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AbstractObjectiveDesignStudy sampleResultsConclusionsExploring applications of automatic speech recognition and text-to-speech technologies in hearing assessment and evaluations of hearing aids.Review protocol was registered at the INPLASY database and was performed following the PRISMA scoping review guidelines. A search in ten databases was conducted in January 2023 and updated in June 2024.Studies that used automatic speech recognition or text-to-speech to assess measures of hearing ability (e.g. speech reception threshold), or to configure hearing aids were retrieved. Of the 2942 records found, 28 met the inclusion criteria.The results indicated that text-to-speech could effectively replace recorded stimuli in speech intelligibility tests, requiring less effort for experimenters, without negatively impacting outcomes (n = 5). Automatic speech recognition captured verbal responses accurately, allowing for reliable speech reception threshold measurements without human supervision (n = 7). Moreover, automatic speech recognition was employed to simulate participants’ hearing, with high correlations between simulated and empirical data (n = 14). Finally, automatic speech recognition was used to optimise hearing aid configurations, leading to higher speech intelligibility for wearers compared to the original configuration (n = 3).There is the potential for automatic speech recognition and text-to-speech systems to enhance accessibility of, and efficiency in, hearing assessments, offering unsupervised testing options, and facilitating hearing aid personalisation. [ABSTRACT FROM AUTHOR]

This study presents a model of automatic speech recognition (ASR) that is designed to diagnose pronunciation issues in children with speech sound disorders (SSDs) to replace manual transcriptions in clinical procedures. Because ASR models trained for general purposes mainly predict input speech into standard spelling words, well-known high-performance ASR models are not suitable for evaluating pronunciation in children with SSDs. We fine-tuned the wav2vec2.0 XLS-R model to recognise words as they are pronounced by children, rather than converting the speech into their standard spelling words. The model was fine-tuned with a speech dataset of 137 children with SSDs pronouncing 73 Korean words that are selected for actual clinical diagnosis. The model’s Phoneme Error Rate (PER) was only 10% when its predictions of children’s pronunciations were compared to human annotations of pronunciations as heard. In contrast, despite its robust performance on general tasks, the state-of-the-art ASR model Whisper showed limitations in recognising the speech of children with SSDs, with a PER of approximately 50%. While the model still requires improvement in terms of the recognition of unclear pronunciation, this study demonstrates that ASR models can streamline complex pronunciation error diagnostic procedures in clinical fields. [ABSTRACT FROM AUTHOR]

Speaking skills generally receive little attention in traditional English as a Foreign Language (EFL) classrooms, and this is especially the case in secondary education in Indonesia. A vocabulary deficit and poor pronunciation skills hinder learners in their efforts to improve speaking proficiency. In the present study, we investigated the effects of using two language learning websites, I Love Indonesia (ILI) and NovoLearning (NOVO). These websites are equipped with Automatic Speech Recognition (ASR) technology, with each website providing different types of immediate feedback. We measured written receptive and productive vocabulary knowledge of 40 target words before and after the intervention in which 146 students practiced with these two ASR-based websites, and compared it to that of a control group (n = 86). The ASR-based websites successfully helped students enhance their receptive vocabulary. Twenty-four students participated in a spoken pre-and post-test pronouncing the same 40 target words. We successfully developed an approach to measure pronunciation skills which showed that the treatment groups outperformed the control group. Our results indicate that our technology is successful in improving vocabulary and pronunciation skills. [ABSTRACT FROM AUTHOR]

AbstractObjectiveDesignStudy sampleResultsConclusionsOne proposed method to improve sound localisation for bilateral cochlear implant (BiCI) users is to synchronise the automatic gain control (AGC) of both audio processors. In this study we tested whether AGC synchronisation in a dual-loop front-end processing scheme with a 3:1 compression ratio improves sound localisation acuity.Source identification in the frontal hemifield was tested in in an anechoic chamber as a function of (roving) presentation level. Three different methods of AGC synchronisation were compared to the standard unsynchronised approach. Both root mean square error (RMSE) and signed bias were calculated to evaluate sound localisation in the horizontal plane.Six BiCI users.None of the three AGC synchronisation methods yielded significant improvements in either localisation error or bias, neither across presentation levels nor for individual presentation levels. For synchronised AGC, the pooled mean (standard deviation) localisation error of the three synchronisation methods was 24.7 (5.8) degrees RMSE, for unsynchronised AGC it was 27.4 (7.5) degrees. The localisation bias was 5.1 (5.5) degrees for synchronised AGC and 5.0 (3.8) for unsynchronised.These findings do not support the hypothesis that the tested AGC synchronisation configurations improves localisation acuity in bilateral users of MED-EL cochlear implants. [ABSTRACT FROM AUTHOR]

This paper intends to model an AGC regulator for a restructured environment using Bare Bone Particle Swarm Optimization (BBPSO). The gain-controlled Proportional–Integral (PI) Controller is designed here to enhance the performance of the BBPSO algorithm along with Gaussian distribution. The practical difficulty in handling the area control error to zero is the sudden variations in load. In practice, the tremendous contribution of deregulation in the power sector causes volatility in frequency and tie-line power deviations. To ensure the robustness of the proposed controller, three different cases of power system transactions have been considered. The performance has been validated by comparing it with Real Coded Genetic Algorithm-tuned PI controller (RCGA-PI) and Differential Evolutionary Algorithm-tuned PI controller (DE-PI) for the five area Thermal-Thermal generation test system. Moreover, the dynamic performance of an extensive range of demands and disturbances of all areas like settling time and overshoot against parametric precariousness has been done on the proposed test system. [ABSTRACT FROM AUTHOR]

We investigated whether chronotype and time-of-day modulate the time course of automatic and controlled semantic processing. Participants performed a category semantic priming task at either the optimal or non-optimal time of day. We varied the prime-target onset asynchrony (100-, 450-, 650-, and 850-ms SOAs) and kept the percentage of unrelated targets constant at 80%. Automatic processing was expected with the short SOA, and controlled processing with longer SOAs. Intermediate-types (Experiment 1) verified that our task was sensitive to capturing both types of processes and served as a reference to assess themin extreme chronotypes. Morning-type and evening-type participants (Experiment 2) differed in the influence of time of testing on priming effects. Morning-types applied control in all conditions, and no performance modulation by time-of-day was observed. In contrast, evening-types were most adversely affected by the time of day to shift from automatic-based to controlled-based responses. Also, they were considerably affected in successfully implementing controlled processing with long intervals, particularly at the non-optimal time of day, with inhibitory priming showing only a marginally significant effect at the longest SOA. These results suggest that extreme chronotypes may be associated with different styles of cognitive control. Morning-types would be driven by a proactive control style, whereas a reactive control style might be applied by evening-types. [ABSTRACT FROM AUTHOR]

