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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Naik, Archana Anjali, M. Ushashree, P. Sooda, Kavitha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hydrabad India B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350360660

This study introduces the concept of anonymous data exchange in banking applications using cloud computing. By intertwining innovative methodologies, the proposed framework bolsters security while introducing novel dimensions to the authentication landscape. Key to the system's architecture is the integration of RSA and SHA algorithms within the framework. In the contemporary digital landscape, the banking sector confronts escalating challenges in establishing robust authentication and security measures to safeguard sensitive customer data and transactions. Color-coded passwords offer a pioneering approach, enhancing traditional authentication methods with added security. Furthermore, the system advocates for integrate multi-factor authentication techniques, thereby reinforcing the authentication process. This integration ensures the confidentiality, integrity, and authenticity of sensitive data transmitted between clients. The performance of this integrated algorithm is compared with other encryption algorithms, and it is observed that the proposed mechanism encryption time is 10% faster. The encrypted message was tested on an online simulator to attempt deciphering and could not be decrypted, confirming its security. © 2024 IEEE.

关键词： Authentication Protocol

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Fostering Global Stability: Machine Learning for Anticipating Terrorism

Fostering Global Stability: Machine Learning for Anticipatin...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Naik, Archana Ushashree, P. Muddapu, Basavaraj Guttedar, Uttkarsh Dutt Venkatesh, Muddam Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering & Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350315455

The threat of terrorism remains a significant global concern, with governments and security agencies continually seeking advanced methods for mitigating and preventing attacks. Machine learning has emerged as a powerful tool for predictive analysis, offering the potential to anticipate and prepare for potential terrorism incidents. This paper aims to develop a machine learning model that can predict the likelihood of a terrorism attack based on historical data and relevant features. The results of the study will offer valuable insights into the potential application of machine learning to enhance counterterrorism efforts. It's important to emphasize that terrorism is a multifaceted issue and should be seen as a supplementary tool rather than a comprehensive solution. © 2023 IEEE.

关键词： Decision Tree KNN Naïve Bayes Random Forest Regression Support Vector Classifier

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Enhanced Hybrid Intrusion Detection System with Attention Mechanism using Deep Learning

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SN computer science 2024年第5期5卷 534页

作者： Chavan, Pundalik Hanumanthappa, H. Satish, E.G. Manoli, Sunil Supreeth, S. Rohith, S. Ramaprasad, H.C. School of Computer Science and Engineering REVA University Bengaluru 560064 India Nitte Meenakshi Institute of Technology Bengaluru 560064 India Department of ECE Nagarjuna College of Engineering and Technology Bengaluru India

The introduction of the Attention mechanism by the Internet of Things—or WSN-IoT—in the sector has greatly enhanced the intrusion detection mechanism capabilities, whereas the deep learning techniques, together with the attention mechanism, enhance the efficacy and efficiency of IDS within WSNs. The proposed “Enhanced Hybrid Intrusion Detection System with Attention Mechanism” (EHID-SCA) underlying insight of this is that the characteristics of WSN data validate this including Convolutional Neural Networks (CNNs) and other deep learning models into an effective and coherent architecture. The design of the deep hybrid network with attention incorporates Channel Attention and Spatial Attention as some of the main components. The model biased capacity is enlarged to choose and stress important spatial and channel information from the sensor input. The method is based on the consideration of these things and it cuts noise. Therefore, the proposed technique can be made to pave the way for enabling the Internet of Things intrusion detection systems to do the automatic extraction of useful information from sensor data through the utilization of deep learning along with the Attention mechanism. The attack might therefore be better situated for mitigation in the network design that allows analysis of the geographical and temporal context of events, which are then more properly termed as intrusion events. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Attention mechanism Deep learning Enhanced Hybrid Intrusion Detection Wireless sensor networks

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Exploratory Analysis of Methods, Techniques, and Metrics to Handle Class Imbalance Problem 2

Exploratory Analysis of Methods, Techniques, and Metrics to ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Sneha, H.R. Annappa, B. Department of Information Science & Engineering Nitte Meenakshi Institute of Technology Bengaluru560064 India Department of Computer Science & Engineering National Institute of Technology Karnataka Surathkal575025 India

