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Prediction of gamma ray spectrum for 22Na source by feed forward back propagation ANN model

RADIATION PHYSICS AND CHEMISTRY 2023年 202卷

作者： Teke, Cagatay Akkurt, Iskender Arslankaya, Seher Ekmekci, Ismail Gunoglu, Kadir Bayburt Univ Bayburt Turkiye Suleyman Demirel Univ Sci Fac Isparta Turkiye Sakarya Univ Sakarya Turkiye Istanbul Ticaret Univ Istanbul Turkiye Isparta Appl Sci Univ Isparta Turkiye

The radiation has been used in a variety of different fields since its discovery and thus its measurement becomes vital in these industries. Different type detector may be used to measure gamma rays depends on the purposes of measurements. Gamma ray energy spectrum is an important to determine either elemental analysing of a sample or radiation shielding purposes. On the other hand, Artificial Neural Network (ANN) may be used to predict and analysing of gamma-ray spectrum. In this study, gamma ray spectrum from 22Na source detected in NaI (Tl) detector was estimated by ANN. There have been installed ten different ANN models to find the network structure that produces the best predictive value for the gamma ray spectrum NaI (Tl) Detector. Estimation study has been continued with the ANN model with be possessed of lowest error value. ANN model was created by using energy, distance and gamma-rays energy spectrum (called Io) values. In the ANN model developed using the feed forward back propagation algorithm, were used artificial neurons two in the input layer, ten in the hidden layer and one in the output layer. For the case of present work, the experimental data was used 70% for education, 20% for validation and 10% for testing. The estimated values obtained with the ANN model were compared with the experimental results and a good correlation has been found between them (R2 = 0.99).

关键词： Gamma ray spectrum 22Na source ANN back propagation algorithm

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Pharmaceutical plant machine availability prediction using Artificial Neural Network

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2022年第2期16卷 325-335页

作者： Garg, Deepika Roy, Nihar Ranjan Khanna, Ashish GD Goenka Univ Sch Engn Gurugram Haryana India Sharda Univ Sch Engn & Technol Greater Noida UP India MAIT Delhi India

During the 2(nd) phase of COVID-19 pandemic, pharmaceutical plant industry is facing lot of production pressure and machine availability plays vital role in maximizing the manufacturing pharmacy product output. In this paper, Artificial Neural Networks (ANNs) based information processing algorithm has been used to provide a solution to this problem and it has been found suitable to predict machines availability as a prediction function. The considered pharmaceutical plants are dealing with production of medicines related common symptoms in case of COVID-19 (fever, coughing, and breathing problems). The pharmaceutical plant data corresponding to different values of repair and failure rates of different subsystems is collected from plant and analyzed with the help of validated neural network value of availability. This configuration of ANNs approach developed in this research allowed simplifying computational complexities of conventional approaches to solve a large plant machines availability problem. The ANNs methodology in the paper permitted making no assumption, no explicit coding of the problem, no complete knowledge of system configuration, only raw input and clean data found to be sufficient to determine the value of machine availability function for different value of failure and repair rates considered in the paper. The results obtained in the paper are useful for the plant leadership, as the value of failure and repair rates of various subsystems can be fine-tuned at a require clear-cut level to achieve higher availability, and avoid considerably loss of production, loss of man power, and by-pass complete breakdown of concerned system.

关键词： Availability prediction Artificial Neural Networks back propagation algorithm failure rate repair rate pandemic

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Over-The-Air Calibration of A Phase Shifter Network

Over-The-Air Calibration of A Phase Shifter Network

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Zhang, Wei Jiang, Yi Fudan Univ Dept Commun Sci & Engn Shanghai Peoples R China

ISBN: (数字)9781665484800

ISBN: (纸本)9781665484800

Phase shifter networks (PSN) are now widely used in multi-input multi-output (MIMO) systems for its low cost and analog signal processing capability. In practice, the phase shifters may be subject to phase deviations, which needs to be properly estimated and calibrated. This paper proposes a novel over-the-air (OTA) approach to estimate the deviations of the phase shifters at each gear. We formulate the PSN calibration model by the so-termed quasi-neural network (quasi-NN). In training the quasi-NN using the back propagation (BP) algorithm, the phase deviations are automatically estimated. The simulation results verify the effectiveness of the proposed algorithm by showing that the root mean square errors (RMSEs) of the phase estimates are close to the Cramer Rao Bounds (CRBs).

