Iraqi Journal for Computers and Informatics最新文献_第2页

PREDICTING MEDICINE DEMAND USING DEEP LEARNING TECHNIQUES 使用深度学习技术预测药品需求

Iraqi Journal for Computers and Informatics Pub Date : 2023-09-14 DOI: 10.25195/ijci.v49i2.427

Bashaer Abdurahman Mousa, Belal Al-Khateeb

{"title":"PREDICTING MEDICINE DEMAND USING DEEP LEARNING TECHNIQUES","authors":"Bashaer Abdurahman Mousa, Belal Al-Khateeb","doi":"10.25195/ijci.v49i2.427","DOIUrl":"https://doi.org/10.25195/ijci.v49i2.427","url":null,"abstract":"Medication supply and storage are essential components of the medical industry and distribution. Most medications have a predetermined expiration date. When the demand is met in large quantities that exceed the actual need, this leads to the accumulation of medicines in the stores, and this leads to the expiration of the materials. If demand is too low, this will have an impact on consumer happiness and drug marketing.Therefore, it is necessary to find a way to predict the actual quantity required for the organization's needs to avoid material spoilage and storage problems. A mathematical prediction model is required to assist any management in achieving the required availability of medicines for customers and safe storage of medicines. The research question is to design a system based on deep learning that can predict the amount of drugs required with high efficiency and accuracy based on the chronology of previous years.Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, and Gated Recurrent Unit (GRU) are used to build prediction models. Those models allow for the optimization of inventory levels, thus reducing costs and potentially increasing sales. Various measures such as mean squared error (MSE), mean absolute squared error (MASE), root mean squared error (RMSE), and others are used to evaluate the prediction models. RNN model achieved the best result with MSE: 0.019 MAE: 0.102, RMSE: 0.0.","PeriodicalId":53384,"journal":{"name":"Iraqi Journal for Computers and Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134973058","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

UNDERGROUND CRUDE OIL PIPELINE LEAKAGE DETECTION USING DEXINED DEEP LEARNING TECHNIQUES AND LAB COLOR SPACE 利用定义深度学习技术和实验室色彩空间进行地下原油管道泄漏检测

Iraqi Journal for Computers and Informatics Pub Date : 2023-08-14 DOI: 10.25195/ijci.v49i2.418

Muhammad H. Obaid

{"title":"UNDERGROUND CRUDE OIL PIPELINE LEAKAGE DETECTION USING DEXINED DEEP LEARNING TECHNIQUES AND LAB COLOR SPACE","authors":"Muhammad H. Obaid","doi":"10.25195/ijci.v49i2.418","DOIUrl":"https://doi.org/10.25195/ijci.v49i2.418","url":null,"abstract":"Computer vision plays a big role in pipeline leakage detection systems and is one of the latest techniques. Still, it requires a powerful image-processing algorithm to detect objects. The purpose of this work is to develop and implement spill detection in oil pipes caused by leakage using images taken by a drone equipped with a Raspberry Pi 4. The acquired images are sent to the base station along with the global positioning system (GPS) location of the captured images via the message queuing telemetry transport Internet of Things (MQTT IoT) protocol. At the base station, images are processed to identify contours by dense extreme inception networks for edge detection(DexiNed) deep learning techniques based on holistically-nested edge detection(HED) and extreme inception (Xception) networks. This algorithm is capable of finding many contours in images. To find a contour with black color, the CIELAB color space (LAB) has been used. The proposed algorithm removes small contours and computes the area of the remaining contours. If the contour is above the threshold value, it is considered a spill; otherwise, it will be saved in a database for further inspection. For testing purposes, three different spill areas were implemented with spill sizes of (1 m^2,2 m^2 ,and 3 m^2). Images have been captured at three different heights (5 m, 10 m, and 15 m) by the drone used to capture the images. The result shows that effective detection has been obtained at 10 meters high. To monitor the entire system, a web application has been integrated into the base station.","PeriodicalId":53384,"journal":{"name":"Iraqi Journal for Computers and Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135308107","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

USING SPECIAL LETTERS AND DIACRITICS IN STEGANOGRAPHY IN HOLY QURAN 在《古兰经》中使用特殊字母和变音符进行隐写

Iraqi Journal for Computers and Informatics Pub Date : 2023-08-14 DOI: 10.25195/ijci.v49i2.417

