An Effective Slant Detection and Correction Method Based on the Tilted Rectangle Method for Telugu Manuscript Terms

This paper proposes an efficient method called tilted rectangle (TR) for detecting and correcting of slant angle of the manuscript Telugu words (MTW). Telugu language is one of India's common languages spoken by over 80 million individuals. The complex characters are attached with some extra marks known as “maatras” and “vatthus,” and it is challenging to detect slant angle. The proposed TR method initially performs preprocessing and identifies a connected component within the given Telugu manuscript word. Then, it estimates the slant angle of each connected component by deriving connected slant lines on the boundary of each connected component. After this process, the proposed TR method estimates the entire word's overall slant angle from the average of estimated slant angle and height of all connected components. The correction of the word's slant angle is done in the reverse direction by applying a simple shear transformation. With 1000 manuscript records of three different kinds, the algorithm is tested. Experimental findings indicate the efficacy of the approach proposed.

A Novel Node Management in Hadoop Cluster by Using DNA

This paper proposes a novel node management for the distributed system using DNA hiding and generating a unique key by combing a unique physical address (MAC) of node and hostname. This mechanism provides better node management for the Hadoop cluster providing adding and deletion node mechanism by using limited computations and providing better node security from hackers. The objective of this paper is to design an algorithm to implement node-sensitive data hiding using DNA sequences and provide security to the node and its data from hackers.

Review of Association Mining Methods for the Extraction of Rules Based on the Frequency and Utility Factors

Association rule defines the relationship among the items and discovers the frequent items using a support-confidence framework. This framework establishes user-interested or strong association rules with two thresholds (i.e., minimum support and minimum confidence). Traditional association rule mining methods (i.e., apriori and frequent pattern growth [FP-growth]) are widely used for discovering of frequent itemsets, and limitation of these methods is that they are not considering the key factors of the items such as profit, quantity, or cost of items during the mining process. Applications like e-commerce, marketing, healthcare, and web recommendations, etc. consist of items with their utility or profit. Such cases, utility-based itemsets mining methods, are playing a vital role in the generation of effective association rules and are also useful in the mining of high utility itemsets. This paper presents the survey on high-utility itemsets mining methods and discusses the observation study of existing methods with their experimental study using benchmarked datasets.

An Extended Fuzzy C-Means Segmentation for an Efficient BTD With the Region of Interest of SCP

Magnetic resonance imaging (MRI) is the primary source to diagnose a brain tumor or masses in the medical sciences. It is emerging to detect the tumors from the scanned MRI brain images at early stages for the best treatments. Existing image segmentation techniques, morphological, fuzzy c-means are wildly successful in the extraction region of interest (ROI) in brain image segmentation. Proper extraction of ROIs is useful for regularizing the regions of tumors from the brain image with effective binarization in the segmentation. However, the existing techniques are limiting the irregular boundaries or shapes in tumor segmentation. Thus, this paper presents the proposed work extending the FCM with the spatial correlated pixel (RSCP), known as FCM-RSCP. It overcomes the problem of irregular boundaries by assessing correlated spatial information during segmentation. Benchmarked MRI brain images are used in the experiment for demonstrating the efficiency of the proposed methodology.

Featured Clustering and Ranking-Based Bad Cluster Removal for Hyperspectral Band Selection and Classification Using Ensemble of Binary SVM Classifiers

The rich spectral and spatial information of hyperspectral images are well known in the literature. The higher dimensionality of HSI creates Hughes's effect and increased computational complexity. This demands reduction for HS images as a pre-processing step. The necessary reduction of bands can be achieved by a proper band selection (BS) technique. The proposed features based unsupervised BS technique follows three subsequent steps: 1) for each band image statistical features are extracted, 2) bands are clustered with a k-means approach using the extracted features, 3) each cluster is ranked using mean entropy measure, 4) bad clusters are removed, and 5) for each selected cluster, a representative band is selected. The proposed method is validated over three widely used standard datasets and six state-of-the-art approaches using an ensemble of binary SVM classifiers. The obtained results strongly suggest the clustering is essential to reduce the redundancy, and removal of cluster is informative to keep the informative bands.

