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Introduction: State-dependent (STD) memory is a process, in which the learned information can be optimally retrieved only when the subject is in the state similar to the encoding phase. This phenomenon has been widely studied with morphine. Several studies have reported that Pentylenetetrazole (PTZ) impairs memory in experimental animal models. Due to certain mechanistic interactions between morphine and PTZ, it is hypothesized that PTZ may interfere with the morphine-STD. The cyclic adenosine monophosphate Response Element-Binding (CREB) is considered as the main downstream marker for long-term memory. This study was designed to determine the possible interaction between PTZ and morphine STD and the presumable changes in CREB mRNA. Methods: In an Inhibitory Avoidance (IA) model, posttraining morphine (2.5, 5, and 7.5 mg/kg-i.p.) was used. The pre-test morphine was evaluated for morphine-induced STD memory. Moreover, the effect of a pre-test PTZ (60 mg/kg-i.p.) was studied along with morphine STD. Locomotion testing was carried out using open-field. Eventually, using real-time-PCR, the CREB mRNA changes in the hippocampus were evaluated. Results: Posttraining MOR (7.5 mg/kg-i.p.) impaired IA memory (P<0.001). The pre-test injection of similar doses of morphine recovered the morphine-induced memory impairment (P<0.001). The pre-test PTZ impaired the IA memory recall (P<0.001); however, the pre-test PTZ along with morphine STD potentiated the morphine-induced STD (P<0.001). Alterations in CREB mRNA were observed in all groups. No difference was seen in the locomotor activity. Conclusion: Presumably, the certain interactive effect of PTZ on morphine-induced STD is mediated through gamma-aminobutyric acid and opioid systems via CREB signaling. [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]

Sentiment analysis is inevitable in the current era. The Internet is growing day-byday. Now-a-days everything is online. We can shop, buy, and sell online. People can give feedback / opinions on the internet. Customers can compare among various products by analyzing the product reviews. As more and more people from different age groups and languages are becoming new internet users, we need it in regional languages. Till date most of the work related to sentiment analysis has been done in the English language. But when it comes to Indian languages, not much research has been done except for a few languages. This paper mainly focuses on performing sentiment analysis in one of the Indian languages i.e. Hindi. Lately, because of the accessibility of voluminous information on the web for Indian dialects, it has become a significant errand to break down this information to recover valuable data. In light of the development of Indian language content, it is helpful to use this blast of information with the end goal of conclusion investigation. This examination portrays a methodical survey in the field of opinion investigation all in all and Indian dialects explicitly. The current status of Indian dialects in estimation examination is grouped by the Indian language families. The periodical development of Indian dialects in the field of supposition examination, wellsprings of chose distributions based on their importance is additionally portrayed. Further, scientific categorization of Indian dialects in estimation examination dependent on strategies, areas, supposition levels and classes has been introduced. This examination work will help specialists in finding the accessible assets, for example, commented on datasets, pre-handling phonetic and lexical assets in Indian dialects for supposition investigation and will likewise uphold in choosing the most appropriate assumption examination strategy in a particular area alongside applicable future exploration bearings. In the event of asset helpless Indian dialects with morphological varieties, one experiences issues of performing estimation examination because of inaccessibility of explained assets, phonetic and lexical apparatuses. Along these lines, to give productive execution utilizing existing feeling examination procedures, the previously mentioned issues ought to be tended to successfully. [ABSTRACT FROM AUTHOR]

Transporting harvested vegetables in the field or greenhouse is labor-intensive. The utilization of small harvest-aid vehicles can reduce non-productive time for farmers and improve harvest efficiency. This paper models the process of harvesting vegetables in response to non-productive waiting delays caused by the scheduling of harvest-aid vehicles. Taking into consideration harvesting speed, harvest-aid vehicle capacity, and scheduling conflicts, a harvest-aid vehicle scheduling model is constructed to minimize non-production waiting time and coordination costs. Subsequently, to meet the collaborative needs of harvesters, this paper develops a discrete multi-objective Jaya optimization algorithm (DMO-Jaya), which combines an opposition-based learning mechanism and a long-term memory library to obtain scheduling schemes suitable for agricultural environments. Experiments show that the studied model can schedule harvest-aid vehicles without conflicts. Compared to the NSGA-II algorithm and the MMOPSO, the DMO-Jaya algorithm demonstrates a better diversity of solutions, resulting in a shorter non-productive waiting time for harvesters. This research provides a reference model for improving the efficiency of vegetable harvesting and transportation. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) has been shown to improve productivity in industry, but its adverse effects (e.g., headaches, eye strain, nausea, and mental workload) on users warrant further investigation. The objective of this study is to investigate the effects of different instruction methods (i.e., HoloLens AR-based and paper-based instructions) and task complexity (low and high-demanding tasks) on cognitive workloads and performance. Twenty-eight healthy males with a mean age of 32.12 (SD 2.45) years were recruited in this study and were randomly divided into two groups. The first group performed the experiment using AR-based instruction, and the second group used paper-based instruction. Performance was measured using total task time (TTT). The cognitive workload was measured using the power of electroencephalograph (EEG) features and the NASA task load index (NASA TLX). The results showed that using AR instructions resulted in a reduction in maintenance times and an increase in mental workload compared to paper instructions, particularly for the more demanding tasks. With AR instruction, 0.45% and 14.94% less time was spent on low- and high-demand tasks, respectively, as compared to paper instructions. According to the EEG features, employing AR to guide employees during highly demanding maintenance tasks increased information processing, which could be linked with an increased germane cognitive load. Increased germane cognitive load means participants can better facilitate long-term knowledge and skill acquisition. These results suggested that AR is superior and recommended for highly demanding maintenance tasks since it speeds up maintenance times and increases the possibility that information is stored in long-term memory and encrypted for recalls. [ABSTRACT FROM AUTHOR]

The ionosphere is an integral element of the Earth and reflects the variations of the Earth's space weather and solar activity. Since extreme weather can cause ionospheric disturbances, changes in the ionosphere can indirectly enable early warning of extreme weather. The major intention of predicting the peak height of the ionospheric F2 layer (hmF2) in this paper is to acquire ionospheric variations over a period of time in a local area to facilitate future extreme weather warning research. In this paper, a dual element LSTM-CNN (long short term memory-convolutional neural network) prediction model is proposed to predict the hmF2. The performance of the proposed model is assessed by comparing it with other popular models such as SARIMA (seasonal differential autoregressive moving average), LSTM (long short term memory), BP (back propagation neural network) and IRI2016 (international reference ionospheric model) models. The outcome demonstrates that the prediction effect with the proposed model is remarkably excellent in comparison with the remaining four models. Furthermore, the proposed model has better sensitivity to rapid changes in parameters. The outcomes indicate that the forecasting model of this study has high prediction capabilities. [ABSTRACT FROM AUTHOR]

