Applied Computational Intelligence and Soft Computing

Parkinson’s disease (PD) is a neurodegenerative disorder that affects millions of people worldwide, necessitating accurate and timely diagnostic methods for effective disease management. This study proposes a novel approach for PD detection using deep features extracted from magnetic resonance imaging (MRI) scans, employing a pattern recognition neural network architecture based on DenseNet201. The developed model demonstrated exceptional performance, achieving validation and test accuracies of 99.4% and 99.2%, respectively, indicating its robustness and efficacy in distinguishing between patients with PD and healthy individuals. Furthermore, the model achieved a precision of 99.5%, recall of 99.3%, and an F1 score of 99.4%. For the test set, the accuracy was 99.2%, with precision at 99.3%, recall at 99.1%, and an F1 score of 99.2%. The rapid convergence of the model during training further underscores its efficiency in learning discriminative features from MRI images. These findings underscore the promising role of deep learning techniques, particularly convolutional neural networks (CNNs), in medical image analysis for disease diagnosis. The proposed approach holds significant potential for assisting clinicians in early PD diagnosis and personalized treatment planning, ultimately improving patient outcomes and quality of life.

Jyoti Kumari

Santi Kumari Behera

Prabira Kumar Sethy

Aziz Nanthaamornphong

Enhanced Parkinson’s Disease Diagnosis via MRI Analysis: Integrating Deep Features From DenseNet201 With Neural Network Techniques

基于MRI分析的帕金森病增强诊断：将DenseNet201的深度特征与神经网络技术相结合

The development of metaheuristic algorithms has led to the solution of various optimization problems. Bioinspired optimization algorithms like the New Caledonian crow learning algorithm (NCCLA) are primarily designed to address continuous problems. As most real‐world problems are discrete, some operators have been proposed to convert continuous algorithms into discrete ones to address these problems. These operators include evolutionary operators such as crossover and mutation, transformation operators such as symmetry, swap, and shift, and K‐opt algorithms such as 2‐opt, 2‐opt and a half, and 3‐opt. Employing some of these operators usually accompanies changing the algorithm’s rules or the movement patterns of its search agents. However, mathematical operators such as modular arithmetic and set theory and random permutation provide an ability to keep the same algorithm’s agent proposed in its continuous version and K‐opt algorithms usually balance the algorithm’s exploration and exploitation capabilities. Thus, this paper converts the NCCLA into a discrete version by utilizing a combination of those mathematical operators and the 3‐opt algorithm. This combination allows the algorithm to maintain a balance between exploration and exploitation. The resulting algorithm, called the discrete New Caledonian crow learning algorithm (DNCCLA), is employed to solve the traveling salesman problem (TSP). In addition, the paper investigates the best combination of mathematical operators with K‐opt algorithms or the symmetry operator. The performance results demonstrate that DNCCLA outperforms state‐of‐the‐art discrete algorithms, exhibiting a good balance between exploration and exploitation. The algorithm successfully solves 20 TSP instances of varying scales, and it consistently achieved the top rank among the tested algorithms.

Ali H. Alsaidi

Wedad Al-Sorori

Abdulqader M. Mohsen

DNCCLA: Discrete New Caledonian Crow Learning Algorithm for Solving Traveling Salesman Problem

DNCCLA：基于离散新喀里多尼亚乌鸦学习算法的旅行商问题求解

This research paper explores the effectiveness of user emotional experience as a predictor for future returning of the user to the website. Traditional web analytics have been limited in their ability to accurately capture the nuances of user experiences. Methods like eye tracking, speech tracking, and surveys, while insightful, often suffer from being overly intrusive, leading to biased results. This study introduces a state of the art, nonintrusive method of measuring user experience: touch‐gesture based emotion measurement. This technique leverages the subconscious nature of touch gestures to gather emotional data, allowing for a more authentic and unbiased user interaction with websites. To leverage this method, we first gained explicit data collection consent and gathered browsing data from 164,527 users across a 1‐year period on a Canadian e‐commerce website visited from a touchscreen device. While the sample size is significantly large, the sample is primarily made up of visitors from Canada, which could limit generalization of the findings. Using this data, we implemented an AI model which predicts whether a user is likely to return to the website or not, primarily based on their emotional touch gestures on their first visit with an accuracy of 91.7%. This approach not only enhances our understanding of user engagement but also opens new avenues for optimizing user experience in untested digital spaces such as e‐learning and mental health.

Alicia Heraz

Nandith Sajith

Abdullah Siddiqi

An Advanced Forecasting Model Leveraging Emotion‐Gesture Correlation to Predict Returning Visitors Surpasses Visit Duration as a Predictive Factor

利用情感-手势关联预测回访者的先进预测模型超越访问时长作为预测因素

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

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

Abbreviation	Appl. Comput. Intell. Soft Comput.
Journal Impact	2.28
Quartiles(Global)	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE(Q3)

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

Publication Information	Publisher: Hindawi Limited，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2016，Proportion of original research papers: 92.06%，Self Citation Rate:0.00%， Gold OA Rate: 99.24%
Journal Fees	$950Details
Average review cycle	网友分享经验：21 Weeks

Applied Computational Intelligence and Soft Computing

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Enhanced Parkinson’s Disease Diagnosis via MRI Analysis: Integrating Deep Features From DenseNet201 With Neural Network Techniques

DNCCLA: Discrete New Caledonian Crow Learning Algorithm for Solving Traveling Salesman Problem

An Advanced Forecasting Model Leveraging Emotion‐Gesture Correlation to Predict Returning Visitors Surpasses Visit Duration as a Predictive Factor

Poverty Analysis and Prediction in South Africa Using Remotely Sensed Data

Reconfigurable Neuromorphic Neural Network Architecture

Student Performance Prediction Using Machine Learning Algorithms

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

Enhanced Parkinson’s Disease Diagnosis via MRI Analysis: Integrating Deep Features From DenseNet201 With Neural Network Techniques

DNCCLA: Discrete New Caledonian Crow Learning Algorithm for Solving Traveling Salesman Problem

An Advanced Forecasting Model Leveraging Emotion‐Gesture Correlation to Predict Returning Visitors Surpasses Visit Duration as a Predictive Factor