Academic literature on the topic 'Resilient backpropagation'

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Journal articles on the topic "Resilient backpropagation"

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Onggrono, Kelvin, Tulus Tulus, and Erna Budhiarti Nababan. "ANALISIS PENGGUNAAN PARALLEL PROCESSING MULTITHREADING PADA RESILIENT BACKPROPAGATION." InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) 2, no. 1 (2017): 33–40. http://dx.doi.org/10.30743/infotekjar.v2i1.146.

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Proses pembelajaran neural network merupakan hal yang penting, bertujuan untuk mengenali lingkungan. Proses pembelajaran neural network membutuhkan waktu untuk dapat mengenali lingkungan. Terutama pada salah satu algoritma neural network yaitu resilient backpropagation. Proses untuk mempercepat pembelajaran resilient backpropagation pada penelitian ini adalah menggunakan teknik parallel processing. Teknik parallel processing yang digunakan adalah multithreading. Teknik parallel ini diterapkan pada bagian hidden layer yaitu membagi jumlah neuron pada hidden layer menjadi beberapa subproses yang dikerjakan secara bersamaan, pembagian yang dilakukan berdasarkan pada jumlah thread. Hasil yang didapatkan dalam penerapan parallel processing menggunakan teknik multithreading ke dalam algoritma resilient backpropagation membantu mempercepat waktu proses pembelajaran resilient backpropagation dengan thread yang digunakan sebanyak 3 buah thread.
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Widyaningrum, Bajeng Nurul, and Lingga Kurnia Ramadhani. "Penerapan Metode Resilient Backpropagation (RPROP) Untuk Prediksi Aktivitas Gempa Bumi Berdasarkan Skala Magnitudo." JURNAL FASILKOM 14, no. 2 (2024): 325–31. https://doi.org/10.37859/jf.v14i2.7296.

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Indonesia merupakan salah satu negara dengan tingkat gempa bumi yang cukup tinggi karena memiliki beberapa lempeng utama. Oleh karena itu, prediksi aktivitas gempa bumi menjadi hal yang penting untuk dilakukan guna mengurangi risiko dan kerugian yang ditimbulkan. Telah banyak penelitian terkait dengan prediksi gempa bumi dengan menggunkan beberapa metode. Salah satu metode yang digunakan adalah Metode Backpropagation. Melihat dari penelitian-penelitian sebelumnya, peneliti mengusulkan penerapan Jaringan Saraf Tiruan dengan Algoritma Resilient Backpropagatio untuk prediksi aktivitas gempa bumi berdasarkan nilai magnitudo di Indonesia. Data yang digunakan yaitu data aktivitas gempa bumi bulanan skala magnitudo 4,5 keatas dari periode waktu tahun 1992 hingga 2023 untuk memastikan keberagaman pola aktivitas gempa bumi yang terjadi di berbagai wilayah di Indonesia. Selain parameter skala magnitudo, informasi penting seperti lokasi episenter, dan waktu kejadian juga dimasukkan sebagai fitur input untuk jaringan saraf. Hasil penelitian menunjukkan bahwa prediksi gempa bumi menggunakan metode Jaringan Saraf Tiruan Resilient Backpropagation dengan model arsitektur 6-10-1 dan learning rate 0,6 menunjukkan tingkat akurasi RMSE 0,04778 dan MAE 0,03509. Hasil prediksi menunjukkan pada bulan Februari dengan 67 kejadian dan tertinggi pada bulan Desember dengan 185 kejadian dengan melihat aktifitas gempa bumi di wilayah Indonesia dengan skala magnitudo diatas 4,5. Penelitian ini menunjukkan bahwa model jaringan saraf tiruan dengan algoritma Backpropagation dengan optimasi Resilient mampu memberikan prediksi aktivitas gempa bumi dengan tingkat akurasi yang memuaskan
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SERPEN, GURSEL, and JOEL CORRA. "TRAINING SIMULTANEOUS RECURRENT NEURAL NETWORK WITH RESILIENT PROPAGATION FOR STATIC OPTIMIZATION." International Journal of Neural Systems 12, no. 03n04 (2002): 203–18. http://dx.doi.org/10.1142/s0129065702001199.

