Document-level sentiment analysis using Jaya chimp optimisation algorithm-enabled deep residual network Manoj L. Bangare, Sampath Arpakkam Karuppan, Debarati Ghosal, Ashwin Perti, Sanjay Nakharu Prasad Kumar International Journal of Intelligent Information and Database Systems, 2026 Document-level sentiment classification automates the process of categorising text reviews on a single topic as representing negative or positive sentiments. Users and customers are intended to share comments and reviews about their products on various social network sites. One of these processing steps is the classification of emotions associated with the reviews. Therefore, this research paper introduces a robust sentiment analysis method, named Jaya chimp optimisation algorithm-enabled deep residual network (JayaChOA-enabled DRN) for document-level sentiment classification. The input is pre-processed and tokenised, and then the key features are extracted. Moreover, the DRN classifier is used for the sentiment classification where the optimal weights are computed using the JayaChOA. Meanwhile, the introduced JayaChOA is implemented by the incorporation of Jaya optimiser and chimp optimisation algorithm (ChOA). The JayaChOA-based DRN obtained the highest precision of 0.914, F-measure of 0.919, and recall of 0.925 using K-fold.
Design and implementation of transition table for token recognizer with a given suffix Utkarsh Raj, Ashwin Perti, Vibhanshu Rajauria, Sanjeev Kumar Punia, Kumar Mishra Progressive Computational Intelligence Information Technology and Networking, 2025 The project involves the design and implementation of a system that can recognize tokens ending in special characters by creating deterministic finite automaton (DFA). The goal is to create a good way to identify these characters by identifying the appropriate states, the transitions between them, and determining which state represents a successful match. This approach, using semantics, provides a scalable solution for identifying tokens based on their results. This approach is particularly useful in applications such as lexical analysis, pattern recognition, and language processing in compilers.
Cognitive Hybrid Deep Learning-based Multi-modal Sentiment Analysis for Online Product Reviews Ashwin Perti, Amit Sinha, Ankit Vidyarthi ACM Transactions on Asian and Low Resource Language Information Processing, 2024 Recently the field of sentiment analysis has gained a lot of attraction in literature. The idea that a machine can dynamically spot the text’s sentiments is fascinating. In this paper, we propose a method to classify the textual sentiments in Twitter feeds. In particular, we focus on analyzing the tweets of products as either positive or negative. The proposed technique utilizes a deep learning schema to learn and predict the sentiment by extracting features directly from the text. Specifically, we use Convolutional Neural Networks with different convolutional layers. Further, we experiment with LSTMs and try an ensemble of multiple models to get the best results. We employ an n-gram-based word embeddings approach to get the machine-level word representations. Testing of the method is conducted on real-world datasets. We have discovered that the ensemble technique yields the best results after conducting experiments on a huge corpus of more than one million tweets. To be specific, we get an accuracy of 84.95%. The proposed method is also compared with several existing methods. An extensive numerical investigation has revealed the superiority of the proposed work in actual deployment scenarios.
Algorithm Transparency and Interpretability for AI-Based Medical Imaging Amit Sinha, Ashwin Perti Computer Assisted Analysis for Digital Medicinal Imagery, 2024 Transparency and interpretability of algorithms are essential factors required while developing and implementing AI-based medical imaging systems. Algorithm transparency ability lies in understanding and interpreting how an AI algorithm arrives at its decisions or predictions. This is the main challenge in implementing AI-based medical imaging techniques. Methods like explainable AI (XAI) are utilized to show how the algorithm makes decisions and points out important parts of the input data. These methods include feature visualization, attention mechanisms, artificial neural networks, and conceptNet. In our work, we perform extensive algorithmic testing and evaluation on various datasets, including external testing using real-world clinical data. The algorithm's advantages and disadvantages require openness in reporting evaluation results
Improved genetic algorithm-based sensor nodes deployment for barrier coverage Subash Harizan, Pratyay Kuila, Rajeev Kumar, Akhilendra Khare, Reeta Clonia, Ashwin Perti International Journal of Sensor Networks, 2023 Barrier coverage is widely used for an intruder detection. However, sensor nodes (SNs) are prone to failure. Hence it is very challenging to construct a barrier with an efficient coverage and connectivity with minimum number of SNs. From a given set of potential positions (PPs), finding minimum number of PPs for the placement of SNs to form a barrier is an NP-complete problem. In this paper, we propose an improved genetic algorithm (GA)-based approach to solve the aforesaid problem. For the better performance and fast convergence of the algorithm, a novel mutation operation is introduced. In our proposed approach chromosomes are efficiently represented along with an efficient fitness function to evaluate the quality. An extensive simulation is conducted on the various scenarios of the network. The efficiency of the proposed algorithm is shown by comparing the simulated results with traditional genetic algorithm (GA), differential evolution (DE) and GreedyCSC algorithms.
Face Recognition from Surveillance Using Sequnetial CNN-Model Ashwin Perti, Mukul Pratap Singh, Harsh Panwar, Harsh Tyagi Icrito 2020 IEEE 8th International Conference on Reliability Infocom Technologies and Optimization Trends and Future Directions, 2020