Developing monitoring system for paper industrial wastewater treatment system is an important route for wastewater reuse and recycling from wastewater, which are regarded as effective way for cleaner production. A novel hybrid deep leaning CLSTMA model, which based on sequential fusion convolutional neural network (CNN), long short term memory (LSTM) and attention mechanism (AM), was developed to monitor the water quality in a full-scale paper industrial wastewater treatment system for energy conservation and emissions reduction. The hybrid CLSTMA model for predicting water quality of paper industrial wastewater treatment system was divided into three steps: spatial information fusion by using CNN module, temporal information fusion by using LSTM module and variable weighted calculation by using AM module. Compare with other models (CNN, LSTM and CLSTM models), RMSE of CLSTMA model for the effluent chemical oxygen demand (COD eff) reduced by 23.3–31.55%, MAE of CLSTMA model reduced by 38.89–74.50%, R of CLSTMA model increased by 8.29–11.86%. For the effluent suspended solids (SS eff), compared with CNN and LSTM models, RMSE of CLSTMA model reduced by 10.26% and 9.92%, MAE of CLSTMA model reduced by 5.37% and 3.44%, R of CLSTMA model increased by 15.13% and 37.21%, respectively. While, R of CLSTMA was consistent with CLSTM model, but RMSE and MAE of CLSTMA model reduced by 16.07% and 7.49% than the CLSTM model. Simulation results demonstrate that the proposed CLSTMA model has a great potential in monitoring paper industrial wastewater treatment system for cleaner production. [Display omitted] • A new deep learning model was proposed for real-time estimation of water quality. • The new model was based on convolutional neural network, long-term short-term memory and attention mechanism. • The new model can guarantee wastewater reuse and reduce the wastewater treatment cost. [ABSTRACT FROM AUTHOR]

Fruit fly species classification is a fine-grained task as there is a small gap between species. In order to effectively identify and improve the recognition of fruit flies, a fine-grained image-recognition method based on a multi-channel self-attention mechanism was studied and a network framework for fine-grained image recognition based on deep learning was designed in this paper. In this framework, long-term and short-term memory networks are used to extract the underlying features in fruit fly fine-grained images. By inputting the underlying features in the multi-channel self-attention mechanism module, the global and local attention feature maps can be obtained.The weighted attention feature map can also be obtained by multiplying the weight of each channel and the attention feature map. The fine-grained image features of fruit flies were obtained by summing the weighted attention feature map. A softmax classifier was used to process the features and complete the recognition of the fruit fly fine-grained images. Two fine-grained image datasets of fruit flies were applied as experimental objects. Dataset 1 and Dataset 2 contain 11,778 images and 20,580 images from 46 different categories of fruit flies, respectively. The Kappa coefficient was used as the evaluation index to identify fruit fly images with different targets using the method proposed herein. The experimental results showed that, as the number of attention channels increased, the Kappa coefficient gradually increased, suggesting an improvement in the accuracy of fine-grained image recognition. The fine-grained image features extracted by introducing a multi-channel self-attention mechanism exhibited more distinct boundaries with a small amount of overlap, demonstrating strong feature extraction capabilities. When dealing with fine-grained images with either simple or complex backgrounds, the method proposed in this paper has good performance and generalization ability. Even if the target is small and varied in shape, it can still achieve highly accurate recognition. [ABSTRACT FROM AUTHOR]

In recent years, various energy crisis and environmental considerations have prompted the use of renewable energy resources. Renewable energy resources like solar, wind, hydro, biomass, etc. have been a continuous source of clean energy. Wind energy is one of the renewable energy resources that has been widely used all over the world. The wind power is mainly dependent on wind speed which is a random variable and its unpredictable behavior creates various challenges for wind farm operators like energy dispatching and system scheduling. Hence, predicting wind power energy becomes crucial. This has led to the development of various forecasting models in the recent decades. The most commonly used deep learning algorithms for wind power prediction are- RNN (Recurrent Neural Network), LSTM (Long Short- Term Memory) and CNN (Convolutional Neural Network). This paper presents the working of these algorithms and provides a timeline review of the research papers that used these algorithms for wind power prediction. [ABSTRACT FROM AUTHOR]

In recent times, video event detection gained high attention in the researcher's community, because of its widespread applications. In this paper, a new model is proposed for detecting different human actions in the video sequences. First, the videos are acquired from the University of Central Florida (UCF) 101, Human Motion Database (HMDB) 51 and Columbia Consumer Video (CCV) datasets. In addition, the DenseNet201 model is implemented for extracting deep feature values from the acquired datasets. Further, the Improved Gray Wolf Optimization (IGWO) algorithm is developed for selecting active/relevant feature values that effectively improve the computational time and system complexity. In the IGWO, leader enhancement and competitive strategies are employed to improve the convergence rate and to prevent the algorithm from falling into the local optima. Finally, the Bi-directional Long Short Term Memory (BiLSTM) network is used for event classification (101 action types in UCF101, 51 action types in HMDB51, and 20 action types in CCV). In the resulting phase, the IGWO-based BiLSTM network achieved 94.73%, 96.53%, and 93.91% accuracy on the UCF101, HMDB51, and CCV datasets. [ABSTRACT FROM AUTHOR]

In this paper, a general test platform for spacecraft data management is designed and constructed. This paper introduces a portable software development environment based on LUA. The technology of space environment data management, comprehensive analysis, parameter correction and visual display of spacecraft is realized. The relationship between continuity, mixed dispersion, variation and indication of remote sensing data is studied. This project uses the integrated Long Short Term Memory network (LSTM) technology to detect anomalies in satellite remote sensing observation data. Give full play to the advantages of laser scanning tunneling microscope in the nonlinear field. The combination of this method and the matrix method can improve the adaptive ability of spacecraft in an operation state to better identify abnormal information in remote sensing data. Experiments show that the algorithm can significantly improve the anomaly detection rate of the system. The system can monitor the front test device and record the data. The method can be connected with the space vehicle's central control and automatic test system. The comprehensive management of the integrated test system of space vehicles is realized. [ABSTRACT FROM AUTHOR]