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This paper proposes a non-recurrent training algorithm, resilient propagation, for the Simultaneous Recurrent Neural network operating in relaxation-mode for computing high quality solutions of static optimization problems. Implementation details related to adaptation of the recurrent neural network weights through the non-recurrent training algorithm, resilient backpropagation, are formulated throughan algebraic approach. Performance of the proposed neuro-optimizer on a well-known static combinatorial optimization problem, the Traveling Salesman Problem, is evaluated on the basis of computational complexity measures and, subsequently, compared to performance of the Simultaneous Recurrent Neural network trained with the standard backpropagation, and recurrent backpropagation for the same static optimization problem. Simulation results indicate that the Simultaneous Recurrent Neural network trained with the resilient backpropagation algorithm is able to locate superior quality solutions through comparable amount of computational effort for the Traveling Salesman Problem.
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Okprana, Harly, Muhammad Ridwan Lubis, and Jaya Tata Hadinata. "Prediksi Kelulusan TOEFL Menggunakan Metode Resilient Backpropagation." Jurnal Edukasi dan Penelitian Informatika (JEPIN) 6, no. 2 (2020): 275. http://dx.doi.org/10.26418/jp.v6i2.41224.

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Prediksi kelulusan TOEFL peserta didik Michigan Computer English Course diperlukan untuk meninjau sejauh mana tingkat pemahaman peserta didik. Backpropagation merupakan salah satu teknik yang baik digunakan untuk prediksi, akan tetapi jika backpropagation dalam training data dengan jumlah besar serta parameter-parameter yang digunakan kurang tepat, akan terjadi proses traning data lebih lambat. Maka diperlukan metode optimasi untuk mempercepat training Bacpropagation dalam memprediksi kelulusan dengan menggunakan metode Resilient Backpropagation. Data yang diolah sebanyak 182 data peserta didik tahun 2016-2018. Tingkat akurasi pengujian semakin baik yakni 100% dengan nilai MSE semakin kecil 0.00342 serta nilai Epoch juga semakin kecil menjadi 5. Sehingga penelitian ini menjadi indikator dalam pengembangan prediksi TOEFL dimasa yang akan datang.
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Abdurrakhman, Arief, Lilik Sutiarso, Makhmudun Ainuri, Mirwan Ushada, and Md Parvez Islam. "Prediction of Biogas Production from Agriculture Waste Biomass Based on Backpropagation Neural Network." BIO Web of Conferences 165 (2025): 06001. https://doi.org/10.1051/bioconf/202516506001.

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An integral aspect of sustainable agriculture involves the implementation of a meticulously planned waste management infrastructure. One strategy to achieve this objective is the utilization of agricultural waste, specifically in the form of biomass, to generate sustainable energy such as biogas. This study aims to provide valuable prediction model for biogas production with many variables which is influenced. The study identifies four variables, namely pH, moisture content, Organic Loading Rate (OLR) and temperature which significantly impact on the biogas production, especially in Indonesia. Any fluctuations in these variables can affect biogas productivity. Therefore, machine learning techniques such as adaptive backpropagation neural network is used to modeling for predition of biogas production. The configuration of the multilayer perceptron model, combined with the Backpropagation Algorithm, establishes the fundamental framework for the proposed advancements. This study explores three different types of training algorithms in the backpropagation neural network, specifically Adaptive Learning Rate, Levenberg-Marquardt, and Resilient Backpropagation. The Resilient Backpropagation approach exhibited exceptional effectiveness, as evidenced by a correlation coefficient of 0.9411 for training and 0.90423 for testing. The best results obtained for Mean Squared Error (MSE) and Mean Absolute Error (MAE) were 0.0038 and 0.0316, respectively. The Standard Deviation was computed to be 0.0615. This study highlights the potential benefits of employing Resilient Backpropagation Neural Network alghoritm to determine the appropriate operational parameters and accurately predict the biogas production
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Rozario, Victor Stany Rozario, and Partha Sutradhar. "In-Depth Case Study on Artificial Neural Network Weights Optimization Using Meta-Heuristic and Heuristic Algorithmic Approach." AIUB Journal of Science and Engineering (AJSE) 21, no. 2 (2022): 98–109. http://dx.doi.org/10.53799/ajse.v21i2.379.

