An Efficient Deadlock Handling Model Based on Neutrosophic Logic: Case Study on Real Time Healthcare Database Systems

Abstract

The adoption of electronic patient records in hospitals will provide numerous benefits, but it will also present new issues. One of these is the creation of deadlocks, which causes delays in conducting tasks such as obtaining patient information. If the underlying system is distributed, the deadlock situation gets even more problematic. The strategy for preventing deadlock requires basic information from the transaction structure and requested resources. However, in most circumstances, this information is either unavailable or ambiguous. Because the cost of processing and storing sophisticated structures (e.g., wait-for graphs) is so high for a system in comparison to an array of structures, most discovery and resolved-deadlock protocols use them. Due to imprecise knowledge in specifying the transactions' attributes, distributed deadlock resolution methods present a new problem in dealing with uncertainty. To address this problem, a variety of fuzzy logic-based techniques have been proposed. However, very little attention has been paid to dealing with ambiguous, vague, incomplete, and inconsistent information about transaction attributes in a single framework. In this paper, we used neutrosophic logic, a generalization of fuzzy logic, to solve the problem of uncertainty in distributed real-time deadlock-resolving systems. The proposed method is structured to reflect multiple types of knowledge and relations among all features and tripartitions the transactions' features include validation factor degree, slackness degree, degree of deadline-missed transaction based on the degree of membership of truthiness, degree of membership of indeterminacy, and degree of membership of falsity. We developed a tool set and conducted experiments using benchmark datasets. There is an increase in detection rate and a large drop in rollback rate when this new strategy is used. Our technology resolved all database deadlocks and significantly increased database performance by up to three orders of magnitude.INDEX TERMS Deadlock recovery, neutrosophic set, healthcare databases, distributed deadlock detection. I. INTRODUCTIONNowadays, we depend heavily on online healthcare systems to ensure that medical treatments are always available. These systems depend largely on an interconnected network for data exchange between diverse entities, including as patients, physicians, and nurses. This results in massive amounts of data being maintained in medical databases, including details of surgeries, drugs, and allergies, among other things. Clinical information systems that are properly developed will allow physicians to obtain pertinent medical information about any patient, anytime they need it. However, one possible issue that users of electronic systems may confront is occasional delays in obtaining information caused by computer freezes. Deadlock is a common cause of a computer seeming to freeze. During the electronic healthcare records (HIMS) pilot phase, and as the number of users increased, the necessity to handle deadlock and concurren...

Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis

“…The purpose of this phase is achieved through the following steps: type 1 or type 2, introduces an overlapping region between classes, leading to ambiguity in the classification. Conversely, NL processing is adept at handling such ambiguity, thereby enhancing the efficiency of the classification process, as illustrated in Figure 2 [12,29,32,33]. Our aim is to enhance the efficiency of classification by using NL with a sentiment lexicon.…”

“…VADER is a Natural Language Toolkit (NLTK) Python package that generates four outputs: the scores of positive, negative, neutral, and compound (i.e., the total score of positive, neutral, and negative normalized between −1 and +1) for each text. VADER was selected due to its demonstrated superior performance when compared to other lexicons such as SentiWordNet, and TextBlob [25,33]. Additionally, it can provide positive and negative sentiment scores, which we incorporated into our NL classifier [34].…”

“…The two inputs are assigned a value between 0 and 1, each with the levels low (L), moderate (M), and high (H) for the truth component according to Equation (1). Meanwhile, for the indeterminacy according to Equation (2) and falsity components according to Equation (3), the levels are low-moderate (L-M) and moderate-high (M-H) [12,33,35]. The output variable was assigned a value ranging from 0 to 1 with three classes (positive, neutral, and negative) inspired by the work introduced in [34].…”

“…In the final phase of our NL model, the neutrosophic output is transformed into a crisp output based on COA (center of the area) [33]. The output is calculated as follows:…”

An Improved Deadlock Detection and Resolution Algorithm for Distributed Computing Systems

“…The deadlock problem has been extensively studied in distributed operating systems [1][2][3][4][5]. Many algorithms for centralized and distributed deadlock detection, prevention, and avoidance have been proposed in the literature [6,7,9,[12][13][14]. This section surveys the research work performed in the area of distributed deadlock detection for detecting skewed deadlock cycles.…”