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This paper addresses issues concerning biometric authentication based on handwritten signatures. Our research aimed to check whether a handwritten signature acquired with a mobile device can effectively verify a user's identity. We present a novel online signature verification method using coordinates of points and pressure values at each point collected with a mobile device. Convolutional neural networks are used for signature verification. In this paper, three neural network models are investigated, i.e., two self-made light SigNet and SigNetExt models and the VGG-16 model commonly used in image processing. The convolutional neural networks aim to determine whether the acquired signature sample matches the class declared by the signer. Thus, the scenario of closed set verification is performed. The effectiveness of our method was tested on signatures acquired with mobile phones. We used the subset of the multimodal database, MobiBits, that was captured using a custom-made application and consists of samples acquired from 53 people of diverse ages. The experimental results on accurate data demonstrate that developed architectures of deep neural networks can be successfully used for online handwritten signature verification. We achieved an equal error rate (EER) of 0.63% for random forgeries and 6.66% for skilled forgeries. [ABSTRACT FROM AUTHOR]

This document provides a summary of a special issue on the application of artificial intelligence (AI) in hydraulic engineering. The papers in this issue cover a range of topics including safety monitoring, optimization algorithms, nondestructive sensors, numerical simulations, and experiments. The research presented in these papers is significant for analyzing and managing the safety of major structures in reservoir dams. The authors hope that these papers will be of interest to researchers, designers, and practitioners in the field and will inspire further research. [Extracted from the article]

Deep learning, a crucial technique for achieving artificial intelligence (AI), has been successfully applied in many fields. The gradual application of the latest architectures of deep learning in the field of time series forecasting (TSF), such as Transformers, has shown excellent performance and results compared to traditional statistical methods. These applications are widely present in academia and in our daily lives, covering many areas including forecasting electricity consumption in power systems, meteorological rainfall, traffic flow, quantitative trading, risk control in finance, sales operations and price predictions for commercial companies, and pandemic prediction in the medical field. Deep learning-based TSF tasks stand out as one of the most valuable AI scenarios for research, playing an important role in explaining complex real-world phenomena. However, deep learning models still face challenges: they need to deal with the challenge of large-scale data in the information age, achieve longer forecasting ranges, reduce excessively high computational complexity, etc. Therefore, novel methods and more effective solutions are essential. In this paper, we review the latest developments in deep learning for TSF. We begin by introducing the recent development trends in the field of TSF and then propose a new taxonomy from the perspective of deep neural network models, comprehensively covering articles published over the past five years. We also organize commonly used experimental evaluation metrics and datasets. Finally, we point out current issues with the existing solutions and suggest promising future directions in the field of deep learning combined with TSF. This paper is the most comprehensive review related to TSF in recent years and will provide a detailed index for researchers in this field and those who are just starting out. [ABSTRACT FROM AUTHOR]

With the rapid growth in the proportion of renewable energy access and the structural complexity of distributed energy systems, traditional microgrid (MG) scheduling methods that rely on mathematical optimization models and expert experience are facing significant challenges. Therefore, it is essential to present a novel scheduling technique with high intelligence and fast decision-making capacity to realize MGs' automatic operation and regulation. This paper proposes an optimal scheduling decision-making method for MGs based on deep neural networks (DNN). Firstly, a typical mathematical scheduling model used for MG operation is introduced, and the limitations of current methods are analyzed. Then, a two-stage optimal scheduling framework comprising day-ahead and intra-day stages is presented. The day-ahead part is solved by mixed integer linear programming (MILP), and the intra-day part uses a convolutional neural network (CNN)—bidirectional long short-term memory (Bi LSTM) for high-speed rolling decision making, with the outputs adjusted by a power correction balance algorithm. Finally, the validity of the model and algorithm of this paper are verified by arithmetic case analysis. [ABSTRACT FROM AUTHOR]

Surface transportation has evolved through technology advancements using parallel knowledge areas such as machine learning (ML). However, the transportation industry has not yet taken full advantage of ML. To evaluate this gap, we utilized a literature review approach to locate, categorize, and synthesize the principal concepts of research papers regarding surface transportation systems using ML algorithms, and we then decomposed them into their fundamental elements. We explored more than 100 articles, literature review papers, and books. The results show that 74% of the papers concentrate on forecasting, while multilayer perceptions, long short-term memory, random forest, supporting vector machine, XGBoost, and deep convolutional neural networks are the most preferred ML algorithms. However, sophisticated ML algorithms have been minimally used. The root-cause analysis revealed a lack of effective collaboration between the ML and transportation experts, resulting in the most accessible transportation applications being used as a case study to test or enhance a given ML algorithm and not necessarily to enhance a mobility or safety issue. Additionally, the transportation community does not define transportation issues clearly and does not provide publicly available transportation datasets. The transportation sector must offer an open-source platform to showcase the sector's concerns and build spatiotemporal datasets for ML experts to accelerate technology advancements. [ABSTRACT FROM AUTHOR]

In the last 10 years, the demand for robot-based depalletization systems has constantly increased due to the growth of sectors such as logistics, storage, and supply chains. Since the scenarios are becoming more and more unstructured, characterized by unknown pallet layouts and stock-keeping unit shapes, the classical depalletization systems based on the knowledge of predefined positions within the pallet frame are going to be substituted by innovative and robust solutions based on 2D/3D vision and Deep Learning (DL) methods. In particular, the Convolutional Neural Networks (CNNs) are deep networks that have proven to be effective in processing 2D/3D images, for example in the automatic object detection task, and robust to the possible variability among the data. However, deep neural networks need a big amount of data to be trained. In this context, whenever deep networks are involved in object detection for supporting depalletization systems, the dataset collection represents one of the main bottlenecks during the commissioning phase. The present work aims at comparing different training strategies to customize an object detection model aiming at minimizing the number of images required for model fitting, while ensuring reliable and robust performances. Different approaches based on a CNN for object detection are proposed, evaluated, and compared in terms of the F1-score. The study was conducted considering different starting conditions in terms of the neural network's weights, the datasets, and the training set sizes. The proposed approaches were evaluated on the detection of different kinds of paper boxes placed on an industrial pallet. The outcome of the work validates that the best strategy is based on fine-tuning of a CNN-based model already trained on the detection of paper boxes, with a median F1-score greater than 85.0 % . [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common causes of death in women. Early signs of breast cancer can be an abnormality depicted on breast images like breast ultrasonography. Unfortunately, ultrasound images contain a lot of noise, which greatly increases the difficulty for doctors to interpret them. In recent years, computer-aided diagnosis (CAD) has been widely used in medical images, reducing the workload of doctors and the probability of misdiagnosis. However, it still faces the following challenges in clinical practice: one is the lack of interpretability, and another is that the accuracy is not high enough. In this paper, we propose a classification model of breast ultrasound images that leverages tumor boundaries as prior knowledge and strengthens the model to guide classification. Furthermore, we employ the advantages of convolutional neural network (CNN) to extract local features and Transformer to extract global features to achieve information balance and complementarity between the two neural network models which increase the recognition performance of the model. Additionally, an explanation method is used to generate visual results, thereby improving the poor interpretability of deep learning models. Finally, we evaluate the model on the BUSI dataset and compare it with other CNN and Transformer models. Experimental results show that the proposed model obtains an accuracy of 0.9870 and an F1 score of 0.9872, achieving state-of-the-art performance. [ABSTRACT FROM AUTHOR]

