Applied Computer Science

The healthcare industry is one of the many out there that could majorly benefit from advancement in the technology it utilizes. Artificial intelligence (AI) technologies are especially integral and specifically deep learning (DL); a highly useful data-driven technology. It is applied in a variety of different methods but it mainly depends on the structure of the available data. However, with varying applications, this technology produces data in different contexts with particular connotations. Reports which are the images of scans play a great role in identifying the existence of the disease in a patient. Further, the automation in processing these images using technology like CNN-based models makes it highly efficient in reducing human errors otherwise resulting in large data. Hence this study presents a hybrid deep learning architecture to classify the histopathology images to identify the presence of cancer in a patient. Further, the proposed models are parallelized using the TensorFlow-GPU framework to accelerate the training of these deep CNN (Convolution Neural Networks) architectures. This study uses the transfer learning technique during training and early stopping criteria are used to avoid overfitting during the training phase. these models use LSTM parallel layer imposed in the model to experiment with four considered architectures such as MobileNet, VGG16, and ResNet with 101 and 152 layers. The experimental results produced by these hybrid models show that the capability of Hybrid ResNet101 and Hybrid ResNet152 architectures are highly suitable with an accuracy of 90% and 92%. Finally, this study concludes that the proposed Hybrid ResNet-152 architecture is highly efficient in classifying the histopathology images. The proposed study has conducted a well-focused and detailed experimental study which will further help researchers to understand the deep CNN architectures to be applied in application development.

Kevin Joy DSOUZA

Zahid Ahmed ANSARI

HISTOPATHOLOGY IMAGE CLASSIFICATION USING HYBRID PARALLEL STRUCTURED DEEP-CNN MODELS

使用混合并行结构深度卷积神经网络模型的组织病理学图像分类

The latest market research (Fanuc Polska 2019) shows that the robotization of the Polish industry is accelerating. More and more companies are investing in robotic production lines, which enable greater efficiency of implemented processes and reduce labour costs. The article presents the possibilities of using virtual reality (VR) for behavioural analysis in open robotic systems with a shared workspace. The aim of the article is to develop a method of verification of programmed movements of an industrial robot in terms of safety and efficiency in systems with a shared workspace. The method of the robot program verification on the digital model of the working cell made in VR will be checked. The obtained research results indicate a great potential of this method in industrial applications as well as for educational purposes.

Jakub ANCZARSKI

Adrian BOCHEN

MArcin GŁĄB

Mikolaj JACHOWICZ

Jacek CABAN

Radosław CECHOWICZ

A METHOD OF VERIFYING THE ROBOT'S TRAJECTORY FOR GOALS WITH A SHARED WORKSPACE

一种验证共享工作空间目标机器人轨迹的方法

In this work, we present a computer simulation model that generates the propagation of sound waves to solve a forward problem in ultrasound transmission tomography. The simulator can be used to create data sets used in the supervised learning process. A solution to the "free-space" boundary problem was proposed, and the memory consumption was significantly optimized from O(n2) to O(n). The given method of simulating wave scattering enables the control of the noise extinction time within the tomographic probe and the permeability of the sound wave. The presented version of the script simulates the classic variant of a circular probe with evenly distributed sensors around the circumference.

Konrad KANIA

Mariusz MAZUREK

Tomasz RYMARCZYK

APPLICATION OF FINITE DIFFERENCE METHOD FOR MEASUREMENT SIMULATION IN ULTRASOUND TRANSMISSION TOMOGRAPHY

有限差分法在超声透射层析成像测量模拟中的应用

Microscopic technology has recently flourished, allowing unparalleled viewing of microscopic elements invisible to the normal eye. Still, the existence of unavoidable constraints led on many occasions to have low contrast scanning electron microscopic (SEM) images. Thus, a noncomplex multiphase (NM) algorithm is proposed in this study to provide better contrast for various SEM images. The developed algorithm contains the following stages: first, the intensities of the degraded image are modified using a two-step regularization procedure. Next, a gamma-corrected cumulative distribution function of the logarithmic uniform distribution approach is applied for contrast enhancement. Finally, an automated histogram expansion technique is used to redistribute the pixels of the image properly. The NM algorithm is applied to natural-contrast distorted SEM images, as well as its results are compared with six algorithms with different processing notions. To assess the quality of images, three modern metrics are utilized, in that each metric measures the quality based on unique aspects. Extensive appraisals revealed the adequate processing abilities of the NM algorithm, as it can process many images suitably and its performances outperformed many available contrast enhancement algorithms in different aspects.

