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The development of increasingly widespread computer networks makes users pay more attention to data security and convenience in accessing it. In addition, heavy data traffic on the network and threats and interference, make users feel worried about exchanging data. In anticipating this, users need a system capable of detecting intrusions on their computer network. This research proposes an intrusion detection system that can be the leading solution for network users. The development of this system certainly requires a reliable detection algorithm for classifying the types of data traffic. Therefore, machine learning techniques, which are very popular in Artificial Intelligence in processing large-scale data, are applied in this study. The development of this Intrusion Detection System adopts CRISP-DM by utilizing a Shallow Neural Network to build a classification model. Furthermore, this method will learn to use intrusion data prepared to produce a Neural Network model capable of detecting attacks based on existing data traffic. This model was developed based on a new dataset called ALLFLOWMETER HIKARI2021. This data is divided into 70% training and 30% testing data. Based on ten modeling experiments, the resulting model is capable of producing an MSE and an accuracy of around 0.04438 and 95.562% at the testing stage so that the developed system can detect various network intrusions that exist during the testing phase, especially in distinguishing between two types of data traffic, namely benign or malicious. [ABSTRACT FROM AUTHOR]

The reasons for pollution and the limitations of fossil fuels have driven the use of electric vehicles. Electric vehicles are synonymous with electric batteries that require periodic charging. There has been an increase in research on battery charging for electric vehicles. There is also the use of renewable energy resources such as photovoltaics for battery charging stations. This paper focuses on prototyping battery charging stations from solar photovoltaic energy using ANN-based MPPT controllers within the framework of optimizing solar energy harvesting and comparing them with conventional MPPT methods. Test results showed that the prototype of the proposed charging system took 33.3 minutes to charge a 7Ah battery. Compared to conventional MPPT methods, the charging time of MPTT methods with Neural Network is 6.01% faster. [ABSTRACT FROM AUTHOR]

Cannabis Sativa L. has been clinically proven to be used for antioxidant, anti-inflammatory, antiemetic, parasitic, antibacterial, antiviral, antifungal, antipyretic, painkiller, etc. Consequently, this plant began to be widely produced as an agricultural crop with selling value. More than that, the production of medicinal cannabis products has been permitted in several countries across the world. Furthermore, detecting the chlorophyll content is very important to monitoring the quality of cannabis leaves since the chlorophyll content plays an essential role in determining photosynthetic capacity and plant growth. Chlorophyll content can be assessed using a bench-top method, which might give an accurate and stable result. However, the bench-top method is destructive, costly, and time-consuming. The hand-held SPAD chlorophyll meter is widely used nowadays as a non-destructive instrument. Nevertheless, the price of this device is not affordable, especially for small holder. This work aims to develop a relatively cheap, non-invasive, and accurate method for chlorophyll content detection. A low-cost Hyperspectral Imaging (HI) based on the mobile phone was used in this research. Low-cost HI can read reflected values at visible (400 - 700 nm) wavelengths by applying digital filters to photographs. The reflectance value at each wavelength was plotted and considered equal with the hyperspectral imaging system method afterward. Then, Artificial Neural Network (ANN) was used to analyze spectral data and generate a model prediction to predict unknown data. The best model predictions were built with 10- and 50-layer sizes. The coefficient determination (R2) exhibited acceptable model predictions of 0.785 and 0.8, with root-mean-squared deviation (RMSE) of 3.396 and 3.439, respectively. A low-cost HI device has great potential to detect chlorophyll content on the plant; hence pre-processing and resolution quality should be improved. [ABSTRACT FROM AUTHOR]

Vertical farming (VF) allows for more plants to be cultivated in a particular land area, maximizing land-use efficiency. VF employs multileveled shelves enabling crops to be planted at each level. This paper aims to develop an optimization model that could generate a required setup of a vertical shelf that would meet the sunlight requirements of strawberries. Firstly, the simulation model was developed using Rhino (Rhinoceros) software with Grasshopper plugin, which can predict the sunlight availability at each VF shelf level. The model is validated by the experiment in Chiang Mai province, Thailand, at an elevation higher than 310 m above mean sea level. The experiment set up of the VF shelf with three levels was constructed, and the solar radiation values were recorded and validated against the radiation predicted by the model. The validated model is used to simulate the sunlight radiation on each level of the shelf for different shelf designs with varying vertical spacings between each level. These sets of predicted radiation values and vertical spacings are used to train the ANN (Artificial Neural Network) model. The ANN simulation model was developed using MATLAB software with Simulink and Neural Net Fitting App. The trained ANN model has an MSE (Mean Squared Error) of 0.00019621 and an R (the correlation coefficient) value of 0.9992. As a result, the optimization model could successfully give out the vertical spacing between each level of the VF shelf with an input of desired sunlight availability on each level. For the PPFD (Photosynthetic Photon Flux Density) of 600 µmol/m2/s on both levels, the spacing should be 51.34 cm from the top to the middle (y1) and 48.69 cm from the middle to the bottom (y2). To receive 600 and 500 µmol/m2/s on the middle and bottom level respectively, the levels should be placed 66.77 cm (y1) and 30.89 cm (y2) apart. [ABSTRACT FROM AUTHOR]