关键词： MIMO quasi-neural network phased array calibration back propagation algorithm

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Segmented parabolic adjustment of the FAST reflector utilizing spatial coordinate rotation transformation

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第10期35卷 106009-106009页

作者： Wang, Fengzhou Kang, Yuchuan Guo, Fenze Jilin Univ Sch Mech & Aerosp Engn Changchun 130025 Peoples R China Sch Disaster Prevent Sci & Technol Coll Earth Sci Beijing 101601 Peoples R China

Since the middle of the twentieth century, the advent of radio telescopes has brought a whole new way and approach to astronomical observation. For Arecibo-type radio telescopes, the tuning optimization of the active reflecting surface (working paraboloid) is the main factor affecting the reflectance calibration. In this study, leveraging the transformation of spatial coordinates through rotation, we introduce an innovative optimization model specifically for the segmented paraboloid of the Five-hundred-meter Aperture Spherical radio Telescope (Hereinafter referred to as FAST) designed by China astronomer and scientist Nan Rendong. This research constructs the equation for an ideal paraboloid and adjusts the working paraboloid to fit within specified constraints such as the orientation of the target star, the adjustment limit of the actuator, and the spatial coordinates. The study employs a combination of coarse and fine grid searches to identify and record the optimal adjustment scheme of the main cable nodes at different angles and the corresponding 2226 actuator coordinates and telescoping length, based on which we build a back propagation model to continuously modify the adjustment scheme. A combination of geometric simulation and Monte Carlo tests were also used for verification. Furthermore, we delve into the impact of variations between adjacent nodes of the modulating actuators, as well as potential longitudinal and radial changes. Compared to the conventional conditioning model, the segmented solution idealized paraboloid we created increases the original reflection efficiency from 77.92% to 95.56% in the working area of 300 m aperture, it will contributes to enhancing the overall performance of FAST.

关键词： reflective surface adjustment spatial coordinate rotation transformation geometric simulation monte carlo test back propagation algorithm

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Analysis on the Parameter of back propagation algorithm with Three Weight Adjustment Structure for Hand Written Digit Recognition

Analysis on the Parameter of Back Propagation Algorithm with...

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10th International Conference on Service Systems and Service Management (ICSSSM)

作者： Kaensar, Chayaporn Ubon Ratchathani Univ Fac Sci Dept Math Stat & Comp Sci Warin Chamrap Ubonratchathani Thailand

ISBN: (纸本)9781467348430

Recently a commonly used method for Recognition of Handwritten Digit Application based on back propagation Neural Network (BPNN) has been widely applied. However, the original algorithm and its modifications contains a number of free parameters which affect particular networks differently and the slight error rate on the selection of these parameters can cause problems. Thus, this paper presents the effect of input parameters on BPNN with three different structures including Simple back propagation, back propagation with momentum terms and back propagation using conjugate gradient descent methods. To do so, this paper determined different parameters such as learning rate, momentum term or even the number of units in the hidden layer that exist in each structure. The data of UCI database is used for experiment in MATLAB program. The result showed that the back propagation with momentum term could perform very well leading to a recognition rate of 99%. The Simple algorithm obtained high recognition rate but it needed to increase learning rate, while back propagation using conjugate gradient descent could provide high result in case of improving hidden neural nodes. Thus, the result confirmed that adjustment of the relevant parameters are significant to obtain better recognition effect and higher accuracy.

关键词： component Handwritten Digit Recognition back propagation algorithm Learning Rate Momentum Term MATLAB

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Development of Spectroscopic Sensor System for an IoT Application of Adulteration Identification on Milk Using Machine Learning

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IEEE ACCESS 2021年 9卷 53979-53995页

作者： Sowmya, N. Ponnusamy, Vijayakumar SRM Inst Sci & Technol Dept ECE Chennai 603203 Tamil Nadu India

