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Very low earth orbit (VLEO) spacecraft have become an attractive proposition with obvious advantages in various missions, including communication and ground observation. Higher requirements for precise orbit decay prediction (PODP) technology are requested, which provides accurate state references for orbit maintenance and necessary database for space situational awareness. The effectiveness of the traditional orbital prediction method for PODP is limited by inaccurate estimation of the spacecraft's physical parameters and space environments. Generalization performance of machine learning techniques (MLTs) is blocked by the universal challenge known as catastrophic forgetting, resulting in limited improvement on PODP. In this study, a method of expandable orbit decay propagator (EODP) for spacecraft PODP in VLEO, based on model‐agnostic MLTs, is proposed. The plasticity of the proposed model is discussed, which originates from the uncertainty of neural network (NN) parameters. The proposed method overcomes the negative effects of uncertain physical parameters and complex space environments. The model achieves at least a 70% improvement in accuracy compared to the high‐precision orbital propagator (HPOP) and presents a novel approach for the future implementation of machine learning–based methods in the field of orbit prediction. [ABSTRACT FROM AUTHOR]

The leading cause of death worldwide today is heart disease (HD). The heart is recognised as the second-most significant organ behind the brain. A successful outcome of treatment can be improved by an early diagnosis which can significantly reduce the chance of death in health care. In this paper, we proposed a method to predict heart disease. We used various machine learning algorithms (MLA), namely, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), Naive Bayes (NB), random forest (RF), and decision tree (DT). With the testing data set, we evaluated the model's accuracy in heart disease prediction. When compared to the other five models, the random forest and k-nearest neighbor approaches perform better. With a 99.04% accuracy rate, the k-nearest neighbor algorithm and random forest provide the best match to the data as compared to other algorithms. Six feature selection algorithms were used for the performance evaluation matrix. MCC parameters for accuracy, precision, recall, and F measure are used to evaluate models. [ABSTRACT FROM AUTHOR]

In order to improve the production quality of the lipstick-filling machines and the performance of the equipment, the structure of a turntable lipstick-filling machine was designed and the servo control was optimized, considering the known production defects. First, based on the lipstick-filling process, the structure of the turntable lipstick-filling machine was designed. Regarding the bubble problem in lipstick filling, an elastic connecting rod vibration device was designed. Based on the principle of PMSM and a position servo system, a mathematical model of the lipstick-filling servo system was established, the system transfer function was calculated and its dynamic performance was analyzed. Combined with the transfer function, the lipstick-filling servo system PID control and particle swarm optimization PID control simulation models were built. The system adjustment time ts was 0.7 s under particle swarm optimization. Finally, a prototype was built to verify the simulation optimization results. The experimental results demonstrate that the qualified rate of lipstick filling, under the particle swarm optimization PID control implementation, is 97%, indicating that the particle swarm algorithm is quite applicable and has an economic impact by improving the accuracy and stability of the lipstick-filling servo system. [ABSTRACT FROM AUTHOR]

The bearing is an essential component of rotating machinery, as its reliability and running state have a direct impact on the machinery's performance. Considering that deep learning-based fault diagnosis methods for bearing require a large amount of labelled sample data, a novel fault diagnosis framework based on digital twin is proposed. In the case of fault data available, self-organizing maps with minimum quantization error and support vector machine are employed to analyze the data. Where fault data is unavailable, a bearing digital twin model is first constructed to simulate the data, and the convolutional neural network combined with transfer learning is utilized to diagnose the bearing faults. Then, the law of bearing performance degradation is investigated. The effectiveness of the proposed method is verified using bearing vibration data. [ABSTRACT FROM AUTHOR]

Washing machines (WMs) are common household appliances that help to save time and effort used in brushing and washing clothes. It is a common practice to use manually operated WMs, and based on their uses, WMs are classified into top and front open cloth washing machines, which operate based on an automatic control mechanism. In this paper, we present the design and simulation of an Arduino fuzzy logic-based WM control system, with an emphasis on improvement in its operating algorithm. Simulations were performed to determine the optimal time the WM takes to wash clothes, the maximum number of clothes the WM can wash per time, the acceptable dirtiness level of cloths, and the type of clothes that the machine can wash. The number of clothes to be washed, the degree of dirtiness, and the type of clothes govern the fuzzy logic control process adopted by the WM. The output voltage of the WM varies as the degree of dirtiness of the water varies from 0 to 1.95 V for very dirty water output from washed clothes and varies from 4 to 4.89 V for low-contaminated water. Another constraint considered was the load current, which increased as the number of clothes increased. The WM's operation time is determined by the amount of voltage and load current used during its operation. As a result, the control of the WM is dependent on the dirtiness level of the clothes and the amount of load. [ABSTRACT FROM AUTHOR]

Machinability investigation of new material is one of the mandatory investigations to complete the purpose of creation of it. Electrical discharge machining (EDM) is one of the promising unconventional machining processes for highly accurate machining performance in difficult-to-cut material even machining complicated profiles. The powder-mixed EDM and nanopowder-mixed EDM are the improved versions of the EDM. The Al–Zn–Mg composite is reinforced with Si3N4 (9 wt. %) for meeting automotive and marine applications. The aluminium nanoparticles enhanced deionised water was used in NPMEDM. The nickel-coated brass and uncoated brass tube electrode were considered for the investigation. Pulse on time (µs), voltage (V), input current (A), and capacitance (nF) were independent variables and varied at 3 levels. The microhole machining performance with a coated and uncoated electrode was investigated. The L18 orthogonal array involved in the experimental design, material removal rate, and electrode wear rate were analysed. The SEM analysis was employed in the surface morphological study of electrodes before and after machining. The input parameters were optimised for the coated electrode for the responses of MRR and EWR. [ABSTRACT FROM AUTHOR]

Breast cancer is a cancer disease that seriously threatens women's health and occupies the first place in female cancer mortality. At present, the incidence rate of breast cancer in China is the first in the world and is on the rise. In view of the serious harm of breast cancer to life and health, researchers and institutions are making unremitting efforts to find a perfect diagnosis and treatment plan. With the improvement of computer performance and machine learning levels, intelligent algorithms have been able to replace human behavior and judgment in some fields. The traditional breast cancer diagnosis process requires medical experts to observe patient data repeatedly. In this case, the algorithm technology is used to quickly feedback a high probability reference result to doctors, which is particularly important to increase the diagnosis efficiency and reduce the burden of doctors. In order to improve the accuracy of existing breast cancer recognition methods, this paper proposes and implements a scheme based on a whale optimization algorithm to iteratively adjust the key parameters of the support vector machine to improve the accuracy of breast cancer recognition. In order to verify the performance of the WOA-SVM algorithm, this paper uses the Wisconsin breast cancer data in the UCI database for performance verification experiments. Experiments show that the WOA-SVM model has higher recognition accuracy than the traditional breast cancer recognition model. [ABSTRACT FROM AUTHOR]

