflieg, Sven, Klaus Neuburg, and Simon Nestler, eds. (Dis)Obedience in Digital Societies. transcript Verlag, 2022. http://dx.doi.org/10.14361/9783839457634.

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Algorithms are not to be regarded as a technical structure but as a social phenomenon - they embed themselves, currently still very subtle, into our political and social system. Algorithms shape human behavior on various levels: they influence not only the aesthetic reception of the world but also the well-being and social interaction of their users. They act and intervene in a political and social context. As algorithms influence individual behavior in these social and political situations, their power should be the subject of critical discourse - or even lead to active disobedience and to th

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Munerman, Viktor, Vadim Borisov, and Aleksandra Kononova. Mass data processing. Algebraic models and methods. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1906037.

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The monograph is devoted to mathematical and algorithmic support of mass data processing based on algebraic models. One of the most common classes of mass processing is considered - processing of highly active structured data. The construction of algebraic models of data and calculations and methods of proving their correspondence are analyzed. Three algebraic systems are studied, which can be used both as data models and as models of calculations. The algebraic and axiomatic methods of proving the correspondence of these models are investigated. A proof of their correspondence is given: homom

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Dubanov, Aleksandr. Simulation of pursuit and parallel approach methods in pursuit problems. Publishing Center RIOR, 2021. http://dx.doi.org/10.29039/02071-5.

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This monograph publishes a description of methods and algorithms for pursuit problems on surfaces. Simulation of tasks in the Mathcad programming environment was made. The development of digital technologies makes it possible to simulate a variety of problems from the theory of differential games. As a result of computer modeling, a lot of animation videos were obtained, which allow you to see the algorithmic solutions proposed by the author in pursuit problems. The monograph can be useful for students of technical universities, graduate students and developers of robotic systems with el

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Yurchuk, Ol'ga, Ol'ga Ul'yanina, Yuliya Tukfeeva, et al. Professional activity of a teacher-psychologist: organizational basis and work protocols: methodological recommendations. Publishing Center RIOR, 2023. http://dx.doi.org/10.29039/02123-1.

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The methodological recommendations consider a wide range of aspects concerning the organizing of psychological and pedagogical support for participants in educational relations. The list of normative legal documents regulating the activity of an educational psychologist (psychologist in the field of education) is presented. Goals and objectives, main types and directions of teacher-psychologist’s activity are given. Forms of reporting documentation based on the results of the professional activity of an educational psychologist (psychologist in the field of education) are presented. The algori

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Gdanskiy, Nikolay. Fundamentals of the theory and algorithms on graphs. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/978686.

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The textbook describes the main theoretical principles of graph theory, the main tasks to be solved using graph structures, and General methods of their solution and specific algorithms, with estimates of their complexity. I covered a lot of the examples given questions to test knowledge and tasks for independent decisions. Along with the control tasks to verify the theoretical training provided practical assignments to develop programs to study topics of graph theory. Meets the requirements of Federal state educational standards of higher education of the last generation. Designed

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P, Banks Stephen. Can Perceptrons find Lyapunov functions?: An algorithmic approach to systems stability. University of Sheffield, Dept. of Control Engineering, 1989.

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Dapporto, Paolo, Paola Paoli, Patrizia Rossi, and Annalisa Guerri. The UTN program. Firenze University Press, 2001. http://dx.doi.org/10.36253/88-8453-032-6.

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We give an algorithm which goal is to find the energy barrier between a given pair of points in a graph which represents the conformational space of a molecule. If the conformational space is homeomorphic to an -dimensional torus, then the graph can be chosen of a particular form. The UTN software, which implements the algorithm in this case, is described in detail. Finally we focus on applications: to show how UTN works, some examples are carried on in detail, with the additional support of graphical animation1 in the twodimensional case. The source code of the program and some data of the ex

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van Es, Karin, and Nanna Verhoeff. Situating Data. Amsterdam University Press, 2023. http://dx.doi.org/10.5117/9789463722971.

