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Rock Paper Scissors is one of the most popular games in the world, because of their easy and simple way to play among young and elderly people. The point of this game is to do the draw or just to find out who loses or wins. The pandemic conditions made people unable to meet face-to-face and could only play this game virtually. To carry out this activity in a virtual way, this research facilitates a model in the form of image classification to distinguish the hand gestures s in the form of rock, paper, and scissors. This classification process utilizes the Convolutional Neural Network (CNN) method. This method is one type of artificial neural network in terms of image classification. CNN uses three stages, namely convolutional layer, pooling layer, and fully connected layer. The implementation of this method for hand gesture classification in the form of rock, scissors, and paper images in this study shows an increased average accuracy towards the previous study from 97.66% to 99%.

The paper classification method aims to correctly divide the paper data according to the similarity of its content. However, how to accurately classify according to the content expressed in the paper has always been a problem that various classification algorithms need to face. At present, there is a kind of paper classification method based on deep learning and implemented by the encoder‐decoder structure. This method inputs the words from a large number of papers into encoder, after calculating by NN (neural network) algorithm, the similarity degree of different papers is compared to achieve the purpose of classification. However, this type of method only considers the similarity between words, a NN algorithm can only calculate a large number of word information once, and it cannot find the regularity of classification through word information. But it has a difference with the similarity of the content. This paper starts from the perspective of considering the content, its label information is extracted, and the input vector of encoder‐decoder structure is formed with labels and words. This improves the original paper classification method based on encoder‐decoder structure. Firstly, the label information is based on the content, which can reflect the content of the paper. Secondly, the classification method which combines label information and word information can reflect the content of the paper comprehensively. Thirdly, the label information is independent of word information and NN algorithm is used separately to make this part of the content more consistent in the encoder‐decoder structure. Finally, the label information and the word information are combined, respectively, with the output values obtained by different NN algorithms to realize the classification of the content. This paper proves the effectiveness of the proposed method by evaluating the paper data in web of science and obtaining relevant experimental results. [ABSTRACT FROM AUTHOR]

To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).

Citation counts are commonly used to evaluate the scientific impact of a publication on the general premise that more citations probably mean more endorsements. However, two questionable assumptions underpin this idea: a) that all authors contributed equally to the paper; and b) that the endorsement is positive. Obviously, neither of these assumptions hold true. Hence, with this study, we examine two components of citations—their purpose, i.e., the reason for the citation, and polarity, being the author's attitude toward the cited work. Our findings provide a new perspective on the scientific impact of highly-cited publications. Our methodology consists of three steps. Firstly, a pre-trained model composed of a Word2Vec—a well-known word embedding approach—and a convolutional neural network (CNN) is used to identify citation polarity and purpose. Secondly, in a set of highly-cited papers, we compare eight categories of purpose from foundational to critical and three categories of polarity: positive, negative, and neutral. We further explore how different types of papers—those discussing discoveries or those discussing utilitarian topics—influence the evaluation of scientific impact of papers. Finally, we mine and discover the knowledge (e.g. method, concept, tool or data) to explain the actual scientific impact of a highly-cited paper. To demonstrate how combining citation polarity with purpose can provide far greater details of a paper's scientific impact, we undertake a case study with 370 highly-cited journal articles spanning "Biochemistry & Molecular Biology" and "Genetics & Heredity". The results yield valuable insights into the assumption about citation counts as a metric for evaluating scientific impact. [ABSTRACT FROM AUTHOR]

Numerous vitamin deficiencies might manifest as one or more easily recognizable signs and symptoms that emerge in various parts of the body. Through examination of photos of their eyes, lips, tongue, and nails, the program gives users the opportunity to identify any vitamin deficiencies they may have without giving blood samples. The CNN algorithm, which is based on deep learning, is used to carry out this operation. Here, the dataset of the eyes, lips, tongue, and lips has been considered. Following the analysis of the dataset, pre-processing is carried out, and the data is subsequently trained using the CNN method. Following training, the model is stored, and OpenCV is used for testing. [ABSTRACT FROM AUTHOR]