Heavy electric quadricycles (L7e) typically have the problem of crash incompatibility because their size and weight are smaller and lighter than other vehicles. The S-rails, located in front of the automotive underbody, are the main components for absorbing the impact energy of the L7e. The design of the L7e's S-rail for crashworthiness is challenging because the design space is relatively limited. High computational resources are commonly required for crash simulation due to a vast amount of data from nonlinear explicit dynamic analyses. Machine learning techniques can more quicky provide and extract accurate crash predictions from a combination of feasible solutions. This work employs a finite element analysis via LS-DYNA to obtain discrete results of the peak crushing force and the specific energy absorption for S-rail impact. The artificial neural network (ANN) module in MATLAB and response surface methodology (RSM) were then applied to predict the crushing-performance surrogate models based on the S-rail's thickness and geometry. To conduct an accurate learning process of the ANN, 81 data points for the crashworthiness simulation were adopted as training inputs with ten test data points. Furthermore, the number of neurons and activation function are examined to acquire the most accurate results for predicting the dynamic crushing of an S-rail. The investigation shows that ANN is more accurate than RSM in predicting the S-rail's peak crash force and specific energy absorption, especially when applying ReLU activation functions. With its high accuracy and capability to predict multiple targets, the ANN-ReLU is recommended to perform surrogate-based model optimization. [ABSTRACT FROM AUTHOR]

Due to favorable strength-to-weight ratio properties, composite materials are widely used in various industrial applications. The manufacturing process, type of fiber, type of matrix, fiber orientation, number of laminations, and volume fraction can significantly affect the mechanical properties of composite materials. Thus, experimental and simulation techniques are required to investigate such material properties. Usually, the strength-to-weight ratio is a crucial factor for analyzing material properties. However, new carbon fiber/epoxy composites may require additional manufacturing with higher costs. To deal with manufacturing costs, an artificial neural network (ANN) is used to predict the flexural strength of such composite materials. ANN training data are the weight percentage, width, manufacturing, ply orientation, and thickness of a graphite filler. To evaluate the desired output with flexural strength, an ANN structure is constructed using a multi-layer feed-forward neural network. ANN mathematics and computing are also compared with Levenberg– Marquardt backpropagation (LMBP) and a scaling conjugate gradient (SCG) to determine the differences between the predicted and actual results. Correlation coefficients for flexural strength represent a range between 0.9791 and 0.9926. Regression analysis signifies an acceptable value of about 0.95, while the mean squared error (MSE) is less than 0.01. Therefore, it can be concluded that the proposed method-based on LMBP is more accurate than the SCG. Numerical results show that the proposed method-based ANN is effective and useful for designing flexural strength of composite materials. [ABSTRACT FROM AUTHOR]

This preliminary research article presents an alternative formulation of machine learning (ML) models for the prediction of the higher heating value (HHV) of biomass. The proposed method aims to improve the accuracy of the decision tree model (DT) while preserving its computational performance. It used 21 HHV prediction correlations augmented with the ML model formulation process rather than the sole use of raw experimental data as in a conventional approach. The method was also tested and compared with other two ML models, including support vector machine (SVM) and artificial neural network (ANN), with 148 experimental data sets. The test results showed that integration of the proposed method with DT had 2.72 times higher accuracy than the conventional model based on the R2 criterion with comparative computational performance. [ABSTRACT FROM AUTHOR]