Purpose: This paper studies the problem that occurs on material purchase quantity in price uncertainty situation. Larger buying quantity when the price at high will increase the purchase amount while smaller buying quantity could risk the inventory level. The decision on the purchase quantity of a cycle takes future price as input from price prediction output. Design/methodology/approach: This paper examines five price prediction models, Classification and Regression Tree (CART), Random Forest Regressor (RFR), Support Vector Regressor (SVR), Seasonal Autoregressive Integrated Moving Average (SARIMA) and Long Short Term Memory (LSTM) to predict four Petrochemical products, Linear Low Density Polyethylene (LLDPE), Low Density Polyethylene (LDPE), Biaxially Oriented Polypropylene (BOPP) and Cast Polypropylene (CPP) using dataset built from weekly datapoints from January 2020 to June 2023. The most performing model is validated with data from July 2023 to September 2023 where the prediction result is fed into Linear Programming Simplex method to minimize the amount of purchase by making advanced or postpone orders. Findings: Result that RFR performs higher at most products tested, while SVR performs higher in LDPE product. The fitting of RFR and SVR models prediction, as predicted price to Linear Programming that decides optimum purchase quantity, delivers a total 2.2% of purchase amount reduction compared to original purchase quantity reflecting base scenario issued by the planner. Research limitations/implications: This study does not include additional prediction factors such as freight cost and the hyperparameters tuning studies on the existing factors. Originality/value: The novelty of this paper is prediction value is followed up by an optimization model that would guide the Procurement team decisions for future anticipation because imported raw materials should be purchased ahead of time. This research will provide a scientific approach input that would counterbalance or strengthen decision making that is typically made by individuals owning years of experience. This combined approach is rarely researched and has not been done to polymer products. [ABSTRACT FROM AUTHOR]

In recent years, various early warning signals of critical transition have been presented, such as autocorrelation at lag 1 [AR(1)], variance, the propagator based on detrended fluctuation analysis (DFA-propagator), and so on. Many studies have shown that the climate system has the characteristics of long-term memory (LTM). Will the LTM characteristics of the climate system change as it approaches possible critical transition points? In view of this, the present paper first studies whether the LTM of several folding (folded bifurcation) models changes consistently as they approach their critical points slowly by the rescaled range (R/S) analysis. The results of numerical experiments show that when the control parameters of the folding model are close to its critical threshold, the Hurst exponent H exhibits an almost monotonic increase (significance level α = 0.05). We compare the performance of R/S with the existing indicators, including AR(1), variance, and DFA-propagator, and find that R/S is a perfectly valid alternative. When there is no extra false noise, AR(1) and variance have good early warning effects. After the addition of extra Gaussian white noise of different intensities, the values of AR(1) and variance change significantly. As a result, the DFA-propagator based on AR(1) calibration also changed significantly. Compared with the other three indicators, the early warning effect of H has stronger ability to resist the interference of external false signals. To further verify the validity of increasing H, paleoclimate reconstruction of Cariaco Basin sediment core grayscale record with long trends filtered out is studied by R/S analysis. The other three early warning signals are calculated in the same way. The data contain a well-known abrupt climate change: the transition between the Younger Dryas (YD) and the Holocene. We find that approximately 300 years before this abrupt climate change occurred, before 11.7 kyr BP, the LTM exponents for Cariaco Basin deglacial grayscale data present an obvious increasing trend at a significant level of α = 0.05. Meanwhile, the variation trend of H and DFA-propagator is basically similar. This shows that increasing H by R/S analysis is an effective early warning signal, which indicates that a dynamic system is approaching its possible critical transition points; H is a completely valid alternative signal for AR(1) and DFA-propagator. The main conclusion of this paper is based on numerical experiments. The precise relationship between H and the stability of the underlying state approaching the transition needs to be further studied. [ABSTRACT FROM AUTHOR]

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Power quality disturbance (PQD) is an important problem affecting the safe and stable operation of power system. Traditional single modal methods not only have a large number of parameters, but also usually focus on only one type of feature, resulting in incomplete information about the extracted features, and it is difficult to identify complex and diverse PQD types in modern power systems. In this regard, this paper proposes a multi-modal parallel feature extraction and classification model. The model pays attention to both temporal and spatial features of PQD, which effectively improves classification accuracy. And a lightweight approach is adopted to reduce the number of parameters of the model. The model uses Long Short Term Memory Neural Network (LSTM) to extract the temporal features of one-dimensional temporal modes of PQD. At the same time, a lightweight residual network (LResNet) is designed to extract the spatial features of the two-dimensional image modality of PQD. Then, the two types of features are fused into multi-modal spatio-temporal features (MSTF). Finally, MSTF is input to a Support Vector Machine (SVM) for classification. Simulation results of 20 PQD signals show that the classification accuracy of the multi-modal model proposed in this paper reaches 99.94%, and the parameter quantity is only 0.08 MB. Compared with ResNet18, the accuracy of the proposed method has been improved by 2.55% and the number of parameters has been reduced by 99.25%. [ABSTRACT FROM AUTHOR]

Adaptation of handover parameters in ultra-dense networks has always been one of the key issues in optimizing network performance. Aiming at the optimization goal of effective handover ratio, this paper proposes a deep Q-learning (DQN) method that dynamically selects handover parameters according to wireless signal fading conditions. This approach seeks good backward compatibility. In order to enhance the efficiency and performance of the DQN method, Long Short Term Memory (LSTM) is used to build a digital twin and assist the DQN algorithm to achieve a more efficient search. Simulation experiments prove that the enhanced method has a faster convergence speed than the ordinary DQN method, and at the same time, achieves an average effective handover ratio increase of 2.7%. Moreover, in different wireless signal fading intervals, the method proposed in this paper has achieved better performance. [ABSTRACT FROM AUTHOR]

With the wide application of the Internet and the rapid development of network technology, microblogs and online shopping platforms are playing an increasingly important role in people's daily life, learning, and communication. The length of these information texts is usually relatively short, and the grammatical structure is not standardized, but it contains rich emotional tendencies of users. The features used by custumal machinery schooling methods are too sparse on the vector space model and lack the semantic information of short texts, which cannot well identify the semantic features and potential emotional features of short texts. In response to the above problems, this paper proposes a bidirectional long-term and short-term memory network model based on emotional multichannel, combining the attention mechanism and convolutional neural network features in deep learning and learning the short text by combining shallow learning and deep learning. The semantic information and potential emotional information of the short text can be improved to promote the effective expression of short-text emotional features and improve the short-text emotional classification effect. Finally, this paper compares the above models on multidomain classification data sets such as NLPIR and NLPCC2014. The accuracy and F1 value of the model proposed in this paper have achieved good improvement in the field of short-text sentiment analysis. [ABSTRACT FROM AUTHOR]