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The Meta-heuristic and Heuristic algorithms that have been introduced for deep neural network optimization is in this paper. Artificial Intelligence, and also the most used Deep Learning methods are all growing in popularity these days, thus we need faster optimization strategies for finding the results of future activities. Neural Network Optimization with Particle Swarm Optimization, Backpropagation (BP), Resilient Propagation (Rprop), and Genetic Algorithm (GA) is used for numerical analysis of different datasets and comparing each other to find out which algorithms work better for finding optimal solutions by reducing training loss. Genetic algorithm and also bio-inspired Particle Swarm Optimization is introduced in this paper. Besides, Resilient Propagation and Conventional Backpropagation algorithms which are application-specific algorithms have also been introduced. Meta-heuristic algorithms GA and PSO are a higher-level formula and problem-independent technique that may be used to a diverse number of challenges. The characteristic of Heuristic algorithms has extremely specific features that vary depending on the problem. The conventional Backpropagation (BP) based optimization, the Particle Swarm Optimization methodology, and Resilient Propagation (Rprop) are all fully presented, and how to apply these procedures in Artificial Deep Neural networks Optimization is also thoroughly described. Applied numerical simulation over several datasets proves that the Meta-heuristic algorithm Particle Swarm Optimization and also Genetic Algorithm performs better than the conventional heuristic algorithm like Backpropagation and Resilient Propagation.
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Christofer, Ariel, Chandrasurya Kusuma, Vincent Pribadi, and Widodo Budiharto. "The Notation Scanner Systems Using Resilient Backpropagation Method." Procedia Computer Science 59 (2015): 98–105. http://dx.doi.org/10.1016/j.procs.2015.07.342.

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Saputra, Widodo, Tulus, Muhammad Zarlis, Rahmat Widia Sembiring, and Dedy Hartama. "Analysis Resilient Algorithm on Artificial Neural Network Backpropagation." Journal of Physics: Conference Series 930 (December 2017): 012035. http://dx.doi.org/10.1088/1742-6596/930/1/012035.

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Saputra, Widodo, Agus Perdana Windarto, and Anjar Wanto. "Analysis of the Resilient Method in Training and Accuracy in the Backpropagation Method." IJICS (International Journal of Informatics and Computer Science) 5, no. 1 (2021): 52. http://dx.doi.org/10.30865/ijics.v5i1.2922.

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Artificial Neural Network (ANN) is one of the clusters of computer science that leads to artificial intelligence, there are several methods in ANN, one of which is the backpropagation method. This method is used in the prediction process. In some cases the backpropagation method can help in problems solving, especially predictions. However, the backpropagation method has weaknesses. The results of the backpropagation method are very influenced by the determination of the parameters so that the convergence becomes very slow. So needed an optimization method to optimize the performance of the bakpropagation method. The resilient backpropgation method is one solution, this method can change the weight and bias of the network with a direct adaptation process of weighting based on local gradient information from learning iterations so that it can provide optimal results. The data used is the Higher Education Gross Enrollment Rate in Indonesia from 2015-2020 by province. The results were obtained from several data testing with architectural experiments 3-5-1, 3-20-1, 3-37-1, 3-19-1, 3-26-4 and 3-4-1 from backpropagation and resilient testing, shows that the data training process can be optimized significantly, but the accuracy is not evenly optimal
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Cahyani, Nita, Rahmat Irsyada, and Rahmawati Mahmuda. "Penerapan Algoritma Neural Network untuk Klasifikasi Diabetes Mellitus: Perbandingan Backpropagation dan Resillient Backpropagation." Digital Transformation Technology 4, no. 2 (2025): 1067–74. https://doi.org/10.47709/digitech.v4i2.5208.

