Max Normalization Research Articles

Empowering Resilience: The Impact of Farmer Field Schools on Smallholder Livestock Farmers’ Climate Change Perceptions in Raymond Local Municipality

Experiential learning and discovery through farmer field schools (FFS) have the potential to empower smallholder livestock farmers who face heightened vulnerability to climate change. However, there are various levels of learning and discovery in FFS that can inform smallholder livestock farmer knowledge and perception. Understanding this is vital, as farmers’ perceptions influence their readiness to adopt climate-smart practices, informing effective resilience-building strategies. Therefore, this study sought to investigate and assess the impact of the FFS approach on smallholder livestock farmers’ perceptions of climate change, taking Raymond Local Municipality in South Africa as a case. The design followed by the study was a longitudinal survey, with three pools each signifying various FFS intervention points. The study utilized simple random sampling to collect data from 80 smallholder livestock farmers using structured questionnaires in each of the three cross-sectional pools, while descriptive statistics, Min–Max Normalization, and t-tests were used for analysis. The results show that there was an increase in the awareness of climate change due to the interventions of the FFS. Furthermore, there are cumulative differences between the knowledge and perception towards climate change between the three pooled cross-sections. In conclusion, participating in FFS had a significant impact on farmers’ level of understanding and adaptation to climate change. The study recommends that the government and policymakers extensively promote FFS and support them financially so that they can provide more support to rural farmers as well as enhance knowledge on climate change. This study recommends the provision of workshops and awareness campaigns on climate change for farmers through FFS as this will assist farmers to be more sustainable on their farming systems and production.

Integration of deep learning in the diagnosis, chemical analysis, and therapeutic approaches for neurodegenerative disorders

Neurodegenerative disorders (NDD) are a group of progressive conditions that primarily affect neurons in the brain. The diseases gradually impair cognitive function, movement, and other neurological processes, leading to a decline in the individual’s quality of life. Reliable biomarkers that accurately detect and track the growth of neurological disorders are crucial for the development of effective therapeutics. People with NDDs have damage to the brainneurons, which causes strange walking patterns. To overcome this problem we proposed Chemical reaction optimization based improved generative adversarial network (CRO-IGAN) method is to improve the patient’s conditions that are affected in neurodegenerative diseases. The patient’s dataset is gathered, here we utilized min max normalization for data preprocessing is used to clean the data. Principal component analysis (PCA) is employed for feature extraction to extract the pre-processed data and remove the unwanted data. The appropriate data is selected using linear discriminant analysis (LDA) for feature selection. The parameter metrics used in this study are recall, sensitivity and specificity. The suggest techniques CRO-IGAN provides high performance of accuracy for diagnosing NDD to improve the patient health which provides a superior performance than other existing methods.

State of Health Prediction in Electric Vehicle Batteries Using a Deep Learning Model

Accurately estimating the state of health (SOH) of lithium-ion batteries plays a significant role in the safe operation of electric vehicles. Deep learning (DL)-based approaches for estimating state of health (SOH) have consistently been the focus of study in recent years. In the current era of electric mobility, the utilization of lithium-ion batteries (LIBs) has evolved into a necessity for energy storage. Ensuring the safe operation of EVs requires a precise assessment of the state-of-health (SOH) of LIBs. To estimate battery SOH accurately, this paper employs a deep learning (DL) algorithm to enhance the estimation accuracy of SOH to obtain accurate SOH measurements. This research introduces the Diffusion Convolutional Recurrent Neural Network (DCRNN) with a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm (DCRNN + SVM-RFE) for enhancing classification and feature selection performance. The data gathered from the dataset were pre-processed using the min–max normalization method. The Center for Advanced Life Cycle Engineering (CALCE) dataset from the University of Maryland was employed to train and evaluate the model. The SVM-RFE algorithm was used for feature selection of pre-processed data. DCRNN algorithm was used for the classification process to enhance prediction precision. The DCRNN + SVM-RFE model’s performance was calculated using Mean Absolute Percentage Error (MAPE), Mean Squared Error (MAE), Mean Squared Error (MSE), and Root MSE (RMSE) metric values. The proposed model generates accurate results for SOH prediction; all RMSEs are within 0.02%, MAEs are within 0.015%, MSEs were within 0.032%, and MAPEs are within 0.41%. The mean values of RMSE, MSE, MAE, and MAPE were 0.014, 0.026, 0.011, and 0.32, respectively. Experiments confirmed that the DCRNN + SVM-RFE model has the highest accuracy among those that predict SOH.

