Search results for: neural regeneration

Authors: P. Karthick, K. Mahesh

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Video has become an increasingly significant component of our digital everyday contact. With the advancement of greater contents and shows of the resolution, its significant volume poses serious obstacles to the objective of receiving, distributing, compressing, and revealing video content of high quality. In this paper, we propose the primary beginning to complete a deep video compression model that jointly upgrades all video compression components. The video compression method involves splitting the video into frames, comparing the images using convolutional neural networks (CNN) to remove duplicates, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using generative adversarial network (GAN) and recorded with long short-term memory (LSTM). Instead of the complete image, the small changes generated using GAN are substituted, which helps in frame level compression. Pixel wise comparison is performed using K-nearest neighbours (KNN) over the frame, clustered with K-means, and singular value decomposition (SVD) is applied for each and every frame in the video for all three color channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors. Video frames are packed with parameters with the aid of a codec and converted to video format, and the results are compared with the original video. Repeated experiments on several videos with different sizes, duration, frames per second (FPS), and quality results demonstrate a significant resampling rate. On average, the result produced had approximately a 10% deviation in quality and more than 50% in size when compared with the original video.

Keywords: video compression, K-means clustering, convolutional neural network, generative adversarial network, singular value decomposition, pixel visualization, stochastic gradient descent, frame per second extraction, RGB channel extraction, self-detection and deciding system

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Authors: Shibo Wei, Ting Jiang

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Convolutional-recurrent neural networks (CRN) have achieved much success recently in the speech enhancement field. The common processing method is to use the convolution layer to compress the feature space by multiple upsampling and then model the compressed features with the LSTM layer. At last, the enhanced speech is obtained by deconvolution operation to integrate the global information of the speech sequence. However, the feature space compression process may cause the loss of information, so we propose to model the upsampling result of each step with the residual LSTM layer, then join it with the output of the deconvolution layer and input them to the next deconvolution layer, by this way, we want to integrate the global information of speech sequence better. The experimental results show the network model (RES-CRN) we introduce can achieve better performance than LSTM without residual and overlaying LSTM simply in the original CRN in terms of scale-invariant signal-to-distortion ratio (SI-SNR), speech quality (PESQ), and intelligibility (STOI).

Keywords: convolutional-recurrent neural networks, speech enhancement, residual LSTM, SI-SNR

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Authors: Venkat S. Somayajula

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Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical features

Keywords: Bayesian long-short term memory neural network, borehole temperature, ground surface temperature history, paleoclimate cycle

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Authors: Silpita Paul, Susanta Sadhukhan, Biswanath Maity, Madhusudan Das

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Neural tube defects (NTDs) are one of the most common forms of birth defect and affect ~300,000 new born worldwide each year. The prevalence is higher in Northern India (11 per 1000 birth) compare to southern India (5 per 1000 birth). NTDs are one of the common birth defects related with low blood folate and Hcy concentration. Though the mechanism is still unknown, but it is now established that, NTDs in human are polygenic in nature and follow the heterogeneous trait. In spite of its heterogeneity, polymorphism in few genes affects significantly the trait of NTDs. Polymorphisms in the genes FOLH1 and MTHFR plays important role in NTDs. In this study, the polymorphisms of these genes were screened by bi-directional sequencing from 30 mothers with NTD babies as case. The result revealed that 26.67% patients had bi-allelic FOLH1 polymorphism. The polymorphism has been identified as p.Y60H and frequent to cause NTDs. The study of MTHFR gene showed 2 different SNPs rs1801131 (at exon 4) and rs1801131 (at exon 7). The study showed 6.67% patients of both mono- and bi-allelic MTHFR-rs1801131 polymorphism and 6.67% patients of bi-allelic MTHFR-rs1801131 polymorphism. These polymorphisms has been responsible for p.A222V and p.E429A change respectively and frequently involved in NTD formation. Those polymorphisms affect mainly the absorption of dietary folate from intestine and the formation of 5-methylenetetrahydrofolate (5 MTHF) from 5,10-methylenetetrahydrofolate (5,10- MTHF), which is the functional folate form in our system. Though the study is not complete yet, but these polymorphisms play crucial roles in the formation of NTDs in other world population. Based on the result till date, it can be concluded that they also play significant role in our population too as in control samples we have not found any changes.

