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Journal articles on the topic 'Weighted adaptive min-max normalization'

Author: Grafiati

Published: 2 June 2025

Last updated: 19 July 2025

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1

Kalluri, Venkata Saiteja, Sai Chakravarthy Malineni, Manjula Seenivasan, Jeevitha Sakkarai, Deepak Kumar, and Bhuvanesh Ananthan. "Enhancing manufacturing efficiency: leveraging CRM data with Lean-based DL approach for early failure detection." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2319–29. https://doi.org/10.11591/eei.v14i3.8757.

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In the pursuit of enhancing manufacturing competitiveness in India, companies are exploring innovative strategies to streamline operations and ensure product quality. Embracing Lean principles has become a focal point for many, aiming to optimize profitability while minimizing waste. As part of this endeavour, researchers have introduced various methodologies grounded in Lean principles to track and mitigate operational inefficiencies. This paper introduces a novel approach leveraging deep learning (DL) techniques to detect early failures in manufacturing systems. Initially, realtime data is c

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Prasetyowati, Sri Arttini Dwi, Munaf Ismail, and Badieah Badieah. "Implementation of Least Mean Square Adaptive Algorithm on Covid-19 Prediction." JUITA: Jurnal Informatika 10, no. 1 (2022): 139. http://dx.doi.org/10.30595/juita.v10i1.11963.

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This study used Corona Virus Disease-19 (Covid-19) data in Indonesia from June to August 2021, consisting of data on people who were infected or positive Covid-19, recovered from Covid-19, and passed away from Covid-19. The data were processed using the adaptive LMS algorithm directly without pre-processing cause calculation errors, because covid-19 data was not balanced. Z-score and min-max normalization were chosen as pre-processing methods. After that, the prediction process can be carried out using the LMS adaptive method. The analysis was done by observing the error prediction that occurr

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Rodríguez, Carlos Gervasio, María Isabel Lamas, Juan de Dios Rodríguez, and Claudio Caccia. "ANALYSIS OF THE PRE-INJECTION CONFIGURATION IN A MARINE ENGINE THROUGH SEVERAL MCDM TECHNIQUES." Brodogradnja 72, no. 4 (2021): 1–17. http://dx.doi.org/10.21278/brod72401.

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The present manuscript describes a computational model employed to characterize the performance and emissions of a commercial marine diesel engine. This model analyzes several pre-injection parameters, such as starting instant, quantity, and duration. The goal is to reduce nitrogen oxides (NOx), as well as its effect on emissions and consumption. Since some of the parameters considered have opposite effects on the results, the present work proposes a MCDM (Multiple-Criteria Decision Making) methodology to determine the most adequate pre-injection configuration. An important issue in MCDM model

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Himsar, Himsar. "Payment System Liquidity Index." Talenta Conference Series: Energy and Engineering (EE) 1, no. 2 (2018): 196–210. http://dx.doi.org/10.32734/ee.v1i2.250.

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ISSP is an index that demonstrates payment system’s stability figuring its liquidity (ISLSP) and its operational capability (IOSP). It was formed using two methods, which are statistical normalization and conversion using empirical normalization Min-Max. Basically, this paper intends to evaluate towards variables used in forming ISLSP and basically as a tool to ensure data sensitivity to important events stated. To get ISLSP that is sensitive to RTGS liquidity condition, we use coefficient from each weighted variable through simultaneous regression. We get parameters simbolized , and that are

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Shantal, Mohammed, Zalinda Othman, and Azuraliza Abu Bakar. "A Novel Approach for Data Feature Weighting Using Correlation Coefficients and Min–Max Normalization." Symmetry 15, no. 12 (2023): 2185. http://dx.doi.org/10.3390/sym15122185.

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In the realm of data analysis and machine learning, achieving an optimal balance of feature importance, known as feature weighting, plays a pivotal role, especially when considering the nuanced interplay between the symmetry of data distribution and the need to assign differential weights to individual features. Also, avoiding the dominance of large-scale traits is essential in data preparation. This step makes choosing an effective normalization approach one of the most challenging aspects of machine learning. In addition to normalization, feature weighting is another strategy to deal with th

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HAFS, Toufik, Hatem ZEHIR, and Ali HAFS. "Enhancing Recognition in Multimodal Biometric Systems: Score Normalization and Fusion of Online Signatures and Fingerprints." Romanian Journal of Information Science and Technology 2024, no. 1 (2024): 37–49. http://dx.doi.org/10.59277/romjist.2024.1.03.

