Lahore Garrison University Research Journal of Computer Science and Information Technology最新文献

Classification of Microscopic Malaria Parasitized Images Using Deep Learning Feature Fusion 基于深度学习特征融合的微观疟疾寄生图像分类

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2023-08-22 DOI: 10.54692/lgurjcsit.2023.0702473

Muhammad Asim

{"title":"Classification of Microscopic Malaria Parasitized Images Using Deep Learning Feature Fusion","authors":"Muhammad Asim","doi":"10.54692/lgurjcsit.2023.0702473","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2023.0702473","url":null,"abstract":"An infectious disease that causes a chronic and potentially life-threatening infection caused by microorganisms of the Plasmodium class, is malaria, or malarial disease. It is critical to detect the presence of Malaria parasites as early as possible to ensure that antimalarial treatment is adequate to cure the particular type of Plasmodium. This is to reduce death rates and to focus on various infections in the event of an adverse outcome. The purpose of this study was to develop an artificial intelligence approach capable of separating parasitized erythrocytes from normal basophilic erythrocytes as well as platelets overlying the red blood cells to overcome the high cost of Ma-laria diagnostic equipment. The tone and texture characteristics of erythrocyte images were extracted using histo-gram thresholds and watershed methods, and then fused with Squeeze Net and ShuffleNet algorithms. The measures included planning, preparing, approving, and testing Deep Convolution Neural Network Segmentation without preparation using a graphic processor unit. A total of 96 percent accuracy and specificity was obtained for the position of malaria in red blood cells based on the results of all of the tests. It has been demonstrated that deep learning can be effective in the field of clinical pathology. This provides new directions for development as well as increasing awareness of researchers in this field.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121411766","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 systematic review A Conversational interface agent for the export business acceleration 一个用于出口业务加速的会话接口代理

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2023-08-21 DOI: 10.54692/lgurjcsit.2023.0702430

Muhammad Bilal Ahmad Jamil, Duryab Shahzadi

{"title":"A systematic review A Conversational interface agent for the export business acceleration","authors":"Muhammad Bilal Ahmad Jamil, Duryab Shahzadi","doi":"10.54692/lgurjcsit.2023.0702430","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2023.0702430","url":null,"abstract":"Conversational agents, which understand, respond to, and learn from each interaction using Automatic Speech Recognition (ASR), Natural Language Processing (NLP), Advanced Dialog Management, and Machine Learning (ML), have become more common in recent years. Conversational agents, also referred to as chatbots, are used to have real-time conversations with individuals. As a result, conversational agents are now being used in a variety of sectors, including those in education, healthcare, marketing, customer assistance, and entertainment. Conversational agents, which are frequently used as chatbots and virtual or AI helpers, show how computational linguistics is used in everyday life. It can be challenging to pinpoint the variables that affect the use of conversational agents for business acceleration and to defend their utility in order to enhance export company. This paper provides a summary of the evolution of conversational agents from a straightforward model to a sophisticated intelligent system, as well as how they are applied in various practical contexts. This study contributes to the body of literature on information systems by contrasting the different conversational agent types based on the export business acceleration interface. This paper also identifies the challenges conversational applications experience today and makes recommendations for further research.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124313498","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

Cloud Computing Services and Security Challenges: A Review 云计算服务和安全挑战:综述

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2023-08-21 DOI: 10.54692/lgurjcsit.2023.0702459

