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International Conference on Inventive Computation Technologies (ICICT)

作者： Prakash J P Joyce Beryl Princess Division of Computer Science and Engineering Karunya Institute of Technology and Science Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Science Coimbatore India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

The ability to get an immediate response from medical emergencies quickly and more effectively is essential for saving both lives and ensuring people's well-being. In the Traditional medical emergency response systems often struggle to correctly assess the seriousness and urgency of emergency response, leading to ineffective resource allocation and prolonged response times. This research work suggests a unique method for medical emergency response systems based on a multi-criteria-based graph neural network to resolve such problems in recent times. The process of choosing the best hospital based on a variety of conditions is optimized by the graph neural network (GNN), which makes use of the graph structure with hospital nodes and accident location nodes. The GNN will examine several conditions to choose the best hospital after identifying the nearby hospitals. It evaluates whether each nearby hospital has the medical resources required to meet the needs of the patient, including available beds, emergency gear, and doctor availability to get a quick medical treatment. To promote quick transportation, the GNN also confirms the availability of ambulances connected to each nearby hospital and alerts the hospital management to arrange needs for patients. The above approach improves the effectiveness and efficiency of medical emergency response systems, guaranteeing that injured persons receive immediate and appropriate medical treatment.

关键词： Ethics Hospitals Navigation Scalability Transportation Emergency services Graph neural networks

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Prevent Data in Embedded Based Customized Wireless Message Transmitting System Using AES Algorithm with artificial Bee Colony Optimisation Techniques 1

Prevent Data in Embedded Based Customized Wireless Message T...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Sathia Priya, M. Bhagavathi Priya, M. Manojkumar, B. Vijayakumar, M. Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Dr.Mahalingam College of Engineering & Technology Department of Artificial Intelligence & Machine Learning Pollachi India Dr. Mahalingam College of Engineering & Technology Department of Artificial Intelligence & Data Science Pollachi India

ISBN: (纸本)9798350359756

The sender encrypts a message before transferring it to the recipient over a channel in a unidirectional, linear process, according to the transmission model of communication. Hardware and software components known as "message transmission systems"are in charge of managing the transmission path and the communication process. It is possible to study transmission data with the traditional information theory, which is based on "emitters,""messages,"and "receivers". The execution of control tasks such data collecting, control algorithm execution, system identification, and actuation frequently takes place in several system nodes. The effectiveness of a status monitoring system where a sensor delivers sporadic status updates to a remote monitor across a network is observed. Additionally, wireless protocol is utilized in embedded devices to convey messages because it is made for lightweight devices and unstable network conditions. The core concept is to develop a new class of applications through direct interaction amongst intelligent techniques, without the requirement for human involvement. Because it embeds user personal data and activates the aims of information, data prevention is crucial for information sharing. This article has covered the issues of using wireless data, messages, or information with an encryption system, as well as a summary of previous studies in the field. A suggested plan of action has also been offered. To protect user data and privacy safer than any other existing approach, the proposed system also uses artificial Bee Colony Optimisation (ABC) to optimise the key used for encrypting the sensor data with the aid of the AES Encryption algorithm. Even so, the suggested solution uses less time to encrypt and decode patent data before saving it to the server. © 2023 IEEE.

关键词： Information theory

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CAUSAL BALANCING FOR DOMAIN GENERALIZATION 11

CAUSAL BALANCING FOR DOMAIN GENERALIZATION

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11th International Conference on learning Representations, ICLR 2023

作者： Wang, Xinyi Saxon, Michael Li, Jiachen Zhang, Hongyang Zhang, Kun Wang, William Yang Department of Computer Science University of California Santa Barbara United States David R. Cheriton School of Computer Science University of Waterloo Canada Department of Philosophy Carnegie Mellon University United States Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

While machine learning models rapidly advance the state-of-the-art on various real-world tasks, out-of-domain (OOD) generalization remains a challenging problem given the vulnerability of these models to spurious correlations. We propose a balanced mini-batch sampling strategy to transform a biased data distribution into a spurious-free balanced distribution, based on the invariance of the underlying causal mechanisms for the data generation process. We argue that the Bayes optimal classifiers trained on such balanced distribution are minimax optimal across a diverse enough environment space. We also provide an identifiability guarantee of the latent variable model of the proposed data generation process, when utilizing enough train environments. Experiments are conducted on DomainBed, demonstrating empirically that our method obtains the best performance across 20 baselines reported on the benchmark. © 2023 11th International Conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Optimization

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learning Discrete Latent Variable Structures with Tensor Rank Conditions 38

Learning Discrete Latent Variable Structures with Tensor Ran...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Zhengming Cai, Ruichu Xie, Feng Qiao, Jie Wu, Anpeng Li, Zijian Hao, Zhifeng Zhang, Kun School of Computer Science Guangdong University of Technology China Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Department of Applied Statistics Beijing Technology and Business University China Department of Computer Science and Technology Zhejiang University China College of Science Shantou University Guangdong Shantou China Department of Philosophy Carnegie Mellon University United States

Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the causal structure of these latent variables is crucial for uncovering data patterns. Most studies focus on the linear latent variable model or impose strict constraints on latent structures, which fail to address cases in discrete data involving non-linear relationships or complex latent structures. To achieve this, we explore a tensor rank condition on contingency tables for an observed variable set Xp, showing that the rank is determined by the minimum support of a specific conditional set (not necessary in Xp) that d-separates all variables in Xp. By this, one can locate the latent variable through probing the rank on different observed variables set, and further identify the latent causal structure under some structure assumptions. We present the corresponding identification algorithm and conduct simulated experiments to verify the effectiveness of our method. Our results elegantly expand the application scope of causal discovery with latent variables. © 2024 Neural information processing systems foundation. All rights reserved.

