Speech is a fundamental aspect of communication, and in India, conversations frequently involve s... more Speech is a fundamental aspect of communication, and in India, conversations frequently involve switching between multiple languages. This poses significant challenges for multilingual speech recognition systems, as accurately identifying the language of spoken words, letters, or sentences is complex. The problem is exacerbated by code-switching, where speakers seamlessly alternate between languages. Existing models, often trained on non-representative corpora of Indian languages, struggle with accuracy in these scenarios. To address these challenges, we propose a novel approach that eliminates the need for users to pre-select spoken or transcribed text languages. Our model automatically detects the language in real-time, recognizes the spoken words, and provides output text in all languages used during the conversation. This method simplifies the user experience and improves the accuracy of multilingual speech recognition, making it more effective and user-friendly in multilingual contexts.
Indonesian Journal of Electrical Engineering and Computer Science, 2022
Heart diseases have a severe impact on human life and health. Cardiovascular deaths and diseases ... more Heart diseases have a severe impact on human life and health. Cardiovascular deaths and diseases have increased at a fast rate worldwide. The early prediction of these diseases is necessary to prevent deaths. Now a day; a considerable amount of medical information is available and collected as databases. An efficient technique is required to analyse this data and predict the disease. Clustering can help medical practitioners in diagnosis by classifying the patient's data collected for a disease. Clustering techniques can analyse such data based on each patient-generated and predict disease. A new prediction model based on salp swarm algorithm and support vector machine is proposed in this research for predicting heart diseases. Salp swarm algorithm is used to select the useful features from the database. Support vector machine classifier is used to predict heart diseases. Results obtained are compared with the other algorithms available in the literature. It is observed that the proposed approach produces better results with accuracy 98.75% and 98.46% with the dataset 1 and 2, respectively. In addition to this, the algorithm converges in significantly less time in comparison to other algorithms. This algorithm might become a perfect supporting tool for medical practitioners.
Artificial Intelligent Systems and Machine Learning, 2015
Ant Colony Optimization (ACO) is a novel and competitive optimization method for numerous combina... more Ant Colony Optimization (ACO) is a novel and competitive optimization method for numerous combinatorial optimization problems. It is already applied to various optimization problems. Normally it proved best in terms of solution quality, accuracy and other parameters. This paper presents an Elitist Ant System (EAS) which uses tuning in pheromone update process to improve the performance of the basic Ant System (AS) approach. Dynamic Traveling Salesman Problem is solved in this research with the various pheromone update strategy for finding the improvements in the results. The results obtained are empirically compared with the results obtained with the basic pheromone update strategy of Ant System.
Over the past few decades, the studies on algorithms inspired by nature have shown that these met... more Over the past few decades, the studies on algorithms inspired by nature have shown that these methods can be efficiently used to eliminate most of the difficulties of classical methods. Nature inspired algorithms are widely used to solve optimization problems with complex nature. Various research works are carried out and algorithms are presented based on that during last few decades. Recently, some new algorithms inspired from nature are proposed to further improve the solutions obtained by the algorithms presented before. In this paper, a survey of five recently introduced Nature inspired algorithms is carried out. They include Firefly algorithm (FA), Cuckoo Search (CS), and Bat Inspired Algorithm (BA). Each of these algorithms are introduced and applied on various numerical optimization functions by various authors. We have tried to review and study the papers published by the authors and present a conclusion of this survey based on the results obtained.
Artificial Intelligent Systems and Machine Learning, 2012
There are varieties of problems in various engineering branches which can’t be solved using tradi... more There are varieties of problems in various engineering branches which can’t be solved using traditional search techniques. These problems are known as NP hard problems. The problem which can’t be solved in a polynomial time is called a NP hard problem. This type of problem normally involved exhaustive searching in side it. Various Nature inspired techniques are developed and employed by the researchers to solve this kind of problems. Evolutionary algorithms (EAs) are one of them. These algorithms are known as optimization algorithms as they give very near optimal solution. In this paper, we investigate the ability of Evolutionary Algorithm (EA) by applying it to Dynamic Traveling Salesman Problem (DTSP). Dynamic Traveling Salesman Problem is a real world TSP where problem changes itself over the period of the time. It is very difficult to solve it by using traditional methods. We apply an optimization method, evolutionary algorithm to it and obtain the results. Experimental results indicate that EAs can overcome many problems encountered by traditional search techniques. The performance of EAs is compared to the results of traditional search techniques.
