Applied Computing and Informatics

Attention has a key role in the flight performance of the aviation pilot, therefore it is among human factors commonly used in the evaluation of candidate pilots. In this context, our work aims to define a single integrated instrument able to measure all the distinctive attention factors and to assist the assessment and the training of aviation pilots. In this paper, we present a battery of seven computerized tests, encompassing classical and innovative solutions inspired by the literature in the field, for the integrated measurement of the attention factors of aviation pilots. The computer software is validated by means of an experimental trial with 50 experienced aviation pilots and 50 untrained people as controls. Statistical analyzes confirm that the instrument can effectively classify aviation pilots, and identify a subset of distinctive attention factors that could be used for monitoring their duty.

Rosario Cannavò

Daniela Conti

Alessandro Di Nuovo

Computer-aided assessment of aviation pilots attention: Design of an integrated test and its empirical validation

航空飞行员注意力计算机辅助评估：综合测试设计及其实证验证

Epilepsy is a chronic neurological condition that affects approximately 70 million people worldwide. Characterised by sudden bursts of excess electricity in the brain, manifesting as seizures, epilepsy is still not well understood when compared with other neurological disorders. Seizures often happen unexpectedly and attempting to predict them has been a research topic for the last 30 years. Electroencephalograms have been integral to these studies, as the recordings that they produce can capture the brain’s electrical signals. The diagnosis of epilepsy is usually made by a neurologist, but can be difficult to make in the early stages. Supporting para-clinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and instigate treatment earlier. However, electroencephalogram capture and interpretation is time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity generalised across different regions of the brain and across multiple subjects may be a solution. This paper explores this idea further and presents a supervised machine learning approach that classifies seizure and non-seizure records using an open dataset containing 342 records (171 seizures and 171 non-seizures). Our approach posits a new method for generalising seizure detection across different subjects without prior knowledge about the focal point of seizures. Our results show an improvement on existing studies with 88% for sensitivity, 88% for specificity and 93% for the area under the curve, with a 12% global error, using the k-NN classifier.

P. Fergus

A. Hussain

David Hignett

D. Al-Jumeily

Khaled Abdel-Aziz

Hani Hamdan

A machine learning system for automated whole-brain seizure detection

用于全脑癫痫自动检测的机器学习系统

Purpose This study aims to assess subjective responses in computer science education to understand students' grasp of core concepts. Extracting key ideas from short answers remains challenging, necessitating an effective method to enhance learning outcomes. Design/methodology/approach This study introduces KeydistilTF, a model to identify essential concepts from student and teacher responses. Using the University of North Texas dataset from Kaggle, consisting of 53 teachers and 1,705 student responses, the model’s performance was evaluated using the F1 score for key concept detection. Findings KeydistilTF outperformed baseline techniques with F1 scores improved by 8, 6 and 4% for student key concept detection and 10, 8 and 6% for teacher key concept detection. These results indicate the model’s effectiveness in capturing crucial concepts and enhancing the understanding of key curriculum content. Originality/value KeydistilTF shows promise in improving the assessment of subjective responses in education, offering insights that can inform teaching methods and learning strategies. Its superior performance over baseline methods underscores its potential as a valuable tool in educational settings.

Zaira Hassan Amur

Yew Kwang Hooi

Gul Muhammad Soomro

Hina Bhanbhro

Revealing essential notions: an algorithmic approach to distilling core concepts from student and teacher responses in computer science education

揭示基本概念：一种从计算机科学教育中提炼学生和教师核心概念的算法方法

Purpose The practical purpose of this research is to propose a candidate for post-quantum signature standard that is free of significant drawback of the finalists of the NIST world competition, which consists in the large size of the signature and the public key. The practical purpose is to propose a fundamentally new method for development of algebraic digital signature algorithms. Design/methodology/approach The proposed method is distinguished by the use of two different finite commutative associative algebras as a single algebraic support of the digital signature scheme and setting two different verification equation for a single signature. A single public key is computed as the first and the second public keys, elements of which are computed exponentiating two different generators of cyclic groups in each of the algebras. Findings Additionally, a scalar multiplication by a private integer is performed as final step of calculation of every element of the public key. The same powers and the same scalar values are used to compute the first and the second public keys by the same mathematic formulas. Due to such design, the said generators are kept in secret, providing resistance to quantum attacks. Two new finite commutative associative algebras, multiplicative group of which possesses four-dimensional cyclicity, have been proposed as a suitable algebraic support. Originality/value The introduced method is novel and includes new techniques for designing algebraic signature schemes that resist quantum attacks. On its base, a new practical post-quantum signature scheme with relatively small size of signature and public key is developed.

