Journal of Intelligent Systems

Patra, Ankita; Biswas, Preesat; Behera, Santi Kumari; Barpanda, Nalini Kanta; Sethy, Prabira Kumar; Nanthaamornphong, Aziz

doi: 10.1515/jisys-2024-0172pmid: N/A

AbstractIn the realm of image-based breast cancer detection and severity assessment, this study delves into the revolutionary potential of sophisticated artificial intelligence (AI) techniques. By investigating image processing, machine learning (ML), and deep learning (DL), the research illuminates their combined impact on transforming breast cancer diagnosis. This integration offers insights into early identification and precise characterization of cancers. With a foundation in 125 research articles, this article presents a comprehensive overview of the current state of image-based breast cancer detection. Synthesizing the transformative role of AI, including image processing, ML, and DL, the review explores how these technologies collectively reshape the landscape of breast cancer diagnosis and severity assessment. An essential aspect highlighted is the synergy between advanced image processing methods and ML algorithms. This combination facilitates the automated examination of medical images, which is crucial for detecting minute anomalies indicative of breast cancer. The utilization of complex neural networks for feature extraction and pattern recognition in DL models further enhances diagnostic precision. Beyond diagnostic improvements, the abstract underscores the substantial influence of AI-driven methods on breast cancer treatment. The integration of AI not only increases diagnostic precision but also opens avenues for individualized treatment planning, marking a paradigm shift toward personalized medicine in breast cancer care. However, challenges persist, with issues related to data quality and interpretability requiring continued research efforts. Looking forward, the abstract envisions future directions for breast cancer identification and diagnosis, emphasizing the adoption of explainable AI techniques and global collaboration for data sharing. These initiatives promise to propel the field into a new era characterized by enhanced efficiency and precision in breast cancer care.

Huang, Xiaojie

doi: 10.1515/jisys-2023-0309pmid: N/A

AbstractIn order to solve the problems that the current English grammar correction algorithms are not effective, the error correction ability is limited, and the error correction accuracy needs to be improved, this study proposes an automatic grammar correction method for business English writing based on two-way long short-term memory (LSTM) and N-gram. First, this study considers article and preposition errors as a special sequence labeling task, and proposes a Grammar error checking (GEC) method for sequence labeling based on bidirectional LSTM. During training, english as a second language (ESL) corpus and supplementary corpus are used to label specific articles or prepositions. Second, for noun simple-plural errors, verb form errors, and subject-verb inconsistency errors, a large number of news corpora are used to count the frequency of N-gram, and a GEC method based on ESL and news corpora N-gram voting strategy is proposed. Experimental results show that the overall F1 value of the method designed in this study on the GEC data of CoNLL2013 is 33.87%, which is higher than the F1 value of UIUC. The F1 value of article error correction is 38.05%, and the F1 value of preposition error correction is 28.89%. It is proved that this method can effectively improve the accuracy of grammar error correction and solve the gradient explosion problem of traditional error correction model, which is of great significance to further strengthen the practicality of automatic grammar error correction technology.

Qiao, Honghui

doi: 10.1515/jisys-2024-0115pmid: N/A

AbstractThe deep fusion of Internet technology and education is constantly pushing forward the reform of university education. Traditional educational ideas, concepts, and models cannot keep pace with the times, and hybrid teaching has become a new way of education in colleges and universities. To improve the teaching effect of physical education classes, the study used a blended teaching model and designed a teaching evaluation and performance prediction model under the blended teaching model based on an improved cluster analysis method and attention mechanism. The lab results indicated that under the blended teaching model, students’ performance increased by 12.89 points, and the level of skill mastery and proficiency increased by 26.52 and 28.55%, respectively, with grades more inclined to high score distribution. “Excellent” grade clustering increased by 77.71%, and “Good” grade clustering increased by 19.01%. The minimum error sum of squares of the improved clustering algorithm was 58.18 and 36.25% lower than the other two algorithms, and the clustering results were more relevant. The two-way attention mechanism algorithm predicted higher accuracy results and performed best on all four evaluation metrics, with a prediction accuracy of 98.23%, an accuracy of 98.42%, and an F1 value of 91.78%. This hybrid teaching model is more in line with the characteristics of the physical education teaching discipline, successfully cultivates students’ independent learning ability, stimulates students’ love for physical education courses, and achieves better teaching results.