Prediction of heart disease using a novel slap swarm optimized multi-objective random forest

Heart disease is a leading cause of death worldwide, becoming a major health concern for many people. Among the most important tasks and regular diagnostic research entails the identified cardiac issues, like heart diseases, valve conditions, etc. Early detection of heart disease may save many lives. The application of machine learning techniques in the medical sector has advanced significantly. A novel Slap Swarm Optimized Multi-Objective Random Forest (SSO-MORF) approach was presented in the proposed work for predicting cardiac disease. For this suggested investigation, heart disease information using individual customer identification at the University of California Irvine (UCI) was utilized, and data mining methods like categorization were applied. Min–max normalization and principal component analysis (PCA) were used for data preparation and feature extraction. As a result, a rather basic supervised machine learning technique may be used to predict heart disease quite accurately and with excellent potential value. According to this study, a classification based on the RF technique produced results with 99.45% accuracy, 98.3% recall, 98.6% precision, and a 98.9% f1 score. This finding demonstrates that our system is more efficient at predicting cardiac disease than other cutting-edge techniques.

A hybrid machine learning-powered intelligent system for enhancing dengue patient safety and care

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 ultimate diagnosis. The study included 50 patients recruited between July to October 2019 and diagnosed with dengue infection. The collected data was preprocessed and features extracted using min max normalization and independent component analysis (ICA). The WKNN-GBDT model had an overallprecision of 0.96%, f1-score of 0.90% accuracy of 0.98%, andrecall of 0.99% in predicting the final diagnosis. As a consequence of seasonality and other variables, model results changed over time. These models might enhance medical decision-making in the field of medical care and offer passive surveillance in dengue affected areas. Considering the unexpected consequences of human-induced climate change and its impact on health, as well as the implications of seasonality and shifting disease prevalence, is crucial.

Porosity Prediction Based on Ensemble Learning for Feature Selection and an Optimized GRU Improved by the PSO Algorithm

Accurate and reliable prediction of porosity forms the foundational basis for evaluating reservoir quality, which is essential for the systematic deployment of oil and gas exploration and development plans. When data quality of samples is low, and critical model parameters are typically determined through subjective experience, resulting in diminished accuracy and reliability of porosity prediction methods utilizing gated recurrent units (GRU), a committee-voting ensemble learning (EL) method, and an enhanced particle swarm optimization (PSO) algorithm are proposed to optimize the GRU-based porosity prediction model. Initially, outliers are eliminated through box plots and the min–max normalization is applied to enhance data quality. To address issues related to model accuracy and high training costs arising from dimensional complexity, substantial noise, and redundant information in logging data, a committee-voting EL strategy based on four feature selection algorithms is introduced. Following data preprocessing, this approach is employed to identify logging parameters highly correlated with porosity, thereby furnishing the most pertinent data samples for the GRU model, mitigating constraints imposed by single-feature selection methods. Second, an improved PSO algorithm is suggested to tackle challenges associated with low convergence accuracy stemming from random population initialization, alongside the absence of global optimal solutions due to overly rapid particle movement during iteration. This algorithm uses a good-point set for population initialization and incorporates a compression factor to devise an adaptive velocity updating strategy, thereby enhancing search efficacy. The enhanced PSO algorithm’s superiority is substantiated through comparison with four alternative swarm intelligent algorithms across 10 benchmark test functions. Ultimately, optimal hyper-parameters for the GRU model are determined using the improved PSO algorithm, thereby minimizing the influence of human factors. Experimental findings based on approximately 15,000 logging data points from well A01 in an operational field validate that, relative to three other deep learning methodologies, the proposed model proficiently extracts spatiotemporal features from logging data, yielding enhanced accuracy in porosity prediction. The mean squared error on the test set was 7.19 × 10–6, the mean absolute error stood at 0.0082, and coefficient of determination reached 0.99, offering novel insights for predicting reservoir porosity.