Nikolay Andreevich Moldovyan

Dmitriy Nikolaevich Moldovyan

A novel method for developing post-quantum cryptoschemes and a practical signature algorithm

一种开发后量子密码方案的新方法及实用签名算法

Purpose This paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO 2 prediction models. The purpose of this paper is to pre-process a relatively large data of NO 2 from Internet of Thing (IoT) sensors with time-corresponding weather and traffic data and to use the data to develop NO 2 prediction models using BA-GS-LSSVM and popular standalone algorithms to allow for a fair comparison. Design/methodology/approach This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO 2 pollution concentration. The authors used big data analytics infrastructure to retrieve the large volume of data collected in tens of seconds for over 5 months. Weather data from the UK meteorology department and traffic data from the department for transport were collected and merged for the corresponding time and location where the pollution sensors exist. Findings The results show that the hybrid BA-GS-LSSVM outperforms all other standalone machine learning predictive Model for NO 2 pollution. Practical implications This paper's hybrid model provides a basis for giving an informed decision on the NO 2 pollutant avoidance system. Originality/value This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO 2 pollution concentration.

Habeeb Balogun

Hafiz Alaka

Christian Nnaemeka Egwim

Boruta-grid-search least square support vector machine for NO<sub>2</sub> pollution prediction using big data analytics and IoT emission sensors

使用大数据分析和物联网排放传感器的NO<sub>2</sub>污染预测的Boruta-网格搜索最小二乘支持向量机

Purpose This study evaluates machine learning (ML) classifiers for diagnosing Parkinson’s disease (PD) using subcortical brain region data from 3D T1 magnetic resonance imaging (MRI) Parkinson’s Progression Markers Initiative (PPMI database). We aim to identify top-performing algorithms and assess gender-related differences in accuracy. Design/methodology/approach Multiple ML algorithms will be compared for their ability to classify PD vs healthy controls using MRI scans of the brain structures like the putamen, thalamus, brainstem, accumbens, amygdala, caudate, hippocampus and pallidum. Analysis will include gender-specific performance comparisons. Findings The study reveals that ML classifier performance in diagnosing PD varies across subcortical brain regions and shows gender differences. The Extra Trees classifier performed best in men (86.36% accuracy in the putamen), while Naive Bayes performed best in women (69.23%, amygdala). Regions like the accumbens, hippocampus and caudate showed moderate accuracy (65–70%) in men and poor performance in women. The results point out a significant gender-based performance gap, highlighting the need for gender-specific models to improve diagnostic precision across complex brain structures. Originality/value This study highlights the significant impact of gender on machine learning diagnosis of PD using data from subcortical brain regions. Our novel focus on these regions uncovers their diagnostic potential, improves model accuracy and emphasizes the need for gender-specific approaches in medical AI. This work could ultimately lead to earlier PD detection and more personalized treatment.

Nair Ul Islam

Ruqaiya Khanam

Gender variability in machine learning based subcortical neuroimaging for Parkinson’s disease diagnosis

基于机器学习的帕金森病诊断中皮质下神经影像的性别变异性

Purpose Accurate diagnosis of brain tumors is crucial for effective treatment and improved patient outcomes. Magnetic resonance imaging (MRI) is a common method for detecting brain malignancies, but interpreting MRI data can be challenging and time-consuming for healthcare professionals. Design/methodology/approach An innovative method is presented that combines deep learning (DL) models with natural language processing (NLP) from ChatGPT to enhance the accuracy of brain tumor detection in MRI scans. The method generates textual descriptions of brain tumor regions, providing clinicians with valuable insights into tumor characteristics for informed decision-making and personalized treatment planning. Findings The evaluation of this approach demonstrates promising outcomes, achieving a notable Dice coefficient score of 0.93 for tumor segmentation, outperforming current state-of-the-art methods. Human validation of the generated descriptions confirms their precision and conciseness. Research limitations/implications While the method showcased advancements in accuracy and understandability, ongoing research is essential for refining the model and addressing limitations in segmenting smaller or atypical tumors. Originality/value These results emphasized the potential of this innovative method in advancing neuroimaging practices and contributing to the effective detection and management of brain tumors.