The base fluids of eco-friendly cutting fluids should contain some vegetable oil. Due to their eco-friendly, thermal, and tribological qualities, these types of machining fluids are becoming increasingly popular among researchers throughout the world. The authors summarized published research studies on the use of eco-friendly machining fluids (including nanofluids) in various metal removal processes such as turning, milling, grinding, and drilling in this review paper. The impact of several biodegradable oil-based machining fluids on performance metrics such as surface irregularity, machining force, tool wear, power demand, and temperature-induced during the machining process is also explored in this paper. According to the previous and current literature, using an eco-friendly nano machining fluid results in better surface features, less cutting tool wear, lower cutting force, less power required, and lower machining zone temperature during the machining operation due to superior cooling and lubrication properties. [ABSTRACT FROM AUTHOR]

Machine translation is different from written translation. How to improve the performance of machine translation has been a research hotspot in current research on machine translation. In this paper, based on the semantic analysis and research of Japanese passive, a joint optimization algorithm of scheduling has been proposed, and the machine translation of Japanese passive has been studied. At present, machine translation is more and more widely used. Machine translation has solved many vocabulary problems, and it can complete a large amount of translation work and save a lot of manual translation time. While improving the translation speed, in the process of Japanese passive translation, it is also found that direct machine translation shows many shortcomings, and the quality of passive translation is not particularly ideal, exposing the basic problems of machine translation, such as semantic errors, syntactic errors, unclear and rigid expressions, and messy structures. In response to the problems above, this paper has improved the machine translation model for scheduling joint optimization algorithms. The paper has proposed several optimization algorithms and used resource awareness and computing power scheduling algorithms to conduct experimental analysis of translation performance. Finally, it is found that, among the two scheduling optimization algorithms, the resource-aware scheduling algorithm has better performance. With the same data, the resource-aware scheduling algorithm has saved 15.5% of the time compared with the computing power scheduling algorithm, and the accuracy of Japanese passive translation was 6%, 5%, and 21% higher than the computing power scheduling algorithm under different data volumes. Not only has the time taken been shortened, but the translation accuracy has also been improved. [ABSTRACT FROM AUTHOR]

Productivity and cost-effectiveness are essential components of any long-term manufacturing system. While quantity and quality are linked to productivity, the economy focuses on energy-efficient processes that produce a high output-to-input ratio. Hard-to-cut materials have always been difficult to machine because of more significant tool wear and power losses. Inconel 625 is a hard material used in aerospace and underwater applications and is milled using biolubricants with nanoparticles. Palm oil is considered a biolubricant, and titanium dioxide (TiO2) and copper oxide (CuO) are selected as nanoparticles. When the combination of biolubricants and nanoparticles is added to the workpiece's surface, it enhanced some properties while machining. Experiments involving four factors with four levels were carried out using the Taguchi design of experiments (DoE). The feed, depth of cut, speed, and coolant with nanoparticle additives were all factors. The responses were surface roughness, spindle vibration along X, Y, and Z axes, and material removal rate. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to alter the multiresponse optimization problem to a single-response optimization problem. The S/N of TOPSIS closeness coefficients was calculated, and the optimal machining conditions were determined. Surface roughness, material removal rate, and spindle vibration were reduced by 3.10%, 6.14%, 7.54% (Vx), and 6.78% (Vz), respectively, due to the TOPSIS optimization. [ABSTRACT FROM AUTHOR]

In this research study, the design and performance evaluation of grid-tied photovoltaic systems has been carried out through experimentation, HelioScope simulation, and black-box machine learning methods for data-driven artificial intelligence system performance assessment and validation. The proposed systems are based on 15 kWp of monoperk and polyperk, which are separately installed in the industrial sector of Faisalabad, Pakistan. The experimental evaluation of the installed PV modules was performed from November 2020 to August 2021. The performance of the PV modules was evaluated by determining the annual average daily final yield (If), performance ratio (PR), and capacity factor (CF). The study showed that the annual average of daily final yield, performance ratio, and capacity factor for 15 kW polyperk was estimated to be 61.94 kWh, 84.17%, and 19.12, respectively. The annual average of daily final yield, performance ratio, and capacity factor for 15 kW monoperk was estimated to be 58.32 kWh, 81.42%, and 18.13, respectively. A comparison of final yield is obtained from simulation and real-time systems obtained from polyperk PV and monoperk. A significant mean error exists between the experimentation and simulation results which lie within the range of 1250 to 1470 kWh and 1600 to 1950 kWh, respectively. Substantial differences between both aforementioned results were initially tested and highlighted by statistical values; i.e., the standard error lies in-between 5 and 45% in polyperk crystalline and 5 and 25% in monocrystalline PV grid-connected module. Machine learning logistical regression evaluated that monoperk crystalline grid-connected system, experimental work was found to be more reliable with error difference reduces in off-peak months as compared to corresponding simulation study and vice versa for polyperk crystalline grid-connected system. Model accuracy after training and testing produced resulted up to 99.5% accuracy for either grid-connected experimentation or simulation outcomes with validation. [ABSTRACT FROM AUTHOR]

Aiming at solving the problem that it is difficult to recognize the quiet period of acoustic emission in rocks, four machine learning algorithms were adopted to develop and improve the recognition method of the quiet period of acoustic emission. In the process of establishing the model, the time domain data of acoustic emission were standardized and processed by box diagram method, so as to clean the abnormal data and reduce the dimension, and the frequency domain data were denoised by wavelet four-layer transform and wavelet packet three-layer energy decomposition, and a group of 8 wavelet packet energy parameters were established as frequency domain characteristic parameters. Based on AE time domain data, frequency domain data, and composite data (time-frequency domain data sets), the grid search traversal parameter technique was used to obtain the optimal parameters of four machine learning models. The accuracy, precision, recall, and F 1 score were used to verify and evaluate the recognition performance of the models. The study results show that the recognition effects of the models are good, the model accuracy of the frequency domain data set is the lowest, and the model accuracy of the composite data set is the highest, with an accuracy of more than 90%. The kernel support vector machine model has the best performance, and its average precision is 0.87. The random forest (RF) model is the best model for recognizing quiet period of acoustic emission. [ABSTRACT FROM AUTHOR]

