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Enhanced Water Security and Resilience in Drought-Prone Zones with IoT and SVM classifier for Early Warning Systems

Enhanced Water Security and Resilience in Drought-Prone Zone...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Anandan, P. Saillaja, V. Kovarasan, Rajesh Kambattan Padmaloshani, P. Ishwarya, M.V. Rajmohan, M. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Department Of Electronics And Communication Engineering Tamil Nadu Chennai India Srm Institute Of Science And Technology Faculty Of Management Studies Kattankulathur Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Department Of Computer Science And Engineering Tamil Nadu Chennai India Department Of Electronics And Communication Engineering Tamil Nadu Namakkal India Agni College Of Technology Artificial Intelligence And Data Science Department Tamil Nadu Chennai India Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350371567

The worldwide risk of droughts to water security and agricultural sustainability is growing. Using Internet of Things (IoT) technology and a Support Vector Machine (SVM) classifier to create drought-prone early warning systems is an innovative solution to these difficulties. Better water resource management and resilience in water-scarce locations are the study goals. The IoT infrastructure collects real-time data from sensors strategically positioned over the research area to monitor soil moisture, weather, and water levels. SVM classifiers, which are effective in classification, get this large dataset. The SVM model uses data to predict and classify drought events, alerting decision-makers and stakeholders. The system shows that this integrated strategy works. The IoT-SVM early warning system reliably predicts droughts and alerts authorities in advance, enabling proactive mitigation and intervention. The system's drought prediction accuracy helps drought-prone areas achieve water security and resilience by allocating resources, managing water sustainably, and reducing effects on agriculture and people. It uses IoT and machine learning to improve drought preparation and response, improving long-term water sustainability and resilience in water-scarce areas. © 2024 IEEE.

关键词： Forecasting

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Leveraging Machine Learning Algorithms to Optimize Diet Maintenance Based on User Food Intake and Fitness data 5

Leveraging Machine Learning Algorithms to Optimize Diet Main...

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5th International Conference on Sustainable Communication networks and Application, ICSCNA 2024

作者： Clarinda, K. Deepa, N. Dhamotharan, R. Krishnan, R. Santhana Raj, J. Relin Fransis Anchitaalagammai, J.V. SCAD College of Engineering and Technology Department of Management Studies Cheranmahadevi India PSNA College of Engineering and Technology Department of Artificial Intelligence and Data Science Dindigul India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798331530013

The increasing prevalence of lifestyle-related health issues has necessitated the development of innovative approaches to diet management. This research introduces the Adaptive Eating Pattern Predictor (AEPP), a novel machine learning algorithm designed to optimize diet maintenance by analyzing user food intake and fitness data. The AEPP combines the predictive capabilities of Artificial Neural networks (ANN) with the probabilistic modeling of Markov Chains, enabling accurate forecasting of future eating patterns based on historical dietary and activity data. A comprehensive simulation analysis was conducted to evaluate the performance of the AEPP against existing algorithms, including traditional recommendation systems and basic machine learning models. Various simulation metrics, such as accuracy, precision, recall, and F1 score, were employed to compare the effectiveness of the AEPP in providing personalized dietary recommendations. The results demonstrate that the AEPP significantly outperforms existing algorithms, highlighting its potential as a valuable tool for individuals seeking to maintain a balanced diet in alignment with their fitness goals. This study contributes to the field of nutritional science by showcasing the advantages of integrating advanced machine learning techniques in personal diet management. © 2024 IEEE.

关键词： Adversarial machine learning

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Detection of Speech Signal for Speaker Recognition in Biometric Systems Using Optical Scanning Holography with Double Random Phase Encoding 4

Detection of Speech Signal for Speaker Recognition in Biomet...