Optimizing federated learning approaches with hybrid Convolutional Neural Networks ‐ Bidirectional Encoder Representations from Transformers for precise estimation of average localization errors in wireless sensor networks

SummaryWireless sensor networks (WSNs) require precise node location in order to function properly, and they are essential in many different applications. In this research, we propose a unique method to maximize the accurate estimate of average localization errors (ALEs) in WSNs by utilizing federated learning (FL) approaches. The suggested approach combines the extraction of spatial features with collection of contextual data through integration of hybrid convolutional neural network–bidirectional encoder representations from transformers (CNN‐BERT) model. Effectively applying min–max normalization to input features minimizes data from flowing into test, validation, and training sets. The technique is centered around a collaborative learning architecture, wherein the weights of the model are iteratively assessed and modified on centralized server. The suggested methodology's effectiveness in an array of settings is illustrated by mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) values for node localization in WSN. Network B demonstrated accuracy with an MAE of 0.05, MSE of 0.03, and RMSE of 0.10; Network C demonstrated strong accuracy with an MAE of 0.09, MSE of 0.08, and RMSE of 0.12; and Network A generated an MAE of 0.04, MSE of 0.06, and RMSE of 0.08. Furthermore, the centralized server, which is crucial for collaborative learning, obtained exceptional MAE of 0.01, MSE of 0.02, and RMSE of 0.99 demonstrating the superiority of the FL‐optimized method in improving localization accuracy. The outcomes demonstrate the FL‐optimized hybrid model's superiority over conventional methods in providing accurate node localization in a variety of WSN settings.

Cloud computing-based framework for heart disease classification using quantum machine learning approach

Abstract Accurate early identification and treatment of cardiovascular diseases can prevent heart failure problems and reduce mortality rates. This study aims to use quantum learning to predict heart problems to increase the accuracy of traditional prediction and classification methods. Machine learning (ML) and deep learning (DL) techniques need quantum learning to quickly and accurately analyze massive volumes of complex data. With quantum computing, the suggested DL and ML algorithms can change their predictions on the basis of changes in the dataset. This approach could help with the early and accurate detection of chronic diseases. The Cleveland heart disease dataset is undergoing preliminary processing to validate missing values to increase the precision rate and prevent incorrect forecasts. This study examined the feasibility of employing and deploying a quantum ML (QML) framework via cloud computing to categorize cardiac conditions. The research was divided into four sections. First, the principal component analysis was used to preprocess the Cleveland dataset, recursive feature elimination was used to select features, and min–max normalization was used to give the dataset a high-dimensional value. Second, we compared traditional classifiers, such as support vector machine (SVM) and artificial neural network, with the quantum approach to verify the quantum approach’s efficiency. Third, we examined two unique QML classification methods: quantum neural networks (QNNs) and quantum SVM (QSVM). Fourth, bagging-QSVM was developed and deployed as an ensemble learning model. Experimental results using the QNN show an accuracy of 77%, a precision of 76%, a recall of 73%, and an F1 score of 75%. With an accuracy of 85%, a precision of 79%, a recall of 90%, and an F1-score of 84%, the QSVM method demonstrated a much better performance than the QNN. Particularly, the Bagging_QSVM model exhibited an outstanding performance, with a flawless score of 100% across all critical performance measures. The study shows that the bagging method for ensemble learning is a solid way of increasing the accuracy of quantum method predictions.

Landslide susceptibility analysis using random forest model with SMOTE-ENN resampling algorithm

Landslide is one of the natural disasters that cause property damages and human injuries. Landslide hazard predictions are crucial measures to reduce the damages and losses. One of the effective approaches in landslide prediction is landslide susceptibility analysis (LSA). In this article, LSA is carried out on the study area, Penang Island. The imbalanced landslide dataset is the most important issue to be solved in this article, four resampling methods were compared for the training set using random forest (RF) as the basic model. To enhance the credibility of the results, the experiments replicate 10 times, and McNemar’s test was applied to analyse statistical significance of classifier performances for the LSA. The results indicated that the differences between the methods were statistically significant; RF combined with the synthetic minority oversampling technique-edited nearest neighbour (SMOTE-ENN) resampling method proposed in this paper has positive effect in LSA as compared with the other resampling methods. The RF and SMOTE-ENN combined model for the LSA using the min–max normalization method achieved a recall of 0.844 and an F2-score of 0.756. The SMOTE-ENN method had a significant impact on the LSA of the imbalanced data in the study area.