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Diabetes Mellitus (DM) adalah gangguan metabolisme yang ditandai dengan hiperglikemia kronis dan kelainan metabolisme karbohidrat, lipid, dan protein yang disebabkan oleh kelainan sekresi insulin, kerja insulin, atau keduanya. Penelitian ini bertujuan untuk membandingkan hasil klasifikasi menggunakan analisis Backpropagation Neural Network (BPNN) dengan Resilient Backpropagation Neural Network (RBPNN) pada kasus Diabetes Mellitus. Metode yang digunakan pada penelitian ini adalah metode analisis BPNN dan RBPNN dengan sumber data yang diperoleh dari RSUD Sosodoro Djatikusumo Bojonegoro. Dari penelitian ini diperoleh hasil penyebab utama faktor-faktor yang mengakibatkan DM adalah faktor keturunan, tekanan darah dan umur. Dari penelitian ini dapat disimpulkan bahwa faktor dominan yang ada pada penderita DM adalah faktor keturunan yang telah dijelaskan oleh model terbaik yaitu RBPNN
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Dissertations / Theses on the topic "Resilient backpropagation"

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Vicentini, Rafael Estéfano. "Uso de redes neurais artificiais para detecção de pele em imagens digitais." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/152329.

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Submitted by Rafael Estefano Vicentini null (rafaelvicentini@dee.feis.unesp.br) on 2017-12-14T18:01:32Z No. of bitstreams: 1 DISSERTAÇÃO-RAFAEL ESTÉFANO VICENTINI.pdf: 15039479 bytes, checksum: 43a2765c1d39e13b3435f194a64198ec (MD5)<br>Approved for entry into archive by Cristina Alexandra de Godoy null (cristina@adm.feis.unesp.br) on 2017-12-18T10:48:16Z (GMT) No. of bitstreams: 1 vicentini_re_me_ilha.pdf: 15039479 bytes, checksum: 43a2765c1d39e13b3435f194a64198ec (MD5)<br>Made available in DSpace on 2017-12-18T10:48:16Z (GMT). No. of bitstreams: 1 vicentini_re_me_ilha.pdf: 15039479 bytes, checksum: 43a2765c1d39e13b3435f194a64198ec (MD5) Previous issue date: 2017-10-20<br>Na última década, o aumento da capacidade de processamento de informação em computadores e dispositivos de uso pessoal possibilitou o desenvolvimento de filtros e classificadores automatizados que operam em tempo real, aplicados em diversas áreas. No âmbito do Processamento Digital de Imagens associado às Redes Neurais Artificiais, os filtros emulam a percepção humana buscando por padrões para identificação de características de interesse. Filtros que têm por objetivo restringir o acesso a conteúdo impróprio partem da identificação de pele - principal indício de presença humana em uma imagem. Independentemente de sua complexidade e/ou robustez, caso o classificador não seja capaz de identificar as diferentes tonalidades de pele sob diferentes condições de captura, sua eficácia é prejudicada. Frente à diversificada forma de descrever uma tonalidade de pele usando diferentes espaços de cor, neste estudo foram destacados os espaços de cor RGB, YCbCr e HSV, amplamente utilizados em equipamentos de captura (por exemplo câmeras fotográficas e filmadoras digitais). A partir de exemplos apresentados durante a etapa de treinamento, as RNAs devem estar aptas para classificar as tonalidades em dois grupos distintos: pele e não pele. Dentre os espaços de cores indicados, seja utilizando ou descartando o aspecto da iluminação (critério amplamente discutido na literatura), este trabalho busca avaliar qual possui a maior taxa de detecção de pele em uma imagem.<br>Over the last decade, the increasing capacity of data processing in personal computers and devices could develop filters and automatic classifiers working in real time and applied in several areas. Considering Digital Image Processing and Artificial Neural Networks, these filters emulate the human perception searching for patterns to identify specific features. Filters which the main goal is to restrict the access to inappropriate content starts identifying skin tones - the main evidence of human presence in a picture. Although being complex and robust, if the classifier is not able to identify distinct skin tones under random capture conditions, the accuracy is minimal. Facing several ways on describing skin tones over different color spaces, this work uses the RGB, YCbCr and HSV color spaces which are widely applied in recording devices (photographic and digital cameras for example). Based on the examples shown during the training phase, the ANNs must be able to classify skin tones into two distinct groups: skin and non skin. Among the different color spaces used, considering or not the luminance aspect (widely discussed on papers), this work intends to evaluate which one has the highest detection accuracy to identify skin tone in such a picture.
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Fick, Machteld. "Neurale netwerke as moontlike woordafkappingstegniek vir Afrikaans." Diss., 2002. http://hdl.handle.net/10500/584.

