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In this work, we introduced ZnO through hydrothermal reaction to improve the breakdown strength and piezoelectric properties of potassium-sodium niobate (KNN) ceramics. The impact of ZnO synthesized at different reaction times on the structure, breakdown strength, dielectric performance, and piezoelectric performance of 0.9KNN-0.1ZnO ceramics was investigated. The findings reveal that the KNN ceramics incorporating ZnO exhibit a dense microstructure, reduced grain size, enhanced breakdown strength, and piezoelectric performance. Notably, the breakdown field strength of KNN-Z-3 ceramic significantly increased to 7.42 kV/mm, exhibiting an 80.98 % improvement compared to KNN ceramics (4.10 kV/mm). Furthermore, the d 33 value of the KNN-Z-3 ceramic also had a significant increase, reaching 104 pC/N, having a 30 % enhancement compared to KNN ceramics (80 pC/N). Such enhancement is ascribed to the increased piezoelectric response due to the stress bending between KNN and ZnO as well as adjacent domains. [ABSTRACT FROM AUTHOR]

This study is primarily aimed at creating three machine learning models: artificial neural network (ANN), random forest (RF), and k-nearest neighbour (KNN), so as to predict the crippling load (CL) of I-shaped steel columns. Five input parameters, namely length of column (L), width of flange (bf), flange thickness (tf), web thickness (tw) and height of column (H), are used to compute the crippling load (CL). A range of performance indicators, including the coefficient of determination (R2), variance account factor (VAF), a-10 index, root mean square error (RMSE), mean absolute error (MAE) and mean absolute deviation (MAD), are used to assess the effectiveness of the established machine learning models. The results show that all of the three ML (machine learning) models can accurately predict the crippling load, but the performance of ANN is superior: it delivers the highest value of R2 = 0.998 and the lowest value of RMSE = 0.008 in the training phase, as well as the highest value of R2 = 0.996 and the smaller value of RMSE = 0.012 in the testing phase. Additional methods, including rank analysis, reliability analysis, regression plot, Taylor diagram and error matrix plot, are employed to assess the models' performance. The reliability index (β) of the models is calculated by using the first-order second moment (FOSM) technique, and the result is compared with the actual value. Additionally, sensitivity analysis is performed to check the impact of the input variables on the output (CL), finding that bf has the greatest impact on the crippling load, followed by tf, tw, H and L, in that order. This study demonstrates that ML techniques are useful for developing a reliable numerical tool for measuring the crippling load of I-shaped steel columns. It is found that the proposed techniques can also be used to predict other kinds of failures as well as different kinds of perforated columns. [ABSTRACT FROM AUTHOR]

In this work, an attempt is made for improving the piezoelectric properties of a lead-free (Na 0. 5 K 0. 4 8 5 Li 0. 0 1 5) (Nb 0. 9 8 V 0. 0 2) O3 ceramic system by doping manganese in it. The Rietveld analysis of XRD micrographs of the ceramics indicates the samples crystallizing into 99.86% of orthorhombic phase and very small traces of tetragonal phase around room temperature. The Curie temperature ( T c ) is hardly affected by the incorporation of Mn 4 + into the system. At the manganese concentration of 0.02 wt.%, the ceramic system attains the peak values in its density, dielectric constant at room temperature ( ε RT ), planar electromechanical coefficient ( k p) , piezoelectric coefficient ( d 3 3 ), and remnant polarization ( P r ). The optimum piezoelectric properties of k p = 4 3 % and d 3 3 = 1 9 3 pC/N are observed for this composition. The study reveals that the modification of (Na 0. 5 K 0. 4 8 5 Li 0. 0 1 5 ) (Nb 0. 9 8 V 0. 0 2) O3 with an appropriate quantity of Mn 4 + can produce the desired changes in the crystallographic properties and densification so as to eventually improve its piezoelectric properties. [ABSTRACT FROM AUTHOR]

Attributed networks containing entity-specific information in node attributes are ubiquitous in modeling social networks, e-commerce, bioinformatics, etc. Their inherent network topology ranges from simple graphs to hypergraphs with high-order interactions and multiplex graphs with separate layers. An important graph mining task is node clustering, aiming to partition the nodes of an attributed network into k disjoint clusters such that intra-cluster nodes are closely connected and share similar attributes, while inter-cluster nodes are far apart and dissimilar. It is highly challenging to capture multi-hop connections via nodes or attributes for effective clustering on multiple types of attributed networks. In this paper, we first present AHCKA as an efficient approach to attributed hypergraph clustering (AHC). AHCKA includes a carefully-crafted K-nearest neighbor augmentation strategy for the optimized exploitation of attribute information on hypergraphs, a joint hypergraph random walk model to devise an effective AHC objective, and an efficient solver with speedup techniques for the objective optimization. The proposed techniques are extensible to various types of attributed networks, and thus, we develop ANCKA as a versatile attributed network clustering framework, capable of attributed graph clustering, attributed multiplex graph clustering, and AHC. Moreover, we devise ANCKA-GPU with algorithmic designs tailored for GPU acceleration to boost efficiency. We have conducted extensive experiments to compare our methods with 19 competitors on 8 attributed hypergraphs, 16 competitors on 6 attributed graphs, and 16 competitors on 3 attributed multiplex graphs, all demonstrating the superb clustering quality and efficiency of our methods. [ABSTRACT FROM AUTHOR]