Stock Market Prediction is a challenging and trending topic for researchers in recent years. Although it contains significant risk, it is frequently utilized in investment schemes that promise big returns. The returns on stocks are quite erratic. They are influenced by a number of variables, including prior stock prices, current market trends, financial news, social media, etc. There are many methods used to forecast stock value, including technical analysis, fundamental analysis, time series analysis, and statistical analysis, however none of these methods has been demonstrated to be a reliable forecasting method. In order to improve the accuracy of stock price prediction, a variety of machine learning approaches and algorithms are examined in this study. [ABSTRACT FROM AUTHOR]

To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI). [ABSTRACT FROM AUTHOR]

With the establishment of the "double carbon" goal, various industries are actively exploring ways to reduce carbon emissions. Cloud data centers, represented by cloud computing, often have the problem of mismatch between load requests and resource supply, resulting in excessive carbon emissions. Based on this, this paper proposes a complete method for cloud computing carbon emission prediction. Firstly, the convolutional neural network and bidirectional long-term and short-term memory neural network (CNN-BiLSTM) combined model are used to predict the cloud computing load. The real-time prediction power is obtained by real-time prediction load of cloud computing, and then the carbon emission prediction is obtained by power calculation. Develop a dynamic server carbon emission prediction model, so that the server carbon emission can change with the change of CPU utilization, so as to achieve the purpose of low carbon emission reduction. In this paper, Google cluster data is used to predict the load. The experimental results show that the CNN-BiLSTM combined model has good prediction effect. Compared with the multi-layer feed forward neural network model (BP), long short-term memory network model (LSTM), bidirectional long short-term memory network model (BiLSTM), modal decomposition and convolution long time series neural network model (CEEMDAN-ConvLSTM), the MSE index decreased by 52 % , 50 % , 34 % and 45 % respectively. [ABSTRACT FROM AUTHOR]

Vision loss and forgetfulness are two aspects of aging and are normal processes. The elderly may be affected by these deformities, which may put them at risk during daily activities. Inappropriate drug use is one of the most dangerous problems. These mistakes pose a great risk to the health and life of the elderly. In addition, current solutions to this problem are designed for professionals or the public, without covering the needs of the elderly. As the first part of a large collection of devices designed specifically for the elderly, imaging devices have been proposed to help fill this lack of assistance. The concept continues to use its form, size and material for steps such as photography and tablet. Colour Tablet data in the local database is used to characterize and store the system during operation. The same feature then calculates and analyses the data during the authentication process to provide users with useful information about the authenticated tablet. [ABSTRACT FROM AUTHOR]

In this paper, research was conducted on anomaly detection of wheel flats. In the railway sector, conducting tests with actual railway vehicles is challenging due to safety concerns for passengers and maintenance issues as it is a public industry. Therefore, dynamics software was utilized. Next, STFT (short-time Fourier transform) was performed to create spectrogram images. In the case of railway vehicles, control, monitoring, and communication are performed through TCMS, but complex analysis and data processing are difficult because there are no devices such as GPUs. Furthermore, there are memory limitations. Therefore, in this paper, the relatively lightweight models LeNet-5, ResNet-20, and MobileNet-V3 were selected for deep learning experiments. At this time, the LeNet-5 and MobileNet-V3 models were modified from the basic architecture. Since railway vehicles are given preventive maintenance, it is difficult to obtain fault data. Therefore, semi-supervised learning was also performed. At this time, the Deep One Class Classification paper was referenced. The evaluation results indicated that the modified LeNet-5 and MobileNet-V3 models achieved approximately 97% and 96% accuracy, respectively. At this point, the LeNet-5 model showed a training time of 12 min faster than the MobileNet-V3 model. In addition, the semi-supervised learning results showed a significant outcome of approximately 94% accuracy when considering the railway maintenance environment. In conclusion, considering the railway vehicle maintenance environment and device specifications, it was inferred that the relatively simple and lightweight LeNet-5 model can be effectively utilized while using small images. [ABSTRACT FROM AUTHOR]

