Pub Date : 2021-09-12DOI: 10.54692/lgurjcsit.2021.0503218
Kausar Parveen
Now a day’s all organizations are moving towards digitalization. These consequences of the use of digital technologies made organizations seek for best and fast digital solutions. All software developer companies are also trying to draw consumer's attention by offering prompt services. In this regard, the critical issue in information technology and other areas of computation is how software can be created easily and rapidly for complex businesses. In this context, the main aim of the research is to show the agile methodology role in the rapid digital transformation. In this paper, we have surveyed different agile methodologies and tools for rapid software development and introduced an agile management tool having a backlog. We identified the key practices of agile methods and after a survey, it is suggested that the agile approach can help to achieve a balance between the applications generated by developers on customer demand. This paper illuminates and translates agile methodologies into agile project management tools for simple and rapid application development. Empirical research based on a case study is provided for better understanding and showing the importance of agility in software development
{"title":"Rapid Digital Transformation Using Agile Methodologies for Software Development Projects","authors":"Kausar Parveen","doi":"10.54692/lgurjcsit.2021.0503218","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0503218","url":null,"abstract":"Now a day’s all organizations are moving towards digitalization. These consequences of the use of digital technologies made organizations seek for best and fast digital solutions. All software developer companies are also trying to draw consumer's attention by offering prompt services. In this regard, the critical issue in information technology and other areas of computation is how software can be created easily and rapidly for complex businesses. In this context, the main aim of the research is to show the agile methodology role in the rapid digital transformation. In this paper, we have surveyed different agile methodologies and tools for rapid software development and introduced an agile management tool having a backlog. We identified the key practices of agile methods and after a survey, it is suggested that the agile approach can help to achieve a balance between the applications generated by developers on customer demand. This paper illuminates and translates agile methodologies into agile project management tools for simple and rapid application development. Empirical research based on a case study is provided for better understanding and showing the importance of agility in software development","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133285031","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 : 2021-09-12DOI: 10.54692/lgurjcsit.2021.0503217
Sohaib Ahmad
The need to process and dealing with a vast amount of data is increasing with the developing technology. One of the leading promising technology is Cloud Computing, enabling one to accomplish desired goals, leading to performance enhancement. Cloud Computing comes into play with the debate on the growing requirements of data capabilities and storage capacities. Not every organization has the financial resources, infrastructure & human capital, but Cloud Computing offers an affordable infrastructure based on availability, scalability, and cost-efficiency. The Cloud can provide services to clients on-demand, making it the most adapted system for virtual storage, but still, it has some issues not adequately addressed and resolved. One of those issues is that load balancing is a primary challenge, and it is required to balance the traffic on every peer adequately rather than overloading an individual node. This paper provides an intelligent workload management algorithm, which systematically balances traffic and homogeneously allocates the load on every node & prevents overloading, and increases the response time for maximum performance enhancement.
{"title":"Load Balancing in Cloud Computing Empowered with Dynamic Divisible Load Scheduling Method","authors":"Sohaib Ahmad","doi":"10.54692/lgurjcsit.2021.0503217","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0503217","url":null,"abstract":"The need to process and dealing with a vast amount of data is increasing with the developing technology. One of the leading promising technology is Cloud Computing, enabling one to accomplish desired goals, leading to performance enhancement. Cloud Computing comes into play with the debate on the growing requirements of data capabilities and storage capacities. Not every organization has the financial resources, infrastructure & human capital, but Cloud Computing offers an affordable infrastructure based on availability, scalability, and cost-efficiency. The Cloud can provide services to clients on-demand, making it the most adapted system for virtual storage, but still, it has some issues not adequately addressed and resolved. One of those issues is that load balancing is a primary challenge, and it is required to balance the traffic on every peer adequately rather than overloading an individual node. This paper provides an intelligent workload management algorithm, which systematically balances traffic and homogeneously allocates the load on every node & prevents overloading, and increases the response time for maximum performance enhancement.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115278572","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 : 2021-09-12DOI: 10.54692/lgurjcsit.2021.0503224
Rana Aamir Raza
In the area of fuzzy rough set theory (FRST), researchers have gained much interest in handling the high-dimensional data. Rough set theory (RST) is one of the important tools used to pre-process the data and helps to obtain a better predictive model, but in RST, the process of discretization may loss useful information. Therefore, fuzzy rough set theory contributes well with the real-valued data. In this paper, an efficient technique is presented based on Fuzzy rough set theory (FRST) to pre-process the large-scale data sets to increase the efficacy of the predictive model. Therefore, a fuzzy rough set-based feature selection (FRSFS) technique is associated with a Random weight neural network (RWNN) classifier to obtain the better generalization ability. Results on different dataset show that the proposed technique performs well and provides better speed and accuracy when compared by associating FRSFS with other machine learning classifiers (i.e., KNN, Naive Bayes, SVM, decision tree and backpropagation neural network).