Adulteration in milk is a common scenario for gaining extra profit, which may cause severe harmful effects on humans. The qualitative spectroscopic technique provides a better solution for detecting the toxic contents of milk and foodstuffs. All the available spectroscopic methods for milk adulterant detection are based on laboratory-based with costly equipment. This laboratory-based detection takes a long time and is more expensive, which may not be afforded by a common man. To overcome this issue, this research work involves the design and development of a low-cost, portable, multispectral, AI-based, non-destructive spectroscopic sensor system that can be used to detect the milk adulterant in real-time. The designed sensor system uses the spectroscopic method with wavelength ranges from (410-940nm) which consists of three different bands Ultraviolet (UV), visible, and Infra-Red(IR) spectrum to improve the accuracy of detection. The sensor system is connected to the internet via the developed IoT application module, which displays the detected adulterant results in a dedicated web page designed for this purpose. This IoT application enables the adulterant detected results published on the internet immediately with location information for bringing transparency. Adulterant detection problem is formulated as a classification problem and solved by machine learning algorithms of a decision tree, Naive Bayes, linear discriminant analysis, support vector machine and neural network model. The average accuracy of linear discriminant analysis, support vector machine, Naive Bayes, decision tree and neural network model are obtained as 88.1%, 90%, 90%, 91.7% and 92.7% respectively. Genetic algorithm framework is formulated for hyperparameter tuning of neural network model which improved the accuracy from 92.7% to 100%. The model is trained for five different classes of four adulterants, namely Sodium Salicylate, Dextrose, Hydrogen Peroxide, Ammonium Sulphate, and one pure mil

关键词： Dairy products Cows Powders Spectroscopy Sensor systems Internet of Things Hydrogen back propagation algorithm K-means clustering machine learning milk adulteration multispectral spectroscopy neural network

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Prediction of emission characteristics of a diesel engine using experimental and artificial neural networks

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APPLIED NANOSCIENCE 2021年第1期13卷 433-442页

作者： Van Hung, Tran Alkhamis, Hussein H. Alrefaei, Abdulwahed F. Sohret, Yasin Brindhadevi, Kathirvel Van Lang Univ 45 Nguyen Khac Nhu StCo Giang Ward Ho Chi Minh City Vietnam King Saud Univ Coll Sci Dept Bot & Microbiol Riyadh 11451 Saudi Arabia King Saud Univ Coll Sci Dept Zool POB 2455 Riyadh 11451 Saudi Arabia Suleyman Demirel Univ Sch Civil Aviat Dept Airframe & Power Plant Maintenance Isparta Turkey Ton Duc Thang Univ Fac Elect & Elect Engn Ho Chi Minh City Vietnam

The focus of the study is to investigate effects of corn blends on exhaust emissions using Artificial Neural Network (ANN) approach. A series of experiments were conducted on the water-cooled multi-cylinder engine to calibrate the emissions of CO, THC, and NOx. The biodiesel was prepared using the transesterification process. Furthermore, the MgO nanoparticles of 10, 15, 20 and 30 ppm was added to the corn blends through ultrasonication. The ANN is developed to anticipate the emission characteristics of the compression ignition engine. As engine load increases, the emission of carbon monoxide and total hydrocarbons decreases significantly. On the contrary, the emission of NOx gases spiked at higher load. The ANN back propagation algorithm is developed with four input network and one output network to predict the results. The blends C10, C15, C20, and C30 were studied with the developed ANN by varying the engine load. Besides, the highest and lowest value of mean square errors and correlation coefficient were found for CO, THC, and NOx. Meanwhile, the optimized regression coefficients for the emission parameters ranged between 0.8875 and 0.9858. The predicted correlation coefficients for CO, THC, and NOx were 0.9985, 0.9978 and 0.9986, respectively.

关键词： Diesel engine ANN back propagation algorithm Biodiesel Emission NOx

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Implementation of ‘Smishing Detector’: An Efficient Model for Smishing Detection Using Neural Network

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SN Computer Science 2022年第3期3卷 189页

作者： Mishra, Sandhya Soni, Devpriya Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Sector-128 Noida India

Neural network creates a neuron-based network similar to the human nervous system to solve classification problems efficiently. The smishing problem is a binary classification problem in which attackers target smartphone users through text messages. As smishing is a remarkable cybersecurity issue that is troubling researchers and smartphone users these days. Addressing this security issue using the most efficient algorithm is the need of the hour. This manuscript presented an algorithm for the model proposed by authors in ‘Smishing Detector’ model and implemented it using Neural Network. The result obtained proves that the neural network is much efficient in detecting smishing problem. Neural Network outperformed other machine learning algorithms with a difference of 1.11%. Neural Network performed with the final accuracy of 97.40%. In this paper, system extracted the most efficient features of smishing SMS (Short Message Service) using the Neural Network. This manuscript also reported the accuracy shown by the system for each feature selected and implemented. It is evident from the implementation that each feature selected is most effective in smishing detection and URL (Uniform Resource Locator) feature is the most effective feature with an accuracy of 94%. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： back propagation algorithm Cyber security Information security Mobile security Phishing

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Bioprospecting of novel peroxidase from Streptomyces coelicolor strain SPR7 for carcinogenic azo dyes decolorization