The wind shear layer is a naturally formed airflow that enables the albatross to soar for six days at almost no cost. The modeling and prediction of the wind shear layer can be very helpful for a long-endurance flight (dynamic soaring), but the existing studies usually ignore the turbulence structures of wind shear layers. In this paper, the wind shear layer on the leeward side of the ridge is simulated by a large eddy simulation (LES) method to analyze the turbulence structures. In the numerical simulation, the three-dimensional (3D) elevation data of the mountain is used as the topography at the bottom and the synthesized turbulent velocity is used as the inlet boundary. Because of the huge computational cost of 3D simulations, a data-driven predicting framework is also established to reduce the cost and maintain the prediction accuracy, which includes an offline training stage and an online forecasting stage. In the offline stage, the proper orthogonal decomposition (POD) is used to extract features from the LES data of wind velocity fields and the obtained POD coefficients are used to train the long short term memory (LSTM) networks. In the online stage, the future wind fields are predicted by the trained LSTM networks in the noisy and real-time environment. In conclusion, this paper analyzed the physical characteristics of the wind shear layer on the leeward side of the ridge and provided the accurate prediction for these characteristics. [ABSTRACT FROM AUTHOR]

With the rapid development of smart grids, the number of various types of power IoT terminal devices has grown by leaps and bounds. An attack on either of the difficult-to-protect end devices or any node in a large and complex network can put the grid at risk. The traffic generated by Distributed Denial of Service (DDoS) attacks is characterised by short bursts of time, making it difficult to apply existing centralised detection methods that rely on manual setting of attack characteristics to changing attack scenarios. In this paper, a DDoS attack detection model based on Bidirectional Long Short-Term Memory (BiLSTM) is proposed by constructing an edge detection framework, which achieves bi-directional contextual information extraction of the network environment using the BiLSTM network and automatically learns the temporal characteristics of the attack traffic in the original data traffic. This paper takes the DDoS attack in the power Internet of Things as the research object. Simulation results show that the model outperforms traditional advanced models such as Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) in terms of accuracy, false detection rate, and time delay. It plays an auxiliary role in the security protection of the power Internet of Things and effectively improves the reliability of the power grid. [ABSTRACT FROM AUTHOR]

It is critical to identify and detect hazardous, flammable, explosive, and poisonous gases in the realms of industrial production and medical diagnostics. To detect and categorize a range of common hazardous gasses, we propose an attention-based Long Short term memory Full Convolutional network (ALSTM-FCN) in this paper. We adjust the network parameters of ALSTM-FCN using the Sparrow search algorithm (SSA) based on this, by comparison, SSA outperforms Particle Swarm Optimization (PSO) Algorithm, Genetic Algorithm (GA), Gray Wolf Optimization (GWO) Algorithm, Cuckoo Search (CS) Algorithm and other traditional optimization algorithms. We evaluate the model using University of California-Irvine (UCI) datasets and compare it with LSTM and FCN. The findings indicate that the ALSTM-FCN hybrid model has a better reliability test accuracy of 99.461% than both LSTM (89.471%) and FCN (96.083%). Furthermore, AdaBoost, logistic regression (LR), extra tree (ET), decision tree (DT), random forest (RF), K-nearest neighbor (KNN) and other models were trained. The suggested approach outperforms the conventional machine learning model in terms of gas categorization accuracy, according to experimental data. The findings indicate a potential for a broad range of polluting gas detection using the suggested ALSTM-FCN model, which is based on SSA optimization. [ABSTRACT FROM AUTHOR]

On-road car accidents are immensely unfortunate but quite common occurrences worldwide. Instant data-centric and informed decisions of crisis management are rarely experienced due to the absence of real-time car accident detection and severity analysis mechanisms. On this background, the current paper presents a deep learning model for car accident detection and analysis of its severity so that the crisis management activities might follow without any delay saving invaluable human lives. The existing works lack in using time-series data, the proper learning model for accurate prediction, and minimizing the time taken in post-accident scenarios for the victims to receive immediate medical help. This paper introduces the Long Short Term Memory (LSTM) model in conjunction with the Gradient Boosted Regression Trees (GBRT) technique for the determination of car accidents with different levels of severity. The proposed model works with the accelerometer and gyroscopic data collected through an application installed in the smartphones of the users inside the car. The LSTM-GBRT hybrid model is proposed to achieve higher accuracy than LSTM which deals with time-variant data. The satisfactory performance of the proposed technique has been reported and the results are extensively investigated in comparison with another hybrid technique such as LSTM with Random Forest (RF) as well. The statistics confirm the superiority of the proposed model over other parallel models in terms of several performance metrics, like Accuracy, Precision, etc. [ABSTRACT FROM AUTHOR]

Short-term load forecasting is a challenging research problem and has a tremendous impact on electricity generation, transmission, and distribution. A robust forecasting algorithm can help power system operators to better tackle the ever-changing electric power demand. This paper presents a novel deep neural network for short-term electric load forecasting for the St. John's campus of Memorial University of Newfoundland (MUN). The electric load data are obtained from the Memorial University of Newfoundland and combined with metrological data from St. John's. This dataset is used to formulate a multivariate time-series forecasting problem. A novel deep learning algorithm is presented, consisting of a 1D Convolutional Neural Network, which is followed by an encoder–decoder-based network with attention. The input used for this model is the electric load consumption and metrological data, while the output is the hourly prediction of the next day. The model is compared with Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM)-based Recurrent Neural Network. A CNN-based encoder–decoder model without attention is also tested. The proposed model shows a lower mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and higher R2 score. These evaluation metrics show an improved performance compared to GRU and LSTM-based RNNs as well as the CNN encoder–decoder model without attention. [ABSTRACT FROM AUTHOR]

Soil temperature is a key parameter in many disciplines, and its research has important practical significance. In recent years, the prediction of soil temperature by deep learning has achieved good results. However, deep learning is difficult to popularize in practical use because of its opacity. This study aims to interpret and analyze the Long Short Term Memory Network (LSTM) model for global soil temperature prediction using SHapley Additive exPlanation (SHAP), Permutation Importance (PI) and Partial Dependence Plot (PDP). The results show that Temperature of air at 2 m above the surface of land has the greatest influence on the prediction of soil temperature, and its SHAP and PI characteristic values have significant seasonality. Meanwhile, radiation also has a certain influence on the prediction results. There was a significant positive correlation between the temperature of 2 m and the soil temperature. The explanatory insights provided in this paper enhance the transparency and confidence of the model, which promotes the applicability of soil temperature prediction models in relevant fields. [ABSTRACT FROM AUTHOR]