Enhanced slime mould optimization with convolutional BLSTM autoencoder based malware classification in intelligent systems

AbstractAutonomous intelligent systems are artificial intelligence (AI) tools that act autonomously without direct human supervision. Cloud computing (CC) and Internet of Things (IoT) technologies find it challenging to deploy sufficient security defences because of the different structures, storage, and limited computing capabilities that make them more vulnerable to attacks. Security threats against IoT structures, devices, and applications are increasing with the demand for IoT technology. The training data available to AI models may be limited, which could impact their performance and generalizability. Adopting AI solutions in real‐world situations may be impeded by compatibility concerns and the requirement for flawless integration. Malware classification errors can occur due to a lack of contextual knowledge, particularly in cases where benign files behave identically to malicious. Various studies were carried out on detecting IoT malware to evade the menaces posed by malicious code. However, prevailing techniques of IoT malware classification supported particular platforms or demanded complicated methods for attaining higher accuracy. This study introduces an enhanced slime mould optimization with a convolutional BLSTM autoencoder‐based malware classification (ESMO‐CBLSTMAE) system in the IoT cloud platform. The projected ESMO‐CBLSTMAE system focuses on detecting and classifying malware in the IoT cloud platform. To achieve that, the ESMO‐CBLSTMAE algorithm employs a min–max normalization technique for scaling the input dataset. The ESMO‐CBLSTMAE method uses a convolutional bidirectional long short‐term memory autoencoder (CBLSTM‐AE) model for the malware detection process. Lastly, the ESMO method is executed for the optimum hyperparameter tuning of the CBLSTM‐AE technique, which boosts the malware classification results. The experimental analysis of the ESMO‐CBLSTMAE method is tested against a benchmark database, and the outcomes portray the greater efficacy of the ESMO‐CBLSTMAE approach over other existing techniques. The proposed malware classification model achieved an accuracy of 98.57 and F Score of 80.77 and outperformed the existing models.

On time demand traffic estimation based on DBN with Horse herd optimization for next generation wireless network

A wireless network's increased demand for mobility means that 5G and later wireless networks must anticipate traffic demand in order to prevent disruptions. The network usage is high in day time in the city areas at that same time, the usage of the network is very low in rural regions. At night the network usage in city areas gradually decreases and gradually increases in the rural areas. When the demand for the network increases, the network traffic also increases. An effective time demand network traffic estimate technique based on deep learning is created to address such problems. The original data are gathered, then preprocessed using two strategies such asgaussian weighted average filter and min–max normalization. A sufficient range of raw data is converted using min–max normalization, and a gaussian weighted average filter is used to transform overall data from low to high quality. After that, use agglomerative clustering to split every base station into a number of groups. Using discrete wavelet transform, separate the traffic data into its high and low frequency constituents. Finally, a modified Deep Belief Network is employed to predict the network traffic. The horse herd optimization is utilized for training the neural network and optimizing the classifier’s weight. According to the simulation research, the proposed strategy can achieve 97 % accuracy with a 0.03 % Error. As a result, the proposed approach performs better than other existing techniques. Thus, the designed model predicted network traffic effective manner.

Version [1.1]- [Handling decision-making in intuitionistic fuzzy environment: PyIFDM package

The paper introduces the update of the Python Intuitionistic Fuzzy Decision Making (PyIFDM) package, emphasizing its focus on Multi-Criteria Decision Analysis (MCDA) with uncertain data modeled as Intuitionistic Fuzzy Sets (IFS). The update enhances PyIFDM’s capabilities, introducing five new IF-MCDA methods and basic mathematical operations on IFS. The update includes the implementation of Max normalization, a Hausdorff measure-based Euclidean distance, and a module for IFS-similarity measures. The release contributes to a significant library upgrade, providing advanced tools for modeling fuzzy data in and outside the MCDA space. PyIFDM now supports a greater variety of popular IF-MCDA methods, offering flexibility and applicability in decision-making scenarios. The added visualization module facilitates the graphical representation of IFS, aiding the interpretation of complex decision models involving intuitionistic fuzzy data. The enhanced version of the PyIFDM library expands its applicability in research methodologies, making it a more efficient tool for conducting multi-criteria decision analysis in an intuitionistic fuzzy environment.