The latest survey results show an increase in accidents on the roads involving pedestrians and cyclists. The reasons for such situations are many, the fault actually lies on both sides. Equipping vehicles, especially autonomous vehicles, with frequency-modulated continuous-wave (FMCW) radar and dedicated algorithms for analyzing signals in the time–frequency domain as well as algorithms for recognizing objects in radar imaging through deep neural networks can positively affect safety. This paper presents a method for recognizing and distinguishing a group of objects based on radar signatures of objects and a special convolutional neural network structure. The proposed approach is based on a database of radar signatures generated on pedestrian, cyclist, and car models in a Matlab environment. The obtained results of simulations and positive tests provide a basis for the application of the system in many sectors and areas of the economy. Innovative aspects of the work include the method of discriminating between multiple objects on a single radar signature, the dedicated architecture of the convolutional neural network, and the use of a method of generating a custom input database. [ABSTRACT FROM AUTHOR]

Accurately predicting the long-term trajectory of a surface drifting buoy (SDB) is challenging. This paper proposes a promising solution to the SDB trajectory prediction based on artificial intelligence (AI) technologies. Initially, a scalable mathematical model for trajectory prediction is developed, transforming the challenge of predicting trajectory points into predicting velocities in eastward and northward directions. Subsequently, a four-layer trajectory prediction calculation framework (FLTPCF) is established, outlining a complete workflow for the real-time online training of marine environment data and SDBs' trajectory prediction. Thirdly, for facilitating accurate long-term trajectory prediction, a hybrid artificial neural network trajectory prediction model, named CNN–BiGRU–Attention, integrates a Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Attention mechanism (AM), tuned for spatiotemporal feature extraction and extended time-series reasoning. Extensive experiments, including ablation studies, comparative analyses with state-of-the-art models like BiLSTM and Transformer, evaluations against numerical methods, and adaptability tests, were conducted for justifying the CNN–BiGRU–Attention model. The results highlight the CNN–BiGRU–Attention model's excellent convergence, accuracy, and generalization capabilities in predicting 24, 48, and 72 h trajectories for SDBs with varying drogue statuses and under different sea conditions. This work has great potential to promote the intelligent degree of marine environmental monitoring. [ABSTRACT FROM AUTHOR]

Maritime traffic is increasing more and more, creating more complex navigation environments for ships. Ship trajectory prediction based on historical AIS data is a vital method of reducing navigation risks and enhancing the efficiency of maritime traffic control. At present, employing machine learning or deep learning techniques to construct predictive models based on AIS data has become a focal point in ship trajectory prediction research. This paper systematically evaluates various trajectory prediction methods, spanning classical machine learning approaches and emerging deep learning techniques, to uncover their respective merits and drawbacks. In this work, a variety of studies were investigated that applied different algorithms in ship trajectory prediction, including regression models (RMs), artificial neural networks (ANNs), Kalman filtering (KF), and random forests (RFs) in machine learning, along with deep learning such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), gate recurrent unit (GRU) networks, and sequence-to-sequence (Seq2seq) networks. The performance of predictive models based on different algorithms in trajectory prediction tasks was graded and analyzed. Among the existing studies, deep learning methods exhibit significant performance and considerable potential application value for maritime traffic systems, which can be assessed by future work on ship trajectory prediction research. [ABSTRACT FROM AUTHOR]

In recent years, with the development of geological prospecting from shallow ore to deep and hidden ore, the difficulty of prospecting is increasing day by day, so the application of computer technology and new methods of geological and mineral exploration is paid more and more attention. The mining and prediction of geological prospecting information based on deep learning have become the frontier field of earth science. However, as a deep artificial intelligence algorithm, deep learning still has many problems to be solved in the big data mining and prediction of geological prospecting, such as the small number of training samples of geological and mineral images, the difficulty of building deep learning network models, and the universal applicability of deep learning models. In this paper, the training samples and convolutional neural network models suitable for geochemical element data mining are constructed to solve the above problems, and the model is successfully applied to the prediction research of gold, silver, lead and zinc polymetallic metallogenic areas in South China. Taking the Pangxidong research area in the west of Guangdong Province as an example, this paper carries out prospecting target prediction research based on a 1:50000 stream sediment survey original data. Firstly, the support vector machine (SVM) model and statistical method were used to determine the ore-related geochemical element assemblage. Secondly, the experimental data of geochemical elements were augmented and a dataset was established. Finally, ResNet-50 neural network model is used for data training and prediction research. The experimental results show that the areas numbered 9, 29, 38, 40, 95, 111, 114, 124, 144 have great metallogenic potential, and this method would be a promising tool for metallogenic prediction. By applying the ResNet-50 neural network in metallogenic prediction, it can provide a new idea for the future exploration of mineral resources. In order to verify the generality of the research method in this paper, we conducted experimental tests on the geochemical dataset of B area, another deposit research area in South China. The results show that 100% of the prediction area obtained by using the proposed method covers the known ore deposit area. This model also provides method support for further delineating the prospecting target area in study area B. [ABSTRACT FROM AUTHOR]

