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Optimising Energy Efficient Routing protocols in Wireless Sensor Networks - A comparative study 2

Optimising Energy Efficient Routing protocols in Wireless Se...

引用

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Manideep, Thotapalli Sri Surya Rohan, Gamidi Saragadam, Sneha Sahay, Apurvanand Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350368413

This study presents a comparative overview of optimization techniques that include, among others, Ant Colony Optimization, Firefly Algorithm, Bat Algorithm, Bee Colony Optimization, Q-learning, Dynamic Programming, and Branch and Bound Algorithm. Addressing each domain in detail, explanations will be offered for the methods described: for example, ACO's foraging behavior, Firefly's light attraction, Bat's echolocation, and BCO's collaborative approach. The Travelling Salesman Problem (TSP) is treated with the DP algorithm and Branch and Bound. Different tests in each algorithm are made, and their efficiency and weaknesses are approximated to guide the selection of a proper approach to be used in future optimizations. This research highlights the strengths and drawbacks of each method, thus enhancing the understanding of how these optimization techniques operate in various fields and contributing to the informed selection of algorithms for specific problems. The results provide practical insights and suggest directions for further improvement and application in real-world scenarios. © 2024 IEEE.

关键词： Ant colony optimization

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Modeling Temple Run Gameplay with Finite State Machines 3

Modeling Temple Run Gameplay with Finite State Machines

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Datta, P. Tapan Lalith Kumar, D. Tanoj, S. Radha, D. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350364569

The ambitious task of implementing the popular Temple Run game in a new approach that is used to create a version of the Temple Run game that is represented in Non-Deterministic Finite Automata (NFA) and Deterministic Finite Automata (DFA). Temple Run, celebrated for its dynamic gameplay and constantly changing obstacles, offers an ideal platform for these automata models in gaming mechanics. The initiative focuses on using Non-Deterministic Finite Automata to generate diverse and evolving pathways within the game environment, ensuring players encounter a variety of challenges and opportunities. Conversely, Deterministic Finite Automata will regulate the behavior of in game elements like obstacles, abilities, and enemies, establishing clear rules and patterns for their movements and interactions. Through the integration of both NFA and DFA models, an immersive Temple Run experience is aimed to be created, seamlessly combining unpredictability with structured gameplay, ensuring each gaming session is both engaging and rewarding for players. © 2024 IEEE.

关键词： Digital elevation model

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Visual Question Answering in Malayalam Text 3

Visual Question Answering in Malayalam Text

引用

3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Joseph, Jose Ram, V Anand YadhuKrishna, P. Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350396157

One of the most important tasks in combining computer vision and natural language understanding is Visual Question Answering (VQA). This paper focuses on the VQA problem in Malayalam, a language known for its unique Dravidian linguistic characteristics. The robust LXMERT model, a bidirectional encoder-decoder architecture capable of handling multimodal input, is used to achieve this goal. Extensive testing on an independent set of Malayalam images and questions highlights the feasibility of building a trustworthy VQA model on a Malayalam dataset. The method shows adaptability when answering questions about Malayalam images, which creates opportunities for image retrieval, annotation, and possibly image-based machine translation. This demonstrates how the model can be applied to real-world situations in a variety of fields. © 2024 IEEE.

关键词： computer vision

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A Novel Image Encryption Technique Leveraging Irrational Expansions and New 3D Chaotic Map 5

A Novel Image Encryption Technique Leveraging Irrational Exp...

引用

5th International Conference on Circuits, Control, Communication and computing, I4C 2024

作者： Aggarwal, Priyanshu Ashwini, K. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798331528539

To solve the significant issues of data security in the digital age, this work investigates the combination of chaotic map-based approaches with irrational expansions for picture encryption. Chaotic maps offer a strong foundation for encrypting picture data, guaranteeing high degrees of security and protection against unwanted access. They are distinguished by their sensitivity to beginning circumstances and pseudorandom behaviour. In addition to exploring the theories and techniques of chaotic map encryption, a brand-new chaotic map has also been put out. To increase the security and effectiveness of digital image processing systems, the study highlights the importance and promise of chaotic maps through a thorough examination of existing methods and new developments. The findings underscore the relevance of continuous research and development to address the growing demands for secure and efficient image data management in various applications. © 2024 IEEE.

