Oghenekome Efijemue, Ifunanya Ejimofor, Omoshola Simon Owolabi
Insider threats have been a major problem for the US banking sector in recent years, costing billions of dollars in damages. To combat this, the implementation of effective cybersecurity measures is essential. This paper investigates the current state of insider threats to banks in the U.S., the associated costs, and the potential measures that can be taken to mitigate this risk. The development of a framework for the adoption of cybersecurity measures within the banking industry is the primary emphasis in order to stop fraud and lessen financial losses. Through a detailed examination of the literature, in-depth interviews with experts in the banking sector, and case studies of existing cybersecurity measures, this paper provides a comprehensive overview of the problem and potential remedies. Analysis of the research reveals that identity and access management, data encryption, and secure authentication are key components of any cybersecurity strategy. Furthermore, it is recommended that banks increase their technical capabilities and improve their employee awareness and training. The study concludes with a series of suggestions for enhancing banking industry cybersecurity and eventually reducing the danger of insider attacks. This paper explores the topic of insider threats in the US banking industry and presents cybersecurity measures to prevent fraud. Insider threats from people with access to sensitive data and systems present serious hazards to the banking industry, resulting in monetary losses, reputational harm, and compromised data integrity.
{"title":"Insider Threat Prevention in the US Banking System","authors":"Oghenekome Efijemue, Ifunanya Ejimofor, Omoshola Simon Owolabi","doi":"10.5121/ijsc.2023.14302","DOIUrl":"https://doi.org/10.5121/ijsc.2023.14302","url":null,"abstract":"Insider threats have been a major problem for the US banking sector in recent years, costing billions of dollars in damages. To combat this, the implementation of effective cybersecurity measures is essential. This paper investigates the current state of insider threats to banks in the U.S., the associated costs, and the potential measures that can be taken to mitigate this risk. The development of a framework for the adoption of cybersecurity measures within the banking industry is the primary emphasis in order to stop fraud and lessen financial losses. Through a detailed examination of the literature, in-depth interviews with experts in the banking sector, and case studies of existing cybersecurity measures, this paper provides a comprehensive overview of the problem and potential remedies. Analysis of the research reveals that identity and access management, data encryption, and secure authentication are key components of any cybersecurity strategy. Furthermore, it is recommended that banks increase their technical capabilities and improve their employee awareness and training. The study concludes with a series of suggestions for enhancing banking industry cybersecurity and eventually reducing the danger of insider attacks. This paper explores the topic of insider threats in the US banking industry and presents cybersecurity measures to prevent fraud. Insider threats from people with access to sensitive data and systems present serious hazards to the banking industry, resulting in monetary losses, reputational harm, and compromised data integrity.","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":"340 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135181400","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}
As the financial sectors in the United States deal with expanding cyberthreats and a rising danger of financial crime, cybersecurity has become a top priority. This paper examines the crucial cybersecurity techniques used by financial institutions to protect client information and counter the growing risk of financial fraud. It proves that understanding common fraud tactics used to defraud financial institutions and customers, putting fraud detection and prevention techniques like anomaly detection and machine learning into practice, and using transaction monitoring and anti-money laundering tactics to spot and stop fraudulent activity are all necessary for preventing financial fraud. The paper begins by reviewing the common cyber dangers affecting the financial industry and the strategies used by cybercriminals to circumvent security precautions and take advantage of weaknesses. After looking at potential risks, the paper highlights the importance of proactive cybersecurity measures and risk mitigation techniques. It highlights crucial components of cybersecurity frameworks, including strong data encryption, multifactor authentication, intrusion detection systems, and ongoing security monitoring. This paper also emphasizes the value of educating and training financial institution staff members to increase cybersecurity resilience. It underlines the significance of building a strong security culture, educating personnel about potential dangers, and encouraging responsible management of client data. The study also explores the advantages of financial organizations working together and exchanging threat knowledge. It examines industry alliances, information-sharing platforms, and public-private partnerships as crucial methods for group protection against cyber threats. This paper highlighted the significance of artificial intelligence and machine learning in cybersecurity domain. It demonstrates how these technologies improve cybersecurity systems' capabilities by spotting irregularities and potential attacks. It emphasizes the significance of taking a proactive and dynamic strategy to securing client information and maintaining faith in the United States’ financial sectors. Overall, this paper provides a thorough overview of cybersecurity tactics crucial for protecting consumer data and avoiding financial fraud in the financial sectors across the United States. By taking a vigilant, team-based, and technology-driven strategy, financial institutions may strengthen their cyber defenses, protect the data of their clients, and defend the integrity of the financial system.
