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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Arunakumari, B.N. Shashidhara, Shrividya Bms Institute of Technology & Managament Department of Computer Science Engineering Karnataka Bangalore560064 India

ISBN: (纸本)9798350370249

There is no surprise that it is known that there have emerged several benefits as well as drawbacks with the progressive growth of industrialization, vehicles-be it public or private, as well as urban transit systems. Obviously, this issue is addressed to requite frustrating especially when there is a need to travel from once place to another within a particular period of time. Thankfully, this is where big data emerges into the frame to help reduce congestion and traffic problems in order to improve the approaches and efforts made towards optimizing transport by proposing a framework that makes use of collecting, aggregating and disseminating data from various kinds of sources. The fundamental approach towards achieving this is making use of reinforcement learning techniques-particularly the application of Deep Q Network Based Scheduling algorithm. By harnessing vast datasets, this algorithm dynamically optimizes traffic signal control, route planning, and resource allocation in real-time. © 2024 IEEE.

关键词： Deep reinforcement learning

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Classification of imbalanced datasets utilizing the synthetic minority oversampling method in conjunction with several machine learning techniques

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Iran Journal of computer science 2024年第1期8卷 51-68页

作者： Datta, Shrayasi Ghosh, Chinmoy Choudhury, J. Pal Department of Information Technology Jalpaiguri Government Engineering College West Bengal Jalpaiguri 735102 India Department of Computer Science & amp Engineering Jalpaiguri Government Engineering College West Bengal Jalpaiguri 735102 India Department of Computer Science & amp Engineering Narula Institute of Technology West Bengal Agarpara 700109 India

Pharmacological datasets like Yeast and Escherichia coli (E. coli) have a massive impact on the healthcare industry for the production of human drugs. Yeast is well recognized as a significant constituent in the production of pharmaceuticals for human use, owing to its cellular structure that bears resemblance to that of human cells. E. coli is widely regarded as the preferred expression host within the biotechnology industry for the efficient and cost-effective production of proteins on a large scale. This preference is particularly evident in the context of non-glycosylated proteins, which are of significant interest for their therapeutic applications. This study aims to determine the efficacy of machine learning (ML) methods in classifying pharmacological datasets like Yeast and E. coli. As Yeast and E. coli both are imbalanced datasets by nature, to handle the imbalanced classification problem, the synthetic minority oversampling technique (SMOTE) is used in this study. The application of the combined feature subset selection (CFSS) approach has been also employed to find out the overall classification performance, but classification with CFFS has not been very promising. According to the findings, classification on SMOTE-oversampled data gives better results and from several performance and evaluation metrics, random forest achieves the best overall prediction performance for both Yeast and E. coli datasets with an accuracy of 87% and 97%, precision of 87% and 97%, recall of 87% and 98%, and f1 score of 85% and 98%, respectively. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Classification E. coli Feature selection SMOTE Yeast

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A Deep Insight on Cricket Video to Text Summarization Using Neural Networks 10

A Deep Insight on Cricket Video to Text Summarization Using ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Gopal, J. Vijay Prasad, K. Sai Kumar, Dhatrika Kamal Prashanth, Maddhe Sai Kumar, Vuppala Praneeth Teja, I. Nikhil Sri Sai Computer Science Engineering Artificial Intelligence and Machine Learning Mlr Institute of Technology Hyderabad India

ISBN: (纸本)9798350384369

In this modern era, the demand for efficient and automated cricket video summarization techniques is rapidly increasing. This paper introduces an innovative and advance neural network system that transforms the way cricket match videos are summarized. Cricket video-to-text summarization system overcomes the limitations of traditional manual summarization approach by utilizing various deep learning techniques to completely automate the summarization process, our system can extract crucial insights from lengthy cricket match footage and convert them into easily readable text formats. The system employs three-pronged approach which involves extraction of visual features using VGG16 Convolutional Neural Network (CNN), scoreboard information is extracted through Optical Character Recognition (OCR) technology, and Text Summarization performed by Long Short-Term Memory (LSTM) network. Our system revolutionizes the way cricket enthusiasts engage with match videos, providing a game-changing experience for fans worldwide. © 2024 IEEE.

关键词： Convolutional neural networks

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Personal Finance Management and Prediction using ML Algorithms 8

Personal Finance Management and Prediction using ML Algorith...

