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Design Simulation and Assessment of Cellular Automata Based Improved Image Segmentation System

A variety of methods may be found in the numerous image segmentation techniques. Here a method of text retrieval conducted is typically to produce a collection of localized features. In computer science, object recognition is the problem of automatically "identifying", or classifying, an object. In certain instances, the awareness of artifacts is deeper into image in image segmentation through image processing. The algorithm used for image segmentation has a direct impact on the outcome of the whole approach, therefore it is important to choose carefully. It is important to choose a segmentation method appropriate for a certain framework. There are several ready-to-use segmentation methods, so one by one evaluate the tools to see which works best. Segmentation algorithms have reached such a level of complexity that a research employing them is often considered impractical. The given research undertakes the process of improved graph cut method to select the foreground and background of image through labelling and segmentation of the image. Results have been compared on the performance parameter to analyse the effectiveness of the proposed algorithm for segmentation of the images

Optimizing the Failure Prediction in Deep Learning

  Avatars are computer-generated digital representations that people may use in the Predicting issues with software systems built from modules is the focus of this research. This data collection was used as a reference in order to accomplish this objective. The evaluation framework for reusable software components is provided by this research. The dataset of factors that play a role in the decision-making process has been run through the PSO algorithm. The primary objective is to provide a clever and time-saving method of choosing components. After filtering for ideal values, the dataset is utilized to train a deep learning model. Accuracy measurements including recall value, precision, and F1 score will be used to evaluate the effectiveness of the optimized component selection model. This research is significant because it provides a high-performance and accurate solution to a major problem in predicting. We have done our best to estimate the number of lines of code, the complexity, the design complexity, the projected time, the difficulty, the intelligence, and the efforts required. A model for discovering mistakes has been developed after the dataset was filtered to account for the ideal value. By keeping just the most crucial characteristics and getting rid of all optimized data, we have made the model more trustworthy. &nbsp

Extending Classical Technology Acceptance Models, A Review of Potential Mobile Device and Consumer Individual Factors to Better Explain Mobile Commerce Acceptance

Purpose - Technology adoption theories are very general, however the factors influencing acceptance could vary on the specific technology and the segments of consumers with their individual traits. This study accomplishes a comprehensive review of literature and to find potential variables to extend classical technology acceptance models specifically in the contexts of mobile technology and mobile commerce with consumer individual traits in mind. Methodology - 1. Methodical Review of key journal articles on Technology Acceptance across multiple key publishers, 2. Review of popular extant models in the context of general technology, 3. Elicit Mobile and Consumer specific considerations 4. Identify theories relevant to mobile devices and consumers as individuals Result - The result showed that the three were multiple mobile device/ commerce and consumer related theories including convenience, perceived risk, trust and deal proneness Study Implications - The theories and the constructs identified in this review could be used by future researchers working to further the acceptance science in the context of mobile devices taking consumer individual factors into consideratio

The Role of E-Government in Bangladesh’s Housing Market: A Study on Bogura, Rajshahi, Bangladesh

Bangladesh is a developing country on e-government sector day by day. Now the E-Government mainly focusing on the housing market sector day by day because it needs a vas attention so that people can easily get this service. Bangladesh has begun to use Information Communication Technology in E-Government sector firms to improve service delivery via improved governance processes. The ICT-based governing process known as e-government provides benefits to governments while also posing obstacles. Because of the government's complexity, adoption of e-government in housing market is challenging, which may subtract from the ultimate result. E-government is the delivery of universal services to citizens. E-government opens up new road for citizens to connect with government in a more direct and convenient way, as long as allowing e-government is providing services to city’s people directly. Individuals and their governments, as well as governments and other government agencies, people and governments, governments and employees, and governments and corporations, are all included by the phrase. This research focus on the impact of Bangladesh e-government on Bangladesh's property markets, and find out the potential consequences and repercussions

Analysis & Numerical Simulation of Indian Food Image Classification Using Convolutional Neural Network