Optimization of DC Microgrid Power and Energy Management in the Presence of Small-Scale Wind Turbine Integration and Renewable Load Shedding

Improvement of Direct Current Microgrid (DCM) Power and Energy Management is the methodical process of increasing the DCM system’s performance, dependability, and effectiveness. In this study, the suggested a Stochastic Dove Swarm Optimized Deep Reinforcement Learning Algorithm approach to estimate the availability of wind energy, allowing for proactive management tactics. Initially, the data was collected and preprocessed by performing min–max normalization to clean the unwanted data. Recursive Feature Elimination is used to determine the greatest informative attributes. This study analyzes the use of significant performance metrics, including accuracy (91%), precision (92%), recall (93%), Root Mean Square Error (0.154), Normalized Root Mean Square Error (0.0526), and Mean Absolute Percentage Error (4.21). The use of optimization methodology focuses on the seamless integration of SWTs and the application of renewable LS methods to ensure the efficient management of power and energy in DC microgrids. The intention is to keep operational expenses to a minimum while providing a stable and robust energy supply. To ensure power balance, which serves as the primary objective of the monitoring, it is considered imperative for carrying out electricity limitations in this instance of the energy excess generated by solar power plants and the small-scale wind turbine.

A Novel Approach for Data Feature Weighting Using Correlation Coefficients and Min–Max Normalization

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 the importance of the different features. One of the strategies to measure the dependency of features is the correlation coefficient. The correlation between features shows the relationship strength between the features. The integration of the normalization method with feature weighting in data transformation for classification has not been extensively studied. The goal is to improve the accuracy of classification methods by striking a balance between the normalization step and assigning greater importance to features with a strong relation to the class feature. To achieve this, we combine Min–Max normalization and weight the features by increasing their values based on their correlation coefficients with the class feature. This paper presents a proposed Correlation Coefficient with Min–Max Weighted (CCMMW) approach. The data being normalized depends on their correlation with the class feature. Logistic regression, support vector machine, k-nearest neighbor, neural network, and naive Bayesian classifiers were used to evaluate the proposed method. Twenty UCI Machine Learning Repository and Kaggle datasets with numerical values were also used in this study. The empirical results showed that the proposed CCMMW significantly improves the classification performance through support vector machine, logistic regression, and neural network classifiers in most datasets.

Hybrid deep model for predicting anti-cancer drug efficacy in colorectal cancer patients

Cancers are genetically diversified, so anticancer treatments have different levels of efficacy on people due to genetic differences. The main objective of this work is to predict the anticancer drug efficiency for colorectal cancer patients to reduce the mortality rates and provides immune energy for the patients. This paper proposes a novel anti-cancer drug efficacy system in colorectal cancer patients. The input data gene is normalized with the Min–Max normalization technique that normalizes the data in distinct scales. Subsequently, proposes an improved entropy-based feature to evaluate the uncertainty distribution of data, in which it induces weight to overcome the issue of computational complexity. Along with this feature, a correlation-based feature and statistical features are also retrieved. Subsequently, proposes a Recursive Feature Elimination with Hybrid Machine Learning (RFEHML) mechanism for selecting the appropriate feature set by eliminating the recursive features with the aid of hybrid Machine Learning strategies that combine decision tree and logistic regression. Also, the Gini impurity is employed for ranking the feature and selecting the maximum importance score by eliminating the least acquired importance score. Further, proposes a hybrid model for predicting the drug efficiency with the trained feature set. The hybrid model comprises of Long Short-Term Memory (LSTM) and Updated Rectified Linear Unit-Deep Convolutional Neural Network (UReLU-DCNN) model, in which DCNN is modified by updating the activation function at the fully connected layer. Consequently, the learned feature predicts the drug efficacy of anti-cancer in colorectal cancer patients by determining whether the patient is a responder or non-responder of the drug. Finally, the performance of the proposed RFEHML model is compared with other traditional approaches. It is found that the developed method has higher accuracy for each learning percentage, with values of 60LP = 92.48%, 70LP = 94.28%, 80LP = 95.24%, and 90LP = 96.86%, respectively.