Introduction: Recently, the Transformer model and its variants have been a great success in terms of computer vision, and have surpassed the performance of convolutional neural networks (CNN). The key to the success of Transformer vision is the acquisition of short-term and long-term visual dependencies through selfattention mechanisms; this technology can efficiently learn global and remote semantic information interactions. However, there are certain challenges associated with the use of Transformers. The computational cost of the global self-attention mechanism increases quadratically, thus hindering the application of Transformers for high-resolution images. Methods: In view of this, this paper proposes a multi-view brain tumor segmentation model based on cross windows and focal self-attention which represents a novel mechanism to enlarge the receptive field by parallel cross windows and improve global dependence by using local fine-grained and global coarse-grained interactions. First, the receiving field is increased by parallelizing the self-attention of horizontal and vertical fringes in the cross window, thus achieving strong modeling capability while limiting the computational cost. Second, the focus on self-attention with regards to local fine-grained and global coarse-grained interactions enables the model to capture short-term and longterm visual dependencies in an efficient manner. Results: Finally, the performance of the model on Brats2021 verification set is as follows: dice Similarity Score of 87.28, 87.35 and 93.28%; Hausdorff Distance (95%) of 4.58 mm, 5.26 mm, 3.78 mm for the enhancing tumor, tumor core and whole tumor, respectively. Discussion: In summary, the model proposed in this paper has achieved excellent performance while limiting the computational cost. [ABSTRACT FROM AUTHOR]

Facial features extracted from deep convolutional networks are susceptible to background, individual identity and other factors. It interferes with facial expression recognition when mixed with useless features. Considering that different scale features have rich semantic and texture information respectively, this paper takes VGG-16 as the basic network structure and combines multiscale features to obtain richer feature information. In addition, the input feature map elements are enhanced or suppressed by the attention module in order to extract salient features more accurately. The proposed method was validated on two commonly used expression data sets CK+ and RAF-DB, and the recognition rates were 98.77 and 82.83%, respectively. Experimental results show the superiority of this method. [ABSTRACT FROM AUTHOR]

Comic inpainting is essential for language localization and adapting comics into animations, involving the removal of speech boxes, textboxes, and sound effects while maintaining artistic integrity. This paper introduces an automated approach leveraging the Manga inpainting method by Xie et al., adapted for western comics like Tex Willer. Tex Willer comics, known for their fusion of Italian art and American Western imagery, pose unique challenges due to their distinct styles. Customized Manga Restoration and Structural Line Extraction codes are employed, along with automated mask generation using deep CNN-based speech balloon detection and segmentation. Our contributions include the development of a precise model tailored to Tex Willer Comics, resulting in visually coherent inpainting results. The paper follows a structured format, covering State-of-the-Art, methodology, results, and conclusions, aiming to streamline the process of western comic inpainting for professionals in the comics and animation industries. [ABSTRACT FROM AUTHOR]

With the increasing complexity and cost of industrial control systems and the rapid development of information technology, the tolerance of industrial control equipment to performance degradation, productivity decline, and hidden safety hazards is getting lower and lower. Immediate detection of failures of industrial control equipment is of great significance for the safety of industrial control systems and reducing maintenance costs. Faced with these challenges, the traditional fault diagnosis technology based on expert knowledge has been insufficient to meet the requirements of accuracy and real-time fault diagnosis of industrial control systems due to its high cost and low efficiency. Aiming at the problem that the fault diagnosis method in the current industrial control environment is not systematic, this paper proposes a Safety Fault diagnosis system for industrial intelligent control equipment based on DevOps concept and deep CNN. Based on the in-depth analysis of the principles of CNN, this paper improves the model's robustness from the perspective of data set enhancement, fault diagnosis performance, and noise immunity analysis. Finally, it is proved through experiments that the fault diagnosis model can effectively deal with the lack of fault samples by adopting the fault data set enhancement method based on periodic overlapping sampling. Our fault diagnosis system based on DevOps and CNN proposed in this paper has high scalability and can accurately predict fault input. [ABSTRACT FROM AUTHOR]