Harmful Algae Blooms (HABs) have emerged as a critical environmental concern, causing significant damage to marine ecosystems and coastal communities worldwide. Sabah, a coastal region in Malaysia, has experienced increased frequency and intensity of HAB events, leading to severe ecological and economic consequences. Traditional methods for detecting and predicting HABs are limited by their manual and time-consuming nature, hindering timely responses and effective mitigation strategies. To address this issue, this study presents an approach for predicting HABs in Sabah using a Deep Learning model. The proposed deep learning model harnesses the potential of artificial intelligence by integrating historical HAB occurrence records. In the first phase, the researcher collected and organized data from the Sabah Fisheries Department, then carefully cleaned and transformed it into a more practical format. After reviewing existing research, the study chose two promising techniques: the Artificial Neural Networks (ANNs) algorithm and the Long Short-Term Memory (LSTM). These techniques were experimented with to determine the most effective one for predicting HABs. Phase three involved rigorous testing of the prediction model using existing data, providing insights into how well it functions with the dataset and revealing the results obtained. In Phase four, the focus shifted to evaluating the model's performance, specifically identifying any reduced accuracy or shortcomings in the prediction percentages. This study adopts various evaluation metrics to assess the model's performance, such as accuracy, precision, recall, and F1-score, on a dataset with distinct training, validation, and testing sets. The authors also conducted usability testing to gather users' feedback on the proposed system. By providing timely predictions, the proposed model empowers stakeholders to take proactive measures to mitigate the ecological and socio-economic impacts of HABs on the marine ecosystem and coastal communities in Sabah. The findings will contribute substantially to the community by improving early warning systems and optimizing resource allocation for monitoring and controlling HABs. [ABSTRACT FROM AUTHOR]

In this publication a method is presented, that provides a shortcut to find material model parameters for rubber-like materials. Here it is shown exemplarily for PET and especially rPET types. This Method uses a simulation approach to create a dataset consisting of a novel process image representation for the blow moulding process and the corresponding material model parameters. This dataset is used to train an artificial neural network to identify the model parameters in the process image representations. Further, it is proposed to use the same process image representation, created from experimental blowing processes, to identify key material model parameters for experimentally tested materials. The suitability of the method is shown by using an artificial neural network trained to predict key material parameters of the Glass-Rubber model. The validity of the neural network and its prediction is exemplarily shown on the example of the inextensibility factor, a key material parameter of the Glass-Rubber-Model. [ABSTRACT FROM AUTHOR]

Through use of a neuro-fuzzy system, a new methodology has been introduced throughout this work this updated strategy is called modified neuro-fuzzy system. in order to numerically resolve fuzzy singular perturbation problems for partial differential equations using boundary conditions. The parameters of the fuzzy neural network are chosen using the hyperbolic tangent activation function. Analytical methods and common training algorithms were compared to the proposed approach. According to our research, the suggested method is distinguished from traditional methods by having a speed that is much faster, high results accuracy, and a low error rate. Several examples are used to illustrate the suggested strategy. [ABSTRACT FROM AUTHOR]

In recent years, considerable progress has been made in genomics and proteomics, resulting in much biological data. To draw inferences from this data, advanced computer analysis techniques are required. Bioinformatics is crucial to interpreting and applying this data in any endeavor. As huge amounts of genomic, proteomic, and other data begin to be collected and integrated, the relevance of this emerging area of study will increase. The detection of variation in Next Generation Sequencing is now a standard and indispensable technique in all areas of the biological sciences. Life-threatening conditions like cancer and other diseases may be caused by DNA mutation sequences (genetic mutations). Thus, it's important to discover these mutations early, classify them, and understand their effects on the DNA sequence. Bioinformatics is mostly concerned with the changes to DNA that will occur in the cells of the next generation. The primary goal of this research is to develop a sophisticated and comprehensive approach for classifying the mutation type and categorizing the influence of known disease-causing genetic variations on an individual's risk of disease using a multi-label multi-class deep learning classification technique. A deep neural network was trained using the TP53 dataset (The TP53 gene is a tumor suppressor gene on chromosome 17) to discover the mutation type and its impact in a unified model, as described. To evaluate the performance of the suggested method, the accuracy, recall, precision, and F1 score evaluation scales were utilized. and the results demonstrate that it can accurately determine the mutation type and its effects with an accuracy of 97.58%, Precision 98%, Recall 95%, and F1 score 96%. [ABSTRACT FROM AUTHOR]

