Security, Trust and Privacy (STP) Model for Federated Identity and Access Management (FIAM) Systems

The federated identity and access management systems facilitate the home domain organization users to access multiple resources (services) in the foreign domain organization by web single sign-on facility. In federated environment the user’s authentication is performed in the beginning of an authentication session and allowed to access multiple resources (services) until the current session is active. In current federated identity and access management systems the main security concerns are: (1) In home domain organization machine platforms bidirectional integrity measurement is not exist, (2) Integrated authentication (i.e., username/password and home domain machine platforms mutual attestation) is not present and (3) The resource (service) authorization in the foreign domain organization is not via the home domain machine platforms bidirectional attestation

Emotion classification in poetry text using deep neural network

Emotion classification from online content has received considerable attention from researchers in recent times. Most of the work in this direction has been carried out on classifying emotions from informal text, such as chat, sms, tweets and other social media content. However, less attention is given to emotion classification from formal text, such as poetry. In this work, we propose an emotion classification system from poetry text using a deep neural network model. For this purpose, the BiLSTM model is implemented on a benchmark poetry dataset. This is capable of classifying poetry into different emotion types, such as love, anger, alone, suicide and surprise. The efficiency of the proposed model is compared with different baseline methods, including machine learning and deep learning models

Applying Deep Neural Networks for Predicting Dark Triad Personality Trait of Online Users

© 2020 IEEE. In the recent times, the social networking sites act as a rich source of information, which is shared among online users, who post comments and express their opinions in the form of likes and dislikes. Such content reflects important clues about the personality and behavior of the online community. The dark triad personality traits, such as the psychopathic behavior of individuals, can be detected using computational models. The earlier studies on the dark triad (psychopath) prediction exploit traditional machine learning techniques with limited dataset size. Therefore, it is required to develop an advanced deep neural network-based technique. In this work, we implement a deep neural network model, namely BILSTM for the efficient prediction of dark triad (psychopath) personality traits regarding online users. Experimental results depict that the proposed model attained an improved AUC (0.82) when compared to the baseline study

Customer churn prediction using composite deep learning technique

Customer churn, a phenomenon that causes large financial losses when customers leave a business, makes it difficult for modern organizations to retain customers. When dissatisfied customers find their present company\u27s services inadequate, they frequently migrate to another service provider. Machine learning and deep learning (ML/DL) approaches have already been used to successfully identify customer churn. In some circumstances, however, ML/DL-based algorithms lacks in delivering promising results for detecting client churn. Previous research on estimating customer churn revealed unexpected forecasts when utilizing machine learning classifiers and traditional feature encoding methodologies. Deep neural networks were also used in these efforts to extract features without taking into account the sequence information. In view of these issues, the current study provides an effective method for predicting customer churn based on a hybrid deep learning model termed BiLSTM-CNN. The goal is to effectively estimate customer churn using benchmark data and increase the churn prediction process\u27s accuracy. The experimental results show that when trained, tested, and validated on the benchmark dataset, the proposed BiLSTM-CNN model attained a remarkable accuracy of 81%

A survey on sentiment analysis in Urdu: A resource-poor language

© 2020 Background/introduction: The dawn of the internet opened the doors to the easy and widespread sharing of information on subject matters such as products, services, events and political opinions. While the volume of studies conducted on sentiment analysis is rapidly expanding, these studies mostly address English language concerns. The primary goal of this study is to present state-of-art survey for identifying the progress and shortcomings saddling Urdu sentiment analysis and propose rectifications. Methods: We described the advancements made thus far in this area by categorising the studies along three dimensions, namely: text pre-processing lexical resources and sentiment classification. These pre-processing operations include word segmentation, text cleaning, spell checking and part-of-speech tagging. An evaluation of sophisticated lexical resources including corpuses and lexicons was carried out, and investigations were conducted on sentiment analysis constructs such as opinion words, modifiers, negations. Results and conclusions: Performance is reported for each of the reviewed study. Based on experimental results and proposals forwarded through this paper provides the groundwork for further studies on Urdu sentiment analysis

