User Activity Classification and Domain-Wise Ranking Through Social Interactions

Twitter has gained a significant prevalence among the users across the numerous domains, in the majority of the countries, and among different age groups. It servers a real-time micro-blogging service for communication and opinion sharing. Twitter is sharing its data for research and study purposes by exposing open APIs that make it the most suitable source of data for social media analytics. Applying data mining and machine learning techniques on tweets is gaining more and more interest. The most prominent enigma in social media analytics is to automatically identify and rank influencers. This research is aimed to detect the user's topics of interest in social media and rank them based on specific topics, domains, etc. Few hybrid parameters are also distinguished in this research based on the post's content, post’s metadata, user’s profile, and user's network feature to capture different aspects of being influential and used in the ranking algorithm. Results concluded that the proposed approach is well effective in both the classification and ranking of individuals in a cluster.

Enhancing Behavioral Dependency for Effective Computing in Software

Software plays an important role in effective computing and communication of any services. It become crucial to identify some critical parts of the software that can lead to enhanced computing and increases efficiency of the software. Dependency plays a significant role in finding relationship amongst classes and predicting change prone classes. This paper aims to enhance Behavioral Dependency by defining 6 types of dependencies amongst classes. These are (i) direct behavioral dependency (ii) indirect behavioral dependency (iii) internal behavioral dependency (iv) external behavioral dependency (v) indirect internal behavioral dependency and (vi) Indirect External Behavioral Dependency. Evaluating these dependencies, gives accurate results for the prediction of change prone classes. Further, paper compares proposed approach with existing methods.

Multi-layer and Clustering based Security Implementation for an IoT Environment

IoT devices are having many constraints related to computation power and memory etc. Many existing cryptographic algorithms of security could not work with IoT devices because of these constraints. Since the sensors are used in large amount to collect the relevant data in an IoT environment, and different sensor devices transmit these data as useful information, the first thing needs to be secure is the identity of devices. The second most important thing is the reliable information transmission between a sensor node and a sink node. While designing the cryptographic method in the IoT environment, programmers need to keep in mind the power limitation of the constraint devices. Mutual authentication between devices and encryption-decryption of messages need some sort of secure key. In the proposed cryptographic environment, there will be a hierarchical clustering, and devices will get registered by the authentication center at the time they enter the cluster. The devices will get mutually authenticated before initiating any conversation and will have to follow the public key protocol.

BERT-BU12 Hate Speech Detection using Bidirectional Encoder-Decoder

In the recent times transfer learning models have known to exhibited good results in the area of text classification for question-answering, summarization, next word prediction but these learning models have not been extensively used for the problem of hate speech detection yet. We anticipate that these networks may give better results in another task of text classification i.e. hate speech detection. This paper introduces a novel method of hate speech detection based on the concept of attention networks using the BERT attention model. We have conducted exhaustive experiments and evaluation over publicly available datasets using various evaluation metrics (precision, recall and F1 score). We show that our model outperforms all the state-of-the-art methods by almost 4%. We have also discussed in detail the technical challenges faced during the implementation of the proposed model.

User’s Activity Classification And Domain Wise Ranking Through Social Interactions

Twitter has gained a significant prevalence among the users across the numerous domains, in the majority of the countries, and among different age groups. It servers a real-time micro-blogging service for communication and opinion sharing. Twitter is sharing its data for research and study purposes by exposing open APIs that make it the most suitable source of data for social media analytics. Applying data mining and machine learning techniques on tweets is gaining more and more interest. The most prominent enigma in social media analytics is to automatically identify and rank influencers. This research is aimed to detect the user's topics of interest in social media and rank them based on specific topics, domains, etc. Few hybrid parameters are also distinguished in this research based on the post's content, post’s metadata, user’s profile, and user's network feature to capture different aspects of being influential and used in the ranking algorithm. Results concluded that the proposed approach is well effective in both the classification and ranking of individuals in a cluster.