RNA (Ribonucleic Aci) plays a crucial role in a variety of physiological and pathological processes. Over the past few decades, researchers have designed many types of RNAs and their derivatives and applied them to different fields of biology. Studies have confirmed that RNA molecules play an important role in tumor growth, metastasis, metabolism and immune escape. Based on the research of tumor-associated RNA and the application of related technologies, RNA has been designed and applied in tumor immunotherapy. Machine learning (ML) algorithms can be used to solve tumor type classification predictions with the large-scale data, some with high prediction accuracy and others with limited accuracy. For the early detection of tumors, this paper proposes to use machine learning and RNA technology to establish a tumor type prediction model to enhance the effect of anti-tumor immunotherapy. The model first preprocesses the data. Then, this paper uses an improved principal component analysis algorithm to perform feature dimensionality reduction and data fusion on the RNA expression and DNA methylation data of patients with 32 tumor types. Finally, the data is fed into a tumor type classifier based on the CNN-LSTM model for training and prediction. The experimental results show that the method proposed in this paper has excellent performance in tumor classification, and has important guiding significance for the early diagnosis and treatment of tumor patients, which further indicates that the intelligent ML method can be used for data analysis in the field of tumor type prediction. [ABSTRACT FROM AUTHOR]

The classification of human face shapes, a pivotal aspect of one's appearance, plays a crucial role in diverse fields like beauty, cosmetics, healthcare, and security. In this paper, we present a multi-step methodology for face shape classification, harnessing the potential of transfer learning and a pretrained EfficientNetV2S neural network. Our approach comprises key phases, including preprocessing, augmentation, training, and testing, ensuring a comprehensive and reliable solution. The preprocessing step involves precise face detection, cropping, and image scaling, laying a solid foundation for accurate feature extraction. Our methodology utilizes a publicly available dataset of female celebrities, comprising five face shape classes: heart, oblong, oval, round, and square. By augmenting this dataset during training, we magnify its diversity, enabling better generalization and enhancing the model's robustness. With the EfficientNetV2S neural network, we employ transfer learning, leveraging pretrained weights to optimize accuracy, training speed, and parameter size. The result is a highly efficient and effective model, which outperforms state-of-the-art approaches on the same dataset, boasting an outstanding overall accuracy of 96.32%. Our findings demonstrate the efficiency of our approach, proving its potential in the field of face shape classification. The success of our methodology holds promise for various applications, offering valuable insights into beauty analysis, cosmetic recommendations, and personalized healthcare. [ABSTRACT FROM AUTHOR]

In modern deep learning, character recognition in images is a very important field of study due to its has many real life applications. The goal of this paper is to create the state-of-the-art character recognition model using a stacking ensemble of convolution neural networks (CNNs).To develop the proposed ensemble model, we evaluated several CNN models. The models were judged on how well they performed on the Chars74k dataset. The dataset contains 74,103 images divided into 62 classes with labels [A-Z], [a-z], and [0-9]. The accuracy distribution based on the dataset's subgroups (uppercase, lowercase, and digit) is shown in results. The proposed ensemble model achieves state-of-the-art performance with a maximum accuracy of 92.31% on complete dataset, 99.22% on Uppercase alphabets, 98.66% on Lowercase alphabets, 99.77% on Digits, 91.97% on Uppercase+Lowercase alphabets. On the complete and partial datasets, a comparison report between the proposed model and other existing approaches is also displayed. A comparative study of the proposed work and the previous methods is also shown in this paper, in order to demonstrate the effectiveness of the proposed work. [ABSTRACT FROM AUTHOR]