International Journal of Computer Science Engineering and Information Technology Research, 2018
Software Testing is one of the expensive activities in software development. But at the same time... more Software Testing is one of the expensive activities in software development. But at the same time, it will assure the quality of the software product. Software testing will take more cost and maximum time which actually increases the cost of the product as well as wastage of time. We have investigated various approaches proposed to addresses the problems in test suite optimization using nature-inspired algorithms. Such problems can be solved using nature-inspired algorithms. This approach is based on Genetic Algorithm, Particle Swarm Optimization, Memetic Algorithm is investigated. The results obtained by different researchers are collected and comparative analysis is prescribed and listed.
International Journal of Computer Science and Engineering, 2020
Particle swarm optimization algorithm is one of the nature inspired algorithms based on the flock... more Particle swarm optimization algorithm is one of the nature inspired algorithms based on the flocking behaviour of a swarm of the birds. The standard Particle swarm optimization algorithm has been successfully used to solve many engineering problems. Each and every algorithm has its own merits and demerits like stagnation and fall in premature convergence in searching space. It is always necessary to handle the issues of exploitation and exploration of the search space. Excessive exploitation leads to premature convergence, while excessive exploration slows down the convergence. In this paper, an improved particle swarm optimization algorithm is proposed to solve the Random Traveling Salesman Problem. Random TSP is a type of the TSP where the TSP problems are defined randomly. The results obtained from this algorithm are compared with the results obtained with other optimization algorithms like GA, MA and ACO. Results shows that the Particle swarm optimization (PSO) algorithm performs very well to solve most of TSP problems, but it can be trapped into local optimum solutions for some of the problems.
Indonesian Journal of Electrical Engineering and Computer Science, 2022
A new hybrid algorithm named discrete salp swarm algorithm that integrates effectiveness of weigh... more A new hybrid algorithm named discrete salp swarm algorithm that integrates effectiveness of weights, Lévy flights, and an excellent classifier, support vector machine (SVM), has been proposed to predict Parkinson's disease. In the proposed algorithm, salp swarm algorithm (SSA) is used as a feature selection tool, which targets to reduce the noise in features of the speech PD dataset to improve the SVM classifier's prediction accuracy. The efficacy and usefulness of the proposed discrete salp swarm algorithm with Lévy flights have been meticulously assessed against the speech PD dataset in terms of G-mean, accuracy, F-measure, specificity, sensitivity, and precision measures. DWLSSA has achieved values of the measures, 97.76%, 98.75%, 98.77%, 97.37%, 98.15%, and 99.39% respectively. Comparison of DWLSSA with other nature inspired algorithms applied to predict Parkinson's shows that the proposed DWLSSA performs better. It can be also said that DWLSSA can be an alternative for solving the NP-hard problems.
Indonesian Journal of Electrical Engineering and Computer Science
A new hybrid algorithm named discrete salp swarm algorithm that integrates effectiveness of weigh... more A new hybrid algorithm named discrete salp swarm algorithm that integrates effectiveness of weights, Lévy flights, and an excellent classifier, support vector machine (SVM), has been proposed to predict Parkinson's disease. In the proposed algorithm, salp swarm algorithm (SSA) is used as a feature selection tool, which targets to reduce the noise in features of the speech PD dataset to improve the SVM classifier's prediction accuracy. The efficacy and usefulness of the proposed discrete salp swarm algorithm with Lévy flights have been meticulously assessed against the speech PD dataset in terms of G-mean, accuracy, F-measure, specificity, sensitivity, and precision measures. DWLSSA has achieved values of the measures, 97.76%, 98.75%, 98.77%, 97.37%, 98.15%, and 99.39% respectively. Comparison of DWLSSA with other nature inspired algorithms applied to predict Parkinson’s shows that the proposed DWLSSA performs better. It can be also said that DWLSSA can be an alternative for ...