Facial emotion recognition (FER) is a computer vision process aimed at detecting and classifying human emotional expressions. FER systems are currently used in a vast range of applications from areas such as education, healthcare, or public safety; therefore, detection and recognition accuracies are very important. Similar to any computer vision task based on image analyses, FER solutions are also suitable for integration with artificial intelligence solutions represented by different neural network varieties, especially deep neural networks that have shown great potential in the last years due to their feature extraction capabilities and computational efficiency over large datasets. In this context, this paper reviews the latest developments in the FER area, with a focus on recent neural network models that implement specific facial image analysis algorithms to detect and recognize facial emotions. This paper's scope is to present from historical and conceptual perspectives the evolution of the neural network architectures that proved significant results in the FER area. This paper endorses convolutional neural network (CNN)-based architectures against other neural network architectures, such as recurrent neural networks or generative adversarial networks, highlighting the key elements and performance of each architecture, and the advantages and limitations of the proposed models in the analyzed papers. Additionally, this paper presents the available datasets that are currently used for emotion recognition from facial expressions and micro-expressions. The usage of FER systems is also highlighted in various domains such as healthcare, education, security, or social IoT. Finally, open issues and future possible developments in the FER area are identified. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is one of the most promising technologies based on machine learning algorithms. In this paper, we propose a workflow for the implementation of deep neural networks. This workflow attempts to combine the flexibility of high-level compilers (HLS)-based networks with the architectural control features of hardware description languages (HDL)-based flows. The architecture consists of a convolutional neural network, SqueezeNet v1.1, and a hard processor system (HPS) that coexists with acceleration hardware to be designed. This methodology allows us to compare solutions based solely on software (PyTorch 1.13.1) and propose heterogeneous inference solutions, taking advantage of the best options within the software and hardware flow. The proposed workflow is implemented on a low-cost field programmable gate array system-on-chip (FPGA SOC) platform, specifically the DE10-Nano development board. We have provided systolic architectural solutions written in OpenCL that are highly flexible and easily tunable to take full advantage of the resources of programmable devices and achieve superior energy efficiencies working with a 32-bit floating point. From a verification point of view, the proposed method is effective, since the reference models in all tests, both for the individual layers and the complete network, have been readily available using packages well known in the development, training, and inference of deep networks. [ABSTRACT FROM AUTHOR]

In recent years, the use of remote sensing data obtained from satellite or unmanned aerial vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield prediction, soil classification or crop mapping. The ready availability of information, with improved temporal, radiometric, and spatial resolution, has resulted in the accumulation of vast amounts of data. Meeting the demands of analysing this data requires innovative solutions, and artificial intelligence techniques offer the necessary support. This systematic review aims to evaluate the effectiveness of deep learning techniques for crop classification using remote sensing data from aerial imagery. The reviewed papers focus on a variety of deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks, transformers, and hybrid CNN-recurrent neural network models, and incorporate techniques such as data augmentation, transfer learning, and multimodal fusion to improve model performance. The review analyses the use of these techniques to boost crop classification accuracy by developing new deep learning architectures or by combining various types of remote sensing data. Additionally, it assesses the impact of factors like spatial and spectral resolution, image annotation, and sample quality on crop classification. Ensembling models or integrating multiple data sources tends to enhance the classification accuracy of deep learning models. Satellite imagery is the most commonly used data source due to its accessibility and typically free availability. The study highlights the requirement for large amounts of training data and the incorporation of non-crop classes to enhance accuracy and provide valuable insights into the current state of deep learning models and datasets for crop classification tasks. [ABSTRACT FROM AUTHOR]

This paper proposes a method for recognizing the origin of Saposhnikovia divaricata using the IResNet model to achieve computer vision-based classification. Firstly, we created a small sample dataset and applied data augmentation techniques to enhance its diversity. After that, we introduced the hierarchical residual connection block in the early stage of the original model to expand the perceptual field of the neural network and enhance the extraction of scale features. Meanwhile, a depth-separable convolution operation was adopted in the later stage of the model to replace the conventional convolution operation and further reduce the time cost of the model. The experimental results demonstrate that the improved network model achieved a 5.03% improvement in accuracy compared to the original model while also significantly reducing the number of parameters required for the model. In our experiments, we compared the accuracy of the proposed model with several classical convolutional neural network models, including ResNet50, Resnest50, Res2net50, RepVggNet_B0, and ConvNext_T. The results showed that our proposed model achieved an accuracy of 93.72%, which outperformed ResNet50 (86.68%), Resnest50 (89.38%), Res2net50 (91.83%), RepVggNet_B0 (88.68%), and ConvNext_T (92.18%). Furthermore, our proposed model achieved the highest accuracy among the compared models, with a transmission frame rate of 158.9 fps and an inference time of only 6.29 ms. The research methodology employed in this study has demonstrated the ability to reduce potential errors caused by manual observation, effectively improving the recognition ability of Saposhnikovia divaricata based on existing data. Furthermore, the findings of this study provide valuable reference and support for future efforts to develop lightweight models in this area. [ABSTRACT FROM AUTHOR]

The presented paper investigates the problem of endoscopic bleeding detection in endoscopic videos in the form of a binary image classification task. A set of definitions of high-level visual features of endoscopic bleeding is introduced, which incorporates domain knowledge from the field. The high-level features are coupled with respective feature descriptors, enabling automatic capture of the features using image processing methods. Each of the proposed feature descriptors outputs a feature activation map in the form of a grayscale image. Acquired feature maps can be appended in a straightforward way to the original color channels of the input image and passed to the input of a convolutional neural network during the training and inference steps. An experimental evaluation is conducted to compare the classification ROC AUC of feature-extended convolutional neural network models with baseline models using regular color image inputs. The advantage of feature-extended models is demonstrated for the Resnet and VGG convolutional neural network architectures. [ABSTRACT FROM AUTHOR]