Nooruldeen Subhi Shakir, Mohammed Salih Mahdi

{"title":"USING SPECIAL LETTERS AND DIACRITICS IN STEGANOGRAPHY IN HOLY QURAN","authors":"Nooruldeen Subhi Shakir, Mohammed Salih Mahdi","doi":"10.25195/ijci.v49i2.417","DOIUrl":"https://doi.org/10.25195/ijci.v49i2.417","url":null,"abstract":"Because of the great development that took place in information transfer and communication technologies, the issue of information transfer security has become a very sensitive and resonant issue, great importance must be given to protecting this confidential information. Steganography is one of the important and effective ways to protect the security of this information while it is being transmitted through the Internet, steganography is a technology to hide information inside an unnoticeable envelope object that can be an image, video, text or sound. The Arabic language has some special features that make it excellent covers to hide information from Through the diversity of the Arabic letters from dotted letters in several forms or vowels or special letters, the Holy Qur’an is considered a cover rich in movements and Arabic grammar, which makes it a wide cover for the purpose of concealing information. The Holy Qur’an is a sacred book where it is not permissible to modify, add or move any of the letters or any diacritical mark to it. The algorithm hides the two bits by uses six special letters of Arabic language. Moreover, it checks for the presence of specific Arabic linguistic features referred Arabic diacritics. The proposed system achieved a high ability to hide as in Surat Al-Baqarah (4524 bits) and also (2576 bits) in Surat Al-Imran and in Surat Al-An’am (2318 bits).","PeriodicalId":53384,"journal":{"name":"Iraqi Journal for Computers and Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135308106","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

A Survey on Cybercrime Using Social Media 利用社交媒体的网络犯罪调查

Iraqi Journal for Computers and Informatics Pub Date : 2023-06-11 DOI: 10.25195/ijci.v49i1.404

Zainab Khyioon Abdalrdha, Abbas Mohsin Al-Bakry, Alaa K. Farhan

引用次数: 0

An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application 基于6-DOF的智能控制器在生物医学应用中的最优轨迹分析综述

Iraqi Journal for Computers and Informatics Pub Date : 2023-06-11 DOI: 10.25195/ijci.v49i1.405

Kian Raheem qasim, Yousif I. Al Mashhadany, Esam T. Yassen

{"title":"An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application","authors":"Kian Raheem qasim, Yousif I. Al Mashhadany, Esam T. Yassen","doi":"10.25195/ijci.v49i1.405","DOIUrl":"https://doi.org/10.25195/ijci.v49i1.405","url":null,"abstract":"With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper.","PeriodicalId":53384,"journal":{"name":"Iraqi Journal for Computers and Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46968780","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

REVIEW ON DETECTION OF RICE PLANT LEAVES DISEASES USING DATA AUGMENTATION AND TRANSFER LEARNING TECHNIQUES 利用数据扩充和迁移学习技术检测水稻叶片病害研究进展

Iraqi Journal for Computers and Informatics Pub Date : 2023-06-11 DOI: 10.25195/ijci.v49i1.381

Osama Alaa Hussein, Mohammed Salih Mahdi

{"title":"REVIEW ON DETECTION OF RICE PLANT LEAVES DISEASES USING DATA AUGMENTATION AND TRANSFER LEARNING TECHNIQUES","authors":"Osama Alaa Hussein, Mohammed Salih Mahdi","doi":"10.25195/ijci.v49i1.381","DOIUrl":"https://doi.org/10.25195/ijci.v49i1.381","url":null,"abstract":"The most important cereal crop in the world is rice (Oryza sativa). Over half of the world's population uses it as a staple food and energy source. Abiotic and biotic factors such as precipitation, soil fertility, temperature, pests, bacteria, and viruses, among others, impact the yield production and quality of rice grain. Farmers spend a lot of time and money managing diseases, and they do so using a bankrupt \"eye\" method that leads to unsanitary farming practices. The development of agricultural technology is greatly conducive to the automatic detection of pathogenic organisms in the leaves of rice plants. Several deep learning algorithms are discussed, and processors for computer vision problems such as image classification, object segmentation, and image analysis are discussed. The paper showed many methods for detecting, characterizing, estimating, and using diseases in a range of crops. The methods of increasing the number of images in the data set were shown. Two methods were presented, the first is traditional reinforcement methods, and the second is generative adversarial networks. And many of the advantages have been demonstrated in the research paper for the work that has been done in the field of deep learning.","PeriodicalId":53384,"journal":{"name":"Iraqi Journal for Computers and Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42950939","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 Detection of Students' Abnormal Behavior in Online Exams Using Facial Landmarks in Conjunction with the YOLOv5 Models 结合YOLOv5模型的面部标志对在线考试学生异常行为的检测

Iraqi Journal for Computers and Informatics Pub Date : 2023-06-11 DOI: 10.25195/ijci.v49i1.380

Muhanad Abdul Elah Alkhalisy, Saad Hameed Abid

{"title":"The Detection of Students' Abnormal Behavior in Online Exams Using Facial Landmarks in Conjunction with the YOLOv5 Models","authors":"Muhanad Abdul Elah Alkhalisy, Saad Hameed Abid","doi":"10.25195/ijci.v49i1.380","DOIUrl":"https://doi.org/10.25195/ijci.v49i1.380","url":null,"abstract":"The popularity of massive open online courses (MOOCs) and other forms of distance learning has increased recently. Schools and institutions are going online to serve their students better. Exam integrity depends on the effectiveness of proctoring remote online exams. Proctoring services powered by computer vision and artificial intelligence have also gained popularity. Such systems should employ methods to guarantee an impartial examination. This research demonstrates how to create a multi-model computer vision system to identify and prevent abnormal student behaviour during exams. The system uses You only look once (YOLO) models and Dlib facial landmarks to recognize faces, objects, eye, hand, and mouth opening movement, gaze sideways, and use a mobile phone. Our approach offered a model that analyzes student behaviour using a deep neural network model learned from our newly produced dataset\" StudentBehavioralDS.\" On the generated dataset, the \"Behavioral Detection Model\" had a mean Average Precision (mAP) of 0.87, while the \"Mouth Opening Detection Model\" and \"Person and Objects Detection Model\" had accuracies of 0.95 and 0.96, respectively. This work demonstrates good detection accuracy. We conclude that using computer vision and deep learning models trained on a private dataset, our idea provides a range of techniques to spot odd student behaviour during online tests.","PeriodicalId":53384,"journal":{"name":"Iraqi Journal for Computers and Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47093407","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

