Pub Date : 2024-03-01DOI: 10.36548/jtcsst.2024.1.002
Tanvi Meet Dhruv
The most prevalent type of cancer among women is breast cancer. According to the statistics given by the World Health Organization (WHO), breast cancer is the reason behind the death of about 2.3 billion women globally in 2020, accounting for 685.9 million deaths. Since they are thought to be useful approaches, machine learning and deep learning techniques have drawn attention from researchers in breast cancer detection. Also, it can significantly assist in the process of prior detection and prediction of breast cancer by extracting handcrafted features. However, in recent years, improvements in artificial intelligence (AI) have enabled the successful use of deep learning strategies like CNN and the transfer learning method for detection of breast cancer. A significantly large dataset is used for deep learning methods. It does not require human intervention for feature extraction, which, as a result, enhances the patient's chances of survival. This review paper is based on breast cancer detection using deep learning and machine learning-based cancer detection techniques to aid in the understanding of trends and challenges in cancer detection.
{"title":"Comparative Study of Artificial Intelligence Models for Breast Cancer Detection","authors":"Tanvi Meet Dhruv","doi":"10.36548/jtcsst.2024.1.002","DOIUrl":"https://doi.org/10.36548/jtcsst.2024.1.002","url":null,"abstract":"The most prevalent type of cancer among women is breast cancer. According to the statistics given by the World Health Organization (WHO), breast cancer is the reason behind the death of about 2.3 billion women globally in 2020, accounting for 685.9 million deaths. Since they are thought to be useful approaches, machine learning and deep learning techniques have drawn attention from researchers in breast cancer detection. Also, it can significantly assist in the process of prior detection and prediction of breast cancer by extracting handcrafted features. However, in recent years, improvements in artificial intelligence (AI) have enabled the successful use of deep learning strategies like CNN and the transfer learning method for detection of breast cancer. A significantly large dataset is used for deep learning methods. It does not require human intervention for feature extraction, which, as a result, enhances the patient's chances of survival. This review paper is based on breast cancer detection using deep learning and machine learning-based cancer detection techniques to aid in the understanding of trends and challenges in cancer detection.","PeriodicalId":484362,"journal":{"name":"Journal of Trends in Computer Science and Smart Technology","volume":"117 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140089337","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 : 2024-03-01DOI: 10.36548/jtcsst.2024.1.001
G. Sivakumar, G. Mogesh, N. Pragatheeswaran, T. Sambathkumar
The importance of developing automated video surveillance systems for public safety and security, particularly in crime analysis, has witnessed significant growth in recent years. This survey delves into the current landscape of automated video surveillance systems, emphasizing advancements in crime analysis and exploring existing methodologies and technologies. The study underscores the significance of employing deep learning models in video analysis. Furthermore, the study suggests a deep learning architecture to address the challenges of the existing methods. The goal of the suggested approach is to help security and law enforcement organizations quickly react to any dangers by precisely identifying unusual occurrences or actions in video sequences. The DenseNet-121 architecture is used for efficient spatial and temporal data acquisition from the video frames. This architecture is characterized by a dense connection structure in which all levels get feature mappings from all layers before them. The characteristics of DenseNet-121 can help in the accurate identification of anomalies in video streams and differentiate between normal and abnormal actions. In addition, the study also delves into the topic of using a cell structure with varied sizes to effectively split video sequences. This allows for flexible analysis and can accommodate different sorts of abnormalities. Anomaly detection accuracy can be further improved by adding size, motion, and location information to prediction and measurement models. This study serves as a foundation for the future research that aims to develop a more robust and efficient automated video surveillance solutions.
{"title":"Video Anomaly Detection in Crime Analysis using Deep learning Architecture- A survey","authors":"G. Sivakumar, G. Mogesh, N. Pragatheeswaran, T. Sambathkumar","doi":"10.36548/jtcsst.2024.1.001","DOIUrl":"https://doi.org/10.36548/jtcsst.2024.1.001","url":null,"abstract":"The importance of developing automated video surveillance systems for public safety and security, particularly in crime analysis, has witnessed significant growth in recent years. This survey delves into the current landscape of automated video surveillance systems, emphasizing advancements in crime analysis and exploring existing methodologies and technologies. The study underscores the significance of employing deep learning models in video analysis. Furthermore, the study suggests a deep learning architecture to address the challenges of the existing methods. The goal of the suggested approach is to help security and law enforcement organizations quickly react to any dangers by precisely identifying unusual occurrences or actions in video sequences. The DenseNet-121 architecture is used for efficient spatial and temporal data acquisition from the video frames. This architecture is characterized by a dense connection structure in which all levels get feature mappings from all layers before them. The characteristics of DenseNet-121 can help in the accurate identification of anomalies in video streams and differentiate between normal and abnormal actions. In addition, the study also delves into the topic of using a cell structure with varied sizes to effectively split video sequences. This allows for flexible analysis and can accommodate different sorts of abnormalities. Anomaly detection accuracy can be further improved by adding size, motion, and location information to prediction and measurement models. This study serves as a foundation for the future research that aims to develop a more robust and efficient automated video surveillance solutions.","PeriodicalId":484362,"journal":{"name":"Journal of Trends in Computer Science and Smart Technology","volume":" July","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140092790","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 : 2023-09-01DOI: 10.36548/jtcsst.2023.3.006
Shivani Kania, Yesha Mehta
Augmented analytics is a type of analytics in which machine learning and artificial intelligence are used to provide users with more advanced and understandable analytical capabilities. Data preparation, analysis, and result interpretation are all automated steps in the analysis method. Natural language processing (NLP), automated data collection, machine learning, data visualization, explainable AI, and collaborative analytics are some of the techniques used in augmented analytics. The goal of augmented analytics technology is to simplify and modernize data analysis, making it more accessible to a wider variety of people and enabling improved decision-making across organizations. NLP is a branch of artificial intelligence (AI) and machine learning (ML) that studies the interactions between technology and people. The purpose of this study is to examine cutting-edge approaches in augmented analytics and natural language processing in order to create a sophisticated natural language generation model for augmented analytics data interpretation.
