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Blockchain-Based Carbon Offset Tracking System for Sustainable Product Management

Blockchain-Based Carbon Offset Tracking System for Sustainab...

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Electrical Power engineering, Communication and computing Technology(ODICON), Odisha International Conference on

作者： A Padmavathi Laxmi Maurya Gauri Santhosh G Charan Reddy Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

The agriculture sector of our country has been an important part of economic activity for a long time. Rather than the increasing demand shown by population growth, profitability remains difficult. This paper introduces an approach aimed at fixing agricultural marketing networks through the integration of blockchain technology. The proposed method uses blockchain to virtualize products and carbon offset certificates, providing a way for agricultural producers to mitigate carbon emissions and point out environmental concerns. With the help of the blockchain in this context, carbon offset credits can be securely tracked, traded, and utilized to promote sustainability practices. This innovative and new integration promises to revolutionize current agricultural product marketing methods, along with promoting environmental sustainability and empowering consumers with comprehensive information about the environmental footprint of their purchases.

关键词： computers Visualization Power engineering Profitability Green products Carbon dioxide Agricultural products Blockchains Sustainable development Carbon

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

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

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Women in computing (InCoWoCo), International Conference for

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

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

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.

关键词： Deep learning Support vector machines Location awareness Magnetic resonance imaging Brain tumors Glands Medical diagnostic imaging Diseases

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Cryptographically Secured Data Transmission Over a Network with NetCAT

Cryptographically Secured Data Transmission Over a Network w...

引用

Electronics, computing, Communication and Control Technology (ICECCC), International Conference on

作者： Subhiksha Suresh Rao Sahil Mishra S Akhilesh Kavitha C.R. Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

This study focuses on cryptically secure data trans-mission on a network with NetCAT and emphasizes the improvement of the Advanced Encryption Standard (AES) algorithm to improve data security. It specifically addresses file encryption using the modified AES and uses the flexibility of NetCAT utility for safe transfer. In particular, the deliberate transformation of intercepted data into incomprehensible gibberish emphasizes the robustness of modified AES against potential surveillance and unauthorized access. This document presents an improved and practical approach to improving data protection during trans-mission, especially in the context of encrypted data transmission through a network with NetCAT. Through the discussion of this specific topic, it contributes significantly to the evolving field of cryptography and emphasizes the need for accurate specifications and a clear focus on the treatment of data security challenges.

关键词： Resistance Surveillance Data protection Solids Robustness Encryption Data communication

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Energy Management System - Deep Reinforcement Learning Based Dueling DQN (Deep Q-Networks)

Energy Management System - Deep Reinforcement Learning Based...

引用

Innovation in Technology (APCIT), Asia Pacific Conference on

作者： Uthej K Kothuru Gurunadh Nagandla Krishna Sai Keerthan Nikhil Kumar Musunuru Amudha J Department of Computer Science & Engineering Amrita School Of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

In today's energy-conscious world, efficient energy management has become a critical challenge. Buildings, being one of the major energy consumers’ usages of light source, humidity control irrespective to surrounding environment conditions and presence of human, requires innovative solutions to optimize energy consumption. The proposed study presents a EMDRL (Energy Management Deep Reinforcement Learning), an energy management system for room optimization that makes use of Deep Reinforcement Learning (DRL) approaches notably Deep Q-Learning, dynamic decision making of a DRL agent with environment interaction. Deep Reinforcement Learning extends the principles of RL by incorporating Deep Neural Networks to learn optimal behaviors. This includes variations of DQN algorithms, such as Deep Q-Network (DQN), Double Deep Q-Network (DDQN), Dueling Q-Network (Dueling DQN) and its hyperparameter tuning and comparative analysis of the three algorithms and an evaluation of their performance. The comparative analysis has contributed valuable insights, providing a viable resolution to the problems associated with modern building energy management and cost efficiency in which Dueling DQN our novel model’s agent that has contributed a significant performance of 136%, 144% more reward gaining and less energy-efficient by saving cost compared to Double DQN and traditional DQN respectively.

关键词： Technological innovation Costs Q-learning Heuristic algorithms Deep reinforcement learning Humidity control Energy management systems

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A Comparative Analysis of Black-Box and White-Box Models for IOT Botnet Detection

A Comparative Analysis of Black-Box and White-Box Models for...

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International Conference for Convergence of Technology (I2CT)

作者： Richa Vivek Savant Spoorthi M Penumarty Krishna Mohan Sreebha Bhaskaran Shinu M. Rajagopal Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Botnets pose a significant threat to network security, especially to the potential server crashes they can cause. The proposed study focuses on methods for identifying the presence of and the type of botnet activity, by implementing machine learning techniques, and consecutively comparing the black-box and white-box approaches employed. Utilizing the CTU-13 and N-BaIoT datasets, the study involves data preprocessing and a comparative analysis of various models. The LGBM Classifier emerges as the top-performing model for detecting the presence of a botnet, while the XGBoost Classifier excels in identifying the specific type of botnet attack based on different performance evaluation metrics.

关键词： Performance evaluation Analytical models Botnet Data preprocessing Closed box Machine learning Network security

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Optimization Techniques for the Traveling Salesman Problem: A Study of Conglomerate of Algorithmic Approaches

Optimization Techniques for the Traveling Salesman Problem: ...