The automatic extraction of bottom sediment annotations in large-scale raster nautical charts has limitations, including an imprecise semantic information description and low efficiency. To overcome them, we propose a convolutional neural network (CNN)-based method for the automatic extraction of bottom sediment annotations in raster nautical charts, using image processing techniques to improve it. First, an adaptive chart partitioning model that considers element completeness is constructed. Second, a principle for the unique identification of elements based on spatial conflicts is designed. Finally, a model for accurately extracting semantic information for bottom sediment annotations is established. To evaluate the effectiveness of the proposed method, we implemented a model based on the PyTorch framework and used the PIL library to analyze the results. We also conducted comparative experiments on multiple CNN models to recommend the selection of such models in the proposed method by comparing their classification and recognition performance. The experimental results indicate that (1) the proposed model can achieve high-precision extraction of bottom sediment annotations in raster nautical charts. (2) Furthermore, the proposed model generally has high recognition accuracy and semantic completeness, with better recognition precision than traditional pattern recognition methods. [ABSTRACT FROM AUTHOR]

ObjectivesMethodsResultsConclusionTo investigate the effects of automatic scene classification (SCAN) on speech perception in noise and real-world functional performance in children using cochlear implants (CIs).We used a within-subjects repeated measures design in two studies. The first study assessed speech perception in noise with or without SCAN enabled in 25 school-aged children. The second study evaluated functional auditory performance in real life. Parents of 18 children provided ratings using the Parents’ Evaluation of Aural/oral Performance of Children (PEACH) questionnaire; and children provided ratings using the Self Evaluation of Listening Function (SELF) questionnaire. Analyses of variance with repeated measures were used to examine the effect of SCAN.On average, speech perception in noise was significantly better with SCAN enabled (mean SRT: −4.1 dB; SD: 4.0), compared to SCAN disabled (mean SRT: 0.5 dB; SD: 3.5). Children’s functional performance in real life was similar between the two device settings.Automatic auditory scene classification provides significant benefits for speech perception in noise (4.6 dB improvement). On average, there were no perceived detrimental or beneficial effects in real life. These findings support the use of SCAN in CIs for young children. [ABSTRACT FROM AUTHOR]

The construction of questions is an essential component in educational assessment and student learning processes. However, manually constructing questions is a complex task that requires not only professional training, substantial experience, and extensive resources from teachers but is also time-consuming. This article introduces an Automatic Question Generation (AQG) technology based on a prompt pattern to alleviate this burden and address the ongoing need for new questions in education. The essence of this method lies in constructing a prompt pattern grounded on a collective knowledge base derived from teachers, thereby enhancing the quality of the questions produced. Practical applications and expert evaluations demonstrate that integrating a prompt pattern with a collective knowledge base into Large Language Models (LLMs) results in high-quality questions with statistically significant results. These questions not only meet educational standards but also approach the quality of manually constructed questions by teachers in certain aspects. Our research further emphasizes the feasibility of AI-teacher collaboration in education. [ABSTRACT FROM AUTHOR]

Frequency is an important index for the stable and secure operation of the power system. In the conventional methods, an inappropriate value of the frequency bias coefficient will lead to over-biasing or under-biasing of generators, causing unintended cross-regional interaction. This paper proposes a fully non-interactive automatic generation control strategy for power systems. Through the derivation of deviation of frequency and tie-line active power under disturbance, it's found that the disturbance can be located from the deviation of frequency and active power of tie-line. Based on this, the conventional AGC control model is modified. With the proposed method, only the AGC units in disturbed areas respond to disturbances, and the AGC units in other areas will not act. Thus, the unintended cross-regional interaction in the conventional AGC control method is avoided. The proposed method only needs minor modification to the conventional AGC control model and hence is easy to implement for onsite application. The effectiveness of the proposed strategy is confirmed via simulation on the Kundur four-machine two-area system. [ABSTRACT FROM AUTHOR]

Borehole imaging, one of the main methods of rock formation detection, is widely used in mining, tunnels, petroleum, and other fields of engineering. However, the method of manually identifying the characteristic of rock formation based on borehole imaging is greatly affected by subjective factors. For example, it should be noted that when the rock layer color is similar, the position of the rock interface may be different. In this paper, a method of quantitatively describing the characteristics of the rock formation is proposed, and the automatic recognition of the rock interface is realized. Firstly, the calculation method of rock dip angle and position based on borehole image with 360° wall surface development is proposed. Secondly, the three-dimensional distribution characteristics of the gray values of the rock layer are used to quantitatively describe the characteristics of the rock layer. Finally, an automatic recognition method of rock formation interface based on change-point detection algorithm is proposed. In addition, the recognition effect of the rock formation interface is analyzed by experiments and applied in the field. Research shows that, compared with method two, method one can determine the rock inclination. But its anti-interference ability is poor. Method two can better determine the position of the rock interface. The method is of great significance to the intelligent development of the borehole imaging system and promotes safe, efficient, and sustainable mining in coal mines [ABSTRACT FROM AUTHOR]

Major depression is a severe psychological disorder typically diagnosed using scale tests and through the subjective assessment of medical professionals. Along with the continuous development of machine learning techniques, computer technology has been increasingly employed to identify depression in recent years. Traditional methods of automatic depression recognition rely on using the patient's physiological data, such as facial expressions, voice, electroencephalography (EEG), and magnetic resonance imaging (MRI) as input. However, the acquisition cost of these data is relatively high, making it unsuitable for large-scale depression screening. Thus, we explore the possibility of utilizing a house-tree-person (HTP) drawing to automatically detect major depression without requiring the patient's physiological data. The dataset we used for this study consisted of 309 drawings depicting individuals at risk of major depression and 290 drawings depicting individuals without depression risk. We classified the eight features extracted from HTP sketches using four machine-learning models and used multiple cross-validations to calculate recognition rates. The best classification accuracy rate among these models reached 97.2%. Additionally, we conducted ablation experiments to analyze the association between features and information on depression pathology. The results of Wilcoxon rank-sum tests showed that seven of the eight features significantly differed between the major depression group and the regular group. We demonstrated significant differences in HTP drawings between patients with severe depression and everyday individuals, and using HTP sketches to identify depression automatically is feasible, providing a new approach for automatic identification and large-scale screening of depression. [ABSTRACT FROM AUTHOR]

Boron Neutron Capture Therapy (BNCT) is a promising radiotherapy technique for cancer treatment. Optimizing dose distribution in tumors is a significant challenge for BNCT, especially for tumors that are deep-seated. The method of optimizing dose distribution through multi-field irradiation holds great potential for clinical applications in BNCT. This study presents an optimization program that couples genetic algorithm and Monte Carlo simulation, which can automatically optimize irradiation field weights for multi-field irradiation, ensuring optimal dose distribution for cancer patients. Using this program, a glioblastoma case was used to analyze the optimization results for different numbers of irradiation fields. The results demonstrate that the program possesses an efficient and accurate automatic optimization feature for multi-field irradiation weights. The minimum dose, mean dose, and D80% of tumor dose improve with the increase in irradiation fields, while the homogeneity index (HI) of tumor dose decreases, without exceeding dose constraints for organs at risk. The developed optimization program provides a method for determining irradiation field weights in BNCT treatment planning systems, which has the potential to improve treatment outcomes. [ABSTRACT FROM AUTHOR]

