Advances in Distributed Computing and Artificial Intelligence Journal

This article aims at presenting Blockchain and Distributed Ledger Technologies from business perspective (although providing adequate technology context) and, especially, highlighting concrete implementations in Agri-Food Supply Chain, bringing security, transparency and robustness to solutions, and enabling the creation of added value through the provisioning of information to consumers which allow them to understand the origin, the transformation and the transportation of agri-food goods. It also brings some examples of European Programmes and Projects that are supporting innovative solutions to reach the market.

Javier Parra Domínguez

Pedro Roseiro

ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL

Blockchain: a brief review of Agri-Food Supply Chain Solutions and Opportunities

区块链：农业食品供应链解决方案与机遇简述

Saudi Arabia is interested in providing health care and ambulatory for all citizens, residents and tourists of the pilgrims and pilgrims, and it is cooperating with the Saudi Red Crescent Authority (SRCA) to provide emergency health care especially for the pilgrims, Ammar - an independent body dealing with this. The efforts of SRCA can be highly noticed during on the Hajj season and public events such as the national day celebrations. The main issue lies in the fact that despite their efforts, the Ambulance Response Time (ART) remains higher than the global standard. Moreover, the reasons behind the high ART are circumstantial and thus hard to maneuver or manipulate. Therefore, to benefit from the Red Crescent’s efforts and the first aid courses they offer, a system where credible first aiders can be summoned to provide proper and faster first aid to the injured is suggested. This study aims to develop an application to request the help of the nearest first aider by the injured or bystanders close to the injured. Also, to develop an interface that shows the route to get to the victim. The result has shown a positive indication of the importance of a system where credible first aiders can be summoned to provide proper and faster first aid to the injured. This study contributes to increase bystanders’ awareness because they have the ability to connect with the nearest registered first aider.

Areej Alshutayria

Nahla Aljojo

Basma Alharbia

Ameen Banjarb

Atheer Alshehria

Mashaiel Alargoubia

Ola Barradha

Rahaf Helabia

An Interactive Mobile Application to Request the Help of the Nearest First Aider by the Injured

受伤者通过互动移动应用程序请求最近急救员帮助

Time series forecasting methods are used by an evolving field of data analytics for the prediction of market trends, sales, and demands. Turkey is the major producer of hazelnut in the world. If Turkey wants to continue its domination of hazelnut and protect the price-setting role, time series forecasting methods could be key factors accordingly. There are a few studies that focused on time series forecasting of hazelnut export quantities of Turkey, and this study uses a recently developed algorithm and implements a power transformation to increase the forecast accuracy. The presented research aims to forecast Turkey’s hazelnut export quantities for the coming 36-months starting from June 2020. The forecasting process was conducted with the help of Facebook’s Prophet algorithm. To improve the forecast accuracy, a Box-Cox power transformation was also implemented to process. To find out the stationarity and periodicity of the data set, the Augmented Dickey-Fuller test and autocorrelation function was applied to the time-series data. The Prophet algorithm, with Box-Cox transformation, projected the hazelnut export quantity could be over five hundred thousand tons from 07/2020 to 06/2023. The export quantities were in an increment trend, and the slope of the trend has increased since June 2008 by 0.66 % per month. The Prophet algorithm also revealed the seasonality of the data set, and the export amounts indicate monthly oscillations. The monthly export volumes start to increase and reach their peak value in October because August is the time for the harvest of hazelnuts in Turkey.

Ersin Aytaç

Forecasting Turkey's Hazelnut Export Quantities with Facebook's Prophet Algorithm and Box-Cox Transformation

使用Facebook的Prophet算法和Box-Cox变换预测土耳其榛子出口量

Students at Saudi universities face difficulty registering for the right course since Student performance there is no support offered to students that uniquely consider each situation. Machine learning techniques could be applied to fill this gap by predicting grades of new courses for each student based on their historical data. This paper experiments with nine different prediction algorithms to predict course grades for public university students. The data-set includes grades for 215 students and 180 various courses. The models utilize grades obtained in semesters between the 2015 and 2018 academic years and evaluated on grades obtained in the 2019 academic year. Our result shows that the K-nearest neighbor with ZScore model outperforms the remaining models with respect to the Percentage of Tick Accuracy (PTA), which is the difference between two consecutive letter grades for the predicted letter grade and the observed letter grade. Our work achieved an 84% accuracy score in PTA2, where the difference between the predicted letter grade and the actual letter grade is less than or equal to two consecutive letter grades.