Keywords: neural tube defects, polymorphism, FOLH1, MTHFR

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Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Zdravko Eškinja, Lovre Miljanić, Ognjen Kuljača

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This paper is an overview of simulation tools used to model specific thermal dynamics that occurs while controlling double skin façade. Research has been conducted on simplified construction with single zone where one side is glazed. Heat flow and temperature responses are simulated in three different simulation tools: IDA-ICE, EnergyPlus and HAMBASE. The excitation of observed system, used in all simulations, was a temperature step of exterior environment. Air infiltration, insulation and other disturbances are excluded from this research. Although such isolated behaviour is not possible in reality, experiments are carried out to gain novel information about heat flow transients which are not observable under regular conditions. Results revealed new possibilities for adapting the parameters of the neural network regulator. Along numerical simulations, the same set-up has been also tested in a real-time experiment with a 1:18 scaled model and thermal chamber. The comparison analysis brings out interesting conclusion about simulation accuracy in this particular case.

Keywords: double skin façade, experimental tests, heat control, heat flow, simulated tests, simulation tools

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Authors: Umair Rashid

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Normally softmax loss is used as the supervision signal in face recognition (FR) system, and it boosts the separability of features. In the last two years, a number of techniques have been proposed by reformulating the original softmax loss to enhance the discriminating power of Deep Convolutional Neural Networks (DCNNs) for FR system. To learn angularly discriminative features Cosine-Margin based softmax has been adjusted as monotonically decreasing angular function, that is the main challenge for angular based softmax. On that issue, we propose monotonically decreasing element for Cosine-Margin based softmax and also, we discussed the effect of different monotonically decreasing parameters on angular Margin softmax for FR system. We train the model on publicly available dataset CASIA- WebFace via our proposed monotonically decreasing parameters for cosine function and the tests on YouTube Faces (YTF, Labeled Face in the Wild (LFW), VGGFace1 and VGGFace2 attain the state-of-the-art performance.

Keywords: deep convolutional neural networks, cosine margin face recognition, softmax loss, monotonically decreasing parameter

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Authors: Bin Ji, Kai Xu, Shunyu Yao, Jingjing Liu, Ye Pan

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Human pose estimation is a popular research area in computer vision for its important application in human-machine interface. In recent years, 2D human pose estimation based on convolution neural network has got great progress and development. However, in more and more practical applications, people often need to deal with tasks based on video. It’s not far-fetched for us to consider how to combine the spatial and temporal information together to achieve a balance between computing cost and accuracy. To address this issue, this study proposes a new spatiotemporal model, namely Spatiotemporal Net (STNet) to combine both temporal and spatial information more rationally. As a result, the predicted keypoints heatmap is potentially more accurate and spatially more precise. Under the condition of ensuring the recognition accuracy, the algorithm deal with spatiotemporal series in a decoupled way, which greatly reduces the computation of the model, thus reducing the resource consumption. This study demonstrate the effectiveness of our network over the Penn Action Dataset, and the results indicate superior performance of our network over the existing methods.

Keywords: convolutional long short-term memory, deep learning, human pose estimation, spatiotemporal series

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Authors: Zhifeng Kong

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Over-parameterized neural networks have attracted a great deal of attention in recent deep learning theory research, as they challenge the classic perspective of over-fitting when the model has excessive parameters and have gained empirical success in various settings. While a number of theoretical works have been presented to demystify properties of such models, the convergence properties of such models are still far from being thoroughly understood. In this work, we study the convergence properties of training two-hidden-layer partially over-parameterized fully connected networks with the Rectified Linear Unit activation via gradient descent. To our knowledge, this is the first theoretical work to understand convergence properties of deep over-parameterized networks without the equally-wide-hidden-layer assumption and other unrealistic assumptions. We provide a probabilistic lower bound of the widths of hidden layers and proved linear convergence rate of gradient descent. We also conducted experiments on synthetic and real-world datasets to validate our theory.

Keywords: over-parameterization, rectified linear units ReLU, convergence, gradient descent, neural networks

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Authors: Ana Neves, John Leander, Ignacio Gonzalez, Raid Karoumi

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Systematic monitoring and inspection are needed to assess the present state of a structure and predict its future condition. If an irregularity is noticed, repair actions may take place and the adequate intervention will most probably reduce the future costs with maintenance, minimize downtime and increase safety by avoiding the failure of the structure as a whole or of one of its structural parts. For this to be possible decisions must be made at the right time, which implies using systems that can detect abnormalities in their early stage. In this sense, Structural Health Monitoring (SHM) is seen as an effective tool for improving the safety and reliability of infrastructures. This paper explores the decision-making problem in SHM regarding the maintenance of civil engineering structures. The aim is to assess the present condition of a bridge based exclusively on measurements using the suggested method in this paper, such that action is taken coherently with the information made available by the monitoring system. Artificial Neural Networks are trained and their ability to predict structural behavior is evaluated in the light of a case study where acceleration measurements are acquired from a bridge located in Stockholm, Sweden. This relatively old bridge is presently still in operation despite experiencing obvious problems already reported in previous inspections. The prediction errors provide a measure of the accuracy of the algorithm and are subjected to further investigation, which comprises concepts like clustering analysis and statistical hypothesis testing. These enable to interpret the obtained prediction errors, draw conclusions about the state of the structure and thus support decision making regarding its maintenance.