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Text in Afrikaans<br>Summaries in Afrikaans and English<br>In Afrikaans, soos in NederJands en Duits, word saamgestelde woorde aanmekaar geskryf. Nuwe woorde word dus voortdurend geskep deur woorde aanmekaar te haak Dit bemoeilik die proses van woordafkapping tydens teksprosessering, wat deesdae deur rekenaars gedoen word, aangesien die verwysingsbron gedurig verander. Daar bestaan verskeie afkappingsalgoritmes en tegnieke, maar die resultate is onbevredigend. Afrikaanse woorde met korrekte lettergreepverdeling is net die elektroniese weergawe van die handwoordeboek van die Afrikaanse Taal (HAT) onttrek. 'n Neutrale netwerk ( vorentoevoer-terugpropagering) is met sowat. 5 000 van hierdie woorde afgerig. Die neurale netwerk is verfyn deur 'n gcskikte afrigtingsalgoritme en oorfragfunksie vir die probleem asook die optimale aantal verborge lae en aantal neurone in elke laag te bepaal. Die neurale netwerk is met 5 000 nuwe woorde getoets en dit het 97,56% van moontlike posisies korrek as of geldige of ongeldige afkappingsposisies geklassifiseer. Verder is 510 woorde uit tydskrifartikels met die neurale netwerk getoets en 98,75% van moontlike posisies is korrek geklassifiseer.<br>In Afrikaans, like in Dutch and German, compound words are written as one word. New words are therefore created by simply joining words. Word hyphenation during typesetting by computer is a problem, because the source of reference changes all the time. Several algorithms and techniques for hyphenation exist, but results are not satisfactory. Afrikaans words with correct syllabification were extracted from the electronic version of the Handwoordeboek van die Afrikaans Taal (HAT). A neural network (feedforward backpropagation) was trained with about 5 000 of these words. The neural network was refined by heuristically finding a suitable training algorithm and transfer function for the problem as well as determining the optimal number of layers and number of neurons in each layer. The neural network was tested with 5 000 words not the training data. It classified 97,56% of possible points in these words correctly as either valid or invalid hyphenation points. Furthermore, 510 words from articles in a magazine were tested with the neural network and 98,75% of possible positions were classified correctly.<br>Computing<br>M.Sc. (Operasionele Navorsing)
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Book chapters on the topic "Resilient backpropagation"

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Wang, Xugang, Hongan Wang, Guozhong Dai, and Zheng Tang. "A Reliable Resilient Backpropagation Method with Gradient Ascent." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-37275-2_31.

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Maarouf, Mustapha, and Blas J. Galván González. "Physical Activity Classification Using Resilient Backpropagation (RPROP) with Multiple Outputs." In Computer Aided Systems Theory - EUROCAST 2013. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-53856-8_10.

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Fernandes, Paula Odete, João Paulo Teixeira, João Ferreira, and Susana Azevedo. "Training Neural Networks by Resilient Backpropagation Algorithm for Tourism Forecasting." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00569-0_6.

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Sandjak, K., M. Ouanani, and T. Messafer. "Bayesian Regularized Backpropagation Neural Network Model to Estimate Resilient Modulus of Unbound Granular Materials for Pavement Design." In Lecture Notes in Networks and Systems. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-21216-1_48.

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Lenka, Saroj Kumar, and Ambarish G. Mohapatra. "Hybrid Decision Model for Weather Dependent Farm Irrigation Using Resilient Backpropagation Based Neural Network Pattern Classification and Fuzzy Logic." In Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 1. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30933-0_30.

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Kapp Jr., Claudio, Eduardo Fávero Caires, and Alaine Margarete Guimarães. "Discriminating Biomass and Nitrogen Status in Wheat Crop by Spectral Reflectance Using ANN Algorithms." In Innovations and Trends in Environmental and Agricultural Informatics. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5978-8.ch007.