Budor Alharbi

Fatmah Assiri

Basma Alharbi

A Comparative Study of Student Performance Prediction using Pre-Course Data

预课数据用于学生成绩预测的比较研究

The increase in the volume and velocity of credit card transactions causes class imbalance and concept deviation problems in data sets where credit card fraud is detected. These problems make it very difficult for traditional approaches to produce robust detection models. In this study, a different perspective has been developed for this problem and a novel approach named Fraud Detection with Image Conversion (FDIC) is proposed. FDIC handles credit card transactions as time series and transforms them into images. These images, which comprise temporal correlations and bilateral relationships of features, are classified by a convolutional neural network architecture as fraudulent or legitimate. When the obtained results are compared with the related studies, FDIC has the best F1-score and recall values, which are 85.49% and 80.35%, respectively. Since the images created during the FDIC process are difficult to interpret, a new explainable artificial intelligence approach is also presented. In this way, feature relationships that have a dominant effect on fraud detection are revealed.

Duygu Sinanc

Umut Demirezen

Şeref Sağıroğlu

Explainable Credit Card Fraud Detection with Image Conversion

可解释的信用卡欺诈检测与图像转换

Leveraging discussion forums as a medium for information exchange has led to a surge in data, making topic clustering in these platforms essential for understanding user interests, preferences, and concerns. This study introduces an innovative methodology for topic clustering by combining text embedding techniques—Latent Dirichlet Allocation (LDA) and BERT—trained on a singular autoencoder. Additionally, it proposes an amalgamation of K-Means and Genetic Algorithms for clustering topics within triadic discussion forum threads. The proposed technique begins with a preprocessing stage to clean and tokenize textual data, which is then transformed into a vector representation using the hybrid text embedding method. Subsequently, the K-Means algorithm clusters these vectorized data points, and Genetic Algorithms optimize the parameters of the K-Means clustering. We assess the efficacy of our approach by computing cosine similarities between topics and comparing performance against coherence and graph visualization. The results confirm that the hybrid text embedding methodology, coupled with evolutionary algorithms, enhances the quality of topic clustering across various discussion forum themes. This investigation contributes significantly to the development of effective methods for clustering discussion forums, with potential applications in diverse domains, including social media analysis, online education, and customer response analysis.

Ibrahim Bouabdallaoui

Fatima Guerouate

Mohammed Sbihi

Hybrid Text Embedding and Evolutionary Algorithm Approach for Topic Clustering in Online Discussion Forums

混合文本嵌入与进化算法方法用于在线讨论论坛的主题聚类

Recently, one of the most common approaches used in speech recognition is deep learning. The most advanced results have been obtained with speech recognition systems created using convolutional neural network (CNN) and recurrent neural networks (RNN). Since CNNs can capture local features effectively, they are applied to tasks with relatively short-term dependencies, such as keyword detection or phoneme- level sequence recognition. This paper presents the development of a deep learning and speech command recognition system. The Google Speech Commands Dataset has been used for training. The dataset contained 65.000 one-second-long words of 30 short English words. That is, %80 of the dataset has been used in the training and %20 of the dataset has been used in the testing. The data set consists of one-second voice commands that have been converted into a spectrogram and used to train different artificial neural network (ANN) models. Various variants of CNN are used in deep learning applications. The performance of the proposed model has reached %94.60.

