https://www.i-scholar.in/index.php/IJSC/issue/feed ICTACT Journal on Soft Computing 2024-02-12T11:10:44+00:00 Editor raghav@ictact.in Open Journal Systems ICTACT Journal on Soft Computing (IJSC) is a peer – reviewed International Journal published quarterly. IJSC welcomes Scientists, Researchers, Academicians and Engineers to submit their original research papers which is neither published nor currently under review by other journals or conferences. Papers should emphasize original results relating to both theoretical and application issues of Soft Computing. Review articles, focusing on multi disciplinary views of Soft Computing, are also welcome. https://www.i-scholar.in/index.php/IJSC/article/view/224321 Detecting Deceptive Reviews: An Integrated Machine Learning Approach 2024-02-07T10:38:42+00:00 Anusuya Krishnan Kennedyraj In recent years, online reviews have become a crucial factor in promoting products and services. However, the rise of fake reviews has posed a significant challenge. Businesses, marketers, and advertisers often resort to embedding fake reviews to attract customers or undermine their competitors. Deceptive reviews have become a common practice, as they serve as a means of promoting one's own business or tarnishing the reputation of rivals. Consequently, the identification of deceptive reviews has emerged as a critical and ongoing research area. This research paper presents a machine learning model approach to detect deceptive reviews. The study focuses on experiments conducted using a deceptive opinion spam corpus dataset, specifically targeting restaurant reviews. An n-gram model combined with max features is developed to identify deceptive content, with a particular emphasis on fake reviews. Additionally, a benchmark study is conducted to explore the performance of two different feature extraction techniques and their application in five machine learning classification techniques. The experimental findings demonstrate that the passive aggressive classifier outperforms other algorithms, achieving the highest accuracy not only in text classification but also in identifying fake reviews. Moreover, the research delves into the identification of deceptive reviews and explores diverse feature extraction and machine learning techniques to improve the model's accuracy. 2023-10-30T00:00:00+00:00 https://www.i-scholar.in/index.php/IJSC/article/view/224323 Evaluation of Lattice Based XAI 2024-02-12T10:31:59+00:00 Bhaskaran Venkatsubramaniam Pallav Kumar Baruah With multiple methods to extract explanations from a black box model, it becomes significant to evaluate the correctness of these Explainable AI (XAI) techniques themselves. While there are many XAI evaluation methods that need manual intervention, in order to be objective, we use computable XAI evaluation methods to test the basic nature and sanity of an XAI technique. We pick four basic axioms and three sanity tests from existing literature that the XAI techniques are expected to satisfy. Axioms like Feature Sensitivity, Implementation Invariance, Symmetry preservation and sanity tests like Model parameter randomization, Model-Outcome relationship, Input transformation invariance are used. After reviewing the axioms and sanity tests, we apply it on existing XAI techniques to check if they satisfy them or not. Thereafter, we evaluate our lattice based XAI technique with these axioms and sanity tests using a mathematical approach. This work proves these axioms and sanity tests to establish the correctness of explanations extracted from our Lattice based XAI technique. 2023-10-30T00:00:00+00:00 https://www.i-scholar.in/index.php/IJSC/article/view/224324 An Integrated Deep Learning Approach for Sentiment Analysis on Twitter Data 2024-02-12T10:34:04+00:00 N. S. Prabakaran S. Karthik Analysis of Sentiment (SA) is a computational technique that seeks to extract subjective evaluations, attitudes, and emotional states from online platforms, specifically social media sites like Twitter. The subject has gained significant traction within the research community. The predominant emphasis of traditional sentiment analysis lies in the analysis of textual data. Twitter is widely recognized as a prominent online social networking platform that facilitates microblogging, wherein users share updates pertaining to various subjects through concise messages known as tweets. Twitter is a widely utilized platform that enables individuals to articulate their perspectives and emotions through the medium of tweets. Sentiment analysis refers to the computational methods of classification of given data in text format, categorizing it as either positive, negative, or neutral. The main goal of this study is to use deep learning methods for SA. The purpose is to guess sentiment and then evaluate the results based on accuracy, recall, and f-score. In this paper, a hybrid approach combining Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) techniques, specifically referred to as PSOGA, is proposed to optimize features for a modified neural network (MNN). The final step is to use the K-fold cross-validation method to assess the results. The dataset was obtained through the utilization of the Ruby Twitter API. The ultimate outcome is juxtaposed with the preceding Cuckoo Search (CS) algorithm that had been terminated. 