Although social networking apps and dictation-based automatic speech recognition (ASR) are now widely available in mobile phones, relatively little is known about whether and how these technological affordances can contribute to EFL pronunciation learning. The purpose of this study is to investigate the effectiveness of feedback from peers and/or ASR in mobile-assisted pronunciation learning. 84 Chinese EFL university students were assigned into three conditions, using WeChat (a multi-purpose mobile app) for autonomous ASR feedback (the Auto-ASR group), peer feedback (the Co-non-ASR group), or peer plus ASR feedback (the Co-ASR group). Quantitative data included the pronunciation pretest, posttest, and delayed posttest, and students' perception questionnaires, while qualitative data included students' interviews. The main findings are: (a) all three groups improved their pronunciation, but the Co-non-ASR and the Co-ASR groups outperformed the Auto-ASR group; (b) the three groups showed no significant difference in perception questionnaires; and (c) the interviews revealed some common and unique technical, social/psychological, and educational affordances and concerns about the three mobile-assisted learning conditions. [ABSTRACT FROM AUTHOR]

With the rapid growth of global urbanization and the rising demand for sustainable development, it is essential to study the performance and durability of building materials. As a traditional and widely employed building material, Chinese clay tiles play a significant role in traditional Chinese architecture. However, traditional methods for assessing structural surface damage necessitate the time-consuming and labor-intensive assessment and judgment of trained professionals. Consequently, it is crucial to employ machine learning techniques for automatic damage type identification. This study identifies the types of damage to Chinese clay tiles in Macau by employing machine learning techniques and the YOLOv4 object detection model. A total of 363 photographs of on-site Chinese clay tiles were used as training samples, and 200 epochs of the model training were performed. The primary findings of this study are as follows: (1) The machine learning method, based on the YOLOv4 model, provides an effective and precise solution for the automatic identification of Chinese clay tiles damage types, overcoming the human and time-cost constraints of conventional evaluation methods. (2) The detection accuracy of the detection model in this study is 95.42% for the detection of Chinese clay tiles cracks, 80.91% for the detection of stains, and 89.34% for the detection of surface wear, with an overall accuracy of 88.98%, which meets the basic detection requirements. (3) The experimental results demonstrate the viability and efficacy of the proposed method for identifying clay tile damage types and provide a method reference for the preservation and sustainable development of historical buildings. [ABSTRACT FROM AUTHOR]

There is a need for robust methodological approaches to improve our capacity to automatically detect plant species from seed samples tohelp support plant management strategies. In this study, we tested different neural network techniques to automatically detect native species from seeds from the Andean páramo region based on optic RGB images. Specifically, we compared i) simple feed-forward networks (SNNs), consisting of feed-forward nets with error back-propagation, holding one hidden layer with different number of neurons; and ii) deep convolutional neural networks (CNNs), which have their convolutional layers-built form multiple 3x3 kernels. First, we sampled 50 seeds from four common plant species in the La Rusia Páramo (Colombia): Espeletia congestiflora, Bucquetia glutinosa, Calamagrostis effusa and Puya santosii. We took RGB images of individual seeds for each species on contrasted white and black backgrounds, and then classified all images under both SNNs and CNNs. Under a double cross-validation scheme, the SNN approach with 14 neurons approached 88% of test accuracy, while CNN achieved 93%. Moreover, when increasing the image sample in the training dataset fed to models, CNN performed with 100% accuracy when used on testing and validation datasets. Overall, the neural network approach explored here suggests a promising methodology for species prediction from seeds based on optical RGB images, with potential for automatic seed recognition and counting on the field. [ABSTRACT FROM AUTHOR]

Coplanar array capacitance imaging is an attractive imaging technique in industrial and detection fields, but low-quality images reduce its practicality and reliability. The inverse problem is difficult to solve and the image reconstruction quality is low, in this paper, an auxiliary optimisation strategy is proposed to transform the inverse problem of image reconstruction into a general minimisation problem, combined with the threshold of automatic parameter selection for feature extraction of capacitive data. The Kullback-Leibler(KL) data error term is combined with the Total Variation (TV) semi-norm to form a new imaging model. Utilising the Chambolle-Pock framework, the image reconstruction problem is transformed into a general minimisation problem to obtain high-quality images. Capacitance data from a 3 $ \times $× 4 coplanar array capacitance sensor is used for feature extraction, serving as the basis for automatic parameter selection in the auxiliary optimisation strategy. The proposed Chambolle-Pock framework with the support of the auxiliary optimisation strategy, validated through simulation and experiments, outperforms popular imaging algorithms in terms of performance. The novel automatic parameter selection optimization algorithm introduced in this paper enhances the accuracy of capacitor image reconstruction in coplanar arrays. [ABSTRACT FROM AUTHOR]

Eddies assume a pivotal role in both the oceanic heat cycle and marine dynamic processes. The development of real-time satellite observations, coupled with advancements in computer intelligence, has propelled automatic eddy detection algorithms to the forefront of ocean remote sensing research. Nevertheless, target omissions and accuracy degradations are inevitable for multiple detection algorithms due to strong morphological variations in ocean eddies. This paper proposes an automatic detection algorithm based on the quadrant angle of the velocity vector in the flow field. Firstly, a rectangular search box is established, and the corresponding quadrant angles at the four vertices are calculated. Secondly, the centres and types of eddies are determined according to the cumulative sum and variety rules of quadrant angles. Then the outermost boundary is identified by regularity of quadrant angles in eight directions expanding outward from the centre of the eddy using the stream function equation. The new algorithm is assessed and verified using geostrophic flow data derived from the CMEMS standard gridded sea level anomaly product. Furthermore, its detection capabilities are demonstrated through a comparative analysis with several other algorithms. All methods exhibit consistent efficacy in detecting the majority of eddies with similar spatial distributions. In cases where the new algorithm identifies a specific eddy not detected by the FF15 and ND10 algorithms, sea surface temperature data is employed for verification. The sea surface temperature distribution map, along with the obtained results, illustrates the superiority of the new algorithm and its adaptability to products with varying resolutions. Additionally, the results undergo verification through a manual detection method, revealing that the new algorithm achieves SDR of 91.73% and an EDR of 3.54%. This percentage significantly surpasses the lower acceptable limit of 80% for the SDR parameter. The new algorithm is applied to study eddies with lifetimes exceeding ten days, spanning from March 2022 to February 2023, within the East China Sea and the South China Sea. The radius of SCS eddies is generally larger than that of ECS eddies, both cyclonic and anticyclonic. This further confirms the significant influence of regional ocean dynamics on the structure of eddies in different regions. Finally, the algorithm is applied to SWOT data to test the adaptability of the algorithm to non-regular grid data. The results show that the algorithm can effectively analyse SWOT data within 60° latitudes. [ABSTRACT FROM AUTHOR]