Machine translation relies on parallel sentences, the number of which is an important factor affecting the performance of machine translation systems, especially in low-resource languages. Recent advances in learning cross-lingual word representations from nonparallel data by machine learning make a new possibility for obtaining bilingual sentences with minimal supervision in low-resource languages. In this paper, we introduce a novel methodology to obtain parallel sentences via only a small-size bilingual seed lexicon about hundreds of entries. We first obtain bilingual semantic by establishing cross-lingual mapping in monolingual languages via a seed lexicon. Then, we construct a deep learning classifier to extract bilingual parallel sentences. We demonstrate the effectiveness of our methodology by harvesting Uyghur-Chinese parallel sentences and constructing a machine translation system. The experiments indicate that our method can obtain large and high-accuracy bilingual parallel sentences in low-resource language pairs. [ABSTRACT FROM AUTHOR]

In this research work, an attempt was made to machine the titanium (Ti6Al4V) alloy utilizing electric discharge machining technique. The distinct process parameters and its impact on the machining performance were identified using the cause-and-effect diagram (CED). The key process parameters identified by CED diagram were current, pulse on time (Ton), aluminium oxide (Al2O3) powder concentration, and gap distance; experiments were conducted by varying the process parameters, experimental runs were designed using the Taguchi mixed orthogonal array. The experimental results revealed that improvement in material removal rate (MRR) was due to the bridging effect; reduction in tool wear rate (TWR) owing to the expansion of spark gap and enhancement in the surface roughness (Ra) was due to the complete flushing of machined debris. The interaction impact was analysed using the contour plot and with the aid of mathematical modelling experimental fits that were identified and the results were validated utilizing the sensitivity analysis. The obtained results were optimized using the technique for order of preference by similarity to ideal solution (TOPSIS) optimization technique. [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy combined kernel relevance vector machine method for the coal spontaneous combustion temperature prediction to avoid the shortcomings of traditional machine learning algorithms, such as the large prediction error, the weak generalization ability of the single kernel function, and the inability to deal with abnormal values. First, build a platform to simulate the coal spontaneous combustion scene and obtain the data of different index gas concentrations and the coal spontaneous combustion temperature through experiments. Second, the fuzzy algorithm is used to give the training samples different membership degrees with attention, so as to reduce the influence of outliers on the model. Third, the combined kernel function weighted by the polynomial kernel function and Gaussian kernel function is used to construct the input sample matrix to map the data from the low-dimensional space to a high-dimensional space, so as to obtain a better training model. Finally, the fuzzy combined kernel relevance vector machine model is constructed and compared with methods based on the radial basis function neural network, least squares support vector machine, Gaussian kernel relevance vector machine, and combined kernel relevance vector machine to verify the effectiveness of prediction for coal spontaneous combustion temperature. The results show that the fuzzy combined kernel relevance vector machine not only has the characteristics of strong generalization ability and weakening the influence of abnormal data, but also with a more sparse model, which is suitable for the prediction of coal spontaneous combustion temperature. [ABSTRACT FROM AUTHOR]

Fourier transformation (FT) and Multiple Signal Classification (MUSIC) method suffer the insufficient ability in diagnosing broken rotor bar (BRB) fault using short-time data. Theoretical and simulation analyses show that the Optimum Resolution of Prescient Direction (ORPD) algorithm has the best frequency resolution performance due to a priori knowledge. The main objective of this paper is to detect BRB faults in induction machines using a condition monitoring architecture based on ORPD algorithm. In the proposed application, the ORPD algorithm with the best frequency resolution performance is used to estimate the fault-sensitive frequencies in the stator current signature. The prior information of BRB fault characteristic distribution is used to construct a weighting matrix in the ORPD algorithm, for acquiring the lowest signal-to-noise ratio resolution threshold. Once frequencies are estimated, their corresponding amplitudes are obtained by using the least squares estimator. The proposed methods were tested using experimental induction motors with different fault severity under the effect of several load levels or supply frequencies. Two types of power supply modes are considered: main and inverter. The results show that compared with traditional method which uses FT and MUSIC algorithm for fault diagnosing, the method based on ORPD algorithm has a higher frequency resolution and identification ability with short-time data, and still has good diagnostic performance even under light loading and lower supply frequencies. [ABSTRACT FROM AUTHOR]

In order to improve the intelligent analysis effect of Bel Canto music and improve the singing skills of Bel Canto, this paper uses the machine semantic recognition algorithm to identify the characteristics of Bel Canto music, builds a Bel Canto music feature recognition model, and analyzes the advantages and disadvantages of each Bel Canto semantic feature clustering method and the applicable data set types. Finally, this paper applies the Bel Canto semantic feature clustering method to multicomponent signal parameter estimation, uses the clustering method to cluster the time-frequency analysis of nonstationary signals, obtains the time-frequency distribution of each signal component, and then estimates the parameters of each single-component signal. The experimental results show that the Bel Canto music feature recognition method based on machine semantics proposed in this paper has a good effect. [ABSTRACT FROM AUTHOR]

Efficiency is an important indicator of risk assessment, but the failure rate changes with time, which makes risk assessment very difficult. Traditional methods often only measure the steady-state and static indicators of relay protection. The traditional method often only measures the steady-state and static indicators of the relay protection, or directly uses the RiskWeibull analysis system to perform reliability analysis on the relevant data. However, this method can only perform quantitative static analysis on the data. Once the system is disturbed, its reliability will be affected. Analysis is not possible. Since the relay protection device has the possibility of malfunction and refusal at the same time, in order to distinguish, the risk is introduced into the reliability evaluation of the relay protection device to achieve the purpose of considering the possibility and consequences of its failure at the same time. Based on the traditional method, this paper proposes a method to calculate the time-varying failure rate of the relay protection device by using the support vector machine model, which improves the influence of hidden risks on the power grid relay protection, and not only realizes the scientific hidden danger evaluation, risk rating. It is of great significance for the development of power grids to evaluate the consequences of failures and to make the transmission lines operate safely and stably. [ABSTRACT FROM AUTHOR]

One of the most former and widely utilized unconventional machining is known as the process of electric discharge machining (EDM). This is a procedure, where a serialized set of electric discharges are applied to eliminate the material from a workpiece (i.e., electrically conductive). This article proposes a bi-objective optimization of process arguments in machining of stainless steel of 630 (SS630) grade using both experimentation and thermal investigation. Here, flushing pressure ( P F ), pulse on-time ( T ON ), peak current ( I p ), and pulse off-time ( T OFF ) are assumed as the input process parameters to conduct a series of experiments on die-sinking EDM (DS-EDM) of SS630 to predict the output characteristics of machining optimization using grey relation analysis (GRA). Both material removal rate (MRR) and surface roughness (SR) are used for evaluating the output response obtained using the machining performance. In addition, thermal investigation is performed using finite element method (FEM) analysis to compute theoretical MRR (T-MRR) for the distribution of temperature on workpiece. Further, GRA is employed to obtain the optimal process parameter combination for best output responses. From the confirmation test results, the optimal combination obtained using GRA approach is at I p = 6 A , T ON = 35 μ s , T OFF = 90 μ s , and P F = 4 MPa. [ABSTRACT FROM AUTHOR]