Handwriting is a biometric behavioral characteristic with evident individual distinctiveness. With the rise of the deep learning trend and demands for forensic identification, handwriting identification has become one of the focal points of research in the field of pattern recognition. Research in handwriting identification for major global languages has matured. However, in China, there is limited attention in the field of writer identification for minority languages such as Mongolian, making it challenging to resolve criminal cases involving handwriting issues. This paper initiates an initial exploration of Mongolian handwriting identification by constructing a structurally simple convolutional neural network. This convolutional neural network, consisting of 12 convolution operations and designed for Mongolian handwriting identification, is referred to as MWInet-12. In this paper, the model evaluation experiments were conducted using a dataset comprising 156,372 samples contributed by 125 writers from the MOLHW dataset. The dataset was divided into training, validation, and test sets in an 8:1:1 ratio. The final results of the experiments reveal impressive accuracy on the test set, achieving a top-1 accuracy of 89.60% and a top-5 accuracy of 97.53%. Furthermore, through comparative experiments involving Resnet50, Fragnet, GRRNN, VGG16, and VGG19 models, this paper establishes that the proposed model yields the most favorable results for Mongolian handwriting identification. The exploratory research on Mongolian handwriting identification in this paper contributes to increasing awareness of information processing for minority languages. It aids in advancing research on classifying writers of Mongolian historical texts and provides technical support for judicial authentication involving handwriting issues. [ABSTRACT FROM AUTHOR]

• Compared the performance of three algorithms in classifying the age of documents. • FTIR combine with chemometrics can classify the documents of different years. • CNN are proved to be an effective technique. A non-destructive method based on Fourier Transformed Infrared Spectroscopy (FT-IR) was proposed to estimate the date of paper from different years in this article. For the paper samples, dated from 1940 to 1980, naturally aged and conserved in library. Partial least squares-discriminate analysis (PLS-DA), Logistic regression and convolutional neural network (CNN), were employed to evaluate the date of paper, with the accuracy 60.74%, 95.31% and 98.77%, respectively. Based on the characteristics of CNN model and with the help of network localization, active variables could be recognized in the whole spectrum. Although the localization of active variables showed a discriminative pattern, the selected spectral regions were similar. Most important variables focused on the 1700–1400 cm−1, were corresponding to cellulose crystallinity, which was consisted with the ageing processing. The present work gave the potential of FT-IR combined with chemometric techniques could estimate the dating of unknown paper. Meanwhile, the analysis of active variables obtained further indicated the worthy of CNN model for document dating. [ABSTRACT FROM AUTHOR]

This paper aims to use deep learning algorithms to identify and study the stability of breakwater armor blocks. It introduces a posture identification model for fender blocks using a Mask Region-based Convolutional Neural Network (R-CNN), which has been enhanced by considering factors affecting breakwater fender blocks. Furthermore, a wave prediction model for breakwaters is developed by integrating Bidirectional Encoder Representations from Transformers (BERTs) with Bidirectional Long Short-Term Memory (BiLSTM). The performance of these models is evaluated. The results show that the accuracy of the Mask R-CNN and its comparison algorithms initially increases and then decreases with higher Intersection Over Union (IOU) thresholds, peaking at 95.16% accuracy at an IOU threshold of 0.5. The BERT-BiLSTM wave prediction model maintains a loss value around 0.01 and an accuracy of approximately 90.00%. These results suggest that the proposed models offer more accurate stability assessments of breakwater armor blocks. By combining the random forest prediction model with BiLSTM, the wave characteristics and fender posture can be predicted better, offering reliable decision support for breakwater engineering. [ABSTRACT FROM AUTHOR]

About five million people lose their lives every year to lung cancer, making it one of the leading causes of mortality worldwide. In the last few years, a lot of methods of detection of lung cancer were improved however these could not efficiently diagnose cancer. In this paper, a convolutional neural network (CNN) of robust deep learning is developed. CNN precision raises the deeper that is, however, it causes overfitting or vanishing gradient problems simultaneously. To solve the issue, the CNN used resort to parallel CNN. It used Pictures the LIDC-IDRI consortium image collection has thoracic CT images that have been annotated for lung cancer diagnosis and screening purposes. The presented model includes result models of CNN which are integrated via ensemble methods and it compare with every model. Results of the simulation illustrate that the presented method's accuracy has developed by 2.18 percent in comparison with the method of the main paper [ABSTRACT FROM AUTHOR]

