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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Sendil Vadivu, D. Rajagopalan, Narendran National Institute of Technology Department of Computer Science and Engineering Puducherry Karaikal India

ISBN: (纸本)9798350371314

As the Internet continues to be the primary driver of modern technology, the need for robust safety procedures increases. Distributed Denial of Service (DDoS) assaults represent an essential threat to network availability and performance, demanding active detection methods. This study explores Machine Learning (ML) techniques to improve the detection of DDoS assaults in SDN environments, using SDN's programmable and centralized framework for efficient traffic monitoring and analysis. This article aims to employ Ryu as the controller and use its skills to coordinate network tasks and allow seamless integration with the Random Forest (RF) ML classifier. The impact of applying ML in real-time DDoS detection is investigated with the flow table entries of the controller. 99.9% accuracy in attack traffic detection and blocking is achieved in Ryu to improve the general resistance of SDN-based networks to malicious activity. © 2024 IEEE.

关键词： Controllers

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Natural Language Processing for Analyzing Social Media Trends 2

Natural Language Processing for Analyzing Social Media Trend...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Hemalatha, Raavi Amulya, Godi Sai Lalitha, Ch.S.N. Vishnu Institute of Technology Department of Computer Science & Engineering Bhimavaram534202 India

ISBN: (纸本)9798350364699

This investigation studies the effectiveness of Natural Language Processing (NLP) in analyzing and interpreting trends in social media. Because social media platforms generate enormous amounts of data daily, extracting useful perceptions from this information creates several important obstacles. To uncover patterns and sentiments within the material of social platforms, the study investigates a variety of natural language processing (NLP), such as sentiment analysis, content modeling, and trend finding. Through the use of these methods, the investigation intends to improve comprehension of the patterns of public opinion and behavior over time. In addition, the study analyzes the efficiency of several natural language processing (NLP) algorithms and tools in the processing and analysis of data from social media platforms, highlighting both their strengths and weaknesses. The findings provide valuable insight into how natural language processing (NLP) might be utilized to monitor and forecast societal trends. They also provide counterarguments for those who are involved in experimentation, policymaking, and marketing. Through the demonstration of how advanced speech analysis may be applied to the dynamic and complicated geography of social media, this study contributes to the larger subject of data wisdom. © 2024 IEEE.

关键词： and data mining computational linguistics digital communication natural language processing online trends sentiment analysis Sentiment analysis social media text mining trend analysis

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Bank Note Recognition using Vision-Based System 15

Bank Note Recognition using Vision-Based System

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Chaitra, H.V. Varshini, N. Himaja, Yennapusala Shruti, R. Department of Computer Science & Engineering Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (纸本)9798331300579

According to the latest estimations by the World Organization from their research, it is known that there is a large population across the world who are not gifted with perfect vision, recently estimated as 2.2 billion. And along with blindness comes around many challenges like walking, accessing the current technology, socializing, identifying basic things such as medicine, reading, cooking, and many more. And one of the most challenging things a visually impaired person can face is recognizing currency notes. The motive of the research is to evaluate the performance of the ground-breaking technologies, i.e., Artificial intelligence and Machine learning in recognizing different kinds of banknotes but also provide the voice assistance to the system. Previous studies include the images of the banknote directly fed to AlexNet and, the currency note picture with various orientations and taken in different environments are fed into the machine learning classification using one of the algorithms such as K-Nearest Neighbours (KNN). © Grenze Scientific Society, 2024.

关键词： Decision trees

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GPS - Enhanced Automated Toll Management System 2

GPS - Enhanced Automated Toll Management System

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Dudhe, Priyanka Tembhurne, Shreya Telghare, Bhagyashree Chaudhary, Abhishek Meshram, Priyanka Waghmare, Sakshi Jhulelal Institute of Technology Department of Computer Science & Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350387933

