Empowering Cybersecurity: A Deep Dive into AI-Driven Security Intelligence Modelling
Smart Incubator for Empowering Poultry Farmers by Maximizing Egg Hatching Success
Early Disease Detection of Black Gram Plant Leaf using Cloud Computing Based YOLO V8 Model
Data Retrieval in Cancer Documents using Various Weighting Schemes
Securing Healthcare Data in Smart Cities: A Machine Learning Approach
Character Recognition wherein a computer is able to interpret human handwriting and recognize it as an alphanumeric character. The input provided to the recognition system is an image of a digit, a word, or generally combinations of such texts. The system accordingly generates an output as an ASCII transcription of the inputted text. This task involves a number of pre-processing steps. This paper analyses and mainly focuses on few pre-processing approaches to recognize handwritten Gujarati characters. The whole character recognition process is logically divided into separate parts like Image acquisition, Preprocessing, Processing, and Post-processing. In the targeted system which will be used to recognize character; the scanned image is first passed through pre-processing modules like Image Acquisition, Smoothing, Boundary tracing, etc., in order to achieve a higher recognition rate.
Mobile Ad Hoc Network (MANET) is a wireless, infrastructureless network where each node acts as a router, transmitter, and data sink. The objective of this paper is to evaluate the performance of reactive, proactive, and hybrid routing protocols of Mobile Ad-Hoc networks (MANET's) for Custom application. The authors’ have evaluated three routing protocols, i.e. AODV, OLSR, and ZRP by using NETSIM simulator tool. The performance of these routing protocols is examined by two application metrics: throughput and delay. The study of protocols will be carried out and finally the results will be presented as to which routing protocol is a better one for MANET.
In Optical Character Recognition, written characters are recognized by computer based system. Such characters might be computer generated or handwritten. In case writing process is over, it is considered as offline writing. This paper proposes offline handwritten Gujarati numerals (0-9) recognition. Offline handwritten character recognition is an important area of pattern recognition. The entire character recognition task is logically divided into Image acquisition, preprocessing, feature extraction, classification, and recognition steps. To recognize Gujarati numeral features, such as hole, straight-line, number of open/end edge and open edge present in different zone(s), etc., are extracted. As per the result of different extracted features for particular Gujarati numeral, classification is carried out. Methodology, Implementation of it, and the results obtained are presented in the paper.
A novel method using clustering algorithm is proposed to recognize the tamil characters in a given document. Many algorithms and processing techniques exist, which are used only for certain languages and hard specific file formats. This does not involve any pre-processing on the documents like contrast adjustments or filtering of noises on the image. Considering all these negativities, a novel method is proposed in this project, where the input can be of any file type which undergoes pre-processing like contrast adjustment before applying the procedure. Above all, this method is used to replace the historical Tamil words in the earlier Tamil documents to the words corresponding to them that are in use today.
Due to the restrictions of image-catching gadgets or the presence of a non-ideal environment, the quality of digital images may get corrupted. Much of the time, these images might demand a certain level of enhancement for satisfactory visual representation. Image enhancement is a procedure to evacuate the undesirable distortion due to deterioration in contrast, unwanted noise, improper intensity saturation, blurring effect, etc., and determine the hidden information that are contained in images. The main goal of Image enhancement is to process an image so that the result is more suitable than original image for particular application. Many image enhancement techniques are based on spatial operations performed on local neighborhoods of input pixels. Conventional global histogram equalization few times becomes a reason for immoderate contrast enhancement, thus local histogram equalization may cause block effect. Therefore to conquer these problems, a new way for image contrast enhancement is presented in this research work. The curiosity of the proposed strategy is that the weighted average of the histogram equalized, gamma corrected, and the original image are combined to obtain the enhanced processed image. The proposed algorithm not only achieves contrast enhancement, but also preserves a sufficient level of brightness level. This study will highlight various image enhancement techniques along with their benchmark results. Empirical study results demonstrate that the proposed algorithm has good performance on enhancing contrast and clarity for a larger part of images.
Big data is the current state-of-the-art topic creating its unique place in the research and industry minds to look into depth of topic to get valuable considerable results needed to meet the future data mining and analysis needs. Big data refers to the fast moving, large size of different structural form data increasing at fast pace. So, there also prevail the need of tools to tackle it as the current and traditional techniques are getting unfit for the current key challenges and security aspects of big data. This paper deals with characteristics of big data, applications of big data in various different fields as it has now became a multi-disciplinary aspect to review. Big data generation, storing, analysis and transmission are increasingly growing which has accompanied it as its characteristics. In this paper, different sections through an overlook on different aspects on big data, such as sources, security challenges, and future directions regarding big data mining and analysis have been discussed.
