Text Sentiment Analysis for Intelligent Transportation Systems by Using Web-Based Traffic Data: A Descriptive Study | Chapter 2 | Recent Advances in Mathematical Research and Computer Science Vol. 10

  In essence, transportation systems serve people, yet modern intelligent transportation systems (ITSs) have failed to consider public opinion. It is required to collect and analyse public wisdom and opinions in order to complete the ITS space. However, only a few research concentrated on the subject of transportation, which failed to meet the severe standards of intelligent transportation systems in terms of safety, efficiency, and information sharing (ITSs). We suggest traffic sentiment analysis (TSA) as a novel approach for addressing this issue, which gives modern ITSs a fresh perspective. This paper proposes TSA methods and models, as well as a comparison of the benefits and drawbacks of rule- and learning-based approaches using online data. We used the rule-based approach to solve real-world challenges, presented an architectural design, built associated bases, illustrated the process, and talked about online data collecting. The "yellow light rule" and China's "fuel price" were used to demonstrate the efficacy of our strategy. Our efforts will contribute to the advancement of TSA and its applications. This research paper proposes a new classification system for future development. To solve this problem, we employ a fuzzy extension known as the Fuzzy Naive Bayes classifier for the opinion mining process, which generalises the meaning of an attribute so that it has a collection of values to a specific degree of truth rather than a single value. This fuzzy naive Bayesian classification uses a hybrid classifier that combines Fuzzy Set Theory with a Naive Bayes classification.

Author(s) Details:

R. Jayavadivel,
Department of Computer Science and Engineering, School of Engineering, Presidency University, Bangalore, Karnataka - 560064, India.


N. Rajkumar,
Department of Computer Science and Engineering, School of Engineering, Presidency University, Bangalore, Karnataka - 560064, India.


B. Prabhu Shankar,
Department of Computer Science and Engineering, School of Engineering, Presidency University, Bangalore, Karnataka - 560064, India.


E. Vetrimani,
Department of Computer Science and Engineering, School of Engineering, Presidency University, Bangalore, Karnataka - 560064, India.


C. Viji,
Department of Computer Science and Engineering, HKBK College of Engineering, Bangalore, Karnataka – 560045, India.

G. Aarthy,
Department of Computer Science and Engineering, HKBK College of Engineering, Bangalore, Karnataka – 560045, India.

Please see the link here: https://stm.bookpi.org/RAMRCS-V10/article/view/6270

A Descriptive Classification of Association Item Sets from Large Data Sets Based on User Awareness Using Hybrid Approach | Chapter 4 | Emerging Trends in Engineering Research and Technology Vol. 11

 There is a large amount of data to be generated in business intelligence due to rising data in business applications. Data interpretation and prediction is a very vigorous idea for determining the outcomes of data based on decision-making analysis. Some of the machine learning-related techniques such as clustering, grouping, neural network-based approaches and association rule-based approaches have historically been used to explore and analyse business data in order to provide efficient data processing. Because of increasing data analysis in business intelligence-related applications, static machine learning approaches were not satisfied with establishing an association in real-time data sets between different attributes. So we suggest Advanced & Hybrid Computer in this article, Learning Methodology (AHMLA) for efficient data analysis of various high-dimensional data related attributes. Our proposed plan improves customer experience, reporting generations of business intelligence applications based on user understanding. An experimental outcome of the proposed method offers better output with regard to various parameters with regard to current approaches. Experimental findings illustrate successful data creation with different relationships between attributes. A further enhancement of the proposed approach is to facilitate the optimization of attribute relationships from large high-dimensional datasets between different item sets.

Author (s) Details

Srihari Varma Mantena
Department of CSE, Acharya Nagarjuna University, Guntur, AP, India.

C. V. P. R. Prasad
Department of CSE, Acharya Nagarjuna University, Guntur, AP, India.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/335

Time Series Variation Based on Power Links and Field Association Words in English and Arabic | Book Publisher International

 Some words have frequency appearance in each text and are known as keywords because they have a close association with the subjects of their texts, these words change frequencies in a given duration with time series variation. However, the significance of frequency shift with time-series variance is not considered in conventional text dealing methods and text search techniques. Traditional methods were therefore unable to correctly determine the popularity index of the word in a given time. A new method is proposed to automatically estimate the stability groups (increasing, relatively constant, and decreasing) that indicate the popularity of the word with variations in time series based on the change in frequency in previous text data. At first, learning data was created by defining five attributes to quantitatively measure the frequency of word shift, automatically extracting these five attributes from electronic texts. These learning data were categorised manually (human) into three classes of stability. These data were then subjected to a decision tree (DT) for automatic determination of the stability groups of research data (test data). However, the accuracy of the decision tree estimate decreases as data numbers are distributed between groups. A new way to use a Random Sampling Approach and a new Data Copying Method to increase the accuracy of decision tree estimation. In addition, the term Field Association (FA) is the smallest word or conceptual unit capable of evaluating a text field in a scheme called field tree. In a given period of time, a modern methodology based on frequencies of particular terms related to the field called Field Association (FA) terms is used. The technique suggests that the stabilisation classes of FA terms are automatically evaluated to point out the popularity of the list of FA terms depending on the time shift and to increase the accuracy of the Decision Tree (DT). In addition, all previous methods are based on keywords in the languages of English and French. The extension of keywords to other languages, such as Arabic, could therefore reinforce further research in that area. This approach incorporates a new method of extracting Arabic keywords from companies based on changes in a text over several periods of time using a decision tree based on their recurrences. The new methodology is applied to the new field of data set (computer science) that differentiates it from historically used approaches. Finally, to examine the co-occurrences of words in the publications of a given topic, Power Links Analysis (PLA) was developed to solve the previous downside of conventional approaches. This approach is focused on both the advanced frequency form and the distance between the various instances of the terms given. A new technique has been proposed that extracts stability groups of field association terms based on automated power relation analysis to improve decision tree accuracy. In this method, we analysed the effects of time variance based on the frequencies of unique FA words that applied to documents in a particular period using power connection.

Author(s) Details

El-Sayed Atlam
Department of Computer Science, Faculty of Science, Tanta University, Tanta, Egypt and College of Computer Science and Engineering at Yanbu, Taibah University, Saudi Arabia.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/312

Selection of Table Scan or Seek Scan on the Basis of Data Size to Reduce Number of Logical Reads | Chapter 3 | Theory and Practice of Mathematics and Computer Science Vol. 1

The information retrieval system is an effort to extract services from a range of knowledge services, which are

specific to the information requirement. You will access this method using passwords or keywords or a user

query. Databases and documents are usually limited to different organizational framework managed by the

DBMS and the Information Retrieval Systems (IRS). Both DBMS and IRS, though, frequently store data on the

same individuals, providing convergence opportunities. In the modern world, it has become necessary to retrieve

the information in very efficient manner and within a very less time. But being availability of data in very large

size, information retrieval form database become slower which may affect the overall performance of the

system. For this, various methodologies have been implemented in all DBMS software. In addition, it becomes

mandatory to use proper technique for the better and faster result. Table scan and seek scan have integrated in

the DBMS for the same purpose. In the current research, we have compared both methods for better

performance [1].

Author (s) Details

S. K. Kharade

Department of Mathematics, Shivaji University, Kolhapur, India.

K. G. Kharade

Department of Computer Science, Shivaji University, Kolhapur, India.

V. S. Kumbhar

Department of Computer Science, Shivaji University, Kolhapur, India.

R. K. Kamat

Department of Electronics, Shivaji University, Kolhapur, India.

View Book :-https://bp.bookpi.org/index.php/bpi/catalog/book/237