Applied Computational Intelligence and Soft Computing

This study aimed to analyse and predict the state of poverty in South Africa using remotely sensed data. In the developing world, trustworthy knowledge is still hard to come by for economic survival. One of the 17 sustainable development goals listed by the United Nations is to eradicate poverty. Ground‐level survey data, such as household consumption and wealth, were historically the primary sources for evaluating poverty. As technology has advanced, one method that is now used by both wealthy and developing nations is to estimate the poverty index of a region using remote sensing data, such as satellite images, and a machine learning algorithm. This method uses a highly structured, reasonably priced, and easily accessible data source. This machine learning method calculates the poverty index for a given year and determines how the index relates to other years. Inception NetV3, ResNet50 and convolutional neural network are trained beforehand to estimate the intensity of the night‐time illumination matching to the input daytime satellite images. The suggested model also forecasted the cluster wealth score and created a link between wealth scores derived from satellite images and data from the Demographic and Health Survey (DHS). The study investigates a possibly more affordable alternative technique for estimating poverty. The findings imply that satellite data and machine learning can be useful techniques for estimating poverty. By analysing the resilience of the generated estimates to user‐specified algorithmic parameters and model specifications, the work adds to the body of the current literature.

Vincent Zibi Mohale

Ibidun Christiana Obagbuwa

Poverty Analysis and Prediction in South Africa Using Remotely Sensed Data

南非贫困分析与预测：基于遥感数据

Neural network (NN), also known as artificial neural network (ANN), provides efficient results in every computation, whether it is pattern recognition, forecasting, customer research, data validation, risk management, data mining, etc. In each computation, NN requires a specific dataset, and according to the datasets, a new NN structure is used to compute the results. Thus, these NN structures mostly depend upon the type of datasets. The current study presents a method that comprises a dedicated architecture known as neuromorphic neural network (NNN), which is made up of optical waveguides that enable high communication and data processing speed at the same time. We have also proposed three algorithms for configuring and building distinct ANN structures from the same architecture. These dedicated structures are not dependent on the datasets and employ the necessary processing element (PE) nodes to function as neurons in the hidden layer. Because specialized resources will be employed to perform operations in the hidden layer, these designs may produce more efficient outcomes than the present logical NN. Furthermore, we assessed our proposed architecture in terms of communication latency, deadlock prevention, energy consumption, and power usage. The simulation results show that deadlocks are avoided to the greatest extent possible, power consumption is reduced by up to 95%, and communicational latency is accomplished in the order of femtoseconds while conversing among PE nodes. The proposed architecture and simulation results promise an alternative for logical NN as well as improved results in terms of speed and efficiency.

Kapil Sharma

Pradeepta Kumar Sarangi

Parth Sharma

Soumya Ranjan Nayak

Srinivas Aluvala

Santosh Kumar Swain

Reconfigurable Neuromorphic Neural Network Architecture

可重构神经形态神经网络架构

Blockchain is a distributed system where transactions are recorded on blocks. The blocks are linked with previous blocks creating a chain of blocks that ensures the integrity of the blockchain. Throughput operates as a pivotal performance indicator in blockchain contributing to its popularity among organizations and individual users. To achieve optimized use of blockchain for an increasing number of users, accurate prediction of performance at different timestamps is necessary. In this paper, two recurrent neural network (RNN)–based predictive models, i.e., long short‐term memory (LSTM) and gated recurrent unit (GRU) are proposed that predict the performance (throughput) of blockchain in different scenarios. Moreover, the study analyzes blockchain data to find optimal block size, impact of block size on performance, and uncover the relationship between performance and block size. The RNN models are evaluated using a dataset collected from Hyperledger Caliper on the HF framework 2.3. This study found that the performance of LSTM is approximately close to GRU regarding evaluation metrics MAE and RMSE. However, GRU is more suitable for practical applications and performs better overall. This study also shows a Spearman correlation value of 0.032, which is indicative of a weak association between the two factors.