Catastrophic forgetting, which means a rapid forgetting of learned representations while learning new data/samples, is one of the main problems of deep neural networks. In this paper, we propose a novel incremental learning framework that can address the forgetting problem by learning new incoming data in an online manner. We develop a new incremental learning framework that can learn extra data or new classes with less catastrophic forgetting. We adopt the hippocampal memory process to the deep neural networks by defining the effective maximum of neural activation and its boundary to represent a feature distribution. In addition, we incorporate incremental QR factorization into the deep neural networks to learn new data with both existing labels and new labels with less forgetting. The QR factorization can provide the accurate subspace prior, and incremental QR factorization can reasonably express the collaboration between new data with both existing classes and new class with less forgetting. In our framework, a set of appropriate features (i.e., nodes) provides improved representation for each class. We apply our method to the convolutional neural network (CNN) for learning Cifar-100 and Cifar-10 datasets. The experimental results show that the proposed method efficiently alleviates the stability and plasticity dilemma in the deep neural networks by providing the performance stability of a trained network while effectively learning unseen data and additional new classes. [ABSTRACT FROM AUTHOR]

In medical and health sciences, the detection of cell injury plays an important role in diagnosis, personal treatment and disease prevention. Despite recent advancements in tools and methods for image classification, it is challenging to classify cell images with higher precision and accuracy. Cell classification based on computer vision offers significant benefits in biomedicine and healthcare. There have been studies reported where cell classification techniques have been complemented by Artificial Intelligence-based classifiers such as Convolutional Neural Networks. These classifiers suffer from the drawback of the scale of computational resources required for training and hence do not offer real-time classification capabilities for an embedded system platform. Field Programmable Gate Arrays (FPGAs) offer the flexibility of hardware reconfiguration and have emerged as a viable platform for algorithm acceleration. Given that the logic resources and on-chip memory available on a single device are still limited, hardware/software co-design is proposed where image pre-processing and network training were performed in software, and trained architectures were mapped onto an FPGA device (Nexys4DDR) for real-time cell classification. This paper demonstrates that the embedded hardware-based cell classifier performs with almost 100% accuracy in detecting different types of damaged kidney cells. [ABSTRACT FROM AUTHOR]

Medical images play an important role in medical diagnosis and treatment. Oncologists analyze images to determine the different characteristics of deadly diseases, plan the therapy, and observe the evolution of the disease. The objective of this paper is to propose a method for the detection of brain tumors. Brain tumors are identified from Magnetic Resonance (MR) images by performing suitable segmentation procedures. The latest technical literature concerning radiographic images of the brain shows that deep learning methods can be implemented to extract specific features of brain tumors, aiding clinical diagnosis. For this reason, most data scientists and AI researchers work on Machine Learning methods for designing automatic screening procedures. Indeed, an automated method would result in quicker segmentation findings, providing a robust output with respect to possible differences in data sources, mostly due to different procedures in data recording and storing, resulting in a more consistent identification of brain tumors. To improve the performance of the segmentation procedure, new architectures are proposed and tested in this paper. We propose deep neural networks for the detection of brain tumors, trained on the MRI scans of patients' brains. The proposed architectures are based on convolutional neural networks and inception modules for brain tumor segmentation. A comparison of these proposed architectures with the baseline reference ones shows very interesting results. MI-Unet showed a performance increase in comparison to baseline Unet architecture by 7.5 % in dice score, 23.91 % insensitivity, and 7.09 % in specificity. Depth-wise separable MI-Unet showed a performance increase by 10.83 % in dice score, 2.97 % in sensitivity, and 12.72 % in specificity as compared to the baseline Unet architecture. Hybrid Unet architecture achieved performance improvement of 9.71 % in dice score, 3.56 % in sensitivity, and 12.6 % in specificity. Whereas the depth-wise separable hybrid Unet architecture outperformed the baseline architecture by 15.45 % in dice score, 20.56 % in sensitivity, and 12.22 % in specificity. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) has been associated with conditions like depression, anxiety, epilepsy, etc., due to its impact on an individual's educational, social, and employment. Since diagnosis is challenging and there is no cure, the goal is to maximize an individual's ability by reducing the symptoms, and early diagnosis plays a role in improving behavior and language development. In this paper, an autism screening analysis for toddlers and adults has been performed using fair AI (feature engineering, SMOTE, optimizations, etc.) and deep learning methods. The analysis considers traditional deep learning methods like Multilayer Perceptron (MLP), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), and also proposes two hybrid deep learning models, i.e., CNN–LSTM with Particle Swarm Optimization (PSO), and a CNN model combined with Gated Recurrent Units (GRU–CNN). The models have been validated using multiple performance metrics, and the analysis confirms that the proposed models perform better than the traditional models. [ABSTRACT FROM AUTHOR]

Arora et al. [[2]] propose a brain tumour segmentation and detection system where experiments are tested on the collected BraTS 2018 dataset. Machine and deep learning techniques are fuelling a revolution in the health domain and are attracting the interest of many cross-disciplinary research groups all over the world. In [[3]], a completely autonomous brain tumour segmentation approach based on deep neural networks is presented. Motor imagery brain-computer interfaces (MI-BCIs) are systems that receive the users' brain activity as an input signal in order to communicate between the brain and the interface or an action to be performed through the detection of the imagination of a movement. [Extracted from the article]

The rapidly evolving field of Virtual Reality (VR)-based Human–Computer Interaction (HCI) presents a significant demand for robust and accurate hand tracking solutions. Current technologies, predominantly based on single-sensing modalities, fall short in providing comprehensive information capture due to susceptibility to occlusions and environmental factors. In this paper, we introduce a novel sensor fusion approach combined with a Long Short-Term Memory (LSTM)-based algorithm for enhanced hand tracking in VR-based HCI. Our system employs six Leap Motion controllers, two RealSense depth cameras, and two Myo armbands to yield a multi-modal data capture. This rich data set is then processed using LSTM, ensuring the accurate real-time tracking of complex hand movements. The proposed system provides a powerful tool for intuitive and immersive interactions in VR environments. [ABSTRACT FROM AUTHOR]

Today, most humanoid mechanical fingers use an underactuated mechanism driven by linkages or tendons, with only a single and fixed grasping trajectory. This paper proposes a new multi-mode humanoid finger mechanism based on linkage and tendon fusion transmission, which is embedded with an adjustable-length tendon mechanism to achieve three types of grasping mode. The structural parameters of the mechanism are optimized according to the kinematic and static models. Furthermore, a discussion was conducted on how to set the speed ratio of the linkage driving motor and the tendon driving motor to adjust the length and tension of the tendon, in order to achieve the switching of the shape-adaptive, coupled-adaptive, and variable coupling-adaptive grasping modes. Finally, the multi-mode functionality of the proposed finger mechanism was verified through multiple grasping experiments. [ABSTRACT FROM AUTHOR]