LUNG CANCER DETECTION IN LOW-RESOLUTION IMAGES 肺癌在低分辨率图像中的检测

Iraqi Journal for Computers and Informatics Pub Date : 2023-06-11 DOI: 10.25195/ijci.v49i1.378

Mostafa K .abd alrahman aladamey, Duha D .salman

{"title":"LUNG CANCER DETECTION IN LOW-RESOLUTION IMAGES","authors":"Mostafa K .abd alrahman aladamey, Duha D .salman","doi":"10.25195/ijci.v49i1.378","DOIUrl":"https://doi.org/10.25195/ijci.v49i1.378","url":null,"abstract":"One of the most important prognostic factors for all lung cancer patients is the accurate detection of metastases. Pathologists, as we all know, examine the body and its tissues. On the existing clinical method, they have a tedious and manual task. Recent analysis has been inspired by these aspects. Deep Learning (DL) algorithms have been used to identify lung cancer. The developed cutting-edge technologies beat pathologists in terms of cancer identification and localization inside pathology images. These technologies, though, are not medically feasible because they need a massive amount of time or computing capabilities to perceive high-resolution images. Image processing techniques are primarily employed for lung cancer prediction and early identification and therapy to avoid lung cancer. This research aimed to assess lung cancer diagnosis by employing DL algorithms and low-resolution images. The goal would be to see if Machine Learning (ML) models might be created that generate higher confidence conclusions while consuming fractional resources by comparing low and high-resolution images. A DL pipeline has been built to a small enough size from compressing high-resolution images to be fed into an or before CNN (Convolutional Neural Network) for binary classification i.e. cancer or normal. Numerous enhancements have been done to increase overall performance, providing data augmentations, including augmenting training data and implementing tissue detection. Finally, the created low-resolution models are practically incapable of handling extremely low-resolution inputs i.e. 299 x 299 to 2048 x 2048 pixels. Considering the lack of classification ability, a substantial reduction in models’ predictable times is only a marginal benefit. Due to an obvious drawback with the methodology, this is disheartening but predicted finding: very low resolutions, essentially expanding out on a slide, preserve only data about macro-cellular structures, which is usually insufficient to diagnose cancer by itself.","PeriodicalId":53384,"journal":{"name":"Iraqi Journal for Computers and Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135005930","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 ROUGH CLASSIFICATION AND TWO THRESHOLD TWO DIVISORS FOR DEDUPLICATION 粗分类和两阈值二除数在重复数据消除中的应用

Iraqi Journal for Computers and Informatics Pub Date : 2023-06-11 DOI: 10.25195/ijci.v49i1.379

Hashem B. Jehlol, Loay E. George

{"title":"THE USE OF ROUGH CLASSIFICATION AND TWO THRESHOLD TWO DIVISORS FOR DEDUPLICATION","authors":"Hashem B. Jehlol, Loay E. George","doi":"10.25195/ijci.v49i1.379","DOIUrl":"https://doi.org/10.25195/ijci.v49i1.379","url":null,"abstract":"The data deduplication technique efficiently reduces and removes redundant data in big data storage systems. The main issue is that the data deduplication requires expensive computational effort to remove duplicate data due to the vast size of big data. The paper attempts to reduce the time and computation required for data deduplication stages. The chunking and hashing stage often requires a lot of calculations and time. This paper initially proposes an efficient new method to exploit the parallel processing of deduplication systems with the best performance. The proposed system is designed to use multicore computing efficiently. First, The proposed method removes redundant data by making a rough classification for the input into several classes using the histogram similarity and k-mean algorithm. Next, a new method for calculating the divisor list for each class was introduced to improve the chunking method and increase the data deduplication ratio. Finally, the performance of the proposed method was evaluated using three datasets as test examples. The proposed method proves that data deduplication based on classes and a multicore processor is much faster than a single-core processor. Moreover, the experimental results showed that the proposed method significantly improved the performance of Two Threshold Two Divisors (TTTD) and Basic Sliding Window BSW algorithms.","PeriodicalId":53384,"journal":{"name":"Iraqi Journal for Computers and Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43927038","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

CASE STUDY FOR MIGRATION FROMON PREMISE TO CLOUD 从本地迁移到云的案例研究

Iraqi Journal for Computers and Informatics Pub Date : 2019-12-30 DOI: 10.25195/20174523

Saif Q. Muhamed

引用次数: 0