Pub Date : 2023-02-08DOI: 10.5815/ijisa.2023.01.02
Anthony Wambua Wambua, G. Wambugu
Regression testing is carried out to ensure that software modifications do not introduce new potential bugs to the existing software. Existing test cases are applied in the testing, such test cases can run into thousands, and there is not much time to execute all of them. Test Case Prioritization (TCP) is a technique to order test cases so that the test cases potentially revealing more faults are performed first. With TCP being deemed an optimization problem, several metaheuristic nature-inspired algorithms such as Bat, Genetic, Ant colony, and Firefly algorithms have been proposed for TCP. These algorithms have been compared theoretically or based on a single metric. This study employed an experimental design to offer an in-depth comparison of bat and genetic algorithms for TCP. Unprioritized test cases and a brute-force approach were used for comparison. Average Percentage Fault Detection (APFD)- a popular metric, execution time and memory usage were used to evaluate the algorithms’ performance. The study underscored the importance of test case prioritization and established the superiority of the Genetic algorithm over the bat algorithm for TCP in APFD. No stark differences were recorded regarding memory usage and execution time for the two algorithms. Both algorithms seemed to scale well with the growth of test cases.
{"title":"A Comparative Analysis of Bat and Genetic Algorithms for Test Case Prioritization in Regression Testing","authors":"Anthony Wambua Wambua, G. Wambugu","doi":"10.5815/ijisa.2023.01.02","DOIUrl":"https://doi.org/10.5815/ijisa.2023.01.02","url":null,"abstract":"Regression testing is carried out to ensure that software modifications do not introduce new potential bugs to the existing software. Existing test cases are applied in the testing, such test cases can run into thousands, and there is not much time to execute all of them. Test Case Prioritization (TCP) is a technique to order test cases so that the test cases potentially revealing more faults are performed first. With TCP being deemed an optimization problem, several metaheuristic nature-inspired algorithms such as Bat, Genetic, Ant colony, and Firefly algorithms have been proposed for TCP. These algorithms have been compared theoretically or based on a single metric. This study employed an experimental design to offer an in-depth comparison of bat and genetic algorithms for TCP. Unprioritized test cases and a brute-force approach were used for comparison. Average Percentage Fault Detection (APFD)- a popular metric, execution time and memory usage were used to evaluate the algorithms’ performance. The study underscored the importance of test case prioritization and established the superiority of the Genetic algorithm over the bat algorithm for TCP in APFD. No stark differences were recorded regarding memory usage and execution time for the two algorithms. Both algorithms seemed to scale well with the growth of test cases.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83586863","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 : 2023-02-08DOI: 10.5815/ijisa.2023.01.03
Dipto Biswas, Md. Samsuddoha, Md. Rashid Al Asif, M. Ahmed
Cloud computing refers to a sophisticated technology that deals with the manipulation of data in internet-based servers dynamically and efficiently. The utilization of the cloud computing has been rapidly increased because of its scalability, accessibility, and incredible flexibility. Dynamic usage and process sharing facilities require task scheduling which is a prominent issue and plays a significant role in developing an optimal cloud computing environment. Round robin is generally an efficient task scheduling algorithm that has a powerful impact on the performance of the cloud computing environment. This paper introduces a new approach for round robin based task scheduling algorithm which is suitable for cloud computing environment. The proposed algorithm determines time quantum dynamically based on the differences among three maximum burst time of tasks in the ready queue for each round. The concerning part of the proposed method is utilizing additive manner among the differences, and the burst times of the processes during determining the time quantum. The experimental results showed that the proposed approach has enhanced the performance of the round robin task scheduling algorithm in reducing average turn-around time, diminishing average waiting time, and minimizing number of contexts switching. Moreover, a comparative study has been conducted which showed that the proposed approach outperforms some of the similar existing round robin approaches. Finally, it can be concluded based on the experiment and comparative study that the proposed dynamic round robin scheduling algorithm is comparatively better, acceptable and optimal for cloud environment.
