Pub Date : 2023-09-30DOI: 10.14445/23488387/ijcse-v10i9p102
Amirali Kerimovs
{"title":"Software Engineering Project Life Cycle Modeling Based on Neural Network Technologies","authors":"Amirali Kerimovs","doi":"10.14445/23488387/ijcse-v10i9p102","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v10i9p102","url":null,"abstract":"","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139332774","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 : 2023-06-25DOI: 10.14445/23488387/ijcse-v10i6p102
Amey Bhandari
- Artificial intelligence is becoming extremely popular in our lives, being used in every sector, from job applications to medical diagnoses. AI is often biased due to various factors, ranging from biased training data to a lack of diversity and the designing and modeling team. Bias in AI is this research paper’s focus, which starts by discussing AI development and a basic understanding of how AI models work. Later, bias in AI and its reasons are discussed with examples, along with a comparison of bias in different AI models. Image generation AI models such as Stable Diffusion and DALL-E 2, along with text generation AIs such as ChatGPT, are analyzed. Bias in AI in different respects, such as Gender, Religion, and Race, has been explored in detail. Towards the end, steps that have been taken to mitigate bias have been discussed.
{"title":"Bias in AI: A Comprehensive Examination of Factors and Improvement Strategies","authors":"Amey Bhandari","doi":"10.14445/23488387/ijcse-v10i6p102","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v10i6p102","url":null,"abstract":"- Artificial intelligence is becoming extremely popular in our lives, being used in every sector, from job applications to medical diagnoses. AI is often biased due to various factors, ranging from biased training data to a lack of diversity and the designing and modeling team. Bias in AI is this research paper’s focus, which starts by discussing AI development and a basic understanding of how AI models work. Later, bias in AI and its reasons are discussed with examples, along with a comparison of bias in different AI models. Image generation AI models such as Stable Diffusion and DALL-E 2, along with text generation AIs such as ChatGPT, are analyzed. Bias in AI in different respects, such as Gender, Religion, and Race, has been explored in detail. Towards the end, steps that have been taken to mitigate bias have been discussed.","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117313299","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 : 2023-06-25DOI: 10.14445/23488387/ijcse-v10i6p105
Vivek Basavegowda Ramu
{"title":"Performance Testing using Machine Learning","authors":"Vivek Basavegowda Ramu","doi":"10.14445/23488387/ijcse-v10i6p105","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v10i6p105","url":null,"abstract":"","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126444289","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 : 2023-06-25DOI: 10.14445/23488387/ijcse-v10i6p104
Prajwal Madhusudhana Reddy
- This research paper aims to predict how Twitter tweets from a specific politician correlate to their winning a seat in a state election. To understand this effect, sentiment analysis has been conducted on tweets by politicians in Karnataka to help predict who will win in the upcoming 2023 Karnataka Legislative Assembly election. Though previous research has already been done in this area, most studies have only focussed on the sentiment analysis of tweets. This paper goes further as it also looks at other factors, including the number of retweets and comments a tweet garners, which measures the tweet's engagement. A model has been created that weighs each factor to help predict who will win an election for a particular constituency. Through this model, a 72.7% accuracy has been achieved. However, the Twitter API severely limited the quantity and quality of data collected. These results can be expanded to help predict elections for other states. They could potentially help understand the effect of positive and negative sentiment on the winnability of a political candidate.
{"title":"Conducting Sentiment Analysis on Twitter Tweets to Predict the Outcomes of the Upcoming Karnataka State Elections","authors":"Prajwal Madhusudhana Reddy","doi":"10.14445/23488387/ijcse-v10i6p104","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v10i6p104","url":null,"abstract":"- This research paper aims to predict how Twitter tweets from a specific politician correlate to their winning a seat in a state election. To understand this effect, sentiment analysis has been conducted on tweets by politicians in Karnataka to help predict who will win in the upcoming 2023 Karnataka Legislative Assembly election. Though previous research has already been done in this area, most studies have only focussed on the sentiment analysis of tweets. This paper goes further as it also looks at other factors, including the number of retweets and comments a tweet garners, which measures the tweet's engagement. A model has been created that weighs each factor to help predict who will win an election for a particular constituency. Through this model, a 72.7% accuracy has been achieved. However, the Twitter API severely limited the quantity and quality of data collected. These results can be expanded to help predict elections for other states. They could potentially help understand the effect of positive and negative sentiment on the winnability of a political candidate.","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122122760","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 : 2023-06-25DOI: 10.14445/23488387/ijcse-v10i6p101
Balagopal Ramdurai, Prasanna Adhithya
{"title":"The Impact, Advancements and Applications of Generative AI","authors":"Balagopal Ramdurai, Prasanna Adhithya","doi":"10.14445/23488387/ijcse-v10i6p101","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v10i6p101","url":null,"abstract":"","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129679662","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 : 2023-06-25DOI: 10.14445/23488387/ijcse-v10i6p103
N. Hoang, Nguyễn Thuỷ Tiên, Le Dinh Lam, Vo Thi Thanh Ha
{"title":"Experimental Study on Lithium-Ion Batteries Remaining Useful Life Prediction by Developing a Feedforward and a Long-Short-Time-Memory (LSTM) Neural Network for Electric Vehicles Application","authors":"N. Hoang, Nguyễn Thuỷ Tiên, Le Dinh Lam, Vo Thi Thanh Ha","doi":"10.14445/23488387/ijcse-v10i6p103","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v10i6p103","url":null,"abstract":"","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"891 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123250615","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 : 2023-06-25DOI: 10.14445/23488387/ijcse-v10i6p106
P. Gupta, Prakashkumar H. Patel
- This article provides a comprehensive overview of various types of available databases and their corresponding use cases. The primary objective of publishing this paper is to examine the different types of databases that exist, the reasons behind their development, and the specific use cases they serve. Databases play a critical role in facilitating the efficient organization and effective management of data in a wide range of applications. The article begins by highlighting the significance of databases in modern data-driven environments and their essential role in ensuring effective data organization and management. It emphasizes the need for a deep understanding of different database types' unique characteristics and intended purposes to address specific requirements effectively. Therefore, it is essential to have a deep understanding of the unique characteristics and intended purposes of different database types to make well-informed decisions during the process of designing and implementing database solutions.