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Kaushik, Sujay G. Kashyap, Sumukha S. Surabhi, B.V. Chaitra, M. Reddy, Santhosh Anitha, N. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

Effective personal finance management is essential for achieving financial stability and long-term financial health. This paper presents a comprehensive approach to summarizing, analyzing, and show monthly expenses using machine learning models. By collecting user inputs such as age, place of stay, number of dependents, monthly expenses on transport, education, subscriptions, savings, medical expenses, and more, the system uses linear regression, decision trees and random forest algorithms to predict financial values. The system provides detailed information of expenses, savings, and overall financial health, along with personal health analysis based on dietary habits. Users have access to a wide range of financial and lifestyle information, allowing the system to provide personalized recommendations to increase financial security and achieve financial goals. The model also evaluates the impact of recurring expenses and notable investments, helping users understand their spending patterns and make informed financial decisions. The results demonstrate the system's capability to deliver accurate predictions and actionable advice, enable users to better manage their finances. This approach has led to significant advances in personal financial management by integrating machine learning techniques to provide insight into people's financial health. © 2024 IEEE.

关键词： Adversarial machine learning

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Supervised Deep Learning based Leaf Disease and Pest Detection using Image Processing 7

Supervised Deep Learning based Leaf Disease and Pest Detecti...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Isravel, Deva Priya Somasundaram, Kumaresan Jestin Josephraj, M. Christopher Paul, L. Johnson, J.P. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Sivaganga India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Kumbakonam India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Dharmapuri India

ISBN: (纸本)9798350397253

The key problems that influence plant health and crop yield quality are leaf disease and pests. To improve crop production many advanced technologies are deployed. One of the predominant technologies is the incorporation of Deep Learning (DL) techniques. DL supports numerous training and testing models that are suitable for smart agriculture. Real-time detection of plant diseases and pest detection is made simpler and more efficient by using DL models. This paper provides an extensive survey of the DL techniques associated with agriculture automation and discusses the latest models focusing on leaf disease detection and pest identification. To select the best features and to optimize the accuracy of the results, an optimizer is identified and an enhanced deep learning model is proposed. The VGG16 and YOLOV5s models are deployed with ADAM optimizer. The results illustrate that the proposed optimized approach achieves an accuracy of 98.71% for leaf disease detection and 97.52% for pest detection. © 2023 IEEE.

关键词： Image processing

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Early Identification of Suicide-Susceptible Students using Computational Techniques 3

Early Identification of Suicide-Susceptible Students using C...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Khan, Adeeb Sachdev, Devanshu Verma, Abhinay Kumar, Divya Motilal Nehru National Institute of Technology Allahabad Department of Computer Science & Engineering Prayagraj India

ISBN: (纸本)9798350354171

In college life, pressure is the main cause of student suicides, highlighting a significant societal and academic challenge that demands urgent attention for early detection systems and support mechanisms. Addressing this issue proactively is crucial. To tackle this, we introduce machine learning models designed to identify suicidal tendencies in students. These models analyze various data points such as depression levels and experiences of discrimination to predict suicide risk levels - high, medium, or low, in the near future. By using predictive analytics, our approach aims to identify those in need so that they can receive the necessary support promptly, thereby reducing the risk of student suicides and promoting a safer, more supportive environment in academic institutions. © 2024 IEEE.

关键词： Predictive analytics

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Criminal Activity Forecasting using Machine Learning 10

Criminal Activity Forecasting using Machine Learning

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Augustin Sam, N. Bamini, A. M. Anusha Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384369

Crime analysis is an important topic that uses cutting-edge machine learning. This study focuses on using machine learning algorithms to examine crime trends and give law enforcement organizations useful information. The ML models may find patterns and trends by using past crime data, which makes it possible to identify possible crime hotspots and allocate the necessary resources. To find hidden patterns in crime data, Random forest algorithm was applied. The algorithm has the ability to automatically group criminal episodes into clusters, categorize them according to various attributes, and spot anomalies-that is, deviations from the norm. Furthermore, by using previous data, machine learning algorithms can be trained to support proactive crime prevention tactics. The accuracy and efficacy of crime analysis models are further improved by the integration of many data sources, such as information. The proposed algorithm makes it easier to monitor crimes in real time and permits prompt decision-making in emergency scenarios. Law enforcement organizations can use their resources more wisely, plan out patrol routes, and create focused crime prevention programs. By giving law enforcement agencies useful insights, machine learning turns crime analysis into a potent weapon. © 2024 IEEE.