关键词： MapReduce

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FER-Net: Leveraging Attention Mechanisms in EfficintNetB5 model for Enhanced Emotion Recognition in Augmented Images 5

FER-Net: Leveraging Attention Mechanisms in EfficintNetB5 mo...

引用

5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Sulochana, Balla Charishma Pragada, Bhavya Sri Reddy, Gaddam Anvith Singh, Rimjhim Padam Palaniswamy, Suja Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Facial emotion recognition is a critical component in the development of advanced human-computer interaction systems, with applications spanning security, healthcare, and social robotics. This study explores the efficacy of advanced deep learning models, specifically EfficientNetB5 model in identifying emotions from facial images. Leveraging with augmented data, our approach integrates attention mechanisms, batch normalization, convolutional layers, global average pooling layers, and dropout techniques to enhance model performance. The proposed model demonstrated significant performance accuracy of 87.5%, highlighting the importance of emphasizing relevant image regions for better emotion detection. Additionally, we conducted a comparative analysis with other prominent deep learning architectures, including AlexNet, XceptionNet, ResNet, MobileNet and EfficientNetV2M and proved the superiority of the proposed model against them. © 2024 IEEE.

关键词： Contrastive Learning

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Deep Learning based Terrain Classification of Mars Raw Images using UNet and FCN Models 18

Deep Learning based Terrain Classification of Mars Raw Image...

引用

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

作者： Samhitha, D. Sai Ashwini, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai India

ISBN: (纸本)9789380544519

The exploration of Mars has consistently been of utmost significance in understanding planetary development, suitability for life, and the possibility of establishing civilizations. Precise classification of Martian terrain is essential for effective navigation, identification of resources, and prevention of potential dangers, to enhance the effectiveness of Mars missions. Traditional methods of terrain categorization frequently depend on manual examination and have constraints in terms of scalability and precision. This paper introduces an innovative method that utilizes deep learning algorithms to completely transform the process of classifying Martian terrain. This study employed the Unet and FCN models to categorize the Martian surface by utilizing a wide variety of images obtained from the AI4MARS dataset, from which a collection of 5K to 15K images are extracted. The results show the effective performance, the FCN model attained an Intersection over Union (IoU) score of 73%, while the Unet model acquired a score of 60%. The results emphasize the capacity of deep learning to revolutionize the classification of the Martian landscape, providing a more precise and adaptable answer in contrast with traditional approaches. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： AI4MARS Deep Learning Fully Convolution Network (FCN) Mars Exploration ResNet50 Terrain Recognition UNet

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Energy Conservation Using Piezo Electric Effect Through Modern Technology 9

Energy Conservation Using Piezo Electric Effect Through Mode...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Durga, Kanaparthi Dhathri, Kammarapalli Sri, Kanchukommula Sai Lavanya, Kandagaddala Naga Lakshmi School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350377972

This paper seeks to establish the energy conservation by using piezo-electric effect through some Modern Technology's and the project aims to investigate the application of modern technology in various fields to reduce energy consumption and increase energy efficiency. We focus on three key areas: buildings, transportation, and industry. In each of these areas, we identify the most significant energy consumption sources and potential opportunities for energy savings using modern technology. This aims to explore innovative solutions for sustainable energy generation by harnessing mechanical energy from human movement. This research focuses on integrating piezoelectric cells into everyday footwear and flooring tiles to convert mechanical stress into electrical energy. The project involves designing and fabricating prototypes of energy-harvesting shoes and tiles. The efficiency of energy conversion, the durability of the materials, and the overall feasibility of integrating such systems into daily life will be evaluated. The study will also address the scalability of this technology and its potential impact on reducing the reliance on traditional power sources. © 2024 IEEE.