{"title":"Cybersecurity Strategies for Safeguarding Customer’s Data and Preventing Financial Fraud in the United States Financial Sectors","authors":"Efijemue Oghenekome Paul, Obunadike Callistus, Olisah Somtobe, Taiwo Esther, Kizor-Akaraiwe Somto, Odooh Clement, Ifunanya Ejimofor","doi":"10.5121/ijsc.2023.14301","DOIUrl":"https://doi.org/10.5121/ijsc.2023.14301","url":null,"abstract":"As the financial sectors in the United States deal with expanding cyberthreats and a rising danger of financial crime, cybersecurity has become a top priority. This paper examines the crucial cybersecurity techniques used by financial institutions to protect client information and counter the growing risk of financial fraud. It proves that understanding common fraud tactics used to defraud financial institutions and customers, putting fraud detection and prevention techniques like anomaly detection and machine learning into practice, and using transaction monitoring and anti-money laundering tactics to spot and stop fraudulent activity are all necessary for preventing financial fraud. The paper begins by reviewing the common cyber dangers affecting the financial industry and the strategies used by cybercriminals to circumvent security precautions and take advantage of weaknesses. After looking at potential risks, the paper highlights the importance of proactive cybersecurity measures and risk mitigation techniques. It highlights crucial components of cybersecurity frameworks, including strong data encryption, multifactor authentication, intrusion detection systems, and ongoing security monitoring. This paper also emphasizes the value of educating and training financial institution staff members to increase cybersecurity resilience. It underlines the significance of building a strong security culture, educating personnel about potential dangers, and encouraging responsible management of client data. The study also explores the advantages of financial organizations working together and exchanging threat knowledge. It examines industry alliances, information-sharing platforms, and public-private partnerships as crucial methods for group protection against cyber threats. This paper highlighted the significance of artificial intelligence and machine learning in cybersecurity domain. It demonstrates how these technologies improve cybersecurity systems' capabilities by spotting irregularities and potential attacks. It emphasizes the significance of taking a proactive and dynamic strategy to securing client information and maintaining faith in the United States’ financial sectors. Overall, this paper provides a thorough overview of cybersecurity tactics crucial for protecting consumer data and avoiding financial fraud in the financial sectors across the United States. By taking a vigilant, team-based, and technology-driven strategy, financial institutions may strengthen their cyber defenses, protect the data of their clients, and defend the integrity of the financial system.","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135181594","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 : 2022-11-28DOI: 10.15849/ijasca.221128.04
Manal Loukili
Abstract Churn risk is one of the most worrying issues in the telecommunications industry. The methods for predicting churn have been improved to a great extent by the remarkable developments in the word of artificial intelligence and machine learning. In this context, a comparative study of four machine learning models was conducted. The first phase consists of data preprocessing, followed by feature analysis. In the third phase, feature selection. Then, the data is split into the training set and the test set. During the prediction phase, some of the commonly used predictive models were adopted, namely k-nearest neighbor, logistic regression, random forest, and support vector machine. Furthermore, we used cross-validation on the training set for hyperparameter adjustment and for avoiding model overfitting. Next, the hyperparameters were adjusted to increase the models' performance. The results obtained on the test set were evaluated using the feature weights, confusion matrix, accuracy score, precision, recall, error rate, and f1 score. Finally, it was found that the support vector machine model outperformed the other prediction models with an accuracy equal to 96.92%. Keywords: Churn Prediction, Classification Algorithms, Hyperparameter Optimization, Machine Learning, Telecommunications.
