Cognitive science is a technology which focuses on analyzing the human brain using the application of DM. The databases are utilized to gather and store the large volume of data. The authenticated information is extracted using measures. This research work is based on detecting the sarcasm from the text data. This research work introduces a scheme to detect sarcasm based on PCA algorithm, - means algorithm, and ensemble classification. The four ensemble classifiers are designed with the objective of detecting the sarcasm. The first ensemble classification algorithm (SKD) is the combination of SVM, KNN, and decision tree. In the second ensemble classifier (SLD), SVM, logistic regression, and decision tree classifiers are combined for the sarcasm detection. In the third ensemble model (MLD), MLP, logistic regression, and decision tree are combined, and the last one (SLM) is the combination of MLP, logistic regression, and SVM. The proposed model is implemented in Python and tested on five datasets of different sizes. The performance of the models is tested with regard to various metrics.

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Ensemble Classification Approach for Sarcasm Detection.

Meta heuristics is an optimization approach that works as an intelligent technique to solve optimization problems. Evolutionary algorithms, human-based algorithms, physics-based algorithms and swarm intelligence are categorized under meta -heuristic algorithms. This study presents a critical review of meta -heuristic algorithms for future reference, including concepts, applications, advantages and disadvantages, before focusing on one specific meta -heuristic algorithm, namely, Emperor Penguin Optimizer (EPO). It is an intelligent algorithm developed after observing the behaviour of emperor penguins during cold winters. This technique was introduced by Dhiman in 2018 and adopted to solve optimization problems. The study reviews the algorithm variants starting from its invention in 2018 until 2022. The literature is comprehensively reviewed to reflect on the progress of the algorithm’s adoption, highlighting a new area for improvement. The most significant result is that the proposed algorithm has been proven an effective technique. The merits and demerits of the algorithm are explored to provide valuable perspectives for future research. This study answers the question regarding meta -heuristic algorithms’ effectiveness, especially EPO. Both beginners and experts of EPO research can use the findings of this study as guidelines for enhancing current concepts and applications of state-of-the-art algorithms for future development works. 

Emperor penguin optimizer: A comprehensive review based on state-of-the-art meta-heuristic algorithms

Sarcasm is the main reason behind the faulty classification of tweets. It brings a challenge in natural language processing (NLP) as it hampers the method of finding people’s actual sentiment. Various feature engineering techniques are being investigated for the automatic detection of sarcasm. However, most related techniques have always concentrated only on the content-based features in sarcastic expression, leaving the contextual information in isolation. This leads to a loss of the semantics of words in the sarcastic expression. Another drawback is the sparsity of the training data. Due to the word limit of microblog, the feature vector’s values for each sample constructed by BoW produces null features. To address the above-named problems, a Multi-feature Fusion Framework is proposed using two classification stages. The first stage classification is constructed with the lexical feature only, extracted using the BoW technique, and trained using five standard classifiers, including SVM, DT, KNN, LR, and RF, to predict the sarcastic tendency. In stage two, the constructed lexical sarcastic tendency feature is fused with eight other proposed features for modelling a context to obtain a final prediction. The effectiveness of the developed framework is tested with various experimental analysis to obtain classifiers’ performance. The evaluation shows that our constructed classification models based on the developed novel feature fusion obtained results with a precision of 0.947 using a Random Forest classifier. Finally, the obtained results were compared with the results of three baseline approaches. The comparison outcome shows the significance of the proposed framework.

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Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach.

Mobile Ad-Hoc Networks are currently experiencing a second youth in terms of research interest as well as providing benefits to our society. Clearly, this has been fostered by the wide range of applications that have become actually feasible thanks to the pervasive and increasing presence of smartphones, drones, sensors and other small devices with communication and sensing capability. Aiming to deploy solutions able to self-organize without the need for any infrastructure support, packet routing remains an open and critical research problem. Several routing solutions have been conceived to guarantee delivery and low overhead in this context. A promising approach, trying to limit the information needed and stored in the network, is represented by stateless solutions, a class of solutions not relying on topology state information. However, since they exploit local knowledge, the achieved performance in terms of packet delivery and latency is not always up to expectations. To this aim, we propose a dynamic routing protocol based on a tabu search approach, relying on local network knowledge and in-packet short-term memory to alleviate the local minima problem. A through experimental assessment is conducted, measuring protocol performance under different configurations and profiles, evidencing its benefits.