Muhammad Hasnain

Imran Ghani

David Smith

Muhammad Asaf

Shahid Saleem

Asad Hayat

Seung Ryul Jeong

Recurrent Neural Network (RNN)–Based Approach to Uncover the Relationship Between Block Size and Blockchain Performance

基于循环神经网络（RNN）的方法揭示区块大小与区块链性能之间的关系

Owing to rapid advancements in information and communication technology, natural language processing (NLP) methods have attracted considerable attention. Estimating uncertainty is a well‐established problem in NLP. Developing systems capable of automatically detecting uncertain terms within a natural language is critical for developing effective text‐analysis algorithms. Consequently, several NLP tools have been developed for this purpose. However, there are several obstacles to the design of fast and effective NLP technologies that effectively analyze ambiguous terms in natural languages. This work focuses on a particular method for detecting uncertainty, termed progressive detection, which can be accomplished using a dynamic probabilistic graphical model to solve these challenges. We iteratively upgraded the Kalman Filter to Labeled Variable Dimension Kalman Filter (LVDKF) based on observations from real datasets. The LVDKF learns two basic features from positive and negative words. The formula was then derived using a dynamic detection system, designed based on conditional probability. Finally, we simulated progressive detection experimental conditions and assessed the LVDKF on two publicly available datasets. It performs better than the baseline methods in these tests, which shows that it is effective at detecting changes over time.

Lingaraj K.

S. Supreeth

Yerriswamy T.

Dayananda P.

Rohith S.

Shruthi G.

Progressive Detection of Uncertainty in Natural Language Processing Using a Labeled Variable Dimension Kalman Filter

使用带标签变维卡尔曼滤波的自然语言处理中不确定性渐进检测

Studies on automatically predicting student learning outcomes often focus on developing and optimizing machine learning algorithms that fit the data captured from different education systems. This approach has a fatal weakness when it is used for disadvantaged groups, such as those with academic warnings or who have dropped out, because these groups are often much smaller than other common groups in number. The imbalanced data that have class distribution skew create a big challenge to training good classification models. The significant approach to tackle this challenge is applying oversampling methods to increase the number of minor classes; however, generating good new samples from the existing instances of a minor class is still a hard issue and requires new investigation. This study presents two new methods of handling data imbalance based on the original algorithms SMOTE and adaptive synthetic sampling (ADASYN), called Improved SMOTE (I_SMOTE) and Improved ADASYN (I_ADASYN). These modifications involve a new selecting fit candidate method based on a new similarity measurement and a roulette wheel selection to generate synthetic data samples. The aim is to rebalance data and therefore improve the prediction accuracy of minor groups. The proposal methods were designed and applied to education datasets, and they were tested on public datasets and a dataset collected from a Vietnamese university for evaluation. The experimental results on learning datasets showed the high potential of novel algorithms, I_SMOTE and I_ADASYN, for student academic performance problems in general and at‐risk student groups especially. Empirical results proved that the recall, precision, and F1‐score of the minority class of I_SMOTE and I_ADASYN are strongly better than the original balancing algorithms. Besides, the I_SMOTE and I_ADASYN also improve relatively by 6.6% and 8.0% of the ROC area compared to the original SMOTE and ADASYN, respectively.

Nguyen Giap Cu

Thi Lich Nghiem

Thi Hoai Ngo

Manh Tuong Lam Nguyen

Hong Quan Phung

Increment of Academic Performance Prediction of At‐Risk Student by Dealing With Data Imbalance Problem

通过处理数据不平衡问题提高风险学生学业成绩预测的增量

Education is crucial for a productive life and providing necessary resources. With the advent of technology like artificial intelligence, higher education institutions are incorporating technology into traditional teaching methods. Predicting academic success has gained interest in education as a strong academic record improves a university’s ranking and increases student employment opportunities. Modern learning institutions face challenges in analyzing performance, providing high-quality education, formulating strategies for evaluating students’ performance, and identifying future needs. E-learning is a rapidly growing and advanced form of education, where students enroll in online courses. Platforms like Intelligent Tutoring Systems (ITS), learning management systems (LMS), and massive open online courses (MOOC) use educational data mining (EDM) to develop automatic grading systems, recommenders, and adaptative systems. However, e-learning is still considered a challenging learning environment due to the lack of direct interaction between students and course instructors. Machine learning (ML) is used in developing adaptive intelligent systems that can perform complex tasks beyond human abilities. Some areas of applications of ML algorithms include cluster analysis, pattern recognition, image processing, natural language processing, and medical diagnostics. In this research work, K-means, a clustering data mining technique using Davies’ Bouldin method, obtains clusters to find important features affecting students’ performance. The study found that the SVM algorithm had the best prediction results after parameter adjustment, with a 96% accuracy rate. In this paper, the researchers have examined the functions of the Support Vector Machine, Decision Tree, naive Bayes, and KNN classifiers. The outcomes of parameter adjustment greatly increased the accuracy of the four prediction models. Naïve Bayes model’s prediction accuracy is the lowest when compared to other prediction methods, as it assumes a strong independent relationship between features.

