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Enhancing Village Ranking: Leveraging Cluster Analysis and Machine Learning 5

Enhancing Village Ranking: Leveraging Cluster Analysis and M...

引用

5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Karthikeya, Moturi Madhan, S. Sha, Akhbar Megha, R. Dhanush Krishna, R. Gopakumar, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

The research presented in this paper addresses the critical challenge of ranking Indian villages based on their potential for socioeconomic growth, inspired by the objectives of the Shyam Prasad Mukerji Rurban Mission (SPMRM). Leveraging a novel algorithm known as ClusterRank, an extension of the PageRank algorithm, we endeavor to provide a timely, efficient, and accurate solution for village ranking. The ClusterRank algorithm, an extension of the well-known PageRank algorithm, serves as the cornerstone of our approach. It excels in its ability to efficiently and accurately rank villages, outperforming alternative ranking methods. Notably, our findings reveal the superior convergence speed of the ClusterRank algorithm, affirming its capacity to produce rapid results. To assess the efficacy of ClusterRank, we employ a range of statistical ranking coefficients, including the Spearman, Kendall, and Point Biserial correlation coefficients. Our analysis demonstrates a strong positive correlation, with a Spearman correlation coefficient of 0.89, between the rankings generated by ClusterRank and the ground truth rankings provided by SPMRM. This correlation underscores the effectiveness of ClusterRank in identifying villages with high growth potential and highlights its alignment with the objectives of the SPMRM. This research presents a compelling case for the adoption of the ClusterRank algorithm by SPMRM as a valuable tool in the identification of rural areas poised for rurban development. Such adoption not only promises substantial time and resource savings but also directs the focus of SPMRM towards the implementation of targeted interventions and initiatives to uplift rural communities. Moreover, our supplementary analyses using Kendall and Point Biserial correlation coefficients further validate the robustness of the ClusterRank algorithm. © 2024 The Authors. Published by Elsevier B.V.

关键词： Cluster analysis

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Enhancing Web Security with Two-Way Authenticated SSL Communication 8

Enhancing Web Security with Two-Way Authenticated SSL Commun...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Kosanam, Akshith Ayalasomayajula, Neeharika Vivek, G. Kavitha, C.R. Amrita School of Computing Bengaluru Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350367904

The use of two-way SSL or Secure Socket Layer communication becomes the key for effectively creating a secure two-way communication between the servers and the clients in web applications. This research delves into an understanding of the objectives and importance of two-way SSL authentication with a focus on security, encryption, as well as protection against most threats including man in the middle attacks. The use of two-way SSL is also demonstrated in a Flask integrated secure login system example that demonstrates how mutual authentication and encryption are implemented so as to provide confidentiality, integrity, and authenticity of data that are being transmitted between the server and different clients. Therefore, this research draws attention to two major areas of concern through demonstrating the relevance of two-way SSL when it comes to issues of security and defending data from unauthorized access and hacking in current complex web platforms. © 2024 IEEE.

关键词： Authentication Protocol

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Grad-CAM-Based Fine-Grained Classification for Celestial Object 3

Grad-CAM-Based Fine-Grained Classification for Celestial Obj...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Handa, Devansh Nikhil, Kindi Krishna Duvarakanath, S. Kanagaraj, Kanaganandini Palaniswamy, Suja Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331528201

This paper proposes a new method of classifying celestial objects using Grad-Class Activation Mapping (Grad-CAM) in conjunction with deep learning models such as VGG16, Google Net, Dense Net, and Efficient Net. The paper looks at the difficulties characterizing the enormous varieties of astronomical objects and the complexity that makes it difficult to classify the objects by conventional methods. From the empirical evaluation, it is clear that the Dense Net model has the ability to predict the right class of celestial objects with the help of Grad-CAM and has the highest accuracy of 99.76% for differentiating between images in the case of nebula. Grad-CAM works well at identifying the features that affect the model's output. Thus, the integration of Grad-CAM with deep learning helps to increase the classification indicators and offers useful information regarding the morphological and spectral properties of the objects, thus, contributing to the further development of observational astronomy. It outperformed Class Activation Mapping (CAM) in terms of performance. © 2024 IEEE.

关键词： Mapping

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Deep Learning Based Fetal Brain Ventricle Segmentation and Size Estimation 15

Deep Learning Based Fetal Brain Ventricle Segmentation and S...

