Mendel Pub Date : 2022-12-20 DOI: 10.13164/mendel.2022.2.054

Werry Febrianti, K. A. Sidarto, N. Sumarti

引用次数: 1

A Novel Light-Weight DCNN Model for Classifying Plant Diseases on Internet of Things Edge Devices 基于物联网边缘设备的植物病害分类轻量级DCNN模型

Mendel Pub Date : 2022-12-20 DOI: 10.13164/mendel.2022.2.041

H. T. Minh, T. P. Anh, Van Nguyen Nhan

{"title":"A Novel Light-Weight DCNN Model for Classifying Plant Diseases on Internet of Things Edge Devices","authors":"H. T. Minh, T. P. Anh, Van Nguyen Nhan","doi":"10.13164/mendel.2022.2.041","DOIUrl":"https://doi.org/10.13164/mendel.2022.2.041","url":null,"abstract":"One of the essential aspects of smart farming and precision agriculture is quickly and accurately identifying diseases. Utilizing plant imaging and recently developed machine learning algorithms, the timely detection of diseases provides many benefits to farmers regarding crop and product quality. Specifically, for farmers in remote areas, disease diagnostics on edge devices is the most effective and optimal method to handle crop damage as quickly as possible. However, the limitations posed by the equipment’s limited resources have reduced the accuracy of disease detection. Consequently, adopting an efficient machine-learning model and decreasing the model size to fit the edge device is an exciting problem that receives significant attention from researchers and developers. This work takes advantage of previous research on deep learning model performance evaluation to present a model that applies to both the Plant-Village laboratory dataset and the Plant-Doc natural-type dataset. The evaluation results indicate that the proposed model is as effective as the current state-of-the-art model. Moreover, due to the quantization technique, the system performance stays the same when the model size is reduced to accommodate the edge device.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91257539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Explanation and Speedup Comparison of Advanced Path-planning Algorithms Presented on Two-dimensional Grid 二维网格上先进路径规划算法的解释与提速比较

Mendel Pub Date : 2022-12-20 DOI: 10.13164/mendel.2022.2.097

Petr Soustek, R. Matousek, J. Dvorak, Lenka Manakova

{"title":"Explanation and Speedup Comparison of Advanced Path-planning Algorithms Presented on Two-dimensional Grid","authors":"Petr Soustek, R. Matousek, J. Dvorak, Lenka Manakova","doi":"10.13164/mendel.2022.2.097","DOIUrl":"https://doi.org/10.13164/mendel.2022.2.097","url":null,"abstract":"Path planning or network route planning problems are an important issue in AI, robotics, or computer games. Appropriate implementation and knowledge of advanced and classical path-planning algorithms can be important for both autonomous navigation systems and computer games. In this paper, we compare advanced path planning algorithms implemented on a two-dimensional grid. Advanced path planning algorithms, including pseudocode, are introduced. The experiments were performed in the Python environment, thus with a significant performance margin over C++ or Rust implementations. The main focus is on the speedup of the algorithms compared to a baseline method, which was chosen to be the well-known Dijkstra's algorithm. All experiments correspond to trajectories on a two-dimensional grid, with variously defined constraints. The motion from each node corresponds to a Moore neighborhood, i.e., it is possible in eight directions. In this paper, three well-known path planning algorithms are described and compared: the Dijkstra, A* and A* /w Bounding Box. And two advanced methods are included, namely Jump Point Search (JPS), incorporated with the Bounding Box variant (JPS+BB), and Simple Subgoal (SS). These advanced methods clearly show their advantage in the context of the speed up of solution time.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"435 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77407685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Use of the Multi-Scale Discrete Wavelet Transform and Deep Neural Networks on ECGs for the Diagnosis of 8 Cardio-Vascular Diseases 多尺度离散小波变换与深度神经网络在心电图诊断中的应用

Mendel Pub Date : 2022-12-20 DOI: 10.13164/mendel.2022.2.062

Mhamed-Amine Soumiaa, Sara Elhabbari, Mohamad Mansouri

{"title":"The Use of the Multi-Scale Discrete Wavelet Transform and Deep Neural Networks on ECGs for the Diagnosis of 8 Cardio-Vascular Diseases","authors":"Mhamed-Amine Soumiaa, Sara Elhabbari, Mohamad Mansouri","doi":"10.13164/mendel.2022.2.062","DOIUrl":"https://doi.org/10.13164/mendel.2022.2.062","url":null,"abstract":"Cardiovascular diseases (CVD) continues to be the leading cause of death worldwide, with over 17 million deaths each year. In 2015, approximately 422 million people suffered from cardiovascular disease (CVD). Reading and analyzing electrocardiograms (ECGs) can be time consuming, and the development of decision support tools based on automated systems can facilitate and speed up the diagnosis of ECGs. In this paper, we propose a 12 leads ECG signals classification using Multi-level Discrete Wavelet Transform and ResNet34 Deep Learning algorithm which classifies 8 types of cardiovascular diseases: Atrial fibrillation (AF), 1st degree atrioventricular block (AV), Left bundle branch block (LBBB), Right bundle branch block (RBBB), Premature ventricular contraction (PVC), Premature atrial contraction (PAC), ST segment depression (STD), and ST segment elevation (STE). The ECGs are preprocessed, and different features are extracted using Multi-level Discrete Wavelet Transform. The model is trained on a database of more than 6000 electrocardiograms which includes 9 types of 12-lead ECGs: a normal type and the 8 abnormal ones which correspond to the diseases mentioned above.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88928768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Segmentation of Chest X-Ray Images Using U-Net Model 基于U-Net模型的胸部x线图像分割