For the efficiency and operator safety issues caused by manual blade replacement in flexible material cutting machines. In order to improve the efficiency of tool changing, the automatic tool changing equipment for the cutting machine has been designed, which includes a clamping mechanism, tool magazine, tool feeding mechanism, and control system after studying the tool changing process and tool changing equipment of the automatic tool changing system in the existing machining center. A prototype and testing platform were built, and the prototype was tested and analyzed. The results show that the automatic tool change equipment of the cutting machine has achieved automation in the process of unloading, receiving, taking, feeding, and loading the cutting machine blade, and the success rate of tool change has achieved the expected effect. [ABSTRACT FROM AUTHOR]

The demand for functionality, performance, safety, and reliability in complex product domains is continuously increasing, leading to growing product complexity. Traditional document-based systems engineering approaches face numerous challenges in terms of communication efficiency, traceability, and maintenance when dealing with complex product system design. Consequently, Model-Based Systems Engineering (MBSE) has been widely adopted. To improve the efficiency of system modelling, an effective method is needed to automate the process by reusing existing design resources. This study proposes a knowledge graph-based approach for the automatic generation of system model diagrams, consisting of three main steps. Firstly, a system model graph is constructed using an existing system model repository. Then, the required modelling elements are extracted from design documents, and reusable model elements are obtained based on the system model graph. Lastly, a mapping relationship is established between system model elements extracted from multi-source heterogeneous data and SysML metamodel, enabling the automatic generation of system model diagrams based on SysML views. This approach effectively leverages the information from the existing system model repository and achieves automated mapping conversion from multi-source heterogeneous data to the SysML system model. A communication satellite case study is presented to demonstrate the capability of this method. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) images have been widely used for surface water identification due to their all-weather capabilities. However, the presence of inherent speckle noise in SAR data poses a challenge for accurate water identification. Additionally, annotating high-quality water body samples requires significant human labour, which can be costly and time-consuming. Aiming at the above problems, a noise-robust automatic water identification architecture without artificial labels is proposed. First, a two-stage automatic sample collection method that utilizes k-means++ clustering and morphological concepts is designed. Then, a weakly supervised noise-resistant SAR water body segmentation method NRM-ACUNet has been developed based on U-Net combined with LNR-Dice loss function and Conditionally Parameterized Convolutions (CondConv) to minimize the impact of sample noises. Experimental results show that the morphological processing can improve water body sample quality compared to k-means++, and compared with U-Net, NRM-ACUNet performs superior with noise-containing pseudo-samples, achieving 96.8% F1 accuracy and 52.06% accuracy improvement. [ABSTRACT FROM AUTHOR]

A computed tomography (CT) scan is an important radiological imaging method in diagnosing pneumonia caused by SARS-CoV-2. Within the scope of the study, three classes of automatic classification – COVID-19 pneumonia, healthy, and other pneumonia – were carried out. Using deep learning as a classifier, a total of 6,377 CT images were used, including 3,364 COVID-19 pneumonia, 1,766 healthy, and 1,247 other pneumonia images. A total of seven architectures, including the most recent convolutional neural network (CNN) architectures, MobileNetV2, ResNet-101, Xception, Inceptionv3, GoogLeNet, EfficientNetB0, and DenseNet201, were used in the study. The classification results were obtained using the CT images, and they were calculated using the feature images obtained by applying local binary patterns on the CT images. The results were then combined with the help of a pipeline algorithm. The results revealed that the best overall accuracy result obtained by using CNN architectures could be improved by 4.87% with a two-step pipeline algorithm. In addition, significant improvements were achieved in all other measurement parameters within the scope of the study. At the end of the study, the highest sensitivity, specificity, accuracy, F-1 score, and Area under the Receiver Operating Characteristic Curve (AUC) values obtained for the COVID-19 pneumonia class were 0.9004, 0.8901, 0.8956, 0.9010, and 0.9600, respectively. The highest overall accuracy value was 0.8332. The most important output of the work carried out is the demonstration that the results obtained with the most successful CNN architectures used in previous studies can be significantly improved thanks to pipeline algorithms. [ABSTRACT FROM AUTHOR]

Well test model identification is a challenging task due to the numerous types of well test interpretation models and the non-uniqueness of pressure responses generated by different reservoir models. An automated framework is crucial to aid in the identification of well test interpretation models. Since the identification of well test interpretation relies primarily on the various flow regimes appeared on different diagnostic plots. A novel approach is proposed for the well test model identification from the pressure transient test data using the syntactic pattern recognition in this study. In this study, the identification process of well test interpretation model is divided into six steps: preprocessing, feature primitive extraction, curve shape tracking, flow regime division, model preliminary inference, and model final validation incorporating TDS technology. The automatic identification framework developed with this method has been able to identify a variety of complex well test interpretation models correctly, and the non-uniqueness of model results can be well resolved by syntactic pattern recognition combined with TDS technology. In general, the findings of this study can help for better understanding of the process by which well test expert completes the task of model identification. [ABSTRACT FROM AUTHOR]

Systems using graphical processing units (GPUs) and field-programmable gate arrays (FPGAs) have increased due to their advantages over central processing units (CPUs). However, such systems require the understanding of hardware-specific technical specifications such as Hardware Description Language (HDL) and compute unified device architecture (CUDA), which is a high hurdle. Based on this background, we previously proposed environment-adaptive software that enables automatic conversion, configuration and high-performance operation of existing code according to the hardware to be placed. As an element of this concept, we also proposed a method of automatically offloading loop statements of application source code for CPUs to GPUs and FPGAs. In this paper, we propose a method for offloading a function block, which is a larger unit, instead of individual loop statements in an application to achieve higher speed by automatically offloading to GPUs and FPGAs. We implemented the proposed method and evaluated it using current applications offloading to GPUs and FPGAs. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) system is analysing human behaviour using mobile health technology. Mobile Health data (MHEALTH) uses electronic devices to collect data and identify the activity of the patient in real-time. Recordings of 10 patients' vital signs from various circumstances are included in the dataset. With a sensor attached to their bodies, they were required to carry out a number of physical tasks. Due to the lack of accuracy in the other state-of-the-art algorithms, we proposed Human Activity Recognition Neural Network (HARNet) architecture for automatic recognition of human activity using CNN and LSTM with the transfer learning of SVM. Here, the human health behaviour was analysed and classified using different ML and DL algorithms. The hybrid techniques of CNN and LSTM are selected across the different DL algorithms and it is used to extract independent and discriminating features, which aids the SVM classifier to attain good classification. When compared to other DL methods, HARNet performed better, achieving 99.8% accuracy. Overall, HAR systems have many potential applications in various fields, including healthcare, wellness, sports and surveillance. They have relationships to many different academic disciplines, including sociology, human–computer interaction, medical and may offer individualized help for different domains. [ABSTRACT FROM AUTHOR]