{"title":"Optimized Round Robin Scheduling Algorithm Using Dynamic Time Quantum Approach in Cloud Computing Environment","authors":"Dipto Biswas, Md. Samsuddoha, Md. Rashid Al Asif, M. Ahmed","doi":"10.5815/ijisa.2023.01.03","DOIUrl":"https://doi.org/10.5815/ijisa.2023.01.03","url":null,"abstract":"Cloud computing refers to a sophisticated technology that deals with the manipulation of data in internet-based servers dynamically and efficiently. The utilization of the cloud computing has been rapidly increased because of its scalability, accessibility, and incredible flexibility. Dynamic usage and process sharing facilities require task scheduling which is a prominent issue and plays a significant role in developing an optimal cloud computing environment. Round robin is generally an efficient task scheduling algorithm that has a powerful impact on the performance of the cloud computing environment. This paper introduces a new approach for round robin based task scheduling algorithm which is suitable for cloud computing environment. The proposed algorithm determines time quantum dynamically based on the differences among three maximum burst time of tasks in the ready queue for each round. The concerning part of the proposed method is utilizing additive manner among the differences, and the burst times of the processes during determining the time quantum. The experimental results showed that the proposed approach has enhanced the performance of the round robin task scheduling algorithm in reducing average turn-around time, diminishing average waiting time, and minimizing number of contexts switching. Moreover, a comparative study has been conducted which showed that the proposed approach outperforms some of the similar existing round robin approaches. Finally, it can be concluded based on the experiment and comparative study that the proposed dynamic round robin scheduling algorithm is comparatively better, acceptable and optimal for cloud environment.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"18 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85749263","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 : 2023-02-08DOI: 10.5815/ijisa.2023.01.05
Samuel M. Alade, O. D. Ninan
A food system is composed of a complex network of activities and processes for production, distribution, transportation and consumption, which interact with each other, thus leading to changeable behaviour. Most existing empirical studies on cassava processing have focused on the technical efficiency analysis of the cassava crop processing techniques among processors indicating that the modelling of the events and operations involved in the processing of the cassava crop is highly limited. In this context, different strategies have been used to solve difficult environmental and agro-informatic systems model-based problems such as system dynamics, agent based, rule-based knowledge and mathematical modeling. However, the structural comprehension and behavioral investigation of this modeling are constrained. In this regard, formal computational modeling is a method that enables modeling and simulation of the dynamical characteristics of these food systems to be examined. In this study, the system specification is designed using Unified Modelling language (UML) to show the structural process and system design modelled and simulated using Coloured Petri Net (CPN), a formal method for analyzing the behavioural properties of complex system because of its efficient analysis. For the purpose of observing and analyzing the behaviour of the cassava food process, a series of simulation runs was proposed.