Multiresolution analysis has wide-ranging applications in signal processing such as fault detection and isolation in sensors. Wavelet transforms can be used for feature extraction and compressing the signals without losing their characteristics. Coefficients of wavelet transform of a signal can be represented in the form of scalogram images. Neural networks helped researchers in replacing human interventions which can also be trained for specific tasks. Researchers have also demonstrated the high level of accuracy and usefulness of Deep Neural Networks(DNNs) in image classification. DNNs pre-trained with millions of images can be used for feature extraction or can be modified and fine-tuned for specific tasks. Scalograms contain the fault signatures of the transformed signal, which can then be classified with the help of a Deep Neural Network(DNN). Though DNNs are efficient in image classification it requires a lot of computing resources for training. To overcome this disadvantage we used transfer learning i.e. modifying and fine-tuning a pre-trained DNN. In this paper, we proposed a model for fault detection and isolation in sensors using transfer learning of DNNs and scalograms. A publicly available temperature sensor dataset was used for fault injection and classification purposes. The proposed model successfully classified sensor stuck faults with 95% accuracy by using scalograms as input to the fine-tuned DNN. [ABSTRACT FROM AUTHOR]

This research employs a novel approach to headcounting by comparing the performance of the ANN algorithm with that of the SVM algorithm. To determine how many individuals are moving under an overhead monitoring system, researchers have developed a revolutionary paper counting approach based on head recognition and tracking. Substances and Techniques: Twenty samples in all were taken from an electronic resource. We maintained a significance level of 0.05, a confidence interval of 95% (95% CI), an enrollment ratio of 1, and a significance level of 80% (G power). The precision value is calculated using the SPSS software. Result: The results of two separate t-tests show that the ANN algorithm outperformed the SVM method, although the difference is not statistically significant (p=0.721; p>0.05). In summary, it seems that the ANN algorithm outperforms the SVM method on the analysed datasets. [ABSTRACT FROM AUTHOR]

The main goal of the study was to detect and classify spot diseases in fruits using artificial neural networks (ANN) more accurately than K-nearest neighbors. The dataset available on Github was used to collect samples of 40 different RGB photos. Using Clinicalc.com, sample size was determined with a pretest power of 80%. The accuracy of these samples was evaluated using ANN and ANN as experimental and control groups, respectively. Based on the accuracy, the effectiveness of ANN and KNN for fruit RGB photos is examined. For artificial neural network and ANN, the accuracy rates are 92.08% and 87.14%, respectively. According to statistical analysis, a significant value of p 0.05 for ANN and independent sample t-test can bring more accurate and desirable results for RGB images. This study finds that ANNs outperform ANN algorithms. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to evaluate the efficacy of the Blaze Pose Algorithm, a new method for human pose detection, in comparison to that of Convolutional Neural Networks. Unlike the current method, which relies on static neural networks, Blazepose is a novel deep learning model for estimating human poses. Pretest power is 0.8, and there are 163 participants in each group. Data was gathered from the MPII Human Pose Dataset, which contains over 25,000 photos taken from various web videos. Researchers found that Blazepose's accuracy was slightly higher than that of a traditional neural network (90.3% vs. 40%). Independent sample T-test results show a 95% level of confidence that there is a statistically significant difference between the proposed and existing groups, with a significance value of P = 0.001 (P 0.05) 2-tailed. The Blazepose outperforms the traditional neural network in terms of improved accuracy, as seen by the comparison findings. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate how feature extraction strategies like GLRLM and SIFT affect artificial neural network-based cancer classification models. To enhance cancer picture categorization, this study proposes using a Gray-level run length matrix (GLRLM). Scale-invariant feature transform (SIFT) is another feature extraction technique compared to the suggested study, with 30 samples used for each group. The 95% confidence interval has a significance level of 0.05. Accuracy is used to evaluate the effectiveness of an artificial neural network (ANN) for detecting cancer. Mean accuracy is 63.32 percent for the gray-level run length matrix and 50.39 percent for the SIFT. Experimental results and analysis show that GLRLM feature extraction combined with ANN outperforms SIFT feature extraction combined with ANN for cancer classification. [ABSTRACT FROM AUTHOR]