{"title":"An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network","authors":"Rana Aamir Raza","doi":"10.54692/lgurjcsit.2021.0503224","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0503224","url":null,"abstract":"In the area of fuzzy rough set theory (FRST), researchers have gained much interest in handling the high-dimensional data. Rough set theory (RST) is one of the important tools used to pre-process the data and helps to obtain a better predictive model, but in RST, the process of discretization may loss useful information. Therefore, fuzzy rough set theory contributes well with the real-valued data. In this paper, an efficient technique is presented based on Fuzzy rough set theory (FRST) to pre-process the large-scale data sets to increase the efficacy of the predictive model. Therefore, a fuzzy rough set-based feature selection (FRSFS) technique is associated with a Random weight neural network (RWNN) classifier to obtain the better generalization ability. Results on different dataset show that the proposed technique performs well and provides better speed and accuracy when compared by associating FRSFS with other machine learning classifiers (i.e., KNN, Naive Bayes, SVM, decision tree and backpropagation neural network).","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129510870","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 : 2021-09-12DOI: 10.54692/lgurjcsit.2021.0503213
Muhammad Usman Ashraf
Cloud computing is one of the ruling storage solutions. However, the cloud computing centralized storage method is not stable. Blockchain, on the other hand, is a decentralized cloud storage system that ensures data security. Cloud environments are vulnerable to several attacks which compromise the basic confidentiality, integrity, availability, and security of the network. This research focus on decentralized, safe data storage, high data availability, and effective use of storage resources. To properly respond to the situation of the blockchain method, we have conducted a comprehensive survey of the most recent and promising blockchain state-of-the-art methods, the P2P network for data dissemination, hash functions for data authentication, and IPFS (InterPlanetary File System) protocol for data integrity. Furthermore, we have discussed a detailed comparison of consensus algorithms of Blockchain concerning security. Also, we have discussed the future of blockchain and cloud computing. The major focus of this study is to secure the data in Cloud computing using blockchain and ease for researchers for further research work.
{"title":"A Survey on Data Security in Cloud Computing Using Blockchain: Challenges, Existing-State-Of-The-Art Methods, And Future Directions","authors":"Muhammad Usman Ashraf","doi":"10.54692/lgurjcsit.2021.0503213","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0503213","url":null,"abstract":"Cloud computing is one of the ruling storage solutions. However, the cloud computing centralized storage method is not stable. Blockchain, on the other hand, is a decentralized cloud storage system that ensures data security. Cloud environments are vulnerable to several attacks which compromise the basic confidentiality, integrity, availability, and security of the network. This research focus on decentralized, safe data storage, high data availability, and effective use of storage resources. To properly respond to the situation of the blockchain method, we have conducted a comprehensive survey of the most recent and promising blockchain state-of-the-art methods, the P2P network for data dissemination, hash functions for data authentication, and IPFS (InterPlanetary File System) protocol for data integrity. Furthermore, we have discussed a detailed comparison of consensus algorithms of Blockchain concerning security. Also, we have discussed the future of blockchain and cloud computing. The major focus of this study is to secure the data in Cloud computing using blockchain and ease for researchers for further research work.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133276242","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 : 2021-09-12DOI: 10.54692/lgurjcsit.2021.0503221
Qaiser Abbas
Information retrieval is acquiring particular information from large resources and presenting it according to the user’s need. The incredible increase in information resources on the Internet formulates the information retrieval procedure, a monotonous and complicated task for users. Due to over access of information, better methodology is required to retrieve the most appropriate information from different sources. The most important information retrieval methods include the probabilistic, fuzzy set, vector space, and boolean models. Each of these models usually are used for evaluating the connection between the question and the retrievable documents. These methods are based on the keyword and use lists of keywords to evaluate the information material. In this paper, we present a survey of these models so that their working methodology and limitations are discussed. This is an important understanding because it makes possible to select an information retrieval technique based on the basic requirements. The survey results showed that the existing model for knowledge recovery is somewhere short of what was planned. We have also discussed different areas of IR application where these models could be used.