Separation of speech and music plays a vital role in multiple fields related to audio and speech processing. The spectrograms of speech and music show distinct patterns. This serves as the motivation for the differentiation of speech and music signals in an audio segment. The patterns have been further emphasized using Sobel edge kernels, Mel-spectrograms. For the inception of this paper, we have made a dataset from "All India Radio" news archives which is having separate and overlapped speech and music data in different languages. The different input features are extracted from these audio segments and further emphasized before feeding them to the different classifiers for distinguishing speech and music frames. We also compared the different classification algorithms for their varied performance in terms of accuracy. We have found that the convolutional neural network based approach on Mel-spectrograms and MFCC-delta-RNN methods have given a significantly better result compared to other approaches. Further, we wanted to see how these approaches work in the audio data of different languages, hence, we have applied the proposed method in three different languages such as Bengali, Punjabi, and Tamil. We have seen that the performance of the proposed method in all languages is consistent. The paper has also attempted to solve the problem of classifying audio segments with overlapped speech and music regions and achieved a good level of accuracy. [ABSTRACT FROM AUTHOR]

Since the dawn of civilization, handwriting has been one of the most important forms of communication. However, as handwriting differs from person to person, writer identification has become a promising application of pattern recognition to identify the actual writer of a handwritten document. Handwriting can be either online or offline, depending on how it was obtained. Users can write directly on tablets, smartphones, touch screens, PDAs, and other devices using input devices with online handwriting, whereas offline handwriting is done with a pen and paper. With the advent of artificial intelligence, and most importantly deep learning techniques, the development of writer identification systems based on offline handwritten documents has gained a lot of attention. Deep learning models have the capability of automatic feature extraction, which results in increased performance. From the literature survey, it was revealed that least attention has been paid towards the development of deep learning-based writer identification systems for offline Urdu handwritten documents, unlike the English and Arabic scripts. Therefore, in this paper, we proposed an offline Urdu handwritten writer identification system using a deep learning model inspired by the VGG-16 model of CNN. The model was trained and tested on a novel Urdu handwritten dataset contributed by 318 distinct Urdu writers, resulting in an overall training accuracy of 98.71% and a testing accuracy of 99.11%. The results achieved showed that the proposed model outperformed the already existing writer identification techniques. [ABSTRACT FROM AUTHOR]

The rapid advancement of autonomous vehicle technology has brought into focus the critical need for enhanced road safety systems, particularly in the areas of road damage detection and surface classification. This paper explores these two essential components, highlighting their importance in autonomous driving. In the domain of road damage detection, this study explores a range of deep learning methods, particularly focusing on one-stage and two-stage detectors. These methodologies, including notable ones like YOLO and SSD for one-stage detection and Faster R-CNN for two-stage detection, are critically analyzed for their efficacy in identifying various road damages under diverse conditions. The review provides insights into their comparative advantages, balancing between real-time processing and accuracy in damage localization. For road surface classification, the paper investigates the classification techniques based on both environmental conditions and material road composition. It highlights the role of different convolutional neural network architectures and innovations at the neural level in enhancing classification accuracy under varying road and weather conditions. The main finding of this work is that it offers a comprehensive overview of the current state of the art, showcasing significant strides in utilizing deep learning for road analysis in autonomous vehicle systems. The study concludes by underscoring the importance of continued research in these areas to further refine and improve the safety and efficiency of autonomous driving. [ABSTRACT FROM AUTHOR]

De-mining operations are of critical importance for humanitarian efforts and safety in conflict-affected regions. In this paper, we address the challenge of enhancing the accuracy and efficiency of mine detection systems. We present an innovative Deep Learning architecture tailored for pulse induction-based Metallic Mine Detectors (MMD), so called DL-MMD. Our methodology leverages deep neural networks to distinguish amongst nine distinct materials with an exceptional validation accuracy of 93.5%. This high level of precision enables us not only to differentiate between anti-personnel mines, without metal plates but also to detect minuscule 0.2-g vertical paper pins in both mineralized soil and non-mineralized environments. Moreover, through comparative analysis, we demonstrate a substantial 3% and 7% improvement (approx.) in accuracy performance compared to the traditional K-Nearest Neighbors and Support Vector Machine classifiers, respectively. The fusion of deep neural networks with the pulse induction-based MMD not only presents a cost-effective solution but also significantly expedites decision-making processes in de-mining operations, ultimately contributing to improved safety and effectiveness in these critical endeavors. [ABSTRACT FROM AUTHOR]