S. Nawaz, Ahmed Naeem Akhtar, Hafiz Burhan, Ul Haq

{"title":"Cloud Computing Services and Security Challenges: A Review","authors":"S. Nawaz, Ahmed Naeem Akhtar, Hafiz Burhan, Ul Haq","doi":"10.54692/lgurjcsit.2023.0702459","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2023.0702459","url":null,"abstract":"An architecture of computing that provides services over the internet on the demand and desires of users that pay for the accessible resources that are shared is refer as the cloud computing. These resources are shared over the cloud and users do not have to acquire them physically. Some of the shared resources are: software, hardware, networks, services, applications and servers. Almost every industry from hospitals to education is moving towards the cloud for storage of data because of managing the effective cost and time of organizing the resources physically on their space. Storage of data over the data centers provided in the form of clouds is the key service of the cloud computing. Users store their desired data on clouds that are publicly available over the internet and away from their boundaries in cost effective manner. Therefore, techniques like encryption is used for obscuring the user’s information before uploading or storing to the shared cloud devices. The main aim of the techniques is to provide security to the data of users from unauthorized and malicious intrusions.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134251416","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

Identification of Finger Vein Images with Deep Neural Networks 基于深度神经网络的手指静脉图像识别

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2023-08-21 DOI: 10.54692/lgurjcsit.2023.0702425

Hana Sharif, Faisal Rehman, Naveed Riaz, Rana Mohtasham Aftab, Adnan Ashraf, Azher Mehmood

引用次数: 0

Classifying Tweets with Keras and TensorFlow using RNN (Bi-LSTM) 基于RNN (Bi-LSTM)的Keras和TensorFlow推文分类

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2023-08-17 DOI: 10.54692/lgurjcsit.2023.0702455

Muhammad Kashif

{"title":"Classifying Tweets with Keras and TensorFlow using RNN (Bi-LSTM)","authors":"Muhammad Kashif","doi":"10.54692/lgurjcsit.2023.0702455","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2023.0702455","url":null,"abstract":"Understanding public opinion, sentiment analysis, and subject recognition have all become more and more important as social media platforms have grown exponentially. The methodology for categorizing tweets using Keras and TensorFlow with a Recurrent Neural Network (RNN) architecture, specifically Long Short-Term Memory (LSTM) units, is presented in this research article. The method uses word embeddings and other properties to improve tweet representation, allowing the model to reliably identify specified categories and capture contextual connections. Our RNN-LSTM model beats baseline methods after extensive testing and evaluation, proving its suitability for tweet classification applications. The model's comprehension of tweet content is further improved by the incorporation of pre-trained word embeddings as well as features like emotion scores and hashtags. The approach offers a thorough framework for using deep learning methods in tweet classification, opening the door for uses cases including sentiment analysis, topic recognition, and opinion mining. By providing knowledge on the possibilities of RNN-LSTM models and their use in comprehending and analysing social media data, this research makes a contribution to the area. The results emphasise how crucial it is to take temporal dynamics and contextual factors into account while handling tweet classification jobs. Future research may concentrate on researching other pre-trained embeddings, investigating advanced RNN architectures, and solving issues with noisy and biassed twitter data. Overall, the large volume of information published on social networking sites like Twitter may now be better understood and analysed thanks to this research. \u0000 ","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123541095","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 Fuzzy Clustering-based Approach for Classifying COVID-19 Patients by Age and Early Symptom Indicators 基于年龄和早期症状指标的COVID-19患者模糊聚类分类方法

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2023-08-04 DOI: 10.54692/lgurjcsit.2023.0702410

Haris Ahmed, Dr. Muhammad Aﬀan Alim, Dr. Waleej Haider, Muhammad Nadeem, Ahsan Masroor

引用次数: 0

Machine Vision Approach for Identification of Four Variant Pakistani Rice Using Multi-Features Dataset 基于多特征数据集的四种变异巴基斯坦稻的机器视觉识别方法

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2023-03-07 DOI: 10.54692/lgurjcsit.2023.0701348

Tanveer Aslam, Hafiz Muhammad Ijaz, Muzammil Ur Rehman, Abdul Razzaq, Syed Ali Nawaz, Salman Qadri

