Applied Computational Intelligence and Soft Computing
 Journal metrics
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Acceptance rate17%
Submission to final decision66 days
Acceptance to publication22 days
CiteScore4.000
Journal Citation Indicator0.410
Impact Factor-

Software Defect Prediction through Neural Network and Feature Selections

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Applied Computational Intelligence and Soft Computing provides a forum for research that connects the disciplines of computer science, engineering, and mathematics using the technologies of computational intelligence and soft computing.

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Applied Computational Intelligence and Soft Computing maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

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Research Article

An Integrated Node Selection Model Using FAHP and FTOPSIS for Data Retrieval in Ubiquitous Computing

Ubiquitous computing (UC) is an advanced computing concept that makes services and computing available everywhere and anytime. In UC, data lies at the heart of all UC applications, and the key technologies that are required to make UC a reality are data and task management. In this context, retrieving data is influenced by the dynamic nature of these systems in addition to human and sensor failures. So the main problem is how to select the most appropriate service provider for retrieving data. Retrieving data is a complex issue that requires an extensive evaluation process and is one of the biggest challenges in UC. In addition, not every eventuality in these systems can be predicted due to their dynamic nature. As a result, there is a strong need to address the uncertainty in context data. In this paper, to assist users to efficiently select their most preferred service provider for retrieving data, a new fuzzy integrated multicriteria decision-making model, which meets quality of context (QoC) and quality of service (QoS) and satisfies user quality requirements and needs, is proposed. The proposed model is based on four stages. In the initial stage, the identification of evaluation criteria is performed due to the varying importance of the selected criteria. In the second stage, a fuzzy Analytical Hierarchy Process (FAHP) procedure is utilized to assign importance weights to each criterion. In the third stage, the fuzzy Technique for Order Preference by Similarity of an Ideal Solution (FTOPSIS) is used to evaluate and measure the performance of each alternative. Finally, sensitivity analysis is performed to check the robustness and the applicability of the proposed model.

Research Article

A Diagnostic Model of Breast Cancer Based on Digital Mammogram Images Using Machine Learning Techniques

Breast cancer disease is one of the most recorded cancers that lead to morbidity and maybe death among women around the world. Recent research statistics have exposed that one from 8 females in the USA and one from 10 females in Europe are contaminated by breast cancer. The challenge with this disease is how to develop a relaxed and fast diagnosing method. One of the attractive ways of early breast cancer diagnosis is based on the mammogram images analysis of the breast using a computer-aided diagnosing (CAD) tool. This paper firstly aimed to propose an efficient method for diagnosing tumors based on mammogram images of breasts using a machine learning approach. Secondly, this paper aimed to the development of a CAD software program for breast cancer diagnosing based on the proposed method in the first step. The followed step-by-step procedure of the proposed method is performed by passing the Mammographic Image Analysis Society (MIAS) through five steps of image preprocessing, image segmentation using seeded region growing (SRG) algorithm, feature extraction using different feature’s extraction classes, and important and effectiveness feature selection using the Sequential Forward Selection (SFS) technique, and finally, the Support Vector Machine (SVM) algorithm is used as a binary classifier in two classification levels. The first level classifier is used to categorize the given image as normal or abnormal while the second-level classifier is used for further classifying the abnormal image as either a malignant or benign cancer. The proposed method is studied and investigated in two phases: the training phase and the testing phase, with the MIAS dataset of mammogram images, using 70% and 30% ratios of dataset images for the training and testing sets, respectively. The practical implementation of the proposed method and the graphical user interface (GUI) CAD tool are carried out using MATLAB software. Experimental results of the proposed method have shown that the accuracy of the proposed method reached 100% in classifying images as normal and abnormal mammogram images while the classification accuracy for benign and malignant is equal to 87.1%.

Review Article

Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review

Recently, educational institutions faced many challenges. One of these challenges is the huge amount of educational data that can be used to discover new insights that have a significant contribution to students, teachers, and administrators. Nowadays, researchers from numerous domains are very interested in increasing the quality of learning in educational institutions in order to improve student success and learning outcomes. Several studies have been made to predict student achievement at various levels. Most of the previous studies were focused on predicting student performance at graduation time or at the level of a specific course. The main objective of this paper is to highlight the recently published studies for predicting student academic performance in higher education. Moreover, this study aims to identify the most commonly used techniques for predicting the student's academic level. In addition, this study summarized the highest influential features used for predicting the student academic performance where identifying the most influential factors on student’s performance level will help the student as well as the policymakers and will give detailed insights into the problem. Finally, the results showed that the RF and ensemble model were the most accurate models as they outperformed other models in many previous studies. In addition, researchers in previous studies did not agree on whether the admission requirements have a strong relationship with students' achievement or not, indicating the need to address this issue. Moreover, it has been noticed that there are few studies which predict the student academic performance using students’ data in arts and humanities major.