Keywords: artificial neural networks, clustering analysis, model-free damage detection, statistical hypothesis testing, structural health monitoring

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Authors: H. Jung, N. Kim, B. Kang, J. Choe

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History matching is a crucial procedure for predicting reservoir performances and making future decisions. However, it is difficult due to uncertainties of initial reservoir models. Therefore, it is important to have reliable initial models for successful history matching of highly heterogeneous reservoirs such as channel reservoirs. In this paper, we proposed a novel scheme for regenerating geological models using support vector machine (SVM) and principal component analysis (PCA). First, we perform PCA for figuring out main geological characteristics of models. Through the procedure, permeability values of each model are transformed to new parameters by principal components, which have eigenvalues of large magnitude. Secondly, the parameters are projected into two-dimensional plane by multi-dimensional scaling (MDS) based on Euclidean distances. Finally, we train an SVM classifier using 20% models which show the most similar or dissimilar well oil production rates (WOPR) with the true values (10% for each). Then, the other 80% models are classified by trained SVM. We select models on side of low WOPR errors. One hundred channel reservoir models are initially generated by single normal equation simulation. By repeating the classification process, we can select models which have similar geological trend with the true reservoir model. The average field of the selected models is utilized as a probability map for regeneration. Newly generated models can preserve correct channel features and exclude wrong geological properties maintaining suitable uncertainty ranges. History matching with the initial models cannot provide trustworthy results. It fails to find out correct geological features of the true model. However, history matching with the regenerated ensemble offers reliable characterization results by figuring out proper channel trend. Furthermore, it gives dependable prediction of future performances with reduced uncertainties. We propose a novel classification scheme which integrates PCA, MDS, and SVM for regenerating reservoir models. The scheme can easily sort out reliable models which have similar channel trend with the reference in lowered dimension space.

Keywords: history matching, principal component analysis, reservoir modelling, support vector machine

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Authors: Kishor Prasad Bhatta, Suman Ghimire, Durga Prasad Joshi

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Invasive Alien Species (IAS) are considered waste forest resources in Nepal. The rapid expansion of IAS is one of the nine main drivers of forest degradation, though the extent and distribution of this species are not well known. Further, the knowledge of the impact of IAS removal on forest plant diversity is hardly known, and the possibilities of income generation from them at the grass-root communities are rarely documented. Systematic sampling of 1% with nested circular plots of 500 square meters was performed in IAS removed and non-removed area, each of 30 hectares in Udayapur Community Forest User Group (CFUG), Chitwan, central Nepal to observe whether the removal of IAS contributed to an increase in plant diversity. In addition, ten entrepreneurs of Udaypur CFUG, involved in the charcoal production, briquette making and marketing were interviewed and interacted as well as their record keeping booklets were reviewed to understand if the charcoal production contributed to their income and employment. The average annual precipitation and temperature of the study area is 2100 mm and 34 degree Celsius respectively with Shorea robusta as main tree species and Eupatorium odoratum as dominant IAS. All the interviewed households were from the ̔below-poverty-line’ category as per Community Forestry Guidelines. A higher Shannon-Weiner plant diversity index at regeneration level was observed in IAS removed areas (2.43) than in control site (1.95). Furthermore, the number of tree seedlings and saplings in the IAS harvested blocks were significantly higher (p < 0.005) compared to the unharvested one. The sale of charcoal produced through the pyrolysis of IAS in ̔ Bio-energy kilns’ contributed for an average increased income of 30.95 % (Nepalese rupees 31,000) of the involved households. Despite above factors, some operational policy hurdles related to charcoal transport and taxation existed at field level. This study suggests that plant diversity could be increased through the removal of IAS, and considerable economic benefits could be achieved if charcoal is substantially produced and utilized.

Keywords: briquette, economic benefits, pyrolysis, regeneration

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Authors: Hanbey Hazar, Hakan Gul

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In this study, Thermal Barrier Coating (TBC) application is performed in order to reduce the engine emissions. Piston, exhaust, and intake valves of a single-cylinder four-cycle gasoline engine were coated with chromium carbide (Cr3C2) at a thickness of 300 µm by using the Plasma Spray coating method which is a TBC method. Gasoline engine was converted into an LPG system. The study was conducted in 4 stages. In the first stage, the piston, exhaust, and intake valves of the gasoline engine were coated with Cr3C2. In the second stage, gasoline engine was converted into the LPG system and the emission values in this engine were recorded. In the third stage, the experiments were repeated under the same conditions with a standard (uncoated) engine and the results were recorded. In the fourth stage, data obtained from both engines were loaded on Artificial Neural Networks (ANN) and estimated values were produced for every revolution. Thus, mathematical modeling of coated and uncoated engines was performed by using ANN. While there was a slight increase in exhaust gas temperature (EGT) of LPG engine due to TBC, carbon monoxide (CO) values decreased.

