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International Conference on science Technology engineering and Management (ICONSTEM)

作者： Murali N D. Beulah David Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamilnadu India Department of Data Analytics Saveetha School of Engineering SIMATS Institute of Information Technology Chennai Tamilnadu

The most pressing issue with ITS is minimal latency in communication between vehicles, especially given that unstable traffic flow and road safety are two of their main concerns. Because to these problems, I have concentrated my study on employing a vehicular ad hoc network to track speedy object detection and provide the quickest warning to drivers. These systems require ongoing monitoring to function properly, making it possible to use machine learning techniques on vast amounts of data produced by various VANET applications. Machine learning is a technique used to prevent accidents for emergency vehicles by automatically learning and improving the system's rapid object detection stage in situations with high traffic. Our methods effectively implement the VANET aim by providing efficient unsupervised learning of the collected data. In this research, we suggested a unique real-time (DT-YOLO) algorithm-based automatic discovering vehicle gap approach for detecting and identifying objects in congested metropolitan regions. By using VANET technologies, we were able to address traffic, communication, and safety concerns in metropolitan areas. We investigated, using DT-YOLO technology, how these problems may be resolved in a transparent object-tracking system via machine learning techniques. Fast-moving EVs have a high probability of finding a gap to let the driver to make a decision. Moreover, a case study demonstrating a VANET-based scenario of nearest-object and gap-finding to flee from congested traffic with the ideal vehicle moment is shown. The paper examined current challenges and future directions.

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Comparative Evaluation of Attribute-Enabled Supervised Classification in Predicting the Air Quality

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Spatial Information Research 2023年第4期31卷 399-407页

作者： Subbulakshmi, P. Vimal, S. Robinson, Y. Harold Verma, Amit Nayak, Janmenjoy School of computer science and Engineering VIT Chennai campus Tamilnadu India Data Analytics Lab Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam India Department of Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India Department of Computer Science & Engineering University Centre for Research & Development Chandigarh University Gharuan Mohali India Department of Computer Science Maharaja Sriram Chandra Bhanja Deo University Odisha India

Air pollution demonstrates the appearance of toxins into the air which is blocking human prosperity and the earth. It will portray as potentially the riskiest threats that humanity anytime faced. It makes hurt animals, harvests to thwart these issues in transportation territories need to expect air quality from pollutions utilizing AI systems and IoT. Along these lines, air quality evaluation and assumption has become a huge target for human health factors and also affect internal organs related to respiratory. The accuracy of Air Pollution prediction has been involved with the machine learning techniques and the best accuracy model is identified. The air quality prediction dataset is used for identifying the meteorology air pollution data while the predicted model is involved the decision tree computation for predicting the toxin contents in the region, the Air quality indicator is used to assess the pollution level and monitoring the air quality. The performance analysis shows that the decision tree technique has produced the better results in the performance metrics of Accuracy, precision, recall, and F1-score with the minimized error values while the comparative evaluation of Attribute-enabled classification has identified the best technique for predicting the air quality.

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AI-Empowered ClimateClean: Wastewater-to-Energy Solutions for a Resilient and Sustainable Future

AI-Empowered ClimateClean: Wastewater-to-Energy Solutions fo...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Saeed Hamood Alsamhi Ammar Hawbani Neil J Rowan Mamoona Naveed Asghar Niall O'Brolchain Eoghan Clifford Insight Centre for Data Analytics University of Galway Galway Ireland School of Computer Science Shenyang Aerospace University Shenyang China Centre for Sustainable Disinfection and Sterilization TUS Athlone Ireland School of Computer Science University of Galway Ireland School of Engineering University of galway Galway Ireland