{"title":"Development of a Computational Model for Cassava Food Processing Using Coloured Petri Net","authors":"Samuel M. Alade, O. D. Ninan","doi":"10.5815/ijisa.2023.01.05","DOIUrl":"https://doi.org/10.5815/ijisa.2023.01.05","url":null,"abstract":"A food system is composed of a complex network of activities and processes for production, distribution, transportation and consumption, which interact with each other, thus leading to changeable behaviour. Most existing empirical studies on cassava processing have focused on the technical efficiency analysis of the cassava crop processing techniques among processors indicating that the modelling of the events and operations involved in the processing of the cassava crop is highly limited. In this context, different strategies have been used to solve difficult environmental and agro-informatic systems model-based problems such as system dynamics, agent based, rule-based knowledge and mathematical modeling. However, the structural comprehension and behavioral investigation of this modeling are constrained. In this regard, formal computational modeling is a method that enables modeling and simulation of the dynamical characteristics of these food systems to be examined. In this study, the system specification is designed using Unified Modelling language (UML) to show the structural process and system design modelled and simulated using Coloured Petri Net (CPN), a formal method for analyzing the behavioural properties of complex system because of its efficient analysis. For the purpose of observing and analyzing the behaviour of the cassava food process, a series of simulation runs was proposed.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88895510","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 : 2023-02-08DOI: 10.5815/ijisa.2023.01.01
D. Hamdi, Ines Elouedi, Maï K. Nguyen, A. Hamouda
This article presents a new approach for image recognition that proposes to combine Conical Radon Transform (CRT) and Convolutional Neural Networks (CNN). In order to evaluate the performance of this approach for pattern recognition task, we have built a Radon descriptor enhancing features extracted by linear, circular and parabolic RT. The main idea consists in exploring the use of Conic Radon transform to define a robust image descriptor. Specifically, the Radon transformation is initially applied on the image. Afterwards, the extracted features are combined with image and then entered as an input into the convolutional layers. Experimental evaluation demonstrates that our descriptor which joins together extraction of features of different shapes and the convolutional neural networks achieves satisfactory results for describing images on public available datasets such as, ETH80, and FLAVIA. Our proposed approach recognizes objects with an accuracy of 96 % when tested on the ETH80 dataset. It also has yielded competitive accuracy than state-of-the-art methods when tested on the FLAVIA dataset with accuracy of 98 %. We also carried out experiments on traffic signs dataset GTSBR. We investigate in this work the use of simple CNN models to focus on the utility of our descriptor. We propose a new lightweight network for traffic signs that does not require a large number of parameters. The objective of this work is to achieve optimal results in terms of accuracy and to reduce network parameters. This approach could be adopted in real time applications. It classified traffic signs with high accuracy of 99%.
{"title":"A Conic Radon-based Convolutional Neural Network for Image Recognition","authors":"D. Hamdi, Ines Elouedi, Maï K. Nguyen, A. Hamouda","doi":"10.5815/ijisa.2023.01.01","DOIUrl":"https://doi.org/10.5815/ijisa.2023.01.01","url":null,"abstract":"This article presents a new approach for image recognition that proposes to combine Conical Radon Transform (CRT) and Convolutional Neural Networks (CNN). In order to evaluate the performance of this approach for pattern recognition task, we have built a Radon descriptor enhancing features extracted by linear, circular and parabolic RT. The main idea consists in exploring the use of Conic Radon transform to define a robust image descriptor. Specifically, the Radon transformation is initially applied on the image. Afterwards, the extracted features are combined with image and then entered as an input into the convolutional layers. Experimental evaluation demonstrates that our descriptor which joins together extraction of features of different shapes and the convolutional neural networks achieves satisfactory results for describing images on public available datasets such as, ETH80, and FLAVIA. Our proposed approach recognizes objects with an accuracy of 96 % when tested on the ETH80 dataset. It also has yielded competitive accuracy than state-of-the-art methods when tested on the FLAVIA dataset with accuracy of 98 %. We also carried out experiments on traffic signs dataset GTSBR. We investigate in this work the use of simple CNN models to focus on the utility of our descriptor. We propose a new lightweight network for traffic signs that does not require a large number of parameters. The objective of this work is to achieve optimal results in terms of accuracy and to reduce network parameters. This approach could be adopted in real time applications. It classified traffic signs with high accuracy of 99%.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"86 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81127793","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 : 2023-02-08DOI: 10.5815/ijisa.2023.01.04
Muhammad Aqeel, Hammad Shahab, M. Naeem, Muhammad Shahbaz, F. Qaisar, M. Shahzad
The innovation of e-metering (Electronic Metering) has experienced fast mechanical progressions and there is expanded interest in a solid and effective Automatic Meter Reading (AMR) framework. GSM Based shrewd vitality meter perusing framework replaces conventional meter perusing techniques. It empowers remote access to the existing vitality meter by the vitality provider. A GSM-based remote correspondence module is incorporated with the electronic vitality meter of every element to have remote access to the utilization of power. A PC with a GSM recipient at the opposite end, which contains the database goes about as the charging point. Live meter perusing from the GSM-empowered vitality meter is sent back to this charging point intermittently and these subtle elements are refreshed in a focal database. The total month-to-month utilization and the due bill are informed back to the client after handling this information. So, GSM-based remote AMR framework is a more successful approach for a traditional charging framework. This framework additionally gives specialists to power organizations to take activities against tolerant clients who have a remarkable contribution; generally, the organization has the ideal to detach the power supply, and it can reconnect the control supply after the affidavit of duty. So, we thought about building such an automatic system. This research is GSM-Based on a smart energy meter reading system to eliminate the conventional way of the reading system. In this paper, the GSM module sends reading information through SMS to the related Authority. There are no chances of any unethical mistake by using this modern technique.