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Taking up the challenges of the datafication of culture, as well as of the scholarship of cultural inquiry itself, this collection contributes to the critical debate about data and algorithms. How can we understand the quality and significance of current socio-technical transformations that result from datafication and algorithmization? How can we explore the changing conditions and contours for living within such new and changing frameworks? How can, or should we, think and act within, but also in response to these conditions? This collection brings together various perspectives on the datafi

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Kazakova, Nataliya, and Lyudmila Permitina. ESG-the concept of production accounting and sustainability management of an industrial organization. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/2049716.

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The monograph is devoted to the formation of the concept of production accounting based on the principles of sustainable development. The stages of formation and transformation of accounting practices of industrial organizations are considered. The ESG model of production accounting and control is described. An algorithm for assessing the sustainability of the development of an industrial organization is proposed. For researchers, researchers, teachers, and degree seekers. It can be used in the system of additional professional education, advanced training, for self-development of manage

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Części książek na temat "CNN ALGORITHM"

Elghazi, Khalid, Hassan Ramchoun, and Tawfik Masrour. "Genetic Algorithm for CNN Architecture Optimization." In Artificial Intelligence and Industrial Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43520-1_8.

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Mishra, Debani Prasad, Anmit Ray, Shashwat Singh, Bijaya Krushna Panda, and Gyanabritish Nayak. "Electrical Theft Detection Using CNN Algorithm." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-6390-0_9.

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Deepa, B., S. Geetha, S. Rabiswaran, S. Rahul, K. Sanjaykumar, and P. Selvarameshkumar. "Skin cancer prediction using modified CNN algorithm." In Challenges in Information, Communication and Computing Technology. CRC Press, 2024. http://dx.doi.org/10.1201/9781003559092-14.

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Su, Te-Jen, Yi Hui, Chiao-Yu Chuang, and Wen-Pin Tsai. "MCSA-CNN Algorithm for Image Noise Cancellation." In Lecture Notes in Electrical Engineering. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74935-8_15.

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Korade, Nilesh B., and Mohd Zuber. "Stock Forecasting Using Multichannel CNN and Firefly Algorithm." In Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2742-5_46.

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Di, Lei, Hongzhong Ma, Yi Luo, and Zhiru Li. "A CNN-Based Information Network Attack Detection Algorithm." In Advances in Wireless Communications and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3486-5_19.

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Wu, Jin, Lei Wang, and Yu Wang. "An Improved CNN-LSTM Model Compression Pruning Algorithm." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89698-0_75.

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Wan, Yanchen, Yu Liu, Yuan Li, and Puhong Zhang. "p-Faster R-CNN Algorithm for Food Detection." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00916-8_13.

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Nandhini, S., R. Suganya, K. Nandhana, S. Varsha, S. Deivalakshmi, and Senthil Kumar Thangavel. "Automatic Detection of Leaf Disease Using CNN Algorithm." In Machine Learning for Predictive Analysis. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7106-0_24.

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Rostovski, Jakob, Mohammad Hasan Ahmadilivani, Andrei Krivošei, Alar Kuusik, and Muhammad Mahtab Alam. "Real-Time Gait Anomaly Detection Using 1D-CNN and LSTM." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_17.

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AbstractAnomaly detection and fall prevention represent one of the key research areas within gait analysis for patients suffering from neurological disorders. Deep Learning has penetrated into healthcare applications, encompassing disease diagnosis and anomaly prediction. Connected wearable medical sensors are emerging due to computationally expensive machine learning tasks, which traditionally require use of remote PC or cloud computing. However, to reduce needs for wireless communication channel throughput, for data processing latency, and increase service reliability and safety, on device m

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Streszczenia konferencji na temat "CNN ALGORITHM"

K, Suvarna Vani, Harika Reddy Kalakota, Vaishnavi Velisala, and Sree Vijaya Lakshmi K. "Medicinal Flower Detection using CNN Algorithm." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714599.

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Shahi, Abhishek, Amitabh Wahi, and Anshu Kumar Dwivedi. "Diabetes Prediction in Healthcare Using CNN Algorithm." In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT). IEEE, 2024. https://doi.org/10.1109/icicat62666.2024.10923198.

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Zhang, Hua. "Face Recognition Algorithm Based on CNN-Transformer." In 2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing (CITSC). IEEE, 2025. https://doi.org/10.1109/citsc64390.2025.00063.