The research aims to determine how well innovative Artificial Neural Network (ANN) and Recurrent Neural Network architectures can classify students' academic performance (RNN). Artificial Neural Networks (ANNs) have been suggested and compared to Recurrent Neural Networks (RNNs) for use in predicting students' academic achievement using data sets. For the purpose of prediction, we use ANN with a sample size of 20, as well as RNN with a sample size of 20. In this research, we looked at the problem of predicting where a student could end up as a college sophomore. In this respect, the effectiveness of an Artificial Neural Network algorithm for enhancing students' performance on work placements has been examined and forecasted. To estimate students' chances of being placed successfully, a methodology is given that uses slope-assisted tree computation. Each group included a total of 72 participants in the study. On the student data set, the RNN achieves a success rate of 69.3 percent in terms of analysing academic achievement, whereas the innovative ANN achieves a success rate of 88.89 percent. Since ANN is trained using a different set of data, it outperforms RNN (RNN). New Artificial Neural Networks (ANNs) outperform Recurrent Neural Networks (RNNs) in terms of performance (RNN). [ABSTRACT FROM AUTHOR]

The purpose of this research is to create a cutting-edge YOLO v4 (You Only Look Once v4) neural network model for detecting objects. Evaluate it alongside TensorFlow SSD MobileNet in terms of precision and latency. Twenty representative photographs were taken from a wide range of categories and labels. In order to get the necessary samples for this research, G-power calculation was used. The maximum allowed margin of error was set at 0.5 and the minimum power of analysis required was set at 0.8. The YOLO v4 algorithm outperformed the SSD MobileNet algorithm by a significant margin (81 percent) when predicting the locations of items in a picture (76 percent). The p-value for significance is 0.781. When compared to the SSD MobileNet method, the YOLO v4 algorithm looks to be more precise. [ABSTRACT FROM AUTHOR]

The aim of the study was to distinguish fingerprints using state-of-the-art artificial neural networks and support vector machines. Materials and methods: This study compared support vector machines and innovative artificial neural network techniques (N=10). The total sample size was calculated using the g-Power software with an alpha of 0.05, an enrollment rate of 0.1, a confidence interval of 95%, and a pretest power of 80%. Results: The ANN achieves 93% accuracy while the SVM classifier achieves 83% accuracy. In SPSS statistical analysis, the accuracy was found to be significant (p<0.05). Conclusion: The novel ANN classifier provides significantly better fingerprinting performance than the SVM classifier. [ABSTRACT FROM AUTHOR]

This work is a relative investigation of novel normal language and counterfeit brain networks calculations for improving voice discovery to lessen the time utilization of time individual collaborators. Materials and Methods: Novel Natural Language Processing (NNLP) calculation and Artificial Neural Networks (ANNs) calculation strategies are reenacted by differing the NNLP boundary and mechanize voice location to upgrade the pH. Test size is determined utilizing G power 80% for two gatherings and there are 40 examples utilized in this work. Results: Based on acquired results NNLP has fundamentally diminished time utilization and the exactness has been worked on around 89.00% contrasted with ANNs with precision of 71.90%. Measurable importance contrast among NNLP and ANNs was viewed as 0.376 (p>0.05). End: NNLP calculations give improved brings about voice acknowledgment than ANNs calculations. [ABSTRACT FROM AUTHOR]

In this investigation, we compare the performance of the state-of-the-art ANN algorithm for vehicle detection with the more conventional SVM algorithm. Twenty representatives were selected at random from the database of the website. The threshold was kept at 0.05, the confidence level at 95%, the enrollment ratio at 1, and the G power at 80%. Accuracy metrics are calculated using the SPSS software. Results from an independent t test showed that the ANN algorithm outperformed the SVM algorithm, with a mean accuracy of 95%. The difference between the two groups was not statistically significant (p=0.11, p>0.05). In this research, it appears that the ANN algorithms perform better than the SVM algorithms when it comes to detecting vehicles in the datasets used. [ABSTRACT FROM AUTHOR]

Using Regression Neural Networks and Probabilistic Neural Networks, implement a comparative overall performance evaluation of estimating food volume and monitoring meal intake for hospitalised affected persons using neural networks. This evaluation will be done using neural networks. Both the Materials and the Methods: Regression neural network and probabilistic neural network, with the same sample size (N=10) for each of the two groups, for the purpose of determining the accuracy of intake food estimation. Results show that the accuracy rate of the Regression neural network is 91.5 percent based on the obtained result, which is higher than the accuracy rate of the probabilistic neural network, which is 89.3 percent. According to the results of an independent sample T test, the statistical significance difference that was observed between two groups was P=0.00 (P0.05), 2-tailed. It has been determined through the research presented in this paper that the Probabilistic Neural Network is less accurate than the Regression Neural Network. [ABSTRACT FROM AUTHOR]