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Wastewater treatment has emerged as a critical area of interest for sustainable resource management due to its ability to provide both environmental repair and produce renewable energy. Using artificial intelligence (AI) to boost efficacy and efficiency, this study explores wastewater-to-energy solutions from the standpoint of sustainable development and climate change mitigation. We analyse the energy outputs, advantages, and climatic implications, including Microbial Fuel Cells (MFC), Thermal Conversion (TC), Anaerobic Digestion (AD), and Hydrothermal Processes (HP) while providing a taxonomy and integrating AI-driven strategies for optimisation. By utilizing state-of-the-art research and practical implementations, we clarify optimal procedures, valuable insights, and critical success elements influencing the implementation. To manage resources sustainably, this paper emphasizes incorporating wastewater-to-energy solutions into current infrastructure wastewater-to-energy paradigms, encouraging multi-stakeholder involvement, and igniting circular economy ideas. It provides strategic guidance for negotiating complex technical and legal environments, pointing the way toward the scalable and significant implementation of wastewater-to-energy breakthroughs to achieve a more sustainable, resilient, and clean future in the face of climate change.

关键词： Climate change Technological innovation Renewable energy sources Scalability Wastewater Resource management Artificial intelligence Wastewater treatment Sustainable development Resilience

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A Rationale-Centric Framework for Human-in-the-loop Machine Learning 60

A Rationale-Centric Framework for Human-in-the-loop Machine ...

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60th Annual Meeting of the Association for Computational Linguistics, ACL 2022

作者： Lu, Jinghui Yang, Linyi Namee, Brian Mac Zhang, Yue The Insight Centre for Data Analytics University College Dublin Ireland School of Computer Science University College Dublin Ireland School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China SenseTime Research

ISBN: (纸本)9781955917216

We present a novel rationale-centric framework with human-in-the-loop - Rationales-centric Double-robustness Learning (RDL) - to boost model out-of-distribution performance in few-shot learning scenarios. By using static semi-factual generation and dynamic human-intervened correction, RDL exploits rationales (i.e. phrases that cause the prediction), human interventions and semi-factual augmentations to decouple spurious associations and bias models towards generally applicable underlying distributions, which enables fast and accurate generalisation. Experimental results show that RDL leads to significant prediction benefits on both in-distribution and out-of-distribution tests compared to many state-of-the-art benchmarks-especially for few-shot learning scenarios. We also perform extensive ablation studies to support in-depth analyses of each component in our framework. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Minimum Separator Reconfiguration 18

Minimum Separator Reconfiguration

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18th International Symposium on Parameterized and Exact Computation, IPEC 2023

作者： Gomes, Guilherme C. M. Legrand-Duchesne, Clément Mahmoud, Reem Mouawad, Amer E. Okamoto, Yoshio Dos Santos, Vinicius F. Van Der Zanden, Tom C. Department of Computer Science Federal University of Minas Gerais Belo Horizonte Brazil LaBRI CNRS Université de Bordeaux France Virginia Commonwealth University RichmondVA United States Department of Computer Science American University of Beirut Beirut Lebanon Graduate School of Informatics and Engineering The University of Electro-Communications Chofu Japan Department of Data Analytics and Digitalisation Maastricht University Netherlands

ISBN: (纸本)9783959773058

We study the problem of reconfiguring one minimum s-t-separator A into another minimum s-tseparator B in some n-vertex graph G containing two non-adjacent vertices s and t. We consider several variants of the problem as we focus on both the token sliding and token jumping models. Our first contribution is a polynomial-time algorithm that computes (if one exists) a minimum-length sequence of slides transforming A into B. We additionally establish that the existence of a sequence of jumps (which need not be of minimum length) can be decided in polynomial time (by an algorithm that also outputs a witnessing sequence when one exists). In contrast, and somewhat surprisingly, we show that deciding if a sequence of at most jumps can transform A into B is an NP-complete problem. To complement this negative result, we investigate the parameterized complexity of what we believe to be the two most natural parameterized counterparts of the latter problem;in particular, we study the problem of computing a minimum-length sequence of jumps when parameterized by the size k of the minimum s-t-separators and when parameterized by the number of jumps. For the first parameterization, we show that the problem is fixed-parameter tractable, but does not admit a polynomial kernel unless NP ⊆ coNP/poly. We complete the picture by designing a kernel with O(2) vertices and edges for the length of the sequence as a parameter. © 2023 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Separators