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CHEMOSPHERE 2023年 310卷 136836页

作者： Preethi, P. Sai Vickram, Sundaram Das, Raja Hariharan, N. M. Rameshpathy, M. Subbaiya, R. Karmegam, N. Kim, Woong Govarthanan, M. Sree Sastha Inst Engn & Technol Dept Biotechnol Chembarambakkam 600123 Tamil Nadu India Saveetha Inst Med & Tech Sci SIMATS Saveetha Sch Engn Dept Biotechnol Chennai 602105 Tamil Nadu India Vellore Inst Technol VIT Sch Adv Sci Vellore 632014 Tamil Nadu India Vellore Inst Technol VIT Sch Bio Sci & Technol Vellore 632014 Tamil Nadu India Copperbelt Univ Sch Math & Nat Sci Dept Biol Sci Jambo DrPOBox Kitwe 21692 Zambia Govt Arts Coll Autonomous PG & Res Dept Bot Salem 636007 Tamil Nadu India Kyungpook Natl Univ Dept Environm Engn Daegu 41566 South Korea

Peroxidase (POX) is a heme-containing oxidoreductase, its voluminous immuno-diagnostic and bioremediatory intuitions have incited optimization and large scale-generation from novel microbial repertoires. Azo dyes are the most detrimental classes of synthetic dyes and they are the common ecotoxic industrial pollutants in wastewater. In addition, azo dyes are refractory to degradation owing to their chemical nature, comprising of azoic linkages, amino moieties with recalcitrant traits. Moreover, they are major carcinogenic and mutagenic on humans and animals, whereby emphasizing the need for decolorization. In the present study, a novel POX from Streptomyces coelicolor strain SPR7 was investigated for the deterioration of ecotoxic dyestuffs. The initial me-dium component screening for POX production was achieved using, One Factor at a Time and Placket-Burman methodologies with starch, casein and temperature as essential parameters. In auxiliary, Response Surface Methodology (RSM) was recruited and followed by model validation using back propagation algorithm (BPA). RSM-BPA composite approach prophesied that combination of starch, casein, and temperature at optimal values 2.5%, 0.035% and 35 degrees C respectively, has resulted in 7 folds enhancement of POX outturn (2.52 U/mL) compared to the unoptimized media (0.36 U/mL). The concentrated enzyme decolorized 75.4% and 90% of the two azo dyes with lignin (10 mM), respectively. Hence, this investigation confirms the potentiality of mangrove actinomycete derived POX for elimination of noxious azo dyes to overcome their carcinogenic, mutagenic and teratogenic effects on humans and aquatic organisms.

关键词： Azo dyes back propagation algorithm Computational docking Peroxidase Statistical modeling Streptomyces coelicolor

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A review on current advances in machine learning based diabetes prediction

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PRIMARY CARE DIABETES 2021年第3期15卷 435-443页

作者： Jaiswal, Varun Negi, Anjli Pal, Tarun Shoolini Univ Sch Elect & Comp Sci Engn Solan 173212 Himachal Prades India Gachon Univ Coll BioNano Technol Dept Food & Nutr Gyeonggi Do 13120 South Korea Vignans Fdn Sci Technol & Res Deemed Univ Dept Biotechnol Guntur 522213 Andhra Pradesh India

Diabetes is a metabolic disorder comprising of high glucose level in blood over a prolonged period in the body as it is not capable of using it properly. The severe complications associated with diabetes include diabetic ketoacidosis, nonketotic hypersmolar coma, cardiovascular disease, stroke, chronic renal failure, retinal damage and foot ulcers. There is a huge increase in the number of patients with diabetes globally and it is considered a major health problem worldwide. Early diagnosis of diabetes is helpful for treatment and reduces the chance of severe complications associated with it. Machine learning algorithms (such as ANN, SVM, Naive Bayes, PLS-DA and deep learning) and data mining techniques are used for detecting interesting patterns for diagnosing and treatment of disease. Current computational methods for diabetes diagnosis have some limitations and are not tested on different datasets or peoples from different countries which limits the practical use of prediction methods. This paper is an effort to summarize the majority of the literature concerned with machine learning and data mining techniques applied for the prediction of diabetes and associated challenges. This report would be helpful for better prediction of disease and improve in understanding the pattern of diabetes. Consequently, the report would be helpful for treatment and reduce risk of other complications of diabetes. (c) 2021 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

关键词： Diabetes Machine learning Artificial neural network SVM Bayesian network Apriori algorithm back propagation algorithm

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