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Multimodal biometrics employs multiple modalities within a single system to address the limitations of unimodal systems, such as incomplete data acquisition or deliberate fraud, while enhancing recognition accuracy. This study explores score normalization and its impact on system performance. To fuse scores effectively, prior normalization is necessary, followed by a weighted sum fusion technique that aligns impostor and genuine scores within a common range. Experiments conducted on three biometric databases demonstrate the promising efficacy of the proposed approach, particularly when combine

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

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Brain tumors are most common in children and the elderly. It is a serious form of cancer caused by uncontrollable brain cell growth inside the skull. Tumor cells are notoriously difficult to classify due to their heterogeneity. Convolutional neural networks (CNNs) are the most widely used machine learning algorithm for visual learning and brain tumor recognition. This study proposed a CNN-based dense EfficientNet using min-max normalization to classify 3260 T1-weighted contrast-enhanced brain magnetic resonance images into four categories (glioma, meningioma, pituitary, and no tumor). The deve

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Patanavijit, Vorapoj. "Denoising performance analysis of adaptive decision based inverse distance weighted interpolation (DBIDWI) algorithm for salt and pepper noise." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 2 (2019): 804. http://dx.doi.org/10.11591/ijeecs.v15.i2.pp804-813.

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<p>Due to its superior performance for denoising an image, which is contaminated by impulsive noise, an adaptive decision based inverse distance weighted interpolation (DBIDWI) algorithm is one of the most dominant and successful denoising algorithm, which is recently proposed in 2017, however this DBIDWI algorithm is not desired for denoising the full dynamic intensity range image, which is comprised of min or max intensity. Consequently, the research article aims to study the performance and its limitation of the DBIDWI algorithm when the DBIDWI algorithm is performed in both general i

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Lu, Kuan, Song Gao, Pang Xiangkun, Zhu lingkai, Xiangrong Meng, and Wenxue Sun. "Multi-layer Long Short-term Memory based Condenser Vacuum Degree Prediction Model on Power Plant." E3S Web of Conferences 136 (2019): 01012. http://dx.doi.org/10.1051/e3sconf/201913601012.

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A multi-layer LSTM (Long short-term memory) model is proposed for condenser vacuum degree prediction of power plants. Firstly, Min-max normalization is used to pre-process the input data. Then, the model proposes the two-layer LSTM architecture to identify the time series pattern effectively. ADAM（Adaptive moment）optimizer is selected to find the optimum parameters for the model during training. Under the proposed forecasting framework, experiments illustrates that the two-layer LSTM model can give a more accurate forecast to the condenser vacuum degree compared with other simple RNN (Recurren

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Sharma, Nikhil, Prateek Jeet Singh Sohi, and Bharat Garg. "An Adaptive Weighted Min-Mid-Max Value Based Filter for Eliminating High Density Impulsive Noise." Wireless Personal Communications 119, no. 3 (2021): 1975–92. http://dx.doi.org/10.1007/s11277-021-08314-5.

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Alhamad, Apriyanto, Azminuddin I. S. Azis, Budy Santoso, and Sunarto Taliki. "Prediksi Penyakit Jantung Menggunakan Metode-Metode Machine Learning Berbasis Ensemble – Weighted Vote." Jurnal Edukasi dan Penelitian Informatika (JEPIN) 5, no. 3 (2019): 352. http://dx.doi.org/10.26418/jp.v5i3.37188.

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Kematian yang disebabkan penyakit jantung masih sangat tinggi, sehingga perlu peningkatan upaya-upaya pencegahannya, misalnya dengan meningkatkan capaian model prediksinya. Penerapan metode-metode machine learning pada dataset publik (Cleveland, Hungary, Switzerland, VA Long Beach, & Statlog) yang umumnya digunakan oleh para peneliti untuk prediksi penyakit jantung, termasuk pengembangan alat bantunya, masih belum menangani missing value, noisy data, unbalanced class, dan bahkan data validation secara efisien. Oleh karena itu, pendekatan imputasi mean/mode diusulkan untuk menangani missing

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Prasetyowati, Sri Arttini Dwi, Munaf Ismail, Eka Nuryanto Budisusila, De Rosal Ignatius Moses Setiadi, and Mauridhi Hery Purnomo. "Dataset Feasibility Analysis Method based on Enhanced Adaptive LMS method with Min-max Normalization and Fuzzy Intuitive Sets." International Journal on Electrical Engineering and Informatics 14, no. 1 (2022): 55–75. http://dx.doi.org/10.15676/ijeei.2022.14.1.4.

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Dai, Jianhua, Ye Liu, and Jiaolong Chen. "Feature selection via max-independent ratio and min-redundant ratio based on adaptive weighted kernel density estimation." Information Sciences 568 (August 2021): 86–112. http://dx.doi.org/10.1016/j.ins.2021.03.049.

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14

Benaliouche, Houda, and Mohamed Touahria. "Comparative Study of Multimodal Biometric Recognition by Fusion of Iris and Fingerprint." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/829369.

