Asian Journal of Research in Computer Science

Hidden Markov Models (HMMs) have become increasingly popular in the last several years due to the fact that, the models are very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications. Various algorithms have been proposed in literature for optimizing the parameters of these models to make them applicable in real-life. However, the performance of these algorithms has remained computationally challenging largely due to slow/premature convergence and their sensitivity to preliminary estimates. In this paper, a hybrid algorithm comprising the Particle Swarm Optimization (PSO), Baum-Welch (BW), and Genetic Algorithms (GA) is proposed and implemented for optimizing the parameters of HMMs. The algorithm not only overcomes the shortcomings of the slow convergence speed of the PSO but also helps the BW escape from local optimal solution whilst improving the performance of GA despite the increase in the search space. Detailed experimental results demonstrates the effectiveness of our proposed approach when compared to other techniques available in literature.

With the exponential growth of the information on the Internet, there is a high demand for making this information readable and processable by machines. For this purpose, there is a need for the Natural Language Processing (NLP) pipeline. Natural language analysis is a tool used by computers to grasp, perceive, and control human language. This paper discusses various techniques addressed by different researchers on NLP and compares their performance. The comparison among the reviewed researches illustrated that good accuracy levels haved been achieved. Adding to that, the researches that depended on the Sentiment Analysis and ontology methods achieved small prediction error.

The syntactic analysis or parsing or syntax analysis is the third stage of the NLP as a conclusion to use NLP technology. This step aims to accurately mean or, from the text, you may state a dictionary meaning. Syntax analysis analyzes the meaning of the text in comparison with the formal grammatical rules.

Biometrics is developing into a technological science in this lifelong technology for the defense of identification. Biometrics is the technology to recognize individuals based on facial features, fingerprints, iris, retina, speech, handprints, etc. Biometric features are used for human recognition and identification. Much research was done in the last years on the biometric system because of a growing need for identification methods. This paper offers an overview of biometric solutions using fingerprint and iris identification, their uses, and Compare the data set, methods, Fusion Level, and the accuracy of the results.

Clients can adequately control PCs and create reports by speaking with the guide of innovation, discourse acknowledgment empowers records to be delivered all the more effectively in light of the fact that the program normally produces words as fast as they expressed, which is typically a lot faster than a human can compose. Discourse acknowledgment is an innovation that consequently finds the words and expressions that best match the contribution of human discourse. The most normal use of discourse acknowledgment is correspondence, where discourse acknowledgment can be utilized to create letters/messages and different reports. Point of discourse handling: - to comprehend discourse as a mechanism of correspondence, to reflect discourse for transmission and propagation; - to inspect discourse for robotized data discovery and extraction-to find some physiological highlights of the speaker. In discourse combination, there are two significant capacities. The first is the interpretation of voice to message. The second is for the content to be converted into human voice.

Today, web services rapidly increased and are accessed by many users, leading to massive traffic on the Internet. Hence, the web server suffers from this problem, and it becomes challenging to manage the total traffic with growing users. It will be overloaded and show response time and bottleneck, so this massive traffic must be shared among several servers. Therefore, the load balancing technologies and server clusters are potent methods for dealing with server bottlenecks. Load balancing techniques distribute the load among servers in the cluster so that it balances all web servers. The motivation of this paper is to give an overview of the several load balancing techniques used to enhance the efficiency of web servers in terms of response time, throughput, and resource utilization. Different algorithms are addressed by researchers and get good results like the pending job, and IP hash algorithms achieve better performance.