AI plays a pivotal role in predicting plant growth in agricultural contexts and in creating optimized environments for cultivation. However, unlike agriculture, the application of AI in aquaculture is predominantly focused on diagnosing animal conditions and monitoring them for users. This paper introduces an Automated Fish-feeding System (AFS) based on Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs), aiming to establish an automated system akin to smart farming in the aquaculture sector. The AFS operates by precisely calculating feed rations through two main modules. The Fish Growth Measurement Module (FGMM) utilizes fish data to assess the current growth status of the fish and transmits this information to the Feed Ration Prediction Module (FRPM). The FRPM integrates sensor data from the fish farm, fish growth data, and current feed ration status as time-series data, calculating the increase or decrease rate of ration based on the present fish conditions. This paper automates feed distribution within fish farms through these two modules and verifies the efficiency of automated feed distribution. Simulation results indicate that the FGMM neural network model effectively identifies fish body length with a minor deviation of less than 0.1%, while the FRPM neural network model demonstrates proficiency in predicting ration using a GRU cell with a structured layout of 64 × 48. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of deep learning technology, a large number of excellent convolutional neural networks (CNNs) have been proposed, many of which are based on improvements to classical methods. Based on the Inception family of methods, depthwise separable convolution was applied to Xception to achieve lightweighting, and Inception-ResNet introduces residual connections to accelerate model convergence. However, existing improvements for the Inception module often neglect further enhancement of its receptive field, while increasing the receptive field of CNNs has been widely studied and proven to be effective in improving classification performance. Motivated by this fact, three effective expansion modules are proposed in this paper. The first expansion module, Inception expand (Inception-e) module, is proposed to improve the classification accuracy by concatenating more and deeper convolutional branches. To reduce the number of parameters for Inception e, this paper proposes a second expansion module—Equivalent Inception-e (Eception) module, which is equivalent to Inception-e in terms of feature extraction capability, but which suppresses the growth of the parameter quantity brought by the expansion by effectively reducing the redundant convolutional layers; on the basis of Eception, this paper proposes a third expansion module—Lightweight Eception (Lception) module, which crosses depthwise convolution with ordinary convolution to further effectively reduce the number of parameters. The three proposed modules have been validated on the Cifar10 dataset. The experimental results show that all these extensions are effective in improving the classification accuracy of the models, and the most significant effect is the Lception module, where Lception (rank = 4) on the Cifar10 dataset improves the accuracy by 1.5% compared to the baseline model (Inception module A) by using only 0.15 M more parameters. [ABSTRACT FROM AUTHOR]

Autism Spectrum Illness (ASD), an evolution of the brain disorder, is commonly related with sensory difficulties, such as excessive or insufficient sensitivity to sounds, scents, or touch. Autism Spectrum Disorder (ASD) is evolving at a faster rate than ever before. By screening tests autism detection is very expensive and time consuming. With the advancement of Deep Learning (DL), autism can be predicted from a young age. In this paper we are using Convolutional Neural Network (CNN) with Transfer Learning (TL) models to classify the disease and we will suggest the precautions if it is detected as autism. Here we consider the Autism Master Dataset (AMD) from kaggle.com website, which contains two classes (Autism, Non_Autism). By using this models, we are obtaining good accuracy. [ABSTRACT FROM AUTHOR]