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This research investigates the comparative performance from three different approaches for multimodal recognition of combined iris and fingerprints: classical sum rule, weighted sum rule, and fuzzy logic method. The scores from the different biometric traits of iris and fingerprint are fused at the matching score and the decision levels. The scores combination approach is used after normalization of both scores using the min-max rule. Our experimental results suggest that the fuzzy logic method for the matching scores combinations at the decision level is the best followed by the classical wei

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Polatgil, Mesut. "Investigation of the Effect of Normalization Methods on ANFIS Success: Forestfire and Diabets Datasets." International Journal of Information Technology and Computer Science 14, no. 1 (2022): 1–8. http://dx.doi.org/10.5815/ijitcs.2022.01.01.

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Machine learning and artificial intelligence techniques are more and more in our lives and studies in this field are increasing day by day. Data is vital for these studies. In order to draw meaningful conclusions from the available data, new methods are proposed and successful results are obtained. The preparation of the obtained data is very important in the studies to be carried out. Data preprocessing is very important in the preparation of data. The most critical stage of the data preprocessing process is the scaling or normalization of the data. Machine learning libraries such as scikit-l

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Hafs, Toufik, Hatem Zehir, Ali Hafs, and Amine Nait-Ali. "Multimodal Biometric System Based on the Fusion in Score of Fingerprint and Online Handwritten Signature." Applied Computer Systems 28, no. 1 (2023): 58–65. http://dx.doi.org/10.2478/acss-2023-0006.

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Abstract Multimodal biometrics is the technique of using multiple modalities on a single system. This allows us to overcome the limitations of unimodal systems, such as the inability to acquire data from certain individuals or intentional fraud, while improving recognition performance. In this paper, a study of score normalization and its impact on the performance of the system is performed. The fusion of scores requires prior normalisation before applying a weighted sum fusion that separates impostor and genuine scores into a common interval with close ranges. The experiments were carried out

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Vinitha, Vinitha, V. Parthasarathy, and R. Santhosh. "Dense-BiGRU: Densely Connected Bi-directional Gated Recurrent Unit based Heart Failure Detection using ECG Signal." Journal of Cybersecurity and Information Management 14, no. 2 (2024): 53–69. http://dx.doi.org/10.54216/jcim.140204.

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Heart failure, a state marked by the heart's inefficiency in pumping blood adequately., can lead to serious health complications and reduced quality of life. Detecting heart failure early is crucial as it allows for timely intervention and management strategies to prevent progression and improve patient outcomes. The effectiveness of integrating ECG and AI for heart failure detection stems from AI's capacity to meticulously analyze extensive ECG datasets, facilitating the early identification of nuanced cardiac irregularities and enhancing diagnostic precision. While the current research lacks

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Zheng, Zhanguang, Kaiming Wan, Tao Xu, and Liping Jiang. "A Statistical Methodology of Cyclic Plasticity Inhomogeneity at Grain Scale." Journal of Modern Mechanical Engineering and Technology 12 (July 15, 2025): 25–33. https://doi.org/10.31875/2409-9848.2025.12.04.

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The inhomogeneous plastic deformation has important effects on the manufacturing process and the fatigue property of mechanical products. To directly and correctly evaluate the deformation inhomogeneity of grain scale under cyclic loading, a statistical method is proposed and named as the normalized standard deviation. The method is comprised of the following steps: (1) Construct a representative volume element (RVE) of polycrystalline by Voronoi tessellation and electron backscatter diffraction, and calculate the grain strain by a constitutive model of crystal cyclical plasticity. (2) Deal wi

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Chen, Shushu. "A Fractional-Order Weighted and Self-Adaptive Max-Min Ant System with 3-Opt Algorithm for Traveling Salesman Problem." International Journal of Intelligent Information Systems 5, no. 4 (2016): 48. http://dx.doi.org/10.11648/j.ijiis.20160504.11.

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Protic, Danijela, Loveleen Gaur, Miomir Stankovic, and Md Anisur Rahman. "Cybersecurity in Smart Cities: Detection of Opposing Decisions on Anomalies in the Computer Network Behavior." Electronics 11, no. 22 (2022): 3718. http://dx.doi.org/10.3390/electronics11223718.

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The increased use of urban technologies in smart cities brings new challenges and issues. Cyber security has become increasingly important as many critical components of information and communication systems depend on it, including various applications and civic infrastructures that use data-driven technologies and computer networks. Intrusion detection systems monitor computer networks for malicious activity. Signature-based intrusion detection systems compare the network traffic pattern to a set of known attack signatures and cannot identify unknown attacks. Anomaly-based intrusion detection

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Bevl Naidu, Krishna Babu Sambaru, Guru Prasad Pasumarthi, Romala Vijaya Srinivas, K. Srinivasa Krishna, and V. Purna Kumari Pechetty. "Solar-Powered Aerobics Training Robot with Adaptive Energy Management for Improved Environmental Sustainability." Journal of Environmental & Earth Sciences 7, no. 6 (2025): 482–96. https://doi.org/10.30564/jees.v7i6.9012.