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4th IEEE International Conference on Mobile networks and Wireless Communications, ICMNWC 2024

作者： Habelalmateen, Mohammed I. Kalshetty, Jagadevi N. Cindhamani, J. Gayathri, G.M. Jebaseeli, S. Kanmani College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Department of Artificial Intelligence and Data Science Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai India Bannari Amman Institute of Technology Department of Humanities Erode India New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350352931

In recent years, the speaker recognition technology is significant system for securing the biometric authentication and enabling remote logins for telephone banking and enterprise applications. Whereas Speech Signal Detection ensure with accurate speaker identification and verification for restricted access and sensitive data protection. The combination of speaker recognition technology with speech signal detection has accompanied in a new era of biometric authentication, providing unparalleled security and accuracy in verifying identities. However, Vulnerability to voice spoofing attacks leads to imitation of speaker such as mimicry or synthetic speech, compromise system security. To overcome these drawbacks, an Optical Scanning Holography with Double Random Phase Encoding (OSH-DRPE) is proposed using Texas Instruments Massachusetts institute of Technology (TIMIT) dataset. After collecting the data, the data is preprocessed through normalization approaches for normalize the data. Then, a Mel-Frequency Cepstral Coefficients (MFCC) is employed for feature extraction to extract relevant features, which produces low computational complexity. At last, OSH-DRPE is used as classifier to detect speech signal and speaker recognition with higher accuracy, efficient computation and flexibility. From the results, it clearly shows that the proposed OSH-DRPE obtained higher values in the context of precision, F1 score and accuracy when compared to existing Hidden Markov Model (HMM). © 2024 IEEE.

关键词： Authentication

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SCIM MILQ: An HPC Quantum Scheduler

SCIM MILQ: An HPC Quantum Scheduler

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Philipp Seitz Manuel Geiger Christian Ufrecht Axel Plinge Christopher Mutschler Daniel D. Scherer Christian B. Mendl Department of Computer Science Technical University of Munich TUM School of Computation Information and Technology Garching Germany Fraunhofer IIS Fraunhofer Institute for Integrated Circuits IIS Division Positioning and Networks Nürnberg Germany Technical University of Munich TUM Institute for Advanced Study

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

With the increasing sophistication and capability of quantum hardware, its integration, and employment in high performance computing (HPC) infrastructure becomes relevant. This opens largely unexplored access models and scheduling questions in such quantum-classical computing environments, going beyond the current cloud access model. SCIM MILQ is a scheduler for quantum tasks in HPC infrastructure. It combines well-established scheduling techniques with methods unique to quantum computing, such as circuit cutting. SCIM MILQ can schedule tasks while minimizing the makespan, i.e., the time that elapses from the start of work to the end, improving on average by 25 %. Additionally, it reduces the noise in the circuit by up to 10 %, increasing the outcome's reliability. We compare it against an existing baseline and show its viability in an HPC environment.

关键词： Measurement Schedules Quantum computing Processor scheduling Computational modeling High performance computing Noise Integrated circuit modeling Integrated circuit reliability Optimization

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Passer Kinematic Cues for Object Weight Prediction in a Simulated Robot-Human Handover

Passer Kinematic Cues for Object Weight Prediction in a Simu...

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IEEE-RAS International Conference on Humanoid Robots

作者： Clara Günter Luis Figueredo Joachim Hermsdörfer David W. Franklin Neuromuscular Diagnostics School of Medicine and Health Technical University of Munich Munich Germany Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich Munich Germany School of Computer Science University of Nottingham Nottingham UK Human Movement Science School of Medicine and Health Technical University of Munich Munich Germany Munich Data Science Institute (MDSI) Technical University of Munich Munich Germany

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Object handovers, a seemingly straightforward action, involve a complex interplay of predictive and reactive control mechanisms in both partners. Understanding the cues that are used by humans to predict object properties is needed for planning natural robot handovers. In human-human interactions, the receiver can extract information from the passer’s movement. Here, we show in a VR simulated agenthuman object handover, that the human receiver can use passer kinematic cues to predict the transported object’s properties, such as weight, and preemptively adapt the grasping strategy towards them. We show that when the agent’s movement is correlated to the object weight, humans can interpret this cue and produce proportional anticipatory grip forces before object release. This adaptation is learned even when objects are presented in a random order and is strengthened with the repeated presentation of the pairing. The outcome of this study contributes to a better understanding of non-verbal cues in handover tasks and enables more transparent and efficient real-world physical robot-human interactions.