{"title":"Intelligent Smart Energy Meter Reading System Using Global System for Mobile Communication","authors":"Muhammad Aqeel, Hammad Shahab, M. Naeem, Muhammad Shahbaz, F. Qaisar, M. Shahzad","doi":"10.5815/ijisa.2023.01.04","DOIUrl":"https://doi.org/10.5815/ijisa.2023.01.04","url":null,"abstract":"The innovation of e-metering (Electronic Metering) has experienced fast mechanical progressions and there is expanded interest in a solid and effective Automatic Meter Reading (AMR) framework. GSM Based shrewd vitality meter perusing framework replaces conventional meter perusing techniques. It empowers remote access to the existing vitality meter by the vitality provider. A GSM-based remote correspondence module is incorporated with the electronic vitality meter of every element to have remote access to the utilization of power. A PC with a GSM recipient at the opposite end, which contains the database goes about as the charging point. Live meter perusing from the GSM-empowered vitality meter is sent back to this charging point intermittently and these subtle elements are refreshed in a focal database. The total month-to-month utilization and the due bill are informed back to the client after handling this information. So, GSM-based remote AMR framework is a more successful approach for a traditional charging framework. This framework additionally gives specialists to power organizations to take activities against tolerant clients who have a remarkable contribution; generally, the organization has the ideal to detach the power supply, and it can reconnect the control supply after the affidavit of duty. So, we thought about building such an automatic system. This research is GSM-Based on a smart energy meter reading system to eliminate the conventional way of the reading system. In this paper, the GSM module sends reading information through SMS to the related Authority. There are no chances of any unethical mistake by using this modern technique.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83880569","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 : 2022-12-08DOI: 10.5815/ijisa.2022.06.05
Ademola A. Adesokan
Facemask wearing is becoming a norm in our daily lives to curb the spread of Covid-19. Ensuring facemasks are worn correctly is a topic of concern worldwide. It could go beyond manual human control and enforcement, leading to the spread of this deadly virus and many cases globally. The main aim of wearing a facemask is to curtail the spread of the covid-19 virus, but the biggest concern of most deep learning research is about who is wearing the mask or not, and not who is incorrectly wearing the facemask while the main objective of mask wearing is to prevent the spread of the covid-19 virus. This paper compares three state-of-the- art object detection approaches: Haarcascade, Multi-task Cascaded Convolutional Networks (MTCNN), and You Only Look Once version 4 (YOLOv4) to classify who is wearing a mask, who is not wearing a mask, and most importantly, who is incorrectly wearing the mask in a real-time video stream using FPS as a benchmark to select the best model. Yolov4 got about 40 Frame Per Seconds (FPS), outperforming Haarcascade with 16 and MTCNN with 1.4. YOLOv4 was later used to compare the two datasets using Intersection over Union (IoU) and mean Average Precision (mAP) as a comparative measure; dataset2 (balanced dataset) performed better than dataset1 (unbalanced dataset). Yolov4 model on dataset2 mapped and detected images of masks worn incorrectly with one correct class label rather than giving them two label classes with uncertainty in dataset1, this work shows the advantage of having a balanced dataset for accuracy. This work would help decrease human interference in enforcing the COVID-19 face mask rules and create awareness for people who do not comply with the facemask policy of wearing it correctly. Hence, significantly reducing the spread of COVID-19.