Zaid ALSAYGH

Zohair AL-AMEEN

CONTRAST ENHANCEMENT OF SCANNING ELECTRON MICROSCOPY IMAGES USING A NONCOMPLEX MULTIPHASE ALGORITHM

非复杂多相算法在扫描电子显微镜图像对比度增强中的应用

Plant diseases are a foremost risk to the safety of food. They have the potential to significantly reduce agricultural products quality and quantity. In agriculture sectors, it is the most prominent challenge to recognize plant diseases. In computer vision, the Convolutional Neural Network (CNN) produces good results when solving image classification tasks. For plant disease diagnosis, many deep learning architectures have been applied. This paper introduces a transfer learning based model for detecting tomato leaf diseases. This study proposes a model of DenseNet201 as a transfer learning-based model and CNN classifier. A comparison study between four deep learning models (VGG16, Inception V3, ResNet152V2 and DenseNet201) done in order to determine the best accuracy in using transfer learning in plant disease detection. The used images dataset contains 22930 photos of tomato leaves in 10 different classes, 9 disorders and one healthy class. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.84% and validation accuracy of 99.30%.

Mahmoud BAKR

Sayed ABDEL-GABER

Mona NASR

Maryam HAZMAN

TOMATO DISEASE DETECTION MODEL BASED ON DENSENET AND TRANSFER LEARNING

基于DenseNet与迁移学习的番茄病害检测模型

In the present work, an automatic R410A refrigerant charger for residential air conditioners is fabricated and tested. The charger operates on the throttling principle and uses the suction pressure of the compressor to estimate the refrigerant charge level. This helps to reduce the risk of compressor damage and ensures the correct composition ratio of R410A refrigerant when charged into the machine. The charging process is controlled by the LabVIEW platform, which provides adequate control and visualization of the charging process. The developed charger meets expectations in solving the technical problems encountered when charging R410A refrigerant for residential air conditioners. It is compact, portable and can be directly controlled through the LabVIEW interface, allowing real-time visualization of the charging process. The present work is expected to make a significant practical contribution, serving as a useful reference for the future manufacturing of compact portable equipment in the residential air conditioning field.

Hong Son Le NGUYEN

Minh Ha NGUYEN

Luan Nguyen THANH

REFRIGERANT CHARGING UNIT FOR THE RESIDENTIAL AIR CONDITIONERS: AN EXPERIMENT

家用空调制冷剂加注装置实验研究

Satellite imagery plays an important role in detecting algal blooms because of its ability to cover larger geographical regions. Excess growth of Sea surface algae, characterized by the presence of Chlorophyll-a (Chl-a), is considered to be harmful. The detection of algal growth at an earlier stage may prevent hazardous effects on the aquatic environment. Semantic segmentation of algal blooms is helpful in the quantization of algal blooms. A rule-based semantic segmentation approach for the segregation of sea surface algal blooms is proposed. Bloom concentrations are classified into three different concentrations, namely, low, medium, and high. The chl_nn band in the Sentinel-3 satellite images is used for experimentation. The chl_nn band has exclusive details of the presence of chlorophyll concentrations. A dataset is proposed for the semantic segmentation of algal blooms. The devised rule-based semantic segmentation approach has produced an average accuracy of 98%. A set of 100 images is randomly selected for testing. The tests are repeated on 5 different image sets. The results are validated by the pixel comparison method. The proposed work is compared with other relevant works. The Arabian Sea near the coastal districts of Udupi and Mangaluru has been considered as the area of study. The methodology can be adapted to monitor the life cycle of blooms and their hazardous effects on aquatic life.

Venkatesh BHANDAGE

Manohara PAI M. M.