关键词： Sufficient conditions Humanoid robots Collaboration Kinematics Receivers Grasping Handover Planning data mining

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Step-by-Step: Controlling Arbitrary Style in Text with Large Language Models 30

Step-by-Step: Controlling Arbitrary Style in Text with Large...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Pusheng Wu, Lianwei Wang, Linyong Guo, Sensen Liu, Yang National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University China Research & Development Institute of Northwestern Polytechnical University Shenzhen China Chongqing Science & Technology Innovation Center of Northwestern Polytechnical University China State Key Laboratory of Integrated Service Networks Xidian University China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University China

ISBN: (纸本)9782493814104

Recently, the autoregressive framework based on large language models (LLMs) has achieved excellent performance in controlling the generated text to adhere to the required style. These methods guide LLMs through prompt learning to generate target text in an autoregressive manner. However, this manner possesses lower controllability and suffers from the challenge of accumulating errors, where early prediction inaccuracies might influence subsequent word generation. Furthermore, existing prompt-based methods overlook specific region editing, resulting in a deficiency of localized control over input text. To overcome these challenges, we propose a novel three-stage prompt-based approach for specific region editing. To alleviate the issue of accumulating errors, we transform the text style transfer task into a text infilling task, guiding the LLMs to modify only a small portion of text within the editing region to achieve style transfer, thus reducing the number of autoregressive iterations. To achieve an effective specific editing region, we adopt both prompt-based and word frequency-based strategies for region selection, subsequently employing a discriminator to validate the efficacy of the selected region. Experiments conducted on several publicly competitive datasets for text style transfer task confirm that our proposed approach achieves state-of-the-art performance. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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DeepMind Trainer: Harnessing Cognitive Neural networks on Raspberry Pi for Advanced Cognitive Enhancement Games 2

DeepMind Trainer: Harnessing Cognitive Neural Networks on Ra...

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

作者： Abirami, P. Antony Joseph Rajan, D. Vivekanandan, T. Saillaja, V. Mohankumar, N. Rajmohan, M. Velammal Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sakunthala Engineering College Vel Tech Multi Tech Dr Rangarajan Dr Department of Mathematics Tamil Nadu Chennai India SRM Institute of Science and Technology Faculty of Management Studies Tamil Nadu Kattankulathur Chennai India B.V. Raju Institute of Technology Department of Electronics and Communication Engineering Telangana India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350371314

The presentation summarizes a groundbreaking cognitive training program designed specifically for those who suffer from cognitive impairments or neurodevelopmental abnormalities. The work creates a dynamic platform for immersive and adaptable cognitive training games by combining cutting-edge Convolutional Neural networks (CNNs) with Raspberry Pi technology. Memory, focus, and ability to think creatively are all qualities directly targeted by these games. The CNN model, trained on a broad dataset, interprets real-time user inputs, delivering quick feedback and altering game complexity depending on an individual's performance. The incorporation of Raspberry Pi guarantees a solution that is both affordable and easily accessible. The capacity of the system to redefine cognitive rehabilitation by providing a solution that is individualizable, interactive, and broadly deployable is the potential source of the system's influence. The goal of the work is to make a substantial contribution to the field, which will eventually be to the advantage of users, caregivers, and healthcare professionals and ultimately improve the cognitive well-being of those coping with cognitive issues. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Real-time Facial Sentiment Recognition Systems through Deep Learning Architectures

Enhancing Real-time Facial Sentiment Recognition Systems thr...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： K Geetha Rani Rajkumar Selvaraj Aravind Swaminathan R Surendran Department of Computer Science and Engineering Jain (Deemed to be University) Bangalore India Department of Data Science Middlesex University (Dubai Campus) Dubai United Arab Emirates Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Facial Emotion Recognition (FER) has emerged as a pivotal domain within computer vision, with applications spanning diverse fields such as human-computer interaction, mental health monitoring, and security systems. While significant strides have been made in recent years, accurate recognition of nuanced emotions, particularly fear, remains a challenging task. This research contributes to the advancement of FER technology, paving the way for more sophisticated and empathetic human-computer interactions. By discussing about the reliable and efficient methods for real-time fear detection, this study enables the potential to revolutionize various applications, including mental health monitoring, security surveillance, and autonomous vehicles.