{"title":"Classical and Probabilistic Information Retrieval Techniques: An Audit","authors":"Qaiser Abbas","doi":"10.54692/lgurjcsit.2021.0503221","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0503221","url":null,"abstract":"Information retrieval is acquiring particular information from large resources and presenting it according to the user’s need. The incredible increase in information resources on the Internet formulates the information retrieval procedure, a monotonous and complicated task for users. Due to over access of information, better methodology is required to retrieve the most appropriate information from different sources. The most important information retrieval methods include the probabilistic, fuzzy set, vector space, and boolean models. Each of these models usually are used for evaluating the connection between the question and the retrievable documents. These methods are based on the keyword and use lists of keywords to evaluate the information material. In this paper, we present a survey of these models so that their working methodology and limitations are discussed. This is an important understanding because it makes possible to select an information retrieval technique based on the basic requirements. The survey results showed that the existing model for knowledge recovery is somewhere short of what was planned. We have also discussed different areas of IR application where these models could be used.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125159120","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 : 2021-06-21DOI: 10.54692/lgurjcsit.2021.0502210
Asif Yaseen
The business industry is generating a lot of data on daily business deals and financial transactions. These businesses are generating intensive-data like they need customer satisfaction on top priority, fulfilling their needs, etc. In every step, Data is being produced. This Data has a great value that is hidden from regular users. Data analytics is used to unhide those values. In our project, we are using a business-related dataset that contains strings and their class (0 or 1). 0 or 1 denotes the positive or negative string labels. To analyze this data, we are using a decision tree classification algorithm (J48 exceptionally) to perform text mining (classification) on our target dataset. Text mining comes under supervised learning (type). In-text mining, generally, we use two datasets. One is used to train the model, and the second dataset is used to predict the missing class labels in the second dataset based on this training model generated using the first dataset.
{"title":"Data Classification Using Decision Trees J48 Algorithm for Text Mining of Business Data","authors":"Asif Yaseen","doi":"10.54692/lgurjcsit.2021.0502210","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0502210","url":null,"abstract":"The business industry is generating a lot of data on daily business deals and financial transactions. These businesses are generating intensive-data like they need customer satisfaction on top priority, fulfilling their needs, etc. In every step, Data is being produced. This Data has a great value that is hidden from regular users. Data analytics is used to unhide those values. In our project, we are using a business-related dataset that contains strings and their class (0 or 1). 0 or 1 denotes the positive or negative string labels. To analyze this data, we are using a decision tree classification algorithm (J48 exceptionally) to perform text mining (classification) on our target dataset. Text mining comes under supervised learning (type). In-text mining, generally, we use two datasets. One is used to train the model, and the second dataset is used to predict the missing class labels in the second dataset based on this training model generated using the first dataset.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123733861","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 : 2021-06-21DOI: 10.54692/lgurjcsit.2021.0502205
Muhammad ilyas
The use of the internet has increased significantly over the past couple of years. Access to the internet has become so common that a person without computer knowledge can also use this facility easily. This ease of availability has provided a lot of benefits to society but on the other hand misuse of the internet for personal or corporate benefits is also increasing. To prosecute cybercriminals and make some lawful checks on everyone's digital activities, digital forensic science comes into the light. In this context, we developed a new framework that improves the digital forensic investigation process. This research paper proposes a method in which we can identify the illegal activities and trace the adversaries. We capture the TCP (Transmission Control Protocol) packets from the servers and workstations. This data collected from the TCP log is stored in the database and preprocessed to eliminate redundant data. Furthermore, the database also contains past data. The proposed framework has three major processes collection of TCP packets, storing and preprocessing of collected data in a database, and mining of the pattern through a digital forensic anomaly collection algorithm. For the evaluation of our proposed framework, we have developed a java based application. The results are shown in the form of reports and tables.