{"title":"Demystifying Databases: Exploring their Use Cases","authors":"P. Gupta, Prakashkumar H. Patel","doi":"10.14445/23488387/ijcse-v10i6p106","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v10i6p106","url":null,"abstract":"- This article provides a comprehensive overview of various types of available databases and their corresponding use cases. The primary objective of publishing this paper is to examine the different types of databases that exist, the reasons behind their development, and the specific use cases they serve. Databases play a critical role in facilitating the efficient organization and effective management of data in a wide range of applications. The article begins by highlighting the significance of databases in modern data-driven environments and their essential role in ensuring effective data organization and management. It emphasizes the need for a deep understanding of different database types' unique characteristics and intended purposes to address specific requirements effectively. Therefore, it is essential to have a deep understanding of the unique characteristics and intended purposes of different database types to make well-informed decisions during the process of designing and implementing database solutions.","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127954466","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 : 2023-05-25DOI: 10.14445/23488387/ijcse-v10i5p107
P. Gupta
- Fraud remains a pervasive global issue, affecting individuals and organizations alike. In the modern technology-driven landscape, the role of machine learning (ML) and artificial intelligence (AI) has become paramount in combating fraud across various sectors. This article critically examines traditional fraud prevention methods, highlighting their limitations in the face of ever-evolving fraudulent tactics. It further explores how ML and AI technologies revolutionise fraud prevention efforts by facilitating rapid digitalization. By harnessing the power of ML algorithms and AI techniques, organizations can effectively analyze massive volumes of data, uncover patterns, and identify abnormal behaviors that often signify fraudulent activities. This article delves into the invaluable role played by ML and AI in augmenting fraud prevention through advanced data analytics, anomaly detection, and predictive modeling. It emphasizes how these technologies enable organizations to detect and mitigate fraud risks proactively, thus safeguarding their operations and stakeholders.
{"title":"Leveraging Machine Learning and Artificial Intelligence for Fraud Prevention","authors":"P. Gupta","doi":"10.14445/23488387/ijcse-v10i5p107","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v10i5p107","url":null,"abstract":"- Fraud remains a pervasive global issue, affecting individuals and organizations alike. In the modern technology-driven landscape, the role of machine learning (ML) and artificial intelligence (AI) has become paramount in combating fraud across various sectors. This article critically examines traditional fraud prevention methods, highlighting their limitations in the face of ever-evolving fraudulent tactics. It further explores how ML and AI technologies revolutionise fraud prevention efforts by facilitating rapid digitalization. By harnessing the power of ML algorithms and AI techniques, organizations can effectively analyze massive volumes of data, uncover patterns, and identify abnormal behaviors that often signify fraudulent activities. This article delves into the invaluable role played by ML and AI in augmenting fraud prevention through advanced data analytics, anomaly detection, and predictive modeling. It emphasizes how these technologies enable organizations to detect and mitigate fraud risks proactively, thus safeguarding their operations and stakeholders.","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124017981","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}
{"title":"A Deep Learning Approach for Enhanced Power Management using Artificial Intelligence","authors":"Dikko Elisha Sylvanus, Shehu Ahmed, Bamanga Mahmud Ahmad, Adepetun Oluwaseun Ibukun","doi":"10.14445/23488387/ijcse-v10i5p104","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v10i5p104","url":null,"abstract":"","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127220519","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 : 2023-05-25DOI: 10.14445/23488387/ijcse-v10i5p103
Ravi Kashyap
- In today's world, data has become a valuable resource for businesses, governments, researchers, and individuals alike. However, to truly extract value from data, it is essential to provide the proper context. Simply collecting and analyzing data without understanding its context can lead to inaccurate conclusions and misguided decision-making. An important factor that drives a successful organization is gathering data that can be analyzed to gain greater insights into the business and enable new opportunities, allowing the business to innovate products/services based on consumer preference. Data is the lifeblood of all businesses, and data-driven decisions can make a significant difference in staying ahead of the competition. Machine learning can be the key to unlocking the value of corporate and customer data, enabling businesses to leverage their data to make more accurate predictions and decisions. It can help businesses to identify patterns and trends in their data that might not be apparent to humans, leading to more accurate predictions and better decisions.
{"title":"Machine Learning in Google Cloud Big Query using SQL","authors":"Ravi Kashyap","doi":"10.14445/23488387/ijcse-v10i5p103","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v10i5p103","url":null,"abstract":"- In today's world, data has become a valuable resource for businesses, governments, researchers, and individuals alike. However, to truly extract value from data, it is essential to provide the proper context. Simply collecting and analyzing data without understanding its context can lead to inaccurate conclusions and misguided decision-making. An important factor that drives a successful organization is gathering data that can be analyzed to gain greater insights into the business and enable new opportunities, allowing the business to innovate products/services based on consumer preference. Data is the lifeblood of all businesses, and data-driven decisions can make a significant difference in staying ahead of the competition. Machine learning can be the key to unlocking the value of corporate and customer data, enabling businesses to leverage their data to make more accurate predictions and decisions. It can help businesses to identify patterns and trends in their data that might not be apparent to humans, leading to more accurate predictions and better decisions.","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131960286","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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