关键词： Human computer interaction Emotion recognition Accuracy Face recognition Surveillance Transfer learning Mental health Real-time systems Security Videos

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CNN based Optimized Autoencoder for Satellite Image Compression

CNN based Optimized Autoencoder for Satellite Image Compress...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： P. Sowmya Sri S. Karthi M. Kalaiyarasi S. Saravanan Computer Science and Engineering (Data Science) B V Raju Institute of Technology Narsapur Telangana India Department of Computer Science and Engineering V.S.B.Engineering College India Institute of AI & ML Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India Department of EEE B V Raju Institute of Technology Telangana India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

This paper introduces an innovative Convolutional Neural Network (CNN) based Optimized Autoencoder for the compression of satellite images, with a focus on high resolution Sentinel-2 and Landsat imagery. In this method, the Satellite images are resized and normalized before an optimal autoencoder architecture is implemented. The encoder compresses the image, while the decoder reconstructs it with minimum quality loss. Convolutional and down sampling layers in the encoder and up sampling layers in the decoder preserve image features. MSE, PSNR, SSIM, and CR were used to assess compression performance. The simulation outcomes show that the CNN-based Autoencoder can retain image quality across images with a high compression ratio of 512, an average MSE of 0.0064, an average PSNR of 25.214 dB, and an SSIM of 0.62. This satellite image compression approach is efficient enough for storage and transmission in satellite data applications.

关键词： Image quality Technological innovation Image coding Satellites Autoencoders Memory Real-time systems Satellite images Decoding Convolutional neural networks

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Advancements in Artificial Intelligence for Better Image Analysis, Recognition, and Interpretation in the Field of Image Processing 2

Advancements in Artificial Intelligence for Better Image Ana...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Saravanan, V. Adaikkammai, A. Sasikala, G. Iyswariya, A. Babisha, A. Latha, M. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nano Electronics Materials and Sensors Tamilnadu Chennai India R.M.D. Engineering College Department of Computer Science and Business Systems India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Avadi Chennai India R.M.K. Engineering College Department of Ece Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science India Srm Institute of Science and Technology Department of It India

ISBN: (纸本)9798350388916

The rapid advancement of artificial intelligence (AI) has brought about a significant revolution in image processing. This revolutionary technology has enabled the analysis, recognition, and interpretation of images in ways that were previously inconceivable. An investigation into the most recent developments in artificial intelligence techniques that have improved the efficiency and precision of image processing tasks is presented in this study. We survey the emergence of transformer architectures and the integration of deep learning models, namely Convolutional Neural networks (CNNs), to explore how these technologies have transformed the understanding and application of images in several industries. An overview of existing image processing methods and the limits of those approaches is presented at the beginning of our research. This highlights the necessity of more advanced AI-driven solutions. After that, we examine the advancements that have been made in artificial intelligence, such as the incorporation of attention processes, the development of more effective CNN architectures, and the utilisation of generative adversarial networks (GANs) for the purpose of picture synthesis and augmentation. The purpose of this research is to investigate the impact that artificial intelligence has on particular applications of image processing, such as autonomous car navigation systems, facial recognition, medical imaging, and satellite images analysis. Specifically, we focus on the enhancements in diagnostic accuracy, environmental monitoring, security, and safety that arose as a result of the adoption of artificial intelligence in these sectors through the use of case studies. Furthermore, we discuss the issues that are associated with artificial intelligence in image processing. These challenges include the necessity for explainable AI models, the requirement for computational resources, and the protection of data privacy. A number of potential solutions to these problems

关键词： Convolutional neural networks

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