Properties of root weld are important for service safety of pipelines, while microstructure and properties of root weld would alter due to heat affect of following welding passes. Evolution of root weld microstructure and properties during automatic girth welding of X80 pipeline was studied. Microstructure of the original root weld primarily consists of acicular ferrite (AF) and side-plate ferrite (SPF), and it is mostly affected by hot pass and first filling pass weld, after which SPF and AF coarsened significantly and some interlock ferrite combined with each other. During subsequent welding, no significant microstructural changes was found. However, impact toughness of root weld slightly increased after hot pass and first filling pass in spite of microstructure coarsening. In contrast, hardness of root weld notably dropped due to microstructure coarsening of hot pass and first filling pass, and then it was retrieved in following welding passes due to tempering effect. [ABSTRACT FROM AUTHOR]

Pre-treatment processing technology plays a crucial role in the overall freshwater fish processing procedure, and automatic head and tail cutting stands out as a significant pre-treatment technique within the industry. The system for removing the head and tail of freshwater fish comprised a Cartesian coordinate manipulator, a fish transfer device, a control system, and an image acquisition device. In the vision system, five image segmentation methods were utilized for fish head and tail image segmentation comparison tests. These methods include U-Net (U-shaped Deep Neural Network), DeeplabV3, PSPNet (Pyramid Scene Parsing Network), FastSCNN (Fast Semantic Segmentation Network), and ICNet (Image Cascade Network), all of which were employed to evaluate their performance. Among the tested segmentation methods, the ICNet demonstrated the most excellent segmentation capability. The experimental results indicated a segmentation accuracy of 99.01%, a mean intersection over union (MIoU) of 82.50%, and an image processing time of 15.25 ms. The results showed that the fish head and tail were successfully cut off using this model for recognition with a circular knife. Consequently, the segmentation model employed in the machine vision system within this study has demonstrated successful applicability in automatically cutting the heads and tails of freshwater fish of various sizes. [ABSTRACT FROM AUTHOR]

Road network selection plays a key role in map generalization for creating multi-scale road network maps. Existing methods usually determine road importance based on road geometric and topological features, few evaluate road importance from the perspective of road utilization based on human travel data, ignoring the functional values of roads, which leads to a mismatch between the generated results and people's needs. This paper develops two functional semantic features (i.e. travel path selection probability and regional attractiveness) to measure the functional importance of roads and proposes an automatic road network selection method based on graph convolutional networks (GCN), which models road network selection as a binary classification. Firstly, we create a dual graph representing the source road network and extract road features including six graphical and two functional semantic features. Then, we develop an extended GCN model with connectivity loss for generating multi-scale road networks and propose a refinement strategy based on the road continuity principle to ensure road topology. Experiments demonstrate the proposed model with functional features improves the quality of selection results, particularly for large and medium scale maps. The proposed method outperforms state-of-the-art methods and provides a meaningful attempt for artificial intelligence models empowering cartography. [ABSTRACT FROM AUTHOR]

Chatbot research has received growing attention due to the rapid diversification of chatbot technology, as demonstrated by the emergence of large language models (LLMs) and their integration with automatic speech recognition. However, among various chatbot types, speech-recognition chatbots have received limited attention in relevant research reviews, despite their increasing potential for language learning. To fill this gap, 32 empirical studies on speech-recognition chatbots for language learning were reviewed. The following information was reviewed for each study: basic publication information, research focus, location of chatbot use, methodology, group design format, participant information, intervention duration, target language, device type adopted, and chatbot role. An upward trend in research quantity starting in 2020 was identified, which accelerated exponentially in 2022. College students were more likely than other groups to be involved in research, and English as a second or foreign language was the most common target language. Most studies focused on participants' perceptions of chatbots and the degree to which using chatbots helped them develop their speaking or listening proficiency. Methodologically, single-chatbot design using mixed methods was the most common design format, and most studies were conducted for more than one month in laboratory or classroom settings. Conventional mobile devices, such as smartphones, tablet PCs, and smart speakers without a screen, were the most frequently adopted device types. The chatbots' most common role was as conversational partner. A detailed discussion of these results and their implications for future research on speech-recognition chatbots, particularly regarding the use of LLM-powered chatbots, is provided. [ABSTRACT FROM AUTHOR]

In this article, we investigate the task of normalizing transcribed texts in Vietnamese Automatic Speech Recognition (ASR) systems in order to improve user readability and the performance of downstream tasks. This task usually consists of two main sub-tasks: predicting and inserting punctuation (i.e., period, comma); and detecting and standardizing named entities (i.e., numbers, person names) from spoken forms to their appropriate written forms. To achieve these goals, we introduce a complete corpus including of 87,700 sentences and investigate conditional joint learning approaches which globally optimize two sub-tasks simultaneously. The experimental results are quite promising. Overall, the proposed architecture outperformed the conventional architecture which trains individual models on the two sub-tasks separately. The joint models are furthered improved when integrated with the surrounding contexts (SCs). Specifically, we obtained 81.13% for the first sub-task and 94.41% for the second sub-task in the F1 scores using the best model. [ABSTRACT FROM AUTHOR]

Polar lows are small, high-latitude, intense maritime cyclones and frequently have severe impacts on the ocean such as strong winds, high waves and heavy rainfall. They are difficult to observe and forecast due to their short lifetime (<48 hours), small horizontal scales (200 ~ 1000 km), and the sparse synoptic observing network that exists in the subarctic and Arctic oceans. Previous studies have identified and monitored polar lows by visual analysis of visible and thermal infrared imagery from satellites. However, this manual inspection method is subjective, time-consuming and inevitably involves errors in polar low detections. In this study, we present an automatic objective procedure which we demonstrate by detecting polar lows using spaceborne active synthetic aperture radar (SAR) and passive microwave radiometer observations. Based on the marker-controlled watershed segmentation method and the morphological image thinning algorithm, the centre locations of polar lows are determined using RADARSAT-2 and Sentinel-1A high-resolution SAR images and total atmospheric water vapour content fields from radiometers (AMSR2, SSM/I, GMI, and WindSat). Furthermore, the trajectories of polar lows are constructed, using detected centres from multi-temporal SAR and radiometer observations. Polar low detections are confirmed by high surface wind speeds from SAR, scatterometer, and radiometer data, the presence of cloud vortex signatures visible in MODIS, AVHRR, and VIIRS thermal infrared imagery, as well as the difference between the sea surface temperature and the air temperature at 500 hPa. These results show that the proposed methods have potential to automatically detect and track polar lows from multisensor data. We also estimate the characteristic parameters of detected polar lows. The diameters, translation speeds, and distances travelled are 189 km and 225 km, 8 m/s and 4.9 m/s, and 318 km and 263 km, respectively. [ABSTRACT FROM AUTHOR]