{"title":"A Literature Review on Augmented Analytics and Natural Language Generation: A Review of State of Art Techniques, Opportunities and Challenges","authors":"Shivani Kania, Yesha Mehta","doi":"10.36548/jtcsst.2023.3.006","DOIUrl":"https://doi.org/10.36548/jtcsst.2023.3.006","url":null,"abstract":"Augmented analytics is a type of analytics in which machine learning and artificial intelligence are used to provide users with more advanced and understandable analytical capabilities. Data preparation, analysis, and result interpretation are all automated steps in the analysis method. Natural language processing (NLP), automated data collection, machine learning, data visualization, explainable AI, and collaborative analytics are some of the techniques used in augmented analytics. The goal of augmented analytics technology is to simplify and modernize data analysis, making it more accessible to a wider variety of people and enabling improved decision-making across organizations. NLP is a branch of artificial intelligence (AI) and machine learning (ML) that studies the interactions between technology and people. The purpose of this study is to examine cutting-edge approaches in augmented analytics and natural language processing in order to create a sophisticated natural language generation model for augmented analytics data interpretation.","PeriodicalId":484362,"journal":{"name":"Journal of Trends in Computer Science and Smart Technology","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135346694","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 : 2023-09-01DOI: 10.36548/jtcsst.2023.3.007
None Sanjay S Tippannavar, None Yashwanth S D
Due to the increasing number of cars on the road and the exponential growth of traffic throughout the globe, regulating traffic has become crucial in the most industrialized countries. The development of technology has led to the current state of traffic management systems that comes with the ability to count, monitor, and predict the speed of vehicles in order to improve the transportation planning. This has also reduced the number of accidents that occur due to worsen traffic conditions. Road traffic surveys have been carried out manually for a long time since automated measures were not often employed due to the difficulty of installation. Machine learning in image processing is widely recognized as a significant approach for real-world applications such as traffic monitoring. The primary benefit of automated vehicle counting is that it allows for the management and evaluation of traffic in the urban transportation system. There are many methods employing distributed acoustic systems on intelligent transportation systems, including YOLO v4 and the Normalized Cross-correlation algorithm, which uses ultrasonic sensors and the algorithms ALPR, YOLO, GDPR, and CNN. The simplest method for identifying a vehicle is to gather information from sensors such as cameras, vibration detectors, ultrasound detectors, or acoustic detectors. These sensors are combined with the proper microcontrollers to determine the amount of traffic using the most recent data and theory. This review article is a quick reference for researchers working on safety-related traffic management systems.
