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A Next-Generation Blockchain Framework for Secure and Tamper-Proof Online Examinations

A Next-Generation Blockchain Framework for Secure and Tamper...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Markala Anjali Sk. Ashraf Vali J. Divya B. Lokesh Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Online exams are a general aspect of education nowadays. However, they are still suffering with several security problems such as data breaches, result-changing cheating, and impersonation scams. Some of the modern systems utilize blockchain technology to improve security but come with problems such as being difficult to integrate, taking time to process, being costly, and lacking adequate privacy protection. This work introduces a blockchain-based structure that addresses these concerns. It utilizes smart contracts in a more improvised manner, employs zero-knowledge proofs to secure privacy. The system provides safe sharing of questions, decentralized storage of answers, and tamper-evident grading. It also has seamless integration with learning management systems (LMS). In comparison to traditional and blockchain-based examination systems, this method provides upgrades in security, efficiency, and speed. With the integration of decentralized technology and state-of-the-art security methods, this system provides internet-based exams that are secure, transparent, and economical.

关键词： Privacy Learning management systems Smart contracts Education Data breach Control systems Blockchains Security Protection Next generation networking

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Image-based System for Blood Group Classification

Image-based System for Blood Group Classification

引用

International Conference on ent Computing and Control Systems (ICICCS)

作者： K. Ramakrishna Reddy G. Indrani N. Pavan Kumar K. Vamshi Krishna Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

It provides a classic method for blood image based system for blood group classification techniques. The suggested approach reduces reliance on traditional processes by automating the analysis of blood sample images through the use of sophisticated image processing algorithms. This strategy seeks to offer a dependable and expandable solution that is especially useful for application in distant or resource-constrained environments.

关键词： Image segmentation Histograms Control systems Classification algorithms Blood

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Colorization of Grayscale Images Using Neural Network Without User Intervention

Colorization of Grayscale Images Using Neural Network Withou...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Vinish Kumar Santosh Verma Gaurav Raj Department of Computer Science and Engineering (AI&ML) RKGIT Ghaziabad UP India Course Mentor-Geeksforgeeks Noida UP India

The task of colorizing grayscale images has been performed manually for a long time. Recently, neural networks have found their use in this domain. In this project we propose a system that takes a grayscale image as an input and produces a colored image of the same. The technique implemented in this project does not require complicated human inputs or image processing techniques, but rather training of the neural network much like a human mind. Where manual colorization methods can take months to color a single image, neural networks, once trained, are much more efficient in terms of time consumption and also comparable levels of accuracy can be achieved which depends upon training of the neural network. The Neural network works on hit and trial method; at first it makes a random prediction for a pixel, calculates the error and then tries to reduce it as much as possible by the method of backpropagation. The classifier, trained using backpropagation of information, predicts the color of a pixel based on the grayscale value of its own as well as the surrounding pixels in the grayscale image.

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Navigating the Future: Deep Reinforcement Learning for Smart Autonomous Vehicles

Navigating the Future: Deep Reinforcement Learning for Smart...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： M. Venkateswarlu G. V. N. Shri Himaja V. Sainath Reddy M. Vineeth Department of Computer Science and Engineering(AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Intelligent transportation is gaining much attention about autonomous driving systems. This research will present a fully developed comprehensive framework to implement the navigation of autonomous vehicles using CARLA. Deep learning-based object detection by the use of YOLOv5 is used in the current project, which identifies both real-time vehicle traffic, traffic lights, and obstacles. In parallel, lane detection is achieved through image processing techniques, such as edge detection and Hough Line Transform. The proposed system integrates these capabilities with decision-making algorithms to facilitate responsive vehicle control in dynamic environments. In addition, the validation of autonomous behaviour from a holistic point of view uses third-party camera views. Multiple vehicle traffic simulations add realism to the environment. A pilot function simulates the actual condition, with a synchronized simulation approach for the correct timing in processing and analysing data. The outputs include saved images, processed frames, and videos demonstrating the efficacy of the system under varying scenarios. This research integrates modern computer vision techniques with simulated environments to create a robust testing platform for autonomous navigation. Future research can extend this framework to real-world applications or incorporate advanced reinforcement learning algorithms for enhanced decision-making capabilities.

关键词： YOLO computer vision Navigation Lane detection Heuristic algorithms Decision making Traffic control Vehicle dynamics Autonomous vehicles Videos

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Hybrid Modeling Approaches in Energy Internet: Bridging Cyber, Physical, and Social Realms

Hybrid Modeling Approaches in Energy Internet: Bridging Cybe...

