2023 4th International Conference on Advancements in Computational Sciences (ICACS)最新文献_第2页

A Machine Learning Based Predictive Model to Diagnose Heart Failure Patients using Imbalanced Classification Problem 基于机器学习的不平衡分类诊断心力衰竭预测模型

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089759

M. Mudassar, Mehtab Afzal, Muhammad Tufail

{"title":"A Machine Learning Based Predictive Model to Diagnose Heart Failure Patients using Imbalanced Classification Problem","authors":"M. Mudassar, Mehtab Afzal, Muhammad Tufail","doi":"10.1109/ICACS55311.2023.10089759","DOIUrl":"https://doi.org/10.1109/ICACS55311.2023.10089759","url":null,"abstract":"Heart failure (HF) is now one of the most common diseases, causing approximately seventeen million death cases every year all over the world. HF occurs due to less pumping ratio of blood by the heart that a normal human being needs to survive. In this regard, research studies have been proposed to predict the causes behind the heart failure of a patient using the 'Heart failure clinical records (HFCR's) dataset. Although, much research has been performed on this dataset, however, there is a lack of construction of a more reliable predictive model that helps to predict HF patients with better prediction results. We aimed to apply imbalance learning to handle the imbalance dataset as a very few researchers applied it. We trained the models using six ensemble and three non-ensemble classifiers with the help of multiple experiments. In the end, we performed an evaluation measure to compare our prediction results with the previous research work. Our proposed model gives a significant increase in accuracy value as well as in precision, recall, and f1-score.","PeriodicalId":357522,"journal":{"name":"2023 4th International Conference on Advancements in Computational Sciences (ICACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130438397","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

Melanoma Lesion Segmentation and Classification Using SegNet 基于SegNet的黑色素瘤病灶分割与分类

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089675

Hareem Kibriya, Iram Abdullah, F. Kousar

{"title":"Melanoma Lesion Segmentation and Classification Using SegNet","authors":"Hareem Kibriya, Iram Abdullah, F. Kousar","doi":"10.1109/ICACS55311.2023.10089675","DOIUrl":"https://doi.org/10.1109/ICACS55311.2023.10089675","url":null,"abstract":"Melanoma is one of the worst forms of skin cancers that should be detected early for proper treatment. Usually the dermatologists inspect lesion region via optical inspection but this method is time-consuming and error-prone. Furthermore, over the past few years, due to advent of Machine Learning (ML) based systems, the researchers have developed automatic skin cancer diagnosis techniques. However, they rely heavily on manual image segmentation and handcrafted feature extraction techniques. Moreover, the performance of these systems is also degraded due to hair, blood vessels, poor contrast and hazy tumor boundaries. In this paper, we propose a deep learning-(DL) based melanoma lesion segmentation framework using SegNet. The proposed technique is trained and evaluated on dermoscopic images taken from ISIC-2016. We also evaluated the performance of our proposed methodology on a cross data scenario using ISIC-2017 database. The performance of the proposed framework is evaluated using various evaluation metrics such as accuracy, precision, Intersection over Union (IoU) and recall. The proposed framework succeeded in achieving 89% accuracy and is robust to presence of artefacts such as blood vessels or hair. The experimental results demonstrate the robustness of the suggested melanoma lesion segmentation and classification method. Hence, the system can be deployed in clinical settings to automatically detect melanoma lesions from dermoscopic images.","PeriodicalId":357522,"journal":{"name":"2023 4th International Conference on Advancements in Computational Sciences (ICACS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133317478","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

Detection of Tomato Leaf Disease Using Deep Convolutional Neural Networks 基于深度卷积神经网络的番茄叶片病害检测