(K,Na)NbO3(KNN)-based ceramics are a promising lead-free piezoelectric material that could replace Pb(Zr1−xTix)O3-based materials due to their higher Curie temperature and superior electrical properties. However, the volatilization of sodium and potassium during conventional high-temperature sintering makes it difficult to obtain KNN ceramics with a dense structure and excellent electrical properties. Herein, a conventional solid-phase method combined with microwave sintering is applied for the preparation of KNN ceramics. The influence of the microwave sintering process on the microstructure and electrical properties of KNN ceramics were studied. The optimal microwave sintering conditions for KNN ceramics were determined to be 1100°C for 50 min. Under these conditions, the ceramic samples exhibited excellent ferroelectric properties, with the maximum polarization (Pmax) of 20.4 μC/cm2 and a remanent polarization (Pr) of 18.1 μC/cm2. Additionally, the samples demonstrated impressive piezoelectric properties, including the maximum electric field-induced strain (Smax) of 0.0251%, a dynamic piezoelectric coefficient (d33*) of 125.5 pm/V, and a piezoelectric coefficient (d33) of 109.8 pC/N. The study has important implications for the use of microwave sintering to achieve the densification of the ceramic with volatile elements such as KNN-based ceramics at lower sintering temperature and short sintering time. [ABSTRACT FROM AUTHOR]

Mango is a highly diverse tropical fruit with numerous varieties that differ in flavor, texture, and chemical composition. Consequently, identifying fraudulent substitutions of mango varieties poses a significant challenge using traditional techniques. Therefore, there is an increasing need for new methods to discriminate between mango varieties. Hyperspectral imaging coupled with machine learning techniques presents a promising approach for varietal discrimination. In this study, mango samples of eleven varieties were collected from a germplasm bank, with four slices obtained from each sample. Hyperspectral images were acquired in the Vis–NIR and NIR ranges for each slice, and spectral profiles were extracted and pretreated. Three discrimination models, linear discriminant analysis, K-nearest neighbor, and artificial neural networks, were implemented and validated using relevant wavelengths selected through a covering array feature selection algorithm. The performance of these models was evaluated using precision, accuracy, and F-score metrics. The average spectral profiles of the studied varieties exhibited a similar behavior with slight differences, which could be used for classification within the evaluated ranges. The optimal number of variables selected to refine the models was 17 for the UV–Vis–NIR range and 21 for the NIR range, with an accuracy ranging between 0.752 and 0.972. This study concludes that hyperspectral imaging combined with machine learning techniques can effectively discriminate between different varieties of mango. [ABSTRACT FROM AUTHOR]

Background/Objectives: Photoplethysmography (PPG) signals, which measure blood volume changes through light absorption, are increasingly used for non-invasive cardiovascular disease (CVD) detection. Analyzing PPG signals can help identify irregular heart patterns and other indicators of CVD. Methods: This research involves a total of 41 subjects sourced from the CapnoBase database, consisting of 21 normal subjects and 20 CVD cases. In the initial stage, heuristic optimization algorithms, such as ABC-PSO, the Cuckoo Search algorithm (CSA), and the Dragonfly algorithm (DFA), were applied to reduce the dimension of the PPG data. Next, these Dimensionally Reduced (DR) PPG data are then fed into various classifiers such as Linear Regression (LR), Linear Regression with Bayesian Linear Discriminant Classifier (LR-BLDC), K-Nearest Neighbors (KNN), PCA-Firefly, Linear Discriminant Analysis (LDA), Kernel LDA (KLDA), Probabilistic LDA (ProbLDA), SVM-Linear, SVM-Polynomial, and SVM-RBF, to identify CVD. Classifier performance is evaluated using Accuracy, Kappa, MCC, F1 Score, Good Detection Rate (GDR), Error rate, and Jaccard Index (JI). Results: The SVM-RBF classifier for ABC PSO dimensionality reduced values outperforms other classifiers, achieving the highest accuracy of 95.12% along with the minimum error rate of 4.88%. In addition to that, it provides an MCC and kappa value of 0.90, a GDR and F1 score of 95%, and a Jaccard Index of 90.48%. Conclusions: This study demonstrated that heuristic-based optimization and machine learning classification of PPG signals are highly effective for the non-invasive detection of cardiovascular disease. [ABSTRACT FROM AUTHOR]

Maritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. This paper presents a novel approach to predictive maintenance in the maritime industry, leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance fault detection and maintenance planning for naval systems. Traditional maintenance strategies, such as corrective and preventive maintenance, are increasingly ineffective in meeting the high safety and efficiency standards required by maritime operations. The proposed model integrates AI-driven methods to process operational data from shipboard systems, enabling more accurate fault diagnosis and early identification of system failures. By analyzing historical operational data, ML algorithms identify patterns and estimate the functional states, helping prevent unplanned failures and costly downtime. This approach is critical in environments where technical failures are a leading cause of incidents, as demonstrated by the high rate of machinery-related accidents in maritime operations. Our study highlights the growing importance of AI and ML in predictive maintenance and offers a practical tool for improving operational safety and efficiency in the naval industry. The paper discusses the development of a fault detection approach, evaluates its performance on real shipboard data-through tests on a seawater cooling system from an oil tanker and concludes with insights into the broader implications of AI-driven maintenance in the maritime sector. [ABSTRACT FROM AUTHOR]