The traditional propeller-based propulsion of underwater robots is inefficient and poorly adapted to practice. By contrast, underwater biomimetic robots show better stability and maneuverability in harsh marine environments. This is particularly true of undulating propulsion biomimetic robots. This paper classifies the existing underwater biomimetic robots and outlines their main contributions to the field. The propulsion mechanisms of underwater biomimetic undulating robots are summarized based on theoretical, numerical and experimental studies. Future perspectives on underwater biomimetic undulating robots are also presented, filling the gaps in the existing literature. [ABSTRACT FROM AUTHOR]

The study of the human visual system not only helps to understand the mechanism of the visual system but also helps to develop visual aid systems to help the visually impaired. As the systematic study of neural signal processing mechanisms in early biological vision continues, the hierarchical structure of the visual system is gradually being dissected, bringing the possibility of building brain-like computational models from a bionic perspective. In this paper, we follow the objective facts of neurobiology and propose a parallel distributed processing computational model of primary visual cortex orientation selection with reference to the complex process of visual signal processing and transmission between the retina to the primary visual cortex, the hierarchical receptive field structure between cells in each layer, and the very fine-grained parallel distributed characteristics of cortical visual computation, which allow for high speed and efficiency. We approach the design from a brain-like chip perspective, map our network model on the field programmable gate array (FPGA), and perform simulation experiments. The results verify the possibility of implementing our proposed model with programmable devices, which can be applied to small wearable devices with low power consumption and low latency. [ABSTRACT FROM AUTHOR]

This paper presents a hydrodynamics study that examines the comparison and collaboration of two swimming modes relevant to the universality of dolphins. This study utilizes a three-dimensional virtual swimmer model resembling a dolphin, which comprises a body and/or caudal fin (BCF) module, as well as a medium and/or paired fin (MPF) module, each equipped with predetermined kinematics. The manipulation of the dolphin to simulate various swimming modes is achieved through the application of overlapping grids in conjunction with the parallel hole cutting technique. The findings demonstrate that the swimming velocity and thrust attained through the single BCF mode consistently surpass those achieved through the single MPF mode and collaborative mode. Interestingly, the involvement of the MPF mode does not necessarily contribute to performance enhancement. Nevertheless, it is encouraging to note that adjusting the phase difference between the two modes can partially mitigate the limitations associated with the MPF mode. To further investigate the potential advantages of dual-mode collaboration, we conducted experiments by increasing the MPF frequency while keeping the BCF frequency constant, thus introducing the concept of frequency ratio (β). In comparison to the single BCF mode, the collaborative mode with a high β exhibits superior swimming velocity and thrust. Although its efficiency experiences a slight decrease, it tends to stabilize. The corresponding flow structure indirectly verifies the favorable impact of collaboration. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area. [ABSTRACT FROM AUTHOR]

The well-being of residents is a top priority for megacities, which is why urban design and sustainable development are crucial topics. Quality of Life (QoL) is used as an effective key performance index (KPI) to measure the efficiency of a city plan's quantity and quality factors. For city dwellers, QoL for pedestrians is also significant. The walkability concept evaluates and analyzes the QoL in a walking scene. However, the traditional questionnaire survey approach is costly, time-consuming, and limited in its evaluation area. To overcome these limitations, the paper proposes using artificial intelligence (AI) technology to evaluate walkability data collected through a questionnaire survey using virtual reality (VR) tools. The proposed method involves knowledge extraction using deep convolutional neural networks (DCNNs) for information extraction and deep learning (DL) models to infer QoL scores. Knowledge distillation (KD) is also applied to reduce the model size and improve real-time performance. The experiment results demonstrate that the proposed approach is practical and can be considered an alternative method for acquiring QoL. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a dangerous eye condition that affects diabetic patients. Without early detection, it can affect the retina and may eventually cause permanent blindness. The early diagnosis of DR is crucial for its treatment. However, the diagnosis of DR is a very difficult process that requires an experienced ophthalmologist. A breakthrough in the field of artificial intelligence called deep learning can help in giving the ophthalmologist a second opinion regarding the classification of the DR by using an autonomous classifier. To accurately train a deep learning model to classify DR, an enormous number of images is required, and this is an important limitation in the DR domain. Transfer learning is a technique that can help in overcoming the scarcity of images. The main idea that is exploited by transfer learning is that a deep learning architecture, previously trained on non-medical images, can be fine-tuned to suit the DR dataset. This paper reviews research papers that focus on DR classification by using transfer learning to present the best existing methods to address this problem. This review can help future researchers to find out existing transfer learning methods to address the DR classification task and to show their differences in terms of performance. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue titled "Deep and Machine Learning for Image Processing: Medical and Non-medical Applications." The special issue explores the use of machine learning and deep learning technologies in image processing, with a focus on medical and non-medical applications. The contributions cover a wide range of topics, including the use of ensemble models for diagnosing myocardial infarction, camouflaged object detection, crime analysis using seasonal autoregressive integrated moving average models, monkeypox diagnosis using metaheuristic optimization algorithms, instance segmentation for power operation monitoring, long-distance person detection, hate-speech detection on social media, perceptual image quality prediction, material discrimination using colorization, compressed sensing magnetic resonance imaging, quantization for low-power accelerators, and computer-aided diagnosis for skin diseases. The document also highlights future research directions, such as refining integrated ML/DL models, broadening predictive modeling, enhancing data fusion, advancing imaging and material discrimination techniques, developing context-aware natural language processing models, improving object detection, optimizing for edge computing, innovating in medical imaging, and assessing multimedia content quality. The special issue aims to showcase the current state of research in ML and DL applications for image processing and inspire further advancements in the field. [Extracted from the article]