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089689

Aima Khalid, Shahzad Akbar, Syed Ale Hassan, Saba Firdous, Sahar Gull

{"title":"Detection of Tomato Leaf Disease Using Deep Convolutional Neural Networks","authors":"Aima Khalid, Shahzad Akbar, Syed Ale Hassan, Saba Firdous, Sahar Gull","doi":"10.1109/ICACS55311.2023.10089689","DOIUrl":"https://doi.org/10.1109/ICACS55311.2023.10089689","url":null,"abstract":"Agriculture is the backbone of many economies throughout the world including Pakistan. Similarly, tomatoes are the most widely cultivated vegetables in the agricultural field. In addition, the tropical weather increases the throughput yield of tomato crops. However, various climatic conditions and other factors affect the growth of the tomato plant. Rather than such climate conditions and natural disasters, plant diseases are the primary reason for the production crisis resulting in less tomato yield and financial disaster. Traditional methods for detecting diseases in tomato leaves failed to produce the expected outcomes, and disease detection seemed static. However, making the vegetable plants healthy with time is becoming very significant. Identifying diseases in vegetable plants is essential before they cause too severe harm to the vegetables. This research proposes three CNN-based models VGG-16, ResNet-152, and EfficientNet-B4, to classify tomato leaf diseases into normal of disease affected. The proposed research is conducted to find the best possible solution for detecting tomato leaf disease using these deep learning approaches. Employing the Plant-Village dataset with 5524 leaf images, ResNet-152 and EfficientNet-B4 achieved 93.75% and 97.27% accuracy respectively, while VGG-16 achieved 98% accuracy. The efficiency of the system makes it capable of becoming a preference in the agricultural field for real-time tomato leave disease detection applications.","PeriodicalId":357522,"journal":{"name":"2023 4th International Conference on Advancements in Computational Sciences (ICACS)","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128307160","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: 3

Artificial Neural Network for Human Object Interaction System Over Aerial Images 基于航拍图像的人-物交互系统的人工神经网络

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089722

Mahwish Pervaiz, Ahmad Jalal

引用次数: 7

Deep Convolutional Neural Network-based Framework for Apple Leaves Disease Detection 基于深度卷积神经网络的苹果叶片病害检测框架

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089774

Saba Firdous, Shahzad Akbar, Syed Ale Hassan, Aima Khalid, Sahar Gull

{"title":"Deep Convolutional Neural Network-based Framework for Apple Leaves Disease Detection","authors":"Saba Firdous, Shahzad Akbar, Syed Ale Hassan, Aima Khalid, Sahar Gull","doi":"10.1109/ICACS55311.2023.10089774","DOIUrl":"https://doi.org/10.1109/ICACS55311.2023.10089774","url":null,"abstract":"Apples are a popular fruit all around the world, nevertheless, they are mostly farmed in Asia. Moreover, approximately 76 million tons of apples are produced annually around the world. Furthermore, Apples may aid in preventing cancer, metabolic syndrome, cardiovascular disease, diabetes, and a variety of other diseases. However, various environmental conditions and other factors affect Apple leaf plant growth. In addition, the primary cause of the production crisis is apple plant disease. ATLDs such as RUST and SCAB are all popular and significantly impact apple leave yield. Therefore, numerous researches have been carried out to detect apple leave diseases automatically. However, there is still room for improvement in efficiency, computation complexity, time consumption, cost, and variety of techniques. This research employs deep convolutional neural network models VGG-19, ResNet-34, and Dense-121-Net to identify apple leave diseases. Besides, pre-processing of the dataset images enhanced the image quality and remove the noise. Furthermore, Data augmentation methods are also utilized to expand the number of images in the dataset. Moreover, models employing VGG-19, Resnet-34, and Dense-121Net are analyzed through the plant village dataset and attained 98.02%, 97.06%, and 99.75% accuracy respectively. An evaluation of networks in the plant village dataset shows that the developed algorithm performs better and has an advanced methodology suitable for real-time agricultural disease detection applications.","PeriodicalId":357522,"journal":{"name":"2023 4th International Conference on Advancements in Computational Sciences (ICACS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116462834","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

Anti-Ant Framework for Android Malware Detection and Prevention Using Supervised Learning 使用监督学习的Android恶意软件检测和预防的反蚂蚁框架

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089629

M. Awais, Muhammad Arham Tariq, Junaid Iqbal, Yasir Masood

{"title":"Anti-Ant Framework for Android Malware Detection and Prevention Using Supervised Learning","authors":"M. Awais, Muhammad Arham Tariq, Junaid Iqbal, Yasir Masood","doi":"10.1109/ICACS55311.2023.10089629","DOIUrl":"https://doi.org/10.1109/ICACS55311.2023.10089629","url":null,"abstract":"Android users have been increasing drastically by the day, therefore apps for android users are being introduced frequently in the market which are currently available on the Play Store, APK Pure, APK Mirror, and other APK stores. Consequently, it is difficult to find apps that don't harm users' privacy, integrity, and intellectual property rights. For that reason, we made a framework named ANTI-ANT that detects malware apps and prevents them from harming the phone. We mainly target that malware which comprises of Botnets, Rootkits, SMS malware, Spywares, app installers, and ransomware. In this paper, we proposed a framework that consists of three layers of detection. The first layer is the application layer, the second layer is the user background layer, and the last one is the package layer, in order to distinguish malicious behaviors of malware. Both static and dynamic detection analyses are used for feature extraction of android malware, and then to classify them as malware or benign applications. The framework consists of the participation of multi-classifiers Logistic Regressor, Decision Tree, Random Forest, and Support Vector Machine. For the training and testing, we used 13,559 samples of malware that are analyzed on the datasets of CCCS-CIC-AndMal-2020 (Canadian Institute for Cybersecurity). We detect the malware in four phases. First, we analyze the features and then perform assessments, and in Phase four, we train the Machine Learning models to detect the malware and prevent it by using the malware block applist generated by our model. We run that framework on 500 android phones which checks their behavior in the background and the permissions that are used in their manifest file. After training, we got the results based on the labeled datasets for our ML Models, the SVM achieved the highest accuracy of 96.64%, along with the accuracy of 91.50% for Logistic Regressor.","PeriodicalId":357522,"journal":{"name":"2023 4th International Conference on Advancements in Computational Sciences (ICACS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126085158","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