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

作者： Chaturvedi, Prateek Thethi, H. Pal Reddy, N. V. Uma Kalaiarasi, M. Al-Rubaye, Taqi Mohammed Khattab Anusha, K. Amity University Greater Noida Department Of Mechanical Engineering India Lovely Professional University Phagwara India New Horizon College Of Engineering Department Of Artificial Intelligence And Machine Learning Bangalore India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India College Of Medical Technologies The Islamic University Department Of Medical Laboratory Technology Najaf Iraq Mlr Institute Of Technology Department Of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350366846

Effective modeling approaches are required for the management and operation of the Energy Internet due to the system's complexity and mobility. New hybrid models that link the Energy Internet's digital, real-world, and social components are the primary focus of this article. We propose a novel approach that integrates adaptive reinforcement learning, social behavior analysis, and multi-agent coordination optimization to enhance the system's performance, robustness, and long-term sustainability. We intend to address issues that arise when several factors, such as evolving technology, user behavior, and social interactions, converge by employing these mixed modeling approaches. Because of this, the Energy Internet setting will run more efficiently and effectively. Through extensive simulations and case studies, we demonstrate that our proposed method outperforms conventional ones. Our main focus is on how technology can enhance energy management, better allocate resources, and back user-centered and socially inclusive policies. To ensure the seamless operation of the Energy Internet environment's cyber, physical, and social components, the results highlight the significance of mixed modeling methodologies. © 2024 IEEE.

关键词： Multi agent systems

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Redefining Agricultural Disease Management using ai and ml

Redefining Agricultural Disease Management using AI and ML

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Venkata Srinivasulu B K.Sai Dixith Keerthi Shivaram Ch.Karthik Reddy Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Hyderabad Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Precision agriculture is a fast growing field that may contemporary issues surrounding agricultural sustainability are increasingly being addressed through innovative technologies. Machine learning, a cutting-edge tool that enhances precision agriculture, enables sophisticated methods for detecting and classifying plant diseases. This paper offers a comprehensive review of how ml and DL techniques are applied in precision agriculture, focusing on disease identification and categorization. A novel classification framework is introduced, organizing relevant studies into distinct categories—Method-wise Classification Based Approaches. First, we categorize the studies method wise into classification (e.,g, image, and video-based studies) or object detection (e. g., bounding box-based studies) algorithms. Moreover, we provide existing archives for the classification and tracking of plant infections. By presenting a systematic framework that groups machine learning techniques according to their methodology, this study seeks to close the gap between the current plant disease classification techniques. We offer a comparative study to identify the best methods for disease identification by assessing the effectiveness of cutting-edge classification and object recognition algorithms on the PlantDoc dataset. In addition to streamlining precision agriculture research, the suggested architecture provides a basis for creating plant disease detection models that are more precise and scalable.

关键词： Precision agriculture Deep learning Plant diseases Machine learning algorithms Cameras Real-time systems Classification algorithms Computational efficiency Object recognition Sustainable development

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GRAPHTRaiL: Translating GNN Predictions into Human-Interpretable Logical Rules 38

GRAPHTRAIL: Translating GNN Predictions into Human-Interpret...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Armgaan, Burouj Dalmia, Manthan Medya, Sourav Ranu, Sayan Department of Computer Science University of Illinois Chicago United States Department of Computer Science & Engineering Yardi School of AI IIT Delhi India

Instance-level explanation of graph neural networks (GNNs) is a well-studied area. These explainers, however, only explain an instance (e.g., a graph) and fail to uncover the combinatorial reasoning learned by a GNN from the training data towards making its predictions. In this work, we introduce GRAPHTRaiL, the first end-to-end, post-hoc, global GNN explainer that translates the functioning of a black-box GNN model to a boolean formula over the (sub)graph-level concepts without relying on local explainers. GRAPHTRaiL is unique in automatically mining the discriminative subgraph-level concepts using Shapley values. Subsequently, the GNN predictions are mapped to a human-interpretable boolean formula over these concepts through symbolic regression. Extensive experiments across diverse datasets and GNN architectures demonstrate significant improvement over existing global explainers in mapping GNN predictions to faithful logical formulae. The robust and accurate performance of GRAPHTRaiL makes it invaluable for improving GNNs and facilitates adoption in domains with strict transparency requirements. © 2024 Neural information processing systems foundation. All rights reserved.

