Sammon Quadratic Recurrent Multilayer Deep Classifier for Legal Document Analytics

In recent years,machine learning algorithms and in particular deep learning has shown promising results when used in the field of legal domain.The legal field is strongly affected by the problem of information overload,due to the large amount of legal material stored in textual form.Legal text processing is essential in the legal domain to analyze the texts of the court events to automatically predict smart decisions.With an increasing number of digitally available documents,legal text processing is essential to analyze documents which helps to automate various legal domain tasks.Legal document classification is a valuable tool in legal services for enhancing the quality and efficiency of legal document review.In this paper,we propose Sammon Keyword Mapping-based Quadratic Discriminant Recurrent Multilayer Perceptive Deep Neural Classifier(SKM-QDRMPDNC),a system that applies deep neural methods to the problem of legal document classification.The SKM-QDRMPDNC technique consists of many layers to perform the keyword extraction and classification.First,the set of legal documents are collected from the dataset.Then the keyword extraction is performed using SammonMapping technique based on the distance measure.With the extracted features,Quadratic Discriminant analysis is applied to performthe document classification based on the likelihood ratio test.Finally,the classified legal documents are obtained at the output layer.This process is repeated until minimum error is attained.The experimental assessment is carried out using various performance metrics such as accuracy,precision,recall,F-measure,and computational time based on several legal documents collected from the dataset.The observed results validated that the proposed SKM-QDRMPDNC technique provides improved performance in terms of achieving higher accuracy,precision,recall,and F-measure with minimum computation time when compared to existing methods.

Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment

The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before.However,the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services.Thus,there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service.Most of the existing techniques—including collaborative filtering(CF),which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems,preventing them from providing high quality recommendations.Inspired by the great success of deep learning in a wide range of fields,this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF recommendations.The proposed deep learning model is designed as a hybrid architecture with three key networks,namely autoencoder(AE),multilayered perceptron(MLP),and generalized matrix factorization(GMF).The model employs two AE networks to learn deep latent feature representations of users and items respectively and in parallel.Next,MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item features.Finally,the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF and MLP networks.We conducted extensive experiments on two benchmark datasets,MoiveLens100K and MovieLens1M,using four standard evaluation metrics.Ablation experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation performance.Comparative analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CF methods with resistance to rating sparsity and cold-start problems.

Complex Function Approximation Based on Multi-ANN Approach

This paper presents a multi-ANN approximation approach to approximate complex non-linear function. Comparing with single-ANN methods the proposed approach improves and increases the approximation and generalization ability, and adaptability greatly in learning processes of networks. The simulation results have been shown that the method can be applied to the modeling and identification of complex dynamic control systems.

Investigation of Android Malware Using Deep Learning Approach

In recent days the usage of android smartphones has increased exten-sively by end-users.There are several applications in different categories bank-ing/finance,social engineering,education,sports andfitness,and many more applications.The android stack is more vulnerable compared to other mobile plat-forms like IOS,Windows,or Blackberry because of the open-source platform.In the Existing system,malware is written using vulnerable system calls to bypass signature detection important drawback is might not work with zero-day exploits and stealth malware.The attackers target the victim with various attacks like adware,backdoor,spyware,ransomware,and zero-day exploits and create threat hunts on the day-to-day basics.In the existing approach,there are various tradi-tional machine learning classifiers for building a decision support system with limitations such as low detection rate and less feature selection.The important contents taken for building model from android applications like Intent Filter,Per-mission Signature,API Calls,and System commands are taken from the manifestfile.The function parameters of various machine and deep learning classifiers like Nave Bayes,k-Nearest Neighbors(k-NN),Support Vector Machine(SVM),Ada Boost,and Multi-Layer Perceptron(MLP)are done for effective results.In our pro-posed work,we have used an unsupervised learning multilayer perceptron with multiple target labels and built a model with a better accuracy rate compared to logistic regression,and rank the best features for detection of applications and clas-sify as malicious or benign can be used as threat model by online antivirus scanners.

Determination of future land use changes using remote sensing imagery and artificial neural network algorithm:A case study of Davao City,Philippines

Land use and land cover(LULC)changes refer to alterations in land use or physical characteristics.These changes can be caused by human activities,such as urbanization,agriculture,and resource extraction,as well as natural phenomena,for example,erosion and climate change.LULC changes significantly impact ecosystem services,biodiversity,and human welfare.In this study,LULC changes in Davao City,Philippines,were simulated,predicted,and projected using a multilayer perception artificial neural network(MLP-ANN)model.The MLP-ANN model was employed to analyze the impact of elevation and proximity to road networks(i.e.,exploratory maps)on changes in LULC from 2017 to 2021.The predicted 2021 LULC map shows a high correlation to the actual LULC map of 2021,with a kappa index of 0.91 and a 96.68%accuracy.The MLP-ANN model was applied to project LULC changes in the future(i.e.,2030 and 2050).The results suggest that in 2030,the built-up area and trees are increasing by 4.50%and 2.31%,respectively.Unfortunately,water will decrease by up to 0.34%,and crops is about to decrease by approximately 3.25%.In the year 2050,the built-up area will continue to increase to 6.89%,while water and crops will decrease by 0.53%and 3.32%,respectively.Overall,the results show that anthropogenic activities influence the land’s alterations.Moreover,the study illustrates how machine learning models can generate a reliable future scenario of land usage changes.

Integrating the artificial intelligence and hybrid machine learning algorithms for improving the accuracy of spatial prediction of landslide hazards in Kurseong Himalayan Region

The aim of the current work is to compare susceptibility maps of landslides produced using machine learning techniques i.e.multilayer perception neural nets(MLP),kernel logistic regression(KLR),random forest(RF),and multivariate adaptive regression splines(MARS);novel ensemble approaches i.e.MLP-Bagging,KLR-Bagging,RFBagging and MARS-Bagging in the Kurseong-Himalayan region.For the ensemble models the RF,KLR,MLP and MARS were used as base classifiers,and Bagging was used as meta classifier.Another objective of the current work is to introduce and evaluate the effectiveness of the novel KLR-Bagging and MARS-Bagging ensembles in susceptibility to landslide.Compiling 303 landslide locations to calibrate and test the models,an inventory map was created.Eighteen LCFs were chosen using the Relief-F and multi-collinearity tests for mapping the landslide susceptibility.Applying receiver operating characteristic(ROC),precision,accuracy,incorrectly categorized proportion,mean-absolute-error(MAE),and root-mean-square-error(RMSE),the LSMs were subsequently verified.The different validation results showed RF-Bagging(AUC training 88.69%&testing 92.28%)with ensemble Meta classifier gives better performance than the MLP,KLR,RF,MARS,MLP-Bagging,KLR-Bagging,and MARSBagging based LSMs.RF model showed that the slope,altitude,rainfall,and geomorphology played the most vital role in landslide occurrence comparing the other LCFs.These results will help to reduce the losses caused by the landslides in the Kurseong and in other areas where geo-environmental and geological conditions more or less similar.