Mobility and Content Retrieval in Vehicular Named Data Network: Challenges and Countermeasures 车辆命名数据网络的移动性与内容检索:挑战与对策

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089761

Muhammad Abdullah, Ayesha Kiran, Ubaid Azam

{"title":"Mobility and Content Retrieval in Vehicular Named Data Network: Challenges and Countermeasures","authors":"Muhammad Abdullah, Ayesha Kiran, Ubaid Azam","doi":"10.1109/ICACS55311.2023.10089761","DOIUrl":"https://doi.org/10.1109/ICACS55311.2023.10089761","url":null,"abstract":"In the ecosystem of connected vehicles, the TCP/IP stack serves a significant role in terms of content dissemination, traffic control, and assignment of vehicle address. Recently, the Internet of Vehicles (IoV) has emerged drastically. Primarily, information sharing between vehicles is carried out for road safety, location sharing, hazard warning, and infotainment services. The traditional TCP/IP model is not appropriate for the transmission of bulky data in this dense and highly dynamic environment. Recently, a Vehicular Named Data Networking (VNDN) approach has been used for efficient information sharing between vehicles. Unlike currently used TCP/IP internet architecture, vehicles demand data in the form of Interest packet and disseminate requested data in a pull-based fashion. Interest packets need to be broadcasted to the potential data producers. Although, with the advent of VNDN, many challenges of IoV have been resolved; however, mobility and content retrieval are still major concerns that may lead to the problem of packet broadcast storm. In this paper, we discussed the major contributions that have been done to eliminate vehicle mobility and content retrieval issues in the VNDN paradigm. Besides, we mention the limitations of the proposed existing solutions implemented in the VNDN scenario. Furthermore, based on existing proposed solutions, we highlight the new challenges and directions for the design of new solutions.","PeriodicalId":357522,"journal":{"name":"2023 4th International Conference on Advancements in Computational Sciences (ICACS)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132023983","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

Abnormal Action Recognition in Crowd Scenes via Deep Data Mining and Random Forest 基于深度数据挖掘和随机森林的人群场景异常动作识别

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089674

Israr Akhter, Ahmad Jalal

引用次数: 5

Software Project Management - Gap between Theory and Practice 软件项目管理-理论与实践之间的差距

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089727

N. Ahmad, Ali Afzal Malik

{"title":"Software Project Management - Gap between Theory and Practice","authors":"N. Ahmad, Ali Afzal Malik","doi":"10.1109/ICACS55311.2023.10089727","DOIUrl":"https://doi.org/10.1109/ICACS55311.2023.10089727","url":null,"abstract":"Proper management of software projects is crucial in preventing budget overruns, schedule slips, requirements creep, and other causes of project failure. The skills required for successfully managing software development projects, however, come with practical experience and rigorous training. Both the industry and the academia, therefore, need to work in unison. A differential in the skill set emphasized in the academia and the skill set required by the industry may lead to sub-optimal results. This research tries to identify the extent of the gap between the theory and practice of software project management by focusing on the landscape of universities and software development companies in Pakistan. The content of software project management-related courses offered by top universities in Pakistan is analysed to determine a set of subtopics emphasized by the academia. This set of subtopics is then used to design and, later, conduct an online survey of employees of various Pakistani software houses to gauge the differential in emphasis. Survey results indicate that a close mapping exists between what is being taught in the academia and what is required by the software development industry implying that this gap is not wide in the context of Pakistan.","PeriodicalId":357522,"journal":{"name":"2023 4th International Conference on Advancements in Computational Sciences (ICACS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117161358","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

Predicting Early Withdrawal of University Students: A Comparative Study between KNN and Decision Tree 预测大学生早期退出:KNN与决策树的比较研究

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089706

Arham Tariq, Ahmad Amin, Yasir Masood, Muhammad Muzaffar, Junaid Iqbal

引用次数: 0