People often engage in the selective remembering of valuable or important information, whether strategic and/or automatic. We examined potential age-related differences in the automatic processing of value during encoding on later remembering by presenting participants with words paired with point values (range: 1–10 twice or 1–20) to remember for a later test. On the first three lists, participants were told that they would receive the points associated with each word if they recalled it on the test (their goal was to maximize their score). On the last three lists, we told participants that all words were worth the same number of points if recalled on the tests, thus making the point value paired with each word meaningless. Results revealed that selective memory may be impaired in older adults using procedures with larger value ranges. Additionally, we demonstrated that the automatic effects of value may have a greater effect on younger adults relative to older adults, but there may be instances where older adults also exhibit these automatic effects. Finally, strategic and automatic processes may not be related within each learner, suggesting that these processes may rely on different cognitive mechanisms. This indicates that these processes could be underpinned by distinct cognitive mechanisms: strategic processes might engage higher-level cognitive operations like imagery, while automatic processes appear to be more perceptually driven. [ABSTRACT FROM AUTHOR]

Learning resources are quite important for online learning while resource provision based on algorithms could not address learners' ubiquitous needs well. Moreover, the structure and content of resources are pre-defined which makes the "Structure" and "Content" coupled closely and could not easily adjust when learners' needs changed. To solve this problem, an automatic resource generation mechanism is needed. In this study, we summarize the main components of resource design and proposed a "Structure-Content Loosely Coupled" resource model (Learning Cell Model). The model separates the structure and content into independent yet connected parts by defining "Dynamic Structure" and "Container". Then, the automatic resource generation mechanism and its supporting system were designed based on the model and used in two 5th Grade classes. Results showed the mechanism and system could generate resources according to learners' needs accurately and improve learners' learning outcomes without increasing their cognitive load. Further, the learners had good attitude, technique acceptance, and satisfaction. Overall, the "Structure-Content Loosely Coupled" model and the proposed mechanism could be used creatively for more flexible and adaptive resource provision. They made the resource generation timely and automatic which helped teachers' resource design. The results are enlightening and foster further research in this field. [ABSTRACT FROM AUTHOR]

During the last decades, Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has been vastly utilized in the military services and civil studies. Because of the sensibility of the SAR images of the imaging in the azimuth dimension, using the multi-aspect SAR image sequence is more practical than the single-aspect SAR image to achieve the superior classification accuracy in an actual SAR ATR task. At present time, multi-aspect SAR ATR models mostly utilize Recurrent Neural Networks (RNN) that depend on the sequence between samples and so suffer from the lack of information. In a practical work, a huge amount of training set is needed to train a deep learning network accurately, but it is so costly work to extract multi-aspect SAR images. So, in this article, a new model of multi-aspect SAR ATR is proposed by using a self-attention model. The proposed self-attention model is utilized to calculate the internal correlation between the original SAR images. At the same time, to develop the anti-noise capability of the proposed model and decrease the dependency on a huge amount of training data, a Convolutional Auto Encoder (CAE) is designed and utilized in the feature extraction section of the proposed model. On the other hand, unlike existing traditional methods, in this work we use both amplitude and phase information of SAR images to devolve the training process of the proposed model. It should be noted that all of the parameters of the proposed network are developed to a complex domain. In addition a complex backpropagation algorithm by using the gradient based model is used for training the network. In the end, experiments is obtained by using two MSTAR data set (the MSTAR-SOC and EOC). The experimental results prove that the proposed model not only can achieve a high recognitions rate on the case of sufficient training sample but also can obtain an acceptable rate in the case of small training samples in different complex situations. In addition, the simulation results demonstrate that by using the encoder of CAE in the proposed model, the whole configuration of the proposed model achieves the anti-noise capability that is a valuable benefit of any practical SAR ATR task. [ABSTRACT FROM AUTHOR]

In this paper, a radial basis function neural network (RBF-NN) automatic sonar target recognition system is proposed. For the RBF-NN training phase, a whale optimisation algorithm (WOA) developed with a fuzzy system has been used (which is called FWOA). The reason for using the fuzzy system is the lack of correct identification of the boundary between the two stages of exploration and exploitation. Thus, the tuning of the effective parameters of the WOA is left to the fuzzy system of the Mamdani type. RBF-NN was trained by chimp optimisation algorithm (ChOA), genetic algorithm (GA), Evolution Strategy (ES), league championship algorithm (LCA), grey wolf algorithms (GWO), gravitational search algorithm (GSA), and WOA to compare the proposed algorithm. The measured criteria are convergence speed, ability to avoid local optimisation, and classification rate. The simulation results showed that FWOA with 97.49% classification accuracy rate in sonar data performed better than the other seven benchmark algorithms. [ABSTRACT FROM AUTHOR]

COVID-19 has seriously affected normal life as well as public safety. It is extremely transmissible and has now infected millions of people worldwide. To obtain more image features of the lungs, Computed Tomography (CT) scans are widely used. However, manual examination of CT images for abnormal areas of COVID-19 disease can be time-consuming, and it is highly subjective to determine whether they are infected. To rapidly screen patients, Machine Learning (ML) can be used to determine whether patients have the disease. In this paper, a hybrid extraction technique is used to extract feature vectors from CT images, which is a mixture of a histogram of orientation gradients (HOG) extraction technique and a local binary pattern (LBP) extraction technique. In this experiment, 960 NON-COVID-19 and 960 COVID-19 were adopted to train the model, and 240 NON-COVID-19 and 240 COVID-19 were used to test the model. And the CT images were scaled to a uniform size. After obtaining the feature vectors using HOG and LBP feature extraction methods, The CT images were classified using a Support Vector Machine (SVM) classifier optimised by Particle Swarm Optimisation (PSO). In the performance evaluation of the presented classification model, the combination of the HOG feature extraction technique and the LBP feature extraction technique resulted in a substantial improvement in the classification effectiveness of the SVM. HOG_LBP PSO SVM improved Accuracy to 97.5%, Precision to 97.75%, Recall to 97.27%, Specificity to 97.25%, F1_score to 97.50%, and Mcc to 95.01%. [ABSTRACT FROM AUTHOR]