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Prediction of Technical Indicators for Stock Markets using HPO-CCOA Based machine learning Algorithm

Prediction of Technical Indicators for Stock Markets using H...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Rao, Sakshith S Kumar Pareek, Piyush Vishwanatha, C.R. Kumar Bhardwaj, Arvind Nitte Meenakshi Institute of Technology Department of Master of Computer Applications Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Capgemini Houston Texas United States

ISBN: (纸本)9798350313413

Predicting the Stock Market's behaviour has become an enormous requirement, making it one of the most active study fields. Stock market forecasting is notoriously difficult and calls for a deep dive into the facts. To address this problem and provide a workable solution, we need to employ targeted statistical models and AI-powered algorithms. Several different deep learning and machine learning algorithms can generate a reliable forecast with little room for mistake. Due to the significant degree of uncertainty surrounding technical indicators, they tend to have a disproportionate impact on stock market forecasts. When compared to other methods created for financial market prediction, AI methods demonstrate superior prediction efficiency. In order to forecast the stock market index, this study employs a machine learning-based Hyper-parameter tuning optimisation procedure (HPO). During pre-processing, data is first cleaned and normalised to improve classification precision. The Crisscross Optimisation Algorithm (CCOA) is then used to fine-tune the HPO once K-Nearest Neighbour (KNN) has been applied. Using three publicly accessible datasets, this study presents a thorough analysis of the methods used to forecast stock prices, discusses the difficulties encountered, and looks ahead to the potential for further research in this area. © 2023 IEEE.

关键词： Crisscross Optimization Algorithm Deep learning algorithms Hyper-parameter tuning optimization K-Nearest Neighbour Stock Market Technical indicators

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Avoidance of Ship Collision Using Deep Reinforcement learning with Bi-Directional Long Short-Term Memory in Continuous Action Spaces

Avoidance of Ship Collision Using Deep Reinforcement Learnin...

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Data science and Network Security (ICDSNS), International Conference on

作者： Revatthy Krishnamurthy Myasar Mundher Adnan Sunil Kumar V T. Aditya Sai Srinivas Srinivas Aluvala Artificial Intelligence & Machine Learning Department Rajalakshmi Engineering College Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic university Najaf Iraq Department of Artificial intelligence and Machine learning Nitte Meenakshi Institute of Technology Bengaluru India Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India School of Computer Science and Engineering SR University Warangal India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Recent days, with the globalization of the world economy the ships count are increasing for marine transportation and the Waterways are growing increasingly overcrowded than previous. This situation may leads to problem of collision of ships which may cause losing of life and damage for property and to nature. Many automatically collision models of ships are implemented but many focused only on the ship-ship encounter situation only. By using a grid sensor which is virtual sensor, agents of Deep Reinforcement learning (DRL) classify approach of a multiple ships. This framework introduces an automatically collision detection algorithm for ships using DRL in continuous action spaces. DRL is used for avoidance of collision with a maximum distance of safe passing between ships. A unique method is developed named inside Obstacle Zone by Target (OZT) used to change learning capability that expands the OZT. Using Bi-directional Long Short-Term Memory (BI-LSTM) cell, network is redesigned and continuous action spaces training is carried out to train a model with longer safe distance of ships. In collision detection model that the bow cross range is effective for COLREGs compliant collision avoidance that is proposed in this model. The propose model also validates a scenario that included more ships and have passed that Imazu problem. The proposed BI-LSTM model achieved 80.43 % of accuracy, 95.67% of precision, 85.89% of recall and 93.54% of f1 score values.

关键词： Accuracy Biological system modeling Bidirectional control Power system stability Deep reinforcement learning Stability analysis Vectors Marine vehicles Collision avoidance Long short term memory

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Deepfake Image and Video Detection Using Deep learning Algorithms

Deepfake Image and Video Detection Using Deep Learning Algor...