This study's goal is to estimate the price range for mobile devices using the Deep Neural Network algorithm and the Random Forest model. When N = 10 sample iterations were used to test the accuracy of the model for the validation of the mobile price range, two groups—the Deep Neural Network algorithm and the Random Forest model— with G power (value = 0.8) were studied. The Deep Neural Network model's accuracy results have a possible range of 84.183 percent, and the Random Forest model has an accuracy of (74.80 percent). The Deep Neural Network and Random Forest had statistically significant differences (p = 0.005). The Deep Neural Network technique outperforms the Random Forest approach in terms of performance and significance, according to the study findings for validating the pricing range for mobile devices. [ABSTRACT FROM AUTHOR]

Developers and consumers frequently regard deep neural networks as a "black box" since they are comparatively poor at communicating their inference procedures and end outputs. Explainability and transparency of Artificial Intelligent (AI) systems become critically valuable for their users, the people who are influenced by Artificial Intelligence judgments, in many real-world applications such as medical diagnosis, investment advice, business decisions, and process optimization. This study contains state-of-the-art strategies of Explainable AI for Convolution Neural Network Models. This may be used when building models to evaluate CNN model predictions. The goal is also to act as a benchmark for algorithms and metrics for new explainability techniques research. This study compares and contrasts common XAI visual methods. The study contains a brief review of each approach, its advantages and disadvantages, and its implementation. Furthermore, the study analyzes the interpretability and trustworthiness of different approaches, as well as advice for picking best visual method for a certain use case. As worked on pre-trained CNN models on ImagNet dataset to obtain the experimental outcome for various visual explanation methods. Based on study and experimentation among all strategies, LayerCAM and HiResCAM generated better results for CNN models. [ABSTRACT FROM AUTHOR]

The rise of cyber-attacks such as Distributed Denial of Service (DDoS) and SQL injection has become a significant concern for organizations and individuals who rely on the internet. Traditional detection methods have become increasingly ineffective in addressing these challenges, necessitating the development of new and innovative solutions. This paper proposes using Convolutional Neural Networks (CNNs) to detect DDoS and SQL injection attacks. Our paper proposes a Convolutional Neural Network model with a multi-layer neural and relu activation function optimizer that reaches a higher degree of precision than previous Deep Learning models. In this study, we tested the relatively new dataset CIC-IDS-2018, which contains different types of attacks. With this dataset, our model achieves an unprecedented accuracy of>96%, minimizing computational time. [ABSTRACT FROM AUTHOR]

Physical disabilities cause significant distress in people's daily routine, especially for leg amputees as their movement is restricted. The civilized society has the responsibility to provide them with better prosthetics so they can live a normal life. Previously, prosthetic legs were developed for lower leg amputees. Today's electronic prosthesis provides a wide range of mobility compared to a mechanical prosthesis. Powered prosthetics are quite expensive and hence not widely used. We have proposed a low-cost control system above-knee powered prosthetics leg that is reliable, and the complexity of the model is less. The prostheses works by taking input from a sensor placed in the model and six FSR402 sensor and an IMU sensor that record force muscle activity of the thigh during variation of leg movement. The value of motion of the joint knee angle is determined by sensors. In this paper, we are discussing and comparing potential deep learning control system namely artificial neural network (ANN) as baseline and recurrent neural network-long short term memory (RNN-LSTM) to predict human gait cycle. A multi-layer perceptron (MLP) method is implemented. In general, network architecture consists of input layer which is represents by six inputs of the FSR402 sensor and an IMU sensor, fully connected layers, and an output layer as joint knee angle. As the result model of ANN as baseline has metric value MAE and R squared are 6.21 and 68.7% respectively whereas model of RNN-LSTM has metric value MAE and R squared are 4.99 and 96.9% respectively. The result indicates the RNN-LSTM model is most likely to perform fewer errors based on the dataset. RNN-LSTM outperforms the task with a greater value compared to the ANN model as the baseline. This present study suggests that deep learning with the recurrent neural network-long short term memory (RNN-LSTM) is suitable for the prediction of human gait cycle movement from the FSR402 and an IMU signal in the overall walking task. [ABSTRACT FROM AUTHOR]

In this study, we compare the effect of different hyperparameters of the deep neural network while solving partial differential equations. We consider feed-forward neural networks, and the hyperparameters such as the number of hidden layers, number of neurons, activation function, optimization methods, and learning rate are studied in this paper. Numerical results for the effect of all the hyperparameters while solving fourth-order partial differential equations are mentioned in this study. [ABSTRACT FROM AUTHOR]