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Location Dynamic Tabu Routing Protocol for MANETs

Generally, battery power is a valuable resource for mobile devices in a Mobile Ad Hoc Network (MANET). Therefore, energy efficiency and network lifetime should be taken into account when developing control strategies. However, designing an energy-efficient routing mechanism necessitates consideration of many nodes from many layers, such as remaining energy, overall traffic load, and channel assumptions. The traditional layered strategy is unsuccessful in dealing with power-related issues that might affect all layers of the stack. In this paper, the Energy Centric Tunicate Swarm Algorithm (ECTSA) is proposed to perform the cross-layer routing over MANET. The fitness metrics considered in the ECTSA to improve the cross-layer routing are residual energy, communication cost, Data Success Rate (DSR), and mobility. Additionally, an Adaptive Competition Window (ACW) adjustment is used for minimizing the energy consumption caused by the contentions. The performance of the proposed ECTSA is analyzed by means of energy consumption, Packet Delivery Ratio (PDR), End-to-End Delay (EED), and routing overhead. Next, the existing techniques, namely, CEELBRP and EECRP-PSO, are used to evaluate the efficiency of the ECTSA method. The energy consumption of the ECTSA is 7.1 joules and prolongs the network lifetime up to 1603 s for 50 nodes, which is better when compared to the existing CEELBRP and EECRP-PSO techniques.

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Sustainable Multipath Routing for Improving Cross-Layer Performance in MANET Using an Energy Centric Tunicate Swarm Algorithm

Geographic routing with hybrid firefly algorithm and galactic swarm optimization for efficient ‘void’ handling in mobile ad hoc networks

Despite its various benefits, the Mobile Ad-hoc Network (MANET) has a number of obstacles due to its mobility, unstable topology, energy efficiency, and other factors. In such an environment, routing protocols are required to transfer packets from source to destination. Due to the limited range of each mobile node's wireless broadcasts, some mobile nodes can operate as intermediary nodes to forward a packet to its destination. In MANET, the multipath routing technique is used to decrease the routing overhead. It can be accomplished by minimizing network traffic. This paper aims to reduce the routing overhead to improve the effectiveness of broadcasting messages successfully. The Position-based Opportunistic Routing protocol is used to identify the optimal routing path in the network. The opportunistic routing system is mostly employed in MANET data transmission to reduce overhead. This paper presented a hybrid routing algorithm with the combination of Emperor Penguin Optimization (EPO) and the Bald Eagle Search (BES) algorithm (EPO–BES) for finding the best-forwarded path to moderate the routing overhead problem in MANET. NS2 platform is utilized to perform the routing operation and compared with existing techniques such as NKR, PSO-GA, PSO-BLAP, QUACS, and SGC-MO. The proposed method has obtained higher PDR, path optimality, end-to-end delay, throughput, routing overhead, load balancing, and energy consumption than other approaches.

Opportunistic Routing Protocol Based EPO–BES in MANET for Optimal Path Selection

Sarcasm is one of the most popular ways of expressing one’s opinion on a specific subject. Sarcasm is an act of conveying a viewpoint with an opposite emotion. Identifying such statements among the textual information is an important challenge present in sentiment analysis. Often, sarcastic statements are misleading. They mislead the whole context of the sentence; hence, detecting sarcasm is important in order to understand the actual meaning involved. The main objective of this paper is to propose a feature ensemble-based learning algorithm for sarcasm detection. Most of the existing works focused on using lexical features for identifying sarcasm. In the proposed work apart from the traditional lexical features, hyperbolic and pragmatic features have been extracted. After extracting the features, a model for detecting sarcasm has been proposed based on stacking-based feature ensemble algorithm. Experimental results show that the feature-based approaches when considered individually, Internet slang-based features attain an accuracy of 62%; interjection features obtained an accuracy of 49%, and emoticon-based features attained 59%, respectively. However, the proposed ensemble learning algorithm attains an overall accuracy of 83%, which is much better than the feature set-based approaches.

Anandhakumar Palanisamy

Papers

Geographic routing with hybrid firefly algorithm and galactic swarm optimization for efficient ‘void’ handling in mobile ad hoc networks

Opportunistic Routing Protocol Based EPO–BES in MANET for Optimal Path Selection

Textual Feature Ensemble-Based Sarcasm Detection in Twitter Data