The biggest issue many students face in today's world is choosing the right career. Especially when there are so many options available to them and the counselling options available are very limited or not very efficient, Career counselling is a very essential process that assists individuals in making informed decisions about their career paths. The use of machine learning in career counselling has gained so much attention due to its potential to analyse vast amounts of data and provide personalised guidance. Previously, there had been so much work done in this field with the help of artificial intelligence and machine learning, but there was a lack of a systematic system where students could explore each and every option thoroughly and get to know what the real outcome would be if they chose that stream. In this study, various factors such as the student's interests, hobbies, past academics and performances, and achievements are taken into consideration to predict the right career option. The model is trained using five different machine learning algorithms: decision tree, Random Forest, Support Vector Machine, Nave Bayes, and K-nearest neighbours Classifier. Out of these, Random Forest gave the highest accuracy of 84.17%, and after hypertuning, it gave the highest accuracy of 85.68%. We also gave some manual inputs to the system and found out that the Random Forest gave the highest accuracy of 85.71%. The prediction results of each algorithm are summarised in this study. [ABSTRACT FROM AUTHOR]

Stress is a crucial factor that causes various health-related issues. Recent research has focused on stress prediction using Electroencephalography (EEG) signal processing. Early detection of stress among the students helps to avoid suicidal thoughts and illness, also proper counseling is given to improve the learning ability of the students. To improve the performance metrics of the classifier model, EEG features such as relative sub-band energies and EEG band ratios were considered. In this study, two levels of classification such as stress and non-stress states were carried out using machine learning techniques. An experimental work with EEG signal acquired from 25 subjects under two conditions as relax mode (non-stress) and during a mental task (stress) using an 8-channel wireless Enobio device. EEG features extracted using discrete wavelet transform technique, relative sub-band energy such as alpha, theta, and beta energies, and the relative band ratios computed from sub-band energies for two states such as arousal index, heart rate, performance enhancement index, cognitive performance attentional resource index (CPARI), CNS arousal and desynchronization. EEG Features were selected by analyzing statistically significant (p < 0.05) for both states of data by using a non-parametric test as the Wilcoxon signed-rank test, and brain functional connectivity analysis was carried out for subband energies. Then, the extracted features were imported to various machine learning classifier algorithms such as decision tree, linear discriminant analysis, Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (kNN), ensemble, and neural network. The classifier performance metrics such as classification accuracy, sensitivity, specificity, and precision were compared for the above classifiers. The experimental result shows that the cubic SVM classifier has achieved the highest accuracy of 95.83%, sensitivity of 96.70%, specificity of 93.10% and precision of 97.78% for detecting stress and non-stress states compared with other classifier algorithms. A classification model was exported for the cubic SVM classifier and tested with an online EEG dataset for 12 subjects with two states as relaxed and during a task. Finally, the result for the exported cubic SVM classifier model was achieved with a classification accuracy of 89.74%. [ABSTRACT FROM AUTHOR]

Cybercrime is one of the fastest-growing crimes worldwide. It is observed that every seven seconds, cyber attackers penetrate cyber systems. While detecting an anomaly or attack, the log system is one of the crucial components of any system storing and managing all the events. It has always been challenging to detect an anomaly in logs. This is because of continuous and ever-changing log events and their mutability property. In this paper, we develop a machine learning-based artificial intelligence approach to address this issue of log analysis by proposing two modules. The first one is anomaly detection using different machine learning models. The second one is a distributed immutable storage system for securely storing the logs. In addition, we present a descriptive and user-friendly web application by integrating all modules using HTML, CSS, and Flask Framework on the Heroku cloud environment. The results demonstrate that the proposed hybrid machine learning models are capable of achieving 99.7% accuracy in detecting network anomalies. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) helps to assess the electrical activities of the brain so that the neuronal activities of the brain are captured effectively. EEG is used to analyze many neurological disorders, as it serves as a low-cost equipment. To diagnose and treat every neurological disorder, lengthy EEG signals are needed, and different machine learning and deep learning techniques have been developed so that the EEG signals could be classified automatically. In this work, five ensemble models are proposed for EEG signal classification, and the main neurological disorder analyzed in this paper is epilepsy. The first proposed ensemble technique utilizes an equidistant assessment and ranking determination mode with the proposed Enhance the Sum of Connection and Distance (ESCD)-based feature selection technique for the classification of EEG signals; the second proposed ensemble technique utilizes the concept of Infinite Independent Component Analysis (I-ICA) and multiple classifiers with majority voting concept; the third proposed ensemble technique utilizes the concept of Genetic Algorithm (GA)-based feature selection technique and bagging Support Vector Machine (SVM)-based classification model. The fourth proposed ensemble technique utilizes the concept of Hilbert Huang Transform (HHT) and multiple classifiers with GA-based multiparameter optimization, and the fifth proposed ensemble technique utilizes the concept of Factor analysis with Ensemble layer K nearest neighbor (KNN) classifier. The best results are obtained when the Ensemble hybrid model using the equidistant assessment and ranking determination method with the proposed ESCD-based feature selection technique and Support Vector Machine (SVM) classifier is utilized, achieving a classification accuracy of 89.98%. [ABSTRACT FROM AUTHOR]