Security, Trust and Privacy (STP) in Federated Identity and Access Management Mode and Trusted Computing (TC) Attestation

The federated identity and access management facilitate the home domain users to access multiple resources (services) at the foreign domain using single sign-on facility. They make use of the secure socket layer, firewalls and multi-factor authentication to diminish the security threats. However, first of all such traditional security gauges do not provide bidirectional protection for the communicating machine's platform integrity in the home domain organization against malevolent programs such as Trojans, worms and viruses. The automated installation of these programs may lead to risks such as the user's login credential theft and the capturing of the user's keyboard inputs remotely. Therefore, the absence of the mutual trust in the communicating machines platform may possibly lead to the security threats in the home and foreign domains. The Trusted Computing solutions such as the trusted platform module and the mutual attestation technique may utilize the integrity measurement architecture to establish the mutual trust and security in the machines platform. However, mutual attestation may lead to the machine's platform security credential privacy concern. Therefore, such concerns demand unified security, trust and privacy solutions in the imminent federated identity and access management mode to collaborate in a secured, trustworthy and privacy-enhanced fashion

A survey on sentiment analysis in Urdu: A resource-poor language

Background/introduction: The dawn of the internet opened the doors to the easy and widespread sharing of information on subject matters such as products, services, events and political opinions. While the volume of studies conducted on sentiment analysis is rapidly expanding, these studies mostly address English language concerns. The primary goal of this study is to present state-of-art survey for identifying the progress and shortcomings saddling Urdu sentiment analysis and propose rectifications. Methods: We described the advancements made thus far in this area by categorising the studies along three dimensions, namely: text pre-processing lexical resources and sentiment classification. These pre-processing operations include word segmentation, text cleaning, spell checking and part-of-speech tagging. An evaluation of sophisticated lexical resources including corpuses and lexicons was carried out, and investigations were conducted on sentiment analysis constructs such as opinion words, modifiers, negations. Results and conclusions: Performance is reported for each of the reviewed study. Based on experimental results and proposals forwarded through this paper provides the groundwork for further studies on Urdu sentiment analysis

Integrated effect of allelochemicals and herbicides on weed suppression and soil microbial activity in wheat (Triticum aestivum L.)

To evaluate the allelopathic effects of major crops and weeds, studies were conducted during 2011–12 and 2012–13 by utilizing water extracts of allelopathic plants namely rice (Oryza sativa L.), Parthenium hysterophorus L., Phragmites australis Cav., and Datura alba L. with reduced rates of fenoxaprop-p-ethyl and bromoxynil plus MCPA to control weeds. Application of Phragmites australis and Parthenium hysterophorus along with lower rates of fenoxaprop-p-ethyl and bromoxynil plus MCPA showed promising results by controlling weeds and improving yield. Parthenium hysterophorus extract with half of the recommended dose of fenoxaprop-p-ethyl and bromoxynil plus MCPA reduced weed density by 51 and 50% during year 1 and year 2, respectively, compared with the control. Phragmites australis extract with half of the recommended dose of fenoxaprop-p-ethyl produced grain yield (5.7 and 6.1\ua0t\ua0ha during year 1 and 2, respectively) in wheat. However, these results were also at par with Parthenium hysterophorus and Datura alba extracts in combinations with half the recommended doses of the above mentioned herbicides. The study of microbial activity showed higher amount of mineralizable carbon in D. alba with half the recommended dose of fenoxaprop-p-ethyl treated plots (0.073\ua0g during both the years). The lowest amount of mineralizable carbon (0.035 and 0.030\ua0g during year 1 and 2, respectively) was observed in the control plots. The presence of allelopathic plants in field crops and subsequent mixing in soil by tillage may create problems in crop production. Therefore, further studies are suggested to fully explore all the possible interactions among allelochemicals and herbicides