This abstract gives a complete review of GPS Integrated Toll Collection' a novel GPS-based toll series gadget, highlighting the important things studies query, primary research reviewed, and the conclusions *** evaluation question at the core of this research pertains to the efficiency and feasibility of imposing the device as a modernized method to toll series. In a world where visitor congestion and conventional tollbooths pose considerable demands for transportation efficiency and environmental sustainability, innovative solutions are urgent. It leverages GPS generation to streamline toll series, eliminating the want for bodily tollbooths and enhancing overall riding *** essential studies were tested to assess its merits. These studies encompassed a variety of fields, together with transportation engineering, GPS technology, visitor management, and user experience. Key components evaluated included the accuracy and reliability of GPS-based total toll collection, its impact on reducing visitor congestion and emissions, fee effectiveness, and consumer *** ability blessings in terms of decreasing site visitor delays, minimizing environmental impact, and enhancing standard user satisfaction are sizeable. However, a hit implementation hinges on addressing positive technical challenges and making sure of widespread adoption amongst drivers. Further studies and pilot research are recommended for high-quality-track the gadget and examining its real-global overall *** represents a promising option to the challenges associated with toll series, supplying the potential for more efficient, environmentally pleasant, and user-pleasant transportation experiences. Future endeavors need to be conscious of refining this modern technique to toll series and facilitating its integration into current transportation infrastructure. © 2024 IEEE.

关键词： Environmental impact

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Improved Set Algebra-Based Heuristic Technique for Training Multiplicative Functional Link Artificial Neural Networks for Financial Time Series Forecasting

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SN computer Science 2024年第5期5卷 1-34页

作者： Behera, Sudersan Kumar, AVS Pavan Nayak, Sarat Chandra Department of Computer Science and Engineering School of Engineering & amp Technology GIET University Gunupur India Department of Computer Science and Engineering School of Technology GITAM University Hyderabad India

The current study is defined by two main aims. An effective strategy for improving local search is to combine the Set Algebra-Based Heuristic Algorithm (SAHA) algorithm with the Nelder-Mead simplex method. The approach outlined above, referred to as the Improved SAHA (ISAHA), and has the ability to produce superior outcomes. The multiplicative functional link artificial neural network (MFLANN) is an improved version of the functional link artificial neural network that promotes exploration by replacing the summing unit in the output layer with a multiplication unit. Moreover, the combination of ISAHA and MFLANN results in the development of ISAHA-MFLANN, a sophisticated hybrid forecasting model. The main assessment of the hybrid model rests on its ability to forecast complex and dynamic financial time series. It's possible to get around the problems that come with traditional learning-based MFLANN techniques by using MFLANN's advanced approximation features and ISAHA's resilient global search features together. Experimental verification using two stock market datasets and three currency exchange rates demonstrates the validity of the idea. The results show that the suggested improved SAHA hybrid learning works well at improving six standard benchmark functions. The ISAHA-MFLANN model is also statistically significant at accurately capturing the volatility that is inherent in the financial time series. In addition, it surpasses other models such as SAHA-MFLANN, Monarch Butterfly Optimization-MFLANN, Particle Swarm Optimization-MFLANN, Genetic Algorithm-MFLANN, Gradient Descent-MFLANN, Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Auto Regressive Integrated Moving Average (ARIMA). © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Exchange rate forecasting Improved Set Algebra-Based Heuristic Algorithm Multiplicative functional link artificial neural network Stock market forecasting

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Analyzing Farmers' Protest Tweets using Topic Modelling Techniques

Analyzing Farmers' Protest Tweets using Topic Modelling Tech...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Sharaff, Aakanksha Papatla, Sai Rohith Rajput, Nandini National Institute of Technology Raipur Department of Computer Science and Engineering Raipur India

ISBN: (纸本)9798350373783

In natural language processing, topic modeling is an effective method for identifying underlying themes in large volumes of textual data. Topic modeling is a useful technique in many fields since it allows for a deeper understanding of the underlying patterns found in textual information through the examination of word distributions and co-occurrences. Topic modeling is a useful concept for social media data analysis. The Indian farmers' protest gained a lot of attention and became popular on Twitter. By creating a new dataset, this article investigates and examines the tweets against farmers' protest. A rudimentary exploratory data analysis is performed on this newly created dataset. Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), Hierarchical Dirichlet Process (HDP), and Non-negative Matrix factorization (NMF) are the four topic modeling techniques that are employed, and these approaches are compared by calculating each method's Coherence score. With a coherence score of 0.5002, the NMF model performed best when seven topics were chosen. © 2024 IEEE.

关键词： Tweets

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Smartserve - Leveraging Generative AI for Multifaceted Services 3

Smartserve - Leveraging Generative AI for Multifaceted Servi...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Kayande, Deepali Malave, Sachin Jagtap, Abhishek Ghaduse, Omkar Bhosale, Vidul Choudhari, Tanvi A.P. Shah Institute of Technology Department of Computer Engineering Thane India

ISBN: (纸本)9798350381931

SmartServe, an integrated AI platform, seeks to address the scattered nature of current AI platforms by efficiently integrating multiple services. To transform user interaction, this all-encompassing solution uses modern deep learning and natural language processing techniques. With the help of OpenAI's generative AI model, SmartServe is able to generate on-demand text, code and images in response to feedback from users. Additionally, the platform features powerful text summarizer and a multilingual translator that uses natural language processing (NLP) to provide cross-cultural and accessible data synthesis is improved Notably with the inclusion of JSearch, a job search feature that collects listings from several portals. To ensure a seamless and efficient AI experience, SmartServe's integrated approach streamlines user interaction and takes financial considerations into account Users can purchase credits for targeted service usage. © 2024 IEEE.