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Precision agriculture has the goal of reducing cost which is difficult when it is related to fertilizer application. Nitrogen (N) is the nutrient absorbed in greater amounts by crops and the N fertilizer application presents significant costs. The use of spectral reflectance sensors has been studied to identify the nutritional status of crops and prescribe varying N rates. This study aimed to contribute to the determination of a model to discriminating biomass and nitrogen status in wheat through two sensors, GreenSeeker and Crop Circle, using the resilient propagation and backpropagation artificial neural networks algorithms. As a result, a strong correlation to the sensor readings with the aboveground biomass production and N extraction by plants was detected. For both algorithms a satisfactory model for estimating wheat dry biomass production was established. The best backpropagation and resilient propagation models defined showed better performance for the GreenSeeker and Crop Circle sensors, respectively.
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Ghosh, Arghyadeep, and Mrinal Das. "Bottlenecked Backpropagation to Train Differentially Private Deep Neural Networks." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240743.

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Deep neural networks often tend to memorize data and can risk information leakage if trained on private data. There has been some attempts to train deep neural networks by contaminating the gradients during backpropagation. Over the years, this has become one of the most prominent techniques to make neural networks differentially private. One downside of this method is that contaminated gradients lead to suboptimal solutions during backpropagation. In this paper, we make an attempt to diminish the contamination effect by proposing a bottlenecked backpropagation technique. The proposed bottlenecked backpropagation technique follows Reny differential privacy, a recently developed more optimized version in the realm of differential privacy. On the other hand, the bottlenecked backpropagation considers the direction and neglects the magnitude of the gradient vectors. The idea is built on top of signed stochastic gradient descent, another recent advancement in the optimization methods for deep learning. By prioritizing gradient direction over magnitude, it minimizes noise impact on model convergence. Experimental results on benchmarks including MNIST, FMNIST, and IMDB datasets demonstrate substantial improvements over the state-of-the-art methods, achieving faster convergence and higher model accuracy, striking a promising balance between privacy and performance. Furthermore, we observe the proposed methods to be resilient against membership inference attacks.
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Nguyen, Loc. "TUTORIAL ON ARTIFICIAL NEURAL NETWORK." In Futuristic Trends in Artificial Intelligence Volume 3 Book 5. Selfypage Developers Private Limited, 2024. http://dx.doi.org/10.58532/v3bbai5p1ch3.

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It is undoubtful that artificial intelligence (AI) is being the trend of computer science and this trend is still ongoing in the far future even though technologies are being developed suddenly fast because computer science does not reach the limitation of approaching biological world yet. Machine learning (ML), which is a branch of AI, is a spearhead but not a key of AI because it sets first bricks to build up an infinitely long bridge from computer to human intelligence, but it is also vulnerable to environmental changes or input errors. There are three typical types of ML such as supervised learning, unsupervised learning, and reinforcement learning (RL) where RL, which is adapt progressively to environmental changes, can alleviate vulnerability of machine learning but only RL is not enough because the resilience of RL is based on iterative adjustment technique, not based on naturally inherent aspects like data mining approaches and moreover, mathematical fundamentals of RL lean forwards swing of stochastic process. Fortunately, artificial neural network, or neural network (NN) in short, can support all three types of ML including supervised learning, unsupervised learning, and RL where the implicitly regressive mechanism with high order through many layers under NN can improve the resilience of ML. Moreover, applications of NN are plentiful and multiform because three ML types are supported by NN; besides, NN training by backpropagation algorithm is simple and effective, especially for sample of data stream. Therefore, this study research is an introduction to NN with easily understandable explanations about mathematical aspects under NN as a beginning of stepping into deep learning which is based on multilayer NN. Deep learning, which is producing amazing results in the world of AI, is undoubtfully being both spearhead and key of ML with expectation that ML improved itself by deep learning will become both spearhead and key of AI, but this expectation is only for ML researchers because there are many AI subdomains are being invented and developed in such a way that we cannot understand exhaustedly. It is more important to recall that NN, which essentially simulates human neuron system, is appropriate to the philosophy of ML that constructs an infinitely long bridge from computer to human intelligence.
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Conference papers on the topic "Resilient backpropagation"

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Roslan, Muhammad Ikhsan, Noor Aida Syakira Ahmad Sabri, Nur Athirah Syafiqah Noramli, Nurlaila Ismail, Zakiah Mohd Yusoff, and Mohd Nasir Taib. "Enhancing NARX Neural Network for Agarwood Oil Grading: A Study on Resilient Backpropagation Training Method." In 2024 IEEE 22nd Student Conference on Research and Development (SCOReD). IEEE, 2024. https://doi.org/10.1109/scored64708.2024.10872669.