{"title":"Supervised Learning Algorithms for Predicting Customer Churn with Hyperparameter Optimization","authors":"Manal Loukili","doi":"10.15849/ijasca.221128.04","DOIUrl":"https://doi.org/10.15849/ijasca.221128.04","url":null,"abstract":"Abstract Churn risk is one of the most worrying issues in the telecommunications industry. The methods for predicting churn have been improved to a great extent by the remarkable developments in the word of artificial intelligence and machine learning. In this context, a comparative study of four machine learning models was conducted. The first phase consists of data preprocessing, followed by feature analysis. In the third phase, feature selection. Then, the data is split into the training set and the test set. During the prediction phase, some of the commonly used predictive models were adopted, namely k-nearest neighbor, logistic regression, random forest, and support vector machine. Furthermore, we used cross-validation on the training set for hyperparameter adjustment and for avoiding model overfitting. Next, the hyperparameters were adjusted to increase the models' performance. The results obtained on the test set were evaluated using the feature weights, confusion matrix, accuracy score, precision, recall, error rate, and f1 score. Finally, it was found that the support vector machine model outperformed the other prediction models with an accuracy equal to 96.92%. Keywords: Churn Prediction, Classification Algorithms, Hyperparameter Optimization, Machine Learning, Telecommunications.","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44251141","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 : 2022-11-28DOI: 10.15849/ijasca.221128.08
Aa Zezen Abidin1
Abstract ATP Tennis stands for the “The Association of Tennis Professionals” which is the primary governing body for male tennis players. ATP was formed in Sep 1972 for professional tennis players. A study has been done on tennis players’ datasets to implement supervised machine learning techniques to illustrate match data and make predictions. An appropriate dataset has been chosen, data cleaning has been implemented to extract anomalies, data is visualized via plotting methods in R language and supervised machine learning models applied. The main models applied are linear regression and decision tree. Results and predictions have been extracted from the applied models. In the linear regression model, the correlation is calculated to find the relation between dependent and independent variables, furthermore the results and prediction are extracted from the linear regression model. Also, three hypotheses are applied for multiple linear regression model. The decision tree modeled the best of 3 or best of 5 sets of matches and predicted which set of matches would be considered best. Keywords: Machine Learning, supervised learning, linear regression, decision tree, R language, Tennis, ATP.
{"title":"LoRa-Based Smart Waste Bins Placement using Clustering Method in Rural Areas of Indonesia","authors":"Aa Zezen Abidin1","doi":"10.15849/ijasca.221128.08","DOIUrl":"https://doi.org/10.15849/ijasca.221128.08","url":null,"abstract":"Abstract ATP Tennis stands for the “The Association of Tennis Professionals” which is the primary governing body for male tennis players. ATP was formed in Sep 1972 for professional tennis players. A study has been done on tennis players’ datasets to implement supervised machine learning techniques to illustrate match data and make predictions. An appropriate dataset has been chosen, data cleaning has been implemented to extract anomalies, data is visualized via plotting methods in R language and supervised machine learning models applied. The main models applied are linear regression and decision tree. Results and predictions have been extracted from the applied models. In the linear regression model, the correlation is calculated to find the relation between dependent and independent variables, furthermore the results and prediction are extracted from the linear regression model. Also, three hypotheses are applied for multiple linear regression model. The decision tree modeled the best of 3 or best of 5 sets of matches and predicted which set of matches would be considered best. Keywords: Machine Learning, supervised learning, linear regression, decision tree, R language, Tennis, ATP.","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44616421","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 : 2022-11-28DOI: 10.15849/ijasca.221128.06
Radwan n Al-Dwairi, Wafa’a Jditawi
Abstract Cloud computing is an effective technology for businesses and government sections to enhance their performance. In many modern countries adoption of cloud computing improves its success in reducing the costs with high level of eservices offered to citizens. However, many developing countries are still reluctant to adopt cloud computing and received very little empirical support. This study reviews the literature of this domain and builds a model to examine the main drivers that help decision makers in adoption of cloud technology with e-government sectors. Based on a sample of 326 respondents data analyzed using the Structural Equation Modelling though Smart Partial Least Squares technique. The study revealed that mobility, cost, backup & disaster recovery, scalability & flexibility are the key drivers that significantly affect employees’ intention to adopt cloud computing for governmental units which in turn positively influence the effectiveness of e-participation Keywords: Cloud computing, E-government, E-participation, Scalability, Mobility, Backup & Disaster recovery.