关键词： Deep learning

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Enhancing Cybersecurity through Advanced Keylogging Techniques 2

Enhancing Cybersecurity through Advanced Keylogging Techniqu...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Sanjay, K. Ratheesh, J.K. Johin, R.R. Krishnamoorthy, A. Vellore Institute of Technology Department of Computer Science Engineering Tamil Nadu India

ISBN: (纸本)9798350368109

As the viruses are increasing efficiently, our project aims to develop a keylogger software. The purpose of this software is to continuously monitor and assess the activities of employees, and to address security concerns that may arise for both children and professionals who can encounter and install unwanted software. Using the Java programming language, keylogger is crafted for specialized testing purposes within this project. This software is a solution for monitoring the activities of employees and children. This program is tested in a controlled environment to attain its efficiency. The outcomes suggest that the software functions effectively, indicating the areas for potential enhancement in future research endeavors. © 2024 IEEE.

关键词： Cyber attacks

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Morphological Classification of Galaxies using Enhanced Deep Learning Model 5

Morphological Classification of Galaxies using Enhanced Deep...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Nemade, Rohan Jha, Suraj Anand, Harsh Chandrasekaran, Saravanan Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379884

Galaxies in astrophysics are one of its fundamental, their morphological classification remains an essential key to unlock countless doors about the structure, formation and history of our Universe. For nearly a century, astronomers have assigned galaxies to perceptual categories such as ellipticals, spirals and irregulars. However, the extraordinary expansion of the galaxy database catalysed and instigated by sky surveys of ever-increasing scales rendered this classification more manual than practical. Because of the learning capability and its scaling nature, to learn hierarchical abstract features from the visual data Convolutional Neural Networks (CNN) are powerful tools to recognize complex patterns and structures in images, such as spiral arms or central bulges that contribute significantly to accurate morphological classification. That is why CNN are particularly well suited to the problem of galaxy classification. While traditional classification work used to solve the problem, it is not feasible for today's huge amounts of generated data. This attempt to build and train a CNN model using galaxy images as our domain. The Enhanced CNN model can achieve the high accuracy to morphological type of galaxies. The Enhanced CNN increase both efficiency and classification accuracy, particularly when processing large dataset. The Galaxy Zoo dataset indicated that our study can provide a tool for other astronomical research in future. The Enhanced CNN gain the overall accuracy rate are 95.29% with the performance indicator were calculated including average precision, recall, F1-score and Area under curve (AUC) is 0.9513, 0.9531, 0.9525 and 0.9853 respectively. The Enhanced CNN outperforms the various existing state-of-the models. © 2024 IEEE.

关键词： Convolutional neural networks

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Cloud-Driven Machine Learning with AWS: A Comprehensive Review of Services 2

Cloud-Driven Machine Learning with AWS: A Comprehensive Revi...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Ravindranathan, Manya K. Sendil Vadivu, D. Rajagopalan, Narendran National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798350306415

With the rise of cloud computing, people now have 24/7 online access to IT resources, which is a transformational force. Amazon Web Services (AWS) is a market leader in the field of cloud service providers, providing a broad range of platform, software, and infrastructure services. In this review article, we explore the domain of cloud-driven Machine Learning (ML) with an emphasis on the extensive resources and support provided by AWS. The ability of the AWS ecosystem to provide scalable and effective ML applications is highlighted in this study. It looks at the range of AWS services created to make building, training, and deploying cloud-based ML applications easier. Our analysis includes an AWS Machine Learning as a Service (MLaaS) product categorization, illuminating the range of resources and options available to ML practitioners. We also take into account findings from comparable studies that have used AWS services for a range of purposes. In conclusion, this article offers a thorough introduction for individuals wishing to explore the world of cloud- driven ML using AWS. It provides insightful information on AWS services, their uses, and how they are influencing ML in the cloud. © 2024 IEEE.

关键词： AWS AWS Free Tier Services Cloud Computing Distributed Computing Machine Learning as a Service

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