Keywords: LPG fuel, thermal barrier coating, artificial neural network, mathematical modelling

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Authors: G. Ravindranath, S. Savitha

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This paper presents heat transfer analysis of single horizontal bare tube and heat transfer analysis of staggered arrangement of bare tube bundles bare tube bundles in gas-solid (air-solid) fluidized bed and predictions are done by using Artificial Neural Network (ANN) based on experimental data. Fluidized bed provide nearly isothermal environment with high heat transfer rate to submerged objects i.e. due to through mixing and large contact area between the gas and the particle, a fully fluidized bed has little temperature variation and gas leaves at a temperature which is close to that of the bed. Measurement of average heat transfer coefficient was made by local thermal simulation technique in a cold bubbling air-fluidized bed of size 0.305 m. x 0.305 m. Studies were conducted for single horizontal Bare Tube of length 305mm and 28.6mm outer diameter and for bare tube bundles of staggered arrangement using beds of large (average particle diameter greater than 1 mm) particle (raagi and mustard). Within the range of experimental conditions influence of bed particle diameter ( Dp), Fluidizing Velocity (U) were studied, which are significant parameters affecting heat transfer. Artificial Neural Networks (ANNs) have been receiving an increasing attention for simulating engineering systems due to some interesting characteristics such as learning capability, fault tolerance, and non-linearity. Here, feed-forward architecture and trained by back-propagation technique is adopted to predict heat transfer analysis found from experimental results. The ANN is designed to suit the present system which has 3 inputs and 2 out puts. The network predictions are found to be in very good agreement with the experimental observed values of bare heat transfer coefficient (hb) and nusselt number of bare tube (Nub).

Keywords: fluidized bed, large particles, particle diameter, ANN

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Authors: Babak Bahri, Fatemeh Yassaee Meybodi, Changiz Eslahchi

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In the pursuit of effective cancer therapies, the exploration of combinatorial drug regimens is crucial to leverage synergistic interactions between drugs, thereby improving treatment efficacy and overcoming drug resistance. However, identifying synergistic drug pairs poses challenges due to the vast combinatorial space and limitations of experimental approaches. This study introduces ClusterSyn, a machine learning (ML)-powered framework for classifying anti-cancer drug synergy scores. ClusterSyn employs a two-step approach involving drug clustering and synergy score prediction using a fully connected deep neural network. For each cell line in the training dataset, a drug graph is constructed, with nodes representing drugs and edge weights denoting synergy scores between drug pairs. Drugs are clustered using the Markov clustering (MCL) algorithm, and vectors representing the similarity of drug pairs to each cluster are input into the deep neural network for synergy score prediction (synergy or antagonism). Clustering results demonstrate effective grouping of drugs based on synergy scores, aligning similar synergy profiles. Subsequently, neural network predictions and synergy scores of the two drugs on others within their clusters are used to predict the synergy score of the considered drug pair. This approach facilitates comparative analysis with clustering and regression-based methods, revealing the superior performance of ClusterSyn over state-of-the-art methods like DeepSynergy and DeepDDS on diverse datasets such as Oniel and Almanac. The results highlight the remarkable potential of ClusterSyn as a versatile tool for predicting anti-cancer drug synergy scores.

Keywords: drug synergy, clustering, prediction, machine learning., deep learning

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Authors: Tahir Nawaz Cheema, Dumitru Baleanu, Ali Raza

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In this research work, we design intelligent computing through Bayesian Regularization artificial neural networks (BRANNs) introduced to solve the mathematical modeling of infectious diseases (Covid-19). The dynamical transmission is due to the interaction of people and its mathematical representation based on the system's nonlinear differential equations. The generation of the dataset of the Covid-19 model is exploited by the power of the explicit Runge Kutta method for different countries of the world like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, testing, and validation processes for every frequent update in Bayesian Regularization backpropagation for numerical behavior of the dynamics of the Covid-19 model. The performance and effectiveness of designed methodology BRANNs are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis.