This study contributes to the Sybil node-detecting algorithm in online social networks (OSNs). As major communication platforms, online social networks are significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. An additional exploration of distinct reputation systems and their practical applications led to this study's discovery of machine learning algorithms, i.e., the KNN, support vector machine, and random forest algorithms, as part of our SybilSocNet. This study details the data-cleansing process for the employed dataset for optimizing the computational demands required to train machine learning algorithms, achieved through dataset partitioning. Such a process led to an explanation and analysis of our conducted experiments and comparing their results. The experiments demonstrated the algorithm's ability to detect Sybil nodes in OSNs (99.9% accuracy in SVM, 99.6% in random forest, and 97% in KNN algorithms), and we propose future research opportunities. [ABSTRACT FROM AUTHOR]

Accurate and robust positioning has become increasingly essential for emerging applications and services. While GPS (global positioning system) is widely used for outdoor environments, indoor positioning remains a challenging task. This paper presents a novel architecture for indoor positioning, leveraging machine learning techniques and a divide-and-conquer strategy to achieve low error estimates. The proposed method achieves an MAE (mean absolute error) of approximately 1 m for latitude and longitude. Our approach provides a precise and practical solution for indoor positioning. Additionally, some insights on the best machine learning techniques for these tasks are also envisaged. [ABSTRACT FROM AUTHOR]

The global shift towards sustainable energy has positioned photovoltaic (PV) systems as a critical component in the renewable energy landscape. However, maintaining the efficiency and longevity of these systems requires effective fault detection and diagnosis mechanisms. Traditional methods, relying on manual inspections and standard electrical measurements, have proven inadequate, especially for large-scale solar installations. The emergence of machine learning (ML) and deep learning (DL) has sparked significant interest in developing computational strategies to enhance the identification and classification of PV system faults. Despite these advancements, challenges remain, particularly due to the limited availability of public datasets for PV fault detection and the complexity of existing artificial-intelligence (AI)-based methods. This study distinguishes itself by proposing a novel AI-based approach that optimizes fault detection and classification in PV systems, addressing existing gaps in AI-driven fault detection, especially in terms of thermal imaging and current–voltage (I-V) curve analysis. This comprehensive survey identifies emerging trends in AI-driven PV fault detection, highlights the most advanced methodologies, and proposes a novel AI-based approach to enhance fault detection and classification capabilities. The findings aim to advance the state of technology in this field, offering insights into more efficient and practical solutions for PV system fault management. [ABSTRACT FROM AUTHOR]

Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from high time complexity. Prior research suggests that clustering algorithms grounded in P systems can mitigate time complexity concerns. Within the realm of membrane systems (P systems), spiking neural P systems (SN P systems), inspired by biological nervous systems, are third-generation neural networks that possess intricate structures and offer substantial parallelism advantages. Thus, this study first improved the DPC by introducing the maximum nearest neighbor distance and K-nearest neighbors (KNN). Moreover, a method based on delayed spiking neural P systems (DSN P systems) was proposed to improve the performance of the algorithm. Subsequently, the DSNP-ANDPC algorithm was proposed. The effectiveness of DSNP–ANDPC was evaluated through comprehensive evaluations across four synthetic datasets and 10 real-world datasets. The proposed method outperformed the other comparison methods in most cases. [ABSTRACT FROM AUTHOR]

Recent cyber security solutions for wireless networks during internet open access have become critically important for personal data security. The newest WPA3 network security protocol has been used to maximize this protection; however, attackers can use an Evil Twin attack to replace a legitimate access point. The article is devoted to solving the problem of intrusion detection at the OSI model's physical layers. To solve this, a hardware–software complex has been developed to collect information about the signal strength from Wi-Fi access points using wireless sensor networks. The collected data were supplemented with a generative algorithm considering all possible combinations of signal strength. The k-nearest neighbor model was trained on the obtained data to distinguish the signal strength of legitimate from illegitimate access points. To verify the authenticity of the data, an Evil Twin attack was physically simulated, and a machine learning model analyzed the data from the sensors. As a result, the Evil Twin attack was successfully identified based on the signal strength in the radio spectrum. The proposed model can be used in open access points as well as in large corporate and home Wi-Fi networks to detect intrusions aimed at substituting devices in the radio spectrum where IEEE 802.11 networking equipment operates. [ABSTRACT FROM AUTHOR]

In the modern world, traffic accidents frequently result in fatalities and serious injuries. The ability of machine learning to foretell the severity of road traffic accidents has shown great promise. The classification of traffic accidents has shown to be a good application for algorithms like random forest. In this paper, performance on a specific dataset has been evaluated using random forest and other models. The dataset used for the analysis came from a publicly accessible source and contained information on several variables like the type of road, the time of day, and the weather. In order to analyze the severity of accidents, a number of algorithms were applied to the dataset, including decision tree, random forest classifier, and logistic regression algorithm. Each model was evaluated on parameters such as model accuracy, precision and recall of the model, and F1 score. The random forest classifier outperformed the other models, achieving an accuracy of 98.48%. The study concludes that machine learning algorithms can accurately predict the severity of road traffic accidents, which could help to reduce the number of accidents and fatalities on the road. [ABSTRACT FROM AUTHOR]

