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FRACTIONAL-ORDER SENSITIVITY ANALYSIS OF LEPTOSPIROSIS TREATMENT DYNAMICS IN THE PRESENCE OF AN ENVIRONMENTAL BACTERIAL RESERVOIR WITH ARTIFICIAL NEURAL NETWORK SUPPORT
(2026-01-01)
Irshad, Ateeq Ur Rehman
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Ullah, Naeem
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Hassaballa, Abaker A.
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Jeelani, Mdi Begum
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Fatima, Nahid
In this study, we develop and analyze a deterministic fractional-order human–animal–environment transmission model using the Caputo fractional-order derivative (CFOD) to investigate leptospirosis transmission dynamics, explicitly incorporating treatment for infected humans in the human population. The model accounts for indirect transmission through an environmental bacterial reservoir and shedding from infected animals. The qualitative features of the suggested model, such as positivity, boundedness, existence and uniqueness of solution, equilibrium points, and biological well-posedness of the solutions, are thoroughly demonstrated. The model captures nonlocal and memory-dependent characteristics that cannot be described by classical integer-order derivatives. The next-generation matrix (NGM) approach is used to determine the basic reproduction number (ℛ<inf>0</inf><sup>FV</sup>). The stability properties of the pathogen-extinction steady state and sustained-transmission steady state are investigated. In particular, Lyapunov function techniques are used to check the global stability of the PESS and STSS under suitable conditions, while Ulam–Hyers stability is established to examine the stability of the model solutions under small perturbations. To better understand the influence of model parameters, normalized forward sensitivity analysis is performed, showing that transmission, treatment, and recovery-related parameters exert the strongest influence on disease burden. Numerical simulations are used to verify theoretical results and investigate the effects of key epidemiological parameters on disease transmission. Finally, an artificial neural network (ANN) is employed only as a supplementary computational tool to reproduce the numerically obtained solution trajectories and to provide a consistency check of the computed results. The findings offer a valuable perspective on the dynamics of leptospirosis transmission and could inform disease control and intervention efforts.
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PREDICTION OF STOCK PRICE USING HYBRID NEURAL NETWORK: A CASE OF COAL PRODUCTION COMPANY
(2025-01-15)
Kiatcharoenpol, Tossapol
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Klongboonjit, Sakon
Stock market prediction is a critical issue in the field of economics. As machine learning technologies advance, an increasing number of algorithms are being utilized to forecast stock price movements. Nonetheless, predicting stock market trends remains a challenging task due to the inherent noise and volatility in stock market data. This paper addresses this challenge by proposing a novel hybrid neural network model designed to predict stock market prices using parameters related to commodity prices and stock indices. A case study company is mainly in coal production business in Thailand, which produce coal, sale, distribute and operate coal-fired power plants as well. The Multiple Linear Regression (MLR) and Back propagation neural network (BPNN) as traditional prediction technique are employed to comparatively investigate the accuracy and performance of the proposed HNN. Experiment results show that the prediction accuracy of HNN is superior to MLR but similar to that of the BPNN model. However, HNN has a good performance both in accuracy, speed and practice. It can help investing analysts and investors make their wise decisions.
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The Study and Analysis of Passive Power Filter to Improve Power Quality by Using Deep Learning
(2022-01-01)
Dangkong, A.
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Boonseng, C.
Passive power filter(PPFs) is widely used in large industries due to their ease of use, durability, and stability. Typically, a passive power filter's lifespan can be up to 14 years, but data has shown that it has a shorter service life because of damage to capacitors for a variety of reasons, such as capacitor's degeneration, overheating in reactor, reactor's vibration, and noise due to the flowing of harmonic current through the power filter excessively. This implies that the passive power filter system is in trouble. Furthermore, it has been found that maintaining the capacitor at a low temperature can keep the %THD level within the IEEE 519-2014 range and extend the life of the capacitor. Eventually, the collected data will be used to create a neural network for capacitor prediction and maintenance for further industrial applications.
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A rule-based training for artificial neural network packet filtering firewall
(2019-11-01)
Khunkitti, Akharin
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Chongsujjatham, Ponsuda
The Artificial Neural Network has been used in many network applications, including firewalls. Training process of neural network is very important to define the intelligence of the systems. Many artificial neural network firewalls used direct network packets for training process, which may be difficult to get training samples and may not follow their firewall's policies. This research work proposes a rule-based training for artificial neural network packet filtering firewall. The developed neural network model is trained by generating samples from legacy firewall ruleset. Each rule has been converted to random training samples. All firewall's rules are used to generate the training sample data, rule by rule. The accuracy results show high accuracy with some behavior studies. The number of samples per rule, number of rules and rule style, including default rule and rule-scope effects, have been studied for the best accuracy results. This study also concludes the styles of firewall ruleset for the best accuracy of the proposed system.
