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Smart Surveillence Drone with Navigation System 5

Smart Surveillence Drone with Navigation System

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Padmavathi, A. Vyasasreevedansh, B. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

At present, in this time of technological advancement and an urgent necessity for advanced spying abilities, the 'Smart Surveillance Drone with Navigation System' program is leading the way in redefining the field of surveillance and safeguarding. The way in which this project is leading is through the integration of the most advanced drone technology, complex navigation systems, and real-time computer vision, striving to create an independent drone model that can be identified and followed by human subjects in real-time while travelling between predetermined waypoints. The key aims of the program involve the assembling and integrating of a range of sensors, such as GPS, IMU, cameras, ultrasonic sensors, and LiDAR, alongside the production of independent navigation algorithms and computer vision technology. By combining cutting-edge technologies such as wireless communication, real-time data processing, and machine learning, the revolutionising of surveillance in several domains is being set out by this project. The adaptability and reliability of the system will be verified by rigorous field testing, while the experience will be enhanced by user feedback and training. With such advancements, the future of disaster response, agriculture, search and rescue operations, and security is promised to be innovative and efficient. This project that's set to change how we think about surveillance and security is captured by this short summary, and the way is being paved for a safer and more secure world. © 2024 IEEE.

关键词： Autonomous Surveillance Navigation System Real-time Object Detection Security Innovation Surveillance Drone

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Interactome: A Platform for Comprehensive Drug-Drug Interaction Analysis 15

Interactome: A Platform for Comprehensive Drug-Drug Interact...

引用

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

作者： Sivakumar, Amrutha Bhanot, Shreya Srinidhi, M. Aiswariya Milan, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

Drug-drug interaction is an important aspect to be taken into consideration when administering multiple drugs to patients with severe diseases. It is important to know if the combination of the drugs causes a positive effect or an ill effect on the person's body. Thus, Interactome roots to offer a comprehensive solution to assess and find out the interactions between commonly used drugs for the treatment of different ailments such as heart diseases or diabetes and provides an understanding of how the combination of these drugs can cause side effects if not provided with enough details about the drug. Thus, it enables the user to know alternatives to using efficient drugs without any side effects by displaying graphs of the percentage of positive and negative effects and charts depicting the effects. By providing negative and positive feedback on the combination of various drugs used for heart and diabetes patients and displaying the percentage risks with the help of graphs and charts, Interactome ensures the prevention of any unnecessary attacks. Also, it saves the lives of an individual by spreading awareness about drug-drug interactions. We incorporate a Trie data structure that efficiently analyzes the input medications against a database of known interactions. It optimizes the retrieval process by organizing the drugs into a hierarchical structure. Then it ensures rapid and accurate results so that healthcare professionals and individuals receive timely and comprehensive insights into potential drug interactions. © 2024 IEEE.

关键词： charts Drug-drug interaction graphs negative effects positive effects Trie data structure

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Deep Learning Based Multi-Class Brain Tumor Detection and Localization Using SVM 1

Deep Learning Based Multi-Class Brain Tumor Detection and Lo...

引用

1st International Conference for Women in computing, InCoWoCo 2024

作者： Yaswanth, Julakanti Sai Venkatesh, K. Jilani, Nemaladdinne Abdul Khadar Afnaan, K. Singh, Tripty Duraisamy, Prakash Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331518943

Brain tumors using MRI images were a long-standing issue for mankind. Early disease detection and treatment leads to a better quality of but relying on human instincts alone is not enough. In recent times many advancements in Deep Learning are showing better results in the medical field and as the number of cases and the risk of death from tumors are on the rise, there's a need for better tools to help in identifying these growths more effectively. So, our work in this paper focuses on using convolutional deep learning and hybrid deep learning approaches to enhance the classification of different brain tumors like glioma, meningioma, and pituitary tumors, from MRI images and also to identify the exact location of brain tumor for the early diagnosis of tumors in patients using MRI images. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Comparative Analysis of Legal Outcome Prediction with Detailed and Summarized Text 9

Comparative Analysis of Legal Outcome Prediction with Detail...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Varshini, Surisetty Hima Varma, Gottimukkala Sarayu Prabhakar, Priyanka Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350394474

Legal Outcome Prediction(LOP) is the process of predicting the possible outcome of a court case based on the contents in the case *** aim of this work is to perform a comparative analysis to assess the effectiveness of popular language models for outcome prediction with the case file as well as its summary *** models of BERT, LegalBERT, DistilBERT and RoBERTa are enhanced with transfer learning on the legal data files and the models are evaluated with accuracy and F-score. It is observed that RoBERTa performs best in LOP for both complete case file text as well as for summary text. © 2024 IEEE.