Quantitative measurement of RNA expression levels through RNA-Seq is an ideal replacement for conventional cancer diagnosis via microscope examination. Currently, cancer-related RNA-Seq studies focus on two aspects: classifying the status and tissue of origin of a sample and discovering marker genes. Existing studies typically identify marker genes by statistically comparing healthy and cancer samples. However, this approach overlooks marker genes with low expression level differences and may be influenced by experimental results. This paper introduces "GENESO," a novel framework for pan-cancer classification and marker gene discovery using the occlusion method in conjunction with deep learning. we first trained a baseline deep LSTM neural network capable of distinguishing the origins and statuses of samples utilizing RNA-Seq data. Then, we propose a novel marker gene discovery method called "Symmetrical Occlusion (SO)". It collaborates with the baseline LSTM network, mimicking the "gain of function" and "loss of function" of genes to evaluate their importance in pan-cancer classification quantitatively. By identifying the genes of utmost importance, we then isolate them to train new neural networks, resulting in higher-performance LSTM models that utilize only a reduced set of highly relevant genes. The baseline neural network achieves an impressive validation accuracy of 96.59% in pan-cancer classification. With the help of SO, the accuracy of the second network reaches 98.30%, while using 67% fewer genes. Notably, our method excels in identifying marker genes that are not differentially expressed. Moreover, we assessed the feasibility of our method using single-cell RNA-Seq data, employing known marker genes as a validation test. [ABSTRACT FROM AUTHOR]

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition. [ABSTRACT FROM AUTHOR]

Since the existing visual question answering model lacks long-term memory modules for answering complex questions, it is easy to cause the loss of effective information. In order to further improve the accuracy of the visual question answering model, this paper applies the multiple attention mechanism combining channel attention and spatial attention to memory networks for the first time and proposes a dynamic memory network model (DMN-MA) based on the multiple attention mechanism. The model uses the multiple attention mechanism in the situational memory module to obtain the most relevant visual vectors for answering questions based on continuous memory updating, storage and iterative inference of the questions, and effectively uses contextual information for answer inference. The experimental results show that the accuracy of the model in this paper reaches 64.57% and 67.18% on the large-scale public datasets COCO-QA and VQA2.0, respectively. [ABSTRACT FROM AUTHOR]

In a previous discussion paper, twenty-six working memory fMRI studies that reported activity in the hippocampus were systematically analyzed. None of these studies provided convincing evidence that the hippocampus was active during the late delay phase, the only period in which working memory can be isolated from long-term memory processes. Based on these results, it was concluded that working memory does not activate the hippocampus. Six commentaries on the discussion paper were received from Courtney (2022), Kessels and Bergmann (2022), Peters and Reithler (2022), Rose and Chao (2022), Stern and Hasselmo (2022), and Wood et al. (2022). Based on these commentaries, the present response paper considered whether there is evidence of sustained hippocampal activity during the working memory delay period based on depth-electrode recording, whether there are activity-silent working memory mechanisms in the hippocampus, and whether there is hippocampal lesion evidence indicating this region is important for working memory. There was no convincing electrophysiological or neuropsychological evidence that the hippocampus is associated with working memory maintenance, and activity-silent mechanisms were arguably speculative. Given that only a small fraction (approximately 5%) of working memory fMRI studies have reported hippocampal activity and lesion evidence indicates the hippocampus is not necessary for working memory, the burden of proof is on proponents of the view that the hippocampus is important for working memory to provide compelling evidence to support their position. To date, from my perspective, there is no convincing evidence that the hippocampus is associated with working memory. [ABSTRACT FROM AUTHOR]

Entity relation extraction mainly extracts relations from text, which is one of the important tasks of natural language processing. At present, some special fields have insufficient data; for example, agriculture, the metallurgical industry, etc. There is a lack of an effective model for entity relationship recognition under the condition of insufficient data. Inspired by this, we constructed a suitable small balanced data set and proposed a multi-neural network collaborative model (RBF, Roberta–Bidirectional Gated Recurrent Unit–Fully Connected). In addition, we also optimized the proposed model. This model uses the Roberta model as the coding layer, which is used to extract the word-level features of the text. This model uses BiGRU (Bidirectional Gated Recurrent Unit)–FC (Fully Connected) as the decoding layer, which is used to obtain the optimal relationship of the text. To further improve the effect, the input layer is optimized by feature fusion, and the learning rate is optimized by the cosine annealing algorithm. The experimental results show that, using the small balanced data set, the F1 value of the RBF model proposed in the paper is 25.9% higher than the traditional Word2vec–BiGRU–FC model. It is 18.6% higher than the recent Bert–BiLSTM (Bidirectional Long Short Term Memory)–FC model. The experimental results show that our model is effective. [ABSTRACT FROM AUTHOR]

The current research into decision-making strategies for air combat focuses on the performance of algorithms, while the selection of actions is often ignored, and the actions are often fixed in amplitude and limited in number in order to improve the convergence efficiency, making the strategy unable to give full play to the maneuverability of the aircraft. In this paper, a decision-making strategy for close-range air combat based on reinforcement learning with variable-scale actions is proposed; the actions are the variable-scale virtual pursuit angles and speeds. Firstly, a trajectory prediction method consisting of a real-time prediction, correction, and judgment of errors is proposed. The back propagation (BP) neural network and the long and short term memory (LSTM) neural network are used as base prediction network and correction prediction network, respectively. Secondly, the past, current, and future positions of the target aircraft are used as virtual pursuit points, and they are converted into virtual pursuit angles as the track angle commands using angle guidance law. Then, the proximity policy optimization (PPO) algorithm is applied to train the agent. The simulation results show that the attacking aircraft that uses the strategy proposed in this paper has a higher win rate during air combat and the attacking aircraft's maneuverability is fully utilized. [ABSTRACT FROM AUTHOR]

Video surveillance involves petabytes of data storage requiring expensive hardware, which might also be time-inefficient. The aim of this article is, therefore, to develop an intelligent system capable of analyzing long sequences of videos captured from CCTV, helping to mitigate catastrophe and mitigate the violent threats faced by citizens every day, economically and efficiently. Existing models have achieved high accuracy on available datasets, the primary focus is to improve speed (time-efficient) of violence detection and use very little storage (economical) such that the system can be used in real-time. The paper presents an end-to-end hybrid solution for detecting violence in real-time video frames incorporating both human and weapon detection algorithms applied in a synchronized way. The focus of this article is to propose a generic HWVd (Human Weapon Violence detection) model to detect all kinds of public violence. HWVd is a three-tier ensemble model to detect violence in videos. The first tier is human detection, which uses a LightTrack framework. In the second tier, a Fast Region-based Convolutional Neural Network (F-RCNN) to detect any weapon in videos is used. The third tier uses a pre-trained VGG 19 (a pre-trained model of CNN) for spatial feature extraction and Long Short Term Memory (LSTM) to detect violent activity. Lastly, the output of this framework is sent to the Support Vector Machine to classify the activity as (i) violence not involving weapon, (ii) violence involving weapon and (iii) non-violent. The accuracy obtained using the proposed model is 98%. [ABSTRACT FROM AUTHOR]