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With the rapid advancement of robotics and Artificial Intelligence (AI), aerobics training companion robots now support eco-friendly fitness by reducing reliance on nonrenewable energy. This study presents a solar-powered aerobics training robot featuring an adaptive energy management system designed for sustainability and efficiency. The robot integrates machine vision with an enhanced Dynamic Cheetah Optimizer and Bayesian Neural Network (DynCO-BNN) to enable precise exercise monitoring and real-time feedback. Solar tracking technology ensures optimal energy absorption, while a microcontroll

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Bagrecha, Chaya, Sachin Goswami, Manjula Jain, and Pompi Das Sengupta. "A framework for predicting stock prices based on a novel deep learning algorithm." Multidisciplinary Science Journal 6 (July 3, 2024): 2024ss0402. http://dx.doi.org/10.31893/multiscience.2024ss0402.

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Stock price forecasting has been a difficult and crucial undertaking in the financial markets. The complex prediction models have been developed as a result of the changing stock values, which are affected by a wide range of variables. The development of deep learning (DL) and improved processing power has made programmed techniques of prediction effective at forecasting stock prices. In this article, we proposed a Stochastic Gradient Descent Weighted Long Short Term Memory (SGD-LSTM) method and a complete framework is used for predicting stock prices, which gives a novel viewpoint on stock ma

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Kumar Reddy, Sama Lenin, C. V. Rao, P. Rajesh Kumar, R. V. G. Anjaneyulu, and B. Gopala Krishna. "An index based road feature extraction from LANDSAT-8 OLI images." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (2021): 1319. http://dx.doi.org/10.11591/ijece.v11i2.pp1319-1336.

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Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating the maps, traffic management, etc. In this paper, a new band combination (B652) to form a road index (RI) from OLI multispectral bands based on the spectral reflectance of asphalt, is presented for road feature extraction. The B652 is converted to road index by normalization. The morphological operators (top-hat or bottom-hat) uses on RI to enhance the roads. To sharpen the edges and for better discrimination of features, shock square filter (

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Sama, Lenin Kumar Reddy, V. Rao C., Rajesh Kumar P., V. G. Anjaneyulu R., and Gopala Krishna B. "An index based road feature extraction from LANDSAT-8 OLI images." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (2021): 1319–36. https://doi.org/10.11591/ijece.v11i2.pp1319-1336.

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Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating the maps, traffic management, etc. In this paper, a new band combination (B652) to form a road index (RI) from OLI multispectral bands based on the spectral reflectance of asphalt, is presented for road feature extraction. The B652 is converted to road index by normalization. The morphological operators (Top-hat or Bottom-hat) uses on RI to enhance the roads. To sharpen the edges and for better discrimination of features, shock square filter (

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Malefaki, Sonia, Dionysios Markatos, Angelos Filippatos, and Spiros Pantelakis. "A Comparative Analysis of Multi-Criteria Decision-Making Methods and Normalization Techniques in Holistic Sustainability Assessment for Engineering Applications." Aerospace 12, no. 2 (2025): 100. https://doi.org/10.3390/aerospace12020100.

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The sustainability evaluation of engineering processes and structures is a multifaceted challenge requiring the integration of diverse and often conflicting criteria. To address this challenge, Multi-Criteria Decision-Making (MCDM) methods have emerged as effective tools. However, the selection of the most suitable MCDM approach for problems involving multiple criteria is critical to ensuring robust, reliable, and actionable outcomes. Equally significant is the choice of a proper normalization technique, which plays a pivotal role in determining the robustness and reliability of the results. T

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Jayabalan, Bhuvana, Rakesh Kumar Yadav, Raman Batra, and Ashendra Kumar Saxena. "An innovative neural network-based technique for identifying power quality issues." Multidisciplinary Science Journal 6 (July 12, 2024): 2024ss0301. http://dx.doi.org/10.31893/multiscience.2024ss0301.

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Power quality (PQ) is defined as a combination of voltage quality and current quality. PQ is fast and difficult to predict. The primary issues that customers in the industry are consider being related to transitory interruptions, drawback include voltage sags, surges, harmonics and interruptions. To overcome this problem, we proposed an Adaptive Feedforward Bidirectional Gated Recurrent Neural Network (AF-BiGRNN) method to improve power quality issues. A PQ measurement shows a much more valuable asset. In the study, we gather the Reference Energy Disaggregation (REDD) dataset. The collected da

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Wenda, Akianus, Antonius R. Kopong Notan, Shalwa Azizah Rananda Sudirman, T. Ferdiansyah Sudirman, Tegar Surya Pratama, and Zurnan Alfian. "Application of K-Means on Human Rights, Demographic, Economic, and Crypto Investment Data." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 3 (2025): 2539–48. https://doi.org/10.59934/jaiea.v4i3.1215.