{"title":"The Role of Cloud Computing on the Governmental Units Performance and EParticipation (Empirical Study)","authors":"Radwan n Al-Dwairi, Wafa’a Jditawi","doi":"10.15849/ijasca.221128.06","DOIUrl":"https://doi.org/10.15849/ijasca.221128.06","url":null,"abstract":"Abstract Cloud computing is an effective technology for businesses and government sections to enhance their performance. In many modern countries adoption of cloud computing improves its success in reducing the costs with high level of eservices offered to citizens. However, many developing countries are still reluctant to adopt cloud computing and received very little empirical support. This study reviews the literature of this domain and builds a model to examine the main drivers that help decision makers in adoption of cloud technology with e-government sectors. Based on a sample of 326 respondents data analyzed using the Structural Equation Modelling though Smart Partial Least Squares technique. The study revealed that mobility, cost, backup & disaster recovery, scalability & flexibility are the key drivers that significantly affect employees’ intention to adopt cloud computing for governmental units which in turn positively influence the effectiveness of e-participation Keywords: Cloud computing, E-government, E-participation, Scalability, Mobility, Backup & Disaster recovery.","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49630477","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 : 2022-11-28DOI: 10.15849/ijasca.221128.11
Shadi AlZu’b, A. Zraiqat, Samar Hendawi
Abstract Recommendation systems are an important feature in the proposed virtual life, where users are often stuck with choices most of the time and need help to be able to find what they are looking for. In this work, contentbased techniques have been employed in the proposed recommender system in two ways, a deep review for content and features contents such as (cast, crew, keywords, and genres) has been conducted. A preprocessing stage using TF-IDF and CountVectorizer methods have been employed efficiently to prepare the dataset for any similarity measurements. Cosine similarity algorithm has been employed as well with and without sigmoid and linear kernals. The achieved result proves that similarities between movies using TF-IDF with - Cosine similarity (sigmoid kernel) overcomes the TF-IDF with - Cosine similarity (linear_kernel) and Cosine similarity with CountVectorizer in collaborative filtering. The accuracy values of different machine learning models are validated with K-fold Cross Validator techniques. The performance evaluation has been measured using ROOT Mean Square Error and Mean Absolute Error. Five Machine learning algorithms (NormalPredictor, SVD, KNNBasic (with k=20 and K=10), KNNBasic (with sim_options), and NMF (in several rating scales)). Accuracies are finally been validated with 3 folds from each validator. The best achieved RMSE and MAE scores are using SVD (RMSE = 90%) and (MAE = 69%), followed by KNNBasic (with sim_options, K= 20), NMF, KNNBasic (K=20), KNNBasic (K=10), ending with KNNBasic(sim_options, K= 10). Keywords: Recommendation System, Sustainable Development, Artificial Intelligence, Collaborative Filtering, Content-Based, Cosine Similarity, Movies Recommendation, NLP, Machine Learning Application.
{"title":"Sustainable Development: A Semantics-aware Trends for Movies Recommendation System using Modern NLP","authors":"Shadi AlZu’b, A. Zraiqat, Samar Hendawi","doi":"10.15849/ijasca.221128.11","DOIUrl":"https://doi.org/10.15849/ijasca.221128.11","url":null,"abstract":"Abstract Recommendation systems are an important feature in the proposed virtual life, where users are often stuck with choices most of the time and need help to be able to find what they are looking for. In this work, contentbased techniques have been employed in the proposed recommender system in two ways, a deep review for content and features contents such as (cast, crew, keywords, and genres) has been conducted. A preprocessing stage using TF-IDF and CountVectorizer methods have been employed efficiently to prepare the dataset for any similarity measurements. Cosine similarity algorithm has been employed as well with and without sigmoid and linear kernals. The achieved result proves that similarities between movies using TF-IDF with - Cosine similarity (sigmoid kernel) overcomes the TF-IDF with - Cosine similarity (linear_kernel) and Cosine similarity with CountVectorizer in collaborative filtering. The accuracy values of different machine learning models are validated with K-fold Cross Validator techniques. The performance