{"title":"Machine Vision Approach for Identification of Four Variant Pakistani Rice Using Multi-Features Dataset","authors":"Tanveer Aslam, Hafiz Muhammad Ijaz, Muzammil Ur Rehman, Abdul Razzaq, Syed Ali Nawaz, Salman Qadri","doi":"10.54692/lgurjcsit.2023.0701348","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2023.0701348","url":null,"abstract":"Crops are the most important and beneficial food source in Pakistan. The demand for food has been an increase in Pakistan due to population growth. Pakistan produced 7,410 million tons of rice according to the financial year survey 2020 (FYS-2020). Pakistani rice has been cultivated in 3,304 hectares of the agricultural land zone, and it is also export around the world. Rice is also increased by 0.6% Gross Domestic Product (GDP) of Pakistan (FYS-2020). The old and manual process of rice classification is more expensive and time-consuming. In this study, we describe a machine vision approach for rice identification. We use four different varieties of rice for the experimental process such as Pakei_Kaynat, Kaynat_Kauchei, and Kauchei_Super_Banaspati and Tootaa_Kauchei (P1, P2, P3, and P4). The 100 images dataset have been used for practical work and total calculated of 400 (4 x 100) image of rice. The different process has been deploying on available datasets such as introduction, preprocessing methodology, and result discussion. A quality enhancement technique has been implementing for clarifying between rice color and shape sampling, and it is also converted color image in gray scale level. Every image has been employing six different non-overlapping regions of interest (ROI’s) and calculated a total of 2400 (6 x 400) ROI’s. Binary (B), Histogram (H) and Texture (T) features have been implemented and extract 43 features on each ROI’s and total calculated 103,200 (2400 x 43) machine learning (ML) features. Best First Search (BFS) Algorithm was used for feature optimization. Different ML classifiers are implementing for experimental process namely; Function Multi-Layer-Perception, Function SMO, Random Tree, J48 Tree, Meta Classifier via Regression and Meta Bagging. The Function Multi-Layer-Perception overall accuracy (OA) has describe better accuracy result is 99.8333%.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"52 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123562376","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

Diabetes Diagnosis through Machine Learning: An Analysis of Classification Algorithms 通过机器学习诊断糖尿病:分类算法分析

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2023-02-16 DOI: 10.54692/lgurjcsit.2023.0701411

H. Ahmed, Muhammad Affan Alim, Waleej Haider, Muhammad Nadeem, Ahsan Masroor, Nadeem Qamar

引用次数: 0

Identification and Classification for Diagnosis of Malaria Disease using Blood Cell Images 利用血细胞图像识别和分类诊断疟疾

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2023-02-03 DOI: 10.54692/lgurjcsit.2023.0701417

H. M. Bilal

{"title":"Identification and Classification for Diagnosis of Malaria Disease using Blood Cell Images","authors":"H. M. Bilal","doi":"10.54692/lgurjcsit.2023.0701417","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2023.0701417","url":null,"abstract":"Machine Learning is a subfield of artificial intelligence that focuses on developing intelligent algorithms capable of learning from available data without requiring constant programming, enabling them to adapt to different environments based on current scenarios. These algorithms are crucial in making intelligent decisions and conducting thorough analyses to uncover intricate patterns concealed within the data. This study used multiple machine-learning classification algorithms to analyze patients' data based explicitly on input images containing parasite-infected and uninfected Malaria samples. AI techniques were utilised to measure the presence of parasites in the images. The image classification system was designed to accurately identify malaria parasites in blood images by generating image features related to color, texture, and cell and parasite geometry. A classifier based on SVM (Support Vector Machine) provided by Weka was employed to differentiate between parasite-infected and non-infected blood images. Through extensive experimentation, it was determined that SVM strategies exhibited significant relevance, achieving a cross-validation accuracy of 99.4% in the basic diagnosis of malaria fever. This finding holds great potential in assisting clinicians with accurate infection diagnoses.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134304982","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

Algorithmic as well as Space and Time comparison of various Deep Learning Algorithms 算法以及各种深度学习算法的时空比较

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2023-01-23 DOI: 10.54692/lgurjcsit.2023.0701361

Waheed Javed, Gulnaz Parveen, Sobia Bilal

引用次数: 0