Keywords: mathematical models, beysian regularization, bayesian-regularization backpropagation networks, regression analysis, numerical computing

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Authors: Mehrdad Shahrbanozadeh, Gholam Abbas Barani, Saeed Shojaee

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In this study, a finite element (Seep3D model) and an artificial neural network (ANN) model were developed to simulate flow through dam foundation. Seep3D model is capable of simulating three-dimensional flow through a heterogeneous and anisotropic, saturated and unsaturated porous media. Flow through the Shahid Abbaspour dam foundation has been used as a case study. The FEM with 24960 triangular elements and 28707 nodes applied to model flow through foundation of this dam. The FEM being made denser in the neighborhood of the curtain screen. The ANN model developed for Shahid Abbaspour dam is a feedforward four layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning. The water level elevations of the upstream and downstream of the dam have been used as input variables and the piezometric heads as the target outputs in the ANN model. The two models are calibrated and verified using the Shahid Abbaspour’s dam piezometric data. Results of the models were compared with those measured by the piezometers which are in good agreement. The model results also revealed that the ANN model performed as good as and in some cases better than the FEM.

Keywords: seepage, dam foundation, finite element method, neural network, seep 3D model

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Deepika Christopher, Garima Anand

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To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition.

Keywords: attrition, retention, predictive modeling, customer segmentation, telecommunications

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Authors: Alena O. Stepanova, Vera S. Chernonosova, Tatyana S. Godovikova, Konstantin A. Bulatov, Andrey Y. Patrushev, Pavel P. Laktionov

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Trauma and arthrosis, not to mention cartilage destruction in overweight and elders put hyaline cartilage lesion among the most frequent diseases of locomotor system. These problems combined with low regeneration potential of the cartilage make regeneration of articular cartilage a high-priority task of tissue engineering. Many types of matrices, the procedures of their installation and autologous chondrocyte implantation protocols were offered, but certain aspects including adhesion of the implant with surrounding cartilage/bone, prevention of the ossification and fibrosis were not resolved. Simplification and acceleration of the procedures resulting in restoration of normal cartilage are also required. We have demonstrated that human chondroblasts can be successfully cultivated at the surface of electrospun scaffolds and produce extracellular matrix components in contrast to chondroblasts grown in homogeneous hydrogels. To restore cartilage we offer to use stacks of electrospun scaffolds fixed with photopolymerized solution of prepared from gelatin and chondroitin-4-sulfate both modified by glycidyl methacrylate and non-toxic photoinitator Darocur 2959. Scaffolds were prepared from nylon 6, polylactide-co-glicolide and their mixtures with modified gelatin. Illumination of chondroblasts in photopolymerized solution using 365 nm LED light had no effect on cell viability at compressive strength of the gel less than0,12 MPa. Stacks of electrospun scaffolds provide good compressive strength and have the potential for substitution with cartilage when biodegradable scaffolds are used. Vascularization can be prevented by introduction of biostable scaffolds in the layers contacting the subchondral bone. Studies of two-component materials (2-3 sheets of electrospun scaffold) implanted in the knee-joints of rabbits and fixed by photopolymerization demonstrated good crush resistance, biocompatibility and good adhesion of the implant with surrounding cartilage. Histological examination of the implants 3 month after implantation demonstrates absence of any inflammation and signs of replacement of the biodegradable scaffolds with normal cartilage. The possibility of intraoperative population of the implants with autologous cells is being investigated.

Keywords: chondroblasts, electrospun scaffolds, hyaline cartilage, photopolymerized gel

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Authors: S. Padma, D. H. Tejavathi

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Somatic embryogenesis is a remarkable illustration of the dictum of plant totipotency where developmental reconstruction of somatic cells takes place towards the embryogenic pathway. It recapitulates the morphological and developmental process that occurs in zygotic embryogenesis. S. androgynous commonly called as multivitamin plant. The leaves are consumed as green leafy vegetable by the Southeast Asian communities due to their rich nutritional profile. Despite being a good nutritional vegetable with proteins, vitamins, minerals, amino acids, it is warned for excessive intake due to the presence of alkoloid called papaverine. Papaverine at higher concentrations is toxic and leads to a syndrome called Bronchiolitis Obliterans. In the present study, morphogenetic potential of shoot tip, leaf and nodal explants of Sauropus androgynous was investigated to develop and enhance the reliable plant regeneration protocol via somatic embryogenesis. Somatic embryos were derived directly from the embryogenic callus derived from shoot tip, node and leaf cultures on Phillips and Collins (L2) medium supplemented with NAA at various concentrations ranging from 5.3 µM/l to 26.85 µM/l within two months of inoculation. Thus obtained embryos were sub cultured to modified L2 media supplemented with increased vitamin level for the further growth. Somatic embryos with well-developed cotyledons were transferred to normal and modified L2 basal medium for conversion. The plantlets thus obtained were subjected to brief acclimatization before transferring them to land. About 95% of survival rate was recorded. The augmentation process of culturing various explants through somatic embryogenesis using synthetic medium with various plant growth regulators under controlled conditions have aggrandized the commercial production of Sauropus making it easily available over the conventional propagation methods. In addition, regeneration process through somatic embryogenesis has ameliorated the development of desired character in Sauropus with low papaverine content thereby providing a valuable resource to the food and pharmaceutical industry. Based on this research, plant tissue culture techniques have shown promise for economical and convenient application in Sauropus androgynous breeding.