In the era of advanced cyber developments, intrusions becomes a common event in any network. Although there are research studies and developers found ways to improve the detection models, there is some problem that persists in the intrusion models such as extracting key features from a large dataset, and delayed detection is a critical issue that needs to be addressed. Hence the proposed study aimed to develop a model that could extract key features from the dataset and use them effectively in the detection of threats. The study incorporates two approaches, one is feature extraction by the K-Nearest Neighbourhood, and feature selection by the K-Best approach. And the other is the balanced Gini-Entropy approach for the Random Forest (RF) classifier. This combined approach by KNN, K-best, and RF is referred to as (KK-RF). This combined approach of feature extraction, selection, and classification results in an effective threat detection model with high accuracy of about 99.61%. Moreover the proposed model has achieved precision and the recall rates of 97.3 and 96.6% respectively. Concurrently, the model attained markable F1-score of 96.6 respectively. Also, from the comparison results, it is observed that the proposed model had higher performance. [ABSTRACT FROM AUTHOR]

Since many years ago, walnuts have been extensively available around the world and come in various quality varieties. The proper variety of walnut can be grown in the right area and is vital to human health. This fruit's production is time-consuming and expensive. However, even specialists find it challenging to differentiate distinct kinds since walnut leaves are so similar in color and feel. There aren't many studies on the classification of walnut leaves in the literature, and the most of them were conducted in laboratories. The classification process can now be carried out automatically from leaf photos thanks to technological advancements. The walnut data set was applied to the suggested deep learning model. There aren't many studies on the classification of walnut leaves in the literature, and the most of them were conducted in laboratories. The walnut data set, which consists of 18 different types of 1751 photos, was used to test the suggested deep learning model. The three most successful algorithms among the commonly utilized CNN algorithms in the literature were first selected for the suggested model. From the Vgg16, Vgg19, and AlexNet CNN algorithms, many features were retrieved. Utilizing the Whale Optimization Algorithm (WOA), a new feature set was produced by choosing the top extracted features. KNN is used to categorize this feature set. An accuracy rating of 92.59% was attained as a consequence of the tests. [ABSTRACT FROM AUTHOR]

The piezoelectric and electromechanical coupling coefficients in conjunction with their temperature stability are the crucial figure-of-merits of piezoelectric materials, due to these properties determine their response sensitivity and operating temperature range, respectively for practical applications. In this work, Sb, (Bi 0.5 Na 0.5)ZrO 3 , and (Bi 0.5 K 0.5)HfO 3 were used to regulate phase transition of KNN-based ceramics, and resultant large piezoelectric constants d 33 ∼ 430 ± 10 pC/N, unipolar strain S uni ∼ 0.18 % at 40 kV/cm, and a high planar electromechanical coupling factor k p ∼ 55 % were observed in the composition with rhombohedral-orthorhombic-tetragonal (R-O-T) multiphase coexistence. Ex-situ electric field XRD measurement and TEM observation indicate the outstanding piezoelectric properties are originated from the electric field-induced phase transition, nanodomains and polar nanoregions, which lower polarization anisotropy and facilitate polarization rotation between different crystallographic symmetries. In addition, the piezoelectric properties of the samples with R-O-T phases coexistence show superior temperature stability and fatigue resistance after 106 bipolar electric cycles. The present study not only helps to understand the structural origin of high piezoresponse, but also lays a foundation for the applications of KNN-based ceramics. [ABSTRACT FROM AUTHOR]

Attention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting millions of people globally, is defined by symptoms of hyperactivity, impulsivity, and inattention that can significantly affect an individual's daily life. The diagnostic process for ADHD is complex, requiring a combination of clinical assessments and subjective evaluations. However, recent advances in artificial intelligence (AI) techniques have shown promise in predicting ADHD and providing an early diagnosis. In this study, we will explore the application of two AI techniques, K-Nearest Neighbors (KNN) and Adaptive Boosting (AdaBoost), in predicting ADHD using the Python programming language. The classification accuracies obtained were 96.5% and 93.47%, respectively, before applying balancing to the data. In addition, 98.59% and 97.18%, respectively, after applying the balancing technique The extreme gradient boosting (XGBoost) technique had been applied to selecting the important features and the Pearson correlation for finding the correlation between features. [ABSTRACT FROM AUTHOR]

In gas‐fired power plants, emissions may reduce turbine blade rotation, thus decreasing power output. This study proposes a hybrid model integrating the Feed forward Neural Network (FFNN) model and Particle Swarm Optimization (PSO) algorithm to predict gas emissions from natural gas power plants. The FFNN predicts gas turbine nitrogen oxides (NOx) and carbon monoxide (CO) emissions, while the PSO optimizes FFNN weights, improving prediction accuracy. The PSO adopts a unique random number selection strategy, incorporating the K‐Nearest Neighbor (KNN) algorithm to reduce prediction errors. Neighbor Component Analysis (NCA) selects parameters most correlated with CO and NOx emissions. The hybrid model is constructed, trained, and testedusing publicly available datasets, evaluating performance with statistical metrics like Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Results show significant improvement in FFNN training with the PSO algorithm, boosting CO and NOx prediction accuracy by 99.18% and 82.11%, respectively. The model achieves the lowest MSE, MAE, and RMSE values for CO and NOx emissions. Overall, the hybrid model achieves high prediction accuracy, particularly with optimized PSO parameter selection using seed random generators. [ABSTRACT FROM AUTHOR]