2023-10-30T00:00:00+00:00 https://www.i-scholar.in/index.php/IJSC/article/view/224327 Integrating Neuro-fuzzy Systems for Enhanced Cancer Data Analysis and Prediction 2024-02-12T11:02:01+00:00 R. Nandhakumar M. Sathya Priya Rotash Kumar Dinesh Kumar Yadav Mohammed Saleh Al Ansari The abstract for Integrating Neuro-Fuzzy structures for more significant most cancers statistics analysis and Prediction describes research carried out to examine using a sort of artificial intelligence, known as a Neuro-Fuzzy device, to analyze and expect facts from most cancer sufferers. by leveraging the strengths of both Neural Networks and Fuzzy common sense structures, Neuro-Fuzzy systems provide a powerful answer for complicated statistics analysis. This research examines and tests the overall performance of Neuro-Fuzzy systems on hard and fast benchmark datasets from most cancers Toolbox Markup Language (TMX). Consequences showed that Neuro-Fuzzy yielded a higher accuracy charge when compared to different device learning algorithms in studying information from a diverse set of patients. Furthermore, the researchers also stated that Neural-Fuzzy systems were able to discern subtypes of cancer in an affected person population, which had not been formerly feasible with different techniques. The work defined in the abstract could have a long way to attaining implications for the remedy and prognosis of most cancer patients. With the promising results of this, have a look at showing that Neuro-Fuzzy structures are able to distinguish between particular forms of cancer correctly; a more precise treatment plan might be created for people living with cancer. Additionally, with the improved accuracy of Neuro-Fuzzy structures, more excellent dependable predictions will be made about the progression of most cancers in a selected patient, helping doctors to plan treatments. Ordinary, the findings of the research summarized in this summary are especially significant as the advanced accuracy and capacity to figure out subtle differences in most cancer types keep the promise of improved remedies and prognoses for people living with cancer. 2023-10-30T00:00:00+00:00 https://www.i-scholar.in/index.php/IJSC/article/view/224326 Optimizing Plant Disease Prediction: A Neuro-fuzzy-genetic Algorithm Approach 2024-02-12T10:52:12+00:00 Sachin Vasant Chaudhari T. S. Sasikala R. K. Gnanamurthy Vijay Kumar Dwivedi Davinder Kumar In this essay, the idea of improving plant disease prediction using a Neuro-Fuzzy-Genetic algorithm (NFGA) technique is explored. The concept of a Neuro-Fuzzy-Genetic algorithm is first described. The advantages of the NFGA technique for predicting plant disorders are next addressed. An example is then given to demonstrate how this technique has been successfully used and the advantages it offers you. A hybrid artificial intelligence technique known as a Neuro-Fuzzy-Genetic set of rules (NFGA) combines the genetic algorithms, fuzzy logic, and neural network algorithms. It entails using a method of organizing fuzzy rules for statistics type, developing a network of neurons to anticipate the level of the group of data points to positive fuzzy training, adjusting the weights of fuzzy classes using a genetic algorithm-based totally optimization method to better fit the data factors, and ultimately identifying and predicting patterns of statistics points. The main benefits of this method for predicting plant diseases are its abilities to analyze various plant characteristics, extract complex relationships between statistics points, identify correlations between various environmental factors and illnesses, choose the best combinations of fuzzy rules for accurate classification, and finally adapt to changing data over time 2023-10-30T00:00:00+00:00 https://www.i-scholar.in/index.php/IJSC/article/view/224328 Ant Colony Optimisation Coupled with Chaotic Data Mining for Enhanced Weather Prediction Analysis 2024-02-07T10:38:44+00:00 J. Santhosh Govind Hanmantrao Balde A. Rajesh Kumar Chandra Mouli Venkata Srinivas Akana Meteorological predictions play a pivotal role in various sectors, from agriculture to disaster management. While traditional weather prediction models exhibit proficiency, challenges persist in accurately capturing the complex and dynamic nature of atmospheric phenomena. Conventional weather prediction models often struggle to adapt to the intricacies of climate patterns, leading to suboptimal forecasting accuracy. The need for more robust methodologies that can effectively extract patterns from vast datasets and optimize model parameters is evident. Existing literature lacks comprehensive studies that seamlessly integrate ACO and Data Mining for weather prediction. This research bridges the gap by proposing a novel framework that leverages ACO optimization capabilities to refine Data Mining models, thereby improving the precision of weather forecasts. The proposed method involves utilizing ACO to optimize the parameters of Data Mining algorithms, such as decision trees and neural networks. ACO ability to find optimal solutions is harnessed to fine-tune the model parameters, enhancing its capability to extract meaningful patterns from historical weather data. Experiments demonstrate promising results, with a significant improvement in the accuracy of weather predictions compared to traditional models. The integrated approach shows particular efficacy in handling non-linear relationships and abrupt changes in weather patterns. 2023-10-30T00:00:00+00:00 https://www.i-scholar.in/index.php/IJSC/article/view/224329 Fuzzy Logic Systems with Data Classification - a Cooperative Approach for Intelligent Decision Support 2024-02-12T11:01:11+00:00 A. Alagu Karthikeyan R. D. Jagadeesha Shrinwantu Raha Harshal Patil Prince Williams In intelligent decision support, the integration of fuzzy logic systems with data classification has emerged as a promising avenue. This cooperative approach seeks to enhance decision-making processes by leveraging the strengths of both fuzzy logic and data classification techniques. However, a critical gap exists in the literature concerning the seamless integration of fuzzy logic systems and data classification for effective decision support. Existing approaches often treat these methodologies in isolation, overlooking the synergies that can arise from their collaborative utilization. Bridging this gap is essential for developing robust decision support systems capable of handling the intricacies of modern datasets. The research aims to address this gap by proposing a cooperative approach that seamlessly integrates fuzzy logic systems and data classification methods. By doing so, it seeks to overcome the limitations of traditional decision support systems and enhance their adaptability to real-world scenarios characterized by uncertainty and complexity. The method involves the development of a hybrid system that combines fuzzy logic rules and data classification algorithms. The fuzzy logic component captures and processes imprecise information, while the data classification component identifies patterns and trends within the data. The cooperative nature of the approach ensures that each method complements the other, resulting in a more robust and effective decision support system. The results demonstrate the improved performance of the proposed cooperative approach compared to traditional decision support systems. The system exhibits enhanced accuracy and adaptability, showcasing its potential to address the challenges posed by modern datasets. 