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A programmable artificial neural network coprocessor for handwritten digit recognition
(2019-07-01)
Wisayataksin, Sumek
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Boonyuu, Geranun
This paper proposes the hardware architecture of an artificial neural network coprocessor that its structure can be programmable. The number of neurons in each layer of a feedforward network can be set by writing configuration registers. The processing unit with four MACs and the sigmoid calculation engine are connected in eight pipeline stages to enhance the processing speed. The application of handwritten digit recognition from the MNIST database was performed to verify the performance of proposed architecture. The design was developed with Verilog HDL and implemented on the Xilinx Artix-7 XC7A35T FPGA. The experimental results revealed that the speed of back-propagation learning and validation process can be up to 47 times faster than computation on ARM Cortex-A4 CPU, while the recognition rate is still the same.
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Stock Analysis System for the Stock Exchange of Thailand
(2019-06-01)
Mueadkhunthod, Krittiyaporn
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Khunmood, Natchaya
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Khittiwichayakul, Sirawit
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Phakphisut, Watid
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Supnithi, Pornchai
In this work, we develop the stock analysis system which consists of data collection, data analytic, database and android application which guides the investors to buy, sell or hold a stock. Herein, we propose the analysis of stock data in Stock Exchange of Thailand (SET) using the economic information, historical stock prices, financial statement and exchange rate. The normalized-cross correlation is used to analyze the tendencies of gross profit. Furthermore, we apply the Artificial Neural Network (ANN) to predict the net profit of a public company in SET Our prediction can provide that the prediction errors are less than 15%.
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A Novel Feature Extraction for American Sign Language Recognition Using Webcam
(2019-01-10)
Thongtawee, Ariya
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Pinsanoh, Onamon
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Kitjaidure, Yuttana
Sign language is physical communication for contributing the meaning instead of using voice to demonstrate communicator's opinion. This paper introduces a simple and efficient algorithm for feature extraction to recognize American Sign Language alphabets from both static and dynamic gestures. The proposed algorithm comprises of four different techniques: Number of white pixels at the edge of the image (NwE), Finger length from the centroid point (Fcen), Angles between fingers (AngF) and Differences of angles between fingers of the first and last frame (delAng). After extracting features from video images, an Artificial Neural Network (ANN) is used to classify the signs. The result of these experiments is achieved up to 95% recognition rate, which is clearly to be the highest accuracy comparing with the other research worked in this field.
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Artificial neural network based nuclei segmentation on cytology pleural effusion images
(2017-07-02)
Win, Khin Yadanar
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Choomchuay, Somsak
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Hamamoto, Kazuhiko
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Raveesunthornkiat, Manasanan
Automated segmentation of cell nuclei is the crucial step towards computer-aided diagnosis system because the morphological features of the cell nuclei are highly associated with the cell abnormality and disease. This paper contributes four main stages required for automatic segmentation of the cell nuclei on cytology pleural effusion images. Initially, the image is preprocessed to enhance the image quality by applying contrast limited adaptive histogram equalization (CLAHE). The segmentation process is relied on a supervised Artificial Neural network (ANN) based pixel classification. Then, the boundaries of the extracted cell nuclei regions are refined by utilizing the morphological operation. Finally, the overlapped or touched nuclei are identified and split by using the marker-controlled watershed method. The proposed method is evaluated with the local dataset containing 35 cytology pleural effusion images. It achieves the performance of 0.95%, 0.86 %, 0.90% and 92% in precision, recall, F-measure and Dice Similarity Coefficient respectively. The average computational time for the entire algorithm took 15 mins per image. To our knowledge, this is the first attempt that utilizes ANN as the segmentation on cytology pleural effusion images.
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Harmonic reduction technique in PWM AC voltage controller using Particle Swarm Optimization and artificial neural network
(2010-12-01)
Piyarungsan, Pairoj
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Kaitwanidvilai, Somyot
This paper proposes a novel harmonic reduction technique for designing a Pulse Width Modulation (PWM) AC voltage controller. In the proposed technique, Total Current Harmonic Distortion (THD<inf>i</inf>), subjected to be minimized, is formulated in a cost function in an optimization problem; the optimal turn on and turn off angles in PWM waveform for a given output voltage are evaluated by Particle Swarm Optimization (PSO) technique. To apply our proposed technique for all output voltages, artificial neural network (ANN) is investigated to approximate the switching angles from sets of optimal angles evolved by PSO. Simulation results show that the proposed technique is suited for designing and gains a better performance compared to the conventional technique.
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A new fuzzy-neural system for time series forecasting
(2005-11-30)
Thammano, Arit
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Palahan, Sirinda
This paper proposes a new time series forecasting system, whose learning algorithm is a hybrid of the fuzzy c-means algorithm, the genetic algorithm, and the backpropagation algorithm. The proposed fuzzy-neural system consists of 5 layers: the input layer, the fuzzification layer, the rule layer, the hidden layer, and the output layer. The fuzzy cmeans algorithm is used to determine the center and width of the fuzzy membership functions. The artificial neural network is used as the fuzzy inference engine, while the genetic algorithm is used to optimize the fuzzy rule-base. This proposed system is tested with five time series data. The results obtained are very encouraging.

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