Pub Date : 2023-08-22DOI: 10.54692/lgurjcsit.2023.0702473
Muhammad Asim
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.
{"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}
Pub Date : 2023-08-21DOI: 10.54692/lgurjcsit.2023.0702430
Muhammad Bilal Ahmad Jamil, Duryab Shahzadi
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.
{"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}
Pub Date : 2023-08-21DOI: 10.54692/lgurjcsit.2023.0702459
S. Nawaz, Ahmed Naeem Akhtar, Hafiz Burhan, Ul Haq
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.
{"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}
To establish identification, individuals often utilize biometrics so that their identity cannot be exploited without their consent. Collecting biometric data is getting easier. Existing smartphones and other intelligent technologies can discreetly acquire biometric information. Authentication through finger vein imaging is a biometric identification technique based on a vein pattern visible under finger's skin. Veins are safeguarded by the epidermis and cannot be duplicated. This research focuses on the consistent characteristics of veins in fingers. We collected invariant characteristics from several cutting-edge deep learning techniques before classifying them using multiclass SVM. We used publicly available image datasets of finger veins for this purpose. Several assessment criteria and a comparison of different deep learning approaches were used to characterize the performance and efficiency of these models on the SDUMLA-HMT dataset.
{"title":"Identification of Finger Vein Images with Deep Neural Networks","authors":"Hana Sharif, Faisal Rehman, Naveed Riaz, Rana Mohtasham Aftab, Adnan Ashraf, Azher Mehmood","doi":"10.54692/lgurjcsit.2023.0702425","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2023.0702425","url":null,"abstract":"To establish identification, individuals often utilize biometrics so that their identity cannot be exploited without their consent. Collecting biometric data is getting easier. Existing smartphones and other intelligent technologies can discreetly acquire biometric information. Authentication through finger vein imaging is a biometric identification technique based on a vein pattern visible under finger's skin. Veins are safeguarded by the epidermis and cannot be duplicated. This research focuses on the consistent characteristics of veins in fingers. We collected invariant characteristics from several cutting-edge deep learning techniques before classifying them using multiclass SVM. We used publicly available image datasets of finger veins for this purpose. Several assessment criteria and a comparison of different deep learning approaches were used to characterize the performance and efficiency of these models on the SDUMLA-HMT dataset. ","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"31 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":"128216728","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}
Pub Date : 2023-08-17DOI: 10.54692/lgurjcsit.2023.0702455
Muhammad Kashif
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.
{"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}
Pub Date : 2023-08-04DOI: 10.54692/lgurjcsit.2023.0702410
Haris Ahmed, Dr. Muhammad Affan Alim, Dr. Waleej Haider, Muhammad Nadeem, Ahsan Masroor
The devastating illness known as Covid-19 has disrupted the lives of individuals all over the globe and left a trail of devastation in its wake. The fact that we are unable to determine the severity of illness (SOI) class of the patient during the early stages of infection is without a doubt the most challenging aspect of this disease. An accurate classifier model has to be constructed in order to ensure that patients diagnosed with Covid-19 get prompt and individualized therapy. Within the scope of this investigation, we propose a useful fuzzy clustering based model for categorizing Covid-19 patients according to their age and the severity of their early symptoms (fever, dry cough, breathing difficulties, headache, smell, and taste disturbance). This method is superior to previous hard clustering tactics in terms of reducing the number of deaths that occur among patients suffering from coronavirus and increasing the likelihood that they will recover fully.
{"title":"A Fuzzy Clustering-based Approach for Classifying COVID-19 Patients by Age and Early Symptom Indicators","authors":"Haris Ahmed, Dr. Muhammad Affan Alim, Dr. Waleej Haider, Muhammad Nadeem, Ahsan Masroor","doi":"10.54692/lgurjcsit.2023.0702410","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2023.0702410","url":null,"abstract":"The devastating illness known as Covid-19 has disrupted the lives of individuals all over the globe and left a trail of devastation in its wake. The fact that we are unable to determine the severity of illness (SOI) class of the patient during the early stages of infection is without a doubt the most challenging aspect of this disease. An accurate classifier model has to be constructed in order to ensure that patients diagnosed with Covid-19 get prompt and individualized therapy. Within the scope of this investigation, we propose a useful fuzzy clustering based model for categorizing Covid-19 patients according to their age and the severity of their early symptoms (fever, dry cough, breathing difficulties, headache, smell, and taste disturbance). This method is superior to previous hard clustering tactics in terms of reducing the number of deaths that occur among patients suffering from coronavirus and increasing the likelihood that they will recover fully.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"6 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123728058","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}
Pub Date : 2023-03-07DOI: 10.54692/lgurjcsit.2023.0701348
Tanveer Aslam, Hafiz Muhammad Ijaz, Muzammil Ur Rehman, Abdul Razzaq, Syed Ali Nawaz, Salman Qadri
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%.
