S. Sofana Reka¹, Ankita Bagelikar², Prakash Venugopal^2,*, V. Ravi², Harimurugan Devarajan³

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 781-794, 2024, DOI:10.32604/cmc.2023.043369

Abstract The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality, flavor and nutritional value. The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers. The impact of rotten fruits can foster harmful bacteria, molds and other microorganisms that can cause food poisoning and other illnesses to the consumers. The overall purpose of the study is to classify rotten fruits, which can affect the taste, texture, and appearance of other fresh fruits, thereby reducing their shelf life. The agriculture and food industries… More >

Mian Muhammad Danyal¹, Sarwar Shah Khan^2,4, Muzammil Khan^2,*, Muhammad Bilal Ghaffar¹, Bilal Khan¹, Muhammad Arshad³

Journal on Big Data, Vol.5, pp. 1-18, 2023, DOI:10.32604/jbd.2023.041319

Abstract Movies are the better source of entertainment. Every year, a great percentage of movies are released. People comment on movies in the form of reviews after watching them. Since it is difficult to read all of the reviews for a movie, summarizing all of the reviews will help make this decision without wasting time in reading all of the reviews. Opinion mining also known as sentiment analysis is the process of extracting subjective information from textual data. Opinion mining involves identifying and extracting the opinions of individuals, which can be positive, neutral, or negative. The task of opinion mining also… More >

Zhifang Liao¹, Hao Gu¹, Shichao Zhang¹, Ronghui Mo¹, Yan Zhang^2,*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 599-618, 2023, DOI:10.32604/iasc.2023.036794

Abstract Context-Sensitive Task (CST) is a complex task type in crowdsourcing, such as handwriting recognition, route plan, and audio transcription. The current result inference algorithms can perform well in simple crowdsourcing tasks, but cannot obtain high-quality inference results for CSTs. The conventional method to solve CSTs is to divide a CST into multiple independent simple subtasks for crowdsourcing, but this method ignores the context correlation among subtasks and reduces the quality of result inference. To solve this problem, we propose a result inference algorithm based on the Partially ordered set and Tree augmented naive Bayes Infer (P&T-Inf) for CSTs. Firstly, we… More >

Khalid M. O. Nahar^1,*, Mohammad Alauthman², Saud Yonbawi³, Ammar Almomani^4,5

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5307-5319, 2023, DOI:10.32604/cmc.2023.031848

Abstract Social media networks are becoming essential to our daily activities, and many issues are due to this great involvement in our lives. Cyberbullying is a social media network issue, a global crisis affecting the victims and society as a whole. It results from a misunderstanding regarding freedom of speech. In this work, we proposed a methodology for detecting such behaviors (bullying, harassment, and hate-related texts) using supervised machine learning algorithms (SVM, Naïve Bayes, Logistic regression, and random forest) and for predicting a topic associated with these text data using unsupervised natural language processing, such as latent Dirichlet allocation. In addition,… More >

Joseph Bamidele Awotunde¹, Sanjay Misra^2,*, Vikash Katta², Oluwafemi Charles Adebayo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 131-154, 2023, DOI:10.32604/cmes.2023.026812

Abstract The task of classifying opinions conveyed in any form of text online is referred to as sentiment analysis. The emergence of social media usage and its spread has given room for sentiment analysis in our daily lives. Social media applications and websites have become the foremost spring of data recycled for reviews for sentimentality in various fields. Various subject matter can be encountered on social media platforms, such as movie product reviews, consumer opinions, and testimonies, among others, which can be used for sentiment analysis. The rapid uncovering of these web contents contains divergence of many benefits like profit-making, which… More >

Kashif Saleem¹, Rashid Naseem¹, Khalil Khan^1,2, Siraj Muhammad³, Ikram Syed^4,*, Jaehyuk Choi^4,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3517-3533, 2023, DOI:10.32604/iasc.2023.036356

Abstract Automation software need to be continuously updated by addressing software bugs contained in their repositories. However, bugs have different levels of importance; hence, it is essential to prioritize bug reports based on their severity and importance. Manually managing the deluge of incoming bug reports faces time and resource constraints from the development team and delays the resolution of critical bugs. Therefore, bug report prioritization is vital. This study proposes a new model for bug prioritization based on average one dependence estimator; it prioritizes bug reports based on severity, which is determined by the number of attributes. The more the number… More >

D. Palanikkumar¹, R. Ramesh Kumar², Mehedi Masud³, Mrim M. Alnfiai⁴, Mohamed Abouhawwash^5,6,*

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2425-2440, 2023, DOI:10.32604/iasc.2023.033879

Abstract The major environmental hazard in this pandemic is the unhygienic disposal of medical waste. Medical wastage is not properly managed it will become a hazard to the environment and humans. Managing medical wastage is a major issue in the city, municipalities in the aspects of the environment, and logistics. An efficient supply chain with edge computing technology is used in managing medical waste. The supply chain operations include processing of waste collection, transportation, and disposal of waste. Many research works have been applied to improve the management of wastage. The main issues in the existing techniques are ineffective and expensive… More >

Hameedur Rahman¹, Junaid Tariq^2,*, M. Ali Masood¹, Ahmad F. Subahi³, Osamah Ibrahim Khalaf⁴, Youseef Alotaibi⁵

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5527-5543, 2023, DOI:10.32604/cmc.2023.033190

Abstract Sentiment Analysis (SA) is often referred to as opinion mining. It is defined as the extraction, identification, or characterization of the sentiment from text. Generally, the sentiment of a textual document is classified into binary classes i.e., positive and negative. However, fine-grained classification provides a better insight into the sentiments. The downside is that fine-grained classification is more challenging as compared to binary. On the contrary, performance deteriorates significantly in the case of multi-class classification. In this study, pre-processing techniques and machine learning models for the multi-class classification of sentiments were explored. To augment the performance, a multi-layer classification model… 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 >

Keungyeup Ji, Youngmi Kwon^*

Computer Systems Science and Engineering, Vol.45, No.1, pp. 201-214, 2023, DOI:10.32604/csse.2023.031270

Abstract As the importance of email increases, the amount of malicious email is also increasing, so the need for malicious email filtering is growing. Since it is more economical to combine commodity hardware consisting of a medium server or PC with a virtual environment to use as a single server resource and filter malicious email using machine learning techniques, we used a Hadoop MapReduce framework and Naïve Bayes among machine learning methods for malicious email filtering. Naïve Bayes was selected because it is one of the top machine learning methods(Support Vector Machine (SVM), Naïve Bayes, K-Nearest Neighbor(KNN), and Decision Tree) in… More >