Hilal Ilgaz

Beyza Akkoyun

Özlem Alpay

M. Ali Akcayol

CNN Based Automatic Speech Recognition: A Comparative Study

基于CNN的自动语音识别：一项比较研究

Feedback loops and other types of information processing structures play a pivotal role in maintaining the internal milieu of living organisms. Although methods of biomedical cybernetics and systems biology help to translate between the structure and function of processing structures, computer simulations are necessary for studying nonlinear systems and the full range of dynamic responses of feedback control systems. Currently, available approaches for modelling and simulation comprise basically domain-specific environments, toolkits for computer algebra systems and custom software written in universal programming languages for a specific purpose, respectively. All of these approaches are faced with certain weaknesses. We therefore developed a cross-platform class library that provides versatile building bricks for writing computer simulations in a universal programming language (CyberUnits Bricks). It supports the definition of models, the simulative analysis of linear and nonlinear systems in the time and frequency domain and the plotting of block diagrams. We compared several programming languages that are commonly used in biomedical research (S in the R implementation and Python) or that are optimized for speed (Swift, C++ and Object Pascal). In benchmarking experiments with two prototypical feedback loops, we found the implementations in Object Pascal to deliver the fastest results. CyberUnits Bricks is available as open-source software that has been optimised for Embarcadero Delphi and the Lazarus IDE for Free Pascal.

Johannes W. Dietrich

Nina Siegmar

Jonas R. Hojjati

Oliver Gardt

Bernhard O. Boehm

CyberUnits Bricks: An Implementation Study of a Class Library for Simulating Nonlinear Biological Feedback Loops

CyberUnits 积木：非线性生物反馈回路模拟类库的实现研究

Numerous industrial applications depend heavily on induction motors and their malfunction causes considerable financial losses. Induction motors in industrial processes have recently expanded dramatically in size, and complexity of defect identification and diagnostics for such systems has increased as well. As a result, research has concentrated on developing novel methods for the quick and accurate identification of induction motor problems.In response to these needs, this paper provides an optimised algorithm for analysing the performance of an induction motor. To analyse the operation of induction motors, an enhanced methodology on Deep Belief Networks (DBN) is introduced for recovering properties from the sensor identified vibration signals. Restricted Boltzmann Machine (RBM) is stacked utilizing multiple units of DBN model, which is then trained adopting Ant colony algorithm.An innovative method of feature extraction for autonomous fault analysis in manufacturing is provided by experimental investigations utilising vibration signals and overall accuracy of 99.8% is obtained, which therefore confirms the efficiency of DBN architecture for features extraction.

Pradeep Katta

K. Karunanithi

S. P. Raja

S. Ramesh

S. Vinoth John Prakash

Deepthi Joseph

Optimized Deep Belief Network for Efficient Fault Detection in Induction Motor

感应电机高效故障检测的优化深度信念网络

The medical condition known as acute lymphoblastic leukemia (ALL) is characterized by an excess of immature lymphocyte production, and it can affect people across all age ranges. Detecting it at an early stage is extremely important to increase the chances of successful treatment. Conventional diagnostic techniques for ALL, such as bone marrow and blood tests, can be expensive and time-consuming. They may be less useful in places with scarce resources. The primary objective of this research is to investigate automated techniques that can be employed to detect ALL at an early stage. This analysis covers both machine learning models (ML), such as support vector machine (SVM) &amp; random forest (RF), as well as deep learning algorithms (DL), including convolution neural network (CNN), AlexNet, ResNet50, ShuffleNet, MobileNet, RNN. The effectiveness of these models in detecting ALL is evident through their ability to enhance accuracy and minimize human errors, which is essential for early diagnosis and successful treatment. In addition, the study also highlights several challenges and limitations in this field, including the scarcity of data available for ALL types, and the significant computational resources required to train and operate deep learning models.

Mohammad Faiz

Bakkanarappa Gari Mounika

Mohd Akbar

Swapnita Srivastava

Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review

急性淋巴细胞白血病诊断中的深度学习与机器学习：综合评述

短名
Journal Impact	1.80
国际分区	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE(Q3)

涉及主题	计算机科学人工智能工程类数学操作系统程序设计语言物理生物经济机器学习哲学地理量子力学政治学法学心理学万维网业务历史考古
出版信息	出版商: University of Salamanca，出版周期: ，期刊类型: journal，开源期刊: 是
基本数据	创刊年份: 2019，原创研究文献占比: 90.48%，自引率:0.00%， Gold OA占比: 89.33%
平均审稿周期	网友分享经验：4 Weeks

Advances in Distributed Computing and Artificial Intelligence Journal

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