Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
Ensuring Software Quality in Engineering Environments
New 3D Face Matching Technique for an Automatic 3D Model Based Face Recognition System
Algorithmic Cost Modeling: Statistical Software Engineering Approach
Prevention of DDoS and SQL Injection Attack By Prepared Statement and IP Blocking
MongoDB is a multi-storage NoSQL database. With the fast growth of technology, the number of large databases has exponentially increased, and relational databases cannot fulfill the need for managing such large amounts of data. To address this issue, intensive research was made on the NoSQL (Not Only SQL) data management model, specifically focusing on MongoDB, and a novel approach to manage large-scale remote sensing data is proposed. Social networking sites generate massive amounts of data, and Structured Query Language (SQL) and NoSQL are predominantly used for data storage. NoSQL proves to be a superior choice for web applications due to its ability to handle large data volumes, unlike Relational Database Management Systems (RDBMS). While various data partitioning techniques such as hash and round-robin exist, they are not efficient for small transactions involving only a few tuples. The results demonstrate that the proposed method of different segmentation overcomes the limitations of conventional approaches, facilitating horizontal expansion of the database and making it more suitable for managing extensive stored data. This research provides an essential technical support for effectively managing large amounts of stored data.
This paper aims to assess the suitability and feasibility of a project for Agile development by evaluating its maturity level based on Key Performance Indicators (KPI). The KPIs will be weighted empirically to determine the feasibility of the project for Agile development. It aims to determine if Agile methodologies can effectively manage change and deliver results quickly, as is often claimed in the IT industry.
Chatbots are a famous answering and text generation tool which became more popular with its real time based answering mechanism; use of Artificial Intelligence (AI) made them widely acceptable. Older version of Open AI's ChatGPT has issues like limited memory, inconsistent responses, and limited domain knowledge compared to ChatGPT- 4. ChatGPT-4 is able to answer like human in real time; it can write essays, poems, generate stories too. At present ChatGPT from OpenAI seems to be leading the trend compared to Bard AI, Bing AI, ChatSonic, OpenAI playground, LaMDA, Socratic, etc. This paper gives a brief knowledge on how ChatGPT got trained, its internal working mechanism, guidelines for effective utilization of ChatGPT and future of such AI based text generation tools.
This paper explores the possibilities of using Bedny's activity theory as a framework for developing new online promotion channels, specifically in the context of Search Engine Optimization (SEO). The goal is to identify the constructs of a behavioristic framework for the elaboration of SEO promotion technique. The case study of Increase Visibility Inc. is used to demonstrate the potential application of activity theory to modern online promotion channels. The findings reveal new constructs and provide an initial understanding of their relationships to other constructs in the context of SEO. Practical implications for promotion managers are also discussed.
Software is a collection of programs designed to untangle complexities and it is a derived programming pathway for different versatile projects as per the needs of the industry. There is a necessary need to increase the stratum to develop the optimality of software in an obligatory way. Developing software for a deprived task with an ideal forecast is the key source to achieve successful software. To attain the key success factors, there is a need to overcome the detachments between the planning, development, and implementation of software. Software development is the ideal approach for corrective and continuous connectivity of planning, amalgamation, exploitation, deliverance, authentication, testing, acquiescence, security, use, conviction, run-time monitoring, and enhancement of the designed modules. To conquer the goals of superior software development, effort needs to be calculated in terms of requisite metrics like volume of the software, outlay of the software, eminence of the software within the budget, and to-do list deliverance of the project. Estimating the optimal effort of software development is a critical task when using traditional methods. Versatile projects need to be developed in a specified manner to predict the effort. To overcome the challenges of effort estimation, upgrading approaches produces accurate results. Adopting the Machine Learning (ML) approach, a new technology, makes it easier to obtain accurate information regarding the Estimation of Effort (EoE) and Effort Estimation of a Software (EEoS) as per the requirements of the current trends in the software industry.
The number of data breaches in India has increased significantly in recent years. This is due to a number of factors, including the growing use of digital technologies, the lack of adequate data security measures, and the increasing sophistication of cybercriminals. The impact of data breaches can be far-reaching. Individuals may suffer financial losses, identity theft, and other forms of harm. Businesses may lose customers, suffer reputational damage, and incur significant costs to remediate the breach. The Indian government has taken some steps to address the problem of data breaches. However, more actions should be taken to strengthen data security measures and to educate businesses and individuals about the risks of data breaches. This research examines the rise of data breaches in India and uses the Air India case study to demonstrate the possible attacks in an airline company's infrastructure and how easily the customers' personal information can be stolen.