Potassium sodium niobate (KNN)‐based piezoelectric ceramics have emerged as a promising alternative to lead‐based systems due to their exceptional properties. While extensive research has focused on improving the electrical properties of KNN‐based ceramics through doping and processing optimization, the concurrent investigation of their mechanical properties has been lacking. This study presents a comprehensive analysis of the mechanical and electrical properties of KNN‐based lead‐free piezoceramics doped with various transition metal oxides and rare earth oxides, based on substantial experimental data. Our findings reveal that the as‐sintered KNN‐based ceramics exhibit not only outstanding electrical properties but also remarkable mechanical robustness compared to conventional toughened lead zirconate titanate (PZT)‐based ceramics. These exceptional electrical and mechanical properties are attributed to the micro‐scale and atomic‐scale structure of the modified KNN‐based ceramics, characterized by a highly condensed structure, an inhomogeneous distribution of nano‐domain structure, and the presence of amorphous intergranular films at grain boundaries. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is a life-threatening cardiac condition caused by inadequate blood flow, resulting in abnormal ECG records, blood clotting, and cardioembolic strokes. In recent years, physicians have been particularly concerned with early detection and diagnosis to overcome cardiogenic stroke. AF can be easily identified at the initial stages due to the development in computer-aided diagnosis. The performance of this method is affected by noise and the variations in pattern of the ECG, which leads to false diagnosis. Current signal processing and shallow machine learning (ML) approaches are severely limited in their ability to detect this condition accurately. Deep neural networks have been shown to be extremely effective at learning nonlinear patterns in a wide variety of problems, which include computer vision tasks. Deep learning models are computationally costly, non-explainable, and require a large quantity of data to discover characteristics. In contrast, ML approaches are explainable and require good feature extraction. In this manuscript, ML based supervised classification method is developed based on feature ensembling. ECG signals are preprocessed (mean subtraction followed by Butterworth filtering and computation of RR intervals) and subjected to feature extraction (by entropy-, wavelets-, & statistical-features). The variations due to AF are effectively captured and selective features are ensembled to perform classification by SVM and KNN. This method is experimented on five different databases (such as: PAF prediction Challenge, Long-Term AF, Intracardiac, AF termination Challenge, and MIT-BIH atrial fibrillation) and the classification performance is found to be the highest compared to the state of art. To evaluate the effectiveness of the proposed technique, AF-specific characteristics are retrieved from the ECG signal in the presence of artificially added noise and the features are fed to classifiers for classification. Performance of the proposed method is compared with the deep learning based approaches. The graphical abstract of the proposed atrial fibrillation detection method is presented. The overall accuracy of the proposed method was found to be 91.88 % and 91.99 % for wavelets-SVM and ensemble wavelet-SVM, respectively. This model attained 100 % accuracy for entropy and statistical features with SVM and KNN, respectively. [ABSTRACT FROM AUTHOR]

Identification of landslide susceptible zones is the preliminary step to plan mitigation measures in landslide-prone mountainous terrains. The use of various machine learning (ML) algorithms has proven their superiority in terms of enhancing the success rate in susceptibility studies. Therefore, the present study focuses on spatial prediction of landslides using integrated supervised and unsupervised machine learning (ML) techniques with reference to Bhagirathi Valley, NW Himalaya. A landslide inventory of 514 landslides and 14 viable causative factors of landslides in the study area have been selected for the analysis. Three efficient supervised ML techniques, i.e., random forest (RF), extreme gradient boosting (XGBoost), and k-nearest neighbour (KNN), have been integrated with an unsupervised ISODATA cluster classification technique to prepare the landslide susceptible maps (LSM) of the study area. All the models depict that the greater part of the high and very high landslide hazard zones lie in the Main Central Thrust zone and its vicinity in the Bhagirathi Valley. The accuracy of each model was determined and compared using several statistical signifiers like sensitivity, specificity, area under curve, accuracy, and Kappa index. The results show that XGBoost and RF models exhibit higher performance accuracy than KNN. The quantitative assessment of prepared LSMs of the study area was also done using frequency ratio (FR) and frequency density (FD). The results indicate the consistency of each model in the prediction of landslide zones in the study area as FR and FD both increase with the increase of landslide susceptibility levels from very low to very high in all the models. [ABSTRACT FROM AUTHOR]

Diabetes is an illness that is widespread throughout the world and is considered a health concern, which requires work to explore advanced predictive techniques for early diagnosis of the illness. This paper discusses diabetes prediction by using the K-Nearest Neighbors (KNN) classifier, which is a widely used algorithm in machine learning. Most studies only dealt with investigating the optimal value of k in the KNN algorithm and did not address the best method to measure distance alone or together with the optimal value of k to improve the efficiency of diabetes prediction. This study simultaneously investigates both the optimal value of k and the optimal method for measuring distance to improve the performance of the KNN technique in predicting diabetes. By using and analyzing the Indian Diabetes PIMA dataset, this study seeks to discover the extent to which different parameters, especially the optimal value of K and distance metrics, affect the performance of the classifier. Through experiments that included applying different values for the K factor and using various distance measures, the study reached insights into maximizing the classifier's accuracy. The study shows that choosing the distance measure greatly affects the accuracy of classification and selecting the optimal K value helps eliminate problems of overfitting and underfitting, which is a feature of robust models for diabetes prediction. The research results showed that the best performance achieved was 80.5% when k=35 and the Euclidean distance measure was used. [ABSTRACT FROM AUTHOR]