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Abstract This study combines the K-Means Clustering and Decision Tree methods to analyze multidomain data covering economic and social human rights, demographics, poverty, crypto investment, and sustainable financing in Indonesia's financial services sector. Data was obtained from various credible sources such as the National Commission on Human Rights (Komnas HAM), the Central Statistics Agency (BPS), the Financial Services Authority (OJK), and scientific publications (2019–2023), then processed through missing value handling, outlier detection, and normalization using Min-Max Scaling and Z-s

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Ira Modifa Tarigan, Muhammad Ade Kurnia Harahap, Endang setyawati, Jimmy Moedjahedy, Ernie C Avila, and Robbi Rahim. "A Multi-Criteria Decision-Making Approach for Warehouse Location Selection using TOPSIS." JINAV: Journal of Information and Visualization 4, no. 1 (2023): 45–52. http://dx.doi.org/10.35877/454ri.jinav1616.

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This research makes use of the Method for Order of Preference by Similarity to Ideal Solution, also known as the TOPSIS approach, in order to discover the most suitable site for a company's warehouse. Following the establishment of the criteria for the selection of the warehouse location, weights were allotted to each of the criteria. The min-max method was utilized to do data normalization once it had been collected for each prospective location. After constructing the decision matrix with the weighted normalized values and determining the ideal and non-ideal solutions for each criterion, the

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Khaled, Khaled. "EfficientDense-ViT: APT Detection via Hybrid Deep Learning Framework with Hybrid Dipper Throated Sine Cosine Optimization Algorithm (HDT-SCO)." Journal of Cybersecurity and Information Management 15, no. 2 (2025): 147–64. https://doi.org/10.54216/jcim.150212.

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Advanced Persistent Threats (APT) are intelligent, sophisticated cyberattacks that frequently evade detection by gradually interfering with vital systems or focusing on sensitive data. It is proposed herein the new approach of the Hybrid Dipper Throated Sine Cosine Optimization Algorithm (HDT-SCO) for APT detection in association with the EfficientDense-ViT model. It handles the class imbalance issue with advanced processing Adaptive Synthetic Minority Oversampling Technique (ADASYN), including min-max scaling for normalization, and median imputation for missing values. In terms of feature eng

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Kumar, C. Sandeep, Karan Ram Lal Gupta, Geetika M. Patel, and J. M. Haria. "A hybrid machine learning-powered intelligent system for enhancing dengue patient safety and care." Multidisciplinary Science Journal 6 (August 2, 2024): 2024ss0602. http://dx.doi.org/10.31893/multiscience.2024ss0602.

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Dengue fever is a significant global health concern, with millions of cases reported each year, leading to considerable morbidity and mortality. Early diagnosis, patient monitoring and timely intervention are crucial for managing dengue patients. This study proposed a hybrid machine learning-powered intelligent system designed to enhance dengue patients safety and care. Utilizing data provided at enrollment, including platelet, age, white cell, genders, hematocrit and lymphocyte counts, a Weighted K Nearest Neighbor fused Gradient Boosting Decision Tree (WKNN-GBDT) was utilized to forecast the

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Althobaiti, Maha M., and José Escorcia-Gutierrez. "Weighted salp swarm algorithm with deep learning-powered cyber-threat detection for robust network security." AIMS Mathematics 9, no. 7 (2024): 17676–95. http://dx.doi.org/10.3934/math.2024859.

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<abstract><p>The fast development of the internet of things has been associated with the complex worldwide problem of protecting interconnected devices and networks. The protection of cyber security is becoming increasingly complicated due to the enormous growth in computer connectivity and the number of new applications related to computers. Consequently, emerging intrusion detection systems could execute a potential cyber security function to identify attacks and variations in computer networks. An efficient data-driven intrusion detection system can be generated utilizing artifi

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Qin, Xinjing, Zhisheng Wang, Manqun Zhang, Yue Feng, and Kexian Li. "Lawn Lamp Design Based on Fuzzy Control and Secondary Optical Optimization." Applied Sciences 13, no. 3 (2023): 1631. http://dx.doi.org/10.3390/app13031631.

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With the emergence of new technologies, the design of urban infrastructure is constantly being innovated, and the lawn lamp as urban lighting infrastructure is an important part of urban infrastructure. For the current lawn lamp function, there are single, large power consumption, low light energy utilization and other shortcomings. Combined with deep learning and optical design, this paper constructs an adaptive lighting control system based on the technology of the Internet. Considering the nonlinear and time-varying characteristics of external factors, a fuzzy control model with ambient lig

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Priyadharshini, M., A. Faritha Banu, Bhisham Sharma, Subrata Chowdhury, Khaled Rabie, and Thokozani Shongwe. "Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning." Sensors 23, no. 15 (2023): 6836. http://dx.doi.org/10.3390/s23156836.