Class imbalance a common challenge in machine learning, often results in skewed predictions and misrepresentative model assessments, highlighting the need for effective countermeasures. Our detailed survey dives into three primary techniques for addressing this issue: data-level interventions, algorithmic modifications, and integrated hybrid solutions. We thoroughly dissect each approach, delineating its merits, drawbacks, and ideal use cases. Data-level methods aim to restructure the dataset for class balance, while algorithmic techniques recalibrate the learning process to better detect the minority class. The hybrid strategies merge the benefits of both for a holistic remedy. The study further emphasizes the importance of precise evaluation metrics, elaborating on both conventional metrics and those tailored for imbalanced scenarios. Our objective is to arm professionals with a deep insight into tackling class imbalance, especially within the big data framework. The insights shared aspire to inspire the creation of resilient, equitable machine learning models adapt at navigating imbalanced data, ensuring enhanced prediction fidelity and consistency. © 2024 Elsevier B.V.. All rights reserved.

关键词： Machine learning

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Enhancing Student Support with AI: A College Assistance Chatbot Using NLP and ANN 2

Enhancing Student Support with AI: A College Assistance Chat...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Abdul Razak, M.S. Manoj Kumar, M.V. Nirmala, C.R. Naseer, R. Prashanth, B.S. Sneha, H.R. Bapuji Institute of Engineering and Technology Department of Computer Science and Engineering Karnataka Davanagere577004 India Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru560064 India

ISBN: (纸本)9798350372892

The rapid adoption of chatbots in various domains has revolutionized user support systems, particularly in educational institutions. This paper presents the development and implementation of a College Assistance Chatbot designed to aid university students in navigating academic and administrative queries. Leveraging Natural Language Processing (NLP) techniques and deep learning models, specifically an Artificial Neural Network (ANN), the chatbot efficiently understands and processes student inquiries. The system's architecture is meticulously crafted to manage communication effectively, ensuring timely and accurate responses. An extensive dataset was collected from college resources and pre-processed to train the neural network. The chatbot's performance was rigorously evaluated through a series of experimental campaigns, achieving an accuracy of 80.07% after 1000 epochs. The results indicate that the chatbot can reliably handle a wide range of student queries, providing a scalable solution for enhancing student support services. This innovative approach offers significant improvements over traditional methods, demonstrating high potential for broader application in educational settings. © 2024 IEEE.

关键词： Students

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A Natural Language Processing System for Truth Detection and Text Summarization 7

A Natural Language Processing System for Truth Detection and...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Rohith, H.P. Sooda, Kavitha Karunakara Rai, B. Srinivas, D.B. Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India Department of Electronics & Communication Engineering Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (纸本)9781665464086

In the futuristic world, differentiating between accurate information and false information would be challenging. To identify the fake news a truth detection system will be extremely useful. Different artificial intelligence models were compared, and the highest performing algorithm was chosen. This model was subsequently updated using a larger data-set in order to achieve better results in more real-world applications. The most essential assumptions are that the user will only utilize the subject field in which the model will work. Earlier works have implemented the process of fake news and truth detection separately from the process of summarizing the said news/information. No method was available for streamlining the process to detect whether given information was true or not and to then summarizes the given information automatically. This study has compared and contrasted the differences in accuracy and overall performance of each algorithm for fake news detection and summarization of text. Using smaller dataset, LSTM cells outperformed all other models with an accuracy of 99%. For the larger dataset, the modified LSTM has worked really well with 98.6 percent accuracy. © 2023 IEEE.

关键词： Natural language processing systems

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Image Segmentation and Classification for Fashion Apparel

Image Segmentation and Classification for Fashion Apparel

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2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Chaitra, H.V. Shadaab, Mohammad Dubey, Khushi Subudhi, Ritika Maurya, Simran Computer Science & Engineering Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (纸本)9781665453424

This paper aims at building an AI solution that can recognize clothes and recommend outfits based on the user's preferences (color, style, type, etc). The idea is to utilize machine learning models for image segmentation and classifying a picture of clothing according to a set of predetermined features (Example: tops, trouser, skirt, etc). It then uses a recommendation system to provide a few combinations of outfits according to the parameters (Example: color, occasion, sleeve length, etc) set by the user. We plan on building a ML model for automatic categorization of fashion apparels. Deep Fashion2 dataset is being used totrain U-net model for classification of input image segments into top, bottom, dress etc on the segmented parts of the image. © 2022 IEEE.

