Samar Elbedwehy^1,3,*, T. Medhat², Taher Hamza³, Mohammed F. Alrahmawy³

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3637-3652, 2023, DOI:10.32604/csse.2023.038376

Abstract One of the issues in Computer Vision is the automatic development of descriptions for images, sometimes known as image captioning. Deep Learning techniques have made significant progress in this area. The typical architecture of image captioning systems consists mainly of an image feature extractor subsystem followed by a caption generation lingual subsystem. This paper aims to find optimized models for these two subsystems. For the image feature extraction subsystem, the research tested eight different concatenations of pairs of vision models to get among them the most expressive extracted feature vector of the image. For the caption generation lingual subsystem, this… More >

Huma Israr^1,*, Safdar Abbas Khan¹, Muhammad Ali Tahir¹, Muhammad Khuram Shahzad¹, Muneer Ahmad¹, Jasni Mohamad Zain^2,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2981-3009, 2023, DOI:10.32604/cmc.2023.035814

Abstract In bilingual translation, attention-based Neural Machine Translation (NMT) models are used to achieve synchrony between input and output sequences and the notion of alignment. NMT model has obtained state-of-the-art performance for several language pairs. However, there has been little work exploring useful architectures for Urdu-to-English machine translation. We conducted extensive Urdu-to-English translation experiments using Long short-term memory (LSTM)/Bidirectional recurrent neural networks (Bi-RNN)/Statistical recurrent unit (SRU)/Gated recurrent unit (GRU)/Convolutional neural network (CNN) and Transformer. Experimental results show that Bi-RNN and LSTM with attention mechanism trained iteratively, with a scalable data set, make precise predictions on unseen data. The trained models yielded… More >

Badriyya B. Al-onazi¹, Abdulkhaleq Q. A. Hassan², Mohamed K. Nour³, Mesfer Al Duhayyim^4,*, Abdullah Mohamed⁵, Amgad Atta Abdelmageed⁶, Ishfaq Yaseen⁶, Gouse Pasha Mohammed⁶

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2575-2591, 2023, DOI:10.32604/cmc.2023.033531

Abstract Sentiment Analysis (SA), a Machine Learning (ML) technique, is often applied in the literature. The SA technique is specifically applied to the data collected from social media sites. The research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction process. In this background, the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets (QPSODL-SAAT). The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in Arabic. Initially, the data pre-processing is performed to convert the raw tweets into a… More >

Yeferson Torres-Berru^1,2,*, Vivian F. Lopez-Batista¹, Lorena Conde Zhingre³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3501-3516, 2023, DOI:10.32604/iasc.2023.035367

Abstract In a public procurement process, corruption can occur at each stage, favoring a participant with a previous agreement, which can result in over-pricing and purchases of substandard products, as well as gender discrimination. This paper’s aim is to detect biased purchases using a Spanish Language corpus, analyzing text from the questions and answers registry platform by applicants in a public procurement process in Ecuador. Additionally, gender bias is detected, promoting both men and women to participate under the same conditions. In order to detect gender bias and favoritism towards certain providers by contracting entities, the study proposes a unique hybrid… More >

Umamaheswari Ramalingam^1,*, Senthil Kumar Murugesan², Karthikeyan Lakshmanan², Chidhambararajan Balasubramaniyan³

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1733-1744, 2023, DOI:10.32604/iasc.2023.032108

Abstract A Deep Neural Sentiment Classification Network (DNSCN) is developed in this work to classify the Twitter data unambiguously. It attempts to extract the negative and positive sentiments in the Twitter database. The main goal of the system is to find the sentiment behavior of tweets with minimum ambiguity. A well-defined neural network extracts deep features from the tweets automatically. Before extracting features deeper and deeper, the text in each tweet is represented by Bag-of-Words (BoW) and Word Embeddings (WE) models. The effectiveness of DNSCN architecture is analyzed using Twitter-Sanders-Apple2 (TSA2), Twitter-Sanders-Apple3 (TSA3), and Twitter-DataSet (TDS). TSA2 and TDS consist of… More >

Sajadul Hassan Kumhar¹, Syed Immamul Ansarullah², Akber Abid Gardezi³, Shafiq Ahmad⁴, Abdelaty Edrees Sayed⁴, Muhammad Shafiq^5,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3899-3912, 2023, DOI:10.32604/cmc.2023.032290

Abstract English to Urdu machine translation is still in its beginning and lacks simple translation methods to provide motivating and adequate English to Urdu translation. In order to make knowledge available to the masses, there should be mechanisms and tools in place to make things understandable by translating from source language to target language in an automated fashion. Machine translation has achieved this goal with encouraging results. When decoding the source text into the target language, the translator checks all the characteristics of the text. To achieve machine translation, rule-based, computational, hybrid and neural machine translation approaches have been proposed to… More >

B. Lalitha^1,*, V. Gomathi²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 173-189, 2023, DOI:10.32604/csse.2023.024118

Abstract IC (Image Captioning) is a crucial part of Visual Data Processing and aims at understanding for providing captions that verbalize an image’s important elements. However, in existing works, because of the complexity in images, neglecting major relation between the object in an image, poor quality image, labelling it remains a big problem for researchers. Hence, the main objective of this work attempts to overcome these challenges by proposing a novel framework for IC. So in this research work the main contribution deals with the framework consists of three phases that is image understanding, textual understanding and decoding. Initially, the image… More >

Mohd Anul Haq¹, Mohd Abdul Rahim Khan^1,*, Mohammed Alshehri²

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 619-635, 2022, DOI:10.32604/iasc.2022.021430

Abstract The growth of edge computing, the Internet of Things (IoT), and cloud computing have been accompanied by new security issues evolving in the information security infrastructure. Recent studies suggest that the cost of insider attacks is higher than the external threats, making it an essential aspect of information security for organizations. Efficient insider threat detection requires state-of-the-art Artificial Intelligence models and utility. Although significant have been made to detect insider threats for more than a decade, there are many limitations, including a lack of real data, low accuracy, and a relatively low false alarm, which are major concerns needing further… More >

S. Neelakandan^1,*, A. Arun², Raghu Ram Bhukya³, Bhalchandra M. Hardas⁴, T. Ch. Anil Kumar⁵, M. Ashok⁶

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1617-1632, 2022, DOI:10.32604/iasc.2022.022209

Abstract In recent years, web crawling has gained a significant attention due to the drastic advancements in the World Wide Web. Web Search Engines have the issue of retrieving massive quantity of web documents. One among the web crawlers is the focused crawler, that intends to selectively gather web pages from the Internet. But the efficiency of the focused crawling can easily be affected by the environment of web pages. In this view, this paper presents an Automated Word Embedding with Parameter Tuned Deep Learning (AWE-PTDL) model for focused web crawling. The proposed model involves different processes namely pre-processing, Incremental Skip-gram… More >

Lelisa Adeba Jilcha¹, Jin Kwak^2,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2883-2899, 2022, DOI:10.32604/cmc.2022.023167

Abstract In the contemporary world, digital content that is subject to copyright is facing significant challenges against the act of copyright infringement. Billions of dollars are lost annually because of this illegal act. The current most effective trend to tackle this problem is believed to be blocking those websites, particularly through affiliated government bodies. To do so, an effective detection mechanism is a necessary first step. Some researchers have used various approaches to analyze the possible common features of suspected piracy websites. For instance, most of these websites serve online advertisement, which is considered as their main source of revenue. In… More >