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Women in computing (InCoWoCo), International Conference for

作者： Yamini Niharika Thirumalaraju Akhila Vemula Vaishnavi Apurvanand Sahay Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

This project investigates Python to study the Traveling Salesman Problem (TSP) and looks at five different algorithms that can be implemented: Brute Force, Greedy, Genetic, Dynamic Programming, and Divide and Conquer. Locating the shortest path that makes accurate stops in each city before returning to the origin city is the task assigned to the TSP. In order to identify the best solution, the Brute Force algorithm thoroughly looks through every combination of city sequences. The nearest unexplored city is chosen at each stage via the Greedy Algorithm, in contrast, which makes locally optimal decisions. By applying genetic operators like crossover and mutation, genetic algorithms generate new generations of possible paths by iteratively improving existing ones, mimicking the principles of natural selection. To cut down on unnecessary computations, dynamic programming divides the problem into smaller subproblems and stores the answers.

关键词： Greedy algorithms Scalability Heuristic algorithms Urban areas Force Traveling salesman problems Dynamic programming Time complexity Optimization Python

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

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

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Sustainable Expert Systems (ICSES), International Conference on

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

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

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.

关键词： Finance Training data Chatbots Load management Encryption Security Servers Resource management Reliability Protection

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Real-Time Object Detection using Mobile Robot Captured Images: A Deep Learning Approach

Real-Time Object Detection using Mobile Robot Captured Image...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Hema Srivarshini Ch N Preeti Sreekar Praneeth Marri Suja Palaniswamy Peeta Basa Pati Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

Robots are becoming more and more prevalent in a variety of industries, such as transportation, agriculture, healthcare, and so forth. Deep learning is an area of study that is rapidly expanding and is applied in many fields including robots. For a robot to understand its environment and carry out tasks, object recognition is essential. This paper proposes a deep learning method that leverages both pre-existing data and real-time data for robotic object recognition which will enable robots to operate in challenging conditions. Using real-time data, CNN with decision tree is tested after being trained on both pre-existing and real-time data which yields 96% accuracy. A model has been created for reliable and accurate object recognition for robots.

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A Comprehensive Exploration of Machine Learning and Explainable AI Techniques for Malware Classification

A Comprehensive Exploration of Machine Learning and Explaina...

引用

Communication & computing (WCONF), World Conference on

作者： Athira Drishya Baburaj Deepa Gupta Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

The rapid evolution of malware presents a significant challenge to cybersecurity, necessitating more effective detection and classification methods. Malware attack is a vulnerable and prevalent threat in cyber security. Awareness about these threats and regular updation of security measures are critical. There are various techniques in which we can detect the malwares and prevent them from occurring. Identification of malware is crucial in this technological era to prevent destruction. In this work, we have used various machine learning classifiers including ensemble techniques such as boosting and bagging classifiers to classify four different types of Android Malwares. Random Forest has outperformed with a F1-score of 88%, and model Explainability has been deployed to reveal the most significant feature.

关键词： Measurement Deep learning Recurrent neural networks Explainable AI Forestry Boosting Malware Convolutional neural networks computer crime Random forests

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AMOSI:Attention-Based Multimodal Sentiment Intensity Analysis with LSTM-CNN Architecture for Online Opinion Videos

AMOSI:Attention-Based Multimodal Sentiment Intensity Analysi...

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International Conference on computing and Networking Technology (ICCNT)

作者： V V V Bhagya Sree G Bharathi Mohan R Prasanna Kumar M Rithani Gundala Pallavi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Through online video sharing websites, people share their thoughts, experiences, and reviews on a daily basis. Researching the subjectivity and sentiment in these opinion videos is becoming more and more popular among academics and industry professionals. Although sentiment analysis works well for text, there isn’t much research done on it when it comes to videos and other multimedia. The largest obstacles to research in this area are the absence of appropriate methodology, baselines, datasets,and statistical analysis of the relationships between data from various modality sources. The Multimodal Opinionlevel Sentiment Intensity dataset (MOSI), the first opinion-level annotated corpus of sentiment and subjectivity analysis in online videos, is presented to the scientific community in this paper. With labels for subjectivity, sentiment intensity, per-frame and per-opinion annotated visual features, the dataset is meticulously annotated. User-generated opinion videos are increasingly prevalent, necessitating advanced sentiment analysis techniques. We present an attention-based LSTM-2D CNN model for multimodal sentiment intensity prediction. Our contributions are three-fold: 1) We collect new annotations of visual gestures and acoustic effects in online videos to augment the MOSI dataset. 2) We design an end-to-end neural architecture to model textual, visual and acoustic modalities. Bi-directional LSTMs capture temporal dependencies in text and audio. 2D CNNs extract spatial visual features. A hierarchical attention mechanism identifies important utterances and frames. 3) Extensive experiments demonstrate 74.7% accuracy, outperforming prior BC-LSTM models by 1.7%. Ablation studies validate the impact of multimodal fusion and attention. Our work provides an improved neural baseline on MOSI, with detailed analysis of multi-view sequential modelling for opinion videos. The additional annotations and strong results will aid future research in this emerging domain.

关键词： Visualization Sentiment analysis Attention mechanisms Annotations computer architecture Predictive models Streaming media Feature extraction Acoustics Data models

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