Received Mar 5, 2021 Revised Aug 6, 2021 Accepted Aug 11, 2021 Machine learning involves the task... more Received Mar 5, 2021 Revised Aug 6, 2021 Accepted Aug 11, 2021 Machine learning involves the task of training systems to be able to make decisions without being explicitly programmed. Important among machine learning tasks is classification involving the process of training machines to make predictions from predefined labels. Classification is broadly categorized into three distinct groups: single-label (SL), multi-class, and multi-label (ML) classification. This research work presents an application of a multi-label classification (MLC) technique in automating Quranic verses labeling. MLC has been gaining attention in recent years. This is due to the increasing amount of works based on real-world classification problems of multi-label data. In traditional classification problems, patterns are associated with a single-label from a set of disjoint labels. However, in MLC, an instance of data is associated with a set of labels. In this paper, three standard MLC methods: binary relevan...
Journal of Computational Mathematics and Data Science
K-medoids clustering algorithm is a simple yet effective algorithm that has been applied to solve... more K-medoids clustering algorithm is a simple yet effective algorithm that has been applied to solve many clustering problems. Instead of using the mean point as the centre of a cluster, K-medoids uses an actual point to represent it. Medoid is the most centrally located object of the cluster, with a minimum sum of distances to other points. K-medoids can correctly represent the cluster centre as it is robust to outliers. However, the K-medoids algorithm is unsuitable for clustering arbitrary shaped groups of objects and large scale datasets. This is because it uses compactness as a clustering criterion instead of connectivity. An improved k-medoids algorithm based on the crow search algorithm is proposed to overcome the above problems. This research uses the crow search algorithm to improve the balance between the exploration and exploitation process of the K-medoids algorithm. Experimental result comparison shows that the proposed improved algorithm performs better than other competitors.
International Journal of Innovative Technology and Exploring Engineering, 2020
The firefly algorithm is a recently developed optimization algorithm, which is suitable for solvi... more The firefly algorithm is a recently developed optimization algorithm, which is suitable for solving any kind of discrete optimization problems. This is an algorithm inspired from the nature. In this paper, a firefly algorithm is proposed to solve random traveling salesman problem. The solution to this problem is already proposed by the algorithms like simulated annealing, genetic algorithms and ant colony algorithms. This algorithm is developed to deal with the issue of accuracy and convergence rate in the solutions provided by those algorithms. A comparison of the results produced by proposed algorithm with the results of simulated annealing, genetic algorithms and ant colony algorithm is given. Finally, the effectiveness of the proposed algorithm is discussed.
Speech is a fundamental aspect of communication, and in India, conversations frequently involve s... more Speech is a fundamental aspect of communication, and in India, conversations frequently involve switching between multiple languages. This poses significant challenges for multilingual speech recognition systems, as accurately identifying the language of spoken words, letters, or sentences is complex. The problem is exacerbated by code-switching, where speakers seamlessly alternate between languages. Existing models, often trained on non-representative corpora of Indian languages, struggle with accuracy in these scenarios. To address these challenges, we propose a novel approach that eliminates the need for users to pre-select spoken or transcribed text languages. Our model automatically detects the language in real-time, recognizes the spoken words, and provides output text in all languages used during the conversation. This method simplifies the user experience and improves the accuracy of multilingual speech recognition, making it more effective and user-friendly in multilingual contexts.
Indonesian Journal of Electrical Engineering and Computer Science, 2022
Heart diseases have a severe impact on human life and health. Cardiovascular deaths and diseases ... more Heart diseases have a severe impact on human life and health. Cardiovascular deaths and diseases have increased at a fast rate worldwide. The early prediction of these diseases is necessary to prevent deaths. Now a day; a considerable amount of medical information is available and collected as databases. An efficient technique is required to analyse this data and predict the disease. Clustering can help medical practitioners in diagnosis by classifying the patient's data collected for a disease. Clustering techniques can analyse such data based on each patient-generated and predict disease. A new prediction model based on salp swarm algorithm and support vector machine is proposed in this research for predicting heart diseases. Salp swarm algorithm is used to select the useful features from the database. Support vector machine classifier is used to predict heart diseases. Results obtained are compared with the other algorithms available in the literature. It is observed that the proposed approach produces better results with accuracy 98.75% and 98.46% with the dataset 1 and 2, respectively. In addition to this, the algorithm converges in significantly less time in comparison to other algorithms. This algorithm might become a perfect supporting tool for medical practitioners.