The present world is badly affected by novel coronavirus (COVID-19). Using medical kits to identify the coronavirus affected persons are very slow. What happens in the next, nobody knows. The world is facing erratic problem and don't know what will happen in near future. This paper is trying to make prognosis of the coronavirus recovery cases using LSTM(Long Short Term Memory). This work exploited data of 258 regions, their latitude and longitude and the number of death of 403 days ranging from 22-01-2020 to 27-02-2021. Specifically, advanced deep learning-based algorithms known as the LSTM, play a great effect on extracting highly essential features for time series data (TSD) analysis.There are lots of methods which already use to analyze propagation prediction. The main task of this paper culminates in analyzing the spreading of Coronavirus across worldwide recovery cases using LSTM deep learning-based architectures. [ABSTRACT FROM AUTHOR]

With the expansion of the scale of wind power integration, the safe operation of the grid is challenged. At present, the research mainly focuses on the prediction of a single wind farm, lacking coordinated control of the cluster, and there is a large prediction error in transitional weather. In view of the above problems, this study proposes an adaptive wind farm cluster prediction model based on transitional weather classification, aiming to improve the prediction accuracy of the cluster under transitional weather conditions. First, the reference wind farm is selected, and then the improved snake algorithm is used to optimize the extreme gradient boosting tree (CBAMSO-XGB) to divide the transitional weather, and the sensitive meteorological factors under typical transitional weather conditions are optimized. A convolutional neural network (CNN) with a multi-layer spatial pyramid pooling (SPP) structure is utilized to extract variable dimensional features. Finally, the attention (ATT) mechanism is used to redistribute the weight of the long and short term memory (LSTM) network output to obtain the predicted value, and the cluster wind power prediction value is obtained by upscaling it. The results show that the classification accuracy of the CBAMSO-XGB algorithm in the transitional weather of the two test periods is 99.5833% and 95.4167%, respectively, which is higher than the snake optimization (SO) before the improvement and the other two algorithms; compared to the CNN-LSTM model, the mean absolute error (MAE) of the adaptive prediction model is decreased by approximately 42.49%-72.91% under various transitional weather conditions. The relative root mean square error (RMSE) of the cluster is lower than that of each reference wind farm and the prediction method without upscaling. The results show that the method proposed in this paper effectively improves the prediction accuracy of wind farm clusters during transitional weather. [ABSTRACT FROM AUTHOR]

In industrial production and engineering operations, the health state of complex systems is critical, and predicting it can ensure normal operation. Complex systems have many monitoring indicators, complex coupling structures, non-linear and time-varying characteristics, so it is a challenge to establish a reliable prediction model. The belief rule base (BRB) can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities. Since each indicator of the complex system can reflect the health state to some extent, the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction. A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper. Firstly, the LSTMis introduced to predict the trend of the indicators in the system. Secondly, theDensity PeakClustering (DPC) algorithmis used to determine referential values of indicators for BRB, which effectively offset the lack of expert knowledge. Then, the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference. Finally, the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump. [ABSTRACT FROM AUTHOR]

Although tsunamis occur less frequently compared to some other natural disasters, they can be extremely devastating in the nearshore environment if they occur. An earthquake of magnitude 6.9 Mw occurred on 30 October 2020 at 12:51 p.m. UTC (2:51 p.m. GMT+03:00) and its epicenter was approximately 23 km south of İzmir province of Turkey, off the Greek island of Samos. The tsunami event triggered by this earthquake is known as the 30 October 2020 İzmir-Samos (Aegean) tsunami, and in this paper, we study the hydrodynamics of this tsunami using some of these artificial intelligence (AI) techniques applied to observational data. More specifically, we use the tsunami time series acquired from the UNESCO data portal at different stations of Bodrum, Syros, Kos, and Kos Marina. Then, we investigate the usage and shortcomings of the Long Short Term Memory (LSTM) DL technique for the prediction of the tsunami time series and its Fourier spectra. More specifically we study the predictability of the offshore water surface elevation dynamics, their spectral frequency and amplitude features, possible prediction success and enhancement of the accurate early prediction time scales. The uses and applicability of our findings and possible research directions are also discussed. [ABSTRACT FROM AUTHOR]

In the realm of Multi-Label Text Classification (MLTC), the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches. Many studies in semantic feature extraction have turned to external knowledge to augment the model's grasp of textual content, often overlooking intrinsic textual cues such as label statistical features. In contrast, these endogenous insights naturally align with the classification task. In our paper, to complement this focus on intrinsic knowledge, we introduce a novel Gate-Attention mechanism. This mechanism adeptly integrates statistical features from the text itself into the semantic fabric, enhancing the model's capacity to understand and represent the data. Additionally, to address the intricate task of mining label correlations, we propose a Dual-end enhancement mechanism. This mechanism effectively mitigates the challenges of information loss and erroneous transmission inherent in traditional long short term memory propagation. We conducted an extensive battery of experiments on the AAPD and RCV1-2 datasets. These experiments serve the dual purpose of confirming the efficacy of both the Gate-Attention mechanism and the Dual-end enhancement mechanism. Our final model unequivocally outperforms the baseline model, attesting to its robustness. These findings emphatically underscore the imperativeness of taking into account not just external knowledge but also the inherent intricacies of textual data when crafting potent MLTC models. [ABSTRACT FROM AUTHOR]

In this paper, a novel ensemble learning model named EWT-EnsemLSTM-SSA, which assembles long short-term memory (LSTM), support vector regression (SVR), and sparrow search algorithm (SSA), is a proposed to deal with long term metro passenger flow volume prediction, which is an essential content of traffic flow prediction problems. Firstly, the empirical wavelet transform (EWT) method is introduced to decompose the original dataset into five wavelet time-sequence data for further prediction. Then, a cluster of LSTMs with diverse hidden layers and neuron counts are employed to explore and exploit the implicit information of the EWT-decomposed signals. Next, the output of LSTMs is aggregated into a nonlinear regression method SVR. Lastly, SSA is utilized to optimize the SVR automatically. The proposed EWT-EnsemLSTM-SSA model is applied in three case studies, using the data collected from the passengers' amount in the Minneapolis, America metro, divided into one hour in one day. Experiment results, which compare the proposed EnsemLSTM-SSA model with five conventional time series forecasting models, show that the proposed model can achieve a better performance than the traditional prediction algorithms. [ABSTRACT FROM AUTHOR]

