Cuckoo Search-Driven Feature Selection for Decision Tree Modelling

Features are fundamental components of decision tree modeling, and their relevance, quality, and selection are crucial determinants of the model's effectiveness and performance. However, decision trees can be computationally expensive, requiring a significant amount of memory to store the trees and their associated data structures. To address this limitation, we present a novel approach that utilizes a Cuckoo Search-based feature selection algorithm to construct efficient and optimal decision trees. The Cuckoo Search algorithm, inspired by the behavior of cuckoo birds, is a powerful metaheuristic algorithm that effectively selects high-quality features and creates accurate decision trees in the subforest. We evaluate the proposed method on a variety of datasets from the standard UCI learning repository with different domains and sizes, and our results demonstrate that the algorithm creates optimal decision trees with high performance

A Review on Various Approach of Speech Recognition Technique

The Speech is most prominent & primary mode of Communication among of human being. The communication among human computer interaction is called human computer interface. Speech has potential of being important mode of interaction with computer. This paper gives an overview of major technological perspective and appreciation of the fundamental progress of speech recognition and also gives overview technique developed in each stage of speech recognition. This paper helps in choosing the technique along with their relative merits & demerits. A comparative study of different technique is done asperstages. After years of research and development the accuracy of automatic speech recognition remains one of the promising research challenges (eg. variation of the context, speakers, and environment). The design of Speech Recognition system requires careful attentions to the following issues: Definition of various types of speech classes, speech representation, feature extraction techniques, speech classifiers, and database and performance evaluation. The problems that are existing in ASR and the various techniques to solve these problems constructed by various research workers have been presented in a chronological order. Real-time speech recognition is a challenging task due to the variability of speech signals and the need for fast and accurate processing. Support Vector Machines (SVMs) are a popular machine learning technique that has been used for speech recognition tasks. In this paper, we present a real-time speech recognition system using SVM. The system is based on a feature extraction process that uses Mel-Frequency Cepstral Coefficients (MFCCs) to represent speech signals. The extracted features are then used as input to the SVM classifier, which is trained to recognize different speech signals. The proposed system was implemented using the Python programming language and the Scikit-learn machine learning library. The performance of the system was evaluated using a dataset of spoken digits. The results showed that the proposed system achieved high recognition accuracy and real-time performance, making it suitable for practical applications. Speech is a unique human characteristic used as a tool to communicate and express ideas. Automatic speech recognition (ASR) finds application in electronic devices that are too small to allow data entry via the commonly used input devices such as keyboards. Personal Digital Assistants (PDA) and cellular phones are such examples in which ASR plays an important role

Serological responses to prednisolone treatment in leprosy reactions: study of TNF-α, antibodies to phenolic glycolipid-1, lipoarabinomanan, ceramide and S100-B.

BACKGROUND: Corticosteroids have been extensively used in the treatment of immunological reactions and neuritis in leprosy. The present study evaluates the serological response to steroid treatment in leprosy reactions and neuritis. METHODS: Seven serological markers [TNF-α, antibodies to Phenolic glycolipid-1 (PGL-1 IgM and IgG), Lipoarabinomannan (LAM IgG1 and IgG3), C2-Ceramide and S100 B] were analyzed longitudinally in 72 leprosy patients before, during and after the reaction. At the onset of reaction these patients received a standard course of prednisolone. The levels of the above markers were measured by Enzyme linked immunosorbent assay (ELISA) and compared with the individuals own value in the month prior to the reaction and presented as percentage increase. RESULTS: One month before the reaction individuals showed a varying increase in the level of different markers such as TNF-α (53%) and antibodies to Ceramide (53%), followed by to PGL-1 (51%), S100B (50%) and LAM (26%). The increase was significantly associated with clinical finding of nerve pain, tenderness and new nerve function impairment. After one month prednisolone therapy, there was a fall in the levels [TNF-α (60%), C2-Ceramide (54%), S100B (67%), PGL-1(47%) and LAM (52%)] with each marker responding differently to steroid. CONCLUSION: Reactions in leprosy are inflammatory processes wherein a rise in set of serological markers can be detected a month before the clinical onset of reaction, some of which remain elevated during their action and steroid treatment induces a variable fall in the levels, and this forms the basis for a variable individual response to steroid therapy

An Evaluation of Pixel-based and Object-based Classification Methods for Land Use Land Cover Analysis Using Geoinformatic Techniques

Land use land cover (LULC) classification is a valuable asset for resource managers; in many fields of study, it has become essential to monitor LULC at different scales. As a result, the primary goal of this work is to compare and contrast the performance of pixel-based and object-based categorization algorithms. The supervised maximum likelihood classifier (MLC) technique was employed in pixel-based classification, while multi-resolution segmentation and the standard nearest neighbor (SNN) algorithm were employed in object-based classification. For the urban and suburban parts of Kolhapur, the Resourcesat-2 LISS-IV image was used, and the entire research region was classified into five LULC groups. The performance of the two approaches was examined by comparing the classification results. For accuracy evaluation, the ground truth data was used, and confusion matrixes were generated. The overall accuracy of the object-based methodology was 84.66%, which was significantly greater than the overall accuracy of the pixel-based categorization methodology, which was 72.66%. The findings of this study show that object-based classification is more appropriate for high-resolution Resourcesat-2 satellite data than MLC of pixel-based classification