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Multi-Agent Systems for Collaborative intelligence (ICMSCI), International Conference on

作者： Srivanth Srinivasan Nischitha P Akshita Chavan Mohana Artificial Intelligence & Machine Learning Engineering RV College of Engineering® Bengaluru India Computer Science & Engineering (Cyber Security) RV College of Engineering® Bengaluru India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to distinguish from reality. The advancements in image and video technology and tools, especially on social media platforms, potentially lead to misuse for malicious purposes like blackmail or defamation. To tackle this issue, several group of researchers tried upon spreading or creating awareness on real or fake data. The proposed approach involves combining Deepfake generation using GANs and Autoencoders with a Deepfake detection method. The aim of this initiative is exclusively to combat disinformation and online fraud for the welfare of the general population. Deepfakes, products of AI, have become increasingly realistic, rendering it nearly difficult to distinguish the content. Auto-encoders with sufficient time can achieve about 92 % accuracy. As the generator improves, the discriminator performance worsens as it struggles to differentiate real or fake data. A perfect generator results in 50% accuracy. With advancements in computational capacity and data availability, the proposed DDM (Deepfake Detection Model) has achieved greater accuracy rate of up to 92.3%.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Streaming media Feature extraction Generators Convolutional neural networks artificial intelligence Research and development

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TempXAI: Explainable AI for Time Series and Data Streams 2024

TempXAI: Explainable AI for Time Series and Data Streams 202...

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2024 Workshop on Explainable AI for Time Series and Data Streams, TempXAI 2024

作者： Abdallah, Zahraa Fumagalli, Fabian Hammer, Barbara Hüllermeier, Eyke Jakobs, Matthias Müller, Emmanuel Muschalik, Maximilian Papapetrou, Panagiotis Saadallah, Amal Tzagkarakis, George University of Bristol United Kingdom Bielefeld University Germany LMU Munich Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany Research Center Trustworthy Data Science and Security Germany Stockholm University Sweden FORTH-ICS Greece

This document is the preface of the proceedings of the TempXAI Workshop Tutorial on Explainable AI for Time Series and Data Streams, held on September 9th, 2024, in Vilnius, Lithuania. We received 5 submissions for peer-review, out of which we accepted 4 papers for this volume. In addition, we publish two extended abstracts of the tutorial that we give as a part of the workshop program. © 2024 CEUR-WS. All rights reserved.

关键词： Data streams

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Spiralizer Strategy with Wolf Pack Algorithm and Temporal Convolution Network – Gated Recurrent Unit for Coconut Crop Yield Prediction

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SN computer science 2024年第8期5卷 1-9页

作者： Niranjan, S.J. Raviprakash, M.L. Babu, J. Ananda Department of Computer Science and Engineering Kalpataru Institute of Technology Tiptur Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of Artificial Intelligence and Machine Learning Kalpataru Institute of Technology Tiptur Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of Information Science and Engineering Malnad College of Engineering Hassan Affiliated to Visvesvaraya Technological University Karnataka Belagavi India

The estimating coconut crop yield across large areas is crucial for ensuring food security and early to predicted. However, current spatial heterogeneity learning methods face limitations, such as selecting irrelevant features across regions and difficulties in achieve better performance in crop yields. In this research, Spiralizer Strategy with Wolf Pack Algorithm (SWPA) for feature selection and Temporal Convolution Network – Gated Recurrent Unit (GRU) for classification combined to performed efficiently and achieve better performance. The SWPA efficiently select relevant feature using a spiral search patter, leading to refined feature set that is fed into classification model. The TCN is designed to learn sequential data by applying convolutional layer that maintain the order of sequence. The GRU technique specifically designed to handling sequential data and long term dependencies, reducing the risk of overfitting. Initially,data acquisition and pre-processing involved Min–Max normalization, label encoder which efficiently scaling and categorized values into 0 and 1. The proposed method achiever better outcomes such as better MAE of 1.50, MAPE of 8.05, MSE of 7.23, RMSE of 0.35, R² of 0.654, respectively. The existing method such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are compared to proposed method. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Coconut crop yield predication Convolutional neural network Long short-term memory Spiralizer strategy with wolf pack algorithm and temporal convolution network – gated recurrent unit

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Handwritten Signature Verification using CNN

Handwritten Signature Verification using CNN

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artificial intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Rutuja Nitin Lanjewar Radha Wasudeo Wande Swapnil Gundewar Faculty of Engineering and Technology Artificial Intelligence and Data Science Sawangi Maharashtra India Faculty of Engineering and Technology Artificial Intelligence and Machine Learning Sawangi Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

A signature is a special kind of a sign made or given by a person on an instrument or document to indicate that he or she fully understands everything written on the document, agrees with them or being bound to fulfill the obligations stated on the document. It is used to verify the genuineness of the writing, to indicate origin and to constrain the signer to the provisions thus provided in the writing. Thus, the field of signature verification is not only valuable for the sphere of commercial banking, used for tools against frauds and approval of transactions, but also for a lot of other fields. Employment of this method is widely seen in legal, financial and governmental sectors to avoid forgery of numerous contracts agreements, legal instruments and other related documents. This paper concentrates on the offline handwritten signature verification using CNN, an advanced deep learning architecture which is famous for its capability to work on image data. CNNs are more appropriate for this task concerning the following characteristics: they are capable to draw out significant features from the province of the images of signatures, and the features like the strokes, texture, and general structure of signatures and so on with the high classification accuracy of genuine and forged signatures. This approach makes the signature verification more efficient and accurate and provides a better and automated solution for many applications for which signature authentication is most essential.

关键词： Deep learning Handwriting recognition Accuracy Recurrent neural networks Instruments Writing Real-time systems Convolutional neural networks Optimization Residual neural networks

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