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In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from different classes when producing synthetic samples. As a result, there can be more class overlap and more noise. To avoid this problem, this work presented an innovative technique called Adaptive Synthetic Data-Based Multi-label Classification (ASDMLC). Adaptive Synthetic (ADASYN) sampling is a sampling strategy for learning from unbalanced data sets.

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Jayanna, Niranjan Shadaksharappa, and Raviprakash Madenur Lingaraju. "Seasonal auto-regressive integrated moving average with bidirectional long short-term memory for coconut yield prediction." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 783. http://dx.doi.org/10.11591/ijece.v15i1.pp783-791.

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Crop yield prediction helps farmers make informed decisions regarding the optimal timing for crop cultivation, taking into account environmental factors to enhance predictive accuracy and maximize yields. The existing methods require a massive amount of data, which is complex to acquire. To overcome this issue, this paper proposed a seasonal auto-regressive integrated moving average-bidirectional long short-term memory (SARIMA-BiLSTM) for coconut yield prediction. The collected dataset is preprocessed through a label encoder and min-max normalization is employed to change non-numeric features

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Wu, Pei-Yi, Yuan-Jin Lin, Yu-Jen Chang, et al. "Deep Learning-Assisted Diagnostic System: Apices and Odontogenic Sinus Floor Level Analysis in Dental Panoramic Radiographs." Bioengineering 12, no. 2 (2025): 134. https://doi.org/10.3390/bioengineering12020134.

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Odontogenic sinusitis is a type of sinusitis caused by apical lesions of teeth near the maxillary sinus floor. Its clinical symptoms are highly like other types of sinusitis, often leading to misdiagnosis as general sinusitis by dentists in the early stages. This misdiagnosis delays treatment and may be accompanied by toothache. Therefore, using artificial intelligence to assist dentists in accurately diagnosing odontogenic sinusitis is crucial. This study introduces an innovative odontogenic sinusitis image processing technique, which is fused with common contrast limited adaptive histogram e

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Liang, Zhenyu, Letian Chen, and Wenbin Xiao. "Compression AutoEncoder for High-Resolution Ocean Sound Speed Profile Data." Journal of Physics: Conference Series 2718, no. 1 (2024): 012067. http://dx.doi.org/10.1088/1742-6596/2718/1/012067.

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Abstract High-resolution ocean sound speed profile (HROSSP) data is essential for ocean acoustic modeling and sonar performance evaluation. However, the large volume and storage requirements of this data severely restrict its practical application in ocean acoustics. In this paper, we propose a compression autoencoder specifically designed for managing HROSSP data (CAE-HROSSP) and investigate the optimal network structure. Experimental results demonstrate that by using the min-max normalization method for input data and the corresponding inverse normalization for output data, along with employ

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Sheliemina, Nataliia, and Ievgen Rekun. "Economic and mathematical modeling of the mutual influence between a country’s economic well-being and healthcare expenditure." Problems and prospects of economics and management, no. 1(41) (May 16, 2025): 111–22. https://doi.org/10.25140/2411-5215-2025-1(41)-111-122.

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This article investigates the relationship between a country's level of economic well-being and its healthcare expenditure using economic and mathematical modeling tools. The relevance of the topic stems from the increasing importance of human capital in transitional economies, demographic challenges, and the growing financial burden on healthcare systems due to post-crisis and pandemic factors. A composite well-being index (WI-5) is developed, integrating five key indicators: GDP per capita, public expenditure on education, life expectancy at birth, unemployment rate (inverted), and the Corru

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Prihandi, Ifan, Sutarto Wijono, Irwan Sembiring, and Evi Maria. "Implementation of ARIMA with Min-Max Normalization for predicting the Price and Production Quantity of Red Chili Peppers in North Sumatra Province considering Rainfall and Sunlight Duration Factors." Engineering, Technology & Applied Science Research 15, no. 2 (2025): 21876–87. https://doi.org/10.48084/etasr.9875.

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Red chili peppers are a vital agricultural commodity in the North Sumatra province, playing a significant role in Indonesia's economy. Fluctuations in chili prices affect farmers, consumers, and overall economic stability. This study leverages time series forecasting using the ARIMA model to predict red chili pepper prices and production, incorporating weather factors such as rainfall and sunlight duration. The dataset spans March 2021 to December 2023 and includes historical records of chili prices, production levels, and weather conditions. The analysis reveals a strong correlation between p

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Abdelaziz, Ahmed, and Alia N. Mahmoud. "Clustered IoT Based Data Fusion model for Smart Healthcare Systems." Journal of Intelligent Systems and Internet of Things 6, no. 2 (2022): 22–31. http://dx.doi.org/10.54216/jisiot.060202.