Research Article

Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction

The heart’s electrical activity is registered by an electrocardiogram (ECG), which consists of a wealth of pathological data on heart diseases such as arrhythmia. However, with increasing complexity and nonlinearity, direct observation of ECG signals and analysis is very tough. The highest accuracy of classification performance for machine learning approaches are 99.7 for neural network with wavelet scattering features extraction and 99.92 for SVM also with wavelet scattering features extraction. Through wavelet cascades with a neural network, the wavelet scattering transform can yield a translation invariant and deflection depictions of ECG signals. We suggested a new wavelet scattering transform-based method for automatically classifying three types of ECG heart diseases as follows: arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR). The bandwidth of the scaling function is used to critically downsample the wavelet scattering transform in time. As a result, each of the scattering paths has 16-time windows. Beat classification performance is classified by utilizing the MIT-BIH arrhythmia dataset. The suggested method is able to conduct high accuracy arrhythmia classification, with a 99.7% and 99.92% accuracy rate of the neural network (NN) and support vector machine (SVM), respectively, and will aid physicians in ECG explanation.

Research Article

Caption Generation Based on Emotions Using CSPDenseNet and BiLSTM with Self-Attention

Automatic image caption generation is an intricate task of describing an image in natural language by gaining insights present in an image. Featuring facial expressions in the conventional image captioning system brings out new prospects to generate pertinent descriptions, revealing the emotional aspects of the image. The proposed work encapsulates the facial emotional features to produce more expressive captions similar to human-annotated ones with the help of Cross Stage Partial Dense Network (CSPDenseNet) and Self-attentive Bidirectional Long Short-Term Memory (BiLSTM) network. The encoding unit captures the facial expressions and dense image features using a Facial Expression Recognition (FER) model and CSPDense neural network, respectively. Further, the word embedding vectors of the ground truth image captions are created and learned using the Word2Vec embedding technique. Then, the extracted image feature vectors and word vectors are fused to form an encoding vector representing the rich image content. The decoding unit employs a self-attention mechanism encompassed with BiLSTM to create more descriptive and relevant captions in natural language. The Flickr11k dataset, a subset of the Flickr30k dataset is used to train, test, and evaluate the present model based on five benchmark image captioning metrics. They are BiLingual Evaluation Understudy (BLEU), Metric for Evaluation of Translation with Explicit Ordering (METEOR), Recall-Oriented Understudy for Gisting Evaluation (ROGUE), Consensus-based Image Description Evaluation (CIDEr), and Semantic Propositional Image Caption Evaluation (SPICE). The experimental analysis indicates that the proposed model enhances the quality of captions with 0.6012(BLEU-1), 0.3992(BLEU-2), 0.2703(BLEU-3), 0.1921(BLEU-4), 0.1932(METEOR), 0.2617(CIDEr), 0.4793(ROUGE-L), and 0.1260(SPICE) scores, respectively, using additive emotional characteristics and behavioral components of the objects present in the image.

Research Article

AI-Enabled Ant-Routing Protocol to Secure Communication in Flying Networks

Artificial intelligence has recently been used in FANET-based routing strategies for decision-making, which is a unique paradigm. For effective communication in flying vehicles that use routing protocols to accomplish tasks collectively, aerial vehicles are used in both civic and military applications. Aerial ad hoc networks are wirelessly connected, and designing routing schemes is difficult due to the rapid mobility. Ground base stations and satellites are frequently used to interconnect UAV ad hoc networks. This paper developed a novel routing protocol with a focus on ant behavior routing, which assists in end-to-end security. For the first time in flying networks, the column mobility model is used to evaluate the performance of routing protocols. While merging with aerial ad hoc networks, AI-based networking is a relatively new field. In simulation results, AntHocNet shows better results in comparison with other contemporary routing algorithms. Pheromone update process is used for data encryption in AntHocNet. This research study is performed on network simulator-2.

Applied Computational Intelligence and Soft Computing
 Journal metrics
See full report
Acceptance rate17%
Submission to final decision66 days
Acceptance to publication22 days
CiteScore4.000
Journal Citation Indicator0.410
Impact Factor-
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