Artificial Intelligent Systems and Machine Learning, 2015
Ant Colony Optimization (ACO) is a novel and competitive optimization method for numerous combina... more Ant Colony Optimization (ACO) is a novel and competitive optimization method for numerous combinatorial optimization problems. It is already applied to various optimization problems. Normally it proved best in terms of solution quality, accuracy and other parameters. This paper presents an Elitist Ant System (EAS) which uses tuning in pheromone update process to improve the performance of the basic Ant System (AS) approach. Dynamic Traveling Salesman Problem is solved in this research with the various pheromone update strategy for finding the improvements in the results. The results obtained are empirically compared with the results obtained with the basic pheromone update strategy of Ant System.
Over the past few decades, the studies on algorithms inspired by nature have shown that these met... more Over the past few decades, the studies on algorithms inspired by nature have shown that these methods can be efficiently used to eliminate most of the difficulties of classical methods. Nature inspired algorithms are widely used to solve optimization problems with complex nature. Various research works are carried out and algorithms are presented based on that during last few decades. Recently, some new algorithms inspired from nature are proposed to further improve the solutions obtained by the algorithms presented before. In this paper, a survey of five recently introduced Nature inspired algorithms is carried out. They include Firefly algorithm (FA), Cuckoo Search (CS), and Bat Inspired Algorithm (BA). Each of these algorithms are introduced and applied on various numerical optimization functions by various authors. We have tried to review and study the papers published by the authors and present a conclusion of this survey based on the results obtained.
Artificial Intelligent Systems and Machine Learning, 2012
There are varieties of problems in various engineering branches which can’t be solved using tradi... more There are varieties of problems in various engineering branches which can’t be solved using traditional search techniques. These problems are known as NP hard problems. The problem which can’t be solved in a polynomial time is called a NP hard problem. This type of problem normally involved exhaustive searching in side it. Various Nature inspired techniques are developed and employed by the researchers to solve this kind of problems. Evolutionary algorithms (EAs) are one of them. These algorithms are known as optimization algorithms as they give very near optimal solution. In this paper, we investigate the ability of Evolutionary Algorithm (EA) by applying it to Dynamic Traveling Salesman Problem (DTSP). Dynamic Traveling Salesman Problem is a real world TSP where problem changes itself over the period of the time. It is very difficult to solve it by using traditional methods. We apply an optimization method, evolutionary algorithm to it and obtain the results. Experimental results indicate that EAs can overcome many problems encountered by traditional search techniques. The performance of EAs is compared to the results of traditional search techniques.
International Journal of Computer Science Engineering and Information Technology Research, 2018
Software Testing is one of the expensive activities in software development. But at the same time... more Software Testing is one of the expensive activities in software development. But at the same time, it will assure the quality of the software product. Software testing will take more cost and maximum time which actually increases the cost of the product as well as wastage of time. We have investigated various approaches proposed to addresses the problems in test suite optimization using nature-inspired algorithms. Such problems can be solved using nature-inspired algorithms. This approach is based on Genetic Algorithm, Particle Swarm Optimization, Memetic Algorithm is investigated. The results obtained by different researchers are collected and comparative analysis is prescribed and listed.
International Journal of Computer Science and Engineering, 2020
Particle swarm optimization algorithm is one of the nature inspired algorithms based on the flock... more Particle swarm optimization algorithm is one of the nature inspired algorithms based on the flocking behaviour of a swarm of the birds. The standard Particle swarm optimization algorithm has been successfully used to solve many engineering problems. Each and every algorithm has its own merits and demerits like stagnation and fall in premature convergence in searching space. It is always necessary to handle the issues of exploitation and exploration of the search space. Excessive exploitation leads to premature convergence, while excessive exploration slows down the convergence. In this paper, an improved particle swarm optimization algorithm is proposed to solve the Random Traveling Salesman Problem. Random TSP is a type of the TSP where the TSP problems are defined randomly. The results obtained from this algorithm are compared with the results obtained with other optimization algorithms like GA, MA and ACO. Results shows that the Particle swarm optimization (PSO) algorithm performs very well to solve most of TSP problems, but it can be trapped into local optimum solutions for some of the problems.