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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2024 IEEE International Conference on Computing, ICOCO 2024

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Radi, Noor Fadhilah Ahmad Rahim, Nadirah Abdul Yahaya, Syarul Heiry Bakar, Mohd Aftar Abu Zakaria, Roslinazairimah School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA Penang Campus Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (纸本)9798331530303

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability. ©2024 IEEE.

关键词： Emotion Recognition

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Lactose prediction in dry milk with hyperspectral imaging: A data analysis competition at the "International Workshop on Spectroscopy and Chemometrics 2024"

Lactose prediction in dry milk with hyperspectral imaging: A...

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作者： Frizzarin, Maria Caponigro, Vicky Domijan, Katarina Molle, Arnaud Aderinola, Timilehin Nguyen, Thach Le Serramazza, Davide Ifrim, Georgiana Konkolewska, Agnieszka Irish Cattle Breeding Federation Highfield House Bandon Ireland School of Biosystems and Food Engineering Belfield Dublin Ireland Department of Pharmacy University of Salerno Fisciano Italy Department of Mathematics and Statistics National University of Ireland Maynooth Ireland Department of Veterinary Science University of Parma Parma Italy School of Computer Science University College Dublin Belfield Dublin Ireland Insight SFI Research Centre for Data Analytics University College Dublin Belfield Dublin Ireland

In April 2024, the Vistamilk SFI Research Centre organized the fourth edition of the "International Workshop on Spectroscopy and Chemometrics — Spectroscopy meets modern Statistics". Within this event, a data challenge was organized among workshop participants, focusing on hyperspectral imaging (HSI) of milk samples. Milk is a complex emulsion comprising of fats, water, proteins, and carbohydrates. Due to the widespread prevalence of lactose intolerance, precise lactose quantification in milk samples became necessary for the dairy industry. The dataset provided to the participants contained spectral data extracted from HSI, without the spatial information, obtained from 72 samples with reference laboratory values for lactose concentration [mg/mL]. The winning strategy was built using ROCKET, a convolutional-based method that was originally designed for time series classification, which achieved a Pearson correlation of 0.86 and RMSE of 9.8 on the test set. The present paper describes the approaches and statistical methods adopted by all the participants to analyse the data and develop the lactose prediction models. © 2024

关键词： Hyperspectral imaging

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A IoT Edge Advanced VANET Technique for Vehicle Communication and Improve Safety in Hill Station Critical Scenario

A IoT Edge Advanced VANET Technique for Vehicle Communicatio...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： Murali N D. Beulah David Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamilnadu India Department of Data Analytics Institute of Information Technology Saveetha School of Engineering SIMATS Chennai Tamilnadu India

The safety of the vehicles is given great importance by advanced intelligent transportation systems. We are here to make sure that swift transit vehicles and infrastructure communicate with one other quickly. The majority of the vehicles need regular monitoring for appropriate warnings and road maps to achieve their ultimate objective in a timely manner in the hill station bad environment, and travel in hairpin bends and risky turning in particular is a duty that is vital for ITS. Here, my study will provide a strategy for preventing accidents by applying a sophisticated early warning system before notifying the car in a certain circumstance. In order to prevent accidents, machine learning is a technique where the system automatically learns and enhances the rapid object detection stage in bobby pin road turning scenarios. Here, my study offers effective unsupervised learning of these characteristics based on the acquired data, which in turn serves as the foundation for the clustering I performed, which successfully builds a mobile ad hoc network. In this work, we suggested an innovative automated discovering vehicle prier that uses the UNetXST technique in real-time to notify warring for vehicle in three-way shining LED light with blinking mode, slowing warring message in display unit, and beep alarm sound. By combining V2I and V2V technologies and sending messages to the central traffic light management system, we highlighted safety and communication. In worst-case scenarios, we employ a v2v technology sensor to detect the vehicle that will cross the junction road, and we investigate two-way merging technology's potential to implement and resolve these problems in transparent object tracking in the turning system. We looked at future direction and hurdles at this time in addition to a case study illustrating a VANET-based scenario of a critical urban road turning, receiving the earliest notice to flee from a traffic collision, and experiencing the perfect EV momen