It has been claimed that artificial intelligence (AI) has transformative potential for the healthcare sector by enabling increased productivity and creative methods of delivering healthcare services. Recently, there has been a major shift to artificial intelligence by businesses, government, and private sectors in general and the health sector in particular. Many studies have proven that artificial intelligence is contributing greatly to the health sector by discovering diseases and determining the best treatments for patients. Dentistry requires new innovative methods that serve both the patient and the service provider in obtaining the best and appropriate medical services. Artificial intelligence has the ability to develop the field of dentistry through early diagnosis and prediction of dental implant cases. This research develops a set of four machine learning algorithms to predict when a patient might need dental implants. These models are the Bayesian network, random forest, AdaBoost algorithm, and improved AdaBoost algorithm. This work shows that the developed algorithms can predict when a patient needs dental implants. Also, we believe that this proposal will advise managers and decision-makers in targeting patients with particular diagnoses. Analysis of the obtained results indicates good performance of the developed machine learning. As a result of this research, we note that the proposed improved AdaBoost algorithm increases the level of prediction accuracy and gives significantly higher performance than the other studied methods with the accuracy for the improved AdaBoost algorithm reaching 91.7%. [ABSTRACT FROM AUTHOR]

Summary: In this work, zirconia composite is machined by the unconventional Electrochemical Discharge Machining (ECDM). Normally, the machining of zirconia is an inconvenient process due to its increased hardness and maximum machining time. The ECDM of zirconia‐silicon nitride is done by varying the input parameters such as electrolytic concentration, voltage, and duty cycle. The measured output machined parameters are material removal rate (MRR), Overcut (OC), and Tool wear rate (TWR). In Response Surface Methodology (RSM), the Box Behnken method is used to plan the experimental design of this work. The parameter optimization is conducted using RSM. Besides, the experimented machining performances are validated using hybrid Deep Neural Network‐based Spotted Hyena optimization (DNN‐SHO) done in MATLAB platform version 2020 a. From the findings, the voltage and electrolytic concentration are identified as signified parameters for improving the ECDM performances from the RSM analysis. The obtained favorable machining performances are 0.371 mg/min of MRR, 162.2 μm of OC, and 0.26 mg/min of TWR. The predicted results from the proposed DNN‐SHO for the MRR are 0.402 mg/min, OC is 152.98 μm, and TWR is 0.21 mg/min. The proposed DNN‐SHO outcomes are in perfect agreement with the experimented values and are more superior to the RSM, DNN, and DNN‐PSO based prediction approaches. [ABSTRACT FROM AUTHOR]

While potential for battery-powered electric vehicles (BEVs) is increasing as the need to minimise greenhouse gas pollution and the utilization of energy production, the restricted driving range and the uninviting market price constitute BEV obstacles to reliable assessment as a conventional car. This paper suggests a double-motor multispeed direct-drive BEV drivetrain to improve average engine running performance in everyday travelling whilst raising any production or control sophistication, effectively saving minimal battery resources and production costs. The characteristics of the proposed drivetrain are first defined through quantitative and visual measurements, which divide the conventional one-engine propulsion to two with different permanent gears to optimise engine performance. Centred on dynamic powertrain modelling in Simulink, the economic change strategy and circulatory system consisting transfer control were planned and checked. Based on the simulation performance, it is stated that substantial increases in energy quality can be accomplished. Thanks to the optimised torque transfer control technique, the vehicle's incredibly low pull is registered during the shifting phase. Finally, it can be inferred that the suggested dual-motor drivetrain is better than the conventional single-motor equivalent in terms of fuel efficiency, driving range, and expense. There is substantial increase in total machine running areas of affordable to solitary machine performance and megacity driving ages, similar to WLTC, FTP-75, and JP 10-15, with 5 to 8 percentage performance enhancement being achieved. [ABSTRACT FROM AUTHOR]

Intended pregnancy is one of the significant indicators of women's well-being. Globally, 74 million women become pregnant every year without planning. Unintended pregnancies account for 28% of all pregnancies among married women in Bangladesh. This study aimed to investigate the performance of six different machine learning (ML) algorithms applied to predict unintended pregnancies among married women in Bangladesh. From BDHS 2017-18, only 1129 pregnant women aged 15–49 were eligible for this study. An independent χ 2 test had performed before we considered six popular ML algorithms, such as logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), naïve Bayes (NB), and elastic net regression (ENR) to predict the unintended pregnancy. Accuracy, sensitivity, specificity, Cohen's Kappa statistic, and area under curve (AUC) value were used as model evaluation. The bivariate analysis result showed that women aged 30–49 years, poor, not educated, and living in male-headed households had a higher percentage of unintended pregnancy. We found various performance parameters for the classification of unintended pregnancy: LR accuracy = 79.29%, LR AUC = 72.12%; RF accuracy = 77.81%, RF AUC = 72.17%; SVM accuracy = 76.92%, SVM AUC = 70.90%; KNN accuracy = 77.22%, KNN AUC = 70.27%; NB accuracy = 78%, NB AUC = 73.06%; and ENR accuracy = 77.51%, ENR AUC = 74.67%. Based on the AUC value, we can conclude that of all the ML algorithms we investigated, the ENR algorithm provides the most accurate classification for predicting unwanted pregnancy among Bangladeshi women. Our findings contribute to a better understanding of how to categorize pregnancy intentions among Bangladeshi women. As a result, the government can initiate an effective campaign to raise contraception awareness. [ABSTRACT FROM AUTHOR]