为了遏制新冠肺炎的传播，戴口罩正成为我们日常生活中的一种常态。确保正确佩戴口罩是全世界关注的一个话题。它可能超出人工控制和强制执行的范围，导致这种致命病毒的传播和全球许多病例。戴口罩的主要目的是遏制新冠病毒的传播，但大多数深度学习研究最关心的是谁戴了口罩，而不是谁戴错了口罩，而戴口罩的主要目的是防止新冠病毒的传播。本文比较了三种最先进的目标检测方法:Haarcascade, Multi-task cascade卷积网络(MTCNN)和You Only Look Once version 4 (YOLOv4)，以FPS为基准，在实时视频流中区分谁戴了面具，谁没有戴面具，最重要的是，谁错误地戴了面具，以选择最佳模型。yolo4的帧率约为每秒40帧，超过了每秒16帧的Haarcascade和每秒1.4帧的MTCNN。随后使用YOLOv4比较两个数据集，使用Intersection over Union (IoU)和mean Average Precision (mAP)作为比较度量;Dataset2(平衡数据集)的性能优于dataset1(不平衡数据集)。dataset2上的Yolov4模型用一个正确的类别标签映射和检测了不正确佩戴的面具图像，而不是在dataset1中给它们两个不确定的标签类别，这项工作显示了拥有平衡数据集的优势。这项工作将有助于减少人为干预执行COVID-19口罩规则，并提高那些不遵守正确佩戴口罩政策的人的意识。从而大大减少COVID-19的传播。
{"title":"Covid-19 Control: Face Mask Detection Using Deep Learning for Balanced and Unbalanced Dataset","authors":"Ademola A. Adesokan","doi":"10.5815/ijisa.2022.06.05","DOIUrl":"https://doi.org/10.5815/ijisa.2022.06.05","url":null,"abstract":"Facemask wearing is becoming a norm in our daily lives to curb the spread of Covid-19. Ensuring facemasks are worn correctly is a topic of concern worldwide. It could go beyond manual human control and enforcement, leading to the spread of this deadly virus and many cases globally. The main aim of wearing a facemask is to curtail the spread of the covid-19 virus, but the biggest concern of most deep learning research is about who is wearing the mask or not, and not who is incorrectly wearing the facemask while the main objective of mask wearing is to prevent the spread of the covid-19 virus. This paper compares three state-of-the- art object detection approaches: Haarcascade, Multi-task Cascaded Convolutional Networks (MTCNN), and You Only Look Once version 4 (YOLOv4) to classify who is wearing a mask, who is not wearing a mask, and most importantly, who is incorrectly wearing the mask in a real-time video stream using FPS as a benchmark to select the best model. Yolov4 got about 40 Frame Per Seconds (FPS), outperforming Haarcascade with 16 and MTCNN with 1.4. YOLOv4 was later used to compare the two datasets using Intersection over Union (IoU) and mean Average Precision (mAP) as a comparative measure; dataset2 (balanced dataset) performed better than dataset1 (unbalanced dataset). Yolov4 model on dataset2 mapped and detected images of masks worn incorrectly with one correct class label rather than giving them two label classes with uncertainty in dataset1, this work shows the advantage of having a balanced dataset for accuracy. This work would help decrease human interference in enforcing the COVID-19 face mask rules and create awareness for people who do not comply with the facemask policy of wearing it correctly. Hence, significantly reducing the spread of COVID-19.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90815376","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 : 2022-12-08DOI: 10.5815/ijisa.2022.06.03
N. A. Al-Majmar, Ayedh abdulaziz Mohsen, Mohammed Sharaf Al-Thulathi
Drug interactions prediction is one of the health critical issues in drug producing and use. Proposing computational model for classifying and predicting interactions of drugs with high precision is a difficult problem. Medicines are classified into two classes: overlapping, non-overlapping. It was suggested an expert system for classifying and predicting interactions of drugs using various information about drugs, interference reasons and common factors between patients and active substance that causes interference, such as: effective dose of the drug, maximum dose, times of use per day and age of patients considering that only adult category selected. The proposed model can classify and predict interactions of drugs through patient's state taking into consideration that when changing one of mentioned factors, the effect of drugs will be changed and it may lead to appear new symptoms on the patients. There is a desktop application related with the mentioned model, which helps users to know drugs and drugs families and its interactions. Proposed model will be implemented in Python using following classifiers: Logistic Regression (LR), Support Vector Machine (SVM) and Neural Network (NN), which divided data according to their similarity related to the factors of occurrence of drug interference. As these techniques showed good results, NN technology is considered one of the best techniques in giving results where MLPClassifier achieved superior performance with 97.12%.