evaluation has been measured using ROOT Mean Square Error and Mean Absolute Error. Five Machine learning algorithms (NormalPredictor, SVD, KNNBasic (with k=20 and K=10), KNNBasic (with sim_options), and NMF (in several rating scales)). Accuracies are finally been validated with 3 folds from each validator. The best achieved RMSE and MAE scores are using SVD (RMSE = 90%) and (MAE = 69%), followed by KNNBasic (with sim_options, K= 20), NMF, KNNBasic (K=20), KNNBasic (K=10), ending with KNNBasic(sim_options, K= 10). Keywords: Recommendation System, Sustainable Development, Artificial Intelligence, Collaborative Filtering, Content-Based, Cosine Similarity, Movies Recommendation, NLP, Machine Learning Application.","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44348276","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 : 2022-11-28DOI: 10.15849/ijasca.221128.07
A. Sheng, J. Jamil, I. Shaharanee
Abstract A lot of applications including event logs and web pages uses XML format for utilizing, keeping, transferring and displaying data. Thus, volume of data expressed in XML has increase rapidly. Numerous research has been done to extract and mine information from XML documents. Mining XML documents allows an understanding to the architecture and composition of XML documents. Generally, frequent subtree mining is one of the methods to mine XML documents. Frequent subtree mining searches the relation between data in a tree structured database. Due to the architecture and the composition of XML format, normal data mining and statistical analysis difficult to be performed. This paper suggests a framework that flattens and converts tree structured data into structured data, while maintaining the information of architecture and the composition of XML format. To gain more information from event logs, converting into structured data from semistructured format grants more ability to perform variety data mining techniques and statistical test. Keywords: Flatten Sequential Structure Model, XML Format Event Logs, Data Mining, Statistical Analysis.
{"title":"Framework to Mine XML Format Event Logs","authors":"A. Sheng, J. Jamil, I. Shaharanee","doi":"10.15849/ijasca.221128.07","DOIUrl":"https://doi.org/10.15849/ijasca.221128.07","url":null,"abstract":"Abstract A lot of applications including event logs and web pages uses XML format for utilizing, keeping, transferring and displaying data. Thus, volume of data expressed in XML has increase rapidly. Numerous research has been done to extract and mine information from XML documents. Mining XML documents allows an understanding to the architecture and composition of XML documents. Generally, frequent subtree mining is one of the methods to mine XML documents. Frequent subtree mining searches the relation between data in a tree structured database. Due to the architecture and the composition of XML format, normal data mining and statistical analysis difficult to be performed. This paper suggests a framework that flattens and converts tree structured data into structured data, while maintaining the information of architecture and the composition of XML format. To gain more information from event logs, converting into structured data from semistructured format grants more ability to perform variety data mining techniques and statistical test. Keywords: Flatten Sequential Structure Model, XML Format Event Logs, Data Mining, Statistical Analysis.","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46043148","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 : 2022-11-28DOI: 10.15849/ijasca.221128.12
M. AlTarawneh, M. Hassan
Abstract This study aimed to assess Readiness of Jordanian Universities for E-learning. For the purpose of the study a questionnaire consisting of (42) items was developed and divided into five domains, namely: organizational readiness, ICT tools, technical resources, faculty members, and students. The statistical analyses have been done using descriptive and interferential analytical approaches by the Statistical Package for Social Sciences. The results indicated that Readiness of Jordanian Universities for e-learning was medium. On one hand, the findings indicate that there were statistically significant differences at the significance level (α≤0.05) in individual responses to the study sample attributed to the type of faculty variable in favor of sciences faculties. On other hand there were no statistically significant differences at the significance level (α≤0.05) in individual responses to the study sample attributed to variable of faculty members by the academic rank. Keywords: E-learning, Jordanian Universities.