When it comes to large-scale renewable energy plants, the future of solar power forecasting is vital to their success. For reliable predictions of solar electricity generation, one must take into consideration changes in weather patterns over time. In this paper, a hybrid model that integrates machine learning and statistical approaches is suggested for predicting future solar energy generation. In order to improve the accuracy of the suggested model, an ensemble of machine learning models was used in this study. The results of the simulation show that the proposed method has reduced placement cost, when compared with existing methods. When comparing the performance of an ensemble model that integrates all of the combination strategies to standard individual models, the suggested ensemble model outperformed the conventional individual models. According to the findings, a hybrid model that made use of both machine learning and statistics outperformed a model that made sole use of machine learning in its performance. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate infected leaf disease images. Precision agriculture's automatic leaf disease detection system employs image acquisition, image processing, image segmentation, feature extraction, and machine learning techniques. An automated disease detection system offers the farmer with a fast and accurate diagnosis of the plant disease. Automation of plant leaf disease detection system is essential for accelerating crop diagnosis. Using machine learning and image processing, this paper describes a framework for detecting leaf illness. An image of a leaf can be used as an input for this framework. To begin, leaf photographs are preprocessed in order to remove noise from their images. The mean filter is used to filter out background noise. Histogram equalization is used to enhance the quality of the image. The division of a single image into multiple portions or segments is referred to as segmentation in photography. It assists in establishing the boundaries of the image. Segmenting the image is accomplished using the K-Means approach. Feature extraction is carried by using the principal component analysis. Following that, images are categorized using techniques such as RBF-SVM, SVM, random forest, and ID3. [ABSTRACT FROM AUTHOR]

To improve the function of machine translation to adapt to global language translation, the work takes deep neural network (DNN) as the basic theory, carries out transfer learning and neural network translation modeling, and optimizes the word alignment function in machine translation performance. First, the work implements a deep learning translation network model for English translation. On this basis, the neural machine translation model is designed under transfer learning. The random shielding method is introduced to implement the language training model, and the machine translation is slightly adjusted as the goal of transfer learning, thereby improving the semantic understanding ability in translation performance. Meanwhile, the work design introduces the method of word alignment optimization and optimizes the performance of word alignment in the transformer system by using word corpus. The experimental results show that the proposed method reduces the average alignment error rate by 8.1%, 24.4%, and 22.1% in EnRo (English-Roman), EnGe (English-German), and EnFr (English-French), respectively, compared with the previous algorithms. Compared with the designed optimization method, the word alignment error rate is lower than that of traditional methods. The modeling and optimization method is feasible, which can effectively solve the problems of insufficient information utilization, large parameter scale, and difficult storage in the process of machine translation. Additionally, it provides a feasible idea and direction for the optimization and improvement in neural machine translation (NMT) system. [ABSTRACT FROM AUTHOR]

In this research work, AA2024/5Al2O3/5Gr hybrid composites fabricated through stir casting were machined utilising an electric discharge machine (EDM). Experiments were performed by varying current, pulse on time (POT), gap voltage (GV), and Al2O3 powder concentration (PC). The experiments were designed using response surface methodology in which material removal rate (MRR), tool wear rate (TWR), and surface roughness (Ra) were recorded as responses. The addition of Al2O3 particles has a positive impact on MRR and Ra, whereas it has a negative impact on TWR. The interaction impact of process parameters (p-p) on responses was thoroughly analyzed using contour plots. A mathematical model was developed and validated for all the responses. The experimental results were compared with the predicted values. It was found that all the values have a maximum deviation of 3.5%. The ANOVA table reveals that the PC was the most influential factor followed by the current. [ABSTRACT FROM AUTHOR]

The steady increase in the world's population has directly influenced global climate change, resulting in catastrophic environmental consequences. This has created an immediate need for scientists from interdisciplinary domains like clean technology innovation in solar energy and computer science to join in the effort to save the world for future generations. As such, the United Nations has set a goal to ensure global access to affordable, sustainable, and clean energy. As a leading influential G20 economy, Saudi Arabia has recently established the Green Saudi initiative to align with the UN goal for enhancing the use of green energy. However, research in this area is sparse and greater effort is still required. This work is among the first to address the issue of enhancing and expanding the use of clean energy by means of studying the data collected from solar plants around Saudi. We used machine learning-based methods to assess the energy output performance of solar plants and employed the collected data to train the models to make early detection of faults. Our models achieved the highest performance at an accuracy score of 98.85% and 0.98 weighted F-score using the J48 model trained on a publicly available dataset of 874 instances collected from 26 different sites across Saudi. We anticipate that the findings of this work to serve as testbed to facilitate further research in this area and enhance the early fault detection in solar energy stations. [ABSTRACT FROM AUTHOR]

In recent times, knee joint pains have become severe enough to make daily tasks difficult. Knee osteoarthritis is a type of arthritis and a leading cause of disability worldwide. The middle of the knee contains a vital portion, the anterior cruciate ligament (ACL). It is necessary to diagnose the ACL ruptured tears early to avoid surgery. The study aimed to perform a comparative analysis of machine learning models to identify the condition of three ACL tears. In contrast to previous studies, this study also considers imbalanced data distributions as machine learning techniques struggle to deal with this problem. The paper applied and analyzed four machine learning classification models, namely, random forest (RF), categorical boosting (Cat Boost), light gradient boosting machines (LGBM), and highly randomized classifier (ETC) on the balanced, structured dataset of ACL. After oversampling a hyperparameter adjustment, the above four models have achieved an average accuracy of 95.72%, 94.98%, 94.98%, and 98.26%. There are 2070 observations and eight features in the collection of three diagnosis ACL classes after oversampling. The area under curve value was approximately 0.998, respectively. Experiments were performed using twelve machine learning algorithms with imbalanced and balanced datasets. However, the accuracy of the imbalanced dataset has remained under 76% for all twelve models. After oversampling, the proposed model may contribute to the investigation of ACL tears on magnetic resonance imaging and other knee ligaments efficiently and automatically without involving radiologists. [ABSTRACT FROM AUTHOR]

In order to reduce the workload of manual grading and improve the efficiency of grading, a computerized intelligent grading system for English translation based on natural language processing is designed. An attention-embedded LSTM English machine translation model is proposed. Firstly, according to the characteristics of the standard LSTM network model that uses fixed dimensional vectors to represent words in the encoding stage, an English machine translation model based on LSTM attention embedding is established; the structure level of the English translation scoring system is constructed. A linguistic model of the English translation scoring system is established, and the probability distribution of a particular sentence sequence or word sequence of the translated text is statistically calculated using the model. The results show that the English machine translation model based on LSTM attention embedding proposed in this study can enhance the representation of the source language contextual information and improve the performance of the English machine translation model and the quality of the translation compared with the English machine translation models constructed by existing neural network structures, such as standard LSTM models, RNN models, and GRU-Attention translation models. [ABSTRACT FROM AUTHOR]