Indonesian Journal of Electrical Engineering and Computer Science, 2022
A new hybrid algorithm named discrete salp swarm algorithm that integrates effectiveness of weigh... more A new hybrid algorithm named discrete salp swarm algorithm that integrates effectiveness of weights, Lévy flights, and an excellent classifier, support vector machine (SVM), has been proposed to predict Parkinson's disease. In the proposed algorithm, salp swarm algorithm (SSA) is used as a feature selection tool, which targets to reduce the noise in features of the speech PD dataset to improve the SVM classifier's prediction accuracy. The efficacy and usefulness of the proposed discrete salp swarm algorithm with Lévy flights have been meticulously assessed against the speech PD dataset in terms of G-mean, accuracy, F-measure, specificity, sensitivity, and precision measures. DWLSSA has achieved values of the measures, 97.76%, 98.75%, 98.77%, 97.37%, 98.15%, and 99.39% respectively. Comparison of DWLSSA with other nature inspired algorithms applied to predict Parkinson's shows that the proposed DWLSSA performs better. It can be also said that DWLSSA can be an alternative for solving the NP-hard problems.
Indonesian Journal of Electrical Engineering and Computer Science
A new hybrid algorithm named discrete salp swarm algorithm that integrates effectiveness of weigh... more A new hybrid algorithm named discrete salp swarm algorithm that integrates effectiveness of weights, Lévy flights, and an excellent classifier, support vector machine (SVM), has been proposed to predict Parkinson's disease. In the proposed algorithm, salp swarm algorithm (SSA) is used as a feature selection tool, which targets to reduce the noise in features of the speech PD dataset to improve the SVM classifier's prediction accuracy. The efficacy and usefulness of the proposed discrete salp swarm algorithm with Lévy flights have been meticulously assessed against the speech PD dataset in terms of G-mean, accuracy, F-measure, specificity, sensitivity, and precision measures. DWLSSA has achieved values of the measures, 97.76%, 98.75%, 98.77%, 97.37%, 98.15%, and 99.39% respectively. Comparison of DWLSSA with other nature inspired algorithms applied to predict Parkinson’s shows that the proposed DWLSSA performs better. It can be also said that DWLSSA can be an alternative for ...
Received Mar 5, 2021 Revised Aug 6, 2021 Accepted Aug 11, 2021 Machine learning involves the task... more Received Mar 5, 2021 Revised Aug 6, 2021 Accepted Aug 11, 2021 Machine learning involves the task of training systems to be able to make decisions without being explicitly programmed. Important among machine learning tasks is classification involving the process of training machines to make predictions from predefined labels. Classification is broadly categorized into three distinct groups: single-label (SL), multi-class, and multi-label (ML) classification. This research work presents an application of a multi-label classification (MLC) technique in automating Quranic verses labeling. MLC has been gaining attention in recent years. This is due to the increasing amount of works based on real-world classification problems of multi-label data. In traditional classification problems, patterns are associated with a single-label from a set of disjoint labels. However, in MLC, an instance of data is associated with a set of labels. In this paper, three standard MLC methods: binary relevan...
Journal of Computational Mathematics and Data Science
K-medoids clustering algorithm is a simple yet effective algorithm that has been applied to solve... more K-medoids clustering algorithm is a simple yet effective algorithm that has been applied to solve many clustering problems. Instead of using the mean point as the centre of a cluster, K-medoids uses an actual point to represent it. Medoid is the most centrally located object of the cluster, with a minimum sum of distances to other points. K-medoids can correctly represent the cluster centre as it is robust to outliers. However, the K-medoids algorithm is unsuitable for clustering arbitrary shaped groups of objects and large scale datasets. This is because it uses compactness as a clustering criterion instead of connectivity. An improved k-medoids algorithm based on the crow search algorithm is proposed to overcome the above problems. This research uses the crow search algorithm to improve the balance between the exploration and exploitation process of the K-medoids algorithm. Experimental result comparison shows that the proposed improved algorithm performs better than other competitors.
International Journal of Innovative Technology and Exploring Engineering, 2020
The firefly algorithm is a recently developed optimization algorithm, which is suitable for solvi... more The firefly algorithm is a recently developed optimization algorithm, which is suitable for solving any kind of discrete optimization problems. This is an algorithm inspired from the nature. In this paper, a firefly algorithm is proposed to solve random traveling salesman problem. The solution to this problem is already proposed by the algorithms like simulated annealing, genetic algorithms and ant colony algorithms. This algorithm is developed to deal with the issue of accuracy and convergence rate in the solutions provided by those algorithms. A comparison of the results produced by proposed algorithm with the results of simulated annealing, genetic algorithms and ant colony algorithm is given. Finally, the effectiveness of the proposed algorithm is discussed.
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