Robust RDH Technique using Sorting and IPVO-based Pairwise PEE for Secure Communication

Nowadays, Reversible Data Hiding (RDH) is used extensively in information sensitive communication domains to protect the integrity of hidden data and the cover medium. However, most of the recently proposed RDH methods lack robustness. Robust RDH methods are required to protect the hidden data from security attacks at the time of communication between the sender and receiver. In this paper, we propose a Robust RDH scheme using IPVO based pairwise embedding. The proposed scheme is designed to prevent unintentional modifications caused to the secret data by JPEG compression. The cover image is decomposed into two planes namely HSB plane and LSB plane. As JPEG compression most likely modifies the LSBs of the cover image during compression, it is best not to hide the secret data into LSB planes. So, the proposed method utilizes a pairwise embedding to embed secret data into HSB plane of the cover image. High fidelity improved pixel value ordering (IPVO) based pairwise embedding ensures that the embedding performance of the proposed method is improved.

Enhancing Behavioral Dependency for Effective Computing in Software

Software plays an important role in effective computing and communication of any services. It become crucial to identify some critical parts of the software that can lead to enhanced computing and increases efficiency of the software. Dependency plays a significant role in finding relationship amongst classes and predicting change prone classes. This paper aims to enhance Behavioral Dependency by defining 6 types of dependencies amongst classes. These are (i) direct behavioral dependency (ii) indirect behavioral dependency (iii) internal behavioral dependency (iv) external behavioral dependency (v) indirect internal behavioral dependency and (vi) Indirect External Behavioral Dependency. Evaluating these dependencies, gives accurate results for the prediction of change prone classes. Further, paper compares proposed approach with existing methods.

Models for Efficient Utilization of Resources for Upgrading Android Mobile Technology

The need of the customers to be connected to the network at all times has led to the evolution of mobile technology. Operating systems play a vitol role when we talk of technology. Nowadays, Android is one of the popularly used operating system in mobile phones. Authors have analysed three stable versions of Android, 6.0, 7.0 and 8.0. Incorporating a change in the version after it is released requires a lot of rework and thus huge amount of costs are incurred. In this paper, the aim is to reduce this rework by identifying certain parts of a version during early phase of development which need careful attention. Machine learning prediction models are developed to identify the parts which are more prone to changes. The accuracy of such models should be high as the developers heavily rely on them. The high dimensionality of the dataset may hamper the accuracy of the models. Thus, the authors explore four dimensionality reduction techniques, which are unexplored in the field of network and communication. The results concluded that the accuracy improves after reducing the features.

Economic Decision-Making and Risk Management

Because of the recent financial crisis in the United States that shook the financial sector, the need for adopting effective Risk Management practices has increased. Essentially, the volatility of the sector calls for an augmented re-evaluation of the framework, as well as the components of uncertainty management practices by commercial banks, regulatory agencies, and scholars. By doing so, the stakeholders in the financial sector would ensure the conformity to the best practices. To further fortify this, the research herein uses the Ames National Corporation (ANC), which is a commercial Bank in Iowa, USA, as a case study. The institution risk profile and risk management practices are evaluated to give insights on conforming to the best international practices. The research also seeks to establish whether effective risk management results in enhanced performance and profitability for financial institutions.Stating areas on which further research should be conducted is how the study is concluded.

Optimizing Energy Consumption in IoT-Based Scalable Wireless Sensor Networks

In recent era of IoT, energy ingesting by sensor nodes in Wireless Sensor Networks (WSN) is one of the key challenges. It is decisive to diminish energy ingesting due to restricted battery lifespan of sensor nodes, Objective of this research is to develop efficient routing protocol/algorithm in IoT based scenario to enhance network performance with QoS parameters. Therefore, keeping this objective in mind, a QoS based Optimized Energy Clustering Routing (QOECR) protocol for IoT based WSN is proposed and evaluated. QOECR discovers optimal path for sink node and provides better selection for sub-sink nodes. Simulation has been done in MATLAB to assess the performance of QOECR with pre-existing routing protocols. Simulation outcomes represent that QOECR reduces E2E delay 30%-35%, enhances throughput 25%-30%, minimizes energy consumption 35%-40%, minimizes packet loss 28%-32%, improves PDR and prolongs network lifetime 32%-38% than CBCCP, HCSM and ZEAL routing protocols.