Heart failure is a chronic cardiac condition characterized by reduced supply of blood to the body due to impaired contractile properties of the muscles of the heart. Like any other cardiac disorder, heart failure is a serious ailment limiting the activities and curtailing the lifespan of the patient, most often resulting in death sooner or later. Detection of survival of patients with heart failure is the path to effective intervention and good prognosis in terms of both treatment and quality of life of the patient. Machine learning techniques can be critical in this regard since they can be used to predict the survival of patients with heart failure in advance, allowing patients to receive appropriate treatment. Hence, six supervised machine learning algorithms have been studied and applied to analyze a dataset of 299 individuals from the UCI Machine Learning Repository and predict their survivability from heart failure. Three distinct approaches have been followed using Decision Tree Classifier, Logistic Regression, Gaussian Naïve Bayes, Random Forest Classifier, K-Nearest Neighbors, and Support Vector Machine algorithms. Data scaling has been performed as a preprocessing step utilizing the standard and min–max scaling method. However, grid search cross-validation and random search cross-validation techniques have been employed to optimize the hyperparameters. Additionally, the synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) data resampling technique are utilized, and the performances of all the approaches have been compared extensively. The experimental results clearly indicate that Random Forest Classifier (RFC) surpasses all other approaches with a test accuracy of 90% when used in combination with SMOTE-ENN and standard scaling technique. Therefore, this comprehensive investigation portrays a vivid visualization of the applicability and compatibility of different machine learning algorithms in such an imbalanced dataset and presents the role of the SMOTE-ENN algorithm and hyperparameter optimization for enhancing the performances of the machine learning algorithms. [ABSTRACT FROM AUTHOR]

The history of data stored can be used to forecast potential patterns and help companies make competitive decisions to increase their success and benefits. Many analysts look at healthcare sector data to identify and forecast illnesses in order to benefit patients and physicians in a variety of ways. This study is concerned with the diagnosis and estimation of heart disease. Heart disease is one of the most dangerous illnesses for humans, leading to death all over the world. Many different groups of researchers have used knowledge exploration methods in diverse fields to forecast heart disease and have shown acceptable degrees of precision. There were no real-time methods for analyzing and forecasting heart disease in its early stages. For the prediction of heart disease, decision trees are used to analyze various training and evaluation datasets. Classification algorithms such as Naive Bayes, ID3, C4.5, and SVM are being investigated. The UCI machinery heart disease data set is used in experimental studies. [ABSTRACT FROM AUTHOR]

Wireless communication systems have evolved and offered more smart and advanced systems like ad hoc and sensor-based infrastructure fewer networks. These networks are evaluated with two fundamental parameters including data rate and spectral efficiency. To achieve a high data rate and robust wireless communication, the most significant task is channel equalization at the receiver side. The transmitted data symbols when passing through the wireless channel suffer from various types of impairments, such as fading, Doppler shifts, and Intersymbol Interference (ISI), and degraded the overall network performance. To mitigate channel-related impairments, many channel equalization algorithms have been proposed for communication systems. The channel equalization problem can also be solved as a classification problem by using Machine Learning (ML) methods. In this paper, channel equalization is performed by using ML techniques in terms of Bit Error Rate (BER) analysis and comparison. Radial Basis Functions (RBFs), Multilayer Perceptron (MLP), Support Vector Machines (SVM), Functional Link Artificial Neural Network (FLANN), Long-Short Term Memory (LSTM), and Polynomial-based Neural Networks (NNs) are adopted for channel equalization. [ABSTRACT FROM AUTHOR]

Phrase identification plays an important role in medical English machine translation. However, the phrases in medical English are complicated in internal structure and semantic relationship, which hinders the identification of machine translation and thus affects the accuracy of translation results. With the aim of breaking through the bottleneck of machine translation in medical field, this paper designed a machine translation model based on the optimized generalized likelihood ratio (GLR) algorithm. Specifically, the model in question established a medical phrase corpus of 250,000 English and 280,000 Chinese words, applied the symbol mapping function to the identification of the phrase's part of speech, and employed the syntactic function of the multioutput analysis table structure to correct the structural ambiguity in the identification of the part of speech, eventually obtaining the final identification result. According to the comprehensive verification, the translation model employing the optimized GLR algorithm was seen to improve the speed, accuracy, and update performance of machine translation and was seen to be more suitable for machine translation in medical field, therefore providing a new perspective for the employment of medical machine translation. [ABSTRACT FROM AUTHOR]

With the continuous progress of modern industry, rotating machinery is gradually developing toward complexity and intelligence. The fault diagnosis technology of rotating machinery is one of the key means to ensure the normal operation of equipment and safe production, which has very important significance. Deep learning is a useful tool for analyzing and processing big data, which has been widely used in various fields. After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault diagnosis of rotating machinery. Then, the commonly used evaluation indicators used to evaluate the performance of rotating machinery fault diagnosis methods are summarized. Finally, according to the current research status in the field of rotating machinery fault diagnosis, the current problems and possible future development and research trends are discussed. [ABSTRACT FROM AUTHOR]

Synchronous reluctance machine (SynRM) is one of the candidates of high‐speed application drives. The rotor of this machine, as the most important and most challenging part of the design, has various tangential and radial ribs. Therefore, the quantity, thickness and length of the ribs are significant SynRM design parameters. If ribs' thickness is increased in order to enhance the mechanical robustness, its magnetic properties will be substantially deteriorated and vice versa. In this paper, by employing interior claws, a new rotor for SynRM is designed in a way that while increasing the mechanical robustness of the rotor for high speeds application, its magnetic characteristics enhanced, too. For this purpose, the proposed design method is applied on a two‐flux barrier rotor of a 24 stator slot SynRM. The designed machine electromagnetic performance is evaluated by finite elements method (FEM) simulation and compared with other designs. Also, the mechanical strength of the proposed rotor with two alternative claws for the speeds up to 50krpm are evaluated and confirmed by structural FE analysis. At the end, the designed interior claw rotor is prototyped and tested. Prototyped SynRM magnetic characteristics that are extracted by experiment confirmed simulations results with acceptable accuracy. [ABSTRACT FROM AUTHOR]

Excessive mental workload affects human health and may lead to accidents. This study is motivated by the need to assess mental workload in the process of human-robot interaction, in particular, when the robot performs a dangerous task. In this study, the use of heart rate variability (HRV) signals with different time scales in mental workload assessment was analyzed. A humanoid dual-arm robot that can perform dangerous work was used as a human-robot interaction object. Electrocardiogram (ECG) signals of six subjects were collected in two states: during the task and in a relaxed state. Multiple time-scale (1, 3, and 5 min) HRV signals were extracted from ECG signals. Then, we extracted the same linear and nonlinear features from the HRV signals at different time scales. The performance of machine learning algorithms using the different time-scale HRV signals obtained during the human-robot interaction was evaluated. The results show that for the per-subject case with a 3 min HRV signal length, the K -nearest neighbor classifier achieved the best mental workload classification performance. For the cross-subject case with a 5 min time-scale signal length, the gentle boost classifier achieved the best mental workload classification accuracy. This study provides a novel research idea for using HRV signals to measure mental workload during human-robot interaction. [ABSTRACT FROM AUTHOR]