{"title":"Real-Time Vehicle Identification for Improving the Traffic Management system-A Review","authors":"None Sanjay S Tippannavar, None Yashwanth S D","doi":"10.36548/jtcsst.2023.3.007","DOIUrl":"https://doi.org/10.36548/jtcsst.2023.3.007","url":null,"abstract":"Due to the increasing number of cars on the road and the exponential growth of traffic throughout the globe, regulating traffic has become crucial in the most industrialized countries. The development of technology has led to the current state of traffic management systems that comes with the ability to count, monitor, and predict the speed of vehicles in order to improve the transportation planning. This has also reduced the number of accidents that occur due to worsen traffic conditions. Road traffic surveys have been carried out manually for a long time since automated measures were not often employed due to the difficulty of installation. Machine learning in image processing is widely recognized as a significant approach for real-world applications such as traffic monitoring. The primary benefit of automated vehicle counting is that it allows for the management and evaluation of traffic in the urban transportation system. There are many methods employing distributed acoustic systems on intelligent transportation systems, including YOLO v4 and the Normalized Cross-correlation algorithm, which uses ultrasonic sensors and the algorithms ALPR, YOLO, GDPR, and CNN. The simplest method for identifying a vehicle is to gather information from sensors such as cameras, vibration detectors, ultrasound detectors, or acoustic detectors. These sensors are combined with the proper microcontrollers to determine the amount of traffic using the most recent data and theory. This review article is a quick reference for researchers working on safety-related traffic management systems.","PeriodicalId":484362,"journal":{"name":"Journal of Trends in Computer Science and Smart Technology","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135889866","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 : 2023-09-01DOI: 10.36548/jtcsst.2023.3.008
S. Ramalakshmi, G. Asha
Today businesses are becoming more productive and their return on investment (ROI) is increasing with the development of new technologies like data science, artificial intelligence and data analytics. In today's trend organizations are dealing with big data and these data can drive the whole organization in many ways. The process of doing data analysis and extracting meaningful insight is known as data science. Most business organizations are taking data driven models to ease their work and for making intelligent business decisions. The life cycle of a data science involves so many steps like understanding the business, data collection, analysis and data modelling etc., and to achieve these steps various new technologies and methods are available. Firstly, the process of data collection has been significantly augmented by artificial intelligence, allowing businesses to gather vast amounts of structured and unstructured data efficiently. This rich pool of data serves as the foundation upon which strategic decisions are made. By leveraging advanced data collection methods, organizations gain invaluable insights into market trends, customer behaviour, and operational patterns, empowering them to make informed, data-driven decisions. Secondly, data analysis, a core element of data science, plays a pivotal role in extracting meaningful insights from the collected data. Through sophisticated analytical techniques, businesses can uncover hidden patterns, correlations, and trends within the data. This deep understanding of the data not only facilitates efficient problem-solving but also enables the identification of opportunities for innovation and growth. Informed by data analysis, businesses can optimize processes, identify cost-saving measures, and enhance overall operational efficiency. Lastly, data visualization techniques such as real-time visualization and augmented analytics empower organizations to transform complex data sets into easily understandable visual representations. Real-time visualization provides businesses with up-to-the-minute insights, enabling them to respond promptly to market changes and emerging trends. Augmented analytics, on the other hand, leverages machine learning algorithms to automate data analysis and present actionable insights in an intuitive manner, further accelerating the decision-making process. In this study the recent trends in data science like artificial intelligence for data collection, augmented analytics and predictive analysis for data analysis and data democratization & real time visualization techniques for data visualization are discussed in detail. This study also presents the tools, key challenges and applications of these recent methods in brief.
{"title":"Embracing Innovative Approaches in Data Science: Investigating Contemporary Trends in Data Collection, Analysis, and Visualization Methods","authors":"S. Ramalakshmi, G. Asha","doi":"10.36548/jtcsst.2023.3.008","DOIUrl":"https://doi.org/10.36548/jtcsst.2023.3.008","url":null,"abstract":"Today businesses are becoming more productive and their return on investment (ROI) is increasing with the development of new technologies like data science, artificial intelligence and data analytics. In today's trend organizations are dealing with big data and these data can drive the whole organization in many ways. The process of doing data analysis and extracting meaningful insight is known as data science. Most business organizations are taking data driven models to ease their work and for making intelligent business decisions. The life cycle of a data science involves so many steps like understanding the business, data collection, analysis and data modelling etc., and to achieve these steps various new technologies and methods are available. Firstly, the process of data collection has been significantly augmented by artificial intelligence, allowing businesses to gather vast amounts of structured and unstructured data efficiently. This rich pool of data serves as the foundation upon which strategic decisions are made. By leveraging advanced data collection methods, organizations gain invaluable insights into market trends, customer behaviour, and operational patterns, empowering them to make informed, data-driven decisions. Secondly, data analysis, a core element of data science, plays a pivotal role in extracting meaningful insights from the collected data. Through sophisticated analytical techniques, businesses can uncover hidden patterns, correlations, and trends within the data. This deep understanding of the data not only facilitates efficient problem-solving but also enables the identification of opportunities for innovation and growth. Informed by data analysis, businesses can optimize processes, identify cost-saving measures, and enhance overall operational efficiency. Lastly, data visualization techniques such as real-time visualization and augmented analytics empower organizations to transform complex data sets into easily understandable visual representations. Real-time visualization provides businesses with up-to-the-minute insights, enabling them to respond promptly to market changes and emerging trends. Augmented analytics, on the other hand, leverages machine learning algorithms to automate data analysis and present actionable insights in an intuitive manner, further accelerating the decision-making process. In this study the recent trends in data science like artificial intelligence for data collection, augmented analytics and predictive analysis for data analysis and data democratization & real time visualization techniques for data visualization are discussed in detail. This study also presents the tools, key challenges and applications of these recent methods in brief.","PeriodicalId":484362,"journal":{"name":"Journal of Trends in Computer Science and Smart Technology","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135737156","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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