{"title":"Development a Model for Drug Interaction Prediction Based on Patient State","authors":"N. A. Al-Majmar, Ayedh abdulaziz Mohsen, Mohammed Sharaf Al-Thulathi","doi":"10.5815/ijisa.2022.06.03","DOIUrl":"https://doi.org/10.5815/ijisa.2022.06.03","url":null,"abstract":"Drug interactions prediction is one of the health critical issues in drug producing and use. Proposing computational model for classifying and predicting interactions of drugs with high precision is a difficult problem. Medicines are classified into two classes: overlapping, non-overlapping. It was suggested an expert system for classifying and predicting interactions of drugs using various information about drugs, interference reasons and common factors between patients and active substance that causes interference, such as: effective dose of the drug, maximum dose, times of use per day and age of patients considering that only adult category selected. The proposed model can classify and predict interactions of drugs through patient's state taking into consideration that when changing one of mentioned factors, the effect of drugs will be changed and it may lead to appear new symptoms on the patients. There is a desktop application related with the mentioned model, which helps users to know drugs and drugs families and its interactions. Proposed model will be implemented in Python using following classifiers: Logistic Regression (LR), Support Vector Machine (SVM) and Neural Network (NN), which divided data according to their similarity related to the factors of occurrence of drug interference. As these techniques showed good results, NN technology is considered one of the best techniques in giving results where MLPClassifier achieved superior performance with 97.12%.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"158 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82915205","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 : 2022-12-08DOI: 10.5815/ijisa.2022.06.02
Debadrita Panda, S. Mukhopadhyay, Rajarshi Saha
This study tries to gain insight into the effect of demographic and psychological variables on the Bottom of the Pyramid (BoP) consumers for making Packaging Influenced Purchase (PIP) decisions by focusing on two specific consumer behaviour theories - compensatory consumption and consumers’ resistance. Being the product's face, packaging contributes heavily to the above mentioned two streams of consumption behaviour. A collection of ten demographic variables and four psychological variables have been administered on a sample of 1400 BoP consumers to explore their effect behind making PIP of selected FMCG products. Various classification techniques have been deployed to capture the impact of these variables. This experimental research design revealed that both demographic and psychological variables affect the PIP. The comparison between urban and rural BoPs potentially comes with the guidelines for practical marketing implications.