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A Smart Electronic Wheelchair Design Using Artificial Intelligence and Intelligent Sensors Association 2

A Smart Electronic Wheelchair Design Using Artificial Intell...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Balamurugan, R. Kanchana, D. Anni Princy, B. Jaisudha, J. Malathi, P. Glory, E. Ajk College of Arts and Science Department of Artificial Intelligence and Machine Learning Tamil Nadu Coimbatore India Srm Institute of Science and Technology Department of Computer Applications Tamil Nadu Chennai India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Tamil Nadu Chennai India Simats Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India Simats Department of Biosciences Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350306415

Since the beginning of the seventeenth century, wheelchairs have been used to transport hospital patients and those with mobility impairments. The power of people is what spreads wheelchairs. Wheelchair users propel themselves forward using their upper bodies or, more commonly, require the assistance of another person to propel them. In this work, we examine how to build an autonomous electric wheelchair for the disabled at the lowest possible cost without sacrificing quality of design or control. This research presents the planning and development of an AI-enhanced electric wheelchair that can be controlled automatically to climb stairs, making it accessible to those with mobility impairments. The chair's controls allow the user to move it in any direction and raise the landing and staircases to varying heights. The chair's DC power source (battery used) may be recharged, allowing it to function for an extended period of time. In addition, a safety system was created that monitors and provides feedback on the user's pulse rate, as well as the wheelchair's functioning state (including acceleration, climate, and inclination position). This proposed AI based Wheelchair (AIWC) design is suitable for any aged people and the proposed model is cross-validated with the conventional wheelchair design using mobile based operations handling system called Mobile-Wheelchair (MWC) to test the efficiency of the proposed design in clear manner. © 2024 IEEE.

关键词： Accelerator AI Artificial Intelligence Batteries DC Power Intelligent Sensors Mobile-Wheelchair MWC Wheelchair

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Synthesizing CTA Image data for Type-B Aortic Dissection using Stable Diffusion Models

Synthesizing CTA Image Data for Type-B Aortic Dissection usi...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ayman Abaid Muhammad Ali Farooq Niamh Hynes Peter Corcoran Ihsan Ullah School of Computer Science University of Galway Ireland School of Engineering College of Science and Engineering University of Galway Ireland University Hospital Galway Newcastle Road University of Galway Ireland Insight SFI Research Center for Data Analytics University of Galway Ireland

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Stable Diffusion (SD) has gained a lot of attention in recent years in the field of Generative AI thus helping in synthesizing medical imaging data with distinct features. The aim is to contribute to the ongoing effort focused on overcoming the limitations of data scarcity and improving the capabilities of ML algorithms for cardiovascular image processing. Therefore, in this study, the possibility of generating synthetic cardiac CTA images was explored by fine-tuning stable diffusion models based on user defined text prompts, using only limited number of CTA images as input. A comprehensive evaluation of the synthetic data was conducted by incorporating both quantitative analysis and qualitative assessment, where a clinician assessed the quality of the generated data. It has been shown that Cardiac CTA images can be successfully generated using using Text to Image (T2I) stable diffusion model. The results demonstrate that the optimized T2I CTA diffusion model successfully render images with features that are typically unique to acute type B aortic dissection (TBAD) medical conditions.

关键词： Medical conditions Statistical analysis Generative AI Image processing Text to image Diffusion models Synthetic data Biomedical imaging

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