At the present early stage of application, automatic train driving systems still require constant supervision by the driver, and the continuous attention level of the driver is very important. This study investigated the changes in drivers' concentration' levels during the automatic driving of a high-speed EMU (electric multiple units) train, as well as the influence of their attention on take-over performance when necessary. It was found that the degree of concentration decreased over time, with the inattention degree ranging from 20% to 57%. Attention was weakly correlated to the take-over time and strongly correlated to the stable running time. When drivers inattention degree was controlled between 20% and 33%, their response speed to a take-over request might be improved, but inattention would affect their judgment of braking power after a take-over. When the inattention degree was over 33%, all take-over performances became worse. [ABSTRACT FROM AUTHOR]

Automatic target recognition and detection in synthetic aperture radar (SAR) images is playing a significant role in military and civilian fields. Traditional methods are weak in migration capabilities and cumbersome in manual design. Existing deep learning-based methods can achieve high recognition and detection accuracy, but most of them have a very large number of parameters and slow recognition and computational efficiency, which makes it difficult for them to migrate to edge computing equipments, and computational efficiency is crucial in rapid maritime rescue and emergency military deployment. Therefore, we propose a lightweight feature extraction block, lightweight split concat block (LSCB), for SAR automatic target recognition and detection. Specifically, LSCB consists of several operators, such as split, depthwise convolution (DC), multiple kernel sizes block (MKSB), and channel-shuffle (CS). MKSB is a novel channel attention mechanism designed by us, which can make feature maps have target features of different scales and adjust their receptive field sizes adaptively. In addition, to assess the performance of LSCB, we use LSCB to build LSCNet-R and LSCNet-D for SAR target recognition and detection, respectively, and conduct extensive experiments on MSTAR, SSDD, and SAR-Ship-Dataset. The experimental results show that our LSCNet-R and LSCNet-D built with LSCB achieve higher recognition and detection accuracy and computational efficiency with few parameters compared with existing excellent methods. This further illustrates that the proposed LSCB can extract fine and stable features with few parameters. [ABSTRACT FROM AUTHOR]

This study examined the difference in adults' pronunciation performance with peer feedback and individual practice when using an automatic speech recognition (ASR) system. The same ASR software was used in both the comparison (n = 31) and the experimental group (n = 33) for 12 weeks. The participants were working adults in Taiwan. During the weekly lesson, the participants dictated a text to the ASR software, Speechnotes, on its website and then practiced misidentified words by themselves (the comparison group) or with feedback from team members (the experimental group). Learners' pronunciation was measured in reading aloud and spontaneous conversation tasks before and after the treatment. Participants' satisfaction and perceptions of the ease of use of the software were also assessed at the end of the course. The results revealed a significant difference in the two groups' pronunciation post-tests, suggesting that peer feedback is more effective than individual practice for the correction of pronunciation. The experimental group was more satisfied than the comparison group with the software. However, no differences in accentedness or perception of ease of use were evident. The findings, ASR software implementation challenges, and implications for adult language learning are discussed. [ABSTRACT FROM AUTHOR]

The topological structure of distribution network system is complex, and the operation state changes frequently, so the obtained distribution network topological information has a high error. Therefore, this article proposes a clustering feature extraction method of load curve based on singular value decomposition. The load curve is given by the invariance of singular vectors to improve the generalization ability of feature processing. On the basis of considering the weight of load feature, the data integrity is ensured by singular value curve, and the clustering accuracy is high, which makes the load feature have practical physical significance. The experimental results show that this method can achieve good clustering effect, reduce the clustering time, improve the reliability of data transmission and communication coverage, and meet the communication access requirements of the power Internet of Things sensing layer. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Improved Artificial Electric Field Algorithm (IAEF)-based AGC in a multi-area power system to minimize the area control error and to obtain an optimal solution. Here, four area AGC systems equipped with the PID controller are examined for the study processes. Also, an optimal gain value for the PID controller is obtained by employing an IAEFA; thus minimizing the area control error (ACE). In addition to this, nine different test functions are employed to evaluate the performances of the proposed IAEF-based AGC of a multi-area power system and are compared with several optimization approaches such as AEF algorithm, BBO Algorithm, PSO Algorithm as well as DE Algorithm. Also, the performance evaluation based on three different simulation strategies such as simulation based on time domain, Random disturbances of the AGC system as well as System Dynamics for maximum frequency deviation and Percentage Improvement for a four area power system. Here, the maximum frequency deviations for four areas are −0.0132, −0.0145, −0.0122 and −0.0120, respectively. Moreover, the percentage improvements with respect to the IAEF with respect to the PID controllers are 67.32%, 57.21%, 47.26% and 55.47%, respectively. The experimental analysis reveals that the proposed approach provides very less tie-line control error and better optimal tuning of the PID controller when compared with all other approaches. [ABSTRACT FROM AUTHOR]

This article introduces a measure of self-condemnatory internal dialogue as an element of the relationship with the self: The Automatic Self-Recrimination Scale (ASRS). Using the construct validation approach to test construction, we describe the initial development of items and report on findings from a clinical and nonclinical sample showing the ASRS is best understood as a multidimensional measure of self-critical internal dialogue composed of one higher-order factor and four lower-order facets: Not Mattering, Self as Failure, Undeserving Self, and Loathsomeness. The overall scale and four subscales demonstrated acceptable internal consistency and test-retest reliability. Moreover, there was evidence of good convergent and incremental validity of the ASRS subscales with measures of perfectionism, self-criticism, and dysfunctional attitudes. Overall, the ASRS appears to be a reliable and valid measure of an automatic self-recriminatory internal dialogue. [ABSTRACT FROM AUTHOR]

AbstractWe consider a linear model which can have a large number of explanatory variables, the errors with an asymmetric distribution or the values of the explained variable are missing at random. In order to take in account these several situations, we consider the non parametric empirical likelihood (EL) estimation method. Because a constraint in EL contains an indicator function then a smoothed function instead of the indicator will be considered. Two smoothed expectile maximum EL methods are proposed, one of which will automatically select the explanatory variables. For each of the methods we obtain the convergence rate of the estimators and their asymptotic normality. The smoothed expectile empirical log-likelihood ratio process follow asymptotically a chi-square distribution and moreover the adaptive LASSO smoothed expectile maximum EL estimator satisfies the sparsity property which guarantees the automatic selection of zero model coefficients. In order to implement these methods, we propose four algorithms. [ABSTRACT FROM AUTHOR]