Lung cancer is a leading cause of cancer-related deaths worldwide, and its diagnosis must be carried out as soon as possible to increase the survival rate. The development of computer-aided diagnosis systems can improve the accuracy of lung cancer diagnosis while reducing the workload of pathologists. The purpose of this study was to develop a learning algorithm (CancerDetecNN) to evaluate the presence or absence of tumor tissue in lung whole-slide images (WSIs) while reducing the computational cost. Three existing deep neural network models, including different versions of the CancerDetecNN algorithm, were trained and tested on datasets of tumor and non-tumor tiles extracted from lung WSIs. The fifth version of CancerDetecNN (CancerDetecNN Version 5) outperformed all existing convolutional neural network (CNN) models in the provided dataset, achieving higher precision (0.972), an area under the curve (AUC) of 0.923, and an F1-score of 0.897, while requiring 1 h and 51 min less for training than the best compared CNN model (ResNet-50). The results for CancerDetecNN Version 5 surpass the results of some architectures used in the literature, but the relatively small size and limited diversity of the dataset used in this study must be considered. This paper demonstrates the potential of CancerDetecNN Version 5 for improving lung cancer diagnosis since it is a dedicated model for lung cancer that leverages domain-specific knowledge and optimized architecture to capture unique characteristics and patterns in lung WSIs, potentially outperforming generic models in this domain and reducing the computational cost. [ABSTRACT FROM AUTHOR]

Waste segregation, management, transportation, and disposal must be carefully managed to reduce the danger to patients, the public, and risks to the environment's health and safety. The previous method of monitoring trash in strategically placed garbage bins is a time-consuming and inefficient method that wastes time, human effort, and money, and is also incompatible with smart city needs. So, the goal is to reduce individual decision-making and increase the productivity of the waste categorization process. Using a convolutional neural network (CNN), the study sought to create an image classifier that recognizes items and classifies trash material. This paper provides an overview of trash monitoring methods, garbage disposal strategies, and the technology used in establishing a waste management system. Finally, an efficient system and waste disposal approach is provided that may be employed in the future to improve performance and cost effectiveness. One of the most significant barriers to efficient waste management can now be overcome with the aid of a deep learning technique. The proposed method outperformed the alternative AlexNet, VGG16, and ResNet34 methods. [ABSTRACT FROM AUTHOR]

Featured Application: This work will be materialized into the Bioimage Diagnostic system in Kaohsiung Branch of Chang Gung Hospital, Taiwan. Image recognition through the use of deep learning (DL) techniques has recently become a hot topic in many fields. Especially for bioimage informatics, DL-based image recognition has been successfully used in several applications, such as cancer and fracture detection. However, few previous studies have focused on detecting scaphoid fractures, and the related effectiveness is also not significant. Aimed at this issue, in this paper, we present a two-stage method for scaphoid fracture recognition by conducting an effectiveness analysis of numerous state-of-the-art artificial neural networks. In the first stage, the scaphoid bone is extracted from the radiograph using object detection techniques. Based on the object extracted, several convolutional neural networks (CNNs), with or without transfer learning, are utilized to recognize the segmented object. Finally, the analytical details on a real data set are given, in terms of various evaluation metrics, including sensitivity, specificity, precision, F1-score, area under the receiver operating curve (AUC), kappa, and accuracy. The experimental results reveal that the CNNs with transfer learning are more effective than those without transfer learning. Moreover, DenseNet201 and ResNet101 are found to be more promising than the other methods, on average. According to the experimental results, DenseNet201 and ResNet101 can be recommended as considerable solutions for scaphoid fracture detection within a bioimage diagnostic system. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic highlighted the importance of health and education and also revealed the need for innovative solutions relative to the challenges confronting these disciplines. This includes the development of interactive learning tools and intelligent tutoring systems that can provide feedback to both teachers and students, as well as the use of data analytics to assess student progress and adjust teaching strategies accordingly. In healthcare, machine learning algorithms have been used to predict the likelihood of disease progression, identify at-risk patients, and personalize treatment plans. Thus, in [[19]], both machine learning models such as support vector machines, decision trees, and neural networks, together with logistic regression, were used to predict likely university dropout rates either at the beginning of the course of study or at the end of the first semester. [Extracted from the article]

Parkinson's disease (PD) is a serious movement disorder that may eventually progress to mild cognitive dysfunction (MCI) and dementia. According to the Parkinson's foundation, one million Americans were diagnosed with PD and almost 10 million individuals suffer from the disease worldwide. An early and precise clinical diagnosis of PD will ensure an early initiation of effective therapeutic treatments, which will potentially slow down the progression of the disease and improve the quality of life for patients and their caregivers. Machine and deep learning are promising technologies that may assist and support clinicians in providing an objective and reliable diagnosis of the disease based upon significant and unique features identified from relevant medical data. In this paper, the author provides a comprehensive review of the artificial intelligence techniques that were recently proposed during the period from 2016 to 2022 for the screening and staging of PD as well as the identification of the biomarkers of the disease based on Electroencephalography (EEG), Magnetic Resonance Imaging (MRI), speech tests, handwriting exams, and sensory data. In addition, the author highlights the current and future trends for PD diagnosis based machine and deep learning and discusses the limitations, challenges, potential future solutions, and recommendations for a reliable application of machine and deep learning for PD detection and screening. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) and Convolutional neural networks (CNNs) have improved accuracy in many Artificial Intelligence (AI) applications. Some of these applications are recognition and detection tasks, such as speech recognition, facial recognition and object detection. On the other hand, CNN computation requires complex arithmetic and a lot of memory access time; thus, designing new hardware that would increase the efficiency and throughput without increasing the hardware cost is much more critical. This area in hardware design is very active and will continue to be in the near future. In this paper, we propose a novel 8T XNOR-SRAM design for Binary/Ternary DNNs (TBNs) directly supporting the XNOR-Network and the TBN DNNs. The proposed SRAM Computing-in-Memory (CIM) can operate in two modes, the first of which is the conventional 6T SRAM, and the second is the XNOR mode. By adding two extra transistors to the conventional 6T structure, our proposed CIM showed an improvement up to 98% for power consumption and 90% for delay compared to the existing state-of-the-art XNOR-CIM. [ABSTRACT FROM AUTHOR]

Microsystems are widely used in 5G, the Internet of Things, smart electronic devices and other fields, and signal integrity (SI) determines their performance. Establishing accurate and fast predictive models and intelligent optimization models for SI in microsystems is extremely essential. Recently, neural networks (NNs) and heuristic optimization algorithms have been widely used to predict the SI performance of microsystems. This paper systematically summarizes the neural network methods applied in the prediction of microsystem SI performance, including artificial neural network (ANN), deep neural network (DNN), recurrent neural network (RNN), convolutional neural network (CNN), etc., as well as intelligent algorithms applied in the optimization of microsystem SI, including genetic algorithm (GA), differential evolution (DE), deep partition tree Bayesian optimization (DPTBO), two stage Bayesian optimization (TSBO), etc., and compares and discusses the characteristics and application fields of the current applied methods. The future development prospects are also predicted. Finally, the article is summarized. [ABSTRACT FROM AUTHOR]