2023-10-30T00:00:00+00:00 https://www.i-scholar.in/index.php/IJSC/article/view/224330 Swarm Intelligence Embedded Data Mining for Precision Agriculture Advancements 2024-02-12T11:04:42+00:00 N. Karthik Sanjay R. Pawar R. Pramodhini Arvind Kumar Shukla The present study investigates the potential of Swarm Intelligence (SI) in driving breakthroughs in Precision Agriculture (PA). It focuses on the research of mining techniques to uncover novel insights and developments in the field of PA. Social informatics (SI) is an academic discipline that focuses on the examination of collective behaviour within both herbal and synthetic structures. In order to gather, analyse, and synthesise information, SI utilises self-sufficient mobile devices known as Autonomous Mobile Agents (AMAs). These entities refer to robotic and computational frameworks that engage in mutual interaction, facilitating the examination of collective intelligence. This essay examines the potential impact of utilising the System of International Units (SI) on enhancing the accuracy and precision of commodity production and control in the field of production agriculture (PA). It also highlights the existing advancements that have been achieved in this regard. This analysis examines possible uses of Swarm Intelligence in the Public Administration (PA) industry, as well as the challenges that need to be solved in order to enhance the efficiency and accuracy of PA operations. 2023-10-30T00:00:00+00:00 https://www.i-scholar.in/index.php/IJSC/article/view/224331 Forecasting Quarterly Rice and Corn Production in the Philippines: A Comparative Study of Seasonal Arima and Holt-winters Models 2024-02-12T11:07:30+00:00 Samuel John E. Parreño Rice and corn are essential crops for the Philippines, playing a critical role in the nation’s economy and food security. However, the agricultural sector faces challenges, including climate variability, land constraints, and the need for imports to meet growing demand. Accurate forecasting of rice and corn production is crucial for informed decision-making and resource allocation. This research applied Seasonal Autoregressive Integrated Moving Average (SARIMA) and Holt-Winters exponential smoothing models to forecast rice and corn production. The study used quarterly production data from 1987 to 2023 obtained from the Philippine Statistics Authority. The Holt-Winters model with additive seasonality outperformed the SARIMA model for both rice and corn production, achieving lower Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. The findings have significant implications for policymakers, agricultural stakeholders, and commodity traders, guiding them in making informed decisions regarding import requirements. The volatility of global food prices and exchange rates can significantly impact the cost of imports, putting a strain on the country’s financial resources. Accurate forecasting models are essential for ensuring food sufficiency and making informed decisions on the amount of imports required. By adopting the Holt-Winters model and continuously improving forecasting methodologies, the Philippines can enhance food sufficiency and promote rural economic growth. The study highlighted the importance of accurate forecasting models in ensuring stable and sufficient rice and corn supplies to meet the nation’s growing demands, contributing to sustainable agricultural development and food security. Continuous research in agricultural forecasting methodologies is necessary to address the challenges posed by evolving agricultural dynamics and further enhance predictive accuracy. 2023-10-30T00:00:00+00:00 https://www.i-scholar.in/index.php/IJSC/article/view/224332 Script Identification from Camera Captured Indian Document Images with CNN Model 2024-02-12T11:10:44+00:00 Satishkumar Mallappa B. V. Dhandra Gururaj Mukarambi Compared to typical scanners, handheld cameras offer convenient, flexible, portable, and noncontact image capture, which enables many new applications and breathes new life into existing ones, but camera-captured documents may suffer from distortions caused by a nonplanar document shape and perspective projection, which lead to the failure of current optical character recognition (OCR) technologies. This paper presents a new CNN model for script identification from camera-captured Indian multilingual document images. To evaluate the performance of the proposed model 9 regional languages, one national language and one international Roman languages are considered. Two languages, Hindi national language, and Roman English language are taken as the common languages with regional language for the study. The proposed method is applied on Bi-script, Tri-script, and Multi-script combinations. The average recognition accuracy for three script combinations is 92.92%, for bi-script 91.33%, and for tri-script 87.33%. is achieved. The proposed method is the unified approach used for identifying the script from bi-script, tri-script and multi-script camera-captured document images and is the novelty of this paper. The proposed model is compared with the Alexnet pretrained CNN model, and it achieved the highest recognition accuracy. 2023-10-30T00:00:00+00:00