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

作者： Prakhalbh Santhosh, M. Surendran, Simi Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

Medical diagnostics with the segmentation and analysis of fetal brain ventricles assumes paramount importance in the prediction and management of neurological disorders. This paper focus on the segmentation and analysis of fetal brain ventricles using four deep-learning models: U-Net, VNet, ResNet, and DeepMedic, on the Feta 2022 dataset of T2weighted MRI scans. Saliency maps were used to visualize key areas in the decision-making processes for these models, which helped clinicians verify the AI output to ensure that it focused on the relevant anatomical features. The results showed very distinct strengths and trade-offs among the models, where ResNet and V-Net generally provided the best segmentation accuracy. This work not only enhances the fetal brain imaging analysis through the integration of and underlines their potential to improve diagnostic workflows for the early detection of fetal brain abnormalities. © 2024 IEEE.

关键词： Deep Learning Medical Image Analysis Ventricle Segmentation

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Smart Refrigerator for Enhancing User Convenience and Food Management 2

Smart Refrigerator for Enhancing User Convenience and Food M...

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

作者： Nandini, Bandaru Jaya Sahithi, Mamidi Leha Harshika, Siddareddy Gari Thangam, S. Gurupriya, M. Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering India

ISBN: (纸本)9798350372892

Many individuals face challenges in effectively managing their household food storage due to limitations in traditional refrigerator technology. These limitations include challenges in monitoring the weights of food, receiving timely reminders for restocking, and incorporating location-based services. Existing solutions lack real-time inventory information, fail to provide timely restocking reminders, and lack integration with location-based services. The proposed Smart Refrigerator solution offers accurate inventory management, remote monitoring and control, location-based notifications, Peltier cooling system, and enhanced user experience through IoT technology and innovative sensor systems. A mobile application complements the system, providing users with remote monitoring, control, and locationbased notifications. Societal benefits include reduced food waste, improved food safety, enhanced user experience, and efficient household inventory management, contributing to sustainability and consumer well-being. The proposed Smart Refrigerator results demonstrated significant improvements in inventory management accuracy and user convenience with DS18B20 temperature sensors, load cells, and gas sensors, reducing food *** remote monitoring, control capabilities, and location-based notifications contributed to increased user satisfaction. © 2024 IEEE.

关键词： Location based services

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A Hybrid Deep Learning Model For Emotion Recognition For Discrete Speech 15

A Hybrid Deep Learning Model For Emotion Recognition For Dis...

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

作者： Sohel, Mohammad Aman Madhukar, Raghupatruni Sai Oyyavuru, Puneeth Kumar Rahul, Sriramoju Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370249

Human emotion recognition is recognized as essential for effective communication, understanding development, and developing meaningful connections. Its importance depends on assisting individuals in recognizing social signs, interpreting varied situations, and fostering psychological well-being. Furthermore, it helps with decision-making, conflict resolution, and general community cohesiveness. Recognizing human emotions in speech is especially important for supporting effective communication, raising awareness, and understanding complex nuances in interpersonal dynamics. This proficiency not only improves mental processes, but it also encourages deeper relationships and more compassionate reactions. The model is based on supervised deep learning principles and was particularly designed for emotion identification using discrete inputs. It employs data augmentation techniques to resolve imbalances in lower instance classes and experimentation using multiple deep learning models implemented that include CNN, LSTM and CLSTM. CLSTM Model reported the best performance with an F1-score of 0.77. © 2024 IEEE.

关键词： Deep learning Model discrete speech emotion detection human-machine interactions RAVDESS dataset voice tone

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Scrutinization of Threats from a Cloud-based Network Traffic Environment 18

Scrutinization of Threats from a Cloud-based Network Traffic...

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Chopra, Shraddha Sreedevi, A.G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9789380544519

In this age of digitalization, securing computer networks is of utmost importance to protect sensitive data and critical infrastructure. Threat scrutinization plays an integral role in identifying and thwarting cyber risks. This study focuses on a pioneering strategy to bolster the network's reliability by harnessing Artificial Intelligence (AI) techniques in conjunction with cloud computing infrastructure, particularly AWS SageMaker. The focus lies in leveraging a comprehensive and realistic cyber dataset, CSE-IDS-2018, to assess the efficacy of the proposed model. The study commences by architecting a threat scrutinization mechanism that harnesses the feasibility of machine learning algorithms, deep learning models, and cloud computing resources. Specifically, the Random Forest algorithm is employed for the multi-class classification of seven types of attacks within the CSE-CICIDS2018 dataset, facilitated by AWS SageMaker's machine-learning capabilities. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Artificial Intelligence (AI) in cybersecurity AWS SageMaker Cloud computing Infrastructure CSE-CIC-IDS-2018 Machine Learning Network Security Random Forest Threat Traffic

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Zero Day Attack Detection and Simulation through Deep Learning Techniques 14

Zero Day Attack Detection and Simulation through Deep Learni...