In the case of pandemics such as COVID-19, the rapid development of medicines addressing the symptoms is necessary to alleviate the pressure on the medical system. One of the key steps in medicine evaluation is the determination of p I C 50 factor, which is a negative logarithmic expression of the half maximal inhibitory concentration ( I C 50 ). Determining this value can be a lengthy and complicated process. A tool allowing for a quick approximation of p I C 50 based on the molecular makeup of medicine could be valuable. In this paper, the creation of the artificial intelligence (AI)-based model is performed using a publicly available dataset of molecules and their p I C 50 values. The modeling algorithms used are artificial and convolutional neural networks (ANN and CNN). Three approaches are tested—modeling using just molecular properties (MP), encoded SMILES representation of the molecule, and the combination of both input types. Models are evaluated using the coefficient of determination ( R 2 ) and mean absolute percentage error (MAPE) in a five-fold cross-validation scheme to assure the validity of the results. The obtained models show that the highest quality regression ( R 2 ¯ = 0.99 ,   σ R 2 ¯ = 0.001 ;   M A P E ¯ = 0.009 % ,   σ M A P E ¯ = 0.009 ), by a large margin, is obtained when using a hybrid neural network trained with both MP and SMILES. [ABSTRACT FROM AUTHOR]

Featured Application: Medical diagnosis and computer-aided diagnosis systems. Breast cancer is one of the most common types of cancer among women. Accurate diagnosis at an early stage can reduce the mortality associated with this disease. Governments and health organizations stress the importance of early detection of breast cancer as it is related to an increase in the number of available treatment options and increased survival. Early detection gives patients the best chance of receiving effective treatment. Different types of images and imaging modalities are used in the detection and diagnosis of breast cancer. One of the imaging types is "infrared thermal" breast imaging, where a screening instrument is used to measure the temperature distribution of breast tissue. Although it has not been used often, compared to mammograms, it showed promising results when used for early detection. It also has many advantages as it is non-invasive, safe, painless, and inexpensive. The literature has indicated that the use of thermal images with deep neural networks improves the accuracy of early diagnosis of breast malformation. Therefore, in this paper, we aim to investigate to what extent convolutional neural networks (CNNs) with attention mechanisms (AMs) can provide satisfactory detection results in thermal breast cancer images. We present a model for breast cancer detection based on deep neural networks with AMs using thermal images from the Database for Research Mastology with Infrared Image (DMR-IR). The model will be evaluated in terms of accuracy, sensitivity and specificity, and will be compared against state-of-the-art breast cancer detection methods. The AMs with the CNN model achieved encouraging test accuracy rates of 99.46%, 99.37%, and 99.30% on the breast thermal dataset. The test accuracy of CNNs without AMs was 92.32%, whereas CNNs with AMs achieved an improvement in accuracy of 7%. Moreover, the proposed models outperformed previous models that were reviewed in the literature. [ABSTRACT FROM AUTHOR]

Energy and power demands for military operations continue to rise as autonomous air, land, and sea platforms are developed and deployed with increasingly energetic weapon systems. The primary limiting capability hindering full integration of such systems is the need to effectively and efficiently manage, generate, and transmit energy across the battlefield. Energy efficiency is primarily dictated by the number of dissimilar energy conversion processes in the system. After combustion, a Compression Ignition (CI) engine must periodically continue to inject fuel to produce mechanical energy, simultaneously generating thermal, acoustic, and fluid energy (in the form of unburnt hydrocarbons, engine coolant, and engine oil). In this paper, we present multiple sets of Shallow Artificial Neural Networks (SANNs), Convolutional Neural Network (CNNs), and K-th Nearest Neighbor (KNN) classifiers, capable of approximating the in-cylinder conditions and informing future optimization and control efforts. The neural networks provide outstanding predictive capabilities of the variables of interest and improve understanding of the energy and power management of a CI engine, leading to improved awareness, efficiency, and resilience at the device and system level. [ABSTRACT FROM AUTHOR]

The digital optical fiber repeater (DOFR) is an important infrastructure in the LTE networks, which solve the problem of poor regional signal quality. Various types of conventional measurement data from the LTE network cannot indicate whether a working DOFR is present in the cell. Currently, the detection of DOFRs relies solely on maintenance engineers for field detection. Manual detection methods are not timely or efficient, because of the large number and wide geographical distribution of DOFRs. Implementing automatic detection of DOFR can reduce the maintenance cost for mobile network operators. We treat the DOFR detection problem as a classification problem and employ a deep convolutional neural network (DCNN) to tackle it. The measurement report (MR) we used in this paper are tabular data, which is not an ideal input for DCNN. We propose a novel MR representation method that takes the overall MR data of a cell as a sample rather than a single record in the table, and represents the MR data as a pseudo-image matrix (PIM). The PIM will be used as the input for training DCNN, and the trained DCNN will be used to perform DOFR detection tasks. We conducted a series of experiments on real MR data, and the classification accuracy can achieve 93%. The proposed AI-based method can effectively detect the DOFR in a cell. [ABSTRACT FROM AUTHOR]

Global food production is being strained by extreme weather conditions, fluctuating temperatures, and geopolitics. Tomato is a staple agricultural product with tens of millions of tons produced every year worldwide. Thus, preserving the tomato plant from diseases will go a long way in reducing economical loss and boost output. Technological innovations have great potential in facilitating disease detection and control. More specifically, artificial intelligence algorithms in the form of deep learning methods have established themselves in many real-life applications in a wide range of disciplines (e.g., medicine, agriculture, or facial recognition, etc.). In this paper, we aim at applying deep transfer learning in the classification of nine tomato diseases (i.e., bacterial spot, early blight, late blight, leaf mold, mosaic virus, septoria leaf spot, spider mites, target spot, and yellow leaf curl virus) in addition to the healthy state. The approach in this work uses leaf images as input, which is fed to convolutional neural network models. No preprocessing, feature extraction, or image processing is required. Moreover, the models are based on transfer learning of well-established deep learning networks. The performance was extensively evaluated using multiple strategies for data split and a number of metrics. In addition, the experiments were repeated 10 times to account for randomness. The ten categories were classified with mean values of 99.3% precision, 99.2% F1 score, 99.1% recall, and 99.4% accuracy. Such results show that it is highly feasible to develop smartphone-based applications that can aid plant pathologists and farmers to quickly and accurately perform disease detection and subsequent control. [ABSTRACT FROM AUTHOR]