Chadaga, Krishnaraj; Khanna, Varada Vivek; Prabhu, Srikanth; Sampathila, Niranjana; Chadaga, Rajagopala; Palkar, Anisha

doi: 10.1515/jisys-2024-0077pmid: N/A

AbstractMultiple sclerosis (MS) is a dangerous illness that strikes the central nervous system. The body’s immune system attacks myelin (an entity above the nerves) and impairs brain-to-body communication. To date, it is not possible to cure MS. However, symptoms can be managed, and treatments can be provided if the disease is diagnosed early. Hence, supervised machine learning (ML) algorithms and several hyperparameter tuning techniques, including Bayesian optimization, have been utilized in this study to predict MS in patients. Descriptive and inferential statistical analysis has been conducted before training the classifiers. The most essential markers were chosen using a technique called mutual information. Among the search techniques, the Bayesian optimization search technique prevailed to be pre-eminent, with an accuracy of 89%. To comprehend the diagnosis generated by the ML classifiers, four techniques of explainable artificial intelligence were utilized. According to them, the crucial attributes are periventricular magnetic resonance imaging (MRI), infratentorial MRI, oligoclonal bands, spinal cord MRI, breastfeeding, varicella disease, and initial symptoms. The models could be deployed in various medical facilities to detect MS in patients. The doctors could also use this framework to get a second opinion regarding the diagnosis.

AL-Gburi, Abbas Fadhil Jasim; Nazri, Mohd Zakree Ahmad; Yaakub, Mohd Ridzwan Bin; Alyasseri, Zaid Abdi Alkareem

doi: 10.1515/jisys-2023-0267pmid: N/A

AbstractIn recent years, the field of data analytics has witnessed a surge in innovative techniques to handle the ever-increasing volume and complexity of data. Among these, nature-inspired algorithms have gained significant attention due to their ability to efficiently mimic natural processes and solve intricate problems. One such algorithm, the symbiotic organisms search (SOS) Algorithm, has emerged as a promising approach for clustering and predictive analytics tasks, drawing inspiration from the symbiotic relationships observed in biological ecosystems. Metaheuristics such as the SOS have been frequently employed in clustering to discover suitable solutions for complicated issues. Despite the numerous research works on clustering and SOS-based predictive techniques, there have been minimal secondary investigations in the field. The aim of this study is to fill this gap by performing a systematic literature review (SLR) on SOS-based clustering models focusing on various aspects, including the adopted clustering approach, feature selection approach, and hybridized algorithms combining K-means algorithm with different SOS algorithms. This review aims to guide researchers to better understand the issues and challenges in this area. The study assesses the unique articles published in journals and conferences over the last ten years (2014–2023). After the abstract and full-text eligibility analysis, a limited number of articles were considered for this SLR. The findings show that various SOS methods were adapted as clustering and feature selection methods in which CSOS, discrete SOS, and multiagent SOS are mostly used for the clustering applications, and binary SOS, binary SOS with S-shaped transfer functions, and BSOSVT are used for feature selection problems. The findings also revealed that, of all the selected studies for this review, only a few studies specifically focused on hybridizing SOS with K-means algorithm for automatic data clustering application. Finally, the study analyzes the study gaps and the research prospects for SOS-based clustering methods.

Feng, Jianjie

doi: 10.1515/jisys-2023-0189pmid: N/A

AbstractIn the investment risk assessment of green finance (GF) projects, the application of multi-attribute perceptual fuzzy information decision technology is taken as the main research object. With the promotion of the concept of environmental protection and the development of green economy, the investment risk assessment of GF projects has become more and more important. However, this requires dealing with a large amount of fuzzy information and multi-attribute decision problems, which is a big challenge for traditional decision techniques. Based on this background, a new decision model, intuitionistic fuzzy preference theory-based tomada de decisão interativa multicritério (IF-PT-TODIM), is adopted, which can better deal with fuzzy information and multi-attribute decision problems by taking two different choices as reference. By knowing the weight distribution of experts, the model can better assess the influence of various factors on the decision. In the research results, the calculated results of expert weights are 0.2796, 0.2221, 0.1914, 0.1328, and 0.1745, respectively, showing that each expert has different degrees of influence on decision-making. In addition, the application of IF-PT-TODIM model can effectively reduce the investment risk. Compared with national bank of Kuwait, systematic review, evolutionary algorithm, the improved method can reduce the risk of venture capital by 28.14, 15.47, and 11.05%, respectively. This result further confirms the advantage of the IF-PT-TODIM model in dealing with fuzzy information and multi-attribute decision problems. This study has practical implications for understanding and improving the investment risk assessment of GF projects. It not only provides a new decision model for risk assessment, but also provides an effective method to deal with fuzzy information and multi-attribute decision problems. This provides new ideas and methods for the risk management of GF projects and also provides a new perspective and reference for research in related fields.