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14th International Conference on Cloud computing, Data science and engineering, Confluence 2024

作者： Arun, Aalap Nair, Anjaly S Sreedevi, A.G. Amrita School of Computing Amrita Vishwa Vidhyapeetam Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350344837

Combating zero-day attacks is essential in the age of ongoing cyber threats. For simulating and identifying these dangers, our research uses Long Short-Term Memory (LSTM) algorithms, which are skilled at collecting temporal data correlations. This novel method signals a paradigm shift in cybersecurity. The project begins by developing a complex framework to model intricate zero-day attacks that imitate previously undiscovered vulnerabilities. Our detection method uses recurrent neural networks and sophisticated gating techniques, and it is LSTM-based. Its capacity to recognize novel assault patterns and pinpoint minute departures from the usual is demonstrated by rigorous testing. The result is a powerful zero-day attack detection system that improves accuracy and responsiveness. This reduces the potential damage that unknown vulnerabilities could inflict. © 2024 IEEE.

关键词： Long short-term memory

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Comprehensive Study: Advancements in Parkinson's Disease Diagnosis with Data-Driven Insights and Machine Learning

Comprehensive Study: Advancements in Parkinson's Disease Dia...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Subhashini, S.M. Sriram, Suthir Nivethitha, V. Amrita School Of Computing Amrita Vishwa Vidyapeetham Department Of Computer Science And Engineering Chennai India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366846

The authors have a unique strategy that has never been used before, suggesting that a wearable device that records data is a combination of data from patients with Parkinson's disease and healthy individuals. Thus, it can accurately record motor features that are especially prevalent in Parkinson's disease, allowing for the actual identification of motor patterns peculiar and symptom intensity assessments. To further innovate in the field of self-assessment and individual evaluation of a patient's Parkinson's disease progression, the study points a significant relationship between clinical tests and quantitative symptom scores. Finally, a planned second opinion system using sensors and machine learning would help reduce the number of people misdiagnosed by comparing individuals with Parkinson's disease and those with similar phenotypic diseases. The differential diagnosis of tremor utilizing machine learning algorithms has been validated in various patient cohorts, significantly enhancing diagnostic specificity. In addition, the current study underscores the game-changing aspect of wearable technology and machine learning for medical care, eliminating resource constraints and allowing for the early identification and treatment of diseases. The experiment aims to improve remedy results and PD sufferers lives by recognizing barriers and encouraging ongoing research and innovation. It features the importance of patient-oriented monitoring methods, which are among the most valuable for PD patients with advanced disease. These techniques integrate quantitative sensor data analysis with handwritten comments and remote video monitoring. Finally, the investigation promotes tailored approaches to diverse patient demands, achieving better PD intervention and care capabilities. © 2024 IEEE.

关键词： Diagnosis

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Efficient data management through ML-based feature extraction 15

Efficient data management through ML-based feature extractio...

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

作者： Krishna, Adwaith E.R. Varundev, M. Nair, Lekha S. Surendran, Sidharth Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

Multimedia data production, such as audio, video, and acoustic measurements in large volumes and speed, has approached the scale of big data in many sensor networks. Multimedia Sensor Networks (MSN) are popular because of their capability to manage and integrate the different domains of sensor outputs, their representations, and the encoding of these measurements. Studies on generic and specific models for detection and tracing events in a sensor network have been established by multiple models. At the same time, there is still the requirement to handle these measurements from the Complex Dataset perspective. In the current world scenario, sensor networks are not limited to producing high-quality and diverse sensor readings but also large volumes at high speed. The most intuitive methods of handling them focus only on the interested range of measurement or store-for-later use. The possibility of data analysis in such an information-rich big data environment can be improved. In this study, we propose a model, "Multilink" that helps to align the multimedia data generated by a sensor network to a standard easy-to-process feature space with the incorporation of Machine Learning centric architecture. ©2024 IEEE.

关键词： Big data

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