The motion smoothness of 3C locking robot directly affects the machining performance. Improving the motion smoothness can optimize the motion trajectory and reduce the processing time. In this paper, a novel machining path optimization model including motion smoothness is built by employing the coordinate boundary of velocity and acceleration after evaluating the machining motion smoothness of the 3C locking robot. Secondly, based on the creation of the ant colony of adaptive function algorithm, the optimization model of the 3C locking robot in the situation of fixed bolt hole position and floating bolt hole position is resolved. Lastly, the proposed approach collects and analyses a huge amount of data to enable robots to make on-the-fly decisions in the middle of production, even when faced with unexpected circumstances. In the Spark distributed environment, we use the conventional K clustering technique to improve the final output utilizing clustering means. The results show that the machining path optimization of fixed hole considering the motion smoothness improves the smoothness but extends the machining path; the cooperative machining path optimization of multiregion floating bolt holes can significantly improve the motion smoothness and effectively reduce the length of the path. The research results provide theoretical support and design guidance for designers. [ABSTRACT FROM AUTHOR]

In the context of cloud computing, one problem that is frequently encountered is task scheduling. This problem has two primary implications, which are the planning of tasks on virtual machines and the attenuation of performance. In order to address the problem of task scheduling in cloud computing, requisite nontraditional optimization attitudes to attain the optima of the problem, the present paper puts forth a hybrid multiple-objective approach called hybrid grey wolf and whale optimization (HGWWO) algorithms, that integrates two algorithms, namely, the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA), with the purpose of conjoining the advantages of each algorithm for minimizing costs, energy consumption, and total execution time needed for task implementation, beside that improving the use of resources. Assessment of the aims of the proposed approach is carried out with the help of the tool known as CloudSim. As pointed out by the results of the experimental work undertaken, the proposed approach has the capability of performing at a superior level by comparison to the original algorithms GWO and WOA on their own with regard to costs, energy consumption, makespan, use of resources, and degree of imbalance. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to present a new hybrid approach to select the appropriate maintenance policy (NET) of machines utilizing analytic network process (ANP), computer simulation, and the concept of virtual cellular manufacturing system (VCMS). Since conventional methods select only one NET policy for all machines or production lines, the performance of machines that do not conform to the selected policy is reduced. In the proposed method, the information of the functional parameters of the machines is extracted by means of computer simulation and there is no need for expert opinion. Next, the appropriate net policy for each machine is selected using the ANP method. To reduce the diversity and complexity of NET applications, machines are grouped using the concept of virtual cellular manufacturing system based on the similarity of NET policies. A NET program is prepared for each group of machines. The proposed approach is used in a production unit with three widely used NET policies, and its efficiency was proved by comparing the results with conventional methods. [ABSTRACT FROM AUTHOR]

The Seq2Seq model and its variants (ConvSeq2Seq and Transformer) emerge as a promising novel solution to the machine translation problem. However, these models only focus on exploiting knowledge from bilingual sentences without paying much attention to utilizing external linguistic knowledge sources such as semantic representations. Not only do semantic representations can help preserve meaning but they also minimize the data sparsity problem. However, to date, semantic information remains rarely integrated into machine translation models. In this study, we examine the effect of abstract meaning representation (AMR) semantic graphs in different machine translation models. Experimental results on the IWSLT15 English-Vietnamese dataset have proven the efficiency of the proposed model, expanding the use of external language knowledge sources to significantly improve the performance of machine translation models, especially in the application of low-resource language pairs. [ABSTRACT FROM AUTHOR]

Accurate risk assessment of high-risk patients is essential in clinical practice. However, there is no practical method to predict or monitor the prognosis of patients with ST-segment elevation myocardial infarction (STEMI) complicated by hyperuricemia. We aimed to evaluate the performance of different machine learning models for the prediction of 1-year mortality in STEMI patients with hyperuricemia. We compared five machine learning models (logistic regression, k -nearest neighbor, CatBoost, random forest, and XGBoost) with the traditional global (GRACE) risk score for acute coronary event registrations. We registered patients aged >18 years diagnosed with STEMI and hyperuricemia at the Affiliated Hospital of Zunyi Medical University between January 2016 and January 2020. Overall, 656 patients were enrolled (average age, 62.5 ± 13.6 years ; 83.6%, male). All patients underwent emergency percutaneous coronary intervention. We evaluated the performance of five machine learning classifiers and the GRACE risk model in predicting 1-year mortality. The area under the curve (AUC) of the six models, including the GRACE risk model, ranged from 0.75 to 0.88. Among all the models, CatBoost had the highest predictive accuracy (0.89), AUC (0.87), precision (0.84), and F1 value (0.44). After hybrid sampling technique optimization, CatBoost had the highest accuracy (0.96), AUC (0.99), precision (0.95), and F1 value (0.97). Machine learning algorithms, especially the CatBoost model, can accurately predict the mortality associated with STEMI complicated by hyperuricemia after a 1-year follow-up. [ABSTRACT FROM AUTHOR]

In recent years, there have been numerous cyber security issues that have caused considerable damage to the society. The development of efficient and reliable Intrusion Detection Systems (IDSs) is an effective countermeasure against the growing cyber threats. In modern high-bandwidth, large-scale network environments, traditional IDSs suffer from a high rate of missed and false alarms. Researchers have introduced machine learning techniques into intrusion detection with good results. However, due to the scarcity of attack data, such methods' training sets are usually unbalanced, affecting the analysis performance. In this paper, we survey and analyze the design principles and shortcomings of existing oversampling methods. Based on the findings, we take the perspective of imbalance and high dimensionality of datasets in the field of intrusion detection and propose an oversampling technique based on Generative Adversarial Networks (GAN) and feature selection. Specifically, we model the complex high-dimensional distribution of attacks based on Gradient Penalty Wasserstein GAN (WGAN-GP) to generate additional attack samples. We then select a subset of features representing the entire dataset based on analysis of variance, ultimately generating a rebalanced low-dimensional dataset for machine learning training. To evaluate the effectiveness of our proposal, we conducted experiments based on the NSL-KDD, UNSW-NB15, and CICIDS-2017 datasets. The experimental results show that our method can effectively improve the detection performance of machine learning models and outperform the baselines. [ABSTRACT FROM AUTHOR]