Sliding principal component and dynamic reward reinforcement learning based IIoT attack detection

The Internet of Things (IoT) involves the gathering of all those devices that connect to the Internet with the purpose of collecting and sharing data. The application of IoT in the different sectors, including health, industry has also picked up the threads to augment over the past few years. The IoT and, by integrity, the IIoT, are found to be highly susceptible to different types of threats and attacks owing to the networks nature that in turn leads to even poor outcomes (i.e., increasing error rate). Hence, it is critical to design attack detection systems that can provide the security of IIoT networks. To overcome this research work of IIoT attack detection in large amount of evolutions is failed to determine the certain attacks resulting in a minimum detection performance, reinforcement learning-based attack detection method called sliding principal component and dynamic reward reinforcement learning (SPC–DRRL) for detecting various IIoT network attacks is introduced. In the first stage of this research methodology, preprocessing of raw TON_IoT dataset is performed by employing min–max normalization scaling function to obtain normalized values with same scale. Next, with the processed sample data as output, to extract data from multi-sources (i.e., different service profiles from the dataset), a robust log likelihood sliding principal component-based feature extraction algorithm is applied with an arbitrary size sliding window to extract computationally-efficient features. Finally, dynamic reward reinforcement learning-based IIoT attack detection model is presented to control the error rate involved in the design. Here, with the design of dynamic reward function and introducing incident repository that not only generates the reward function in an arbitrary fashion but also stores the action results in the incident repository for the next training, therefore reducing the attack detection error rate. Moreover, an IIoT attack detection system based on SPC–DRRL is constructed. Finally, we verify the algorithm on the ToN_IoT dataset of University of New South Wales Australia. The experimental results show that the IIoT attack detection time and overhead along with the error rate are reduced considerably with higher accuracy than that of traditional reinforcement learning methods.

Honey-block: Edge assisted ensemble learning model for intrusion detection and prevention using defense mechanism in IoT

The Internet of Things (IoT) has gained popularity with interconnected devices and diverse network applications, leading to increased vulnerability of sensitive data to security threats. Many researchers have focused on intrusion detection without considering prevention mechanisms. To overcome these issues, we propose the honeypot and blockchain-based intrusion detection and prevention (HB-IDP) model, in which edge computing is introduced to reduce the latency during communication. Initially, three-fold authentication is performed for entities (users, devices, and gateway) to ensure legitimacy using the camellia encryption algorithm (CEA), which provides secret keys. The proposed datasets (i.e., UNSW-NB15 and BoT-IoT) are pre-processed at the gateway using min–max normalization to reduce redundancy and complexity during feature extraction and classification. Signature-based intrusion detection is performed on the pre-processed data, with known attacks classified into three classes (normal, malicious, and suspicious) using the improved isolation forest (IIF) algorithm. Suspicious data are forwarded for anomaly detection to the edge level; here, a honeypot is deployed to attract the attacker’s patterns. Ensemble learning technique, including multi-layer perceptron (MLP), general adversarial network (GAN), and lightweight convolutional neural Network (LCNN), is applied to classify suspicious packet behaviors. Once intrusions are detected, the proposed work prevents future intrusions by generating reports, which are then encrypted by the CEA algorithm and provided to legitimate users. All transactions (i.e., key generation, report generation, and attacker patterns) are stored in the blockchain. The HB-IDP model’s performance and effectiveness were evaluated using network simulator 3.26 (NS-3.26), showcasing its superiority over existing approaches.