农作物是巴基斯坦最重要和最有益的食物来源。由于人口增长，巴基斯坦对食品的需求一直在增加。根据2020财政年度调查(FYS-2020)，巴基斯坦生产了74.1亿吨大米。巴基斯坦水稻已在3304公顷的农业用地上种植，并出口到世界各地。巴基斯坦的国内生产总值(GDP)也增加了0.6% (FYS-2020)。旧的手工大米分类过程更加昂贵和耗时。在本研究中，我们描述了一种用于水稻识别的机器视觉方法。我们使用Pakei_Kaynat、Kaynat_Kauchei和kauche_super_banaspati和Tootaa_Kauchei (P1、P2、P3和P4)四个不同的水稻品种进行实验。将100幅图像数据集用于实际工作，共计算出400幅(4 × 100)水稻图像。不同的过程已经部署在可用的数据集上，如介绍、预处理方法和结果讨论。本文提出了一种图像质量增强技术，用于澄清米色和形状采样之间的关系，并对彩色图像进行灰度级转换。每张图像都使用6个不同的不重叠感兴趣区域(ROI’s)，并计算出总共2400 (6 × 400)个ROI’s。已经实现了二进制(B)，直方图(H)和纹理(T)特征，并在每个ROI上提取了43个特征，总共计算了103,200 (2400 x 43)个机器学习(ML)特征。采用Best First Search (BFS)算法进行特征优化。不同的机器学习分类器分别在实验过程中实现;函数多层感知，函数SMO，随机树，J48树，基于回归和Meta Bagging的Meta分类器。功能多层感知整体准确率(OA)描述了较好的准确率结果，达到99.8333%。
{"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}
Pub Date : 2023-02-16DOI: 10.54692/lgurjcsit.2023.0701411
H. Ahmed, Muhammad Affan Alim, Waleej Haider, Muhammad Nadeem, Ahsan Masroor, Nadeem Qamar
Diabetes is a serious and chronic disease characterized by high levels of sugar in the blood. If left untreated, it can lead to numerous complications. In the past, diagnosing diabetes required a visit to a diagnostic center and consultation with a doctor. However, the use of machine learning can help to identify the disease earlier and more accurately. This study aimed to create a model that can accurately predict the likelihood of diabetes in patients using three machine learning classification algorithms: Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB). The model was tested on the Pima Indians Diabetes Database (PIDD) from the UCI machine learning repository and the performance of the algorithms was evaluated using various metrics such as accuracy, precision, F-measure, and recall. The results showed that Logistic Regression had the highest accuracy at 71.39% outperforming the other algorithms.
{"title":"Diabetes Diagnosis through Machine Learning: An Analysis of Classification Algorithms","authors":"H. Ahmed, Muhammad Affan Alim, Waleej Haider, Muhammad Nadeem, Ahsan Masroor, Nadeem Qamar","doi":"10.54692/lgurjcsit.2023.0701411","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2023.0701411","url":null,"abstract":"Diabetes is a serious and chronic disease characterized by high levels of sugar in the blood. If left untreated, it can lead to numerous complications. In the past, diagnosing diabetes required a visit to a diagnostic center and consultation with a doctor. However, the use of machine learning can help to identify the disease earlier and more accurately. This study aimed to create a model that can accurately predict the likelihood of diabetes in patients using three machine learning classification algorithms: Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB). The model was tested on the Pima Indians Diabetes Database (PIDD) from the UCI machine learning repository and the performance of the algorithms was evaluated using various metrics such as accuracy, precision, F-measure, and recall. The results showed that Logistic Regression had the highest accuracy at 71.39% outperforming the other algorithms.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115509322","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}
Pub Date : 2023-02-03DOI: 10.54692/lgurjcsit.2023.0701417
H. M. Bilal
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.
{"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}
Pub Date : 2023-01-23DOI: 10.54692/lgurjcsit.2023.0701361
Waheed Javed, Gulnaz Parveen, Sobia Bilal
Deep learning is an artificial intelligence subfield within machine learning. Now- a-days, deep learning has been used in various applications like computer vision, natural language processing, speech recognition, social network filtering, neural machine translation, etc. Deep learning, Convolutional Neural Network (CNN) is a set of deep neural networks mainly designed for image analysis. Deep learning strong ability is mainly due to multiple feature extraction. In this pa- per, we will discuss and compare AlexNet,VGGNet-16,Residual Network(ResNet-50,101,152).
{"title":"Algorithmic as well as Space and Time comparison of various Deep Learning Algorithms","authors":"Waheed Javed, Gulnaz Parveen, Sobia Bilal","doi":"10.54692/lgurjcsit.2023.0701361","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2023.0701361","url":null,"abstract":"Deep learning is an artificial intelligence subfield within machine learning. Now- a-days, deep learning has been used in various applications like computer vision, natural language processing, speech recognition, social network filtering, neural machine translation, etc. Deep learning, Convolutional Neural Network (CNN) is a set of deep neural networks mainly designed for image analysis. Deep learning strong ability is mainly due to multiple feature extraction. In this pa- per, we will discuss and compare AlexNet,VGGNet-16,Residual Network(ResNet-50,101,152).","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130228434","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}
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