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Futuristic sustainable computing solutions in e-healthcare applications were depends on the Internet of Things (IoT) and cloud computing (CC), has provided several features and realistic services. IoT-related medical devices gather the necessary data like recurrent transmissions in health limitations and upgrade the exactness of health limitations all inside a standard period. These data can be generated from different types of sensors in different formats. As a result, the data fusion is a big challenge to handle these IoT-based data. Moreover, IoT gadgets and medical parameters based on sens

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Mohammed, Gouse Pasha, Naif Alasmari, Hadeel Alsolai, Saud S. Alotaibi, Najm Alotaibi, and Heba Mohsen. "Autonomous Short-Term Traffic Flow Prediction Using Pelican Optimization with Hybrid Deep Belief Network in Smart Cities." Applied Sciences 12, no. 21 (2022): 10828. http://dx.doi.org/10.3390/app122110828.

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Accurate and timely traffic flow prediction not just allows traffic controllers to evade traffic congestion and guarantee standard traffic functioning, it even assists travelers to take advantage of planning ahead of schedule and modifying travel routes promptly. Therefore, short-term traffic flow prediction utilizing artificial intelligence (AI) techniques has received significant attention in smart cities. This manuscript introduces an autonomous short-term traffic flow prediction using optimal hybrid deep belief network (AST2FP-OHDBN) model. The presented AST2FP-OHDBN model majorly focuses

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Fan, Ke-Jun, Bo-Yuan Liu, and Wen-Hao Su. "Discrimination of Deoxynivalenol Levels of Barley Kernels Using Hyperspectral Imaging in Tandem with Optimized Convolutional Neural Network." Sensors 23, no. 5 (2023): 2668. http://dx.doi.org/10.3390/s23052668.

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Deoxynivalenol (DON) in raw and processed grain poses significant risks to human and animal health. In this study, the feasibility of classifying DON levels in different genetic lines of barley kernels was evaluated using hyperspectral imaging (HSI) (382–1030 nm) in tandem with an optimized convolutional neural network (CNN). Machine learning methods including logistic regression, support vector machine, stochastic gradient descent, K nearest neighbors, random forest, and CNN were respectively used to develop the classification models. Spectral preprocessing methods including wavelet transform

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Alsubayhay, Abraheem Mohammed Sulayman, Mohamed A. E. Abdalla, and Ali A. Salem Buras. "Adaptive HCI Systems with GRU-Based User Emotion Recognition and Response Prediction." International Science and Technology Journal 35, no. 1 (2024): 1–19. http://dx.doi.org/10.62341/amam2098.

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Human-Computer Interaction (HCI) has evolved to incorporate sophisticated systems capable of recognizing and responding to user emotions, enhancing the user experience by making interactions more intuitive and engaging. This paper explores the development of adaptive HCI systems utilizing Gated Recurrent Unit (GRU) neural networks for emotion recognition and response prediction. The core objective is to create interfaces that dynamically adjust based on the user's emotional state, thereby improving usability and satisfaction across various applications, including healthcare, education, and cus

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Vaijayanthimala, J., and T. Padma. "Synthesis Score Level Fusion Based Multifarious Classifier for Multi-Biometrics Applications." Journal of Medical Imaging and Health Informatics 9, no. 8 (2019): 1673–80. http://dx.doi.org/10.1166/jmihi.2019.2762.

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In this paper, we are presenting a face and signature recognition method from a large dataset with the different pose and multiple features. Initially, Face and corresponding signature are detected from devices for further pre-processing. Face recognition is the first stage of a system then the signature verification will be done. The proposed Legion feature based verification method will be developed using four important steps like, (i) feature extraction from face and data glove signals using feature Extraction. The various Features like Local binary pattern, shape and geometrical features o

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Guo, Xia. "Research on interactive english classroom teaching based on biosensor technology: Analysis of biological indicators." Molecular & Cellular Biomechanics 22, no. 2 (2025): 935. https://doi.org/10.62617/mcb935.

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With the advancement of educational technology, biosensors are becoming valuable in enhancing classroom interactivity and adapting teaching strategies. In English language classrooms, maintaining student engagement and managing learning anxiety is essential for effective learning; traditional methods fail to offer real-time insights into student engagement and emotional states. The objective of the research was to enhance language instruction effectiveness by monitoring learners’ cognitive states using biosensor technology. Initially, biosensors were used to collect physiological data such as

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Jayanna, Niranjan Shadaksharappa, and Raviprakash Madenur Lingaraju. "Seasonal auto-regressive integrated moving average with bidirectional long short-term memory for coconut yield prediction." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 783–91. https://doi.org/10.11591/ijece.v15i1.pp783-791.