Flood risk assessment using an indicator based approach combined with flood risk maps and grid data

The increasing frequency of tropical cyclones, such as typhoons and localized heavy rainfall, increases the risk of flood damage in urban areas around the world. Countries implementing flood prevention projects use qualitative risk assessments, such as flood risk assessments, to support decision-making. However, existing flood risk assessment methods are not able to select and reflect only the damage targets that are directly exposed to flood risk. In addition, existing indexing methods are limited in improving the skewness of the raw data distribution, which distorts the calculated flood risk index. This study conducted a flood risk assessment based on an indicator-based approach using flood risk maps and grid data utilizing indicators (buildings, road area, etc.) from 2016 to 2019 for 44 cities and counties in the Gyeongsang-do Province. The flood risk assessment in this study consists of four items (Hazard, Exposure, Vulnerability, and Capacity) and nine indicators. Instead of using statistical data, we used the overlapped grids with the flood risk map for Exposure and Vulnerability indicators to reflect the flood risk exposed targets in the risk assessment. The limitations of existing indexing methods, such as min–max normalization, were improved by dividing the distribution into ten quantiles and scoring each quantile interval when indexing the indicators. Finally, entropy weights and Euclidean distance were applied to calculate the annual flood risk index of 44 cities and counties. The annual flood risk indices for local governments were arranged in descending order and then, the rankings of flood damage amount in 2019 were compared with the calculated index rankings. The sum and mean of absolute errors were 386 and 9.897, and the root mean square error was calculated as 11.930, which showed an improvement over the existing method. Using the calculated indices, it is possible to create Hazard vs. Capacity graph, Exposure vs. Vulnerability graph, or annual flood risk index graph. These graphs can be utilized to understand the level of exposure to flood risk of local governments and to assess the level of response through the Capacity index that contributes to damage reduction. It is believed that the flood risk assessment method presented in this study not only accurately identify the flood risk for local governments but can also be used to support decision-making processes to strengthen the government’s disaster prevention capabilities.

Numerical Study of Dig Sequence Effects during Large-Scale Excavation

The appropriate excavation sequence can improve the overall stability of a foundation pit. In this study, eight schemes were created using FLAC3D to examine the impact of the excavation sequence on a foundation pit by analyzing a deep foundation pit in Nanjing, which had an irregular large rectangle shape. The results show that different excavation sequence schemes and different phases of the foundation pit can change the displacement values and the horizontal displacement type. The min–max normalization method was used to score the schemes in terms of six parameters and confirm the best excavation sequence scheme. In addition, the irregular shape of the foundation pit also leads to local differences in the stability of a foundation pit; the wide end is only slightly longer than the narrow end, but its displacement is significantly higher than that of the narrow end, so attention should be paid to reinforcing the weak parts when carrying out the support. This study can inform the selection of the excavation sequence for actual construction processes.

Novel deep learning models with novel integrated activation functions for autism screening: AutiNet and MinAutiNet

Deep Learning (DL) particularly CNN has the potential to outplay conventional Machine Learning (ML) procedures in the realm of computer vision. With the advent of DL, profound research on autism utilizing fMRI data has declared advancements in autism screening. Yet, DL models are inadequate for heterogeneous datasets encompassing complex fMRI data. To resolve these problems, two cutting-edge AutiNet and MinAutiNet models are proposed with a minimum number of layers and neurons in classifying the autistic subjects. Both AutiNet and MinAutiNet models are the two refined forms of the traditional LeNet-5 model. AutiNet model includes two convolutional layers, two average pooling layers, a flatten layer, and one fully connected layer. One convolutional layer, one average pooling layer, one flatten layer, and one fully connected layer make up the MinAutiNet model. Pre-processing is a pre-requisite phase in the AutiNet and MinAutiNet models where denoising of raw fMRI data is done with slice time correction, realignment, min–max normalization and one hot encoding to obtain numerical values. As inspired by the promising results of non-handcrafted feature extraction, the proposed DL models have been used for feature extraction. Two novel integrated activation functions namely Li-ReLU and S-RReLU are developed and incorporated into the proposed DL models towards improving the performance of autism screening. Both AutiNet and MinAutiNet models surpass the standard accuracy in classifying autistic subjects with minimum number of layers and neurons. Experimental results reveal that the proposed AutiNet attains the maximum accuracy of 77.78% with Caltech and KKI datasets and the proposed MinAutiNet achieves the maximum accuracy of 88.89% with SBL dataset. The proposed models are compared with other state-of-the-art works to ensure its superiority.