关键词： Forecasting

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Building a Secure and Scalable Finance Chatbot: Client-Server Architecture, Load Balancing, and TLS Security 4

Building a Secure and Scalable Finance Chatbot: Client-Serve...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Reddy, T. Anisha Akhilesh, S. Manogna, Sodum Bhaskaran, Sreebha Kavitha, C.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331540364

This research introduces a new and improved model for a financial chatbot based on deep learning, machine learning, and natural language processing to provide suitable financial services and increase the financial literacy of users. It utilizes a detailed financial database, another complex machine learning structure for market forecasts., and natural language processing to parse user questions. Moreover, there is the customer resource management feature that applies machine learning in the analysis of financial data and natural language processing for understanding the user's intent. The paper also looks into the use of transport layer security (TLS) encryption to secure the flow of the sensitive information, the data that is to be transmitted from the chatbot server to the user clients. By integrating the dimensions of concepts such as financial analysis, personal finance advice, and data transmission, this research provides an extensive and secure approach to improving one's financial situation. © 2024 IEEE.

关键词： Financial data processing

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Can Technical Festivals Help in Attaining Undergraduate engineering Programme Outcomes? 13

Can Technical Festivals Help in Attaining Undergraduate Engi...

引用

13th IEEE International Conference on Teaching, Assessment and Learning for engineering, TALE 2024

作者： Dhanalakshmy, Dhanya M Murali, Ritwik Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350376234

The curriculum framework of an undergraduate engineering programme contains clearly defined learning outcomes. It is expected that students who graduate from a specific degree / diploma are able to attain these goals. Towards this, while not mandatory, every institution is encouraged to also host technical events such as technical festivals, workshops, technical competitions, hackathons, etc. This work explores the challenges and merits in leveraging undergraduate technical festivals to enable an evaluation of students towards the attainment of qualitative Programme Outcome. Using qualitative analysis strategies including surveys and one-on-one interviews, this work shows that technical festivals provide unparalleled hands-on experience in medium to large-scale project management, encompassing crucial disciplines that are often overlooked in the classroom evaluation rubrics. The major takeaways from organizing a technical festival can be summarized as large-scale organizing of events, better time and people management, improved communication skills, and the positive attitude / ability to learn new things. This work also mapped the specific programme outcomes to the actual learning that could be attained though the organization of a technical festival. This work also highlights the importance of such events in order to bridge the gap between industry & academia while being able to assess qualitative outcomes effectively. © 2024 IEEE.

关键词： Project management

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A Comparative Analysis of ML & DL Models for Fetal Health Classification Using Cardiotocographic Data

A Comparative Analysis of ML & DL Models for Fetal Health Cl...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Kaushik, Tata Sri Vani, Tunuguntla Aasritha Vithyatharshana, N. Khare, Sangita Amrita Vishwa Vidyapeetam Amrita School of Computing Bengaluru Department of Computer Science and Engineering India

ISBN: (纸本)9798350361537

Traditional diagnostic methods in fetal health are frequently hindered by class imbalance and complex data, which puts early intervention and optimal perinatal outcomes at risk. This study fills this important gap by conducting a thorough comparative analysis of several ML and DL models that have been effective in fetal health research. This project goes beyond the obvious applications and investigates how model performance is affected by Principal Component Analysis (PCA) and Chi-Square for feature extraction and feature selection respectively and Conditional Generative Adversarial Networks (CGANs) for data augmentation. This project seeks to determine the best model or ensemble for dependable and accurate fetal health prediction by methodically assessing model performance across all its forms, CGAN-augmented versions, integrated CGAN - PCA and CGAN - Chi-Square configurations. This will provide medical professionals with a data-driven tool for early diagnosis and well-informed clinical decisions. This thorough study has the potential to greatly enhance perinatal outcomes and improve the health of moms and their unborn children. It is positioned to transform prenatal care. In addition to identifying the best model, this comparative analysis lays the foundation for future research examining the integration of explainable AI techniques to gain deeper insights into fetal health dynamics and to understand the internal decision-making processes of the model. © 2024 IEEE.