关键词： Adversarial machine learning

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Fraud Detection in Audit Data Using Machine Learning Algorithm 2

Fraud Detection in Audit Data Using Machine Learning Algorit...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Ajitha, E. Monishkumaran, S. Kumar, S. Nithish St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350365269

Accounting records are regarded as analytical documents that financial businesses produce on a regular basis to provide multiple viewpoints on their financial condition. Since many investors and auditors rely heavily on these reports to make decisions, certain institutions may falsify them in order to deceive the public and perpetrate fraud. The principal goals of discovery of accounting records forgery are to recognize anomalies brought about by these biases and separate reports that are likely to include fraud from those that are not. Among the most common approaches for analyzing data in this field is binary classification, however it requires an established tagged datasets, which is sometimes unobtainable in real-world situations because fraudulent samples are uncommon. This study presents a novel strategy that addresses the great degree of scale in the parameter area and resolves the shortage of non-fraudulent samples. It is based on machine learning and ensemble models. Additionally, a new datasets is created by gathering ten Iranian bank's annual financial statements and then extracting the three kinds of attributes this study suggests. © 2024 IEEE.

关键词： Smart contract

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Modeling of Trust in Social Relationships for Secure Communication in Social Internet of Things 4

Modeling of Trust in Social Relationships for Secure Communi...

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4th International Conference on Blockchain technology and Information Security, ICBCTIS 2024

作者： Khan, Salabat Amin, Farhan Khan, Mansoor School of Computer and Information Engineering Qilu Institute of Technology Shandong Jinan China School of Computer Science and Engineering Yeungnam University Korea Republic of School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Shandong Jinan China

ISBN: (纸本)9798331528324

Herein, a social trust model is presented for investigating social relationships and social networks in the real world. Our proposal addresses the design of conceptual concepts to easily implement and develop complex applications that require direct interaction among objects. The secure social relationship paradigm guarantees network navigability, even if there are more objects on the traditional Internet of Things (IoT). This paradigm shift is very interesting, but very limited researchers have worked in this direction. Therefore, the core objective of our article is to study and classify these objects and emphasize the importance and the characteristics of trust in social objects and their relationships. Herein, five types of secure social relationships were identified and explained with real scenarios. Finding new social relationships and understanding different features and characteristics helps the researcher understand their structure and, thus, build trust between smart objects. Based on these social relationships we propose secure social relationship modeling. The achieved experimental result shows that our proposed model outperforms as compared to the state-of-the-art model. The efficiency is measured based on processing time and precision. We present a critical alternative vision based on the most recent literature. We discuss social relations along with strengths and weaknesses. Our critical analysis guides the reader to understand this paradigm in-depth and its impact on future technologies, i.e., artificial social intelligence. We also present the major ongoing research activities and highlight the most important technical and critical challenges associated with these objects, such as inter-object relations, support and definition of new communication primitives, and security and privacy concerns. © 2024 IEEE.

关键词： Secure relationships Secure social network analysis Security Social Network Analytics Social networks Trust

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Translating Words to Pixels: An Effective Text-To-Image System Utilizing Upsampling and CLIP Models

Translating Words to Pixels: An Effective Text-To-Image Syst...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Saeed, Nuaima Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Department of Artificial Intelligence Topi Pakistan

ISBN: (纸本)9798350349719

Generative AI and text-based image synthesis are being pursued with more interest than ever and adopt a growing audience. This work offers a new way of producing images within a broad range of quality levels, deducing the extra value of the increase resolution. The system applies sophisticated technologies such as diffusion and upscaling models that are guided by the pre-Training of the Contrastive Language-Image PreTraining (CLIP). However, the model shines primarily in its ability to generate visually cohesive and artistic images which are a good illustration of each specified textual prompt. Upsampling technique resolved the major translation problems by generating more high resolution and detailed resultant images of base images. Beyond that, the system also thoroughly illustrated its diversity by accurately producing a comprehensive collection of images based on the text input. This work would largely be a demonstration of the suitability of it for different fields like digital art, advertising, and content creation. Moreover, it delimits the chances of producing innovative and unique content. Future development of many other applications of the system will include the improvement of the system handling more complex textual descriptions, the addition of user-interactive generation features, the optimization of system performance and expanding to multimodal content generation. Our results here only represent a small but important step in this exciting direction. By means of this survey we enrich further knowledges on the field and its farther issues. © 2024 IEEE.

关键词： Signal sampling

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