A full connected world is expected to be introduced in the sixth generation mobile network (6G). As a typical fully connected scenario, the internet of vehicle (IoV) enables intelligent vehicle operations via artificial intelligence (AI) and edge computing technologies. Thus, integrating intelligence into edge computing is, no doubt, a promising development trend. In the future of vehicular networks, a massive variety of services need powerful computing resources and higher quality of service (QoS). Existing computing resources are insufficient to match those increasing requirements. Most works on this problem focused on finding the power-delay's trade-off, ignoring QoS and stable load balance. In this study, we found that the computing power and redundancy of vehicles' in IoV is increasing. So, those redundant computing resources are possible to be used to solve the shortage of computing resource. CNN is a typical AI technique. This technology is very suitable for solving the problems in this article. So, we adopted CNN technique of AI to design and algorithm of convolutional long short-term memory (CN_LSTM) based traffic prediction (ACLBTP). ACLBTP was designed to gain the predicted number of vehicles belonging to the edge node. Secondly, according to the problem of insufficient computing resources on remote servers, we found that a large amount of redundant computing resources exist in edge nodes. So, we used edge computing technique to solve the problem of insufficient computing resources on remote servers. ASOBCL was designed to distribute computing tasks to edge nodes. Meanwhile, an intelligent service offloading framework was provided in this article. Based on the framework, an algorithm of improved gradient descent (AIGD) was created to accelerate the speed of iteration. So, the ACLBTP's convergence of convolutional neural network (CNN) based on AIGD was able to be accelerated too. In ASOBCL, a sorting technique was adopted to speed up the offloading work. Simulation results demonstrated the fact that the prediction strategy designed in this paper had high accuracy. The low offloading time and maintaining stable load balance were gained via running ASOBCL. Low offloading time means short response time. Additionally, the QoS was guaranteed. So, these strategies designed in this paper were effective and valuable. [ABSTRACT FROM AUTHOR]

In recent years, the increasing popularity of smart mobile devices has made the interaction between devices and users, particularly through voice interaction, more crucial. By enabling smart devices to better understand users' emotional states through voice data, it becomes possible to provide more personalized services. This paper proposes a novel machine learning model for speech emotion recognition called CLDNN, which combines convolutional neural networks (CNN), long short-term memory neural networks (LSTM), and deep neural networks (DNN). To design a system that closely resembles the human auditory system in recognizing audio signals, this article uses the Mel-frequency cepstral coefficients (MFCCs) of audio data as the input of the machine learning model. First, the MFCCs of the voice signal are extracted as the input of the model. Local feature learning blocks (LFLBs) composed of one-dimensional CNNs are employed to calculate the feature values of the data. As audio signals are time-series data, the resulting feature values from LFLBs are then fed into the LSTM layer to enhance learning on the time-series level. Finally, fully connected layers are used for classification and prediction. The experimental evaluation of the proposed model utilizes three databases: RAVDESS, EMO-DB, and IEMOCAP. The results demonstrate that the LSTM model effectively models the features extracted from the 1D CNN due to the time-series characteristics of speech signals. Additionally, the data augmentation method applied in this paper proves beneficial in improving the recognition accuracy and stability of the systems for different databases. Furthermore, according to the experimental results, the proposed system achieves superior recognition rates compared to related research in speech emotion recognition. [ABSTRACT FROM AUTHOR]

Personalized recommendation systems learn user preference characteristics by analyzing behavioral data such as ratings and comments generated by users in the Internet, and provide precise recommendations for individual users accordingly. However, in real life, users often conduct group activities like group buying and traveling together. How to recommend for groups has become a heated research topic in recent years. Most existing group recommendation algorithms are recommended for given divided groups by collectively combining the preferences of members in the group. However, in most cases, users' group properties are fickle. As the results of group detection are decisive to the performance of group recommendation, group detection is particularly important to the group recommendation algorithm. After analyzing problems of existing group recommendation algorithms, this paper proposes the density peak clustering group detection algorithm based on GRU-CNN and the group recommendation algorithm based on the mechanism. With respect to group detection, most of the existing group detection algorithms suffer from certain deficiencies: First, depending solely on the users' static preference features while ignoring the variation of users' interest over time when finding the group structure in the network; second, group division based on users' topic features extracted from reviews is difficult to support further digging of the in-depth features in reviews. To address the above-mentioned problems, this paper proposes a density peak clustering group detection algorithm based on CNN-GRU. It would first extract representative keywords in the reviews with LDA topic model, and then model time series information based on GRU attaining users' dynamic topic features. Coupling with deeper characteristics cored out by CNN, density peak clustering algorithm completes its group detection finally. Experiments on real dataset indicate that the features mined by the fusion depth neural network model effectively capture users' dynamic preferences, and yield better results of group detection than that of existing algorithms. [ABSTRACT FROM AUTHOR]