Soha Rawas

Cerine Tafran

Duaa AlSaeed

ChatGPT-powered deep learning: elevating brain tumor detection in MRI scans

ChatGPT驱动的深度学习：提升MRI扫描中的脑肿瘤检测

Purpose Plant cultivation holds a pivotal role in agriculture, necessitating precise disease identification for the overall health of plants. This research conducts a comprehensive comparative analysis between two prominent deep learning algorithms, convolutional neural network (CNN) and DenseNet121, with the goal of enhancing disease identification in tomato plant leaves. Design/methodology/approach The dataset employed in this investigation is a fusion of primary data and publicly available data, covering 13 distinct disease labels and a total of 18,815 images for model training. The data pre-processing workflow prioritized activities such as normalizing pixel dimensions, implementing data augmentation and achieving dataset balance, which were subsequently followed by the modeling and testing phases. Findings Experimental findings elucidated the superior performance of the DenseNet121 model over the CNN model in disease classification on tomato leaves. The DenseNet121 model attained a training accuracy of 98.27%, a validation accuracy of 87.47% and average recall, precision and F1-score metrics of 87, 88 and 87%, respectively. The ultimate aim was to implement the optimal classifier for a mobile application, namely Tanamin.id, and, therefore, DenseNet121 was the preferred choice. Originality/value The integration of private and public data significantly contributes to determining the optimal method. The CNN method achieves a training accuracy of 90.41% and a validation accuracy of 83.33%, whereas the DenseNet121 method excels with a training accuracy of 98.27% and a validation accuracy of 87.47%. The DenseNet121 architecture, comprising 121 layers, a global average pooling (GAP) layer and a dropout layer, showcases its effectiveness. Leveraging categorical_crossentropy as the loss function and utilizing the stochastic gradien descent (SGD) Optimizer with a learning rate of 0.001 guides the course of the training process. The experimental results unequivocally demonstrate the superior performance of DenseNet121 over CNN.

Heru Agus Santoso

Brylian Fandhi Safsalta

Nanang Febrianto

Galuh Wilujeng Saraswati

Su-Cheng Haw

Comparative analysis of convolutional neural network and DenseNet121 transfer learning in agriculture focusing on crop leaf disease identification

卷积神经网络与DenseNet121迁移学习在农业中针对作物叶片病害识别的比较分析

Purpose The present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods. Design/methodology/approach The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments. Findings The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. Research limitations/implications The rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments. Originality/value The originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

Atef Gharbi

Bi-directional adaptive enhanced A* algorithm for mobile robot navigation

移动机器人导航的双向自适应增强A*算法

短名	ACI
Journal Impact	12.50
国际分区	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q1)

ISSN	2210-8327, 2634-1964
h-index	38

涉及主题	计算机科学工程类数学人工智能操作系统物理程序设计语言机器学习哲学心理学法学政治学经济地理
出版信息	出版商: Emerald Group Publishing Ltd.，出版周期: ，期刊类型: journal，开源期刊: 是
基本数据	创刊年份: 2014，原创研究文献占比: 0.00%，自引率:0.00%， Gold OA占比: 94.23%
平均审稿周期	网友分享经验：39 Weeks

Applied Computing and Informatics

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Computer-aided assessment of aviation pilots attention: Design of an integrated test and its empirical validation

A machine learning system for automated whole-brain seizure detection

Revealing essential notions: an algorithmic approach to distilling core concepts from student and teacher responses in computer science education

A novel method for developing post-quantum cryptoschemes and a practical signature algorithm

Boruta-grid-search least square support vector machine for NO₂ pollution prediction using big data analytics and IoT emission sensors

Revealing essential notions: an algorithmic approach to distilling core concepts from student and teacher responses in computer science education

Gender variability in machine learning based subcortical neuroimaging for Parkinson’s disease diagnosis

ChatGPT-powered deep learning: elevating brain tumor detection in MRI scans

Comparative analysis of convolutional neural network and DenseNet121 transfer learning in agriculture focusing on crop leaf disease identification

Bi-directional adaptive enhanced A* algorithm for mobile robot navigation

Applied Computing and Informatics

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

Computer-aided assessment of aviation pilots attention: Design of an integrated test and its empirical validation

A machine learning system for automated whole-brain seizure detection

Revealing essential notions: an algorithmic approach to distilling core concepts from student and teacher responses in computer science education

A novel method for developing post-quantum cryptoschemes and a practical signature algorithm

Boruta-grid-search least square support vector machine for NO2 pollution prediction using big data analytics and IoT emission sensors

Revealing essential notions: an algorithmic approach to distilling core concepts from student and teacher responses in computer science education

Gender variability in machine learning based subcortical neuroimaging for Parkinson’s disease diagnosis

ChatGPT-powered deep learning: elevating brain tumor detection in MRI scans

Comparative analysis of convolutional neural network and DenseNet121 transfer learning in agriculture focusing on crop leaf disease identification

Bi-directional adaptive enhanced A* algorithm for mobile robot navigation

Boruta-grid-search least square support vector machine for NO₂ pollution prediction using big data analytics and IoT emission sensors