{"title":"Readiness of Higher Education Institutions for E-learning Case of Jordanian Universities","authors":"M. AlTarawneh, M. Hassan","doi":"10.15849/ijasca.221128.12","DOIUrl":"https://doi.org/10.15849/ijasca.221128.12","url":null,"abstract":"Abstract This study aimed to assess Readiness of Jordanian Universities for E-learning. For the purpose of the study a questionnaire consisting of (42) items was developed and divided into five domains, namely: organizational readiness, ICT tools, technical resources, faculty members, and students. The statistical analyses have been done using descriptive and interferential analytical approaches by the Statistical Package for Social Sciences. The results indicated that Readiness of Jordanian Universities for e-learning was medium. On one hand, the findings indicate that there were statistically significant differences at the significance level (α≤0.05) in individual responses to the study sample attributed to the type of faculty variable in favor of sciences faculties. On other hand there were no statistically significant differences at the significance level (α≤0.05) in individual responses to the study sample attributed to variable of faculty members by the academic rank. Keywords: E-learning, Jordanian Universities.","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47630394","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 : 2022-11-28DOI: 10.15849/ijasca.221128.10
J. Fadila, M. Hariyadi, Ajib Hanani, Johan Ericka W.P., Okta Aziz
Abstract I-Device (Intelligent Devices) is one of the fastest growing devices since the beginning of this decade. Some of its major problems are accuracy and performance. This study aims to present an improvement in the performance of those devices. We used a simulation application for I-Devices to conduct the experiment. The simulation was built based on classifying results using Logarithmic learning for Generalized Classifier Neural Networks (L-GCNN). The output was a simulation that will be implemented on a smart mosque system. L-GCNN itself was a modification method of GCNN to improve the processing speed and have high accuracy as a classifier method. This method will take a role when the given parameters meet the conditions of the devices to take an action. To simplify the understanding of the simulation models, we used a game application to make an interactive simulation for our project in an environment that represents the real-world condition of the mosque. The result of this study shows that the devices could make a decision by themselves accurately. Additionally, using LGCNN models, we could reduce the processing iteration compared to other models. The experiment results show that LGCNN has an average value of 90% in accuracy, precision, recall, and f1. Keywords: Automation, Classifier, L-GCNN, Neural Network, Decision.
{"title":"Improvement on I-Devices Using L-GCNN Classifier for Smart Mosque Simulation","authors":"J. Fadila, M. Hariyadi, Ajib Hanani, Johan Ericka W.P., Okta Aziz","doi":"10.15849/ijasca.221128.10","DOIUrl":"https://doi.org/10.15849/ijasca.221128.10","url":null,"abstract":"Abstract I-Device (Intelligent Devices) is one of the fastest growing devices since the beginning of this decade. Some of its major problems are accuracy and performance. This study aims to present an improvement in the performance of those devices. We used a simulation application for I-Devices to conduct the experiment. The simulation was built based on classifying results using Logarithmic learning for Generalized Classifier Neural Networks (L-GCNN). The output was a simulation that will be implemented on a smart mosque system. L-GCNN itself was a modification method of GCNN to improve the processing speed and have high accuracy as a classifier method. This method will take a role when the given parameters meet the conditions of the devices to take an action. To simplify the understanding of the simulation models, we used a game application to make an interactive simulation for our project in an environment that represents the real-world condition of the mosque. The result of this study shows that the devices could make a decision by themselves accurately. Additionally, using LGCNN models, we could reduce the processing iteration compared to other models. The experiment results show that LGCNN has an average value of 90% in accuracy, precision, recall, and f1. Keywords: Automation, Classifier, L-GCNN, Neural Network, Decision.","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43581682","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 : 2022-11-28DOI: 10.15849/ijasca.221128.05
Murinto Prahara, E.I.H. Ujianto
Abstract The image segmentatation technique that is often used is thresholding. Image segmentation is a process of dividing the image into different regions according to their similar characteristics. This research proposes a multilevel thresholding algorithm using modified particle swarm optimization to solve a segmentation problem. The threshold optimal values are determined by maximizing Otsu’s objective function using optimization technique namely particle swarm optimization based on the logarithmic decreasing inertia weight (LogDIWPSO). The proposed method reduces the computational time to find the optimum thresholds of multilevel thresholding which evaluated on several grayscale images. A detailed comparison analysis with other multilevel thresholding based techniques namely particle swarm optimization (PSO), iterative particle swarm optimization (IPSO), and genetic algorithms (GA), From the experiments, Modified particle swarm optimization (MoPSO) produces better performance compared to the other methods in terms of fitness value, robustness and convergence. Therefore, it can be concluded that MoPSO is a good approach in finding the optimal threshold value. Keywords: grayscale image, inertia weight, image segmentation, particle swarm optimization.
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