关键词： Classification (of information)

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Advancing Music Genre Identification Through Deep Learning Techniques

Advancing Music Genre Identification Through Deep Learning T...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Shetty, Chinmai Debnath, Sanjoy Kumar Adithya Falleiro, Malden Joel Sarojadevi, H. Srikanteswara, Ramya Dept of Information Science and Engineering Nitte India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350300857

Music plays a huge role in human's lives, inspiring a wide range of feelings including enthusiasm and nostalgia. 97 million songs have been recorded globally;an incredible figure that is only increasing exponentially as more people use music applications. Many people love particular musical genres, such as classical, hip-hop, or disco, and want for an easy way to arrange their music in accordance with their preferences. This gave rise to the idea of 'music genre classification'. In the current environment, where there are many music recordings available both online and offline, proper genre categorization has become essential. Several machine learning methods may be used to categorize the different types of music. This model's emphasis is on using deep learning methods to identify the musical genre. The Mel-frequency cepstral coefficients (MFCC), a technique that works exceptionally well for music files, are used to extract key elements. The proposed model is excellent at categorizing musical subgenres and provides default settings as well as interactive options depending on user preferences. The simple web interface built using Flask allows users to submit audio files and view the anticipated genre. The system also has the capability of recommending new genres to the user. © 2023 IEEE.

关键词： Music

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Cloud Data Security Using Hybrid Encryption with Blockchain 8th

Cloud Data Security Using Hybrid Encryption with Blockchain

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8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Soundappan, Kalaiselvi Shenoy, Govind Sreekar MSC- Computer Science Liverpool John Moors University Chennai India Dept. of Information Science Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9789819976324

Securing cloud computing is a significant challenge for the cloud services sector. If someone is using the cloud to store data, choosing an encryption method that would not slow things down too much while providing a high-security level is essential. The world is going through a massive transformation from the electrical to the digital era. Though the mobile industry is quickly increasing, it has not yet fully advanced in terms of capability, storage, and capacity to meet customer demands. This paper focusses on cloud data security using hybrid encryption (RSA + proxy-re-encryption) with Blockchain technology (BCT). BCT provides high data security, decentralization, an open and transparent network design, and minimal operational costs. The proposed method is also focused on BC-based decentralized consortium traceable anonymous cloud data storage and sharing system. It alleviates the blockchain’s massive data storage load using an "on-chain & off-chain" paradigm. The proposed solution enables fine-grained access control of outsourced data by presenting an upgraded proxy re-encryption with the RSA algorithm. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Innovative Horizons in Agricultural technology with TSA Based StrawberrySqueezeNet Classification Model 2

Innovative Horizons in Agricultural Technology with TSA Base...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Manoj, H.M. Pawar, Priyanka Pramod Krupa, R. Pareek, Piyush Kumar Kumar, Deepak Vindhya, G.B. Bms Institute of Technology and Management Computer Science and Engineering Bengaluru India University of the Cumberlands Information Technology United States Nitte Meenakshi Institute of Technology Mechanical Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Channabasaveshwara Institute of Technology Tumkur Gubbi India

ISBN: (纸本)9798350373110

A fruit plant with sufficient value is the strawberry plant. High concentrations of fibre, antioxidants, potassium, vitamin C, and folic acid can be found in strawberry fruit. Because bacteria, pests, and microbes frequently impact strawberry plants, cultivating strawberries is a simple operation. Digital image processing will be used to introduce strawberry disease initially and limit its spread in strawberry plants. To improve strawberry disease diagnosis, a multi-pronged strategy was used, starting with the application of a carefully selected dataset. The dataset was then enriched and diversified using data augmentation techniques, which made it easier to build a reliable and complex model. For classification, the StrawberrySqueezeNet architecture was used, utilising its inherent capacity to identify complex patterns in the data. For hyperparameter tuning, a Tangent Search technique was used to maximise the model's performance. In order to get higher classification accuracy, the iterative optimisation procedure dynamically adjusted parameters as it explored the hyperparameter space. The result of these efforts was an astounding accuracy of 98.3%, which beat the industry standards set by other models. © 2024 IEEE.

关键词： Fruits

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