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Bridging Horizons in Diabetes Prediction: A Comparative Exploration of Machine Learning and Deep Learning Approaches in Pima Indian Women 2

Bridging Horizons in Diabetes Prediction: A Comparative Expl...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Chandra Sekhar Reddy, L. Gottipalli, Monikadevi Sravanthi, P. Rajanikanth, J. Yalamarthi, Ganesh Gurrapu, Neelima Hyderabad India Sir C R Reddy College of Engineering Department of CSE Eluru India CMR College of Engineering & Technology Department of CSE Hyderabad India S R K R Engineering College Department of CSE Bhimavaram India SIR C R Ramalinga Reddy Polytechnic College Department of AI&ML Eluru India SR University Department of computer science and Artificial intelligence Warangal India

ISBN: (纸本)9798350371314

This study has demonstrated that diabetes prediction models will require powerful machine learning and anomaly detection methodologies in practice. Researchers are exploring novel outlier identification algorithms to enhance predictions. This study has addressed various concerns outlined in prior research. According to this study, diagnostic records, when combined with powerful machine learning such as Adaboost and XGBoost, can offer a precise forecast of the likelihood of diabetes. RNNs also improved deep learning, according to experts. Experts use IQR for the straightened subspace to detect outliers. The stand appears to have a highly positive impact on prediction models. IQR-boosted RNNs outperform XGBoost and AdaBoost in terms of prediction and efficiency. This suggests that combining outlier detection with advanced machine learning could potentially improve diabetes diagnosis and care. The identification of IQR outliers, using RNNs cultivated through iteration with a different line of machine learning, may improve diabetes prediction outcomes. RNN produces better results, with 93% accuracy. The study demonstrated that enhanced machine learning and outlier detection led to improvements in diabetes prediction algorithms. © 2024 IEEE.

关键词： Recurrent neural networks

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Statistically Inspired Discrepancy Detection for Anomalous Spatio-Temporal Graphs (Extended Abstract)

Statistically Inspired Discrepancy Detection for Anomalous S...

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International Conference on Data science and Advanced Analytics (DSAA)

作者： Atharv Tiwari Shreyash Chatterjee Siddharth Padmakumar Sushanth Nair Bhaskarjyoti Das Department of Computer Science and Engineering in AI & ML PES University Bangalore India

ISBN: (数字)9798350364941

ISBN: (纸本)9798350364958

Anomaly detection in dynamic graphs is a critical topic with applications in many fields, such as fraud detection and network security. This paper tackles the difficulties in locating abnormalities in time-varying graphs by presenting a novel divide-and-conquer method. We combine Graph Convolutional Networks (GCN) and Recurrent Neural Networks (RNN) to predict future node values on temporal graphs, followed by a macro and micro-level analysis. At the macro level, we present a novel algorithm to extract correlation-based subgraphs. The values obtained for each subgraph allow us to concentrate on subgraphs that show significant anomalies, effectively minimizing the challenges in node-level anomaly detection. This is followed by a micro-level analysis of the node contributions and temporal properties within the identified anomalous subgraphs. By combining macro and micro-level analysis with a machine learning-based approach, our method provides an efficient approach for zoning in on anomalies, significantly speeding up computation.

关键词： Recurrent neural networks Graph convolutional networks Network security Data science Prediction algorithms Fraud Computational efficiency Anomaly detection

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Enhancing Disease Impact Analysis Through Deep Learning Methods for Temperature–Based Crop Disease Detection

Enhancing Disease Impact Analysis Through Deep Learning Meth...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： K. Maheswari S. Kirubakaran Jinugu Ranjith Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Department of Computer Science and Engineering (AI&ML) CMR College of Engineering & Technology Hyderabad Telangana India Department of Computer Science and Engineering CMR College of Engineering & Technology Hyderabad Telangana India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

Through the utilization of thermal images data, this research endeavors to evaluate the possible uses of deep learning (DL) in the identification and diagnosis of crop illnesses. When it comes to capturing the complex and spatially dependent fluctuations that are associated with the course of disease, traditional methodologies frequently depend on single point temperatures, which may prove to be insufficient. Through the use of a dataset consisting of thermal images that have been labeled for both healthy and pathological states, we present a unique image-based deep learning model. With the help of this model, the complete thermal image is analyzed, and complex details that go beyond conventional temperature averages are extracted. A 92.98% accuracy rate is achieved by our model, which exhibits greater performance in comparison to other methods that are currently in use. Furthermore, the model demonstrates a high level of precision (93.38 percent) and area under the curve (99.46 percent), which indicates that it is able to differentiate among healthy and unhealthy crops. Through the visualization of the model's predictions accompanying thermal images, we further investigate the interpretability of the information. Because of this analysis, certain thermal patterns that are related particular disease have been revealed, which may make it possible to implement specific remedies based on individual thermal signatures. A significant step forward in the detection of crop diseases through the use of DL is represented by this work. The integration of real-time sensors for temperature, weather data, and multispectral photography is one of the future topics of research that will be developed in order to produce a comprehensive picture of crop health.

关键词： Deep learning Temperature sensors Photography Analytical models Temperature distribution Accuracy Crops Predictive models Thermal analysis Diseases

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