关键词： Generative adversarial networks

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Automated Classification and Size Estimation of Fetal Ventriculomegaly from MRI Images: A Comparative Study of Deep Learning Segmentation Approaches 5

Automated Classification and Size Estimation of Fetal Ventri...

引用

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

作者： Gopikrishna, K. Niranjan, N.R. Maurya, Siddharth Uday Krishnan, V.G. Surendran, Simi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Fetal ventriculomegaly is one of the major risks in prenatal diagnosis, which is an enlargement of the ventricles of the developing fetus's brain. Timely prediction of these brain disorders helps patients and healthcare providers make informed decisions and treatment plans. This paper investigates the classification of ventriculomegaly from MRI images using a deep learning approach. Segmentation of ventricles is performed using DeeplabV3+ and U-Net architectures and the accuracy, dice coefficient, and loss of these two approaches are compared. Additionally, a novel approach for size estimation was developed, utilizing contour detection to delineate ventricle boundaries and compute their areas by enclosing them within rectangles. A threshold-based classification method was then applied to differentiate between abnormal ventriculomegaly and normal cases. This research contributes to the assessment and comparison of the segmentation algorithms for aiding in the identification and classification of ventriculomegaly cases, offering potential implications for clinical diagnosis and treatment planning. © 2024 The Authors. Published by Elsevier B.V.

关键词： Clinical research

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Pedestrian Intention Detection for Autonomous Driving: A Novel Object Detection Approach 10

Pedestrian Intention Detection for Autonomous Driving: A Nov...

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Padmavathi, A. Pullela, Dheeraj Reddy Nazeer, Shaik Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350353778

Detecting pedestrian intentions is crucial for safer interactions between autonomous driving and pedestrians. This study addresses this challenge by proposing an object detection model for autonomous driving using YOLOv8. Our model identifies pedestrians and categorizes their intentions as "waiting"or "crossing"based on bounding box analysis. Utilizing YOLOv8, the model recognizes pedestrians in real-time video streams and interprets their actions to determine their intentions. The model's performance is evaluated through metrics such as precision, recall, and F1 score, achieving high accuracy. This approach enables proactive decision-making based on pedestrian intentions, significantly enhancing autonomous driving and driver assistance system safety. The results highlight the model's potential to improve pedestrian safety in traffic, contribute to autonomous driving technology, and benefit connected vehicles. © 2024 IEEE.

关键词： Pedestrian safety

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Malware Image Classification with Enhanced Feature Extraction Using VGG16 and Spatial Pyramid Pooling 8

Malware Image Classification with Enhanced Feature Extractio...

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8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Ryan, Collin Diviya, M. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798331531539

In the present era, the rise in number and complexity of malware poses a major threat leading to the rise in the importance of smarter Malware detection systems to understand and detect modern malwares. Modern systems are often very slow and inefficient with outdated machine learning models and feature selection techniques. These inefficiencies are overcome by the use of a multiclass detection system with CNN application to automatically classify malware. The system represents malware binaries in image format and uses VGG16 based network architecture with Spatial Pyramid Pooling (SPP) and ReLU activation to efficiently retrieve multiscale features from images of variable dimensionality. The model also includes dropout regularization, categorical cross entropy and Adam optimizer for better performance, adaptability and generalization. This approach works with a well balanced dataset with extensive training and achieved an accuracy of 95.94% making it a reliable and scalable solution for real time threat detection in a dynamic environment. © 2024 IEEE.

关键词： Convolutional neural networks

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