Over the past few years, Internet applications have become more advanced and widely used. This has increased the need for Internet networks to be secured. Intrusion detection systems (IDSs), which employ artificial intelligence (AI) methods, are vital to ensuring network security. As a branch of AI, deep learning (DL) algorithms are now effectively applied in IDSs. Among deep learning neural networks, the convolutional neural network (CNN) is a well-known structure designed to process complex data. The CNN overcomes the typical limitations of conventional machine learning approaches and is mainly used in IDSs. Several CNN-based approaches are employed in IDSs to handle privacy issues and security threats. However, there are no comprehensive surveys of IDS schemes that have utilized CNN to the best of our knowledge. Hence, in this study, our primary focus is on CNN-based IDSs so as to increase our understanding of various uses of the CNN in detecting network intrusions, anomalies, and other types of attacks. This paper innovatively organizes the studied CNN-IDS approaches into multiple categories and describes their primary capabilities and contributions. The main features of these approaches, such as the dataset, architecture, input shape, evaluated metrics, performance, feature extraction, and classifier method, are compared. Because different datasets are used in CNN-IDS research, their experimental results are not comparable. Hence, this study also conducted an empirical experiment to compare different approaches based on standard datasets, and the comparative results are presented in detail. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely used as effective energy storage and have become the main component of power supply systems. Accurate battery state prediction is key to ensuring reliability and has significant guidance for optimizing the performance of battery power systems and replacement. Due to the complex and dynamic operations of lithium-ion batteries, the state parameters change with either the working condition or the aging process. The accuracy of online state prediction is difficult to improve, which is an urgent issue that needs to be solved to ensure a reliable and safe power supply. Currently, with the emergence of artificial intelligence (AI), battery state prediction methods based on data-driven methods have high precision and robustness to improve state prediction accuracy. The demanding characteristics of test time are reduced, and this has become the research focus in the related fields. Therefore, the convolutional neural network (CNN) was improved in the data modeling process to establish a deep convolutional neural network ensemble transfer learning (DCNN-ETL) method, which plays a significant role in battery state prediction. This paper reviews and compares several mathematical DCNN models. The key features are identified on the basis of the modeling capability for the state prediction. Then, the prediction methods are classified on the basis of the identified features. In the process of deep learning (DL) calculation, specific criteria for evaluating different modeling accuracy levels are defined. The identified features of the state prediction model are taken advantage of to give relevant conclusions and suggestions. The DCNN-ETL method is selected to realize the reliable state prediction of lithium-ion batteries. [ABSTRACT FROM AUTHOR]

The physical properties of water cause an underwater image to degrade. Enhancing damaged underwater photos is an important topic of study. Several academics have used various picture-enhancing methods and models. We have included a hyper-Laplacian reflectance priors enhancement technique for underwater images and a Convolutional Neural Network (CNN) for the classification of images. The classification is performed on the real images and the enhanced images when passed through an HLRP code. [ABSTRACT FROM AUTHOR]

Convolution Neural Networks (CNNs) are gaining ground in deep learning and Artificial Intelligence (AI) domains, and they can benefit from rapid prototyping in order to produce efficient and low-power hardware designs. The inference process of a Deep Neural Network (DNN) is considered a computationally intensive process that requires hardware accelerators to operate in real-world scenarios due to the low latency requirements of real-time applications. As a result, High-Level Synthesis (HLS) tools are gaining popularity since they provide attractive ways to reduce design time complexity directly in register transfer level (RTL). In this paper, we implement a MobileNetV2 model using a state-of-the-art HLS tool in order to conduct a design space exploration and to provide insights on complex hardware designs which are tailored for DNN inference. Our goal is to combine design methodologies with sparsification techniques to produce hardware accelerators that achieve comparable error metrics within the same order of magnitude with the corresponding state-of-the-art systems while also significantly reducing the inference latency and resource utilization. Toward this end, we apply sparse matrix techniques on a MobileNetV2 model for efficient data representation, and we evaluate our designs in two different weight pruning approaches. Experimental results are evaluated with respect to the CIFAR-10 data set using several different design methodologies in order to fully explore their effects on the performance of the model under examination. [ABSTRACT FROM AUTHOR]

One-stage production of carbohydrate-enriched microalgae biomass in wastewater is a promising option to obtain biofuels. Understanding the interaction of water quality parameters such as nutrients, carbon, internal carbohydrates, and microbial composition in the culture is crucial for efficient operation and viable large-scale cultivation. Bioprocess models are an essential tool for studying the simultaneous effect of complex factors on carbohydrate accumulation, optimizing the process, and reducing operational costs. In this sense, we use a dataset obtained from an empirical model that analyzed the accumulation of carbohydrates in a single process (simultaneous growth and accumulation) from real wastewater. In this experiment, there were no ideal conditions (limiting nutrient conditions), but rather these limitations are guaranteed by the operating conditions (hydraulic retention times/nutrient or carbon loads). Thus, the model integrates 18 variables that are affected and not only carbohydrates. The effect of these variables directly influences the accumulation of carbohydrates. Therefore, this paper analyzes artificial intelligence (AI) algorithms to develop a model to forecast biomass production in wastewater treatment systems. Carbohydrates were modeled using five artificial intelligence methods: (1) Artificial Neural Networks (ANNs), (2) Convolutional Neural Networks (CNN), (3) Long Short-Term Memory Network (LSTMs), (4) K-Nearest Neighbors (kNN), and (5) Random Forest (RF)). The AI methods allow learning how several components interact and if their combinations work faster than building the physical experiments over the same period of time. After comparing the five learning models, the CNN-1D model obtained the best results with an MSE (Mean Squared Error) = 0.0028. This result shows that the model adequately approximates the system's dynamics. [ABSTRACT FROM AUTHOR]

Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area. [ABSTRACT FROM AUTHOR]