Comparative study on sentimental analysis using machine learning techniques

With the advancement of the Internet and the world wide web (WWW), it is observed that there is an exponential growth of data and information across the internet. In addition, there is a huge growth in digital or textual data generation. This is because users post the reply comments in social media websites based on the experiences about an event or product. Furthermore, people are interested to know whether the majority of potential buyers will have a positive or negative experience on the event or the product. This kind of classification in general can be attained through Sentiment Analysis which inputs unstructured text comments about the product reviews, events, etc., from all the reviews or comments posted by users. This further classifies the data into different categories namely positive, negative or neutral opinions. Sentiment analysis can be performed by different machine learning models like CNN, Naive Bayes, Decision Tree, XgBoost, Logistic Regression etc. The proposed work is compared with the existing solutions in terms of different performance metrics and XgBoost outperforms out of all other methods

Algorithms for Finding Influential People with Mixed Centrality in Social Networks

Identifying the seed nodes in networks is an important task for understanding the dynamics of information diffusion. It has many applications, such as energy usage/consumption, rumor control, viral marketing, and opinion monitoring. When compared to other nodes, seed nodes have the potential to spread information in the majority of networks. To identify seed nodes, researchers gave centrality measures based on network structures. Centrality measures based on local structure are degree, semi-local, Pagerank centralities, etc. Centrality measures based on global structure are betweenness, closeness, eigenvector, etc. Very few centrality measures exist based on the network’s local and global structure. We define mixed centrality measures based on the local and global structure of the network. We propose a measure based on degree, the shortest path between vertices, and any global centrality. We generalized the definition of our mixed centrality, where we can use any measure defined on a network’s global structure. By using this mixed centrality, we identify the seed nodes of various real-world networks. We also show that this mixed centrality gives good results compared with existing basic centrality measures. We also tune the different real-world parameters to study the effect of their maximum influence

A novel approach to minimize the Black Hole attacks in Vehicular IoT Networks

Vehicular Ad-hoc IoT Networks (VA-IOT) have gained significant attention due to their ability to enable the distributed data transmission between vehicles to vehicle. However, VA-IOT are susceptible to various security threats, including the Black Hole attack. With Black Hole attacks, an intruder or malicious node attracts the internet traffic by broadcasting fake messages and drops all the received packets, which can significantly impact the network\u27s performance. To mitigate, this paper presents an new mechanism to minimize the Black Hole attack on VANETs by combining two techniques: A trust management system and an intrusion detection system. The proposed approach involves assigning trust values to each vehicle based on their past behavior and routing packets through only trusted nodes. Additionally, an intrusion detection system is used to identify malicious nodes that violate the trust threshold and to take appropriate measures. The performance of the proposed approach is outperformed in terms of achievable end-To-end throughput and minimized network delays

Redundant Transmission Control Algorithm for Information-Centric Vehicular IoT Networks

Vehicular Adhoc Networks (VANETs) enable vehicles to act as mobile nodes that can fetch, share, and disseminate information about vehicle safety, emergency events, warning messages, and passenger infotainment. However, the continuous dissemination of information from vehicles and their one-hop neighbor nodes, Road Side Units (RSUs), and VANET infrastructures can lead to performance degradation of VANETs in the existing host-centric IP-based network. Therefore, Information Centric Networks (ICN) are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile, dynamic, and error-prone domains. In ICN-based Vehicular-IoT networks, consumer mobility is implicitly supported, but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular nodes. This paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information dissemination. The proposed protocol, called Network Cording Multiple Solutions Scheduling (NCMSS), is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information sources. Experimental results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching, data retrieval delay, and end-to-end application throughput. The end-to-end throughput in proposed NCMSS is 22% higher (for 1024 byte data) than existing solutions whereas delay in NCMSS is reduced by 5% in comparison with existing solutions