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Kaur, Sukhdeep, Ajay Shiv Sharma, Harpreet Kaur, and Karamjit Singh. "Gene selection for tumor classification using resilient backpropagation neural network." In 2016 2nd International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Fall). IEEE, 2016. http://dx.doi.org/10.1109/icaccaf.2016.7748988.

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Etemad, Seyed Ali, and Ali Arya. "3D human action recognition and style transformation using resilient backpropagation neural networks." In 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2009). IEEE, 2009. http://dx.doi.org/10.1109/icicisys.2009.5357690.

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Annas, Suwardi, Aswi Aswi, Muhammad Abdy, Bobby Poerwanto, and Riska Yanu Fa’rifah. "Stroke type classification model based on risk factors using resilient backpropagation neural networks." In 4TH INTERNATIONAL SCIENTIFIC CONFERENCE OF ALKAFEEL UNIVERSITY (ISCKU 2022). AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0181745.

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Dutta, M., A. Chatterjee, and A. Rakshit. "A Resilient Backpropagation Neural Network based Phase Correction System for Automatic Digital AC Bridges." In 2004 Conference on Precision Electromagnetic Measurements. IEEE, 2004. http://dx.doi.org/10.1109/cpem.2004.305621.

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Annas, S., and R. Arisandi. "Improving the accuracy of rainfall forecasting using multivariate transfer function and resilient backpropagation neural network." In 3RD ELECTRONIC AND GREEN MATERIALS INTERNATIONAL CONFERENCE 2017 (EGM 2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5002378.

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Mohammed, N. "Application of artificial neural networks (ANN) to evaluate centrifugal pump characteristics." In Civil and Environmental Engineering for Resilient, Smart and Sustainable Solutions. Materials Research Forum LLC, 2025. https://doi.org/10.21741/9781644903414-96.

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Abstract. This paper uses an Artificial Neural Network (ANN) technique to give an experimental and comparative examination of centrifugal pump characteristics. Comprehensive physical testing is a common component of traditional pump performance evaluation techniques, which may be expensive and time-consuming. In this study, we created an ANN model to forecast important performance metrics, such as speed, torque, pressure based on input data. These metrics include flow discharge, height, hydraulic power, motor power, and efficiency. The ANN model was trained and validated using experimental data that came from a series of carefully monitored experiments conducted on a typical centrifugal pump. Using a Levenberg Marquardt backpropagation approach, the ANN model was trained to attain high prediction accuracy by refining the topology of the network found to be high R-squared value of 0.98 and low RMSE by effectively predicting important parameters. Next, in order to assess the ANN model's predictive power, its performance was contrasted with the outcomes of the experiments. The results show that there is a strong degree of correlation between the predicted and experimental data, indicating that the ANN technique offers a dependable and effective way to forecast centrifugal pump characteristics.
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Kaur, Gurpreet, and Gurmeet Kaur. "Fuzzy-Neuro Network in a CO-OFDM system: Various Membership Functions Comparison." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.46.

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Fuzzy-Neuro Network based nonlinear equalizer (FNN-NLE) has been used for the extenuation of nonlinearities in optical communication systems. Until now, many membership functions with resilient backpropagation activation function was used for making FNN-NLE in a coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. Despite this, no research is reflecting the comparison of different membership functions (MFs). In this paper, various membership functions such as gaussian MF, gaussian combination MF, triangular MF, difference between two sigmoidal functions MF, pi shaped MF, generalized bell shaped MF, trapezoidal MF and product of two sigmoid functions MF has been compared. From this study, the maximum performance in terms of BER is achieved with gaussian membership function has been concluded.
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Canbolat, S., M. Cicek, and E. Artun. "Data-Driven Reservoir Performance Forecasting: Leveraging Machine Learning for Complex Reservoirs." In SPE Europe Energy Conference and Exhibition. SPE, 2025. https://doi.org/10.2118/225507-ms.