{"title":"Detection of Crime Patterns in Digital Forensic Investigation to Trace the Adversaries","authors":"Muhammad ilyas","doi":"10.54692/lgurjcsit.2021.0502205","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0502205","url":null,"abstract":"The use of the internet has increased significantly over the past couple of years. Access to the internet has become so common that a person without computer knowledge can also use this facility easily. This ease of availability has provided a lot of benefits to society but on the other hand misuse of the internet for personal or corporate benefits is also increasing. To prosecute cybercriminals and make some lawful checks on everyone's digital activities, digital forensic science comes into the light. In this context, we developed a new framework that improves the digital forensic investigation process. This research paper proposes a method in which we can identify the illegal activities and trace the adversaries. We capture the TCP (Transmission Control Protocol) packets from the servers and workstations. This data collected from the TCP log is stored in the database and preprocessed to eliminate redundant data. Furthermore, the database also contains past data. The proposed framework has three major processes collection of TCP packets, storing and preprocessing of collected data in a database, and mining of the pattern through a digital forensic anomaly collection algorithm. For the evaluation of our proposed framework, we have developed a java based application. The results are shown in the form of reports and tables.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116793717","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 : 2021-06-21DOI: 10.54692/lgurjcsit.2021.0502207
Qaiser Abbas
This paper presents a technique for optical recognition of Urdu characters using template matching based on a probabilistic N-Gram language model. Dataset used has the collection of both printed and typed text. This model is able to perform three types of segmentations including line, ligature and character using horizontal projection, connected component labeling, corners and pointers techniques, respectively. A separate stochastic lexicon is built from a collected corpus, which contains the probability values of grams. By using template matching and the N-Gram language model, our study predicts complete segmented words with the promising result, particularly in case of bigrams. It outperforms three out of four existing models with an accuracy rate of 97.33%. Results achieved on our test dataset are encouraging in one perspective but provide direction to work for further improvement in this model.
{"title":"Template Matching Based Probabilistic Optical Character Recognition for Urdu Nastaliq Script","authors":"Qaiser Abbas","doi":"10.54692/lgurjcsit.2021.0502207","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0502207","url":null,"abstract":"This paper presents a technique for optical recognition of Urdu characters using template matching based on a probabilistic N-Gram language model. Dataset used has the collection of both printed and typed text. This model is able to perform three types of segmentations including line, ligature and character using horizontal projection, connected component labeling, corners and pointers techniques, respectively. A separate stochastic lexicon is built from a collected corpus, which contains the probability values of grams. By using template matching and the N-Gram language model, our study predicts complete segmented words with the promising result, particularly in case of bigrams. It outperforms three out of four existing models with an accuracy rate of 97.33%. Results achieved on our test dataset are encouraging in one perspective but provide direction to work for further improvement in this model.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116980543","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 : 2021-06-21DOI: 10.54692/lgurjcsit.2021.0502206
Bushra Idrees
From recent past years, Weed identification remained a hot topic for researchers. Majority of work focused on the detection of weed but we are trying to identify the weed via weed name. The unrivaled successes of deep learning make the researchers able to evaluate different weed species in the complex rangeland climate. Nowadays, with an increasing population, farming productivity needs to be increased a lot to meet the demand for accurate weed detection. Increased demand for an increase in the use of herbicides, resulting in environmental harm. In this research work, the picture of weed helps to detect and differentiate as per area, and its name. The main aim of this research is the identification of weed so that fewer herbicides can use. This research work will contribute toreducing the higher use of herbicides by helping clear identification of weed names through its features. We use transfer learning in machine learning. The deep Weeds dataset is used for the evaluation. For this, we use the deep learning model ResNet50 to get better results. The Deep Weeds dataset contains 17,509 images that are label and eight nationally recognized species of weed belonged to 8 across northern Australia locations. This paper declares a baseline for classification performance on the dataset of weed while utilizing the deep learning model ResNet-50 and it is a benchmark too. Deep learning model ResNet-50 attained an average accuracy classification of 96.16. The findings are high enough to make effective use of weed control methods in Pakistan for futurefield implementation. The results confirm that our System offers more effective Weed recognition than many other systems.