Keywords: L2 medium, multivitamin plant, NAA, papaverine

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Authors: Avinash Malladhi

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Forensic accounting is a specialized field that involves the application of accounting principles, investigative skills, and legal knowledge to detect and prevent fraud. With the rise of big data and technological advancements, artificial intelligence (AI) and machine learning (ML) algorithms have emerged as powerful tools for forensic accountants to enhance their fraud detection capabilities. In this paper, we review and analyze various AI/ML algorithms that are commonly used in forensic accounting, including supervised and unsupervised learning, deep learning, natural language processing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Support Vector Machines (SVMs), Decision Trees, and Random Forests. We discuss their underlying principles, strengths, and limitations and provide empirical evidence from existing research studies demonstrating their effectiveness in detecting financial fraud. We also highlight potential ethical considerations and challenges associated with using AI/ML in forensic accounting. Furthermore, we highlight the benefits of these technologies in improving fraud detection and prevention in forensic accounting.

Keywords: AI, machine learning, forensic accounting & fraud detection, anti money laundering, Benford's law, fraud triangle theory

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Authors: Ksenia Meshkova

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With increasing amount of multimedia data, it is very important to automate and speed up the process of obtaining meta. This process means not just recognition of some object or its movement, but recognition of the entire scene versus separate frames and having timeline segmentation as a final result. Labeling datasets is time consuming, besides, attributing characteristics to particular scenes is clearly difficult due to their nature. In this article, we will consider autoencoders application to unsupervised scene recognition and clusterization based on interpretable features. Further, we will focus on particular types of auto encoders that relevant to our study. We will take a look at the specificity of deep learning related to information theory and rate-distortion theory and describe the solutions empowering poor interpretability of deep learning in media content processing. As a conclusion, we will present the results of the work of custom framework, based on autoencoders, capable of scene recognition as was deeply studied above, with highlights generation resulted out of this recognition. We will not describe in detail the mathematical description of neural networks work but will clarify the necessary concepts and pay attention to important nuances.

Keywords: neural networks, computer vision, representation learning, autoencoders

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Authors: Tomas Premoli, Sareh Rowlands

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In this study, we investigate the impact of various convolutional neural network (CNN) architectures on the accuracy of diagnosing Alzheimer’s disease (AD) using patient MRI scans. Alzheimer’s disease is a debilitating neurodegenerative disorder that affects millions worldwide. Early, accurate, and non-invasive diagnostic methods are required for providing optimal care and symptom management. Deep learning techniques, particularly CNNs, have shown great promise in enhancing this diagnostic process. We aim to contribute to the ongoing research in this field by comparing the effectiveness of different CNN architectures and providing insights for future studies. Our methodology involved preprocessing MRI data, implementing multiple CNN architectures, and evaluating the performance of each model. We employed intensity normalization, linear registration, and skull stripping for our preprocessing. The selected architectures included VGG, ResNet, and DenseNet models, all implemented using the Keras library. We employed transfer learning and trained models from scratch to compare their effectiveness. Our findings demonstrated significant differences in performance among the tested architectures, with DenseNet201 achieving the highest accuracy of 86.4%. Transfer learning proved to be helpful in improving model performance. We also identified potential areas for future research, such as experimenting with other architectures, optimizing hyperparameters, and employing fine-tuning strategies. By providing a comprehensive analysis of the selected CNN architectures, we offer a solid foundation for future research in Alzheimer’s disease diagnosis using deep learning techniques. Our study highlights the potential of CNNs as a valuable diagnostic tool and emphasizes the importance of ongoing research to develop more accurate and effective models.

