Manar Mohamed Hafez^1,*, Essam Eldin F. Elfakharany¹, Amr A. Abohany², Mostafa Thabet³

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 943-958, 2023, DOI:10.32604/cmc.2023.034078

Abstract Big data is usually unstructured, and many applications require the analysis in real-time. Decision tree (DT) algorithm is widely used to analyze big data. Selecting the optimal depth of DT is time-consuming process as it requires many iterations. In this paper, we have designed a modified version of a (DT). The tree aims to achieve optimal depth by self-tuning running parameters and improving the accuracy. The efficiency of the modified (DT) was verified using two datasets (airport and fire datasets). The airport dataset has 500000 instances and the fire dataset has 600000 instances. A comparison has been made between the… More >

T. Merlin Inbamalar^1,*, Dhandapani Samiappan², R. Ramesh³

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1899-1913, 2023, DOI:10.32604/iasc.2023.031202

Abstract Conferring to the American Association of Neurological Surgeons (AANS) survey, 85% to 99% of people are affected by spinal cord tumors. The symptoms are varied depending on the tumor’s location and size. Up-to-the-minute, back pain is one of the essential symptoms, but it does not have a specific symptom to recognize at the earlier stage. Numerous significant research studies have been conducted to improve spine tumor recognition accuracy. Nevertheless, the traditional systems are consuming high time to extract the specific region and features. Improper identification of the tumor region affects the predictive tumor rate and causes the maximum error-classification problem.… More >

M. Priyadharsini^1,*, K. Karuppasamy²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2187-2205, 2023, DOI:10.32604/csse.2023.031115

Abstract Big Data applications face different types of complexities in classifications. Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed data. The existing scheme has many disadvantages including continuity in training, more samples and training time in feature selections and increased classification execution times. Recently ensemble methods have made a mark in classification tasks as combine multiple results into a single representation. When comparing to a single model, this technique offers for improved prediction. Ensemble based feature selections parallel multiple expert’s judgments on a… More >

Kuldeep Chouhan¹, Mukesh Yadav², Ranjeet Kumar Rout³, Kshira Sagar Sahoo⁴, NZ Jhanjhi^5,*, Mehedi Masud⁶, Sultan Aljahdali⁶

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1113-1128, 2023, DOI:10.32604/csse.2023.030842

Abstract Twitter is a radiant platform with a quick and effective technique to analyze users’ perceptions of activities on social media. Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group. The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools. An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine (SVM). This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres… More >

Ayodeji O. J Ibitoye^1,*, Olufade F. W Onifade²

Journal on Big Data, Vol.4, No.2, pp. 87-95, 2022, DOI:10.32604/jbd.2022.024533

Abstract Community based churn prediction, or the assignment of recognising the influence of a customer’s community in churn prediction has become an important concern for firms in many different industries. While churn prediction until recent times have focused only on transactional dataset (targeted approach), the untargeted approach through product advisement, digital marketing and expressions in customer’s opinion on the social media like Twitter, have not been fully harnessed. Although this data source has become an important influencing factor with lasting impact on churn management. Since Social Network Analysis (SNA) has become a blended approach for churn prediction and management in modern… More >

A. S. Harish^*, C. Malathy

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 589-600, 2023, DOI:10.32604/iasc.2023.032030

Abstract Retailing is a dynamic business domain where commodities and goods are sold in small quantities directly to the customers. It deals with the end user customers of a supply-chain network and therefore has to accommodate the needs and desires of a large group of customers over varied utilities. The volume and volatility of the business makes it one of the prospective fields for analytical study and data modeling. This is also why customer segmentation drives a key role in multiple retail business decisions such as marketing budgeting, customer targeting, customized offers, value proposition etc. The segmentation could be on various… More >

Mohammad Al-Omari¹, Fadi Qutaishat¹, Majdi Rawashdeh¹, Samah H. Alajmani², Mehedi Masud^3,*

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 515-527, 2023, DOI:10.32604/iasc.2023.030374

Abstract Business Analytics is one of the vital processes that must be incorporated into any business. It supports decision-makers in analyzing and predicting future trends based on facts (Data-driven decisions), especially when dealing with a massive amount of business data. Decision Trees are essential for business analytics to predict business opportunities and future trends that can retain corporations’ competitive advantage and survival and improve their business value. This research proposes a tree-based predictive model for business analytics. The model is developed based on ranking business features and gradient-boosted trees. For validation purposes, the model is tested on a real-world dataset of… More >

Sandeep Kumar¹, Muhammad Badruddin Khan², Mozaherul Hoque Abul Hasanat², Abdul Khader Jilani Saudagar^2,*, Abdullah AlTameem², Mohammed AlKhathami²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 897-914, 2023, DOI:10.32604/cmc.2023.031867

Abstract Social media, like Twitter, is a data repository, and people exchange views on global issues like the COVID-19 pandemic. Social media has been shown to influence the low acceptance of vaccines. This work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individual’s sensitivities and feelings that lead to achievement. This work proposes a method to analyze the opinion of an individual’s tweet about the COVID-19 vaccines. This paper introduces a sigmoidal particle swarm optimization (SPSO) algorithm. First, the performance of SPSO is measured on a set of 12 benchmark problems, and later it is deployed… More >

Farhan Amin¹, Omar M. Barukab², Gyu Sang Choi^1,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 489-502, 2023, DOI:10.32604/cmc.2023.030615

Abstract The networks are fundamental to our modern world and they appear throughout science and society. Access to a massive amount of data presents a unique opportunity to the researcher’s community. As networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep pace. Therefore, this paper initiates a discussion on graph signal processing for large-scale data analysis. We first provide a comprehensive overview of core ideas in Graph signal processing (GSP) and their connection to conventional digital signal processing (DSP). We then summarize… More >

Rebecca Gedda^1,*, Larisa Beilina², Ruomu Tan³

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1737-1759, 2023, DOI:10.32604/cmes.2022.019764

Abstract Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner. In the context of process data analytics, change points in the time series of process variables may have an important indication about the process operation. For example, in a batch process, the change points can correspond to the operations and phases defined by the batch recipe. Hence identifying change points can assist labelling the time series data. Various unsupervised algorithms have been developed for change point detection, including the optimisation approach which minimises a cost function with certain… More > Graphic Abstract