Smart contracts are programs that reside and execute on a blockchain, like any transaction. They are automatically executed when preprogrammed terms and conditions are met. Although the smart contract (SC) must be presented in the blockchain for the integrity of data and transactions stored within it, it is highly exposed to several vulnerabilities attackers exploit to access the data. In this paper, classification and detection of vulnerabilities targeting smart contracts are performed using deep learning algorithms over two datasets containing 12,253 smart contracts. These contracts are converted into RGB and Grayscale images and then inserted into Residual Network (ResNet50), Visual Geometry Group-19 (VGG19), Dense Convolutional Network (DenseNet201), k-nearest Neighbors (KNN), and Random Forest (RF) algorithms for binary and multi-label classification. A comprehensive analysis is conducted to detect and classify vulnerabilities using different performance metrics. The performance of these algorithms was outstanding, accurately classifying vulnerabilities with high F1 scores and accuracy rates. For binary classification, RF emerged in RGB images as the best algorithm based on the highest F1 score of 86.66% and accuracy of 86.66%. Moving on to multi-label classification, VGG19 stood out in RGB images as the standout algorithm, achieving an impressive accuracy of 89.14% and an F1 score of 85.87%. To the best of our knowledge, and according to the available literature, this study is the first to investigate binary classification of vulnerabilities targeting Ethereum smart contracts, and the experimental results of the proposed methodology for multi-label vulnerability classification outperform existing literature. [ABSTRACT FROM AUTHOR]

Fintech is an industry that uses technology to enhance and automate financial services. Fintech firms use software, mobile apps, and digital technologies to provide financial services that are faster, more efficient, and more accessible than those provided by traditional banks and financial institutions. Fintech companies take care of processes such as lending, payment processing, personal finance, and insurance, among other financial services. A data breach refers to a security liability when unapproved individuals gain access to or pilfer susceptible data. Data breaches pose a significant financial, reputational, and legal liability for companies. In 2017, Equifax suffered a data breach that revealed the personal information of over 143 million customers. Combining federated learning (FL) and blockchain can provide financial institutions with additional insurance and safeguards. Blockchain technology can provide a transparent and secure platform for FL, allowing financial institutions to collaborate on machine learning (ML) models while maintaining the confidentiality and integrity of their data. Utilizing blockchain technology, FL can provide an immutable and auditable record of all transactions and data exchanges. This can ensure that all parties adhere to the protocols and standards agreed upon for data sharing and collaboration. We propose the implementation of an FL framework that uses multiple ML models to protect consumers against fraudulent transactions through blockchain. The framework is intended to preserve customer privacy because it does not mandate the exchange of private customer data between participating institutions. Each bank trains its local models using data from its consumers, which are then combined on a centralised federated server to produce a unified global model. Data is neither stored nor exchanged between institutions, while models are trained on each institution's data. [ABSTRACT FROM AUTHOR]

Early diagnosis of kidney as well as pre-kidney disease is crucial for patients because it allows them to take control of their condition and could potentially avoid or delay more significant consequences that could lower their quality of life. The chance of developing a major disease might be decreased with its assistance. Almost every part of the body could be impacted by chronic kidney disease (CKD). Fluid retention in the lungs, high blood pressure, and swelling of the legs and arms are all potential side effects. This study proposes a model that makes use of machine learning (ML) algorithms for diagnosing kidney disease. The preprocessing dataset, which contains missing values and is preprocessed with the use of mean, delete, and median approaches before data scaling, is the foundation of the suggested model. To achieve the highest classification accuracy, the preprocessing stage receives the results of missing values. Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) are the two classification algorithms used to classify whether kidney disease is present or absent. Classify the dataset into testing and training (40% and 60%, respectively). The accuracy, F1-score, recall, and precision have been utilized for evaluating the suggested model. The kidney disease data-set has been used to test the outcomes of the suggested model. Without preprocessing any missing values in the dataset, the algorithms SVM and K-NN obtained maximum accuracy (95% and %89). Through deleting missing values from the dataset, the algorithms SVM and K-NN obtained maximum accuracy (%96 and %93). K-NN and SVM algorithms reached a maximum accuracy of %98 when using a mean technique; when using a median method, such algorithms attained an accuracy ranging from %95 to %98.

Generation Z is a group that is very connected to digital technology, especially social media such as Twitter. Their widespread presence on these platforms creates a unique opportunity to understand their behavioural patterns and personalities. However, research on personality prediction on social media is still limited and focused on certain platforms or different age groups. Personality prediction can help to find out someone's personality by just looking at tweets on social media. This research aims at two things: first, to build a Gen-Z personality prediction model on Twitter based on the Big Five Personality Model with the K-Nearest Neighbor (KNN) algorithm and Support Vector Machine (SVM). Second, test and compare the performance of previously generated personality prediction models with various evaluation metrics. The research results show that the KNN algorithm has an accuracy rate of 0.73%, precision of 0.73%, recall of 0.73%, and score of 0.72%. Based on the test results, the SVM algorithm obtained the best accuracy, which received an accuracy of 0.78%, precision of 0.82%, recall of 0.78%, and F1-score of 0.78%. This research contributes in two ways: first, scientifically, by understanding Gen-Z personalities on Twitter, and second, by developing new prediction methods and insights into Gen-Z behaviour. Second, practically, by helping with communication and marketing strategies, product/service development and social interventions for Gen-Z.