Esmael Ahmed

Student Performance Prediction Using Machine Learning Algorithms

学生表现预测使用机器学习算法

Breast cancer imaging is paramount to quickly detecting and accurately evaluating the disease. The scarcity of annotated mammogram data presents a significant obstacle when building deep learning models that can produce reliable outcomes. This paper proposes a novel approach that utilizes deep convolutional generative adversarial networks (DCGANs) to effectively tackle the issue of limited data availability. The main goal is to produce synthetic mammograms that accurately reproduce the intrinsic patterns observed in real data, enhancing the current dataset. The proposed synthesis method is supported by thorough experimentation, demonstrating its ability to reproduce diverse viewpoints of the breast accurately. A mean similarity assessment with a standard deviation was performed to evaluate the credibility of the synthesized images and establish the clinical significance of the data obtained. A thorough evaluation of the uniformity within each class was conducted, and any deviations from each class’s mean values were measured. Including outlier removal using a specified threshold is a crucial process element. This procedure improves the accuracy level of each image cluster and strengthens the synthetic dataset’s general dependability. The visualization of the class clustering results highlights the alignment between the produced images and the inherent distribution of the data. After removing outliers, distinct and consistent clusters of homogeneous data points were observed. The proposed similarity assessment demonstrates noteworthy effectiveness, eliminating redundant and dissimilar images from all classes. Specifically, there are 505 instances in the normal class, 495 instances in the benign class, and 490 instances in the malignant class out of 600 synthetic mammograms for each class. To check the further validity of the proposed model, human experts visually inspected and validated synthetic images. This highlights the effectiveness of our methodology in identifying substantial outliers.

Dilawar Shah

Mohammad Asmat Ullah Khan

Mohammad Abrar

Reliable Breast Cancer Diagnosis with Deep Learning: DCGAN-Driven Mammogram Synthesis and Validity Assessment

基于深度学习的可靠乳腺癌诊断：DCGAN驱动的乳腺X线合成与有效性评估

短名	Appl. Comput. Intell. Soft Comput.
Journal Impact	2.28
国际分区	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE(Q3)

ISSN	1687-9724, 1687-9732
h-index	22

涉及主题	计算机科学人工智能数学机器学习工程类哲学物理程序设计语言算法生物心理学操作系统统计经济语言学量子力学数学优化计算机视觉认知心理学模式识别（心理学）人工神经网络地理数据挖掘地质学
出版信息	出版商: Hindawi Limited，出版周期: ，期刊类型: journal，开源期刊: 是
基本数据	创刊年份: 2016，原创研究文献占比: 92.06%，自引率:0.00%， Gold OA占比: 99.24%
期刊费用	$950详情
平均审稿周期	网友分享经验：21 Weeks

Applied Computational Intelligence and Soft Computing

期刊引文格式

有 1 位以上作者的期刊

有 2 位作者的期刊

有 3 位作者的期刊

有 5 位以上作者的期刊

书籍引用格式

学位论文引用格式

网页引用格式

专利引用格式

手工熬夜修改参考文献？研飞自动匹配期刊，一键轻松成稿，支持 Word/WPS

投稿经验分享

Poverty Analysis and Prediction in South Africa Using Remotely Sensed Data

Reconfigurable Neuromorphic Neural Network Architecture

Recurrent Neural Network (RNN)–Based Approach to Uncover the Relationship Between Block Size and Blockchain Performance

Progressive Detection of Uncertainty in Natural Language Processing Using a Labeled Variable Dimension Kalman Filter

Increment of Academic Performance Prediction of At‐Risk Student by Dealing With Data Imbalance Problem

Student Performance Prediction Using Machine Learning Algorithms

Reliable Breast Cancer Diagnosis with Deep Learning: DCGAN-Driven Mammogram Synthesis and Validity Assessment

Poverty Analysis and Prediction in South Africa Using Remotely Sensed Data

Reconfigurable Neuromorphic Neural Network Architecture

Recurrent Neural Network (RNN)–Based Approach to Uncover the Relationship Between Block Size and Blockchain Performance