Improving Network Security Based on Trust-Aware Routing Protocols Using Long Short-Term Memory-Queuing Segment-Routing Algorithms

Defending all single connection failures for a particular system, segment routing issue, the switch will focus on the problems of selecting a small subset of trust-aware routing to improve the deep learning (DL). In the end, even if there were multiple path failures, these paths may introduce long-term, unnecessary overload in the proposed long short-term memory networks-based queuing routing segmentation (LSTM-QRS) experience of reducing traffic delays and adjusting traffic length by reducing network bandwidth. The critical factor is a novel traffic repair technique used to create a traffic repair path that switches to software-defined network (SDN) using multiple routing and providing additional flexibility in re-routing using long short-term memory networks (LSTM)-based queuing routing segment (LSTM-QRS) algorithms. It reduces the repair path length and recommends replacing the target-based traffic with the connection-based traffic fault detection router to avoid targeted traffic network congestion.

Proposing Knowledge Sharing as a Mediator Between Positive Gossip and Success of IT Projects

Using evolutionary perspective of gossip and knowledge-based view, the study proposes knowledge sharing behaviour as the underlying mechanism that helps in establishing the relationship between positive workplace gossip and success of IT projects. The study used multi-source and multi-layered data collection technique; data were collected from 304 employees working in information technology (IT) projects within Pakistan. The study used peer reports for knowledge sharing behaviour, while gossip and project success were self-reported. Results of the study show that positive workplace gossip has a positive relation with project success. The study also found support of partial mediation of knowledge sharing behaviour between positive workplace gossip and project success. These results have crucial implications for policy makers and project managers of IT projects. The findings reveal the importance of informal social networks of project team members towards project success. Managers shall device such mechanisms that allow for propagation of informal networks and knowledge sharing. Keywords Critical Success Factors, Information Technology Projects, Knowledge Sharing Behaviour, Positive Workplace Gossip, Project Success.

Optimal Fuzzy Scheduling and Sequencing of Work-Intensive Multiple Projects Under Normal and Unexpected Events

This research proposed optimization models for task scheduling and sequencing in work-intensive multiple projects under normal and unexpected events. The objectives of scheduling model were minimizing the total overtime/under-time costs and maximizing satisfaction values on tasks due dates and processing standard times. Further, the sequencing model aimed to minimize the sum of tasks' start times, maximize resource utilization, and maximize satisfaction on project completion times. Illustrations of the proposed scheduling and sequencing optimization models were provided where the results showed effective scheduling and sequencing of project tasks at minimal costs and achieved the desired satisfaction levels on tasks and projects and significantly enhanced resource efficiency at minimal overtime and under-time costs. Further, optimization models were modified to deal with unexpected events. In conclusion, the proposed models may support project managers in planning project tasks in a cost-effective manner under normal and unexpected events.

Shared Leadership

Shared leadership refers to an emergent, collective team process where leadership is distributed among and stems from team members, rather than a single designated leader. While shared leadership has gained considerable attention in the literature in recent years, it has received little attention in information systems (IS) project teams. In this study, a theoretical moderation framework in IS project teams that portrays the positive relationship between shared leadership and IS project success is presented. Moreover, project-centered moderators (i.e., project complexity and project task interdependence) and team-centered moderators (i.e., team autonomy and team supportive culture) have been proposed to influence the shared leadership-IS project success relationship. This study proposes that this relationship is stronger when projects are more complex and more interdependent, and when teams have a higher level of autonomy and supportive culture.

Which Organizational and Individual Factors Predict Success vs. Failure in Procurement Projects

Project manager (PM) certification and other commonly-tested independent factors were deductively examined using logistic regression to develop an explanatory model of high-priced public procurement project success versus failure. Overall 59% were successful, and 41% failed. The model correctly classified 67.3% of the 2,692 projects, yielding a significant 12% effect size. Individual factors, PM experience, certification, and contractor quality, impacted performance, but contractor size, revenue, industry type, PM age, gender, and education had negligible effect on performance. Quality was in the opposite hypothesized direction. Certified PMs were more likely to achieve successful projects rather than encounter failures. Future research advice was offered.