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Abstract Recent advancements in data collection, storage, and processing have facilitated the widespread use of data-driven models for forecasting reservoir and well performance, which is essential for evaluating hydrocarbon assets, making sound economic choices, and optimizing reservoir management. Data analytics workflows play a crucial role by supporting descriptive analysis, identifying variable relationships through diagnostic insights, utilizing predictive models through machine learning, and using prescriptive analytics to guide strategic decision-making for oil and gas reservoirs. This study presents an application of an integrated workflow (Artun et al. 2025) that leverages data analytics and machine learning to improve reservoir management and characterization, particularly in complex, highly fractured, and faulted reservoirs. The workflow begins with data collection and analysis, focusing on the spatial estimation of reservoir properties from well logs. A performance prediction model, based on artificial neural networks (ANNs), was developed using a data set from 19 wells. This model utilized 33 input parameters, that consisted raw well logs, estimated reservoir properties and operational/geographical features, to predict six key performance indicators related to production. The ANN model, comprising three hidden layers with a resilient backpropagation learning algorithm, demonstrated satisfactory accuracy with high R² values for both training and testing sets. The distance to the water-oil contact, active production days, porosity, depth, and permeability were identified as the most correlated variables for well performance. The importance of input parameters in the developed forecasting model, including well log and operational data, aligned with exploratory data analysis findings, confirming the model's reliability. The model was successfully applied to estimate the performance through a spatial grid, with the help of spatially estimated reservoir properties. By combining these with operational inputs, the forecasting model identified potential high-production zones in the reservoir for 2 years of production. The model particularly suggested the central and northeastern regions, and accurately predicted the performance of in-fill wells drilled in recent years.
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Gunel, B. T., E. Artun, S. Gul, B. Kulga, Y. D. Pak, and A. O. Herekeli. "Machine Learning-Based Estimation of Solids Content in Drilling Fluids Through Utilization of a Comprehensive Mud-Report Database." In SPE Europe Energy Conference and Exhibition. SPE, 2025. https://doi.org/10.2118/225536-ms.

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Abstract Characterization and optimization of total solids content of drilling fluids is critical for the efficiency and success of drilling operations. Traditional solids content analysis methods, such as retort analysis, require substantial human intervention and time, which can lead to inaccuracies, time-management issues, and increased operational risks. To address these issues and complement automated rheological property measurements during drilling, this study aims to develop and validate a machine learning-based framework for estimating solids content in drilling fluids from readily available rheological parameters. A comprehensive data set was compiled from more than 1600 laboratory reports of drilling fluid analyses across 130 oil wells globally. Text mining packages were used to convert laboratory reports into a numeric data set which consists of drilling fluid properties. Pre-processing steps were taken to clean, filter and reduce the dimensionality of the data. Unrealistic measurements, and missing data were filtered out and several feature-selection approaches such as least absolute shrinkage and selection operator (LASSO) regression, permutation feature importance, and correlation coefficient matrix were employed to remove redundant variables from the data set. Cleaned and dimensionality-optimized data set was then used for machine-learning model development. Due to their ability to capture non-linear relationships among multiple variables, artificial neural networks employing the resilient backpropagation algorithm were selected to fit regression models for estimating total solids content. Various configurations were tested, including different numbers of layers and neurons, as well as alternative data partitioning schemes. Considering reasonable values of rheological properties based on domain knowledge, filtering was applied to remove outliers which indicate experimental error. As a result of combining the observations from different feature importance analyses and considering physical relationships between rheological variables, redundant features as well as reports with many missing values were removed from the data set. Five features, including mud weight, chloride content, plastic viscosity, oil-water ratio and API fluid loss, were selected to carry over into the modeling stage. As a result of experimenting with 120 different neural network configurations, a two-hidden layer neural network with 40-30 neurons in each hidden layer was selected. The model achieved an R2 value of 0.96 for the training set and 0.89 for the testing set. The root-mean-square error (RMSE) was 0.87% for training and 1.27% for testing, both of which were considered acceptable levels of error. An analysis of the network weights indicated that mud weight (MW) and oil–water ratio (OWR) were the most influential features, contributing more significantly to the model’s predictions compared to other input variables.
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