Lead-free K 0.5 Na 0.5 NbO 3 (KNN) ferroelectric film and transparent La 0.06 Ba 0.94 SnO 3 (LBSO) bottom electrode are fabricated on (001)-oriented SrTiO 3 (STO) substrate by sol-gel. The characterization results confirm an epitaxial relationship between the films and the substrate, as well as a uniform structure and good crystallization quality of the films. The optical measurement shows that the film heterostructure exhibit a high transmittance with a maximum transmittance of ∼80 %. The polarization-electric field (P-E) curves demonstrate that the twice remanent polarization value of the ∼500 nm thick KNN film reaches up to 28 μC/cm2 under an electric field of 800 kV/cm, and the effective piezoelectric strain constant (d 33 ∗) is measured as 24.8 p.m./V. The dielectric properties of the film are displayed, and the leakage behavior can be divided into three stages of Ohmic conduction, Schottky emission and Poole-Frenkel emission with increasing the applied electric field. This study indicates that transparent lead-free ferroelectric KNN heterostructures can be prepared using a cost-effective sol-gel method and shows promise for future applications. [ABSTRACT FROM AUTHOR]

The prevalence of diabetes is increasing globally, necessitating efficient methods to enhance the timely identification and treatment of diabetes, Focusing on early detection and effective management strategies for complications is essential. This study presents an integrated solution comprising two modules: diabetic detection and diabetic retinopathy detection. The diabetic detection module employs machine learning techniques like decision trees, random forests, and KNN for forecasting presence of diabetes based on patient data. The diabetic retinopathy detection module utilizes deep learning techniques, specifically the ResNet50 model architecture, to analyze retinal images and identify signs of diabetic retinopathy. A comprehensive implementation of both modules, including data preprocessing, model training, and evaluation, using Python libraries such as TensorFlow, Keras, and scikit- learn. The trained models are then integrated into a web application. This web application allows users to input their medical data and retinal images, and receive real- time predictions regarding their diabetic status and risk of diabetic retinopathy. The integration of these modules into a web application provides an intuitive interface tailored for both healthcare professionals and patients to assess diabetic risks conveniently. Furthermore, it facilitates early intervention and management of diabetic complications, ultimately improving patient outcomes and reducing healthcare burdens. [ABSTRACT FROM AUTHOR]

Parkinson’s disease is accepted as one of the most important diseases in the world. Parkinson’s disease can be diagnosed in various conventional techniques. Recently, these techniques have been replaced by artificial intelligence systems. This study proposes a feature selection and classification technique for Parkinson’s disease based on speech signals using a meta-heuristic algorithm. The proposed method selects the features from the data set including speech signal data that most accurately represent the problem using the efficient search strategies of the immune plasma algorithm (IPA). The experimental results are promising compared to other competing methods for diagnosing Parkinson’s disease in the literature.

Perkembangan teknologi memaksa pelaku bisnis untuk memberikan layanan terbaik dengan menjadikan sistem rekomendasi sebagai salah satu solusi untuk menjaga loyalitas konsumen. Sudah banyak dilakukan penelitian terkait dengan sistem rekomendasi untuk mengatasi permasalahan Cold-Start ataupun Serendipitous Problem. Penelitian ini melakukan Hybrid Collaborative Filtering dan Content Based filtering dengan menggunakan Switching method sebagai media untuk memilih data dan atribut yang tepat. Selanjutnya data diproses menggunakan algoritma TF-IDF dan KNN. Penelitian ini melakukan beberapa pengujian dengan menggunakan berbagai macam nilai K serta komposisi data training dan testing. Hasil pengujian menunjukkan bahwa akurasi tertinggi yang dihasilkan oleh model yang telah dikembangkan adalah 83.62% untuk metode switching dengan atribut product category sebagai variable label, dan 74.9% untuk metode switching dengan atribut rating sebagai variable label. Rasio data training dan testing yang digunakan dalam penelitian ini adalah 70:30 dengan nilai K=3. Hasil penelitian juga menemukan bahwa ada korelasi signifikan antara nilai K dengan nilai akurasi dimana nilai K yang tinggi akan menghasilkan akurasi yang tinggi juga.

Abstract Heart attack is a life‐threatening condition which is mostly caused due to coronary disease resulting in death in human beings. Detecting the risk of heart diseases is one of the most important problems in medical science that can be prevented and treated with early detection and appropriate medical management; it can also help to predict a large number of medical needs and reduce expenses for treatment. Predicting the occurrence of heart diseases by machine learning (ML) algorithms has become significant work in healthcare industry. This study aims to create a such system that is used for predicting whether a patient is likely to develop heart attacks, by analysing various data sources including electronic health records and clinical diagnosis reports from hospital clinics. ML is used as a process in which computers learn from data in order to make predictions about new datasets. The algorithms created for predictive data analysis are often used for commercial purposes. This paper presents an overview to forecast the likelihood of a heart attack for which many ML methodologies and techniques are applied. In order to improve medical diagnosis, the paper compares various algorithms such as Random Forest, Regression models, K‐nearest neighbour imputation (KNN), Naïve Bayes algorithm etc. It is found that the Random Forest algorithm provides a better accuracy of 88.52% in forecasting heart attack risk, which could herald a revolution in the diagnosis and treatment of cardiovascular illnesses.

This paper mainly concentrates and discusses on sugarcane crop, the variety of cane seeds available for sowing; various cane diseases and its early detection using different approaches. Machine Learning (ML) and Deep Learning (DL) techniques are used to analyze agricultural data like temperature, soil quality, yield prediction, selling price forecasts, etc. and avoid crop damage from a variety of sources, including diseases. In the proposed work, with particular reference to eight specific sugarcane crop diseases and including healthy crop database, the neural network algorithms are tested and verified in terms quality metrics like accuracy, F1 score, recall and precision.