Ahmed, Wasan Saad; Mustafa AL-Ta’I, Ziyad Tariq; Abegaz, Tamirat; Mahmood, Ghassan Sabeeh

doi: 10.1515/jisys-2024-0109pmid: N/A

AbstractIn the face of escalating cyber threats, a real-time automated security evidence collection system for cloud-based digital forensics investigations is essential for identifying and mitigating malicious activities. However, the substantial volumes of data generated by modern cloud-based digital systems pose difficulties in collecting and analyzing evidence promptly and systematically. To address these challenges, this research introduces an architecture that combines a security lake and a modern data lake. The primary objective of this architecture is to overcome the obstacles associated with gathering evidence from multiple cloud-based accounts and regions while ensuring the flexibility and scalability required to manage the ever-expanding data volumes encountered in cloud-based digital forensics investigations. This work focuses on gathering security events from multiple accounts and regions within a cloud environment in real-time while maintaining the integrity of the evidence and storing them in lakes, providing investigators with the flexibility to move between these lakes for analysis to get quick results. This is achieved through the utilization of security lake and modern data architecture. To validate the system, we tested it within a university system comprising numerous accounts spread across different regions within an AWS environment. Overall, the proposed system effectively gathers evidence from various sources and consolidates all data lakes into a single account. These lakes were then utilized for analyzing the evidence using Athena and Wazuh.

Ma, Xiangfen

doi: 10.1515/jisys-2023-0319pmid: N/A

AbstractWith the widespread popularity of intelligent education, artificial intelligence plays an important role in the field of education. Currently, there are issues such as low accuracy and low adaptability. By comparing algorithms such as logistic regression, decision tree, random forest (RF), support vector machine, and long short-term memory (LSTM) recurrent neural network (RNN), this article adopted a multi-classification fusion strategy and fully considered the adaptability of the algorithm to evaluate and grade students in two scenarios with different grades and teaching quality. By encoding and normalizing student grades, six evaluation parameters were selected for the evaluation criteria of teaching quality through principal component analysis feature selection. Multi-classifier models were used to fuse the five models in pairs, improving the accuracy of the experimental evaluation. Finally, the experimental data of the six fused multi-classification models in the scenarios of student performance estimation and teaching quality estimation were compared, and the experimental effects of education evaluation and grading under different models were analyzed. The experimental results showed that the LSTM RNN-RF model had the strongest adaptability in the scenario of student performance estimation, with an estimation accuracy of 98.5%, which was 12.9% higher than a single RF model. This experiment was closely related to educational scenarios and fully considered the adaptability of different machine learning algorithms to different scenarios, improving the prediction and classification accuracy of the model.

Chen, Shan; Xiao, Yingmei

doi: 10.1515/jisys-2023-0170pmid: N/A

AbstractThis article proposes an English grammar intelligent error correction model based on the attention mechanism and Recurrent Neural Network (RNN) algorithm. It aims to improve the accuracy and effectiveness of error correction by combining the powerful context-capturing ability of the attention mechanism with the sequential modeling ability of RNN. First, based on the improvement of recurrent neural networks, a bidirectional gated recurrent network is added to form a dual encoder structure. The encoder is responsible for reading and understanding the input text, while the decoder is responsible for generating the corrected text. Second, the attention mechanism is introduced into the decoder to convert the output of the encoder into the attention probability distribution for integration. This allows the model to focus on the relevant input word as it generates each corrected word. The results of the study showed that the model was 2.35% points higher than statistical machine translation–neural machine translation in the CoNLL-2014 test set, and only 1.24 points lower than the human assessment score, almost close to the human assessment level. The model proposed in this study not only created a new way of English grammar error correction based on the attention mechanism and RNN algorithm in theory but also effectively improved the accuracy and efficiency of English grammar error correction in practice. It further provides English learners with higher-quality intelligent error correction tools, which can help them learn and improve their English level more effectively.

Yang, Liu

doi: 10.1515/jisys-2023-0236pmid: N/A

AbstractAnalyzing online learning behavior helps to understand students’ progress, difficulties, and needs during the learning process, making it easier for teachers to provide timely feedback and personalized guidance. However, the classroom behavior (CB) of online teaching is complex and variable, and relying on traditional classroom supervision methods, teachers find it difficult to comprehensively pay attention to the learning behavior of each student. In this regard, a dual stream network was designed to capture and analyze CB by integrating AlphaPose human keypoint detection method and image data method. The experimental results show that when the learning rate of the model parameters is set to 0.001, the accuracy of the model is as high as 92.3%. When the batch size is 8, the accuracy of the model is as high as 90.8%. The accuracy of the fusion model in capturing upright sitting behavior reached 97.3%, but the accuracy in capturing hand raising behavior decreased to only 74.8%. The fusion model performs well in terms of accuracy and recall, with recall rates of 88.3, 86.2, and 85.1% for capturing standing up, raising hands, and sitting upright behaviors, respectively. And the maximum F1 value is 0.931. The dual stream network effectively integrates the advantages of two types of data, improves the performance of behavior capture, and improves the robustness of the algorithm. The successful application of the model is beneficial for teachers’ classroom observation and research activities, providing a favorable path for their professional development, and thereby improving the overall teaching quality of teachers.