Networks are exposed to an increasing number of cyberattacks due to their vulnerabilities. So, cybersecurity strives to make networks as safe as possible, by introducing defense systems to detect any suspicious activities. However, firewalls and classical intrusion detection systems (IDSs) suffer from continuous updating of their defined databases to detect threats. The new directions of the IDSs aim to leverage the machine learning models to design more robust systems with higher detection rates and lower false alarm rates. This research presents a novel network IDS, which plays an important role in network security and faces the current cyberattacks on networks using the UNSW-NB15 dataset benchmark. Our proposed system is a dynamically scalable multiclass machine learning-based network IDS. It consists of several stages based on supervised machine learning. It starts with the Synthetic Minority Oversampling Technique (SMOTE) method to solve the imbalanced classes problem in the dataset and then selects the important features for each class existing in the dataset by the Gini Impurity criterion using the Extremely Randomized Trees Classifier (Extra Trees Classifier). After that, a pretrained extreme learning machine (ELM) model is responsible for detecting the attacks separately, "One-Versus-All" as a binary classifier for each of them. Finally, the ELM classifier outputs become the inputs to a fully connected layer in order to learn from all their combinations, followed by a logistic regression layer to make soft decisions for all classes. Results show that our proposed system performs better than related works in terms of accuracy, false alarm rate, Receiver Operating Characteristic (ROC), and Precision-Recall Curves (PRCs). [ABSTRACT FROM AUTHOR]

Information and communication technologies are well thought-out as probable assets for the development of socioeconomics in developing countries. Studies have shown that enhanced infrastructure of telecommunication has facilitated means for underserved population development by various ways. Among the existing applications of ICT, the digital library systems provide with better solutions and respond to a variety of unmet needs of research institutions, scientific communities, and developments. With the development of digital library technology, the parallel database system has become the main tool for efficient information processing in the digital library system. On this basis, based on the parallel environment of the computer cluster, by coordinating the communication in the parallel environment, the coordinator, the collection machine, and the query processor can complete the operation of distribution, load, and maintenance, which has high efficiency and saves much precious time, supports the digital library to meet user requirements effectively, and meets the digital library's performance requirements for data, and also, the key problem in the parallel algorithm has been solved. The experimental results show that this parallel technique has very good performance and efficiency. [ABSTRACT FROM AUTHOR]

To design and develop AI-based cybersecurity systems (e.g., intrusion detection system (IDS)), users can justifiably trust, one needs to evaluate the impact of trust using machine learning and deep learning technologies. To guide the design and implementation of trusted AI-based systems in IDS, this paper provides a comparison among machine learning and deep learning models to investigate the trust impact based on the accuracy of the trusted AI-based systems regarding the malicious data in IDs. The four machine learning techniques are decision tree (DT), K nearest neighbour (KNN), random forest (RF), and naïve Bayes (NB). The four deep learning techniques are LSTM (one and two layers) and GRU (one and two layers). Two datasets are used to classify the IDS attack type, including wireless sensor network detection system (WSN-DS) and KDD Cup network intrusion dataset. A detailed comparison of the eight techniques' performance using all features and selected features is made by measuring the accuracy, precision, recall, and F1-score. Considering the findings related to the data, methodology, and expert accountability, interpretability for AI-based solutions also becomes demanded to enhance trust in the IDS. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a fundamental task for text classification problems. Text feature selection aims to represent documents using the most relevant features. This process can reduce the size of datasets and improve the performance of the machine learning algorithms. Many researchers have focused on elaborating efficient FS techniques. However, most of the proposed approaches are evaluated for small datasets and validated using single machines. As textual data dimensionality becomes higher, traditional FS methods must be improved and parallelized to handle textual big data. This paper proposes a distributed approach for feature selection based on mutual information (MI) method, which is widely applied in pattern recognition and machine learning. A drawback of MI is that it ignores the frequency of the terms during the selection of features. The proposal introduces a distributed FS method, namely, Maximum Term Frequency-Mutual Information (MTF-MI), based on term frequency and mutual information techniques to improve the quality of the selected features. The proposed approach is implemented on Hadoop using the MapReduce programming model. The effectiveness of MTF-MI is demonstrated through several text classification experiments using the multinomial Naïve Bayes classifier on three datasets. Through a series of tests, the results reveal that the proposed MTF-MI method improves the classification results compared with four state-of-the-art methods in terms of macro-F1 and micro-F1 measures. [ABSTRACT FROM AUTHOR]

Summary: Flux intensifying interior permanent magnet synchronous machine (FI‐IPMSM) was designed to improve the characteristic of conventional permanent magnet machine, especially, in field weakening region. Increasing a saliency ratio is the main method to achieve this target which leads to increasing reluctance torque than magnetic torque and so improving the machine performance. In this paper, the characteristics of FI‐IPMSM are investigated. The effect of PM material, PM width, PM thickness, flux barriers width, and iron bridge thickness on the machine parameters is studied. Machine inductances, no‐load and full‐load characteristics are analyzed. A comparison is carried out between the FI‐IPMSM and conventional IPMSM to evaluate the improvement in machine performance. Two‐dimensional time‐steeping finite element method (FEM) is employed in analysis of the FI‐IPMSM and conventional IPMSM. [ABSTRACT FROM AUTHOR]

The article titled "Retracted: Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection" has been retracted by Hindawi due to evidence of systematic manipulation of the publication process. The investigation revealed discrepancies in scope, description of the research, availability of data, inappropriate citations, incoherent content, and compromised peer review. Hindawi acknowledges that the usual quality checks did not identify these issues before publication and has implemented additional measures to ensure research integrity. The corresponding author has been given the opportunity to respond to the retraction. [Extracted from the article]

The main objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Boosting Trees (Boosted) algorithms, considering the influence of various training to testing ratios in predicting the soil shear strength, one of the most critical geotechnical engineering properties in civil engineering design and construction. For this aim, a database of 538 soil samples collected from the Long Phu 1 power plant project, Vietnam, was utilized to generate the datasets for the modeling process. Different ratios (i.e., 10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20, and 90/10) were used to divide the datasets into the training and testing datasets for the performance assessment of models. Popular statistical indicators, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R), were employed to evaluate the predictive capability of the models under different training and testing ratios. Besides, Monte Carlo simulation was simultaneously carried out to evaluate the performance of the proposed models, taking into account the random sampling effect. The results showed that although all three ML models performed well, the ANN was the most accurate and statistically stable model after 1000 Monte Carlo simulations (Mean R = 0.9348) compared with other models such as Boosted (Mean R = 0.9192) and ELM (Mean R = 0.8703). Investigation on the performance of the models showed that the predictive capability of the ML models was greatly affected by the training/testing ratios, where the 70/30 one presented the best performance of the models. Concisely, the results presented herein showed an effective manner in selecting the appropriate ratios of datasets and the best ML model to predict the soil shear strength accurately, which would be helpful in the design and engineering phases of construction projects. [ABSTRACT FROM AUTHOR]