SEMANTIC SEGMENTATION OF ALGAL BLOOMS ON THE OCEAN SURFACE USING SENTINEL 3 CHL_NN BAND IMAGERY

利用哨兵3号CHL_NN波段影像对海洋表面藻华进行语义分割

Forecasting violence has become a critical obstacle in the field of video monitoring to guarantee public safety. Lately, YOLO (You Only Look Once) has become a popular and effective method for detecting weapons. However, identifying and forecasting violence remains a challenging endeavor. Additionally, the classification results had to be enhanced with semantic information. This study suggests a method for forecasting violent incidents by utilizing Yolov9 and ontology. The authors employed Yolov9 to identify and categorize weapons and individuals carrying them. Ontology is utilized for semantic prediction to assist in predicting violence. Semantic prediction happens through the application of a SPARQL query to the identified frame label. The authors developed a Threat Events Ontology (TEO) to gain semantic significance. The system was tested with a fresh dataset obtained from a variety of security cameras and websites. The VP Dataset comprises 8739 images categorized into 9 classes. The authors examined the outcomes of using Yolov9 in conjunction with ontology in comparison to using Yolov9 alone. The findings show that by combining Yolov9 with ontology, the violence prediction system's semantics and dependability are enhanced. The suggested system achieved a mean Average Precision (mAP) of 83.7 %, 88% for precision, and 76.4% for recall. However, the mAP of Yolov9 without TEO ontology achieved a score of 80.4%. It suggests that this method has a lot of potential for enhancing public safety. The authors finished all training and testing processes on Google Colab's GPU. That reduced the average duration by approximately 90.9%. The result of this work is a next level of object detectors that utilize ontology to improve the semantic significance for real-time end-to-end object detection.

Esraa Alaa MAHAREEK

Doaa Rizk FATHY

Eman Karm ELSAYED

Nahed ELDESOUKY

Kamal Abdelraouf ELDAHSHAN

VIOLENCE PREDICTION IN SURVEILLANCE VIDEOS

监控视频中的暴力预测

Parkinson's Disease (PD) is a neurodegenerative disorder that impacts movement, speech, dexterity, and cognition. Clinical assessments primarily diagnose PD, but symptoms' variability often leads to misdiagnosis. This study examines ML algorithms to distinguish Healthy People (HP) from People with Parkinson's Disease (PPD). Data from 106 HP and 106 PPD participants, who underwent the Parkinson’s Disease Sleep Test (PDST), Hopkin’s Verbal Learning Test (HVLT), and Clock Drawing Test (CDT) from the Parkinson's Progression Markers Initiative (PPMI) were used. A custom HYBRID dataset was also created by integrating these 3 datasets. Various Machine Learning (ML) Classification Algorithms (CA) were also studied: Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR). Multiple feature sets: the first quartile (Q1: 25 % most important features), second quartile (Q2: 50 % most important features), third quartile (Q3: 75 % most important features), and fourth quartile (Q4: All 100 % features) were generated using various Feature Selection (FS) algorithms and ensemble mechanisms. Results showed that all the ML CA achieved over 73±8.4 % accuracy with individual datasets, while the proposed HYBRID dataset achieved a remarkable accuracy of 98±0.6 %. This study identified the optimal quantity of non-motor features, dataset, the best FS and CA in hierarchical approach for early PD diagnosis and also proved that PD may be diagnosed with great accuracy by analyzing non-motor PD parameters using ML algorithms. This suggests that extended data collection could serve as a digital biomarker for PD diagnosis in the future.

Anitha Rani PALAKAYALA

Kuppusamy P

A QUALITATIVE AND QUANTITATIVE APPROACH USING MACHINE LEARNING AND NON-MOTOR SYMPTOMS FOR PARKINSON’S DISEASE CLASSIFICATION. A HIERARCHICAL STUDY

使用机器学习和非运动症状的定性与定量方法进行帕金森病分类：一项分层研究

The purpose of this study is to examine the suitability of machine learning (ML) techniques for predicting students’ performance. By analyzing various ML algorithms, the authors assess the accuracy and reliability of these approaches, considering factors such as data quality, feature selection, and model complexity. The findings indicate that certain ML methods are more effective for student performance forecasting, emphasizing the need for a deliberate evaluation of these factors. This study provides significant contributions to the field of education and reinforces the growing use of ML in decision-making and student performance prediction.

Bilal OWAIDAT

EXPLORING THE ACCURACY AND RELIABILITY OF MACHINE LEARNING APPROACHES FOR STUDENT PERFORMANCE

探究机器学习方法在学生表现预测中的准确性与可靠性

Publication Information	Publisher: Polish Association for Knowledge Promotion，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2019，Proportion of original research papers: 0.00%，Self Citation Rate:N.A.， Gold OA Rate: 0.00%
Journal Fees	$Details
Average review cycle	8 weeks

Applied Computer Science

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