关键词： Piezoelectricity

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Graph-Based Approaches to Detect Network Anomalies and to Predict Attack Spread 15

Graph-Based Approaches to Detect Network Anomalies and to Pr...

引用

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

作者： Rithuh Subhakkrith, S. Kumar, Y. Jagadish Harish, R. Dheeraj, B. Vishnu Vidhya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

Real world applications of data structures is enormous. Queues, stacks, trees, and graphs are commonly applied and implemented across various fields. To improve the efficiency of such implementations, optimisation through the underlying data structure can be considered. computer networks inherently are based on graphs with communicating systems as nodes and communication channels as edges. Information flows between the communicating systems through the communication channel. To detect suspicious connections and information flow, an optimal graph structure is implemented by modelling the information flow as edges, and retaining the communicating systems as nodes. Graph analytics is performed to capture graphical relationships, and the final graph structure is formed with the features of the information flow split into node features and edge features. Then this graph structure is passed to Graph Neural Networks (GNN) that captures hidden relationships of the graph, and transforms these graph features in such a way that basic Machine Learning models like k-Nearest Neighbors (KNN), Support Vector Classifier (SVC), and Random Forest can train themselves and model into better anomalous connection classifiers. Eleven baseline models are trained using the traditional way, and using GNN transformed embeddings, and their performances are observed. Testing shows that all models show an improvement in the evaluatory scores. Finally, a logic is provided to build an attack sequence graph to track the route in which the attack spreads among infected systems. © 2024 IEEE.

关键词： Attack Sequence Graph Edge Classification Graph Analytics Graph Neural Networks Network Security

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Banana Freshness Classification: A Deep Learning Approach with VGG16 6th

Banana Freshness Classification: A Deep Learning Approach wi...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Patre, Falguni Vasant Arya, Aditya Saranya, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819774227

In the food sector, ensuring the safety and quality of banana products is crucial. The classification of bananas into "fresh banana" and "rotten banana" categories is the main objective of this study. The studied banana varieties are cavendish, lady fingers, and red bananas. We use the VGG16 deep convolutional neural network with a dataset of high-resolution banana images. Our method includes training-testing ratios, picture enhancement, and rigorous data preprocessing. The results indicate how well the VGG16 model performs in classifying freshness, with good recall, accuracy, precision, and F1-score. Additionally, the model successfully differentiates between cavendish, lady finger, and red bananas, highlighting its capacity to handle minute variations. This work expands the application of image classification to other fruit varieties by offering a dependable technique for quality control and automated evaluation of banana freshness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Food industry

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Epilepsy Classification using Multi-Sequence MRI's: An Ensemble Approach 15

Epilepsy Classification using Multi-Sequence MRI's: An Ensem...

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

作者： Rajasekhar, Alamanda Hima Naga Kavya, Gurram Sriya, Kota Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Epilepsy is a major threat, when it comes to human health. Classifying brain anomalies on MR images as a pathological clue of Epilepsy is crucial for diagnosis. Electroencephalography (EEG) and Magnetic resonance (MR) imaging are only two of the diagnostic tools that have been proposed for use in screening for epileptic seizures. The diagnosis can be made with the use of many MR images taken at different times by a neuroradiologist. The dataset under consideration is comprised of axial multi-sequences of MR images, and it features both epileptic and nonepileptic aberrant brain structures from a wide range of people. This article performs a comparative study among the models - CNN, MobileNet, InceptionV3, VGG16, VGG19, LSTM, GRU and also the Ensemble models - 'CNN + MobileNet', 'VGG16 + LSTM', 'VGG16 + GRU', 'VGG19 + LSTM' and 'VGG19 + GRU'. The Epilepsy prediction for user upload images is achieved through an ensemble approach. We have demonstrated the effectiveness of these models in correctly detecting epileptic patients based on multi-sequence MRI data by achieving extremely accurate and robust classification findings through extended experimentation. The experiments demonstrated the classification best accuracy of 97.16% for 'VGG19 + LSTM', while the lowest score was 69.93 for InceptionV3 among all the twelve models. © 2024 IEEE.

关键词： Electroencephalography

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