In this paper proposes the design and implementation of the back-propagation algorithm (BPA) based neural network direct memory access (DMA) controller for use of multimedia applications. The proposed DMA controller work with the back propagation-training algorithm. The advantages of the BPA it will be work on the gradient loss w.r.t the network weights. So, this BPA is used as training algorithm for the DMA controller. The proposed method is test with the different workload characteristics like heavy workload, medium workload and normal workload. The performance parameters are considered here is like accuracy, precision, recall, and F1-score. The proposed method is compared with existing methods like convolutional neural network (CNN), recurrent neural network (RNN), long sort term memory (LSTM), and gated recurrent unit (GRU). Finally, the proposed design will give the better performance than existing methods. [ABSTRACT FROM AUTHOR]

Healthcare cost is an issue of concern right now. While many complex machine learning algorithms have been proposed to analyze healthcare cost and address the shortcomings of linear regression and reliance on expert analyses, these algorithms do not take into account whether each characteristic variable contained in the healthcare data has a positive effect on predicting healthcare cost. This paper uses hybrid machine learning algorithms to predict healthcare cost. First, network structure learning algorithms (a score-based algorithm, constraint-based algorithm, and hybrid algorithm) for a Conditional Gaussian Bayesian Network (CGBN) are used to learn the isolated characteristic variables in healthcare data without changing the data properties (i.e., discrete or continuous). Then, the isolated characteristic variables are removed from the original data and the remaining data used to train regression algorithms. Two public healthcare datasets are used to test the performance of the proposed hybrid machine learning algorithm model. Experiments show that when compared to popular single machine learning algorithms (Long Short Term Memory, Random Forest, etc.) the proposed scheme can obtain similar or higher prediction accuracy with a reduced amount of data. [ABSTRACT FROM AUTHOR]

In recent times, there is an increasing interest in employing technology to process natural language with the aim of providing information that can benefit society. Language identification refers to the process of detecting which speech a speaker appears to be using. This paper presents an audio-based Ethio-semitic language identification system using Recurrent Neural Network. Identifying the features that can accurately differentiate between various languages is a difficult task because of the very high similarity between characters of each language. Recurrent Neural Network (RNN) was used in this paper in relation to the Mel-frequency cepstral coefficients (MFCCs) features to bring out the key features which helps provide good results. The primary goal of this research is to find the best model for the identification of Ethio-semitic languages such as Amharic, Geez, Guragigna, and Tigrigna. The models were tested using an 8-h collection of audio recording. Experiments were carried out using our unique dataset with an extended version of RNN, Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory (BLSTM), for 5 and 10 s, respectively. According to the results, Bidirectional Long Short Term Memory (BLSTM) with a 5 s delay outperformed Long Short Term Memory (LSTM). The BLSTM model achieved average results of 98.1, 92.9, and 89.9% for training, validation, and testing accuracy, respectively. As a result, we can infer that the best performing method for the selected Ethio-Semitic language dataset was the BLSTM algorithm with MFCCs feature running for 5 s. [ABSTRACT FROM AUTHOR]

Recurrent neural networks (RNNs), along with long short-term memory networks (LSTMs), have been successfully used on a wide range of sequential data problems and have been entitled as extraordinarily powerful tools for learning and processing such data. However, the search for a new or derived architecture that would model very long-term dependencies is still an active area of research. In this paper, a relatively psychologically plausible architecture named event buffering JANET (EB-JANET) is proposed. The architecture is derived from the forgetgate-only version of the LSTM, which is also called just another network (JANET). The new architecture implements a new working memory mechanism that operates on information represented as dynamic events. The event buffer, as a container of events, is a reference to the state of the relevant pre-activation values on the basis of which historical candidate values were generated relative to the current timestep. The buffer is emptied as needed and depending on the context of information. The proposed architecture has achieved world-class results and it outperforms JANET on multiple benchmark datasets. Moreover, the new architecture is applicable to a wider class of problems and showed superior resilience when processing longer sequences, as opposed to JANET which experienced catastrophic failures on certain tasks. [ABSTRACT FROM AUTHOR]

Recently, interest in the Cyber-Physical System (CPS) has been increasing in the manufacturing industry environment. Various manufacturing intelligence studies are being conducted to enable faster decision-making through various reliable indicators collected from the manufacturing process. Artificial intelligence (AI) and Machine Learning (ML) have advanced enough to give various possibilities of predicting manufacturing time, which can help implement CPS in manufacturing environments, but it is difficult to secure reliability because it is difficult to understand how AI works, and although it can offer good results, it is often not applied to industries. In this paper, Bidirectional Long Short Term Memory (BI-LSTM) is used to predict process execution time, which is an indicator that can be used as a basis for CPS in the manufacturing process, and the Shapley Additive Explanations (SHAP) algorithm is used to explain how artificial intelligence works. The experimental results of this paper, applying manufacturing data, prove that the results derived from SHAP are effective for workers and AI to collaborate. [ABSTRACT FROM AUTHOR]

DNS tunneling is the attempt to create a hidden tunnel through a domain name service. Such a tunnel would jeopardize the targeted network and open the door for illegal access, control, and data exfiltration. The information security research community showed the variety of techniques that have been proposed to detect the tunnel. The majority of these efforts were relying on machine learning techniques where features of tunneling are considered such as length of DNS query, size, and entropy of the query. However, an additional analysis of the lexical information of the DNS query has been depicted recently and showed remarkable performance. This paper aims to examine the role of Long Short Term Memory (LSTM) model in terms of DNS lexical analysis. Two benchmark datasets related to DNS have been used. In addition, a character mapping mechanism has been used to replace every possible character with an integer number. Consequentially, the mapped representation has been fed into an LSTM model for DNS tunneling detection. Results showed that the proposed method was able to obtain a weighted average F1-score of 98% for both datasets respectively. Such results are competitive in the context of the state of the art and demonstrate the efficacy of the lexical analysis within the DNS tunneling detection task. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the automated diagnosis of physiological curvature in the cervical spine, with an emphasis on feature point localization. Cervical spine deformity is prevalent, and the Cobb angle is widely recognized as the gold standard for diagnosing and treating it. However, manual measurement is time-consuming, labor-intensive, and heavily reliant on clinical experience. Therefore, there is an urgent need for a high-precision automatically detecting algorithm to meet the clinical requirements of orthopedic surgeons. Traditional methods are constrained by complex steps and limited data, which pose challenges. Therefore, we propose an efficient framework that formulates an automatic diagnosis of cervical spine physiological curvature based on a novel deep neural network. By leveraging the excellent properties of neural memory Ordinary Differential Equation (nmODE) in long-term memory retention and nonlinear representation capabilities, we effectively improve the network's performance in keypoint detection branching tasks. Additionally, we integrate a novel hybrid transformer based on residual structures and a multi-stage dilated dynamic convolution to alleviate false detections caused by X-ray obstruction and shadows, and the integration also captures the relationship between vertebrae and landmarks to compensate for the lack of detailed information. We constructed a dataset named CSL-947X, comprising 947 cervical spine lateral X-ray images of patients to train and evaluate our proposed model. Extensive experiments on CSL-947X demonstrate that our framework achieves higher accuracy and outperforms most state-of-the-art methods. These results highlight the effectiveness of the proposed architecture and its potential feasibility as a clinical decision-making tool for healthcare professionals. [ABSTRACT FROM AUTHOR]