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Sakthy, S. Susila, A. Albina, A. Shanmathi, and A. Akshaya. "Attendance System Using CNN Face Recognition Algorithm." In 2025 International Conference on Computing and Communication Technologies (ICCCT). IEEE, 2025. https://doi.org/10.1109/iccct63501.2025.11020058.

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Yang, Ziyi, and Minchen Yang. "Apple leaf scab recognition using CNN and transfer learning." In Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), edited by Hui Yuan and Lu Leng. SPIE, 2025. https://doi.org/10.1117/12.3055902.

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Wu, Zhiqiang. "Rolling bearing fault diagnosis based on VMD-CNN-BiLSTM." In Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), edited by Hui Yuan and Lu Leng. SPIE, 2025. https://doi.org/10.1117/12.3055915.

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Xing, Jinhui, Zhang Chen, Hui Zhang, Yanfang Fu, Guochuang Yan, and Jiang Du. "LWLC-CNN: Ultra-lightweight Network Traffic Classification Algorithm." In 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN). IEEE, 2024. https://doi.org/10.1109/indin58382.2024.10774251.

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Huang, Xiaofei, Fei Shu, Zhiqiang Peng, Kunsan Zhang, and Wei Ma. "Intrusion Detection Algorithm Based on CNN and LSTM." In 2024 International Conference on HVDC (HVDC). IEEE, 2024. http://dx.doi.org/10.1109/hvdc62448.2024.10723071.

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Ingle, Yashwant S., and Nuzhat Faiz Shaikh. "Skin Cancer Recognition Using Attention-Based CNN Algorithm." In 2024 IEEE Pune Section International Conference (PuneCon). IEEE, 2024. https://doi.org/10.1109/punecon63413.2024.10895882.

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Guhan, T., RM Abiraj, M. Muneshwaran, V. Sowndharya Lakshmi, and M. Suriyanarayanan. "Classification of Knee Osteoarthritis Using R-CNN Algorithm." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894085.

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Raporty organizacyjne na temat "CNN ALGORITHM"

Panta, Manisha, Md Tamjidul Hoque, Kendall Niles, Joe Tom, Mahdi Abdelguerfi, and Maik Flanagin. Deep learning approach for accurate segmentation of sand boils in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49460.

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Sand boils can contribute to the liquefaction of a portion of the levee, leading to levee failure. Accurately detecting and segmenting sand boils is crucial for effectively monitoring and maintaining levee systems. This paper presents SandBoilNet, a fully convolutional neural network with skip connections designed for accurate pixel-level classification or semantic segmentation of sand boils from images in levee systems. In this study, we explore the use of transfer learning for fast training and detecting sand boils through semantic segmentation. By utilizing a pretrained CNN model with ResNe

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Tipton, Kelley, Brian F. Leas, Emilia Flores, et al. Impact of Healthcare Algorithms on Racial and Ethnic Disparities in Health and Healthcare. Agency for Healthcare Research and Quality (AHRQ), 2023. http://dx.doi.org/10.23970/ahrqepccer268.

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Objectives. To examine the evidence on whether and how healthcare algorithms (including algorithm-informed decision tools) exacerbate, perpetuate, or reduce racial and ethnic disparities in access to healthcare, quality of care, and health outcomes, and examine strategies that mitigate racial and ethnic bias in the development and use of algorithms. Data sources. We searched published and grey literature for relevant studies published between January 2011 and February 2023. Based on expert guidance, we determined that earlier articles are unlikely to reflect current algorithms. We also hand-se

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Baader, Franz, Jan Hladik, and Rafael Peñaloza. PSpace Automata with Blocking for Description Logics. Aachen University of Technology, 2006. http://dx.doi.org/10.25368/2022.157.

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In Description Logics (DLs), both tableau-based and automatabased algorithms are frequently used to show decidability and complexity results for basic inference problems such as satisfiability of concepts. Whereas tableau-based algorithms usually yield worst-case optimal algorithms in the case of PSpace-complete logics, it is often very hard to design optimal tableau-based algorithms for ExpTime-complete DLs. In contrast, the automata-based approach is usually well-suited to prove ExpTime upper-bounds, but its direct application will usually also yield an ExpTime-algorithm for a PSpace-complet

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Lewis, Dustin, ed. A Compilation of Materials Apparently Reflective of States’ Views on International Legal Issues pertaining to the Use of Algorithmic and Data-reliant Socio-technical Systems in Armed Conflict. Harvard Law School Program on International Law and Armed Conflict, 2020. http://dx.doi.org/10.54813/cawz3627.