Background: The aim of the study was to evaluate the effect of automatic gas control (AGC) on sevoflurane gas monitoring, safety, and recovery of patients during pancreaticoduodenectomy operation. Methods: Forty patients scheduled for the pancreatico-duodenectomy operation were allocated into group I manual gas control group (MGC, n = 20) and group II AGC (n = 20) group. In (the MGC group): The vaporizer set ranged from 3 to 5% Sevoflurane until reaching 1 MAC, fresh gas flow (FGF) 1-2 L/min, and FiO2 of 0.4 was set. In (the AGC group): Set target FiO2 of 0.4, end-tidal anesthetic agent (EtSev was set to 1.5-2%) with safely ventilate the patient with an FGF down to 0.3 liters per minute. Hemodynamics, anesthesia gas analysis (FiSev, EtSev, FiO2, and EtO2), total gas consumption, extubation time, incidence of perioperative hypercapnia, hypoxia, and accidental awareness were recorded. Results: The volume of sevoflurane administered in the MGC group was in a mean ± standard deviation of 81.20 ± 16.47 ml which was statistically significantly greater than that administered in the AGC group (58.80 ± 10.54), P = 0.001. ETSevo, FISevo, and the EtO2 were significantly larger in the MGC group than in the AGC group. The extubation time was statistically prolonged in the MGC group than in the AGC group (14.10 ± 4.75 versus 7.70 ± 1.59 min, P < 0.001). No patient developed hypoxia, hypercapnia, or awareness in both groups. Conclusion: AGC maintained the targeted end-tidal sevoflurane concentration with the least sevoflurane consumption. It reduced the manual adjustment of delivered sevoflurane and oxygen. General anesthesia with manual and AGC is safe and maintains hemodynamic stability. [ABSTRACT FROM AUTHOR]

The increasing use of Remote Sensing (RS) technology has resulted in the availability of large volumes of satellite imaging data, necessitating efficient, and scalable solutions for rapid analysis and classification in various interdisciplinary fields. This research focuses on addressing these challenges by examining different training and optimization techniques to achieve the highest classification accuracy on both labelled and unlabelled data from the Large-Scale Cloud Photos Dataset for Meteorology Research (LSCIDMR), which contains 10 high-resolution classes. A key aspect of this study is the implementation of a unique deep learning model based on the ResNet101 architecture, designed to handle the inter-class similarity problem through architectural improvements and imbalanced class distributions using data augmentation techniques. Furthermore, this model integrates a novel approach that combines self-learning with semi-supervised learning to process large amounts of unlabelled data. An iterative self-learning process is employed for continuous refinement, using pseudo-labels generated from the unlabelled data. To ensure higher quality and relevance, confidence-based selection of pseudo-labels is applied. Our model demonstrates superior performance in classification compared to recently reported deep learning algorithms, offering a robust solution for the automated categorization of satellite images. In this study, our ResNet101 model achieved a mean accuracy of 0.917 (±0.02), a mean precision of 0.917 (±0.01), with a mean F1 score of 0.9300 (±0.015) over five folds. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is a microvascular complication of diabetes that leads to visual blindness. Early identification of DR can prevent the loss of sight. The first visible sign of DR is the appearance of micro aneurysms (MAs). Micro aneurysms are seen as small red circular spots on the retinal surface. The very small size of micro aneurysms proves to be challenging in its proper detection. In this research, DeTrac Deep Convolutional Neural Network m classifier with Woodpecker Mating Algorithm is proposed for the detection of MAs. By using this technique, every image is classified as either MAs or non-MAs pixel to automatically detect micro aneurysms from the retinal images. Experimental results are evaluated on diabetic-retinopathy-detection (DRD) dataset of the Kaggle website. Extensive simulations on this dataset shows an improved performance over the existing methods with 0.98 mean sensitivity, 0.97 mean specificity, and 0.98 mean accuracy in detecting the MAs irrespective of their intrinsic properties. [ABSTRACT FROM AUTHOR]

Owing to the epidemic growth of diabetes, ophthalmologists need to examine the huge fundus images for diagnosing the disease of Diabetic Retinopathy (DR). Without proper knowledge, people are too lethargic to detect the DR. Therefore, the early diagnosis system is requisite for treating ailments in the medical industry. Therefore, a novel deep model-based DR detection structure is recommended to fix the aforementioned difficulties. The developed deep model-based diabetic retinopathy detection process is performed adaptively. The DR detection process is imitated by garnering the images from benchmark sources. The gathered images are further preceded by the abnormality segmentation phase. Here, the Residual TransUNet with Enhanced loss function is used to employ the abnormality segmentation, and the loss function in this structure may be helpful to lessen the error in the segmentation procedure. Further, the segmented images are passed to the final phase of retinopathy detection. At this phase, the detection is carried out through the Adaptive Multiscale MobileNet. The variables in the AMMNet are optimized by the Adaptive Puzzle Optimization to obtain better detection performance. Finally, the effectiveness of the offered approach is confirmed by the experimentation procedure over various performance indices. [ABSTRACT FROM AUTHOR]

The task of monitoring the plant leaves is considered to be error-prone, inconsistent, and unreliable. Thus, certain deep learning algorithms are developed to detect plant leaf diseases, but most deep learning techniques were designed with restricted-resolution images based on convolutional neural networks (CNNs). Hence, this article decides to develop an optimized nine-layer (ONL)-CNN-based plant leaf disease classification (PLDC) model for ensuring accurate results. The leaf segmented images are given into the abnormality segmentation with the "Adaptive Fuzzy C-Means" (A-FCM) technique for getting the abnormality segmented images. The segmented images are inserted into the classification stage, in which the optimized nine layer-CNN is utilized for leaf disease classification. Here, the parameter optimization takes place in FCM and ONL-CNN for enhancing the performance of the developed PLDC model using the developed hybrid heuristic optimization algorithm with hybrid leader cat swarm optimization (HLCSO). The experimental analysis is conducted with the developed PLDC model with different baseline methods to put forward the effectiveness of the developed model. Throughout the analysis, the given designed method achieved a 96% accuracy rate. Therefore, the proposed model ensures its effective performance regarding accuracy metric and also helps to detect early diagnosis of leaf diseases. [ABSTRACT FROM AUTHOR]

\nHIGHLIGHTSActivity schedule results from a complex decision-making process characterized by several interrelated decisions. Different facets of an activity schedule such as activity type, timing, duration, etc. influence each other and this makes modeling activity schedules a complex task. This complexity has compelled researchers to explore different approaches for modeling activity schedules, among which two predominant approaches can be identified: the utility-maximization theory based econometric approach and the computational process modeling approach. Despite their advantages and a few successful practical applications, challenges still remain leaving avenues for exploration of new approaches. This paper contributes in this direction by reviewing the relationship between language, grammar, and machines in the context of sequence analysis for activity sequence generation. Following that, the paper presents a stochastic Finite State Machine that can generate activity sequences to match the frequency distribution of sequences from a given data set. Our results show that the proposed algorithm can not only generate activity sequences with a distribution similar to that of original data but can also efficiently generate new patterns not in the original data. Presents the language, grammar, and machine framework for use in activity sequence generationDevelops a stochastic version of sequence generator with a case study to show the details of sequence generation techniquePresents the language, grammar, and machine framework for use in activity sequence generationDevelops a stochastic version of sequence generator with a case study to show the details of sequence generation technique [ABSTRACT FROM AUTHOR]