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Crop yield prediction helps farmers make informed decisions regarding the optimal timing for crop cultivation, taking into account environmental factors to enhance predictive accuracy and maximize yields. The existing methods require a massive amount of data, which is complex to acquire. To overcome this issue, this paper proposed a seasonal auto-regressive integrated moving average-bidirectional long short-term memory (SARIMA-BiLSTM) for coconut yield prediction. The collected dataset is preprocessed through a label encoder and min-max normalization is emplo

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Rohini. C. "Intelligent Edge Healthcare Using Federated Learning and Clustering with Kepler-Optimized Steerable Graph Neural Networks." Journal of Information Systems Engineering and Management 10, no. 39s (2025): 1–13. https://doi.org/10.52783/jisem.v10i39s.7054.

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Nowadays, people frequently use smart healthcare systems (SHS) to use a variety of smart devices to monitor their health. The SHS uses Internet of Things (IoT) and cloud infrastructure for data collection, transmission via smart devices, data processing, storage, and medical advice. It can be difficult to process so much data from so many IoT devices in a short period. Therefore, in SHS, technical frameworks like fog computing or edge computing can be utilized as mediators between the user and the cloud. It shortens response times for lower-level (edge-level) data processing. If anomalous data

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Gupta, Rajesh, Aditya Sharma, Charu Wadhwa, and Yogananthan S. "Improving supply chain performance administration using a novel deep learning algorithm." Multidisciplinary Science Journal 6 (July 3, 2024): 2024ss0410. http://dx.doi.org/10.31893/multiscience.2024ss0410.

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Businesses and economic growth together depend on effective supply chain management. The threats posed by poor supply chain management are beyond the reach of the present methods of managing them. To study the improvement in supply chain efficiency management, we propose a learning and neural-network-based supply chain risk management model called the Integrated Tunicate Swarm Algorithm Adaptive Multilayer Feed Forward Neural Networks (ITSA-AMFNN) method for determining strategies to manage supply chains. The initial step of our methodology is to gather both historical and current supply chain

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Wei, Lifei, Ziran Yuan, Yanfei Zhong, Lanfang Yang, Xin Hu, and Yangxi Zhang. "An Improved Gradient Boosting Regression Tree Estimation Model for Soil Heavy Metal (Arsenic) Pollution Monitoring Using Hyperspectral Remote Sensing." Applied Sciences 9, no. 9 (2019): 1943. http://dx.doi.org/10.3390/app9091943.

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Hyperspectral remote sensing can be used to effectively identify contaminated elements in soil. However, in the field of monitoring soil heavy metal pollution, hyperspectral remote sensing has the characteristics of high dimensionality and high redundancy, which seriously affect the accuracy and stability of hyperspectral inversion models. To resolve the problem, a gradient boosting regression tree (GBRT) hyperspectral inversion algorithm for heavy metal (Arsenic (As)) content in soils based on Spearman’s rank correlation analysis (SCA) coupled with competitive adaptive reweighted sampling (CA

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Aljuhni, Abdullah, Amer Aljaedi, Adel R. Alharbi, Ahmed Mubaraki, and Moahd K. Alghuson. "Hybrid Dynamic Galois Field with Quantum Resilience for Secure IoT Data Management and Transmission in Smart Cities Using Reed–Solomon (RS) Code." Symmetry 17, no. 2 (2025): 259. https://doi.org/10.3390/sym17020259.

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The Internet of Things (IoT), which is characteristic of the current industrial revolutions, is the connection of physical devices through different protocols and sensors to share information. Even though the IoT provides revolutionary opportunities, its connection to the current Internet for smart cities brings new opportunities for security threats, especially with the appearance of new threats like quantum computing. Current approaches to protect IoT data are not immune to quantum attacks and are not designed to offer the best data management for smart city applications. Thus, post-quantum

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Bashynska, Iryna, and Ihor Bashynskyi. "THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE SMARTIZATION OF ENTERPRISES." Смарт-економіка, підприємництво та безпека 2, no. 2 (2024): 17–25. https://doi.org/10.60022/sis.2.(02).2.

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The increasing role of Artificial Intelligence (AI) in enterprise transformation necessitates compre- hensive assessment models that capture the full spectrum of AI-driven smartization. This study introduces the AI-Enterprise Smartization Index (AIES-Index) — a structured framework designed to evaluate AI adoption across five key dimensions: AI adoption level, decision-making autonomy, explainability and transparency, operational efficiency, and sustainability contributions. Unlike existing AI maturity models, which primarily focus on strategic or financial aspects, AIES-Index integrates quant

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