Recognition of Indian food can be assumed to be a fine-grained visual task owing to recognition property of various food classes. It is therefore important to provide an optimized approach to segmentation and classification for different applications based on food recognition. Food computation mainly utilizes a computer science approach which needs food data from various data outlets like real-time images, social flat-forms, food journaling, food datasets etc, for different modalities. In order to consider Indian food images for a number of applications we need a proper analysis of food images with state-of-art-techniques. The appropriate segmentation and classification methods are required to forecast the relevant and upgraded analysis. As accurate segmentation lead to proper recognition and identification, in essence we have considered segmentation of food items from images. Considering the basic convolution neural network (CNN) model, there are edge and shape constraints that influence the outcome of segmentation on the edge side. Approaches that can solve the problem of edges need to be developed; an edge-adaptive As we have solved the problem of food segmentation with CNN, we also have difficulty in classifying food, which has been an important area for various types of applications. Food analysis is the primary component of health-related applications and is needed in our day to day life. It has the proficiency to directly predict the score function from image pixels, input layer to produce the tensor outputs and convolution layer is used for self- learning kernel through back-propagation. In this method, feature extraction and Max-Pooling is considered with multiple layers, and outputs are obtained using softmax functionality. The proposed implementation tests 92.89% accuracy by considering some data from yummly dataset and by own prepared dataset. Consequently, it is seen that some more improvement is needed in food image classification. We therefore consider the segmented feature of EA-CNN and concatenated it with the feature of our custom Inception-V3 to provide an optimized classification. It enhances the capacity of important features for further classification process. In extension we have considered south Indian food classes, with our own collected food image dataset and got 96.27% accuracy. The obtained accuracy for the considered dataset is very well in comparison with our foregoing method and state-of-the-art techniques.

A Study on the Impact of Sri Lankan Crisis on Kerala Tourism

Tourism is a major sector in the world that has a significant role in the economic development of various countries. Tourism sector also helps in the generation of employment opportunities. Sri Lanka and Kerala are two major tourist destinations in South Asia. Millions of tourists visit Sri Lanka and Kerala every year. Sri Lanka as a nation suffered from a brutal civil war and it adversely affected all the major sectors in the country. After the end of civil war, Sri Lanka achieved significant growth and development in multiple fields. Millions of tourists visited the nation and Sri Lanka received foreign exchange earnings worth billions of Dollars. But the terrorist attacks in the year 2019, COVID-19 pandemic and ongoing economic and political crisis shattered the nation and especially the tourism sector. Kerala, the southern state of India is currently benefitting from the Sri Lankan crisis. Kerala is a major rival to Sri Lanka in terms of tourism. Both Sri Lanka and Kerala has similar climate, landscape, food cuisine, geography etc. As a result of the Sri Lankan crisis, international tourists started preferring Kerala over Sri Lanka. This article aims to analyze the impact of Sri Lankan crisis on Kerala tourism

Liver Cancer Identification Grid Search RFC Model using Machine Learning

Liver is essential to the body's digestion of sugar and fats, absorption, and immunological system. This substance is present in almost everything a man takes in, breathes, or absorbs through his skin. Liver disorders are a significant health burden. It is increasing daily and is difficult to detect in its early stages since the liver may function normally even when partially damaged. Doctors have widely employed machine learning algorithms to diagnose liver illness in order to increase the efficiency of medical diagnosis. The study's primary aim is to evaluate how machine learning algorithms may be used to prevent postponing medical care, accurately diagnose liver illness, and minimize the number of erroneous diagnoses provided to sick patients. The main objective is to ensure that liver patients receive an accurate diagnosis as soon as possible

Investigating Role of Deep Learning in Metaverse

  Avatars are computer-generated digital representations that people may use in the metaverse to communicate and interact with one another as well as with digital goods. Imagine a setting that combines elements of virtual reality, an online performance game, and the World Wide Web. In the modern world, one does not have the option of avoiding the usage of bitcoin. In this rapidly evolving hybrid setting, Bitcoin is the proper medium of exchange because of the inherent decentralisation it has. In addition to this, it is essential to integrate data compression and safety precautions. Compression is an area of study that is constantly undergoing new developments as well as technological leaps and bounds. This study looks on other aspects of the metaverse as well, such as data compression and security concerns related to the metaverse. Before training and testing the DL model, an image processing approach was included in order to reduce its size. This was done so that object identification may be improved even more. &nbsp

Investigating Role of Data Mining in Software Engineering

  Companies that focus on software development produce vast volumes of data. Every stage of software development, from gathering requirements to ongoing upkeep, generates its own set of data. To better the software, efforts are undertaken to collect and store data produced in software repositories. Data mining techniques are used to the massive amounts of data found in software repositories in order to extract previously unseen patterns and insights. Researchers from the fields of Software Engineering and Data Mining have lately made this area of study a top priority. This research aims to examine the many uses of data mining in software engineering, the many types of software engineering data that can be mined, and the many data mining techniques that are available and have been used by researchers to solve the problems that this research focuses on. The next step is to use this classification to determine which subfield within software engineering has the highest scholarly interest.   &nbsp