Quality of life (QoL) of patients has grown as a result of thoughtful medical care systems where many stakeholders remotely review records. Data privacy is highly at risk due to open communication channels, which also has an impact on how models are trained using centralized servers' acquired data. An emerging idea called federated learning (FL) provides a workable remedy to this problem. There hasn't been a comprehensive or in-depth study of FL in the field of health informatics (HI), in contrast to previous studies that mainly focused on the role of FL in diverse applications. In this proposed approach, a Class Topper Optimization (CTO) based federated learning approach is developed. Clinical data's uploaded by clients are taken as input for this proposed work. Stratified sampling is employed to select clients according to their metadata, preventing contacts with clients that aren't relevant. In this paper, clients are selected based on the CTO approach utilizing a variety of criteria's. The server then receives the newly created parameters from each selected clients, which is then utilized for the training process of the local model. Two different algorithms named as Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) are utilized as a local model to train the homogeneous client. The global model is further improved periodically by utilizing the updates from the locally trained instances. Long Short Term Memory (LSTM) is employed as a global model here. The proposed approach achieves 93% accuracy and 92% precision. Thus, the proposed optimization based client selection approach is the best choice for federated learning. [ABSTRACT FROM AUTHOR]

The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average man. Analyzing this data can be critical for any organization. Sentiments are often expressed with different intensity and topics which can provide great insight into how something affects society. Sentiment analysis in Twitter mitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in twitter. Resources used for analyzing tweet emotions are also briefly presented in literature survey section. In this paper, hybrid combination of different model’s LSTM-CNN have been proposed where LSTM is Long Short Term Memory and CNN represents Convolutional Neural Network. Furthermore, the main contribution of our work is to compare various deep learning and machine learning models and categorization based on the techniques used. The main drawback of LSTM is that it’s a time-consuming process whereas CNN do not express content information in an accurate way, thus our proposed hybrid technique improves the precision rate and helps in achieving better results. Initial step of our mentioned technique is to preprocess the data in order to remove stop words and unnecessary data to improve the efficiency in terms of time and accuracy also it shows optimal results when it is compared with predefined approaches. [ABSTRACT FROM AUTHOR]

We introduce and summarize reviews and research papers by speakers at a discussion meeting on 'Long-term potentiation: 50 years on' held at the Royal Society, London, on 20–21 November 2023. The meeting followed earlier discussion meetings marking the 30th and 40th anniversaries of the discovery of long-term potentiation. These new contributions give an overview of current research and controversies in a vibrant branch of neuroscience with important implications for our understanding of the neurobiological basis of many forms of learning and memory and a wide spectrum of neurological and cognitive disorders. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'. [ABSTRACT FROM AUTHOR]

Global food security can be influenced by the diseases in crop plants as several diseases straightforwardly influence the quality of the grains, vegetables, fruits, etc., which also results in affecting of agricultural productivity. Like other plants, the mango tree is also affected by several diseases, and also the identification of multi-disease classification with a single leaf is more complex, and also it is impossible to detect diseases with bare eyes. Based on the other plants, the mango tree is also affected by various diseases, which is more difficult to detect the disorders with bare eyes. It is error-prone, inconsistent, and unreliable. Here, the mango trees are affected during the production, and also affect the plant health regarding multi-diseases. When the plants are affected by the diseases, it may cause fewer amounts of productivity, as a result, impacting the economy. However, it is more critical to detect plant diseases with the large varieties of trees and plants. Various research tasks on deep learning approaches focus on identifying the diseases in plants including leaves and fruits. Thus, the main objective of this paper is to implement an effective and appropriate technique for diagnosing mango tree diseases and their symptoms through fruit and leaf images, and thus, there is a need for an appropriate system for cost-effective and early solutions to this problem. Hence, the main intention of this work is to implement an efficient and suitable technique for diagnosing mango tree diseases and also identify the symptoms through fruit and leaf images. Intending to overcome the existing challenges, there is a need for an appropriate system for achieving cost-effectiveness and also creating an early solution to resolve this problem. This paper intends to present novel deep learning models for mango tree multi-disease classification. Initially, the data collection is done for gathering the diseased parts of the mango tree in terms of leaf and fruit images. Then, the contrast enhancement of the images is performed by the "Contrast-Limited Adaptive Histogram Equalization (CLAHE)". For the deep feature extraction of leaf images, and fruit images, Convolutional Neural Network (CNN) is employed, and the features from both inputs are concatenated for further processing. Further, the weighted feature selection is adopted for selecting the most significant features by the Adaptive Squirrel-Grey Wolf Search Optimization (AS-GWSO). Enhanced "Long Short Term Memory (LSTM)" is applied in the classification part with parameter optimization using the same AS-GWSO for enhancing classification accuracy. At last, the results of the designed system on various mango tree diseases verify that the designed approach has yielded the highest accuracy by evaluating conventional approaches. Therefore, it would also alleviate and treat the affected mango leaf diseases accurately. [ABSTRACT FROM AUTHOR]

Aiming at the problems of large lag and inertia in the PH control system of gypsum wet desulfurization in thermal power units, this paper proposes a cascade multi-step feed forward predictive control strategy based on proportional integral derivative (PID). Firstly, the paper establishes mechanism model through the transfer function of PH value influence factor to ensure a strong correlation between the control system parameters and the PH value; Secondly, based on the traditional PID control strategy, the paper introduces cascade and multi-step feed forward control mechanisms to solve the strong inertia of the control system; Then, the three-step predictive value of SO2 content in raw flue gas based on long short term memory network (LSTM) is introduced as the feed forward value, so as to better eliminate system lag characteristics and track the PH setting value; Finally, the simulation experiment is carried out using the desulfurization data of the plant. The simulation results show that, compared with the traditional PID and cascade PID control strategies, the control strategy proposed in this paper achieves more accurate and stable control of the PH value of desulfurization slurry in thermal power units, and improve the desulfurization efficiency. [ABSTRACT FROM AUTHOR]