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This document is a compilation of materials that at least appear to be reflective of one or more states’ views on international legal issues pertaining to the actual or possible use of algorithmic and data-reliant socio-technical systems in armed conflict. In September of 2018, the Harvard Law School Program on International Law and Armed Conflict (HLS PILAC) commenced a project titled “International Legal and Policy Dimensions of War Algorithms: Enduring and Emerging Concerns.”[1] The project builds on the program’s earlier research and policy initiative on war-algorithm accountability. A goa

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Baader, Franz, Oliver Fernández Gil, and Barbara Morawska. Hybrid Unification in the Description Logic EL. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.197.

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Unification in Description Logics (DLs) has been proposed as an inference service that can, for example, be used to detect redundancies in ontologies. For the DL EL, which is used to define several large biomedical ontologies, unification is NP-complete. However, the unification algorithms for EL developed until recently could not deal with ontologies containing general concept inclusions (GCIs). In a series of recent papers we have made some progress towards addressing this problem, but the ontologies the developed unification algorithms can deal with need to satisfy a certain cycle restricti

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Bergeron, Augustin, Arnaud Fournier, John Kabeya Kabeya, Gabriel Tourek, and Jonathan L. Weigel. Using Machine Learning to Create a Property Tax Roll: Evidence from the City of Kananga, DR Congo'. Institute of Development Studies, 2023. http://dx.doi.org/10.19088/ictd.2023.053.

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Developing countries often lack the financial resources to provide public goods. Property taxation has been identified as a promising source of local revenue, because it is relatively efficient, captures growth in real estate value, and can be progressive. However, many low income countries do not collect property taxes effectively due to missing or incomplete property tax rolls. We use machine learning and computer vision models to construct a property tax roll in a large Congolese city. To train the algorithm and predict the value of all properties in the city, we rely on the value of 1,654

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Allende López, Marcos, Diego López, Sergio Cerón, et al. Quantum-Resistance in Blockchain Networks. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003313.

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This paper describes the work carried out by the Inter-American Development Bank, the IDB Lab, LACChain, Cambridge Quantum Computing (CQC), and Tecnológico de Monterrey to identify and eliminate quantum threats in blockchain networks. The advent of quantum computing threatens internet protocols and blockchain networks because they utilize non-quantum resistant cryptographic algorithms. When quantum computers become robust enough to run Shor's algorithm on a large scale, the most used asymmetric algorithms, utilized for digital signatures and message encryption, such as RSA, (EC)DSA, and (EC)DH

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Baader, Franz, Stefan Borgwardt, and Barbara Morawska. Unification in the Description Logic EL w.r.t. Cycle-Restricted TBoxes. Technische Universität Dresden, 2011. http://dx.doi.org/10.25368/2022.183.

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Unification in Description Logics (DLs) has been proposed as an inference service that can, for example, be used to detect redundancies in ontologies. The inexpressive Description Logic EL is of particular interest in this context since, on the one hand, several large biomedical ontologies are defined using EL. On the other hand, unification in EL has recently been shown to be NP-complete, and thus of significantly lower complexity than unification in other DLs of similarly restricted expressive power. However, the unification algorithms for EL developed so far cannot deal with general concept

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti

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Borgwardt, Stefan, Walter Forkel, and Alisa Kovtunova. Finding New Diamonds: Temporal Minimal-World Query Answering over Sparse ABoxes. Technische Universität Dresden, 2019. http://dx.doi.org/10.25368/2023.223.

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Lightweight temporal ontology languages have become a very active field of research in recent years. Many real-world applications, like processing electronic health records (EHRs), inherently contain a temporal dimension, and require efficient reasoning algorithms. Moreover, since medical data is not recorded on a regular basis, reasoners must deal with sparse data with potentially large temporal gaps. In this paper, we introduce a temporal extension of the tractable language ELH⊥, which features a new class of convex diamond operators that can be used to bridge temporal gaps. We develop a com

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