{"title":"Weed Identification Methodology by using Transfer Learning","authors":"Bushra Idrees","doi":"10.54692/lgurjcsit.2021.0502206","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0502206","url":null,"abstract":"From recent past years, Weed identification remained a hot topic for researchers. Majority of work focused on the detection of weed but we are trying to identify the weed via weed name. The unrivaled successes of deep learning make the researchers able to evaluate different weed species in the complex rangeland climate. Nowadays, with an increasing population, farming productivity needs to be increased a lot to meet the demand for accurate weed detection. Increased demand for an increase in the use of herbicides, resulting in environmental harm. In this research work, the picture of weed helps to detect and differentiate as per area, and its name. The main aim of this research is the identification of weed so that fewer herbicides can use. This research work will contribute toreducing the higher use of herbicides by helping clear identification of weed names through its features. We use transfer learning in machine learning. The deep Weeds dataset is used for the evaluation. For this, we use the deep learning model ResNet50 to get better results. The Deep Weeds dataset contains 17,509 images that are label and eight nationally recognized species of weed belonged to 8 across northern Australia locations. This paper declares a baseline for classification performance on the dataset of weed while utilizing the deep learning model ResNet-50 and it is a benchmark too. Deep learning model ResNet-50 attained an average accuracy classification of 96.16. The findings are high enough to make effective use of weed control methods in Pakistan for futurefield implementation. The results confirm that our System offers more effective Weed recognition than many other systems.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125667263","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 : 2021-06-21DOI: 10.54692/lgurjcsit.2021.0502209
Asif Yaseen
With the swift increase of mobile devices such as personal digital assistants, smartphones and tablets, mobile commerce is broadly considered to be a driving force for the next wave of ecommerce. The power of mobile commerce is primarily due to the anytime-anywhere connectivity and the use of mobile technology, which creates enormous opportunities to attract and engage customers. Many believe that in an era of m-commerce especially in the telecommunication business retaining customers is a big challenge. In the face of an extremely competitive telecommunication industry, the value of acquiring new customers is very much expensive than retaining the existing customer. Therefore, it has become imperative to pay much attention to retaining the existing customers in order to get stabilized in a market comprised of vibrant service providers. In the current market, a number of prevailing statistical techniques for customer churn management are replaced by more machine learning and predictive analysis techniques. In this study, we employed the feature selection technique to identify the most influencing factors in customer churn prediction. We adopt the wrapper-based feature selection approach where Particle Swarm Optimization (PSO) is used for search purposes and different classifiers like Decision Tree (DT), Naïve Bayes, k-NN and Logistic regression is used for evaluation purposes to assess the enactment on optimally sampled and abridged dataset. Lastly, it is witnessed through simulations that our suggested method accomplishes fairly thriving for forecasting churners and hence could be advantageous for exponentially increasing competition in the telecommunication sector.
{"title":"Next-Wave of E-commerce: Mobile Customers Churn Prediction using Machine Learning","authors":"Asif Yaseen","doi":"10.54692/lgurjcsit.2021.0502209","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0502209","url":null,"abstract":"With the swift increase of mobile devices such as personal digital assistants, smartphones and tablets, mobile commerce is broadly considered to be a driving force for the next wave of ecommerce. The power of mobile commerce is primarily due to the anytime-anywhere connectivity and the use of mobile technology, which creates enormous opportunities to attract and engage customers. Many believe that in an era of m-commerce especially in the telecommunication business retaining customers is a big challenge. In the face of an extremely competitive telecommunication industry, the value of acquiring new customers is very much expensive than retaining the existing customer. Therefore, it has become imperative to pay much attention to retaining the existing customers in order to get stabilized in a market comprised of vibrant service providers. In the current market, a number of prevailing statistical techniques for customer churn management are replaced by more machine learning and predictive analysis techniques. In this study, we employed the feature selection technique to identify the most influencing factors in customer churn prediction. We adopt the wrapper-based feature selection approach where Particle Swarm Optimization (PSO) is used for search purposes and different classifiers like Decision Tree (DT), Naïve Bayes, k-NN and Logistic regression is used for evaluation purposes to assess the enactment on optimally sampled and abridged dataset. Lastly, it is witnessed through simulations that our suggested method accomplishes fairly thriving for forecasting churners and hence could be advantageous for exponentially increasing competition in the telecommunication sector.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120887337","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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