Keywords: Alzheimer’s disease, convolutional neural networks, deep learning, medical imaging, MRI

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Authors: Dongkwon Han, Sangho Kim, Sunil Kwon

Abstract:

Unconventional gas resources have dramatically changed the future energy landscape. Unlike conventional gas resources, the key challenges in unconventional gas have been the requirement that applies to advanced approaches for production forecasting due to uncertainty and complexity of fluid flow. In this study, artificial neural network (ANN) model which integrates machine learning and data-driven approach was developed to predict productivity in shale gas. The database of 129 wells of Eagle Ford shale basin used for testing and training of the ANN model. The Input data related to hydraulic fracturing, well completion and productivity of shale gas were selected and the output data is a cumulative production. The performance of the ANN using all data sets, clustering and variables importance (VI) models were compared in the mean absolute percentage error (MAPE). ANN model using all data sets, clustering, and VI were obtained as 44.22%, 10.08% (cluster 1), 5.26% (cluster 2), 6.35%(cluster 3), and 32.23% (ANN VI), 23.19% (SVM VI), respectively. The results showed that the pre-trained ANN model provides more accurate results than the ANN model using all data sets.

Keywords: unconventional gas, artificial neural network, machine learning, clustering, variables importance

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Authors: J. K. Adedeji, M. O. Oyekanmi

Abstract:

This paper has critically examined the use of Machine Learning procedures in curbing unauthorized access into valuable areas of an organization. The use of passwords, pin codes, user’s identification in recent times has been partially successful in curbing crimes involving identities, hence the need for the design of a system which incorporates biometric characteristics such as DNA and pattern recognition of variations in facial expressions. The facial model used is the OpenCV library which is based on the use of certain physiological features, the Raspberry Pi 3 module is used to compile the OpenCV library, which extracts and stores the detected faces into the datasets directory through the use of camera. The model is trained with 50 epoch run in the database and recognized by the Local Binary Pattern Histogram (LBPH) recognizer contained in the OpenCV. The training algorithm used by the neural network is back propagation coded using python algorithmic language with 200 epoch runs to identify specific resemblance in the exclusive OR (XOR) output neurons. The research however confirmed that physiological parameters are better effective measures to curb crimes relating to identities.

Keywords: biometric characters, facial recognition, neural network, OpenCV

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Authors: Parisa Mansour

Abstract:

Understanding how vision works in both health and disease involves understanding the anatomy and physiology of the eye as well as the neural pathways involved in visual perception. The development of imaging techniques for the visual system is essential for understanding the neural foundation of visual function or impairment. MRI provides a way to examine neural circuit structure and function without invasive procedures, allowing for the detection of brain tissue abnormalities in real time. One of the advanced MRI methods is manganese-enhanced MRI (MEMRI), which utilizes active manganese contrast agents to enhance brain tissue signals in T1-weighted imaging, showcasing connectivity and activity levels. The way manganese ions build up in the eye, and visual pathways can be due to their spread throughout the body or by moving locally along axons in a forward direction and entering neurons through calcium channels that are voltage-gated. The paramagnetic manganese contrast is utilized in MRI for various applications in the visual system, such as imaging neurodevelopment and evaluating neurodegeneration, neuroplasticity, neuroprotection, and neuroregeneration. In this assessment, we outline four key areas of scientific research where MEMRI can play a crucial role - understanding brain structure, mapping nerve pathways, monitoring nerve cell function, and distinguishing between different types of glial cell activity. We discuss various studies that have utilized MEMRI to investigate the visual system, including delivery methods, spatiotemporal features, and biophysical analysis. Based on this literature, we have pinpointed key issues in the field related to toxicity, as well as sensitivity and specificity of manganese enhancement. We will also examine the drawbacks and other options to MEMRI that could offer new possibilities for future exploration.

Keywords: glial activity, manganese-enhanced magnetic resonance imaging, neuroarchitecture, neuronal activity, neuronal tract tracing, visual pathway, eye

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Authors: Laheeb M. Ibrahim, Ibrahim A. Salih

Abstract:

Biometrics is a technology that has been widely used in many official and commercial identification applications. The increased concerns in security during recent years (especially during the last decades) have essentially resulted in more attention being given to biometric-based verification techniques. Here, a novel fusion approach of palmprint, dental traits has been suggested. These traits which are authentication techniques have been employed in a range of biometric applications that can identify any postmortem PM person and antemortem AM. Besides improving the accuracy, the fusion of biometrics has several advantages such as increasing, deterring spoofing activities and reducing enrolment failure. In this paper, a first unimodel biometric system has been made by using (palmprint and dental) traits, for each one classification applying an artificial neural network and a hybrid technique that combines swarm intelligence and neural network together, then attempt has been made to combine palmprint and dental biometrics. Principally, the fusion of palmprint and dental biometrics and their potential application has been explored as biometric identifiers. To address this issue, investigations have been carried out about the relative performance of several statistical data fusion techniques for integrating the information in both unimodal and multimodal biometrics. Also the results of the multimodal approach have been compared with each one of these two traits authentication approaches. This paper studies the features and decision fusion levels in multimodal biometrics. To determine the accuracy of GAR to parallel system decision-fusion including (AND, OR, Majority fating) has been used. The backpropagation method has been used for classification and has come out with result (92%, 99%, 97%) respectively for GAR, while the GAR) for this algorithm using hybrid technique for classification (95%, 99%, 98%) respectively. To determine the accuracy of the multibiometric system for feature level fusion has been used, while the same preceding methods have been used for classification. The results have been (98%, 99%) respectively while to determine the GAR of feature level different methods have been used and have come out with (98%).