{"title":"BoPMLPIP: Application of Classification Techniques to Explore the Impact of PIP among BoPs","authors":"Debadrita Panda, S. Mukhopadhyay, Rajarshi Saha","doi":"10.5815/ijisa.2022.06.02","DOIUrl":"https://doi.org/10.5815/ijisa.2022.06.02","url":null,"abstract":"This study tries to gain insight into the effect of demographic and psychological variables on the Bottom of the Pyramid (BoP) consumers for making Packaging Influenced Purchase (PIP) decisions by focusing on two specific consumer behaviour theories - compensatory consumption and consumers’ resistance. Being the product's face, packaging contributes heavily to the above mentioned two streams of consumption behaviour. A collection of ten demographic variables and four psychological variables have been administered on a sample of 1400 BoP consumers to explore their effect behind making PIP of selected FMCG products. Various classification techniques have been deployed to capture the impact of these variables. This experimental research design revealed that both demographic and psychological variables affect the PIP. The comparison between urban and rural BoPs potentially comes with the guidelines for practical marketing implications.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75073308","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 : 2022-12-08DOI: 10.5815/ijisa.2022.06.04
V. Kakulapati
A relatively effective training system and advancements in data science demonstrate their evolutionary algorithm power to discover defects and abnormalities in the specified learning process. This work employs a fast and precise fault modelling environment to enhance genetic input implantable devices defect diagnostics. We offer a genetic data technique that incorporates phylogenetic analysis operations and faulty efficiency analysis. This study contributes to fault training in three different ways: 1) it exposes communicative training categories of information formulating adhesion, 2) it introduces a hierarchical system dissemination processing principles to design the fault aggregative, and 3) it indicates forecasting the genetic data sector that corresponds to complicated fault training. The proposed algorithm analyses methods that combine automatically generated fault detection development with massive data testing by non-repetitive fault instances. Analyzing data from validation challenges, infrastructure blowouts, and failure uncertainty make our algorithm more productive in the health sector.
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Pub Date : 2022-12-08DOI: 10.5815/ijisa.2022.06.01
Shaimaa Mahmoud, Mohamed Gaber, Gamal Farouk, A. Keshk
Particularly compared to other diseases, heart disease (HD) claims the lives of the greatest number of people worldwide. Many priceless lives can be saved with the help of early and effective disease identification. Medical tests, an electrocardiogram (ECG) signal, heart sounds, computed tomography (CT) images, etc. can all be used to identify HD. Of all sorts, HD signal recognition from ECG signals is crucial. The ECG samples from the participants were taken into consideration as the necessary inputs for the HD detection model in this study. Many researchers analyzed the risk factors of heart disease and used machine learning or deep learning techniques for the early detection of heart patients. In this paper, we propose a modified version of the LeNet-5 model to be used as a transfer model for cardiovascular disease patients. The modified version is compared to the standard version using four evaluation metrics: accuracy, precision, recall, and F1-score. The achieved results indicated that when the LeNet-5 model was modified by increasing the number of used filters, this increased the model's ability to handle the ECGs dataset and extract the most important features from it. The results also showed that the modified version of the LeNet-5 model based on the ECGs image dataset improved accuracy by 9.14 percentage points compared to the standard LeNet-5 model.
{"title":"Heart Disease Prediction Using Modified Version of LeNet-5 Model","authors":"Shaimaa Mahmoud, Mohamed Gaber, Gamal Farouk, A. Keshk","doi":"10.5815/ijisa.2022.06.01","DOIUrl":"https://doi.org/10.5815/ijisa.2022.06.01","url":null,"abstract":"Particularly compared to other diseases, heart disease (HD) claims the lives of the greatest number of people worldwide. Many priceless lives can be saved with the help of early and effective disease identification. Medical tests, an electrocardiogram (ECG) signal, heart sounds, computed tomography (CT) images, etc. can all be used to identify HD. Of all sorts, HD signal recognition from ECG signals is crucial. The ECG samples from the participants were taken into consideration as the necessary inputs for the HD detection model in this study. Many researchers analyzed the risk factors of heart disease and used machine learning or deep learning techniques for the early detection of heart patients. In this paper, we propose a modified version of the LeNet-5 model to be used as a transfer model for cardiovascular disease patients. The modified version is compared to the standard version using four evaluation metrics: accuracy, precision, recall, and F1-score. The achieved results indicated that when the LeNet-5 model was modified by increasing the number of used filters, this increased the model's ability to handle the ECGs dataset and extract the most important features from it. The results also showed that the modified version of the LeNet-5 model based on the ECGs image dataset improved accuracy by 9.14 percentage points compared to the standard LeNet-5 model.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85002857","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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