Mendel Pub Date : 2022-12-20 DOI: 10.13164/mendel.2022.2.049

Mohammed Y. Kamil, Sahar A. Hashem

引用次数: 0

Meta-Heuristics Based Inverse Kinematics of Robot Manipulator’s Path Tracking Capability Under Joint Limits 基于元启发式的关节限制下机器人机械手路径跟踪能力逆运动学

Mendel Pub Date : 2022-06-30 DOI: 10.13164/mendel.2022.1.041

G. Kanagaraj, S. S. Sheik Masthan, V. Yu

{"title":"Meta-Heuristics Based Inverse Kinematics of Robot Manipulator’s Path Tracking Capability Under Joint Limits","authors":"G. Kanagaraj, S. S. Sheik Masthan, V. Yu","doi":"10.13164/mendel.2022.1.041","DOIUrl":"https://doi.org/10.13164/mendel.2022.1.041","url":null,"abstract":"In robot-assisted manufacturing or assembly, following a predefined path became a critical aspect. In general, inverse kinematics offers the solution to control the movement of manipulator while following the trajectory. The main problem with the inverse kinematics approach is that inverse kinematics are computationally complex. For a redundant manipulator, this complexity is further increased. Instead of employing inverse kinematics, the complexity can be reduced by using a heuristic algorithm. Therefore, a heuristic-based approach can be used to solve the inverse kinematics of the robot manipulator end effector, guaranteeing that the desired paths are accurately followed. This paper compares the performance of four such heuristic-based approaches to solving the inverse kinematics problem. They are Bat Algorithm (BAT), Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA). The performance of these algorithms is evaluated based on their ability to accurately follow a predefined trajectory. Extensive simulations show that BAT and GSA outperform PSO and WOA in all aspects considered in this work related to inverse kinematic problems.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77368369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images 使用增量学习算法从胸部x线图像诊断COVID-19

Mendel Pub Date : 2022-06-30 DOI: 10.13164/mendel.2022.1.001

Rimah Amami, Suleiman Ali Al Saif, Rim Amami, Hassan Ahmed Eleraky, Fatma Melouli, Mariem Baazaoui

{"title":"The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images","authors":"Rimah Amami, Suleiman Ali Al Saif, Rim Amami, Hassan Ahmed Eleraky, Fatma Melouli, Mariem Baazaoui","doi":"10.13164/mendel.2022.1.001","DOIUrl":"https://doi.org/10.13164/mendel.2022.1.001","url":null,"abstract":"The new Coronavirus or simply Covid-19 causes an acute deadly disease. It has spread rapidly across the world, which has caused serious consequences for health professionals and researchers. This is due to many reasons including the lack of vaccine, shortage of testing kits and resources. Therefore, the main purpose of this study is to present an inexpensive alternative diagnostic tool for the detection of Covid-19 infection by using chest radiographs and Deep Convolutional Neural Network (DCNN) technique. In this paper, we have proposed a reliable and economical solution to detect COVID-19. This will be achieved by using X-rays of patients and an Incremental-DCNN (I-DCNN) based on ResNet-101 architecture. The datasets used in this study were collected from publicly available chest radiographs on medical repositories. The proposed I-DCNN method will help in diagnosing the positive Covid-19 patient by utilising three chest X-ray imagery groups, these will be: Covid-19, viral pneumonia, and healthy cases. Furthermore, the main contribution of this paper resides on the use of incremental learning in order to accommodate the detection system. This has high computational energy requirements, time consuming challenges, while working with large-scale and regularly evolving images. The incremental learning process will allow the recognition system to learn new datasets, while keeping the convolutional layers learned previously. The overall Covid-19 detection rate obtained using the proposed I-DCNN was of 98.70% which undeniably can contribute effectively to the detection of COVID-19 infection.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83988645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Intelligent Sampling of Anterior Human Nasal Swabs using a Collaborative Robotic Arm 使用协作机械臂的人类前鼻拭子智能采样

Mendel Pub Date : 2022-06-30 DOI: 10.13164/mendel.2022.1.032

Roman Parák, M. Juricek

引用次数: 1

Grouped Pointwise Convolutions Reduce Parameters in Convolutional Neural Networks 分组点卷积减少卷积神经网络中的参数

Mendel Pub Date : 2022-06-30 DOI: 10.13164/mendel.2022.1.023

Joao Paulo Schwarz Schuler, S. Romaní, M. Abdel-Nasser, Hatem A. Rashwan, D. Puig

引用次数: 12

Improving Initial Aerofoil Geometry Using Aerofoil Particle Swarm Optimisation 利用粒子群优化改进初始翼型几何形状

Mendel Pub Date : 2022-06-30 DOI: 10.13164/mendel.2022.1.063

J. Muller

引用次数: 3

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