Keywords: back propagation neural network BP ANN, multibiometric system, parallel system decision-fusion, practical swarm intelligent PSO

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Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

Abstract:

The most aggressive form of brain tumor is called glioma. Glioma is kind of tumor that arises from glial tissue of the brain and occurs quite often. A fully automatic 2D-CNN model for brain tumor segmentation is presented in this paper. We performed pre-processing steps to remove noise and intensity variances using N4ITK and standard intensity correction, respectively. We used Keras open-source library with Theano as backend for fast implementation of CNN model. In addition, we used BRATS 2015 MRI dataset to evaluate our proposed model. Furthermore, we have used SimpleITK open-source library in our proposed model to analyze images. Moreover, we have extracted random 2D patches for proposed 2D-CNN model for efficient brain segmentation. Extracting 2D patched instead of 3D due to less dimensional information present in 2D which helps us in reducing computational time. Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.77 for complete, 0.76 for core, 0.77 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, LGG

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Authors: Shanna Swart, Caryn Fenner, Athanasios Kotsiopoulos, Susan Harrison

Abstract:

Linear alkanes are produced as by-products from the increasing use of gas-to-liquid fuel technologies for synthetic fuel production and offer great potential for value addition. Their current use as low-value fuels and solvents do not maximize this potential. Therefore, attention has been drawn towards direct activation of these aliphatic alkanes to more useful products such as alcohols, aldehydes, carboxylic acids and derivatives. Cytochrome P450 monooxygenases (P450s) can be used for activation of these aliphatic alkanes using whole-cells or cell-free systems. Some limitations of whole-cell systems include reduced mass transfer, stability and possible side reactions. Since the P450 systems are little studied as cell-free systems, they form the focus of this study. Challenges of a cell-free system include co-factor regeneration, substrate availability and enzyme stability. Enzyme immobilization offers a positive outlook on this dilemma, as it may enhance stability of the enzyme. In the present study, 2 different P450s (CYP153A6 and CYP102A1) as well as the relevant accessory enzymes required for electron transfer (ferredoxin and ferredoxin reductase) and co-factor regeneration (glucose dehydrogenase) have been expressed in E. coli and purified by metal affinity chromatography. Glucose dehydrogenase (GDH), was used as a model enzyme to assess the potential of various enzyme immobilization strategies including; surface attachment on MagReSyn® microspheres with various functionalities and on electrospun nanofibers, using self-assembly based methods forming Cross Linked Enzymes (CLE), Cross Linked Enzyme Aggregates (CLEAs) and spherezymes as well as in a sol gel. The nanofibers were synthesized by electrospinning, which required the building of an electrospinning machine. The nanofiber morphology has been analyzed by SEM and binding will be further verified by FT-IR. Covalent attachment based methods showed limitations where only ferredoxin reductase and GDH retained activity after immobilization which were largely attributed to insufficient electron transfer and inactivation caused by the crosslinkers (60% and 90% relative activity loss for the free enzyme when using 0.5% glutaraldehyde and glutaraldehyde/ethylenediamine (1:1 v/v), respectively). So far, initial experiments with GDH have shown the most potential when immobilized via their His-tag onto the surface of MagReSyn® microspheres functionalized with Ni-NTA. It was found that Crude GDH could be simultaneously purified and immobilized with sufficient activity retention. Immobilized pure and crude GDH could be recycled 9 and 10 times, respectively, with approximately 10% activity remaining. The immobilized GDH was also more stable than the free enzyme after storage for 14 days at 4˚C. This immobilization strategy will also be applied to the P450s and optimized with regards to enzyme loading and immobilization time, as well as characterized and compared with the free enzymes. It is anticipated that the proposed immobilization set-up will offer enhanced enzyme stability (as well as reusability and easy recovery), minimal mass transfer limitation, with continuous co-factor regeneration and minimal enzyme leaching. All of which provide a positive outlook on this robust multi-enzyme system for efficient activation of linear alkanes as well as the potential for immobilization of various multiple enzymes, including multimeric enzymes for different bio-catalytic applications beyond alkane activation.

Keywords: alkane activation, cytochrome P450 monooxygenase, enzyme catalysis, enzyme immobilization

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